From e059dd82c25a84c704229b1a247c85307fef6cc0 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 16 Sep 2025 10:42:42 -0700 Subject: [PATCH 001/335] Tomo base plotting fixes --- src/quantem/tomography/tomography_base.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 0d937020..35c69b34 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -4,6 +4,7 @@ from numpy.typing import NDArray from torch._tensor import Tensor from tqdm.auto import tqdm +from typing import Tuple from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.io.serialize import AutoSerialize @@ -387,6 +388,7 @@ def plot_projections( fft: bool = False, norm: str = "log_auto", figax: tuple[plt.Figure, plt.Axes] | None = None, + fft_vmax: Tuple[float, float] = (0, 40), **kwargs, ): """ @@ -427,13 +429,15 @@ def plot_projections( if fft: fig, ax = plt.subplots(ncols=3, figsize=(25, 8)) - + print(fft_vmax) show_2d( np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=0)))), figax=(fig, ax[0]), cmap=cmap, title="Y-X Projection FFT", - norm=norm, + # norm=norm, + vmin = fft_vmax[0], + vmax = fft_vmax[1], ) show_2d( @@ -441,14 +445,18 @@ def plot_projections( figax=(fig, ax[1]), cmap=cmap, title="Z-X Projection FFT", - norm=norm, + vmin = fft_vmax[0], + vmax = fft_vmax[1], + # norm=norm, ) show_2d( np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=2)))), figax=(fig, ax[2]), cmap=cmap, title="Z-Y Projection FFT", - norm=norm, + vmin = fft_vmax[0], + vmax = fft_vmax[1], + # norm=norm, ) def plot_slice( From 993ba862451803983bc2eda2f23bb26e44030d6e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 16 Sep 2025 22:13:23 -0700 Subject: [PATCH 002/335] Semi-working NeRF implementation; bug where it's not converging correctly --- src/quantem/tomography/models.py | 59 ++ src/quantem/tomography/tomography_dataset.py | 64 +- src/quantem/tomography/tomography_nerf.py | 662 +++++++++++++++++++ 3 files changed, 778 insertions(+), 7 deletions(-) create mode 100644 src/quantem/tomography/models.py create mode 100644 src/quantem/tomography/tomography_nerf.py diff --git a/src/quantem/tomography/models.py b/src/quantem/tomography/models.py new file mode 100644 index 00000000..0829712e --- /dev/null +++ b/src/quantem/tomography/models.py @@ -0,0 +1,59 @@ +import torch +from torch import nn +import numpy as np +import math +from torch.nn import init + +class SineLayer(nn.Module): + def __init__(self, in_features, out_features, bias=True, is_first=False, omega_0=30, hsiren=False, alpha=1.0): + super().__init__() + self.omega_0 = omega_0 + self.is_first = is_first + self.hsiren = hsiren + self.in_features = in_features + self.alpha = alpha + self.linear = nn.Linear(in_features, out_features, bias=bias) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + if self.is_first: + # Scale the first layer initialization by alpha + self.linear.weight.uniform_(-self.alpha / self.in_features, + self.alpha / self.in_features) + else: + # Scale the hidden layer initialization by alpha + self.linear.weight.uniform_(-self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, + self.alpha * np.sqrt(6 / self.in_features) / self.omega_0) + + def forward(self, input): + if self.is_first and self.hsiren: + out = torch.sin(self.omega_0 * torch.sinh(2*self.linear(input))) + else: + out = torch.sin(self.omega_0 * self.linear(input)) + return out + +class HSiren(nn.Module): + def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, + first_omega_0=30, hidden_omega_0=30, alpha=1.0): + super().__init__() + self.net = [] + self.net.append(SineLayer(in_features, hidden_features, is_first=True, + omega_0=first_omega_0, hsiren=True, alpha=alpha)) + + for i in range(hidden_layers): + self.net.append(SineLayer(hidden_features, hidden_features, is_first=False, + omega_0=hidden_omega_0, alpha=alpha)) + + final_linear = nn.Linear(hidden_features, out_features) + with torch.no_grad(): + # Final layer keeps original initialization (no alpha scaling) + final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, + np.sqrt(6 / hidden_features) / hidden_omega_0) + self.net.append(final_linear) + self.net.append(nn.Softplus()) + self.net = nn.Sequential(*self.net) + + def forward(self, coords): + output = self.net(coords) + return output diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index 816259b6..967fb0f0 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -9,8 +9,10 @@ validate_tensor, ) +from torch.utils.data import Dataset -class TomographyDataset(AutoSerialize): + +class TomographyDataset(AutoSerialize, Dataset): _token = object() """ @@ -39,6 +41,16 @@ def __init__( self._initial_z1_angles = z1_angles.clone() self._initial_z3_angles = z3_angles.clone() self._initial_shifts = shifts.clone() + + # Move everything to CPU + self.to("cpu") + + # Number of indices (?) + + + # Enforce normalization of tilt series + tilt_percentile = torch.quantile(self._tilt_series, .95) + self._tilt_series = self._tilt_series / tilt_percentile # --- Class Methods --- @classmethod @@ -49,12 +61,6 @@ def from_data( z1_angles: NDArray | Tensor | None = None, z3_angles: NDArray | Tensor | None = None, shifts: NDArray | Tensor | None = None, - # name: str | None = None, - # origin: NDArray | tuple | list | float | int | None = None, - # sampling: NDArray | tuple | list | float | int | None = None, - # units: list[str] | tuple | list | None = None, - # signal_units: str = "arb. units", - # _token: object | None = None, ): """ tilt_series: (N, H, W) @@ -145,6 +151,50 @@ def initial_z3_angles(self) -> Tensor: @property def initial_shifts(self) -> Tensor: return self._initial_shifts + + @property + def num_projections(self) -> int: + + return self._tilt_series.shape[0] + + @property + def num_pixels(self) -> int: + + return self._tilt_series.shape[0] * self._tilt_series.shape[1] * self._tilt_series.shape[2] + + @property + def dims(self) -> tuple[int, int, int]: + + return self._tilt_series.shape[0], self._tilt_series.shape[1], self._tilt_series.shape[2] + + def __len__(self) -> int: + + return self.num_pixels + + def __getitem__(self, idx): + + actual_idx = idx + + projection_idx = actual_idx // (self.dims[1] * self.dims[2]) + remaining = actual_idx % (self.dims[1] * self.dims[2]) + + # TODO: What if non-square tilt images? + if self.dims[1] != self.dims[2]: + raise NotImplementedError("Non-square tilt images are not supported yet.") + + pixel_i = remaining // self.dims[1] + pixel_j = remaining % self.dims[1] + + target_value = self._tilt_series[projection_idx, pixel_i, pixel_j] + phi = self._tilt_angles[projection_idx] + + return { + 'projection_idx': projection_idx, + 'pixel_i': pixel_i, + 'pixel_j': pixel_j, + 'target_value': target_value, + 'phi': phi + } # --- Setters --- diff --git a/src/quantem/tomography/tomography_nerf.py b/src/quantem/tomography/tomography_nerf.py new file mode 100644 index 00000000..e83d8e88 --- /dev/null +++ b/src/quantem/tomography/tomography_nerf.py @@ -0,0 +1,662 @@ +from torch.utils.data import Dataset, DataLoader, DistributedSampler +import torch +import torch.distributed as dist +import numpy as np + +from quantem.tomography.tomography_dataset import TomographyDataset +from quantem.tomography.models import HSiren + +from torch.utils.tensorboard import SummaryWriter +import matplotlib.pyplot as plt + + +import os + +def get_accumulation_steps(epoch): + """Increase accumulation steps at specific epochs.""" + # schedule = config.get("accumulation_schedule", { + # 0: 1, + # 200: 4, + # 500: 28, + # }) + + schedule = { + 0: 1, + 200: 4, + 500: 28, + } + + accumulation_steps = 1 + for epoch_threshold, steps in sorted(schedule.items()): + if epoch >= epoch_threshold: + accumulation_steps = steps + + return accumulation_steps + +def get_num_samples_per_ray(epoch): + """Increase number of samples per ray at specific epochs.""" + # schedule = config.get("num_samples_schedule", { + # 0: 64, + # 200: 128, + # 500: 256, + # }) + + # TODO: Maybe delete + schedule = { + 0: 25, + 2: 50, + 4: 100, + 6: 200, + } + + num_samples = 64 + for epoch_threshold, samples in sorted(schedule.items()): + if epoch >= epoch_threshold: + num_samples = samples + + return num_samples + +class AuxiliaryParams(torch.nn.Module): + def __init__(self, num_tilts, device, zero_tilt_idx=None): + super().__init__() + + if zero_tilt_idx is None: + # If not provided, assume first projection is reference + zero_tilt_idx = 0 + + self.zero_tilt_idx = zero_tilt_idx + self.num_tilts = num_tilts + + # Shifts: only parameterize non-reference tilts + num_param_tilts = num_tilts - 1 + self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) + + # Fixed zero shifts for reference + self.shifts_ref = torch.zeros(1, 2, device=device) + + # Z1 and Z3: parameterize all tilts EXCEPT the reference + self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + + # Fixed zeros for reference tilt + self.z1_ref = torch.zeros(1, device=device) + # self.z3_ref = torch.zeros(1, device=device) + + def forward(self, dummy_input=None): + # Reconstruct full arrays with zeros at reference position + before_shifts = self.shifts_param[:self.zero_tilt_idx] + after_shifts = self.shifts_param[self.zero_tilt_idx:] + shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) + + before_z1 = self.z1_param[:self.zero_tilt_idx] + after_z1 = self.z1_param[self.zero_tilt_idx:] + z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) + + # before_z3 = self.z3_param[:self.zero_tilt_idx] + # after_z3 = self.z3_param[self.zero_tilt_idx:] + # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) + + return shifts, z1, -z1 + + +class TomographyNerf(): + + def __init__( + self, + tomo_dataset: TomographyDataset, + + ): + + self.setup_distributed() + self.tomo_dataset = tomo_dataset + self.setup_logging() + + def setup_logging(self): + """Setup logging directory and TensorBoard writer.""" + + # base_logdir = self.config.get("logdir", "exp-logs") + + # hidden_features = self.config["hidden_features"] + # hidden_layers = self.config["hidden_layers"] + # lr = self.config["train_lr"] + # aux_lr = self.config["aux_lr"] + # bs = self.config["ray_batch_size"] + # fbs = self.config["fbs"] + # tv = self.config["tv_factor"] + + # lr_str = f"{lr:.1e}" + # tv_str = f"{tv:.1e}" + # aux_lr_str = f"{aux_lr:.1e}" + + + # descriptive_name = f"h{hidden_features}_l{hidden_layers}_lr{lr_str}_auxlr{aux_lr_str}_bs{bs}_tv{tv_str}_omega{fbs}" + # self.logdir = os.path.join(base_logdir, descriptive_name) + + if self.global_rank == 0: + os.makedirs("exp-logs", exist_ok=True) + self.writer = SummaryWriter() # Should put a logdir here + + # config_path = os.path.join(self.logdir, "config.yaml") + # with open(config_path, "w") as f: + # yaml.dump(self.config, f) + + print(f"Logging to: Need to fix") + else: + self.writer = None + def setup_distributed(self): + + """Setup distributed training if available, otherwise use single GPU/CPU.""" + + # Check if we're in a distributed environment + + + if "RANK" in os.environ: + # Distributed training + if not dist.is_initialized(): + dist.init_process_group(backend='nccl' if torch.cuda.is_available() else 'gloo', init_method='env://') + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + print(f"Global rank: {self.global_rank}") + self.local_rank = int(os.environ["LOCAL_RANK"]) + + torch.cuda.set_device(self.local_rank) + self.device = torch.device("cuda", self.local_rank) + + else: + # Single GPU/CPU training + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + self.device = torch.device("cuda:0") + torch.cuda.set_device(0) + print("Single GPU training") + else: + self.device = torch.device("cpu") + print("CPU training") + + # Optional performance optimizations (only for CUDA) + if self.device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + def create_volume(self): + N = self.tomo_dataset.dims[1] + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + + # Get the underlying model if using DDP + model = self.model.module if hasattr(self.model, 'module') else self.model + + samples_per_gpu = N**3 // self.world_size + start_idx = self.global_rank * samples_per_gpu + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx].to(self.device) + outputs = model(inputs_subset) + + if outputs.dim() > 1: + outputs = outputs.squeeze(-1) + + if self.world_size > 1: + # Ensure consistent tensor sizes across all ranks + gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] + dist.all_gather(gathered_outputs, outputs.contiguous()) + + # Explicitly order by rank + pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() + else: + pred_full = outputs.reshape(N, N, N).float() + + return pred_full + + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): + """Scale learning rate based on world size.""" + if self.world_size == 1: + return base_lr + + if scaling_rule == "linear": + # Linear scaling: lr = base_lr * world_size + return base_lr * self.world_size + elif scaling_rule == "sqrt": + # Square root scaling: lr = base_lr * sqrt(world_size) + return base_lr * np.sqrt(self.world_size) + else: + return base_lr + + + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + + # Step 1: Apply shifts + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + # Rotation 1: Z(-z3) + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + # Rotation 2: X(x) + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + # Rotation 3: Z(-z1) + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + # Stack the final result + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + def save_checkpoint(self, epoch, aux_params): + """Save model and auxiliary parameters checkpoint (rank 0 only).""" + if self.global_rank == 0: + model_to_save = self.model.module if hasattr(self.model, 'module') else self.model + aux_params_to_save = aux_params.module if hasattr(aux_params, 'module') else aux_params + + checkpoint = { + "model_state_dict": model_to_save.state_dict(), + "aux_params_state_dict": aux_params_to_save.state_dict(), + "epoch": epoch, + # "config": self.config, + } + checkpoint_path = os.path.join("exp-logs", f"checkpoint_epoch_{epoch:04d}.pt") + torch.save(checkpoint, checkpoint_path) + print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") + + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): + """Create projection rays for entire batch simultaneously.""" + batch_size = len(pixel_i) + + # Convert all pixels to normalized coordinates + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + + # Create z coordinates + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + + # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + + # Fill coordinates efficiently + rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray + rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray + rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + + return rays + + def train( + self, + batch_size: int, + num_workers: int, + omega_0 : int, + hidden_omega_0 : int, + train_lr : float, + aux_lr : float, + warmup_epochs : int, + epochs : int, + use_amp : bool, + sampling_rate : float, + tv_weight : float, + viz_freq : int, + checkpoint_freq : int, + # hidden_layers: int, + # hidden_features: int, + # alpha: float, + ): + + ### DataLoader setup + batch_size = batch_size + num_workers = num_workers + + if self.world_size > 1: + self.train_sampler = DistributedSampler( + self.tomo_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False + ) + shuffle = False + else: + self.train_sampler = None + shuffle = True # Need this!! + + self.dataloader = DataLoader( + self.tomo_dataset, + batch_size=batch_size, + sampler=self.train_sampler, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=True if self.device.type == "cuda" else False, + drop_last=True, + persistent_workers=False if num_workers == 0 else True + ) + + # Print statistics + if self.global_rank == 0: + # total_pixels = len(self.tomo_dataset.tilt_angles) * self.config["N"] ** 2 + train_pixels = self.tomo_dataset.num_pixels + effective_batch_size = batch_size * self.world_size + + print(f"Dataloader setup complete:") + print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") + print(f" Grid size: {self.tomo_dataset.dims[1]}×{self.tomo_dataset.dims[2]}") + print(f" Total pixels: {train_pixels:,}") + + print(f" Local batch size (train): {batch_size}") + print(f" Global batch size: {effective_batch_size}") + print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + + ### + + ### Model Setup + + self.model = HSiren( + in_features = 3, + out_features = 1, + hidden_features = 512, + hidden_layers = 4, + first_omega_0 = omega_0, + hidden_omega_0 = omega_0, # Remove hidden_omega_0. + ).to(self.device) + + if self.world_size > 1: + self.model = torch.nn.parallel.DistributedDataParallel( + self.model, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + bucket_cap_mb=100, + gradient_as_bucket_view=True, # TODO: Check if this is needed. + ) + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully.") + else: + print("Model built and compiled successfully.") + + ### Initializing optimizers, and auxiliary parameters + + # Torch module on z1, z3, and shifts as module + + phis = self.tomo_dataset.tilt_angles + zero_tilt_idx = torch.argmin(torch.abs(phis)).item() + + if self.global_rank == 0: + print(f"Using projection {zero_tilt_idx} (angle={phis[zero_tilt_idx]:.2f}°) as reference") + + + self.aux_params = AuxiliaryParams( + num_tilts = len(phis), + device = self.device, + zero_tilt_idx = zero_tilt_idx + ) + + + if self.world_size > 1: + self.aux_params = torch.nn.parallel.DistributedDataParallel( + self.aux_params, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True + ) + if self.global_rank == 0: + print("Auxiliary parameters wrapped with DDP") + + # Learning rates + scaled_train_lr = train_lr*np.sqrt(self.world_size) + scaled_aux_lr = aux_lr*np.sqrt(self.world_size) + + # Optimizers + optimizer = torch.optim.Adam(self.model.parameters(), lr=scaled_train_lr, fused = True) + aux_optimizer = torch.optim.Adam(self.aux_params.parameters(), lr=scaled_aux_lr) + + optimizer.zero_grad() + aux_optimizer.zero_grad() + + warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.001, total_iters=warmup_epochs) # TODO: Start factor play around with this. + cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs - warmup_epochs, eta_min=scaled_train_lr/100) + scheduler_model = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler_model, cosine_scheduler_model], milestones=[warmup_epochs]) + + scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR(aux_optimizer, T_max=epochs, eta_min=scaled_aux_lr/100) + + + accumulated_batches = 0 # TODO: Do this? Gradient accumulation + device_type = self.device.type + autocast_dtype = torch.bfloat16 if use_amp else None + + for epoch in range(epochs): + current_accumulation_steps = get_accumulation_steps(epoch) + num_samples_per_ray = get_num_samples_per_ray(epoch) + + if epoch > 0: + prev_steps = get_accumulation_steps(epoch - 1) + prev_samples = get_num_samples_per_ray(epoch - 1) + + if current_accumulation_steps != prev_steps and self.global_rank == 0: + print(f"Epoch {epoch}: Changing accumulation steps from {prev_steps} to {current_accumulation_steps}") + if num_samples_per_ray != prev_samples and self.global_rank == 0: + print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") + + if self.train_sampler is not None: + self.train_sampler.set_epoch(epoch) + + epoch_loss = 0.0 + epoch_mse_loss = 0.0 + epoch_tv_loss = 0.0 + epoch_z1_loss = 0.0 + + num_batches = 0 + + for batch_idx, batch in enumerate(self.dataloader): + + projection_indices = batch['projection_idx'] + + pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) + pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) + target_values = batch['target_value'].to(self.device, non_blocking=True) + phis = batch['phi'].to(self.device, non_blocking=True) + projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = self.aux_params(None) # NONE just for DDP + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + + with torch.no_grad(): # For every value of the tilts, shifts, and phi figure out where ray enters and exits and sample along that line. Ray has to be -1 to 1 + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, self.tomo_dataset.dims[1], num_samples_per_ray + ) + + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=self.tomo_dataset.dims[1], + sampling_rate=1 # TODO: make input into train + ) + + all_coords = transformed_rays.view(-1, 3) + all_densities = self.model(all_coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) # [N, 1] → [N] + valid_mask = ( # TODO: Needs to be fixed edge of boxes + (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & + (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & + (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) + ).float() + + all_densities = all_densities * valid_mask + if tv_weight > 0: + + num_tv_samples = min(10000, all_coords.shape[0]) + tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] + + tv_coords = all_coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True + )[0] + + + grad_norm = torch.norm(grad_outputs, dim=1) + tv_loss = tv_weight * grad_norm.mean() + else: + tv_loss = torch.tensor(0.0, device=self.device) + + ray_densities = all_densities.view(len(target_values), num_samples_per_ray) + + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + + tv_loss_z1 = torch.tensor(0.0, device=self.device) + + loss = mse_loss + tv_loss_z1 + tv_loss + loss = loss / current_accumulation_steps + + loss.backward() + + epoch_loss += loss.detach() + epoch_mse_loss += mse_loss.detach() + epoch_tv_loss += tv_loss.detach() + epoch_z1_loss += tv_loss_z1.detach() + + num_batches += 1 + if epoch >= warmup_epochs: + + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + optimizer.step() + optimizer.zero_grad() + + aux_norm = torch.nn.utils.clip_grad_norm_(self.aux_params.parameters(), max_norm = 1) + aux_optimizer.step() + aux_optimizer.zero_grad() + else: + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + optimizer.step() + optimizer.zero_grad() + + aux_optimizer.zero_grad() + + scheduler_model.step() + if epoch >= warmup_epochs: + scheduler_aux.step() + + if self.global_rank == 0: + print(f"Epoch Complete {epoch}") + + + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : + + with torch.no_grad(): + + pred_full = self.create_volume().cpu() + avg_loss = epoch_loss.item() / num_batches + avg_mse_loss = epoch_mse_loss.item() / num_batches + avg_tv_loss = epoch_tv_loss.item() / num_batches + avg_z1_loss = epoch_z1_loss.item() / num_batches + + metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) + if self.world_size > 1: + torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) + avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): + with torch.no_grad(): + current_lr = scheduler_model.get_last_lr()[0] + + # Log metrics + if self.writer is not None: + self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) + self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) + if tv_weight > 0: + self.writer.add_scalar("train/tv_loss", avg_tv_loss, epoch) + if self.writer is not None: + self.writer.add_scalar("train/total_loss", avg_loss, epoch) + if self.writer is not None: + self.writer.add_scalar("train/model_grad_norm", model_norm.item(), epoch) + + if hasattr(self, "aux_norm"): + if self.writer is not None: + self.writer.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) + else: + self.writer.add_scalar("train/aux_grad_norm", 0, epoch) + if self.writer is not None: + self.writer.add_scalar("train/lr", current_lr, epoch) + self.writer.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) + + fig, axes = plt.subplots(1, 3, figsize=(36, 12)) + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) + axes[0].set_title('Sum over Z-axis') + + axes[1].matshow(pred_full[self.tomo_dataset.dims[1]//2].cpu().numpy(), cmap='turbo', vmin=0) + axes[1].set_title(f'Slice at Z={self.tomo_dataset.dims[1]//2}') + + slice_start = max(0, self.tomo_dataset.dims[1]//2 - 5) + slice_end = min(self.tomo_dataset.dims[1], self.tomo_dataset.dims[1]//2 + 6) + thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() + axes[2].matshow(thick_slice, cmap='turbo', vmin=0) + axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') + + if self.writer is not None: + self.writer.add_figure("train/viz", fig, epoch, close=True) + plt.close(fig) + + if epoch % checkpoint_freq == 0 or epoch == epochs - 1: + with torch.no_grad(): + if self.global_rank == 0: + save_path = os.path.join("exp-logs", f"volume_epoch_{epoch:04d}.pt") + torch.save(pred_full.cpu(), save_path) + + self.save_checkpoint(epoch, self.aux_params) + + if self.global_rank == 0: + print("Training complete.") + + + \ No newline at end of file From 66502b2db064feed63c1781c1b8a7e7b068f090d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 17 Sep 2025 15:08:09 -0700 Subject: [PATCH 003/335] NeRF kinda working; bugs with convergence --- src/quantem/tomography/tomography_dataset.py | 2 +- src/quantem/tomography/tomography_nerf.py | 52 +++++++++----------- 2 files changed, 24 insertions(+), 30 deletions(-) diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index 967fb0f0..ec51080f 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -12,7 +12,7 @@ from torch.utils.data import Dataset -class TomographyDataset(AutoSerialize, Dataset): +class TomographyDataset(Autoserialize, Dataset): _token = object() """ diff --git a/src/quantem/tomography/tomography_nerf.py b/src/quantem/tomography/tomography_nerf.py index e83d8e88..1cf40bec 100644 --- a/src/quantem/tomography/tomography_nerf.py +++ b/src/quantem/tomography/tomography_nerf.py @@ -365,7 +365,7 @@ def train( print(f"Dataloader setup complete:") print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") - print(f" Grid size: {self.tomo_dataset.dims[1]}×{self.tomo_dataset.dims[2]}") + print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") print(f" Total pixels: {train_pixels:,}") print(f" Local batch size (train): {batch_size}") @@ -382,8 +382,8 @@ def train( out_features = 1, hidden_features = 512, hidden_layers = 4, - first_omega_0 = omega_0, - hidden_omega_0 = omega_0, # Remove hidden_omega_0. + first_omega_0 = 30, + hidden_omega_0 = 30, # Remove hidden_omega_0. ).to(self.device) if self.world_size > 1: @@ -442,17 +442,18 @@ def train( optimizer = torch.optim.Adam(self.model.parameters(), lr=scaled_train_lr, fused = True) aux_optimizer = torch.optim.Adam(self.aux_params.parameters(), lr=scaled_aux_lr) + aux_norm = torch.tensor(0.0, device=self.device) + model_norm = torch.tensor(0.0, device=self.device) + optimizer.zero_grad() aux_optimizer.zero_grad() - warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.001, total_iters=warmup_epochs) # TODO: Start factor play around with this. + warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, total_iters=warmup_epochs) # TODO: Start factor play around with this. cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs - warmup_epochs, eta_min=scaled_train_lr/100) scheduler_model = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler_model, cosine_scheduler_model], milestones=[warmup_epochs]) scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR(aux_optimizer, T_max=epochs, eta_min=scaled_aux_lr/100) - - accumulated_batches = 0 # TODO: Do this? Gradient accumulation device_type = self.device.type autocast_dtype = torch.bfloat16 if use_amp else None @@ -593,58 +594,51 @@ def train( if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : - with torch.no_grad(): - pred_full = self.create_volume().cpu() avg_loss = epoch_loss.item() / num_batches avg_mse_loss = epoch_mse_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches avg_z1_loss = epoch_z1_loss.item() / num_batches - + + metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) if self.world_size > 1: torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): with torch.no_grad(): current_lr = scheduler_model.get_last_lr()[0] - + # Log metrics - if self.writer is not None: - self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) - self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) + self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) + + + self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) if tv_weight > 0: self.writer.add_scalar("train/tv_loss", avg_tv_loss, epoch) - if self.writer is not None: self.writer.add_scalar("train/total_loss", avg_loss, epoch) - if self.writer is not None: - self.writer.add_scalar("train/model_grad_norm", model_norm.item(), epoch) - - if hasattr(self, "aux_norm"): - if self.writer is not None: - self.writer.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) - else: - self.writer.add_scalar("train/aux_grad_norm", 0, epoch) - if self.writer is not None: - self.writer.add_scalar("train/lr", current_lr, epoch) + self.writer.add_scalar("train/model_grad_norm", model_norm.item(), epoch) + self.writer.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) + self.writer.add_scalar("train/lr", current_lr, epoch) self.writer.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) - + fig, axes = plt.subplots(1, 3, figsize=(36, 12)) axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) axes[0].set_title('Sum over Z-axis') axes[1].matshow(pred_full[self.tomo_dataset.dims[1]//2].cpu().numpy(), cmap='turbo', vmin=0) axes[1].set_title(f'Slice at Z={self.tomo_dataset.dims[1]//2}') - + slice_start = max(0, self.tomo_dataset.dims[1]//2 - 5) slice_end = min(self.tomo_dataset.dims[1], self.tomo_dataset.dims[1]//2 + 6) thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() axes[2].matshow(thick_slice, cmap='turbo', vmin=0) axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') - - if self.writer is not None: - self.writer.add_figure("train/viz", fig, epoch, close=True) + + self.writer.add_figure("train/viz", fig, epoch, close=True) plt.close(fig) if epoch % checkpoint_freq == 0 or epoch == epochs - 1: From bcb7d1da5d0fc0789ad1b32e15cb71c945812838 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 17 Sep 2025 16:03:48 -0700 Subject: [PATCH 004/335] TV loss squeeze axis --- src/quantem/tomography/models.py | 12 ++++++------ src/quantem/tomography/tomography_dataset.py | 5 ++++- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/src/quantem/tomography/models.py b/src/quantem/tomography/models.py index 0829712e..a138990c 100644 --- a/src/quantem/tomography/models.py +++ b/src/quantem/tomography/models.py @@ -37,12 +37,12 @@ class HSiren(nn.Module): def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, first_omega_0=30, hidden_omega_0=30, alpha=1.0): super().__init__() - self.net = [] - self.net.append(SineLayer(in_features, hidden_features, is_first=True, + self.net_list = [] + self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, omega_0=first_omega_0, hsiren=True, alpha=alpha)) for i in range(hidden_layers): - self.net.append(SineLayer(hidden_features, hidden_features, is_first=False, + self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, omega_0=hidden_omega_0, alpha=alpha)) final_linear = nn.Linear(hidden_features, out_features) @@ -50,9 +50,9 @@ def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_featur # Final layer keeps original initialization (no alpha scaling) final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, np.sqrt(6 / hidden_features) / hidden_omega_0) - self.net.append(final_linear) - self.net.append(nn.Softplus()) - self.net = nn.Sequential(*self.net) + self.net_list.append(final_linear) + self.net_list.append(nn.Softplus()) + self.net = nn.Sequential(*self.net_list) def forward(self, coords): output = self.net(coords) diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index ec51080f..682f0de3 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -12,7 +12,7 @@ from torch.utils.data import Dataset -class TomographyDataset(Autoserialize, Dataset): +class TomographyDataset(AutoSerialize, Dataset): _token = object() """ @@ -32,6 +32,9 @@ def __init__( shifts: Tensor, ): self._tilt_series = tilt_series + # Enforce Positivity + self._tilt_series = torch.clamp(self._tilt_series, min=0) + self._tilt_angles = tilt_angles self._z1_angles = z1_angles self._z3_angles = z3_angles From 0bb881bb9f0785fe113ab748376b84517c4627a2 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 17 Sep 2025 17:28:09 -0700 Subject: [PATCH 005/335] NeRF works, barebones. Start writing into quantem style --- src/quantem/tomography/models.py | 2 +- src/quantem/tomography/tomography_dataset.py | 3 +- src/quantem/tomography/tomography_nerf.py | 569 ++++++++------- src/quantem/tomography/tomography_nerf_old.py | 658 ++++++++++++++++++ 4 files changed, 932 insertions(+), 300 deletions(-) create mode 100644 src/quantem/tomography/tomography_nerf_old.py diff --git a/src/quantem/tomography/models.py b/src/quantem/tomography/models.py index a138990c..212385ee 100644 --- a/src/quantem/tomography/models.py +++ b/src/quantem/tomography/models.py @@ -51,7 +51,7 @@ def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_featur final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, np.sqrt(6 / hidden_features) / hidden_omega_0) self.net_list.append(final_linear) - self.net_list.append(nn.Softplus()) + self.net_list.append(nn.Softplus()) self.net = nn.Sequential(*self.net_list) def forward(self, coords): diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index 682f0de3..348964ad 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -33,7 +33,6 @@ def __init__( ): self._tilt_series = tilt_series # Enforce Positivity - self._tilt_series = torch.clamp(self._tilt_series, min=0) self._tilt_angles = tilt_angles self._z1_angles = z1_angles @@ -54,6 +53,8 @@ def __init__( # Enforce normalization of tilt series tilt_percentile = torch.quantile(self._tilt_series, .95) self._tilt_series = self._tilt_series / tilt_percentile + self._tilt_series = torch.clamp(self._tilt_series, min=0) + # --- Class Methods --- @classmethod diff --git a/src/quantem/tomography/tomography_nerf.py b/src/quantem/tomography/tomography_nerf.py index 1cf40bec..5251d2d9 100644 --- a/src/quantem/tomography/tomography_nerf.py +++ b/src/quantem/tomography/tomography_nerf.py @@ -11,37 +11,11 @@ import os +from pathlib import Path -def get_accumulation_steps(epoch): - """Increase accumulation steps at specific epochs.""" - # schedule = config.get("accumulation_schedule", { - # 0: 1, - # 200: 4, - # 500: 28, - # }) - - schedule = { - 0: 1, - 200: 4, - 500: 28, - } - - accumulation_steps = 1 - for epoch_threshold, steps in sorted(schedule.items()): - if epoch >= epoch_threshold: - accumulation_steps = steps - - return accumulation_steps def get_num_samples_per_ray(epoch): """Increase number of samples per ray at specific epochs.""" - # schedule = config.get("num_samples_schedule", { - # 0: 64, - # 200: 128, - # 500: 256, - # }) - - # TODO: Maybe delete schedule = { 0: 25, 2: 50, @@ -104,52 +78,24 @@ class TomographyNerf(): def __init__( self, tomo_dataset: TomographyDataset, - + batch_size: int, + num_workers: int = 0, ): - self.setup_distributed() self.tomo_dataset = tomo_dataset - self.setup_logging() + self.setup_distributed() + self.setup_dataloader( + batch_size = batch_size, + num_workers = num_workers, + ) + self.build_model() - def setup_logging(self): - """Setup logging directory and TensorBoard writer.""" - - # base_logdir = self.config.get("logdir", "exp-logs") - - # hidden_features = self.config["hidden_features"] - # hidden_layers = self.config["hidden_layers"] - # lr = self.config["train_lr"] - # aux_lr = self.config["aux_lr"] - # bs = self.config["ray_batch_size"] - # fbs = self.config["fbs"] - # tv = self.config["tv_factor"] - - # lr_str = f"{lr:.1e}" - # tv_str = f"{tv:.1e}" - # aux_lr_str = f"{aux_lr:.1e}" - - - # descriptive_name = f"h{hidden_features}_l{hidden_layers}_lr{lr_str}_auxlr{aux_lr_str}_bs{bs}_tv{tv_str}_omega{fbs}" - # self.logdir = os.path.join(base_logdir, descriptive_name) - - if self.global_rank == 0: - os.makedirs("exp-logs", exist_ok=True) - self.writer = SummaryWriter() # Should put a logdir here - - # config_path = os.path.join(self.logdir, "config.yaml") - # with open(config_path, "w") as f: - # yaml.dump(self.config, f) - - print(f"Logging to: Need to fix") - else: - self.writer = None + # --- Setup ---- + def setup_distributed(self): - """Setup distributed training if available, otherwise use single GPU/CPU.""" # Check if we're in a distributed environment - - if "RANK" in os.environ: # Distributed training if not dist.is_initialized(): @@ -157,7 +103,6 @@ def setup_distributed(self): self.world_size = dist.get_world_size() self.global_rank = dist.get_rank() - print(f"Global rank: {self.global_rank}") self.local_rank = int(os.environ["LOCAL_RANK"]) torch.cuda.set_device(self.local_rank) @@ -182,39 +127,118 @@ def setup_distributed(self): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True + + def build_model(self): + + self.model = HSiren( + in_features = 3, + out_features = 1, + hidden_layers = 4, + hidden_features = 512, + first_omega_0 = 30, + alpha = 1.0, + ).to(self.device) + + if self.world_size > 1: + self.model = torch.nn.parallel.DistributedDataParallel( + self.model, + device_ids = [self.local_rank], + output_device = self.local_rank, + find_unused_parameters = False, + broadcast_buffers = True, + bucket_cap_mb = 100, + gradient_as_bucket_view = True, + ) + + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully") + + else: + print("Model built, compiled successfully") + + def setup_dataloader( + self, + batch_size: int, + num_workers: int = 0, + ): + + if self.world_size > 1: + sampler = DistributedSampler( + self.tomo_dataset, + num_replicas = self.world_size, + rank = self.global_rank, + shuffle = True, + ) + shuffle = False + else: + sampler = None + shuffle = True + self.dataloader = DataLoader( + self.tomo_dataset, + batch_size = batch_size, + num_workers = num_workers, + sampler = sampler, + shuffle = shuffle, + pin_memory = True if self.device.type == "cuda" else False, + drop_last = True, + persistent_workers = False if num_workers == 0 else True, + ) + + self.sampler = sampler + + + if self.global_rank == 0: + print(f"Dataloader setup complete:") + print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") + print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") + print(f" Total pixels: {self.tomo_dataset.num_pixels:,}") + + print(f" Local batch size (train): {batch_size}") + print(f" Global batch size: {batch_size*self.world_size}") + print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + + # --- Creating Volume --- def create_volume(self): - N = self.tomo_dataset.dims[1] + + N = max(self.tomo_dataset.dims) + with torch.no_grad(): coords_1d = torch.linspace(-1, 1, N) x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - # Get the underlying model if using DDP + model = self.model.module if hasattr(self.model, 'module') else self.model - + samples_per_gpu = N**3 // self.world_size start_idx = self.global_rank * samples_per_gpu end_idx = start_idx + samples_per_gpu - + inputs_subset = inputs[start_idx:end_idx].to(self.device) + + # TODO: torch.nn.functional.softplus here + # outputs = torch.nn.functional.softplus(model(inputs_subset)) outputs = model(inputs_subset) - + if outputs.dim() > 1: outputs = outputs.squeeze(-1) - + if self.world_size > 1: - # Ensure consistent tensor sizes across all ranks gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] dist.all_gather(gathered_outputs, outputs.contiguous()) - - # Explicitly order by rank + pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() else: pred_full = outputs.reshape(N, N, N).float() - + return pred_full - + + # --- Scaling LR --- def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): """Scale learning rate based on world size.""" if self.world_size == 1: @@ -227,10 +251,46 @@ def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): # Square root scaling: lr = base_lr * sqrt(world_size) return base_lr * np.sqrt(self.world_size) else: - return base_lr + raise ValueError(f"Invalid scaling rule: {scaling_rule}") + # --- Creating Optimizer --- + def create_optimizer( + self, + params, + lr, + fused = True, + ): - @torch.compile(mode="reduce-overhead") + return torch.optim.Adam( + params, + lr = lr, + fused = fused, + ) + + # Batch Projection Rays --- + + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): + """Create projection rays for entire batch simultaneously.""" + batch_size = len(pixel_i) + + # Convert all pixels to normalized coordinates + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + + # Create z coordinates + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + + # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + + # Fill coordinates efficiently + rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray + rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray + rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + + return rays + + @torch.compile(mode="reduce-overhead") def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): # Step 1: Apply shifts @@ -273,174 +333,61 @@ def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_r return transformed_rays - def save_checkpoint(self, epoch, aux_params): - """Save model and auxiliary parameters checkpoint (rank 0 only).""" - if self.global_rank == 0: - model_to_save = self.model.module if hasattr(self.model, 'module') else self.model - aux_params_to_save = aux_params.module if hasattr(aux_params, 'module') else aux_params - - checkpoint = { - "model_state_dict": model_to_save.state_dict(), - "aux_params_state_dict": aux_params_to_save.state_dict(), - "epoch": epoch, - # "config": self.config, - } - checkpoint_path = os.path.join("exp-logs", f"checkpoint_epoch_{epoch:04d}.pt") - torch.save(checkpoint, checkpoint_path) - print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") - - def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): - """Create projection rays for entire batch simultaneously.""" - batch_size = len(pixel_i) - - # Convert all pixels to normalized coordinates - x_coords = (pixel_j / (N - 1)) * 2 - 1 - y_coords = (pixel_i / (N - 1)) * 2 - 1 - - # Create z coordinates - z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - - # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] - rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - - # Fill coordinates efficiently - rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray - rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray - rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - - return rays - + # --- Training --- def train( self, - batch_size: int, - num_workers: int, - omega_0 : int, - hidden_omega_0 : int, - train_lr : float, - aux_lr : float, - warmup_epochs : int, - epochs : int, - use_amp : bool, - sampling_rate : float, - tv_weight : float, - viz_freq : int, - checkpoint_freq : int, - # hidden_layers: int, - # hidden_features: int, - # alpha: float, + train_lr: float = 1e-5, + epochs: int = 20, + warmup_epochs: int = 10, + use_amp: bool = True, + tv_weight: float = 0.0, + viz_freq: int = 1, + checkpoint_freq: int = 5, ): + log_path = Path("runs/test_1") + if not log_path.exists(): + log_path.mkdir(parents=True, exist_ok=True) + self.writer = SummaryWriter(log_dir=log_path) - ### DataLoader setup - batch_size = batch_size - num_workers = num_workers + # Find index closest to zero degrees + zero_tilt_idx = torch.argmin(torch.abs(self.tomo_dataset.tilt_angles)).item() - if self.world_size > 1: - self.train_sampler = DistributedSampler( - self.tomo_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=False - ) - shuffle = False - else: - self.train_sampler = None - shuffle = True # Need this!! - - self.dataloader = DataLoader( - self.tomo_dataset, - batch_size=batch_size, - sampler=self.train_sampler, - shuffle=shuffle, - num_workers=num_workers, - pin_memory=True if self.device.type == "cuda" else False, - drop_last=True, - persistent_workers=False if num_workers == 0 else True - ) - - # Print statistics if self.global_rank == 0: - # total_pixels = len(self.tomo_dataset.tilt_angles) * self.config["N"] ** 2 - train_pixels = self.tomo_dataset.num_pixels - effective_batch_size = batch_size * self.world_size - - print(f"Dataloader setup complete:") - print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") - print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") - print(f" Total pixels: {train_pixels:,}") - - print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {effective_batch_size}") - print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + print(f"Using projection {zero_tilt_idx} (angle={self.tomo_dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference") - - ### - - ### Model Setup - - self.model = HSiren( - in_features = 3, - out_features = 1, - hidden_features = 512, - hidden_layers = 4, - first_omega_0 = 30, - hidden_omega_0 = 30, # Remove hidden_omega_0. - ).to(self.device) - - if self.world_size > 1: - self.model = torch.nn.parallel.DistributedDataParallel( - self.model, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - bucket_cap_mb=100, - gradient_as_bucket_view=True, # TODO: Check if this is needed. - ) - if self.global_rank == 0: - print("Model wrapped with DDP") - - if self.world_size > 1: - if self.global_rank == 0: - print("Model built, distributed, and compiled successfully.") - else: - print("Model built and compiled successfully.") - - ### Initializing optimizers, and auxiliary parameters - - # Torch module on z1, z3, and shifts as module - - phis = self.tomo_dataset.tilt_angles - zero_tilt_idx = torch.argmin(torch.abs(phis)).item() - - if self.global_rank == 0: - print(f"Using projection {zero_tilt_idx} (angle={phis[zero_tilt_idx]:.2f}°) as reference") - - - self.aux_params = AuxiliaryParams( - num_tilts = len(phis), + aux_params = AuxiliaryParams( + num_tilts = len(self.tomo_dataset.tilt_angles), device = self.device, - zero_tilt_idx = zero_tilt_idx + zero_tilt_idx = zero_tilt_idx, ) - if self.world_size > 1: - self.aux_params = torch.nn.parallel.DistributedDataParallel( - self.aux_params, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True + aux_params = torch.nn.parallel.DistributedDataParallel( + aux_params, + device_ids = [self.local_rank], + output_device = self.local_rank, + find_unused_parameters = False, + broadcast_buffers = True, ) + if self.global_rank == 0: print("Auxiliary parameters wrapped with DDP") - # Learning rates - scaled_train_lr = train_lr*np.sqrt(self.world_size) - scaled_aux_lr = aux_lr*np.sqrt(self.world_size) + scaled_train_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") + scaled_aux_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") - # Optimizers - optimizer = torch.optim.Adam(self.model.parameters(), lr=scaled_train_lr, fused = True) - aux_optimizer = torch.optim.Adam(self.aux_params.parameters(), lr=scaled_aux_lr) + optimizer = self.create_optimizer( + self.model.parameters(), + scaled_train_lr, + fused = True, + ) + + aux_optimizer = self.create_optimizer( + aux_params.parameters(), + scaled_aux_lr, + fused = True, + ) aux_norm = torch.tensor(0.0, device=self.device) model_norm = torch.tensor(0.0, device=self.device) @@ -448,31 +395,47 @@ def train( optimizer.zero_grad() aux_optimizer.zero_grad() - warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, total_iters=warmup_epochs) # TODO: Start factor play around with this. - cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs - warmup_epochs, eta_min=scaled_train_lr/100) - scheduler_model = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler_model, cosine_scheduler_model], milestones=[warmup_epochs]) - - scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR(aux_optimizer, T_max=epochs, eta_min=scaled_aux_lr/100) + warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR( + optimizer, + start_factor = 0.001, + total_iters = warmup_epochs, + ) + cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max = epochs - warmup_epochs, + eta_min = scaled_train_lr / 100, + ) - device_type = self.device.type + scheduler_model = torch.optim.lr_scheduler.SequentialLR( + optimizer, + schedulers = [warmup_scheduler_model, cosine_scheduler_model], + milestones = [warmup_epochs], + ) + + scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR( + aux_optimizer, + T_max = epochs, + eta_min = scaled_aux_lr / 100, + ) + + N = max(self.tomo_dataset.dims) + + device_type = self.device.type autocast_dtype = torch.bfloat16 if use_amp else None for epoch in range(epochs): - current_accumulation_steps = get_accumulation_steps(epoch) num_samples_per_ray = get_num_samples_per_ray(epoch) + # Log the change if it happens if epoch > 0: - prev_steps = get_accumulation_steps(epoch - 1) prev_samples = get_num_samples_per_ray(epoch - 1) - - if current_accumulation_steps != prev_steps and self.global_rank == 0: - print(f"Epoch {epoch}: Changing accumulation steps from {prev_steps} to {current_accumulation_steps}") + if num_samples_per_ray != prev_samples and self.global_rank == 0: print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") - if self.train_sampler is not None: - self.train_sampler.set_epoch(epoch) - + if self.sampler is not None: + self.sampler.set_epoch(epoch) + epoch_loss = 0.0 epoch_mse_loss = 0.0 epoch_tv_loss = 0.0 @@ -489,52 +452,60 @@ def train( target_values = batch['target_value'].to(self.device, non_blocking=True) phis = batch['phi'].to(self.device, non_blocking=True) projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = self.aux_params(None) # NONE just for DDP + + shifts, z1_params, z3_params = aux_params(None) batch_shifts = torch.index_select(shifts, 0, projection_indices) batch_z1 = torch.index_select(z1_params, 0, projection_indices) batch_z3 = torch.index_select(z3_params, 0, projection_indices) - + with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): - with torch.no_grad(): # For every value of the tilts, shifts, and phi figure out where ray enters and exits and sample along that line. Ray has to be -1 to 1 + with torch.no_grad(): batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, self.tomo_dataset.dims[1], num_samples_per_ray + pixel_i, pixel_j, N, num_samples_per_ray ) - + transformed_rays = self.transform_batch_ray_coordinates( batch_ray_coords, z1=batch_z1, x=phis, z3=batch_z3, shifts=batch_shifts, - N=self.tomo_dataset.dims[1], - sampling_rate=1 # TODO: make input into train + N=N, + sampling_rate=1.0 ) - + all_coords = transformed_rays.view(-1, 3) + # TODO: torch.nn.functional.softplus here + # all_densities = torch.nn.functional.softplus(self.model(all_coords)) all_densities = self.model(all_coords) - + if all_densities.dim() > 1: all_densities = all_densities.squeeze(-1) # [N, 1] → [N] - valid_mask = ( # TODO: Needs to be fixed edge of boxes + + # Create mask for valid coordinates (within [-1, 1]^3) + valid_mask = ( (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) ).float() all_densities = all_densities * valid_mask + if tv_weight > 0: - num_tv_samples = min(10000, all_coords.shape[0]) tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] - + + # Rerun forward for gradient tracking tv_coords = all_coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) + # TODO: torch.nn.functional.softplus here + # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this + tv_densities_recomputed = self.model(tv_coords) if tv_densities_recomputed.dim() > 1: tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - + + # Compute gradients grad_outputs = torch.autograd.grad( outputs=tv_densities_recomputed, inputs=tv_coords, @@ -542,40 +513,40 @@ def train( create_graph=True )[0] - grad_norm = torch.norm(grad_outputs, dim=1) tv_loss = tv_weight * grad_norm.mean() else: tv_loss = torch.tensor(0.0, device=self.device) - - ray_densities = all_densities.view(len(target_values), num_samples_per_ray) - + + ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) - + predicted_values = ray_densities.sum(dim=1) * step_size - + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - + tv_loss_z1 = torch.tensor(0.0, device=self.device) - + # tv_loss_z1 = tv_loss_1d(z1_params, factor = 1e-3) + tv_loss_1d(z3_params, factor = 1e-3) + # tv_loss_z1 = 1e-5*(torch.sum(z1_params**2) + torch.sum(z3_params**2)) + + # Combine losses loss = mse_loss + tv_loss_z1 + tv_loss - loss = loss / current_accumulation_steps loss.backward() - + epoch_loss += loss.detach() epoch_mse_loss += mse_loss.detach() epoch_tv_loss += tv_loss.detach() epoch_z1_loss += tv_loss_z1.detach() - + num_batches += 1 + if epoch >= warmup_epochs: - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) optimizer.step() optimizer.zero_grad() - aux_norm = torch.nn.utils.clip_grad_norm_(self.aux_params.parameters(), max_norm = 1) + aux_norm = torch.nn.utils.clip_grad_norm_(aux_params.parameters(), max_norm = 1) aux_optimizer.step() aux_optimizer.zero_grad() else: @@ -584,15 +555,12 @@ def train( optimizer.zero_grad() aux_optimizer.zero_grad() - + scheduler_model.step() + if epoch >= warmup_epochs: scheduler_aux.step() - - if self.global_rank == 0: - print(f"Epoch Complete {epoch}") - - + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : with torch.no_grad(): pred_full = self.create_volume().cpu() @@ -600,14 +568,13 @@ def train( avg_mse_loss = epoch_mse_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches avg_z1_loss = epoch_z1_loss.item() / num_batches - - + metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) if self.world_size > 1: torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - + # Logging and visualization if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): with torch.no_grad(): current_lr = scheduler_model.get_last_lr()[0] @@ -615,7 +582,6 @@ def train( # Log metrics self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) - self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) if tv_weight > 0: self.writer.add_scalar("train/tv_loss", avg_tv_loss, epoch) @@ -629,28 +595,35 @@ def train( axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) axes[0].set_title('Sum over Z-axis') - axes[1].matshow(pred_full[self.tomo_dataset.dims[1]//2].cpu().numpy(), cmap='turbo', vmin=0) - axes[1].set_title(f'Slice at Z={self.tomo_dataset.dims[1]//2}') + axes[1].matshow(pred_full[N//2].cpu().numpy(), cmap='turbo', vmin=0) + axes[1].set_title(f'Slice at Z={N//2}') - slice_start = max(0, self.tomo_dataset.dims[1]//2 - 5) - slice_end = min(self.tomo_dataset.dims[1], self.tomo_dataset.dims[1]//2 + 6) + slice_start = max(0, N//2 - 5) + slice_end = min(N, N//2 + 6) thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() axes[2].matshow(thick_slice, cmap='turbo', vmin=0) axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') self.writer.add_figure("train/viz", fig, epoch, close=True) plt.close(fig) - + if epoch % checkpoint_freq == 0 or epoch == epochs - 1: - with torch.no_grad(): - if self.global_rank == 0: - save_path = os.path.join("exp-logs", f"volume_epoch_{epoch:04d}.pt") - torch.save(pred_full.cpu(), save_path) - - self.save_checkpoint(epoch, self.aux_params) - + self.save_checkpoint(epoch, aux_params, log_path) + if self.global_rank == 0: print("Training complete.") - - - \ No newline at end of file + + def save_checkpoint(self, epoch, aux_params, log_path): + """Save model and auxiliary parameters checkpoint (rank 0 only).""" + if self.global_rank == 0: + model_to_save = self.model.module if hasattr(self.model, 'module') else self.model + aux_params_to_save = aux_params.module if hasattr(aux_params, 'module') else aux_params + + checkpoint = { + "model_state_dict": model_to_save.state_dict(), + "aux_params_state_dict": aux_params_to_save.state_dict(), + "epoch": epoch, + } + checkpoint_path = os.path.join(log_path, f"checkpoint_epoch_{epoch:04d}.pt") + torch.save(checkpoint, checkpoint_path) + print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") \ No newline at end of file diff --git a/src/quantem/tomography/tomography_nerf_old.py b/src/quantem/tomography/tomography_nerf_old.py new file mode 100644 index 00000000..adecfd6f --- /dev/null +++ b/src/quantem/tomography/tomography_nerf_old.py @@ -0,0 +1,658 @@ +from torch.utils.data import Dataset, DataLoader, DistributedSampler +import torch +import torch.distributed as dist +import numpy as np + +from quantem.tomography.tomography_dataset import TomographyDataset +from quantem.tomography.models import HSiren + +from torch.utils.tensorboard import SummaryWriter +import matplotlib.pyplot as plt + + +import os + +def get_accumulation_steps(epoch): + """Increase accumulation steps at specific epochs.""" + # schedule = config.get("accumulation_schedule", { + # 0: 1, + # 200: 4, + # 500: 28, + # }) + + schedule = { + 0: 1, + 200: 4, + 500: 28, + } + + accumulation_steps = 1 + for epoch_threshold, steps in sorted(schedule.items()): + if epoch >= epoch_threshold: + accumulation_steps = steps + + return accumulation_steps + +def get_num_samples_per_ray(epoch): + """Increase number of samples per ray at specific epochs.""" + # schedule = config.get("num_samples_schedule", { + # 0: 64, + # 200: 128, + # 500: 256, + # }) + + # TODO: Maybe delete + schedule = { + 0: 25, + 2: 50, + 4: 100, + 6: 200, + } + + num_samples = 64 + for epoch_threshold, samples in sorted(schedule.items()): + if epoch >= epoch_threshold: + num_samples = samples + + return num_samples + +class AuxiliaryParams(torch.nn.Module): + def __init__(self, num_tilts, device, zero_tilt_idx=None): + super().__init__() + + if zero_tilt_idx is None: + # If not provided, assume first projection is reference + zero_tilt_idx = 0 + + self.zero_tilt_idx = zero_tilt_idx + self.num_tilts = num_tilts + + # Shifts: only parameterize non-reference tilts + num_param_tilts = num_tilts - 1 + self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) + + # Fixed zero shifts for reference + self.shifts_ref = torch.zeros(1, 2, device=device) + + # Z1 and Z3: parameterize all tilts EXCEPT the reference + self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + + # Fixed zeros for reference tilt + self.z1_ref = torch.zeros(1, device=device) + # self.z3_ref = torch.zeros(1, device=device) + + def forward(self, dummy_input=None): + # Reconstruct full arrays with zeros at reference position + before_shifts = self.shifts_param[:self.zero_tilt_idx] + after_shifts = self.shifts_param[self.zero_tilt_idx:] + shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) + + before_z1 = self.z1_param[:self.zero_tilt_idx] + after_z1 = self.z1_param[self.zero_tilt_idx:] + z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) + + # before_z3 = self.z3_param[:self.zero_tilt_idx] + # after_z3 = self.z3_param[self.zero_tilt_idx:] + # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) + + return shifts, z1, -z1 + + +class TomographyNerf(): + + def __init__( + self, + tomo_dataset: TomographyDataset, + + ): + + self.setup_distributed() + self.tomo_dataset = tomo_dataset + self.setup_logging() + + def setup_logging(self): + """Setup logging directory and TensorBoard writer.""" + + # base_logdir = self.config.get("logdir", "exp-logs") + + # hidden_features = self.config["hidden_features"] + # hidden_layers = self.config["hidden_layers"] + # lr = self.config["train_lr"] + # aux_lr = self.config["aux_lr"] + # bs = self.config["ray_batch_size"] + # fbs = self.config["fbs"] + # tv = self.config["tv_factor"] + + # lr_str = f"{lr:.1e}" + # tv_str = f"{tv:.1e}" + # aux_lr_str = f"{aux_lr:.1e}" + + + # descriptive_name = f"h{hidden_features}_l{hidden_layers}_lr{lr_str}_auxlr{aux_lr_str}_bs{bs}_tv{tv_str}_omega{fbs}" + # self.logdir = os.path.join(base_logdir, descriptive_name) + + if self.global_rank == 0: + os.makedirs("exp-logs", exist_ok=True) + self.writer = SummaryWriter() # Should put a logdir here + + # config_path = os.path.join(self.logdir, "config.yaml") + # with open(config_path, "w") as f: + # yaml.dump(self.config, f) + + print(f"Logging to: Need to fix") + else: + self.writer = None + def setup_distributed(self): + + """Setup distributed training if available, otherwise use single GPU/CPU.""" + + # Check if we're in a distributed environment + + + if "RANK" in os.environ: + # Distributed training + if not dist.is_initialized(): + dist.init_process_group(backend='nccl' if torch.cuda.is_available() else 'gloo', init_method='env://') + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + print(f"Global rank: {self.global_rank}") + self.local_rank = int(os.environ["LOCAL_RANK"]) + + torch.cuda.set_device(self.local_rank) + self.device = torch.device("cuda", self.local_rank) + + else: + # Single GPU/CPU training + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + self.device = torch.device("cuda:0") + torch.cuda.set_device(0) + print("Single GPU training") + else: + self.device = torch.device("cpu") + print("CPU training") + + # Optional performance optimizations (only for CUDA) + if self.device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + def create_volume(self): + N = self.tomo_dataset.dims[1] + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + + # Get the underlying model if using DDP + model = self.model.module if hasattr(self.model, 'module') else self.model + + samples_per_gpu = N**3 // self.world_size + start_idx = self.global_rank * samples_per_gpu + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx].to(self.device) + outputs = model(inputs_subset) + + if outputs.dim() > 1: + outputs = outputs.squeeze(-1) + + if self.world_size > 1: + # Ensure consistent tensor sizes across all ranks + gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] + dist.all_gather(gathered_outputs, outputs.contiguous()) + + # Explicitly order by rank + pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() + else: + pred_full = outputs.reshape(N, N, N).float() + + return pred_full + + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): + """Scale learning rate based on world size.""" + if self.world_size == 1: + return base_lr + + if scaling_rule == "linear": + # Linear scaling: lr = base_lr * world_size + return base_lr * self.world_size + elif scaling_rule == "sqrt": + # Square root scaling: lr = base_lr * sqrt(world_size) + return base_lr * np.sqrt(self.world_size) + else: + return base_lr + + + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + + # Step 1: Apply shifts + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + # Rotation 1: Z(-z3) + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + # Rotation 2: X(x) + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + # Rotation 3: Z(-z1) + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + # Stack the final result + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + def save_checkpoint(self, epoch, aux_params): + """Save model and auxiliary parameters checkpoint (rank 0 only).""" + if self.global_rank == 0: + model_to_save = self.model.module if hasattr(self.model, 'module') else self.model + aux_params_to_save = aux_params.module if hasattr(aux_params, 'module') else aux_params + + checkpoint = { + "model_state_dict": model_to_save.state_dict(), + "aux_params_state_dict": aux_params_to_save.state_dict(), + "epoch": epoch, + # "config": self.config, + } + checkpoint_path = os.path.join("exp-logs", f"checkpoint_epoch_{epoch:04d}.pt") + torch.save(checkpoint, checkpoint_path) + print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") + + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): + """Create projection rays for entire batch simultaneously.""" + batch_size = len(pixel_i) + + # Convert all pixels to normalized coordinates + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + + # Create z coordinates + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + + # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + + # Fill coordinates efficiently + rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray + rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray + rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + + return rays + + def train( + self, + batch_size: int, + num_workers: int, + omega_0 : int, + hidden_omega_0 : int, + train_lr : float, + aux_lr : float, + warmup_epochs : int, + epochs : int, + use_amp : bool, + sampling_rate : float, + tv_weight : float, + viz_freq : int, + checkpoint_freq : int, + # hidden_layers: int, + # hidden_features: int, + # alpha: float, + ): + + ### DataLoader setup + batch_size = batch_size + num_workers = num_workers + + if self.world_size > 1: + self.train_sampler = DistributedSampler( + self.tomo_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False + ) + shuffle = False + else: + self.train_sampler = None + shuffle = True # Need this!! + + self.dataloader = DataLoader( + self.tomo_dataset, + batch_size=batch_size, + sampler=self.train_sampler, + shuffle=shuffle, + num_workers=num_workers, + pin_memory=True if self.device.type == "cuda" else False, + drop_last=True, + persistent_workers=False if num_workers == 0 else True + ) + + # Print statistics + if self.global_rank == 0: + # total_pixels = len(self.tomo_dataset.tilt_angles) * self.config["N"] ** 2 + train_pixels = self.tomo_dataset.num_pixels + effective_batch_size = batch_size * self.world_size + + print(f"Dataloader setup complete:") + print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") + print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") + print(f" Total pixels: {train_pixels:,}") + + print(f" Local batch size (train): {batch_size}") + print(f" Global batch size: {effective_batch_size}") + print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + + ### + + ### Model Setup + + self.model = HSiren( + in_features = 3, + out_features = 1, + hidden_features = 512, + hidden_layers = 4, + first_omega_0 = 30, + hidden_omega_0 = 30, # Remove hidden_omega_0. + ).to(self.device) + + if self.world_size > 1: + self.model = torch.nn.parallel.DistributedDataParallel( + self.model, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + bucket_cap_mb=100, + gradient_as_bucket_view=True, # TODO: Check if this is needed. + ) + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully.") + else: + print("Model built and compiled successfully.") + + ### Initializing optimizers, and auxiliary parameters + + # Torch module on z1, z3, and shifts as module + + phis = self.tomo_dataset.tilt_angles + zero_tilt_idx = torch.argmin(torch.abs(phis)).item() + + if self.global_rank == 0: + print(f"Using projection {zero_tilt_idx} (angle={phis[zero_tilt_idx]:.2f}°) as reference") + + + self.aux_params = AuxiliaryParams( + num_tilts = len(phis), + device = self.device, + zero_tilt_idx = zero_tilt_idx + ) + + + if self.world_size > 1: + self.aux_params = torch.nn.parallel.DistributedDataParallel( + self.aux_params, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True + ) + if self.global_rank == 0: + print("Auxiliary parameters wrapped with DDP") + + # Learning rates + scaled_train_lr = train_lr*np.sqrt(self.world_size) + scaled_aux_lr = aux_lr*np.sqrt(self.world_size) + + # Optimizers + optimizer = torch.optim.Adam(self.model.parameters(), lr=scaled_train_lr, fused = True) + aux_optimizer = torch.optim.Adam(self.aux_params.parameters(), lr=scaled_aux_lr) + + aux_norm = torch.tensor(0.0, device=self.device) + model_norm = torch.tensor(0.0, device=self.device) + + optimizer.zero_grad() + aux_optimizer.zero_grad() + + warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, total_iters=warmup_epochs) # TODO: Start factor play around with this. + cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs - warmup_epochs, eta_min=scaled_train_lr/100) + scheduler_model = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler_model, cosine_scheduler_model], milestones=[warmup_epochs]) + + scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR(aux_optimizer, T_max=epochs, eta_min=scaled_aux_lr/100) + + device_type = self.device.type + autocast_dtype = torch.bfloat16 if use_amp else None + + for epoch in range(epochs): + current_accumulation_steps = get_accumulation_steps(epoch) + num_samples_per_ray = get_num_samples_per_ray(epoch) + + if epoch > 0: + prev_steps = get_accumulation_steps(epoch - 1) + prev_samples = get_num_samples_per_ray(epoch - 1) + + if current_accumulation_steps != prev_steps and self.global_rank == 0: + print(f"Epoch {epoch}: Changing accumulation steps from {prev_steps} to {current_accumulation_steps}") + if num_samples_per_ray != prev_samples and self.global_rank == 0: + print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") + + if self.train_sampler is not None: + self.train_sampler.set_epoch(epoch) + + self.model.train() + epoch_loss = 0.0 + epoch_mse_loss = 0.0 + epoch_tv_loss = 0.0 + epoch_z1_loss = 0.0 + + num_batches = 0 + + for batch_idx, batch in enumerate(self.dataloader): + + projection_indices = batch['projection_idx'] + + pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) + pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) + target_values = batch['target_value'].to(self.device, non_blocking=True) + phis = batch['phi'].to(self.device, non_blocking=True) + projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = self.aux_params(None) # NONE just for DDP + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + + with torch.no_grad(): # For every value of the tilts, shifts, and phi figure out where ray enters and exits and sample along that line. Ray has to be -1 to 1 + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, self.tomo_dataset.dims[1], num_samples_per_ray + ) + + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=self.tomo_dataset.dims[1], + sampling_rate=1 # TODO: make input into train + ) + + all_coords = transformed_rays.view(-1, 3) + all_densities = self.model(all_coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) # [N, 1] → [N] + + valid_mask = ( # TODO: Needs to be fixed edge of boxes + (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & + (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & + (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) + ).float() + + all_densities = all_densities * valid_mask + if tv_weight > 0: + + num_tv_samples = min(10000, all_coords.shape[0]) + tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] + + tv_coords = all_coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True + )[0] + + + grad_norm = torch.norm(grad_outputs, dim=1) + tv_loss = tv_weight * grad_norm.mean() + else: + tv_loss = torch.tensor(0.0, device=self.device) + + ray_densities = all_densities.view(len(target_values), num_samples_per_ray) + + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + + tv_loss_z1 = torch.tensor(0.0, device=self.device) + + loss = mse_loss + tv_loss_z1 + tv_loss + loss = loss / current_accumulation_steps + + loss.backward() + + epoch_loss += loss.detach() + epoch_mse_loss += mse_loss.detach() + epoch_tv_loss += tv_loss.detach() + epoch_z1_loss += tv_loss_z1.detach() + + num_batches += 1 + if epoch >= warmup_epochs: + + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + optimizer.step() + optimizer.zero_grad() + + aux_norm = torch.nn.utils.clip_grad_norm_(self.aux_params.parameters(), max_norm = 1) + aux_optimizer.step() + aux_optimizer.zero_grad() + else: + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + optimizer.step() + optimizer.zero_grad() + + aux_optimizer.zero_grad() + + scheduler_model.step() + if epoch >= warmup_epochs: + scheduler_aux.step() + + if self.global_rank == 0: + print(f"Epoch Complete {epoch}") + + + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : + with torch.no_grad(): + pred_full = self.create_volume().cpu() + avg_loss = epoch_loss.item() / num_batches + avg_mse_loss = epoch_mse_loss.item() / num_batches + avg_tv_loss = epoch_tv_loss.item() / num_batches + avg_z1_loss = epoch_z1_loss.item() / num_batches + + + metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) + if self.world_size > 1: + torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) + avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + + + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): + with torch.no_grad(): + current_lr = scheduler_model.get_last_lr()[0] + + # Log metrics + self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) + + + self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) + if tv_weight > 0: + self.writer.add_scalar("train/tv_loss", avg_tv_loss, epoch) + self.writer.add_scalar("train/total_loss", avg_loss, epoch) + self.writer.add_scalar("train/model_grad_norm", model_norm.item(), epoch) + self.writer.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) + self.writer.add_scalar("train/lr", current_lr, epoch) + self.writer.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) + + fig, axes = plt.subplots(1, 3, figsize=(36, 12)) + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) + axes[0].set_title('Sum over Z-axis') + + axes[1].matshow(pred_full[self.tomo_dataset.dims[1]//2].cpu().numpy(), cmap='turbo', vmin=0) + axes[1].set_title(f'Slice at Z={self.tomo_dataset.dims[1]//2}') + + slice_start = max(0, self.tomo_dataset.dims[1]//2 - 5) + slice_end = min(self.tomo_dataset.dims[1], self.tomo_dataset.dims[1]//2 + 6) + thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() + axes[2].matshow(thick_slice, cmap='turbo', vmin=0) + axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') + + self.writer.add_figure("train/viz", fig, epoch, close=True) + plt.close(fig) + + if epoch % checkpoint_freq == 0 or epoch == epochs - 1: + with torch.no_grad(): + if self.global_rank == 0: + save_path = os.path.join("exp-logs", f"volume_epoch_{epoch:04d}.pt") + torch.save(pred_full.cpu(), save_path) + + self.save_checkpoint(epoch, self.aux_params) + + if self.global_rank == 0: + print("Training complete.") + + + \ No newline at end of file From a6a701458b6c857ee71b2f0aa1228c288785358c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 22 Sep 2025 10:12:44 -0700 Subject: [PATCH 006/335] Pre-transferring to quantem code --- src/quantem/tomography/object_models.py | 11 +++++++++++ src/quantem/tomography/tomography.py | 4 +++- src/quantem/tomography/tomography_dataset.py | 7 ++++++- 3 files changed, 20 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d103087d..00f9a901 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -536,5 +536,16 @@ def _pretrain( self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) pbar.set_description(f"Epoch {a0 + 1}/{num_epochs}, Loss: {loss.item():.4f}, ") +class ObjectINN(ObjectConstraints): + """ + Object model for INN objects. + + - VolumeDataset (?) dependent on the object + """ + + def __init__( + self + ): + pass ObjectModelType = Union[ObjectVoxelwise] # | ObjectDIP | ObjectImplicit (ObjectFFN?) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ef70d5f2..b144d4a2 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -90,7 +90,9 @@ def sirt_recon( if plot_loss: self.plot_loss() - + + # TODO: ML Recon which has NeRF and AD depending on the object type. + def ad_recon( self, optimizer_params: dict, diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index 348964ad..251538d0 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -11,6 +11,7 @@ from torch.utils.data import Dataset +import numpy as np class TomographyDataset(AutoSerialize, Dataset): _token = object() @@ -51,7 +52,10 @@ def __init__( # Enforce normalization of tilt series - tilt_percentile = torch.quantile(self._tilt_series, .95) + try: + tilt_percentile = torch.quantile(self._tilt_series, .95) + except: + tilt_percentile = np.quantile(self._tilt_series, .95) self._tilt_series = self._tilt_series / tilt_percentile self._tilt_series = torch.clamp(self._tilt_series, min=0) @@ -186,6 +190,7 @@ def __getitem__(self, idx): if self.dims[1] != self.dims[2]: raise NotImplementedError("Non-square tilt images are not supported yet.") + #TODO: row, column pixel_i = remaining // self.dims[1] pixel_j = remaining % self.dims[1] From e162dde36dbf6c2a4a8fdfe522335af3268273b9 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 22 Sep 2025 17:37:58 -0700 Subject: [PATCH 007/335] Baseline working Nerf reconstructions; created a TomoDDP class --- src/quantem/tomography/tomography.py | 460 +++++++++++++++++++++- src/quantem/tomography/tomography_base.py | 2 +- src/quantem/tomography/tomography_ddp.py | 128 ++++++ 3 files changed, 587 insertions(+), 3 deletions(-) create mode 100644 src/quantem/tomography/tomography_ddp.py diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index b144d4a2..e9e9cc35 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -7,10 +7,76 @@ from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_conv import TomographyConv from quantem.tomography.tomography_ml import TomographyML +from quantem.tomography.tomography_ddp import TomographyDDP from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ - -class Tomography(TomographyConv, TomographyML, TomographyBase): +# Temporary imports for TomographyNERF +from quantem.tomography.models import HSiren +from torch.utils.tensorboard import SummaryWriter +import numpy as np +import matplotlib.pyplot as plt +import torch.distributed as dist + +# Temporary aux class for TomographyNERF +def get_num_samples_per_ray(epoch): + """Increase number of samples per ray at specific epochs.""" + schedule = { + 0: 25, + 2: 50, + 4: 100, + 6: 200, + } + + num_samples = 64 + for epoch_threshold, samples in sorted(schedule.items()): + if epoch >= epoch_threshold: + num_samples = samples + + return num_samples + +class AuxiliaryParams(torch.nn.Module): + def __init__(self, num_tilts, device, zero_tilt_idx=None): + super().__init__() + + if zero_tilt_idx is None: + # If not provided, assume first projection is reference + zero_tilt_idx = 0 + + self.zero_tilt_idx = zero_tilt_idx + self.num_tilts = num_tilts + + # Shifts: only parameterize non-reference tilts + num_param_tilts = num_tilts - 1 + self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) + + # Fixed zero shifts for reference + self.shifts_ref = torch.zeros(1, 2, device=device) + + # Z1 and Z3: parameterize all tilts EXCEPT the reference + self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + + # Fixed zeros for reference tilt + self.z1_ref = torch.zeros(1, device=device) + # self.z3_ref = torch.zeros(1, device=device) + + def forward(self, dummy_input=None): + # Reconstruct full arrays with zeros at reference position + before_shifts = self.shifts_param[:self.zero_tilt_idx] + after_shifts = self.shifts_param[self.zero_tilt_idx:] + shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) + + before_z1 = self.z1_param[:self.zero_tilt_idx] + after_z1 = self.z1_param[self.zero_tilt_idx:] + z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) + + # before_z3 = self.z3_param[:self.zero_tilt_idx] + # after_z3 = self.z3_param[self.zero_tilt_idx:] + # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) + + return shifts, z1, -z1 + +class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): """ Top level class for either using conventional or ML-based reconstruction methods for tomography. @@ -92,7 +158,397 @@ def sirt_recon( self.plot_loss() # TODO: ML Recon which has NeRF and AD depending on the object type. + # TODO: Temporary + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): + if scaling_rule == "sqrt": + return base_lr * np.sqrt(self.world_size) + elif scaling_rule == "linear": + return base_lr * self.world_size + else: + raise ValueError(f"Invalid scaling rule: {scaling_rule}") + + def create_optimizer(self, params, lr, fused = True): + + return torch.optim.Adam( + params, + lr = lr, + fused = fused, + ) + + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): + """Create projection rays for entire batch simultaneously.""" + batch_size = len(pixel_i) + + # Convert all pixels to normalized coordinates + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + + # Create z coordinates + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + + # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + + # Fill coordinates efficiently + rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray + rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray + rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + + return rays + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + + # Step 1: Apply shifts + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + # Rotation 1: Z(-z3) + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + # Rotation 2: X(x) + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + # Rotation 3: Z(-z1) + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + # Stack the final result + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + # --- Creating Volume --- + def create_volume(self): + + N = max(self.dataset.dims) + + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + + model = self.model.module if hasattr(self.model, 'module') else self.model + + samples_per_gpu = N**3 // self.world_size + start_idx = self.global_rank * samples_per_gpu + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx].to(self.device) + + # TODO: torch.nn.functional.softplus here + # outputs = torch.nn.functional.softplus(model(inputs_subset)) + outputs = model(inputs_subset) + + if outputs.dim() > 1: + outputs = outputs.squeeze(-1) + + if self.world_size > 1: + gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] + dist.all_gather(gathered_outputs, outputs.contiguous()) + + pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() + else: + pred_full = outputs.reshape(N, N, N).float() + + return pred_full + + def recon( + self, + model: HSiren, + batch_size: int, + num_workers: int = 0, + train_lr = 1e-5, + aux_lr = 1e-5, + epochs = 20, + warmup_epochs = 10, + use_amp = True, + tv_weight = 0.0, + viz_freq = 1, + checkpoint_freq = 5, + ): + + + + self.setup_distributed() + self.setup_dataloader(self.dataset, batch_size, num_workers) + self.build_model(model) + # TODO: Temp-logger + if self.global_rank == 0: + logger = SummaryWriter() + else: + logger = None + zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() + + if self.global_rank == 0: + print(f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference") + + aux_params = AuxiliaryParams( + num_tilts = len(self.dataset.tilt_angles), + device = self.device, + zero_tilt_idx = zero_tilt_idx, + ) + + scaled_train_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") + scaled_aux_lr = self.get_scaled_lr(aux_lr, scaling_rule="sqrt") + + optimizer = self.create_optimizer( + self.model.parameters(), + scaled_train_lr, + fused = True, + ) + + aux_optimizer = self.create_optimizer( + aux_params.parameters(), + scaled_aux_lr, + fused = True, + ) + + aux_norm = torch.tensor(0.0, device=self.device) + model_norm = torch.tensor(0.0, device=self.device) + + optimizer.zero_grad() + aux_optimizer.zero_grad() + + + warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR( + optimizer, + start_factor = 0.001, + total_iters = warmup_epochs, + ) + + + cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max = epochs - warmup_epochs, + eta_min = scaled_train_lr / 100, + ) + + + scheduler_model = torch.optim.lr_scheduler.SequentialLR( + optimizer, + schedulers = [warmup_scheduler_model, cosine_scheduler_model], + milestones = [warmup_epochs], + ) + + + scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR( + aux_optimizer, + T_max = epochs, + eta_min = scaled_aux_lr / 100, + ) + + N = max(self.dataset.dims) + + device_type = self.device.type + autocast_dtype = torch.bfloat16 if use_amp else None + + + for epoch in range(epochs): + num_samples_per_ray = get_num_samples_per_ray(epoch) + + # Log the change if it happens + if epoch > 0: + prev_samples = get_num_samples_per_ray(epoch - 1) + + if num_samples_per_ray != prev_samples and self.global_rank == 0: + print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") + + if self.sampler is not None: + self.sampler.set_epoch(epoch) + + epoch_loss = 0.0 + epoch_mse_loss = 0.0 + epoch_tv_loss = 0.0 + epoch_z1_loss = 0.0 + + num_batches = 0 + + for batch_idx, batch in enumerate(self.dataloader): + + projection_indices = batch['projection_idx'] + + pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) + pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) + target_values = batch['target_value'].to(self.device, non_blocking=True) + phis = batch['phi'].to(self.device, non_blocking=True) + projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = aux_params(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + + with torch.no_grad(): + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, N, num_samples_per_ray + ) + + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + + all_coords = transformed_rays.view(-1, 3) + # TODO: torch.nn.functional.softplus here + # all_densities = torch.nn.functional.softplus(self.model(all_coords)) + all_densities = self.model(all_coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) # [N, 1] → [N] + + # Create mask for valid coordinates (within [-1, 1]^3) + valid_mask = ( + (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & + (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & + (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) + ).float() + + all_densities = all_densities * valid_mask + + if tv_weight > 0: + num_tv_samples = min(10000, all_coords.shape[0]) + tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] + + # Rerun forward for gradient tracking + tv_coords = all_coords[tv_indices].detach().requires_grad_(True) + + # TODO: torch.nn.functional.softplus here + # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this + tv_densities_recomputed = self.model(tv_coords) + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + # Compute gradients + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + tv_loss = tv_weight * grad_norm.mean() + else: + tv_loss = torch.tensor(0.0, device=self.device) + + ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + tv_loss_z1 = torch.tensor(0.0, device=self.device) + + loss = mse_loss + tv_loss + tv_loss_z1 + + loss.backward() + + epoch_loss += loss.detach() + epoch_mse_loss += mse_loss.detach() + epoch_tv_loss += tv_loss.detach() + epoch_z1_loss += tv_loss_z1.detach() + num_batches += 1 + + if epoch >= warmup_epochs: + + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + optimizer.step() + optimizer.zero_grad() + + aux_norm = torch.nn.utils.clip_grad_norm_(aux_params.parameters(), max_norm = 1) + aux_optimizer.step() + aux_optimizer.zero_grad() + else: + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + optimizer.step() + optimizer.zero_grad() + + aux_optimizer.zero_grad() + + scheduler_model.step() + + if epoch >= warmup_epochs: + scheduler_aux.step() + + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : + with torch.no_grad(): + pred_full = self.create_volume().cpu() + avg_loss = epoch_loss.item() / num_batches + avg_mse_loss = epoch_mse_loss.item() / num_batches + avg_tv_loss = epoch_tv_loss.item() / num_batches + avg_z1_loss = epoch_z1_loss.item() / num_batches + + metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) + if self.world_size > 1: + dist.all_reduce(metrics, op=dist.ReduceOp.AVG) + avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): + with torch.no_grad(): + current_lr = scheduler_model.get_last_lr()[0] + + # Log metrics + logger.add_scalar("train/mse_loss", avg_mse_loss, epoch) + + logger.add_scalar("train/z1_loss", avg_z1_loss, epoch) + if tv_weight > 0: + logger.add_scalar("train/tv_loss", avg_tv_loss, epoch) + logger.add_scalar("train/total_loss", avg_loss, epoch) + logger.add_scalar("train/model_grad_norm", model_norm.item(), epoch) + logger.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) + logger.add_scalar("train/lr", current_lr, epoch) + logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) + + fig, axes = plt.subplots(1, 3, figsize=(36, 12)) + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) + axes[0].set_title('Sum over Z-axis') + + axes[1].matshow(pred_full[N//2].cpu().numpy(), cmap='turbo', vmin=0) + axes[1].set_title(f'Slice at Z={N//2}') + + slice_start = max(0, N//2 - 5) + slice_end = min(N, N//2 + 6) + thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() + axes[2].matshow(thick_slice, cmap='turbo', vmin=0) + axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') + + logger.add_figure("train/viz", fig, epoch, close=True) + plt.close(fig) + + if self.global_rank == 0: + print("Training complete.") + + # print("Successfully setup DDP and dataloader") + def ad_recon( self, optimizer_params: dict, diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 35c69b34..61fb7937 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -85,7 +85,7 @@ def from_data( shifts=shifts, ) - dataset.to(device) + # dataset.to(device) if volume_obj is None: max_shape = max(dataset.tilt_series.shape) diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py new file mode 100644 index 00000000..f733e012 --- /dev/null +++ b/src/quantem/tomography/tomography_ddp.py @@ -0,0 +1,128 @@ +import torch.distributed as dist +import torch +from torch.utils.data import Dataset, DataLoader, DistributedSampler +import torch.nn as nn +import os + +from quantem.tomography.tomography_dataset import TomographyDataset + +class TomographyDDP: + """ + Initializing DDP stuff for tomo class. + """ + def __init__( + self, + ): + + self.setup_distributed() + + + def setup_distributed(self): + + # Check if in distributed env + if "RANK" in os.environ: + # Distributed training + if not dist.is_initialized(): + dist.init_process_group(backend='nccl' if torch.cuda.is_available() else 'gloo', init_method='env://') + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + self.local_rank = int(os.environ["LOCAL_RANK"]) + + torch.cuda.set_device(self.local_rank) + self.device = torch.device("cuda", self.local_rank) + + else: + # Single GPU/CPU training + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + self.device = torch.device("cuda:0") + torch.cuda.set_device(0) + print("Single GPU training") + else: + self.device = torch.device("cpu") + print("CPU training") + + # Optional performance optimizations (only for CUDA) + if self.device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + def build_model( + self, + model: nn.Module, + ): + + # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? + self.model = model.to(self.device) + + if self.world_size > 1: + self.model = torch.nn.parallel.DistributedDataParallel( + self.model, + device_ids = [self.local_rank], + output_device = self.local_rank, + find_unused_parameters = False, + broadcast_buffers = True, + bucket_cap_mb = 100, + gradient_as_bucket_view = True, + ) + + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully") + + else: + print("Model built, compiled successfully") + + def setup_dataloader( + self, + tomo_dataset: TomographyDataset, + batch_size: int, + num_workers: int = 0, + ): + + if self.world_size > 1: + sampler = DistributedSampler( + tomo_dataset, + num_replicas = self.world_size, + rank = self.global_rank, + shuffle = True, + ) + shuffle = False + + else: + sampler = None + shuffle = True + + self.dataloader = DataLoader( + tomo_dataset, + batch_size = batch_size, + num_workers = num_workers, + sampler = sampler, + shuffle = shuffle, + pin_memory = True if self.device.type == "cuda" else False, + drop_last = True, + persistent_workers = False if num_workers == 0 else True, + ) + + self.sampler = sampler + + if self.global_rank == 0: + print(f"Dataloader setup complete:") + print(f" Total projections: {len(tomo_dataset.tilt_angles)}") + print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") + print(f" Total pixels: {tomo_dataset.num_pixels:,}") + + print(f" Local batch size (train): {batch_size}") + print(f" Global batch size: {batch_size*self.world_size}") + print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + \ No newline at end of file From bb40c4b940175251c4b037760125a64297270b1d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 22 Sep 2025 22:54:43 -0700 Subject: [PATCH 008/335] Tomo optimizers + schedulers working; need to do object_models --- src/quantem/tomography/object_models.py | 13 +- src/quantem/tomography/tomography.py | 184 +++++-------------- src/quantem/tomography/tomography_dataset.py | 61 ++++++ src/quantem/tomography/tomography_ddp.py | 36 +++- src/quantem/tomography/tomography_ml.py | 30 ++- 5 files changed, 184 insertions(+), 140 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 00f9a901..284f743e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -78,7 +78,7 @@ def reset(self): pass @abstractmethod - def to(self, device: str): + def to(self, device: str):z pass @abstractmethod @@ -544,8 +544,15 @@ class ObjectINN(ObjectConstraints): """ def __init__( - self + self, + model: torch.nn.Module, ): pass + + def forward( + self, + rays, + + ): -ObjectModelType = Union[ObjectVoxelwise] # | ObjectDIP | ObjectImplicit (ObjectFFN?) +ObjectModelType = ObjectVoxelwise | ObjectINN # | ObjectDIP | ObjectImplicit (ObjectFFN?) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index e9e9cc35..d35a19b1 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -34,47 +34,7 @@ def get_num_samples_per_ray(epoch): return num_samples -class AuxiliaryParams(torch.nn.Module): - def __init__(self, num_tilts, device, zero_tilt_idx=None): - super().__init__() - if zero_tilt_idx is None: - # If not provided, assume first projection is reference - zero_tilt_idx = 0 - - self.zero_tilt_idx = zero_tilt_idx - self.num_tilts = num_tilts - - # Shifts: only parameterize non-reference tilts - num_param_tilts = num_tilts - 1 - self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) - - # Fixed zero shifts for reference - self.shifts_ref = torch.zeros(1, 2, device=device) - - # Z1 and Z3: parameterize all tilts EXCEPT the reference - self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - - # Fixed zeros for reference tilt - self.z1_ref = torch.zeros(1, device=device) - # self.z3_ref = torch.zeros(1, device=device) - - def forward(self, dummy_input=None): - # Reconstruct full arrays with zeros at reference position - before_shifts = self.shifts_param[:self.zero_tilt_idx] - after_shifts = self.shifts_param[self.zero_tilt_idx:] - shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) - - before_z1 = self.z1_param[:self.zero_tilt_idx] - after_z1 = self.z1_param[self.zero_tilt_idx:] - z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) - - # before_z3 = self.z3_param[:self.zero_tilt_idx] - # after_z3 = self.z3_param[self.zero_tilt_idx:] - # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) - - return shifts, z1, -z1 class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): """ @@ -90,7 +50,6 @@ def __init__( _token, ): super().__init__(dataset, volume_obj, device, _token) - # --- Reconstruction Method --- def sirt_recon( @@ -159,13 +118,7 @@ def sirt_recon( # TODO: ML Recon which has NeRF and AD depending on the object type. # TODO: Temporary - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): - if scaling_rule == "sqrt": - return base_lr * np.sqrt(self.world_size) - elif scaling_rule == "linear": - return base_lr * self.world_size - else: - raise ValueError(f"Invalid scaling rule: {scaling_rule}") + def create_optimizer(self, params, lr, fused = True): @@ -274,90 +227,63 @@ def create_volume(self): return pred_full + + # TODO: Temp logger + def setup_logger(self): + if self.global_rank == 0: + self.temp_logger = SummaryWriter() + else: + self.temp_logger = None + def recon( self, model: HSiren, batch_size: int, num_workers: int = 0, - train_lr = 1e-5, - aux_lr = 1e-5, epochs = 20, - warmup_epochs = 10, use_amp = True, tv_weight = 0.0, viz_freq = 1, checkpoint_freq = 5, + optimizer_params: dict = None, + scheduler_params: dict = None, ): - self.setup_distributed() self.setup_dataloader(self.dataset, batch_size, num_workers) self.build_model(model) - # TODO: Temp-logger - if self.global_rank == 0: - logger = SummaryWriter() - else: - logger = None + + + if not hasattr(self, "temp_logger"): + self.setup_logger() + zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() if self.global_rank == 0: print(f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference") - - aux_params = AuxiliaryParams( - num_tilts = len(self.dataset.tilt_angles), - device = self.device, - zero_tilt_idx = zero_tilt_idx, - ) - scaled_train_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") - scaled_aux_lr = self.get_scaled_lr(aux_lr, scaling_rule="sqrt") + # Auxiliary params setup + self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device) + aux_params = self.dataset.auxiliary_params - optimizer = self.create_optimizer( - self.model.parameters(), - scaled_train_lr, - fused = True, - ) + # Scaling learning rates to account for distributed training + if optimizer_params is not None: + optimizer_params = self.scale_lr(optimizer_params) + self.optimizer_params = optimizer_params + self.set_optimizers() + + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params, num_iter = epochs) - aux_optimizer = self.create_optimizer( - aux_params.parameters(), - scaled_aux_lr, - fused = True, - ) aux_norm = torch.tensor(0.0, device=self.device) model_norm = torch.tensor(0.0, device=self.device) - optimizer.zero_grad() - aux_optimizer.zero_grad() - - - warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor = 0.001, - total_iters = warmup_epochs, - ) - - - cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max = epochs - warmup_epochs, - eta_min = scaled_train_lr / 100, - ) - - - scheduler_model = torch.optim.lr_scheduler.SequentialLR( - optimizer, - schedulers = [warmup_scheduler_model, cosine_scheduler_model], - milestones = [warmup_epochs], - ) - - scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR( - aux_optimizer, - T_max = epochs, - eta_min = scaled_aux_lr / 100, - ) + for _, opt in self.optimizers.items(): + opt.zero_grad() N = max(self.dataset.dims) @@ -477,28 +403,20 @@ def recon( epoch_tv_loss += tv_loss.detach() epoch_z1_loss += tv_loss_z1.detach() num_batches += 1 - - if epoch >= warmup_epochs: - - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) - optimizer.step() - optimizer.zero_grad() - - aux_norm = torch.nn.utils.clip_grad_norm_(aux_params.parameters(), max_norm = 1) - aux_optimizer.step() - aux_optimizer.zero_grad() - else: - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) - optimizer.step() - optimizer.zero_grad() + + for key, opt in self.optimizers.items(): - aux_optimizer.zero_grad() + if key == "model": + model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + elif key == "aux_params": + aux_norm = torch.nn.utils.clip_grad_norm_(self.dataset.auxiliary_params.parameters(), max_norm = 1) + opt.step() + opt.zero_grad() - scheduler_model.step() - - if epoch >= warmup_epochs: - scheduler_aux.step() + for key, sched in self.schedulers.items(): + sched.step() + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : with torch.no_grad(): pred_full = self.create_volume().cpu() @@ -514,19 +432,19 @@ def recon( if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): with torch.no_grad(): - current_lr = scheduler_model.get_last_lr()[0] + current_lr = self.schedulers["model"].get_last_lr()[0] # Log metrics - logger.add_scalar("train/mse_loss", avg_mse_loss, epoch) + self.temp_logger.add_scalar("train/mse_loss", avg_mse_loss, epoch) - logger.add_scalar("train/z1_loss", avg_z1_loss, epoch) + self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, epoch) if tv_weight > 0: - logger.add_scalar("train/tv_loss", avg_tv_loss, epoch) - logger.add_scalar("train/total_loss", avg_loss, epoch) - logger.add_scalar("train/model_grad_norm", model_norm.item(), epoch) - logger.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) - logger.add_scalar("train/lr", current_lr, epoch) - logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) + self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, epoch) + self.temp_logger.add_scalar("train/total_loss", avg_loss, epoch) + self.temp_logger.add_scalar("train/model_grad_norm", model_norm.item(), epoch) + self.temp_logger.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) + self.temp_logger.add_scalar("train/lr", current_lr, epoch) + self.temp_logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) fig, axes = plt.subplots(1, 3, figsize=(36, 12)) axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) @@ -541,7 +459,7 @@ def recon( axes[2].matshow(thick_slice, cmap='turbo', vmin=0) axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') - logger.add_figure("train/viz", fig, epoch, close=True) + self.temp_logger.add_figure("train/viz", fig, epoch, close=True) plt.close(fig) if self.global_rank == 0: diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index 251538d0..c66b9e61 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -13,6 +13,49 @@ import numpy as np +class AuxiliaryParams(torch.nn.Module): + def __init__(self, num_tilts, device, zero_tilt_idx=None): + super().__init__() + + if zero_tilt_idx is None: + # If not provided, assume first projection is reference + zero_tilt_idx = 0 + + self.zero_tilt_idx = zero_tilt_idx + self.num_tilts = num_tilts + + # Shifts: only parameterize non-reference tilts + num_param_tilts = num_tilts - 1 + self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) + + # Fixed zero shifts for reference + self.shifts_ref = torch.zeros(1, 2, device=device) + + # Z1 and Z3: parameterize all tilts EXCEPT the reference + self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + + # Fixed zeros for reference tilt + self.z1_ref = torch.zeros(1, device=device) + # self.z3_ref = torch.zeros(1, device=device) + + def forward(self, dummy_input=None): + # Reconstruct full arrays with zeros at reference position + before_shifts = self.shifts_param[:self.zero_tilt_idx] + after_shifts = self.shifts_param[self.zero_tilt_idx:] + shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) + + before_z1 = self.z1_param[:self.zero_tilt_idx] + after_z1 = self.z1_param[self.zero_tilt_idx:] + z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) + + # before_z3 = self.z3_param[:self.zero_tilt_idx] + # after_z3 = self.z3_param[self.zero_tilt_idx:] + # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) + + return shifts, z1, -z1 + + class TomographyDataset(AutoSerialize, Dataset): _token = object() @@ -276,6 +319,24 @@ def initial_z3_angles(self, z3_angles: NDArray | Tensor) -> None: def initial_shifts(self, shifts: NDArray | Tensor) -> None: self._initial_shifts = shifts + # TODO: Temp auxiliary params + + def setup_auxiliary_params(self, zero_tilt_idx: int = None, device: str = "cpu") -> None: + + if not hasattr(self, "_auxiliary_params"): + self._auxiliary_params = AuxiliaryParams( + num_tilts = len(self.tilt_angles), + device = device, + zero_tilt_idx = zero_tilt_idx, + ) + + else: + print("Auxiliary params already set") + + @property + def auxiliary_params(self) -> AuxiliaryParams: + return self._auxiliary_params + # --- RESET --- def reset(self) -> None: diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index f733e012..ec093a32 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -3,7 +3,7 @@ from torch.utils.data import Dataset, DataLoader, DistributedSampler import torch.nn as nn import os - +import numpy as np from quantem.tomography.tomography_dataset import TomographyDataset class TomographyDDP: @@ -81,7 +81,8 @@ def build_model( else: print("Model built, compiled successfully") - + + def setup_dataloader( self, tomo_dataset: TomographyDataset, @@ -124,5 +125,36 @@ def setup_dataloader( print(f" Local batch size (train): {batch_size}") print(f" Global batch size: {batch_size*self.world_size}") print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): + if scaling_rule == "sqrt": + return base_lr * np.sqrt(self.world_size) + elif scaling_rule == "linear": + return base_lr * self.world_size + else: + raise ValueError(f"Invalid scaling rule: {scaling_rule}") + def scale_lr( + self, + optimizer_params: dict, + ): + + new_optimizer_params = {} + for key, value in optimizer_params.items(): + + if "original_lr" in value: + new_optimizer_params[key] = { + "type": value["type"], + "lr": self.get_scaled_lr(value["original_lr"]), + "original_lr": value["original_lr"], + } + else: + new_optimizer_params[key] = { + "type": value["type"], + "lr": self.get_scaled_lr(value["lr"]), + "original_lr": value["lr"], + } + + return new_optimizer_params + \ No newline at end of file diff --git a/src/quantem/tomography/tomography_ml.py b/src/quantem/tomography/tomography_ml.py index 4a902887..4953e45c 100644 --- a/src/quantem/tomography/tomography_ml.py +++ b/src/quantem/tomography/tomography_ml.py @@ -10,7 +10,7 @@ class TomographyML(TomographyBase): Class for handling conventional reconstruction methods of tomography data. """ - OPTIMIZABLE_VALS = ["volume", "z1", "x", "z3", "shifts"] + OPTIMIZABLE_VALS = ["volume", "z1", "x", "z3", "shifts", "model", "aux_params"] DEFAULT_LRS = { "volume": 1e-2, "z1": 1e-1, @@ -81,6 +81,18 @@ def set_optimizers(self): self._add_optimizer(key, self.dataset.tilt_angles, self._optimizer_params[key]) elif key == "z3": self._add_optimizer(key, self.dataset.z3_angles, self._optimizer_params[key]) + elif key == "model": + + if self._optimizer_params[key]["original_lr"] is not None: + optimizer_params = self._optimizer_params[key].copy() + optimizer_params.pop("original_lr", None) + self._add_optimizer(key, self.model.parameters(), optimizer_params) + elif key == "aux_params": + + if self._optimizer_params[key]["original_lr"] is not None: + optimizer_params = self._optimizer_params[key].copy() + optimizer_params.pop("original_lr", None) + self._add_optimizer(key, self.dataset.auxiliary_params.parameters(), optimizer_params) else: raise ValueError( f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" @@ -151,7 +163,7 @@ def scheduler_params(self, d: dict) -> None: raise ValueError( f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" ) - if v["type"] not in ["cyclic", "plateau", "exp", "gamma", "none"]: + if v["type"] not in ["cyclic", "plateau", "exp", "gamma", "linear", "cosine_annealing", "none"]: raise ValueError( f"Unknown scheduler type: {v['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" ) @@ -166,6 +178,8 @@ def schedulers( torch.optim.lr_scheduler.CyclicLR | torch.optim.lr_scheduler.ReduceLROnPlateau | torch.optim.lr_scheduler.ExponentialLR + | torch.optim.lr_scheduler.LinearLR + | torch.optim.lr_scheduler.CosineAnnealingLR | None ), ]: @@ -271,6 +285,18 @@ def _get_scheduler( base_LR = optimizer.param_groups[0]["lr"] if sched_type == "none": scheduler = None + elif sched_type == "linear": + scheduler = torch.optim.lr_scheduler.LinearLR( + optimizer, + start_factor=params.get("start_factor", 0.1), + total_iters=params.get("total_iters", num_iter), + ) + elif sched_type == "cosine_annealing": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=params.get("T_max", num_iter), + eta_min=params.get("eta_min", base_LR / 100), + ) elif sched_type == "cyclic": scheduler = torch.optim.lr_scheduler.CyclicLR( optimizer, From c704ce3b89737c6146b4b5bbbe05d081c1c1277f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 23 Sep 2025 00:16:18 -0700 Subject: [PATCH 009/335] Working DDP with objects and stuff --- src/quantem/tomography/object_models.py | 86 ++++++++++++++++++++++-- src/quantem/tomography/tomography.py | 50 ++++++++------ src/quantem/tomography/tomography_ddp.py | 8 ++- src/quantem/tomography/tomography_ml.py | 2 +- 4 files changed, 116 insertions(+), 30 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 284f743e..5ef08894 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -11,6 +11,8 @@ from quantem.core.utils.validators import validate_gt, validate_tensor from quantem.tomography.utils import get_TV_loss +import torch.nn as nn + class ObjectBase(AutoSerialize): """ @@ -78,7 +80,7 @@ def reset(self): pass @abstractmethod - def to(self, device: str):z + def to(self, device: str): pass @abstractmethod @@ -545,14 +547,88 @@ class ObjectINN(ObjectConstraints): def __init__( self, - model: torch.nn.Module, + model: nn.Module, + volume_shape = tuple[int, int, int], + device: str = "cuda", ): - pass + + super().__init__( + volume_shape = volume_shape, + device = device, + offset_obj = 0, + ) + + self._model = model + + + # --- Properties --- + @property + def obj(self): + + raise NotImplementedError - def forward( + @property + def model(self): + + return self._model + + @model.setter + def model(self, model): + + self._model = model + + def apply_soft_constraints( self, - rays, + coords: torch.Tensor, + tv_weight: float = 0.0, + ): + if tv_weight > 0: + + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device = coords.device)[:num_tv_samples] + + # Rerun forward for gradient tracking + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.model(tv_coords) + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + # Compute gradients + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True + )[0] + + grad_norm = torch.norm(grad_outputs, dim = 1) + tv_loss = tv_weight * grad_norm.mean() + else: + tv_loss = torch.tensor(0.0, device = self.device) + return tv_loss + + def forward( + self, + all_coords: torch.Tensor, ): + + all_densities = self.model(all_coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) + + valid_mask = ( + (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & + (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & + (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) + ).float() + + all_densities = all_densities * valid_mask + + return all_densities + + ObjectModelType = ObjectVoxelwise | ObjectINN # | ObjectDIP | ObjectImplicit (ObjectFFN?) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index d35a19b1..78567ddb 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -12,6 +12,7 @@ # Temporary imports for TomographyNERF from quantem.tomography.models import HSiren +from quantem.tomography.object_models import ObjectINN from torch.utils.tensorboard import SummaryWriter import numpy as np import matplotlib.pyplot as plt @@ -193,7 +194,7 @@ def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_r return transformed_rays # --- Creating Volume --- - def create_volume(self): + def create_volume(self, model): N = max(self.dataset.dims) @@ -202,7 +203,7 @@ def create_volume(self): x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - model = self.model.module if hasattr(self.model, 'module') else self.model + model = model.module if hasattr(model, 'module') else model samples_per_gpu = N**3 // self.world_size start_idx = self.global_rank * samples_per_gpu @@ -237,7 +238,7 @@ def setup_logger(self): def recon( self, - model: HSiren, + obj: ObjectINN, batch_size: int, num_workers: int = 0, epochs = 20, @@ -247,13 +248,14 @@ def recon( checkpoint_freq = 5, optimizer_params: dict = None, scheduler_params: dict = None, - ): - + ): self.setup_distributed() self.setup_dataloader(self.dataset, batch_size, num_workers) - self.build_model(model) + obj.model = self.build_model(obj._model) + + self.obj = obj if not hasattr(self, "temp_logger"): self.setup_logger() @@ -346,20 +348,26 @@ def recon( all_coords = transformed_rays.view(-1, 3) # TODO: torch.nn.functional.softplus here # all_densities = torch.nn.functional.softplus(self.model(all_coords)) - all_densities = self.model(all_coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) # [N, 1] → [N] - - # Create mask for valid coordinates (within [-1, 1]^3) - valid_mask = ( - (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & - (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & - (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) - ).float() - all_densities = all_densities * valid_mask + # TODO: I don't think this work, object needs to be distributed to all devices? + all_densities = self.obj.forward(all_coords) + # all_densities = self.model(all_coords) + + # if all_densities.dim() > 1: + # all_densities = all_densities.squeeze(-1) # [N, 1] → [N] + + # # Create mask for valid coordinates (within [-1, 1]^3) + # valid_mask = ( + # (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & + # (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & + # (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) + # ).float() + # all_densities = all_densities * valid_mask + # tv_loss = obj.apply_soft_constraints( + # coords = all_coords, + # tv_weight = 1e-5, + # ) if tv_weight > 0: num_tv_samples = min(10000, all_coords.shape[0]) tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] @@ -393,7 +401,7 @@ def recon( mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) tv_loss_z1 = torch.tensor(0.0, device=self.device) - + loss = mse_loss + tv_loss + tv_loss_z1 loss.backward() @@ -407,7 +415,7 @@ def recon( for key, opt in self.optimizers.items(): if key == "model": - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + model_norm = torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm = 1) elif key == "aux_params": aux_norm = torch.nn.utils.clip_grad_norm_(self.dataset.auxiliary_params.parameters(), max_norm = 1) opt.step() @@ -419,7 +427,7 @@ def recon( if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : with torch.no_grad(): - pred_full = self.create_volume().cpu() + pred_full = self.create_volume(self.obj.model).cpu() avg_loss = epoch_loss.item() / num_batches avg_mse_loss = epoch_mse_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index ec093a32..58e4158d 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -58,11 +58,11 @@ def build_model( ): # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? - self.model = model.to(self.device) + model = model.to(self.device) if self.world_size > 1: - self.model = torch.nn.parallel.DistributedDataParallel( - self.model, + model = torch.nn.parallel.DistributedDataParallel( + model, device_ids = [self.local_rank], output_device = self.local_rank, find_unused_parameters = False, @@ -81,6 +81,8 @@ def build_model( else: print("Model built, compiled successfully") + + return model def setup_dataloader( diff --git a/src/quantem/tomography/tomography_ml.py b/src/quantem/tomography/tomography_ml.py index 4953e45c..47e479d8 100644 --- a/src/quantem/tomography/tomography_ml.py +++ b/src/quantem/tomography/tomography_ml.py @@ -86,7 +86,7 @@ def set_optimizers(self): if self._optimizer_params[key]["original_lr"] is not None: optimizer_params = self._optimizer_params[key].copy() optimizer_params.pop("original_lr", None) - self._add_optimizer(key, self.model.parameters(), optimizer_params) + self._add_optimizer(key, self.obj.model.parameters(), optimizer_params) elif key == "aux_params": if self._optimizer_params[key]["original_lr"] is not None: From 1960f35414100d59706f59700ea341c1723809b9 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 23 Sep 2025 00:22:44 -0700 Subject: [PATCH 010/335] Soft constraints also work --- src/quantem/tomography/tomography.py | 53 ++++++++++++++-------------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 78567ddb..bdec96fb 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -368,32 +368,33 @@ def recon( # coords = all_coords, # tv_weight = 1e-5, # ) - if tv_weight > 0: - num_tv_samples = min(10000, all_coords.shape[0]) - tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] + # if tv_weight > 0: + # num_tv_samples = min(10000, all_coords.shape[0]) + # tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] - # Rerun forward for gradient tracking - tv_coords = all_coords[tv_indices].detach().requires_grad_(True) + # # Rerun forward for gradient tracking + # tv_coords = all_coords[tv_indices].detach().requires_grad_(True) - # TODO: torch.nn.functional.softplus here - # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this - tv_densities_recomputed = self.model(tv_coords) - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - # Compute gradients - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - tv_loss = tv_weight * grad_norm.mean() - else: - tv_loss = torch.tensor(0.0, device=self.device) + # # TODO: torch.nn.functional.softplus here + # # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this + # tv_densities_recomputed = self.model(tv_coords) + # if tv_densities_recomputed.dim() > 1: + # tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + # # Compute gradients + # grad_outputs = torch.autograd.grad( + # outputs=tv_densities_recomputed, + # inputs=tv_coords, + # grad_outputs=torch.ones_like(tv_densities_recomputed), + # create_graph=True + # )[0] + # grad_norm = torch.norm(grad_outputs, dim=1) + # tv_loss = tv_weight * grad_norm.mean() + # else: + # tv_loss = torch.tensor(0.0, device=self.device) + + tv_loss = self.obj.apply_soft_constraints(all_coords, tv_weight = 1e-5) ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) @@ -446,9 +447,9 @@ def recon( self.temp_logger.add_scalar("train/mse_loss", avg_mse_loss, epoch) self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, epoch) - if tv_weight > 0: - self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, epoch) - self.temp_logger.add_scalar("train/total_loss", avg_loss, epoch) + # if tv_weight > 0: + self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, epoch) + self.temp_logger.add_scalar("train/total_loss", avg_loss, epoch) self.temp_logger.add_scalar("train/model_grad_norm", model_norm.item(), epoch) self.temp_logger.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) self.temp_logger.add_scalar("train/lr", current_lr, epoch) From b0ccd8c8bd93953169f8b4e0907d191442ed2fe5 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 23 Sep 2025 00:43:08 -0700 Subject: [PATCH 011/335] Multi-step training with different schedulers kind of working. Need to figure out if I should just instantiate both aux_params, and model then change the LR after one chckpoint --- src/quantem/tomography/tomography.py | 96 +++++++++------------------- 1 file changed, 30 insertions(+), 66 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index bdec96fb..0e963347 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -51,6 +51,11 @@ def __init__( _token, ): super().__init__(dataset, volume_obj, device, _token) + + # TODO: More elegant way of doing this. + self.global_epochs = 0 + self.ddp_instantiated = False + # --- Reconstruction Method --- def sirt_recon( @@ -250,12 +255,13 @@ def recon( scheduler_params: dict = None, ): - self.setup_distributed() - self.setup_dataloader(self.dataset, batch_size, num_workers) - - obj.model = self.build_model(obj._model) + if not self.ddp_instantiated: + self.setup_distributed() + self.setup_dataloader(self.dataset, batch_size, num_workers) - self.obj = obj + if not hasattr(self, "obj"): + obj.model = self.build_model(obj._model) + self.obj = obj if not hasattr(self, "temp_logger"): self.setup_logger() @@ -294,15 +300,16 @@ def recon( for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(epoch) + num_samples_per_ray = get_num_samples_per_ray(self.global_epochs) # Log the change if it happens - if epoch > 0: - prev_samples = get_num_samples_per_ray(epoch - 1) + if self.global_epochs >= 0: + prev_samples = get_num_samples_per_ray(self.global_epochs) if num_samples_per_ray != prev_samples and self.global_rank == 0: print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") - + + if self.sampler is not None: self.sampler.set_epoch(epoch) @@ -346,54 +353,9 @@ def recon( ) all_coords = transformed_rays.view(-1, 3) - # TODO: torch.nn.functional.softplus here - # all_densities = torch.nn.functional.softplus(self.model(all_coords)) - - # TODO: I don't think this work, object needs to be distributed to all devices? - all_densities = self.obj.forward(all_coords) - # all_densities = self.model(all_coords) - # if all_densities.dim() > 1: - # all_densities = all_densities.squeeze(-1) # [N, 1] → [N] + all_densities = self.obj.forward(all_coords) - # # Create mask for valid coordinates (within [-1, 1]^3) - # valid_mask = ( - # (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & - # (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & - # (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) - # ).float() - - # all_densities = all_densities * valid_mask - # tv_loss = obj.apply_soft_constraints( - # coords = all_coords, - # tv_weight = 1e-5, - # ) - # if tv_weight > 0: - # num_tv_samples = min(10000, all_coords.shape[0]) - # tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] - - # # Rerun forward for gradient tracking - # tv_coords = all_coords[tv_indices].detach().requires_grad_(True) - - # # TODO: torch.nn.functional.softplus here - # # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this - # tv_densities_recomputed = self.model(tv_coords) - # if tv_densities_recomputed.dim() > 1: - # tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - # # Compute gradients - # grad_outputs = torch.autograd.grad( - # outputs=tv_densities_recomputed, - # inputs=tv_coords, - # grad_outputs=torch.ones_like(tv_densities_recomputed), - # create_graph=True - # )[0] - - # grad_norm = torch.norm(grad_outputs, dim=1) - # tv_loss = tv_weight * grad_norm.mean() - # else: - # tv_loss = torch.tensor(0.0, device=self.device) - tv_loss = self.obj.apply_soft_constraints(all_coords, tv_weight = 1e-5) ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) @@ -412,6 +374,7 @@ def recon( epoch_tv_loss += tv_loss.detach() epoch_z1_loss += tv_loss_z1.detach() num_batches += 1 + for key, opt in self.optimizers.items(): @@ -421,7 +384,7 @@ def recon( aux_norm = torch.nn.utils.clip_grad_norm_(self.dataset.auxiliary_params.parameters(), max_norm = 1) opt.step() opt.zero_grad() - + for key, sched in self.schedulers.items(): sched.step() @@ -444,16 +407,16 @@ def recon( current_lr = self.schedulers["model"].get_last_lr()[0] # Log metrics - self.temp_logger.add_scalar("train/mse_loss", avg_mse_loss, epoch) + self.temp_logger.add_scalar("train/mse_loss", avg_mse_loss, self.global_epochs) - self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, epoch) + self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) # if tv_weight > 0: - self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, epoch) - self.temp_logger.add_scalar("train/total_loss", avg_loss, epoch) - self.temp_logger.add_scalar("train/model_grad_norm", model_norm.item(), epoch) - self.temp_logger.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) - self.temp_logger.add_scalar("train/lr", current_lr, epoch) - self.temp_logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) + self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) + self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) + self.temp_logger.add_scalar("train/model_grad_norm", model_norm.item(), self.global_epochs) + self.temp_logger.add_scalar("train/aux_grad_norm", aux_norm.item(), self.global_epochs) + self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) + self.temp_logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, self.global_epochs) fig, axes = plt.subplots(1, 3, figsize=(36, 12)) axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) @@ -468,9 +431,10 @@ def recon( axes[2].matshow(thick_slice, cmap='turbo', vmin=0) axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') - self.temp_logger.add_figure("train/viz", fig, epoch, close=True) + self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) plt.close(fig) - + self.global_epochs += 1 + if self.global_rank == 0: print("Training complete.") From f9a8afaddee2fa075c73a0e91e89f83c819ee95c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 23 Sep 2025 09:44:54 -0700 Subject: [PATCH 012/335] Implemented ObjectINN with create volume in it (create_volume new version from Corneel's most recent code). Also implemented the soft constraints, thing left to abstract away is the logging. Need to look at auxiliary params, maybe looking at ray instantiation? --- src/quantem/tomography/object_models.py | 99 +++++++++++++++++++++++-- src/quantem/tomography/tomography.py | 59 +++------------ 2 files changed, 102 insertions(+), 56 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 5ef08894..bec78138 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -12,6 +12,7 @@ from quantem.tomography.utils import get_TV_loss import torch.nn as nn +import torch.distributed as dist class ObjectBase(AutoSerialize): @@ -560,12 +561,96 @@ def __init__( self._model = model + # --- Properties --- @property def obj(self): - raise NotImplementedError + return self._obj + + def create_volume( + self, + world_size: int, + global_rank: int, + ray_size: int, + ): + N = max(self._shape) + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + + # Underlying model if using DDP + model = self.model.module if hasattr(self.model, 'module') else self.model + + # Batch size for inference + # 5x larger batches no gradients needed + + inference_batch_size = 5 * N * ray_size + + # Distribute work across GPUs + total_samples = N**3 + samples_per_gpu = total_samples // world_size + remainder = total_samples % world_size + + # Handle uneven distribution + if global_rank < remainder: + start_idx = global_rank * (samples_per_gpu + 1) + end_idx = start_idx + samples_per_gpu + 1 + else: + start_idx = global_rank * samples_per_gpu + remainder + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx] + num_samples = inputs_subset.shape[0] + outputs_list = [] + + for batch_start in range(0, num_samples, inference_batch_size): + batch_end = min(batch_start + inference_batch_size, num_samples) + batch_coords = inputs_subset[batch_start:batch_end].to(self.device, non_blocking=True) + + batch_outputs = model(batch_coords) + if batch_outputs.dim() > 1: + batch_outputs = batch_outputs.squeeze(-1) + + outputs_list.append(batch_outputs.cpu()) + + outputs = torch.cat(outputs_list, dim=0) + + if world_size > 1: + # Gather from all ranks + # handle potentially different sizes + output_size = torch.tensor(outputs.shape[0], device=self.device, dtype=torch.long) + all_sizes = [torch.zeros(1, device=self.device, dtype=torch.long) for _ in range(world_size)] + dist.all_gather(all_sizes, output_size) + + # Create gather list with correct sizes + max_size = max(size.item() for size in all_sizes) + + # Pad if necessary for gathering + if outputs.shape[0] < max_size: + padding = torch.zeros(max_size - outputs.shape[0], device=outputs.device, dtype=outputs.dtype) + outputs_padded = torch.cat([outputs, padding], dim=0).to(self.device) + else: + outputs_padded = outputs.to(self.device) + + gathered_outputs = [torch.empty(max_size, device=self.device, dtype=outputs.dtype) + for _ in range(world_size)] + dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + + # Trim padding and concatenate + trimmed_outputs = [] + for rank, size in enumerate(all_sizes): + trimmed_outputs.append(gathered_outputs[rank][:size.item()]) + + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N).float() + else: + pred_full = outputs.reshape(N, N, N).float() + + + self._obj = pred_full.detach().cpu() + @property def model(self): @@ -577,12 +662,14 @@ def model(self, model): self._model = model + def apply_soft_constraints( self, coords: torch.Tensor, - tv_weight: float = 0.0, ): - if tv_weight > 0: + + soft_loss = torch.tensor(0.0, device = coords.device) + if self.soft_constraints["tv_vol"] > 0: num_tv_samples = min(10000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device = coords.device)[:num_tv_samples] @@ -603,11 +690,9 @@ def apply_soft_constraints( )[0] grad_norm = torch.norm(grad_outputs, dim = 1) - tv_loss = tv_weight * grad_norm.mean() - else: - tv_loss = torch.tensor(0.0, device = self.device) + soft_loss += self.soft_constraints["tv_vol"] * grad_norm.mean() - return tv_loss + return soft_loss def forward( self, diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 0e963347..b30f137b 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -124,15 +124,6 @@ def sirt_recon( # TODO: ML Recon which has NeRF and AD depending on the object type. # TODO: Temporary - - - def create_optimizer(self, params, lr, fused = True): - - return torch.optim.Adam( - params, - lr = lr, - fused = fused, - ) def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): """Create projection rays for entire batch simultaneously.""" @@ -197,43 +188,7 @@ def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_r transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) return transformed_rays - - # --- Creating Volume --- - def create_volume(self, model): - - N = max(self.dataset.dims) - - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - model = model.module if hasattr(model, 'module') else model - - samples_per_gpu = N**3 // self.world_size - start_idx = self.global_rank * samples_per_gpu - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx].to(self.device) - - # TODO: torch.nn.functional.softplus here - # outputs = torch.nn.functional.softplus(model(inputs_subset)) - outputs = model(inputs_subset) - - if outputs.dim() > 1: - outputs = outputs.squeeze(-1) - - if self.world_size > 1: - gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] - dist.all_gather(gathered_outputs, outputs.contiguous()) - - pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() - else: - pred_full = outputs.reshape(N, N, N).float() - - return pred_full - - + # TODO: Temp logger def setup_logger(self): if self.global_rank == 0: @@ -248,21 +203,25 @@ def recon( num_workers: int = 0, epochs = 20, use_amp = True, - tv_weight = 0.0, viz_freq = 1, checkpoint_freq = 5, optimizer_params: dict = None, scheduler_params: dict = None, + soft_constraints: dict = None, ): if not self.ddp_instantiated: self.setup_distributed() self.setup_dataloader(self.dataset, batch_size, num_workers) + self.ddp_instantiated = True if not hasattr(self, "obj"): obj.model = self.build_model(obj._model) self.obj = obj + if soft_constraints is not None: + self.obj.soft_constraints = soft_constraints + if not hasattr(self, "temp_logger"): self.setup_logger() @@ -356,7 +315,7 @@ def recon( all_densities = self.obj.forward(all_coords) - tv_loss = self.obj.apply_soft_constraints(all_coords, tv_weight = 1e-5) + tv_loss = self.obj.apply_soft_constraints(all_coords) ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) @@ -391,7 +350,9 @@ def recon( if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : with torch.no_grad(): - pred_full = self.create_volume(self.obj.model).cpu() + + self.obj.create_volume(world_size = self.world_size, global_rank = self.global_rank, ray_size = num_samples_per_ray) + pred_full = self.obj.obj avg_loss = epoch_loss.item() / num_batches avg_mse_loss = epoch_mse_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches From f3b5b86e84936301c8ac5a3f5ed43a36be11fa5a Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 23 Sep 2025 10:17:08 -0700 Subject: [PATCH 013/335] Tomo-NeRF working fully; just need to clean up a little bit --- src/quantem/tomography/tomography.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index b30f137b..d31964b4 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -19,6 +19,7 @@ import torch.distributed as dist # Temporary aux class for TomographyNERF +# TODO: Maybe put this in INN? def get_num_samples_per_ray(epoch): """Increase number of samples per ray at specific epochs.""" schedule = { @@ -132,7 +133,7 @@ def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray) # Convert all pixels to normalized coordinates x_coords = (pixel_j / (N - 1)) * 2 - 1 y_coords = (pixel_i / (N - 1)) * 2 - 1 - + # TODO: maybe pixel_j.device? # Create z coordinates z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) @@ -214,11 +215,10 @@ def recon( self.setup_distributed() self.setup_dataloader(self.dataset, batch_size, num_workers) self.ddp_instantiated = True - - if not hasattr(self, "obj"): obj.model = self.build_model(obj._model) self.obj = obj + if soft_constraints is not None: self.obj.soft_constraints = soft_constraints @@ -248,7 +248,6 @@ def recon( aux_norm = torch.tensor(0.0, device=self.device) model_norm = torch.tensor(0.0, device=self.device) - for _, opt in self.optimizers.items(): opt.zero_grad() @@ -300,7 +299,8 @@ def recon( batch_ray_coords = self.create_batch_projection_rays( pixel_i, pixel_j, N, num_samples_per_ray ) - + + # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate transformed_rays = self.transform_batch_ray_coordinates( batch_ray_coords, z1=batch_z1, From 867bb8e51ac76e07ed7adf91862511f218d1a154 Mon Sep 17 00:00:00 2001 From: cophus Date: Tue, 7 Oct 2025 12:54:50 -0700 Subject: [PATCH 014/335] Initial commit for background subtraction --- src/quantem/core/utils/imaging_utils.py | 195 +++++++++++++++++++++++- 1 file changed, 193 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 7592dee4..125b0d30 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,12 +1,17 @@ # Utilities for processing images - -from typing import Optional, Tuple +from __future__ import annotations +from typing import Optional, Tuple, Union import numpy as np from numpy.typing import NDArray from scipy.ndimage import gaussian_filter +from scipy.special import comb from quantem.core.utils.utils import generate_batches +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.visualization import show_2d + +ArrayOrDS = Union[NDArray, Dataset2d] def dft_upsample( @@ -360,3 +365,189 @@ def fourier_cropping( result[-h2:, -w2:] = corner_centered_array[-h2:, -w2:] return result + + +def _as_array(x: ArrayOrDS) -> NDArray: + return x.array if isinstance(x, Dataset2d) else np.asarray(x) + + +def _like_dataset2d(arr: NDArray, template: Dataset2d, name: str) -> Dataset2d: + return Dataset2d.from_array( + arr, + name=name, + origin=getattr(template, "origin", None), + sampling=getattr(template, "sampling", None), + units=getattr(template, "units", None), + ) + + +def _bernstein_basis_1d(n: int, t: NDArray) -> NDArray: + """ + Bernstein basis B_k^n(t) for k=0..n, evaluated at t in [0,1]. + Returns shape (t.size, n+1). + """ + k = np.arange(n + 1, dtype=int) + return comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) + + +def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray: + """ + Builds A with shape (H*W, (ou+1)*(ov+1)) via a Kronecker product of 1D Bernstein bases. + """ + H, W = im_shape + ou, ov = int(order[0]), int(order[1]) + u = np.linspace(0.0, 1.0, H) + v = np.linspace(0.0, 1.0, W) + Bu = _bernstein_basis_1d(ou, u) # (H, ou+1) + Bv = _bernstein_basis_1d(ov, v) # (W, ov+1) + basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) # (H, W, ou+1, ov+1) + A = basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) + return A + + +def background_subtract( + image: ArrayOrDS, + mask: Optional[ArrayOrDS] = None, + thresh_bg: Optional[float] = None, + order: Tuple[int, int] = (2, 2), + sigma: Optional[float] = None, + num_iter: int = 10, + plot_result: bool = True, + cmap: str = "turbo", + return_background: bool = False, + return_mask: bool = False, + **show_kwargs, +): + """ + Background subtraction using bi-variate Bernstein (Bezier) polynomial fitting + with iterative background pixel selection. + + Parameters + ---------- + image : np.ndarray or Dataset2d + Input 2D image. + mask : np.ndarray or Dataset2d, optional + Boolean mask selecting valid pixels (True = valid). If None, all pixels valid. + thresh_bg : float, optional + Threshold on residual (image - background) to classify background pixels. + If None, initialized to median(image[mask]) and reused each iteration. + order : (int, int), default (2,2) + Polynomial orders (row_order, col_order) for the Bernstein basis. + sigma : float, optional + Gaussian sigma (in pixels) for smoothing residuals before thresholding. + num_iter : int, default 10 + Number of fit/update iterations. + plot_result : bool, default True + If True, displays input, background, and background-subtracted images using show_2d. + cmap : str, default "turbo" + Colormap for plotting. + return_background : bool, default False + If True, also return the background image. + return_mask : bool, default False + If True, also return the final background mask (numpy bool array). + **show_kwargs + Passed through to `show_2d` (e.g., to enable scalebars if supported). + + Returns + ------- + image_sub : same type as `image` (np.ndarray or Dataset2d) + Background-subtracted image. + image_bg : same type as `image` (optional) + Estimated background image (returned if `return_background=True`). + mask_bg : np.ndarray of bool (optional) + Final background mask (returned if `return_mask=True`). + """ + # --- normalize inputs --- + is_dataset = isinstance(image, Dataset2d) + im = _as_array(image).astype(float, copy=True) + + if im.ndim != 2: + raise ValueError("`image` must be a 2D numpy array or Dataset2d") + + if mask is None: + mask_arr = np.ones_like(im, dtype=bool) + else: + mask_arr = _as_array(mask).astype(bool, copy=False) + if mask_arr.shape != im.shape: + raise ValueError("`mask` must have the same shape as `image`.") + + # --- build basis once --- + order = (int(order[0]), int(order[1])) + A_full = _build_basis_matrix(im.shape, order) # (H*W, K) + H, W = im.shape + im_flat = im.ravel() + + # --- initialize background & thresholds --- + im_bg = np.zeros_like(im) + if thresh_bg is None: + thresh_val = np.median(im[mask_arr]) + else: + thresh_val = float(thresh_bg) + + resid = im - im_bg + if sigma is not None and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + mask_bg = resid < thresh_val + mask_bg &= mask_arr + + # --- iterate fit/update --- + for _ in range(int(num_iter)): + idx = mask_bg.ravel() + if not np.any(idx): + idx = mask_arr.ravel() # ensure solvable if mask collapses + + coefs, *_ = np.linalg.lstsq(A_full[idx, :], im_flat[idx], rcond=None) + im_bg = (A_full @ coefs).reshape(H, W) + + resid = im - im_bg + if sigma is not None and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + + thr = thresh_val if thresh_bg is None else float(thresh_bg) + mask_bg = (resid < thr) + mask_bg &= mask_arr + + # --- final subtraction --- + im_sub = im - im_bg + + # --- plotting with quantem.show_2d (for scalebar support when available) --- + if plot_result: + vmin = float(np.min(im_sub[mask_arr])) + vmax = float(np.max(im_sub[mask_arr])) + disp = [ + im - np.mean(im_bg), + (im_bg - np.mean(im_bg)) * mask_bg, + im_sub, + ] + # Add default titles only if the caller didn't specify titles + local_kwargs = dict(vmin=vmin, vmax=vmax, cmap=cmap) + local_kwargs.update(show_kwargs) + if "title" not in local_kwargs: + local_kwargs["title"] = [ + "Input Image", + "Background Image (masked)", + "Background Subtracted", + ] + show_2d(disp, **local_kwargs) + + # --- package outputs in the same type as input --- + if is_dataset: + sub_ds = _like_dataset2d(im_sub, image, name=getattr(image, "name", "image") + " (background subtracted)") + bg_ds = _like_dataset2d(im_bg, image, name=getattr(image, "name", "image") + " (background)") + if return_background and return_mask: + return sub_ds, bg_ds, mask_bg + elif return_background: + return sub_ds, bg_ds + elif return_mask: + return sub_ds, mask_bg + else: + return sub_ds + else: + if return_background and return_mask: + return im_sub, im_bg, mask_bg + elif return_background: + return im_sub, im_bg + elif return_mask: + return im_sub, mask_bg + else: + return im_sub From 0d09a393b57cbac0e45d2b268cdcf8b6049c3e86 Mon Sep 17 00:00:00 2001 From: cophus Date: Tue, 7 Oct 2025 14:04:10 -0700 Subject: [PATCH 015/335] Updating plots for background subtraction --- src/quantem/core/utils/imaging_utils.py | 170 ++++++++++-------------- 1 file changed, 68 insertions(+), 102 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 125b0d30..c5f96b77 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -6,6 +6,7 @@ from numpy.typing import NDArray from scipy.ndimage import gaussian_filter from scipy.special import comb +from matplotlib import cm from quantem.core.utils.utils import generate_batches from quantem.core.datastructures.dataset2d import Dataset2d @@ -367,33 +368,16 @@ def fourier_cropping( return result -def _as_array(x: ArrayOrDS) -> NDArray: +def _as_array(x: NDArray | Dataset2d) -> NDArray: return x.array if isinstance(x, Dataset2d) else np.asarray(x) -def _like_dataset2d(arr: NDArray, template: Dataset2d, name: str) -> Dataset2d: - return Dataset2d.from_array( - arr, - name=name, - origin=getattr(template, "origin", None), - sampling=getattr(template, "sampling", None), - units=getattr(template, "units", None), - ) - - def _bernstein_basis_1d(n: int, t: NDArray) -> NDArray: - """ - Bernstein basis B_k^n(t) for k=0..n, evaluated at t in [0,1]. - Returns shape (t.size, n+1). - """ k = np.arange(n + 1, dtype=int) return comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray: - """ - Builds A with shape (H*W, (ou+1)*(ov+1)) via a Kronecker product of 1D Bernstein bases. - """ H, W = im_shape ou, ov = int(order[0]), int(order[1]) u = np.linspace(0.0, 1.0, H) @@ -401,88 +385,53 @@ def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> ND Bu = _bernstein_basis_1d(ou, u) # (H, ou+1) Bv = _bernstein_basis_1d(ov, v) # (W, ov+1) basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) # (H, W, ou+1, ov+1) - A = basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) - return A + return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) def background_subtract( - image: ArrayOrDS, - mask: Optional[ArrayOrDS] = None, + image: NDArray | Dataset2d, + mask: Optional[NDArray] = None, # boolean numpy array or None thresh_bg: Optional[float] = None, order: Tuple[int, int] = (2, 2), sigma: Optional[float] = None, num_iter: int = 10, plot_result: bool = True, + axsize: Tuple[int, int] = (3.1,3), cmap: str = "turbo", return_background: bool = False, return_mask: bool = False, **show_kwargs, ): """ - Background subtraction using bi-variate Bernstein (Bezier) polynomial fitting - with iterative background pixel selection. - - Parameters - ---------- - image : np.ndarray or Dataset2d - Input 2D image. - mask : np.ndarray or Dataset2d, optional - Boolean mask selecting valid pixels (True = valid). If None, all pixels valid. - thresh_bg : float, optional - Threshold on residual (image - background) to classify background pixels. - If None, initialized to median(image[mask]) and reused each iteration. - order : (int, int), default (2,2) - Polynomial orders (row_order, col_order) for the Bernstein basis. - sigma : float, optional - Gaussian sigma (in pixels) for smoothing residuals before thresholding. - num_iter : int, default 10 - Number of fit/update iterations. - plot_result : bool, default True - If True, displays input, background, and background-subtracted images using show_2d. - cmap : str, default "turbo" - Colormap for plotting. - return_background : bool, default False - If True, also return the background image. - return_mask : bool, default False - If True, also return the final background mask (numpy bool array). - **show_kwargs - Passed through to `show_2d` (e.g., to enable scalebars if supported). + Background subtraction via bi-variate Bernstein (Bezier) polynomial fitting. Returns ------- - image_sub : same type as `image` (np.ndarray or Dataset2d) - Background-subtracted image. - image_bg : same type as `image` (optional) - Estimated background image (returned if `return_background=True`). - mask_bg : np.ndarray of bool (optional) - Final background mask (returned if `return_mask=True`). + im_sub : np.ndarray + im_bg : np.ndarray (optional, if return_background=True) + mask_bg: np.ndarray bool (optional, if return_mask=True) """ - # --- normalize inputs --- - is_dataset = isinstance(image, Dataset2d) + # --- inputs --- im = _as_array(image).astype(float, copy=True) - if im.ndim != 2: raise ValueError("`image` must be a 2D numpy array or Dataset2d") if mask is None: mask_arr = np.ones_like(im, dtype=bool) else: - mask_arr = _as_array(mask).astype(bool, copy=False) + mask_arr = np.asarray(mask, dtype=bool) if mask_arr.shape != im.shape: raise ValueError("`mask` must have the same shape as `image`.") - # --- build basis once --- + # --- basis --- order = (int(order[0]), int(order[1])) A_full = _build_basis_matrix(im.shape, order) # (H*W, K) H, W = im.shape im_flat = im.ravel() - # --- initialize background & thresholds --- + # --- init background & mask --- im_bg = np.zeros_like(im) - if thresh_bg is None: - thresh_val = np.median(im[mask_arr]) - else: - thresh_val = float(thresh_bg) + thresh_val = np.median(im[mask_arr]) if thresh_bg is None else float(thresh_bg) resid = im - im_bg if sigma is not None and sigma > 0: @@ -490,7 +439,7 @@ def background_subtract( mask_bg = resid < thresh_val mask_bg &= mask_arr - # --- iterate fit/update --- + # --- iterations --- for _ in range(int(num_iter)): idx = mask_bg.ravel() if not np.any(idx): @@ -510,44 +459,61 @@ def background_subtract( # --- final subtraction --- im_sub = im - im_bg - # --- plotting with quantem.show_2d (for scalebar support when available) --- + # --- plotting (right panel: zero-centered RdBu_r with symmetric ±min(|min|,|max|) from im_sub[mask]) --- if plot_result: - vmin = float(np.min(im_sub[mask_arr])) - vmax = float(np.max(im_sub[mask_arr])) + # Use only the masked region to set the range + vals = im_sub[mask_arr] + vals = vals[np.isfinite(vals)] + if vals.size == 0: + vals = np.array([0.0]) + + vmin_sub = float(np.min(vals)) + vmax_sub = float(np.max(vals)) + # vrange = float(np.minimum(abs(vmin_sub), abs(vmax_sub))) + vrange = float(np.maximum(abs(vmin_sub), abs(vmax_sub))) + if vrange <= 0: + vrange = 1e-12 # avoid zero-width range + + # Background panel: show only fitted region; black elsewhere + bg_disp = (im_bg - np.mean(im_bg)).copy() + bg_disp[~mask_bg] = np.nan + + # Colormaps + cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") # NaNs -> black + cmap_div = "RdBu_r" # diverging; center (white) at 0 via centered interval + + # Panels: input, background(masked), background-subtracted disp = [ im - np.mean(im_bg), - (im_bg - np.mean(im_bg)) * mask_bg, + bg_disp, im_sub, ] - # Add default titles only if the caller didn't specify titles - local_kwargs = dict(vmin=vmin, vmax=vmax, cmap=cmap) - local_kwargs.update(show_kwargs) - if "title" not in local_kwargs: - local_kwargs["title"] = [ - "Input Image", - "Background Image (masked)", - "Background Subtracted", - ] - show_2d(disp, **local_kwargs) - - # --- package outputs in the same type as input --- - if is_dataset: - sub_ds = _like_dataset2d(im_sub, image, name=getattr(image, "name", "image") + " (background subtracted)") - bg_ds = _like_dataset2d(im_bg, image, name=getattr(image, "name", "image") + " (background)") - if return_background and return_mask: - return sub_ds, bg_ds, mask_bg - elif return_background: - return sub_ds, bg_ds - elif return_mask: - return sub_ds, mask_bg - else: - return sub_ds + + # Per-panel normalization: + # - Panels 1 & 2: manual min/max from masked im_sub + # - Panel 3 : centered at 0.0 with half_range = vrange (white == 0.0) + norm = [ + {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, # input + {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, # background + {"interval_type": "centered", "stretch_type": "linear", "vcenter": 0.0, "half_range": vrange}, # zero-centered + ] + + show_2d( + disp, + cmap=[cmap_base, cmap_base, cmap_div], + norm=norm, + cbar=[False, False, True], # colorbar only on right panel + title=["Input Image", "Background (fit region)", "Background Subtracted"], + axsize=axsize, + **show_kwargs, + ) + + # --- outputs (numpy arrays) --- + if return_background and return_mask: + return im_sub, im_bg, mask_bg + elif return_background: + return im_sub, im_bg + elif return_mask: + return im_sub, mask_bg else: - if return_background and return_mask: - return im_sub, im_bg, mask_bg - elif return_background: - return im_sub, im_bg - elif return_mask: - return im_sub, mask_bg - else: - return im_sub + return im_sub \ No newline at end of file From f34b9e158a56268135a0bd5ec07e255b17c215eb Mon Sep 17 00:00:00 2001 From: cophus Date: Tue, 7 Oct 2025 14:16:19 -0700 Subject: [PATCH 016/335] Adding function overloading --- src/quantem/core/utils/imaging_utils.py | 153 ++++++++++++++++++++---- 1 file changed, 129 insertions(+), 24 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index c5f96b77..89459b74 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,6 +1,6 @@ # Utilities for processing images from __future__ import annotations -from typing import Optional, Tuple, Union +from typing import Optional, Tuple, Union, Literal, overload import numpy as np from numpy.typing import NDArray @@ -388,6 +388,66 @@ def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> ND return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) +# --- Overloads: NDArray input --- +@overload +def background_subtract( + image: NDArray, + mask: Optional[NDArray] = ..., + thresh_bg: Optional[float] = ..., + order: Tuple[int, int] = ..., + sigma: Optional[float] = ..., + num_iter: int = ..., + plot_result: bool = ..., + axsize: Tuple[int, int] = ..., + cmap: str = ..., + return_background_and_mask: Literal[False] = ..., + **show_kwargs, +) -> NDArray: ... +@overload +def background_subtract( + image: NDArray, + mask: Optional[NDArray] = ..., + thresh_bg: Optional[float] = ..., + order: Tuple[int, int] = ..., + sigma: Optional[float] = ..., + num_iter: int = ..., + plot_result: bool = ..., + axsize: Tuple[int, int] = ..., + cmap: str = ..., + return_background_and_mask: Literal[True] = ..., + **show_kwargs, +) -> Tuple[NDArray, NDArray, NDArray]: ... + +# --- Overloads: Dataset2d input (mask stays NDArray) --- +@overload +def background_subtract( + image: Dataset2d, + mask: Optional[NDArray] = ..., + thresh_bg: Optional[float] = ..., + order: Tuple[int, int] = ..., + sigma: Optional[float] = ..., + num_iter: int = ..., + plot_result: bool = ..., + axsize: Tuple[int, int] = ..., + cmap: str = ..., + return_background_and_mask: Literal[False] = ..., + **show_kwargs, +) -> Dataset2d: ... +@overload +def background_subtract( + image: Dataset2d, + mask: Optional[NDArray] = ..., + thresh_bg: Optional[float] = ..., + order: Tuple[int, int] = ..., + sigma: Optional[float] = ..., + num_iter: int = ..., + plot_result: bool = ..., + axsize: Tuple[int, int] = ..., + cmap: str = ..., + return_background_and_mask: Literal[True] = ..., + **show_kwargs, +) -> Tuple[Dataset2d, Dataset2d, NDArray]: ... + def background_subtract( image: NDArray | Dataset2d, mask: Optional[NDArray] = None, # boolean numpy array or None @@ -396,20 +456,62 @@ def background_subtract( sigma: Optional[float] = None, num_iter: int = 10, plot_result: bool = True, - axsize: Tuple[int, int] = (3.1,3), + axsize: Tuple[int, int] = (3.1, 3), cmap: str = "turbo", - return_background: bool = False, - return_mask: bool = False, + return_background_and_mask: bool = False, **show_kwargs, ): """ - Background subtraction via bi-variate Bernstein (Bezier) polynomial fitting. + Background subtraction via bi-variate Bernstein (Bézier) polynomial fitting. + + Parameters + ---------- + image : numpy.ndarray or Dataset2d + 2D input image. If a Dataset2d is provided, only its array is used for the fit; + outputs are returned as Dataset2d (same metadata preserved). + mask : numpy.ndarray of bool, optional + Boolean mask (same shape as `image`) indicating valid pixels for fitting and for + range calculations in the diagnostic plot. If None, all pixels are valid. + thresh_bg : float, optional + Background threshold to classify pixels as background during robust iterations. + If None, initialized from the median of `image[mask]`. + order : tuple[int, int], default (2, 2) + Polynomial order (u, v) for the Bernstein basis along the two axes. + Number of basis terms = (order[0] + 1) * (order[1] + 1). + sigma : float, optional + Gaussian sigma (pixels) to smooth residuals before thresholding at each iteration. + If None or 0, no smoothing is applied. + num_iter : int, default 10 + Number of robust fitting iterations (refit background, update mask). + plot_result : bool, default True + If True, displays a 3-panel diagnostic: + (1) Input (mean-centered by background mean), + (2) Fitted background shown only where `mask_bg` is True (else black), + (3) Background-subtracted image with `RdBu_r`, zero-centered, symmetric ±min(|min|,|max|) + computed from `im_sub[mask]`. Colorbar is shown on panel (3). + axsize : tuple[int, int], default (3.1, 3) + Panel size in inches, forwarded to `show_2d`. + cmap : str, default "turbo" + Colormap for panels (1) and (2). Panel (3) always uses "RdBu_r". + return_background_and_mask : bool, default False + If True, also return the fitted background and the final background mask. + + **show_kwargs + Additional arguments passed to `quantem.core.visualization.show_2d`. Returns ------- - im_sub : np.ndarray - im_bg : np.ndarray (optional, if return_background=True) - mask_bg: np.ndarray bool (optional, if return_mask=True) + im_sub : numpy.ndarray or Dataset2d + Background-subtracted image (same type as `image`). + im_bg : numpy.ndarray or Dataset2d, optional + Fitted background (same type as `image`). Returned if `return_background_and_mask=True`. + mask_bg : numpy.ndarray of bool, optional + Final background mask (always NumPy). Returned if `return_background_and_mask=True`. + + Notes + ----- + - The right-panel plot uses `im_sub[mask]` to compute a symmetric, zero-centered range + so white corresponds to 0.0 in RdBu_r. """ # --- inputs --- im = _as_array(image).astype(float, copy=True) @@ -459,9 +561,9 @@ def background_subtract( # --- final subtraction --- im_sub = im - im_bg - # --- plotting (right panel: zero-centered RdBu_r with symmetric ±min(|min|,|max|) from im_sub[mask]) --- + # --- plotting (right panel: zero-centered RdBu_r; limits from im_sub[mask]) --- if plot_result: - # Use only the masked region to set the range + # Range from masked region vals = im_sub[mask_arr] vals = vals[np.isfinite(vals)] if vals.size == 0: @@ -469,10 +571,9 @@ def background_subtract( vmin_sub = float(np.min(vals)) vmax_sub = float(np.max(vals)) - # vrange = float(np.minimum(abs(vmin_sub), abs(vmax_sub))) - vrange = float(np.maximum(abs(vmin_sub), abs(vmax_sub))) + vrange = float(np.maximum(abs(vmin_sub), abs(vmax_sub))) # ±min(|min|,|max|) if vrange <= 0: - vrange = 1e-12 # avoid zero-width range + vrange = 1e-12 # Background panel: show only fitted region; black elsewhere bg_disp = (im_bg - np.mean(im_bg)).copy() @@ -480,7 +581,7 @@ def background_subtract( # Colormaps cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") # NaNs -> black - cmap_div = "RdBu_r" # diverging; center (white) at 0 via centered interval + cmap_div = "RdBu_r" # Panels: input, background(masked), background-subtracted disp = [ @@ -490,8 +591,6 @@ def background_subtract( ] # Per-panel normalization: - # - Panels 1 & 2: manual min/max from masked im_sub - # - Panel 3 : centered at 0.0 with half_range = vrange (white == 0.0) norm = [ {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, # input {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, # background @@ -508,12 +607,18 @@ def background_subtract( **show_kwargs, ) - # --- outputs (numpy arrays) --- - if return_background and return_mask: - return im_sub, im_bg, mask_bg - elif return_background: - return im_sub, im_bg - elif return_mask: - return im_sub, mask_bg + # --- outputs (match image type for images; mask always ndarray) --- + if isinstance(image, Dataset2d): + meta = dict(origin=image.origin, sampling=image.sampling, units=image.units) + name_base = getattr(image, "name", "image") + im_sub_ds = Dataset2d.from_array(im_sub, name=f"{name_base} (bg-sub)", **meta) + im_bg_ds = Dataset2d.from_array(im_bg, name=f"{name_base} (bg-fit)", **meta) + if return_background_and_mask: + return im_sub_ds, im_bg_ds, mask_bg + else: + return im_sub_ds else: - return im_sub \ No newline at end of file + if return_background_and_mask: + return im_sub, im_bg, mask_bg + else: + return im_sub From 9f9ef88a6051f2b6746efb1a038a633c17775df4 Mon Sep 17 00:00:00 2001 From: cophus Date: Tue, 7 Oct 2025 14:18:11 -0700 Subject: [PATCH 017/335] More conservative fitting order --- src/quantem/core/utils/imaging_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 89459b74..c6f893cb 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -452,7 +452,7 @@ def background_subtract( image: NDArray | Dataset2d, mask: Optional[NDArray] = None, # boolean numpy array or None thresh_bg: Optional[float] = None, - order: Tuple[int, int] = (2, 2), + order: Tuple[int, int] = (1, 1), sigma: Optional[float] = None, num_iter: int = 10, plot_result: bool = True, From da1055139acf4490a7c8dc72c0acf7eabd21e05a Mon Sep 17 00:00:00 2001 From: cophus Date: Fri, 10 Oct 2025 15:52:31 -0700 Subject: [PATCH 018/335] Switching from @overload to TypeVar --- src/quantem/core/utils/imaging_utils.py | 221 +++++------------------- 1 file changed, 44 insertions(+), 177 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index c6f893cb..4e3ca537 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,6 +1,6 @@ # Utilities for processing images from __future__ import annotations -from typing import Optional, Tuple, Union, Literal, overload +from typing import Any, Optional, Tuple, TypeVar import numpy as np from numpy.typing import NDArray @@ -8,11 +8,12 @@ from scipy.special import comb from matplotlib import cm -from quantem.core.utils.utils import generate_batches from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.visualization import show_2d -ArrayOrDS = Union[NDArray, Dataset2d] +# single TypeVar: works for both numpy and Dataset2d +ImageType = TypeVar("ImageType", NDArray[Any], Dataset2d) +BoolArray = NDArray[np.bool_] def dft_upsample( @@ -368,89 +369,27 @@ def fourier_cropping( return result -def _as_array(x: NDArray | Dataset2d) -> NDArray: +def _as_array(x: ImageType) -> NDArray[Any]: return x.array if isinstance(x, Dataset2d) else np.asarray(x) - -def _bernstein_basis_1d(n: int, t: NDArray) -> NDArray: +def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: k = np.arange(n + 1, dtype=int) return comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) - -def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray: +def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray[Any]: H, W = im_shape ou, ov = int(order[0]), int(order[1]) u = np.linspace(0.0, 1.0, H) v = np.linspace(0.0, 1.0, W) - Bu = _bernstein_basis_1d(ou, u) # (H, ou+1) - Bv = _bernstein_basis_1d(ov, v) # (W, ov+1) - basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) # (H, W, ou+1, ov+1) + Bu = _bernstein_basis_1d(ou, u) + Bv = _bernstein_basis_1d(ov, v) + basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) -# --- Overloads: NDArray input --- -@overload -def background_subtract( - image: NDArray, - mask: Optional[NDArray] = ..., - thresh_bg: Optional[float] = ..., - order: Tuple[int, int] = ..., - sigma: Optional[float] = ..., - num_iter: int = ..., - plot_result: bool = ..., - axsize: Tuple[int, int] = ..., - cmap: str = ..., - return_background_and_mask: Literal[False] = ..., - **show_kwargs, -) -> NDArray: ... -@overload -def background_subtract( - image: NDArray, - mask: Optional[NDArray] = ..., - thresh_bg: Optional[float] = ..., - order: Tuple[int, int] = ..., - sigma: Optional[float] = ..., - num_iter: int = ..., - plot_result: bool = ..., - axsize: Tuple[int, int] = ..., - cmap: str = ..., - return_background_and_mask: Literal[True] = ..., - **show_kwargs, -) -> Tuple[NDArray, NDArray, NDArray]: ... - -# --- Overloads: Dataset2d input (mask stays NDArray) --- -@overload -def background_subtract( - image: Dataset2d, - mask: Optional[NDArray] = ..., - thresh_bg: Optional[float] = ..., - order: Tuple[int, int] = ..., - sigma: Optional[float] = ..., - num_iter: int = ..., - plot_result: bool = ..., - axsize: Tuple[int, int] = ..., - cmap: str = ..., - return_background_and_mask: Literal[False] = ..., - **show_kwargs, -) -> Dataset2d: ... -@overload -def background_subtract( - image: Dataset2d, - mask: Optional[NDArray] = ..., - thresh_bg: Optional[float] = ..., - order: Tuple[int, int] = ..., - sigma: Optional[float] = ..., - num_iter: int = ..., - plot_result: bool = ..., - axsize: Tuple[int, int] = ..., - cmap: str = ..., - return_background_and_mask: Literal[True] = ..., - **show_kwargs, -) -> Tuple[Dataset2d, Dataset2d, NDArray]: ... - def background_subtract( - image: NDArray | Dataset2d, - mask: Optional[NDArray] = None, # boolean numpy array or None + image: ImageType, + mask: Optional[BoolArray] = None, thresh_bg: Optional[float] = None, order: Tuple[int, int] = (1, 1), sigma: Optional[float] = None, @@ -460,165 +399,93 @@ def background_subtract( cmap: str = "turbo", return_background_and_mask: bool = False, **show_kwargs, -): +) -> ImageType | Tuple[ImageType, NDArray[Any], BoolArray]: """ - Background subtraction via bi-variate Bernstein (Bézier) polynomial fitting. - - Parameters - ---------- - image : numpy.ndarray or Dataset2d - 2D input image. If a Dataset2d is provided, only its array is used for the fit; - outputs are returned as Dataset2d (same metadata preserved). - mask : numpy.ndarray of bool, optional - Boolean mask (same shape as `image`) indicating valid pixels for fitting and for - range calculations in the diagnostic plot. If None, all pixels are valid. - thresh_bg : float, optional - Background threshold to classify pixels as background during robust iterations. - If None, initialized from the median of `image[mask]`. - order : tuple[int, int], default (2, 2) - Polynomial order (u, v) for the Bernstein basis along the two axes. - Number of basis terms = (order[0] + 1) * (order[1] + 1). - sigma : float, optional - Gaussian sigma (pixels) to smooth residuals before thresholding at each iteration. - If None or 0, no smoothing is applied. - num_iter : int, default 10 - Number of robust fitting iterations (refit background, update mask). - plot_result : bool, default True - If True, displays a 3-panel diagnostic: - (1) Input (mean-centered by background mean), - (2) Fitted background shown only where `mask_bg` is True (else black), - (3) Background-subtracted image with `RdBu_r`, zero-centered, symmetric ±min(|min|,|max|) - computed from `im_sub[mask]`. Colorbar is shown on panel (3). - axsize : tuple[int, int], default (3.1, 3) - Panel size in inches, forwarded to `show_2d`. - cmap : str, default "turbo" - Colormap for panels (1) and (2). Panel (3) always uses "RdBu_r". - return_background_and_mask : bool, default False - If True, also return the fitted background and the final background mask. - - **show_kwargs - Additional arguments passed to `quantem.core.visualization.show_2d`. + Background subtraction via bivariate Bernstein polynomial fitting. Returns ------- - im_sub : numpy.ndarray or Dataset2d - Background-subtracted image (same type as `image`). - im_bg : numpy.ndarray or Dataset2d, optional - Fitted background (same type as `image`). Returned if `return_background_and_mask=True`. - mask_bg : numpy.ndarray of bool, optional - Final background mask (always NumPy). Returned if `return_background_and_mask=True`. - - Notes - ----- - - The right-panel plot uses `im_sub[mask]` to compute a symmetric, zero-centered range - so white corresponds to 0.0 in RdBu_r. + - If `return_background_and_mask=False`: ImageType (same as input) + - If `True`: (ImageType, numpy.ndarray, numpy.ndarray[bool]) + where background and mask are always NumPy. """ - # --- inputs --- im = _as_array(image).astype(float, copy=True) if im.ndim != 2: - raise ValueError("`image` must be a 2D numpy array or Dataset2d") + raise ValueError("`image` must be 2D") - if mask is None: - mask_arr = np.ones_like(im, dtype=bool) - else: - mask_arr = np.asarray(mask, dtype=bool) - if mask_arr.shape != im.shape: - raise ValueError("`mask` must have the same shape as `image`.") + mask_arr: BoolArray = np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) + if mask_arr.shape != im.shape: + raise ValueError("`mask` must match `image` shape") - # --- basis --- order = (int(order[0]), int(order[1])) - A_full = _build_basis_matrix(im.shape, order) # (H*W, K) + A_full = _build_basis_matrix(im.shape, order) H, W = im.shape im_flat = im.ravel() - # --- init background & mask --- im_bg = np.zeros_like(im) thresh_val = np.median(im[mask_arr]) if thresh_bg is None else float(thresh_bg) resid = im - im_bg - if sigma is not None and sigma > 0: + if sigma and sigma > 0: resid = gaussian_filter(resid, sigma=sigma, mode="nearest") - mask_bg = resid < thresh_val - mask_bg &= mask_arr + mask_bg: BoolArray = (resid < thresh_val) & mask_arr - # --- iterations --- for _ in range(int(num_iter)): idx = mask_bg.ravel() if not np.any(idx): - idx = mask_arr.ravel() # ensure solvable if mask collapses - + idx = mask_arr.ravel() coefs, *_ = np.linalg.lstsq(A_full[idx, :], im_flat[idx], rcond=None) im_bg = (A_full @ coefs).reshape(H, W) resid = im - im_bg - if sigma is not None and sigma > 0: + if sigma and sigma > 0: resid = gaussian_filter(resid, sigma=sigma, mode="nearest") thr = thresh_val if thresh_bg is None else float(thresh_bg) - mask_bg = (resid < thr) - mask_bg &= mask_arr + mask_bg = (resid < thr) & mask_arr - # --- final subtraction --- - im_sub = im - im_bg + im_sub_np = im - im_bg - # --- plotting (right panel: zero-centered RdBu_r; limits from im_sub[mask]) --- if plot_result: - # Range from masked region - vals = im_sub[mask_arr] + vals = im_sub_np[mask_arr] vals = vals[np.isfinite(vals)] if vals.size == 0: vals = np.array([0.0]) - vmin_sub = float(np.min(vals)) vmax_sub = float(np.max(vals)) - vrange = float(np.maximum(abs(vmin_sub), abs(vmax_sub))) # ±min(|min|,|max|) - if vrange <= 0: - vrange = 1e-12 + vrange = float(max(abs(vmin_sub), abs(vmax_sub))) or 1e-12 - # Background panel: show only fitted region; black elsewhere bg_disp = (im_bg - np.mean(im_bg)).copy() bg_disp[~mask_bg] = np.nan - # Colormaps - cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") # NaNs -> black - cmap_div = "RdBu_r" + cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") + cmap_div = "RdBu_r" - # Panels: input, background(masked), background-subtracted - disp = [ - im - np.mean(im_bg), - bg_disp, - im_sub, - ] - - # Per-panel normalization: + disp = [im - np.mean(im_bg), bg_disp, im_sub_np] norm = [ - {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, # input - {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, # background - {"interval_type": "centered", "stretch_type": "linear", "vcenter": 0.0, "half_range": vrange}, # zero-centered + {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, + {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, + {"interval_type": "centered", "stretch_type": "linear", "vcenter": 0.0, "half_range": vrange}, ] show_2d( disp, cmap=[cmap_base, cmap_base, cmap_div], norm=norm, - cbar=[False, False, True], # colorbar only on right panel + cbar=[False, False, True], title=["Input Image", "Background (fit region)", "Background Subtracted"], axsize=axsize, **show_kwargs, ) - # --- outputs (match image type for images; mask always ndarray) --- + # preserve Dataset2d if needed if isinstance(image, Dataset2d): meta = dict(origin=image.origin, sampling=image.sampling, units=image.units) name_base = getattr(image, "name", "image") - im_sub_ds = Dataset2d.from_array(im_sub, name=f"{name_base} (bg-sub)", **meta) - im_bg_ds = Dataset2d.from_array(im_bg, name=f"{name_base} (bg-fit)", **meta) - if return_background_and_mask: - return im_sub_ds, im_bg_ds, mask_bg - else: - return im_sub_ds + im_sub: ImageType = Dataset2d.from_array(im_sub_np, name=f"{name_base} (bg-sub)", **meta) # type: ignore[assignment] else: - if return_background_and_mask: - return im_sub, im_bg, mask_bg - else: - return im_sub + im_sub = im_sub_np # type: ignore[assignment] + + if return_background_and_mask: + return im_sub, im_bg, mask_bg + return im_sub From f1dc677e600b7ff38667b643f5523d5170be169e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 09:43:55 -0700 Subject: [PATCH 019/335] Tomo changes --- src/quantem/tomography/tilt_series_dataset.py | 153 ---- src/quantem/tomography/tomography.py | 35 +- src/quantem/tomography/tomography_base.py | 2 +- src/quantem/tomography/tomography_nerf_old.py | 658 ------------------ 4 files changed, 30 insertions(+), 818 deletions(-) delete mode 100644 src/quantem/tomography/tilt_series_dataset.py delete mode 100644 src/quantem/tomography/tomography_nerf_old.py diff --git a/src/quantem/tomography/tilt_series_dataset.py b/src/quantem/tomography/tilt_series_dataset.py deleted file mode 100644 index f6bff836..00000000 --- a/src/quantem/tomography/tilt_series_dataset.py +++ /dev/null @@ -1,153 +0,0 @@ -from typing import Any, List, Self - -import numpy as np -import torch -from numpy.typing import NDArray - -from quantem.core.datastructures.dataset3d import Dataset2d, Dataset3d -from quantem.core.utils.validators import ensure_valid_array - -# from quantem.tomography.alignment import tilt_series_cross_cor_align, compute_com_tilt_series - - -# DEPRECATED: Use TomographyDataset instead. -class TiltSeries(Dataset3d): - def __init__( - self, - array: NDArray | Any, # Assumes a input tilt series [phis, x, y] - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, - tilt_angles: list | NDArray, - z1_angles: list | NDArray, - z3_angles: list | NDArray, - shifts: list[tuple[float, float]] | NDArray, - signal_units: str = "arb. units", - _token: object | None = None, - ): - super().__init__( - array=array, - name=name, - origin=origin, - sampling=sampling, - units=units, - signal_units=signal_units, - _token=_token, - ) - self._tilt_angles = tilt_angles - self._z1_angles = z1_angles - self._z3_angles = z3_angles - self._shifts = shifts - - @classmethod - def from_array( - cls, - array: NDArray | List[Dataset2d] | Any, - tilt_angles: list | NDArray = None, - z1_angles: list | NDArray = None, - z3_angles: list | NDArray = None, - shifts: list[tuple[float, float]] | NDArray = None, - name: str | None = None, - origin: NDArray | tuple | list | float | int | None = None, - sampling: NDArray | tuple | list | float | int | None = None, - units: list[str] | tuple | list | None = None, - signal_units: str = "arb. units", - ) -> Self: - if tilt_angles is not None: - validated_tilt_angles = ensure_valid_array(tilt_angles, ndim=1) - else: - validated_tilt_angles = None - - # array = np.transpose(array, axes=(2, 0, 1)) - - if z1_angles is not None: - validated_z1_angles = ensure_valid_array(z1_angles, ndim=1) - else: - validated_z1_angles = torch.zeros(len(validated_tilt_angles)) - - if z3_angles is not None: - validated_z3_angles = ensure_valid_array(z3_angles, ndim=1) - else: - validated_z3_angles = torch.zeros(len(validated_tilt_angles)) - - if shifts is not None: - validated_shifts = ensure_valid_array(shifts, ndim=2) - else: - validated_shifts = torch.zeros((len(validated_tilt_angles), 2)) - - array = torch.from_numpy(array) - - return cls( - array=array, - tilt_angles=validated_tilt_angles - if validated_tilt_angles is not None - else ["duck" for _ in range(array.shape[0])], - z1_angles=validated_z1_angles, - z3_angles=validated_z3_angles, - shifts=validated_shifts, - name=name if name is not None else "Tilt Series Dataset", - origin=origin if origin is not None else np.zeros(3), - sampling=sampling if sampling is not None else np.ones(3), - units=units if units is not None else ["index", "pixels", "pixels"], - signal_units=signal_units, - _token=cls._token, - ) - - # --- Properties --- - - @property - def tilt_angles(self) -> NDArray: - """Get the tilt angles of the dataset.""" - return self._tilt_angles - - @property - def tilt_angles_rad(self) -> NDArray: - """Get the tilt angles of the dataset in radians.""" - return np.deg2rad(self._tilt_angles) - - @tilt_angles.setter - def tilt_angles(self, angles: NDArray | list) -> None: - """Set the tilt angles of the dataset.""" - if len(angles) != self.shape[0]: - raise ValueError("Tilt angles must match the number of projections.") - - # Convert to numpy array if not already - if isinstance(self._tilt_angles, NDArray): - self._tilt_angles = np.array(angles) - else: - self._tilt_angles = angles - - @property - def z1_angles(self) -> NDArray: - """Get the z1 angles of the dataset.""" - return self._z1_angles - - @z1_angles.setter - def z1_angles(self, angles: NDArray | list) -> None: - """Set the z1 angles of the dataset.""" - if len(angles) != self.shape[0]: - raise ValueError("Z1 angles must match the number of projections.") - self._z1_angles = angles - - @property - def z3_angles(self) -> NDArray: - """Get the z3 angles of the dataset.""" - return self._z3_angles - - @z3_angles.setter - def z3_angles(self, angles: NDArray | list) -> None: - """Set the z3 angles of the dataset.""" - if len(angles) != self.shape[0]: - raise ValueError("Z3 angles must match the number of projections.") - self._z3_angles = angles - - @property - def shifts(self) -> NDArray: - """Get the shifts of the dataset.""" - return self._shifts - - @shifts.setter - def shifts(self, shifts: list[tuple[float, float]] | NDArray) -> None: - """Set the shifts of the dataset.""" - self._shifts = shifts diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index d31964b4..ffb16d15 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -27,6 +27,7 @@ def get_num_samples_per_ray(epoch): 2: 50, 4: 100, 6: 200, + 8: 350, } num_samples = 64 @@ -100,6 +101,8 @@ def sirt_recon( else: gaussian_kernel = None + print("Devices", sirt_tilt_series.device, proj_forward.device, self.dataset.tilt_angles.device) + for iter in pbar: proj_forward, loss = self._sirt_run_epoch( tilt_series=sirt_tilt_series, @@ -196,7 +199,8 @@ def setup_logger(self): self.temp_logger = SummaryWriter() else: self.temp_logger = None - + + def recon( self, obj: ObjectINN, @@ -260,13 +264,19 @@ def recon( for epoch in range(epochs): num_samples_per_ray = get_num_samples_per_ray(self.global_epochs) + # num_samples_per_ray = get_num_samples_per_ray(epoch) # Log the change if it happens - if self.global_epochs >= 0: - prev_samples = get_num_samples_per_ray(self.global_epochs) + if self.global_rank == 0: + print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - if num_samples_per_ray != prev_samples and self.global_rank == 0: + + if self.global_rank == 0 and self.global_epochs > 0: + prev_samples = get_num_samples_per_ray(self.global_epochs - 1) + + if num_samples_per_ray != prev_samples: print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") - + + if self.sampler is not None: self.sampler.set_epoch(epoch) @@ -379,7 +389,7 @@ def recon( self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) self.temp_logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, self.global_epochs) - fig, axes = plt.subplots(1, 3, figsize=(36, 12)) + fig, axes = plt.subplots(1, 5, figsize=(36, 12)) axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) axes[0].set_title('Sum over Z-axis') @@ -391,9 +401,22 @@ def recon( thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() axes[2].matshow(thick_slice, cmap='turbo', vmin=0) axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') + + axes[3].matshow(pred_full.sum(dim = 1).cpu().numpy(), cmap='turbo', vmin=0) + axes[3].set_title('Sum over Y-axis') + + axes[4].matshow(pred_full.sum(dim = 2).cpu().numpy(), cmap='turbo', vmin=0) + axes[4].set_title('Sum over X-axis') self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) plt.close(fig) + + if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: + with torch.no_grad(): + if self.global_rank == 0: + save_path = f"new_dataset_logs/volume_epoch_{self.global_epochs:04d}.pt" + torch.save(pred_full.cpu(), save_path) + self.global_epochs += 1 if self.global_rank == 0: diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 61fb7937..35c69b34 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -85,7 +85,7 @@ def from_data( shifts=shifts, ) - # dataset.to(device) + dataset.to(device) if volume_obj is None: max_shape = max(dataset.tilt_series.shape) diff --git a/src/quantem/tomography/tomography_nerf_old.py b/src/quantem/tomography/tomography_nerf_old.py deleted file mode 100644 index adecfd6f..00000000 --- a/src/quantem/tomography/tomography_nerf_old.py +++ /dev/null @@ -1,658 +0,0 @@ -from torch.utils.data import Dataset, DataLoader, DistributedSampler -import torch -import torch.distributed as dist -import numpy as np - -from quantem.tomography.tomography_dataset import TomographyDataset -from quantem.tomography.models import HSiren - -from torch.utils.tensorboard import SummaryWriter -import matplotlib.pyplot as plt - - -import os - -def get_accumulation_steps(epoch): - """Increase accumulation steps at specific epochs.""" - # schedule = config.get("accumulation_schedule", { - # 0: 1, - # 200: 4, - # 500: 28, - # }) - - schedule = { - 0: 1, - 200: 4, - 500: 28, - } - - accumulation_steps = 1 - for epoch_threshold, steps in sorted(schedule.items()): - if epoch >= epoch_threshold: - accumulation_steps = steps - - return accumulation_steps - -def get_num_samples_per_ray(epoch): - """Increase number of samples per ray at specific epochs.""" - # schedule = config.get("num_samples_schedule", { - # 0: 64, - # 200: 128, - # 500: 256, - # }) - - # TODO: Maybe delete - schedule = { - 0: 25, - 2: 50, - 4: 100, - 6: 200, - } - - num_samples = 64 - for epoch_threshold, samples in sorted(schedule.items()): - if epoch >= epoch_threshold: - num_samples = samples - - return num_samples - -class AuxiliaryParams(torch.nn.Module): - def __init__(self, num_tilts, device, zero_tilt_idx=None): - super().__init__() - - if zero_tilt_idx is None: - # If not provided, assume first projection is reference - zero_tilt_idx = 0 - - self.zero_tilt_idx = zero_tilt_idx - self.num_tilts = num_tilts - - # Shifts: only parameterize non-reference tilts - num_param_tilts = num_tilts - 1 - self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) - - # Fixed zero shifts for reference - self.shifts_ref = torch.zeros(1, 2, device=device) - - # Z1 and Z3: parameterize all tilts EXCEPT the reference - self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - - # Fixed zeros for reference tilt - self.z1_ref = torch.zeros(1, device=device) - # self.z3_ref = torch.zeros(1, device=device) - - def forward(self, dummy_input=None): - # Reconstruct full arrays with zeros at reference position - before_shifts = self.shifts_param[:self.zero_tilt_idx] - after_shifts = self.shifts_param[self.zero_tilt_idx:] - shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) - - before_z1 = self.z1_param[:self.zero_tilt_idx] - after_z1 = self.z1_param[self.zero_tilt_idx:] - z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) - - # before_z3 = self.z3_param[:self.zero_tilt_idx] - # after_z3 = self.z3_param[self.zero_tilt_idx:] - # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) - - return shifts, z1, -z1 - - -class TomographyNerf(): - - def __init__( - self, - tomo_dataset: TomographyDataset, - - ): - - self.setup_distributed() - self.tomo_dataset = tomo_dataset - self.setup_logging() - - def setup_logging(self): - """Setup logging directory and TensorBoard writer.""" - - # base_logdir = self.config.get("logdir", "exp-logs") - - # hidden_features = self.config["hidden_features"] - # hidden_layers = self.config["hidden_layers"] - # lr = self.config["train_lr"] - # aux_lr = self.config["aux_lr"] - # bs = self.config["ray_batch_size"] - # fbs = self.config["fbs"] - # tv = self.config["tv_factor"] - - # lr_str = f"{lr:.1e}" - # tv_str = f"{tv:.1e}" - # aux_lr_str = f"{aux_lr:.1e}" - - - # descriptive_name = f"h{hidden_features}_l{hidden_layers}_lr{lr_str}_auxlr{aux_lr_str}_bs{bs}_tv{tv_str}_omega{fbs}" - # self.logdir = os.path.join(base_logdir, descriptive_name) - - if self.global_rank == 0: - os.makedirs("exp-logs", exist_ok=True) - self.writer = SummaryWriter() # Should put a logdir here - - # config_path = os.path.join(self.logdir, "config.yaml") - # with open(config_path, "w") as f: - # yaml.dump(self.config, f) - - print(f"Logging to: Need to fix") - else: - self.writer = None - def setup_distributed(self): - - """Setup distributed training if available, otherwise use single GPU/CPU.""" - - # Check if we're in a distributed environment - - - if "RANK" in os.environ: - # Distributed training - if not dist.is_initialized(): - dist.init_process_group(backend='nccl' if torch.cuda.is_available() else 'gloo', init_method='env://') - - self.world_size = dist.get_world_size() - self.global_rank = dist.get_rank() - print(f"Global rank: {self.global_rank}") - self.local_rank = int(os.environ["LOCAL_RANK"]) - - torch.cuda.set_device(self.local_rank) - self.device = torch.device("cuda", self.local_rank) - - else: - # Single GPU/CPU training - self.world_size = 1 - self.global_rank = 0 - self.local_rank = 0 - - if torch.cuda.is_available(): - self.device = torch.device("cuda:0") - torch.cuda.set_device(0) - print("Single GPU training") - else: - self.device = torch.device("cpu") - print("CPU training") - - # Optional performance optimizations (only for CUDA) - if self.device.type == "cuda": - torch.backends.cudnn.benchmark = True - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - - def create_volume(self): - N = self.tomo_dataset.dims[1] - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - # Get the underlying model if using DDP - model = self.model.module if hasattr(self.model, 'module') else self.model - - samples_per_gpu = N**3 // self.world_size - start_idx = self.global_rank * samples_per_gpu - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx].to(self.device) - outputs = model(inputs_subset) - - if outputs.dim() > 1: - outputs = outputs.squeeze(-1) - - if self.world_size > 1: - # Ensure consistent tensor sizes across all ranks - gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] - dist.all_gather(gathered_outputs, outputs.contiguous()) - - # Explicitly order by rank - pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() - else: - pred_full = outputs.reshape(N, N, N).float() - - return pred_full - - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): - """Scale learning rate based on world size.""" - if self.world_size == 1: - return base_lr - - if scaling_rule == "linear": - # Linear scaling: lr = base_lr * world_size - return base_lr * self.world_size - elif scaling_rule == "sqrt": - # Square root scaling: lr = base_lr * sqrt(world_size) - return base_lr * np.sqrt(self.world_size) - else: - return base_lr - - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): - - # Step 1: Apply shifts - shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) - shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - - rays_x = rays[:, :, 0] - shift_x_norm - rays_y = rays[:, :, 1] - shift_y_norm - rays_z = rays[:, :, 2] - - # Rotation 1: Z(-z3) - theta = torch.deg2rad(-z3).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot1 = cos_t * rays_x - sin_t * rays_y - rays_y_rot1 = sin_t * rays_x + cos_t * rays_y - rays_z_rot1 = rays_z - - # Rotation 2: X(x) - theta = torch.deg2rad(x).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot2 = rays_x_rot1 - rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 - rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - - # Rotation 3: Z(-z1) - theta = torch.deg2rad(-z1).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 - rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 - rays_z_final = rays_z_rot2 - - # Stack the final result - transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - - return transformed_rays - - def save_checkpoint(self, epoch, aux_params): - """Save model and auxiliary parameters checkpoint (rank 0 only).""" - if self.global_rank == 0: - model_to_save = self.model.module if hasattr(self.model, 'module') else self.model - aux_params_to_save = aux_params.module if hasattr(aux_params, 'module') else aux_params - - checkpoint = { - "model_state_dict": model_to_save.state_dict(), - "aux_params_state_dict": aux_params_to_save.state_dict(), - "epoch": epoch, - # "config": self.config, - } - checkpoint_path = os.path.join("exp-logs", f"checkpoint_epoch_{epoch:04d}.pt") - torch.save(checkpoint, checkpoint_path) - print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") - - def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): - """Create projection rays for entire batch simultaneously.""" - batch_size = len(pixel_i) - - # Convert all pixels to normalized coordinates - x_coords = (pixel_j / (N - 1)) * 2 - 1 - y_coords = (pixel_i / (N - 1)) * 2 - 1 - - # Create z coordinates - z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - - # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] - rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - - # Fill coordinates efficiently - rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray - rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray - rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - - return rays - - def train( - self, - batch_size: int, - num_workers: int, - omega_0 : int, - hidden_omega_0 : int, - train_lr : float, - aux_lr : float, - warmup_epochs : int, - epochs : int, - use_amp : bool, - sampling_rate : float, - tv_weight : float, - viz_freq : int, - checkpoint_freq : int, - # hidden_layers: int, - # hidden_features: int, - # alpha: float, - ): - - ### DataLoader setup - batch_size = batch_size - num_workers = num_workers - - if self.world_size > 1: - self.train_sampler = DistributedSampler( - self.tomo_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=False - ) - shuffle = False - else: - self.train_sampler = None - shuffle = True # Need this!! - - self.dataloader = DataLoader( - self.tomo_dataset, - batch_size=batch_size, - sampler=self.train_sampler, - shuffle=shuffle, - num_workers=num_workers, - pin_memory=True if self.device.type == "cuda" else False, - drop_last=True, - persistent_workers=False if num_workers == 0 else True - ) - - # Print statistics - if self.global_rank == 0: - # total_pixels = len(self.tomo_dataset.tilt_angles) * self.config["N"] ** 2 - train_pixels = self.tomo_dataset.num_pixels - effective_batch_size = batch_size * self.world_size - - print(f"Dataloader setup complete:") - print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") - print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") - print(f" Total pixels: {train_pixels:,}") - - print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {effective_batch_size}") - print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - - - ### - - ### Model Setup - - self.model = HSiren( - in_features = 3, - out_features = 1, - hidden_features = 512, - hidden_layers = 4, - first_omega_0 = 30, - hidden_omega_0 = 30, # Remove hidden_omega_0. - ).to(self.device) - - if self.world_size > 1: - self.model = torch.nn.parallel.DistributedDataParallel( - self.model, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - bucket_cap_mb=100, - gradient_as_bucket_view=True, # TODO: Check if this is needed. - ) - if self.global_rank == 0: - print("Model wrapped with DDP") - - if self.world_size > 1: - - if self.global_rank == 0: - print("Model built, distributed, and compiled successfully.") - else: - print("Model built and compiled successfully.") - - ### Initializing optimizers, and auxiliary parameters - - # Torch module on z1, z3, and shifts as module - - phis = self.tomo_dataset.tilt_angles - zero_tilt_idx = torch.argmin(torch.abs(phis)).item() - - if self.global_rank == 0: - print(f"Using projection {zero_tilt_idx} (angle={phis[zero_tilt_idx]:.2f}°) as reference") - - - self.aux_params = AuxiliaryParams( - num_tilts = len(phis), - device = self.device, - zero_tilt_idx = zero_tilt_idx - ) - - - if self.world_size > 1: - self.aux_params = torch.nn.parallel.DistributedDataParallel( - self.aux_params, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True - ) - if self.global_rank == 0: - print("Auxiliary parameters wrapped with DDP") - - # Learning rates - scaled_train_lr = train_lr*np.sqrt(self.world_size) - scaled_aux_lr = aux_lr*np.sqrt(self.world_size) - - # Optimizers - optimizer = torch.optim.Adam(self.model.parameters(), lr=scaled_train_lr, fused = True) - aux_optimizer = torch.optim.Adam(self.aux_params.parameters(), lr=scaled_aux_lr) - - aux_norm = torch.tensor(0.0, device=self.device) - model_norm = torch.tensor(0.0, device=self.device) - - optimizer.zero_grad() - aux_optimizer.zero_grad() - - warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR(optimizer, start_factor=0.01, total_iters=warmup_epochs) # TODO: Start factor play around with this. - cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs - warmup_epochs, eta_min=scaled_train_lr/100) - scheduler_model = torch.optim.lr_scheduler.SequentialLR(optimizer, schedulers=[warmup_scheduler_model, cosine_scheduler_model], milestones=[warmup_epochs]) - - scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR(aux_optimizer, T_max=epochs, eta_min=scaled_aux_lr/100) - - device_type = self.device.type - autocast_dtype = torch.bfloat16 if use_amp else None - - for epoch in range(epochs): - current_accumulation_steps = get_accumulation_steps(epoch) - num_samples_per_ray = get_num_samples_per_ray(epoch) - - if epoch > 0: - prev_steps = get_accumulation_steps(epoch - 1) - prev_samples = get_num_samples_per_ray(epoch - 1) - - if current_accumulation_steps != prev_steps and self.global_rank == 0: - print(f"Epoch {epoch}: Changing accumulation steps from {prev_steps} to {current_accumulation_steps}") - if num_samples_per_ray != prev_samples and self.global_rank == 0: - print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") - - if self.train_sampler is not None: - self.train_sampler.set_epoch(epoch) - - self.model.train() - epoch_loss = 0.0 - epoch_mse_loss = 0.0 - epoch_tv_loss = 0.0 - epoch_z1_loss = 0.0 - - num_batches = 0 - - for batch_idx, batch in enumerate(self.dataloader): - - projection_indices = batch['projection_idx'] - - pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) - pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) - target_values = batch['target_value'].to(self.device, non_blocking=True) - phis = batch['phi'].to(self.device, non_blocking=True) - projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = self.aux_params(None) # NONE just for DDP - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): - - with torch.no_grad(): # For every value of the tilts, shifts, and phi figure out where ray enters and exits and sample along that line. Ray has to be -1 to 1 - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, self.tomo_dataset.dims[1], num_samples_per_ray - ) - - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=self.tomo_dataset.dims[1], - sampling_rate=1 # TODO: make input into train - ) - - all_coords = transformed_rays.view(-1, 3) - all_densities = self.model(all_coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) # [N, 1] → [N] - - valid_mask = ( # TODO: Needs to be fixed edge of boxes - (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & - (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & - (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) - ).float() - - all_densities = all_densities * valid_mask - if tv_weight > 0: - - num_tv_samples = min(10000, all_coords.shape[0]) - tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] - - tv_coords = all_coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True - )[0] - - - grad_norm = torch.norm(grad_outputs, dim=1) - tv_loss = tv_weight * grad_norm.mean() - else: - tv_loss = torch.tensor(0.0, device=self.device) - - ray_densities = all_densities.view(len(target_values), num_samples_per_ray) - - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - - tv_loss_z1 = torch.tensor(0.0, device=self.device) - - loss = mse_loss + tv_loss_z1 + tv_loss - loss = loss / current_accumulation_steps - - loss.backward() - - epoch_loss += loss.detach() - epoch_mse_loss += mse_loss.detach() - epoch_tv_loss += tv_loss.detach() - epoch_z1_loss += tv_loss_z1.detach() - - num_batches += 1 - if epoch >= warmup_epochs: - - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) - optimizer.step() - optimizer.zero_grad() - - aux_norm = torch.nn.utils.clip_grad_norm_(self.aux_params.parameters(), max_norm = 1) - aux_optimizer.step() - aux_optimizer.zero_grad() - else: - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) - optimizer.step() - optimizer.zero_grad() - - aux_optimizer.zero_grad() - - scheduler_model.step() - if epoch >= warmup_epochs: - scheduler_aux.step() - - if self.global_rank == 0: - print(f"Epoch Complete {epoch}") - - - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : - with torch.no_grad(): - pred_full = self.create_volume().cpu() - avg_loss = epoch_loss.item() / num_batches - avg_mse_loss = epoch_mse_loss.item() / num_batches - avg_tv_loss = epoch_tv_loss.item() / num_batches - avg_z1_loss = epoch_z1_loss.item() / num_batches - - - metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) - if self.world_size > 1: - torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) - avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - - - if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): - with torch.no_grad(): - current_lr = scheduler_model.get_last_lr()[0] - - # Log metrics - self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) - - - self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) - if tv_weight > 0: - self.writer.add_scalar("train/tv_loss", avg_tv_loss, epoch) - self.writer.add_scalar("train/total_loss", avg_loss, epoch) - self.writer.add_scalar("train/model_grad_norm", model_norm.item(), epoch) - self.writer.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) - self.writer.add_scalar("train/lr", current_lr, epoch) - self.writer.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) - - fig, axes = plt.subplots(1, 3, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) - axes[0].set_title('Sum over Z-axis') - - axes[1].matshow(pred_full[self.tomo_dataset.dims[1]//2].cpu().numpy(), cmap='turbo', vmin=0) - axes[1].set_title(f'Slice at Z={self.tomo_dataset.dims[1]//2}') - - slice_start = max(0, self.tomo_dataset.dims[1]//2 - 5) - slice_end = min(self.tomo_dataset.dims[1], self.tomo_dataset.dims[1]//2 + 6) - thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap='turbo', vmin=0) - axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') - - self.writer.add_figure("train/viz", fig, epoch, close=True) - plt.close(fig) - - if epoch % checkpoint_freq == 0 or epoch == epochs - 1: - with torch.no_grad(): - if self.global_rank == 0: - save_path = os.path.join("exp-logs", f"volume_epoch_{epoch:04d}.pt") - torch.save(pred_full.cpu(), save_path) - - self.save_checkpoint(epoch, self.aux_params) - - if self.global_rank == 0: - print("Training complete.") - - - \ No newline at end of file From a57d885060dcc3be315803a5f389f555c216e683 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 16 Oct 2025 15:41:12 -0700 Subject: [PATCH 020/335] Enforcing positivity optional in tomography_dataset with clamp flag --- src/quantem/core/utils/imaging_utils.py | 35 ++++++-- src/quantem/tomography/tomography_base.py | 17 ++-- src/quantem/tomography/tomography_dataset.py | 92 ++++++++++---------- 3 files changed, 83 insertions(+), 61 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 4e3ca537..25d1610d 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,14 +1,16 @@ # Utilities for processing images from __future__ import annotations + from typing import Any, Optional, Tuple, TypeVar import numpy as np +from matplotlib import cm from numpy.typing import NDArray from scipy.ndimage import gaussian_filter from scipy.special import comb -from matplotlib import cm from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.utils.utils import generate_batches from quantem.core.visualization import show_2d # single TypeVar: works for both numpy and Dataset2d @@ -372,9 +374,13 @@ def fourier_cropping( def _as_array(x: ImageType) -> NDArray[Any]: return x.array if isinstance(x, Dataset2d) else np.asarray(x) + def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: k = np.arange(n + 1, dtype=int) - return comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) + return ( + comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) + ) + def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray[Any]: H, W = im_shape @@ -413,7 +419,9 @@ def background_subtract( if im.ndim != 2: raise ValueError("`image` must be 2D") - mask_arr: BoolArray = np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) + mask_arr: BoolArray = ( + np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) + ) if mask_arr.shape != im.shape: raise ValueError("`mask` must match `image` shape") @@ -463,9 +471,24 @@ def background_subtract( disp = [im - np.mean(im_bg), bg_disp, im_sub_np] norm = [ - {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, - {"interval_type": "manual", "stretch_type": "linear", "vmin": vmin_sub, "vmax": vmax_sub}, - {"interval_type": "centered", "stretch_type": "linear", "vcenter": 0.0, "half_range": vrange}, + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "centered", + "stretch_type": "linear", + "vcenter": 0.0, + "half_range": vrange, + }, ] show_2d( diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 35c69b34..b05e51fe 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1,10 +1,11 @@ +from typing import Tuple + import matplotlib.pyplot as plt import numpy as np import torch from numpy.typing import NDArray from torch._tensor import Tensor from tqdm.auto import tqdm -from typing import Tuple from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.io.serialize import AutoSerialize @@ -74,6 +75,7 @@ def from_data( shifts: NDArray | Tensor | None = None, volume_obj: NDArray | Dataset3d | ObjectModelType | None = None, device: str = "cpu", + clamp: bool = True, ): device = device.lower() @@ -83,6 +85,7 @@ def from_data( z1_angles=z1_angles, z3_angles=z3_angles, shifts=shifts, + clamp=clamp, ) dataset.to(device) @@ -436,8 +439,8 @@ def plot_projections( cmap=cmap, title="Y-X Projection FFT", # norm=norm, - vmin = fft_vmax[0], - vmax = fft_vmax[1], + vmin=fft_vmax[0], + vmax=fft_vmax[1], ) show_2d( @@ -445,8 +448,8 @@ def plot_projections( figax=(fig, ax[1]), cmap=cmap, title="Z-X Projection FFT", - vmin = fft_vmax[0], - vmax = fft_vmax[1], + vmin=fft_vmax[0], + vmax=fft_vmax[1], # norm=norm, ) show_2d( @@ -454,8 +457,8 @@ def plot_projections( figax=(fig, ax[2]), cmap=cmap, title="Z-Y Projection FFT", - vmin = fft_vmax[0], - vmax = fft_vmax[1], + vmin=fft_vmax[0], + vmax=fft_vmax[1], # norm=norm, ) diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index c66b9e61..fe5dab7c 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -1,6 +1,8 @@ +import numpy as np import torch from numpy.typing import NDArray from torch._tensor import Tensor +from torch.utils.data import Dataset from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.io.serialize import AutoSerialize @@ -9,9 +11,6 @@ validate_tensor, ) -from torch.utils.data import Dataset - -import numpy as np class AuxiliaryParams(torch.nn.Module): def __init__(self, num_tilts, device, zero_tilt_idx=None): @@ -41,12 +40,12 @@ def __init__(self, num_tilts, device, zero_tilt_idx=None): def forward(self, dummy_input=None): # Reconstruct full arrays with zeros at reference position - before_shifts = self.shifts_param[:self.zero_tilt_idx] - after_shifts = self.shifts_param[self.zero_tilt_idx:] + before_shifts = self.shifts_param[: self.zero_tilt_idx] + after_shifts = self.shifts_param[self.zero_tilt_idx :] shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) - before_z1 = self.z1_param[:self.zero_tilt_idx] - after_z1 = self.z1_param[self.zero_tilt_idx:] + before_z1 = self.z1_param[: self.zero_tilt_idx] + after_z1 = self.z1_param[self.zero_tilt_idx :] z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) # before_z3 = self.z3_param[:self.zero_tilt_idx] @@ -54,8 +53,8 @@ def forward(self, dummy_input=None): # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) return shifts, z1, -z1 - - + + class TomographyDataset(AutoSerialize, Dataset): _token = object() @@ -74,10 +73,11 @@ def __init__( z1_angles: Tensor, z3_angles: Tensor, shifts: Tensor, + clamp: bool, ): self._tilt_series = tilt_series # Enforce Positivity - + self._tilt_angles = tilt_angles self._z1_angles = z1_angles self._z3_angles = z3_angles @@ -87,21 +87,21 @@ def __init__( self._initial_z1_angles = z1_angles.clone() self._initial_z3_angles = z3_angles.clone() self._initial_shifts = shifts.clone() - + # Move everything to CPU self.to("cpu") - + # Number of indices (?) - - + # Enforce normalization of tilt series try: - tilt_percentile = torch.quantile(self._tilt_series, .95) - except: - tilt_percentile = np.quantile(self._tilt_series, .95) + tilt_percentile = torch.quantile(self._tilt_series, 0.95) + except (AttributeError, RuntimeError): + tilt_percentile = np.quantile(self._tilt_series, 0.95) + self._tilt_series = self._tilt_series / tilt_percentile - self._tilt_series = torch.clamp(self._tilt_series, min=0) - + if clamp: + self._tilt_series = torch.clamp(self._tilt_series, min=0) # --- Class Methods --- @classmethod @@ -112,6 +112,7 @@ def from_data( z1_angles: NDArray | Tensor | None = None, z3_angles: NDArray | Tensor | None = None, shifts: NDArray | Tensor | None = None, + clamp: bool = True, ): """ tilt_series: (N, H, W) @@ -151,6 +152,7 @@ def from_data( z1_angles=validated_z1_angles, z3_angles=validated_z3_angles, shifts=validated_shifts, + clamp=clamp, # name=name, # origin=origin, # sampling=sampling, @@ -202,50 +204,45 @@ def initial_z3_angles(self) -> Tensor: @property def initial_shifts(self) -> Tensor: return self._initial_shifts - + @property def num_projections(self) -> int: - return self._tilt_series.shape[0] - + @property def num_pixels(self) -> int: - return self._tilt_series.shape[0] * self._tilt_series.shape[1] * self._tilt_series.shape[2] - + @property def dims(self) -> tuple[int, int, int]: - return self._tilt_series.shape[0], self._tilt_series.shape[1], self._tilt_series.shape[2] - + def __len__(self) -> int: - return self.num_pixels - + def __getitem__(self, idx): - actual_idx = idx - + projection_idx = actual_idx // (self.dims[1] * self.dims[2]) remaining = actual_idx % (self.dims[1] * self.dims[2]) - + # TODO: What if non-square tilt images? if self.dims[1] != self.dims[2]: raise NotImplementedError("Non-square tilt images are not supported yet.") - - #TODO: row, column + + # TODO: row, column pixel_i = remaining // self.dims[1] pixel_j = remaining % self.dims[1] - + target_value = self._tilt_series[projection_idx, pixel_i, pixel_j] phi = self._tilt_angles[projection_idx] - + return { - 'projection_idx': projection_idx, - 'pixel_i': pixel_i, - 'pixel_j': pixel_j, - 'target_value': target_value, - 'phi': phi + "projection_idx": projection_idx, + "pixel_i": pixel_i, + "pixel_j": pixel_j, + "target_value": target_value, + "phi": phi, } # --- Setters --- @@ -320,23 +317,22 @@ def initial_shifts(self, shifts: NDArray | Tensor) -> None: self._initial_shifts = shifts # TODO: Temp auxiliary params - + def setup_auxiliary_params(self, zero_tilt_idx: int = None, device: str = "cpu") -> None: - if not hasattr(self, "_auxiliary_params"): self._auxiliary_params = AuxiliaryParams( - num_tilts = len(self.tilt_angles), - device = device, - zero_tilt_idx = zero_tilt_idx, + num_tilts=len(self.tilt_angles), + device=device, + zero_tilt_idx=zero_tilt_idx, ) - + else: print("Auxiliary params already set") - + @property def auxiliary_params(self) -> AuxiliaryParams: return self._auxiliary_params - + # --- RESET --- def reset(self) -> None: From 078f3519d1fe27a3e28aaa4c04abefbdf88479f4 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 14:39:40 -0700 Subject: [PATCH 021/335] Added SIREN, HSIREN, and Finer with allowed complex inputs --- src/quantem/core/ml/activation_functions.py | 32 ++++ src/quantem/core/ml/blocks.py | 129 ++++++++++++-- src/quantem/core/ml/{cnn3d.py => cnn.py} | 188 +++++++++++++++++++- src/quantem/core/ml/cnn2d.py | 187 ------------------- src/quantem/core/ml/inr.py | 87 +++++++++ 5 files changed, 419 insertions(+), 204 deletions(-) rename src/quantem/core/ml/{cnn3d.py => cnn.py} (50%) delete mode 100644 src/quantem/core/ml/cnn2d.py create mode 100644 src/quantem/core/ml/inr.py diff --git a/src/quantem/core/ml/activation_functions.py b/src/quantem/core/ml/activation_functions.py index 1f21fdd1..a4bea716 100644 --- a/src/quantem/core/ml/activation_functions.py +++ b/src/quantem/core/ml/activation_functions.py @@ -83,6 +83,37 @@ def PhaseReLU( return f.type(torch.complex64) +class FinerActivation(nn.Module): + """ + Finer Activation function for complex and real inputs. + + - Similar to sine activation function. + """ + def __init__( + self, + omega: float = 1, + ): + super().__init__() + self.omega = omega + + def generate_alpha(self, x): + + with torch.no_grad(): + return torch.abs(x) + 1 + + def forward(self, x): + if x.is_complex(): # Complex Input Check + with torch.no_grad(): + alpha_real = torch.abs(x.real) + 1 + alpha_imag = torch.abs(x.imag) + 1 + x.real = x.real * alpha_real + x.imag = x.imag * alpha_imag + return torch.sin(self.omega * self.generate_alpha(x) * x) + else: + return torch.sin(self.omega * self.generate_alpha(x) * x) + + + def get_activation_function( activation_type: str | Callable, @@ -151,6 +182,7 @@ def forward(self, x): activation = nn.Sigmoid() elif activation_type in ["softplus"]: activation = nn.Softplus() + elif activation_type in ["finer"]: else: raise ValueError(f"Unknown activation type {activation_type}") diff --git a/src/quantem/core/ml/blocks.py b/src/quantem/core/ml/blocks.py index bea0f8d2..6dd1ffa8 100644 --- a/src/quantem/core/ml/blocks.py +++ b/src/quantem/core/ml/blocks.py @@ -2,9 +2,12 @@ import numpy as np +import math + + from quantem.core import config -from .activation_functions import Complex_ReLU +from .activation_functions import Complex_ReLU, FinerActivation if TYPE_CHECKING: import torch @@ -17,6 +20,9 @@ import torch.nn.functional as F + +# ---- Convolutional Layers ---- + def complex_pool(z, m, **kwargs): return m(z.real) + 1.0j * m(z.imag) @@ -111,18 +117,6 @@ def forward(self, x): return output -class ComplexLinear(nn.Module): - def __init__(self, in_features: int, out_features: int): - super().__init__() - linear = nn.Linear(in_features, out_features, dtype=torch.cfloat) - self.weight = nn.Parameter(torch.view_as_real(linear.weight)) - self.bias = nn.Parameter(torch.view_as_real(linear.bias)) - - def forward(self, x): - weight = torch.view_as_complex(self.weight) - bias = torch.view_as_complex(self.bias) - return F.linear(x, weight, bias) - class Upsample2dBlock(nn.Module): """ @@ -337,3 +331,112 @@ def __init__(self, num_features): def forward(self, x): return torch.complex(self.real_bn(x.real), self.imag_bn(x.imag)) + +# ---- Linear Layers ---- + +class ComplexLinear(nn.Module): + def __init__(self, in_features: int, out_features: int): + super().__init__() + linear = nn.Linear(in_features, out_features, dtype=torch.cfloat) + self.weight = nn.Parameter(torch.view_as_real(linear.weight)) + self.bias = nn.Parameter(torch.view_as_real(linear.bias)) + + def forward(self, x): + weight = torch.view_as_complex(self.weight) + bias = torch.view_as_complex(self.bias) + return F.linear(x, weight, bias) + +## ---- Siren Family of Layers ---- + +def init_weights(m: nn.Module, omega: float = 1., c: float = 1., is_first: bool = False): + if hasattr(m, 'weight'): + fan_in = m.weight.size(-1) + if is_first: + bound = 1 / fan_in # SIREN + else: + bound = math.sqrt(c / fan_in) / omega + nn.init.uniform_(m.weight, -bound, bound) + +def init_bias(m: nn.Module, k: float): + if hasattr(m, 'bias'): + nn.init.uniform_(m.bias, -k, k) + +class SineLayer(nn.Module): + """ + + Sine layer for H-Siren, and SIREN implementations. + + Note: H-Siren uses the hyperbolic sine function only for the first layer. + """ + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + is_first: bool = False, + omega_0: float = 30, + hsiren: bool = False, + alpha: float = 1.0, + ): + super().__init__() + self.omega_0 = omega_0 + self.is_first = is_first + self.hsiren = hsiren + self.in_features = in_features + self.alpha = alpha + self.linear = nn.Linear(in_features, out_features, bias=bias) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + if self.is_first: + # Scale the first layer initialization by alpha + self.linear.weight.uniform_(-self.alpha / self.in_features, + self.alpha / self.in_features) + else: + # Scale the hidden layer initialization by alpha + self.linear.weight.uniform_(-self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, + self.alpha * np.sqrt(6 / self.in_features) / self.omega_0) + + def forward(self, input): + if self.is_first and self.hsiren: + out = torch.sin(self.omega_0 * torch.sinh(2*self.linear(input))) + else: + out = torch.sin(self.omega_0 * self.linear(input)) + return out + +class FinerLayer(nn.Module): + + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + omega: float = 30, + is_first: bool = False, + is_last: bool = False, + init_method: str = 'sine', + init_gain: float = 1, + fbs: bool = None, + hbs = None, + alphaType = None, + alphaReqGrad = False, + ): + + super().__init__() + self.omega = omega + self.is_last = is_last + self.alphaType = alphaType + self.alphaReqGrad = alphaReqGrad + self.linear = nn.Linear(in_features, out_features, bias=bias) + + # init weights + init_weights(self.linear, omega, init_gain, is_first) + # init bias + init_bias(self.linear, fbs, is_first) + + def forward(self, input): + wx_b = self.linear(input) + if not self.is_last: + return FinerActivation(wx_b, self.omega) + return wx_b # is_last==True \ No newline at end of file diff --git a/src/quantem/core/ml/cnn3d.py b/src/quantem/core/ml/cnn.py similarity index 50% rename from src/quantem/core/ml/cnn3d.py rename to src/quantem/core/ml/cnn.py index 8247b05b..b2f6d56b 100644 --- a/src/quantem/core/ml/cnn3d.py +++ b/src/quantem/core/ml/cnn.py @@ -1,10 +1,190 @@ -from typing import Callable +from typing import TYPE_CHECKING, Callable -import torch -import torch.nn as nn +from quantem.core import config from .activation_functions import get_activation_function -from .blocks import Conv3dBlock, Upsample3dBlock, complex_pool, passfunc +from .blocks import Conv2dBlock, Upsample2dBlock, Conv3dBlock, Upsample3dBlock, complex_pool, passfunc + +if TYPE_CHECKING: + import torch + import torch.nn as nn +else: + if config.get("has_torch"): + import torch + import torch.nn as nn + + +class CNN2d(nn.Module): + """ """ + + def __init__( + self, + in_channels: int, # input channels (C_in, H, W) + out_channels: int | None = None, # output channels (C_out, H, W) + start_filters: int = 16, + num_layers: int = 3, # num_layers + num_per_layer: int = 2, # number conv per layer + use_skip_connections: bool = True, + dtype: torch.dtype = torch.float32, + dropout: float = 0, + activation: str | Callable = "relu", + final_activation: str | Callable = nn.Identity(), + use_batchnorm: bool = True, + ): + super().__init__() + self.in_channels = int(in_channels) + self.out_channels = int(out_channels) if out_channels is not None else int(in_channels) + self.start_filters = start_filters + self.num_layers = num_layers + self._num_per_layer = num_per_layer + if use_skip_connections and num_per_layer < 2: + raise ValueError( + "If using skip connections, num_per_layer must be at least 2 to allow for " + + "channel concatenation." + ) + self.use_skip_connections = use_skip_connections + self.dtype = dtype + self.dropout = dropout + self._use_batchnorm = use_batchnorm + + if self.dtype.is_complex: + self.pool = complex_pool + else: + self.pool = passfunc + self._pooler = nn.MaxPool2d(kernel_size=2, stride=2) + + self.concat = torch.cat + self.flatten = nn.Flatten() + + self.activation = activation + self.final_activation = final_activation + + self._build() + + @property + def activation(self) -> Callable: + return self._activation + + @activation.setter + def activation(self, act: str | Callable): + if callable(act): + self._activation = act + else: + self._activation = get_activation_function(act, self.dtype) + + @property + def final_activation(self) -> Callable: + return self._final_activation + + @final_activation.setter + def final_activation(self, act: str | Callable): + if callable(act): + self._final_activation = act + else: + self._final_activation = get_activation_function(act, self.dtype) + + def _build(self): + self.down_conv_blocks = nn.ModuleList() + self.up_conv_blocks = nn.ModuleList() + self.upsample_blocks = nn.ModuleList() + + in_channels = self.in_channels + out_channels = self.start_filters + for a0 in range(self.num_layers): + if a0 != 0: + out_channels = in_channels * 2 + self.down_conv_blocks.append( + Conv2dBlock( + nb_layers=self._num_per_layer, + input_channels=in_channels, + output_channels=out_channels, + use_batchnorm=self._use_batchnorm, + dropout=self.dropout, + dtype=self.dtype, + activation=self.activation, + ) + ) + in_channels = out_channels + + out_channels = in_channels * 2 + self.bottleneck = Conv2dBlock( + nb_layers=self._num_per_layer, + input_channels=in_channels, + output_channels=out_channels, + use_batchnorm=self._use_batchnorm, + dropout=self.dropout, + dtype=self.dtype, + activation=self.activation, + ) + in_channels = out_channels + + for a0 in range(self.num_layers): + out_channels = self.start_filters if a0 == self.num_layers - 1 else in_channels // 2 + + in_channels2 = in_channels if self.use_skip_connections else out_channels + + self.upsample_blocks.append( + Upsample2dBlock( + in_channels, out_channels, use_batchnorm=self._use_batchnorm, dtype=self.dtype + ) + ) + + self.up_conv_blocks.append( + Conv2dBlock( + nb_layers=self._num_per_layer, + input_channels=in_channels2, + output_channels=out_channels, + use_batchnorm=self._use_batchnorm, + dropout=self.dropout, + dtype=self.dtype, + activation=self.activation, + ) + ) + + in_channels = out_channels + + self.final_conv = Conv2dBlock( + nb_layers=1, + input_channels=self.start_filters, + output_channels=self.out_channels, + use_batchnorm=False, + dropout=self.dropout, + dtype=self.dtype, + activation=self.final_activation, + ) + return + + def forward(self, x: torch.Tensor) -> torch.Tensor: + skips = [] + for down_block in self.down_conv_blocks: + x = down_block(x) + if self.use_skip_connections: + skips.append(x) + x = self.pool(x, self._pooler) + + x = self.bottleneck(x) + for upsample_block, up_conv_block in zip(self.upsample_blocks, self.up_conv_blocks): + x = upsample_block(x) + if self.use_skip_connections: + skip = skips.pop() + x = torch.cat((x, skip), dim=1) + x = up_conv_block(x) + + y = self.final_conv(x) + + return y + + def reset_weights(self): + """ + Reset all weights. + """ + + def _reset(m: nn.Module) -> None: + reset_parameters = getattr(m, "reset_parameters", None) + if callable(reset_parameters): + reset_parameters() + + self.apply(_reset) class CNN3d(nn.Module): diff --git a/src/quantem/core/ml/cnn2d.py b/src/quantem/core/ml/cnn2d.py deleted file mode 100644 index ce7fa53f..00000000 --- a/src/quantem/core/ml/cnn2d.py +++ /dev/null @@ -1,187 +0,0 @@ -from typing import TYPE_CHECKING, Callable - -from quantem.core import config - -from .activation_functions import get_activation_function -from .blocks import Conv2dBlock, Upsample2dBlock, complex_pool, passfunc - -if TYPE_CHECKING: - import torch - import torch.nn as nn -else: - if config.get("has_torch"): - import torch - import torch.nn as nn - - -class CNN2d(nn.Module): - """ """ - - def __init__( - self, - in_channels: int, # input channels (C_in, H, W) - out_channels: int | None = None, # output channels (C_out, H, W) - start_filters: int = 16, - num_layers: int = 3, # num_layers - num_per_layer: int = 2, # number conv per layer - use_skip_connections: bool = True, - dtype: torch.dtype = torch.float32, - dropout: float = 0, - activation: str | Callable = "relu", - final_activation: str | Callable = nn.Identity(), - use_batchnorm: bool = True, - ): - super().__init__() - self.in_channels = int(in_channels) - self.out_channels = int(out_channels) if out_channels is not None else int(in_channels) - self.start_filters = start_filters - self.num_layers = num_layers - self._num_per_layer = num_per_layer - if use_skip_connections and num_per_layer < 2: - raise ValueError( - "If using skip connections, num_per_layer must be at least 2 to allow for " - + "channel concatenation." - ) - self.use_skip_connections = use_skip_connections - self.dtype = dtype - self.dropout = dropout - self._use_batchnorm = use_batchnorm - - if self.dtype.is_complex: - self.pool = complex_pool - else: - self.pool = passfunc - self._pooler = nn.MaxPool2d(kernel_size=2, stride=2) - - self.concat = torch.cat - self.flatten = nn.Flatten() - - self.activation = activation - self.final_activation = final_activation - - self._build() - - @property - def activation(self) -> Callable: - return self._activation - - @activation.setter - def activation(self, act: str | Callable): - if callable(act): - self._activation = act - else: - self._activation = get_activation_function(act, self.dtype) - - @property - def final_activation(self) -> Callable: - return self._final_activation - - @final_activation.setter - def final_activation(self, act: str | Callable): - if callable(act): - self._final_activation = act - else: - self._final_activation = get_activation_function(act, self.dtype) - - def _build(self): - self.down_conv_blocks = nn.ModuleList() - self.up_conv_blocks = nn.ModuleList() - self.upsample_blocks = nn.ModuleList() - - in_channels = self.in_channels - out_channels = self.start_filters - for a0 in range(self.num_layers): - if a0 != 0: - out_channels = in_channels * 2 - self.down_conv_blocks.append( - Conv2dBlock( - nb_layers=self._num_per_layer, - input_channels=in_channels, - output_channels=out_channels, - use_batchnorm=self._use_batchnorm, - dropout=self.dropout, - dtype=self.dtype, - activation=self.activation, - ) - ) - in_channels = out_channels - - out_channels = in_channels * 2 - self.bottleneck = Conv2dBlock( - nb_layers=self._num_per_layer, - input_channels=in_channels, - output_channels=out_channels, - use_batchnorm=self._use_batchnorm, - dropout=self.dropout, - dtype=self.dtype, - activation=self.activation, - ) - in_channels = out_channels - - for a0 in range(self.num_layers): - out_channels = self.start_filters if a0 == self.num_layers - 1 else in_channels // 2 - - in_channels2 = in_channels if self.use_skip_connections else out_channels - - self.upsample_blocks.append( - Upsample2dBlock( - in_channels, out_channels, use_batchnorm=self._use_batchnorm, dtype=self.dtype - ) - ) - - self.up_conv_blocks.append( - Conv2dBlock( - nb_layers=self._num_per_layer, - input_channels=in_channels2, - output_channels=out_channels, - use_batchnorm=self._use_batchnorm, - dropout=self.dropout, - dtype=self.dtype, - activation=self.activation, - ) - ) - - in_channels = out_channels - - self.final_conv = Conv2dBlock( - nb_layers=1, - input_channels=self.start_filters, - output_channels=self.out_channels, - use_batchnorm=False, - dropout=self.dropout, - dtype=self.dtype, - activation=self.final_activation, - ) - return - - def forward(self, x: torch.Tensor) -> torch.Tensor: - skips = [] - for down_block in self.down_conv_blocks: - x = down_block(x) - if self.use_skip_connections: - skips.append(x) - x = self.pool(x, self._pooler) - - x = self.bottleneck(x) - for upsample_block, up_conv_block in zip(self.upsample_blocks, self.up_conv_blocks): - x = upsample_block(x) - if self.use_skip_connections: - skip = skips.pop() - x = torch.cat((x, skip), dim=1) - x = up_conv_block(x) - - y = self.final_conv(x) - - return y - - def reset_weights(self): - """ - Reset all weights. - """ - - def _reset(m: nn.Module) -> None: - reset_parameters = getattr(m, "reset_parameters", None) - if callable(reset_parameters): - reset_parameters() - - self.apply(_reset) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py new file mode 100644 index 00000000..2c0bcd19 --- /dev/null +++ b/src/quantem/core/ml/inr.py @@ -0,0 +1,87 @@ +from .blocks import SineLayer, FinerLayer +from torch import nn +import torch + +class Siren(nn.Module): + def __init__( + self, + in_features: int = 2, + out_features: int = 3, + hidden_layers=3, + hidden_features: int = 256, + first_omega_0: float = 30.0, + hidden_omega_0: float = 30.0, + alpha: float = 1.0, + ): + super().__init__() + self.net = [] + self.net.append(SineLayer(in_features, hidden_features, is_first=True, + omega_0=first_omega_0, alpha=alpha)) + + for i in range(hidden_layers): + self.net.append(SineLayer(hidden_features, hidden_features, is_first=False, + omega_0=hidden_omega_0, alpha=alpha)) + + final_linear = nn.Linear(hidden_features, out_features) + with torch.no_grad(): + # Final layer keeps original initialization (no alpha scaling) + final_linear.weight.uniform_(-torch.sqrt(6 / hidden_features) / hidden_omega_0, + torch.sqrt(6 / hidden_features) / hidden_omega_0) + self.net.append(final_linear) + + self.net = nn.Sequential(*self.net) + + def forward(self, coords): + output = self.net(coords) + return output + +class HSiren(nn.Module): + def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, + first_omega_0=30, hidden_omega_0=30, alpha=1.0): + super().__init__() + self.net = [] + self.net.append(SineLayer(in_features, hidden_features, is_first=True, + omega_0=first_omega_0, hsiren=True, alpha=alpha)) + + for i in range(hidden_layers): + self.net.append(SineLayer(hidden_features, hidden_features, is_first=False, + omega_0=hidden_omega_0, alpha=alpha)) + + final_linear = nn.Linear(hidden_features, out_features) + with torch.no_grad(): + # Final layer keeps original initialization (no alpha scaling) + final_linear.weight.uniform_(-torch.sqrt(6 / hidden_features) / hidden_omega_0, + torch.sqrt(6 / hidden_features) / hidden_omega_0) + self.net.append(final_linear) + + self.net = nn.Sequential(*self.net) + + def forward(self, coords): + output = self.net(coords) + return output + +class Finer(nn.Module): + def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, + first_omega=30, hidden_omega=30, + init_method='sine', init_gain=1, fbs=None, hbs=None, + alphaType=None, alphaReqGrad=False): + super().__init__() + self.net = [] + self.net.append(FinerLayer(in_features, hidden_features, is_first=True, + omega=first_omega, + init_method=init_method, init_gain=init_gain, fbs=fbs, + alphaType=alphaType, alphaReqGrad=alphaReqGrad)) + + for i in range(hidden_layers): + self.net.append(FinerLayer(hidden_features, hidden_features, + omega=hidden_omega, + init_method=init_method, init_gain=init_gain, hbs=hbs, + alphaType=alphaType, alphaReqGrad=alphaReqGrad)) + + self.net.append(FinerLayer(hidden_features, out_features, is_last=True, + omega=hidden_omega, + init_method=init_method, init_gain=init_gain, hbs=hbs)) # omega: For weight init + self.net = nn.Sequential(*self.net) + + def forward(self, coords): + return self.net(coords) \ No newline at end of file From d83deb1f87f425d69b4a7d55593c8a781a5fce9c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 14:53:25 -0700 Subject: [PATCH 022/335] Added FinerActivation to get_activation_function --- src/quantem/core/ml/activation_functions.py | 1 + src/quantem/core/ml/blocks.py | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/activation_functions.py b/src/quantem/core/ml/activation_functions.py index a4bea716..c9c27896 100644 --- a/src/quantem/core/ml/activation_functions.py +++ b/src/quantem/core/ml/activation_functions.py @@ -183,6 +183,7 @@ def forward(self, x): elif activation_type in ["softplus"]: activation = nn.Softplus() elif activation_type in ["finer"]: + activation = FinerActivation() else: raise ValueError(f"Unknown activation type {activation_type}") diff --git a/src/quantem/core/ml/blocks.py b/src/quantem/core/ml/blocks.py index 6dd1ffa8..f9fa81ad 100644 --- a/src/quantem/core/ml/blocks.py +++ b/src/quantem/core/ml/blocks.py @@ -4,7 +4,6 @@ import math - from quantem.core import config from .activation_functions import Complex_ReLU, FinerActivation From 4227e582e0afbbdfb1e95019c9d772a0e268ad76 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 14:57:42 -0700 Subject: [PATCH 023/335] Fixed PtychoLite to call from cnn.py instead of cnn2d (now removed) --- src/quantem/diffractive_imaging/ptychography_lite.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/diffractive_imaging/ptychography_lite.py b/src/quantem/diffractive_imaging/ptychography_lite.py index 985e80ef..ebf1caf0 100644 --- a/src/quantem/diffractive_imaging/ptychography_lite.py +++ b/src/quantem/diffractive_imaging/ptychography_lite.py @@ -7,7 +7,7 @@ from quantem.core import config from quantem.core.datastructures import Dataset4dstem -from quantem.core.ml.cnn2d import CNN2d +from quantem.core.ml.cnn import CNN2d from quantem.diffractive_imaging.dataset_models import PtychographyDatasetRaster from quantem.diffractive_imaging.detector_models import DetectorPixelated from quantem.diffractive_imaging.logger_ptychography import LoggerPtychography From 2e70de35494aff9ea7d96bc6bfdda1d4309b76b7 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 15:00:22 -0700 Subject: [PATCH 024/335] Siren + HSiren need np.sqrt instead of torch.sqrt for .uniform_ --- src/quantem/core/ml/inr.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 2c0bcd19..68976f8a 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -1,6 +1,7 @@ from .blocks import SineLayer, FinerLayer from torch import nn import torch +import numpy as np class Siren(nn.Module): def __init__( @@ -25,8 +26,8 @@ def __init__( final_linear = nn.Linear(hidden_features, out_features) with torch.no_grad(): # Final layer keeps original initialization (no alpha scaling) - final_linear.weight.uniform_(-torch.sqrt(6 / hidden_features) / hidden_omega_0, - torch.sqrt(6 / hidden_features) / hidden_omega_0) + final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, + np.sqrt(6 / hidden_features) / hidden_omega_0) self.net.append(final_linear) self.net = nn.Sequential(*self.net) @@ -50,8 +51,8 @@ def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_featur final_linear = nn.Linear(hidden_features, out_features) with torch.no_grad(): # Final layer keeps original initialization (no alpha scaling) - final_linear.weight.uniform_(-torch.sqrt(6 / hidden_features) / hidden_omega_0, - torch.sqrt(6 / hidden_features) / hidden_omega_0) + final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, + np.sqrt(6 / hidden_features) / hidden_omega_0) self.net.append(final_linear) self.net = nn.Sequential(*self.net) From 9bbb54efde65e3f09ae77eb269870ac50b080b7b Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 15:18:59 -0700 Subject: [PATCH 025/335] net_list fix on Siren models --- src/quantem/core/ml/inr.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 68976f8a..d1a4a9b2 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -15,12 +15,12 @@ def __init__( alpha: float = 1.0, ): super().__init__() - self.net = [] - self.net.append(SineLayer(in_features, hidden_features, is_first=True, + self.net_list = [] + self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, omega_0=first_omega_0, alpha=alpha)) for i in range(hidden_layers): - self.net.append(SineLayer(hidden_features, hidden_features, is_first=False, + self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, omega_0=hidden_omega_0, alpha=alpha)) final_linear = nn.Linear(hidden_features, out_features) @@ -28,9 +28,9 @@ def __init__( # Final layer keeps original initialization (no alpha scaling) final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, np.sqrt(6 / hidden_features) / hidden_omega_0) - self.net.append(final_linear) + self.net_list.append(final_linear) - self.net = nn.Sequential(*self.net) + self.net = nn.Sequential(*self.net_list) def forward(self, coords): output = self.net(coords) @@ -40,12 +40,12 @@ class HSiren(nn.Module): def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, first_omega_0=30, hidden_omega_0=30, alpha=1.0): super().__init__() - self.net = [] - self.net.append(SineLayer(in_features, hidden_features, is_first=True, + self.net_list = [] + self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, omega_0=first_omega_0, hsiren=True, alpha=alpha)) for i in range(hidden_layers): - self.net.append(SineLayer(hidden_features, hidden_features, is_first=False, + self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, omega_0=hidden_omega_0, alpha=alpha)) final_linear = nn.Linear(hidden_features, out_features) @@ -53,9 +53,9 @@ def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_featur # Final layer keeps original initialization (no alpha scaling) final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, np.sqrt(6 / hidden_features) / hidden_omega_0) - self.net.append(final_linear) + self.net_list.append(final_linear) - self.net = nn.Sequential(*self.net) + self.net = nn.Sequential(*self.net_list) def forward(self, coords): output = self.net(coords) @@ -67,22 +67,22 @@ def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_featur init_method='sine', init_gain=1, fbs=None, hbs=None, alphaType=None, alphaReqGrad=False): super().__init__() - self.net = [] - self.net.append(FinerLayer(in_features, hidden_features, is_first=True, + self.net_list = [] + self.net_list.append(FinerLayer(in_features, hidden_features, is_first=True, omega=first_omega, init_method=init_method, init_gain=init_gain, fbs=fbs, alphaType=alphaType, alphaReqGrad=alphaReqGrad)) for i in range(hidden_layers): - self.net.append(FinerLayer(hidden_features, hidden_features, + self.net_list.append(FinerLayer(hidden_features, hidden_features, omega=hidden_omega, init_method=init_method, init_gain=init_gain, hbs=hbs, alphaType=alphaType, alphaReqGrad=alphaReqGrad)) - self.net.append(FinerLayer(hidden_features, out_features, is_last=True, + self.net_list.append(FinerLayer(hidden_features, out_features, is_last=True, omega=hidden_omega, init_method=init_method, init_gain=init_gain, hbs=hbs)) # omega: For weight init - self.net = nn.Sequential(*self.net) + self.net = nn.Sequential(*self.net_list) def forward(self, coords): return self.net(coords) \ No newline at end of file From 0aec97528049e0266e63d5af012eed855bac18a8 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 14 Oct 2025 15:43:50 -0700 Subject: [PATCH 026/335] Softplus missing from self.net_list --- src/quantem/core/ml/inr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index d1a4a9b2..8b5a4239 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -29,7 +29,7 @@ def __init__( final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, np.sqrt(6 / hidden_features) / hidden_omega_0) self.net_list.append(final_linear) - + self.net_list.append(nn.Softplus()) self.net = nn.Sequential(*self.net_list) def forward(self, coords): @@ -54,7 +54,7 @@ def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_featur final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, np.sqrt(6 / hidden_features) / hidden_omega_0) self.net_list.append(final_linear) - + self.net_list.append(nn.Softplus()) self.net = nn.Sequential(*self.net_list) def forward(self, coords): From 35eae4444e416becca52f0901600858ba0eacedf Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 23 Oct 2025 12:51:08 -0700 Subject: [PATCH 027/335] Print pred on cross_Correlation align stack --- src/quantem/tomography/tomography.py | 21 ++++++++++++--------- src/quantem/tomography/utils.py | 4 +++- 2 files changed, 15 insertions(+), 10 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ffb16d15..457aa89a 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -23,11 +23,12 @@ def get_num_samples_per_ray(epoch): """Increase number of samples per ray at specific epochs.""" schedule = { - 0: 25, - 2: 50, - 4: 100, - 6: 200, - 8: 350, + 0: 500, + # 2: 500, + # 4: 500, + # 6: 500, + # 8: 500, + 10: 500, } num_samples = 64 @@ -194,9 +195,9 @@ def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_r return transformed_rays # TODO: Temp logger - def setup_logger(self): + def setup_logger(self, log_path): if self.global_rank == 0: - self.temp_logger = SummaryWriter() + self.temp_logger = SummaryWriter(log_dir=log_path) else: self.temp_logger = None @@ -213,6 +214,8 @@ def recon( optimizer_params: dict = None, scheduler_params: dict = None, soft_constraints: dict = None, + vol_save_path: str = None, # TODO: TEMPORARY + log_path = None, ): if not self.ddp_instantiated: @@ -227,7 +230,7 @@ def recon( self.obj.soft_constraints = soft_constraints if not hasattr(self, "temp_logger"): - self.setup_logger() + self.setup_logger(log_path=log_path) zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() @@ -414,7 +417,7 @@ def recon( if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: with torch.no_grad(): if self.global_rank == 0: - save_path = f"new_dataset_logs/volume_epoch_{self.global_epochs:04d}.pt" + save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" torch.save(pred_full.cpu(), save_path) self.global_epochs += 1 diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index d93f15e0..2725fe20 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -249,7 +249,7 @@ def estimate_background( return mu -def cross_correlation_align_stack(ref_img, stack): +def cross_correlation_align_stack(ref_img, stack, print_pred = False): """ Aligns a stack of images to a reference image using cross-correlation. @@ -264,6 +264,8 @@ def cross_correlation_align_stack(ref_img, stack): prev_img = ref_img for img in tqdm(stack): shift_pred = cross_correlation_shift(prev_img, img) + if print_pred: + print(f'Shift prediction: {shift_pred}') shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) pred_shifts.append(shift_pred) From 0a99af5f9967be5a054a1f6862684d1ac882534c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 11 Nov 2025 22:22:36 -0800 Subject: [PATCH 028/335] Adding back in validation set --- src/quantem/core/ml/blocks.py | 4 +- src/quantem/core/ml/inr.py | 6 +- src/quantem/tomography/tomography.py | 108 +++++++++++++++++-- src/quantem/tomography/tomography_dataset.py | 65 +++++++++++ src/quantem/tomography/tomography_ddp.py | 100 +++++++++++++---- 5 files changed, 248 insertions(+), 35 deletions(-) diff --git a/src/quantem/core/ml/blocks.py b/src/quantem/core/ml/blocks.py index f9fa81ad..955b8ed7 100644 --- a/src/quantem/core/ml/blocks.py +++ b/src/quantem/core/ml/blocks.py @@ -376,6 +376,7 @@ def __init__( omega_0: float = 30, hsiren: bool = False, alpha: float = 1.0, + r: int = 2, ): super().__init__() self.omega_0 = omega_0 @@ -384,6 +385,7 @@ def __init__( self.in_features = in_features self.alpha = alpha self.linear = nn.Linear(in_features, out_features, bias=bias) + self.r = r self.init_weights() def init_weights(self): @@ -399,7 +401,7 @@ def init_weights(self): def forward(self, input): if self.is_first and self.hsiren: - out = torch.sin(self.omega_0 * torch.sinh(2*self.linear(input))) + out = torch.sin(self.omega_0 * torch.sinh(self.r*self.linear(input))) else: out = torch.sin(self.omega_0 * self.linear(input)) return out diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 8b5a4239..8c831f0d 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -38,15 +38,15 @@ def forward(self, coords): class HSiren(nn.Module): def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, - first_omega_0=30, hidden_omega_0=30, alpha=1.0): + first_omega_0=30, hidden_omega_0=30, alpha=1.0, r_first_layer = 3, r_hidden_layers = 2): super().__init__() self.net_list = [] self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, - omega_0=first_omega_0, hsiren=True, alpha=alpha)) + omega_0=first_omega_0, hsiren=True, alpha=alpha, r=r_first_layer)) for i in range(hidden_layers): self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, - omega_0=hidden_omega_0, alpha=alpha)) + omega_0=hidden_omega_0, alpha=alpha, r=r_hidden_layers)) final_linear = nn.Linear(hidden_features, out_features) with torch.no_grad(): diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 457aa89a..6ee5688e 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -23,13 +23,21 @@ def get_num_samples_per_ray(epoch): """Increase number of samples per ray at specific epochs.""" schedule = { - 0: 500, - # 2: 500, - # 4: 500, - # 6: 500, + 0: 20, + 2: 100, + 4: 200, + 6: 400, # 8: 500, 10: 500, } + # schedule = { + # 0: 20, + # 2: 50, + # 4: 100, + # 6: 150, + # 8: 200, + # 10: 200, + # } num_samples = 64 for epoch_threshold, samples in sorted(schedule.items()): @@ -216,11 +224,12 @@ def recon( soft_constraints: dict = None, vol_save_path: str = None, # TODO: TEMPORARY log_path = None, + val_fraction: float = 0.0, ): if not self.ddp_instantiated: self.setup_distributed() - self.setup_dataloader(self.dataset, batch_size, num_workers) + self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) self.ddp_instantiated = True obj.model = self.build_model(obj._model) self.obj = obj @@ -370,19 +379,27 @@ def recon( avg_mse_loss = epoch_mse_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches avg_z1_loss = epoch_z1_loss.item() / num_batches - + shifts, z1, z3 = self.dataset.auxiliary_params.forward() + shifts = shifts.detach().cpu() + z1 = z1.detach().cpu() + z3 = z3.detach().cpu() metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) if self.world_size > 1: dist.all_reduce(metrics, op=dist.ReduceOp.AVG) avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + + if hasattr(self, "val_dataloader") and self.val_dataloader is not None: + val_loss = self.validate(aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N) + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): with torch.no_grad(): current_lr = self.schedulers["model"].get_last_lr()[0] # Log metrics self.temp_logger.add_scalar("train/mse_loss", avg_mse_loss, self.global_epochs) - + if hasattr(self, "val_dataloader") and self.val_dataloader is not None: + self.temp_logger.add_scalar("val/mse_loss", val_loss, self.global_epochs) self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) # if tv_weight > 0: self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) @@ -412,6 +429,14 @@ def recon( axes[4].set_title('Sum over X-axis') self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) + + fig, axes = plt.subplots(ncols = 3, figsize = (10, 5)) + axes[0].plot(shifts[:, 0].cpu().numpy(), label = 'Shifts X') + axes[0].plot(shifts[:, 1].cpu().numpy(), label = 'Shifts Y') + axes[0].legend() + axes[1].plot(z1.cpu().numpy(), label = 'Z1') + axes[2].plot(z3.cpu().numpy(), label = 'Z3') + self.temp_logger.add_figure("train/auxiliary_params", fig, self.global_epochs, close=True) plt.close(fig) if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: @@ -424,9 +449,74 @@ def recon( if self.global_rank == 0: print("Training complete.") - - # print("Successfully setup DDP and dataloader") + torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") + # print("Successfully setup DDP and dataloader") + def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): + """Validate the model on the validation set.""" + self.obj.model.eval() + aux_params.eval() + + val_loss = 0.0 + num_batches = 0 + + with torch.no_grad(): + for batch in self.val_dataloader: + pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) + pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) + target_values = batch['target_value'].to(self.device, non_blocking=True) + phis = batch['phi'].to(self.device, non_blocking=True) + projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = aux_params(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + with torch.no_grad(): + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, N, num_samples_per_ray + ) + + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + + all_coords = transformed_rays.view(-1, 3) + + all_densities = self.obj.forward(all_coords) + + ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + + loss = mse_loss + + val_loss += loss.detach() + num_batches += 1 + + avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 + + if self.world_size > 1: + val_loss_tensor = avg_val_loss.detach().clone() + dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) + avg_val_loss = val_loss_tensor.item() + + self.obj.model.train() + aux_params.train() + + return avg_val_loss + def ad_recon( self, optimizer_params: dict, diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index fe5dab7c..8992d57b 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -167,6 +167,7 @@ def to(self, device: str): self._z3_angles = self._z3_angles.to(device) self._shifts = self._shifts.to(device) + # --- Properties --- @property @@ -340,3 +341,67 @@ def reset(self) -> None: self._z1_angles = self._initial_z1_angles.clone() self._z3_angles = self._initial_z3_angles.clone() self._shifts = self._initial_shifts.clone() + +# TODO: Temporary for use when only doing validation +class TomographyRayDataset(Dataset): + """ + Dataset for ray-based tomography training. + Each sample contains: target pixel value, pixel coordinates, phi angle, and auxiliary info. + """ + + def __init__(self, tilt_series, phis, N, val_ratio=0.0, mode='train', seed=42): + """ + Args: + tilt_series: [M, N, N] tensor of projections + phis: [M] tensor of tilt angles + N: Grid size + """ + self.tilt_series = tilt_series.cpu() + + self.tilt_series = torch.clamp(self.tilt_series, min=0.0) + + # tilt_percentile = torch.quantile(self.tilt_series, .95) + tilt_percentile = np.quantile(self.tilt_series.numpy(), 0.95) + + self.tilt_series = self.tilt_series / tilt_percentile + + self.phis = phis.cpu() + self.N = N + self.M = tilt_series.shape[0] + self.total_pixels = self.M * self.N * self.N + + + # Create train/val split + torch.manual_seed(seed) + all_indices = torch.randperm(self.total_pixels) + + num_val = int(self.total_pixels * val_ratio) + if mode == 'val': + self.indices = all_indices[:num_val] + else: # train + self.indices = all_indices[num_val:] + + self.mode = mode + + def __len__(self): + return len(self.indices) + + def __getitem__(self, idx): + + actual_idx = self.indices[idx].item() + + projection_idx = actual_idx // (self.N * self.N) + remaining = actual_idx % (self.N * self.N) + pixel_i = remaining // self.N + pixel_j = remaining % self.N + + target_value = self.tilt_series[projection_idx, pixel_i, pixel_j] + phi = self.phis[projection_idx] + + return { + 'projection_idx': projection_idx, + 'pixel_i': pixel_i, + 'pixel_j': pixel_j, + 'target_value': target_value, + 'phi': phi + } \ No newline at end of file diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index 58e4158d..8ecedaad 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -4,7 +4,9 @@ import torch.nn as nn import os import numpy as np -from quantem.tomography.tomography_dataset import TomographyDataset +from quantem.tomography.tomography_dataset import TomographyDataset, TomographyRayDataset + +from torch.utils.data import random_split class TomographyDDP: """ @@ -90,40 +92,94 @@ def setup_dataloader( tomo_dataset: TomographyDataset, batch_size: int, num_workers: int = 0, + val_fraction: float = 0.0, ): - + pin_mem = self.device.type == "cuda" + persist = num_workers > 0 + + # Split dataset if validation fraction > 0 + if val_fraction > 0.0: + # TODO: Temporary for when only doing validation, current TomographyDataset doesn't work correctly + train_dataset = TomographyRayDataset( + tomo_dataset.tilt_series.detach().clone(), + tomo_dataset.tilt_angles.detach().clone(), + 500, # TODO: TEMPORARY + val_ratio=val_fraction, + mode='train', + seed=42, + ) + val_dataset = TomographyRayDataset( + tomo_dataset.tilt_series.detach().clone(), + tomo_dataset.tilt_angles.detach().clone(), + 500, # TODO: TEMPORARY + val_ratio=val_fraction, + mode='val', + seed=42, + ) + else: + train_dataset, val_dataset = tomo_dataset, None + + # Samplers for distributed training if self.world_size > 1: - sampler = DistributedSampler( - tomo_dataset, - num_replicas = self.world_size, - rank = self.global_rank, - shuffle = True, - ) + train_sampler = DistributedSampler( + train_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, + ) + if val_dataset: + val_sampler = DistributedSampler( + val_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False, + ) shuffle = False - else: - sampler = None + train_sampler = None + val_sampler = None shuffle = True - + + # Main dataloader self.dataloader = DataLoader( - tomo_dataset, - batch_size = batch_size, - num_workers = num_workers, - sampler = sampler, - shuffle = shuffle, - pin_memory = True if self.device.type == "cuda" else False, - drop_last = True, - persistent_workers = False if num_workers == 0 else True, + train_dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler=train_sampler, + shuffle=shuffle, + pin_memory=pin_mem, + drop_last=True, + persistent_workers=persist, ) - - self.sampler = sampler + + # Validation dataloader if applicable + if val_dataset: + self.val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size * 4, + num_workers=num_workers, + sampler=val_sampler, + shuffle=False, + pin_memory=pin_mem, + drop_last=False, + persistent_workers=persist, + ) + self.val_sampler = val_sampler + + + self.sampler = train_sampler + if self.global_rank == 0: print(f"Dataloader setup complete:") print(f" Total projections: {len(tomo_dataset.tilt_angles)}") + if val_fraction > 0.0: + print(f" Total projections (val): {len(val_dataset)}") print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") print(f" Total pixels: {tomo_dataset.num_pixels:,}") - + if val_fraction > 0.0: + print(f" Total pixels (val): {len(val_dataset):,}") + print(f" Total pixels (train): {len(train_dataset):,}") print(f" Local batch size (train): {batch_size}") print(f" Global batch size: {batch_size*self.world_size}") print(f" Train batches per GPU per epoch: {len(self.dataloader)}") From 397148944a560cb07e1e58a3047d554348952c79 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Fri, 21 Nov 2025 15:41:00 -0800 Subject: [PATCH 029/335] Added pretraininig functionality, in core ML added custom loss functions more torchlike (need to ask Arthur's opinion) --- src/quantem/core/ml/loss_functions.py | 50 ++++++ src/quantem/tomography/object_models.py | 15 ++ src/quantem/tomography/tomography.py | 180 ++++++++++++++++--- src/quantem/tomography/tomography_base.py | 4 + src/quantem/tomography/tomography_dataset.py | 83 +++++++-- src/quantem/tomography/tomography_ddp.py | 50 +++++- 6 files changed, 350 insertions(+), 32 deletions(-) diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index daf96919..720d087c 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -2,6 +2,8 @@ from quantem.core import config +import torch.nn as nn + if TYPE_CHECKING: import torch else: @@ -65,3 +67,51 @@ def combined_l2(pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.7) -> comp_l2 = complex_l2(pred, target) amp_ph_l2 = amp_phase_l2(pred, target) return alpha * amp_ph_l2 + (1 - alpha) * comp_l2 + + +# TODO: Better loss function implementation? More torch-like. + +class L1Loss(nn.Module): + + def __init__( + self, + reduction: str = "mean", + ): + super(L1Loss, self).__init__() + self.reduction = reduction + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.l1_loss(pred, target, reduction=self.reduction) + + +class MSELoss(nn.Module): + + def __init__( + self, + reduction: str = "mean", + ): + super(MSELoss, self).__init__() + self.reduction = reduction + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + return torch.nn.functional.mse_loss(pred, target, reduction=self.reduction) + +class MSELogMSELoss(nn.Module): + + def __init__( + self, + eps: float = 1e-8, + reduction: str = 'mean', + ): + super(MSELogMSELoss, self).__init__() + self.eps = eps + self.reduction = reduction + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + mse = (pred - target) ** 2 + log_mse = mse * torch.log(pred + self.eps) + if self.reduction == 'mean': + return log_mse.mean() + elif self.reduction == 'sum': + return log_mse.sum() + return log_mse \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index bec78138..ff36eb54 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,5 +1,6 @@ from abc import abstractmethod from copy import deepcopy +from pathlib import Path from typing import Any, Callable, Union import numpy as np @@ -539,6 +540,13 @@ def _pretrain( self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) pbar.set_description(f"Epoch {a0 + 1}/{num_epochs}, Loss: {loss.item():.4f}, ") + + +# INR stuff + + + + class ObjectINN(ObjectConstraints): """ Object model for INN objects. @@ -713,7 +721,14 @@ def forward( all_densities = all_densities * valid_mask return all_densities + ObjectModelType = ObjectVoxelwise | ObjectINN # | ObjectDIP | ObjectImplicit (ObjectFFN?) + + +# Pretrain Volume Dataset + + + \ No newline at end of file diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 6ee5688e..a42191bd 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -7,9 +7,10 @@ from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_conv import TomographyConv from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.tomography_ddp import TomographyDDP +from quantem.tomography.tomography_ddp import PretrainVolumeDataset, TomographyDDP from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ +from quantem.core.ml.loss_functions import L1Loss, MSELoss, MSELogMSELoss # Temporary imports for TomographyNERF from quantem.tomography.models import HSiren from quantem.tomography.object_models import ObjectINN @@ -22,13 +23,21 @@ # TODO: Maybe put this in INN? def get_num_samples_per_ray(epoch): """Increase number of samples per ray at specific epochs.""" + # schedule = { + # 0: 20, + # 2: 100, + # 4: 200, + # 6: 400, + # # 8: 500, + # 10: 500, + # } schedule = { 0: 20, 2: 100, - 4: 200, - 6: 400, + 4: 150, + 6: 250, # 8: 500, - 10: 500, + 10: 300, } # schedule = { # 0: 20, @@ -66,6 +75,7 @@ def __init__( # TODO: More elegant way of doing this. self.global_epochs = 0 self.ddp_instantiated = False + self.pretraining_instantiated = False # --- Reconstruction Method --- @@ -209,7 +219,119 @@ def setup_logger(self, log_path): else: self.temp_logger = None + + def pretrain( + self, + volume_dataset: PretrainVolumeDataset, + obj: ObjectINN, + batch_size: int, + soft_constraints: dict = None, + log_path: str = None, + optimizer_params: dict = None, + epochs: int = 100, + viz_freq: int = 1, + l1_loss: bool = False, + ): + if not self.pretraining_instantiated: + self.setup_distributed() + self.setup_pretraining_dataloader(volume_dataset, batch_size) + self.pretraining_instantiated = True + obj.model = self.build_model(obj._model) + self.obj = obj + + if soft_constraints is not None: + self.obj.soft_constraints = soft_constraints + + if not hasattr(self, "temp_logger"): + self.setup_logger(log_path=log_path) + + if optimizer_params is not None: + optimizer_params = self.scale_lr(optimizer_params) + self.optimizer_params = optimizer_params + self.set_optimizers() + device_type = self.device.type + + for epoch in range(epochs): + if self.pretraining_sampler is not None: + self.pretraining_sampler.set_epoch(epoch) + + self.obj.model.train() + + epoch_loss = 0.0 + epoch_consistency_loss = 0.0 + epoch_tv_loss = 0.0 + num_batches = 0 + + for batch_idx, batch in enumerate(self.pretraining_dataloader): + coords = batch['coords'].to(self.device, non_blocking=True) + target = batch['target'].to(self.device, non_blocking=True) + + for _, opt in self.optimizers.items(): + opt.zero_grad() + + with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): + + outputs = self.obj.forward(coords) + + if l1_loss: + consistency_loss = torch.nn.functional.l1_loss(outputs, target) + else: + consistency_loss = torch.nn.functional.mse_loss(outputs, target) + tv_loss = self.obj.apply_soft_constraints(coords) + + loss = consistency_loss + tv_loss + + loss.backward() + + epoch_loss += loss.detach() + epoch_consistency_loss += consistency_loss.detach() + epoch_tv_loss += tv_loss.detach() + + torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm = 1.0) + + for _, opt in self.optimizers.items(): + opt.step() + epoch_loss += loss.detach() + num_batches +=1 + if epoch % viz_freq == 0 or epoch == epochs - 1: + avg_loss = epoch_loss / num_batches + with torch.no_grad(): + self.obj.create_volume(world_size = self.world_size, global_rank = self.global_rank, ray_size = volume_dataset.N) + pred_full = self.obj.obj + loss_tensor = avg_loss.clone().detach() + avg_loss = loss_tensor.item() + avg_tv_loss = epoch_tv_loss / num_batches + avg_consistency_loss = epoch_consistency_loss / num_batches + + metrics = torch.tensor([avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device) + if self.world_size > 1: + dist.all_reduce(metrics, op = dist.ReduceOp.AVG) + avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() + + if self.global_rank == 0: + self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) + self.temp_logger.add_scalar("Pretrain/consistency_loss", avg_consistency_loss, epoch) + self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) + + # current_lr = self.schedulers["model"].get_last_lr()[0] + # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) + + fig, ax = plt.subplots(ncols = 4, figsize = (36, 12)) + ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) + ax[0].set_title('Sum over Z-axis') + ax[1].matshow(pred_full[volume_dataset.N//2].cpu().numpy(), cmap='turbo', vmin=0) + ax[1].set_title(f'Slice at Z={volume_dataset.N//2}') + ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap='turbo', vmin=0) + ax[2].set_title('Sum over Y-axis') + ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap='turbo', vmin=0) + ax[3].set_title('Sum over X-axis') + self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) + + plt.close(fig) + print(f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}") + + def recon( self, obj: ObjectINN, @@ -225,14 +347,19 @@ def recon( vol_save_path: str = None, # TODO: TEMPORARY log_path = None, val_fraction: float = 0.0, + learn_shifts: bool = True, + # l1_loss: bool = False, + consistency_criterion: str = 'mse', ): if not self.ddp_instantiated: - self.setup_distributed() + if self.pretraining_instantiated == False: + self.setup_distributed() + obj.model = self.build_model(obj._model) + self.obj = obj + self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) self.ddp_instantiated = True - obj.model = self.build_model(obj._model) - self.obj = obj if soft_constraints is not None: @@ -245,9 +372,10 @@ def recon( if self.global_rank == 0: print(f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference") + print(f"Using consistency criterion: {consistency_criterion}") # Auxiliary params setup - self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device) + self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) aux_params = self.dataset.auxiliary_params # Scaling learning rates to account for distributed training @@ -294,12 +422,21 @@ def recon( self.sampler.set_epoch(epoch) epoch_loss = 0.0 - epoch_mse_loss = 0.0 + epoch_consistency_loss = 0.0 epoch_tv_loss = 0.0 epoch_z1_loss = 0.0 num_batches = 0 - + consistency_loss_fn = None + if consistency_criterion == 'mse': + consistency_loss_fn = MSELoss() + elif consistency_criterion == 'l1': + consistency_loss_fn = L1Loss() + elif consistency_criterion == 'mse_log': + consistency_loss_fn = MSELogMSELoss() + else: + raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + for batch_idx, batch in enumerate(self.dataloader): projection_indices = batch['projection_idx'] @@ -343,15 +480,15 @@ def recon( predicted_values = ray_densities.sum(dim=1) * step_size - mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + consistency_loss = consistency_loss_fn(predicted_values, target_values) tv_loss_z1 = torch.tensor(0.0, device=self.device) - loss = mse_loss + tv_loss + tv_loss_z1 + loss = consistency_loss + tv_loss + tv_loss_z1 loss.backward() epoch_loss += loss.detach() - epoch_mse_loss += mse_loss.detach() + epoch_consistency_loss += consistency_loss.detach() epoch_tv_loss += tv_loss.detach() epoch_z1_loss += tv_loss_z1.detach() num_batches += 1 @@ -365,9 +502,12 @@ def recon( aux_norm = torch.nn.utils.clip_grad_norm_(self.dataset.auxiliary_params.parameters(), max_norm = 1) opt.step() opt.zero_grad() - + for key, sched in self.schedulers.items(): - sched.step() + if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): + sched.step(epoch_loss) + else: + sched.step() if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : @@ -376,17 +516,17 @@ def recon( self.obj.create_volume(world_size = self.world_size, global_rank = self.global_rank, ray_size = num_samples_per_ray) pred_full = self.obj.obj avg_loss = epoch_loss.item() / num_batches - avg_mse_loss = epoch_mse_loss.item() / num_batches + avg_consistency_loss = epoch_consistency_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches avg_z1_loss = epoch_z1_loss.item() / num_batches shifts, z1, z3 = self.dataset.auxiliary_params.forward() shifts = shifts.detach().cpu() z1 = z1.detach().cpu() z3 = z3.detach().cpu() - metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) + metrics = torch.tensor([avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], device=self.device) if self.world_size > 1: dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() if hasattr(self, "val_dataloader") and self.val_dataloader is not None: @@ -397,9 +537,9 @@ def recon( current_lr = self.schedulers["model"].get_last_lr()[0] # Log metrics - self.temp_logger.add_scalar("train/mse_loss", avg_mse_loss, self.global_epochs) + self.temp_logger.add_scalar(f"train/consistency_loss_{consistency_criterion}", avg_consistency_loss, self.global_epochs) if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - self.temp_logger.add_scalar("val/mse_loss", val_loss, self.global_epochs) + self.temp_logger.add_scalar(f"val/consistency_loss_{consistency_criterion}", val_loss, self.global_epochs) self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) # if tv_weight > 0: self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index b05e51fe..d3b94427 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -392,6 +392,7 @@ def plot_projections( norm: str = "log_auto", figax: tuple[plt.Figure, plt.Axes] | None = None, fft_vmax: Tuple[float, float] = (0, 40), + vmin: float = 0, **kwargs, ): """ @@ -416,18 +417,21 @@ def plot_projections( figax=(fig, ax[0]), cmap=cmap, title="Y-X Projection", + vmin=vmin, ) show_2d( volume_obj_np.sum(axis=1), figax=(fig, ax[1]), cmap=cmap, title="Z-X Projection", + vmin=vmin, ) show_2d( volume_obj_np.sum(axis=2), figax=(fig, ax[2]), cmap=cmap, title="Z-Y Projection", + vmin=vmin, ) if fft: diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography/tomography_dataset.py index 8992d57b..31898db7 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography/tomography_dataset.py @@ -11,11 +11,15 @@ validate_tensor, ) +from pathlib import Path + class AuxiliaryParams(torch.nn.Module): - def __init__(self, num_tilts, device, zero_tilt_idx=None): + def __init__(self, num_tilts, device, zero_tilt_idx=None, learn_shifts: bool = True): super().__init__() + self.learn_shifts = learn_shifts + if zero_tilt_idx is None: # If not provided, assume first projection is reference zero_tilt_idx = 0 @@ -25,18 +29,18 @@ def __init__(self, num_tilts, device, zero_tilt_idx=None): # Shifts: only parameterize non-reference tilts num_param_tilts = num_tilts - 1 - self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) + self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device), requires_grad = learn_shifts) # Fixed zero shifts for reference self.shifts_ref = torch.zeros(1, 2, device=device) # Z1 and Z3: parameterize all tilts EXCEPT the reference self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) + self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) # Fixed zeros for reference tilt self.z1_ref = torch.zeros(1, device=device) - # self.z3_ref = torch.zeros(1, device=device) + self.z3_ref = torch.zeros(1, device=device) def forward(self, dummy_input=None): # Reconstruct full arrays with zeros at reference position @@ -48,11 +52,11 @@ def forward(self, dummy_input=None): after_z1 = self.z1_param[self.zero_tilt_idx :] z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) - # before_z3 = self.z3_param[:self.zero_tilt_idx] - # after_z3 = self.z3_param[self.zero_tilt_idx:] - # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) + before_z3 = self.z3_param[:self.zero_tilt_idx] + after_z3 = self.z3_param[self.zero_tilt_idx:] + z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) - return shifts, z1, -z1 + return shifts, z1, z3 class TomographyDataset(AutoSerialize, Dataset): @@ -319,12 +323,13 @@ def initial_shifts(self, shifts: NDArray | Tensor) -> None: # TODO: Temp auxiliary params - def setup_auxiliary_params(self, zero_tilt_idx: int = None, device: str = "cpu") -> None: + def setup_auxiliary_params(self, zero_tilt_idx: int = None, device: str = "cpu", learn_shifts: bool = True) -> None: if not hasattr(self, "_auxiliary_params"): self._auxiliary_params = AuxiliaryParams( num_tilts=len(self.tilt_angles), device=device, zero_tilt_idx=zero_tilt_idx, + learn_shifts=learn_shifts, ) else: @@ -404,4 +409,62 @@ def __getitem__(self, idx): 'pixel_j': pixel_j, 'target_value': target_value, 'phi': phi - } \ No newline at end of file + } + + +# Pretrain Volume Dataset +class PretrainVolumeDataset(Dataset): + + def __init__( + self, + volume_path: Path | str, + N: int, + ): + self._N = N + if str(volume_path).endswith(".npy"): + data = torch.from_numpy(np.load(volume_path)).float() + elif str(volume_path).endswith(".pt"): + data = torch.load(volume_path).float() + else: + raise ValueError(f"Unsupported file format: {volume_path}") + + # Normalize data + total_elements = data.numel() + if total_elements > 1e6: + sample_size = min(int(1e6), total_elements) + flat_data = data.flatten() + indices = torch.randperm(total_elements)[:sample_size] + sampled_data = flat_data[indices] + data_quantile = torch.quantile(sampled_data, 0.95) + else: + data_quantile = torch.quantile(data, 0.95) + + data = data / data_quantile + data = torch.permute(data, (2, 1, 0)) + # data = torch.flip(data, dims=(2,)) + + self.volume = data.cpu() + self.N = N + self.total_samples = N**3 + + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + self.coords = torch.stack([x, y, z], dim=-1).reshape(-1, 3).cpu() + self.targets = self.volume.reshape(-1).cpu() + + def __len__(self): + return self.total_samples + + def __getitem__(self, idx): + return { + 'coords': self.coords[idx], + 'target': self.targets[idx] + } + + @property + def N(self): + return self._N + + @N.setter + def N(self, N: int): + self._N = N \ No newline at end of file diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index 8ecedaad..f4f652fc 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -4,7 +4,7 @@ import torch.nn as nn import os import numpy as np -from quantem.tomography.tomography_dataset import TomographyDataset, TomographyRayDataset +from quantem.tomography.tomography_dataset import TomographyDataset, TomographyRayDataset, PretrainVolumeDataset from torch.utils.data import random_split @@ -86,7 +86,7 @@ def build_model( return model - + # Setup Tomo DataLoader def setup_dataloader( self, tomo_dataset: TomographyDataset, @@ -183,6 +183,52 @@ def setup_dataloader( print(f" Local batch size (train): {batch_size}") print(f" Global batch size: {batch_size*self.world_size}") print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + + # Setup pretraining dataloader + + def setup_pretraining_dataloader( + self, + volume_dataset: PretrainVolumeDataset, + batch_size: int, + ): + #TODO: temp + + num_workers = 0 + if self.world_size > 1: + sampler = DistributedSampler( + volume_dataset, + num_replicas = self.world_size, + rank = self.global_rank, + shuffle = True, + drop_last = True, + ) + shuffle = False + else: + sampler = None + shuffle = True + + self.pretraining_dataloader = DataLoader( + volume_dataset, + batch_size = batch_size, + sampler = sampler, + shuffle = shuffle, + pin_memory = self.device.type == "cuda", + drop_last = True, + persistent_workers = num_workers > 0, + ) + + self.pretraining_sampler = sampler + + if self.global_rank == 0: + print(f"Pretraining dataloader setup complete:") + print(f" Total samples: {len(volume_dataset)}") + print(f" Grid size: {volume_dataset.N**3}") + print(f" Local batch size: {batch_size}") + print(f" Global batch size: {batch_size*self.world_size}") + print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") + + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): if scaling_rule == "sqrt": From 69efcfdde10b6294dca1d992d46b3d871b86ad14 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Fri, 5 Dec 2025 21:17:59 -0800 Subject: [PATCH 030/335] Updates --- src/quantem/core/ml/loss_functions.py | 51 +++++++++- src/quantem/tomography/tomography.py | 117 ++++++++++++++--------- src/quantem/tomography/tomography_ddp.py | 59 +++++++++++- 3 files changed, 180 insertions(+), 47 deletions(-) diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index 720d087c..d136ca20 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -109,9 +109,56 @@ def __init__( def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: mse = (pred - target) ** 2 - log_mse = mse * torch.log(pred + self.eps) + log_mse = -mse * torch.log(mse + self.eps) if self.reduction == 'mean': return log_mse.mean() elif self.reduction == 'sum': return log_mse.sum() - return log_mse \ No newline at end of file + return log_mse + +class LLMSELoss(nn.Module): + """ + Logarithmic Linear Mean Squared Error (LLMSE) loss: + L = -log(1 - |y - y_hat| / max(|y - y_hat|)) + """ + + def __init__(self, eps: float = 1e-8, reduction: str = 'mean'): + super().__init__() + self.eps = eps + self.reduction = reduction + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + # Absolute residual + abs_diff = torch.abs(pred - target) + + # Normalization by max error in batch (avoid div-by-zero) + max_diff = torch.max(abs_diff.detach()) + self.eps + norm_diff = abs_diff / max_diff + + # Apply -log(1 - normalized_error) + loss = -torch.log(1.0 - norm_diff + self.eps) + + # Reduce + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + return loss + + +class CharbonnierLoss(nn.Module): + def __init__(self, epsilon=1e-12, reduction='mean'): + super(CharbonnierLoss, self).__init__() + self.epsilon = epsilon + self.reduction = reduction + + def forward(self, prediction, target): + diff = prediction - target + loss = torch.sqrt(diff * diff + self.epsilon**2) + + if self.reduction == 'mean': + return torch.mean(loss) + elif self.reduction == 'sum': + return torch.sum(loss) + else: # 'none' + return loss \ No newline at end of file diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index a42191bd..3e630a91 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,3 +1,6 @@ +from typing import Any + + import torch # from torch_radon.radon import ParallelBeam as Radon @@ -7,10 +10,11 @@ from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_conv import TomographyConv from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.tomography_ddp import PretrainVolumeDataset, TomographyDDP +from quantem.tomography.tomography_ddp import PretrainVolumeDataset, TomographyDDP, AdaptiveSmoothL1LossDDP from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ -from quantem.core.ml.loss_functions import L1Loss, MSELoss, MSELogMSELoss +from quantem.core.ml.loss_functions import L1Loss, MSELoss, MSELogMSELoss, LLMSELoss, CharbonnierLoss +from torch.nn import SmoothL1Loss # Temporary imports for TomographyNERF from quantem.tomography.models import HSiren from quantem.tomography.object_models import ObjectINN @@ -21,35 +25,21 @@ # Temporary aux class for TomographyNERF # TODO: Maybe put this in INN? -def get_num_samples_per_ray(epoch): +def get_num_samples_per_ray(N: int, epoch: int): """Increase number of samples per ray at specific epochs.""" - # schedule = { - # 0: 20, - # 2: 100, - # 4: 200, - # 6: 400, - # # 8: 500, - # 10: 500, - # } - schedule = { - 0: 20, - 2: 100, - 4: 150, - 6: 250, - # 8: 500, - 10: 300, - } - # schedule = { - # 0: 20, - # 2: 50, - # 4: 100, - # 6: 150, - # 8: 200, - # 10: 200, - # } - - num_samples = 64 - for epoch_threshold, samples in sorted(schedule.items()): + # Exponential schedule + + epochs = np.linspace(0, 10, 5, dtype=int) + # schedule = np.linspace(20, N, 5, dtype=int) + # schedule = np.array([20, 100, 150, 200, 200]) + # schedule = np.array([200, 200, 200, 200, 200]) + schedule = np.array([20, 100, 250, 500, 500]) + # schedule = np.array([500, 500, 500, 500, 500]) + # schedule = np.array([300, 300, 300, 300, 300]) + # schedule = np.exp(schedule) + schedule_warmup = dict[int, int](zip(epochs, schedule)) + + for epoch_threshold, samples in schedule_warmup.items(): if epoch >= epoch_threshold: num_samples = samples @@ -230,7 +220,7 @@ def pretrain( optimizer_params: dict = None, epochs: int = 100, viz_freq: int = 1, - l1_loss: bool = False, + consistency_criterion: str = 'mse', ): if not self.pretraining_instantiated: self.setup_distributed() @@ -250,6 +240,23 @@ def pretrain( self.optimizer_params = optimizer_params self.set_optimizers() device_type = self.device.type + consistency_loss_fn = None + if consistency_criterion[0].lower() == 'mse': + consistency_loss_fn = MSELoss() + elif consistency_criterion[0].lower() == 'l1': + consistency_loss_fn = L1Loss() + elif consistency_criterion[0].lower() == 'mse_log': + consistency_loss_fn = MSELogMSELoss() + elif consistency_criterion[0].lower() == 'llmse': + consistency_loss_fn = LLMSELoss() + elif consistency_criterion[0].lower() == 'smooth_l1': + consistency_loss_fn = SmoothL1Loss(beta = consistency_criterion[1]) + elif consistency_criterion[0].lower() == 'adaptive_smooth_l1': + consistency_loss_fn = AdaptiveSmoothL1LossDDP(beta_init = consistency_criterion[1], ema_factor = 0.99, eps = 1e-8) + elif consistency_criterion[0].lower() == 'charbonnier': + consistency_loss_fn = CharbonnierLoss(epsilon = 1e-12, reduction = 'mean') + else: + raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") for epoch in range(epochs): if self.pretraining_sampler is not None: @@ -272,11 +279,7 @@ def pretrain( with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): outputs = self.obj.forward(coords) - - if l1_loss: - consistency_loss = torch.nn.functional.l1_loss(outputs, target) - else: - consistency_loss = torch.nn.functional.mse_loss(outputs, target) + consistency_loss = consistency_loss_fn(outputs, target) tv_loss = self.obj.apply_soft_constraints(coords) loss = consistency_loss + tv_loss @@ -330,7 +333,11 @@ def pretrain( plt.close(fig) print(f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}") + self.global_epochs += 1 + if self.global_rank == 0: + print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") + torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") def recon( self, @@ -350,11 +357,24 @@ def recon( learn_shifts: bool = True, # l1_loss: bool = False, consistency_criterion: str = 'mse', + model_weights_path: str = None, ): if not self.ddp_instantiated: if self.pretraining_instantiated == False: self.setup_distributed() + if model_weights_path is not None: + print(f"Loading model weights from {model_weights_path}") + state_dict = torch.load(model_weights_path, map_location='cpu') + + # Handle DataParallel/DDP checkpoints + if any(k.startswith('module.') for k in state_dict.keys()): + new_state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()} + else: + new_state_dict = state_dict + + obj.model.load_state_dict(new_state_dict) + print("Model weights loaded successfully") obj.model = self.build_model(obj._model) self.obj = obj @@ -402,7 +422,7 @@ def recon( for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(self.global_epochs) + num_samples_per_ray = get_num_samples_per_ray(N = N, epoch = self.global_epochs) # num_samples_per_ray = get_num_samples_per_ray(epoch) # Log the change if it happens @@ -411,7 +431,7 @@ def recon( if self.global_rank == 0 and self.global_epochs > 0: - prev_samples = get_num_samples_per_ray(self.global_epochs - 1) + prev_samples = get_num_samples_per_ray(N = N, epoch = self.global_epochs - 1) if num_samples_per_ray != prev_samples: print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") @@ -428,12 +448,20 @@ def recon( num_batches = 0 consistency_loss_fn = None - if consistency_criterion == 'mse': + if consistency_criterion[0].lower() == 'mse': consistency_loss_fn = MSELoss() - elif consistency_criterion == 'l1': + elif consistency_criterion[0].lower() == 'l1': consistency_loss_fn = L1Loss() - elif consistency_criterion == 'mse_log': + elif consistency_criterion[0].lower() == 'mse_log': consistency_loss_fn = MSELogMSELoss() + elif consistency_criterion[0].lower() == 'llmse': + consistency_loss_fn = LLMSELoss() + elif consistency_criterion[0].lower() == 'smooth_l1': + consistency_loss_fn = SmoothL1Loss(beta = consistency_criterion[1]) + elif consistency_criterion[0].lower() == 'adaptive_smooth_l1': + consistency_loss_fn = AdaptiveSmoothL1LossDDP(beta_init = consistency_criterion[1], ema_factor = 0.99, eps = 1e-8) + elif consistency_criterion[0].lower() == 'charbonnier': + consistency_loss_fn = CharbonnierLoss(epsilon = 1e-12, reduction = 'mean') else: raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") @@ -537,9 +565,9 @@ def recon( current_lr = self.schedulers["model"].get_last_lr()[0] # Log metrics - self.temp_logger.add_scalar(f"train/consistency_loss_{consistency_criterion}", avg_consistency_loss, self.global_epochs) + self.temp_logger.add_scalar(f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", avg_consistency_loss, self.global_epochs) if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - self.temp_logger.add_scalar(f"val/consistency_loss_{consistency_criterion}", val_loss, self.global_epochs) + self.temp_logger.add_scalar(f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", val_loss, self.global_epochs) self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) # if tv_weight > 0: self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) @@ -548,7 +576,8 @@ def recon( self.temp_logger.add_scalar("train/aux_grad_norm", aux_norm.item(), self.global_epochs) self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) self.temp_logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, self.global_epochs) - + if consistency_criterion[0].lower() == 'adaptive_smooth_l1': + self.temp_logger.add_scalar("train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs) fig, axes = plt.subplots(1, 5, figsize=(36, 12)) axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) axes[0].set_title('Sum over Z-axis') diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index f4f652fc..86c4f59e 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -261,4 +261,61 @@ def scale_lr( return new_optimizer_params - \ No newline at end of file + + + # TODO: Temporary Adaptive L1 Smooth Loss + +class AdaptiveSmoothL1Loss(nn.Module): + def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): + """ + Adaptive smooth L1 loss with EMA-based beta adaptation. + + Args: + beta_init (float): optional initial β value; if None, starts as 1.0 + ema_factor (float): smoothing factor for EMA (1 - 1/N_b in Eq. 38) + eps (float): small constant for numerical stability + """ + super().__init__() + self.register_buffer('beta2', torch.tensor(beta_init**2 if beta_init else 1.0)) + self.ema_factor = ema_factor + self.eps = eps + + def forward(self, pred, target): + diff = pred - target + abs_diff = diff.abs() + + # compute current batch MSE (Eq. 38) + mse_batch = torch.mean(diff ** 2) + + # update β² adaptively using Eq. (39) + with torch.no_grad(): + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min(self.beta2, mse_batch) + + beta = torch.sqrt(self.beta2 + self.eps) + + # Smooth L1 (Eq. 36) + loss = torch.where( + abs_diff < beta, + 0.5 * (diff ** 2) / (beta + self.eps), + abs_diff - 0.5 * beta + ) + return loss.mean() + +class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): + def forward(self, pred, target): + diff = pred - target + abs_diff = diff.abs() + mse_batch = torch.mean(diff ** 2) + + # Synchronize β across all GPUs + if dist.is_initialized(): + mse_batch_all = mse_batch.clone() + dist.all_reduce(mse_batch_all, op=dist.ReduceOp.AVG) + mse_batch = mse_batch_all / dist.get_world_size() + + with torch.no_grad(): + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min(self.beta2, mse_batch) + + beta = torch.sqrt(self.beta2 + self.eps) + loss = torch.where(abs_diff < beta, 0.5 * (diff ** 2) / (beta + self.eps), abs_diff - 0.5 * beta) + return loss.mean() From 9937bc55cf968a6b141da2aebcd147b9ef756e41 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 15 Dec 2025 15:25:47 -0800 Subject: [PATCH 031/335] Test --- src/quantem/tomography/tomography.py | 406 +++++++++++++---------- src/quantem/tomography/tomography_ddp.py | 134 ++++---- 2 files changed, 297 insertions(+), 243 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 3e630a91..5064cd3f 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,27 +1,33 @@ -from typing import Any - - +import matplotlib.pyplot as plt +import numpy as np import torch +import torch.distributed as dist +from torch.nn import SmoothL1Loss +from torch.utils.tensorboard import SummaryWriter # from torch_radon.radon import ParallelBeam as Radon from tqdm.auto import tqdm -from quantem.tomography.object_models import ObjectVoxelwise +from quantem.core.ml.loss_functions import ( + CharbonnierLoss, + L1Loss, + LLMSELoss, + MSELogMSELoss, + MSELoss, +) + +# Temporary imports for TomographyNERF +from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_conv import TomographyConv +from quantem.tomography.tomography_ddp import ( + AdaptiveSmoothL1LossDDP, + PretrainVolumeDataset, + TomographyDDP, +) from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.tomography_ddp import PretrainVolumeDataset, TomographyDDP, AdaptiveSmoothL1LossDDP from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ -from quantem.core.ml.loss_functions import L1Loss, MSELoss, MSELogMSELoss, LLMSELoss, CharbonnierLoss -from torch.nn import SmoothL1Loss -# Temporary imports for TomographyNERF -from quantem.tomography.models import HSiren -from quantem.tomography.object_models import ObjectINN -from torch.utils.tensorboard import SummaryWriter -import numpy as np -import matplotlib.pyplot as plt -import torch.distributed as dist # Temporary aux class for TomographyNERF # TODO: Maybe put this in INN? @@ -31,9 +37,9 @@ def get_num_samples_per_ray(N: int, epoch: int): epochs = np.linspace(0, 10, 5, dtype=int) # schedule = np.linspace(20, N, 5, dtype=int) - # schedule = np.array([20, 100, 150, 200, 200]) + schedule = np.array([20, 100, 150, 200, 200]) # schedule = np.array([200, 200, 200, 200, 200]) - schedule = np.array([20, 100, 250, 500, 500]) + # schedule = np.array([20, 100, 250, 500, 500]) # schedule = np.array([500, 500, 500, 500, 500]) # schedule = np.array([300, 300, 300, 300, 300]) # schedule = np.exp(schedule) @@ -46,7 +52,6 @@ def get_num_samples_per_ray(N: int, epoch: int): return num_samples - class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): """ Top level class for either using conventional or ML-based reconstruction methods @@ -61,12 +66,12 @@ def __init__( _token, ): super().__init__(dataset, volume_obj, device, _token) - + # TODO: More elegant way of doing this. self.global_epochs = 0 self.ddp_instantiated = False self.pretraining_instantiated = False - + # --- Reconstruction Method --- def sirt_recon( @@ -110,8 +115,13 @@ def sirt_recon( else: gaussian_kernel = None - print("Devices", sirt_tilt_series.device, proj_forward.device, self.dataset.tilt_angles.device) - + print( + "Devices", + sirt_tilt_series.device, + proj_forward.device, + self.dataset.tilt_angles.device, + ) + for iter in pbar: proj_forward, loss = self._sirt_run_epoch( tilt_series=sirt_tilt_series, @@ -134,10 +144,10 @@ def sirt_recon( if plot_loss: self.plot_loss() - + # TODO: ML Recon which has NeRF and AD depending on the object type. - # TODO: Temporary - + # TODO: Temporary + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): """Create projection rays for entire batch simultaneously.""" batch_size = len(pixel_i) @@ -158,10 +168,9 @@ def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray) rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray return rays - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): # Step 1: Apply shifts shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) @@ -208,7 +217,6 @@ def setup_logger(self, log_path): self.temp_logger = SummaryWriter(log_dir=log_path) else: self.temp_logger = None - def pretrain( self, @@ -220,7 +228,7 @@ def pretrain( optimizer_params: dict = None, epochs: int = 100, viz_freq: int = 1, - consistency_criterion: str = 'mse', + consistency_criterion: str = "mse", ): if not self.pretraining_instantiated: self.setup_distributed() @@ -231,30 +239,32 @@ def pretrain( if soft_constraints is not None: self.obj.soft_constraints = soft_constraints - + if not hasattr(self, "temp_logger"): self.setup_logger(log_path=log_path) - + if optimizer_params is not None: optimizer_params = self.scale_lr(optimizer_params) self.optimizer_params = optimizer_params self.set_optimizers() device_type = self.device.type consistency_loss_fn = None - if consistency_criterion[0].lower() == 'mse': + if consistency_criterion[0].lower() == "mse": consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == 'l1': + elif consistency_criterion[0].lower() == "l1": consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == 'mse_log': + elif consistency_criterion[0].lower() == "mse_log": consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == 'llmse': + elif consistency_criterion[0].lower() == "llmse": consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == 'smooth_l1': - consistency_loss_fn = SmoothL1Loss(beta = consistency_criterion[1]) - elif consistency_criterion[0].lower() == 'adaptive_smooth_l1': - consistency_loss_fn = AdaptiveSmoothL1LossDDP(beta_init = consistency_criterion[1], ema_factor = 0.99, eps = 1e-8) - elif consistency_criterion[0].lower() == 'charbonnier': - consistency_loss_fn = CharbonnierLoss(epsilon = 1e-12, reduction = 'mean') + elif consistency_criterion[0].lower() == "smooth_l1": + consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) + elif consistency_criterion[0].lower() == "adaptive_smooth_l1": + consistency_loss_fn = AdaptiveSmoothL1LossDDP( + beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 + ) + elif consistency_criterion[0].lower() == "charbonnier": + consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") else: raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") @@ -270,18 +280,17 @@ def pretrain( num_batches = 0 for batch_idx, batch in enumerate(self.pretraining_dataloader): - coords = batch['coords'].to(self.device, non_blocking=True) - target = batch['target'].to(self.device, non_blocking=True) + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) for _, opt in self.optimizers.items(): opt.zero_grad() with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): - outputs = self.obj.forward(coords) consistency_loss = consistency_loss_fn(outputs, target) tv_loss = self.obj.apply_soft_constraints(coords) - + loss = consistency_loss + tv_loss loss.backward() @@ -290,52 +299,63 @@ def pretrain( epoch_consistency_loss += consistency_loss.detach() epoch_tv_loss += tv_loss.detach() - torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm = 1.0) + torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) for _, opt in self.optimizers.items(): opt.step() epoch_loss += loss.detach() - num_batches +=1 - + num_batches += 1 + if epoch % viz_freq == 0 or epoch == epochs - 1: avg_loss = epoch_loss / num_batches with torch.no_grad(): - self.obj.create_volume(world_size = self.world_size, global_rank = self.global_rank, ray_size = volume_dataset.N) + self.obj.create_volume( + world_size=self.world_size, + global_rank=self.global_rank, + ray_size=volume_dataset.N, + ) pred_full = self.obj.obj loss_tensor = avg_loss.clone().detach() avg_loss = loss_tensor.item() avg_tv_loss = epoch_tv_loss / num_batches avg_consistency_loss = epoch_consistency_loss / num_batches - metrics = torch.tensor([avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device) + metrics = torch.tensor( + [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device + ) if self.world_size > 1: - dist.all_reduce(metrics, op = dist.ReduceOp.AVG) + dist.all_reduce(metrics, op=dist.ReduceOp.AVG) avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() - + if self.global_rank == 0: self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) - self.temp_logger.add_scalar("Pretrain/consistency_loss", avg_consistency_loss, epoch) + self.temp_logger.add_scalar( + "Pretrain/consistency_loss", avg_consistency_loss, epoch + ) self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) - + # current_lr = self.schedulers["model"].get_last_lr()[0] # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) - fig, ax = plt.subplots(ncols = 4, figsize = (36, 12)) - ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) - ax[0].set_title('Sum over Z-axis') - ax[1].matshow(pred_full[volume_dataset.N//2].cpu().numpy(), cmap='turbo', vmin=0) - ax[1].set_title(f'Slice at Z={volume_dataset.N//2}') - ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap='turbo', vmin=0) - ax[2].set_title('Sum over Y-axis') - ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap='turbo', vmin=0) - ax[3].set_title('Sum over X-axis') + fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) + ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + ax[0].set_title("Sum over Z-axis") + ax[1].matshow( + pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 + ) + ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") + ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) + ax[2].set_title("Sum over Y-axis") + ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) + ax[3].set_title("Sum over X-axis") self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) plt.close(fig) - print(f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}") + print( + f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" + ) self.global_epochs += 1 if self.global_rank == 0: - print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") @@ -344,32 +364,34 @@ def recon( obj: ObjectINN, batch_size: int, num_workers: int = 0, - epochs = 20, - use_amp = True, - viz_freq = 1, - checkpoint_freq = 5, + epochs=20, + use_amp=True, + viz_freq=1, + checkpoint_freq=5, optimizer_params: dict = None, scheduler_params: dict = None, soft_constraints: dict = None, - vol_save_path: str = None, # TODO: TEMPORARY - log_path = None, + vol_save_path: str = None, # TODO: TEMPORARY + log_path=None, val_fraction: float = 0.0, learn_shifts: bool = True, # l1_loss: bool = False, - consistency_criterion: str = 'mse', + consistency_criterion: str = "mse", model_weights_path: str = None, - ): - + force_cpu: bool = False, + ): if not self.ddp_instantiated: - if self.pretraining_instantiated == False: + if not self.pretraining_instantiated: self.setup_distributed() if model_weights_path is not None: print(f"Loading model weights from {model_weights_path}") - state_dict = torch.load(model_weights_path, map_location='cpu') + state_dict = torch.load(model_weights_path, map_location="cpu") # Handle DataParallel/DDP checkpoints - if any(k.startswith('module.') for k in state_dict.keys()): - new_state_dict = {k.replace('module.', ''): v for k, v in state_dict.items()} + if any(k.startswith("module.") for k in state_dict.keys()): + new_state_dict = { + k.replace("module.", ""): v for k, v in state_dict.items() + } else: new_state_dict = state_dict @@ -380,64 +402,61 @@ def recon( self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) self.ddp_instantiated = True - - + if soft_constraints is not None: self.obj.soft_constraints = soft_constraints - + if not hasattr(self, "temp_logger"): self.setup_logger(log_path=log_path) zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() - + if self.global_rank == 0: - print(f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference") + print( + f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" + ) print(f"Using consistency criterion: {consistency_criterion}") - + # Auxiliary params setup self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) aux_params = self.dataset.auxiliary_params - # Scaling learning rates to account for distributed training if optimizer_params is not None: optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params + self.optimizer_params = optimizer_params self.set_optimizers() if scheduler_params is not None: self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter = epochs) - - + self.set_schedulers(self.scheduler_params, num_iter=epochs) + aux_norm = torch.tensor(0.0, device=self.device) model_norm = torch.tensor(0.0, device=self.device) - + for _, opt in self.optimizers.items(): opt.zero_grad() - + N = max(self.dataset.dims) - + device_type = self.device.type autocast_dtype = torch.bfloat16 if use_amp else None - - + for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(N = N, epoch = self.global_epochs) - + num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) + # num_samples_per_ray = get_num_samples_per_ray(epoch) # Log the change if it happens if self.global_rank == 0: print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - - + if self.global_rank == 0 and self.global_epochs > 0: - prev_samples = get_num_samples_per_ray(N = N, epoch = self.global_epochs - 1) - + prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) + if num_samples_per_ray != prev_samples: - print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") - - - + print( + f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" + ) + if self.sampler is not None: self.sampler.set_epoch(epoch) @@ -448,45 +467,47 @@ def recon( num_batches = 0 consistency_loss_fn = None - if consistency_criterion[0].lower() == 'mse': + if consistency_criterion[0].lower() == "mse": consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == 'l1': + elif consistency_criterion[0].lower() == "l1": consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == 'mse_log': + elif consistency_criterion[0].lower() == "mse_log": consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == 'llmse': + elif consistency_criterion[0].lower() == "llmse": consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == 'smooth_l1': - consistency_loss_fn = SmoothL1Loss(beta = consistency_criterion[1]) - elif consistency_criterion[0].lower() == 'adaptive_smooth_l1': - consistency_loss_fn = AdaptiveSmoothL1LossDDP(beta_init = consistency_criterion[1], ema_factor = 0.99, eps = 1e-8) - elif consistency_criterion[0].lower() == 'charbonnier': - consistency_loss_fn = CharbonnierLoss(epsilon = 1e-12, reduction = 'mean') + elif consistency_criterion[0].lower() == "smooth_l1": + consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) + elif consistency_criterion[0].lower() == "adaptive_smooth_l1": + consistency_loss_fn = AdaptiveSmoothL1LossDDP( + beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 + ) + elif consistency_criterion[0].lower() == "charbonnier": + consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") else: raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") for batch_idx, batch in enumerate(self.dataloader): + projection_indices = batch["projection_idx"] - projection_indices = batch['projection_idx'] - - pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) - pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) - target_values = batch['target_value'].to(self.device, non_blocking=True) - phis = batch['phi'].to(self.device, non_blocking=True) - projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) shifts, z1_params, z3_params = aux_params(None) batch_shifts = torch.index_select(shifts, 0, projection_indices) batch_z1 = torch.index_select(z1_params, 0, projection_indices) batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): with torch.no_grad(): batch_ray_coords = self.create_batch_projection_rays( pixel_i, pixel_j, N, num_samples_per_ray ) - + # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate transformed_rays = self.transform_batch_ray_coordinates( batch_ray_coords, @@ -503,7 +524,9 @@ def recon( all_densities = self.obj.forward(all_coords) tv_loss = self.obj.apply_soft_constraints(all_coords) - ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) predicted_values = ray_densities.sum(dim=1) * step_size @@ -512,7 +535,7 @@ def recon( tv_loss_z1 = torch.tensor(0.0, device=self.device) loss = consistency_loss + tv_loss + tv_loss_z1 - + loss.backward() epoch_loss += loss.detach() @@ -520,28 +543,32 @@ def recon( epoch_tv_loss += tv_loss.detach() epoch_z1_loss += tv_loss_z1.detach() num_batches += 1 - for key, opt in self.optimizers.items(): - if key == "model": - model_norm = torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm = 1) + model_norm = torch.nn.utils.clip_grad_norm_( + self.obj.model.parameters(), max_norm=1 + ) elif key == "aux_params": - aux_norm = torch.nn.utils.clip_grad_norm_(self.dataset.auxiliary_params.parameters(), max_norm = 1) + aux_norm = torch.nn.utils.clip_grad_norm_( + self.dataset.auxiliary_params.parameters(), max_norm=1 + ) opt.step() opt.zero_grad() - + for key, sched in self.schedulers.items(): if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): sched.step(epoch_loss) - else: + else: sched.step() - - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: with torch.no_grad(): - - self.obj.create_volume(world_size = self.world_size, global_rank = self.global_rank, ray_size = num_samples_per_ray) + self.obj.create_volume( + world_size=self.world_size, + global_rank=self.global_rank, + ray_size=num_samples_per_ray, + ) pred_full = self.obj.obj avg_loss = epoch_loss.item() / num_batches avg_consistency_loss = epoch_consistency_loss.item() / num_batches @@ -551,76 +578,99 @@ def recon( shifts = shifts.detach().cpu() z1 = z1.detach().cpu() z3 = z3.detach().cpu() - metrics = torch.tensor([avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], device=self.device) + metrics = torch.tensor( + [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], + device=self.device, + ) if self.world_size > 1: dist.all_reduce(metrics, op=dist.ReduceOp.AVG) avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - val_loss = self.validate(aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N) + val_loss = self.validate( + aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N + ) if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): with torch.no_grad(): current_lr = self.schedulers["model"].get_last_lr()[0] # Log metrics - self.temp_logger.add_scalar(f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", avg_consistency_loss, self.global_epochs) + self.temp_logger.add_scalar( + f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", + avg_consistency_loss, + self.global_epochs, + ) if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - self.temp_logger.add_scalar(f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", val_loss, self.global_epochs) + self.temp_logger.add_scalar( + f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", + val_loss, + self.global_epochs, + ) self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) # if tv_weight > 0: self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) - self.temp_logger.add_scalar("train/model_grad_norm", model_norm.item(), self.global_epochs) - self.temp_logger.add_scalar("train/aux_grad_norm", aux_norm.item(), self.global_epochs) + self.temp_logger.add_scalar( + "train/model_grad_norm", model_norm.item(), self.global_epochs + ) + self.temp_logger.add_scalar( + "train/aux_grad_norm", aux_norm.item(), self.global_epochs + ) self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) - self.temp_logger.add_scalar("train/num_samples_per_ray", num_samples_per_ray, self.global_epochs) - if consistency_criterion[0].lower() == 'adaptive_smooth_l1': - self.temp_logger.add_scalar("train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs) + self.temp_logger.add_scalar( + "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs + ) + if consistency_criterion[0].lower() == "adaptive_smooth_l1": + self.temp_logger.add_scalar( + "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs + ) fig, axes = plt.subplots(1, 5, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) - axes[0].set_title('Sum over Z-axis') + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + axes[0].set_title("Sum over Z-axis") - axes[1].matshow(pred_full[N//2].cpu().numpy(), cmap='turbo', vmin=0) - axes[1].set_title(f'Slice at Z={N//2}') + axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) + axes[1].set_title(f"Slice at Z={N // 2}") - slice_start = max(0, N//2 - 5) - slice_end = min(N, N//2 + 6) + slice_start = max(0, N // 2 - 5) + slice_end = min(N, N // 2 + 6) thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap='turbo', vmin=0) - axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') - - axes[3].matshow(pred_full.sum(dim = 1).cpu().numpy(), cmap='turbo', vmin=0) - axes[3].set_title('Sum over Y-axis') - - axes[4].matshow(pred_full.sum(dim = 2).cpu().numpy(), cmap='turbo', vmin=0) - axes[4].set_title('Sum over X-axis') + axes[2].matshow(thick_slice, cmap="turbo", vmin=0) + axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") + + axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) + axes[3].set_title("Sum over Y-axis") + + axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) + axes[4].set_title("Sum over X-axis") self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) - fig, axes = plt.subplots(ncols = 3, figsize = (10, 5)) - axes[0].plot(shifts[:, 0].cpu().numpy(), label = 'Shifts X') - axes[0].plot(shifts[:, 1].cpu().numpy(), label = 'Shifts Y') + fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) + axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") + axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") axes[0].legend() - axes[1].plot(z1.cpu().numpy(), label = 'Z1') - axes[2].plot(z3.cpu().numpy(), label = 'Z3') - self.temp_logger.add_figure("train/auxiliary_params", fig, self.global_epochs, close=True) + axes[1].plot(z1.cpu().numpy(), label="Z1") + axes[2].plot(z3.cpu().numpy(), label="Z3") + self.temp_logger.add_figure( + "train/auxiliary_params", fig, self.global_epochs, close=True + ) plt.close(fig) - + if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: with torch.no_grad(): if self.global_rank == 0: save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" torch.save(pred_full.cpu(), save_path) - + self.global_epochs += 1 if self.global_rank == 0: print("Training complete.") - + torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") # print("Successfully setup DDP and dataloader") + def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): """Validate the model on the validation set.""" self.obj.model.eval() @@ -631,18 +681,20 @@ def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, with torch.no_grad(): for batch in self.val_dataloader: - pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) - pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) - target_values = batch['target_value'].to(self.device, non_blocking=True) - phis = batch['phi'].to(self.device, non_blocking=True) - projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) shifts, z1_params, z3_params = aux_params(None) batch_shifts = torch.index_select(shifts, 0, projection_indices) batch_z1 = torch.index_select(z1_params, 0, projection_indices) batch_z3 = torch.index_select(z3_params, 0, projection_indices) - with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): with torch.no_grad(): batch_ray_coords = self.create_batch_projection_rays( pixel_i, pixel_j, N, num_samples_per_ray @@ -662,7 +714,9 @@ def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, all_densities = self.obj.forward(all_coords) - ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) predicted_values = ray_densities.sum(dim=1) * step_size diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index 86c4f59e..cfb59117 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -1,31 +1,36 @@ -import torch.distributed as dist -import torch -from torch.utils.data import Dataset, DataLoader, DistributedSampler -import torch.nn as nn import os + import numpy as np -from quantem.tomography.tomography_dataset import TomographyDataset, TomographyRayDataset, PretrainVolumeDataset +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.utils.data import DataLoader, DistributedSampler + +from quantem.tomography.tomography_dataset import ( + PretrainVolumeDataset, + TomographyDataset, + TomographyRayDataset, +) -from torch.utils.data import random_split class TomographyDDP: """ Initializing DDP stuff for tomo class. """ + def __init__( self, ): - self.setup_distributed() - - + def setup_distributed(self): - # Check if in distributed env if "RANK" in os.environ: # Distributed training if not dist.is_initialized(): - dist.init_process_group(backend='nccl' if torch.cuda.is_available() else 'gloo', init_method='env://') + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" + ) self.world_size = dist.get_world_size() self.global_rank = dist.get_rank() @@ -33,7 +38,7 @@ def setup_distributed(self): torch.cuda.set_device(self.local_rank) self.device = torch.device("cuda", self.local_rank) - + else: # Single GPU/CPU training self.world_size = 1 @@ -41,49 +46,47 @@ def setup_distributed(self): self.local_rank = 0 if torch.cuda.is_available(): - self.device = torch.device("cuda:0") + self.device = torch.device("cuda") torch.cuda.set_device(0) print("Single GPU training") else: self.device = torch.device("cpu") print("CPU training") - + # Optional performance optimizations (only for CUDA) if self.device.type == "cuda": torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True - + def build_model( self, model: nn.Module, ): - # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? model = model.to(self.device) - + if self.world_size > 1: model = torch.nn.parallel.DistributedDataParallel( model, - device_ids = [self.local_rank], - output_device = self.local_rank, - find_unused_parameters = False, - broadcast_buffers = True, - bucket_cap_mb = 100, - gradient_as_bucket_view = True, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, + bucket_cap_mb=100, + gradient_as_bucket_view=True, ) - + if self.global_rank == 0: print("Model wrapped with DDP") - + if self.world_size > 1: - if self.global_rank == 0: print("Model built, distributed, and compiled successfully") - + else: print("Model built, compiled successfully") - + return model # Setup Tomo DataLoader @@ -103,17 +106,17 @@ def setup_dataloader( train_dataset = TomographyRayDataset( tomo_dataset.tilt_series.detach().clone(), tomo_dataset.tilt_angles.detach().clone(), - 500, # TODO: TEMPORARY + 500, # TODO: TEMPORARY val_ratio=val_fraction, - mode='train', + mode="train", seed=42, ) val_dataset = TomographyRayDataset( tomo_dataset.tilt_series.detach().clone(), tomo_dataset.tilt_angles.detach().clone(), - 500, # TODO: TEMPORARY + 500, # TODO: TEMPORARY val_ratio=val_fraction, - mode='val', + mode="val", seed=42, ) else: @@ -166,12 +169,10 @@ def setup_dataloader( ) self.val_sampler = val_sampler - self.sampler = train_sampler - if self.global_rank == 0: - print(f"Dataloader setup complete:") + print("Dataloader setup complete:") print(f" Total projections: {len(tomo_dataset.tilt_angles)}") if val_fraction > 0.0: print(f" Total projections (val): {len(val_dataset)}") @@ -181,10 +182,9 @@ def setup_dataloader( print(f" Total pixels (val): {len(val_dataset):,}") print(f" Total pixels (train): {len(train_dataset):,}") print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {batch_size*self.world_size}") + print(f" Global batch size: {batch_size * self.world_size}") print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - # Setup pretraining dataloader def setup_pretraining_dataloader( @@ -192,16 +192,16 @@ def setup_pretraining_dataloader( volume_dataset: PretrainVolumeDataset, batch_size: int, ): - #TODO: temp + # TODO: temp num_workers = 0 if self.world_size > 1: sampler = DistributedSampler( volume_dataset, - num_replicas = self.world_size, - rank = self.global_rank, - shuffle = True, - drop_last = True, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, + drop_last=True, ) shuffle = False else: @@ -210,26 +210,24 @@ def setup_pretraining_dataloader( self.pretraining_dataloader = DataLoader( volume_dataset, - batch_size = batch_size, - sampler = sampler, - shuffle = shuffle, - pin_memory = self.device.type == "cuda", - drop_last = True, - persistent_workers = num_workers > 0, + batch_size=batch_size, + sampler=sampler, + shuffle=shuffle, + pin_memory=self.device.type == "cuda", + drop_last=True, + persistent_workers=num_workers > 0, ) self.pretraining_sampler = sampler if self.global_rank == 0: - print(f"Pretraining dataloader setup complete:") + print("Pretraining dataloader setup complete:") print(f" Total samples: {len(volume_dataset)}") print(f" Grid size: {volume_dataset.N**3}") print(f" Local batch size: {batch_size}") - print(f" Global batch size: {batch_size*self.world_size}") + print(f" Global batch size: {batch_size * self.world_size}") print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") - - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): if scaling_rule == "sqrt": return base_lr * np.sqrt(self.world_size) @@ -237,15 +235,13 @@ def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): return base_lr * self.world_size else: raise ValueError(f"Invalid scaling rule: {scaling_rule}") - + def scale_lr( self, optimizer_params: dict, ): - new_optimizer_params = {} for key, value in optimizer_params.items(): - if "original_lr" in value: new_optimizer_params[key] = { "type": value["type"], @@ -258,13 +254,12 @@ def scale_lr( "lr": self.get_scaled_lr(value["lr"]), "original_lr": value["lr"], } - + return new_optimizer_params - - # TODO: Temporary Adaptive L1 Smooth Loss + class AdaptiveSmoothL1Loss(nn.Module): def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): """ @@ -276,7 +271,7 @@ def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): eps (float): small constant for numerical stability """ super().__init__() - self.register_buffer('beta2', torch.tensor(beta_init**2 if beta_init else 1.0)) + self.register_buffer("beta2", torch.tensor(beta_init**2 if beta_init else 1.0)) self.ema_factor = ema_factor self.eps = eps @@ -285,27 +280,28 @@ def forward(self, pred, target): abs_diff = diff.abs() # compute current batch MSE (Eq. 38) - mse_batch = torch.mean(diff ** 2) + mse_batch = torch.mean(diff**2) # update β² adaptively using Eq. (39) with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min(self.beta2, mse_batch) + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( + self.beta2, mse_batch + ) beta = torch.sqrt(self.beta2 + self.eps) # Smooth L1 (Eq. 36) loss = torch.where( - abs_diff < beta, - 0.5 * (diff ** 2) / (beta + self.eps), - abs_diff - 0.5 * beta + abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta ) return loss.mean() + class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): def forward(self, pred, target): diff = pred - target abs_diff = diff.abs() - mse_batch = torch.mean(diff ** 2) + mse_batch = torch.mean(diff**2) # Synchronize β across all GPUs if dist.is_initialized(): @@ -314,8 +310,12 @@ def forward(self, pred, target): mse_batch = mse_batch_all / dist.get_world_size() with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min(self.beta2, mse_batch) + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( + self.beta2, mse_batch + ) beta = torch.sqrt(self.beta2 + self.eps) - loss = torch.where(abs_diff < beta, 0.5 * (diff ** 2) / (beta + self.eps), abs_diff - 0.5 * beta) + loss = torch.where( + abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta + ) return loss.mean() From 700f2055474f760f8ea6e75ed4bac6333cc4d8a0 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 16 Dec 2025 15:49:17 -0800 Subject: [PATCH 032/335] Outlining dataset_models.py, and the top level Tomography class --- src/quantem/core/ml/constraints.py | 99 ++ src/quantem/tomography/dataset_models.py | 156 +++ src/quantem/tomography/models.py | 59 -- src/quantem/tomography/object_models.py | 734 --------------- src/quantem/tomography/tomography.py | 890 +----------------- src/quantem/tomography/tomography_base.py | 513 ---------- src/quantem/tomography/tomography_conv.py | 235 ----- src/quantem/tomography/tomography_ddp.py | 321 ------- src/quantem/tomography/tomography_ml.py | 330 ------- src/quantem/tomography/utils.py | 300 ------ .../preprocess => tomography_old}/__init__.py | 0 src/quantem/tomography_old/models.py | 96 ++ src/quantem/tomography_old/object_models.py | 723 ++++++++++++++ .../preprocess}/__init__.py | 0 .../preprocess/drift.py | 0 src/quantem/tomography_old/radon/__init__.py | 0 .../radon/radon.py | 0 src/quantem/tomography_old/tomography.py | 886 +++++++++++++++++ src/quantem/tomography_old/tomography_base.py | 514 ++++++++++ src/quantem/tomography_old/tomography_conv.py | 235 +++++ .../tomography_dataset.py | 50 +- src/quantem/tomography_old/tomography_ddp.py | 321 +++++++ .../tomography_logger.py | 0 src/quantem/tomography_old/tomography_ml.py | 338 +++++++ .../tomography_nerf.py | 331 +++---- src/quantem/tomography_old/utils.py | 300 ++++++ 26 files changed, 3871 insertions(+), 3560 deletions(-) create mode 100644 src/quantem/core/ml/constraints.py create mode 100644 src/quantem/tomography/dataset_models.py delete mode 100644 src/quantem/tomography/models.py rename src/quantem/{tomography/preprocess => tomography_old}/__init__.py (100%) create mode 100644 src/quantem/tomography_old/models.py create mode 100644 src/quantem/tomography_old/object_models.py rename src/quantem/{tomography/radon => tomography_old/preprocess}/__init__.py (100%) rename src/quantem/{tomography => tomography_old}/preprocess/drift.py (100%) create mode 100644 src/quantem/tomography_old/radon/__init__.py rename src/quantem/{tomography => tomography_old}/radon/radon.py (100%) create mode 100644 src/quantem/tomography_old/tomography.py create mode 100644 src/quantem/tomography_old/tomography_base.py create mode 100644 src/quantem/tomography_old/tomography_conv.py rename src/quantem/{tomography => tomography_old}/tomography_dataset.py (95%) create mode 100644 src/quantem/tomography_old/tomography_ddp.py rename src/quantem/{tomography => tomography_old}/tomography_logger.py (100%) create mode 100644 src/quantem/tomography_old/tomography_ml.py rename src/quantem/{tomography => tomography_old}/tomography_nerf.py (78%) create mode 100644 src/quantem/tomography_old/utils.py diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py new file mode 100644 index 00000000..755122c6 --- /dev/null +++ b/src/quantem/core/ml/constraints.py @@ -0,0 +1,99 @@ +from abc import ABC, abstractmethod +from typing import Any + +import torch + +from quantem.core import config + + +class BaseConstraints(ABC): + """Base class for constraint management with common functionality.""" + + # Subclasses should define their own DEFAULT_CONSTRAINTS + DEFAULT_CONSTRAINTS: dict[str, Any] = {} + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self._soft_constraint_loss = {} + self._constraints = self.DEFAULT_CONSTRAINTS.copy() + self._epoch_constraint_losses = {} + + @property + def constraints(self) -> dict[str, Any]: + return self._constraints + + @constraints.setter + def constraints(self, c: dict[str, Any]): + allowed_keys = self.DEFAULT_CONSTRAINTS.keys() + constraint_type = self.__class__.__name__.lower().replace("constraints", "") + + for key, value in c.items(): + if key not in allowed_keys: + raise KeyError( + f"Invalid {constraint_type} constraint key '{key}', allowed keys are {list(allowed_keys)}" + ) + self._constraints[key] = value + + @property + def soft_constraint_loss(self) -> dict[str, torch.Tensor | float]: + return self._soft_constraint_loss + + def add_constraint(self, key: str, value: Any): + allowed_keys = self.DEFAULT_CONSTRAINTS.keys() + constraint_type = self.__class__.__name__.lower().replace("constraints", "") + + if key not in allowed_keys: + raise KeyError( + f"Invalid {constraint_type} constraint key '{key}', allowed keys are {list(allowed_keys)}" + ) + self._constraints[key] = value + + @abstractmethod + def apply_soft_constraints(self, *args, **kwargs) -> torch.Tensor: + """Apply soft constraints and return total constraint loss.""" + pass + + def _get_zero_loss_tensor(self) -> torch.Tensor: + """Helper method to create a zero loss tensor with proper device and dtype.""" + device = getattr(self, "device", "cpu") + return torch.tensor(0, device=device, dtype=getattr(torch, config.get("dtype_real"))) + + # --- helpers for consistent loss logging --- + def reset_soft_constraint_losses(self) -> None: + self._soft_constraint_loss = {} + + def add_soft_constraint_loss(self, name: str, value: torch.Tensor | float) -> None: + """Record a single soft-constraint loss for logging without holding the graph.""" + if isinstance(value, torch.Tensor): + val = value.detach() + if val.ndim != 0: + val = val.mean() + self._soft_constraint_loss[name] = val + else: + self._soft_constraint_loss[name] = float(value) + + def accumulate_constraint_losses( + self, batch_constraint_losses: dict[str, torch.Tensor | float] | None = None + ) -> None: + """Accumulate constraint losses across batches.""" + if batch_constraint_losses is None: + batch_constraint_losses = self.soft_constraint_loss + + for loss_name, loss_value in batch_constraint_losses.items(): + if isinstance(loss_value, torch.Tensor): + try: + v = loss_value.item() + except Exception: + print("loss value not singular: ", loss_value) # TODO remove + v = loss_value.detach().mean().item() + else: + v = float(loss_value) + self._epoch_constraint_losses[loss_name] = ( + self._epoch_constraint_losses.get(loss_name, 0.0) + v + ) + + def get_epoch_constraint_losses(self) -> dict[str, float]: + return getattr(self, "_epoch_constraint_losses", {}) # TODO clean this up + + def reset_epoch_constraint_losses(self) -> None: + self._epoch_constraint_losses = {} diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py new file mode 100644 index 00000000..994d3356 --- /dev/null +++ b/src/quantem/tomography/dataset_models.py @@ -0,0 +1,156 @@ +from abc import abstractmethod +from dataclasses import dataclass +from typing import Any + +import torch +import torch.nn as nn +from numpy.typing import NDArray +from torch.utils.data import Dataset + +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml.optimizer_mixin import OptimizerMixin + + +@dataclass +class DatasetValue: + """ + Class for storing the forward call for both PixDataset and INRDataset. + """ + + target: torch.Tensor + tilt_angle: int | float + pixel_loc: tuple[int, int] | None = None # Only for INRDataset + + +class TomographyDatasetBase(AutoSerialize, OptimizerMixin, nn.Module): + """ + Base tomography dataset class for all tomography datasets to inherit from. + """ + + _token = object() + + DEFAULT_LRS = { + "pose_lr": 5e-2, + } + + def __init__( + self, + tilt_stack: Dataset3d, + tilt_angles: NDArray | torch.Tensor, + learn_pose: bool = True, + _token: object | None = None, + ): + AutoSerialize.__init__(self) + OptimizerMixin.__init__(self) + nn.Module.__init__(self) + + if _token is not self._token: + raise RuntimeError("Use TomographyDatasetBase.from_* to instantiate this class.") + + if not ( + tilt_stack.shape[0] < tilt_stack.shape[1] or tilt_stack.shape[0] < tilt_stack.shape[2] + ): + raise ValueError( + "The number of tilt projections should be in the first dimension of the dataset." + ) + + self.tilt_stack = tilt_stack + self.tilt_angles = tilt_angles + self.learn_pose = learn_pose + + self._reference_tilt_angle_idx = torch.argmin(torch.abs(self.tilt_angles)) + + @abstractmethod + def forward( + self, + dummy_input: Any = None, # Note all nn.Modules require some input. + ): + """ + Forward pass should be implemented in subclasses. + """ + raise NotImplementedError("This method should be implemented in subclasses.") + + +class TomographyPixDataset( + TomographyDatasetBase +): # Dataset): TODO: Does this need to be a dataset? + """ + Dataset class for pixel-based tomography, i.e AD, SIRT, WBP, etc... + + TODO: + - What should the forward pass return? In AD, it should be both the tilt image and the pose. + In SIRT, it's only the tilt image. + """ + + pass + + def __init__( + self, + ): + pass + + def forward( + self, + proj_idx: int, + ): + return DatasetValue( + target=self.tilt_stack[proj_idx], + tilt_angle=self.tilt_angles[proj_idx], + pixel_loc=None, + ) + + +class TomographyINRDataset(TomographyDatasetBase, Dataset): + """ + Dataset class for INR-based tomography. + + The two main methods here are that the `forward` call will return the relative pose parameters, + while `__getitem__` will actually return the pixel values of the tilt stack. + """ + + def __init__( + self, + ): + pass + + def forward(self, dummy_input: Any = None): + """ + Forward pass for INR-based tomography. In the forward pass, the only parameters that + are passed will be the shifts, z1 and z3 Euler angles. + """ + pass + + def __getitem__( + self, + idx: int, + ) -> dict[str, Any]: + """ + Gets the item for INR i.e, the project index, pixel value at (i, j), and the tilt angle. + """ + pass + + actual_idx = idx + + projection_idx = actual_idx // (self.tilt_stack.shape[1] * self.tilt_stack.shape[2]) + remaining = actual_idx % (self.tilt_stack.shape[1] * self.tilt_stack.shape[2]) + + pixel_i = remaining // self.tilt_stack.shape[1] + pixel_j = remaining % self.tilt_stack.shape[1] + + return DatasetValue( + target=self.tilt_stack[projection_idx, pixel_i, pixel_j], + tilt_angle=self.tilt_angles[projection_idx], + pixel_loc=(pixel_i, pixel_j), + ) + + def __len__( + self, + ): + """ + Returns the number of pixels in the tilt stack. + """ + pass + + +DatasetModelType = TomographyINRDataset diff --git a/src/quantem/tomography/models.py b/src/quantem/tomography/models.py deleted file mode 100644 index 212385ee..00000000 --- a/src/quantem/tomography/models.py +++ /dev/null @@ -1,59 +0,0 @@ -import torch -from torch import nn -import numpy as np -import math -from torch.nn import init - -class SineLayer(nn.Module): - def __init__(self, in_features, out_features, bias=True, is_first=False, omega_0=30, hsiren=False, alpha=1.0): - super().__init__() - self.omega_0 = omega_0 - self.is_first = is_first - self.hsiren = hsiren - self.in_features = in_features - self.alpha = alpha - self.linear = nn.Linear(in_features, out_features, bias=bias) - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - if self.is_first: - # Scale the first layer initialization by alpha - self.linear.weight.uniform_(-self.alpha / self.in_features, - self.alpha / self.in_features) - else: - # Scale the hidden layer initialization by alpha - self.linear.weight.uniform_(-self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, - self.alpha * np.sqrt(6 / self.in_features) / self.omega_0) - - def forward(self, input): - if self.is_first and self.hsiren: - out = torch.sin(self.omega_0 * torch.sinh(2*self.linear(input))) - else: - out = torch.sin(self.omega_0 * self.linear(input)) - return out - -class HSiren(nn.Module): - def __init__(self, in_features=2, out_features=3, hidden_layers=3, hidden_features=256, - first_omega_0=30, hidden_omega_0=30, alpha=1.0): - super().__init__() - self.net_list = [] - self.net_list.append(SineLayer(in_features, hidden_features, is_first=True, - omega_0=first_omega_0, hsiren=True, alpha=alpha)) - - for i in range(hidden_layers): - self.net_list.append(SineLayer(hidden_features, hidden_features, is_first=False, - omega_0=hidden_omega_0, alpha=alpha)) - - final_linear = nn.Linear(hidden_features, out_features) - with torch.no_grad(): - # Final layer keeps original initialization (no alpha scaling) - final_linear.weight.uniform_(-np.sqrt(6 / hidden_features) / hidden_omega_0, - np.sqrt(6 / hidden_features) / hidden_omega_0) - self.net_list.append(final_linear) - self.net_list.append(nn.Softplus()) - self.net = nn.Sequential(*self.net_list) - - def forward(self, coords): - output = self.net(coords) - return output diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index ff36eb54..e69de29b 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,734 +0,0 @@ -from abc import abstractmethod -from copy import deepcopy -from pathlib import Path -from typing import Any, Callable, Union - -import numpy as np -import torch -from tqdm.auto import tqdm - -from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml.blocks import reset_weights -from quantem.core.utils.validators import validate_gt, validate_tensor -from quantem.tomography.utils import get_TV_loss - -import torch.nn as nn -import torch.distributed as dist - - -class ObjectBase(AutoSerialize): - """ - Base class for all ObjectModels to inherit from. - """ - - def __init__( - self, - volume_shape: tuple[int, int, int], - device: str, - offset_obj: float = 1e-5, - ): - self._shape = volume_shape - - self._obj = torch.zeros(self._shape, device=device, dtype=torch.float32) + offset_obj - self._offset_obj = offset_obj - self._device = device - self._hard_constraints = {} - self._soft_constraints = {} # One big dicitonary - - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape - - @property - def offset_obj(self) -> float: - return self._offset_obj - - @offset_obj.setter - def offset_obj(self, offset_obj: float): - self._offset_obj = offset_obj - - @property - def obj(self) -> torch.Tensor: - pass - - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj - - @property - def device(self) -> str: - return self._device - - @device.setter - def device(self, device: str): - self._device = device - - @abstractmethod - def forward( - self, z1: torch.Tensor, z3: torch.Tensor, shift_x: torch.Tensor, shift_y: torch.Tensor - ): - pass - - @abstractmethod - def obj(self): - pass - - @abstractmethod - def reset(self): - pass - - @abstractmethod - def to(self, device: str): - pass - - @abstractmethod - def name(self) -> str: - pass - - @abstractmethod - def params(self) -> torch.Tensor: - pass - - -class ObjectConstraints(ObjectBase): - DEFAULT_HARD_CONSTRAINTS = { - "fourier_filter": False, - "positivity": False, - "shrinkage": False, - "circular_mask": False, - } - - DEFAULT_SOFT_CONSTRAINTS = { - "tv_vol": 0, - } - - @property - def hard_constraints(self) -> dict[str, Any]: - return self._hard_constraints - - @hard_constraints.setter - def hard_constraints(self, hard_constraints: dict[str, Any]): - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - for key, value in hard_constraints.items(): - if key not in gkeys: # This might be redundant since add_constraint is checking. - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._hard_constraints[key] = value - - @property - def soft_constraints(self) -> dict[str, Any]: - return self._soft_constraints - - @soft_constraints.setter - def soft_constraints(self, soft_constraints: dict[str, Any]): - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - for key, value in soft_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._soft_constraints[key] = value - - def add_hard_constraint(self, constraint: str, value: Any): - """Add constraints to the object model.""" - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - if constraint not in gkeys: - raise KeyError( - f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" - ) - self._hard_constraints[constraint] = value - - def add_soft_constraint(self, constraint: str, value: Any): - """Add constraints to the object model.""" - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - if constraint not in gkeys: - raise KeyError( - f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" - ) - self._soft_constraints[constraint] = value - - def apply_hard_constraints( - self, - obj: torch.Tensor, - ) -> torch.Tensor: - """ - Apply constraints to the object model. - """ - obj2 = obj.clone() - if self.hard_constraints["positivity"]: - obj2 = torch.clamp(obj, min=0.0, max=None) - if self.hard_constraints["shrinkage"]: - obj2 = torch.max(obj2 - self.hard_constraints["shrinkage"], torch.zeros_like(obj2)) - - return obj2 - - def apply_soft_constraints( - self, - obj: torch.Tensor, - ) -> torch.Tensor: - """ - 'Applies' soft constraints to the object model. This will return additional loss terms. - """ - soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) - if self.soft_constraints["tv_vol"] > 0: - tv_loss = get_TV_loss( - obj.unsqueeze(0).unsqueeze(0), factor=self.soft_constraints["tv_vol"] - ) - - soft_loss += tv_loss - - return soft_loss - - -class ObjectVoxelwise(ObjectConstraints): - """ - Object model for voxelwise objects. - """ - - def __init__( - self, - volume_shape: tuple[int, int, int], - device: str, - initial_volume: torch.Tensor | None = None, - ): - super().__init__( - volume_shape=volume_shape, - device=device, - ) - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - if initial_volume is not None: - self._initial_obj = initial_volume - else: - self.initial_obj = ( - torch.zeros(self._shape, device=self._device, dtype=torch.float32) - + self.offset_obj - ) - - @property - def obj(self): - return self.apply_hard_constraints(self._obj) - - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj - - @property - def initial_obj(self): - return self._initial_obj - - @initial_obj.setter - def initial_obj(self, initial_obj: torch.Tensor): - if not isinstance(initial_obj, torch.Tensor): - raise ValueError("initial_obj must be a torch.Tensor") - - self._initial_obj = initial_obj - - def forward(self): - return self.obj - - def reset(self): - self._obj = ( - torch.zeros(self._shape, device=self._device, dtype=torch.float32) + self.offset_obj - ) - - def to(self, device: str): - self._device = device - self._obj = self._obj.to(self._device) - - @property - def name(self) -> str: - return "ObjectVoxelwise" - - @property - def params(self) -> torch.Tensor: - return self._obj - - @property - def soft_loss(self) -> torch.Tensor: - return self.apply_soft_constraints(self._obj) - - -class ObjectDIP(ObjectConstraints): - """ - Object model for DIP objects. - """ - - def __init__( - self, - model: torch.nn.Module, - volume_shape: tuple[int, int, int], - model_input: torch.Tensor - | None = None, # Determines output size, model input pretraining target - input_noise_std: float = 0.0, - device: str = "cpu", - ): - super().__init__( - volume_shape=volume_shape, - device=device, - ) - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - if model_input is None: - self.model_input = torch.randn(1, 1, volume_shape[0], volume_shape[1], volume_shape[2]) - else: - self.model_input = model_input.clone().detach() - - self.pretrain_target = model_input.clone().detach() - - self._model = model - self._optimizer = None - self._scheduler = None - self._pretrain_losses = [] - self._pretrain_lrs = [] - self._model_input_noise_std = input_noise_std - - @property - def name(self) -> str: - return "ObjectDIP" - - @property - def model(self) -> torch.nn.Module: - return self._model - - @model.setter - def model(self, model: torch.nn.Module): - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Model must be a torch.nn.Module, got {type(model)}") - self._model = model.to(self._device) - self.set_pretrained_weights(self._model) - - @property - def pretrained_weights(self) -> dict[str, torch.Tensor]: - return self._pretrained_weights - - def set_pretrained_weights(self, model: torch.nn.Module): - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") - self._pretrained_weights = deepcopy(model.state_dict()) - - @property - def model_input(self) -> torch.Tensor: - return self._model_input - - @model_input.setter - def model_input(self, input_tensor: torch.Tensor): - inp = validate_tensor( - input_tensor, - name="model_input", - dtype=torch.float32, - ndim=5, - expand_dims=True, - ) - self._model_input = inp.to(self._device) - - @property - def pretrain_target(self) -> torch.Tensor: - return self._pretrain_target - - @pretrain_target.setter - def pretrain_target(self, target: torch.Tensor): - if target.ndim == 5: - target = target.squeeze(0).squeeze(0) - - target = validate_tensor( - target, - name="pretrain_target", - ndim=3, - dtype=torch.float32, - expand_dims=True, - ) - if target.shape[-3:] != self.model_input.shape[-3:]: - raise ValueError( - f"Pretrain target shape {target.shape} does not match model input shape {self.model_input.shape}" - ) - self._pretrain_target = target.to(self._device) - - @property - def _model_input_noise_std(self) -> float: - """standard deviation of the gaussian noise added to the model input each forward call""" - return self._input_noise_std - - @_model_input_noise_std.setter - def _model_input_noise_std(self, std: float): - validate_gt(std, 0.0, "input_noise_std", geq=True) - self._input_noise_std = std - - @property - def optimizer(self) -> torch.optim.Optimizer: - """get the optimizer for the DIP model""" - if self._optimizer is None: - raise ValueError("Optimizer is not set. Use set_optimizer() to set it.") - return self._optimizer - - def set_optimizer(self, opt_params: dict): - opt_type = opt_params.pop("type") - if isinstance(opt_type, torch.optim.Optimizer): - self._optimizer = opt_type - elif isinstance(opt_type, type): - self._optimizer = opt_type(self.model.parameters(), **opt_params) - elif opt_type == "adam": - self._optimizer = torch.optim.Adam(self.model.parameters(), **opt_params) - elif opt_type == "adamw": - self._optimizer = torch.optim.AdamW(self.model.parameters(), **opt_params) - elif opt_type == "sgd": - self._optimizer = torch.optim.SGD(self.model.parameters(), **opt_params) - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") - - @property - def scheduler( - self, - ) -> ( - torch.optim.lr_scheduler._LRScheduler - | torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ): - return self._scheduler - - def set_scheduler(self, params: dict, num_iter: int | None = None) -> None: - sched_type: str = params["type"].lower() - optimizer = self.optimizer - base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - scheduler = None - elif sched_type == "cyclic": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 20), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params.keys(): - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.999 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") - self._scheduler = scheduler - - @property - def pretrain_losses(self) -> np.ndarray: - return np.array(self._pretrain_losses) - - @property - def pretrain_lrs(self) -> np.ndarray: - return np.array(self._pretrain_lrs) - - @property - def obj(self): - obj = self.model(self._model_input)[0] - return self.apply_hard_constraints(obj) - - def forward(self): - return self.model(self._model_input) - - def to(self, device: str): - self.device = device - self._model = self._model.to(self.device) - self._model_input = self._model_input.to(self.device) - self._pretrain_target = self._pretrain_target.to(self.device) - - @property - def params(self): - return self._model.parameters() - - def reset(self): - self.model.load_state_dict(self.pretrained_weights.copy()) - - def pretrain( - self, - model_input: torch.Tensor, - pretrain_target: torch.Tensor, - reset: bool = True, - num_epochs: int = 100, - optimizer_params: dict | None = None, - scheduler_params: dict | None = None, - loss_fn: Callable | str = "l2", - apply_constraints: bool = False, - show: bool = True, - ): - model_input.to(self.device) - pretrain_target.to(self.device) - - if optimizer_params is not None: - self.set_optimizer(optimizer_params) - - if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_epochs) - - if reset: - self._model.apply(reset_weights) - self._pretrain_losses = [] - self._pretrain_lrs = [] - - if model_input is not None: - self.model_input = model_input - - if pretrain_target.shape[-3:] != self.model_input.shape[-3:]: - raise ValueError( - f"Pretrain target shape {pretrain_target.shape} does not match model input shape {self.model_input.shape}" - ) - self.pretrain_target = pretrain_target.clone().detach().to(self.device) - - loss_fn = torch.nn.functional.mse_loss - - self._pretrain( - num_epochs=num_epochs, - loss_fn=loss_fn, - apply_constraints=apply_constraints, - show=show, - ) - self.set_pretrained_weights(self.model) - - def _pretrain( - self, - num_epochs: int, - loss_fn: Callable, - apply_constraints: bool = False, - show: bool = False, - ): - if not hasattr(self, "pretrain_target"): - raise ValueError("Pretrain target is not set. Use pretrain_target to set it.") - - self.model.train() - optimizer = self.optimizer - sch = self.scheduler - pbar = tqdm(range(num_epochs)) - output = self.obj - - for a0 in pbar: - if apply_constraints: - output = self.obj - else: - output = self.model(self.model_input).squeeze(0).squeeze(0) - - loss = loss_fn(output, self.pretrain_target) - loss.backward() - optimizer.step() - optimizer.zero_grad() - - if sch is not None: - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(loss.item()) - else: - sch.step() - - self._pretrain_losses.append(loss.item()) - self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) - pbar.set_description(f"Epoch {a0 + 1}/{num_epochs}, Loss: {loss.item():.4f}, ") - - - -# INR stuff - - - - -class ObjectINN(ObjectConstraints): - """ - Object model for INN objects. - - - VolumeDataset (?) dependent on the object - """ - - def __init__( - self, - model: nn.Module, - volume_shape = tuple[int, int, int], - device: str = "cuda", - ): - - super().__init__( - volume_shape = volume_shape, - device = device, - offset_obj = 0, - ) - - self._model = model - - - - # --- Properties --- - @property - def obj(self): - - return self._obj - - def create_volume( - self, - world_size: int, - global_rank: int, - ray_size: int, - ): - N = max(self._shape) - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - # Underlying model if using DDP - model = self.model.module if hasattr(self.model, 'module') else self.model - - # Batch size for inference - # 5x larger batches no gradients needed - - inference_batch_size = 5 * N * ray_size - - # Distribute work across GPUs - total_samples = N**3 - samples_per_gpu = total_samples // world_size - remainder = total_samples % world_size - - # Handle uneven distribution - if global_rank < remainder: - start_idx = global_rank * (samples_per_gpu + 1) - end_idx = start_idx + samples_per_gpu + 1 - else: - start_idx = global_rank * samples_per_gpu + remainder - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx] - num_samples = inputs_subset.shape[0] - outputs_list = [] - - for batch_start in range(0, num_samples, inference_batch_size): - batch_end = min(batch_start + inference_batch_size, num_samples) - batch_coords = inputs_subset[batch_start:batch_end].to(self.device, non_blocking=True) - - batch_outputs = model(batch_coords) - if batch_outputs.dim() > 1: - batch_outputs = batch_outputs.squeeze(-1) - - outputs_list.append(batch_outputs.cpu()) - - outputs = torch.cat(outputs_list, dim=0) - - if world_size > 1: - # Gather from all ranks - # handle potentially different sizes - output_size = torch.tensor(outputs.shape[0], device=self.device, dtype=torch.long) - all_sizes = [torch.zeros(1, device=self.device, dtype=torch.long) for _ in range(world_size)] - dist.all_gather(all_sizes, output_size) - - # Create gather list with correct sizes - max_size = max(size.item() for size in all_sizes) - - # Pad if necessary for gathering - if outputs.shape[0] < max_size: - padding = torch.zeros(max_size - outputs.shape[0], device=outputs.device, dtype=outputs.dtype) - outputs_padded = torch.cat([outputs, padding], dim=0).to(self.device) - else: - outputs_padded = outputs.to(self.device) - - gathered_outputs = [torch.empty(max_size, device=self.device, dtype=outputs.dtype) - for _ in range(world_size)] - dist.all_gather(gathered_outputs, outputs_padded.contiguous()) - - # Trim padding and concatenate - trimmed_outputs = [] - for rank, size in enumerate(all_sizes): - trimmed_outputs.append(gathered_outputs[rank][:size.item()]) - - pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N).float() - else: - pred_full = outputs.reshape(N, N, N).float() - - - self._obj = pred_full.detach().cpu() - - - @property - def model(self): - - return self._model - - @model.setter - def model(self, model): - - self._model = model - - - def apply_soft_constraints( - self, - coords: torch.Tensor, - ): - - soft_loss = torch.tensor(0.0, device = coords.device) - if self.soft_constraints["tv_vol"] > 0: - - num_tv_samples = min(10000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device = coords.device)[:num_tv_samples] - - # Rerun forward for gradient tracking - tv_coords = coords[tv_indices].detach().requires_grad_(True) - - tv_densities_recomputed = self.model(tv_coords) - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - # Compute gradients - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True - )[0] - - grad_norm = torch.norm(grad_outputs, dim = 1) - soft_loss += self.soft_constraints["tv_vol"] * grad_norm.mean() - - return soft_loss - - def forward( - self, - all_coords: torch.Tensor, - ): - - all_densities = self.model(all_coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) - - valid_mask = ( - (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & - (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & - (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) - ).float() - - all_densities = all_densities * valid_mask - - return all_densities - - - - -ObjectModelType = ObjectVoxelwise | ObjectINN # | ObjectDIP | ObjectImplicit (ObjectFFN?) - - -# Pretrain Volume Dataset - - - \ No newline at end of file diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 5064cd3f..79525364 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,886 +1,16 @@ -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.distributed as dist -from torch.nn import SmoothL1Loss -from torch.utils.tensorboard import SummaryWriter - -# from torch_radon.radon import ParallelBeam as Radon -from tqdm.auto import tqdm - -from quantem.core.ml.loss_functions import ( - CharbonnierLoss, - L1Loss, - LLMSELoss, - MSELogMSELoss, - MSELoss, -) - -# Temporary imports for TomographyNERF -from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise -from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.tomography_conv import TomographyConv -from quantem.tomography.tomography_ddp import ( - AdaptiveSmoothL1LossDDP, - PretrainVolumeDataset, - TomographyDDP, -) -from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ - - -# Temporary aux class for TomographyNERF -# TODO: Maybe put this in INN? -def get_num_samples_per_ray(N: int, epoch: int): - """Increase number of samples per ray at specific epochs.""" - # Exponential schedule - - epochs = np.linspace(0, 10, 5, dtype=int) - # schedule = np.linspace(20, N, 5, dtype=int) - schedule = np.array([20, 100, 150, 200, 200]) - # schedule = np.array([200, 200, 200, 200, 200]) - # schedule = np.array([20, 100, 250, 500, 500]) - # schedule = np.array([500, 500, 500, 500, 500]) - # schedule = np.array([300, 300, 300, 300, 300]) - # schedule = np.exp(schedule) - schedule_warmup = dict[int, int](zip(epochs, schedule)) - - for epoch_threshold, samples in schedule_warmup.items(): - if epoch >= epoch_threshold: - num_samples = samples - - return num_samples - - -class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): +class Tomography: """ - Top level class for either using conventional or ML-based reconstruction methods - for tomography. + Class for handling all ML tomography reconstruction methods. + Automatic handling between AD and INR-based tomography. """ - def __init__( - self, - dataset, - volume_obj, - device, - _token, - ): - super().__init__(dataset, volume_obj, device, _token) - - # TODO: More elegant way of doing this. - self.global_epochs = 0 - self.ddp_instantiated = False - self.pretraining_instantiated = False - - # --- Reconstruction Method --- - - def sirt_recon( - self, - num_iterations: int = 10, - inline_alignment: bool = False, - enforce_positivity: bool = True, - volume_shape: tuple = None, - reset: bool = True, - smoothing_sigma: float = None, - shrinkage: float = None, - filter_name: str = "hamming", - circle: bool = True, - plot_loss: bool = False, - ): - num_angles, num_rows, num_cols = self.dataset.tilt_series.shape - sirt_tilt_series = self.dataset.tilt_series.clone() - sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) - - hard_constraints = { - "positivity": enforce_positivity, - "shrinkage": shrinkage, - } - self.volume_obj.hard_constraints = hard_constraints - - if volume_shape is None: - volume_shape = (num_rows, num_rows, num_rows) - else: - D, H, W = volume_shape - - if reset: - self.volume_obj.reset() - self.loss = [] - - proj_forward = torch.zeros_like(self.dataset.tilt_series) - - pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") - - if smoothing_sigma is not None: - gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) - else: - gaussian_kernel = None - - print( - "Devices", - sirt_tilt_series.device, - proj_forward.device, - self.dataset.tilt_angles.device, - ) - - for iter in pbar: - proj_forward, loss = self._sirt_run_epoch( - tilt_series=sirt_tilt_series, - proj_forward=proj_forward, - angles=self.dataset.tilt_angles, - inline_alignment=iter > 0 and inline_alignment, - filter_name=filter_name, - gaussian_kernel=gaussian_kernel, - circle=circle, - ) - - pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") - - self.loss.append(loss.item()) - - self.sirt_recon_vol = self.volume_obj - - # Permutation due to sinogram ordering. - self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) - - if plot_loss: - self.plot_loss() - - # TODO: ML Recon which has NeRF and AD depending on the object type. - # TODO: Temporary - - def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): - """Create projection rays for entire batch simultaneously.""" - batch_size = len(pixel_i) - - # Convert all pixels to normalized coordinates - x_coords = (pixel_j / (N - 1)) * 2 - 1 - y_coords = (pixel_i / (N - 1)) * 2 - 1 - # TODO: maybe pixel_j.device? - # Create z coordinates - z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - - # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] - rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - - # Fill coordinates efficiently - rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray - rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray - rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - - return rays - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): - # Step 1: Apply shifts - shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) - shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - - rays_x = rays[:, :, 0] - shift_x_norm - rays_y = rays[:, :, 1] - shift_y_norm - rays_z = rays[:, :, 2] - - # Rotation 1: Z(-z3) - theta = torch.deg2rad(-z3).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot1 = cos_t * rays_x - sin_t * rays_y - rays_y_rot1 = sin_t * rays_x + cos_t * rays_y - rays_z_rot1 = rays_z - - # Rotation 2: X(x) - theta = torch.deg2rad(x).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot2 = rays_x_rot1 - rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 - rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - - # Rotation 3: Z(-z1) - theta = torch.deg2rad(-z1).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 - rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 - rays_z_final = rays_z_rot2 - - # Stack the final result - transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - - return transformed_rays - - # TODO: Temp logger - def setup_logger(self, log_path): - if self.global_rank == 0: - self.temp_logger = SummaryWriter(log_dir=log_path) - else: - self.temp_logger = None - - def pretrain( - self, - volume_dataset: PretrainVolumeDataset, - obj: ObjectINN, - batch_size: int, - soft_constraints: dict = None, - log_path: str = None, - optimizer_params: dict = None, - epochs: int = 100, - viz_freq: int = 1, - consistency_criterion: str = "mse", - ): - if not self.pretraining_instantiated: - self.setup_distributed() - self.setup_pretraining_dataloader(volume_dataset, batch_size) - self.pretraining_instantiated = True - obj.model = self.build_model(obj._model) - self.obj = obj - - if soft_constraints is not None: - self.obj.soft_constraints = soft_constraints - - if not hasattr(self, "temp_logger"): - self.setup_logger(log_path=log_path) - - if optimizer_params is not None: - optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params - self.set_optimizers() - device_type = self.device.type - consistency_loss_fn = None - if consistency_criterion[0].lower() == "mse": - consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == "l1": - consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == "mse_log": - consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == "llmse": - consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == "smooth_l1": - consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) - elif consistency_criterion[0].lower() == "adaptive_smooth_l1": - consistency_loss_fn = AdaptiveSmoothL1LossDDP( - beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 - ) - elif consistency_criterion[0].lower() == "charbonnier": - consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") - else: - raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - - for epoch in range(epochs): - if self.pretraining_sampler is not None: - self.pretraining_sampler.set_epoch(epoch) - - self.obj.model.train() - - epoch_loss = 0.0 - epoch_consistency_loss = 0.0 - epoch_tv_loss = 0.0 - num_batches = 0 - - for batch_idx, batch in enumerate(self.pretraining_dataloader): - coords = batch["coords"].to(self.device, non_blocking=True) - target = batch["target"].to(self.device, non_blocking=True) - - for _, opt in self.optimizers.items(): - opt.zero_grad() - - with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): - outputs = self.obj.forward(coords) - consistency_loss = consistency_loss_fn(outputs, target) - tv_loss = self.obj.apply_soft_constraints(coords) - - loss = consistency_loss + tv_loss - - loss.backward() - - epoch_loss += loss.detach() - epoch_consistency_loss += consistency_loss.detach() - epoch_tv_loss += tv_loss.detach() - - torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) - - for _, opt in self.optimizers.items(): - opt.step() - epoch_loss += loss.detach() - num_batches += 1 - - if epoch % viz_freq == 0 or epoch == epochs - 1: - avg_loss = epoch_loss / num_batches - with torch.no_grad(): - self.obj.create_volume( - world_size=self.world_size, - global_rank=self.global_rank, - ray_size=volume_dataset.N, - ) - pred_full = self.obj.obj - loss_tensor = avg_loss.clone().detach() - avg_loss = loss_tensor.item() - avg_tv_loss = epoch_tv_loss / num_batches - avg_consistency_loss = epoch_consistency_loss / num_batches - - metrics = torch.tensor( - [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device - ) - if self.world_size > 1: - dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() - - if self.global_rank == 0: - self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) - self.temp_logger.add_scalar( - "Pretrain/consistency_loss", avg_consistency_loss, epoch - ) - self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) - - # current_lr = self.schedulers["model"].get_last_lr()[0] - # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) - - fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) - ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - ax[0].set_title("Sum over Z-axis") - ax[1].matshow( - pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 - ) - ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") - ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) - ax[2].set_title("Sum over Y-axis") - ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) - ax[3].set_title("Sum over X-axis") - self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) - - plt.close(fig) - print( - f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" - ) - self.global_epochs += 1 - if self.global_rank == 0: - print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") - torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") - - def recon( - self, - obj: ObjectINN, - batch_size: int, - num_workers: int = 0, - epochs=20, - use_amp=True, - viz_freq=1, - checkpoint_freq=5, - optimizer_params: dict = None, - scheduler_params: dict = None, - soft_constraints: dict = None, - vol_save_path: str = None, # TODO: TEMPORARY - log_path=None, - val_fraction: float = 0.0, - learn_shifts: bool = True, - # l1_loss: bool = False, - consistency_criterion: str = "mse", - model_weights_path: str = None, - force_cpu: bool = False, - ): - if not self.ddp_instantiated: - if not self.pretraining_instantiated: - self.setup_distributed() - if model_weights_path is not None: - print(f"Loading model weights from {model_weights_path}") - state_dict = torch.load(model_weights_path, map_location="cpu") - - # Handle DataParallel/DDP checkpoints - if any(k.startswith("module.") for k in state_dict.keys()): - new_state_dict = { - k.replace("module.", ""): v for k, v in state_dict.items() - } - else: - new_state_dict = state_dict - - obj.model.load_state_dict(new_state_dict) - print("Model weights loaded successfully") - obj.model = self.build_model(obj._model) - self.obj = obj - - self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) - self.ddp_instantiated = True - - if soft_constraints is not None: - self.obj.soft_constraints = soft_constraints - - if not hasattr(self, "temp_logger"): - self.setup_logger(log_path=log_path) - - zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() - - if self.global_rank == 0: - print( - f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" - ) - print(f"Using consistency criterion: {consistency_criterion}") - - # Auxiliary params setup - self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) - aux_params = self.dataset.auxiliary_params - # Scaling learning rates to account for distributed training - if optimizer_params is not None: - optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params - self.set_optimizers() - - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=epochs) - - aux_norm = torch.tensor(0.0, device=self.device) - model_norm = torch.tensor(0.0, device=self.device) - - for _, opt in self.optimizers.items(): - opt.zero_grad() - - N = max(self.dataset.dims) - - device_type = self.device.type - autocast_dtype = torch.bfloat16 if use_amp else None - - for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) + pass - # num_samples_per_ray = get_num_samples_per_ray(epoch) - # Log the change if it happens - if self.global_rank == 0: - print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - if self.global_rank == 0 and self.global_epochs > 0: - prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) - - if num_samples_per_ray != prev_samples: - print( - f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" - ) - - if self.sampler is not None: - self.sampler.set_epoch(epoch) - - epoch_loss = 0.0 - epoch_consistency_loss = 0.0 - epoch_tv_loss = 0.0 - epoch_z1_loss = 0.0 - - num_batches = 0 - consistency_loss_fn = None - if consistency_criterion[0].lower() == "mse": - consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == "l1": - consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == "mse_log": - consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == "llmse": - consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == "smooth_l1": - consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) - elif consistency_criterion[0].lower() == "adaptive_smooth_l1": - consistency_loss_fn = AdaptiveSmoothL1LossDDP( - beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 - ) - elif consistency_criterion[0].lower() == "charbonnier": - consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") - else: - raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - - for batch_idx, batch in enumerate(self.dataloader): - projection_indices = batch["projection_idx"] - - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - - all_densities = self.obj.forward(all_coords) - - tv_loss = self.obj.apply_soft_constraints(all_coords) - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - consistency_loss = consistency_loss_fn(predicted_values, target_values) - tv_loss_z1 = torch.tensor(0.0, device=self.device) - - loss = consistency_loss + tv_loss + tv_loss_z1 - - loss.backward() - - epoch_loss += loss.detach() - epoch_consistency_loss += consistency_loss.detach() - epoch_tv_loss += tv_loss.detach() - epoch_z1_loss += tv_loss_z1.detach() - num_batches += 1 - - for key, opt in self.optimizers.items(): - if key == "model": - model_norm = torch.nn.utils.clip_grad_norm_( - self.obj.model.parameters(), max_norm=1 - ) - elif key == "aux_params": - aux_norm = torch.nn.utils.clip_grad_norm_( - self.dataset.auxiliary_params.parameters(), max_norm=1 - ) - opt.step() - opt.zero_grad() - - for key, sched in self.schedulers.items(): - if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): - sched.step(epoch_loss) - else: - sched.step() - - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: - with torch.no_grad(): - self.obj.create_volume( - world_size=self.world_size, - global_rank=self.global_rank, - ray_size=num_samples_per_ray, - ) - pred_full = self.obj.obj - avg_loss = epoch_loss.item() / num_batches - avg_consistency_loss = epoch_consistency_loss.item() / num_batches - avg_tv_loss = epoch_tv_loss.item() / num_batches - avg_z1_loss = epoch_z1_loss.item() / num_batches - shifts, z1, z3 = self.dataset.auxiliary_params.forward() - shifts = shifts.detach().cpu() - z1 = z1.detach().cpu() - z3 = z3.detach().cpu() - metrics = torch.tensor( - [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], - device=self.device, - ) - if self.world_size > 1: - dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - val_loss = self.validate( - aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N - ) - - if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): - with torch.no_grad(): - current_lr = self.schedulers["model"].get_last_lr()[0] - - # Log metrics - self.temp_logger.add_scalar( - f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", - avg_consistency_loss, - self.global_epochs, - ) - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - self.temp_logger.add_scalar( - f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", - val_loss, - self.global_epochs, - ) - self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) - # if tv_weight > 0: - self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) - self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) - self.temp_logger.add_scalar( - "train/model_grad_norm", model_norm.item(), self.global_epochs - ) - self.temp_logger.add_scalar( - "train/aux_grad_norm", aux_norm.item(), self.global_epochs - ) - self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) - self.temp_logger.add_scalar( - "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs - ) - if consistency_criterion[0].lower() == "adaptive_smooth_l1": - self.temp_logger.add_scalar( - "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs - ) - fig, axes = plt.subplots(1, 5, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - axes[0].set_title("Sum over Z-axis") - - axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) - axes[1].set_title(f"Slice at Z={N // 2}") - - slice_start = max(0, N // 2 - 5) - slice_end = min(N, N // 2 + 6) - thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap="turbo", vmin=0) - axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") - - axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) - axes[3].set_title("Sum over Y-axis") - - axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) - axes[4].set_title("Sum over X-axis") - - self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) - - fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) - axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") - axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") - axes[0].legend() - axes[1].plot(z1.cpu().numpy(), label="Z1") - axes[2].plot(z3.cpu().numpy(), label="Z3") - self.temp_logger.add_figure( - "train/auxiliary_params", fig, self.global_epochs, close=True - ) - plt.close(fig) - - if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: - with torch.no_grad(): - if self.global_rank == 0: - save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" - torch.save(pred_full.cpu(), save_path) - - self.global_epochs += 1 - - if self.global_rank == 0: - print("Training complete.") - - torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") - # print("Successfully setup DDP and dataloader") - - def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): - """Validate the model on the validation set.""" - self.obj.model.eval() - aux_params.eval() - - val_loss = 0.0 - num_batches = 0 - - with torch.no_grad(): - for batch in self.val_dataloader: - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - - all_densities = self.obj.forward(all_coords) - - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - - loss = mse_loss - - val_loss += loss.detach() - num_batches += 1 - - avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 - - if self.world_size > 1: - val_loss_tensor = avg_val_loss.detach().clone() - dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) - avg_val_loss = val_loss_tensor.item() - - self.obj.model.train() - aux_params.train() - - return avg_val_loss - - def ad_recon( - self, - optimizer_params: dict, - num_iter: int = 0, - reset: bool = False, - scheduler_params: dict | None = None, - hard_constraints: dict | None = None, - soft_constraints: dict | None = None, - # store_iterations: bool | None = None, - # store_iterations_every: int | None = None, - # autograd: bool = True, - ): - if reset: - self.reset_recon() - - self.hard_constraints = hard_constraints - self.soft_constraints = soft_constraints - - # Make sure everything is in the correct device, might be redundant/cleaner way to do this - self.dataset.to(self.device) - self.volume_obj.to(self.device) - - # Making optimizable parameters into leaf tensors. - self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) - self.dataset.z1_angles = ( - self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) - ) - self.dataset.z3_angles = ( - self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) - ) - - if optimizer_params is not None: - self.optimizer_params = optimizer_params - self.set_optimizers() - - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=num_iter) - - if hard_constraints is not None: - self.volume_obj.hard_constraints = hard_constraints - if soft_constraints is not None: - self.volume_obj.soft_constraints = soft_constraints - - pbar = tqdm(range(num_iter), desc="AD Reconstruction") - - for a0 in pbar: - total_loss = 0.0 - tilt_series_loss = 0.0 - - pred_volume = self.volume_obj.forward() - - for i in range(len(self.dataset.tilt_series)): - forward_projection = self.projection_operator( - vol=pred_volume, - z1=self.dataset.z1_angles[i], - x=self.dataset.tilt_angles[i], - z3=self.dataset.z3_angles[i], - shift_x=self.dataset.shifts[i, 0], - shift_y=self.dataset.shifts[i, 1], - device=self.device, - ) - - tilt_series_loss += torch.nn.functional.mse_loss( - forward_projection, self.dataset.tilt_series[i] - ) - tilt_series_loss /= len(self.dataset.tilt_series) - - total_loss = tilt_series_loss + self.volume_obj.soft_loss - self.loss.append(total_loss.item()) - - total_loss.backward() - - for opt in self.optimizers.values(): - opt.step() - opt.zero_grad() - - if self.schedulers is not None: - for sch in self.schedulers.values(): - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(total_loss) - elif sch is not None: - sch.step() - - pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") - - if self.logger is not None: - self.logger.log_scalar("loss/total", total_loss.item(), a0) - self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) - self.logger.log_scalar( - "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 - ) - - if a0 % self.logger.log_images_every == 0: - self.logger.projection_images( - volume_obj=self.volume_obj, - epoch=a0, - ) - self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) - - self.logger.flush() - - self.ad_recon_vol = self.volume_obj.forward() - - return self - - def reset_recon(self) -> None: - if isinstance(self.volume_obj, ObjectVoxelwise): - self.volume_obj.reset() - - self.ad_recon_vol = None - - # --- Projection Operators ---- - def projection_operator( - self, - vol, - z1, - x, - z3, - shift_x, - shift_y, - device, - ): - projection = ( - rot_ZXZ( - mags=vol.unsqueeze(0), # Add batch dimension - z1=z1, - x=-x, - z3=z3, - device=device, - mode="bilinear", - ) - .squeeze() - .sum(axis=0) - ) - - shifted_projection = differentiable_shift_2d( - image=projection, - shift_x=shift_x, - shift_y=shift_y, - sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit - ) +class TomographyConventional: + """ + Class for handling all conventional tomography reconstruction methods. + Will also handle choosing the appropriate dataset model to use. + """ - return shifted_projection + pass diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index d3b94427..8b137891 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1,514 +1 @@ -from typing import Tuple -import matplotlib.pyplot as plt -import numpy as np -import torch -from numpy.typing import NDArray -from torch._tensor import Tensor -from tqdm.auto import tqdm - -from quantem.core.datastructures.dataset3d import Dataset3d -from quantem.core.io.serialize import AutoSerialize -from quantem.core.visualization.visualization import show_2d -from quantem.imaging.drift import cross_correlation_shift -from quantem.tomography.object_models import ObjectModelType, ObjectVoxelwise -from quantem.tomography.tomography_dataset import TomographyDataset -from quantem.tomography.tomography_logger import LoggerTomography - - -class TomographyBase(AutoSerialize): - _token = object() - - DEFAULT_HARD_CONSTRAINTS = { - "positivity": False, - "shrinkage": False, - "circular_mask": False, - "fourier_filter": False, - } - - DEFAULT_SOFT_CONSTRAINTS = { - "tv_vol": 0.0, - } - - def __init__( - self, - dataset: TomographyDataset, - volume_obj: ObjectModelType, # ObjectDIP? - device: str = "cuda", - # ABF/HAADF property - logger: LoggerTomography | None = None, - _token: object | None = None, - ): - """Initialize a Tomography object. - - Parameters - ---------- - array : NDArray | Any - The underlying 3D array data - name : str - A descriptive name for the dataset - """ - - # if _token is not self._token: - # raise RuntimeError( - # "This class is not meant to be instantiated directly. Use the from_data method." - # ) - - self._device = device - self._dataset = dataset - self._volume_obj = volume_obj - self._loss = [] - self._mode = [] - - self._hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self._soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - self._logger = None - - @classmethod - def from_data( - cls, - tilt_series: Dataset3d | NDArray | Tensor, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - volume_obj: NDArray | Dataset3d | ObjectModelType | None = None, - device: str = "cpu", - clamp: bool = True, - ): - device = device.lower() - - dataset = TomographyDataset.from_data( - tilt_series=tilt_series, - tilt_angles=tilt_angles, - z1_angles=z1_angles, - z3_angles=z3_angles, - shifts=shifts, - clamp=clamp, - ) - - dataset.to(device) - - if volume_obj is None: - max_shape = max(dataset.tilt_series.shape) - volume_obj = ObjectVoxelwise( - volume_shape=(max_shape, max_shape, max_shape), - device=device, - ) - elif isinstance(volume_obj, Dataset3d): - volume = torch.from_numpy(volume_obj.array) - volume_obj = ObjectVoxelwise( - volume_shape=volume_obj.shape, device=device, initial_volume=volume - ) - volume_obj.obj = volume - elif isinstance(volume_obj, np.ndarray): - volume = torch.from_numpy(volume_obj) - volume_obj = ObjectVoxelwise( - volume_shape=volume_obj.shape, device=device, initial_volume=volume - ) - volume_obj.obj = volume - else: - raise ValueError("volume_obj must be a Dataset3d, NDArray ObjectModelType") - - return cls( - dataset=dataset, - volume_obj=volume_obj, - device=device, - _token=cls._token, - ) - - # --- Properties --- - @property - def dataset(self) -> TomographyDataset: - """Tomography dataset.""" - - return self._dataset - - @dataset.setter - def dataset( - self, - tilt_series: Dataset3d | NDArray | TomographyDataset, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - # name: str | None = None, - # origin: NDArray | tuple | list | float | int | None = None, - # sampling: NDArray | tuple | list | float | int | None = None, - # units: list[str] | tuple | list | None = None, - # signal_units: str = "arb. units", - ): - """Set the tilt series dataset.""" - - if not isinstance(tilt_series, TomographyDataset): - dataset = TomographyDataset.from_array( - array=tilt_series, - tilt_angles=tilt_angles, - z1_angles=z1_angles, - z3_angles=z3_angles, - shifts=shifts, - ) - - self._dataset = dataset - - @property - def volume_obj(self) -> Dataset3d | ObjectModelType | None: - """Reconstruction volume dataset.""" - - return self._volume_obj - - @volume_obj.setter - # TODO: add support for ObjectModelType - def volume_obj(self, volume_obj: Dataset3d | NDArray): - """Set the reconstruction volume dataset.""" - if isinstance(volume_obj, ObjectModelType): - self._volume_obj = volume_obj - elif not isinstance(volume_obj, Dataset3d): - volume_obj = Dataset3d.from_array( - array=volume_obj, - # name=self._tilt_series.name, - # origin=self._tilt_series.origin, - # sampling=self._tilt_series.sampling, - # units=self._tilt_series.units, - # signal_units=self._tilt_series.signal_units, - ) - elif isinstance(volume_obj, Dataset3d): - self._volume_obj = volume_obj - else: - raise ValueError("volume_obj must be a Dataset3d or ObjectModelType") - - @property - def device(self) -> str: - """Computation device.""" - - return self._device - - @device.setter - def device(self, device: str): - """Set the computation device.""" - - # if "cuda" not in device or "gpu" not in device: - # raise NotImplementedError("Tomography not currently supported on CPU.") - - self._device = device - - @property - def loss(self) -> list: - """List of loss values during reconstruction.""" - - return self._loss - - @loss.setter - def loss(self, loss: list): - """Set the loss values during reconstruction.""" - - if not isinstance(loss, list): - raise TypeError("Loss must be a list.") - - self._loss = loss - - @property - def mode(self) -> list: - """List of modes used during reconstruction.""" - - return self._mode - - @property - def epochs(self) -> int: - """Number of epochs used during reconstruction.""" - return len(self.loss) - - @property - def logger(self) -> LoggerTomography: - return self._logger - - @logger.setter - def logger(self, logger: LoggerTomography): - if not isinstance(logger, LoggerTomography): - raise TypeError("Logger must be a LoggerTomography") - - self._logger = logger - - # --- Constraints --- - - @property - def hard_constraints(self) -> dict: - """Hard constraints for the reconstruction.""" - return self._hard_constraints - - @hard_constraints.setter - def hard_constraints(self, hard_constraints: dict): - """Set the hard constraints for the reconstruction.""" - - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - for key, value in hard_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._hard_constraints[key] = value - - self._hard_constraints = hard_constraints - - @property - def soft_constraints(self) -> dict: - """Soft constraints for the reconstruction.""" - return self._soft_constraints - - @soft_constraints.setter - def soft_constraints(self, soft_constraints: dict): - """Set the soft constraints for the reconstruction.""" - - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - for key, value in soft_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._soft_constraints[key] = value - - self._soft_constraints = soft_constraints - - # --- RESET --- - - def reset_recon(self) -> None: - self.volume_obj.reset() - self.dataset.reset() - self.loss = [] - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - self._optimizers = {} - self._schedulers = {} - - # --- Preprocessing --- - - """ - TODO - 1. Implement tilt series cross-correlation alignment - 2. Background subtraction (for ABF) - 3. COM Alignment - 4. Masking - 5. Drift Correction - """ - - def cross_corr_alignment( - self, - upsample_factor: int = 1, - overwrite: bool = False, - ): - # TODO: This needs to be able to work with torch tensors. - - placeholder_tilt_series = self.dataset.tilt_series.clone().detach().cpu().numpy() - - aligned_tilt_series = np.zeros_like(placeholder_tilt_series) - aligned_tilt_series[0] = placeholder_tilt_series[0] - shifts = [] - num_imgs = placeholder_tilt_series.shape[1] - - pbar = tqdm(range(num_imgs - 1), desc="Cross-correlation alignment") - - for i in pbar: - shift, aligned_img = cross_correlation_shift( - placeholder_tilt_series[i], - placeholder_tilt_series[i + 1], - upsample_factor=upsample_factor, - return_shifted_image=True, - ) - - aligned_tilt_series[i + 1] = aligned_img - shifts.append(shift) - - if overwrite: - # TODO: Check this overwrite idea, maybe also need to save the relative shifts? - self.dataset.tilt_series = np.array(aligned_tilt_series) - - return np.array(aligned_tilt_series), np.array(shifts) - - # --- Postprocessing --- - - """ - TODO - 1. Apply circular mask - """ - - def circular_mask(self, shape, radius, center=None, dtype=torch.float32, device="cpu"): - """Generate a 2D circular mask of given shape and radius.""" - H, W = shape - - if center is None: - center = (H // 2, W // 2) - y = torch.arange(H, dtype=dtype, device=device).view(-1, 1) - x = torch.arange(W, dtype=dtype, device=device).view(1, -1) - dist_sq = (x - center[1]) ** 2 + (y - center[0]) ** 2 - return (dist_sq <= radius**2).to(dtype) - - def recon_vol_circular_mask(self, radii): - """ - Apply 2D circular masks along all three axes of a 3D volume. - - Args: - volume (torch.Tensor): 3D tensor of shape (H, W, D) - radii (tuple): (r0, r1, r2) for axes 0, 1, 2 - Returns: - masked_volume: tensor with all masks applied - """ - H, W, D = self.volume_obj.array.shape - device = self.device - dtype = torch.float32 - volume_obj = torch.tensor( - self.volume_obj.array, - device=self.device, - dtype=dtype, - ) - # Masks for each axis - mask0 = self.circular_mask((W, D), radii[0], dtype=dtype, device=device).unsqueeze( - 0 - ) # shape (1, W, D) - mask1 = self.circular_mask((H, D), radii[1], dtype=dtype, device=device).unsqueeze( - 1 - ) # shape (H, 1, D) - mask2 = self.circular_mask((H, W), radii[2], dtype=dtype, device=device).unsqueeze( - 2 - ) # shape (H, W, 1) - - # Broadcast and multiply all masks together - total_mask = mask0 * mask1 * mask2 # shape (H, W, D) - - volume_obj = volume_obj * total_mask - volume_obj = volume_obj.detach().cpu().numpy() - self.volume_obj = Dataset3d.from_array( - array=volume_obj, - # name=self.volume_obj.name, - # origin=self.volume_obj.origin, - # sampling=self.volume_obj.sampling, - # units=self.volume_obj.units, - # signal_units=self.volume_obj.signal_units, - ) - - # --- Visualizations --- - - def plot_projections( - self, - cmap: str = "turbo", - fft: bool = False, - norm: str = "log_auto", - figax: tuple[plt.Figure, plt.Axes] | None = None, - fft_vmax: Tuple[float, float] = (0, 40), - vmin: float = 0, - **kwargs, - ): - """ - Plots the projections of the volume object. - Note that the volume object is in the order of (z, y, x). - Parameters - ---------- - cmap : str - The colormap to use for the projections. - fft : bool - """ - - volume_obj_np = self.volume_obj.obj.detach().cpu().numpy() - - if figax is None: - fig, ax = plt.subplots(ncols=3, figsize=(20, 8)) - else: - fig, ax = figax - - show_2d( - volume_obj_np.sum(axis=0), - figax=(fig, ax[0]), - cmap=cmap, - title="Y-X Projection", - vmin=vmin, - ) - show_2d( - volume_obj_np.sum(axis=1), - figax=(fig, ax[1]), - cmap=cmap, - title="Z-X Projection", - vmin=vmin, - ) - show_2d( - volume_obj_np.sum(axis=2), - figax=(fig, ax[2]), - cmap=cmap, - title="Z-Y Projection", - vmin=vmin, - ) - - if fft: - fig, ax = plt.subplots(ncols=3, figsize=(25, 8)) - print(fft_vmax) - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=0)))), - figax=(fig, ax[0]), - cmap=cmap, - title="Y-X Projection FFT", - # norm=norm, - vmin=fft_vmax[0], - vmax=fft_vmax[1], - ) - - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=1)))), - figax=(fig, ax[1]), - cmap=cmap, - title="Z-X Projection FFT", - vmin=fft_vmax[0], - vmax=fft_vmax[1], - # norm=norm, - ) - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=2)))), - figax=(fig, ax[2]), - cmap=cmap, - title="Z-Y Projection FFT", - vmin=fft_vmax[0], - vmax=fft_vmax[1], - # norm=norm, - ) - - def plot_slice( - self, - cmap="turbo", - slice_index: int = 0, - vmin: float = 0, - figax: tuple[plt.Figure, plt.Axes] | None = None, - ): - if figax is None: - fig, ax = plt.subplots(figsize=(15, 8), ncols=3) - else: - fig, ax = figax - - show_2d( - self.volume_obj.obj[slice_index, :, :], - figax=(fig, ax[0]), - cmap=cmap, - vmin=vmin, - ) - show_2d( - self.volume_obj.obj[:, slice_index, :], - figax=(fig, ax[1]), - cmap=cmap, - vmin=vmin, - ) - - show_2d( - self.volume_obj.obj[:, :, slice_index], - figax=(fig, ax[2]), - cmap=cmap, - vmin=vmin, - ) - - def plot_loss( - self, - figsize: tuple = (8, 4), - figax: tuple[plt.Figure, plt.Axes] | None = None, - ): - if figax is None: - fig, ax = plt.subplots(figsize=figsize) - else: - fig, ax = figax - - ax.semilogy( - self.loss, - label="Loss", - ) diff --git a/src/quantem/tomography/tomography_conv.py b/src/quantem/tomography/tomography_conv.py index 1056da17..e69de29b 100644 --- a/src/quantem/tomography/tomography_conv.py +++ b/src/quantem/tomography/tomography_conv.py @@ -1,235 +0,0 @@ -import numpy as np -import torch - -# from torch_radon.radon import ParallelBeam as Radon -from quantem.tomography.radon.radon import iradon_torch, radon_torch -from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.utils import gaussian_filter_2d_stack, torch_phase_cross_correlation - - -class TomographyConv(TomographyBase): - """ - Class for handling conventional reconstruction methods of tomography data. - """ - - # --- Reconstruction Methods --- - - def _sirt_run_epoch( - self, - tilt_series: torch.Tensor, - proj_forward: torch.Tensor, - angles: torch.Tensor, - inline_alignment: bool, - filter_name: str, - circle: bool, - gaussian_kernel: torch.Tensor | None, - ): - loss = 0 - - if inline_alignment: - for ind in range(len(self.dataset.tilt_angles)): - im_proj = proj_forward[ind] - im_meas = tilt_series[ind] - - shift = torch_phase_cross_correlation(im_proj, im_meas) - if torch.linalg.norm(shift) <= 32: - shifted = torch.fft.ifft2( - torch.fft.fft2(im_meas) - * torch.exp( - -2j - * np.pi - * ( - shift[0] - * torch.fft.fftfreq( - im_meas.shape[0], device=im_meas.device - ).unsqueeze(1) - + shift[1] - * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) - ) - ) - ).real - - proj_forward[ind] = shifted - - # Forward projection - - sinogram_est = radon_torch(self.volume_obj.obj, theta=angles, device=self.device) - # proj_forward = sinogram_est.permute(1, 2, 0) - # error = (tilt_series - proj_forward).permute(2, 0, 1) - proj_forward = sinogram_est - error = tilt_series - proj_forward - - correction = iradon_torch( - error, theta=angles, device=self.device, filter_name=filter_name, circle=circle - ) - - normalization = iradon_torch( - torch.ones_like(error), - theta=angles, - device=self.device, - filter_name=None, - circle=circle, - ) - - normalization[normalization == 0] = 1e-6 - - correction /= normalization - - self.volume_obj._obj += correction - - loss = torch.mean(torch.abs(error)) - - # for z in tqdm(range(self.volume_obj.obj.shape[0]), desc="SIRT Reconstruction"): - # slice_estimate = self.volume_obj.obj[z] - # sinogram_est = radon_torch(slice_estimate, theta=angles, device=self.device) - - # sinogram_true = tilt_series[:, :, z] - - # error = sinogram_true - sinogram_est - - # correction = iradon_torch( - # error, theta=angles, device=self.device, filter_name=filter_name, circle=circle - # ) - - # # I'm pretty sure this implementation of normalization is wrong - # normalization = iradon_torch( - # torch.ones_like(error), - # theta=angles, - # device=self.device, - # filter_name=None, - # circle=circle, - # ) - # normalization[normalization == 0] = 1e-6 - - # correction /= normalization - - # self.volume_obj._obj[z] += correction - - # proj_forward[:, :, z] = sinogram_est - - # loss += torch.mean(torch.abs(error)) - - # loss /= self.volume_obj._obj.shape[0] - - if gaussian_kernel is not None: - self.volume_obj.obj = gaussian_filter_2d_stack(self.volume_obj.obj, gaussian_kernel) - - return proj_forward, loss - - # Deprecated torch_radon implementations - # def _sirt_run_epoch( - # self, - # radon: Radon, - # stack_recon: torch.Tensor, - # stack_torch: torch.Tensor, - # proj_forward: torch.Tensor, - # step_size: float = 0.25, - # gaussian_kernel: torch.Tensor = None, - # inline_alignment=True, - # enforce_positivity=True, - # shrinkage: float = None, - # ): - # loss = 0 - - # if inline_alignment: - # for ind in range(len(self.tilt_series.tilt_angles)): - # im_proj = proj_forward[:, ind, :] - # im_meas = stack_torch[:, ind, :] - - # shift = torch_phase_cross_correlation(im_proj, im_meas) - # if torch.linalg.norm(shift) <= 32: - # shifted = torch.fft.ifft2( - # torch.fft.fft2(im_meas) - # * torch.exp( - # -2j - # * np.pi - # * ( - # shift[0] - # * torch.fft.fftfreq( - # im_meas.shape[0], device=im_meas.device - # ).unsqueeze(1) - # + shift[1] - # * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) - # ) - # ) - # ).real - - # stack_torch[:, ind, :] = shifted - - # proj_forward = radon.forward(stack_recon) - - # proj_diff = stack_torch - proj_forward - - # loss = torch.mean(torch.abs(proj_diff)) - - # recon_slice_update = radon.backward( - # radon.filter_sinogram( - # proj_diff, - # ) - # ) - - # stack_recon += step_size * recon_slice_update - # if enforce_positivity: - # stack_recon = torch.clamp(stack_recon, min=0) - - # if gaussian_kernel is not None: - # stack_recon = gaussian_filter_2d_stack( - # stack_recon, - # gaussian_kernel, - # ) - - # if shrinkage is not None: - # stack_recon = torch.max( - # stack_recon - shrinkage, - # torch.zeros_like(stack_recon), - # ) - - # return stack_recon, loss - - # def _sirt_serial_run_epoch( - # self, - # radon: Radon, - # stack_recon: torch.Tensor, - # stack_torch: torch.Tensor, - # proj_forward: torch.Tensor, - # step_size: float = 0.25, - # gaussian_kernel: torch.Tensor = None, - # inline_alignment=True, - # enforce_positivity=True, - # ): - # recon_slice_update = torch.zeros_like(stack_recon).to(self.device) - - # loss = 0 - - # for i in range(stack_recon.shape[0]): - # proj_forward[i] = radon.forward(stack_recon[i]) - - # proj_diff = stack_torch - proj_forward - - # loss = torch.mean(torch.abs(proj_diff)) - - # for i in range(stack_recon.shape[0]): - # recon_slice_update[i] = radon.backward( - # radon.filter_sinogram( - # proj_diff[i], - # ) - # ) - - # stack_recon += step_size * recon_slice_update - - # if enforce_positivity: - # stack_recon = torch.clamp(stack_recon, min=0) - - # return stack_recon, loss - - # --- Properties --- - # @property - # def reconstruction_method(self) -> str: - # """Get the reconstruction method.""" - # return self._reconstruction_method - # @reconstruction_method.setter - # def reconstruction_method(self, value: str): - # """Set the reconstruction method.""" - # if value not in ["SIRT", "FBP"]: - # raise ValueError("Invalid reconstruction method. Choose 'SIRT' or 'FBP'.") - # self._reconstruction_method = value diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index cfb59117..e69de29b 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -1,321 +0,0 @@ -import os - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from torch.utils.data import DataLoader, DistributedSampler - -from quantem.tomography.tomography_dataset import ( - PretrainVolumeDataset, - TomographyDataset, - TomographyRayDataset, -) - - -class TomographyDDP: - """ - Initializing DDP stuff for tomo class. - """ - - def __init__( - self, - ): - self.setup_distributed() - - def setup_distributed(self): - # Check if in distributed env - if "RANK" in os.environ: - # Distributed training - if not dist.is_initialized(): - dist.init_process_group( - backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" - ) - - self.world_size = dist.get_world_size() - self.global_rank = dist.get_rank() - self.local_rank = int(os.environ["LOCAL_RANK"]) - - torch.cuda.set_device(self.local_rank) - self.device = torch.device("cuda", self.local_rank) - - else: - # Single GPU/CPU training - self.world_size = 1 - self.global_rank = 0 - self.local_rank = 0 - - if torch.cuda.is_available(): - self.device = torch.device("cuda") - torch.cuda.set_device(0) - print("Single GPU training") - else: - self.device = torch.device("cpu") - print("CPU training") - - # Optional performance optimizations (only for CUDA) - if self.device.type == "cuda": - torch.backends.cudnn.benchmark = True - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - - def build_model( - self, - model: nn.Module, - ): - # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? - model = model.to(self.device) - - if self.world_size > 1: - model = torch.nn.parallel.DistributedDataParallel( - model, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True, - bucket_cap_mb=100, - gradient_as_bucket_view=True, - ) - - if self.global_rank == 0: - print("Model wrapped with DDP") - - if self.world_size > 1: - if self.global_rank == 0: - print("Model built, distributed, and compiled successfully") - - else: - print("Model built, compiled successfully") - - return model - - # Setup Tomo DataLoader - def setup_dataloader( - self, - tomo_dataset: TomographyDataset, - batch_size: int, - num_workers: int = 0, - val_fraction: float = 0.0, - ): - pin_mem = self.device.type == "cuda" - persist = num_workers > 0 - - # Split dataset if validation fraction > 0 - if val_fraction > 0.0: - # TODO: Temporary for when only doing validation, current TomographyDataset doesn't work correctly - train_dataset = TomographyRayDataset( - tomo_dataset.tilt_series.detach().clone(), - tomo_dataset.tilt_angles.detach().clone(), - 500, # TODO: TEMPORARY - val_ratio=val_fraction, - mode="train", - seed=42, - ) - val_dataset = TomographyRayDataset( - tomo_dataset.tilt_series.detach().clone(), - tomo_dataset.tilt_angles.detach().clone(), - 500, # TODO: TEMPORARY - val_ratio=val_fraction, - mode="val", - seed=42, - ) - else: - train_dataset, val_dataset = tomo_dataset, None - - # Samplers for distributed training - if self.world_size > 1: - train_sampler = DistributedSampler( - train_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=True, - ) - if val_dataset: - val_sampler = DistributedSampler( - val_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=False, - ) - shuffle = False - else: - train_sampler = None - val_sampler = None - shuffle = True - - # Main dataloader - self.dataloader = DataLoader( - train_dataset, - batch_size=batch_size, - num_workers=num_workers, - sampler=train_sampler, - shuffle=shuffle, - pin_memory=pin_mem, - drop_last=True, - persistent_workers=persist, - ) - - # Validation dataloader if applicable - if val_dataset: - self.val_dataloader = DataLoader( - val_dataset, - batch_size=batch_size * 4, - num_workers=num_workers, - sampler=val_sampler, - shuffle=False, - pin_memory=pin_mem, - drop_last=False, - persistent_workers=persist, - ) - self.val_sampler = val_sampler - - self.sampler = train_sampler - - if self.global_rank == 0: - print("Dataloader setup complete:") - print(f" Total projections: {len(tomo_dataset.tilt_angles)}") - if val_fraction > 0.0: - print(f" Total projections (val): {len(val_dataset)}") - print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") - print(f" Total pixels: {tomo_dataset.num_pixels:,}") - if val_fraction > 0.0: - print(f" Total pixels (val): {len(val_dataset):,}") - print(f" Total pixels (train): {len(train_dataset):,}") - print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {batch_size * self.world_size}") - print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - - # Setup pretraining dataloader - - def setup_pretraining_dataloader( - self, - volume_dataset: PretrainVolumeDataset, - batch_size: int, - ): - # TODO: temp - - num_workers = 0 - if self.world_size > 1: - sampler = DistributedSampler( - volume_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=True, - drop_last=True, - ) - shuffle = False - else: - sampler = None - shuffle = True - - self.pretraining_dataloader = DataLoader( - volume_dataset, - batch_size=batch_size, - sampler=sampler, - shuffle=shuffle, - pin_memory=self.device.type == "cuda", - drop_last=True, - persistent_workers=num_workers > 0, - ) - - self.pretraining_sampler = sampler - - if self.global_rank == 0: - print("Pretraining dataloader setup complete:") - print(f" Total samples: {len(volume_dataset)}") - print(f" Grid size: {volume_dataset.N**3}") - print(f" Local batch size: {batch_size}") - print(f" Global batch size: {batch_size * self.world_size}") - print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") - - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): - if scaling_rule == "sqrt": - return base_lr * np.sqrt(self.world_size) - elif scaling_rule == "linear": - return base_lr * self.world_size - else: - raise ValueError(f"Invalid scaling rule: {scaling_rule}") - - def scale_lr( - self, - optimizer_params: dict, - ): - new_optimizer_params = {} - for key, value in optimizer_params.items(): - if "original_lr" in value: - new_optimizer_params[key] = { - "type": value["type"], - "lr": self.get_scaled_lr(value["original_lr"]), - "original_lr": value["original_lr"], - } - else: - new_optimizer_params[key] = { - "type": value["type"], - "lr": self.get_scaled_lr(value["lr"]), - "original_lr": value["lr"], - } - - return new_optimizer_params - - # TODO: Temporary Adaptive L1 Smooth Loss - - -class AdaptiveSmoothL1Loss(nn.Module): - def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): - """ - Adaptive smooth L1 loss with EMA-based beta adaptation. - - Args: - beta_init (float): optional initial β value; if None, starts as 1.0 - ema_factor (float): smoothing factor for EMA (1 - 1/N_b in Eq. 38) - eps (float): small constant for numerical stability - """ - super().__init__() - self.register_buffer("beta2", torch.tensor(beta_init**2 if beta_init else 1.0)) - self.ema_factor = ema_factor - self.eps = eps - - def forward(self, pred, target): - diff = pred - target - abs_diff = diff.abs() - - # compute current batch MSE (Eq. 38) - mse_batch = torch.mean(diff**2) - - # update β² adaptively using Eq. (39) - with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( - self.beta2, mse_batch - ) - - beta = torch.sqrt(self.beta2 + self.eps) - - # Smooth L1 (Eq. 36) - loss = torch.where( - abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta - ) - return loss.mean() - - -class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): - def forward(self, pred, target): - diff = pred - target - abs_diff = diff.abs() - mse_batch = torch.mean(diff**2) - - # Synchronize β across all GPUs - if dist.is_initialized(): - mse_batch_all = mse_batch.clone() - dist.all_reduce(mse_batch_all, op=dist.ReduceOp.AVG) - mse_batch = mse_batch_all / dist.get_world_size() - - with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( - self.beta2, mse_batch - ) - - beta = torch.sqrt(self.beta2 + self.eps) - loss = torch.where( - abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta - ) - return loss.mean() diff --git a/src/quantem/tomography/tomography_ml.py b/src/quantem/tomography/tomography_ml.py index 47e479d8..e69de29b 100644 --- a/src/quantem/tomography/tomography_ml.py +++ b/src/quantem/tomography/tomography_ml.py @@ -1,330 +0,0 @@ -from typing import Any, Generator, Iterator, Sequence - -import torch - -from quantem.tomography.tomography_base import TomographyBase - - -class TomographyML(TomographyBase): - """ - Class for handling conventional reconstruction methods of tomography data. - """ - - OPTIMIZABLE_VALS = ["volume", "z1", "x", "z3", "shifts", "model", "aux_params"] - DEFAULT_LRS = { - "volume": 1e-2, - "z1": 1e-1, - "x": 1e-1, - "z3": 1e-1, - "shifts": 1e-1, - "tv_weight_vol": 0, - "tv_weight_z1": 0, - "tv_weight_x": 0, - "tv_weight_z3": 0, - } - DEFAULT_OPTIMIZER_TYPE = "adam" - - # --- Properties --- - - @property - def optimizer_params(self) -> dict[str, dict]: - """Returns the parameters used to set the optimizers.""" - return self._optimizer_params - - @optimizer_params.setter - def optimizer_params(self, d: dict) -> None: - """ - # Takes a dictionary {key: torch.optim.Adam(params=[blah], lr=[blah]), ...} - Takes a dictionary: - { - "key1": { - "type": "adam", - "lr": 0.001, - }, - "key2": { - ... - }, - ... - } - """ - # resets _optimizers as well - self._optimizers = {} - self._optimizer_params = {} - if isinstance(d, (tuple, list)): - d = {k: {} for k in d} - - for k, v in d.items(): - if k not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if "type" not in v.keys(): - v["type"] = self.DEFAULT_OPTIMIZER_TYPE - if "lr" not in v.keys(): - v["lr"] = self.DEFAULT_LRS[k] - self._optimizer_params[k] = v - - @property - def optimizers(self) -> dict[str, torch.optim.Adam | torch.optim.AdamW]: - return self._optimizers - - def set_optimizers(self): - """Reset all optimizers and set them according to the optimizer_params.""" - for key, _ in self._optimizer_params.items(): - if key == "volume": - self._add_optimizer(key, self.volume_obj.params, self._optimizer_params[key]) - elif key == "shifts": - self._add_optimizer(key, self.dataset.shifts, self._optimizer_params[key]) - elif key == "z1": - self._add_optimizer(key, self.dataset.z1_angles, self._optimizer_params[key]) - elif key == "x": - self._add_optimizer(key, self.dataset.tilt_angles, self._optimizer_params[key]) - elif key == "z3": - self._add_optimizer(key, self.dataset.z3_angles, self._optimizer_params[key]) - elif key == "model": - - if self._optimizer_params[key]["original_lr"] is not None: - optimizer_params = self._optimizer_params[key].copy() - optimizer_params.pop("original_lr", None) - self._add_optimizer(key, self.obj.model.parameters(), optimizer_params) - elif key == "aux_params": - - if self._optimizer_params[key]["original_lr"] is not None: - optimizer_params = self._optimizer_params[key].copy() - optimizer_params.pop("original_lr", None) - self._add_optimizer(key, self.dataset.auxiliary_params.parameters(), optimizer_params) - else: - raise ValueError( - f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - - def remove_optimizer(self, key: str) -> None: - self._optimizers.pop(key, None) - self._optimizer_params.pop(key, None) - return - - def _add_optimizer( - self, - key: str, - params: "torch.Tensor|Sequence[torch.Tensor]|Iterator[torch.Tensor]", - opt_params: dict, - ) -> None: - """Can be used to add an optimizer without resetting the other optimizers.""" - - if key not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if isinstance(params, torch.Tensor): - params = [params] - elif isinstance(params, Generator): - params = list(params) - [p.requires_grad_(True) for p in params] - self.optimizer_params[key] = opt_params - opt_params = opt_params.copy() - opt_type = opt_params.pop("type") - if isinstance(opt_type, type): - opt = opt_type(params, **opt_params) - elif opt_type == "adam": - opt = torch.optim.Adam(params, **opt_params) - elif opt_type == "adamw": - opt = torch.optim.AdamW(params, **opt_params) # TODO pass all other kwargs - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") - # if key in self.optimizers.keys(): - # self.vprint(f"Key {key} is already in optimizers, overwriting.") - self._optimizers[key] = opt - - @property - def scheduler_params(self) -> dict[str, dict]: - """Returns the parameters used to set the schedulers.""" - return self._scheduler_params - - @scheduler_params.setter - def scheduler_params(self, d: dict) -> None: - """ - Takes a dictionary: - { - "key1": { - "type": "cyclic", - "base_lr": 0.001, - }, - "key2": { - ... - }, - ... - } - """ - self._schedulers = {} - for k, v in d.items(): - if not any(v): - continue - if k not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if v["type"] not in ["cyclic", "plateau", "exp", "gamma", "linear", "cosine_annealing", "none"]: - raise ValueError( - f"Unknown scheduler type: {v['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" - ) - self._scheduler_params = d - - @property - def schedulers( - self, - ) -> dict[ - str, - ( - torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | torch.optim.lr_scheduler.LinearLR - | torch.optim.lr_scheduler.CosineAnnealingLR - | None - ), - ]: - return self._schedulers - - def set_schedulers( - self, - params: dict[str, dict], - num_iter: int | None = None, - ): - """ - TODO allow for new schedulers to be passed in when adding new optimizers without - removing the old schedulers or overwrtiting them. Not entirely sure what usecases there - will be for this. - - Sets the schedulers for the optimizer from a dictionary. Expects a dictionary of the form: - { - "optimizable_key1": { - "type": "scheduler_type", - "scheduler_kwarg": scheduler_kwarg_value, - ... - }, - "optimizable_key2": { - "type": "scheduler_type", - "scheduler_kwarg": scheduler_kwarg_value, - ... - }, - ... - } - where the keys are the same as the keys in self.OPTIMIZABLE_VALS. - - The scheduler type can be one of the following: - - "cyclic" - - "plateau" or "reducelronplateau" - - "exponential" - - None - - The num_iter kwarg is only used for exponential schedulers and if a "factor" is given - as a scheduler_kwarg instead of gamma. In that case, the gamma is calculated from num_iter - and the factor. - - TODO could update this to allow passing key:optimizer directly, would likely need to - rewrite get_schedulers to check the tpye - """ - if not any(self.optimizers): - raise NameError("self.optimizers have not yet been set.") - self._schedulers = self._get_schedulers( - params=params, - optimizers=self.optimizers, - num_iter=num_iter, - ) - - def _get_schedulers( - self, - params: dict[str, dict], - optimizers: dict, - num_iter: int | None = None, - ) -> dict[ - str, - ( - torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ), - ]: - """ - return schedulers for a given set of optimizers. Kept seperate from schedulers.setter so - that it can be called for pre-training - """ - schedulers = {} - for opt_key, p in params.items(): - if not any(p): - continue - elif opt_key not in self.OPTIMIZABLE_VALS: - raise KeyError( - f"Scheduler got bad key {opt_key}, schedulers can only be attached to one of {self.OPTIMIZABLE_VALS}" - ) - elif opt_key not in optimizers.keys(): - raise KeyError(f"optimizers does not have an optimizer for: {opt_key}") - else: - schedulers[opt_key] = self._get_scheduler( - optimizer=optimizers[opt_key], params=p, num_iter=num_iter - ) - return schedulers - - def _get_scheduler( - self, - optimizer: torch.optim.Adam, - params: dict[str, Any] | torch.optim.lr_scheduler._LRScheduler, - num_iter: int | None = None, - ) -> ( - torch.optim.lr_scheduler._LRScheduler - | torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ): - if isinstance(params, torch.optim.lr_scheduler._LRScheduler): - return params - - sched_type: str = params["type"].lower() - base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - scheduler = None - elif sched_type == "linear": - scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=params.get("start_factor", 0.1), - total_iters=params.get("total_iters", num_iter), - ) - elif sched_type == "cosine_annealing": - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max=params.get("T_max", num_iter), - eta_min=params.get("eta_min", base_LR / 100), - ) - elif sched_type == "cyclic": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 50), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params.keys(): - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.999 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") - return scheduler diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index 2725fe20..e69de29b 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -1,300 +0,0 @@ -import numpy as np -import torch -import torch.nn.functional as F -from scipy.ndimage import center_of_mass, gaussian_filter, shift -from scipy.stats import norm -from tqdm.auto import tqdm - -from quantem.core.utils.imaging_utils import cross_correlation_shift - -# --- Projection Operator Utils --- - - -def rot_ZXZ(mags, z1, x, z3, device, mode="bilinear"): - if not isinstance(x, torch.Tensor) or not isinstance(z1, torch.Tensor): - z1 = torch.tensor(z1, dtype=torch.float32, device=device) - x = torch.tensor(x, dtype=torch.float32, device=device) - z3 = torch.tensor(z3, dtype=torch.float32, device=device) - curr_mags = mags - - curr_mags = differentiable_rotz_vectorized(curr_mags, z1, mode) - curr_mags = differentiable_rotx_vectorized(curr_mags, x, mode) - - curr_mags = differentiable_rotz_vectorized(curr_mags, z3, mode) - - return curr_mags - - -def differentiable_rotz_vectorized(mags, theta, mode="bilinear"): - _, dimz, dimy, dimx = mags.shape - - if theta.dim() == 0: - theta = theta.unsqueeze(0) - - theta_rad = torch.deg2rad(theta) - - cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) - affine_matrix = torch.stack( - [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 - ).view(-1, 2, 3) - - mags = mags.permute(1, 0, 2, 3) - - def transform_slice(mag_slice): - grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) - return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( - 0 - ) - - rotated_mags = torch.vmap(transform_slice)(mags) - return rotated_mags.permute(1, 0, 2, 3) - - -def differentiable_rotx_vectorized(mags, theta, mode="bilinear"): - _, dimz, dimy, dimx = mags.shape - - if theta.dim() == 0: - theta = theta.unsqueeze(0) - - theta_rad = torch.deg2rad(theta) - - cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) - affine_matrix = torch.stack( - [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 - ).view(-1, 2, 3) - - mags = mags.permute(3, 0, 1, 2) - - def transform_slice(mag_slice): - grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) - return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( - 0 - ) - - rotated_mags = torch.vmap(transform_slice)(mags) - return rotated_mags.permute(1, 2, 3, 0) - - -def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): - """ - Shifts a 2D image using grid_sample in a differentiable manner. - - Args: - image: Tensor of shape [H, W] - shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) - shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) - sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts - - Returns: - Shifted image of shape [H, W] - """ - H, W = image.shape - - # Convert physical shift to pixel shift - shift_x_pixel = shift_x - shift_y_pixel = shift_y - - # Normalize shift for grid_sample (assuming align_corners=True) - normalized_shift_x = shift_x_pixel * 2 / (W - 1) - normalized_shift_y = shift_y_pixel * 2 / (H - 1) - - # Create normalized grid - grid_y, grid_x = torch.meshgrid( - torch.linspace(-1, 1, H, device=image.device), - torch.linspace(-1, 1, W, device=image.device), - indexing="ij", - ) - - grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] - - # Apply shift (ensure it's differentiable) - grid[:, :, :, 0] -= normalized_shift_x - grid[:, :, :, 1] -= normalized_shift_y - - # Add batch and channel dimensions - image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] - - # Sample using grid_sample (fully differentiable) - shifted_image = F.grid_sample( - image, grid, mode="bicubic", padding_mode="zeros", align_corners=True - ) - - return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] - - -# --- TV loss --- - - -def get_TV_loss(tensor, factor=1e-3): - tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() - tv_loss = tv_d + tv_h + tv_w - - return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) - - -# --- Gaussian filters --- - - -def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: - radius = np.ceil(num_sigmas * sigma) - support = torch.arange(-radius, radius + 1, dtype=torch.float) - kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() - # Ensure kernel weights sum to 1, so that image brightness is not altered - return kernel.mul_(1 / kernel.sum()) - - -def gaussian_filter_2d( - img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor -) -> torch.Tensor: # Add kernel_1d as an argument - # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function - padding = len(kernel_1d) // 2 # Ensure that image size does not change - img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` - # Convolve along columns and rows - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - return img.squeeze_(0).squeeze_(0) # Make 2D again - - -def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: - """ - Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. - - Args: - stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms - kernel_1d (torch.Tensor): 1D Gaussian kernel - - Returns: - torch.Tensor: Blurred stack of same shape (H, N, W) - """ - H, N, W = stack.shape - padding = len(kernel_1d) // 2 - - # Reshape to (N, 1, H, W) for conv2d - stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) - - # Apply separable conv2d: vertical then horizontal - out = torch.nn.functional.conv2d( - stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) - ) - out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - - # Restore shape to (H, N, W) - return out.squeeze(1).permute(1, 0, 2) - - -# Circular mask - - -def torch_phase_cross_correlation(im1, im2): - f1 = torch.fft.fft2(im1) - f2 = torch.fft.fft2(im2) - cc = torch.fft.ifft2(f1 * torch.conj(f2)) - cc_abs = torch.abs(cc) - - max_idx = torch.argmax(cc_abs) - shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() - - for i, dim in enumerate(im1.shape): - if shifts[i] > dim // 2: - shifts[i] -= dim - - # return shifts.flip(0) # (dx, dy) - return shifts - - -# --- Tilt Series Processing Utility Functions --- - - -def fourier_cropping(img, crop_size): - """ - Crop the img in Fourier space to the specified size. - """ - center = np.array(img.shape) // 2 - - fft_img = np.fft.fftshift(np.fft.fft2(img)) - - cropped_fft = fft_img[ - center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, - center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, - ] - cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real - return cropped_img - - -def estimate_background( - img, - num_iterations=10, - cutoff=3, - smoothing_sigma=1.0, -): - """ - Estimate the background of the image using a Gaussian filter. - """ - if smoothing_sigma > 0: - img = gaussian_filter(img, sigma=smoothing_sigma) - pixel_vals = img.ravel() - - for i in range(num_iterations): - mu, std = norm.fit(pixel_vals) - - # Set cutoff threshold (e.g., 3 standard deviations) - lower = mu - cutoff * std - upper = mu + cutoff * std - - # Mask pixel values within ±3σ - pixel_vals = pixel_vals[(pixel_vals >= lower) & (pixel_vals <= upper)] - - return mu - - -def cross_correlation_align_stack(ref_img, stack, print_pred = False): - """ - Aligns a stack of images to a reference image using cross-correlation. - - This function assumes the stack does not contain the reference image itself. - - Stack shape should be (N, H, W) where N is the number of images. - """ - - new_images = [] - pred_shifts = [] - - prev_img = ref_img - for img in tqdm(stack): - shift_pred = cross_correlation_shift(prev_img, img) - if print_pred: - print(f'Shift prediction: {shift_pred}') - shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) - - pred_shifts.append(shift_pred) - new_images.append(shifted_image) - - prev_img = shifted_image - - return new_images, pred_shifts - - -def centering_com_alignment(image_stack): - """ - Aligns the image stack to the center of mass of the whole image_stack to the - image center. This is useful for aligning the tilt series to the invariant line. - """ - - aligned_stack = np.zeros_like(image_stack) - h, w = image_stack.shape[1:] - image_center = np.array([h // 2, w // 2]) - - com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) - - for i, img in enumerate(image_stack): - com_img = np.array(center_of_mass(img)) - shift_vec = com_reference - com_img - aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) - - final_shift = image_center - com_reference - for i in range(aligned_stack.shape[0]): - aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) - - return aligned_stack diff --git a/src/quantem/tomography/preprocess/__init__.py b/src/quantem/tomography_old/__init__.py similarity index 100% rename from src/quantem/tomography/preprocess/__init__.py rename to src/quantem/tomography_old/__init__.py diff --git a/src/quantem/tomography_old/models.py b/src/quantem/tomography_old/models.py new file mode 100644 index 00000000..a972400c --- /dev/null +++ b/src/quantem/tomography_old/models.py @@ -0,0 +1,96 @@ +import numpy as np +import torch +from torch import nn + + +class SineLayer(nn.Module): + def __init__( + self, + in_features, + out_features, + bias=True, + is_first=False, + omega_0=30, + hsiren=False, + alpha=1.0, + ): + super().__init__() + self.omega_0 = omega_0 + self.is_first = is_first + self.hsiren = hsiren + self.in_features = in_features + self.alpha = alpha + self.linear = nn.Linear(in_features, out_features, bias=bias) + self.init_weights() + + def init_weights(self): + with torch.no_grad(): + if self.is_first: + # Scale the first layer initialization by alpha + self.linear.weight.uniform_( + -self.alpha / self.in_features, self.alpha / self.in_features + ) + else: + # Scale the hidden layer initialization by alpha + self.linear.weight.uniform_( + -self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, + self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, + ) + + def forward(self, input): + if self.is_first and self.hsiren: + out = torch.sin(self.omega_0 * torch.sinh(2 * self.linear(input))) + else: + out = torch.sin(self.omega_0 * self.linear(input)) + return out + + +class HSiren(nn.Module): + def __init__( + self, + in_features=2, + out_features=3, + hidden_layers=3, + hidden_features=256, + first_omega_0=30, + hidden_omega_0=30, + alpha=1.0, + ): + super().__init__() + self.net_list = [] + self.net_list.append( + SineLayer( + in_features, + hidden_features, + is_first=True, + omega_0=first_omega_0, + hsiren=True, + alpha=alpha, + ) + ) + + for i in range(hidden_layers): + self.net_list.append( + SineLayer( + hidden_features, + hidden_features, + is_first=False, + omega_0=hidden_omega_0, + alpha=alpha, + ) + ) + + final_linear = nn.Linear(hidden_features, out_features) + with torch.no_grad(): + # Final layer keeps original initialization (no alpha scaling) + final_linear.weight.uniform_( + -np.sqrt(6 / hidden_features) / hidden_omega_0, + np.sqrt(6 / hidden_features) / hidden_omega_0, + ) + self.net_list.append(final_linear) + self.net_list.append(nn.Softplus()) + self.net = nn.Sequential(*self.net_list) + + def forward(self, coords): + output = self.net(coords) + return output diff --git a/src/quantem/tomography_old/object_models.py b/src/quantem/tomography_old/object_models.py new file mode 100644 index 00000000..57192e27 --- /dev/null +++ b/src/quantem/tomography_old/object_models.py @@ -0,0 +1,723 @@ +from abc import abstractmethod +from copy import deepcopy +from typing import Any, Callable + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from tqdm.auto import tqdm + +from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml.blocks import reset_weights +from quantem.core.utils.validators import validate_gt, validate_tensor +from quantem.tomography.utils import get_TV_loss + + +class ObjectBase(AutoSerialize): + """ + Base class for all ObjectModels to inherit from. + """ + + def __init__( + self, + volume_shape: tuple[int, int, int], + device: str, + offset_obj: float = 1e-5, + ): + self._shape = volume_shape + + self._obj = torch.zeros(self._shape, device=device, dtype=torch.float32) + offset_obj + self._offset_obj = offset_obj + self._device = device + self._hard_constraints = {} + self._soft_constraints = {} # One big dicitonary + + @property + def shape(self) -> tuple[int, int, int]: + return self._shape + + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape + + @property + def offset_obj(self) -> float: + return self._offset_obj + + @offset_obj.setter + def offset_obj(self, offset_obj: float): + self._offset_obj = offset_obj + + @property + def obj(self) -> torch.Tensor: + pass + + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + + @property + def device(self) -> str: + return self._device + + @device.setter + def device(self, device: str): + self._device = device + + @abstractmethod + def forward( + self, z1: torch.Tensor, z3: torch.Tensor, shift_x: torch.Tensor, shift_y: torch.Tensor + ): + pass + + @abstractmethod + def obj(self): + pass + + @abstractmethod + def reset(self): + pass + + @abstractmethod + def to(self, device: str): + pass + + @abstractmethod + def name(self) -> str: + pass + + @abstractmethod + def params(self) -> torch.Tensor: + pass + + +class ObjectConstraints(ObjectBase): + DEFAULT_HARD_CONSTRAINTS = { + "fourier_filter": False, + "positivity": False, + "shrinkage": False, + "circular_mask": False, + } + + DEFAULT_SOFT_CONSTRAINTS = { + "tv_vol": 0, + } + + @property + def hard_constraints(self) -> dict[str, Any]: + return self._hard_constraints + + @hard_constraints.setter + def hard_constraints(self, hard_constraints: dict[str, Any]): + gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() + for key, value in hard_constraints.items(): + if key not in gkeys: # This might be redundant since add_constraint is checking. + raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") + self._hard_constraints[key] = value + + @property + def soft_constraints(self) -> dict[str, Any]: + return self._soft_constraints + + @soft_constraints.setter + def soft_constraints(self, soft_constraints: dict[str, Any]): + gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() + for key, value in soft_constraints.items(): + if key not in gkeys: + raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") + self._soft_constraints[key] = value + + def add_hard_constraint(self, constraint: str, value: Any): + """Add constraints to the object model.""" + gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() + if constraint not in gkeys: + raise KeyError( + f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" + ) + self._hard_constraints[constraint] = value + + def add_soft_constraint(self, constraint: str, value: Any): + """Add constraints to the object model.""" + gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() + if constraint not in gkeys: + raise KeyError( + f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" + ) + self._soft_constraints[constraint] = value + + def apply_hard_constraints( + self, + obj: torch.Tensor, + ) -> torch.Tensor: + """ + Apply constraints to the object model. + """ + obj2 = obj.clone() + if self.hard_constraints["positivity"]: + obj2 = torch.clamp(obj, min=0.0, max=None) + if self.hard_constraints["shrinkage"]: + obj2 = torch.max(obj2 - self.hard_constraints["shrinkage"], torch.zeros_like(obj2)) + + return obj2 + + def apply_soft_constraints( + self, + obj: torch.Tensor, + ) -> torch.Tensor: + """ + 'Applies' soft constraints to the object model. This will return additional loss terms. + """ + soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) + if self.soft_constraints["tv_vol"] > 0: + tv_loss = get_TV_loss( + obj.unsqueeze(0).unsqueeze(0), factor=self.soft_constraints["tv_vol"] + ) + + soft_loss += tv_loss + + return soft_loss + + +class ObjectVoxelwise(ObjectConstraints): + """ + Object model for voxelwise objects. + """ + + def __init__( + self, + volume_shape: tuple[int, int, int], + device: str, + initial_volume: torch.Tensor | None = None, + ): + super().__init__( + volume_shape=volume_shape, + device=device, + ) + self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() + self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() + + if initial_volume is not None: + self._initial_obj = initial_volume + else: + self.initial_obj = ( + torch.zeros(self._shape, device=self._device, dtype=torch.float32) + + self.offset_obj + ) + + @property + def obj(self): + return self.apply_hard_constraints(self._obj) + + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + + @property + def initial_obj(self): + return self._initial_obj + + @initial_obj.setter + def initial_obj(self, initial_obj: torch.Tensor): + if not isinstance(initial_obj, torch.Tensor): + raise ValueError("initial_obj must be a torch.Tensor") + + self._initial_obj = initial_obj + + def forward(self): + return self.obj + + def reset(self): + self._obj = ( + torch.zeros(self._shape, device=self._device, dtype=torch.float32) + self.offset_obj + ) + + def to(self, device: str): + self._device = device + self._obj = self._obj.to(self._device) + + @property + def name(self) -> str: + return "ObjectVoxelwise" + + @property + def params(self) -> torch.Tensor: + return self._obj + + @property + def soft_loss(self) -> torch.Tensor: + return self.apply_soft_constraints(self._obj) + + +class ObjectDIP(ObjectConstraints): + """ + Object model for DIP objects. + """ + + def __init__( + self, + model: torch.nn.Module, + volume_shape: tuple[int, int, int], + model_input: torch.Tensor + | None = None, # Determines output size, model input pretraining target + input_noise_std: float = 0.0, + device: str = "cpu", + ): + super().__init__( + volume_shape=volume_shape, + device=device, + ) + self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() + self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() + + if model_input is None: + self.model_input = torch.randn(1, 1, volume_shape[0], volume_shape[1], volume_shape[2]) + else: + self.model_input = model_input.clone().detach() + + self.pretrain_target = model_input.clone().detach() + + self._model = model + self._optimizer = None + self._scheduler = None + self._pretrain_losses = [] + self._pretrain_lrs = [] + self._model_input_noise_std = input_noise_std + + @property + def name(self) -> str: + return "ObjectDIP" + + @property + def model(self) -> torch.nn.Module: + return self._model + + @model.setter + def model(self, model: torch.nn.Module): + if not isinstance(model, torch.nn.Module): + raise TypeError(f"Model must be a torch.nn.Module, got {type(model)}") + self._model = model.to(self._device) + self.set_pretrained_weights(self._model) + + @property + def pretrained_weights(self) -> dict[str, torch.Tensor]: + return self._pretrained_weights + + def set_pretrained_weights(self, model: torch.nn.Module): + if not isinstance(model, torch.nn.Module): + raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") + self._pretrained_weights = deepcopy(model.state_dict()) + + @property + def model_input(self) -> torch.Tensor: + return self._model_input + + @model_input.setter + def model_input(self, input_tensor: torch.Tensor): + inp = validate_tensor( + input_tensor, + name="model_input", + dtype=torch.float32, + ndim=5, + expand_dims=True, + ) + self._model_input = inp.to(self._device) + + @property + def pretrain_target(self) -> torch.Tensor: + return self._pretrain_target + + @pretrain_target.setter + def pretrain_target(self, target: torch.Tensor): + if target.ndim == 5: + target = target.squeeze(0).squeeze(0) + + target = validate_tensor( + target, + name="pretrain_target", + ndim=3, + dtype=torch.float32, + expand_dims=True, + ) + if target.shape[-3:] != self.model_input.shape[-3:]: + raise ValueError( + f"Pretrain target shape {target.shape} does not match model input shape {self.model_input.shape}" + ) + self._pretrain_target = target.to(self._device) + + @property + def _model_input_noise_std(self) -> float: + """standard deviation of the gaussian noise added to the model input each forward call""" + return self._input_noise_std + + @_model_input_noise_std.setter + def _model_input_noise_std(self, std: float): + validate_gt(std, 0.0, "input_noise_std", geq=True) + self._input_noise_std = std + + @property + def optimizer(self) -> torch.optim.Optimizer: + """get the optimizer for the DIP model""" + if self._optimizer is None: + raise ValueError("Optimizer is not set. Use set_optimizer() to set it.") + return self._optimizer + + def set_optimizer(self, opt_params: dict): + opt_type = opt_params.pop("type") + if isinstance(opt_type, torch.optim.Optimizer): + self._optimizer = opt_type + elif isinstance(opt_type, type): + self._optimizer = opt_type(self.model.parameters(), **opt_params) + elif opt_type == "adam": + self._optimizer = torch.optim.Adam(self.model.parameters(), **opt_params) + elif opt_type == "adamw": + self._optimizer = torch.optim.AdamW(self.model.parameters(), **opt_params) + elif opt_type == "sgd": + self._optimizer = torch.optim.SGD(self.model.parameters(), **opt_params) + else: + raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") + + @property + def scheduler( + self, + ) -> ( + torch.optim.lr_scheduler._LRScheduler + | torch.optim.lr_scheduler.CyclicLR + | torch.optim.lr_scheduler.ReduceLROnPlateau + | torch.optim.lr_scheduler.ExponentialLR + | None + ): + return self._scheduler + + def set_scheduler(self, params: dict, num_iter: int | None = None) -> None: + sched_type: str = params["type"].lower() + optimizer = self.optimizer + base_LR = optimizer.param_groups[0]["lr"] + if sched_type == "none": + scheduler = None + elif sched_type == "cyclic": + scheduler = torch.optim.lr_scheduler.CyclicLR( + optimizer, + base_lr=params.get("base_lr", base_LR / 4), + max_lr=params.get("max_lr", base_LR * 4), + step_size_up=params.get("step_size_up", 100), + mode=params.get("mode", "triangular2"), + cycle_momentum=params.get("momentum", False), + ) + elif sched_type.startswith(("plat", "reducelronplat")): + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=params.get("factor", 0.5), + patience=params.get("patience", 10), + threshold=params.get("threshold", 1e-3), + min_lr=params.get("min_lr", base_LR / 20), + cooldown=params.get("cooldown", 20), + ) + elif sched_type in ["exp", "gamma", "exponential"]: + if "gamma" in params.keys(): + gamma = params["gamma"] + elif num_iter is not None: + fac = params.get("factor", 0.01) + gamma = fac ** (1.0 / num_iter) + else: + gamma = 0.999 + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) + else: + raise ValueError(f"Unknown scheduler type: {sched_type}") + self._scheduler = scheduler + + @property + def pretrain_losses(self) -> np.ndarray: + return np.array(self._pretrain_losses) + + @property + def pretrain_lrs(self) -> np.ndarray: + return np.array(self._pretrain_lrs) + + @property + def obj(self): + obj = self.model(self._model_input)[0] + return self.apply_hard_constraints(obj) + + def forward(self): + return self.model(self._model_input) + + def to(self, device: str): + self.device = device + self._model = self._model.to(self.device) + self._model_input = self._model_input.to(self.device) + self._pretrain_target = self._pretrain_target.to(self.device) + + @property + def params(self): + return self._model.parameters() + + def reset(self): + self.model.load_state_dict(self.pretrained_weights.copy()) + + def pretrain( + self, + model_input: torch.Tensor, + pretrain_target: torch.Tensor, + reset: bool = True, + num_epochs: int = 100, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + loss_fn: Callable | str = "l2", + apply_constraints: bool = False, + show: bool = True, + ): + model_input.to(self.device) + pretrain_target.to(self.device) + + if optimizer_params is not None: + self.set_optimizer(optimizer_params) + + if scheduler_params is not None: + self.set_scheduler(scheduler_params, num_epochs) + + if reset: + self._model.apply(reset_weights) + self._pretrain_losses = [] + self._pretrain_lrs = [] + + if model_input is not None: + self.model_input = model_input + + if pretrain_target.shape[-3:] != self.model_input.shape[-3:]: + raise ValueError( + f"Pretrain target shape {pretrain_target.shape} does not match model input shape {self.model_input.shape}" + ) + self.pretrain_target = pretrain_target.clone().detach().to(self.device) + + loss_fn = torch.nn.functional.mse_loss + + self._pretrain( + num_epochs=num_epochs, + loss_fn=loss_fn, + apply_constraints=apply_constraints, + show=show, + ) + self.set_pretrained_weights(self.model) + + def _pretrain( + self, + num_epochs: int, + loss_fn: Callable, + apply_constraints: bool = False, + show: bool = False, + ): + if not hasattr(self, "pretrain_target"): + raise ValueError("Pretrain target is not set. Use pretrain_target to set it.") + + self.model.train() + optimizer = self.optimizer + sch = self.scheduler + pbar = tqdm(range(num_epochs)) + output = self.obj + + for a0 in pbar: + if apply_constraints: + output = self.obj + else: + output = self.model(self.model_input).squeeze(0).squeeze(0) + + loss = loss_fn(output, self.pretrain_target) + loss.backward() + optimizer.step() + optimizer.zero_grad() + + if sch is not None: + if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): + sch.step(loss.item()) + else: + sch.step() + + self._pretrain_losses.append(loss.item()) + self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) + pbar.set_description(f"Epoch {a0 + 1}/{num_epochs}, Loss: {loss.item():.4f}, ") + + +# INR stuff + + +class ObjectINN(ObjectConstraints): + """ + Object model for INN objects. + + - VolumeDataset (?) dependent on the object + """ + + def __init__( + self, + model: nn.Module, + volume_shape=tuple[int, int, int], + device: str = "cuda", + ): + super().__init__( + volume_shape=volume_shape, + device=device, + offset_obj=0, + ) + + self._model = model + + # --- Properties --- + @property + def obj(self): + return self._obj + + def create_volume( + self, + world_size: int, + global_rank: int, + ray_size: int, + ): + N = max(self._shape) + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + + # Underlying model if using DDP + model = self.model.module if hasattr(self.model, "module") else self.model + + # Batch size for inference + # 5x larger batches no gradients needed + + inference_batch_size = 5 * N * ray_size + + # Distribute work across GPUs + total_samples = N**3 + samples_per_gpu = total_samples // world_size + remainder = total_samples % world_size + + # Handle uneven distribution + if global_rank < remainder: + start_idx = global_rank * (samples_per_gpu + 1) + end_idx = start_idx + samples_per_gpu + 1 + else: + start_idx = global_rank * samples_per_gpu + remainder + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx] + num_samples = inputs_subset.shape[0] + outputs_list = [] + + for batch_start in range(0, num_samples, inference_batch_size): + batch_end = min(batch_start + inference_batch_size, num_samples) + batch_coords = inputs_subset[batch_start:batch_end].to( + self.device, non_blocking=True + ) + + batch_outputs = model(batch_coords) + if batch_outputs.dim() > 1: + batch_outputs = batch_outputs.squeeze(-1) + + outputs_list.append(batch_outputs.cpu()) + + outputs = torch.cat(outputs_list, dim=0) + + if world_size > 1: + # Gather from all ranks + # handle potentially different sizes + output_size = torch.tensor(outputs.shape[0], device=self.device, dtype=torch.long) + all_sizes = [ + torch.zeros(1, device=self.device, dtype=torch.long) for _ in range(world_size) + ] + dist.all_gather(all_sizes, output_size) + + # Create gather list with correct sizes + max_size = max(size.item() for size in all_sizes) + + # Pad if necessary for gathering + if outputs.shape[0] < max_size: + padding = torch.zeros( + max_size - outputs.shape[0], device=outputs.device, dtype=outputs.dtype + ) + outputs_padded = torch.cat([outputs, padding], dim=0).to(self.device) + else: + outputs_padded = outputs.to(self.device) + + gathered_outputs = [ + torch.empty(max_size, device=self.device, dtype=outputs.dtype) + for _ in range(world_size) + ] + dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + + # Trim padding and concatenate + trimmed_outputs = [] + for rank, size in enumerate(all_sizes): + trimmed_outputs.append(gathered_outputs[rank][: size.item()]) + + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N).float() + else: + pred_full = outputs.reshape(N, N, N).float() + + self._obj = pred_full.detach().cpu() + + @property + def model(self): + return self._model + + @model.setter + def model(self, model): + self._model = model + + def apply_soft_constraints( + self, + coords: torch.Tensor, + ): + soft_loss = torch.tensor(0.0, device=coords.device) + if self.soft_constraints["tv_vol"] > 0: + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + # Rerun forward for gradient tracking + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.model(tv_coords) + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + # Compute gradients + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + soft_loss += self.soft_constraints["tv_vol"] * grad_norm.mean() + + return soft_loss + + def forward( + self, + all_coords: torch.Tensor, + ): + all_densities = self.model(all_coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) + + valid_mask = ( + (all_coords[:, 0] >= -1) + & (all_coords[:, 0] <= 1) + & (all_coords[:, 1] >= -1) + & (all_coords[:, 1] <= 1) + & (all_coords[:, 2] >= -1) + & (all_coords[:, 2] <= 1) + ).float() + + all_densities = all_densities * valid_mask + + return all_densities + + +ObjectModelType = ObjectVoxelwise | ObjectINN # | ObjectDIP | ObjectImplicit (ObjectFFN?) + + +# Pretrain Volume Dataset diff --git a/src/quantem/tomography/radon/__init__.py b/src/quantem/tomography_old/preprocess/__init__.py similarity index 100% rename from src/quantem/tomography/radon/__init__.py rename to src/quantem/tomography_old/preprocess/__init__.py diff --git a/src/quantem/tomography/preprocess/drift.py b/src/quantem/tomography_old/preprocess/drift.py similarity index 100% rename from src/quantem/tomography/preprocess/drift.py rename to src/quantem/tomography_old/preprocess/drift.py diff --git a/src/quantem/tomography_old/radon/__init__.py b/src/quantem/tomography_old/radon/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography_old/radon/radon.py similarity index 100% rename from src/quantem/tomography/radon/radon.py rename to src/quantem/tomography_old/radon/radon.py diff --git a/src/quantem/tomography_old/tomography.py b/src/quantem/tomography_old/tomography.py new file mode 100644 index 00000000..5064cd3f --- /dev/null +++ b/src/quantem/tomography_old/tomography.py @@ -0,0 +1,886 @@ +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.distributed as dist +from torch.nn import SmoothL1Loss +from torch.utils.tensorboard import SummaryWriter + +# from torch_radon.radon import ParallelBeam as Radon +from tqdm.auto import tqdm + +from quantem.core.ml.loss_functions import ( + CharbonnierLoss, + L1Loss, + LLMSELoss, + MSELogMSELoss, + MSELoss, +) + +# Temporary imports for TomographyNERF +from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise +from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.tomography_conv import TomographyConv +from quantem.tomography.tomography_ddp import ( + AdaptiveSmoothL1LossDDP, + PretrainVolumeDataset, + TomographyDDP, +) +from quantem.tomography.tomography_ml import TomographyML +from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ + + +# Temporary aux class for TomographyNERF +# TODO: Maybe put this in INN? +def get_num_samples_per_ray(N: int, epoch: int): + """Increase number of samples per ray at specific epochs.""" + # Exponential schedule + + epochs = np.linspace(0, 10, 5, dtype=int) + # schedule = np.linspace(20, N, 5, dtype=int) + schedule = np.array([20, 100, 150, 200, 200]) + # schedule = np.array([200, 200, 200, 200, 200]) + # schedule = np.array([20, 100, 250, 500, 500]) + # schedule = np.array([500, 500, 500, 500, 500]) + # schedule = np.array([300, 300, 300, 300, 300]) + # schedule = np.exp(schedule) + schedule_warmup = dict[int, int](zip(epochs, schedule)) + + for epoch_threshold, samples in schedule_warmup.items(): + if epoch >= epoch_threshold: + num_samples = samples + + return num_samples + + +class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): + """ + Top level class for either using conventional or ML-based reconstruction methods + for tomography. + """ + + def __init__( + self, + dataset, + volume_obj, + device, + _token, + ): + super().__init__(dataset, volume_obj, device, _token) + + # TODO: More elegant way of doing this. + self.global_epochs = 0 + self.ddp_instantiated = False + self.pretraining_instantiated = False + + # --- Reconstruction Method --- + + def sirt_recon( + self, + num_iterations: int = 10, + inline_alignment: bool = False, + enforce_positivity: bool = True, + volume_shape: tuple = None, + reset: bool = True, + smoothing_sigma: float = None, + shrinkage: float = None, + filter_name: str = "hamming", + circle: bool = True, + plot_loss: bool = False, + ): + num_angles, num_rows, num_cols = self.dataset.tilt_series.shape + sirt_tilt_series = self.dataset.tilt_series.clone() + sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) + + hard_constraints = { + "positivity": enforce_positivity, + "shrinkage": shrinkage, + } + self.volume_obj.hard_constraints = hard_constraints + + if volume_shape is None: + volume_shape = (num_rows, num_rows, num_rows) + else: + D, H, W = volume_shape + + if reset: + self.volume_obj.reset() + self.loss = [] + + proj_forward = torch.zeros_like(self.dataset.tilt_series) + + pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") + + if smoothing_sigma is not None: + gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) + else: + gaussian_kernel = None + + print( + "Devices", + sirt_tilt_series.device, + proj_forward.device, + self.dataset.tilt_angles.device, + ) + + for iter in pbar: + proj_forward, loss = self._sirt_run_epoch( + tilt_series=sirt_tilt_series, + proj_forward=proj_forward, + angles=self.dataset.tilt_angles, + inline_alignment=iter > 0 and inline_alignment, + filter_name=filter_name, + gaussian_kernel=gaussian_kernel, + circle=circle, + ) + + pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") + + self.loss.append(loss.item()) + + self.sirt_recon_vol = self.volume_obj + + # Permutation due to sinogram ordering. + self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) + + if plot_loss: + self.plot_loss() + + # TODO: ML Recon which has NeRF and AD depending on the object type. + # TODO: Temporary + + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): + """Create projection rays for entire batch simultaneously.""" + batch_size = len(pixel_i) + + # Convert all pixels to normalized coordinates + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + # TODO: maybe pixel_j.device? + # Create z coordinates + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + + # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + + # Fill coordinates efficiently + rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray + rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray + rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + + return rays + + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + # Step 1: Apply shifts + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + # Rotation 1: Z(-z3) + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + # Rotation 2: X(x) + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + # Rotation 3: Z(-z1) + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + # Stack the final result + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + # TODO: Temp logger + def setup_logger(self, log_path): + if self.global_rank == 0: + self.temp_logger = SummaryWriter(log_dir=log_path) + else: + self.temp_logger = None + + def pretrain( + self, + volume_dataset: PretrainVolumeDataset, + obj: ObjectINN, + batch_size: int, + soft_constraints: dict = None, + log_path: str = None, + optimizer_params: dict = None, + epochs: int = 100, + viz_freq: int = 1, + consistency_criterion: str = "mse", + ): + if not self.pretraining_instantiated: + self.setup_distributed() + self.setup_pretraining_dataloader(volume_dataset, batch_size) + self.pretraining_instantiated = True + obj.model = self.build_model(obj._model) + self.obj = obj + + if soft_constraints is not None: + self.obj.soft_constraints = soft_constraints + + if not hasattr(self, "temp_logger"): + self.setup_logger(log_path=log_path) + + if optimizer_params is not None: + optimizer_params = self.scale_lr(optimizer_params) + self.optimizer_params = optimizer_params + self.set_optimizers() + device_type = self.device.type + consistency_loss_fn = None + if consistency_criterion[0].lower() == "mse": + consistency_loss_fn = MSELoss() + elif consistency_criterion[0].lower() == "l1": + consistency_loss_fn = L1Loss() + elif consistency_criterion[0].lower() == "mse_log": + consistency_loss_fn = MSELogMSELoss() + elif consistency_criterion[0].lower() == "llmse": + consistency_loss_fn = LLMSELoss() + elif consistency_criterion[0].lower() == "smooth_l1": + consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) + elif consistency_criterion[0].lower() == "adaptive_smooth_l1": + consistency_loss_fn = AdaptiveSmoothL1LossDDP( + beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 + ) + elif consistency_criterion[0].lower() == "charbonnier": + consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") + else: + raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + + for epoch in range(epochs): + if self.pretraining_sampler is not None: + self.pretraining_sampler.set_epoch(epoch) + + self.obj.model.train() + + epoch_loss = 0.0 + epoch_consistency_loss = 0.0 + epoch_tv_loss = 0.0 + num_batches = 0 + + for batch_idx, batch in enumerate(self.pretraining_dataloader): + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) + + for _, opt in self.optimizers.items(): + opt.zero_grad() + + with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): + outputs = self.obj.forward(coords) + consistency_loss = consistency_loss_fn(outputs, target) + tv_loss = self.obj.apply_soft_constraints(coords) + + loss = consistency_loss + tv_loss + + loss.backward() + + epoch_loss += loss.detach() + epoch_consistency_loss += consistency_loss.detach() + epoch_tv_loss += tv_loss.detach() + + torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) + + for _, opt in self.optimizers.items(): + opt.step() + epoch_loss += loss.detach() + num_batches += 1 + + if epoch % viz_freq == 0 or epoch == epochs - 1: + avg_loss = epoch_loss / num_batches + with torch.no_grad(): + self.obj.create_volume( + world_size=self.world_size, + global_rank=self.global_rank, + ray_size=volume_dataset.N, + ) + pred_full = self.obj.obj + loss_tensor = avg_loss.clone().detach() + avg_loss = loss_tensor.item() + avg_tv_loss = epoch_tv_loss / num_batches + avg_consistency_loss = epoch_consistency_loss / num_batches + + metrics = torch.tensor( + [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device + ) + if self.world_size > 1: + dist.all_reduce(metrics, op=dist.ReduceOp.AVG) + avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() + + if self.global_rank == 0: + self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) + self.temp_logger.add_scalar( + "Pretrain/consistency_loss", avg_consistency_loss, epoch + ) + self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) + + # current_lr = self.schedulers["model"].get_last_lr()[0] + # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) + + fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) + ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + ax[0].set_title("Sum over Z-axis") + ax[1].matshow( + pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 + ) + ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") + ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) + ax[2].set_title("Sum over Y-axis") + ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) + ax[3].set_title("Sum over X-axis") + self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) + + plt.close(fig) + print( + f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" + ) + self.global_epochs += 1 + if self.global_rank == 0: + print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") + torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") + + def recon( + self, + obj: ObjectINN, + batch_size: int, + num_workers: int = 0, + epochs=20, + use_amp=True, + viz_freq=1, + checkpoint_freq=5, + optimizer_params: dict = None, + scheduler_params: dict = None, + soft_constraints: dict = None, + vol_save_path: str = None, # TODO: TEMPORARY + log_path=None, + val_fraction: float = 0.0, + learn_shifts: bool = True, + # l1_loss: bool = False, + consistency_criterion: str = "mse", + model_weights_path: str = None, + force_cpu: bool = False, + ): + if not self.ddp_instantiated: + if not self.pretraining_instantiated: + self.setup_distributed() + if model_weights_path is not None: + print(f"Loading model weights from {model_weights_path}") + state_dict = torch.load(model_weights_path, map_location="cpu") + + # Handle DataParallel/DDP checkpoints + if any(k.startswith("module.") for k in state_dict.keys()): + new_state_dict = { + k.replace("module.", ""): v for k, v in state_dict.items() + } + else: + new_state_dict = state_dict + + obj.model.load_state_dict(new_state_dict) + print("Model weights loaded successfully") + obj.model = self.build_model(obj._model) + self.obj = obj + + self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) + self.ddp_instantiated = True + + if soft_constraints is not None: + self.obj.soft_constraints = soft_constraints + + if not hasattr(self, "temp_logger"): + self.setup_logger(log_path=log_path) + + zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() + + if self.global_rank == 0: + print( + f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" + ) + print(f"Using consistency criterion: {consistency_criterion}") + + # Auxiliary params setup + self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) + aux_params = self.dataset.auxiliary_params + # Scaling learning rates to account for distributed training + if optimizer_params is not None: + optimizer_params = self.scale_lr(optimizer_params) + self.optimizer_params = optimizer_params + self.set_optimizers() + + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params, num_iter=epochs) + + aux_norm = torch.tensor(0.0, device=self.device) + model_norm = torch.tensor(0.0, device=self.device) + + for _, opt in self.optimizers.items(): + opt.zero_grad() + + N = max(self.dataset.dims) + + device_type = self.device.type + autocast_dtype = torch.bfloat16 if use_amp else None + + for epoch in range(epochs): + num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) + + # num_samples_per_ray = get_num_samples_per_ray(epoch) + # Log the change if it happens + if self.global_rank == 0: + print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") + + if self.global_rank == 0 and self.global_epochs > 0: + prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) + + if num_samples_per_ray != prev_samples: + print( + f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" + ) + + if self.sampler is not None: + self.sampler.set_epoch(epoch) + + epoch_loss = 0.0 + epoch_consistency_loss = 0.0 + epoch_tv_loss = 0.0 + epoch_z1_loss = 0.0 + + num_batches = 0 + consistency_loss_fn = None + if consistency_criterion[0].lower() == "mse": + consistency_loss_fn = MSELoss() + elif consistency_criterion[0].lower() == "l1": + consistency_loss_fn = L1Loss() + elif consistency_criterion[0].lower() == "mse_log": + consistency_loss_fn = MSELogMSELoss() + elif consistency_criterion[0].lower() == "llmse": + consistency_loss_fn = LLMSELoss() + elif consistency_criterion[0].lower() == "smooth_l1": + consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) + elif consistency_criterion[0].lower() == "adaptive_smooth_l1": + consistency_loss_fn = AdaptiveSmoothL1LossDDP( + beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 + ) + elif consistency_criterion[0].lower() == "charbonnier": + consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") + else: + raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + + for batch_idx, batch in enumerate(self.dataloader): + projection_indices = batch["projection_idx"] + + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = aux_params(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): + with torch.no_grad(): + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, N, num_samples_per_ray + ) + + # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + + all_coords = transformed_rays.view(-1, 3) + + all_densities = self.obj.forward(all_coords) + + tv_loss = self.obj.apply_soft_constraints(all_coords) + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + consistency_loss = consistency_loss_fn(predicted_values, target_values) + tv_loss_z1 = torch.tensor(0.0, device=self.device) + + loss = consistency_loss + tv_loss + tv_loss_z1 + + loss.backward() + + epoch_loss += loss.detach() + epoch_consistency_loss += consistency_loss.detach() + epoch_tv_loss += tv_loss.detach() + epoch_z1_loss += tv_loss_z1.detach() + num_batches += 1 + + for key, opt in self.optimizers.items(): + if key == "model": + model_norm = torch.nn.utils.clip_grad_norm_( + self.obj.model.parameters(), max_norm=1 + ) + elif key == "aux_params": + aux_norm = torch.nn.utils.clip_grad_norm_( + self.dataset.auxiliary_params.parameters(), max_norm=1 + ) + opt.step() + opt.zero_grad() + + for key, sched in self.schedulers.items(): + if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): + sched.step(epoch_loss) + else: + sched.step() + + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: + with torch.no_grad(): + self.obj.create_volume( + world_size=self.world_size, + global_rank=self.global_rank, + ray_size=num_samples_per_ray, + ) + pred_full = self.obj.obj + avg_loss = epoch_loss.item() / num_batches + avg_consistency_loss = epoch_consistency_loss.item() / num_batches + avg_tv_loss = epoch_tv_loss.item() / num_batches + avg_z1_loss = epoch_z1_loss.item() / num_batches + shifts, z1, z3 = self.dataset.auxiliary_params.forward() + shifts = shifts.detach().cpu() + z1 = z1.detach().cpu() + z3 = z3.detach().cpu() + metrics = torch.tensor( + [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], + device=self.device, + ) + if self.world_size > 1: + dist.all_reduce(metrics, op=dist.ReduceOp.AVG) + avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + + if hasattr(self, "val_dataloader") and self.val_dataloader is not None: + val_loss = self.validate( + aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N + ) + + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): + with torch.no_grad(): + current_lr = self.schedulers["model"].get_last_lr()[0] + + # Log metrics + self.temp_logger.add_scalar( + f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", + avg_consistency_loss, + self.global_epochs, + ) + if hasattr(self, "val_dataloader") and self.val_dataloader is not None: + self.temp_logger.add_scalar( + f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", + val_loss, + self.global_epochs, + ) + self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) + # if tv_weight > 0: + self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) + self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) + self.temp_logger.add_scalar( + "train/model_grad_norm", model_norm.item(), self.global_epochs + ) + self.temp_logger.add_scalar( + "train/aux_grad_norm", aux_norm.item(), self.global_epochs + ) + self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) + self.temp_logger.add_scalar( + "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs + ) + if consistency_criterion[0].lower() == "adaptive_smooth_l1": + self.temp_logger.add_scalar( + "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs + ) + fig, axes = plt.subplots(1, 5, figsize=(36, 12)) + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + axes[0].set_title("Sum over Z-axis") + + axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) + axes[1].set_title(f"Slice at Z={N // 2}") + + slice_start = max(0, N // 2 - 5) + slice_end = min(N, N // 2 + 6) + thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() + axes[2].matshow(thick_slice, cmap="turbo", vmin=0) + axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") + + axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) + axes[3].set_title("Sum over Y-axis") + + axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) + axes[4].set_title("Sum over X-axis") + + self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) + + fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) + axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") + axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") + axes[0].legend() + axes[1].plot(z1.cpu().numpy(), label="Z1") + axes[2].plot(z3.cpu().numpy(), label="Z3") + self.temp_logger.add_figure( + "train/auxiliary_params", fig, self.global_epochs, close=True + ) + plt.close(fig) + + if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: + with torch.no_grad(): + if self.global_rank == 0: + save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" + torch.save(pred_full.cpu(), save_path) + + self.global_epochs += 1 + + if self.global_rank == 0: + print("Training complete.") + + torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") + # print("Successfully setup DDP and dataloader") + + def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): + """Validate the model on the validation set.""" + self.obj.model.eval() + aux_params.eval() + + val_loss = 0.0 + num_batches = 0 + + with torch.no_grad(): + for batch in self.val_dataloader: + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = aux_params(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): + with torch.no_grad(): + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, N, num_samples_per_ray + ) + + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + + all_coords = transformed_rays.view(-1, 3) + + all_densities = self.obj.forward(all_coords) + + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + + loss = mse_loss + + val_loss += loss.detach() + num_batches += 1 + + avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 + + if self.world_size > 1: + val_loss_tensor = avg_val_loss.detach().clone() + dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) + avg_val_loss = val_loss_tensor.item() + + self.obj.model.train() + aux_params.train() + + return avg_val_loss + + def ad_recon( + self, + optimizer_params: dict, + num_iter: int = 0, + reset: bool = False, + scheduler_params: dict | None = None, + hard_constraints: dict | None = None, + soft_constraints: dict | None = None, + # store_iterations: bool | None = None, + # store_iterations_every: int | None = None, + # autograd: bool = True, + ): + if reset: + self.reset_recon() + + self.hard_constraints = hard_constraints + self.soft_constraints = soft_constraints + + # Make sure everything is in the correct device, might be redundant/cleaner way to do this + self.dataset.to(self.device) + self.volume_obj.to(self.device) + + # Making optimizable parameters into leaf tensors. + self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) + self.dataset.z1_angles = ( + self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) + ) + self.dataset.z3_angles = ( + self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) + ) + + if optimizer_params is not None: + self.optimizer_params = optimizer_params + self.set_optimizers() + + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params, num_iter=num_iter) + + if hard_constraints is not None: + self.volume_obj.hard_constraints = hard_constraints + if soft_constraints is not None: + self.volume_obj.soft_constraints = soft_constraints + + pbar = tqdm(range(num_iter), desc="AD Reconstruction") + + for a0 in pbar: + total_loss = 0.0 + tilt_series_loss = 0.0 + + pred_volume = self.volume_obj.forward() + + for i in range(len(self.dataset.tilt_series)): + forward_projection = self.projection_operator( + vol=pred_volume, + z1=self.dataset.z1_angles[i], + x=self.dataset.tilt_angles[i], + z3=self.dataset.z3_angles[i], + shift_x=self.dataset.shifts[i, 0], + shift_y=self.dataset.shifts[i, 1], + device=self.device, + ) + + tilt_series_loss += torch.nn.functional.mse_loss( + forward_projection, self.dataset.tilt_series[i] + ) + tilt_series_loss /= len(self.dataset.tilt_series) + + total_loss = tilt_series_loss + self.volume_obj.soft_loss + self.loss.append(total_loss.item()) + + total_loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad() + + if self.schedulers is not None: + for sch in self.schedulers.values(): + if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): + sch.step(total_loss) + elif sch is not None: + sch.step() + + pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") + + if self.logger is not None: + self.logger.log_scalar("loss/total", total_loss.item(), a0) + self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) + self.logger.log_scalar( + "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 + ) + + if a0 % self.logger.log_images_every == 0: + self.logger.projection_images( + volume_obj=self.volume_obj, + epoch=a0, + ) + self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) + + self.logger.flush() + + self.ad_recon_vol = self.volume_obj.forward() + + return self + + def reset_recon(self) -> None: + if isinstance(self.volume_obj, ObjectVoxelwise): + self.volume_obj.reset() + + self.ad_recon_vol = None + + # --- Projection Operators ---- + def projection_operator( + self, + vol, + z1, + x, + z3, + shift_x, + shift_y, + device, + ): + projection = ( + rot_ZXZ( + mags=vol.unsqueeze(0), # Add batch dimension + z1=z1, + x=-x, + z3=z3, + device=device, + mode="bilinear", + ) + .squeeze() + .sum(axis=0) + ) + + shifted_projection = differentiable_shift_2d( + image=projection, + shift_x=shift_x, + shift_y=shift_y, + sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit + ) + + return shifted_projection diff --git a/src/quantem/tomography_old/tomography_base.py b/src/quantem/tomography_old/tomography_base.py new file mode 100644 index 00000000..d3b94427 --- /dev/null +++ b/src/quantem/tomography_old/tomography_base.py @@ -0,0 +1,514 @@ +from typing import Tuple + +import matplotlib.pyplot as plt +import numpy as np +import torch +from numpy.typing import NDArray +from torch._tensor import Tensor +from tqdm.auto import tqdm + +from quantem.core.datastructures.dataset3d import Dataset3d +from quantem.core.io.serialize import AutoSerialize +from quantem.core.visualization.visualization import show_2d +from quantem.imaging.drift import cross_correlation_shift +from quantem.tomography.object_models import ObjectModelType, ObjectVoxelwise +from quantem.tomography.tomography_dataset import TomographyDataset +from quantem.tomography.tomography_logger import LoggerTomography + + +class TomographyBase(AutoSerialize): + _token = object() + + DEFAULT_HARD_CONSTRAINTS = { + "positivity": False, + "shrinkage": False, + "circular_mask": False, + "fourier_filter": False, + } + + DEFAULT_SOFT_CONSTRAINTS = { + "tv_vol": 0.0, + } + + def __init__( + self, + dataset: TomographyDataset, + volume_obj: ObjectModelType, # ObjectDIP? + device: str = "cuda", + # ABF/HAADF property + logger: LoggerTomography | None = None, + _token: object | None = None, + ): + """Initialize a Tomography object. + + Parameters + ---------- + array : NDArray | Any + The underlying 3D array data + name : str + A descriptive name for the dataset + """ + + # if _token is not self._token: + # raise RuntimeError( + # "This class is not meant to be instantiated directly. Use the from_data method." + # ) + + self._device = device + self._dataset = dataset + self._volume_obj = volume_obj + self._loss = [] + self._mode = [] + + self._hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() + self._soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() + + self._logger = None + + @classmethod + def from_data( + cls, + tilt_series: Dataset3d | NDArray | Tensor, + tilt_angles: NDArray | Tensor, + z1_angles: NDArray | Tensor | None = None, + z3_angles: NDArray | Tensor | None = None, + shifts: NDArray | Tensor | None = None, + volume_obj: NDArray | Dataset3d | ObjectModelType | None = None, + device: str = "cpu", + clamp: bool = True, + ): + device = device.lower() + + dataset = TomographyDataset.from_data( + tilt_series=tilt_series, + tilt_angles=tilt_angles, + z1_angles=z1_angles, + z3_angles=z3_angles, + shifts=shifts, + clamp=clamp, + ) + + dataset.to(device) + + if volume_obj is None: + max_shape = max(dataset.tilt_series.shape) + volume_obj = ObjectVoxelwise( + volume_shape=(max_shape, max_shape, max_shape), + device=device, + ) + elif isinstance(volume_obj, Dataset3d): + volume = torch.from_numpy(volume_obj.array) + volume_obj = ObjectVoxelwise( + volume_shape=volume_obj.shape, device=device, initial_volume=volume + ) + volume_obj.obj = volume + elif isinstance(volume_obj, np.ndarray): + volume = torch.from_numpy(volume_obj) + volume_obj = ObjectVoxelwise( + volume_shape=volume_obj.shape, device=device, initial_volume=volume + ) + volume_obj.obj = volume + else: + raise ValueError("volume_obj must be a Dataset3d, NDArray ObjectModelType") + + return cls( + dataset=dataset, + volume_obj=volume_obj, + device=device, + _token=cls._token, + ) + + # --- Properties --- + @property + def dataset(self) -> TomographyDataset: + """Tomography dataset.""" + + return self._dataset + + @dataset.setter + def dataset( + self, + tilt_series: Dataset3d | NDArray | TomographyDataset, + tilt_angles: NDArray | Tensor, + z1_angles: NDArray | Tensor | None = None, + z3_angles: NDArray | Tensor | None = None, + shifts: NDArray | Tensor | None = None, + # name: str | None = None, + # origin: NDArray | tuple | list | float | int | None = None, + # sampling: NDArray | tuple | list | float | int | None = None, + # units: list[str] | tuple | list | None = None, + # signal_units: str = "arb. units", + ): + """Set the tilt series dataset.""" + + if not isinstance(tilt_series, TomographyDataset): + dataset = TomographyDataset.from_array( + array=tilt_series, + tilt_angles=tilt_angles, + z1_angles=z1_angles, + z3_angles=z3_angles, + shifts=shifts, + ) + + self._dataset = dataset + + @property + def volume_obj(self) -> Dataset3d | ObjectModelType | None: + """Reconstruction volume dataset.""" + + return self._volume_obj + + @volume_obj.setter + # TODO: add support for ObjectModelType + def volume_obj(self, volume_obj: Dataset3d | NDArray): + """Set the reconstruction volume dataset.""" + if isinstance(volume_obj, ObjectModelType): + self._volume_obj = volume_obj + elif not isinstance(volume_obj, Dataset3d): + volume_obj = Dataset3d.from_array( + array=volume_obj, + # name=self._tilt_series.name, + # origin=self._tilt_series.origin, + # sampling=self._tilt_series.sampling, + # units=self._tilt_series.units, + # signal_units=self._tilt_series.signal_units, + ) + elif isinstance(volume_obj, Dataset3d): + self._volume_obj = volume_obj + else: + raise ValueError("volume_obj must be a Dataset3d or ObjectModelType") + + @property + def device(self) -> str: + """Computation device.""" + + return self._device + + @device.setter + def device(self, device: str): + """Set the computation device.""" + + # if "cuda" not in device or "gpu" not in device: + # raise NotImplementedError("Tomography not currently supported on CPU.") + + self._device = device + + @property + def loss(self) -> list: + """List of loss values during reconstruction.""" + + return self._loss + + @loss.setter + def loss(self, loss: list): + """Set the loss values during reconstruction.""" + + if not isinstance(loss, list): + raise TypeError("Loss must be a list.") + + self._loss = loss + + @property + def mode(self) -> list: + """List of modes used during reconstruction.""" + + return self._mode + + @property + def epochs(self) -> int: + """Number of epochs used during reconstruction.""" + return len(self.loss) + + @property + def logger(self) -> LoggerTomography: + return self._logger + + @logger.setter + def logger(self, logger: LoggerTomography): + if not isinstance(logger, LoggerTomography): + raise TypeError("Logger must be a LoggerTomography") + + self._logger = logger + + # --- Constraints --- + + @property + def hard_constraints(self) -> dict: + """Hard constraints for the reconstruction.""" + return self._hard_constraints + + @hard_constraints.setter + def hard_constraints(self, hard_constraints: dict): + """Set the hard constraints for the reconstruction.""" + + gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() + for key, value in hard_constraints.items(): + if key not in gkeys: + raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") + self._hard_constraints[key] = value + + self._hard_constraints = hard_constraints + + @property + def soft_constraints(self) -> dict: + """Soft constraints for the reconstruction.""" + return self._soft_constraints + + @soft_constraints.setter + def soft_constraints(self, soft_constraints: dict): + """Set the soft constraints for the reconstruction.""" + + gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() + for key, value in soft_constraints.items(): + if key not in gkeys: + raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") + self._soft_constraints[key] = value + + self._soft_constraints = soft_constraints + + # --- RESET --- + + def reset_recon(self) -> None: + self.volume_obj.reset() + self.dataset.reset() + self.loss = [] + self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() + self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() + + self._optimizers = {} + self._schedulers = {} + + # --- Preprocessing --- + + """ + TODO + 1. Implement tilt series cross-correlation alignment + 2. Background subtraction (for ABF) + 3. COM Alignment + 4. Masking + 5. Drift Correction + """ + + def cross_corr_alignment( + self, + upsample_factor: int = 1, + overwrite: bool = False, + ): + # TODO: This needs to be able to work with torch tensors. + + placeholder_tilt_series = self.dataset.tilt_series.clone().detach().cpu().numpy() + + aligned_tilt_series = np.zeros_like(placeholder_tilt_series) + aligned_tilt_series[0] = placeholder_tilt_series[0] + shifts = [] + num_imgs = placeholder_tilt_series.shape[1] + + pbar = tqdm(range(num_imgs - 1), desc="Cross-correlation alignment") + + for i in pbar: + shift, aligned_img = cross_correlation_shift( + placeholder_tilt_series[i], + placeholder_tilt_series[i + 1], + upsample_factor=upsample_factor, + return_shifted_image=True, + ) + + aligned_tilt_series[i + 1] = aligned_img + shifts.append(shift) + + if overwrite: + # TODO: Check this overwrite idea, maybe also need to save the relative shifts? + self.dataset.tilt_series = np.array(aligned_tilt_series) + + return np.array(aligned_tilt_series), np.array(shifts) + + # --- Postprocessing --- + + """ + TODO + 1. Apply circular mask + """ + + def circular_mask(self, shape, radius, center=None, dtype=torch.float32, device="cpu"): + """Generate a 2D circular mask of given shape and radius.""" + H, W = shape + + if center is None: + center = (H // 2, W // 2) + y = torch.arange(H, dtype=dtype, device=device).view(-1, 1) + x = torch.arange(W, dtype=dtype, device=device).view(1, -1) + dist_sq = (x - center[1]) ** 2 + (y - center[0]) ** 2 + return (dist_sq <= radius**2).to(dtype) + + def recon_vol_circular_mask(self, radii): + """ + Apply 2D circular masks along all three axes of a 3D volume. + + Args: + volume (torch.Tensor): 3D tensor of shape (H, W, D) + radii (tuple): (r0, r1, r2) for axes 0, 1, 2 + Returns: + masked_volume: tensor with all masks applied + """ + H, W, D = self.volume_obj.array.shape + device = self.device + dtype = torch.float32 + volume_obj = torch.tensor( + self.volume_obj.array, + device=self.device, + dtype=dtype, + ) + # Masks for each axis + mask0 = self.circular_mask((W, D), radii[0], dtype=dtype, device=device).unsqueeze( + 0 + ) # shape (1, W, D) + mask1 = self.circular_mask((H, D), radii[1], dtype=dtype, device=device).unsqueeze( + 1 + ) # shape (H, 1, D) + mask2 = self.circular_mask((H, W), radii[2], dtype=dtype, device=device).unsqueeze( + 2 + ) # shape (H, W, 1) + + # Broadcast and multiply all masks together + total_mask = mask0 * mask1 * mask2 # shape (H, W, D) + + volume_obj = volume_obj * total_mask + volume_obj = volume_obj.detach().cpu().numpy() + self.volume_obj = Dataset3d.from_array( + array=volume_obj, + # name=self.volume_obj.name, + # origin=self.volume_obj.origin, + # sampling=self.volume_obj.sampling, + # units=self.volume_obj.units, + # signal_units=self.volume_obj.signal_units, + ) + + # --- Visualizations --- + + def plot_projections( + self, + cmap: str = "turbo", + fft: bool = False, + norm: str = "log_auto", + figax: tuple[plt.Figure, plt.Axes] | None = None, + fft_vmax: Tuple[float, float] = (0, 40), + vmin: float = 0, + **kwargs, + ): + """ + Plots the projections of the volume object. + Note that the volume object is in the order of (z, y, x). + Parameters + ---------- + cmap : str + The colormap to use for the projections. + fft : bool + """ + + volume_obj_np = self.volume_obj.obj.detach().cpu().numpy() + + if figax is None: + fig, ax = plt.subplots(ncols=3, figsize=(20, 8)) + else: + fig, ax = figax + + show_2d( + volume_obj_np.sum(axis=0), + figax=(fig, ax[0]), + cmap=cmap, + title="Y-X Projection", + vmin=vmin, + ) + show_2d( + volume_obj_np.sum(axis=1), + figax=(fig, ax[1]), + cmap=cmap, + title="Z-X Projection", + vmin=vmin, + ) + show_2d( + volume_obj_np.sum(axis=2), + figax=(fig, ax[2]), + cmap=cmap, + title="Z-Y Projection", + vmin=vmin, + ) + + if fft: + fig, ax = plt.subplots(ncols=3, figsize=(25, 8)) + print(fft_vmax) + show_2d( + np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=0)))), + figax=(fig, ax[0]), + cmap=cmap, + title="Y-X Projection FFT", + # norm=norm, + vmin=fft_vmax[0], + vmax=fft_vmax[1], + ) + + show_2d( + np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=1)))), + figax=(fig, ax[1]), + cmap=cmap, + title="Z-X Projection FFT", + vmin=fft_vmax[0], + vmax=fft_vmax[1], + # norm=norm, + ) + show_2d( + np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=2)))), + figax=(fig, ax[2]), + cmap=cmap, + title="Z-Y Projection FFT", + vmin=fft_vmax[0], + vmax=fft_vmax[1], + # norm=norm, + ) + + def plot_slice( + self, + cmap="turbo", + slice_index: int = 0, + vmin: float = 0, + figax: tuple[plt.Figure, plt.Axes] | None = None, + ): + if figax is None: + fig, ax = plt.subplots(figsize=(15, 8), ncols=3) + else: + fig, ax = figax + + show_2d( + self.volume_obj.obj[slice_index, :, :], + figax=(fig, ax[0]), + cmap=cmap, + vmin=vmin, + ) + show_2d( + self.volume_obj.obj[:, slice_index, :], + figax=(fig, ax[1]), + cmap=cmap, + vmin=vmin, + ) + + show_2d( + self.volume_obj.obj[:, :, slice_index], + figax=(fig, ax[2]), + cmap=cmap, + vmin=vmin, + ) + + def plot_loss( + self, + figsize: tuple = (8, 4), + figax: tuple[plt.Figure, plt.Axes] | None = None, + ): + if figax is None: + fig, ax = plt.subplots(figsize=figsize) + else: + fig, ax = figax + + ax.semilogy( + self.loss, + label="Loss", + ) diff --git a/src/quantem/tomography_old/tomography_conv.py b/src/quantem/tomography_old/tomography_conv.py new file mode 100644 index 00000000..1056da17 --- /dev/null +++ b/src/quantem/tomography_old/tomography_conv.py @@ -0,0 +1,235 @@ +import numpy as np +import torch + +# from torch_radon.radon import ParallelBeam as Radon +from quantem.tomography.radon.radon import iradon_torch, radon_torch +from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.utils import gaussian_filter_2d_stack, torch_phase_cross_correlation + + +class TomographyConv(TomographyBase): + """ + Class for handling conventional reconstruction methods of tomography data. + """ + + # --- Reconstruction Methods --- + + def _sirt_run_epoch( + self, + tilt_series: torch.Tensor, + proj_forward: torch.Tensor, + angles: torch.Tensor, + inline_alignment: bool, + filter_name: str, + circle: bool, + gaussian_kernel: torch.Tensor | None, + ): + loss = 0 + + if inline_alignment: + for ind in range(len(self.dataset.tilt_angles)): + im_proj = proj_forward[ind] + im_meas = tilt_series[ind] + + shift = torch_phase_cross_correlation(im_proj, im_meas) + if torch.linalg.norm(shift) <= 32: + shifted = torch.fft.ifft2( + torch.fft.fft2(im_meas) + * torch.exp( + -2j + * np.pi + * ( + shift[0] + * torch.fft.fftfreq( + im_meas.shape[0], device=im_meas.device + ).unsqueeze(1) + + shift[1] + * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) + ) + ) + ).real + + proj_forward[ind] = shifted + + # Forward projection + + sinogram_est = radon_torch(self.volume_obj.obj, theta=angles, device=self.device) + # proj_forward = sinogram_est.permute(1, 2, 0) + # error = (tilt_series - proj_forward).permute(2, 0, 1) + proj_forward = sinogram_est + error = tilt_series - proj_forward + + correction = iradon_torch( + error, theta=angles, device=self.device, filter_name=filter_name, circle=circle + ) + + normalization = iradon_torch( + torch.ones_like(error), + theta=angles, + device=self.device, + filter_name=None, + circle=circle, + ) + + normalization[normalization == 0] = 1e-6 + + correction /= normalization + + self.volume_obj._obj += correction + + loss = torch.mean(torch.abs(error)) + + # for z in tqdm(range(self.volume_obj.obj.shape[0]), desc="SIRT Reconstruction"): + # slice_estimate = self.volume_obj.obj[z] + # sinogram_est = radon_torch(slice_estimate, theta=angles, device=self.device) + + # sinogram_true = tilt_series[:, :, z] + + # error = sinogram_true - sinogram_est + + # correction = iradon_torch( + # error, theta=angles, device=self.device, filter_name=filter_name, circle=circle + # ) + + # # I'm pretty sure this implementation of normalization is wrong + # normalization = iradon_torch( + # torch.ones_like(error), + # theta=angles, + # device=self.device, + # filter_name=None, + # circle=circle, + # ) + # normalization[normalization == 0] = 1e-6 + + # correction /= normalization + + # self.volume_obj._obj[z] += correction + + # proj_forward[:, :, z] = sinogram_est + + # loss += torch.mean(torch.abs(error)) + + # loss /= self.volume_obj._obj.shape[0] + + if gaussian_kernel is not None: + self.volume_obj.obj = gaussian_filter_2d_stack(self.volume_obj.obj, gaussian_kernel) + + return proj_forward, loss + + # Deprecated torch_radon implementations + # def _sirt_run_epoch( + # self, + # radon: Radon, + # stack_recon: torch.Tensor, + # stack_torch: torch.Tensor, + # proj_forward: torch.Tensor, + # step_size: float = 0.25, + # gaussian_kernel: torch.Tensor = None, + # inline_alignment=True, + # enforce_positivity=True, + # shrinkage: float = None, + # ): + # loss = 0 + + # if inline_alignment: + # for ind in range(len(self.tilt_series.tilt_angles)): + # im_proj = proj_forward[:, ind, :] + # im_meas = stack_torch[:, ind, :] + + # shift = torch_phase_cross_correlation(im_proj, im_meas) + # if torch.linalg.norm(shift) <= 32: + # shifted = torch.fft.ifft2( + # torch.fft.fft2(im_meas) + # * torch.exp( + # -2j + # * np.pi + # * ( + # shift[0] + # * torch.fft.fftfreq( + # im_meas.shape[0], device=im_meas.device + # ).unsqueeze(1) + # + shift[1] + # * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) + # ) + # ) + # ).real + + # stack_torch[:, ind, :] = shifted + + # proj_forward = radon.forward(stack_recon) + + # proj_diff = stack_torch - proj_forward + + # loss = torch.mean(torch.abs(proj_diff)) + + # recon_slice_update = radon.backward( + # radon.filter_sinogram( + # proj_diff, + # ) + # ) + + # stack_recon += step_size * recon_slice_update + # if enforce_positivity: + # stack_recon = torch.clamp(stack_recon, min=0) + + # if gaussian_kernel is not None: + # stack_recon = gaussian_filter_2d_stack( + # stack_recon, + # gaussian_kernel, + # ) + + # if shrinkage is not None: + # stack_recon = torch.max( + # stack_recon - shrinkage, + # torch.zeros_like(stack_recon), + # ) + + # return stack_recon, loss + + # def _sirt_serial_run_epoch( + # self, + # radon: Radon, + # stack_recon: torch.Tensor, + # stack_torch: torch.Tensor, + # proj_forward: torch.Tensor, + # step_size: float = 0.25, + # gaussian_kernel: torch.Tensor = None, + # inline_alignment=True, + # enforce_positivity=True, + # ): + # recon_slice_update = torch.zeros_like(stack_recon).to(self.device) + + # loss = 0 + + # for i in range(stack_recon.shape[0]): + # proj_forward[i] = radon.forward(stack_recon[i]) + + # proj_diff = stack_torch - proj_forward + + # loss = torch.mean(torch.abs(proj_diff)) + + # for i in range(stack_recon.shape[0]): + # recon_slice_update[i] = radon.backward( + # radon.filter_sinogram( + # proj_diff[i], + # ) + # ) + + # stack_recon += step_size * recon_slice_update + + # if enforce_positivity: + # stack_recon = torch.clamp(stack_recon, min=0) + + # return stack_recon, loss + + # --- Properties --- + # @property + # def reconstruction_method(self) -> str: + # """Get the reconstruction method.""" + # return self._reconstruction_method + # @reconstruction_method.setter + # def reconstruction_method(self, value: str): + # """Set the reconstruction method.""" + # if value not in ["SIRT", "FBP"]: + # raise ValueError("Invalid reconstruction method. Choose 'SIRT' or 'FBP'.") + # self._reconstruction_method = value diff --git a/src/quantem/tomography/tomography_dataset.py b/src/quantem/tomography_old/tomography_dataset.py similarity index 95% rename from src/quantem/tomography/tomography_dataset.py rename to src/quantem/tomography_old/tomography_dataset.py index 31898db7..c66f1d9d 100644 --- a/src/quantem/tomography/tomography_dataset.py +++ b/src/quantem/tomography_old/tomography_dataset.py @@ -1,3 +1,5 @@ +from pathlib import Path + import numpy as np import torch from numpy.typing import NDArray @@ -11,8 +13,6 @@ validate_tensor, ) -from pathlib import Path - class AuxiliaryParams(torch.nn.Module): def __init__(self, num_tilts, device, zero_tilt_idx=None, learn_shifts: bool = True): @@ -29,7 +29,9 @@ def __init__(self, num_tilts, device, zero_tilt_idx=None, learn_shifts: bool = T # Shifts: only parameterize non-reference tilts num_param_tilts = num_tilts - 1 - self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device), requires_grad = learn_shifts) + self.shifts_param = torch.nn.Parameter( + torch.zeros(num_param_tilts, 2, device=device), requires_grad=learn_shifts + ) # Fixed zero shifts for reference self.shifts_ref = torch.zeros(1, 2, device=device) @@ -52,8 +54,8 @@ def forward(self, dummy_input=None): after_z1 = self.z1_param[self.zero_tilt_idx :] z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) - before_z3 = self.z3_param[:self.zero_tilt_idx] - after_z3 = self.z3_param[self.zero_tilt_idx:] + before_z3 = self.z3_param[: self.zero_tilt_idx] + after_z3 = self.z3_param[self.zero_tilt_idx :] z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) return shifts, z1, z3 @@ -171,7 +173,6 @@ def to(self, device: str): self._z3_angles = self._z3_angles.to(device) self._shifts = self._shifts.to(device) - # --- Properties --- @property @@ -323,7 +324,9 @@ def initial_shifts(self, shifts: NDArray | Tensor) -> None: # TODO: Temp auxiliary params - def setup_auxiliary_params(self, zero_tilt_idx: int = None, device: str = "cpu", learn_shifts: bool = True) -> None: + def setup_auxiliary_params( + self, zero_tilt_idx: int = None, device: str = "cpu", learn_shifts: bool = True + ) -> None: if not hasattr(self, "_auxiliary_params"): self._auxiliary_params = AuxiliaryParams( num_tilts=len(self.tilt_angles), @@ -347,6 +350,7 @@ def reset(self) -> None: self._z3_angles = self._initial_z3_angles.clone() self._shifts = self._initial_shifts.clone() + # TODO: Temporary for use when only doing validation class TomographyRayDataset(Dataset): """ @@ -354,7 +358,7 @@ class TomographyRayDataset(Dataset): Each sample contains: target pixel value, pixel coordinates, phi angle, and auxiliary info. """ - def __init__(self, tilt_series, phis, N, val_ratio=0.0, mode='train', seed=42): + def __init__(self, tilt_series, phis, N, val_ratio=0.0, mode="train", seed=42): """ Args: tilt_series: [M, N, N] tensor of projections @@ -375,13 +379,12 @@ def __init__(self, tilt_series, phis, N, val_ratio=0.0, mode='train', seed=42): self.M = tilt_series.shape[0] self.total_pixels = self.M * self.N * self.N - # Create train/val split torch.manual_seed(seed) all_indices = torch.randperm(self.total_pixels) num_val = int(self.total_pixels * val_ratio) - if mode == 'val': + if mode == "val": self.indices = all_indices[:num_val] else: # train self.indices = all_indices[num_val:] @@ -392,7 +395,6 @@ def __len__(self): return len(self.indices) def __getitem__(self, idx): - actual_idx = self.indices[idx].item() projection_idx = actual_idx // (self.N * self.N) @@ -404,17 +406,16 @@ def __getitem__(self, idx): phi = self.phis[projection_idx] return { - 'projection_idx': projection_idx, - 'pixel_i': pixel_i, - 'pixel_j': pixel_j, - 'target_value': target_value, - 'phi': phi + "projection_idx": projection_idx, + "pixel_i": pixel_i, + "pixel_j": pixel_j, + "target_value": target_value, + "phi": phi, } # Pretrain Volume Dataset class PretrainVolumeDataset(Dataset): - def __init__( self, volume_path: Path | str, @@ -443,23 +444,20 @@ def __init__( data = torch.permute(data, (2, 1, 0)) # data = torch.flip(data, dims=(2,)) - self.volume = data.cpu() + self.volume = data.cpu() self.N = N self.total_samples = N**3 coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") self.coords = torch.stack([x, y, z], dim=-1).reshape(-1, 3).cpu() self.targets = self.volume.reshape(-1).cpu() - + def __len__(self): return self.total_samples - + def __getitem__(self, idx): - return { - 'coords': self.coords[idx], - 'target': self.targets[idx] - } + return {"coords": self.coords[idx], "target": self.targets[idx]} @property def N(self): @@ -467,4 +465,4 @@ def N(self): @N.setter def N(self, N: int): - self._N = N \ No newline at end of file + self._N = N diff --git a/src/quantem/tomography_old/tomography_ddp.py b/src/quantem/tomography_old/tomography_ddp.py new file mode 100644 index 00000000..cfb59117 --- /dev/null +++ b/src/quantem/tomography_old/tomography_ddp.py @@ -0,0 +1,321 @@ +import os + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.utils.data import DataLoader, DistributedSampler + +from quantem.tomography.tomography_dataset import ( + PretrainVolumeDataset, + TomographyDataset, + TomographyRayDataset, +) + + +class TomographyDDP: + """ + Initializing DDP stuff for tomo class. + """ + + def __init__( + self, + ): + self.setup_distributed() + + def setup_distributed(self): + # Check if in distributed env + if "RANK" in os.environ: + # Distributed training + if not dist.is_initialized(): + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" + ) + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + self.local_rank = int(os.environ["LOCAL_RANK"]) + + torch.cuda.set_device(self.local_rank) + self.device = torch.device("cuda", self.local_rank) + + else: + # Single GPU/CPU training + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + self.device = torch.device("cuda") + torch.cuda.set_device(0) + print("Single GPU training") + else: + self.device = torch.device("cpu") + print("CPU training") + + # Optional performance optimizations (only for CUDA) + if self.device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + def build_model( + self, + model: nn.Module, + ): + # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? + model = model.to(self.device) + + if self.world_size > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, + bucket_cap_mb=100, + gradient_as_bucket_view=True, + ) + + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully") + + else: + print("Model built, compiled successfully") + + return model + + # Setup Tomo DataLoader + def setup_dataloader( + self, + tomo_dataset: TomographyDataset, + batch_size: int, + num_workers: int = 0, + val_fraction: float = 0.0, + ): + pin_mem = self.device.type == "cuda" + persist = num_workers > 0 + + # Split dataset if validation fraction > 0 + if val_fraction > 0.0: + # TODO: Temporary for when only doing validation, current TomographyDataset doesn't work correctly + train_dataset = TomographyRayDataset( + tomo_dataset.tilt_series.detach().clone(), + tomo_dataset.tilt_angles.detach().clone(), + 500, # TODO: TEMPORARY + val_ratio=val_fraction, + mode="train", + seed=42, + ) + val_dataset = TomographyRayDataset( + tomo_dataset.tilt_series.detach().clone(), + tomo_dataset.tilt_angles.detach().clone(), + 500, # TODO: TEMPORARY + val_ratio=val_fraction, + mode="val", + seed=42, + ) + else: + train_dataset, val_dataset = tomo_dataset, None + + # Samplers for distributed training + if self.world_size > 1: + train_sampler = DistributedSampler( + train_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, + ) + if val_dataset: + val_sampler = DistributedSampler( + val_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False, + ) + shuffle = False + else: + train_sampler = None + val_sampler = None + shuffle = True + + # Main dataloader + self.dataloader = DataLoader( + train_dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler=train_sampler, + shuffle=shuffle, + pin_memory=pin_mem, + drop_last=True, + persistent_workers=persist, + ) + + # Validation dataloader if applicable + if val_dataset: + self.val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size * 4, + num_workers=num_workers, + sampler=val_sampler, + shuffle=False, + pin_memory=pin_mem, + drop_last=False, + persistent_workers=persist, + ) + self.val_sampler = val_sampler + + self.sampler = train_sampler + + if self.global_rank == 0: + print("Dataloader setup complete:") + print(f" Total projections: {len(tomo_dataset.tilt_angles)}") + if val_fraction > 0.0: + print(f" Total projections (val): {len(val_dataset)}") + print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") + print(f" Total pixels: {tomo_dataset.num_pixels:,}") + if val_fraction > 0.0: + print(f" Total pixels (val): {len(val_dataset):,}") + print(f" Total pixels (train): {len(train_dataset):,}") + print(f" Local batch size (train): {batch_size}") + print(f" Global batch size: {batch_size * self.world_size}") + print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + # Setup pretraining dataloader + + def setup_pretraining_dataloader( + self, + volume_dataset: PretrainVolumeDataset, + batch_size: int, + ): + # TODO: temp + + num_workers = 0 + if self.world_size > 1: + sampler = DistributedSampler( + volume_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, + drop_last=True, + ) + shuffle = False + else: + sampler = None + shuffle = True + + self.pretraining_dataloader = DataLoader( + volume_dataset, + batch_size=batch_size, + sampler=sampler, + shuffle=shuffle, + pin_memory=self.device.type == "cuda", + drop_last=True, + persistent_workers=num_workers > 0, + ) + + self.pretraining_sampler = sampler + + if self.global_rank == 0: + print("Pretraining dataloader setup complete:") + print(f" Total samples: {len(volume_dataset)}") + print(f" Grid size: {volume_dataset.N**3}") + print(f" Local batch size: {batch_size}") + print(f" Global batch size: {batch_size * self.world_size}") + print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") + + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): + if scaling_rule == "sqrt": + return base_lr * np.sqrt(self.world_size) + elif scaling_rule == "linear": + return base_lr * self.world_size + else: + raise ValueError(f"Invalid scaling rule: {scaling_rule}") + + def scale_lr( + self, + optimizer_params: dict, + ): + new_optimizer_params = {} + for key, value in optimizer_params.items(): + if "original_lr" in value: + new_optimizer_params[key] = { + "type": value["type"], + "lr": self.get_scaled_lr(value["original_lr"]), + "original_lr": value["original_lr"], + } + else: + new_optimizer_params[key] = { + "type": value["type"], + "lr": self.get_scaled_lr(value["lr"]), + "original_lr": value["lr"], + } + + return new_optimizer_params + + # TODO: Temporary Adaptive L1 Smooth Loss + + +class AdaptiveSmoothL1Loss(nn.Module): + def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): + """ + Adaptive smooth L1 loss with EMA-based beta adaptation. + + Args: + beta_init (float): optional initial β value; if None, starts as 1.0 + ema_factor (float): smoothing factor for EMA (1 - 1/N_b in Eq. 38) + eps (float): small constant for numerical stability + """ + super().__init__() + self.register_buffer("beta2", torch.tensor(beta_init**2 if beta_init else 1.0)) + self.ema_factor = ema_factor + self.eps = eps + + def forward(self, pred, target): + diff = pred - target + abs_diff = diff.abs() + + # compute current batch MSE (Eq. 38) + mse_batch = torch.mean(diff**2) + + # update β² adaptively using Eq. (39) + with torch.no_grad(): + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( + self.beta2, mse_batch + ) + + beta = torch.sqrt(self.beta2 + self.eps) + + # Smooth L1 (Eq. 36) + loss = torch.where( + abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta + ) + return loss.mean() + + +class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): + def forward(self, pred, target): + diff = pred - target + abs_diff = diff.abs() + mse_batch = torch.mean(diff**2) + + # Synchronize β across all GPUs + if dist.is_initialized(): + mse_batch_all = mse_batch.clone() + dist.all_reduce(mse_batch_all, op=dist.ReduceOp.AVG) + mse_batch = mse_batch_all / dist.get_world_size() + + with torch.no_grad(): + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( + self.beta2, mse_batch + ) + + beta = torch.sqrt(self.beta2 + self.eps) + loss = torch.where( + abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta + ) + return loss.mean() diff --git a/src/quantem/tomography/tomography_logger.py b/src/quantem/tomography_old/tomography_logger.py similarity index 100% rename from src/quantem/tomography/tomography_logger.py rename to src/quantem/tomography_old/tomography_logger.py diff --git a/src/quantem/tomography_old/tomography_ml.py b/src/quantem/tomography_old/tomography_ml.py new file mode 100644 index 00000000..5b130e7f --- /dev/null +++ b/src/quantem/tomography_old/tomography_ml.py @@ -0,0 +1,338 @@ +from typing import Any, Generator, Iterator, Sequence + +import torch + +from quantem.tomography.tomography_base import TomographyBase + + +class TomographyML(TomographyBase): + """ + Class for handling conventional reconstruction methods of tomography data. + """ + + OPTIMIZABLE_VALS = ["volume", "z1", "x", "z3", "shifts", "model", "aux_params"] + DEFAULT_LRS = { + "volume": 1e-2, + "z1": 1e-1, + "x": 1e-1, + "z3": 1e-1, + "shifts": 1e-1, + "tv_weight_vol": 0, + "tv_weight_z1": 0, + "tv_weight_x": 0, + "tv_weight_z3": 0, + } + DEFAULT_OPTIMIZER_TYPE = "adam" + + # --- Properties --- + + @property + def optimizer_params(self) -> dict[str, dict]: + """Returns the parameters used to set the optimizers.""" + return self._optimizer_params + + @optimizer_params.setter + def optimizer_params(self, d: dict) -> None: + """ + # Takes a dictionary {key: torch.optim.Adam(params=[blah], lr=[blah]), ...} + Takes a dictionary: + { + "key1": { + "type": "adam", + "lr": 0.001, + }, + "key2": { + ... + }, + ... + } + """ + # resets _optimizers as well + self._optimizers = {} + self._optimizer_params = {} + if isinstance(d, (tuple, list)): + d = {k: {} for k in d} + + for k, v in d.items(): + if k not in self.OPTIMIZABLE_VALS: + raise ValueError( + f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" + ) + if "type" not in v.keys(): + v["type"] = self.DEFAULT_OPTIMIZER_TYPE + if "lr" not in v.keys(): + v["lr"] = self.DEFAULT_LRS[k] + self._optimizer_params[k] = v + + @property + def optimizers(self) -> dict[str, torch.optim.Adam | torch.optim.AdamW]: + return self._optimizers + + def set_optimizers(self): + """Reset all optimizers and set them according to the optimizer_params.""" + for key, _ in self._optimizer_params.items(): + if key == "volume": + self._add_optimizer(key, self.volume_obj.params, self._optimizer_params[key]) + elif key == "shifts": + self._add_optimizer(key, self.dataset.shifts, self._optimizer_params[key]) + elif key == "z1": + self._add_optimizer(key, self.dataset.z1_angles, self._optimizer_params[key]) + elif key == "x": + self._add_optimizer(key, self.dataset.tilt_angles, self._optimizer_params[key]) + elif key == "z3": + self._add_optimizer(key, self.dataset.z3_angles, self._optimizer_params[key]) + elif key == "model": + if self._optimizer_params[key]["original_lr"] is not None: + optimizer_params = self._optimizer_params[key].copy() + optimizer_params.pop("original_lr", None) + self._add_optimizer(key, self.obj.model.parameters(), optimizer_params) + elif key == "aux_params": + if self._optimizer_params[key]["original_lr"] is not None: + optimizer_params = self._optimizer_params[key].copy() + optimizer_params.pop("original_lr", None) + self._add_optimizer( + key, self.dataset.auxiliary_params.parameters(), optimizer_params + ) + else: + raise ValueError( + f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" + ) + + def remove_optimizer(self, key: str) -> None: + self._optimizers.pop(key, None) + self._optimizer_params.pop(key, None) + return + + def _add_optimizer( + self, + key: str, + params: "torch.Tensor|Sequence[torch.Tensor]|Iterator[torch.Tensor]", + opt_params: dict, + ) -> None: + """Can be used to add an optimizer without resetting the other optimizers.""" + + if key not in self.OPTIMIZABLE_VALS: + raise ValueError( + f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" + ) + if isinstance(params, torch.Tensor): + params = [params] + elif isinstance(params, Generator): + params = list(params) + [p.requires_grad_(True) for p in params] + self.optimizer_params[key] = opt_params + opt_params = opt_params.copy() + opt_type = opt_params.pop("type") + if isinstance(opt_type, type): + opt = opt_type(params, **opt_params) + elif opt_type == "adam": + opt = torch.optim.Adam(params, **opt_params) + elif opt_type == "adamw": + opt = torch.optim.AdamW(params, **opt_params) # TODO pass all other kwargs + else: + raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") + # if key in self.optimizers.keys(): + # self.vprint(f"Key {key} is already in optimizers, overwriting.") + self._optimizers[key] = opt + + @property + def scheduler_params(self) -> dict[str, dict]: + """Returns the parameters used to set the schedulers.""" + return self._scheduler_params + + @scheduler_params.setter + def scheduler_params(self, d: dict) -> None: + """ + Takes a dictionary: + { + "key1": { + "type": "cyclic", + "base_lr": 0.001, + }, + "key2": { + ... + }, + ... + } + """ + self._schedulers = {} + for k, v in d.items(): + if not any(v): + continue + if k not in self.OPTIMIZABLE_VALS: + raise ValueError( + f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" + ) + if v["type"] not in [ + "cyclic", + "plateau", + "exp", + "gamma", + "linear", + "cosine_annealing", + "none", + ]: + raise ValueError( + f"Unknown scheduler type: {v['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" + ) + self._scheduler_params = d + + @property + def schedulers( + self, + ) -> dict[ + str, + ( + torch.optim.lr_scheduler.CyclicLR + | torch.optim.lr_scheduler.ReduceLROnPlateau + | torch.optim.lr_scheduler.ExponentialLR + | torch.optim.lr_scheduler.LinearLR + | torch.optim.lr_scheduler.CosineAnnealingLR + | None + ), + ]: + return self._schedulers + + def set_schedulers( + self, + params: dict[str, dict], + num_iter: int | None = None, + ): + """ + TODO allow for new schedulers to be passed in when adding new optimizers without + removing the old schedulers or overwrtiting them. Not entirely sure what usecases there + will be for this. + + Sets the schedulers for the optimizer from a dictionary. Expects a dictionary of the form: + { + "optimizable_key1": { + "type": "scheduler_type", + "scheduler_kwarg": scheduler_kwarg_value, + ... + }, + "optimizable_key2": { + "type": "scheduler_type", + "scheduler_kwarg": scheduler_kwarg_value, + ... + }, + ... + } + where the keys are the same as the keys in self.OPTIMIZABLE_VALS. + + The scheduler type can be one of the following: + - "cyclic" + - "plateau" or "reducelronplateau" + - "exponential" + - None + + The num_iter kwarg is only used for exponential schedulers and if a "factor" is given + as a scheduler_kwarg instead of gamma. In that case, the gamma is calculated from num_iter + and the factor. + + TODO could update this to allow passing key:optimizer directly, would likely need to + rewrite get_schedulers to check the tpye + """ + if not any(self.optimizers): + raise NameError("self.optimizers have not yet been set.") + self._schedulers = self._get_schedulers( + params=params, + optimizers=self.optimizers, + num_iter=num_iter, + ) + + def _get_schedulers( + self, + params: dict[str, dict], + optimizers: dict, + num_iter: int | None = None, + ) -> dict[ + str, + ( + torch.optim.lr_scheduler.CyclicLR + | torch.optim.lr_scheduler.ReduceLROnPlateau + | torch.optim.lr_scheduler.ExponentialLR + | None + ), + ]: + """ + return schedulers for a given set of optimizers. Kept seperate from schedulers.setter so + that it can be called for pre-training + """ + schedulers = {} + for opt_key, p in params.items(): + if not any(p): + continue + elif opt_key not in self.OPTIMIZABLE_VALS: + raise KeyError( + f"Scheduler got bad key {opt_key}, schedulers can only be attached to one of {self.OPTIMIZABLE_VALS}" + ) + elif opt_key not in optimizers.keys(): + raise KeyError(f"optimizers does not have an optimizer for: {opt_key}") + else: + schedulers[opt_key] = self._get_scheduler( + optimizer=optimizers[opt_key], params=p, num_iter=num_iter + ) + return schedulers + + def _get_scheduler( + self, + optimizer: torch.optim.Adam, + params: dict[str, Any] | torch.optim.lr_scheduler._LRScheduler, + num_iter: int | None = None, + ) -> ( + torch.optim.lr_scheduler._LRScheduler + | torch.optim.lr_scheduler.CyclicLR + | torch.optim.lr_scheduler.ReduceLROnPlateau + | torch.optim.lr_scheduler.ExponentialLR + | None + ): + if isinstance(params, torch.optim.lr_scheduler._LRScheduler): + return params + + sched_type: str = params["type"].lower() + base_LR = optimizer.param_groups[0]["lr"] + if sched_type == "none": + scheduler = None + elif sched_type == "linear": + scheduler = torch.optim.lr_scheduler.LinearLR( + optimizer, + start_factor=params.get("start_factor", 0.1), + total_iters=params.get("total_iters", num_iter), + ) + elif sched_type == "cosine_annealing": + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=params.get("T_max", num_iter), + eta_min=params.get("eta_min", base_LR / 100), + ) + elif sched_type == "cyclic": + scheduler = torch.optim.lr_scheduler.CyclicLR( + optimizer, + base_lr=params.get("base_lr", base_LR / 4), + max_lr=params.get("max_lr", base_LR * 4), + step_size_up=params.get("step_size_up", 100), + mode=params.get("mode", "triangular2"), + cycle_momentum=params.get("momentum", False), + ) + elif sched_type.startswith(("plat", "reducelronplat")): + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + mode="min", + factor=params.get("factor", 0.5), + patience=params.get("patience", 10), + threshold=params.get("threshold", 1e-3), + min_lr=params.get("min_lr", base_LR / 20), + cooldown=params.get("cooldown", 50), + ) + elif sched_type in ["exp", "gamma", "exponential"]: + if "gamma" in params.keys(): + gamma = params["gamma"] + elif num_iter is not None: + fac = params.get("factor", 0.01) + gamma = fac ** (1.0 / num_iter) + else: + gamma = 0.999 + scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) + else: + raise ValueError(f"Unknown scheduler type: {sched_type}") + return scheduler diff --git a/src/quantem/tomography/tomography_nerf.py b/src/quantem/tomography_old/tomography_nerf.py similarity index 78% rename from src/quantem/tomography/tomography_nerf.py rename to src/quantem/tomography_old/tomography_nerf.py index 5251d2d9..2aa27dc8 100644 --- a/src/quantem/tomography/tomography_nerf.py +++ b/src/quantem/tomography_old/tomography_nerf.py @@ -1,17 +1,15 @@ -from torch.utils.data import Dataset, DataLoader, DistributedSampler +import os +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np import torch import torch.distributed as dist -import numpy as np - -from quantem.tomography.tomography_dataset import TomographyDataset -from quantem.tomography.models import HSiren - +from torch.utils.data import DataLoader, DistributedSampler from torch.utils.tensorboard import SummaryWriter -import matplotlib.pyplot as plt - -import os -from pathlib import Path +from quantem.tomography.models import HSiren +from quantem.tomography.tomography_dataset import TomographyDataset def get_num_samples_per_ray(epoch): @@ -30,6 +28,7 @@ def get_num_samples_per_ray(epoch): return num_samples + class AuxiliaryParams(torch.nn.Module): def __init__(self, num_tilts, device, zero_tilt_idx=None): super().__init__() @@ -58,12 +57,12 @@ def __init__(self, num_tilts, device, zero_tilt_idx=None): def forward(self, dummy_input=None): # Reconstruct full arrays with zeros at reference position - before_shifts = self.shifts_param[:self.zero_tilt_idx] - after_shifts = self.shifts_param[self.zero_tilt_idx:] + before_shifts = self.shifts_param[: self.zero_tilt_idx] + after_shifts = self.shifts_param[self.zero_tilt_idx :] shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) - before_z1 = self.z1_param[:self.zero_tilt_idx] - after_z1 = self.z1_param[self.zero_tilt_idx:] + before_z1 = self.z1_param[: self.zero_tilt_idx] + after_z1 = self.z1_param[self.zero_tilt_idx :] z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) # before_z3 = self.z3_param[:self.zero_tilt_idx] @@ -73,25 +72,23 @@ def forward(self, dummy_input=None): return shifts, z1, -z1 -class TomographyNerf(): - +class TomographyNerf: def __init__( self, tomo_dataset: TomographyDataset, batch_size: int, num_workers: int = 0, ): - self.tomo_dataset = tomo_dataset self.setup_distributed() self.setup_dataloader( - batch_size = batch_size, - num_workers = num_workers, + batch_size=batch_size, + num_workers=num_workers, ) self.build_model() - + # --- Setup ---- - + def setup_distributed(self): """Setup distributed training if available, otherwise use single GPU/CPU.""" @@ -99,7 +96,9 @@ def setup_distributed(self): if "RANK" in os.environ: # Distributed training if not dist.is_initialized(): - dist.init_process_group(backend='nccl' if torch.cuda.is_available() else 'gloo', init_method='env://') + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" + ) self.world_size = dist.get_world_size() self.global_rank = dist.get_rank() @@ -129,50 +128,47 @@ def setup_distributed(self): torch.backends.cudnn.allow_tf32 = True def build_model(self): - self.model = HSiren( - in_features = 3, - out_features = 1, - hidden_layers = 4, - hidden_features = 512, - first_omega_0 = 30, - alpha = 1.0, + in_features=3, + out_features=1, + hidden_layers=4, + hidden_features=512, + first_omega_0=30, + alpha=1.0, ).to(self.device) - + if self.world_size > 1: self.model = torch.nn.parallel.DistributedDataParallel( self.model, - device_ids = [self.local_rank], - output_device = self.local_rank, - find_unused_parameters = False, - broadcast_buffers = True, - bucket_cap_mb = 100, - gradient_as_bucket_view = True, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, + bucket_cap_mb=100, + gradient_as_bucket_view=True, ) - + if self.global_rank == 0: print("Model wrapped with DDP") - + if self.world_size > 1: - if self.global_rank == 0: print("Model built, distributed, and compiled successfully") - + else: print("Model built, compiled successfully") - + def setup_dataloader( self, batch_size: int, num_workers: int = 0, ): - if self.world_size > 1: sampler = DistributedSampler( self.tomo_dataset, - num_replicas = self.world_size, - rank = self.global_rank, - shuffle = True, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, ) shuffle = False else: @@ -180,64 +176,61 @@ def setup_dataloader( shuffle = True self.dataloader = DataLoader( self.tomo_dataset, - batch_size = batch_size, - num_workers = num_workers, - sampler = sampler, - shuffle = shuffle, - pin_memory = True if self.device.type == "cuda" else False, - drop_last = True, - persistent_workers = False if num_workers == 0 else True, + batch_size=batch_size, + num_workers=num_workers, + sampler=sampler, + shuffle=shuffle, + pin_memory=True if self.device.type == "cuda" else False, + drop_last=True, + persistent_workers=False if num_workers == 0 else True, ) - + self.sampler = sampler - - + if self.global_rank == 0: - print(f"Dataloader setup complete:") + print("Dataloader setup complete:") print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") print(f" Total pixels: {self.tomo_dataset.num_pixels:,}") - + print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {batch_size*self.world_size}") + print(f" Global batch size: {batch_size * self.world_size}") print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - # --- Creating Volume --- def create_volume(self): - N = max(self.tomo_dataset.dims) - + with torch.no_grad(): coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing='ij') + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - model = self.model.module if hasattr(self.model, 'module') else self.model - + + model = self.model.module if hasattr(self.model, "module") else self.model + samples_per_gpu = N**3 // self.world_size start_idx = self.global_rank * samples_per_gpu end_idx = start_idx + samples_per_gpu - + inputs_subset = inputs[start_idx:end_idx].to(self.device) - + # TODO: torch.nn.functional.softplus here # outputs = torch.nn.functional.softplus(model(inputs_subset)) outputs = model(inputs_subset) - + if outputs.dim() > 1: outputs = outputs.squeeze(-1) - + if self.world_size > 1: gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] dist.all_gather(gathered_outputs, outputs.contiguous()) - + pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() else: pred_full = outputs.reshape(N, N, N).float() - + return pred_full - + # --- Scaling LR --- def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): """Scale learning rate based on world size.""" @@ -252,23 +245,22 @@ def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): return base_lr * np.sqrt(self.world_size) else: raise ValueError(f"Invalid scaling rule: {scaling_rule}") - + # --- Creating Optimizer --- def create_optimizer( self, params, lr, - fused = True, + fused=True, ): - return torch.optim.Adam( params, - lr = lr, - fused = fused, + lr=lr, + fused=fused, ) - + # Batch Projection Rays --- - + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): """Create projection rays for entire batch simultaneously.""" batch_size = len(pixel_i) @@ -289,10 +281,9 @@ def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray) rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray return rays - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): # Step 1: Apply shifts shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) @@ -332,7 +323,7 @@ def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_r transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) return transformed_rays - + # --- Training --- def train( self, @@ -348,90 +339,93 @@ def train( if not log_path.exists(): log_path.mkdir(parents=True, exist_ok=True) self.writer = SummaryWriter(log_dir=log_path) - + # Find index closest to zero degrees zero_tilt_idx = torch.argmin(torch.abs(self.tomo_dataset.tilt_angles)).item() - + if self.global_rank == 0: - print(f"Using projection {zero_tilt_idx} (angle={self.tomo_dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference") - + print( + f"Using projection {zero_tilt_idx} (angle={self.tomo_dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" + ) aux_params = AuxiliaryParams( - num_tilts = len(self.tomo_dataset.tilt_angles), - device = self.device, - zero_tilt_idx = zero_tilt_idx, + num_tilts=len(self.tomo_dataset.tilt_angles), + device=self.device, + zero_tilt_idx=zero_tilt_idx, ) - + if self.world_size > 1: aux_params = torch.nn.parallel.DistributedDataParallel( aux_params, - device_ids = [self.local_rank], - output_device = self.local_rank, - find_unused_parameters = False, - broadcast_buffers = True, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, ) - + if self.global_rank == 0: print("Auxiliary parameters wrapped with DDP") - + scaled_train_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") scaled_aux_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") - + optimizer = self.create_optimizer( self.model.parameters(), scaled_train_lr, - fused = True, + fused=True, ) - + aux_optimizer = self.create_optimizer( aux_params.parameters(), scaled_aux_lr, - fused = True, + fused=True, ) - + aux_norm = torch.tensor(0.0, device=self.device) model_norm = torch.tensor(0.0, device=self.device) - + optimizer.zero_grad() aux_optimizer.zero_grad() - + warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR( optimizer, - start_factor = 0.001, - total_iters = warmup_epochs, + start_factor=0.001, + total_iters=warmup_epochs, ) cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, - T_max = epochs - warmup_epochs, - eta_min = scaled_train_lr / 100, + T_max=epochs - warmup_epochs, + eta_min=scaled_train_lr / 100, ) - + scheduler_model = torch.optim.lr_scheduler.SequentialLR( optimizer, - schedulers = [warmup_scheduler_model, cosine_scheduler_model], - milestones = [warmup_epochs], + schedulers=[warmup_scheduler_model, cosine_scheduler_model], + milestones=[warmup_epochs], ) - + scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR( aux_optimizer, - T_max = epochs, - eta_min = scaled_aux_lr / 100, + T_max=epochs, + eta_min=scaled_aux_lr / 100, ) - + N = max(self.tomo_dataset.dims) - + device_type = self.device.type autocast_dtype = torch.bfloat16 if use_amp else None - + for epoch in range(epochs): num_samples_per_ray = get_num_samples_per_ray(epoch) - + # Log the change if it happens if epoch > 0: prev_samples = get_num_samples_per_ray(epoch - 1) - + if num_samples_per_ray != prev_samples and self.global_rank == 0: - print(f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}") + print( + f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" + ) if self.sampler is not None: self.sampler.set_epoch(epoch) @@ -442,24 +436,24 @@ def train( epoch_z1_loss = 0.0 num_batches = 0 - - for batch_idx, batch in enumerate(self.dataloader): - projection_indices = batch['projection_idx'] + for batch_idx, batch in enumerate(self.dataloader): + projection_indices = batch["projection_idx"] - pixel_i = batch['pixel_i'].float().to(self.device, non_blocking=True) - pixel_j = batch['pixel_j'].float().to(self.device, non_blocking=True) - target_values = batch['target_value'].to(self.device, non_blocking=True) - phis = batch['phi'].to(self.device, non_blocking=True) - projection_indices = batch['projection_idx'].to(self.device, non_blocking=True) + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) shifts, z1_params, z3_params = aux_params(None) batch_shifts = torch.index_select(shifts, 0, projection_indices) batch_z1 = torch.index_select(z1_params, 0, projection_indices) batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast(device_type=device_type, dtype=autocast_dtype, enabled=use_amp): + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): with torch.no_grad(): batch_ray_coords = self.create_batch_projection_rays( pixel_i, pixel_j, N, num_samples_per_ray @@ -472,7 +466,7 @@ def train( z3=batch_z3, shifts=batch_shifts, N=N, - sampling_rate=1.0 + sampling_rate=1.0, ) all_coords = transformed_rays.view(-1, 3) @@ -485,20 +479,25 @@ def train( # Create mask for valid coordinates (within [-1, 1]^3) valid_mask = ( - (all_coords[:, 0] >= -1) & (all_coords[:, 0] <= 1) & - (all_coords[:, 1] >= -1) & (all_coords[:, 1] <= 1) & - (all_coords[:, 2] >= -1) & (all_coords[:, 2] <= 1) + (all_coords[:, 0] >= -1) + & (all_coords[:, 0] <= 1) + & (all_coords[:, 1] >= -1) + & (all_coords[:, 1] <= 1) + & (all_coords[:, 2] >= -1) + & (all_coords[:, 2] <= 1) ).float() - + all_densities = all_densities * valid_mask - + if tv_weight > 0: num_tv_samples = min(10000, all_coords.shape[0]) - tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[:num_tv_samples] + tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[ + :num_tv_samples + ] # Rerun forward for gradient tracking tv_coords = all_coords[tv_indices].detach().requires_grad_(True) - + # TODO: torch.nn.functional.softplus here # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this tv_densities_recomputed = self.model(tv_coords) @@ -510,15 +509,17 @@ def train( outputs=tv_densities_recomputed, inputs=tv_coords, grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True + create_graph=True, )[0] - + grad_norm = torch.norm(grad_outputs, dim=1) tv_loss = tv_weight * grad_norm.mean() else: tv_loss = torch.tensor(0.0, device=self.device) - ray_densities = all_densities.view(len(target_values), num_samples_per_ray) # Reshape rays and integarte + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte step_size = 2.0 / (num_samples_per_ray - 1) predicted_values = ray_densities.sum(dim=1) * step_size @@ -542,34 +543,40 @@ def train( num_batches += 1 if epoch >= warmup_epochs: - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + model_norm = torch.nn.utils.clip_grad_norm_( + self.model.parameters(), max_norm=1 + ) optimizer.step() optimizer.zero_grad() - - aux_norm = torch.nn.utils.clip_grad_norm_(aux_params.parameters(), max_norm = 1) + + aux_norm = torch.nn.utils.clip_grad_norm_(aux_params.parameters(), max_norm=1) aux_optimizer.step() aux_optimizer.zero_grad() else: - model_norm = torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm = 1) + model_norm = torch.nn.utils.clip_grad_norm_( + self.model.parameters(), max_norm=1 + ) optimizer.step() optimizer.zero_grad() - + aux_optimizer.zero_grad() - + scheduler_model.step() - + if epoch >= warmup_epochs: scheduler_aux.step() - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0 : + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: with torch.no_grad(): pred_full = self.create_volume().cpu() avg_loss = epoch_loss.item() / num_batches avg_mse_loss = epoch_mse_loss.item() / num_batches avg_tv_loss = epoch_tv_loss.item() / num_batches avg_z1_loss = epoch_z1_loss.item() / num_batches - - metrics = torch.tensor([avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device) + + metrics = torch.tensor( + [avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device + ) if self.world_size > 1: torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() @@ -592,17 +599,17 @@ def train( self.writer.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) fig, axes = plt.subplots(1, 3, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap='turbo', vmin=0) - axes[0].set_title('Sum over Z-axis') + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + axes[0].set_title("Sum over Z-axis") - axes[1].matshow(pred_full[N//2].cpu().numpy(), cmap='turbo', vmin=0) - axes[1].set_title(f'Slice at Z={N//2}') + axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) + axes[1].set_title(f"Slice at Z={N // 2}") - slice_start = max(0, N//2 - 5) - slice_end = min(N, N//2 + 6) + slice_start = max(0, N // 2 - 5) + slice_end = min(N, N // 2 + 6) thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap='turbo', vmin=0) - axes[2].set_title(f'Thick slice sum (Z={slice_start}:{slice_end-1})') + axes[2].matshow(thick_slice, cmap="turbo", vmin=0) + axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") self.writer.add_figure("train/viz", fig, epoch, close=True) plt.close(fig) @@ -616,8 +623,8 @@ def train( def save_checkpoint(self, epoch, aux_params, log_path): """Save model and auxiliary parameters checkpoint (rank 0 only).""" if self.global_rank == 0: - model_to_save = self.model.module if hasattr(self.model, 'module') else self.model - aux_params_to_save = aux_params.module if hasattr(aux_params, 'module') else aux_params + model_to_save = self.model.module if hasattr(self.model, "module") else self.model + aux_params_to_save = aux_params.module if hasattr(aux_params, "module") else aux_params checkpoint = { "model_state_dict": model_to_save.state_dict(), @@ -626,4 +633,4 @@ def save_checkpoint(self, epoch, aux_params, log_path): } checkpoint_path = os.path.join(log_path, f"checkpoint_epoch_{epoch:04d}.pt") torch.save(checkpoint, checkpoint_path) - print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") \ No newline at end of file + print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") diff --git a/src/quantem/tomography_old/utils.py b/src/quantem/tomography_old/utils.py new file mode 100644 index 00000000..82c1c9d5 --- /dev/null +++ b/src/quantem/tomography_old/utils.py @@ -0,0 +1,300 @@ +import numpy as np +import torch +import torch.nn.functional as F +from scipy.ndimage import center_of_mass, gaussian_filter, shift +from scipy.stats import norm +from tqdm.auto import tqdm + +from quantem.core.utils.imaging_utils import cross_correlation_shift + +# --- Projection Operator Utils --- + + +def rot_ZXZ(mags, z1, x, z3, device, mode="bilinear"): + if not isinstance(x, torch.Tensor) or not isinstance(z1, torch.Tensor): + z1 = torch.tensor(z1, dtype=torch.float32, device=device) + x = torch.tensor(x, dtype=torch.float32, device=device) + z3 = torch.tensor(z3, dtype=torch.float32, device=device) + curr_mags = mags + + curr_mags = differentiable_rotz_vectorized(curr_mags, z1, mode) + curr_mags = differentiable_rotx_vectorized(curr_mags, x, mode) + + curr_mags = differentiable_rotz_vectorized(curr_mags, z3, mode) + + return curr_mags + + +def differentiable_rotz_vectorized(mags, theta, mode="bilinear"): + _, dimz, dimy, dimx = mags.shape + + if theta.dim() == 0: + theta = theta.unsqueeze(0) + + theta_rad = torch.deg2rad(theta) + + cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) + affine_matrix = torch.stack( + [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 + ).view(-1, 2, 3) + + mags = mags.permute(1, 0, 2, 3) + + def transform_slice(mag_slice): + grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) + return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( + 0 + ) + + rotated_mags = torch.vmap(transform_slice)(mags) + return rotated_mags.permute(1, 0, 2, 3) + + +def differentiable_rotx_vectorized(mags, theta, mode="bilinear"): + _, dimz, dimy, dimx = mags.shape + + if theta.dim() == 0: + theta = theta.unsqueeze(0) + + theta_rad = torch.deg2rad(theta) + + cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) + affine_matrix = torch.stack( + [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 + ).view(-1, 2, 3) + + mags = mags.permute(3, 0, 1, 2) + + def transform_slice(mag_slice): + grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) + return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( + 0 + ) + + rotated_mags = torch.vmap(transform_slice)(mags) + return rotated_mags.permute(1, 2, 3, 0) + + +def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): + """ + Shifts a 2D image using grid_sample in a differentiable manner. + + Args: + image: Tensor of shape [H, W] + shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) + shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) + sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts + + Returns: + Shifted image of shape [H, W] + """ + H, W = image.shape + + # Convert physical shift to pixel shift + shift_x_pixel = shift_x + shift_y_pixel = shift_y + + # Normalize shift for grid_sample (assuming align_corners=True) + normalized_shift_x = shift_x_pixel * 2 / (W - 1) + normalized_shift_y = shift_y_pixel * 2 / (H - 1) + + # Create normalized grid + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=image.device), + torch.linspace(-1, 1, W, device=image.device), + indexing="ij", + ) + + grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] + + # Apply shift (ensure it's differentiable) + grid[:, :, :, 0] -= normalized_shift_x + grid[:, :, :, 1] -= normalized_shift_y + + # Add batch and channel dimensions + image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] + + # Sample using grid_sample (fully differentiable) + shifted_image = F.grid_sample( + image, grid, mode="bicubic", padding_mode="zeros", align_corners=True + ) + + return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] + + +# --- TV loss --- + + +def get_TV_loss(tensor, factor=1e-3): + tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + + +# --- Gaussian filters --- + + +def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: + radius = np.ceil(num_sigmas * sigma) + support = torch.arange(-radius, radius + 1, dtype=torch.float) + kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() + # Ensure kernel weights sum to 1, so that image brightness is not altered + return kernel.mul_(1 / kernel.sum()) + + +def gaussian_filter_2d( + img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor +) -> torch.Tensor: # Add kernel_1d as an argument + # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function + padding = len(kernel_1d) // 2 # Ensure that image size does not change + img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` + # Convolve along columns and rows + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + return img.squeeze_(0).squeeze_(0) # Make 2D again + + +def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: + """ + Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. + + Args: + stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms + kernel_1d (torch.Tensor): 1D Gaussian kernel + + Returns: + torch.Tensor: Blurred stack of same shape (H, N, W) + """ + H, N, W = stack.shape + padding = len(kernel_1d) // 2 + + # Reshape to (N, 1, H, W) for conv2d + stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) + + # Apply separable conv2d: vertical then horizontal + out = torch.nn.functional.conv2d( + stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) + ) + out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + + # Restore shape to (H, N, W) + return out.squeeze(1).permute(1, 0, 2) + + +# Circular mask + + +def torch_phase_cross_correlation(im1, im2): + f1 = torch.fft.fft2(im1) + f2 = torch.fft.fft2(im2) + cc = torch.fft.ifft2(f1 * torch.conj(f2)) + cc_abs = torch.abs(cc) + + max_idx = torch.argmax(cc_abs) + shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() + + for i, dim in enumerate(im1.shape): + if shifts[i] > dim // 2: + shifts[i] -= dim + + # return shifts.flip(0) # (dx, dy) + return shifts + + +# --- Tilt Series Processing Utility Functions --- + + +def fourier_cropping(img, crop_size): + """ + Crop the img in Fourier space to the specified size. + """ + center = np.array(img.shape) // 2 + + fft_img = np.fft.fftshift(np.fft.fft2(img)) + + cropped_fft = fft_img[ + center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, + center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, + ] + cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real + return cropped_img + + +def estimate_background( + img, + num_iterations=10, + cutoff=3, + smoothing_sigma=1.0, +): + """ + Estimate the background of the image using a Gaussian filter. + """ + if smoothing_sigma > 0: + img = gaussian_filter(img, sigma=smoothing_sigma) + pixel_vals = img.ravel() + + for i in range(num_iterations): + mu, std = norm.fit(pixel_vals) + + # Set cutoff threshold (e.g., 3 standard deviations) + lower = mu - cutoff * std + upper = mu + cutoff * std + + # Mask pixel values within ±3σ + pixel_vals = pixel_vals[(pixel_vals >= lower) & (pixel_vals <= upper)] + + return mu + + +def cross_correlation_align_stack(ref_img, stack, print_pred=False): + """ + Aligns a stack of images to a reference image using cross-correlation. + + This function assumes the stack does not contain the reference image itself. + + Stack shape should be (N, H, W) where N is the number of images. + """ + + new_images = [] + pred_shifts = [] + + prev_img = ref_img + for img in tqdm(stack): + shift_pred = cross_correlation_shift(prev_img, img) + if print_pred: + print(f"Shift prediction: {shift_pred}") + shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) + + pred_shifts.append(shift_pred) + new_images.append(shifted_image) + + prev_img = shifted_image + + return new_images, pred_shifts + + +def centering_com_alignment(image_stack): + """ + Aligns the image stack to the center of mass of the whole image_stack to the + image center. This is useful for aligning the tilt series to the invariant line. + """ + + aligned_stack = np.zeros_like(image_stack) + h, w = image_stack.shape[1:] + image_center = np.array([h // 2, w // 2]) + + com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) + + for i, img in enumerate(image_stack): + com_img = np.array(center_of_mass(img)) + shift_vec = com_reference - com_img + aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) + + final_shift = image_center - com_reference + for i in range(aligned_stack.shape[0]): + aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) + + return aligned_stack From b90499d2ffcf8222d61c87f42b425f19ff57f281 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 16 Dec 2025 16:28:35 -0800 Subject: [PATCH 033/335] Need to think about tomography_ddp a little bit more, also what should live in tomography_base. Need to implement object_models for incorporating different forward calls --- src/quantem/tomography/dataset_models.py | 52 +++- src/quantem/tomography/logger_tomography.py | 21 ++ src/quantem/tomography/tomography_base.py | 25 ++ src/quantem/tomography/tomography_ddp.py | 327 ++++++++++++++++++++ 4 files changed, 418 insertions(+), 7 deletions(-) create mode 100644 src/quantem/tomography/logger_tomography.py diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 994d3356..b83d89d7 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -1,6 +1,6 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import Any +from typing import Any, Literal import torch import torch.nn as nn @@ -21,6 +21,7 @@ class DatasetValue: target: torch.Tensor tilt_angle: int | float pixel_loc: tuple[int, int] | None = None # Only for INRDataset + # pose: tuple[torch.nn.Parameter, torch.nn.Parameter, torch.nn.Parameter] | None = None # If there is pose optimization. class TomographyDatasetBase(AutoSerialize, OptimizerMixin, nn.Module): @@ -36,7 +37,7 @@ class TomographyDatasetBase(AutoSerialize, OptimizerMixin, nn.Module): def __init__( self, - tilt_stack: Dataset3d, + tilt_stack: Dataset3d | NDArray | torch.Tensor, tilt_angles: NDArray | torch.Tensor, learn_pose: bool = True, _token: object | None = None, @@ -56,10 +57,14 @@ def __init__( ) self.tilt_stack = tilt_stack + self.volume_size = (int(tilt_stack.shape.max()), tilt_stack.shape[1], tilt_stack.shape[2]) self.tilt_angles = tilt_angles self.learn_pose = learn_pose + # The reference tilt angle is the one with the smallest absolute tilt angle. + # I.e, the pose will not be optimized for the reference tilt angle. self._reference_tilt_angle_idx = torch.argmin(torch.abs(self.tilt_angles)) + # TODO: Implement AuxParams from old tomography_dataset.py here. @abstractmethod def forward( @@ -93,7 +98,11 @@ def __init__( def forward( self, proj_idx: int, - ): + ) -> DatasetValue: + """ + Forward pass for pixel-based tomography. + Returns the full tilt image for the given projection index, and the tilt angle. + """ return DatasetValue( target=self.tilt_stack[proj_idx], tilt_angle=self.tilt_angles[proj_idx], @@ -111,8 +120,29 @@ class TomographyINRDataset(TomographyDatasetBase, Dataset): def __init__( self, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_pose: bool = True, + val_ratio: float = 0.0, + mode: Literal["train", "val"] = "train", + seed: int = 42, + _token: object | None = None, ): - pass + super().__init__(tilt_stack, tilt_angles, learn_pose, _token) + + self._total_pixels = self.volume_size[0] * self.volume_size[1] * self.volume_size[2] + + # Create train/val split + torch.manual_seed(seed) + all_indices = torch.randperm(self.total_pixels) + + num_val = int(self.total_pixels * val_ratio) + if mode == "val": + self.indices = all_indices[:num_val] + else: # train + self.indices = all_indices[num_val:] + + self._mode = mode def forward(self, dummy_input: Any = None): """ @@ -124,7 +154,7 @@ def forward(self, dummy_input: Any = None): def __getitem__( self, idx: int, - ) -> dict[str, Any]: + ) -> DatasetValue: """ Gets the item for INR i.e, the project index, pixel value at (i, j), and the tilt angle. """ @@ -150,7 +180,15 @@ def __len__( """ Returns the number of pixels in the tilt stack. """ - pass + + return self._total_pixels + + @property + def mode(self) -> Literal["train", "val"]: + """ + Returns the mode of the dataset. + """ + return self._mode -DatasetModelType = TomographyINRDataset +DatasetModelType = TomographyINRDataset | TomographyPixDataset diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py new file mode 100644 index 00000000..d9f0f8bb --- /dev/null +++ b/src/quantem/tomography/logger_tomography.py @@ -0,0 +1,21 @@ +from quantem.core.ml.logger import LoggerBase + + +class LoggerTomography(LoggerBase): + """ + Logger for ML-based tomography reconstructions. + """ + + def __init__( + self, + log_dir: str, + run_prefix: str, + run_suffix: str = None, + log_images_every: int = 10, + ): + super().__init__(log_dir, run_prefix, run_suffix, log_images_every) + + def log_epoch(self, epoch: int, loss: float, tilt_series_loss: float, soft_loss: float): + self.log_scalar("loss/total", loss, epoch) + self.log_scalar("loss/tilt_series", tilt_series_loss, epoch) + self.log_scalar("loss/soft", soft_loss, epoch) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 8b137891..cb70c15c 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1 +1,26 @@ +import numpy as np +from quantem.core.io.serialize import AutoSerialize +from quantem.core.utils.rng import RNGMixin +from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.logger_tomography import LoggerTomography + + +class TomographyBase(AutoSerialize, RNGMixin): + """ + A base class for performing electron tomography reconstructions. + + Should have all the default attributes needed for tomography reconstructions. + """ + + _token = object() + + def __init__( + self, + dset: DatasetModelType, + logger: LoggerTomography | None = None, + device: str = "cuda", + rng: np.random.Generator | int | None = None, + _token: object | None = None, + ): + pass diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index e69de29b..52d2e783 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -0,0 +1,327 @@ +import os + +import numpy as np +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.utils.data import DataLoader, DistributedSampler + +from quantem.tomography.dataset_models import ( + TomographyINRDataset, # Only INR dataset is supported for DDP +) + +# from quantem.tomography.tomography_dataset import ( +# PretrainVolumeDataset, +# TomographyDataset, +# TomographyRayDataset, +# ) + + +class TomographyDDP: + """ + Initializing DDP stuff for tomo class. + """ + + def __init__( + self, + ): + self.setup_distributed() + + def setup_distributed(self): + # Check if in distributed env + if "RANK" in os.environ: + # Distributed training + if not dist.is_initialized(): + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" + ) + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + self.local_rank = int(os.environ["LOCAL_RANK"]) + + torch.cuda.set_device(self.local_rank) + self.device = torch.device("cuda", self.local_rank) + + else: + # Single GPU/CPU training + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + self.device = torch.device("cuda") + torch.cuda.set_device(0) + print("Single GPU training") + else: + self.device = torch.device("cpu") + print("CPU training") + + # Optional performance optimizations (only for CUDA) + if self.device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + def build_model( + self, + model: nn.Module, + ): + # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? + model = model.to(self.device) + + if self.world_size > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, + bucket_cap_mb=100, + gradient_as_bucket_view=True, + ) + + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully") + + else: + print("Model built, compiled successfully") + + return model + + # Setup Tomo DataLoader + def setup_dataloader( + self, + tomo_dataset: TomographyINRDataset, + batch_size: int, + num_workers: int = 0, + val_fraction: float = 0.0, + volume_size: int = 500, # Assuming N^3 volume size + ): + pin_mem = self.device.type == "cuda" + persist = num_workers > 0 + + # Split dataset if validation fraction > 0 + if val_fraction > 0.0: + # TODO: Temporary for when only doing validation, current TomographyDataset doesn't work correctly + train_dataset = TomographyINRDataset( + tomo_dataset.tilt_series.detach().clone(), + tomo_dataset.tilt_angles.detach().clone(), + volume_size, # TODO: TEMPORARY + val_ratio=val_fraction, + mode="train", + seed=42, + ) + val_dataset = TomographyINRDataset( + tomo_dataset.tilt_series.detach().clone(), + tomo_dataset.tilt_angles.detach().clone(), + volume_size, # TODO: TEMPORARY + val_ratio=val_fraction, + mode="val", + seed=42, + ) + else: + train_dataset, val_dataset = tomo_dataset, None + + # Samplers for distributed training + if self.world_size > 1: + train_sampler = DistributedSampler( + train_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, + ) + if val_dataset: + val_sampler = DistributedSampler( + val_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False, + ) + shuffle = False + else: + train_sampler = None + val_sampler = None + shuffle = True + + # Main dataloader + self.dataloader = DataLoader( + train_dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler=train_sampler, + shuffle=shuffle, + pin_memory=pin_mem, + drop_last=True, + persistent_workers=persist, + ) + + # Validation dataloader if applicable + if val_dataset: + self.val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size * 4, + num_workers=num_workers, + sampler=val_sampler, + shuffle=False, + pin_memory=pin_mem, + drop_last=False, + persistent_workers=persist, + ) + self.val_sampler = val_sampler + + self.sampler = train_sampler + + if self.global_rank == 0: + print("Dataloader setup complete:") + print(f" Total projections: {len(tomo_dataset.tilt_angles)}") + if val_fraction > 0.0: + print(f" Total projections (val): {len(val_dataset)}") + print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") + print(f" Total pixels: {tomo_dataset.num_pixels:,}") + if val_fraction > 0.0: + print(f" Total pixels (val): {len(val_dataset):,}") + print(f" Total pixels (train): {len(train_dataset):,}") + print(f" Local batch size (train): {batch_size}") + print(f" Global batch size: {batch_size * self.world_size}") + print(f" Train batches per GPU per epoch: {len(self.dataloader)}") + + # Setup pretraining dataloader + + # def setup_pretraining_dataloader( + # self, + # volume_dataset: PretrainVolumeDataset, + # batch_size: int, + # ): + # # TODO: temp + + # num_workers = 0 + # if self.world_size > 1: + # sampler = DistributedSampler( + # volume_dataset, + # num_replicas=self.world_size, + # rank=self.global_rank, + # shuffle=True, + # drop_last=True, + # ) + # shuffle = False + # else: + # sampler = None + # shuffle = True + + # self.pretraining_dataloader = DataLoader( + # volume_dataset, + # batch_size=batch_size, + # sampler=sampler, + # shuffle=shuffle, + # pin_memory=self.device.type == "cuda", + # drop_last=True, + # persistent_workers=num_workers > 0, + # ) + + # self.pretraining_sampler = sampler + + # if self.global_rank == 0: + # print("Pretraining dataloader setup complete:") + # print(f" Total samples: {len(volume_dataset)}") + # print(f" Grid size: {volume_dataset.N**3}") + # print(f" Local batch size: {batch_size}") + # print(f" Global batch size: {batch_size * self.world_size}") + # print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") + + def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): + if scaling_rule == "sqrt": + return base_lr * np.sqrt(self.world_size) + elif scaling_rule == "linear": + return base_lr * self.world_size + else: + raise ValueError(f"Invalid scaling rule: {scaling_rule}") + + def scale_lr( + self, + optimizer_params: dict, + ): + new_optimizer_params = {} + for key, value in optimizer_params.items(): + if "original_lr" in value: + new_optimizer_params[key] = { + "type": value["type"], + "lr": self.get_scaled_lr(value["original_lr"]), + "original_lr": value["original_lr"], + } + else: + new_optimizer_params[key] = { + "type": value["type"], + "lr": self.get_scaled_lr(value["lr"]), + "original_lr": value["lr"], + } + + return new_optimizer_params + + +# TODO: Temporary Adaptive L1 Smooth Loss + + +class AdaptiveSmoothL1Loss(nn.Module): + def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): + """ + Adaptive smooth L1 loss with EMA-based beta adaptation. + + Args: + beta_init (float): optional initial β value; if None, starts as 1.0 + ema_factor (float): smoothing factor for EMA (1 - 1/N_b in Eq. 38) + eps (float): small constant for numerical stability + """ + super().__init__() + self.register_buffer("beta2", torch.tensor(beta_init**2 if beta_init else 1.0)) + self.ema_factor = ema_factor + self.eps = eps + + def forward(self, pred, target): + diff = pred - target + abs_diff = diff.abs() + + # compute current batch MSE (Eq. 38) + mse_batch = torch.mean(diff**2) + + # update β² adaptively using Eq. (39) + with torch.no_grad(): + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( + self.beta2, mse_batch + ) + + beta = torch.sqrt(self.beta2 + self.eps) + + # Smooth L1 (Eq. 36) + loss = torch.where( + abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta + ) + return loss.mean() + + +class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): + def forward(self, pred, target): + diff = pred - target + abs_diff = diff.abs() + mse_batch = torch.mean(diff**2) + + # Synchronize β across all GPUs + if dist.is_initialized(): + mse_batch_all = mse_batch.clone() + dist.all_reduce(mse_batch_all, op=dist.ReduceOp.AVG) + mse_batch = mse_batch_all / dist.get_world_size() + + with torch.no_grad(): + self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( + self.beta2, mse_batch + ) + + beta = torch.sqrt(self.beta2 + self.eps) + loss = torch.where( + abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta + ) + return loss.mean() From d73c49869d46e8a8c1033ba32885c58b4f78cfcc Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 18 Dec 2025 16:16:26 -0800 Subject: [PATCH 034/335] SIRT Reconstructions working. TomographyConventional looks a little clunky --- src/quantem/core/ml/constraints.py | 126 +++----- .../diffractive_imaging/object_models.py | 3 +- src/quantem/tomography/dataset_models.py | 52 ++- src/quantem/tomography/object_models.py | 267 ++++++++++++++++ .../radon/__init__.py | 0 .../radon/radon.py | 0 src/quantem/tomography/tomography.py | 145 ++++++++- src/quantem/tomography/tomography_base.py | 96 +++++- src/quantem/tomography/utils.py | 300 ++++++++++++++++++ 9 files changed, 898 insertions(+), 91 deletions(-) rename src/quantem/{tomography_old => tomography}/radon/__init__.py (100%) rename src/quantem/{tomography_old => tomography}/radon/radon.py (100%) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 755122c6..e6d995f2 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -1,99 +1,75 @@ from abc import ABC, abstractmethod +from dataclasses import dataclass from typing import Any import torch -from quantem.core import config + +@dataclass +class Constraints(ABC): + """ + Needs to be implemented in all object models that inherit from BaseConstraints. + """ + + pass class BaseConstraints(ABC): - """Base class for constraint management with common functionality.""" + """ + Base class for constraints. + """ - # Subclasses should define their own DEFAULT_CONSTRAINTS - DEFAULT_CONSTRAINTS: dict[str, Any] = {} + # Default constraints are the dataclasses themselves. + DEFAULT_CONSTRAINTS = Constraints() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self._soft_constraint_loss = {} - self._constraints = self.DEFAULT_CONSTRAINTS.copy() self._epoch_constraint_losses = {} + self.constraints = self.DEFAULT_CONSTRAINTS.copy() @property - def constraints(self) -> dict[str, Any]: + def constraints(self) -> Constraints: + """ + Constraints for the object model. + """ return self._constraints @constraints.setter - def constraints(self, c: dict[str, Any]): - allowed_keys = self.DEFAULT_CONSTRAINTS.keys() - constraint_type = self.__class__.__name__.lower().replace("constraints", "") - - for key, value in c.items(): - if key not in allowed_keys: - raise KeyError( - f"Invalid {constraint_type} constraint key '{key}', allowed keys are {list(allowed_keys)}" - ) - self._constraints[key] = value - - @property - def soft_constraint_loss(self) -> dict[str, torch.Tensor | float]: - return self._soft_constraint_loss + def constraints(self, constraints: Constraints | dict[str, Any]): + """ + Setter for constraints class, can be a Constraints instance or a dictionary. + """ + if isinstance(constraints, Constraints): + self._constraints = constraints + elif isinstance(constraints, dict): + for key, value in constraints.items(): + if key not in self._constraints.allowed_keys: + raise ValueError(f"Invalid constraint key: {key}") + setattr(self._constraints, key, value) + else: + raise ValueError(f"Invalid constraints type: {type(constraints)}") def add_constraint(self, key: str, value: Any): - allowed_keys = self.DEFAULT_CONSTRAINTS.keys() - constraint_type = self.__class__.__name__.lower().replace("constraints", "") - - if key not in allowed_keys: - raise KeyError( - f"Invalid {constraint_type} constraint key '{key}', allowed keys are {list(allowed_keys)}" - ) - self._constraints[key] = value + """ + Add a constraint to the constraints class. + Note the allowed keys should be implemented for each constraint subclass. + """ + if key not in self._constraints.allowed_keys: + raise ValueError(f"Invalid constraint key: {key}") + setattr(self._constraints, key, value) + + # --- Required methods tha tneeds to implemented in subclasses --- + @abstractmethod + def apply_hard_constraints(self, *args, **kwargs) -> torch.Tensor: + """ + Apply hard constraints to the object model. + """ + raise NotImplementedError @abstractmethod def apply_soft_constraints(self, *args, **kwargs) -> torch.Tensor: - """Apply soft constraints and return total constraint loss.""" - pass - - def _get_zero_loss_tensor(self) -> torch.Tensor: - """Helper method to create a zero loss tensor with proper device and dtype.""" - device = getattr(self, "device", "cpu") - return torch.tensor(0, device=device, dtype=getattr(torch, config.get("dtype_real"))) - - # --- helpers for consistent loss logging --- - def reset_soft_constraint_losses(self) -> None: - self._soft_constraint_loss = {} - - def add_soft_constraint_loss(self, name: str, value: torch.Tensor | float) -> None: - """Record a single soft-constraint loss for logging without holding the graph.""" - if isinstance(value, torch.Tensor): - val = value.detach() - if val.ndim != 0: - val = val.mean() - self._soft_constraint_loss[name] = val - else: - self._soft_constraint_loss[name] = float(value) - - def accumulate_constraint_losses( - self, batch_constraint_losses: dict[str, torch.Tensor | float] | None = None - ) -> None: - """Accumulate constraint losses across batches.""" - if batch_constraint_losses is None: - batch_constraint_losses = self.soft_constraint_loss - - for loss_name, loss_value in batch_constraint_losses.items(): - if isinstance(loss_value, torch.Tensor): - try: - v = loss_value.item() - except Exception: - print("loss value not singular: ", loss_value) # TODO remove - v = loss_value.detach().mean().item() - else: - v = float(loss_value) - self._epoch_constraint_losses[loss_name] = ( - self._epoch_constraint_losses.get(loss_name, 0.0) + v - ) - - def get_epoch_constraint_losses(self) -> dict[str, float]: - return getattr(self, "_epoch_constraint_losses", {}) # TODO clean this up - - def reset_epoch_constraint_losses(self) -> None: - self._epoch_constraint_losses = {} + """ + Apply soft constraints to the object model. + """ + raise NotImplementedError diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index f3a225d6..c1d533f5 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -7,6 +7,7 @@ import numpy as np import torch import torch.nn as nn +from torch.nn.parameter import Parameter from tqdm.auto import tqdm from quantem.core import config @@ -927,7 +928,7 @@ def get_optimization_parameters(self): """Get the parameters that should be optimized for this model.""" # Since model is registered as a submodule, we can use the parent's parameters() method # which will automatically include all submodule parameters - return list(self.parameters()) + return list[Parameter](self.parameters()) def reset(self): """Reset the object model to its initial or pre-trained state""" diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index b83d89d7..0530df0f 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -2,6 +2,7 @@ from dataclasses import dataclass from typing import Any, Literal +import numpy as np import torch import torch.nn as nn from numpy.typing import NDArray @@ -21,7 +22,9 @@ class DatasetValue: target: torch.Tensor tilt_angle: int | float pixel_loc: tuple[int, int] | None = None # Only for INRDataset - # pose: tuple[torch.nn.Parameter, torch.nn.Parameter, torch.nn.Parameter] | None = None # If there is pose optimization. + pose: tuple[torch.nn.Parameter, torch.nn.Parameter, torch.nn.Parameter] | None = ( + None # If there is pose optimization. + ) class TomographyDatasetBase(AutoSerialize, OptimizerMixin, nn.Module): @@ -45,9 +48,8 @@ def __init__( AutoSerialize.__init__(self) OptimizerMixin.__init__(self) nn.Module.__init__(self) - - if _token is not self._token: - raise RuntimeError("Use TomographyDatasetBase.from_* to instantiate this class.") + # if _token is not self._token: TODO: Idk why this isn't working. + # raise RuntimeError("Use TomographyPixDataset.from_* to instantiate this class.") if not ( tilt_stack.shape[0] < tilt_stack.shape[1] or tilt_stack.shape[0] < tilt_stack.shape[2] @@ -56,8 +58,19 @@ def __init__( "The number of tilt projections should be in the first dimension of the dataset." ) + self.volume_size = (int(max(tilt_stack.shape)), tilt_stack.shape[1], tilt_stack.shape[2]) + + # TODO: Maybe have the validation in here too. + max_val = np.quantile(tilt_stack, 0.95) + if type(tilt_stack) is not torch.Tensor: + tilt_stack = torch.from_numpy(tilt_stack) + if type(tilt_angles) is not torch.Tensor: + tilt_angles = torch.from_numpy(tilt_angles) + + # Tilt stack normalization + tilt_stack = tilt_stack / max_val + self.tilt_stack = tilt_stack - self.volume_size = (int(tilt_stack.shape.max()), tilt_stack.shape[1], tilt_stack.shape[2]) self.tilt_angles = tilt_angles self.learn_pose = learn_pose @@ -76,6 +89,11 @@ def forward( """ raise NotImplementedError("This method should be implemented in subclasses.") + # --- Helper Functions --- + def to(self, device: str): + self.tilt_stack = self.tilt_stack.to(device) + self.tilt_angles = self.tilt_angles.to(device) + class TomographyPixDataset( TomographyDatasetBase @@ -88,12 +106,16 @@ class TomographyPixDataset( In SIRT, it's only the tilt image. """ - pass - def __init__( self, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_pose: bool = False, + _token: object | None = None, ): - pass + super().__init__( + tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_pose=learn_pose, _token=_token + ) def forward( self, @@ -103,12 +125,22 @@ def forward( Forward pass for pixel-based tomography. Returns the full tilt image for the given projection index, and the tilt angle. """ + return DatasetValue( target=self.tilt_stack[proj_idx], tilt_angle=self.tilt_angles[proj_idx], pixel_loc=None, ) + @classmethod + def from_data( + cls, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_pose: bool = False, + ): + return cls(tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_pose=learn_pose) + class TomographyINRDataset(TomographyDatasetBase, Dataset): """ @@ -126,9 +158,9 @@ def __init__( val_ratio: float = 0.0, mode: Literal["train", "val"] = "train", seed: int = 42, - _token: object | None = None, + token: object | None = None, ): - super().__init__(tilt_stack, tilt_angles, learn_pose, _token) + super().__init__(tilt_stack, tilt_angles, learn_pose, token) self._total_pixels = self.volume_size[0] * self.volume_size[1] * self.volume_size[2] diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index e69de29b..d59f2b5d 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -0,0 +1,267 @@ +from abc import abstractmethod +from copy import deepcopy +from dataclasses import dataclass +from typing import Self + +import numpy as np +import torch +import torch.nn as nn + +from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml.constraints import BaseConstraints, Constraints +from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.utils.rng import RNGMixin + + +@dataclass +class ConstraintsTomography(Constraints): + """ + Data class for all constraints that can be applied to the object model. + """ + + # Hard Constraints + positivity: bool = True + shrinkage: float = 0.0 + circular_mask: bool = False + fourier_filter: str | None = None # Hamming, etc... + + # Soft Constraints + tv_vol: float = 0.0 + + @property + def hard_constraint_keys(self) -> list[str]: + """ + List of hard constraint keys. + """ + return ["positivity", "shrinkage", "circular_mask", "fourier_filter"] + + @property + def soft_constraint_keys(self) -> list[str]: + """ + List of soft constraint keys. + """ + return ["tv_vol"] + + @property + def allowed_keys(self) -> list[str]: + """ + List of all allowed keys. + """ + return self.hard_constraint_keys + self.soft_constraint_keys + + def copy(self) -> Self: + """ + Copy the constraints. + """ + return deepcopy(self) + + def __str__(self) -> str: + return f"""Constraints: + Positivity: {self.positivity} + Shrinkage: {self.shrinkage} + Circular Mask: {self.circular_mask} + Fourier Filter: {self.fourier_filter} + TV Volume: {self.tv_vol} + """ + + +class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): + DEFAULT_LRS = { + "object": 8e-6, + } + _token = object() + """ + Base class for all ObjectModels to inherit from. + """ + + def __init__( + self, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError("Use a factory method to instantiate this class.") + + self._shape = shape + + # Initialize dependencies + nn.Module.__init__(self) + RNGMixin.__init__(self, rng=rng, device=device) + OptimizerMixin.__init__(self) + + # Initialize a torch.zeros volume with the given shape + self._obj = torch.zeros(self._shape, device=device, dtype=torch.float32) + + # --- Properties --- + @property + def shape(self) -> tuple[int, int, int]: + """ + Shape of the object (x, y, z). + """ + return self._shape + + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape + + @property + def obj(self) -> torch.Tensor: + """ + Returns the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @abstractmethod + def forward(self, *args, **kwargs) -> torch.Tensor: + """ + Forward pass, should be implemented in subclasses. Note for any nn.Module this is + a required method. + """ + raise NotImplementedError + + @abstractmethod + def reset(self) -> None: + """ + Reset the object, should be implemented in subclasses. + """ + raise NotImplementedError + + # --- Helper Functions --- + def get_optimization_parameters(self) -> list[nn.Parameter]: + """ + Get the parameters that should be optimized for this model. + + TODO: I have no idea what this does. + """ + return list[nn.Parameter](self.parameters()) + + @abstractmethod # Each subclass should implement this. + def to(self, *args, **kwargs): + """ + Move the object to a device + """ + + raise NotImplementedError + + +class ObjectConstraints(ObjectBase, BaseConstraints): + DEFAULT_CONSTRAINTS = ConstraintsTomography() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.constraints = self.DEFAULT_CONSTRAINTS.copy() + + def apply_hard_constraints( + self, + obj: torch.Tensor, + ) -> torch.Tensor: + """ + Apply hard constraints to the object model. + + Only hard constraint here is the positivity and shrinkage. TODO: Add the other hard constraints. + """ + obj2 = obj.clone() + if self.constraints.positivity: + obj2 = torch.clamp(obj2, min=0.0, max=None) + if self.constraints.shrinkage: + obj2 = torch.max(obj2 - self.constraints.shrinkage, torch.zeros_like(obj2)) + + # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. + return obj2 + + def apply_soft_constraints( + self, + obj: torch.Tensor, + ) -> torch.Tensor: + """ + Apply soft constraints to the object model. + + Only soft constraint here is the TV loss. + """ + + soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) + if self.constraints.tv_vol > 0: + tv_loss = self.get_tv_loss( + obj.unsqueeze(0).unsqueeze(0), factor=self.constraints.tv_vol + ) + soft_loss += tv_loss + return soft_loss + + @abstractmethod + def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 0.0) -> torch.Tensor: + """ + Get the TV loss for the object model. Must be implemented in each subclass. + """ + raise NotImplementedError + + +class ObjectPixelated(ObjectConstraints): + """ + Object model for pixelated objects. + + Supports: Conventional algorithms (SIRT, FBP), and AD-based reconstructions. + """ + + def __init__( + self, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + super().__init__( + shape=shape, + device=device, + rng=rng, + _token=self._token, + ) + + # --- Properties ---- + @property + def obj(self) -> torch.Tensor: + return self.apply_hard_constraints(self._obj) + + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + + @property + def shape(self) -> tuple[int, int, int]: + return self._shape + + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape + + @property + def soft_loss(self) -> torch.Tensor: + return self.apply_soft_constraints(self._obj) + + @property + def name(self) -> str: + return "ObjectPixelated" + + @property + def obj_type(self) -> str: + return "pixelated" + + # --- Forward method --- + def forward(self, dummy_input=None) -> torch.Tensor: + return self.obj + + # --- Defining the TV loss --- + def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tensor: + tv_d = torch.pow(obj[:, :, 1:, :, :] - obj[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(obj[:, :, :, 1:, :] - obj[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(obj[:, :, :, :, 1:] - obj[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * tv_weight / (torch.prod(torch.tensor(obj.shape))) + + # --- Helper Functions --- + def to(self, device: str): + self._obj = self._obj.to(device) + + +ObjectModelType = ObjectPixelated diff --git a/src/quantem/tomography_old/radon/__init__.py b/src/quantem/tomography/radon/__init__.py similarity index 100% rename from src/quantem/tomography_old/radon/__init__.py rename to src/quantem/tomography/radon/__init__.py diff --git a/src/quantem/tomography_old/radon/radon.py b/src/quantem/tomography/radon/radon.py similarity index 100% rename from src/quantem/tomography_old/radon/radon.py rename to src/quantem/tomography/radon/radon.py diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 79525364..609cbe5c 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,16 +1,155 @@ +from typing import Literal, Self + +import numpy as np +import torch +from tqdm.auto import tqdm + +from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.logger_tomography import LoggerTomography +from quantem.tomography.object_models import ObjectModelType +from quantem.tomography.radon.radon import iradon_torch, radon_torch +from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.utils import torch_phase_cross_correlation + + class Tomography: """ Class for handling all ML tomography reconstruction methods. Automatic handling between AD and INR-based tomography. """ - pass + @classmethod + def from_models( + cls, + dset: DatasetModelType, + obj_model: ObjectModelType, + logger: LoggerTomography | None = None, + device: str = "cuda", + rng: np.random.Generator | int | None = None, + ) -> Self: + return cls( + dset=dset, + obj_model=obj_model, + logger=logger, + device=device, + ) -class TomographyConventional: +class TomographyConventional(TomographyBase): """ Class for handling all conventional tomography reconstruction methods. Will also handle choosing the appropriate dataset model to use. """ - pass + @classmethod + def from_models( + cls, + dset: DatasetModelType, + obj_model: ObjectModelType, + logger: LoggerTomography | None = None, + device: str = "cuda", + rng: np.random.Generator | int | None = None, + ) -> Self: + return cls( + dset=dset, + obj_model=obj_model, + logger=logger, + device=device, + rng=rng, + ) + + def reconstruct( + self, + num_iter: int = 10, + mode: Literal["sirt", "fbp"] = "sirt", + reset: bool = False, + inline_alignment: bool = False, + ): + pbar = tqdm(range(num_iter), desc=f"{mode} Reconstruction") + if mode == "sirt" or mode == "fbp": + proj_forward = torch.zeros_like(self.dset.tilt_stack).permute(2, 0, 1) + else: + proj_forward = torch.zeros_like(self.dset.tilt_stack) + + for iter in pbar: + proj_forward, loss = self._reconstruction_epoch( + inline_alignment=inline_alignment, + mode=mode, + proj_forward=proj_forward, + ) + + pbar.set_description(f"{mode} Reconstruction | Loss: {loss.item():.4f}") + + self._epoch_losses.append(loss.item()) + + if mode == "fbp": + break + + # --- Conventional reconstruction method --- + + def _reconstruction_epoch( + self, + inline_alignment: bool, + mode: Literal["sirt", "fbp"], + proj_forward: torch.Tensor, + ): + loss = 0 + + if inline_alignment: + for ind in range(len(self.dset.tilt_angles)): + im_proj = proj_forward[:, ind, :] + im_meas = self.dset.forward(ind).target + shift = torch_phase_cross_correlation(im_proj, im_meas) + if torch.linalg.norm(shift) <= 32: + shifted = torch.fft.ifft2( + torch.fft.fft2(im_meas) + * torch.exp( + -2j + * np.pi + * ( + shift[0] + * torch.fft.fftfreq( + im_meas.shape[0], device=im_meas.device + ).unsqueeze(1) + + shift[1] + * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) + ) + ) + ).real + + proj_forward[:, ind, :] = shifted + + if mode == "sirt" or mode == "fbp": + proj_forward = radon_torch( + self.obj_model.obj, + theta=self.dset.tilt_angles, + device=self.device, + ) + + error = self.dset.tilt_stack.permute(2, 0, 1) - proj_forward + + correction = iradon_torch( + error, + theta=self.dset.tilt_angles, + device=self.device, + filter_name="ramp", + circle=True, + ) + + normalization = iradon_torch( + torch.ones_like(error), + theta=self.dset.tilt_angles, + device=self.device, + filter_name=None, + circle=True, + ) + + normalization[normalization == 0] = 1e-6 + + correction /= normalization + + self.obj_model.obj += correction + + loss = torch.mean(torch.abs(error)) + + return proj_forward, loss diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index cb70c15c..fec0852b 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1,9 +1,11 @@ import numpy as np +from numpy.typing import NDArray from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.rng import RNGMixin -from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.dataset_models import DatasetModelType, TomographyDatasetBase from quantem.tomography.logger_tomography import LoggerTomography +from quantem.tomography.object_models import ConstraintsTomography, ObjectModelType class TomographyBase(AutoSerialize, RNGMixin): @@ -18,9 +20,99 @@ class TomographyBase(AutoSerialize, RNGMixin): def __init__( self, dset: DatasetModelType, + obj_model: ObjectModelType, logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, _token: object | None = None, ): - pass + # if _token is not self._token: # TODO: Idk why this isn't working. + # raise RuntimeError("Use Dataset.from_* to instantiate this class.") + + super().__init__() + + self.dset = dset + self.obj_model = obj_model + self.rng = rng + self.device = device + self.logger = logger + + # Loss + self._epoch_losses: list[float] = [] + + # --- Properties --- + @property + def dset(self) -> DatasetModelType: + return self._dset + + @dset.setter + def dset(self, new_dset: DatasetModelType): + if not isinstance(new_dset, TomographyDatasetBase) and "TomographyDataset" not in str( + type(new_dset) + ): + raise TypeError(f"dset should be a TomographyDataset, got {type(new_dset)}") + self._dset = new_dset + + @property + def obj_type(self) -> str: + return self.obj_model.obj_type + + @property + def obj_model(self) -> ObjectModelType: + return self._obj_model + + @obj_model.setter + def obj_model(self, obj_model: ObjectModelType): + if not isinstance(obj_model, ObjectModelType): + raise TypeError(f"obj_model should be a ObjectModelType, got {type(obj_model)}") + self._obj_model = obj_model + + @property + def constraints(self) -> ConstraintsTomography: + return self.obj_model.constraints + + @constraints.setter + def constraints(self, constraints: ConstraintsTomography): + if not isinstance(constraints, ConstraintsTomography): + raise TypeError( + f"constraints should be a ConstraintsTomography, got {type(constraints)}" + ) + self.obj_model.constraints = constraints + + @property + def logger(self) -> LoggerTomography | None: + return self._logger + + @logger.setter + def logger(self, logger: LoggerTomography | None): + if not isinstance(logger, LoggerTomography) and logger is not None: + raise TypeError(f"logger should be a LoggerTomography, got {type(logger)}") + self._logger = logger + + @property + def device(self) -> str: + return self._device + + @device.setter + def device(self, device: str): + if not isinstance(device, str): + raise TypeError(f"device should be a str, got {type(device)}") + self._device = device + self.to(device) + + @property + def epoch_losses(self) -> NDArray: + """ + Returns the fidelity loss for each epoch ran. + """ + return np.array(self._epoch_losses) + + @property + def num_epochs(self) -> int: + return len(self._epoch_losses) + + # --- Helper Functions --- + + def to(self, device: str): + self.obj_model.to(device) + self.dset.to(device) diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index e69de29b..82c1c9d5 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -0,0 +1,300 @@ +import numpy as np +import torch +import torch.nn.functional as F +from scipy.ndimage import center_of_mass, gaussian_filter, shift +from scipy.stats import norm +from tqdm.auto import tqdm + +from quantem.core.utils.imaging_utils import cross_correlation_shift + +# --- Projection Operator Utils --- + + +def rot_ZXZ(mags, z1, x, z3, device, mode="bilinear"): + if not isinstance(x, torch.Tensor) or not isinstance(z1, torch.Tensor): + z1 = torch.tensor(z1, dtype=torch.float32, device=device) + x = torch.tensor(x, dtype=torch.float32, device=device) + z3 = torch.tensor(z3, dtype=torch.float32, device=device) + curr_mags = mags + + curr_mags = differentiable_rotz_vectorized(curr_mags, z1, mode) + curr_mags = differentiable_rotx_vectorized(curr_mags, x, mode) + + curr_mags = differentiable_rotz_vectorized(curr_mags, z3, mode) + + return curr_mags + + +def differentiable_rotz_vectorized(mags, theta, mode="bilinear"): + _, dimz, dimy, dimx = mags.shape + + if theta.dim() == 0: + theta = theta.unsqueeze(0) + + theta_rad = torch.deg2rad(theta) + + cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) + affine_matrix = torch.stack( + [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 + ).view(-1, 2, 3) + + mags = mags.permute(1, 0, 2, 3) + + def transform_slice(mag_slice): + grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) + return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( + 0 + ) + + rotated_mags = torch.vmap(transform_slice)(mags) + return rotated_mags.permute(1, 0, 2, 3) + + +def differentiable_rotx_vectorized(mags, theta, mode="bilinear"): + _, dimz, dimy, dimx = mags.shape + + if theta.dim() == 0: + theta = theta.unsqueeze(0) + + theta_rad = torch.deg2rad(theta) + + cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) + affine_matrix = torch.stack( + [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 + ).view(-1, 2, 3) + + mags = mags.permute(3, 0, 1, 2) + + def transform_slice(mag_slice): + grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) + return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( + 0 + ) + + rotated_mags = torch.vmap(transform_slice)(mags) + return rotated_mags.permute(1, 2, 3, 0) + + +def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): + """ + Shifts a 2D image using grid_sample in a differentiable manner. + + Args: + image: Tensor of shape [H, W] + shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) + shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) + sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts + + Returns: + Shifted image of shape [H, W] + """ + H, W = image.shape + + # Convert physical shift to pixel shift + shift_x_pixel = shift_x + shift_y_pixel = shift_y + + # Normalize shift for grid_sample (assuming align_corners=True) + normalized_shift_x = shift_x_pixel * 2 / (W - 1) + normalized_shift_y = shift_y_pixel * 2 / (H - 1) + + # Create normalized grid + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=image.device), + torch.linspace(-1, 1, W, device=image.device), + indexing="ij", + ) + + grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] + + # Apply shift (ensure it's differentiable) + grid[:, :, :, 0] -= normalized_shift_x + grid[:, :, :, 1] -= normalized_shift_y + + # Add batch and channel dimensions + image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] + + # Sample using grid_sample (fully differentiable) + shifted_image = F.grid_sample( + image, grid, mode="bicubic", padding_mode="zeros", align_corners=True + ) + + return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] + + +# --- TV loss --- + + +def get_TV_loss(tensor, factor=1e-3): + tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + + +# --- Gaussian filters --- + + +def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: + radius = np.ceil(num_sigmas * sigma) + support = torch.arange(-radius, radius + 1, dtype=torch.float) + kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() + # Ensure kernel weights sum to 1, so that image brightness is not altered + return kernel.mul_(1 / kernel.sum()) + + +def gaussian_filter_2d( + img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor +) -> torch.Tensor: # Add kernel_1d as an argument + # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function + padding = len(kernel_1d) // 2 # Ensure that image size does not change + img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` + # Convolve along columns and rows + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + return img.squeeze_(0).squeeze_(0) # Make 2D again + + +def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: + """ + Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. + + Args: + stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms + kernel_1d (torch.Tensor): 1D Gaussian kernel + + Returns: + torch.Tensor: Blurred stack of same shape (H, N, W) + """ + H, N, W = stack.shape + padding = len(kernel_1d) // 2 + + # Reshape to (N, 1, H, W) for conv2d + stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) + + # Apply separable conv2d: vertical then horizontal + out = torch.nn.functional.conv2d( + stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) + ) + out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + + # Restore shape to (H, N, W) + return out.squeeze(1).permute(1, 0, 2) + + +# Circular mask + + +def torch_phase_cross_correlation(im1, im2): + f1 = torch.fft.fft2(im1) + f2 = torch.fft.fft2(im2) + cc = torch.fft.ifft2(f1 * torch.conj(f2)) + cc_abs = torch.abs(cc) + + max_idx = torch.argmax(cc_abs) + shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() + + for i, dim in enumerate(im1.shape): + if shifts[i] > dim // 2: + shifts[i] -= dim + + # return shifts.flip(0) # (dx, dy) + return shifts + + +# --- Tilt Series Processing Utility Functions --- + + +def fourier_cropping(img, crop_size): + """ + Crop the img in Fourier space to the specified size. + """ + center = np.array(img.shape) // 2 + + fft_img = np.fft.fftshift(np.fft.fft2(img)) + + cropped_fft = fft_img[ + center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, + center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, + ] + cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real + return cropped_img + + +def estimate_background( + img, + num_iterations=10, + cutoff=3, + smoothing_sigma=1.0, +): + """ + Estimate the background of the image using a Gaussian filter. + """ + if smoothing_sigma > 0: + img = gaussian_filter(img, sigma=smoothing_sigma) + pixel_vals = img.ravel() + + for i in range(num_iterations): + mu, std = norm.fit(pixel_vals) + + # Set cutoff threshold (e.g., 3 standard deviations) + lower = mu - cutoff * std + upper = mu + cutoff * std + + # Mask pixel values within ±3σ + pixel_vals = pixel_vals[(pixel_vals >= lower) & (pixel_vals <= upper)] + + return mu + + +def cross_correlation_align_stack(ref_img, stack, print_pred=False): + """ + Aligns a stack of images to a reference image using cross-correlation. + + This function assumes the stack does not contain the reference image itself. + + Stack shape should be (N, H, W) where N is the number of images. + """ + + new_images = [] + pred_shifts = [] + + prev_img = ref_img + for img in tqdm(stack): + shift_pred = cross_correlation_shift(prev_img, img) + if print_pred: + print(f"Shift prediction: {shift_pred}") + shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) + + pred_shifts.append(shift_pred) + new_images.append(shifted_image) + + prev_img = shifted_image + + return new_images, pred_shifts + + +def centering_com_alignment(image_stack): + """ + Aligns the image stack to the center of mass of the whole image_stack to the + image center. This is useful for aligning the tilt series to the invariant line. + """ + + aligned_stack = np.zeros_like(image_stack) + h, w = image_stack.shape[1:] + image_center = np.array([h // 2, w // 2]) + + com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) + + for i, img in enumerate(image_stack): + com_img = np.array(center_of_mass(img)) + shift_vec = com_reference - com_img + aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) + + final_shift = image_center - com_reference + for i in range(aligned_stack.shape[0]): + aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) + + return aligned_stack From bc98f4b1b12a65ae68d69076d4ef52fe5164f8f4 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 18 Dec 2025 16:28:34 -0800 Subject: [PATCH 035/335] Implemented tomography_opt.py --- src/quantem/tomography/tomography.py | 3 +- src/quantem/tomography/tomography_opt.py | 146 +++++++++++++++++++++++ 2 files changed, 148 insertions(+), 1 deletion(-) create mode 100644 src/quantem/tomography/tomography_opt.py diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 609cbe5c..32af65a0 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -9,10 +9,11 @@ from quantem.tomography.object_models import ObjectModelType from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.tomography_opt import TomographyOpt from quantem.tomography.utils import torch_phase_cross_correlation -class Tomography: +class Tomography(TomographyBase, TomographyOpt): """ Class for handling all ML tomography reconstruction methods. Automatic handling between AD and INR-based tomography. diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py new file mode 100644 index 00000000..77b19cfc --- /dev/null +++ b/src/quantem/tomography/tomography_opt.py @@ -0,0 +1,146 @@ +import torch + +from quantem.tomography.tomography_base import TomographyBase + + +class TomographyOpt(TomographyBase): + """ + Class for handling all the optimizers and schedulers for the tomography reconstruction. + """ + + OPTIMIZABLE_VALS = ["object", "pose"] + DEFAULT_OPTIMIZER_TYPE = "adam" + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def _get_default_lr(self, key: str) -> float: + """Get default learning rate for a given optimization key.""" + if key == "object": + return self.obj_model.DEFAULT_LRS.get("object", 1e-5) + elif key == "pose": + return self.dset.DEFAULT_LRS.get("pose", 5e-2) + else: + raise ValueError(f"Unknown optimization key: {key}") + + @property + def optimizer_params(self) -> dict[str, dict]: + return { + key: params + for key, params in [ + ("object", self.obj_model.optimizer_params), + ("pose", self.dset.optimizer_params), + ] + if params + } + + @optimizer_params.setter + def optimizer_params(self, d: dict): + """Set the optimizer parameters.""" + self._optimizer_params = d.copy() if d else {} + + for key in self.OPTIMIZABLE_VALS: + if key not in d: + d[key] = {"type": "none"} + + for k, v in d.items(): + if "type" not in v.keys(): + v["type"] = self.DEFAULT_OPTIMIZER_TYPE + if "lr" not in v.keys(): + v["lr"] = self._get_default_lr(k) + if k == "object": + self.obj_model.optimizer_params = v + elif k == "pose": + self.dset.optimizer_params = v + else: + raise ValueError(f"Unknown optimization key: {k}") + + @property + def optimizers(self) -> dict[str, torch.optim.Optimizer]: + return { + "object": self.obj_model.optimizer, + "pose": self.dset.optimizer, + } + + def set_optimizers(self): + for key, params in self.optimizer_params.items(): + if key == "object": + self.obj_model.set_optimizer(params) + elif key == "pose": + self.dset.set_optimizer(params) + else: + raise ValueError(f"Unknown optimization key: {key}") + + def remove_optimizer(self, key: str): + if key == "object": + self.obj_model.remove_optimizer() + elif key == "pose": + self.dset.remove_optimizer() + else: + raise ValueError(f"Unknown optimization key: {key}") + + @property + def scheduler_params(self) -> dict[str, dict]: + """Returns the parameters used to set the schedulers.""" + return { + "object": self.obj_model.scheduler_params, + "pose": self.dset.scheduler_params, + } + + @scheduler_params.setter + def scheduler_params(self, d: dict): + """Set the scheduler parameters.""" + self._scheduler_params = d.copy() if d else {} + + for key in self.OPTIMIZABLE_VALS: + if key not in d: + d[key] = {} + + for k, v in d.items(): + if k == "object": + self.obj_model.scheduler_params = v + elif k == "pose": + self.dset.scheduler_params = v + else: + raise ValueError(f"Unknown optimization key: {k}") + + @property + def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: + return { + "object": self.obj_model.scheduler, + "pose": self.dset.scheduler, + } + + def set_schedulers(self, params: dict[str, dict], num_iter: int | None = None): + for key, scheduler_params in params.items(): + if key == "object": + self.obj_model.set_scheduler(scheduler_params, num_iter) + elif key == "pose": + self.dset.set_scheduler(scheduler_params, num_iter) + + def step_optimizers(self): + for key in self.optimizer_params.keys(): + if key == "object" and self.obj_model.has_optimizer(): + self.obj_model.step_optimizer() + elif key == "pose" and self.dset.has_optimizer(): + self.dset.step_optimizer() + else: + raise ValueError(f"Unknown optimization key: {key}") + + def zero_grad_all(self): + for key in self.optimizer_params.keys(): + if key == "object" and self.obj_model.has_optimizer(): + self.obj_model.zero_optimizer_grad() + elif key == "pose" and self.dset.has_optimizer(): + self.dset.zero_optimizer_grad() + else: + raise ValueError(f"Unknown optimization key: {key}") + + def step_schedulers(self, loss: float | None = None): + for key in self.scheduler_params.keys(): + if key == "object" and self.obj_model.scheduler is not None: + self.obj_model.step_scheduler(loss) + elif key == "pose" and self.dset.scheduler is not None: + self.dset.step_scheduler(loss) + else: + raise ValueError(f"Unknown optimization key: {key}") From 0fa3740905431c7ba2a640c0638e9b5a6dc7eeff Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 18 Dec 2025 16:42:50 -0800 Subject: [PATCH 036/335] Starting to write the ML methods for Tomography; Need to figure out how to nicely do DDP i.e, implementations in both Dataset and Object. --- src/quantem/tomography/object_models.py | 6 +++++- src/quantem/tomography/tomography.py | 19 ++++++++++++++++++- 2 files changed, 23 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d59f2b5d..69263a2d 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -264,4 +264,8 @@ def to(self, device: str): self._obj = self._obj.to(device) -ObjectModelType = ObjectPixelated +class ObjectINR(ObjectConstraints): + pass + + +ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 32af65a0..ebc36738 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,4 +1,4 @@ -from typing import Literal, Self +from typing import Literal, Optional, Self, Tuple import numpy as np import torch @@ -35,6 +35,23 @@ def from_models( device=device, ) + def reconstruct( + self, + obj_model: ObjectModelType, + dset: DatasetModelType, + num_iter: int = 10, + reset: bool = False, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + constraints=None, # TODO: What to pass into the constraints? + loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), + ): + raise NotImplementedError + """ + This function should be able to handle both AD and INR-based tomography reconstruction methods. + I.e, auto-detection through the obj model type, while both share the same pose optimization. + """ + class TomographyConventional(TomographyBase): """ From f65d5d485472ea9976af81e17fbce1c39afbf2db Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Thu, 8 Jan 2026 10:26:35 -0800 Subject: [PATCH 037/335] add pull request template --- .../pull_request_template.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) create mode 100644 .github/PULL_REQUEST_TEMPLATE/pull_request_template.md diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md new file mode 100644 index 00000000..1099d862 --- /dev/null +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -0,0 +1,15 @@ +### What problem does this PR address? + + + +### What should the reviewer(s) do? + + + + From 9e83cbc9ee5125820a3a0d8c88fd3622fe142e46 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 10 Jan 2026 00:30:08 -0800 Subject: [PATCH 038/335] docs: explicit add test for functional/algorithmic changes per feedback --- .github/PULL_REQUEST_TEMPLATE/pull_request_template.md | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md index 1099d862..e3c0d0d0 100644 --- a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -9,6 +9,7 @@ Should be instantiated in the object? Where does `HSIREN` get instantiated? - model = model.to(self.device) - - if self.world_size > 1: - model = torch.nn.parallel.DistributedDataParallel( - model, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True, - bucket_cap_mb=100, - gradient_as_bucket_view=True, - ) - - if self.global_rank == 0: - print("Model wrapped with DDP") - - if self.world_size > 1: - if self.global_rank == 0: - print("Model built, distributed, and compiled successfully") - - else: - print("Model built, compiled successfully") - - return model - - # Setup Tomo DataLoader - def setup_dataloader( - self, - tomo_dataset: TomographyINRDataset, - batch_size: int, - num_workers: int = 0, - val_fraction: float = 0.0, - volume_size: int = 500, # Assuming N^3 volume size - ): - pin_mem = self.device.type == "cuda" - persist = num_workers > 0 - - # Split dataset if validation fraction > 0 - if val_fraction > 0.0: - # TODO: Temporary for when only doing validation, current TomographyDataset doesn't work correctly - train_dataset = TomographyINRDataset( - tomo_dataset.tilt_series.detach().clone(), - tomo_dataset.tilt_angles.detach().clone(), - volume_size, # TODO: TEMPORARY - val_ratio=val_fraction, - mode="train", - seed=42, - ) - val_dataset = TomographyINRDataset( - tomo_dataset.tilt_series.detach().clone(), - tomo_dataset.tilt_angles.detach().clone(), - volume_size, # TODO: TEMPORARY - val_ratio=val_fraction, - mode="val", - seed=42, - ) - else: - train_dataset, val_dataset = tomo_dataset, None - - # Samplers for distributed training - if self.world_size > 1: - train_sampler = DistributedSampler( - train_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=True, - ) - if val_dataset: - val_sampler = DistributedSampler( - val_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=False, - ) - shuffle = False - else: - train_sampler = None - val_sampler = None - shuffle = True - - # Main dataloader - self.dataloader = DataLoader( - train_dataset, - batch_size=batch_size, - num_workers=num_workers, - sampler=train_sampler, - shuffle=shuffle, - pin_memory=pin_mem, - drop_last=True, - persistent_workers=persist, - ) - - # Validation dataloader if applicable - if val_dataset: - self.val_dataloader = DataLoader( - val_dataset, - batch_size=batch_size * 4, - num_workers=num_workers, - sampler=val_sampler, - shuffle=False, - pin_memory=pin_mem, - drop_last=False, - persistent_workers=persist, - ) - self.val_sampler = val_sampler - - self.sampler = train_sampler - - if self.global_rank == 0: - print("Dataloader setup complete:") - print(f" Total projections: {len(tomo_dataset.tilt_angles)}") - if val_fraction > 0.0: - print(f" Total projections (val): {len(val_dataset)}") - print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") - print(f" Total pixels: {tomo_dataset.num_pixels:,}") - if val_fraction > 0.0: - print(f" Total pixels (val): {len(val_dataset):,}") - print(f" Total pixels (train): {len(train_dataset):,}") - print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {batch_size * self.world_size}") - print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - - # Setup pretraining dataloader - - # def setup_pretraining_dataloader( - # self, - # volume_dataset: PretrainVolumeDataset, - # batch_size: int, - # ): - # # TODO: temp - - # num_workers = 0 - # if self.world_size > 1: - # sampler = DistributedSampler( - # volume_dataset, - # num_replicas=self.world_size, - # rank=self.global_rank, - # shuffle=True, - # drop_last=True, - # ) - # shuffle = False - # else: - # sampler = None - # shuffle = True - - # self.pretraining_dataloader = DataLoader( - # volume_dataset, - # batch_size=batch_size, - # sampler=sampler, - # shuffle=shuffle, - # pin_memory=self.device.type == "cuda", - # drop_last=True, - # persistent_workers=num_workers > 0, - # ) - - # self.pretraining_sampler = sampler - - # if self.global_rank == 0: - # print("Pretraining dataloader setup complete:") - # print(f" Total samples: {len(volume_dataset)}") - # print(f" Grid size: {volume_dataset.N**3}") - # print(f" Local batch size: {batch_size}") - # print(f" Global batch size: {batch_size * self.world_size}") - # print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") - - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): - if scaling_rule == "sqrt": - return base_lr * np.sqrt(self.world_size) - elif scaling_rule == "linear": - return base_lr * self.world_size - else: - raise ValueError(f"Invalid scaling rule: {scaling_rule}") - - def scale_lr( - self, - optimizer_params: dict, - ): - new_optimizer_params = {} - for key, value in optimizer_params.items(): - if "original_lr" in value: - new_optimizer_params[key] = { - "type": value["type"], - "lr": self.get_scaled_lr(value["original_lr"]), - "original_lr": value["original_lr"], - } - else: - new_optimizer_params[key] = { - "type": value["type"], - "lr": self.get_scaled_lr(value["lr"]), - "original_lr": value["lr"], - } - - return new_optimizer_params - - -# TODO: Temporary Adaptive L1 Smooth Loss - - -class AdaptiveSmoothL1Loss(nn.Module): - def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): - """ - Adaptive smooth L1 loss with EMA-based beta adaptation. - - Args: - beta_init (float): optional initial β value; if None, starts as 1.0 - ema_factor (float): smoothing factor for EMA (1 - 1/N_b in Eq. 38) - eps (float): small constant for numerical stability - """ - super().__init__() - self.register_buffer("beta2", torch.tensor(beta_init**2 if beta_init else 1.0)) - self.ema_factor = ema_factor - self.eps = eps - - def forward(self, pred, target): - diff = pred - target - abs_diff = diff.abs() - - # compute current batch MSE (Eq. 38) - mse_batch = torch.mean(diff**2) - - # update β² adaptively using Eq. (39) - with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( - self.beta2, mse_batch - ) - - beta = torch.sqrt(self.beta2 + self.eps) - - # Smooth L1 (Eq. 36) - loss = torch.where( - abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta - ) - return loss.mean() - - -class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): - def forward(self, pred, target): - diff = pred - target - abs_diff = diff.abs() - mse_batch = torch.mean(diff**2) - - # Synchronize β across all GPUs - if dist.is_initialized(): - mse_batch_all = mse_batch.clone() - dist.all_reduce(mse_batch_all, op=dist.ReduceOp.AVG) - mse_batch = mse_batch_all / dist.get_world_size() - - with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( - self.beta2, mse_batch - ) - - beta = torch.sqrt(self.beta2 + self.eps) - loss = torch.where( - abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta - ) - return loss.mean() From 70ffb317b8ad48142a46aea45dfb77ebb0d3b341 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 20 Jan 2026 12:04:54 -0800 Subject: [PATCH 040/335] Implementing pretraining for object_models, along with added functionality in TomographyDDP (i.e, setting up distributed stuff for dataloaders and models). Dataset models also has an included INRPretrainDataset which I'm not sure is a good idea ~ check with Arthur --- src/quantem/tomography/dataset_models.py | 41 +++++++ src/quantem/tomography/object_models.py | 140 ++++++++++++++++++++++- src/quantem/tomography/tomography_ddp.py | 134 ++++++++++++++++++++++ 3 files changed, 312 insertions(+), 3 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 0530df0f..c3b59a56 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -223,4 +223,45 @@ def mode(self) -> Literal["train", "val"]: return self._mode +class TomographyINRPretrainDataset(Dataset): + """ + Dataset class for pretraining INR models. + """ + + def __init__( + self, + pretrain_target: torch.Tensor, + ): + data = pretrain_target.float() + + total_elements = data.numel() + if total_elements > 1e6: + sample_size = min(int(1e6), total_elements) + flat_data = data.flatten() + indices = torch.randperm(total_elements)[:sample_size] + sampled_data = flat_data[indices] + data_quantile = torch.quantile(sampled_data, 0.95) + else: + data_quantile = torch.quantile(data, 0.95) + + data = data / data_quantile + data = torch.permute(data, (2, 1, 0)) + # data = torch.flip(data, dims=(2,)) + + self.volume = data.cpu() + self.N = pretrain_target.shape[0] # Assumes cubic volume. + self.total_samples = pretrain_target.shape[0] ** 3 + + coords_1d = torch.linspace(-1, 1, self.N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + self.coords = torch.stack([x, y, z], dim=-1).reshape(-1, 3).cpu() + self.targets = self.volume.reshape(-1).cpu() + + def __len__(self): + return self.total_samples + + def __getitem__(self, idx): + return {"coords": self.coords[idx], "target": self.targets[idx]} + + DatasetModelType = TomographyINRDataset | TomographyPixDataset diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 69263a2d..cc6ce585 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Self +from typing import Callable, Self import numpy as np import torch @@ -11,6 +11,8 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin +from quantem.tomography.dataset_models import TomographyINRPretrainDataset +from quantem.tomography.tomography_ddp import TomographyDDP @dataclass @@ -264,8 +266,140 @@ def to(self, device: str): self._obj = self._obj.to(device) -class ObjectINR(ObjectConstraints): - pass +class ObjectINR(ObjectConstraints, TomographyDDP): + """ + Object model for INR objects. + """ + + def __init__( + self, + model: nn.Module, + volume_shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + pass + + @classmethod + def from_model( + cls, + model: "torch.nn.Module", + volume_shape: tuple[int, int, int], + device: str = None, # Have to make device a requirement here in distinguishing GPU vs CPU training + rng: np.random.Generator | int | None = None, + ): + obj_model = cls( + model=model, + volume_shape=volume_shape, + device=device, + rng=rng, + _token=cls._token, + ) + + # Initialize DDP params and build the model. TODO: Not sure if this is the best way to do this? But to pretrain we need the model to be wrapped in DDP. + obj_model.setup_distributed(device=device) + obj_model._model = obj_model.build_model(model) + + return obj_model + + # --- Properties --- + + @property + def model(self) -> "nn.Module": + """ + Returns the INR model. + """ + return self._model + + @model.setter + def model(self, model: "nn.Module"): + """ + This doesn't work -- can't have setters for torch sub modules + https://github.com/pytorch/pytorch/issues/52664 + + For now, upon initialization private variable `._model` is set to the built model. + """ + raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + + # Pretraining + @property + def pretrained_weights(self) -> dict[str, torch.Tensor]: + """get the pretrained weights of the INR model""" + return self._pretrained_weights + + def _set_pretrained_weights(self, model: "torch.nn.Module"): + """set the pretrained weights of the INR model""" + if not isinstance(model, torch.nn.Module): + raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") + self._pretrained_weights = deepcopy(model.state_dict()) + + @property + def pretrain_target(self) -> TomographyINRPretrainDataset: + """get the pretrain target""" + return self._pretrain_target + + @pretrain_target.setter + def pretrain_target(self, target: TomographyINRPretrainDataset): + """set the pretrain target""" + self._pretrain_target = target + + # --- Helper Functions --- + + # Reset method that goes back to the pretrained weights. + def reset(self): + """reset the model to the pretrained weights""" + self._model = self.build_model( + self._model, self._pretrained_weights + ) # Since loading the pretrained weights needs to be done in build_model. + + # --- Forward Method --- + + def forward(self, coords: torch.Tensor) -> torch.Tensor: + """forward pass for the INR model""" + + all_densities = self.model(coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) + + valid_mask = ( + (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) + ).float() + + all_densities = all_densities * valid_mask + + return all_densities + + # Pretrain Loop + + def pretrain( + self, + pretrain_dataset: TomographyINRPretrainDataset, + batch_size: int, + reset: bool = False, + num_iters: int = 10, + num_workers: int = 0, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + loss_fn: Callable | str = "l1", + show: bool = True, + ): + """ + Pretrain the INR model to fit target volume. + """ + + if ( + pretrain_dataset is not None + ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. + self.pretrain_dataset = pretrain_dataset + self.pretraining_dataloader = self.setup_dataloader( + pretrain_dataset, batch_size, num_workers=num_workers + ) + + if optimizer_params is not None: + self.set_optimizer(optimizer_params) + if scheduler_params is not None: + self.set_scheduler(scheduler_params, num_iters) ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/tomography/tomography_ddp.py index 8b137891..9a8a4a3f 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/tomography/tomography_ddp.py @@ -1 +1,135 @@ +import os +import torch +import torch.distributed as dist +import torch.nn as nn +from torch.utils.data import DataLoader, Dataset, DistributedSampler + + +class TomographyDDP: + """ + Class for setting up all distributed training. + + - + """ + + def __init__( + self, + ): + self.setup_distributed() + + def setup_distributed(self, device: str | None = None): + """ + Initializes parameters depending if multiple-GPU training, single-GPU training, or CPU training. + """ + if "RANK" in os.environ: + if not dist.is_initialized(): + dist.init_process_group( + backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" + ) + + self.world_size = dist.get_world_size() + self.global_rank = dist.get_rank() + self.local_rank = int(os.environ["LOCAL_RANK"]) + + torch.cuda.set_device(self.local_rank) + self.device = torch.device("cuda", self.local_rank) + else: + self.world_size = 1 + self.global_rank = 0 + self.local_rank = 0 + + if torch.cuda.is_available(): + self.device = torch.device("cuda:0" if device is None else device) + torch.cuda.set_device(self.device.index) + print("Single GPU training") + else: + self.device = torch.device("cpu") + print("CPU training") + + if self.device.type == "cuda": + torch.backends.cudnn.benchmark = True + torch.backends.cuda.matmul.allow_tf32 = True + torch.backends.cudnn.allow_tf32 = True + + def setup_dataloader( + self, + dataset: Dataset, + batch_size: int, + num_workers: int = 0, + # val_fraction: float = 0.0, + ): + pin_mem = self.device.type == "cuda" + persist = num_workers > 0 + + # TODO: Implement validation dataloader + + if self.world_size > 1: + train_sampler = DistributedSampler( + dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=True, + ) + + else: + train_sampler = None + shuffle = True + + train_dataloader = DataLoader( + dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler=train_sampler, + shuffle=shuffle, + pin_memory=pin_mem, + drop_last=True, + persistent_workers=persist, + ) + + if self.global_rank == 0: + print("Dataloader setup complete:") + print(f" Total samples: {len(dataset)}") + print(f" Local batch size: {batch_size}") + print(f" Global batch size: {batch_size * self.world_size}") + print(f" Train batches per GPU per epoch: {len(train_dataloader)}") + + return train_dataloader + + def build_model( + self, + model: nn.Module, + pretrained_weights: dict[str, torch.Tensor] | None = None, + ) -> nn.Module | nn.parallel.DistributedDataParallel: + """ + Wraps the model with DistributedDataParallel if mulitple GPUs are available. + + Returns the model. + """ + + model = model.to(self.device) + if pretrained_weights is not None: + model.load_state_dict(pretrained_weights.copy()) + + if self.world_size > 1: + model = torch.nn.parallel.DistributedDataParallel( + model, + device_ids=[self.local_rank], + output_device=self.local_rank, + find_unused_parameters=False, + broadcast_buffers=True, + bucket_cap_mb=100, + gradient_as_bucket_view=True, + ) + + if self.global_rank == 0: + print("Model wrapped with DDP") + + if self.world_size > 1: + if self.global_rank == 0: + print("Model built, distributed, and compiled successfully") + + else: + print("Model built, compiled successfully") + + return model From 0ed758a412f9329668ea8cd4e1fabac97c59c273 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 20 Jan 2026 14:11:59 -0800 Subject: [PATCH 041/335] Object pretraining INR working --- src/quantem/tomography/object_models.py | 156 ++++++++++++++++++++++-- src/quantem/tomography/tomography.py | 5 +- 2 files changed, 152 insertions(+), 9 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index cc6ce585..dca9a78a 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,10 +1,11 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Callable, Self +from typing import Any, Callable, Self import numpy as np import torch +import torch.distributed as dist import torch.nn as nn from quantem.core.io.serialize import AutoSerialize @@ -130,14 +131,19 @@ def reset(self) -> None: """ raise NotImplementedError + @property + def params(self) -> list[nn.Parameter]: + """ + Get the parameters that should be optimized for this model. + """ + raise NotImplementedError + # --- Helper Functions --- def get_optimization_parameters(self) -> list[nn.Parameter]: """ Get the parameters that should be optimized for this model. - - TODO: I have no idea what this does. """ - return list[nn.Parameter](self.parameters()) + return list[nn.Parameter](self.params()) @abstractmethod # Each subclass should implement this. def to(self, *args, **kwargs): @@ -278,12 +284,19 @@ def __init__( device: str = "cpu", rng: np.random.Generator | int | None = None, ): - pass + super().__init__( + shape=volume_shape, + device=device, + rng=rng, + _token=self._token, + ) + self._pretrain_losses = [] + self._pretrain_lrs = [] @classmethod def from_model( cls, - model: "torch.nn.Module", + model: "nn.Module", volume_shape: tuple[int, int, int], device: str = None, # Have to make device a requirement here in distinguishing GPU vs CPU training rng: np.random.Generator | int | None = None, @@ -293,7 +306,6 @@ def from_model( volume_shape=volume_shape, device=device, rng=rng, - _token=cls._token, ) # Initialize DDP params and build the model. TODO: Not sure if this is the best way to do this? But to pretrain we need the model to be wrapped in DDP. @@ -321,6 +333,10 @@ def model(self, model: "nn.Module"): """ raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + @property + def params(self): + return self.model.parameters() + # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: @@ -352,6 +368,9 @@ def reset(self): self._model, self._pretrained_weights ) # Since loading the pretrained weights needs to be done in build_model. + def get_optimization_parameters(self): + return self.params + # --- Forward Method --- def forward(self, coords: torch.Tensor) -> torch.Tensor: @@ -382,6 +401,7 @@ def pretrain( optimizer_params: dict | None = None, scheduler_params: dict | None = None, loss_fn: Callable | str = "l1", + apply_constraints: bool = False, show: bool = True, ): """ @@ -401,5 +421,127 @@ def pretrain( if scheduler_params is not None: self.set_scheduler(scheduler_params, num_iters) + if reset: + raise NotImplementedError( + "TODO: Resseting the model to the pretrained weights is not implemented yet. To make this work I would have to reinstantiate the model I think." + ) + + if loss_fn == "l1": + loss_fn = nn.functional.l1_loss + + self._pretrain( + num_iters=num_iters, + loss_fn=loss_fn, + apply_constraints=apply_constraints, + ) + + def _pretrain( + self, + num_iters: int, + loss_fn: Callable, + apply_constraints: bool, + ): + self.model.train() + optimizer = self.optimizer + scheduler = self.scheduler + + for a0 in range(num_iters): + for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) + + with torch.autocast( + device_type=self.device.type, dtype=torch.bfloat16, enabled=True + ): + outputs = self.forward(coords) + loss = loss_fn(outputs, target) + + loss.backward() + optimizer.step() + optimizer.zero_grad() + + if scheduler is not None: + if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + scheduler.step(loss.item()) + else: + scheduler.step() + + self._pretrain_losses.append(loss.item()) + print(f"Pretrain Loss: {loss.item():.4f}") + self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) + + def create_volume( + self, + ): + N = max(self._shape) + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + + model = self.model.module if hasattr(self.model, "module") else self.model + + inference_batch_size = 5 * N * N + total_samples = N**3 + samples_per_gpu = total_samples // self.world_size + remainder = total_samples % self.world_size + if self.global_rank < remainder: + start_idx = self.global_rank * (samples_per_gpu + 1) + end_idx = start_idx + samples_per_gpu + 1 + else: + start_idx = self.global_rank * samples_per_gpu + remainder + end_idx = start_idx + samples_per_gpu + inputs_subset = inputs[start_idx:end_idx] + num_samples = inputs_subset.shape[0] + outputs_list = [] + for batch_start in range(0, num_samples, inference_batch_size): + batch_end = min(batch_start + inference_batch_size, num_samples) + batch_coords = inputs_subset[batch_start:batch_end].to( + self.device, non_blocking=True + ) + batch_outputs = model(batch_coords) + if batch_outputs.dim() > 1: + batch_outputs = batch_outputs.squeeze(-1) + outputs_list.append(batch_outputs.cpu()) + + outputs = torch.cat(outputs_list, dim=0) + + if self.world_size > 1: + output_size = torch.tensor(outputs.shape[0], device=self.device, dtype=torch.long) + all_sizes = [ + torch.zeros(1, device=self.device, dtype=torch.long) + for _ in range(self.world_size) + ] + dist.all_gather(all_sizes, output_size) + + max_size = max(size.item() for size in all_sizes) + + if outputs.shape[0] < max_size: + padding = torch.zeros( + max_size - outputs.shape[0], device=outputs.device, dtype=outputs.dtype + ) + outputs_padded = torch.cat([outputs, padding], dim=0).to(self.device) + else: + outputs_padded = outputs.to(self.device) + + gathered_outputs = [ + torch.empty(max_size, device=self.device, dtype=outputs.dtype) + for _ in range(self.world_size) + ] + dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + + trimmed_outputs = [] + for rank, size in enumerate(all_sizes): + trimmed_outputs.append(gathered_outputs[rank][: size.item()]) + + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N).float() + else: + pred_full = outputs.reshape(N, N, N).float() + + self._obj = pred_full.detach().cpu() + + def get_tv_loss(self): + pass + ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ebc36738..0595c402 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -13,7 +13,7 @@ from quantem.tomography.utils import torch_phase_cross_correlation -class Tomography(TomographyBase, TomographyOpt): +class Tomography(TomographyOpt, TomographyBase): """ Class for handling all ML tomography reconstruction methods. Automatic handling between AD and INR-based tomography. @@ -88,7 +88,8 @@ def reconstruct( proj_forward = torch.zeros_like(self.dset.tilt_stack).permute(2, 0, 1) else: proj_forward = torch.zeros_like(self.dset.tilt_stack) - + print("proj_forward.shape", proj_forward.shape) + print("self.dset.tilt_stack.shape", self.dset.tilt_stack.shape) for iter in pbar: proj_forward, loss = self._reconstruction_epoch( inline_alignment=inline_alignment, From c90984c6c9d9f55a1789b7c3c1d01519fb945e12 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 21 Jan 2026 14:53:48 -0800 Subject: [PATCH 042/335] DDPMixin in ML, pretraining working for objects --- .../tomography_ddp.py => core/ml/ddp.py} | 3 ++- src/quantem/core/ml/inr.py | 14 ++++++++++- src/quantem/tomography/object_models.py | 24 ++++++++++++------- src/quantem/tomography/tomography.py | 14 +++++++++++ 4 files changed, 44 insertions(+), 11 deletions(-) rename src/quantem/{tomography/tomography_ddp.py => core/ml/ddp.py} (99%) diff --git a/src/quantem/tomography/tomography_ddp.py b/src/quantem/core/ml/ddp.py similarity index 99% rename from src/quantem/tomography/tomography_ddp.py rename to src/quantem/core/ml/ddp.py index 9a8a4a3f..7b42a641 100644 --- a/src/quantem/tomography/tomography_ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -6,7 +6,8 @@ from torch.utils.data import DataLoader, Dataset, DistributedSampler -class TomographyDDP: +# Rename DDPMixin +class DDPMixin: """ Class for setting up all distributed training. diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 24ee45e4..3091a9f0 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -23,6 +23,7 @@ def __init__( hsiren: bool = False, dtype: torch.dtype = torch.float32, final_activation: str | Callable = "identity", + winner_initialization: bool = False, ) -> None: """Initialize Siren. @@ -59,7 +60,7 @@ def __init__( self.alpha = alpha self.hsiren = hsiren self.dtype = dtype - + self.winner_initialization = winner_initialization self.final_activation = final_activation self._build() @@ -109,6 +110,15 @@ def _build(self) -> None: net_list.append(self._final_activation) self.net = nn.Sequential(*net_list) + if self.winner_initialization: + with torch.no_grad(): + self.net[0].linear.weight += ( + torch.randn_like(self.net[0].linear.weight) * 1 / self.first_omega_0 + ) + self.net[1].linear.weight += ( + torch.randn_like(self.net[1].linear.weight) * 0.01 / self.hidden_omega_0 + ) + def forward(self, coords: torch.Tensor) -> torch.Tensor: output = self.net(coords) return output @@ -182,6 +192,7 @@ def __init__( alpha: float = 1.0, dtype: torch.dtype = torch.float32, final_activation: str | Callable = "identity", + winner_initialization: bool = False, ) -> None: """Initialize HSiren. @@ -217,4 +228,5 @@ def __init__( hsiren=True, dtype=dtype, final_activation=final_activation, + winner_initialization=winner_initialization, ) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index dca9a78a..67276433 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -10,10 +10,10 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.constraints import BaseConstraints, Constraints +from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -from quantem.tomography.tomography_ddp import TomographyDDP @dataclass @@ -272,7 +272,7 @@ def to(self, device: str): self._obj = self._obj.to(device) -class ObjectINR(ObjectConstraints, TomographyDDP): +class ObjectINR(ObjectConstraints, DDPMixin): """ Object model for INR objects. """ @@ -428,6 +428,8 @@ def pretrain( if loss_fn == "l1": loss_fn = nn.functional.l1_loss + elif loss_fn == "l2": + loss_fn = nn.functional.mse_loss self._pretrain( num_iters=num_iters, @@ -446,6 +448,7 @@ def _pretrain( scheduler = self.scheduler for a0 in range(num_iters): + epoch_loss = 0 for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): coords = batch["coords"].to(self.device, non_blocking=True) target = batch["target"].to(self.device, non_blocking=True) @@ -457,17 +460,20 @@ def _pretrain( loss = loss_fn(outputs, target) loss.backward() + epoch_loss += loss.item() optimizer.step() optimizer.zero_grad() - if scheduler is not None: - if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): - scheduler.step(loss.item()) - else: - scheduler.step() + if scheduler is not None: + if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + scheduler.step(epoch_loss) + else: + scheduler.step() - self._pretrain_losses.append(loss.item()) - print(f"Pretrain Loss: {loss.item():.4f}") + self._pretrain_losses.append(epoch_loss / len(self.pretraining_dataloader)) + print( + f"Epoch {a0 + 1}/{num_iters}, Pretrain Loss: {epoch_loss / len(self.pretraining_dataloader):.4f}" + ) self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) def create_volume( diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 0595c402..97dd2bf2 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -11,6 +11,7 @@ from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_opt import TomographyOpt from quantem.tomography.utils import torch_phase_cross_correlation +from quantem.tomography_old.utils import gaussian_filter_2d_stack, gaussian_kernel_1d class Tomography(TomographyOpt, TomographyBase): @@ -82,6 +83,7 @@ def reconstruct( mode: Literal["sirt", "fbp"] = "sirt", reset: bool = False, inline_alignment: bool = False, + smoothing_sigma: float | None = None, ): pbar = tqdm(range(num_iter), desc=f"{mode} Reconstruction") if mode == "sirt" or mode == "fbp": @@ -90,11 +92,18 @@ def reconstruct( proj_forward = torch.zeros_like(self.dset.tilt_stack) print("proj_forward.shape", proj_forward.shape) print("self.dset.tilt_stack.shape", self.dset.tilt_stack.shape) + + if smoothing_sigma is not None: + gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) + else: + gaussian_kernel = None + for iter in pbar: proj_forward, loss = self._reconstruction_epoch( inline_alignment=inline_alignment, mode=mode, proj_forward=proj_forward, + gaussian_kernel=gaussian_kernel, ) pbar.set_description(f"{mode} Reconstruction | Loss: {loss.item():.4f}") @@ -111,6 +120,7 @@ def _reconstruction_epoch( inline_alignment: bool, mode: Literal["sirt", "fbp"], proj_forward: torch.Tensor, + gaussian_kernel: torch.Tensor | None = None, ): loss = 0 @@ -169,6 +179,10 @@ def _reconstruction_epoch( self.obj_model.obj += correction + if gaussian_kernel is not None: + print(self.obj_model.obj.shape) + self.obj_model.obj = gaussian_filter_2d_stack(self.obj_model.obj, gaussian_kernel) + loss = torch.mean(torch.abs(error)) return proj_forward, loss From 97c80d6df8e392f73d7a8b2571f5f032e05b29cb Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 21 Jan 2026 15:06:11 -0800 Subject: [PATCH 043/335] Added cosine annealing to set_scheduler in OptimizerMixin --- src/quantem/core/ml/optimizer_mixin.py | 6 ++++++ src/quantem/tomography/object_models.py | 21 +++++++++++++++++---- 2 files changed, 23 insertions(+), 4 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index e2d2e89a..7c39e39d 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -179,6 +179,12 @@ def set_scheduler( end_factor=params.get("end_factor", 1.0), total_iters=params.get("total_iters", num_iter), ) + elif sched_type == "cosine_annealing": + self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + T_max=params.get("T_max", num_iter), + eta_min=params.get("eta_min", base_LR), + ) else: raise ValueError(f"Unknown scheduler type: {sched_type}") diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 67276433..b21759fa 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -11,6 +11,7 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin +from quantem.core.ml.loss_functions import get_loss_function from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset @@ -116,6 +117,13 @@ def obj(self) -> torch.Tensor: """ raise NotImplementedError + @abstractmethod + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + raise NotImplementedError + @abstractmethod def forward(self, *args, **kwargs) -> torch.Tensor: """ @@ -359,6 +367,14 @@ def pretrain_target(self, target: TomographyINRPretrainDataset): """set the pretrain target""" self._pretrain_target = target + @property + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + # TODO: This is a temporary solution to get the dtype of the object. + return torch.float32 + # --- Helper Functions --- # Reset method that goes back to the pretrained weights. @@ -426,10 +442,7 @@ def pretrain( "TODO: Resseting the model to the pretrained weights is not implemented yet. To make this work I would have to reinstantiate the model I think." ) - if loss_fn == "l1": - loss_fn = nn.functional.l1_loss - elif loss_fn == "l2": - loss_fn = nn.functional.mse_loss + loss_fn = get_loss_function(loss_fn, self.dtype) self._pretrain( num_iters=num_iters, From 9900fc3fb9e710e62cd7145ed9cb3785e91e357a Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 21 Jan 2026 17:09:17 -0800 Subject: [PATCH 044/335] Reworking TomographyINRDatasets; need to figure out what to do for aux params --- src/quantem/core/ml/inr.py | 2 +- src/quantem/tomography/dataset_models.py | 46 +- src/quantem/tomography/object_models.py | 29 +- src/quantem/tomography_old/tomography.py | 1769 +++++++++++----------- 4 files changed, 942 insertions(+), 904 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 3091a9f0..4cf4fc70 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -116,7 +116,7 @@ def _build(self) -> None: torch.randn_like(self.net[0].linear.weight) * 1 / self.first_omega_0 ) self.net[1].linear.weight += ( - torch.randn_like(self.net[1].linear.weight) * 0.01 / self.hidden_omega_0 + torch.randn_like(self.net[1].linear.weight) * 0.1 / self.hidden_omega_0 ) def forward(self, coords: torch.Tensor) -> torch.Tensor: diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index c3b59a56..656520da 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -79,6 +79,8 @@ def __init__( self._reference_tilt_angle_idx = torch.argmin(torch.abs(self.tilt_angles)) # TODO: Implement AuxParams from old tomography_dataset.py here. + self.z1_param = torch + @abstractmethod def forward( self, @@ -89,6 +91,32 @@ def forward( """ raise NotImplementedError("This method should be implemented in subclasses.") + # --- Properties --- + @property + def reference_tilt_idx(self) -> int: + return self._reference_tilt_angle_idx + + @reference_tilt_idx.setter + def reference_tilt_idx(self, reference_tilt_idx: int): + self._reference_tilt_angle_idx = reference_tilt_idx + + @property + def learnable_tilts(self) -> int: + return self.tilt_angles.shape[0] - 1 + + @learnable_tilts.setter + def learnable_tilts(self, learnable_tilts: int): + self._learnable_tilts = learnable_tilts + + @property + def params(self) -> dict[str, torch.nn.Parameter]: + """ + Returns the parameters that should be optimized for this dataset. + + Should be implemented in subclasses. + """ + raise NotImplementedError("This method should be implemented in subclasses.") + # --- Helper Functions --- def to(self, device: str): self.tilt_stack = self.tilt_stack.to(device) @@ -148,6 +176,8 @@ class TomographyINRDataset(TomographyDatasetBase, Dataset): The two main methods here are that the `forward` call will return the relative pose parameters, while `__getitem__` will actually return the pixel values of the tilt stack. + + TODO: I think TomographyINRDataset shouldn't handle the train/val split and will be handled later? Yea this is handled in setup_dataloader in DDP """ def __init__( @@ -155,26 +185,12 @@ def __init__( tilt_stack: Dataset3d | NDArray | torch.Tensor, tilt_angles: NDArray | torch.Tensor, learn_pose: bool = True, - val_ratio: float = 0.0, - mode: Literal["train", "val"] = "train", seed: int = 42, token: object | None = None, ): super().__init__(tilt_stack, tilt_angles, learn_pose, token) - self._total_pixels = self.volume_size[0] * self.volume_size[1] * self.volume_size[2] - - # Create train/val split - torch.manual_seed(seed) - all_indices = torch.randperm(self.total_pixels) - - num_val = int(self.total_pixels * val_ratio) - if mode == "val": - self.indices = all_indices[:num_val] - else: # train - self.indices = all_indices[num_val:] - - self._mode = mode + self.z1_param = torch.nn.Parameter(torch.zeros(self.learnable_tilts)) def forward(self, dummy_input: Any = None): """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index b21759fa..d4d8aab1 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -559,8 +559,33 @@ def create_volume( self._obj = pred_full.detach().cpu() - def get_tv_loss(self): - pass + def get_tv_loss( + self, + coords: torch.Tensor, + ): + tv_loss = torch.tensor(0.0, device=coords.device) + + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.forward(tv_coords) + + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + + tv_loss += self.constraints.tv_vol * grad_norm.mean() + return tv_loss ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography_old/tomography.py b/src/quantem/tomography_old/tomography.py index 5064cd3f..7be24397 100644 --- a/src/quantem/tomography_old/tomography.py +++ b/src/quantem/tomography_old/tomography.py @@ -1,886 +1,883 @@ -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.distributed as dist -from torch.nn import SmoothL1Loss -from torch.utils.tensorboard import SummaryWriter - -# from torch_radon.radon import ParallelBeam as Radon -from tqdm.auto import tqdm - -from quantem.core.ml.loss_functions import ( - CharbonnierLoss, - L1Loss, - LLMSELoss, - MSELogMSELoss, - MSELoss, -) - -# Temporary imports for TomographyNERF -from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise -from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.tomography_conv import TomographyConv -from quantem.tomography.tomography_ddp import ( - AdaptiveSmoothL1LossDDP, - PretrainVolumeDataset, - TomographyDDP, -) -from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ - - -# Temporary aux class for TomographyNERF -# TODO: Maybe put this in INN? -def get_num_samples_per_ray(N: int, epoch: int): - """Increase number of samples per ray at specific epochs.""" - # Exponential schedule - - epochs = np.linspace(0, 10, 5, dtype=int) - # schedule = np.linspace(20, N, 5, dtype=int) - schedule = np.array([20, 100, 150, 200, 200]) - # schedule = np.array([200, 200, 200, 200, 200]) - # schedule = np.array([20, 100, 250, 500, 500]) - # schedule = np.array([500, 500, 500, 500, 500]) - # schedule = np.array([300, 300, 300, 300, 300]) - # schedule = np.exp(schedule) - schedule_warmup = dict[int, int](zip(epochs, schedule)) - - for epoch_threshold, samples in schedule_warmup.items(): - if epoch >= epoch_threshold: - num_samples = samples - - return num_samples - - -class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): - """ - Top level class for either using conventional or ML-based reconstruction methods - for tomography. - """ - - def __init__( - self, - dataset, - volume_obj, - device, - _token, - ): - super().__init__(dataset, volume_obj, device, _token) - - # TODO: More elegant way of doing this. - self.global_epochs = 0 - self.ddp_instantiated = False - self.pretraining_instantiated = False - - # --- Reconstruction Method --- - - def sirt_recon( - self, - num_iterations: int = 10, - inline_alignment: bool = False, - enforce_positivity: bool = True, - volume_shape: tuple = None, - reset: bool = True, - smoothing_sigma: float = None, - shrinkage: float = None, - filter_name: str = "hamming", - circle: bool = True, - plot_loss: bool = False, - ): - num_angles, num_rows, num_cols = self.dataset.tilt_series.shape - sirt_tilt_series = self.dataset.tilt_series.clone() - sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) - - hard_constraints = { - "positivity": enforce_positivity, - "shrinkage": shrinkage, - } - self.volume_obj.hard_constraints = hard_constraints - - if volume_shape is None: - volume_shape = (num_rows, num_rows, num_rows) - else: - D, H, W = volume_shape - - if reset: - self.volume_obj.reset() - self.loss = [] - - proj_forward = torch.zeros_like(self.dataset.tilt_series) - - pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") - - if smoothing_sigma is not None: - gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) - else: - gaussian_kernel = None - - print( - "Devices", - sirt_tilt_series.device, - proj_forward.device, - self.dataset.tilt_angles.device, - ) - - for iter in pbar: - proj_forward, loss = self._sirt_run_epoch( - tilt_series=sirt_tilt_series, - proj_forward=proj_forward, - angles=self.dataset.tilt_angles, - inline_alignment=iter > 0 and inline_alignment, - filter_name=filter_name, - gaussian_kernel=gaussian_kernel, - circle=circle, - ) - - pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") - - self.loss.append(loss.item()) - - self.sirt_recon_vol = self.volume_obj - - # Permutation due to sinogram ordering. - self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) - - if plot_loss: - self.plot_loss() - - # TODO: ML Recon which has NeRF and AD depending on the object type. - # TODO: Temporary - - def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): - """Create projection rays for entire batch simultaneously.""" - batch_size = len(pixel_i) - - # Convert all pixels to normalized coordinates - x_coords = (pixel_j / (N - 1)) * 2 - 1 - y_coords = (pixel_i / (N - 1)) * 2 - 1 - # TODO: maybe pixel_j.device? - # Create z coordinates - z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - - # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] - rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - - # Fill coordinates efficiently - rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray - rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray - rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - - return rays - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): - # Step 1: Apply shifts - shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) - shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - - rays_x = rays[:, :, 0] - shift_x_norm - rays_y = rays[:, :, 1] - shift_y_norm - rays_z = rays[:, :, 2] - - # Rotation 1: Z(-z3) - theta = torch.deg2rad(-z3).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot1 = cos_t * rays_x - sin_t * rays_y - rays_y_rot1 = sin_t * rays_x + cos_t * rays_y - rays_z_rot1 = rays_z - - # Rotation 2: X(x) - theta = torch.deg2rad(x).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot2 = rays_x_rot1 - rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 - rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - - # Rotation 3: Z(-z1) - theta = torch.deg2rad(-z1).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 - rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 - rays_z_final = rays_z_rot2 - - # Stack the final result - transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - - return transformed_rays - - # TODO: Temp logger - def setup_logger(self, log_path): - if self.global_rank == 0: - self.temp_logger = SummaryWriter(log_dir=log_path) - else: - self.temp_logger = None - - def pretrain( - self, - volume_dataset: PretrainVolumeDataset, - obj: ObjectINN, - batch_size: int, - soft_constraints: dict = None, - log_path: str = None, - optimizer_params: dict = None, - epochs: int = 100, - viz_freq: int = 1, - consistency_criterion: str = "mse", - ): - if not self.pretraining_instantiated: - self.setup_distributed() - self.setup_pretraining_dataloader(volume_dataset, batch_size) - self.pretraining_instantiated = True - obj.model = self.build_model(obj._model) - self.obj = obj - - if soft_constraints is not None: - self.obj.soft_constraints = soft_constraints - - if not hasattr(self, "temp_logger"): - self.setup_logger(log_path=log_path) - - if optimizer_params is not None: - optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params - self.set_optimizers() - device_type = self.device.type - consistency_loss_fn = None - if consistency_criterion[0].lower() == "mse": - consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == "l1": - consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == "mse_log": - consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == "llmse": - consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == "smooth_l1": - consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) - elif consistency_criterion[0].lower() == "adaptive_smooth_l1": - consistency_loss_fn = AdaptiveSmoothL1LossDDP( - beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 - ) - elif consistency_criterion[0].lower() == "charbonnier": - consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") - else: - raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - - for epoch in range(epochs): - if self.pretraining_sampler is not None: - self.pretraining_sampler.set_epoch(epoch) - - self.obj.model.train() - - epoch_loss = 0.0 - epoch_consistency_loss = 0.0 - epoch_tv_loss = 0.0 - num_batches = 0 - - for batch_idx, batch in enumerate(self.pretraining_dataloader): - coords = batch["coords"].to(self.device, non_blocking=True) - target = batch["target"].to(self.device, non_blocking=True) - - for _, opt in self.optimizers.items(): - opt.zero_grad() - - with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): - outputs = self.obj.forward(coords) - consistency_loss = consistency_loss_fn(outputs, target) - tv_loss = self.obj.apply_soft_constraints(coords) - - loss = consistency_loss + tv_loss - - loss.backward() - - epoch_loss += loss.detach() - epoch_consistency_loss += consistency_loss.detach() - epoch_tv_loss += tv_loss.detach() - - torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) - - for _, opt in self.optimizers.items(): - opt.step() - epoch_loss += loss.detach() - num_batches += 1 - - if epoch % viz_freq == 0 or epoch == epochs - 1: - avg_loss = epoch_loss / num_batches - with torch.no_grad(): - self.obj.create_volume( - world_size=self.world_size, - global_rank=self.global_rank, - ray_size=volume_dataset.N, - ) - pred_full = self.obj.obj - loss_tensor = avg_loss.clone().detach() - avg_loss = loss_tensor.item() - avg_tv_loss = epoch_tv_loss / num_batches - avg_consistency_loss = epoch_consistency_loss / num_batches - - metrics = torch.tensor( - [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device - ) - if self.world_size > 1: - dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() - - if self.global_rank == 0: - self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) - self.temp_logger.add_scalar( - "Pretrain/consistency_loss", avg_consistency_loss, epoch - ) - self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) - - # current_lr = self.schedulers["model"].get_last_lr()[0] - # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) - - fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) - ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - ax[0].set_title("Sum over Z-axis") - ax[1].matshow( - pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 - ) - ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") - ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) - ax[2].set_title("Sum over Y-axis") - ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) - ax[3].set_title("Sum over X-axis") - self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) - - plt.close(fig) - print( - f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" - ) - self.global_epochs += 1 - if self.global_rank == 0: - print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") - torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") - - def recon( - self, - obj: ObjectINN, - batch_size: int, - num_workers: int = 0, - epochs=20, - use_amp=True, - viz_freq=1, - checkpoint_freq=5, - optimizer_params: dict = None, - scheduler_params: dict = None, - soft_constraints: dict = None, - vol_save_path: str = None, # TODO: TEMPORARY - log_path=None, - val_fraction: float = 0.0, - learn_shifts: bool = True, - # l1_loss: bool = False, - consistency_criterion: str = "mse", - model_weights_path: str = None, - force_cpu: bool = False, - ): - if not self.ddp_instantiated: - if not self.pretraining_instantiated: - self.setup_distributed() - if model_weights_path is not None: - print(f"Loading model weights from {model_weights_path}") - state_dict = torch.load(model_weights_path, map_location="cpu") - - # Handle DataParallel/DDP checkpoints - if any(k.startswith("module.") for k in state_dict.keys()): - new_state_dict = { - k.replace("module.", ""): v for k, v in state_dict.items() - } - else: - new_state_dict = state_dict - - obj.model.load_state_dict(new_state_dict) - print("Model weights loaded successfully") - obj.model = self.build_model(obj._model) - self.obj = obj - - self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) - self.ddp_instantiated = True - - if soft_constraints is not None: - self.obj.soft_constraints = soft_constraints - - if not hasattr(self, "temp_logger"): - self.setup_logger(log_path=log_path) - - zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() - - if self.global_rank == 0: - print( - f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" - ) - print(f"Using consistency criterion: {consistency_criterion}") - - # Auxiliary params setup - self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) - aux_params = self.dataset.auxiliary_params - # Scaling learning rates to account for distributed training - if optimizer_params is not None: - optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params - self.set_optimizers() - - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=epochs) - - aux_norm = torch.tensor(0.0, device=self.device) - model_norm = torch.tensor(0.0, device=self.device) - - for _, opt in self.optimizers.items(): - opt.zero_grad() - - N = max(self.dataset.dims) - - device_type = self.device.type - autocast_dtype = torch.bfloat16 if use_amp else None - - for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) - - # num_samples_per_ray = get_num_samples_per_ray(epoch) - # Log the change if it happens - if self.global_rank == 0: - print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - - if self.global_rank == 0 and self.global_epochs > 0: - prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) - - if num_samples_per_ray != prev_samples: - print( - f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" - ) - - if self.sampler is not None: - self.sampler.set_epoch(epoch) - - epoch_loss = 0.0 - epoch_consistency_loss = 0.0 - epoch_tv_loss = 0.0 - epoch_z1_loss = 0.0 - - num_batches = 0 - consistency_loss_fn = None - if consistency_criterion[0].lower() == "mse": - consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == "l1": - consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == "mse_log": - consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == "llmse": - consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == "smooth_l1": - consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) - elif consistency_criterion[0].lower() == "adaptive_smooth_l1": - consistency_loss_fn = AdaptiveSmoothL1LossDDP( - beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 - ) - elif consistency_criterion[0].lower() == "charbonnier": - consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") - else: - raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - - for batch_idx, batch in enumerate(self.dataloader): - projection_indices = batch["projection_idx"] - - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - - all_densities = self.obj.forward(all_coords) - - tv_loss = self.obj.apply_soft_constraints(all_coords) - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - consistency_loss = consistency_loss_fn(predicted_values, target_values) - tv_loss_z1 = torch.tensor(0.0, device=self.device) - - loss = consistency_loss + tv_loss + tv_loss_z1 - - loss.backward() - - epoch_loss += loss.detach() - epoch_consistency_loss += consistency_loss.detach() - epoch_tv_loss += tv_loss.detach() - epoch_z1_loss += tv_loss_z1.detach() - num_batches += 1 - - for key, opt in self.optimizers.items(): - if key == "model": - model_norm = torch.nn.utils.clip_grad_norm_( - self.obj.model.parameters(), max_norm=1 - ) - elif key == "aux_params": - aux_norm = torch.nn.utils.clip_grad_norm_( - self.dataset.auxiliary_params.parameters(), max_norm=1 - ) - opt.step() - opt.zero_grad() - - for key, sched in self.schedulers.items(): - if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): - sched.step(epoch_loss) - else: - sched.step() - - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: - with torch.no_grad(): - self.obj.create_volume( - world_size=self.world_size, - global_rank=self.global_rank, - ray_size=num_samples_per_ray, - ) - pred_full = self.obj.obj - avg_loss = epoch_loss.item() / num_batches - avg_consistency_loss = epoch_consistency_loss.item() / num_batches - avg_tv_loss = epoch_tv_loss.item() / num_batches - avg_z1_loss = epoch_z1_loss.item() / num_batches - shifts, z1, z3 = self.dataset.auxiliary_params.forward() - shifts = shifts.detach().cpu() - z1 = z1.detach().cpu() - z3 = z3.detach().cpu() - metrics = torch.tensor( - [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], - device=self.device, - ) - if self.world_size > 1: - dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - val_loss = self.validate( - aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N - ) - - if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): - with torch.no_grad(): - current_lr = self.schedulers["model"].get_last_lr()[0] - - # Log metrics - self.temp_logger.add_scalar( - f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", - avg_consistency_loss, - self.global_epochs, - ) - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - self.temp_logger.add_scalar( - f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", - val_loss, - self.global_epochs, - ) - self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) - # if tv_weight > 0: - self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) - self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) - self.temp_logger.add_scalar( - "train/model_grad_norm", model_norm.item(), self.global_epochs - ) - self.temp_logger.add_scalar( - "train/aux_grad_norm", aux_norm.item(), self.global_epochs - ) - self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) - self.temp_logger.add_scalar( - "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs - ) - if consistency_criterion[0].lower() == "adaptive_smooth_l1": - self.temp_logger.add_scalar( - "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs - ) - fig, axes = plt.subplots(1, 5, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - axes[0].set_title("Sum over Z-axis") - - axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) - axes[1].set_title(f"Slice at Z={N // 2}") - - slice_start = max(0, N // 2 - 5) - slice_end = min(N, N // 2 + 6) - thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap="turbo", vmin=0) - axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") - - axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) - axes[3].set_title("Sum over Y-axis") - - axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) - axes[4].set_title("Sum over X-axis") - - self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) - - fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) - axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") - axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") - axes[0].legend() - axes[1].plot(z1.cpu().numpy(), label="Z1") - axes[2].plot(z3.cpu().numpy(), label="Z3") - self.temp_logger.add_figure( - "train/auxiliary_params", fig, self.global_epochs, close=True - ) - plt.close(fig) - - if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: - with torch.no_grad(): - if self.global_rank == 0: - save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" - torch.save(pred_full.cpu(), save_path) - - self.global_epochs += 1 - - if self.global_rank == 0: - print("Training complete.") - - torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") - # print("Successfully setup DDP and dataloader") - - def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): - """Validate the model on the validation set.""" - self.obj.model.eval() - aux_params.eval() - - val_loss = 0.0 - num_batches = 0 - - with torch.no_grad(): - for batch in self.val_dataloader: - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - - all_densities = self.obj.forward(all_coords) - - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - - loss = mse_loss - - val_loss += loss.detach() - num_batches += 1 - - avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 - - if self.world_size > 1: - val_loss_tensor = avg_val_loss.detach().clone() - dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) - avg_val_loss = val_loss_tensor.item() - - self.obj.model.train() - aux_params.train() - - return avg_val_loss - - def ad_recon( - self, - optimizer_params: dict, - num_iter: int = 0, - reset: bool = False, - scheduler_params: dict | None = None, - hard_constraints: dict | None = None, - soft_constraints: dict | None = None, - # store_iterations: bool | None = None, - # store_iterations_every: int | None = None, - # autograd: bool = True, - ): - if reset: - self.reset_recon() - - self.hard_constraints = hard_constraints - self.soft_constraints = soft_constraints - - # Make sure everything is in the correct device, might be redundant/cleaner way to do this - self.dataset.to(self.device) - self.volume_obj.to(self.device) - - # Making optimizable parameters into leaf tensors. - self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) - self.dataset.z1_angles = ( - self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) - ) - self.dataset.z3_angles = ( - self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) - ) - - if optimizer_params is not None: - self.optimizer_params = optimizer_params - self.set_optimizers() - - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=num_iter) - - if hard_constraints is not None: - self.volume_obj.hard_constraints = hard_constraints - if soft_constraints is not None: - self.volume_obj.soft_constraints = soft_constraints - - pbar = tqdm(range(num_iter), desc="AD Reconstruction") - - for a0 in pbar: - total_loss = 0.0 - tilt_series_loss = 0.0 - - pred_volume = self.volume_obj.forward() - - for i in range(len(self.dataset.tilt_series)): - forward_projection = self.projection_operator( - vol=pred_volume, - z1=self.dataset.z1_angles[i], - x=self.dataset.tilt_angles[i], - z3=self.dataset.z3_angles[i], - shift_x=self.dataset.shifts[i, 0], - shift_y=self.dataset.shifts[i, 1], - device=self.device, - ) - - tilt_series_loss += torch.nn.functional.mse_loss( - forward_projection, self.dataset.tilt_series[i] - ) - tilt_series_loss /= len(self.dataset.tilt_series) - - total_loss = tilt_series_loss + self.volume_obj.soft_loss - self.loss.append(total_loss.item()) - - total_loss.backward() - - for opt in self.optimizers.values(): - opt.step() - opt.zero_grad() - - if self.schedulers is not None: - for sch in self.schedulers.values(): - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(total_loss) - elif sch is not None: - sch.step() - - pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") - - if self.logger is not None: - self.logger.log_scalar("loss/total", total_loss.item(), a0) - self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) - self.logger.log_scalar( - "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 - ) - - if a0 % self.logger.log_images_every == 0: - self.logger.projection_images( - volume_obj=self.volume_obj, - epoch=a0, - ) - self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) - - self.logger.flush() - - self.ad_recon_vol = self.volume_obj.forward() - - return self - - def reset_recon(self) -> None: - if isinstance(self.volume_obj, ObjectVoxelwise): - self.volume_obj.reset() - - self.ad_recon_vol = None - - # --- Projection Operators ---- - def projection_operator( - self, - vol, - z1, - x, - z3, - shift_x, - shift_y, - device, - ): - projection = ( - rot_ZXZ( - mags=vol.unsqueeze(0), # Add batch dimension - z1=z1, - x=-x, - z3=z3, - device=device, - mode="bilinear", - ) - .squeeze() - .sum(axis=0) - ) - - shifted_projection = differentiable_shift_2d( - image=projection, - shift_x=shift_x, - shift_y=shift_y, - sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit - ) - - return shifted_projection +# import matplotlib.pyplot as plt +# import numpy as np +# import torch +# import torch.distributed as dist +# from torch.nn import SmoothL1Loss +# from torch.utils.tensorboard import SummaryWriter + +# # from torch_radon.radon import ParallelBeam as Radon +# from tqdm.auto import tqdm + +# from quantem.core.ml.loss_functions import ( +# CharbonnierLoss, +# L1Loss, +# LLMSELoss, +# MSELogMSELoss, +# MSELoss, +# ) + +# # Temporary imports for TomographyNERF +# from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise +# from quantem.tomography.tomography_base import TomographyBase +# from quantem.tomography.tomography_conv import TomographyConv +# from quantem.tomography.tomography_ml import TomographyML +# from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ + + +# # Temporary aux class for TomographyNERF +# # TODO: Maybe put this in INN? +# def get_num_samples_per_ray(N: int, epoch: int): +# """Increase number of samples per ray at specific epochs.""" +# # Exponential schedule + +# epochs = np.linspace(0, 10, 5, dtype=int) +# # schedule = np.linspace(20, N, 5, dtype=int) +# schedule = np.array([20, 100, 150, 200, 200]) +# # schedule = np.array([200, 200, 200, 200, 200]) +# # schedule = np.array([20, 100, 250, 500, 500]) +# # schedule = np.array([500, 500, 500, 500, 500]) +# # schedule = np.array([300, 300, 300, 300, 300]) +# # schedule = np.exp(schedule) +# schedule_warmup = dict[int, int](zip(epochs, schedule)) + +# for epoch_threshold, samples in schedule_warmup.items(): +# if epoch >= epoch_threshold: +# num_samples = samples + +# return num_samples + + +# class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): +# """ +# Top level class for either using conventional or ML-based reconstruction methods +# for tomography. +# """ + +# def __init__( +# self, +# dataset, +# volume_obj, +# device, +# _token, +# ): +# super().__init__(dataset, volume_obj, device, _token) + +# # TODO: More elegant way of doing this. +# self.global_epochs = 0 +# self.ddp_instantiated = False +# self.pretraining_instantiated = False + +# # --- Reconstruction Method --- + +# def sirt_recon( +# self, +# num_iterations: int = 10, +# inline_alignment: bool = False, +# enforce_positivity: bool = True, +# volume_shape: tuple = None, +# reset: bool = True, +# smoothing_sigma: float = None, +# shrinkage: float = None, +# filter_name: str = "hamming", +# circle: bool = True, +# plot_loss: bool = False, +# ): +# num_angles, num_rows, num_cols = self.dataset.tilt_series.shape +# sirt_tilt_series = self.dataset.tilt_series.clone() +# sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) + +# hard_constraints = { +# "positivity": enforce_positivity, +# "shrinkage": shrinkage, +# } +# self.volume_obj.hard_constraints = hard_constraints + +# if volume_shape is None: +# volume_shape = (num_rows, num_rows, num_rows) +# else: +# D, H, W = volume_shape + +# if reset: +# self.volume_obj.reset() +# self.loss = [] + +# proj_forward = torch.zeros_like(self.dataset.tilt_series) + +# pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") + +# if smoothing_sigma is not None: +# gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) +# else: +# gaussian_kernel = None + +# print( +# "Devices", +# sirt_tilt_series.device, +# proj_forward.device, +# self.dataset.tilt_angles.device, +# ) + +# for iter in pbar: +# proj_forward, loss = self._sirt_run_epoch( +# tilt_series=sirt_tilt_series, +# proj_forward=proj_forward, +# angles=self.dataset.tilt_angles, +# inline_alignment=iter > 0 and inline_alignment, +# filter_name=filter_name, +# gaussian_kernel=gaussian_kernel, +# circle=circle, +# ) + +# pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") + +# self.loss.append(loss.item()) + +# self.sirt_recon_vol = self.volume_obj + +# # Permutation due to sinogram ordering. +# self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) + +# if plot_loss: +# self.plot_loss() + +# # TODO: ML Recon which has NeRF and AD depending on the object type. +# # TODO: Temporary + +# def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): +# """Create projection rays for entire batch simultaneously.""" +# batch_size = len(pixel_i) + +# # Convert all pixels to normalized coordinates +# x_coords = (pixel_j / (N - 1)) * 2 - 1 +# y_coords = (pixel_i / (N - 1)) * 2 - 1 +# # TODO: maybe pixel_j.device? +# # Create z coordinates +# z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + +# # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] +# rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + +# # Fill coordinates efficiently +# rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray +# rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray +# rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + +# return rays + +# @torch.compile(mode="reduce-overhead") +# def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): +# # Step 1: Apply shifts +# shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) +# shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + +# rays_x = rays[:, :, 0] - shift_x_norm +# rays_y = rays[:, :, 1] - shift_y_norm +# rays_z = rays[:, :, 2] + +# # Rotation 1: Z(-z3) +# theta = torch.deg2rad(-z3).view(-1, 1) +# cos_t = torch.cos(theta) +# sin_t = torch.sin(theta) + +# rays_x_rot1 = cos_t * rays_x - sin_t * rays_y +# rays_y_rot1 = sin_t * rays_x + cos_t * rays_y +# rays_z_rot1 = rays_z + +# # Rotation 2: X(x) +# theta = torch.deg2rad(x).view(-1, 1) +# cos_t = torch.cos(theta) +# sin_t = torch.sin(theta) + +# rays_x_rot2 = rays_x_rot1 +# rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 +# rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + +# # Rotation 3: Z(-z1) +# theta = torch.deg2rad(-z1).view(-1, 1) +# cos_t = torch.cos(theta) +# sin_t = torch.sin(theta) + +# rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 +# rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 +# rays_z_final = rays_z_rot2 + +# # Stack the final result +# transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + +# return transformed_rays + +# # TODO: Temp logger +# def setup_logger(self, log_path): +# if self.global_rank == 0: +# self.temp_logger = SummaryWriter(log_dir=log_path) +# else: +# self.temp_logger = None + +# def pretrain( +# self, +# # volume_dataset: PretrainVolumeDataset, +# obj: ObjectINN, +# batch_size: int, +# soft_constraints: dict = None, +# log_path: str = None, +# optimizer_params: dict = None, +# epochs: int = 100, +# viz_freq: int = 1, +# consistency_criterion: str = "mse", +# ): +# if not self.pretraining_instantiated: +# self.setup_distributed() +# # self.setup_pretraining_dataloader(volume_dataset, batch_size) +# self.pretraining_instantiated = True +# obj.model = self.build_model(obj._model) +# self.obj = obj + +# if soft_constraints is not None: +# self.obj.soft_constraints = soft_constraints + +# if not hasattr(self, "temp_logger"): +# self.setup_logger(log_path=log_path) + +# if optimizer_params is not None: +# optimizer_params = self.scale_lr(optimizer_params) +# self.optimizer_params = optimizer_params +# self.set_optimizers() +# device_type = self.device.type +# consistency_loss_fn = None +# if consistency_criterion[0].lower() == "mse": +# consistency_loss_fn = MSELoss() +# elif consistency_criterion[0].lower() == "l1": +# consistency_loss_fn = L1Loss() +# elif consistency_criterion[0].lower() == "mse_log": +# consistency_loss_fn = MSELogMSELoss() +# elif consistency_criterion[0].lower() == "llmse": +# consistency_loss_fn = LLMSELoss() +# elif consistency_criterion[0].lower() == "smooth_l1": +# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) +# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": +# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( +# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 +# # ) +# pass +# elif consistency_criterion[0].lower() == "charbonnier": +# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") +# else: +# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + +# for epoch in range(epochs): +# if self.pretraining_sampler is not None: +# self.pretraining_sampler.set_epoch(epoch) + +# self.obj.model.train() + +# epoch_loss = 0.0 +# epoch_consistency_loss = 0.0 +# epoch_tv_loss = 0.0 +# num_batches = 0 + +# for batch_idx, batch in enumerate(self.pretraining_dataloader): +# coords = batch["coords"].to(self.device, non_blocking=True) +# target = batch["target"].to(self.device, non_blocking=True) + +# for _, opt in self.optimizers.items(): +# opt.zero_grad() + +# with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): +# outputs = self.obj.forward(coords) +# consistency_loss = consistency_loss_fn(outputs, target) +# tv_loss = self.obj.apply_soft_constraints(coords) + +# loss = consistency_loss + tv_loss + +# loss.backward() + +# epoch_loss += loss.detach() +# epoch_consistency_loss += consistency_loss.detach() +# epoch_tv_loss += tv_loss.detach() + +# torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) + +# for _, opt in self.optimizers.items(): +# opt.step() +# epoch_loss += loss.detach() +# num_batches += 1 + +# if epoch % viz_freq == 0 or epoch == epochs - 1: +# avg_loss = epoch_loss / num_batches +# with torch.no_grad(): +# self.obj.create_volume( +# world_size=self.world_size, +# global_rank=self.global_rank, +# # ray_size=volume_dataset.N, +# ) +# pred_full = self.obj.obj +# loss_tensor = avg_loss.clone().detach() +# avg_loss = loss_tensor.item() +# avg_tv_loss = epoch_tv_loss / num_batches +# avg_consistency_loss = epoch_consistency_loss / num_batches + +# metrics = torch.tensor( +# [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device +# ) +# if self.world_size > 1: +# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) +# avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() + +# if self.global_rank == 0: +# self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) +# self.temp_logger.add_scalar( +# "Pretrain/consistency_loss", avg_consistency_loss, epoch +# ) +# self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) + +# # current_lr = self.schedulers["model"].get_last_lr()[0] +# # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) + +# fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) +# ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) +# ax[0].set_title("Sum over Z-axis") +# ax[1].matshow( +# # pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 +# # ) +# # ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") +# ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) +# ax[2].set_title("Sum over Y-axis") +# ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) +# ax[3].set_title("Sum over X-axis") +# self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) + +# plt.close(fig) +# print( +# f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" +# ) +# self.global_epochs += 1 +# if self.global_rank == 0: +# print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") +# torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") + +# def recon( +# self, +# obj: ObjectINN, +# batch_size: int, +# num_workers: int = 0, +# epochs=20, +# use_amp=True, +# viz_freq=1, +# checkpoint_freq=5, +# optimizer_params: dict = None, +# scheduler_params: dict = None, +# soft_constraints: dict = None, +# vol_save_path: str = None, # TODO: TEMPORARY +# log_path=None, +# val_fraction: float = 0.0, +# learn_shifts: bool = True, +# # l1_loss: bool = False, +# consistency_criterion: str = "mse", +# model_weights_path: str = None, +# force_cpu: bool = False, +# ): +# if not self.ddp_instantiated: +# if not self.pretraining_instantiated: +# self.setup_distributed() +# if model_weights_path is not None: +# print(f"Loading model weights from {model_weights_path}") +# state_dict = torch.load(model_weights_path, map_location="cpu") + +# # Handle DataParallel/DDP checkpoints +# if any(k.startswith("module.") for k in state_dict.keys()): +# new_state_dict = { +# k.replace("module.", ""): v for k, v in state_dict.items() +# } +# else: +# new_state_dict = state_dict + +# obj.model.load_state_dict(new_state_dict) +# print("Model weights loaded successfully") +# obj.model = self.build_model(obj._model) +# self.obj = obj + +# self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) +# self.ddp_instantiated = True + +# if soft_constraints is not None: +# self.obj.soft_constraints = soft_constraints + +# if not hasattr(self, "temp_logger"): +# self.setup_logger(log_path=log_path) + +# zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() + +# if self.global_rank == 0: +# print( +# f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" +# ) +# print(f"Using consistency criterion: {consistency_criterion}") + +# # Auxiliary params setup +# self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) +# aux_params = self.dataset.auxiliary_params +# # Scaling learning rates to account for distributed training +# if optimizer_params is not None: +# optimizer_params = self.scale_lr(optimizer_params) +# self.optimizer_params = optimizer_params +# self.set_optimizers() + +# if scheduler_params is not None: +# self.scheduler_params = scheduler_params +# self.set_schedulers(self.scheduler_params, num_iter=epochs) + +# aux_norm = torch.tensor(0.0, device=self.device) +# model_norm = torch.tensor(0.0, device=self.device) + +# for _, opt in self.optimizers.items(): +# opt.zero_grad() + +# N = max(self.dataset.dims) + +# device_type = self.device.type +# autocast_dtype = torch.bfloat16 if use_amp else None + +# for epoch in range(epochs): +# num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) + +# # num_samples_per_ray = get_num_samples_per_ray(epoch) +# # Log the change if it happens +# if self.global_rank == 0: +# print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") + +# if self.global_rank == 0 and self.global_epochs > 0: +# prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) + +# if num_samples_per_ray != prev_samples: +# print( +# f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" +# ) + +# if self.sampler is not None: +# self.sampler.set_epoch(epoch) + +# epoch_loss = 0.0 +# epoch_consistency_loss = 0.0 +# epoch_tv_loss = 0.0 +# epoch_z1_loss = 0.0 + +# num_batches = 0 +# consistency_loss_fn = None +# if consistency_criterion[0].lower() == "mse": +# consistency_loss_fn = MSELoss() +# elif consistency_criterion[0].lower() == "l1": +# consistency_loss_fn = L1Loss() +# elif consistency_criterion[0].lower() == "mse_log": +# consistency_loss_fn = MSELogMSELoss() +# elif consistency_criterion[0].lower() == "llmse": +# consistency_loss_fn = LLMSELoss() +# elif consistency_criterion[0].lower() == "smooth_l1": +# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) +# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": +# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( +# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 +# # ) +# pass +# elif consistency_criterion[0].lower() == "charbonnier": +# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") +# else: +# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + +# for batch_idx, batch in enumerate(self.dataloader): +# projection_indices = batch["projection_idx"] + +# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) +# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) +# target_values = batch["target_value"].to(self.device, non_blocking=True) +# phis = batch["phi"].to(self.device, non_blocking=True) +# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + +# shifts, z1_params, z3_params = aux_params(None) +# batch_shifts = torch.index_select(shifts, 0, projection_indices) +# batch_z1 = torch.index_select(z1_params, 0, projection_indices) +# batch_z3 = torch.index_select(z3_params, 0, projection_indices) + +# with torch.autocast( +# device_type=device_type, dtype=autocast_dtype, enabled=use_amp +# ): +# with torch.no_grad(): +# batch_ray_coords = self.create_batch_projection_rays( +# pixel_i, pixel_j, N, num_samples_per_ray +# ) + +# # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate +# transformed_rays = self.transform_batch_ray_coordinates( +# batch_ray_coords, +# z1=batch_z1, +# x=phis, +# z3=batch_z3, +# shifts=batch_shifts, +# N=N, +# sampling_rate=1.0, +# ) + +# all_coords = transformed_rays.view(-1, 3) + +# all_densities = self.obj.forward(all_coords) + +# tv_loss = self.obj.apply_soft_constraints(all_coords) +# ray_densities = all_densities.view( +# len(target_values), num_samples_per_ray +# ) # Reshape rays and integarte +# step_size = 2.0 / (num_samples_per_ray - 1) + +# predicted_values = ray_densities.sum(dim=1) * step_size + +# consistency_loss = consistency_loss_fn(predicted_values, target_values) +# tv_loss_z1 = torch.tensor(0.0, device=self.device) + +# loss = consistency_loss + tv_loss + tv_loss_z1 + +# loss.backward() + +# epoch_loss += loss.detach() +# epoch_consistency_loss += consistency_loss.detach() +# epoch_tv_loss += tv_loss.detach() +# epoch_z1_loss += tv_loss_z1.detach() +# num_batches += 1 + +# for key, opt in self.optimizers.items(): +# if key == "model": +# model_norm = torch.nn.utils.clip_grad_norm_( +# self.obj.model.parameters(), max_norm=1 +# ) +# elif key == "aux_params": +# aux_norm = torch.nn.utils.clip_grad_norm_( +# self.dataset.auxiliary_params.parameters(), max_norm=1 +# ) +# opt.step() +# opt.zero_grad() + +# for key, sched in self.schedulers.items(): +# if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): +# sched.step(epoch_loss) +# else: +# sched.step() + +# if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: +# with torch.no_grad(): +# self.obj.create_volume( +# world_size=self.world_size, +# global_rank=self.global_rank, +# ray_size=num_samples_per_ray, +# ) +# pred_full = self.obj.obj +# avg_loss = epoch_loss.item() / num_batches +# avg_consistency_loss = epoch_consistency_loss.item() / num_batches +# avg_tv_loss = epoch_tv_loss.item() / num_batches +# avg_z1_loss = epoch_z1_loss.item() / num_batches +# shifts, z1, z3 = self.dataset.auxiliary_params.forward() +# shifts = shifts.detach().cpu() +# z1 = z1.detach().cpu() +# z3 = z3.detach().cpu() +# metrics = torch.tensor( +# [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], +# device=self.device, +# ) +# if self.world_size > 1: +# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) +# avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + +# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: +# val_loss = self.validate( +# aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N +# ) + +# if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): +# with torch.no_grad(): +# current_lr = self.schedulers["model"].get_last_lr()[0] + +# # Log metrics +# self.temp_logger.add_scalar( +# f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", +# avg_consistency_loss, +# self.global_epochs, +# ) +# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: +# self.temp_logger.add_scalar( +# f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", +# val_loss, +# self.global_epochs, +# ) +# self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) +# # if tv_weight > 0: +# self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) +# self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) +# self.temp_logger.add_scalar( +# "train/model_grad_norm", model_norm.item(), self.global_epochs +# ) +# self.temp_logger.add_scalar( +# "train/aux_grad_norm", aux_norm.item(), self.global_epochs +# ) +# self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) +# self.temp_logger.add_scalar( +# "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs +# ) +# if consistency_criterion[0].lower() == "adaptive_smooth_l1": +# self.temp_logger.add_scalar( +# "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs +# ) +# fig, axes = plt.subplots(1, 5, figsize=(36, 12)) +# axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) +# axes[0].set_title("Sum over Z-axis") + +# axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) +# axes[1].set_title(f"Slice at Z={N // 2}") + +# slice_start = max(0, N // 2 - 5) +# slice_end = min(N, N // 2 + 6) +# thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() +# axes[2].matshow(thick_slice, cmap="turbo", vmin=0) +# axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") + +# axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) +# axes[3].set_title("Sum over Y-axis") + +# axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) +# axes[4].set_title("Sum over X-axis") + +# self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) + +# fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) +# axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") +# axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") +# axes[0].legend() +# axes[1].plot(z1.cpu().numpy(), label="Z1") +# axes[2].plot(z3.cpu().numpy(), label="Z3") +# self.temp_logger.add_figure( +# "train/auxiliary_params", fig, self.global_epochs, close=True +# ) +# plt.close(fig) + +# if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: +# with torch.no_grad(): +# if self.global_rank == 0: +# save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" +# torch.save(pred_full.cpu(), save_path) + +# self.global_epochs += 1 + +# if self.global_rank == 0: +# print("Training complete.") + +# torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") +# # print("Successfully setup DDP and dataloader") + +# def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): +# """Validate the model on the validation set.""" +# self.obj.model.eval() +# aux_params.eval() + +# val_loss = 0.0 +# num_batches = 0 + +# with torch.no_grad(): +# for batch in self.val_dataloader: +# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) +# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) +# target_values = batch["target_value"].to(self.device, non_blocking=True) +# phis = batch["phi"].to(self.device, non_blocking=True) +# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + +# shifts, z1_params, z3_params = aux_params(None) +# batch_shifts = torch.index_select(shifts, 0, projection_indices) +# batch_z1 = torch.index_select(z1_params, 0, projection_indices) +# batch_z3 = torch.index_select(z3_params, 0, projection_indices) + +# with torch.autocast( +# device_type=device_type, dtype=autocast_dtype, enabled=use_amp +# ): +# with torch.no_grad(): +# batch_ray_coords = self.create_batch_projection_rays( +# pixel_i, pixel_j, N, num_samples_per_ray +# ) + +# transformed_rays = self.transform_batch_ray_coordinates( +# batch_ray_coords, +# z1=batch_z1, +# x=phis, +# z3=batch_z3, +# shifts=batch_shifts, +# N=N, +# sampling_rate=1.0, +# ) + +# all_coords = transformed_rays.view(-1, 3) + +# all_densities = self.obj.forward(all_coords) + +# ray_densities = all_densities.view( +# len(target_values), num_samples_per_ray +# ) # Reshape rays and integarte +# step_size = 2.0 / (num_samples_per_ray - 1) + +# predicted_values = ray_densities.sum(dim=1) * step_size + +# mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + +# loss = mse_loss + +# val_loss += loss.detach() +# num_batches += 1 + +# avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 + +# if self.world_size > 1: +# val_loss_tensor = avg_val_loss.detach().clone() +# dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) +# avg_val_loss = val_loss_tensor.item() + +# self.obj.model.train() +# aux_params.train() + +# return avg_val_loss + +# def ad_recon( +# self, +# optimizer_params: dict, +# num_iter: int = 0, +# reset: bool = False, +# scheduler_params: dict | None = None, +# hard_constraints: dict | None = None, +# soft_constraints: dict | None = None, +# # store_iterations: bool | None = None, +# # store_iterations_every: int | None = None, +# # autograd: bool = True, +# ): +# if reset: +# self.reset_recon() + +# self.hard_constraints = hard_constraints +# self.soft_constraints = soft_constraints + +# # Make sure everything is in the correct device, might be redundant/cleaner way to do this +# self.dataset.to(self.device) +# self.volume_obj.to(self.device) + +# # Making optimizable parameters into leaf tensors. +# self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) +# self.dataset.z1_angles = ( +# self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) +# ) +# self.dataset.z3_angles = ( +# self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) +# ) + +# if optimizer_params is not None: +# self.optimizer_params = optimizer_params +# self.set_optimizers() + +# if scheduler_params is not None: +# self.scheduler_params = scheduler_params +# self.set_schedulers(self.scheduler_params, num_iter=num_iter) + +# if hard_constraints is not None: +# self.volume_obj.hard_constraints = hard_constraints +# if soft_constraints is not None: +# self.volume_obj.soft_constraints = soft_constraints + +# pbar = tqdm(range(num_iter), desc="AD Reconstruction") + +# for a0 in pbar: +# total_loss = 0.0 +# tilt_series_loss = 0.0 + +# pred_volume = self.volume_obj.forward() + +# for i in range(len(self.dataset.tilt_series)): +# forward_projection = self.projection_operator( +# vol=pred_volume, +# z1=self.dataset.z1_angles[i], +# x=self.dataset.tilt_angles[i], +# z3=self.dataset.z3_angles[i], +# shift_x=self.dataset.shifts[i, 0], +# shift_y=self.dataset.shifts[i, 1], +# device=self.device, +# ) + +# tilt_series_loss += torch.nn.functional.mse_loss( +# forward_projection, self.dataset.tilt_series[i] +# ) +# tilt_series_loss /= len(self.dataset.tilt_series) + +# total_loss = tilt_series_loss + self.volume_obj.soft_loss +# self.loss.append(total_loss.item()) + +# total_loss.backward() + +# for opt in self.optimizers.values(): +# opt.step() +# opt.zero_grad() + +# if self.schedulers is not None: +# for sch in self.schedulers.values(): +# if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): +# sch.step(total_loss) +# elif sch is not None: +# sch.step() + +# pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") + +# if self.logger is not None: +# self.logger.log_scalar("loss/total", total_loss.item(), a0) +# self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) +# self.logger.log_scalar( +# "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 +# ) + +# if a0 % self.logger.log_images_every == 0: +# self.logger.projection_images( +# volume_obj=self.volume_obj, +# epoch=a0, +# ) +# self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) + +# self.logger.flush() + +# self.ad_recon_vol = self.volume_obj.forward() + +# return self + +# def reset_recon(self) -> None: +# if isinstance(self.volume_obj, ObjectVoxelwise): +# self.volume_obj.reset() + +# self.ad_recon_vol = None + +# # --- Projection Operators ---- +# def projection_operator( +# self, +# vol, +# z1, +# x, +# z3, +# shift_x, +# shift_y, +# device, +# ): +# projection = ( +# rot_ZXZ( +# mags=vol.unsqueeze(0), # Add batch dimension +# z1=z1, +# x=-x, +# z3=z3, +# device=device, +# mode="bilinear", +# ) +# .squeeze() +# .sum(axis=0) +# ) + +# shifted_projection = differentiable_shift_2d( +# image=projection, +# shift_x=shift_x, +# shift_y=shift_y, +# sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit +# ) + +# return shifted_projection From 4bda57fbad9411c9ef0a419a249998cbae336448 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 22 Jan 2026 17:02:39 -0800 Subject: [PATCH 045/335] Some device switching bugs that need to be addressed. --- src/quantem/core/ml/ddp.py | 14 +- src/quantem/tomography/dataset_models.py | 243 +++++++++++++++++++--- src/quantem/tomography/object_models.py | 44 ++-- src/quantem/tomography/tomography.py | 85 +++++++- src/quantem/tomography/tomography_base.py | 33 ++- 5 files changed, 350 insertions(+), 69 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 7b42a641..0d38e67f 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -34,25 +34,27 @@ def setup_distributed(self, device: str | None = None): self.local_rank = int(os.environ["LOCAL_RANK"]) torch.cuda.set_device(self.local_rank) - self.device = torch.device("cuda", self.local_rank) + device = torch.device("cuda", self.local_rank) else: self.world_size = 1 self.global_rank = 0 self.local_rank = 0 if torch.cuda.is_available(): - self.device = torch.device("cuda:0" if device is None else device) - torch.cuda.set_device(self.device.index) + device = torch.device("cuda:0" if device is None else device) + torch.cuda.set_device(device.index) print("Single GPU training") else: - self.device = torch.device("cpu") + device = torch.device("cpu") print("CPU training") - if self.device.type == "cuda": + if device.type == "cuda": torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True + self.device = device + def setup_dataloader( self, dataset: Dataset, @@ -95,7 +97,7 @@ def setup_dataloader( print(f" Global batch size: {batch_size * self.world_size}") print(f" Train batches per GPU per epoch: {len(train_dataloader)}") - return train_dataloader + return train_dataloader, train_sampler def build_model( self, diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 656520da..e5b0d009 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -1,6 +1,6 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import Any, Literal +from typing import Any import numpy as np import torch @@ -22,8 +22,9 @@ class DatasetValue: target: torch.Tensor tilt_angle: int | float pixel_loc: tuple[int, int] | None = None # Only for INRDataset + projection_idx: int | None = None # Only for INRDataset pose: tuple[torch.nn.Parameter, torch.nn.Parameter, torch.nn.Parameter] | None = ( - None # If there is pose optimization. + None # If there is pose optimization. # Pose is tuple (shifts, z1, z3) ) @@ -58,8 +59,6 @@ def __init__( "The number of tilt projections should be in the first dimension of the dataset." ) - self.volume_size = (int(max(tilt_stack.shape)), tilt_stack.shape[1], tilt_stack.shape[2]) - # TODO: Maybe have the validation in here too. max_val = np.quantile(tilt_stack, 0.95) if type(tilt_stack) is not torch.Tensor: @@ -79,8 +78,33 @@ def __init__( self._reference_tilt_angle_idx = torch.argmin(torch.abs(self.tilt_angles)) # TODO: Implement AuxParams from old tomography_dataset.py here. - self.z1_param = torch + # TODO: The parameters won't be initialized unless .to(device) is called. + self._z1_angles = torch.zeros(self.learnable_tilts) + self._z3_angles = torch.zeros(self.learnable_tilts) + self._shifts = torch.zeros(self.learnable_tilts, 2) + + # Fixed zeros for reference tilt + self._z1_ref = torch.zeros(1) + self._z3_ref = torch.zeros(1) + self._shifts_ref = torch.zeros(1, 2) + # --- Class methods --- + @classmethod + def from_data( + cls, + tilt_stack: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, + learn_pose: bool = False, + ): + return cls(tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_pose=learn_pose) + + # --- Optimization Parameters --- + # @property + # def params(self): + # # TODO: Need to double check if this is correct way, also need to implement get_optimization_parameters @Arthur!! + # return self.parameters() + + # --- Forward pass --- @abstractmethod def forward( self, @@ -92,6 +116,30 @@ def forward( raise NotImplementedError("This method should be implemented in subclasses.") # --- Properties --- + @property + def tilt_stack(self) -> torch.Tensor: + return self._tilt_stack + + @tilt_stack.setter + def tilt_stack(self, tilt_stack: torch.Tensor): + self._tilt_stack = tilt_stack + + @property + def tilt_angles(self) -> torch.Tensor: + return self._tilt_angles + + @tilt_angles.setter + def tilt_angles(self, tilt_angles: torch.Tensor): + self._tilt_angles = tilt_angles + + @property + def learn_pose(self) -> bool: + return self._learn_pose + + @learn_pose.setter + def learn_pose(self, learn_pose: bool): + self._learn_pose = learn_pose + @property def reference_tilt_idx(self) -> int: return self._reference_tilt_angle_idx @@ -117,15 +165,48 @@ def params(self) -> dict[str, torch.nn.Parameter]: """ raise NotImplementedError("This method should be implemented in subclasses.") + @property + def z1_params(self) -> torch.nn.Parameter: + return self._z1_params + + @z1_params.setter + def z1_params(self, z1_angles: torch.Tensor, device: str): + self._z1_params = nn.Parameter(z1_angles.to(device)) + + @property + def z3_params(self) -> torch.nn.Parameter: + return self._z3_params + + @z3_params.setter + def z3_params(self, z3_angles: torch.Tensor, device: str): + self._z3_params = nn.Parameter(z3_angles.to(device)) + + @property + def shifts_params(self) -> torch.nn.Parameter: + return self._shifts_params + + @shifts_params.setter + def shifts_params(self, shifts: torch.Tensor, device: str): + self._shifts_params = nn.Parameter(shifts.to(device)) + # --- Helper Functions --- def to(self, device: str): + """ + Moves the dataset to the device, and also insantiates the aux params to the device. + """ self.tilt_stack = self.tilt_stack.to(device) self.tilt_angles = self.tilt_angles.to(device) + self._z1_params = nn.Parameter(self._z1_angles.to(device)) + self._z3_params = nn.Parameter(self._z3_angles.to(device)) + self._shifts_params = nn.Parameter(self._shifts.to(device)) + + self._z1_ref = self._z1_ref.to(device) + self._z3_ref = self._z3_ref.to(device) + self._shifts_ref = self._shifts_ref.to(device) + -class TomographyPixDataset( - TomographyDatasetBase -): # Dataset): TODO: Does this need to be a dataset? +class TomographyPixDataset(TomographyDatasetBase): """ Dataset class for pixel-based tomography, i.e AD, SIRT, WBP, etc... @@ -160,15 +241,6 @@ def forward( pixel_loc=None, ) - @classmethod - def from_data( - cls, - tilt_stack: Dataset3d | NDArray | torch.Tensor, - tilt_angles: NDArray | torch.Tensor, - learn_pose: bool = False, - ): - return cls(tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_pose=learn_pose) - class TomographyINRDataset(TomographyDatasetBase, Dataset): """ @@ -190,15 +262,137 @@ def __init__( ): super().__init__(tilt_stack, tilt_angles, learn_pose, token) - self.z1_param = torch.nn.Parameter(torch.zeros(self.learnable_tilts)) - + # --- Forward Pass w/ Params Method for OptimizerMixin --- def forward(self, dummy_input: Any = None): """ Forward pass for INR-based tomography. In the forward pass, the only parameters that are passed will be the shifts, z1 and z3 Euler angles. """ - pass + first_half_shifts = self.shifts_params[: self.reference_tilt_idx] + second_half_shifts = self.shifts_params[self.reference_tilt_idx :] + shifts = torch.cat([first_half_shifts, self._shifts_ref, second_half_shifts], dim=0) + + first_half_z1 = self.z1_params[: self.reference_tilt_idx] + second_half_z1 = self.z1_params[self.reference_tilt_idx :] + z1 = torch.cat([first_half_z1, self._z1_ref, second_half_z1], dim=0) + + first_half_z3 = self.z3_params[: self.reference_tilt_idx] + second_half_z3 = self.z3_params[self.reference_tilt_idx :] + z3 = torch.cat([first_half_z3, self._z3_ref, second_half_z3], dim=0) + + return DatasetValue( + target=None, + tilt_angle=None, + pixel_loc=None, + pose=(shifts, z1, z3), + ) + + @property + def params(self): + return self.parameters() + + def get_coords( + self, batch: dict[str, torch.Tensor], N: int, num_samples_per_ray: int + ) -> torch.Tensor: + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + # target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + + with torch.no_grad(): + batch_ray_coords = self.create_batch_rays(pixel_i, pixel_j, N, num_samples_per_ray) + + shifts, z1_params, z3_params = self.forward(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + transformed_rays = self.transform_batch_rays( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + all_coords = transformed_rays.view(-1, 3) + return all_coords + + @staticmethod + def create_batch_rays( + pixel_i: torch.Tensor, pixel_j: torch.Tensor, N: int, num_samples_per_ray: int + ) -> torch.Tensor: + batch_size = len(pixel_i) + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=pixel_i.device) + + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=pixel_i.device) + + rays[:, :, 0] = x_coords.unsqueeze(1) + rays[:, :, 1] = y_coords.unsqueeze(1) + rays[:, :, 2] = z_coords.unsqueeze(0) + + return rays + + @staticmethod + @torch.compile(mode="reduce-overhead") + def transform_batch_rays( + rays: torch.Tensor, + z1: torch.Tensor, + x: torch.Tensor, + z3: torch.Tensor, + shifts: torch.Tensor, + N: int, + sampling_rate: float, + ) -> torch.Tensor: + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + @staticmethod + def integrate_rays(rays: torch.Tensor, num_samples_per_ray: int) -> torch.Tensor: + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = rays.sum(dim=1) * step_size + + return predicted_values + + # --- Torch Dataset Methods --- def __getitem__( self, idx: int, @@ -206,7 +400,6 @@ def __getitem__( """ Gets the item for INR i.e, the project index, pixel value at (i, j), and the tilt angle. """ - pass actual_idx = idx @@ -219,6 +412,7 @@ def __getitem__( return DatasetValue( target=self.tilt_stack[projection_idx, pixel_i, pixel_j], tilt_angle=self.tilt_angles[projection_idx], + projection_idx=projection_idx, pixel_loc=(pixel_i, pixel_j), ) @@ -231,13 +425,6 @@ def __len__( return self._total_pixels - @property - def mode(self) -> Literal["train", "val"]: - """ - Returns the mode of the dataset. - """ - return self._mode - class TomographyINRPretrainDataset(Dataset): """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d4d8aab1..f35aae77 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -281,16 +281,12 @@ def to(self, device: str): class ObjectINR(ObjectConstraints, DDPMixin): - """ - Object model for INR objects. - """ - def __init__( self, - model: nn.Module, volume_shape: tuple[int, int, int], device: str = "cpu", rng: np.random.Generator | int | None = None, + model: nn.Module | None = None, ): super().__init__( shape=volume_shape, @@ -300,26 +296,30 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] + self.device = device + + # Register the network submodule (important: real nn.Module attribute) + if model is not None: + self.setup_distributed(device=device) + self._model = self.build_model(model) @classmethod def from_model( cls, - model: "nn.Module", + model: nn.Module, volume_shape: tuple[int, int, int], - device: str = None, # Have to make device a requirement here in distinguishing GPU vs CPU training + device: str = "cpu", rng: np.random.Generator | int | None = None, ): obj_model = cls( - model=model, volume_shape=volume_shape, device=device, rng=rng, + model=model, # ✅ build/register in __init__ ) - # Initialize DDP params and build the model. TODO: Not sure if this is the best way to do this? But to pretrain we need the model to be wrapped in DDP. obj_model.setup_distributed(device=device) - obj_model._model = obj_model.build_model(model) - + obj_model.to(device) return obj_model # --- Properties --- @@ -331,15 +331,15 @@ def model(self) -> "nn.Module": """ return self._model - @model.setter - def model(self, model: "nn.Module"): - """ - This doesn't work -- can't have setters for torch sub modules - https://github.com/pytorch/pytorch/issues/52664 + # @model.setter + # def model(self, model: "nn.Module"): + # """ + # This doesn't work -- can't have setters for torch sub modules + # https://github.com/pytorch/pytorch/issues/52664 - For now, upon initialization private variable `._model` is set to the built model. - """ - raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + # For now, upon initialization private variable `._model` is set to the built model. + # """ + # raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") @property def params(self): @@ -428,7 +428,7 @@ def pretrain( pretrain_dataset is not None ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. self.pretrain_dataset = pretrain_dataset - self.pretraining_dataloader = self.setup_dataloader( + self.pretraining_dataloader, self.pretraining_sampler = self.setup_dataloader( pretrain_dataset, batch_size, num_workers=num_workers ) @@ -587,5 +587,9 @@ def get_tv_loss( tv_loss += self.constraints.tv_vol * grad_norm.mean() return tv_loss + def to(self, device: str): + # self._model = self._model.to(device) + self._obj = self._obj.to(device) + ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 97dd2bf2..e9f45a20 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -4,6 +4,7 @@ import torch from tqdm.auto import tqdm +from quantem.core.ml.ddp import DDPMixin from quantem.tomography.dataset_models import DatasetModelType from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ObjectModelType @@ -14,7 +15,7 @@ from quantem.tomography_old.utils import gaussian_filter_2d_stack, gaussian_kernel_1d -class Tomography(TomographyOpt, TomographyBase): +class Tomography(TomographyOpt, TomographyBase, DDPMixin): """ Class for handling all ML tomography reconstruction methods. Automatic handling between AD and INR-based tomography. @@ -38,21 +39,95 @@ def from_models( def reconstruct( self, - obj_model: ObjectModelType, - dset: DatasetModelType, + # obj_model: ObjectModelType, + # dset: DatasetModelType, num_iter: int = 10, + batch_size: int = 1024, + num_workers: int = 32, reset: bool = False, optimizer_params: dict | None = None, scheduler_params: dict | None = None, - constraints=None, # TODO: What to pass into the constraints? + constraints: dict = {}, # TODO: What to pass into the constraints? loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), ): - raise NotImplementedError """ This function should be able to handle both AD and INR-based tomography reconstruction methods. I.e, auto-detection through the obj model type, while both share the same pose optimization. """ + # TODO: Prior to reconstruction, it is assumed that object + dataset are both in the correct devices. Need to implement a way to check this. + + if self.obj_model.device != self.dset.device: + raise ValueError( + f"Should never happen! obj_model and dset must be on the same device, got {self.obj_model.device} and {self.dset.device}" + ) + + if reset: + raise NotImplementedError("Reset is not implemented yet.") + + if optimizer_params is not None: + self.optimizer_params = optimizer_params + self.set_optimizers() + + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers() + + new_scheduler = reset + if new_scheduler: + raise NotImplementedError("New schedulers are not implemented yet.") + + # Setting up DDP + if not hasattr(self, "dataloader"): + self.dataloader, self.sampler = self.setup_dataloader( + self.dset, batch_size, num_workers=num_workers + ) + + self.obj_model.model.train() + + N = max(self.obj_model.obj.shape) + num_samples_per_ray = max(self.obj_model.obj.shape) + print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") + + for a0 in range(num_iter): + consistency_loss = 0.0 + total_loss = 0.0 + # self._reset_iter_constraints() + + if self.sampler is not None: + self.sampler.set_epoch(a0) + + for batch_idx, batch in enumerate(self.dataloader): + self.zero_grad_all() + with torch.autocast( + device_type=self.device.type, + dtype=torch.bfloat16, + enabled=True, + ): + all_coords = self.dset.get_coords(batch, N, num_samples_per_ray) + + all_densities = self.obj_model.forward(all_coords) + + integrated_densities = self.dset.integrate_rays( + all_densities, num_samples_per_ray + ) + + # batch_consistency_loss = loss_func(integrated_densities, batch["target_value"]) + batch_consistency_loss = torch.nn.functional.mse_loss( + integrated_densities, batch["target_value"] + ) + + # soft_constraints_loss = self._soft_constraints() + batch_loss = batch_consistency_loss # + soft_constraints_loss + batch_loss.backward() + self.step_optimizers() + total_loss += batch_loss.item() + consistency_loss += batch_consistency_loss.item() + + total_loss = total_loss / len(self.dataloader) + consistency_loss = consistency_loss / len(self.dataloader) + print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") + class TomographyConventional(TomographyBase): """ diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index fec0852b..c90d9ba6 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -2,13 +2,14 @@ from numpy.typing import NDArray from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml.ddp import DDPMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import DatasetModelType, TomographyDatasetBase from quantem.tomography.logger_tomography import LoggerTomography -from quantem.tomography.object_models import ConstraintsTomography, ObjectModelType +from quantem.tomography.object_models import ConstraintsTomography, ObjectBase, ObjectPixelated -class TomographyBase(AutoSerialize, RNGMixin): +class TomographyBase(AutoSerialize, RNGMixin, DDPMixin): """ A base class for performing electron tomography reconstructions. @@ -20,7 +21,7 @@ class TomographyBase(AutoSerialize, RNGMixin): def __init__( self, dset: DatasetModelType, - obj_model: ObjectModelType, + obj_model: ObjectBase, logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, @@ -30,15 +31,25 @@ def __init__( # raise RuntimeError("Use Dataset.from_* to instantiate this class.") super().__init__() + self.obj_model = obj_model self.dset = dset - self.obj_model = obj_model self.rng = rng self.device = device self.logger = logger # Loss self._epoch_losses: list[float] = [] + # DDP Initialization + # print("Checking if obj_model is a ObjectPixelated: ", not isinstance(obj_model, ObjectPixelated)) + if not isinstance(obj_model, ObjectPixelated): + print("Setting up DDP for obj_model") + self.setup_distributed(device=device) + # self._obj_model._model = self.build_model(obj_model) # Assuming when object is initialized it's already wrapped in DDP? + print("After DDP Setup", self._obj_model) + + self.dset = dset + self.dset.to(device) # --- Properties --- @property @@ -58,14 +69,15 @@ def obj_type(self) -> str: return self.obj_model.obj_type @property - def obj_model(self) -> ObjectModelType: + def obj_model(self) -> ObjectBase: return self._obj_model @obj_model.setter - def obj_model(self, obj_model: ObjectModelType): - if not isinstance(obj_model, ObjectModelType): - raise TypeError(f"obj_model should be a ObjectModelType, got {type(obj_model)}") + def obj_model(self, obj_model: ObjectBase): + if not isinstance(obj_model, ObjectBase): + raise TypeError(f"obj_model should be a ObjectBase, got {type(obj_model)}") self._obj_model = obj_model + print(self._obj_model) @property def constraints(self) -> ConstraintsTomography: @@ -95,8 +107,9 @@ def device(self) -> str: @device.setter def device(self, device: str): - if not isinstance(device, str): - raise TypeError(f"device should be a str, got {type(device)}") + print("Device trying to set: ", device) + # if not isinstance(device, str): + # raise TypeError(f"device should be a str, got {type(device)}") self._device = device self.to(device) From cc9188f79ecc3168286a4c28f5145c9c42f21711 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 22 Jan 2026 17:41:19 -0800 Subject: [PATCH 046/335] Working reconstruction loop, need to figure out this device stuff and can start including soft loss and general clean up. --- src/quantem/tomography/dataset_models.py | 65 ++++++++++++++++++------ src/quantem/tomography/object_models.py | 16 ++++-- src/quantem/tomography/tomography.py | 29 ++++++----- src/quantem/tomography/tomography_opt.py | 2 + 4 files changed, 80 insertions(+), 32 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index e5b0d009..fef3cf9d 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -189,6 +189,14 @@ def shifts_params(self) -> torch.nn.Parameter: def shifts_params(self, shifts: torch.Tensor, device: str): self._shifts_params = nn.Parameter(shifts.to(device)) + @property + def device(self) -> str: + return self._device + + @device.setter + def device(self, device: str): + self._device = device + # --- Helper Functions --- def to(self, device: str): """ @@ -205,6 +213,8 @@ def to(self, device: str): self._z3_ref = self._z3_ref.to(device) self._shifts_ref = self._shifts_ref.to(device) + self.device = device + class TomographyPixDataset(TomographyDatasetBase): """ @@ -281,12 +291,13 @@ def forward(self, dummy_input: Any = None): second_half_z3 = self.z3_params[self.reference_tilt_idx :] z3 = torch.cat([first_half_z3, self._z3_ref, second_half_z3], dim=0) - return DatasetValue( - target=None, - tilt_angle=None, - pixel_loc=None, - pose=(shifts, z1, z3), - ) + # return DatasetValue( + # target=None, + # tilt_angle=None, + # pixel_loc=None, + # pose=(shifts, z1, z3), + # ) + return shifts, z1, z3 @property def params(self): @@ -385,10 +396,16 @@ def transform_batch_rays( return transformed_rays @staticmethod - def integrate_rays(rays: torch.Tensor, num_samples_per_ray: int) -> torch.Tensor: + def integrate_rays( + rays: torch.Tensor, num_samples_per_ray: int, target_values_len: int + ) -> torch.Tensor: + ray_densities = rays.view( + target_values_len, + num_samples_per_ray, + ) step_size = 2.0 / (num_samples_per_ray - 1) - predicted_values = rays.sum(dim=1) * step_size + predicted_values = ray_densities.sum(dim=1) * step_size return predicted_values @@ -409,12 +426,19 @@ def __getitem__( pixel_i = remaining // self.tilt_stack.shape[1] pixel_j = remaining % self.tilt_stack.shape[1] - return DatasetValue( - target=self.tilt_stack[projection_idx, pixel_i, pixel_j], - tilt_angle=self.tilt_angles[projection_idx], - projection_idx=projection_idx, - pixel_loc=(pixel_i, pixel_j), - ) + # return DatasetValue( + # target=self.tilt_stack[projection_idx, pixel_i, pixel_j], + # tilt_angle=self.tilt_angles[projection_idx], + # projection_idx=projection_idx, + # pixel_loc=(pixel_i, pixel_j), + # ) + return { + "projection_idx": torch.tensor(projection_idx), + "pixel_i": torch.tensor(pixel_i), + "pixel_j": torch.tensor(pixel_j), + "phi": self.tilt_angles[projection_idx], # tensor + "target_value": self.tilt_stack[projection_idx, pixel_i, pixel_j], # tensor + } def __len__( self, @@ -422,8 +446,19 @@ def __len__( """ Returns the number of pixels in the tilt stack. """ + N = max(self.tilt_stack.shape) + return self.tilt_stack.shape[0] * N * N + + def to(self, device: str): + self._z1_params = nn.Parameter(self._z1_angles.to(device)) + self._z3_params = nn.Parameter(self._z3_angles.to(device)) + self._shifts_params = nn.Parameter(self._shifts.to(device)) + + self._z1_ref = self._z1_ref.to(device) + self._z3_ref = self._z3_ref.to(device) + self._shifts_ref = self._shifts_ref.to(device) - return self._total_pixels + self.device = device class TomographyINRPretrainDataset(Dataset): diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index f35aae77..cd156a23 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -283,13 +283,13 @@ def to(self, device: str): class ObjectINR(ObjectConstraints, DDPMixin): def __init__( self, - volume_shape: tuple[int, int, int], + shape: tuple[int, int, int], device: str = "cpu", rng: np.random.Generator | int | None = None, model: nn.Module | None = None, ): super().__init__( - shape=volume_shape, + shape=shape, device=device, rng=rng, _token=self._token, @@ -307,12 +307,12 @@ def __init__( def from_model( cls, model: nn.Module, - volume_shape: tuple[int, int, int], + shape: tuple[int, int, int], device: str = "cpu", rng: np.random.Generator | int | None = None, ): obj_model = cls( - volume_shape=volume_shape, + shape=shape, device=device, rng=rng, model=model, # ✅ build/register in __init__ @@ -375,6 +375,14 @@ def dtype(self) -> torch.dtype: # TODO: This is a temporary solution to get the dtype of the object. return torch.float32 + @property + def shape(self) -> tuple[int, int, int]: + return self._shape + + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape + # --- Helper Functions --- # Reset method that goes back to the pretrained weights. diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index e9f45a20..074d5891 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -57,10 +57,10 @@ def reconstruct( # TODO: Prior to reconstruction, it is assumed that object + dataset are both in the correct devices. Need to implement a way to check this. - if self.obj_model.device != self.dset.device: - raise ValueError( - f"Should never happen! obj_model and dset must be on the same device, got {self.obj_model.device} and {self.dset.device}" - ) + # if self.obj_model.device != self.dset.device: + # raise ValueError( + # f"Should never happen! obj_model and dset must be on the same device, got {self.obj_model.device} and {self.dset.device}" + # ) if reset: raise NotImplementedError("Reset is not implemented yet.") @@ -71,7 +71,7 @@ def reconstruct( if scheduler_params is not None: self.scheduler_params = scheduler_params - self.set_schedulers() + self.set_schedulers(scheduler_params) new_scheduler = reset if new_scheduler: @@ -85,8 +85,8 @@ def reconstruct( self.obj_model.model.train() - N = max(self.obj_model.obj.shape) - num_samples_per_ray = max(self.obj_model.obj.shape) + N = max(self.obj_model.shape) + num_samples_per_ray = max(self.obj_model.shape) print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") for a0 in range(num_iter): @@ -105,20 +105,23 @@ def reconstruct( enabled=True, ): all_coords = self.dset.get_coords(batch, N, num_samples_per_ray) + all_coords = all_coords.to( + device=self.device, dtype=torch.float32, non_blocking=True + ) all_densities = self.obj_model.forward(all_coords) integrated_densities = self.dset.integrate_rays( - all_densities, num_samples_per_ray + all_densities, num_samples_per_ray, len(batch["target_value"]) ) # batch_consistency_loss = loss_func(integrated_densities, batch["target_value"]) - batch_consistency_loss = torch.nn.functional.mse_loss( - integrated_densities, batch["target_value"] - ) + pred = integrated_densities.float() + target = batch["target_value"].to(self.device, non_blocking=True).float() + batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) - # soft_constraints_loss = self._soft_constraints() - batch_loss = batch_consistency_loss # + soft_constraints_loss + # soft_constraints_loss = self._soft_constraints() + batch_loss = batch_consistency_loss.float() # + soft_constraints_loss batch_loss.backward() self.step_optimizers() total_loss += batch_loss.item() diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 77b19cfc..2a148433 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -117,6 +117,8 @@ def set_schedulers(self, params: dict[str, dict], num_iter: int | None = None): self.obj_model.set_scheduler(scheduler_params, num_iter) elif key == "pose": self.dset.set_scheduler(scheduler_params, num_iter) + else: + raise ValueError(f"Unknown optimization key: {key}") def step_optimizers(self): for key in self.optimizer_params.keys(): From 7a46ff13dbd951e3fb9d6e0f7de63c5d4a454e72 Mon Sep 17 00:00:00 2001 From: smribet Date: Mon, 26 Jan 2026 08:43:13 -0800 Subject: [PATCH 047/335] change kwargs in read4stem --- src/quantem/core/io/file_readers.py | 28 ++++++++++++++++------------ 1 file changed, 16 insertions(+), 12 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index cb36f1de..7cb6fb5e 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -30,7 +30,7 @@ def read_4dstem( Index of the dataset to load if file contains multiple datasets. If None, automatically selects the first 4D dataset found. **kwargs: dict - Additional keyword arguments to pass to the Dataset4dstem constructor. + Additional keyword arguments to pass to the file reader. Returns -------- @@ -39,8 +39,12 @@ def read_4dstem( if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") + sampling_override = kwargs.pop("sampling", None) + origin_override = kwargs.pop("origin", None) + units_override = kwargs.pop("units", None) + file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader - data_list = file_reader(file_path) + data_list = file_reader(file_path, **kwargs) # If specific index provided, use it if dataset_index is not None: @@ -69,17 +73,18 @@ def read_4dstem( imported_axes = imported_data["axes"] - sampling = kwargs.pop( - "sampling", - [ax["scale"] for ax in imported_axes], + sampling = ( + sampling_override + if sampling_override is not None + else [ax["scale"] for ax in imported_axes] ) - origin = kwargs.pop( - "origin", - [ax["offset"] for ax in imported_axes], + origin = ( + origin_override if origin_override is not None else [ax["offset"] for ax in imported_axes] ) - units = kwargs.pop( - "units", - ["pixels" if ax["units"] == "1" else ax["units"] for ax in imported_axes], + units = ( + units_override + if units_override is not None + else ["pixels" if ax["units"] == "1" else ax["units"] for ax in imported_axes] ) dataset = Dataset4dstem.from_array( @@ -87,7 +92,6 @@ def read_4dstem( sampling=sampling, origin=origin, units=units, - **kwargs, ) return dataset From d08c334bd66e52a56bed969c9940bd3a03ad13a7 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 26 Jan 2026 14:05:37 -0800 Subject: [PATCH 048/335] Various u[dates --- src/quantem/core/ml/inr.py | 2 +- src/quantem/tomography/dataset_models.py | 10 +++- src/quantem/tomography/object_models.py | 66 +++++++++++++++++------ src/quantem/tomography/tomography.py | 41 ++++++++++---- src/quantem/tomography/tomography_base.py | 7 ++- 5 files changed, 95 insertions(+), 31 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 4cf4fc70..de43b6c4 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -113,7 +113,7 @@ def _build(self) -> None: if self.winner_initialization: with torch.no_grad(): self.net[0].linear.weight += ( - torch.randn_like(self.net[0].linear.weight) * 1 / self.first_omega_0 + torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 ) self.net[1].linear.weight += ( torch.randn_like(self.net[1].linear.weight) * 0.1 / self.hidden_omega_0 diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index fef3cf9d..bf09cb70 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -102,7 +102,7 @@ def from_data( # @property # def params(self): # # TODO: Need to double check if this is correct way, also need to implement get_optimization_parameters @Arthur!! - # return self.parameters() + # raise NotImplementedError("This method should be implemented in subclasses.") # --- Forward pass --- @abstractmethod @@ -215,6 +215,12 @@ def to(self, device: str): self.device = device + def get_optimization_parameters(self) -> list[nn.Parameter]: + """ + Get the parameters that should be optimized for this model. + """ + return list[nn.Parameter](self.params) + class TomographyPixDataset(TomographyDatasetBase): """ @@ -330,6 +336,8 @@ def get_coords( sampling_rate=1.0, ) all_coords = transformed_rays.view(-1, 3) + + all_coords = all_coords.to(self.device, dtype=torch.float32, non_blocking=True) return all_coords @staticmethod diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index cd156a23..0ce879f6 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -187,23 +187,17 @@ def apply_hard_constraints( # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. return obj2 - def apply_soft_constraints( - self, - obj: torch.Tensor, - ) -> torch.Tensor: - """ - Apply soft constraints to the object model. - - Only soft constraint here is the TV loss. - """ + # def apply_soft_constraints( + # self, + # obj: torch.Tensor, + # ) -> torch.Tensor: + # """ + # TODO: Already in BaseConstraints class. + # Apply soft constraints to the object model. - soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) - if self.constraints.tv_vol > 0: - tv_loss = self.get_tv_loss( - obj.unsqueeze(0).unsqueeze(0), factor=self.constraints.tv_vol - ) - soft_loss += tv_loss - return soft_loss + # Only soft constraint here is the TV loss. + # """ + # return NotImplementedError("Subclasses must implement this method.") @abstractmethod def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 0.0) -> torch.Tensor: @@ -262,6 +256,15 @@ def name(self) -> str: def obj_type(self) -> str: return "pixelated" + def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) + if self.constraints.tv_vol > 0: + tv_loss = self.get_tv_loss( + obj.unsqueeze(0).unsqueeze(0), factor=self.constraints.tv_vol + ) + soft_loss += tv_loss + return soft_loss + # --- Forward method --- def forward(self, dummy_input=None) -> torch.Tensor: return self.obj @@ -341,6 +344,33 @@ def model(self) -> "nn.Module": # """ # raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + def apply_soft_constraints( + self, + coords: torch.Tensor, + ) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=coords.device) + if self.constraints.tv_vol > 0: + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.model(tv_coords) + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + soft_loss += self.constraints.tv_vol * grad_norm.mean() + + return soft_loss + @property def params(self): return self.model.parameters() @@ -482,6 +512,10 @@ def _pretrain( loss.backward() epoch_loss += loss.item() + + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) + optimizer.step() optimizer.zero_grad() diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 074d5891..1f85b698 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,4 +1,4 @@ -from typing import Literal, Optional, Self, Tuple +from typing import List, Literal, Optional, Self, Tuple import numpy as np import torch @@ -49,6 +49,7 @@ def reconstruct( scheduler_params: dict | None = None, constraints: dict = {}, # TODO: What to pass into the constraints? loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), + num_samples_per_ray: int | List[Tuple[int, int]] = None, ): """ This function should be able to handle both AD and INR-based tomography reconstruction methods. @@ -73,6 +74,9 @@ def reconstruct( self.scheduler_params = scheduler_params self.set_schedulers(scheduler_params) + if constraints is not None: + self.obj_model.constraints = constraints + new_scheduler = reset if new_scheduler: raise NotImplementedError("New schedulers are not implemented yet.") @@ -86,7 +90,15 @@ def reconstruct( self.obj_model.model.train() N = max(self.obj_model.shape) - num_samples_per_ray = max(self.obj_model.shape) + + if num_samples_per_ray is None: + num_samples_per_ray = max(self.obj_model.shape) + else: + if isinstance(num_samples_per_ray, int): + num_samples_per_ray = num_samples_per_ray + else: + print("num_samples_per_ray schedule provided.") + print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") for a0 in range(num_iter): @@ -97,6 +109,12 @@ def reconstruct( if self.sampler is not None: self.sampler.set_epoch(a0) + if isinstance(num_samples_per_ray, list): + print(f"num_samples_per_ray[a0][1]: {num_samples_per_ray[a0][1]}") + curr_num_samples_per_ray = num_samples_per_ray[a0][1] + else: + curr_num_samples_per_ray = num_samples_per_ray + print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") for batch_idx, batch in enumerate(self.dataloader): self.zero_grad_all() with torch.autocast( @@ -104,15 +122,15 @@ def reconstruct( dtype=torch.bfloat16, enabled=True, ): - all_coords = self.dset.get_coords(batch, N, num_samples_per_ray) - all_coords = all_coords.to( - device=self.device, dtype=torch.float32, non_blocking=True - ) + all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) + # all_coords = all_coords.to( + # device=self.device, dtype=torch.float32, non_blocking=True + # ) all_densities = self.obj_model.forward(all_coords) integrated_densities = self.dset.integrate_rays( - all_densities, num_samples_per_ray, len(batch["target_value"]) + all_densities, curr_num_samples_per_ray, len(batch["target_value"]) ) # batch_consistency_loss = loss_func(integrated_densities, batch["target_value"]) @@ -120,9 +138,14 @@ def reconstruct( target = batch["target_value"].to(self.device, non_blocking=True).float() batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) - # soft_constraints_loss = self._soft_constraints() - batch_loss = batch_consistency_loss.float() # + soft_constraints_loss + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords) + print(f"soft_constraints_loss: {soft_constraints_loss.item()}") + batch_loss = batch_consistency_loss.float() + soft_constraints_loss batch_loss.backward() + + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.obj_model.model.parameters(), max_norm=1.0) + self.step_optimizers() total_loss += batch_loss.item() consistency_loss += batch_consistency_loss.item() diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index c90d9ba6..c4bfcd3d 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -46,7 +46,7 @@ def __init__( print("Setting up DDP for obj_model") self.setup_distributed(device=device) # self._obj_model._model = self.build_model(obj_model) # Assuming when object is initialized it's already wrapped in DDP? - print("After DDP Setup", self._obj_model) + # print("After DDP Setup", self._obj_model) self.dset = dset self.dset.to(device) @@ -74,10 +74,9 @@ def obj_model(self) -> ObjectBase: @obj_model.setter def obj_model(self, obj_model: ObjectBase): - if not isinstance(obj_model, ObjectBase): - raise TypeError(f"obj_model should be a ObjectBase, got {type(obj_model)}") + # if not isinstance(obj_model, ObjectBase): + # raise TypeError(f"obj_model should be a ObjectBase, got {type(obj_model)}") self._obj_model = obj_model - print(self._obj_model) @property def constraints(self) -> ConstraintsTomography: From 9a37e05de8c376b16ee1fc5ac3f7937552caa801 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 26 Jan 2026 17:09:47 -0800 Subject: [PATCH 049/335] Logger implementation --- src/quantem/core/ml/constraints.py | 52 +++++++++++++------ src/quantem/tomography/logger_tomography.py | 57 +++++++++++++++++++++ src/quantem/tomography/object_models.py | 53 +++++-------------- src/quantem/tomography/tomography.py | 30 +++++++++-- src/quantem/tomography/tomography_base.py | 22 ++++++-- 5 files changed, 148 insertions(+), 66 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index e6d995f2..990c240e 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -1,17 +1,45 @@ from abc import ABC, abstractmethod +from copy import deepcopy from dataclasses import dataclass -from typing import Any +from typing import Any, Self +import numpy as np import torch -@dataclass +@dataclass(slots=True) class Constraints(ABC): """ Needs to be implemented in all object models that inherit from BaseConstraints. """ - pass + soft_constraint_keys = [] + hard_constraint_keys = [] + + @property + def allowed_keys(self) -> list[str]: + """ + List of all allowed keys. + """ + return self.hard_constraint_keys + self.soft_constraint_keys + + def copy(self) -> Self: + """ + Copy the constraints. + """ + return deepcopy(self) + + def __str__(self) -> str: + hard = "\n".join(f"{key}: {getattr(self, key)}" for key in self.hard_constraint_keys) + soft = "\n".join(f"{key}: {getattr(self, key)}" for key in self.soft_constraint_keys) + + return ( + "Constraints:\n" + " Hard constraints:\n" + f" {hard.replace('\n', '\n ')}\n" + " Soft constraints:\n" + f" {soft.replace('\n', '\n ')}" + ) class BaseConstraints(ABC): @@ -24,10 +52,13 @@ class BaseConstraints(ABC): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self._soft_constraint_loss = {} - self._epoch_constraint_losses = {} + self._soft_constraint_losses = [] self.constraints = self.DEFAULT_CONSTRAINTS.copy() + @property + def soft_constraint_losses(self) -> list[float]: + return np.array(self._soft_constraint_losses) + @property def constraints(self) -> Constraints: """ @@ -44,21 +75,10 @@ def constraints(self, constraints: Constraints | dict[str, Any]): self._constraints = constraints elif isinstance(constraints, dict): for key, value in constraints.items(): - if key not in self._constraints.allowed_keys: - raise ValueError(f"Invalid constraint key: {key}") setattr(self._constraints, key, value) else: raise ValueError(f"Invalid constraints type: {type(constraints)}") - def add_constraint(self, key: str, value: Any): - """ - Add a constraint to the constraints class. - Note the allowed keys should be implemented for each constraint subclass. - """ - if key not in self._constraints.allowed_keys: - raise ValueError(f"Invalid constraint key: {key}") - setattr(self._constraints, key, value) - # --- Required methods tha tneeds to implemented in subclasses --- @abstractmethod def apply_hard_constraints(self, *args, **kwargs) -> torch.Tensor: diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index d9f0f8bb..256e47df 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -1,4 +1,9 @@ +import matplotlib.pyplot as plt +import torch + from quantem.core.ml.logger import LoggerBase +from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.object_models import ObjectModelType class LoggerTomography(LoggerBase): @@ -19,3 +24,55 @@ def log_epoch(self, epoch: int, loss: float, tilt_series_loss: float, soft_loss: self.log_scalar("loss/total", loss, epoch) self.log_scalar("loss/tilt_series", tilt_series_loss, epoch) self.log_scalar("loss/soft", soft_loss, epoch) + + def log_iter( + self, + object_model: ObjectModelType, + iter: int, + consistency_loss: float, + total_loss: float, + num_samples_per_ray: int | None = None, + ): + self.log_scalar("loss/consistency", consistency_loss, iter) + self.log_scalar("loss/total", total_loss, iter) + self.log_scalar("loss/soft", object_model._soft_constraint_losses[-1], iter) + self.log_scalar("num_samples_per_ray", num_samples_per_ray, iter) + + def log_iter_images( + self, + object_model: ObjectModelType, + dataset_model: DatasetModelType, + iter: int, + logger_cmap: str = "turbo", + ): + with torch.no_grad(): + vol = object_model.create_volume(return_vol=True) + z1_vals = dataset_model.z1_params.cpu().numpy() + z3_vals = dataset_model.z3_params.cpu().numpy() + shifts_vals = dataset_model.shifts_params.cpu().numpy() + + self.log_image("volume/sum_z", vol.sum(axis=0), iter, logger_cmap) + self.log_image("volume/sum_y", vol.sum(axis=1), iter, logger_cmap) + self.log_image("volume/sum_x", vol.sum(axis=2), iter, logger_cmap) + + # Plotting z1 and z3 vals + fig, ax = plt.subplots() + ax.plot(z1_vals, label="Z1") + ax.plot(z3_vals, label="Z3") + ax.legend() + ax.set_title("Z1 and Z3 Angles") + ax.set_xlabel("Tilt Image") + ax.set_ylabel("Degree") + self.log_figure("z1_z3_angles", fig, iter) + plt.close(fig) + + # Plotting shifts + fig, ax = plt.subplots() + ax.plot(shifts_vals[:, 0], label="Shifts X") + ax.plot(shifts_vals[:, 1], label="Shifts Y") + ax.legend() + ax.set_title("Shifts") + ax.set_xlabel("Tilt Image") + ax.set_ylabel("Pixel") + self.log_figure("shifts", fig, iter) + plt.close(fig) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0ce879f6..d01f2918 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Self +from typing import Any, Callable import numpy as np import torch @@ -17,8 +17,8 @@ from quantem.tomography.dataset_models import TomographyINRPretrainDataset -@dataclass -class ConstraintsTomography(Constraints): +@dataclass(slots=True) +class DefaultConstraintsTomography(Constraints): """ Data class for all constraints that can be applied to the object model. """ @@ -32,41 +32,8 @@ class ConstraintsTomography(Constraints): # Soft Constraints tv_vol: float = 0.0 - @property - def hard_constraint_keys(self) -> list[str]: - """ - List of hard constraint keys. - """ - return ["positivity", "shrinkage", "circular_mask", "fourier_filter"] - - @property - def soft_constraint_keys(self) -> list[str]: - """ - List of soft constraint keys. - """ - return ["tv_vol"] - - @property - def allowed_keys(self) -> list[str]: - """ - List of all allowed keys. - """ - return self.hard_constraint_keys + self.soft_constraint_keys - - def copy(self) -> Self: - """ - Copy the constraints. - """ - return deepcopy(self) - - def __str__(self) -> str: - return f"""Constraints: - Positivity: {self.positivity} - Shrinkage: {self.shrinkage} - Circular Mask: {self.circular_mask} - Fourier Filter: {self.fourier_filter} - TV Volume: {self.tv_vol} - """ + soft_constraint_keys = ["tv_vol"] + hard_constraint_keys = ["positivity", "shrinkage", "circular_mask", "fourier_filter"] class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): @@ -162,8 +129,8 @@ def to(self, *args, **kwargs): raise NotImplementedError -class ObjectConstraints(ObjectBase, BaseConstraints): - DEFAULT_CONSTRAINTS = ConstraintsTomography() +class ObjectConstraints(BaseConstraints, ObjectBase): + DEFAULT_CONSTRAINTS = DefaultConstraintsTomography() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -533,6 +500,7 @@ def _pretrain( def create_volume( self, + return_vol: bool = False, ): N = max(self._shape) with torch.no_grad(): @@ -599,7 +567,10 @@ def create_volume( else: pred_full = outputs.reshape(N, N, N).float() - self._obj = pred_full.detach().cpu() + if return_vol: + return pred_full.detach().cpu() + else: + self._obj = pred_full.detach().cpu() def get_tv_loss( self, diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 1f85b698..49987edd 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -104,13 +104,13 @@ def reconstruct( for a0 in range(num_iter): consistency_loss = 0.0 total_loss = 0.0 + epoch_soft_constraint_loss = 0.0 # self._reset_iter_constraints() if self.sampler is not None: self.sampler.set_epoch(a0) if isinstance(num_samples_per_ray, list): - print(f"num_samples_per_ray[a0][1]: {num_samples_per_ray[a0][1]}") curr_num_samples_per_ray = num_samples_per_ray[a0][1] else: curr_num_samples_per_ray = num_samples_per_ray @@ -136,10 +136,10 @@ def reconstruct( # batch_consistency_loss = loss_func(integrated_densities, batch["target_value"]) pred = integrated_densities.float() target = batch["target_value"].to(self.device, non_blocking=True).float() - batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) + batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords) - print(f"soft_constraints_loss: {soft_constraints_loss.item()}") + epoch_soft_constraint_loss += soft_constraints_loss.item() batch_loss = batch_consistency_loss.float() + soft_constraints_loss batch_loss.backward() @@ -150,10 +150,33 @@ def reconstruct( total_loss += batch_loss.item() consistency_loss += batch_consistency_loss.item() + # TODO: Maybe reorganize the losses so that the order makes sense lol. total_loss = total_loss / len(self.dataloader) consistency_loss = consistency_loss / len(self.dataloader) + epoch_soft_constraint_loss = epoch_soft_constraint_loss / len(self.dataloader) print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") + self._epoch_losses.append(total_loss) + self._consistency_losses.append(consistency_loss) + self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) + + if self.logger is not None and self.global_rank == 0: + self.logger.log_iter( + object_model=self.obj_model, + iter=a0, + consistency_loss=consistency_loss, + total_loss=total_loss, + num_samples_per_ray=curr_num_samples_per_ray, + ) + if self.logger.log_images_every > 0 and a0 % self.logger.log_images_every == 0: + self.logger.log_iter_images( + object_model=self.obj_model, + dataset_model=self.dset, + iter=a0, + ) + + self.logger.flush() + class TomographyConventional(TomographyBase): """ @@ -281,7 +304,6 @@ def _reconstruction_epoch( self.obj_model.obj += correction if gaussian_kernel is not None: - print(self.obj_model.obj.shape) self.obj_model.obj = gaussian_filter_2d_stack(self.obj_model.obj, gaussian_kernel) loss = torch.mean(torch.abs(error)) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index c4bfcd3d..2f8d6fc7 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -6,7 +6,11 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import DatasetModelType, TomographyDatasetBase from quantem.tomography.logger_tomography import LoggerTomography -from quantem.tomography.object_models import ConstraintsTomography, ObjectBase, ObjectPixelated +from quantem.tomography.object_models import ( + DefaultConstraintsTomography, + ObjectBase, + ObjectPixelated, +) class TomographyBase(AutoSerialize, RNGMixin, DDPMixin): @@ -40,6 +44,7 @@ def __init__( # Loss self._epoch_losses: list[float] = [] + self._consistency_losses: list[float] = [] # DDP Initialization # print("Checking if obj_model is a ObjectPixelated: ", not isinstance(obj_model, ObjectPixelated)) if not isinstance(obj_model, ObjectPixelated): @@ -79,14 +84,14 @@ def obj_model(self, obj_model: ObjectBase): self._obj_model = obj_model @property - def constraints(self) -> ConstraintsTomography: + def constraints(self) -> DefaultConstraintsTomography: return self.obj_model.constraints @constraints.setter - def constraints(self, constraints: ConstraintsTomography): - if not isinstance(constraints, ConstraintsTomography): + def constraints(self, constraints: DefaultConstraintsTomography): + if not isinstance(constraints, DefaultConstraintsTomography): raise TypeError( - f"constraints should be a ConstraintsTomography, got {type(constraints)}" + f"constraints should be a DefaultConstraintsTomography, got {type(constraints)}" ) self.obj_model.constraints = constraints @@ -119,6 +124,13 @@ def epoch_losses(self) -> NDArray: """ return np.array(self._epoch_losses) + @property + def consistency_losses(self) -> NDArray: + """ + Returns the consistency loss for each epoch ran. + """ + return np.array(self._consistency_losses) + @property def num_epochs(self) -> int: return len(self._epoch_losses) From cfee59a1fc4b63a3cf68f4a6f80a6337c41f6cfe Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 27 Jan 2026 14:24:45 -0800 Subject: [PATCH 050/335] DDP bug where some projection idx's don't get optimized. --- src/quantem/core/ml/ddp.py | 4 +- src/quantem/tomography/dataset_models.py | 2 +- src/quantem/tomography/logger_tomography.py | 19 +- src/quantem/tomography/object_models.py | 2 - src/quantem/tomography/tomography.py | 58 +- src/quantem/tomography_old/tomography.py | 1766 +++++++++---------- 6 files changed, 938 insertions(+), 913 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 0d38e67f..b6ec536f 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -68,12 +68,14 @@ def setup_dataloader( # TODO: Implement validation dataloader if self.world_size > 1: + shuffle = True train_sampler = DistributedSampler( dataset, num_replicas=self.world_size, rank=self.global_rank, - shuffle=True, + shuffle=shuffle, ) + shuffle = False else: train_sampler = None diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index bf09cb70..b18090f1 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -317,7 +317,7 @@ def get_coords( # target_values = batch["target_value"].to(self.device, non_blocking=True) phis = batch["phi"].to(self.device, non_blocking=True) projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - + print(f"projection_indices: {projection_indices}") with torch.no_grad(): batch_ray_coords = self.create_batch_rays(pixel_i, pixel_j, N, num_samples_per_ray) diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index 256e47df..16e6d9f3 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -40,22 +40,24 @@ def log_iter( def log_iter_images( self, - object_model: ObjectModelType, + pred_volume: torch.Tensor, dataset_model: DatasetModelType, iter: int, logger_cmap: str = "turbo", ): + with torch.no_grad(): - vol = object_model.create_volume(return_vol=True) - z1_vals = dataset_model.z1_params.cpu().numpy() - z3_vals = dataset_model.z3_params.cpu().numpy() - shifts_vals = dataset_model.shifts_params.cpu().numpy() + z1_vals = dataset_model.z1_params.detach().cpu().numpy() + z3_vals = dataset_model.z3_params.detach().cpu().numpy() + shifts_vals = dataset_model.shifts_params.detach().cpu().numpy() - self.log_image("volume/sum_z", vol.sum(axis=0), iter, logger_cmap) - self.log_image("volume/sum_y", vol.sum(axis=1), iter, logger_cmap) - self.log_image("volume/sum_x", vol.sum(axis=2), iter, logger_cmap) + print("Logging volume...") + self.log_image("volume/sum_z", pred_volume.sum(axis=0), iter, logger_cmap) + self.log_image("volume/sum_y", pred_volume.sum(axis=1), iter, logger_cmap) + self.log_image("volume/sum_x", pred_volume.sum(axis=2), iter, logger_cmap) # Plotting z1 and z3 vals + print("Plotting z1 and z3 angles...") fig, ax = plt.subplots() ax.plot(z1_vals, label="Z1") ax.plot(z3_vals, label="Z3") @@ -67,6 +69,7 @@ def log_iter_images( plt.close(fig) # Plotting shifts + print("Plotting shifts...") fig, ax = plt.subplots() ax.plot(shifts_vals[:, 0], label="Shifts X") ax.plot(shifts_vals[:, 1], label="Shifts Y") diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d01f2918..6ab64067 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -542,7 +542,6 @@ def create_volume( for _ in range(self.world_size) ] dist.all_gather(all_sizes, output_size) - max_size = max(size.item() for size in all_sizes) if outputs.shape[0] < max_size: @@ -558,7 +557,6 @@ def create_volume( for _ in range(self.world_size) ] dist.all_gather(gathered_outputs, outputs_padded.contiguous()) - trimmed_outputs = [] for rank, size in enumerate(all_sizes): trimmed_outputs.append(gathered_outputs[rank][: size.item()]) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 49987edd..b0dfc92f 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -13,6 +13,7 @@ from quantem.tomography.tomography_opt import TomographyOpt from quantem.tomography.utils import torch_phase_cross_correlation from quantem.tomography_old.utils import gaussian_filter_2d_stack, gaussian_kernel_1d +import torch.distributed as dist class Tomography(TomographyOpt, TomographyBase, DDPMixin): @@ -100,6 +101,8 @@ def reconstruct( print("num_samples_per_ray schedule provided.") print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") + + for a0 in range(num_iter): consistency_loss = 0.0 @@ -114,7 +117,9 @@ def reconstruct( curr_num_samples_per_ray = num_samples_per_ray[a0][1] else: curr_num_samples_per_ray = num_samples_per_ray - print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") + + if self.global_rank == 0: + print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") for batch_idx, batch in enumerate(self.dataloader): self.zero_grad_all() with torch.autocast( @@ -123,9 +128,6 @@ def reconstruct( enabled=True, ): all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) - # all_coords = all_coords.to( - # device=self.device, dtype=torch.float32, non_blocking=True - # ) all_densities = self.obj_model.forward(all_coords) @@ -133,7 +135,6 @@ def reconstruct( all_densities, curr_num_samples_per_ray, len(batch["target_value"]) ) - # batch_consistency_loss = loss_func(integrated_densities, batch["target_value"]) pred = integrated_densities.float() target = batch["target_value"].to(self.device, non_blocking=True).float() @@ -151,30 +152,51 @@ def reconstruct( consistency_loss += batch_consistency_loss.item() # TODO: Maybe reorganize the losses so that the order makes sense lol. + total_loss = total_loss / len(self.dataloader) consistency_loss = consistency_loss / len(self.dataloader) epoch_soft_constraint_loss = epoch_soft_constraint_loss / len(self.dataloader) - print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") + + metrics = torch.tensor( + [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device + ) + + if self.world_size > 1: + dist.all_reduce(metrics, dist.ReduceOp.AVG) + + total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() + + if self.global_rank == 0: + print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") self._epoch_losses.append(total_loss) self._consistency_losses.append(consistency_loss) self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) - if self.logger is not None and self.global_rank == 0: - self.logger.log_iter( - object_model=self.obj_model, - iter=a0, - consistency_loss=consistency_loss, - total_loss=total_loss, - num_samples_per_ray=curr_num_samples_per_ray, - ) - if self.logger.log_images_every > 0 and a0 % self.logger.log_images_every == 0: - self.logger.log_iter_images( + if self.logger is not None: + if self.logger.log_images_every > 0 and self.num_epochs % self.logger.log_images_every == 0: + print("Creating volume...") + pred_full = self.obj_model.create_volume(return_vol=True) + + if self.global_rank == 0: + print("Logging images...") + self.logger.log_iter_images( + pred_volume=pred_full, + dataset_model=self.dset, + iter=self.num_epochs, + ) + + + if self.global_rank == 0: + self.logger.log_iter( object_model=self.obj_model, - dataset_model=self.dset, - iter=a0, + iter=self.num_epochs, + consistency_loss=consistency_loss, + total_loss=total_loss, + num_samples_per_ray=curr_num_samples_per_ray, ) + self.logger.flush() diff --git a/src/quantem/tomography_old/tomography.py b/src/quantem/tomography_old/tomography.py index 7be24397..ec8c5db3 100644 --- a/src/quantem/tomography_old/tomography.py +++ b/src/quantem/tomography_old/tomography.py @@ -1,883 +1,883 @@ -# import matplotlib.pyplot as plt -# import numpy as np -# import torch -# import torch.distributed as dist -# from torch.nn import SmoothL1Loss -# from torch.utils.tensorboard import SummaryWriter - -# # from torch_radon.radon import ParallelBeam as Radon -# from tqdm.auto import tqdm - -# from quantem.core.ml.loss_functions import ( -# CharbonnierLoss, -# L1Loss, -# LLMSELoss, -# MSELogMSELoss, -# MSELoss, -# ) - -# # Temporary imports for TomographyNERF -# from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise -# from quantem.tomography.tomography_base import TomographyBase -# from quantem.tomography.tomography_conv import TomographyConv -# from quantem.tomography.tomography_ml import TomographyML -# from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ - - -# # Temporary aux class for TomographyNERF -# # TODO: Maybe put this in INN? -# def get_num_samples_per_ray(N: int, epoch: int): -# """Increase number of samples per ray at specific epochs.""" -# # Exponential schedule - -# epochs = np.linspace(0, 10, 5, dtype=int) -# # schedule = np.linspace(20, N, 5, dtype=int) -# schedule = np.array([20, 100, 150, 200, 200]) -# # schedule = np.array([200, 200, 200, 200, 200]) -# # schedule = np.array([20, 100, 250, 500, 500]) -# # schedule = np.array([500, 500, 500, 500, 500]) -# # schedule = np.array([300, 300, 300, 300, 300]) -# # schedule = np.exp(schedule) -# schedule_warmup = dict[int, int](zip(epochs, schedule)) - -# for epoch_threshold, samples in schedule_warmup.items(): -# if epoch >= epoch_threshold: -# num_samples = samples - -# return num_samples - - -# class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): -# """ -# Top level class for either using conventional or ML-based reconstruction methods -# for tomography. -# """ - -# def __init__( -# self, -# dataset, -# volume_obj, -# device, -# _token, -# ): -# super().__init__(dataset, volume_obj, device, _token) - -# # TODO: More elegant way of doing this. -# self.global_epochs = 0 -# self.ddp_instantiated = False -# self.pretraining_instantiated = False - -# # --- Reconstruction Method --- - -# def sirt_recon( -# self, -# num_iterations: int = 10, -# inline_alignment: bool = False, -# enforce_positivity: bool = True, -# volume_shape: tuple = None, -# reset: bool = True, -# smoothing_sigma: float = None, -# shrinkage: float = None, -# filter_name: str = "hamming", -# circle: bool = True, -# plot_loss: bool = False, -# ): -# num_angles, num_rows, num_cols = self.dataset.tilt_series.shape -# sirt_tilt_series = self.dataset.tilt_series.clone() -# sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) - -# hard_constraints = { -# "positivity": enforce_positivity, -# "shrinkage": shrinkage, -# } -# self.volume_obj.hard_constraints = hard_constraints - -# if volume_shape is None: -# volume_shape = (num_rows, num_rows, num_rows) -# else: -# D, H, W = volume_shape - -# if reset: -# self.volume_obj.reset() -# self.loss = [] - -# proj_forward = torch.zeros_like(self.dataset.tilt_series) - -# pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") - -# if smoothing_sigma is not None: -# gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) -# else: -# gaussian_kernel = None - -# print( -# "Devices", -# sirt_tilt_series.device, -# proj_forward.device, -# self.dataset.tilt_angles.device, -# ) - -# for iter in pbar: -# proj_forward, loss = self._sirt_run_epoch( -# tilt_series=sirt_tilt_series, -# proj_forward=proj_forward, -# angles=self.dataset.tilt_angles, -# inline_alignment=iter > 0 and inline_alignment, -# filter_name=filter_name, -# gaussian_kernel=gaussian_kernel, -# circle=circle, -# ) - -# pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") - -# self.loss.append(loss.item()) - -# self.sirt_recon_vol = self.volume_obj - -# # Permutation due to sinogram ordering. -# self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) - -# if plot_loss: -# self.plot_loss() - -# # TODO: ML Recon which has NeRF and AD depending on the object type. -# # TODO: Temporary - -# def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): -# """Create projection rays for entire batch simultaneously.""" -# batch_size = len(pixel_i) - -# # Convert all pixels to normalized coordinates -# x_coords = (pixel_j / (N - 1)) * 2 - 1 -# y_coords = (pixel_i / (N - 1)) * 2 - 1 -# # TODO: maybe pixel_j.device? -# # Create z coordinates -# z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - -# # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] -# rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - -# # Fill coordinates efficiently -# rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray -# rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray -# rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - -# return rays - -# @torch.compile(mode="reduce-overhead") -# def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): -# # Step 1: Apply shifts -# shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) -# shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - -# rays_x = rays[:, :, 0] - shift_x_norm -# rays_y = rays[:, :, 1] - shift_y_norm -# rays_z = rays[:, :, 2] - -# # Rotation 1: Z(-z3) -# theta = torch.deg2rad(-z3).view(-1, 1) -# cos_t = torch.cos(theta) -# sin_t = torch.sin(theta) - -# rays_x_rot1 = cos_t * rays_x - sin_t * rays_y -# rays_y_rot1 = sin_t * rays_x + cos_t * rays_y -# rays_z_rot1 = rays_z - -# # Rotation 2: X(x) -# theta = torch.deg2rad(x).view(-1, 1) -# cos_t = torch.cos(theta) -# sin_t = torch.sin(theta) - -# rays_x_rot2 = rays_x_rot1 -# rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 -# rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - -# # Rotation 3: Z(-z1) -# theta = torch.deg2rad(-z1).view(-1, 1) -# cos_t = torch.cos(theta) -# sin_t = torch.sin(theta) - -# rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 -# rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 -# rays_z_final = rays_z_rot2 - -# # Stack the final result -# transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - -# return transformed_rays - -# # TODO: Temp logger -# def setup_logger(self, log_path): -# if self.global_rank == 0: -# self.temp_logger = SummaryWriter(log_dir=log_path) -# else: -# self.temp_logger = None - -# def pretrain( -# self, -# # volume_dataset: PretrainVolumeDataset, -# obj: ObjectINN, -# batch_size: int, -# soft_constraints: dict = None, -# log_path: str = None, -# optimizer_params: dict = None, -# epochs: int = 100, -# viz_freq: int = 1, -# consistency_criterion: str = "mse", -# ): -# if not self.pretraining_instantiated: -# self.setup_distributed() -# # self.setup_pretraining_dataloader(volume_dataset, batch_size) -# self.pretraining_instantiated = True -# obj.model = self.build_model(obj._model) -# self.obj = obj - -# if soft_constraints is not None: -# self.obj.soft_constraints = soft_constraints - -# if not hasattr(self, "temp_logger"): -# self.setup_logger(log_path=log_path) - -# if optimizer_params is not None: -# optimizer_params = self.scale_lr(optimizer_params) -# self.optimizer_params = optimizer_params -# self.set_optimizers() -# device_type = self.device.type -# consistency_loss_fn = None -# if consistency_criterion[0].lower() == "mse": -# consistency_loss_fn = MSELoss() -# elif consistency_criterion[0].lower() == "l1": -# consistency_loss_fn = L1Loss() -# elif consistency_criterion[0].lower() == "mse_log": -# consistency_loss_fn = MSELogMSELoss() -# elif consistency_criterion[0].lower() == "llmse": -# consistency_loss_fn = LLMSELoss() -# elif consistency_criterion[0].lower() == "smooth_l1": -# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) -# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": -# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( -# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 -# # ) -# pass -# elif consistency_criterion[0].lower() == "charbonnier": -# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") -# else: -# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - -# for epoch in range(epochs): -# if self.pretraining_sampler is not None: -# self.pretraining_sampler.set_epoch(epoch) - -# self.obj.model.train() - -# epoch_loss = 0.0 -# epoch_consistency_loss = 0.0 -# epoch_tv_loss = 0.0 -# num_batches = 0 - -# for batch_idx, batch in enumerate(self.pretraining_dataloader): -# coords = batch["coords"].to(self.device, non_blocking=True) -# target = batch["target"].to(self.device, non_blocking=True) - -# for _, opt in self.optimizers.items(): -# opt.zero_grad() - -# with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): -# outputs = self.obj.forward(coords) -# consistency_loss = consistency_loss_fn(outputs, target) -# tv_loss = self.obj.apply_soft_constraints(coords) - -# loss = consistency_loss + tv_loss - -# loss.backward() - -# epoch_loss += loss.detach() -# epoch_consistency_loss += consistency_loss.detach() -# epoch_tv_loss += tv_loss.detach() - -# torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) - -# for _, opt in self.optimizers.items(): -# opt.step() -# epoch_loss += loss.detach() -# num_batches += 1 - -# if epoch % viz_freq == 0 or epoch == epochs - 1: -# avg_loss = epoch_loss / num_batches -# with torch.no_grad(): -# self.obj.create_volume( -# world_size=self.world_size, -# global_rank=self.global_rank, -# # ray_size=volume_dataset.N, -# ) -# pred_full = self.obj.obj -# loss_tensor = avg_loss.clone().detach() -# avg_loss = loss_tensor.item() -# avg_tv_loss = epoch_tv_loss / num_batches -# avg_consistency_loss = epoch_consistency_loss / num_batches - -# metrics = torch.tensor( -# [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device -# ) -# if self.world_size > 1: -# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) -# avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() - -# if self.global_rank == 0: -# self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) -# self.temp_logger.add_scalar( -# "Pretrain/consistency_loss", avg_consistency_loss, epoch -# ) -# self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) - -# # current_lr = self.schedulers["model"].get_last_lr()[0] -# # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) - -# fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) -# ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) -# ax[0].set_title("Sum over Z-axis") -# ax[1].matshow( -# # pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 -# # ) -# # ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") -# ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) -# ax[2].set_title("Sum over Y-axis") -# ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) -# ax[3].set_title("Sum over X-axis") -# self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) - -# plt.close(fig) -# print( -# f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" -# ) -# self.global_epochs += 1 -# if self.global_rank == 0: -# print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") -# torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") - -# def recon( -# self, -# obj: ObjectINN, -# batch_size: int, -# num_workers: int = 0, -# epochs=20, -# use_amp=True, -# viz_freq=1, -# checkpoint_freq=5, -# optimizer_params: dict = None, -# scheduler_params: dict = None, -# soft_constraints: dict = None, -# vol_save_path: str = None, # TODO: TEMPORARY -# log_path=None, -# val_fraction: float = 0.0, -# learn_shifts: bool = True, -# # l1_loss: bool = False, -# consistency_criterion: str = "mse", -# model_weights_path: str = None, -# force_cpu: bool = False, -# ): -# if not self.ddp_instantiated: -# if not self.pretraining_instantiated: -# self.setup_distributed() -# if model_weights_path is not None: -# print(f"Loading model weights from {model_weights_path}") -# state_dict = torch.load(model_weights_path, map_location="cpu") - -# # Handle DataParallel/DDP checkpoints -# if any(k.startswith("module.") for k in state_dict.keys()): -# new_state_dict = { -# k.replace("module.", ""): v for k, v in state_dict.items() -# } -# else: -# new_state_dict = state_dict - -# obj.model.load_state_dict(new_state_dict) -# print("Model weights loaded successfully") -# obj.model = self.build_model(obj._model) -# self.obj = obj - -# self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) -# self.ddp_instantiated = True - -# if soft_constraints is not None: -# self.obj.soft_constraints = soft_constraints - -# if not hasattr(self, "temp_logger"): -# self.setup_logger(log_path=log_path) - -# zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() - -# if self.global_rank == 0: -# print( -# f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" -# ) -# print(f"Using consistency criterion: {consistency_criterion}") - -# # Auxiliary params setup -# self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) -# aux_params = self.dataset.auxiliary_params -# # Scaling learning rates to account for distributed training -# if optimizer_params is not None: -# optimizer_params = self.scale_lr(optimizer_params) -# self.optimizer_params = optimizer_params -# self.set_optimizers() - -# if scheduler_params is not None: -# self.scheduler_params = scheduler_params -# self.set_schedulers(self.scheduler_params, num_iter=epochs) - -# aux_norm = torch.tensor(0.0, device=self.device) -# model_norm = torch.tensor(0.0, device=self.device) - -# for _, opt in self.optimizers.items(): -# opt.zero_grad() - -# N = max(self.dataset.dims) - -# device_type = self.device.type -# autocast_dtype = torch.bfloat16 if use_amp else None - -# for epoch in range(epochs): -# num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) - -# # num_samples_per_ray = get_num_samples_per_ray(epoch) -# # Log the change if it happens -# if self.global_rank == 0: -# print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - -# if self.global_rank == 0 and self.global_epochs > 0: -# prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) - -# if num_samples_per_ray != prev_samples: -# print( -# f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" -# ) - -# if self.sampler is not None: -# self.sampler.set_epoch(epoch) - -# epoch_loss = 0.0 -# epoch_consistency_loss = 0.0 -# epoch_tv_loss = 0.0 -# epoch_z1_loss = 0.0 - -# num_batches = 0 -# consistency_loss_fn = None -# if consistency_criterion[0].lower() == "mse": -# consistency_loss_fn = MSELoss() -# elif consistency_criterion[0].lower() == "l1": -# consistency_loss_fn = L1Loss() -# elif consistency_criterion[0].lower() == "mse_log": -# consistency_loss_fn = MSELogMSELoss() -# elif consistency_criterion[0].lower() == "llmse": -# consistency_loss_fn = LLMSELoss() -# elif consistency_criterion[0].lower() == "smooth_l1": -# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) -# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": -# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( -# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 -# # ) -# pass -# elif consistency_criterion[0].lower() == "charbonnier": -# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") -# else: -# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - -# for batch_idx, batch in enumerate(self.dataloader): -# projection_indices = batch["projection_idx"] - -# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) -# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) -# target_values = batch["target_value"].to(self.device, non_blocking=True) -# phis = batch["phi"].to(self.device, non_blocking=True) -# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - -# shifts, z1_params, z3_params = aux_params(None) -# batch_shifts = torch.index_select(shifts, 0, projection_indices) -# batch_z1 = torch.index_select(z1_params, 0, projection_indices) -# batch_z3 = torch.index_select(z3_params, 0, projection_indices) - -# with torch.autocast( -# device_type=device_type, dtype=autocast_dtype, enabled=use_amp -# ): -# with torch.no_grad(): -# batch_ray_coords = self.create_batch_projection_rays( -# pixel_i, pixel_j, N, num_samples_per_ray -# ) - -# # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate -# transformed_rays = self.transform_batch_ray_coordinates( -# batch_ray_coords, -# z1=batch_z1, -# x=phis, -# z3=batch_z3, -# shifts=batch_shifts, -# N=N, -# sampling_rate=1.0, -# ) - -# all_coords = transformed_rays.view(-1, 3) - -# all_densities = self.obj.forward(all_coords) - -# tv_loss = self.obj.apply_soft_constraints(all_coords) -# ray_densities = all_densities.view( -# len(target_values), num_samples_per_ray -# ) # Reshape rays and integarte -# step_size = 2.0 / (num_samples_per_ray - 1) - -# predicted_values = ray_densities.sum(dim=1) * step_size - -# consistency_loss = consistency_loss_fn(predicted_values, target_values) -# tv_loss_z1 = torch.tensor(0.0, device=self.device) - -# loss = consistency_loss + tv_loss + tv_loss_z1 - -# loss.backward() - -# epoch_loss += loss.detach() -# epoch_consistency_loss += consistency_loss.detach() -# epoch_tv_loss += tv_loss.detach() -# epoch_z1_loss += tv_loss_z1.detach() -# num_batches += 1 - -# for key, opt in self.optimizers.items(): -# if key == "model": -# model_norm = torch.nn.utils.clip_grad_norm_( -# self.obj.model.parameters(), max_norm=1 -# ) -# elif key == "aux_params": -# aux_norm = torch.nn.utils.clip_grad_norm_( -# self.dataset.auxiliary_params.parameters(), max_norm=1 -# ) -# opt.step() -# opt.zero_grad() - -# for key, sched in self.schedulers.items(): -# if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): -# sched.step(epoch_loss) -# else: -# sched.step() - -# if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: -# with torch.no_grad(): -# self.obj.create_volume( -# world_size=self.world_size, -# global_rank=self.global_rank, -# ray_size=num_samples_per_ray, -# ) -# pred_full = self.obj.obj -# avg_loss = epoch_loss.item() / num_batches -# avg_consistency_loss = epoch_consistency_loss.item() / num_batches -# avg_tv_loss = epoch_tv_loss.item() / num_batches -# avg_z1_loss = epoch_z1_loss.item() / num_batches -# shifts, z1, z3 = self.dataset.auxiliary_params.forward() -# shifts = shifts.detach().cpu() -# z1 = z1.detach().cpu() -# z3 = z3.detach().cpu() -# metrics = torch.tensor( -# [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], -# device=self.device, -# ) -# if self.world_size > 1: -# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) -# avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - -# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: -# val_loss = self.validate( -# aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N -# ) - -# if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): -# with torch.no_grad(): -# current_lr = self.schedulers["model"].get_last_lr()[0] - -# # Log metrics -# self.temp_logger.add_scalar( -# f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", -# avg_consistency_loss, -# self.global_epochs, -# ) -# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: -# self.temp_logger.add_scalar( -# f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", -# val_loss, -# self.global_epochs, -# ) -# self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) -# # if tv_weight > 0: -# self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) -# self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) -# self.temp_logger.add_scalar( -# "train/model_grad_norm", model_norm.item(), self.global_epochs -# ) -# self.temp_logger.add_scalar( -# "train/aux_grad_norm", aux_norm.item(), self.global_epochs -# ) -# self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) -# self.temp_logger.add_scalar( -# "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs -# ) -# if consistency_criterion[0].lower() == "adaptive_smooth_l1": -# self.temp_logger.add_scalar( -# "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs -# ) -# fig, axes = plt.subplots(1, 5, figsize=(36, 12)) -# axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) -# axes[0].set_title("Sum over Z-axis") - -# axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) -# axes[1].set_title(f"Slice at Z={N // 2}") - -# slice_start = max(0, N // 2 - 5) -# slice_end = min(N, N // 2 + 6) -# thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() -# axes[2].matshow(thick_slice, cmap="turbo", vmin=0) -# axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") - -# axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) -# axes[3].set_title("Sum over Y-axis") - -# axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) -# axes[4].set_title("Sum over X-axis") - -# self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) - -# fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) -# axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") -# axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") -# axes[0].legend() -# axes[1].plot(z1.cpu().numpy(), label="Z1") -# axes[2].plot(z3.cpu().numpy(), label="Z3") -# self.temp_logger.add_figure( -# "train/auxiliary_params", fig, self.global_epochs, close=True -# ) -# plt.close(fig) - -# if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: -# with torch.no_grad(): -# if self.global_rank == 0: -# save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" -# torch.save(pred_full.cpu(), save_path) - -# self.global_epochs += 1 - -# if self.global_rank == 0: -# print("Training complete.") - -# torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") -# # print("Successfully setup DDP and dataloader") - -# def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): -# """Validate the model on the validation set.""" -# self.obj.model.eval() -# aux_params.eval() - -# val_loss = 0.0 -# num_batches = 0 - -# with torch.no_grad(): -# for batch in self.val_dataloader: -# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) -# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) -# target_values = batch["target_value"].to(self.device, non_blocking=True) -# phis = batch["phi"].to(self.device, non_blocking=True) -# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - -# shifts, z1_params, z3_params = aux_params(None) -# batch_shifts = torch.index_select(shifts, 0, projection_indices) -# batch_z1 = torch.index_select(z1_params, 0, projection_indices) -# batch_z3 = torch.index_select(z3_params, 0, projection_indices) - -# with torch.autocast( -# device_type=device_type, dtype=autocast_dtype, enabled=use_amp -# ): -# with torch.no_grad(): -# batch_ray_coords = self.create_batch_projection_rays( -# pixel_i, pixel_j, N, num_samples_per_ray -# ) - -# transformed_rays = self.transform_batch_ray_coordinates( -# batch_ray_coords, -# z1=batch_z1, -# x=phis, -# z3=batch_z3, -# shifts=batch_shifts, -# N=N, -# sampling_rate=1.0, -# ) - -# all_coords = transformed_rays.view(-1, 3) - -# all_densities = self.obj.forward(all_coords) - -# ray_densities = all_densities.view( -# len(target_values), num_samples_per_ray -# ) # Reshape rays and integarte -# step_size = 2.0 / (num_samples_per_ray - 1) - -# predicted_values = ray_densities.sum(dim=1) * step_size - -# mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - -# loss = mse_loss - -# val_loss += loss.detach() -# num_batches += 1 - -# avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 - -# if self.world_size > 1: -# val_loss_tensor = avg_val_loss.detach().clone() -# dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) -# avg_val_loss = val_loss_tensor.item() - -# self.obj.model.train() -# aux_params.train() - -# return avg_val_loss - -# def ad_recon( -# self, -# optimizer_params: dict, -# num_iter: int = 0, -# reset: bool = False, -# scheduler_params: dict | None = None, -# hard_constraints: dict | None = None, -# soft_constraints: dict | None = None, -# # store_iterations: bool | None = None, -# # store_iterations_every: int | None = None, -# # autograd: bool = True, -# ): -# if reset: -# self.reset_recon() - -# self.hard_constraints = hard_constraints -# self.soft_constraints = soft_constraints - -# # Make sure everything is in the correct device, might be redundant/cleaner way to do this -# self.dataset.to(self.device) -# self.volume_obj.to(self.device) - -# # Making optimizable parameters into leaf tensors. -# self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) -# self.dataset.z1_angles = ( -# self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) -# ) -# self.dataset.z3_angles = ( -# self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) -# ) - -# if optimizer_params is not None: -# self.optimizer_params = optimizer_params -# self.set_optimizers() - -# if scheduler_params is not None: -# self.scheduler_params = scheduler_params -# self.set_schedulers(self.scheduler_params, num_iter=num_iter) - -# if hard_constraints is not None: -# self.volume_obj.hard_constraints = hard_constraints -# if soft_constraints is not None: -# self.volume_obj.soft_constraints = soft_constraints - -# pbar = tqdm(range(num_iter), desc="AD Reconstruction") - -# for a0 in pbar: -# total_loss = 0.0 -# tilt_series_loss = 0.0 - -# pred_volume = self.volume_obj.forward() - -# for i in range(len(self.dataset.tilt_series)): -# forward_projection = self.projection_operator( -# vol=pred_volume, -# z1=self.dataset.z1_angles[i], -# x=self.dataset.tilt_angles[i], -# z3=self.dataset.z3_angles[i], -# shift_x=self.dataset.shifts[i, 0], -# shift_y=self.dataset.shifts[i, 1], -# device=self.device, -# ) - -# tilt_series_loss += torch.nn.functional.mse_loss( -# forward_projection, self.dataset.tilt_series[i] -# ) -# tilt_series_loss /= len(self.dataset.tilt_series) - -# total_loss = tilt_series_loss + self.volume_obj.soft_loss -# self.loss.append(total_loss.item()) - -# total_loss.backward() - -# for opt in self.optimizers.values(): -# opt.step() -# opt.zero_grad() - -# if self.schedulers is not None: -# for sch in self.schedulers.values(): -# if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): -# sch.step(total_loss) -# elif sch is not None: -# sch.step() - -# pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") - -# if self.logger is not None: -# self.logger.log_scalar("loss/total", total_loss.item(), a0) -# self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) -# self.logger.log_scalar( -# "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 -# ) - -# if a0 % self.logger.log_images_every == 0: -# self.logger.projection_images( -# volume_obj=self.volume_obj, -# epoch=a0, -# ) -# self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) - -# self.logger.flush() - -# self.ad_recon_vol = self.volume_obj.forward() - -# return self - -# def reset_recon(self) -> None: -# if isinstance(self.volume_obj, ObjectVoxelwise): -# self.volume_obj.reset() - -# self.ad_recon_vol = None - -# # --- Projection Operators ---- -# def projection_operator( -# self, -# vol, -# z1, -# x, -# z3, -# shift_x, -# shift_y, -# device, -# ): -# projection = ( -# rot_ZXZ( -# mags=vol.unsqueeze(0), # Add batch dimension -# z1=z1, -# x=-x, -# z3=z3, -# device=device, -# mode="bilinear", -# ) -# .squeeze() -# .sum(axis=0) -# ) - -# shifted_projection = differentiable_shift_2d( -# image=projection, -# shift_x=shift_x, -# shift_y=shift_y, -# sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit -# ) - -# return shifted_projection +import matplotlib.pyplot as plt +import numpy as np +import torch +import torch.distributed as dist +from torch.nn import SmoothL1Loss +from torch.utils.tensorboard import SummaryWriter + +# from torch_radon.radon import ParallelBeam as Radon +from tqdm.auto import tqdm + +from quantem.core.ml.loss_functions import ( + CharbonnierLoss, + L1Loss, + LLMSELoss, + MSELogMSELoss, + MSELoss, +) + +# Temporary imports for TomographyNERF +from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise +from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.tomography_conv import TomographyConv +from quantem.tomography.tomography_ml import TomographyML +from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ + + +# Temporary aux class for TomographyNERF +# TODO: Maybe put this in INN? +def get_num_samples_per_ray(N: int, epoch: int): + """Increase number of samples per ray at specific epochs.""" + # Exponential schedule + + epochs = np.linspace(0, 10, 5, dtype=int) + # schedule = np.linspace(20, N, 5, dtype=int) + schedule = np.array([20, 100, 150, 200, 200]) + # schedule = np.array([200, 200, 200, 200, 200]) + # schedule = np.array([20, 100, 250, 500, 500]) + # schedule = np.array([500, 500, 500, 500, 500]) + # schedule = np.array([300, 300, 300, 300, 300]) + # schedule = np.exp(schedule) + schedule_warmup = dict[int, int](zip(epochs, schedule)) + + for epoch_threshold, samples in schedule_warmup.items(): + if epoch >= epoch_threshold: + num_samples = samples + + return num_samples + + +class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): + """ + Top level class for either using conventional or ML-based reconstruction methods + for tomography. + """ + + def __init__( + self, + dataset, + volume_obj, + device, + _token, + ): + super().__init__(dataset, volume_obj, device, _token) + + # TODO: More elegant way of doing this. + self.global_epochs = 0 + self.ddp_instantiated = False + self.pretraining_instantiated = False + + # --- Reconstruction Method --- + + def sirt_recon( + self, + num_iterations: int = 10, + inline_alignment: bool = False, + enforce_positivity: bool = True, + volume_shape: tuple = None, + reset: bool = True, + smoothing_sigma: float = None, + shrinkage: float = None, + filter_name: str = "hamming", + circle: bool = True, + plot_loss: bool = False, + ): + num_angles, num_rows, num_cols = self.dataset.tilt_series.shape + sirt_tilt_series = self.dataset.tilt_series.clone() + sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) + + hard_constraints = { + "positivity": enforce_positivity, + "shrinkage": shrinkage, + } + self.volume_obj.hard_constraints = hard_constraints + + if volume_shape is None: + volume_shape = (num_rows, num_rows, num_rows) + else: + D, H, W = volume_shape + + if reset: + self.volume_obj.reset() + self.loss = [] + + proj_forward = torch.zeros_like(self.dataset.tilt_series) + + pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") + + if smoothing_sigma is not None: + gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) + else: + gaussian_kernel = None + + print( + "Devices", + sirt_tilt_series.device, + proj_forward.device, + self.dataset.tilt_angles.device, + ) + + for iter in pbar: + proj_forward, loss = self._sirt_run_epoch( + tilt_series=sirt_tilt_series, + proj_forward=proj_forward, + angles=self.dataset.tilt_angles, + inline_alignment=iter > 0 and inline_alignment, + filter_name=filter_name, + gaussian_kernel=gaussian_kernel, + circle=circle, + ) + + pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") + + self.loss.append(loss.item()) + + self.sirt_recon_vol = self.volume_obj + + # Permutation due to sinogram ordering. + self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) + + if plot_loss: + self.plot_loss() + + # TODO: ML Recon which has NeRF and AD depending on the object type. + # TODO: Temporary + + def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): + """Create projection rays for entire batch simultaneously.""" + batch_size = len(pixel_i) + + # Convert all pixels to normalized coordinates + x_coords = (pixel_j / (N - 1)) * 2 - 1 + y_coords = (pixel_i / (N - 1)) * 2 - 1 + # TODO: maybe pixel_j.device? + # Create z coordinates + z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + + # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] + rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + + # Fill coordinates efficiently + rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray + rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray + rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + + return rays + + @torch.compile(mode="reduce-overhead") + def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): + # Step 1: Apply shifts + shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) + shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + + rays_x = rays[:, :, 0] - shift_x_norm + rays_y = rays[:, :, 1] - shift_y_norm + rays_z = rays[:, :, 2] + + # Rotation 1: Z(-z3) + theta = torch.deg2rad(-z3).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot1 = cos_t * rays_x - sin_t * rays_y + rays_y_rot1 = sin_t * rays_x + cos_t * rays_y + rays_z_rot1 = rays_z + + # Rotation 2: X(x) + theta = torch.deg2rad(x).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_rot2 = rays_x_rot1 + rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 + rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + + # Rotation 3: Z(-z1) + theta = torch.deg2rad(-z1).view(-1, 1) + cos_t = torch.cos(theta) + sin_t = torch.sin(theta) + + rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 + rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 + rays_z_final = rays_z_rot2 + + # Stack the final result + transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + + return transformed_rays + + # TODO: Temp logger + def setup_logger(self, log_path): + if self.global_rank == 0: + self.temp_logger = SummaryWriter(log_dir=log_path) + else: + self.temp_logger = None + + def pretrain( + self, + # volume_dataset: PretrainVolumeDataset, + obj: ObjectINN, + batch_size: int, + soft_constraints: dict = None, + log_path: str = None, + optimizer_params: dict = None, + epochs: int = 100, + viz_freq: int = 1, + consistency_criterion: str = "mse", + ): + if not self.pretraining_instantiated: + self.setup_distributed() + # self.setup_pretraining_dataloader(volume_dataset, batch_size) + self.pretraining_instantiated = True + obj.model = self.build_model(obj._model) + self.obj = obj + + if soft_constraints is not None: + self.obj.soft_constraints = soft_constraints + + if not hasattr(self, "temp_logger"): + self.setup_logger(log_path=log_path) + + if optimizer_params is not None: + optimizer_params = self.scale_lr(optimizer_params) + self.optimizer_params = optimizer_params + self.set_optimizers() + device_type = self.device.type + consistency_loss_fn = None + if consistency_criterion[0].lower() == "mse": + consistency_loss_fn = MSELoss() + elif consistency_criterion[0].lower() == "l1": + consistency_loss_fn = L1Loss() + elif consistency_criterion[0].lower() == "mse_log": + consistency_loss_fn = MSELogMSELoss() + elif consistency_criterion[0].lower() == "llmse": + consistency_loss_fn = LLMSELoss() + elif consistency_criterion[0].lower() == "smooth_l1": + consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) + elif consistency_criterion[0].lower() == "adaptive_smooth_l1": + # consistency_loss_fn = AdaptiveSmoothL1LossDDP( + # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 + # ) + pass + elif consistency_criterion[0].lower() == "charbonnier": + consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") + else: + raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + + for epoch in range(epochs): + if self.pretraining_sampler is not None: + self.pretraining_sampler.set_epoch(epoch) + + self.obj.model.train() + + epoch_loss = 0.0 + epoch_consistency_loss = 0.0 + epoch_tv_loss = 0.0 + num_batches = 0 + + for batch_idx, batch in enumerate(self.pretraining_dataloader): + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) + + for _, opt in self.optimizers.items(): + opt.zero_grad() + + with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): + outputs = self.obj.forward(coords) + consistency_loss = consistency_loss_fn(outputs, target) + tv_loss = self.obj.apply_soft_constraints(coords) + + loss = consistency_loss + tv_loss + + loss.backward() + + epoch_loss += loss.detach() + epoch_consistency_loss += consistency_loss.detach() + epoch_tv_loss += tv_loss.detach() + + torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) + + for _, opt in self.optimizers.items(): + opt.step() + epoch_loss += loss.detach() + num_batches += 1 + + if epoch % viz_freq == 0 or epoch == epochs - 1: + avg_loss = epoch_loss / num_batches + with torch.no_grad(): + self.obj.create_volume( + world_size=self.world_size, + global_rank=self.global_rank, + # ray_size=volume_dataset.N, + ) + pred_full = self.obj.obj + loss_tensor = avg_loss.clone().detach() + avg_loss = loss_tensor.item() + avg_tv_loss = epoch_tv_loss / num_batches + avg_consistency_loss = epoch_consistency_loss / num_batches + + metrics = torch.tensor( + [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device + ) + if self.world_size > 1: + dist.all_reduce(metrics, op=dist.ReduceOp.AVG) + avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() + + if self.global_rank == 0: + self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) + self.temp_logger.add_scalar( + "Pretrain/consistency_loss", avg_consistency_loss, epoch + ) + self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) + + # current_lr = self.schedulers["model"].get_last_lr()[0] + # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) + + fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) + ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + ax[0].set_title("Sum over Z-axis") + ax[1].matshow( + # pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 + # ) + # ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") + ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) + ax[2].set_title("Sum over Y-axis") + ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) + ax[3].set_title("Sum over X-axis") + self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) + + plt.close(fig) + print( + f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" + ) + self.global_epochs += 1 + if self.global_rank == 0: + print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") + torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") + + def recon( + self, + obj: ObjectINN, + batch_size: int, + num_workers: int = 0, + epochs=20, + use_amp=True, + viz_freq=1, + checkpoint_freq=5, + optimizer_params: dict = None, + scheduler_params: dict = None, + soft_constraints: dict = None, + vol_save_path: str = None, # TODO: TEMPORARY + log_path=None, + val_fraction: float = 0.0, + learn_shifts: bool = True, + # l1_loss: bool = False, + consistency_criterion: str = "mse", + model_weights_path: str = None, + force_cpu: bool = False, + ): + if not self.ddp_instantiated: + if not self.pretraining_instantiated: + self.setup_distributed() + if model_weights_path is not None: + print(f"Loading model weights from {model_weights_path}") + state_dict = torch.load(model_weights_path, map_location="cpu") + + # Handle DataParallel/DDP checkpoints + if any(k.startswith("module.") for k in state_dict.keys()): + new_state_dict = { + k.replace("module.", ""): v for k, v in state_dict.items() + } + else: + new_state_dict = state_dict + + obj.model.load_state_dict(new_state_dict) + print("Model weights loaded successfully") + obj.model = self.build_model(obj._model) + self.obj = obj + + self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) + self.ddp_instantiated = True + + if soft_constraints is not None: + self.obj.soft_constraints = soft_constraints + + if not hasattr(self, "temp_logger"): + self.setup_logger(log_path=log_path) + + zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() + + if self.global_rank == 0: + print( + f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" + ) + print(f"Using consistency criterion: {consistency_criterion}") + + # Auxiliary params setup + self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) + aux_params = self.dataset.auxiliary_params + # Scaling learning rates to account for distributed training + if optimizer_params is not None: + optimizer_params = self.scale_lr(optimizer_params) + self.optimizer_params = optimizer_params + self.set_optimizers() + + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params, num_iter=epochs) + + aux_norm = torch.tensor(0.0, device=self.device) + model_norm = torch.tensor(0.0, device=self.device) + + for _, opt in self.optimizers.items(): + opt.zero_grad() + + N = max(self.dataset.dims) + + device_type = self.device.type + autocast_dtype = torch.bfloat16 if use_amp else None + + for epoch in range(epochs): + num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) + + # num_samples_per_ray = get_num_samples_per_ray(epoch) + # Log the change if it happens + if self.global_rank == 0: + print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") + + if self.global_rank == 0 and self.global_epochs > 0: + prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) + + if num_samples_per_ray != prev_samples: + print( + f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" + ) + + if self.sampler is not None: + self.sampler.set_epoch(epoch) + + epoch_loss = 0.0 + epoch_consistency_loss = 0.0 + epoch_tv_loss = 0.0 + epoch_z1_loss = 0.0 + + num_batches = 0 + consistency_loss_fn = None + if consistency_criterion[0].lower() == "mse": + consistency_loss_fn = MSELoss() + elif consistency_criterion[0].lower() == "l1": + consistency_loss_fn = L1Loss() + elif consistency_criterion[0].lower() == "mse_log": + consistency_loss_fn = MSELogMSELoss() + elif consistency_criterion[0].lower() == "llmse": + consistency_loss_fn = LLMSELoss() + elif consistency_criterion[0].lower() == "smooth_l1": + consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) + elif consistency_criterion[0].lower() == "adaptive_smooth_l1": + # consistency_loss_fn = AdaptiveSmoothL1LossDDP( + # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 + # ) + pass + elif consistency_criterion[0].lower() == "charbonnier": + consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") + else: + raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + + for batch_idx, batch in enumerate(self.dataloader): + projection_indices = batch["projection_idx"] + + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = aux_params(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): + with torch.no_grad(): + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, N, num_samples_per_ray + ) + + # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + + all_coords = transformed_rays.view(-1, 3) + + all_densities = self.obj.forward(all_coords) + + tv_loss = self.obj.apply_soft_constraints(all_coords) + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + consistency_loss = consistency_loss_fn(predicted_values, target_values) + tv_loss_z1 = torch.tensor(0.0, device=self.device) + + loss = consistency_loss + tv_loss + tv_loss_z1 + + loss.backward() + + epoch_loss += loss.detach() + epoch_consistency_loss += consistency_loss.detach() + epoch_tv_loss += tv_loss.detach() + epoch_z1_loss += tv_loss_z1.detach() + num_batches += 1 + + for key, opt in self.optimizers.items(): + if key == "model": + model_norm = torch.nn.utils.clip_grad_norm_( + self.obj.model.parameters(), max_norm=1 + ) + elif key == "aux_params": + aux_norm = torch.nn.utils.clip_grad_norm_( + self.dataset.auxiliary_params.parameters(), max_norm=1 + ) + opt.step() + opt.zero_grad() + + for key, sched in self.schedulers.items(): + if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): + sched.step(epoch_loss) + else: + sched.step() + + if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: + with torch.no_grad(): + self.obj.create_volume( + world_size=self.world_size, + global_rank=self.global_rank, + ray_size=num_samples_per_ray, + ) + pred_full = self.obj.obj + avg_loss = epoch_loss.item() / num_batches + avg_consistency_loss = epoch_consistency_loss.item() / num_batches + avg_tv_loss = epoch_tv_loss.item() / num_batches + avg_z1_loss = epoch_z1_loss.item() / num_batches + shifts, z1, z3 = self.dataset.auxiliary_params.forward() + shifts = shifts.detach().cpu() + z1 = z1.detach().cpu() + z3 = z3.detach().cpu() + metrics = torch.tensor( + [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], + device=self.device, + ) + if self.world_size > 1: + dist.all_reduce(metrics, op=dist.ReduceOp.AVG) + avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + + if hasattr(self, "val_dataloader") and self.val_dataloader is not None: + val_loss = self.validate( + aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N + ) + + if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): + with torch.no_grad(): + current_lr = self.schedulers["model"].get_last_lr()[0] + + # Log metrics + self.temp_logger.add_scalar( + f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", + avg_consistency_loss, + self.global_epochs, + ) + if hasattr(self, "val_dataloader") and self.val_dataloader is not None: + self.temp_logger.add_scalar( + f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", + val_loss, + self.global_epochs, + ) + self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) + # if tv_weight > 0: + self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) + self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) + self.temp_logger.add_scalar( + "train/model_grad_norm", model_norm.item(), self.global_epochs + ) + self.temp_logger.add_scalar( + "train/aux_grad_norm", aux_norm.item(), self.global_epochs + ) + self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) + self.temp_logger.add_scalar( + "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs + ) + if consistency_criterion[0].lower() == "adaptive_smooth_l1": + self.temp_logger.add_scalar( + "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs + ) + fig, axes = plt.subplots(1, 5, figsize=(36, 12)) + axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) + axes[0].set_title("Sum over Z-axis") + + axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) + axes[1].set_title(f"Slice at Z={N // 2}") + + slice_start = max(0, N // 2 - 5) + slice_end = min(N, N // 2 + 6) + thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() + axes[2].matshow(thick_slice, cmap="turbo", vmin=0) + axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") + + axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) + axes[3].set_title("Sum over Y-axis") + + axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) + axes[4].set_title("Sum over X-axis") + + self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) + + fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) + axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") + axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") + axes[0].legend() + axes[1].plot(z1.cpu().numpy(), label="Z1") + axes[2].plot(z3.cpu().numpy(), label="Z3") + self.temp_logger.add_figure( + "train/auxiliary_params", fig, self.global_epochs, close=True + ) + plt.close(fig) + + if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: + with torch.no_grad(): + if self.global_rank == 0: + save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" + torch.save(pred_full.cpu(), save_path) + + self.global_epochs += 1 + + if self.global_rank == 0: + print("Training complete.") + + torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") + # print("Successfully setup DDP and dataloader") + + def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): + """Validate the model on the validation set.""" + self.obj.model.eval() + aux_params.eval() + + val_loss = 0.0 + num_batches = 0 + + with torch.no_grad(): + for batch in self.val_dataloader: + pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) + pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) + target_values = batch["target_value"].to(self.device, non_blocking=True) + phis = batch["phi"].to(self.device, non_blocking=True) + projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + + shifts, z1_params, z3_params = aux_params(None) + batch_shifts = torch.index_select(shifts, 0, projection_indices) + batch_z1 = torch.index_select(z1_params, 0, projection_indices) + batch_z3 = torch.index_select(z3_params, 0, projection_indices) + + with torch.autocast( + device_type=device_type, dtype=autocast_dtype, enabled=use_amp + ): + with torch.no_grad(): + batch_ray_coords = self.create_batch_projection_rays( + pixel_i, pixel_j, N, num_samples_per_ray + ) + + transformed_rays = self.transform_batch_ray_coordinates( + batch_ray_coords, + z1=batch_z1, + x=phis, + z3=batch_z3, + shifts=batch_shifts, + N=N, + sampling_rate=1.0, + ) + + all_coords = transformed_rays.view(-1, 3) + + all_densities = self.obj.forward(all_coords) + + ray_densities = all_densities.view( + len(target_values), num_samples_per_ray + ) # Reshape rays and integarte + step_size = 2.0 / (num_samples_per_ray - 1) + + predicted_values = ray_densities.sum(dim=1) * step_size + + mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + + loss = mse_loss + + val_loss += loss.detach() + num_batches += 1 + + avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 + + if self.world_size > 1: + val_loss_tensor = avg_val_loss.detach().clone() + dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) + avg_val_loss = val_loss_tensor.item() + + self.obj.model.train() + aux_params.train() + + return avg_val_loss + + def ad_recon( + self, + optimizer_params: dict, + num_iter: int = 0, + reset: bool = False, + scheduler_params: dict | None = None, + hard_constraints: dict | None = None, + soft_constraints: dict | None = None, + # store_iterations: bool | None = None, + # store_iterations_every: int | None = None, + # autograd: bool = True, + ): + if reset: + self.reset_recon() + + self.hard_constraints = hard_constraints + self.soft_constraints = soft_constraints + + # Make sure everything is in the correct device, might be redundant/cleaner way to do this + self.dataset.to(self.device) + self.volume_obj.to(self.device) + + # Making optimizable parameters into leaf tensors. + self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) + self.dataset.z1_angles = ( + self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) + ) + self.dataset.z3_angles = ( + self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) + ) + + if optimizer_params is not None: + self.optimizer_params = optimizer_params + self.set_optimizers() + + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params, num_iter=num_iter) + + if hard_constraints is not None: + self.volume_obj.hard_constraints = hard_constraints + if soft_constraints is not None: + self.volume_obj.soft_constraints = soft_constraints + + pbar = tqdm(range(num_iter), desc="AD Reconstruction") + + for a0 in pbar: + total_loss = 0.0 + tilt_series_loss = 0.0 + + pred_volume = self.volume_obj.forward() + + for i in range(len(self.dataset.tilt_series)): + forward_projection = self.projection_operator( + vol=pred_volume, + z1=self.dataset.z1_angles[i], + x=self.dataset.tilt_angles[i], + z3=self.dataset.z3_angles[i], + shift_x=self.dataset.shifts[i, 0], + shift_y=self.dataset.shifts[i, 1], + device=self.device, + ) + + tilt_series_loss += torch.nn.functional.mse_loss( + forward_projection, self.dataset.tilt_series[i] + ) + tilt_series_loss /= len(self.dataset.tilt_series) + + total_loss = tilt_series_loss + self.volume_obj.soft_loss + self.loss.append(total_loss.item()) + + total_loss.backward() + + for opt in self.optimizers.values(): + opt.step() + opt.zero_grad() + + if self.schedulers is not None: + for sch in self.schedulers.values(): + if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): + sch.step(total_loss) + elif sch is not None: + sch.step() + + pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") + + if self.logger is not None: + self.logger.log_scalar("loss/total", total_loss.item(), a0) + self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) + self.logger.log_scalar( + "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 + ) + + if a0 % self.logger.log_images_every == 0: + self.logger.projection_images( + volume_obj=self.volume_obj, + epoch=a0, + ) + self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) + + self.logger.flush() + + self.ad_recon_vol = self.volume_obj.forward() + + return self + + def reset_recon(self) -> None: + if isinstance(self.volume_obj, ObjectVoxelwise): + self.volume_obj.reset() + + self.ad_recon_vol = None + + # --- Projection Operators ---- + def projection_operator( + self, + vol, + z1, + x, + z3, + shift_x, + shift_y, + device, + ): + projection = ( + rot_ZXZ( + mags=vol.unsqueeze(0), # Add batch dimension + z1=z1, + x=-x, + z3=z3, + device=device, + mode="bilinear", + ) + .squeeze() + .sum(axis=0) + ) + + shifted_projection = differentiable_shift_2d( + image=projection, + shift_x=shift_x, + shift_y=shift_y, + sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit + ) + + return shifted_projection From 370934aa833db611843794b943959d0c45fc5666 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 27 Jan 2026 22:14:34 -0800 Subject: [PATCH 051/335] DDP projection indices fixed; added hard constraints to the forward model of INR --- src/quantem/tomography/dataset_models.py | 1 - src/quantem/tomography/object_models.py | 16 ++++++++++++++++ 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index b18090f1..efd7e9d1 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -317,7 +317,6 @@ def get_coords( # target_values = batch["target_value"].to(self.device, non_blocking=True) phis = batch["phi"].to(self.device, non_blocking=True) projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - print(f"projection_indices: {projection_indices}") with torch.no_grad(): batch_ray_coords = self.create_batch_rays(pixel_i, pixel_j, N, num_samples_per_ray) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 6ab64067..a603c089 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -338,6 +338,19 @@ def apply_soft_constraints( return soft_loss + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: + """ + Apply hard constraints to the predicted values of the INR model. + """ + + if self.constraints.positivity: + pred = torch.clamp(pred, min=0.0, max=None) + if self.constraints.shrinkage: + pred = torch.max(pred - self.constraints.shrinkage, torch.zeros_like(pred)) + + + return pred + @property def params(self): return self.model.parameters() @@ -408,6 +421,7 @@ def forward(self, coords: torch.Tensor) -> torch.Tensor: all_densities = all_densities * valid_mask + all_densities = self.apply_hard_constraints(all_densities) return all_densities # Pretrain Loop @@ -529,6 +543,7 @@ def create_volume( self.device, non_blocking=True ) batch_outputs = model(batch_coords) + batch_outputs = self.apply_hard_constraints(batch_outputs) if batch_outputs.dim() > 1: batch_outputs = batch_outputs.squeeze(-1) outputs_list.append(batch_outputs.cpu()) @@ -566,6 +581,7 @@ def create_volume( pred_full = outputs.reshape(N, N, N).float() if return_vol: + return pred_full.detach().cpu() else: self._obj = pred_full.detach().cpu() From b80cd68c4e3b6e7de11d31911624c5fb8ce058ac Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 28 Jan 2026 00:00:27 -0800 Subject: [PATCH 052/335] NVIDIA Profiling testing added in the reconstruction loop in tomography.py --- src/quantem/tomography/tomography.py | 214 ++++++++++++++++----------- 1 file changed, 130 insertions(+), 84 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index b0dfc92f..33abc011 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,7 +1,9 @@ +from contextlib import contextmanager from typing import List, Literal, Optional, Self, Tuple import numpy as np import torch +import torch.distributed as dist from tqdm.auto import tqdm from quantem.core.ml.ddp import DDPMixin @@ -13,7 +15,6 @@ from quantem.tomography.tomography_opt import TomographyOpt from quantem.tomography.utils import torch_phase_cross_correlation from quantem.tomography_old.utils import gaussian_filter_2d_stack, gaussian_kernel_1d -import torch.distributed as dist class Tomography(TomographyOpt, TomographyBase, DDPMixin): @@ -51,6 +52,7 @@ def reconstruct( constraints: dict = {}, # TODO: What to pass into the constraints? loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), num_samples_per_ray: int | List[Tuple[int, int]] = None, + profiling_mode: bool = False, ): """ This function should be able to handle both AD and INR-based tomography reconstruction methods. @@ -64,16 +66,33 @@ def reconstruct( # f"Should never happen! obj_model and dset must be on the same device, got {self.obj_model.device} and {self.dset.device}" # ) + if profiling_mode: + if self.global_rank == 0: + print("Profiling mode enabled.") + import torch.cuda.nvtx as nvtx + + @contextmanager + def nvtx_range(enabled: bool, name: str): + if enabled: + nvtx.range_push(name) + try: + yield + finally: + if enabled: + nvtx.range_pop() + if reset: raise NotImplementedError("Reset is not implemented yet.") if optimizer_params is not None: - self.optimizer_params = optimizer_params - self.set_optimizers() + with nvtx_range(profiling_mode, "Setting Optimizer Params"): + self.optimizer_params = optimizer_params + self.set_optimizers() if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(scheduler_params) + with nvtx_range(profiling_mode, "Setting Scheduler Params"): + self.scheduler_params = scheduler_params + self.set_schedulers(scheduler_params) if constraints is not None: self.obj_model.constraints = constraints @@ -84,9 +103,10 @@ def reconstruct( # Setting up DDP if not hasattr(self, "dataloader"): - self.dataloader, self.sampler = self.setup_dataloader( - self.dset, batch_size, num_workers=num_workers - ) + with nvtx_range(profiling_mode, "Setting Dataloader"): + self.dataloader, self.sampler = self.setup_dataloader( + self.dset, batch_size, num_workers=num_workers + ) self.obj_model.model.train() @@ -101,103 +121,129 @@ def reconstruct( print("num_samples_per_ray schedule provided.") print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") - - for a0 in range(num_iter): - consistency_loss = 0.0 - total_loss = 0.0 - epoch_soft_constraint_loss = 0.0 - # self._reset_iter_constraints() + with nvtx_range(profiling_mode, f"Epoch {a0}"): + consistency_loss = 0.0 + total_loss = 0.0 + epoch_soft_constraint_loss = 0.0 + # self._reset_iter_constraints() - if self.sampler is not None: - self.sampler.set_epoch(a0) + if self.sampler is not None: + self.sampler.set_epoch(a0) - if isinstance(num_samples_per_ray, list): - curr_num_samples_per_ray = num_samples_per_ray[a0][1] - else: - curr_num_samples_per_ray = num_samples_per_ray + if isinstance(num_samples_per_ray, list): + curr_num_samples_per_ray = num_samples_per_ray[a0][1] + else: + curr_num_samples_per_ray = num_samples_per_ray - if self.global_rank == 0: - print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") - for batch_idx, batch in enumerate(self.dataloader): - self.zero_grad_all() - with torch.autocast( - device_type=self.device.type, - dtype=torch.bfloat16, - enabled=True, - ): - all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) - - all_densities = self.obj_model.forward(all_coords) - - integrated_densities = self.dset.integrate_rays( - all_densities, curr_num_samples_per_ray, len(batch["target_value"]) - ) + if self.global_rank == 0: + print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") + for batch_idx, batch in enumerate(self.dataloader): + with nvtx_range(profiling_mode, f"batch_{batch_idx}"): + self.zero_grad_all() + with torch.autocast( + device_type=self.device.type, + dtype=torch.bfloat16, + enabled=True, + ): + with nvtx_range(profiling_mode, "Getting Coords"): + all_coords = self.dset.get_coords( + batch, N, curr_num_samples_per_ray + ) + + with nvtx_range(profiling_mode, "Forwarding"): + all_densities = self.obj_model.forward(all_coords) + + with nvtx_range(profiling_mode, "Integrating"): + integrated_densities = self.dset.integrate_rays( + all_densities, + curr_num_samples_per_ray, + len(batch["target_value"]), + ) + + pred = integrated_densities.float() + + with nvtx_range(profiling_mode, "Getting Target"): + target = ( + batch["target_value"].to(self.device, non_blocking=True).float() + ) - pred = integrated_densities.float() - target = batch["target_value"].to(self.device, non_blocking=True).float() + with nvtx_range(profiling_mode, "Calculating Loss"): + batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) - batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords) - epoch_soft_constraint_loss += soft_constraints_loss.item() - batch_loss = batch_consistency_loss.float() + soft_constraints_loss - batch_loss.backward() + with nvtx_range(profiling_mode, "Applying Soft Constraints"): + soft_constraints_loss = self.obj_model.apply_soft_constraints( + all_coords + ) - # Clip gradients - torch.nn.utils.clip_grad_norm_(self.obj_model.model.parameters(), max_norm=1.0) + epoch_soft_constraint_loss += soft_constraints_loss.item() - self.step_optimizers() - total_loss += batch_loss.item() - consistency_loss += batch_consistency_loss.item() + with nvtx_range(profiling_mode, "Calculating Batch Loss"): + batch_loss = batch_consistency_loss.float() + soft_constraints_loss - # TODO: Maybe reorganize the losses so that the order makes sense lol. + batch_loss.backward() - total_loss = total_loss / len(self.dataloader) - consistency_loss = consistency_loss / len(self.dataloader) - epoch_soft_constraint_loss = epoch_soft_constraint_loss / len(self.dataloader) + # Clip gradients + torch.nn.utils.clip_grad_norm_( + self.obj_model.model.parameters(), max_norm=1.0 + ) - metrics = torch.tensor( - [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device - ) + self.step_optimizers() + total_loss += batch_loss.item() + consistency_loss += batch_consistency_loss.item() - if self.world_size > 1: - dist.all_reduce(metrics, dist.ReduceOp.AVG) + # TODO: Maybe reorganize the losses so that the order makes sense lol. - total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() + total_loss = total_loss / len(self.dataloader) + consistency_loss = consistency_loss / len(self.dataloader) + epoch_soft_constraint_loss = epoch_soft_constraint_loss / len(self.dataloader) - if self.global_rank == 0: - print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") - - self._epoch_losses.append(total_loss) - self._consistency_losses.append(consistency_loss) - self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) - - if self.logger is not None: - if self.logger.log_images_every > 0 and self.num_epochs % self.logger.log_images_every == 0: - print("Creating volume...") - pred_full = self.obj_model.create_volume(return_vol=True) - - if self.global_rank == 0: - print("Logging images...") - self.logger.log_iter_images( - pred_volume=pred_full, - dataset_model=self.dset, - iter=self.num_epochs, - ) + metrics = torch.tensor( + [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device + ) + if self.world_size > 1: + dist.all_reduce(metrics, dist.ReduceOp.AVG) + + total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() if self.global_rank == 0: - self.logger.log_iter( - object_model=self.obj_model, - iter=self.num_epochs, - consistency_loss=consistency_loss, - total_loss=total_loss, - num_samples_per_ray=curr_num_samples_per_ray, + print( + f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}" ) + self._epoch_losses.append(total_loss) + self._consistency_losses.append(consistency_loss) + self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) + + with nvtx_range(profiling_mode, "Logging"): + if self.logger is not None: + if ( + self.logger.log_images_every > 0 + and self.num_epochs % self.logger.log_images_every == 0 + ): + print("Creating volume...") + pred_full = self.obj_model.create_volume(return_vol=True) + + if self.global_rank == 0: + print("Logging images...") + self.logger.log_iter_images( + pred_volume=pred_full, + dataset_model=self.dset, + iter=self.num_epochs, + ) + + if self.global_rank == 0: + self.logger.log_iter( + object_model=self.obj_model, + iter=self.num_epochs, + consistency_loss=consistency_loss, + total_loss=total_loss, + num_samples_per_ray=curr_num_samples_per_ray, + ) - self.logger.flush() + self.logger.flush() class TomographyConventional(TomographyBase): From a61cce332faab6b24b34c11e303e4ee674e6973c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 28 Jan 2026 22:50:02 -0800 Subject: [PATCH 053/335] Starting profiling of the reconstruction loop; need to move stuff over to conventional tomography file for conv recons --- src/quantem/tomography/dataset_models.py | 3 +- src/quantem/tomography/tomography.py | 42 +- src/quantem/tomography_old/tomography.py | 1766 +++++++++++----------- 3 files changed, 912 insertions(+), 899 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index efd7e9d1..a506d128 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -340,6 +340,7 @@ def get_coords( return all_coords @staticmethod + @torch.compile(mode="reduce-overhead") def create_batch_rays( pixel_i: torch.Tensor, pixel_j: torch.Tensor, N: int, num_samples_per_ray: int ) -> torch.Tensor: @@ -357,7 +358,6 @@ def create_batch_rays( return rays @staticmethod - @torch.compile(mode="reduce-overhead") def transform_batch_rays( rays: torch.Tensor, z1: torch.Tensor, @@ -403,6 +403,7 @@ def transform_batch_rays( return transformed_rays @staticmethod + @torch.compile(mode="reduce-overhead") def integrate_rays( rays: torch.Tensor, num_samples_per_ray: int, target_values_len: int ) -> torch.Tensor: diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 33abc011..0c1b7a49 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -177,27 +177,39 @@ def nvtx_range(enabled: bool, name: str): all_coords ) - epoch_soft_constraint_loss += soft_constraints_loss.item() + with nvtx_range( + profiling_mode, "Adding soft constraint loss to epoch loss" + ): + epoch_soft_constraint_loss += soft_constraints_loss.detach() with nvtx_range(profiling_mode, "Calculating Batch Loss"): - batch_loss = batch_consistency_loss.float() + soft_constraints_loss - - batch_loss.backward() - - # Clip gradients - torch.nn.utils.clip_grad_norm_( - self.obj_model.model.parameters(), max_norm=1.0 - ) + batch_loss = ( + batch_consistency_loss.float() + soft_constraints_loss.detach() + ) - self.step_optimizers() - total_loss += batch_loss.item() - consistency_loss += batch_consistency_loss.item() + with nvtx_range(profiling_mode, "Backwarding"): + batch_loss.backward() + with nvtx_range(profiling_mode, "Clipping Gradients"): + # Clip gradients + torch.nn.utils.clip_grad_norm_( + self.obj_model.model.parameters(), max_norm=1.0 + ) + with nvtx_range(profiling_mode, "Stepping Optimizers"): + self.step_optimizers() + with nvtx_range(profiling_mode, "Adding batch loss to total loss"): + total_loss += batch_loss.detach() + with nvtx_range( + profiling_mode, "Adding batch consistency loss to consistency loss" + ): + consistency_loss += batch_consistency_loss.detach() # TODO: Maybe reorganize the losses so that the order makes sense lol. - total_loss = total_loss / len(self.dataloader) - consistency_loss = consistency_loss / len(self.dataloader) - epoch_soft_constraint_loss = epoch_soft_constraint_loss / len(self.dataloader) + total_loss = total_loss.item() / len(self.dataloader) + consistency_loss = consistency_loss.item() / len(self.dataloader) + epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len( + self.dataloader + ) metrics = torch.tensor( [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device diff --git a/src/quantem/tomography_old/tomography.py b/src/quantem/tomography_old/tomography.py index ec8c5db3..7be24397 100644 --- a/src/quantem/tomography_old/tomography.py +++ b/src/quantem/tomography_old/tomography.py @@ -1,883 +1,883 @@ -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.distributed as dist -from torch.nn import SmoothL1Loss -from torch.utils.tensorboard import SummaryWriter - -# from torch_radon.radon import ParallelBeam as Radon -from tqdm.auto import tqdm - -from quantem.core.ml.loss_functions import ( - CharbonnierLoss, - L1Loss, - LLMSELoss, - MSELogMSELoss, - MSELoss, -) - -# Temporary imports for TomographyNERF -from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise -from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.tomography_conv import TomographyConv -from quantem.tomography.tomography_ml import TomographyML -from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ - - -# Temporary aux class for TomographyNERF -# TODO: Maybe put this in INN? -def get_num_samples_per_ray(N: int, epoch: int): - """Increase number of samples per ray at specific epochs.""" - # Exponential schedule - - epochs = np.linspace(0, 10, 5, dtype=int) - # schedule = np.linspace(20, N, 5, dtype=int) - schedule = np.array([20, 100, 150, 200, 200]) - # schedule = np.array([200, 200, 200, 200, 200]) - # schedule = np.array([20, 100, 250, 500, 500]) - # schedule = np.array([500, 500, 500, 500, 500]) - # schedule = np.array([300, 300, 300, 300, 300]) - # schedule = np.exp(schedule) - schedule_warmup = dict[int, int](zip(epochs, schedule)) - - for epoch_threshold, samples in schedule_warmup.items(): - if epoch >= epoch_threshold: - num_samples = samples - - return num_samples - - -class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): - """ - Top level class for either using conventional or ML-based reconstruction methods - for tomography. - """ - - def __init__( - self, - dataset, - volume_obj, - device, - _token, - ): - super().__init__(dataset, volume_obj, device, _token) - - # TODO: More elegant way of doing this. - self.global_epochs = 0 - self.ddp_instantiated = False - self.pretraining_instantiated = False - - # --- Reconstruction Method --- - - def sirt_recon( - self, - num_iterations: int = 10, - inline_alignment: bool = False, - enforce_positivity: bool = True, - volume_shape: tuple = None, - reset: bool = True, - smoothing_sigma: float = None, - shrinkage: float = None, - filter_name: str = "hamming", - circle: bool = True, - plot_loss: bool = False, - ): - num_angles, num_rows, num_cols = self.dataset.tilt_series.shape - sirt_tilt_series = self.dataset.tilt_series.clone() - sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) - - hard_constraints = { - "positivity": enforce_positivity, - "shrinkage": shrinkage, - } - self.volume_obj.hard_constraints = hard_constraints - - if volume_shape is None: - volume_shape = (num_rows, num_rows, num_rows) - else: - D, H, W = volume_shape - - if reset: - self.volume_obj.reset() - self.loss = [] - - proj_forward = torch.zeros_like(self.dataset.tilt_series) - - pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") - - if smoothing_sigma is not None: - gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) - else: - gaussian_kernel = None - - print( - "Devices", - sirt_tilt_series.device, - proj_forward.device, - self.dataset.tilt_angles.device, - ) - - for iter in pbar: - proj_forward, loss = self._sirt_run_epoch( - tilt_series=sirt_tilt_series, - proj_forward=proj_forward, - angles=self.dataset.tilt_angles, - inline_alignment=iter > 0 and inline_alignment, - filter_name=filter_name, - gaussian_kernel=gaussian_kernel, - circle=circle, - ) - - pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") - - self.loss.append(loss.item()) - - self.sirt_recon_vol = self.volume_obj - - # Permutation due to sinogram ordering. - self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) - - if plot_loss: - self.plot_loss() - - # TODO: ML Recon which has NeRF and AD depending on the object type. - # TODO: Temporary - - def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): - """Create projection rays for entire batch simultaneously.""" - batch_size = len(pixel_i) - - # Convert all pixels to normalized coordinates - x_coords = (pixel_j / (N - 1)) * 2 - 1 - y_coords = (pixel_i / (N - 1)) * 2 - 1 - # TODO: maybe pixel_j.device? - # Create z coordinates - z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - - # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] - rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - - # Fill coordinates efficiently - rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray - rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray - rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - - return rays - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): - # Step 1: Apply shifts - shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) - shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - - rays_x = rays[:, :, 0] - shift_x_norm - rays_y = rays[:, :, 1] - shift_y_norm - rays_z = rays[:, :, 2] - - # Rotation 1: Z(-z3) - theta = torch.deg2rad(-z3).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot1 = cos_t * rays_x - sin_t * rays_y - rays_y_rot1 = sin_t * rays_x + cos_t * rays_y - rays_z_rot1 = rays_z - - # Rotation 2: X(x) - theta = torch.deg2rad(x).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot2 = rays_x_rot1 - rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 - rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - - # Rotation 3: Z(-z1) - theta = torch.deg2rad(-z1).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 - rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 - rays_z_final = rays_z_rot2 - - # Stack the final result - transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - - return transformed_rays - - # TODO: Temp logger - def setup_logger(self, log_path): - if self.global_rank == 0: - self.temp_logger = SummaryWriter(log_dir=log_path) - else: - self.temp_logger = None - - def pretrain( - self, - # volume_dataset: PretrainVolumeDataset, - obj: ObjectINN, - batch_size: int, - soft_constraints: dict = None, - log_path: str = None, - optimizer_params: dict = None, - epochs: int = 100, - viz_freq: int = 1, - consistency_criterion: str = "mse", - ): - if not self.pretraining_instantiated: - self.setup_distributed() - # self.setup_pretraining_dataloader(volume_dataset, batch_size) - self.pretraining_instantiated = True - obj.model = self.build_model(obj._model) - self.obj = obj - - if soft_constraints is not None: - self.obj.soft_constraints = soft_constraints - - if not hasattr(self, "temp_logger"): - self.setup_logger(log_path=log_path) - - if optimizer_params is not None: - optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params - self.set_optimizers() - device_type = self.device.type - consistency_loss_fn = None - if consistency_criterion[0].lower() == "mse": - consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == "l1": - consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == "mse_log": - consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == "llmse": - consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == "smooth_l1": - consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) - elif consistency_criterion[0].lower() == "adaptive_smooth_l1": - # consistency_loss_fn = AdaptiveSmoothL1LossDDP( - # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 - # ) - pass - elif consistency_criterion[0].lower() == "charbonnier": - consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") - else: - raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - - for epoch in range(epochs): - if self.pretraining_sampler is not None: - self.pretraining_sampler.set_epoch(epoch) - - self.obj.model.train() - - epoch_loss = 0.0 - epoch_consistency_loss = 0.0 - epoch_tv_loss = 0.0 - num_batches = 0 - - for batch_idx, batch in enumerate(self.pretraining_dataloader): - coords = batch["coords"].to(self.device, non_blocking=True) - target = batch["target"].to(self.device, non_blocking=True) - - for _, opt in self.optimizers.items(): - opt.zero_grad() - - with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): - outputs = self.obj.forward(coords) - consistency_loss = consistency_loss_fn(outputs, target) - tv_loss = self.obj.apply_soft_constraints(coords) - - loss = consistency_loss + tv_loss - - loss.backward() - - epoch_loss += loss.detach() - epoch_consistency_loss += consistency_loss.detach() - epoch_tv_loss += tv_loss.detach() - - torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) - - for _, opt in self.optimizers.items(): - opt.step() - epoch_loss += loss.detach() - num_batches += 1 - - if epoch % viz_freq == 0 or epoch == epochs - 1: - avg_loss = epoch_loss / num_batches - with torch.no_grad(): - self.obj.create_volume( - world_size=self.world_size, - global_rank=self.global_rank, - # ray_size=volume_dataset.N, - ) - pred_full = self.obj.obj - loss_tensor = avg_loss.clone().detach() - avg_loss = loss_tensor.item() - avg_tv_loss = epoch_tv_loss / num_batches - avg_consistency_loss = epoch_consistency_loss / num_batches - - metrics = torch.tensor( - [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device - ) - if self.world_size > 1: - dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() - - if self.global_rank == 0: - self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) - self.temp_logger.add_scalar( - "Pretrain/consistency_loss", avg_consistency_loss, epoch - ) - self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) - - # current_lr = self.schedulers["model"].get_last_lr()[0] - # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) - - fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) - ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - ax[0].set_title("Sum over Z-axis") - ax[1].matshow( - # pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 - # ) - # ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") - ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) - ax[2].set_title("Sum over Y-axis") - ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) - ax[3].set_title("Sum over X-axis") - self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) - - plt.close(fig) - print( - f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" - ) - self.global_epochs += 1 - if self.global_rank == 0: - print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") - torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") - - def recon( - self, - obj: ObjectINN, - batch_size: int, - num_workers: int = 0, - epochs=20, - use_amp=True, - viz_freq=1, - checkpoint_freq=5, - optimizer_params: dict = None, - scheduler_params: dict = None, - soft_constraints: dict = None, - vol_save_path: str = None, # TODO: TEMPORARY - log_path=None, - val_fraction: float = 0.0, - learn_shifts: bool = True, - # l1_loss: bool = False, - consistency_criterion: str = "mse", - model_weights_path: str = None, - force_cpu: bool = False, - ): - if not self.ddp_instantiated: - if not self.pretraining_instantiated: - self.setup_distributed() - if model_weights_path is not None: - print(f"Loading model weights from {model_weights_path}") - state_dict = torch.load(model_weights_path, map_location="cpu") - - # Handle DataParallel/DDP checkpoints - if any(k.startswith("module.") for k in state_dict.keys()): - new_state_dict = { - k.replace("module.", ""): v for k, v in state_dict.items() - } - else: - new_state_dict = state_dict - - obj.model.load_state_dict(new_state_dict) - print("Model weights loaded successfully") - obj.model = self.build_model(obj._model) - self.obj = obj - - self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) - self.ddp_instantiated = True - - if soft_constraints is not None: - self.obj.soft_constraints = soft_constraints - - if not hasattr(self, "temp_logger"): - self.setup_logger(log_path=log_path) - - zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() - - if self.global_rank == 0: - print( - f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" - ) - print(f"Using consistency criterion: {consistency_criterion}") - - # Auxiliary params setup - self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) - aux_params = self.dataset.auxiliary_params - # Scaling learning rates to account for distributed training - if optimizer_params is not None: - optimizer_params = self.scale_lr(optimizer_params) - self.optimizer_params = optimizer_params - self.set_optimizers() - - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=epochs) - - aux_norm = torch.tensor(0.0, device=self.device) - model_norm = torch.tensor(0.0, device=self.device) - - for _, opt in self.optimizers.items(): - opt.zero_grad() - - N = max(self.dataset.dims) - - device_type = self.device.type - autocast_dtype = torch.bfloat16 if use_amp else None - - for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) - - # num_samples_per_ray = get_num_samples_per_ray(epoch) - # Log the change if it happens - if self.global_rank == 0: - print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - - if self.global_rank == 0 and self.global_epochs > 0: - prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) - - if num_samples_per_ray != prev_samples: - print( - f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" - ) - - if self.sampler is not None: - self.sampler.set_epoch(epoch) - - epoch_loss = 0.0 - epoch_consistency_loss = 0.0 - epoch_tv_loss = 0.0 - epoch_z1_loss = 0.0 - - num_batches = 0 - consistency_loss_fn = None - if consistency_criterion[0].lower() == "mse": - consistency_loss_fn = MSELoss() - elif consistency_criterion[0].lower() == "l1": - consistency_loss_fn = L1Loss() - elif consistency_criterion[0].lower() == "mse_log": - consistency_loss_fn = MSELogMSELoss() - elif consistency_criterion[0].lower() == "llmse": - consistency_loss_fn = LLMSELoss() - elif consistency_criterion[0].lower() == "smooth_l1": - consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) - elif consistency_criterion[0].lower() == "adaptive_smooth_l1": - # consistency_loss_fn = AdaptiveSmoothL1LossDDP( - # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 - # ) - pass - elif consistency_criterion[0].lower() == "charbonnier": - consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") - else: - raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - - for batch_idx, batch in enumerate(self.dataloader): - projection_indices = batch["projection_idx"] - - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - - all_densities = self.obj.forward(all_coords) - - tv_loss = self.obj.apply_soft_constraints(all_coords) - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - consistency_loss = consistency_loss_fn(predicted_values, target_values) - tv_loss_z1 = torch.tensor(0.0, device=self.device) - - loss = consistency_loss + tv_loss + tv_loss_z1 - - loss.backward() - - epoch_loss += loss.detach() - epoch_consistency_loss += consistency_loss.detach() - epoch_tv_loss += tv_loss.detach() - epoch_z1_loss += tv_loss_z1.detach() - num_batches += 1 - - for key, opt in self.optimizers.items(): - if key == "model": - model_norm = torch.nn.utils.clip_grad_norm_( - self.obj.model.parameters(), max_norm=1 - ) - elif key == "aux_params": - aux_norm = torch.nn.utils.clip_grad_norm_( - self.dataset.auxiliary_params.parameters(), max_norm=1 - ) - opt.step() - opt.zero_grad() - - for key, sched in self.schedulers.items(): - if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): - sched.step(epoch_loss) - else: - sched.step() - - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: - with torch.no_grad(): - self.obj.create_volume( - world_size=self.world_size, - global_rank=self.global_rank, - ray_size=num_samples_per_ray, - ) - pred_full = self.obj.obj - avg_loss = epoch_loss.item() / num_batches - avg_consistency_loss = epoch_consistency_loss.item() / num_batches - avg_tv_loss = epoch_tv_loss.item() / num_batches - avg_z1_loss = epoch_z1_loss.item() / num_batches - shifts, z1, z3 = self.dataset.auxiliary_params.forward() - shifts = shifts.detach().cpu() - z1 = z1.detach().cpu() - z3 = z3.detach().cpu() - metrics = torch.tensor( - [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], - device=self.device, - ) - if self.world_size > 1: - dist.all_reduce(metrics, op=dist.ReduceOp.AVG) - avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - val_loss = self.validate( - aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N - ) - - if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): - with torch.no_grad(): - current_lr = self.schedulers["model"].get_last_lr()[0] - - # Log metrics - self.temp_logger.add_scalar( - f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", - avg_consistency_loss, - self.global_epochs, - ) - if hasattr(self, "val_dataloader") and self.val_dataloader is not None: - self.temp_logger.add_scalar( - f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", - val_loss, - self.global_epochs, - ) - self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) - # if tv_weight > 0: - self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) - self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) - self.temp_logger.add_scalar( - "train/model_grad_norm", model_norm.item(), self.global_epochs - ) - self.temp_logger.add_scalar( - "train/aux_grad_norm", aux_norm.item(), self.global_epochs - ) - self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) - self.temp_logger.add_scalar( - "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs - ) - if consistency_criterion[0].lower() == "adaptive_smooth_l1": - self.temp_logger.add_scalar( - "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs - ) - fig, axes = plt.subplots(1, 5, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - axes[0].set_title("Sum over Z-axis") - - axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) - axes[1].set_title(f"Slice at Z={N // 2}") - - slice_start = max(0, N // 2 - 5) - slice_end = min(N, N // 2 + 6) - thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap="turbo", vmin=0) - axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") - - axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) - axes[3].set_title("Sum over Y-axis") - - axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) - axes[4].set_title("Sum over X-axis") - - self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) - - fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) - axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") - axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") - axes[0].legend() - axes[1].plot(z1.cpu().numpy(), label="Z1") - axes[2].plot(z3.cpu().numpy(), label="Z3") - self.temp_logger.add_figure( - "train/auxiliary_params", fig, self.global_epochs, close=True - ) - plt.close(fig) - - if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: - with torch.no_grad(): - if self.global_rank == 0: - save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" - torch.save(pred_full.cpu(), save_path) - - self.global_epochs += 1 - - if self.global_rank == 0: - print("Training complete.") - - torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") - # print("Successfully setup DDP and dataloader") - - def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): - """Validate the model on the validation set.""" - self.obj.model.eval() - aux_params.eval() - - val_loss = 0.0 - num_batches = 0 - - with torch.no_grad(): - for batch in self.val_dataloader: - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - - all_densities = self.obj.forward(all_coords) - - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - - loss = mse_loss - - val_loss += loss.detach() - num_batches += 1 - - avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 - - if self.world_size > 1: - val_loss_tensor = avg_val_loss.detach().clone() - dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) - avg_val_loss = val_loss_tensor.item() - - self.obj.model.train() - aux_params.train() - - return avg_val_loss - - def ad_recon( - self, - optimizer_params: dict, - num_iter: int = 0, - reset: bool = False, - scheduler_params: dict | None = None, - hard_constraints: dict | None = None, - soft_constraints: dict | None = None, - # store_iterations: bool | None = None, - # store_iterations_every: int | None = None, - # autograd: bool = True, - ): - if reset: - self.reset_recon() - - self.hard_constraints = hard_constraints - self.soft_constraints = soft_constraints - - # Make sure everything is in the correct device, might be redundant/cleaner way to do this - self.dataset.to(self.device) - self.volume_obj.to(self.device) - - # Making optimizable parameters into leaf tensors. - self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) - self.dataset.z1_angles = ( - self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) - ) - self.dataset.z3_angles = ( - self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) - ) - - if optimizer_params is not None: - self.optimizer_params = optimizer_params - self.set_optimizers() - - if scheduler_params is not None: - self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=num_iter) - - if hard_constraints is not None: - self.volume_obj.hard_constraints = hard_constraints - if soft_constraints is not None: - self.volume_obj.soft_constraints = soft_constraints - - pbar = tqdm(range(num_iter), desc="AD Reconstruction") - - for a0 in pbar: - total_loss = 0.0 - tilt_series_loss = 0.0 - - pred_volume = self.volume_obj.forward() - - for i in range(len(self.dataset.tilt_series)): - forward_projection = self.projection_operator( - vol=pred_volume, - z1=self.dataset.z1_angles[i], - x=self.dataset.tilt_angles[i], - z3=self.dataset.z3_angles[i], - shift_x=self.dataset.shifts[i, 0], - shift_y=self.dataset.shifts[i, 1], - device=self.device, - ) - - tilt_series_loss += torch.nn.functional.mse_loss( - forward_projection, self.dataset.tilt_series[i] - ) - tilt_series_loss /= len(self.dataset.tilt_series) - - total_loss = tilt_series_loss + self.volume_obj.soft_loss - self.loss.append(total_loss.item()) - - total_loss.backward() - - for opt in self.optimizers.values(): - opt.step() - opt.zero_grad() - - if self.schedulers is not None: - for sch in self.schedulers.values(): - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(total_loss) - elif sch is not None: - sch.step() - - pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") - - if self.logger is not None: - self.logger.log_scalar("loss/total", total_loss.item(), a0) - self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) - self.logger.log_scalar( - "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 - ) - - if a0 % self.logger.log_images_every == 0: - self.logger.projection_images( - volume_obj=self.volume_obj, - epoch=a0, - ) - self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) - - self.logger.flush() - - self.ad_recon_vol = self.volume_obj.forward() - - return self - - def reset_recon(self) -> None: - if isinstance(self.volume_obj, ObjectVoxelwise): - self.volume_obj.reset() - - self.ad_recon_vol = None - - # --- Projection Operators ---- - def projection_operator( - self, - vol, - z1, - x, - z3, - shift_x, - shift_y, - device, - ): - projection = ( - rot_ZXZ( - mags=vol.unsqueeze(0), # Add batch dimension - z1=z1, - x=-x, - z3=z3, - device=device, - mode="bilinear", - ) - .squeeze() - .sum(axis=0) - ) - - shifted_projection = differentiable_shift_2d( - image=projection, - shift_x=shift_x, - shift_y=shift_y, - sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit - ) - - return shifted_projection +# import matplotlib.pyplot as plt +# import numpy as np +# import torch +# import torch.distributed as dist +# from torch.nn import SmoothL1Loss +# from torch.utils.tensorboard import SummaryWriter + +# # from torch_radon.radon import ParallelBeam as Radon +# from tqdm.auto import tqdm + +# from quantem.core.ml.loss_functions import ( +# CharbonnierLoss, +# L1Loss, +# LLMSELoss, +# MSELogMSELoss, +# MSELoss, +# ) + +# # Temporary imports for TomographyNERF +# from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise +# from quantem.tomography.tomography_base import TomographyBase +# from quantem.tomography.tomography_conv import TomographyConv +# from quantem.tomography.tomography_ml import TomographyML +# from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ + + +# # Temporary aux class for TomographyNERF +# # TODO: Maybe put this in INN? +# def get_num_samples_per_ray(N: int, epoch: int): +# """Increase number of samples per ray at specific epochs.""" +# # Exponential schedule + +# epochs = np.linspace(0, 10, 5, dtype=int) +# # schedule = np.linspace(20, N, 5, dtype=int) +# schedule = np.array([20, 100, 150, 200, 200]) +# # schedule = np.array([200, 200, 200, 200, 200]) +# # schedule = np.array([20, 100, 250, 500, 500]) +# # schedule = np.array([500, 500, 500, 500, 500]) +# # schedule = np.array([300, 300, 300, 300, 300]) +# # schedule = np.exp(schedule) +# schedule_warmup = dict[int, int](zip(epochs, schedule)) + +# for epoch_threshold, samples in schedule_warmup.items(): +# if epoch >= epoch_threshold: +# num_samples = samples + +# return num_samples + + +# class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): +# """ +# Top level class for either using conventional or ML-based reconstruction methods +# for tomography. +# """ + +# def __init__( +# self, +# dataset, +# volume_obj, +# device, +# _token, +# ): +# super().__init__(dataset, volume_obj, device, _token) + +# # TODO: More elegant way of doing this. +# self.global_epochs = 0 +# self.ddp_instantiated = False +# self.pretraining_instantiated = False + +# # --- Reconstruction Method --- + +# def sirt_recon( +# self, +# num_iterations: int = 10, +# inline_alignment: bool = False, +# enforce_positivity: bool = True, +# volume_shape: tuple = None, +# reset: bool = True, +# smoothing_sigma: float = None, +# shrinkage: float = None, +# filter_name: str = "hamming", +# circle: bool = True, +# plot_loss: bool = False, +# ): +# num_angles, num_rows, num_cols = self.dataset.tilt_series.shape +# sirt_tilt_series = self.dataset.tilt_series.clone() +# sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) + +# hard_constraints = { +# "positivity": enforce_positivity, +# "shrinkage": shrinkage, +# } +# self.volume_obj.hard_constraints = hard_constraints + +# if volume_shape is None: +# volume_shape = (num_rows, num_rows, num_rows) +# else: +# D, H, W = volume_shape + +# if reset: +# self.volume_obj.reset() +# self.loss = [] + +# proj_forward = torch.zeros_like(self.dataset.tilt_series) + +# pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") + +# if smoothing_sigma is not None: +# gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) +# else: +# gaussian_kernel = None + +# print( +# "Devices", +# sirt_tilt_series.device, +# proj_forward.device, +# self.dataset.tilt_angles.device, +# ) + +# for iter in pbar: +# proj_forward, loss = self._sirt_run_epoch( +# tilt_series=sirt_tilt_series, +# proj_forward=proj_forward, +# angles=self.dataset.tilt_angles, +# inline_alignment=iter > 0 and inline_alignment, +# filter_name=filter_name, +# gaussian_kernel=gaussian_kernel, +# circle=circle, +# ) + +# pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") + +# self.loss.append(loss.item()) + +# self.sirt_recon_vol = self.volume_obj + +# # Permutation due to sinogram ordering. +# self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) + +# if plot_loss: +# self.plot_loss() + +# # TODO: ML Recon which has NeRF and AD depending on the object type. +# # TODO: Temporary + +# def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): +# """Create projection rays for entire batch simultaneously.""" +# batch_size = len(pixel_i) + +# # Convert all pixels to normalized coordinates +# x_coords = (pixel_j / (N - 1)) * 2 - 1 +# y_coords = (pixel_i / (N - 1)) * 2 - 1 +# # TODO: maybe pixel_j.device? +# # Create z coordinates +# z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) + +# # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] +# rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) + +# # Fill coordinates efficiently +# rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray +# rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray +# rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray + +# return rays + +# @torch.compile(mode="reduce-overhead") +# def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): +# # Step 1: Apply shifts +# shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) +# shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) + +# rays_x = rays[:, :, 0] - shift_x_norm +# rays_y = rays[:, :, 1] - shift_y_norm +# rays_z = rays[:, :, 2] + +# # Rotation 1: Z(-z3) +# theta = torch.deg2rad(-z3).view(-1, 1) +# cos_t = torch.cos(theta) +# sin_t = torch.sin(theta) + +# rays_x_rot1 = cos_t * rays_x - sin_t * rays_y +# rays_y_rot1 = sin_t * rays_x + cos_t * rays_y +# rays_z_rot1 = rays_z + +# # Rotation 2: X(x) +# theta = torch.deg2rad(x).view(-1, 1) +# cos_t = torch.cos(theta) +# sin_t = torch.sin(theta) + +# rays_x_rot2 = rays_x_rot1 +# rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 +# rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 + +# # Rotation 3: Z(-z1) +# theta = torch.deg2rad(-z1).view(-1, 1) +# cos_t = torch.cos(theta) +# sin_t = torch.sin(theta) + +# rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 +# rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 +# rays_z_final = rays_z_rot2 + +# # Stack the final result +# transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) + +# return transformed_rays + +# # TODO: Temp logger +# def setup_logger(self, log_path): +# if self.global_rank == 0: +# self.temp_logger = SummaryWriter(log_dir=log_path) +# else: +# self.temp_logger = None + +# def pretrain( +# self, +# # volume_dataset: PretrainVolumeDataset, +# obj: ObjectINN, +# batch_size: int, +# soft_constraints: dict = None, +# log_path: str = None, +# optimizer_params: dict = None, +# epochs: int = 100, +# viz_freq: int = 1, +# consistency_criterion: str = "mse", +# ): +# if not self.pretraining_instantiated: +# self.setup_distributed() +# # self.setup_pretraining_dataloader(volume_dataset, batch_size) +# self.pretraining_instantiated = True +# obj.model = self.build_model(obj._model) +# self.obj = obj + +# if soft_constraints is not None: +# self.obj.soft_constraints = soft_constraints + +# if not hasattr(self, "temp_logger"): +# self.setup_logger(log_path=log_path) + +# if optimizer_params is not None: +# optimizer_params = self.scale_lr(optimizer_params) +# self.optimizer_params = optimizer_params +# self.set_optimizers() +# device_type = self.device.type +# consistency_loss_fn = None +# if consistency_criterion[0].lower() == "mse": +# consistency_loss_fn = MSELoss() +# elif consistency_criterion[0].lower() == "l1": +# consistency_loss_fn = L1Loss() +# elif consistency_criterion[0].lower() == "mse_log": +# consistency_loss_fn = MSELogMSELoss() +# elif consistency_criterion[0].lower() == "llmse": +# consistency_loss_fn = LLMSELoss() +# elif consistency_criterion[0].lower() == "smooth_l1": +# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) +# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": +# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( +# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 +# # ) +# pass +# elif consistency_criterion[0].lower() == "charbonnier": +# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") +# else: +# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + +# for epoch in range(epochs): +# if self.pretraining_sampler is not None: +# self.pretraining_sampler.set_epoch(epoch) + +# self.obj.model.train() + +# epoch_loss = 0.0 +# epoch_consistency_loss = 0.0 +# epoch_tv_loss = 0.0 +# num_batches = 0 + +# for batch_idx, batch in enumerate(self.pretraining_dataloader): +# coords = batch["coords"].to(self.device, non_blocking=True) +# target = batch["target"].to(self.device, non_blocking=True) + +# for _, opt in self.optimizers.items(): +# opt.zero_grad() + +# with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): +# outputs = self.obj.forward(coords) +# consistency_loss = consistency_loss_fn(outputs, target) +# tv_loss = self.obj.apply_soft_constraints(coords) + +# loss = consistency_loss + tv_loss + +# loss.backward() + +# epoch_loss += loss.detach() +# epoch_consistency_loss += consistency_loss.detach() +# epoch_tv_loss += tv_loss.detach() + +# torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) + +# for _, opt in self.optimizers.items(): +# opt.step() +# epoch_loss += loss.detach() +# num_batches += 1 + +# if epoch % viz_freq == 0 or epoch == epochs - 1: +# avg_loss = epoch_loss / num_batches +# with torch.no_grad(): +# self.obj.create_volume( +# world_size=self.world_size, +# global_rank=self.global_rank, +# # ray_size=volume_dataset.N, +# ) +# pred_full = self.obj.obj +# loss_tensor = avg_loss.clone().detach() +# avg_loss = loss_tensor.item() +# avg_tv_loss = epoch_tv_loss / num_batches +# avg_consistency_loss = epoch_consistency_loss / num_batches + +# metrics = torch.tensor( +# [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device +# ) +# if self.world_size > 1: +# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) +# avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() + +# if self.global_rank == 0: +# self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) +# self.temp_logger.add_scalar( +# "Pretrain/consistency_loss", avg_consistency_loss, epoch +# ) +# self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) + +# # current_lr = self.schedulers["model"].get_last_lr()[0] +# # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) + +# fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) +# ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) +# ax[0].set_title("Sum over Z-axis") +# ax[1].matshow( +# # pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 +# # ) +# # ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") +# ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) +# ax[2].set_title("Sum over Y-axis") +# ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) +# ax[3].set_title("Sum over X-axis") +# self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) + +# plt.close(fig) +# print( +# f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" +# ) +# self.global_epochs += 1 +# if self.global_rank == 0: +# print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") +# torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") + +# def recon( +# self, +# obj: ObjectINN, +# batch_size: int, +# num_workers: int = 0, +# epochs=20, +# use_amp=True, +# viz_freq=1, +# checkpoint_freq=5, +# optimizer_params: dict = None, +# scheduler_params: dict = None, +# soft_constraints: dict = None, +# vol_save_path: str = None, # TODO: TEMPORARY +# log_path=None, +# val_fraction: float = 0.0, +# learn_shifts: bool = True, +# # l1_loss: bool = False, +# consistency_criterion: str = "mse", +# model_weights_path: str = None, +# force_cpu: bool = False, +# ): +# if not self.ddp_instantiated: +# if not self.pretraining_instantiated: +# self.setup_distributed() +# if model_weights_path is not None: +# print(f"Loading model weights from {model_weights_path}") +# state_dict = torch.load(model_weights_path, map_location="cpu") + +# # Handle DataParallel/DDP checkpoints +# if any(k.startswith("module.") for k in state_dict.keys()): +# new_state_dict = { +# k.replace("module.", ""): v for k, v in state_dict.items() +# } +# else: +# new_state_dict = state_dict + +# obj.model.load_state_dict(new_state_dict) +# print("Model weights loaded successfully") +# obj.model = self.build_model(obj._model) +# self.obj = obj + +# self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) +# self.ddp_instantiated = True + +# if soft_constraints is not None: +# self.obj.soft_constraints = soft_constraints + +# if not hasattr(self, "temp_logger"): +# self.setup_logger(log_path=log_path) + +# zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() + +# if self.global_rank == 0: +# print( +# f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" +# ) +# print(f"Using consistency criterion: {consistency_criterion}") + +# # Auxiliary params setup +# self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) +# aux_params = self.dataset.auxiliary_params +# # Scaling learning rates to account for distributed training +# if optimizer_params is not None: +# optimizer_params = self.scale_lr(optimizer_params) +# self.optimizer_params = optimizer_params +# self.set_optimizers() + +# if scheduler_params is not None: +# self.scheduler_params = scheduler_params +# self.set_schedulers(self.scheduler_params, num_iter=epochs) + +# aux_norm = torch.tensor(0.0, device=self.device) +# model_norm = torch.tensor(0.0, device=self.device) + +# for _, opt in self.optimizers.items(): +# opt.zero_grad() + +# N = max(self.dataset.dims) + +# device_type = self.device.type +# autocast_dtype = torch.bfloat16 if use_amp else None + +# for epoch in range(epochs): +# num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) + +# # num_samples_per_ray = get_num_samples_per_ray(epoch) +# # Log the change if it happens +# if self.global_rank == 0: +# print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") + +# if self.global_rank == 0 and self.global_epochs > 0: +# prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) + +# if num_samples_per_ray != prev_samples: +# print( +# f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" +# ) + +# if self.sampler is not None: +# self.sampler.set_epoch(epoch) + +# epoch_loss = 0.0 +# epoch_consistency_loss = 0.0 +# epoch_tv_loss = 0.0 +# epoch_z1_loss = 0.0 + +# num_batches = 0 +# consistency_loss_fn = None +# if consistency_criterion[0].lower() == "mse": +# consistency_loss_fn = MSELoss() +# elif consistency_criterion[0].lower() == "l1": +# consistency_loss_fn = L1Loss() +# elif consistency_criterion[0].lower() == "mse_log": +# consistency_loss_fn = MSELogMSELoss() +# elif consistency_criterion[0].lower() == "llmse": +# consistency_loss_fn = LLMSELoss() +# elif consistency_criterion[0].lower() == "smooth_l1": +# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) +# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": +# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( +# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 +# # ) +# pass +# elif consistency_criterion[0].lower() == "charbonnier": +# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") +# else: +# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") + +# for batch_idx, batch in enumerate(self.dataloader): +# projection_indices = batch["projection_idx"] + +# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) +# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) +# target_values = batch["target_value"].to(self.device, non_blocking=True) +# phis = batch["phi"].to(self.device, non_blocking=True) +# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + +# shifts, z1_params, z3_params = aux_params(None) +# batch_shifts = torch.index_select(shifts, 0, projection_indices) +# batch_z1 = torch.index_select(z1_params, 0, projection_indices) +# batch_z3 = torch.index_select(z3_params, 0, projection_indices) + +# with torch.autocast( +# device_type=device_type, dtype=autocast_dtype, enabled=use_amp +# ): +# with torch.no_grad(): +# batch_ray_coords = self.create_batch_projection_rays( +# pixel_i, pixel_j, N, num_samples_per_ray +# ) + +# # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate +# transformed_rays = self.transform_batch_ray_coordinates( +# batch_ray_coords, +# z1=batch_z1, +# x=phis, +# z3=batch_z3, +# shifts=batch_shifts, +# N=N, +# sampling_rate=1.0, +# ) + +# all_coords = transformed_rays.view(-1, 3) + +# all_densities = self.obj.forward(all_coords) + +# tv_loss = self.obj.apply_soft_constraints(all_coords) +# ray_densities = all_densities.view( +# len(target_values), num_samples_per_ray +# ) # Reshape rays and integarte +# step_size = 2.0 / (num_samples_per_ray - 1) + +# predicted_values = ray_densities.sum(dim=1) * step_size + +# consistency_loss = consistency_loss_fn(predicted_values, target_values) +# tv_loss_z1 = torch.tensor(0.0, device=self.device) + +# loss = consistency_loss + tv_loss + tv_loss_z1 + +# loss.backward() + +# epoch_loss += loss.detach() +# epoch_consistency_loss += consistency_loss.detach() +# epoch_tv_loss += tv_loss.detach() +# epoch_z1_loss += tv_loss_z1.detach() +# num_batches += 1 + +# for key, opt in self.optimizers.items(): +# if key == "model": +# model_norm = torch.nn.utils.clip_grad_norm_( +# self.obj.model.parameters(), max_norm=1 +# ) +# elif key == "aux_params": +# aux_norm = torch.nn.utils.clip_grad_norm_( +# self.dataset.auxiliary_params.parameters(), max_norm=1 +# ) +# opt.step() +# opt.zero_grad() + +# for key, sched in self.schedulers.items(): +# if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): +# sched.step(epoch_loss) +# else: +# sched.step() + +# if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: +# with torch.no_grad(): +# self.obj.create_volume( +# world_size=self.world_size, +# global_rank=self.global_rank, +# ray_size=num_samples_per_ray, +# ) +# pred_full = self.obj.obj +# avg_loss = epoch_loss.item() / num_batches +# avg_consistency_loss = epoch_consistency_loss.item() / num_batches +# avg_tv_loss = epoch_tv_loss.item() / num_batches +# avg_z1_loss = epoch_z1_loss.item() / num_batches +# shifts, z1, z3 = self.dataset.auxiliary_params.forward() +# shifts = shifts.detach().cpu() +# z1 = z1.detach().cpu() +# z3 = z3.detach().cpu() +# metrics = torch.tensor( +# [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], +# device=self.device, +# ) +# if self.world_size > 1: +# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) +# avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() + +# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: +# val_loss = self.validate( +# aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N +# ) + +# if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): +# with torch.no_grad(): +# current_lr = self.schedulers["model"].get_last_lr()[0] + +# # Log metrics +# self.temp_logger.add_scalar( +# f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", +# avg_consistency_loss, +# self.global_epochs, +# ) +# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: +# self.temp_logger.add_scalar( +# f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", +# val_loss, +# self.global_epochs, +# ) +# self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) +# # if tv_weight > 0: +# self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) +# self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) +# self.temp_logger.add_scalar( +# "train/model_grad_norm", model_norm.item(), self.global_epochs +# ) +# self.temp_logger.add_scalar( +# "train/aux_grad_norm", aux_norm.item(), self.global_epochs +# ) +# self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) +# self.temp_logger.add_scalar( +# "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs +# ) +# if consistency_criterion[0].lower() == "adaptive_smooth_l1": +# self.temp_logger.add_scalar( +# "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs +# ) +# fig, axes = plt.subplots(1, 5, figsize=(36, 12)) +# axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) +# axes[0].set_title("Sum over Z-axis") + +# axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) +# axes[1].set_title(f"Slice at Z={N // 2}") + +# slice_start = max(0, N // 2 - 5) +# slice_end = min(N, N // 2 + 6) +# thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() +# axes[2].matshow(thick_slice, cmap="turbo", vmin=0) +# axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") + +# axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) +# axes[3].set_title("Sum over Y-axis") + +# axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) +# axes[4].set_title("Sum over X-axis") + +# self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) + +# fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) +# axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") +# axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") +# axes[0].legend() +# axes[1].plot(z1.cpu().numpy(), label="Z1") +# axes[2].plot(z3.cpu().numpy(), label="Z3") +# self.temp_logger.add_figure( +# "train/auxiliary_params", fig, self.global_epochs, close=True +# ) +# plt.close(fig) + +# if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: +# with torch.no_grad(): +# if self.global_rank == 0: +# save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" +# torch.save(pred_full.cpu(), save_path) + +# self.global_epochs += 1 + +# if self.global_rank == 0: +# print("Training complete.") + +# torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") +# # print("Successfully setup DDP and dataloader") + +# def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): +# """Validate the model on the validation set.""" +# self.obj.model.eval() +# aux_params.eval() + +# val_loss = 0.0 +# num_batches = 0 + +# with torch.no_grad(): +# for batch in self.val_dataloader: +# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) +# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) +# target_values = batch["target_value"].to(self.device, non_blocking=True) +# phis = batch["phi"].to(self.device, non_blocking=True) +# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) + +# shifts, z1_params, z3_params = aux_params(None) +# batch_shifts = torch.index_select(shifts, 0, projection_indices) +# batch_z1 = torch.index_select(z1_params, 0, projection_indices) +# batch_z3 = torch.index_select(z3_params, 0, projection_indices) + +# with torch.autocast( +# device_type=device_type, dtype=autocast_dtype, enabled=use_amp +# ): +# with torch.no_grad(): +# batch_ray_coords = self.create_batch_projection_rays( +# pixel_i, pixel_j, N, num_samples_per_ray +# ) + +# transformed_rays = self.transform_batch_ray_coordinates( +# batch_ray_coords, +# z1=batch_z1, +# x=phis, +# z3=batch_z3, +# shifts=batch_shifts, +# N=N, +# sampling_rate=1.0, +# ) + +# all_coords = transformed_rays.view(-1, 3) + +# all_densities = self.obj.forward(all_coords) + +# ray_densities = all_densities.view( +# len(target_values), num_samples_per_ray +# ) # Reshape rays and integarte +# step_size = 2.0 / (num_samples_per_ray - 1) + +# predicted_values = ray_densities.sum(dim=1) * step_size + +# mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) + +# loss = mse_loss + +# val_loss += loss.detach() +# num_batches += 1 + +# avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 + +# if self.world_size > 1: +# val_loss_tensor = avg_val_loss.detach().clone() +# dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) +# avg_val_loss = val_loss_tensor.item() + +# self.obj.model.train() +# aux_params.train() + +# return avg_val_loss + +# def ad_recon( +# self, +# optimizer_params: dict, +# num_iter: int = 0, +# reset: bool = False, +# scheduler_params: dict | None = None, +# hard_constraints: dict | None = None, +# soft_constraints: dict | None = None, +# # store_iterations: bool | None = None, +# # store_iterations_every: int | None = None, +# # autograd: bool = True, +# ): +# if reset: +# self.reset_recon() + +# self.hard_constraints = hard_constraints +# self.soft_constraints = soft_constraints + +# # Make sure everything is in the correct device, might be redundant/cleaner way to do this +# self.dataset.to(self.device) +# self.volume_obj.to(self.device) + +# # Making optimizable parameters into leaf tensors. +# self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) +# self.dataset.z1_angles = ( +# self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) +# ) +# self.dataset.z3_angles = ( +# self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) +# ) + +# if optimizer_params is not None: +# self.optimizer_params = optimizer_params +# self.set_optimizers() + +# if scheduler_params is not None: +# self.scheduler_params = scheduler_params +# self.set_schedulers(self.scheduler_params, num_iter=num_iter) + +# if hard_constraints is not None: +# self.volume_obj.hard_constraints = hard_constraints +# if soft_constraints is not None: +# self.volume_obj.soft_constraints = soft_constraints + +# pbar = tqdm(range(num_iter), desc="AD Reconstruction") + +# for a0 in pbar: +# total_loss = 0.0 +# tilt_series_loss = 0.0 + +# pred_volume = self.volume_obj.forward() + +# for i in range(len(self.dataset.tilt_series)): +# forward_projection = self.projection_operator( +# vol=pred_volume, +# z1=self.dataset.z1_angles[i], +# x=self.dataset.tilt_angles[i], +# z3=self.dataset.z3_angles[i], +# shift_x=self.dataset.shifts[i, 0], +# shift_y=self.dataset.shifts[i, 1], +# device=self.device, +# ) + +# tilt_series_loss += torch.nn.functional.mse_loss( +# forward_projection, self.dataset.tilt_series[i] +# ) +# tilt_series_loss /= len(self.dataset.tilt_series) + +# total_loss = tilt_series_loss + self.volume_obj.soft_loss +# self.loss.append(total_loss.item()) + +# total_loss.backward() + +# for opt in self.optimizers.values(): +# opt.step() +# opt.zero_grad() + +# if self.schedulers is not None: +# for sch in self.schedulers.values(): +# if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): +# sch.step(total_loss) +# elif sch is not None: +# sch.step() + +# pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") + +# if self.logger is not None: +# self.logger.log_scalar("loss/total", total_loss.item(), a0) +# self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) +# self.logger.log_scalar( +# "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 +# ) + +# if a0 % self.logger.log_images_every == 0: +# self.logger.projection_images( +# volume_obj=self.volume_obj, +# epoch=a0, +# ) +# self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) + +# self.logger.flush() + +# self.ad_recon_vol = self.volume_obj.forward() + +# return self + +# def reset_recon(self) -> None: +# if isinstance(self.volume_obj, ObjectVoxelwise): +# self.volume_obj.reset() + +# self.ad_recon_vol = None + +# # --- Projection Operators ---- +# def projection_operator( +# self, +# vol, +# z1, +# x, +# z3, +# shift_x, +# shift_y, +# device, +# ): +# projection = ( +# rot_ZXZ( +# mags=vol.unsqueeze(0), # Add batch dimension +# z1=z1, +# x=-x, +# z3=z3, +# device=device, +# mode="bilinear", +# ) +# .squeeze() +# .sum(axis=0) +# ) + +# shifted_projection = differentiable_shift_2d( +# image=projection, +# shift_x=shift_x, +# shift_y=shift_y, +# sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit +# ) + +# return shifted_projection From 8f6cb02aa0dee2e24e1f235249f486ad4e8a849a Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 29 Jan 2026 23:03:03 -0800 Subject: [PATCH 054/335] Val + train test split implemented - cuBLAS error after adding this need to track down; scheduler stepping fixed prior implementations was not stepping schedulers; end result volume saving, figure out protocol for how to save model weights, volume, etc...; added nvtx wrapper in core/ml; starting implementation of TomographyLite --- src/quantem/core/ml/ddp.py | 46 ++++++-- src/quantem/core/ml/optimizer_mixin.py | 5 +- src/quantem/core/ml/profiling.py | 14 +++ src/quantem/tomography/logger_tomography.py | 9 +- src/quantem/tomography/object_models.py | 12 ++- src/quantem/tomography/tomography.py | 112 +++++++++++++++----- src/quantem/tomography/tomography_base.py | 1 + src/quantem/tomography/tomography_lite.py | 0 src/quantem/tomography/tomography_opt.py | 37 ++++--- 9 files changed, 182 insertions(+), 54 deletions(-) create mode 100644 src/quantem/core/ml/profiling.py create mode 100644 src/quantem/tomography/tomography_lite.py diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index b6ec536f..5cc28188 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -3,7 +3,7 @@ import torch import torch.distributed as dist import torch.nn as nn -from torch.utils.data import DataLoader, Dataset, DistributedSampler +from torch.utils.data import DataLoader, Dataset, DistributedSampler, random_split # Rename DDPMixin @@ -60,29 +60,42 @@ def setup_dataloader( dataset: Dataset, batch_size: int, num_workers: int = 0, - # val_fraction: float = 0.0, + val_fraction: float = 0.0, ): pin_mem = self.device.type == "cuda" persist = num_workers > 0 - # TODO: Implement validation dataloader + if val_fraction > 0.0: + train_dataset, val_dataset = random_split(dataset, [1 - val_fraction, val_fraction]) + else: + train_dataset = dataset + val_dataset = None if self.world_size > 1: shuffle = True train_sampler = DistributedSampler( - dataset, + train_dataset, num_replicas=self.world_size, rank=self.global_rank, shuffle=shuffle, ) + + if val_dataset: + val_sampler = DistributedSampler( + val_dataset, + num_replicas=self.world_size, + rank=self.global_rank, + shuffle=False, + ) shuffle = False else: train_sampler = None + val_sampler = None shuffle = True train_dataloader = DataLoader( - dataset, + train_dataset, batch_size=batch_size, num_workers=num_workers, sampler=train_sampler, @@ -92,14 +105,33 @@ def setup_dataloader( persistent_workers=persist, ) + if val_dataset: + val_dataloader = DataLoader( + val_dataset, + batch_size=batch_size * 4, + num_workers=num_workers, + sampler=val_sampler, + shuffle=False, + pin_memory=pin_mem, + drop_last=False, + persistent_workers=persist, + ) + val_dataloader = val_dataloader + else: + val_dataloader = None + if self.global_rank == 0: print("Dataloader setup complete:") - print(f" Total samples: {len(dataset)}") + print(f" Total train samples: {len(train_dataset)}") print(f" Local batch size: {batch_size}") print(f" Global batch size: {batch_size * self.world_size}") print(f" Train batches per GPU per epoch: {len(train_dataloader)}") - return train_dataloader, train_sampler + if val_dataset: + print(f" Total val samples: {len(val_dataset)}") + print(f" Val batches per GPU per epoch: {len(val_dataloader)}") + + return train_dataloader, train_sampler, val_dataloader, val_sampler def build_model( self, diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 7c39e39d..92db06f1 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -61,10 +61,11 @@ def scheduler_params(self, params: dict): "exp", "gamma", "linear", + "cosine_annealing", "none", ]: raise ValueError( - f"Unknown scheduler type: {params['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" + f"Unknown scheduler type: {params['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'linear', 'cosine_annealing', 'none']" ) self._scheduler_params = params.copy() else: @@ -183,7 +184,7 @@ def set_scheduler( self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( optimizer, T_max=params.get("T_max", num_iter), - eta_min=params.get("eta_min", base_LR), + eta_min=params.get("eta_min", 1e-7), ) else: raise ValueError(f"Unknown scheduler type: {sched_type}") diff --git a/src/quantem/core/ml/profiling.py b/src/quantem/core/ml/profiling.py new file mode 100644 index 00000000..aec6e5ed --- /dev/null +++ b/src/quantem/core/ml/profiling.py @@ -0,0 +1,14 @@ +from contextlib import contextmanager + +import torch.cuda.nvtx as nvtx + + +@contextmanager +def nvtx_range(enabled: bool, name: str): + if enabled: + nvtx.range_push(name) + try: + yield + finally: + if enabled: + nvtx.range_pop() diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index 16e6d9f3..4d850e18 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -31,12 +31,18 @@ def log_iter( iter: int, consistency_loss: float, total_loss: float, - num_samples_per_ray: int | None = None, + learning_rates: dict[str, float], + num_samples_per_ray: int, + val_loss: float | None = None, ): self.log_scalar("loss/consistency", consistency_loss, iter) self.log_scalar("loss/total", total_loss, iter) self.log_scalar("loss/soft", object_model._soft_constraint_losses[-1], iter) self.log_scalar("num_samples_per_ray", num_samples_per_ray, iter) + for param_name, lr_value in learning_rates.items(): + self.log_scalar(f"learning_rate/{param_name}", float(lr_value), iter) + if val_loss is not None: + self.log_scalar("loss/val", val_loss, iter) def log_iter_images( self, @@ -45,7 +51,6 @@ def log_iter_images( iter: int, logger_cmap: str = "turbo", ): - with torch.no_grad(): z1_vals = dataset_model.z1_params.detach().cpu().numpy() z3_vals = dataset_model.z3_params.detach().cpu().numpy() diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index a603c089..aba0bc78 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -311,13 +311,17 @@ def model(self) -> "nn.Module": # """ # raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + @property + def obj(self) -> torch.Tensor: + return self._obj + def apply_soft_constraints( self, coords: torch.Tensor, ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=coords.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(10000, coords.shape[0]) + num_tv_samples = min(1000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices].detach().requires_grad_(True) @@ -348,7 +352,6 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: if self.constraints.shrinkage: pred = torch.max(pred - self.constraints.shrinkage, torch.zeros_like(pred)) - return pred @property @@ -581,10 +584,9 @@ def create_volume( pred_full = outputs.reshape(N, N, N).float() if return_vol: - return pred_full.detach().cpu() - else: - self._obj = pred_full.detach().cpu() + + self._obj = pred_full.detach().cpu() def get_tv_loss( self, diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 0c1b7a49..d2e3eb3c 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,4 +1,3 @@ -from contextlib import contextmanager from typing import List, Literal, Optional, Self, Tuple import numpy as np @@ -7,6 +6,7 @@ from tqdm.auto import tqdm from quantem.core.ml.ddp import DDPMixin +from quantem.core.ml.profiling import nvtx_range from quantem.tomography.dataset_models import DatasetModelType from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ObjectModelType @@ -41,8 +41,6 @@ def from_models( def reconstruct( self, - # obj_model: ObjectModelType, - # dset: DatasetModelType, num_iter: int = 10, batch_size: int = 1024, num_workers: int = 32, @@ -53,6 +51,7 @@ def reconstruct( loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), num_samples_per_ray: int | List[Tuple[int, int]] = None, profiling_mode: bool = False, + val_fraction: float = 0.0, ): """ This function should be able to handle both AD and INR-based tomography reconstruction methods. @@ -65,51 +64,45 @@ def reconstruct( # raise ValueError( # f"Should never happen! obj_model and dset must be on the same device, got {self.obj_model.device} and {self.dset.device}" # ) - if profiling_mode: if self.global_rank == 0: print("Profiling mode enabled.") - import torch.cuda.nvtx as nvtx - - @contextmanager - def nvtx_range(enabled: bool, name: str): - if enabled: - nvtx.range_push(name) - try: - yield - finally: - if enabled: - nvtx.range_pop() if reset: raise NotImplementedError("Reset is not implemented yet.") + new_scheduler = reset + if optimizer_params is not None: with nvtx_range(profiling_mode, "Setting Optimizer Params"): self.optimizer_params = optimizer_params self.set_optimizers() + new_scheduler = True if scheduler_params is not None: with nvtx_range(profiling_mode, "Setting Scheduler Params"): self.scheduler_params = scheduler_params - self.set_schedulers(scheduler_params) + new_scheduler = True if constraints is not None: - self.obj_model.constraints = constraints + with nvtx_range(profiling_mode, "Setting Constraints"): + self.obj_model.constraints = constraints - new_scheduler = reset if new_scheduler: - raise NotImplementedError("New schedulers are not implemented yet.") + with nvtx_range(profiling_mode, "Setting Schedulers"): + self.set_schedulers(scheduler_params, num_iter=num_iter) # Setting up DDP if not hasattr(self, "dataloader"): with nvtx_range(profiling_mode, "Setting Dataloader"): - self.dataloader, self.sampler = self.setup_dataloader( - self.dset, batch_size, num_workers=num_workers + self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( + self.setup_dataloader( + self.dset, + batch_size, + num_workers=num_workers, + val_fraction=val_fraction, + ) ) - - self.obj_model.model.train() - N = max(self.obj_model.shape) if num_samples_per_ray is None: @@ -121,12 +114,13 @@ def nvtx_range(enabled: bool, name: str): print("num_samples_per_ray schedule provided.") print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") - for a0 in range(num_iter): with nvtx_range(profiling_mode, f"Epoch {a0}"): consistency_loss = 0.0 total_loss = 0.0 epoch_soft_constraint_loss = 0.0 + self.obj_model.model.train() + self.dset.train() # self._reset_iter_constraints() if self.sampler is not None: @@ -151,7 +145,6 @@ def nvtx_range(enabled: bool, name: str): all_coords = self.dset.get_coords( batch, N, curr_num_samples_per_ray ) - with nvtx_range(profiling_mode, "Forwarding"): all_densities = self.obj_model.forward(all_coords) @@ -203,6 +196,8 @@ def nvtx_range(enabled: bool, name: str): ): consistency_loss += batch_consistency_loss.detach() + with nvtx_range(profiling_mode, "Stepping Schedulers"): + self.step_schedulers(loss=total_loss) # TODO: Maybe reorganize the losses so that the order makes sense lol. total_loss = total_loss.item() / len(self.dataloader) @@ -211,6 +206,53 @@ def nvtx_range(enabled: bool, name: str): self.dataloader ) + if self.val_dataloader is not None: + print("Validating...") + self.obj_model.model.eval() + self.dset.eval() + with torch.no_grad(): + val_loss = 0.0 + + for batch in self.val_dataloader: + with torch.autocast( + device_type=self.device.type, + dtype=torch.bfloat16, + enabled=True, + ): + with nvtx_range(profiling_mode, "Getting Coords"): + all_coords = self.dset.get_coords( + batch, N, curr_num_samples_per_ray + ) + + with nvtx_range(profiling_mode, "Forwarding"): + all_densities = self.obj_model.forward(all_coords) + + with nvtx_range(profiling_mode, "Integrating"): + integrated_densities = self.dset.integrate_rays( + all_densities, + curr_num_samples_per_ray, + len(batch["target_value"]), + ) + + with nvtx_range(profiling_mode, "Getting Target"): + target = ( + batch["target_value"] + .to(self.device, non_blocking=True) + .float() + ) + + with nvtx_range(profiling_mode, "Calculating Loss"): + batch_val_loss = torch.nn.functional.mse_loss( + integrated_densities, target + ) + + with nvtx_range(profiling_mode, "Adding batch loss to total loss"): + val_loss += ( + batch_val_loss.detach() + soft_constraints_loss.detach() + ) + + avg_val_loss = val_loss.item() / len(self.val_dataloader) + metrics = torch.tensor( [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device ) @@ -225,9 +267,14 @@ def nvtx_range(enabled: bool, name: str): f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}" ) + if self.val_dataloader: + print(f"Validation loss: {avg_val_loss:4f}") + self._epoch_losses.append(total_loss) self._consistency_losses.append(consistency_loss) self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) + if self.val_dataloader is not None: + self._val_losses.append(avg_val_loss) with nvtx_range(profiling_mode, "Logging"): if self.logger is not None: @@ -252,11 +299,24 @@ def nvtx_range(enabled: bool, name: str): iter=self.num_epochs, consistency_loss=consistency_loss, total_loss=total_loss, + learning_rates=self.get_current_lrs(), num_samples_per_ray=curr_num_samples_per_ray, + val_loss=avg_val_loss if self.val_dataloader is not None else None, ) self.logger.flush() + # --- Helper Functions --- + + def save_volume(self, path: str = "recon_volume.npz"): + # TODO: Temporary, need to talk to Arthur what the correct way of saving results is. + if self.global_rank == 0: + print(f"Saving volume to {path}") + + self.obj_model.create_volume() + + np.savez(path, volume=self.obj_model.obj.detach().cpu().numpy()) + class TomographyConventional(TomographyBase): """ diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 2f8d6fc7..6b517f03 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -45,6 +45,7 @@ def __init__( # Loss self._epoch_losses: list[float] = [] self._consistency_losses: list[float] = [] + self._val_losses: list[float] = [] # DDP Initialization # print("Checking if obj_model is a ObjectPixelated: ", not isinstance(obj_model, ObjectPixelated)) if not isinstance(obj_model, ObjectPixelated): diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py new file mode 100644 index 00000000..e69de29b diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 2a148433..c7bc6b38 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -37,11 +37,8 @@ def optimizer_params(self) -> dict[str, dict]: @optimizer_params.setter def optimizer_params(self, d: dict): """Set the optimizer parameters.""" - self._optimizer_params = d.copy() if d else {} - - for key in self.OPTIMIZABLE_VALS: - if key not in d: - d[key] = {"type": "none"} + if isinstance(d, (tuple, list)): + d = {k: {} for k in d} for k, v in d.items(): if "type" not in v.keys(): @@ -71,6 +68,20 @@ def set_optimizers(self): else: raise ValueError(f"Unknown optimization key: {key}") + def get_current_lrs(self) -> dict[str, float]: + if self.obj_model.has_optimizer(): + obj_lr = self.obj_model.get_current_lr() + else: + obj_lr = 0.0 + if self.dset.has_optimizer(): + pose_lr = self.dset.get_current_lr() + else: + pose_lr = 0.0 + return { + "object": obj_lr, + "pose": pose_lr, + } + def remove_optimizer(self, key: str): if key == "object": self.obj_model.remove_optimizer() @@ -106,10 +117,14 @@ def scheduler_params(self, d: dict): @property def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: - return { - "object": self.obj_model.scheduler, - "pose": self.dset.scheduler, - } + schedulers = {} + + if self.obj_model.scheduler is not None: + schedulers["object"] = self.obj_model.scheduler + if self.dset.scheduler is not None: + schedulers["pose"] = self.dset.scheduler + + return schedulers def set_schedulers(self, params: dict[str, dict], num_iter: int | None = None): for key, scheduler_params in params.items(): @@ -142,7 +157,5 @@ def step_schedulers(self, loss: float | None = None): for key in self.scheduler_params.keys(): if key == "object" and self.obj_model.scheduler is not None: self.obj_model.step_scheduler(loss) - elif key == "pose" and self.dset.scheduler is not None: + elif self.dset.scheduler is not None and key == "pose": self.dset.step_scheduler(loss) - else: - raise ValueError(f"Unknown optimization key: {key}") From 791a644de042f357526f3985c69464c107c22906 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Fri, 30 Jan 2026 00:28:22 -0800 Subject: [PATCH 055/335] Small updates --- src/quantem/tomography/object_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index aba0bc78..c5315848 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -321,7 +321,7 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=coords.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(1000, coords.shape[0]) + num_tv_samples = min(10000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices].detach().requires_grad_(True) From 4b7f1086ded434e1503fdd50bbd6fc764fca6bcd Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Fri, 30 Jan 2026 17:28:29 -0800 Subject: [PATCH 056/335] Implemented a working TomographyLite, need to test AutoSerialize, and move conventional algorithm .forward calls. --- src/quantem/core/ml/ddp.py | 1 - src/quantem/tomography/tomography.py | 7 +- src/quantem/tomography/tomography_conv.py | 0 src/quantem/tomography/tomography_lite.py | 121 +++ src/quantem/tomography/tomography_ml.py | 0 src/quantem/tomography_old/__init__.py | 0 src/quantem/tomography_old/models.py | 96 -- src/quantem/tomography_old/object_models.py | 723 -------------- .../tomography_old/preprocess/__init__.py | 0 .../tomography_old/preprocess/drift.py | 0 src/quantem/tomography_old/tomography.py | 883 ------------------ src/quantem/tomography_old/tomography_base.py | 514 ---------- src/quantem/tomography_old/tomography_conv.py | 235 ----- .../tomography_old/tomography_dataset.py | 468 ---------- src/quantem/tomography_old/tomography_ddp.py | 321 ------- .../tomography_old/tomography_logger.py | 68 -- src/quantem/tomography_old/tomography_ml.py | 338 ------- src/quantem/tomography_old/tomography_nerf.py | 636 ------------- src/quantem/tomography_old/utils.py | 300 ------ 19 files changed, 126 insertions(+), 4585 deletions(-) delete mode 100644 src/quantem/tomography/tomography_conv.py delete mode 100644 src/quantem/tomography/tomography_ml.py delete mode 100644 src/quantem/tomography_old/__init__.py delete mode 100644 src/quantem/tomography_old/models.py delete mode 100644 src/quantem/tomography_old/object_models.py delete mode 100644 src/quantem/tomography_old/preprocess/__init__.py delete mode 100644 src/quantem/tomography_old/preprocess/drift.py delete mode 100644 src/quantem/tomography_old/tomography.py delete mode 100644 src/quantem/tomography_old/tomography_base.py delete mode 100644 src/quantem/tomography_old/tomography_conv.py delete mode 100644 src/quantem/tomography_old/tomography_dataset.py delete mode 100644 src/quantem/tomography_old/tomography_ddp.py delete mode 100644 src/quantem/tomography_old/tomography_logger.py delete mode 100644 src/quantem/tomography_old/tomography_ml.py delete mode 100644 src/quantem/tomography_old/tomography_nerf.py delete mode 100644 src/quantem/tomography_old/utils.py diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 5cc28188..df43c2e5 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -6,7 +6,6 @@ from torch.utils.data import DataLoader, Dataset, DistributedSampler, random_split -# Rename DDPMixin class DDPMixin: """ Class for setting up all distributed training. diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index d2e3eb3c..aaf48d17 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -13,8 +13,11 @@ from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_opt import TomographyOpt -from quantem.tomography.utils import torch_phase_cross_correlation -from quantem.tomography_old.utils import gaussian_filter_2d_stack, gaussian_kernel_1d +from quantem.tomography.utils import ( + gaussian_filter_2d_stack, + gaussian_kernel_1d, + torch_phase_cross_correlation, +) class Tomography(TomographyOpt, TomographyBase, DDPMixin): diff --git a/src/quantem/tomography/tomography_conv.py b/src/quantem/tomography/tomography_conv.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index e69de29b..b78ea4ff 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -0,0 +1,121 @@ +import os +from typing import Literal, Self + +import numpy as np + +from quantem.core.ml.inr import HSiren +from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.logger_tomography import LoggerTomography +from quantem.tomography.object_models import ObjectINR +from quantem.tomography.tomography import Tomography + + +class TomographyLiteINR(Tomography): + """ + A lite version of the Tomography class. + """ + + @classmethod + def from_dataset( + cls, + dset: DatasetModelType, + device: str = "cuda", + log_dir: os.PathLike | str | None = None, + log_images_every: int = 10, + rng: np.random.Generator | int | None = None, + ) -> Self: + dset_model = dset + + # Define the object model + model = HSiren(alpha=1, winner_initialization=True) + obj_model = ObjectINR.from_model( + model=model, + shape=( + max(dset_model.tilt_stack.shape), + max(dset_model.tilt_stack.shape), + max(dset_model.tilt_stack.shape), + ), + device=device, + rng=rng, + ) + + # TODO: Implement pretrain + + if log_dir is not None: + logger = LoggerTomography( + log_dir=log_dir, + run_prefix="tomography_lite_inr", + run_suffix="", + log_images_every=log_images_every, + ) + else: + logger = None + + tomography = cls.from_models( + dset=dset_model, + obj_model=obj_model, + device=device, + rng=rng, + logger=logger, + ) + + return tomography + + def reconstruct( + self, + num_iter: int = 10, + reset: bool = False, + obj_lr: float = 1e-4, + pose_lr: float = 1e-2, + batch_size: int = 1024, + num_workers: int = 32, + learn_pose: bool = True, + warmup_routine: bool = True, + scheduler_type: Literal[ + "exp", "cyclic", "plateau", "cosine_annealing", "linear", "full_warmup" + ] = "none", + scheduler_factor: float = 0.5, + new_optimizers: bool = False, + constraints: dict = {}, + ): + if self.num_epochs == 0: + opt_params = { + "object": { + "type": "adam", + "lr": obj_lr, + }, + } + + scheduler_params = { + "object": { + "type": scheduler_type, + "factor": scheduler_factor, + }, + } + + if learn_pose: + opt_params["pose"] = { + "type": "adam", + "lr": pose_lr, + } + scheduler_params["pose"] = { + "type": scheduler_type, + "factor": scheduler_factor, + } + + else: + opt_params = None + scheduler_params = None + + constraints = constraints + num_samples_per_ray = int(max(self.dset.tilt_stack.shape)) + return super().reconstruct( + num_iter=num_iter, + batch_size=batch_size, + num_workers=num_workers, + reset=reset, + num_samples_per_ray=num_samples_per_ray, + optimizer_params=opt_params, + scheduler_params=scheduler_params, + constraints=constraints, + ) diff --git a/src/quantem/tomography/tomography_ml.py b/src/quantem/tomography/tomography_ml.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography_old/__init__.py b/src/quantem/tomography_old/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography_old/models.py b/src/quantem/tomography_old/models.py deleted file mode 100644 index a972400c..00000000 --- a/src/quantem/tomography_old/models.py +++ /dev/null @@ -1,96 +0,0 @@ -import numpy as np -import torch -from torch import nn - - -class SineLayer(nn.Module): - def __init__( - self, - in_features, - out_features, - bias=True, - is_first=False, - omega_0=30, - hsiren=False, - alpha=1.0, - ): - super().__init__() - self.omega_0 = omega_0 - self.is_first = is_first - self.hsiren = hsiren - self.in_features = in_features - self.alpha = alpha - self.linear = nn.Linear(in_features, out_features, bias=bias) - self.init_weights() - - def init_weights(self): - with torch.no_grad(): - if self.is_first: - # Scale the first layer initialization by alpha - self.linear.weight.uniform_( - -self.alpha / self.in_features, self.alpha / self.in_features - ) - else: - # Scale the hidden layer initialization by alpha - self.linear.weight.uniform_( - -self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, - self.alpha * np.sqrt(6 / self.in_features) / self.omega_0, - ) - - def forward(self, input): - if self.is_first and self.hsiren: - out = torch.sin(self.omega_0 * torch.sinh(2 * self.linear(input))) - else: - out = torch.sin(self.omega_0 * self.linear(input)) - return out - - -class HSiren(nn.Module): - def __init__( - self, - in_features=2, - out_features=3, - hidden_layers=3, - hidden_features=256, - first_omega_0=30, - hidden_omega_0=30, - alpha=1.0, - ): - super().__init__() - self.net_list = [] - self.net_list.append( - SineLayer( - in_features, - hidden_features, - is_first=True, - omega_0=first_omega_0, - hsiren=True, - alpha=alpha, - ) - ) - - for i in range(hidden_layers): - self.net_list.append( - SineLayer( - hidden_features, - hidden_features, - is_first=False, - omega_0=hidden_omega_0, - alpha=alpha, - ) - ) - - final_linear = nn.Linear(hidden_features, out_features) - with torch.no_grad(): - # Final layer keeps original initialization (no alpha scaling) - final_linear.weight.uniform_( - -np.sqrt(6 / hidden_features) / hidden_omega_0, - np.sqrt(6 / hidden_features) / hidden_omega_0, - ) - self.net_list.append(final_linear) - self.net_list.append(nn.Softplus()) - self.net = nn.Sequential(*self.net_list) - - def forward(self, coords): - output = self.net(coords) - return output diff --git a/src/quantem/tomography_old/object_models.py b/src/quantem/tomography_old/object_models.py deleted file mode 100644 index 57192e27..00000000 --- a/src/quantem/tomography_old/object_models.py +++ /dev/null @@ -1,723 +0,0 @@ -from abc import abstractmethod -from copy import deepcopy -from typing import Any, Callable - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from tqdm.auto import tqdm - -from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml.blocks import reset_weights -from quantem.core.utils.validators import validate_gt, validate_tensor -from quantem.tomography.utils import get_TV_loss - - -class ObjectBase(AutoSerialize): - """ - Base class for all ObjectModels to inherit from. - """ - - def __init__( - self, - volume_shape: tuple[int, int, int], - device: str, - offset_obj: float = 1e-5, - ): - self._shape = volume_shape - - self._obj = torch.zeros(self._shape, device=device, dtype=torch.float32) + offset_obj - self._offset_obj = offset_obj - self._device = device - self._hard_constraints = {} - self._soft_constraints = {} # One big dicitonary - - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape - - @property - def offset_obj(self) -> float: - return self._offset_obj - - @offset_obj.setter - def offset_obj(self, offset_obj: float): - self._offset_obj = offset_obj - - @property - def obj(self) -> torch.Tensor: - pass - - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj - - @property - def device(self) -> str: - return self._device - - @device.setter - def device(self, device: str): - self._device = device - - @abstractmethod - def forward( - self, z1: torch.Tensor, z3: torch.Tensor, shift_x: torch.Tensor, shift_y: torch.Tensor - ): - pass - - @abstractmethod - def obj(self): - pass - - @abstractmethod - def reset(self): - pass - - @abstractmethod - def to(self, device: str): - pass - - @abstractmethod - def name(self) -> str: - pass - - @abstractmethod - def params(self) -> torch.Tensor: - pass - - -class ObjectConstraints(ObjectBase): - DEFAULT_HARD_CONSTRAINTS = { - "fourier_filter": False, - "positivity": False, - "shrinkage": False, - "circular_mask": False, - } - - DEFAULT_SOFT_CONSTRAINTS = { - "tv_vol": 0, - } - - @property - def hard_constraints(self) -> dict[str, Any]: - return self._hard_constraints - - @hard_constraints.setter - def hard_constraints(self, hard_constraints: dict[str, Any]): - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - for key, value in hard_constraints.items(): - if key not in gkeys: # This might be redundant since add_constraint is checking. - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._hard_constraints[key] = value - - @property - def soft_constraints(self) -> dict[str, Any]: - return self._soft_constraints - - @soft_constraints.setter - def soft_constraints(self, soft_constraints: dict[str, Any]): - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - for key, value in soft_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._soft_constraints[key] = value - - def add_hard_constraint(self, constraint: str, value: Any): - """Add constraints to the object model.""" - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - if constraint not in gkeys: - raise KeyError( - f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" - ) - self._hard_constraints[constraint] = value - - def add_soft_constraint(self, constraint: str, value: Any): - """Add constraints to the object model.""" - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - if constraint not in gkeys: - raise KeyError( - f"Invalid object constraint key '{constraint}', allowed keys are {gkeys}" - ) - self._soft_constraints[constraint] = value - - def apply_hard_constraints( - self, - obj: torch.Tensor, - ) -> torch.Tensor: - """ - Apply constraints to the object model. - """ - obj2 = obj.clone() - if self.hard_constraints["positivity"]: - obj2 = torch.clamp(obj, min=0.0, max=None) - if self.hard_constraints["shrinkage"]: - obj2 = torch.max(obj2 - self.hard_constraints["shrinkage"], torch.zeros_like(obj2)) - - return obj2 - - def apply_soft_constraints( - self, - obj: torch.Tensor, - ) -> torch.Tensor: - """ - 'Applies' soft constraints to the object model. This will return additional loss terms. - """ - soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) - if self.soft_constraints["tv_vol"] > 0: - tv_loss = get_TV_loss( - obj.unsqueeze(0).unsqueeze(0), factor=self.soft_constraints["tv_vol"] - ) - - soft_loss += tv_loss - - return soft_loss - - -class ObjectVoxelwise(ObjectConstraints): - """ - Object model for voxelwise objects. - """ - - def __init__( - self, - volume_shape: tuple[int, int, int], - device: str, - initial_volume: torch.Tensor | None = None, - ): - super().__init__( - volume_shape=volume_shape, - device=device, - ) - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - if initial_volume is not None: - self._initial_obj = initial_volume - else: - self.initial_obj = ( - torch.zeros(self._shape, device=self._device, dtype=torch.float32) - + self.offset_obj - ) - - @property - def obj(self): - return self.apply_hard_constraints(self._obj) - - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj - - @property - def initial_obj(self): - return self._initial_obj - - @initial_obj.setter - def initial_obj(self, initial_obj: torch.Tensor): - if not isinstance(initial_obj, torch.Tensor): - raise ValueError("initial_obj must be a torch.Tensor") - - self._initial_obj = initial_obj - - def forward(self): - return self.obj - - def reset(self): - self._obj = ( - torch.zeros(self._shape, device=self._device, dtype=torch.float32) + self.offset_obj - ) - - def to(self, device: str): - self._device = device - self._obj = self._obj.to(self._device) - - @property - def name(self) -> str: - return "ObjectVoxelwise" - - @property - def params(self) -> torch.Tensor: - return self._obj - - @property - def soft_loss(self) -> torch.Tensor: - return self.apply_soft_constraints(self._obj) - - -class ObjectDIP(ObjectConstraints): - """ - Object model for DIP objects. - """ - - def __init__( - self, - model: torch.nn.Module, - volume_shape: tuple[int, int, int], - model_input: torch.Tensor - | None = None, # Determines output size, model input pretraining target - input_noise_std: float = 0.0, - device: str = "cpu", - ): - super().__init__( - volume_shape=volume_shape, - device=device, - ) - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - if model_input is None: - self.model_input = torch.randn(1, 1, volume_shape[0], volume_shape[1], volume_shape[2]) - else: - self.model_input = model_input.clone().detach() - - self.pretrain_target = model_input.clone().detach() - - self._model = model - self._optimizer = None - self._scheduler = None - self._pretrain_losses = [] - self._pretrain_lrs = [] - self._model_input_noise_std = input_noise_std - - @property - def name(self) -> str: - return "ObjectDIP" - - @property - def model(self) -> torch.nn.Module: - return self._model - - @model.setter - def model(self, model: torch.nn.Module): - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Model must be a torch.nn.Module, got {type(model)}") - self._model = model.to(self._device) - self.set_pretrained_weights(self._model) - - @property - def pretrained_weights(self) -> dict[str, torch.Tensor]: - return self._pretrained_weights - - def set_pretrained_weights(self, model: torch.nn.Module): - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") - self._pretrained_weights = deepcopy(model.state_dict()) - - @property - def model_input(self) -> torch.Tensor: - return self._model_input - - @model_input.setter - def model_input(self, input_tensor: torch.Tensor): - inp = validate_tensor( - input_tensor, - name="model_input", - dtype=torch.float32, - ndim=5, - expand_dims=True, - ) - self._model_input = inp.to(self._device) - - @property - def pretrain_target(self) -> torch.Tensor: - return self._pretrain_target - - @pretrain_target.setter - def pretrain_target(self, target: torch.Tensor): - if target.ndim == 5: - target = target.squeeze(0).squeeze(0) - - target = validate_tensor( - target, - name="pretrain_target", - ndim=3, - dtype=torch.float32, - expand_dims=True, - ) - if target.shape[-3:] != self.model_input.shape[-3:]: - raise ValueError( - f"Pretrain target shape {target.shape} does not match model input shape {self.model_input.shape}" - ) - self._pretrain_target = target.to(self._device) - - @property - def _model_input_noise_std(self) -> float: - """standard deviation of the gaussian noise added to the model input each forward call""" - return self._input_noise_std - - @_model_input_noise_std.setter - def _model_input_noise_std(self, std: float): - validate_gt(std, 0.0, "input_noise_std", geq=True) - self._input_noise_std = std - - @property - def optimizer(self) -> torch.optim.Optimizer: - """get the optimizer for the DIP model""" - if self._optimizer is None: - raise ValueError("Optimizer is not set. Use set_optimizer() to set it.") - return self._optimizer - - def set_optimizer(self, opt_params: dict): - opt_type = opt_params.pop("type") - if isinstance(opt_type, torch.optim.Optimizer): - self._optimizer = opt_type - elif isinstance(opt_type, type): - self._optimizer = opt_type(self.model.parameters(), **opt_params) - elif opt_type == "adam": - self._optimizer = torch.optim.Adam(self.model.parameters(), **opt_params) - elif opt_type == "adamw": - self._optimizer = torch.optim.AdamW(self.model.parameters(), **opt_params) - elif opt_type == "sgd": - self._optimizer = torch.optim.SGD(self.model.parameters(), **opt_params) - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") - - @property - def scheduler( - self, - ) -> ( - torch.optim.lr_scheduler._LRScheduler - | torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ): - return self._scheduler - - def set_scheduler(self, params: dict, num_iter: int | None = None) -> None: - sched_type: str = params["type"].lower() - optimizer = self.optimizer - base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - scheduler = None - elif sched_type == "cyclic": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 20), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params.keys(): - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.999 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") - self._scheduler = scheduler - - @property - def pretrain_losses(self) -> np.ndarray: - return np.array(self._pretrain_losses) - - @property - def pretrain_lrs(self) -> np.ndarray: - return np.array(self._pretrain_lrs) - - @property - def obj(self): - obj = self.model(self._model_input)[0] - return self.apply_hard_constraints(obj) - - def forward(self): - return self.model(self._model_input) - - def to(self, device: str): - self.device = device - self._model = self._model.to(self.device) - self._model_input = self._model_input.to(self.device) - self._pretrain_target = self._pretrain_target.to(self.device) - - @property - def params(self): - return self._model.parameters() - - def reset(self): - self.model.load_state_dict(self.pretrained_weights.copy()) - - def pretrain( - self, - model_input: torch.Tensor, - pretrain_target: torch.Tensor, - reset: bool = True, - num_epochs: int = 100, - optimizer_params: dict | None = None, - scheduler_params: dict | None = None, - loss_fn: Callable | str = "l2", - apply_constraints: bool = False, - show: bool = True, - ): - model_input.to(self.device) - pretrain_target.to(self.device) - - if optimizer_params is not None: - self.set_optimizer(optimizer_params) - - if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_epochs) - - if reset: - self._model.apply(reset_weights) - self._pretrain_losses = [] - self._pretrain_lrs = [] - - if model_input is not None: - self.model_input = model_input - - if pretrain_target.shape[-3:] != self.model_input.shape[-3:]: - raise ValueError( - f"Pretrain target shape {pretrain_target.shape} does not match model input shape {self.model_input.shape}" - ) - self.pretrain_target = pretrain_target.clone().detach().to(self.device) - - loss_fn = torch.nn.functional.mse_loss - - self._pretrain( - num_epochs=num_epochs, - loss_fn=loss_fn, - apply_constraints=apply_constraints, - show=show, - ) - self.set_pretrained_weights(self.model) - - def _pretrain( - self, - num_epochs: int, - loss_fn: Callable, - apply_constraints: bool = False, - show: bool = False, - ): - if not hasattr(self, "pretrain_target"): - raise ValueError("Pretrain target is not set. Use pretrain_target to set it.") - - self.model.train() - optimizer = self.optimizer - sch = self.scheduler - pbar = tqdm(range(num_epochs)) - output = self.obj - - for a0 in pbar: - if apply_constraints: - output = self.obj - else: - output = self.model(self.model_input).squeeze(0).squeeze(0) - - loss = loss_fn(output, self.pretrain_target) - loss.backward() - optimizer.step() - optimizer.zero_grad() - - if sch is not None: - if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): - sch.step(loss.item()) - else: - sch.step() - - self._pretrain_losses.append(loss.item()) - self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) - pbar.set_description(f"Epoch {a0 + 1}/{num_epochs}, Loss: {loss.item():.4f}, ") - - -# INR stuff - - -class ObjectINN(ObjectConstraints): - """ - Object model for INN objects. - - - VolumeDataset (?) dependent on the object - """ - - def __init__( - self, - model: nn.Module, - volume_shape=tuple[int, int, int], - device: str = "cuda", - ): - super().__init__( - volume_shape=volume_shape, - device=device, - offset_obj=0, - ) - - self._model = model - - # --- Properties --- - @property - def obj(self): - return self._obj - - def create_volume( - self, - world_size: int, - global_rank: int, - ray_size: int, - ): - N = max(self._shape) - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - # Underlying model if using DDP - model = self.model.module if hasattr(self.model, "module") else self.model - - # Batch size for inference - # 5x larger batches no gradients needed - - inference_batch_size = 5 * N * ray_size - - # Distribute work across GPUs - total_samples = N**3 - samples_per_gpu = total_samples // world_size - remainder = total_samples % world_size - - # Handle uneven distribution - if global_rank < remainder: - start_idx = global_rank * (samples_per_gpu + 1) - end_idx = start_idx + samples_per_gpu + 1 - else: - start_idx = global_rank * samples_per_gpu + remainder - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx] - num_samples = inputs_subset.shape[0] - outputs_list = [] - - for batch_start in range(0, num_samples, inference_batch_size): - batch_end = min(batch_start + inference_batch_size, num_samples) - batch_coords = inputs_subset[batch_start:batch_end].to( - self.device, non_blocking=True - ) - - batch_outputs = model(batch_coords) - if batch_outputs.dim() > 1: - batch_outputs = batch_outputs.squeeze(-1) - - outputs_list.append(batch_outputs.cpu()) - - outputs = torch.cat(outputs_list, dim=0) - - if world_size > 1: - # Gather from all ranks - # handle potentially different sizes - output_size = torch.tensor(outputs.shape[0], device=self.device, dtype=torch.long) - all_sizes = [ - torch.zeros(1, device=self.device, dtype=torch.long) for _ in range(world_size) - ] - dist.all_gather(all_sizes, output_size) - - # Create gather list with correct sizes - max_size = max(size.item() for size in all_sizes) - - # Pad if necessary for gathering - if outputs.shape[0] < max_size: - padding = torch.zeros( - max_size - outputs.shape[0], device=outputs.device, dtype=outputs.dtype - ) - outputs_padded = torch.cat([outputs, padding], dim=0).to(self.device) - else: - outputs_padded = outputs.to(self.device) - - gathered_outputs = [ - torch.empty(max_size, device=self.device, dtype=outputs.dtype) - for _ in range(world_size) - ] - dist.all_gather(gathered_outputs, outputs_padded.contiguous()) - - # Trim padding and concatenate - trimmed_outputs = [] - for rank, size in enumerate(all_sizes): - trimmed_outputs.append(gathered_outputs[rank][: size.item()]) - - pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N).float() - else: - pred_full = outputs.reshape(N, N, N).float() - - self._obj = pred_full.detach().cpu() - - @property - def model(self): - return self._model - - @model.setter - def model(self, model): - self._model = model - - def apply_soft_constraints( - self, - coords: torch.Tensor, - ): - soft_loss = torch.tensor(0.0, device=coords.device) - if self.soft_constraints["tv_vol"] > 0: - num_tv_samples = min(10000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - # Rerun forward for gradient tracking - tv_coords = coords[tv_indices].detach().requires_grad_(True) - - tv_densities_recomputed = self.model(tv_coords) - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - # Compute gradients - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - soft_loss += self.soft_constraints["tv_vol"] * grad_norm.mean() - - return soft_loss - - def forward( - self, - all_coords: torch.Tensor, - ): - all_densities = self.model(all_coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) - - valid_mask = ( - (all_coords[:, 0] >= -1) - & (all_coords[:, 0] <= 1) - & (all_coords[:, 1] >= -1) - & (all_coords[:, 1] <= 1) - & (all_coords[:, 2] >= -1) - & (all_coords[:, 2] <= 1) - ).float() - - all_densities = all_densities * valid_mask - - return all_densities - - -ObjectModelType = ObjectVoxelwise | ObjectINN # | ObjectDIP | ObjectImplicit (ObjectFFN?) - - -# Pretrain Volume Dataset diff --git a/src/quantem/tomography_old/preprocess/__init__.py b/src/quantem/tomography_old/preprocess/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography_old/preprocess/drift.py b/src/quantem/tomography_old/preprocess/drift.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/quantem/tomography_old/tomography.py b/src/quantem/tomography_old/tomography.py deleted file mode 100644 index 7be24397..00000000 --- a/src/quantem/tomography_old/tomography.py +++ /dev/null @@ -1,883 +0,0 @@ -# import matplotlib.pyplot as plt -# import numpy as np -# import torch -# import torch.distributed as dist -# from torch.nn import SmoothL1Loss -# from torch.utils.tensorboard import SummaryWriter - -# # from torch_radon.radon import ParallelBeam as Radon -# from tqdm.auto import tqdm - -# from quantem.core.ml.loss_functions import ( -# CharbonnierLoss, -# L1Loss, -# LLMSELoss, -# MSELogMSELoss, -# MSELoss, -# ) - -# # Temporary imports for TomographyNERF -# from quantem.tomography.object_models import ObjectINN, ObjectVoxelwise -# from quantem.tomography.tomography_base import TomographyBase -# from quantem.tomography.tomography_conv import TomographyConv -# from quantem.tomography.tomography_ml import TomographyML -# from quantem.tomography.utils import differentiable_shift_2d, gaussian_kernel_1d, rot_ZXZ - - -# # Temporary aux class for TomographyNERF -# # TODO: Maybe put this in INN? -# def get_num_samples_per_ray(N: int, epoch: int): -# """Increase number of samples per ray at specific epochs.""" -# # Exponential schedule - -# epochs = np.linspace(0, 10, 5, dtype=int) -# # schedule = np.linspace(20, N, 5, dtype=int) -# schedule = np.array([20, 100, 150, 200, 200]) -# # schedule = np.array([200, 200, 200, 200, 200]) -# # schedule = np.array([20, 100, 250, 500, 500]) -# # schedule = np.array([500, 500, 500, 500, 500]) -# # schedule = np.array([300, 300, 300, 300, 300]) -# # schedule = np.exp(schedule) -# schedule_warmup = dict[int, int](zip(epochs, schedule)) - -# for epoch_threshold, samples in schedule_warmup.items(): -# if epoch >= epoch_threshold: -# num_samples = samples - -# return num_samples - - -# class Tomography(TomographyConv, TomographyML, TomographyBase, TomographyDDP): -# """ -# Top level class for either using conventional or ML-based reconstruction methods -# for tomography. -# """ - -# def __init__( -# self, -# dataset, -# volume_obj, -# device, -# _token, -# ): -# super().__init__(dataset, volume_obj, device, _token) - -# # TODO: More elegant way of doing this. -# self.global_epochs = 0 -# self.ddp_instantiated = False -# self.pretraining_instantiated = False - -# # --- Reconstruction Method --- - -# def sirt_recon( -# self, -# num_iterations: int = 10, -# inline_alignment: bool = False, -# enforce_positivity: bool = True, -# volume_shape: tuple = None, -# reset: bool = True, -# smoothing_sigma: float = None, -# shrinkage: float = None, -# filter_name: str = "hamming", -# circle: bool = True, -# plot_loss: bool = False, -# ): -# num_angles, num_rows, num_cols = self.dataset.tilt_series.shape -# sirt_tilt_series = self.dataset.tilt_series.clone() -# sirt_tilt_series = sirt_tilt_series.permute(2, 0, 1) - -# hard_constraints = { -# "positivity": enforce_positivity, -# "shrinkage": shrinkage, -# } -# self.volume_obj.hard_constraints = hard_constraints - -# if volume_shape is None: -# volume_shape = (num_rows, num_rows, num_rows) -# else: -# D, H, W = volume_shape - -# if reset: -# self.volume_obj.reset() -# self.loss = [] - -# proj_forward = torch.zeros_like(self.dataset.tilt_series) - -# pbar = tqdm(range(num_iterations), desc="SIRT Reconstruction") - -# if smoothing_sigma is not None: -# gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) -# else: -# gaussian_kernel = None - -# print( -# "Devices", -# sirt_tilt_series.device, -# proj_forward.device, -# self.dataset.tilt_angles.device, -# ) - -# for iter in pbar: -# proj_forward, loss = self._sirt_run_epoch( -# tilt_series=sirt_tilt_series, -# proj_forward=proj_forward, -# angles=self.dataset.tilt_angles, -# inline_alignment=iter > 0 and inline_alignment, -# filter_name=filter_name, -# gaussian_kernel=gaussian_kernel, -# circle=circle, -# ) - -# pbar.set_description(f"SIRT Reconstruction | Loss: {loss.item():.4f}") - -# self.loss.append(loss.item()) - -# self.sirt_recon_vol = self.volume_obj - -# # Permutation due to sinogram ordering. -# self.sirt_recon_vol.obj = self.sirt_recon_vol.obj.permute(1, 2, 0) - -# if plot_loss: -# self.plot_loss() - -# # TODO: ML Recon which has NeRF and AD depending on the object type. -# # TODO: Temporary - -# def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): -# """Create projection rays for entire batch simultaneously.""" -# batch_size = len(pixel_i) - -# # Convert all pixels to normalized coordinates -# x_coords = (pixel_j / (N - 1)) * 2 - 1 -# y_coords = (pixel_i / (N - 1)) * 2 - 1 -# # TODO: maybe pixel_j.device? -# # Create z coordinates -# z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - -# # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] -# rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - -# # Fill coordinates efficiently -# rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray -# rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray -# rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - -# return rays - -# @torch.compile(mode="reduce-overhead") -# def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): -# # Step 1: Apply shifts -# shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) -# shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - -# rays_x = rays[:, :, 0] - shift_x_norm -# rays_y = rays[:, :, 1] - shift_y_norm -# rays_z = rays[:, :, 2] - -# # Rotation 1: Z(-z3) -# theta = torch.deg2rad(-z3).view(-1, 1) -# cos_t = torch.cos(theta) -# sin_t = torch.sin(theta) - -# rays_x_rot1 = cos_t * rays_x - sin_t * rays_y -# rays_y_rot1 = sin_t * rays_x + cos_t * rays_y -# rays_z_rot1 = rays_z - -# # Rotation 2: X(x) -# theta = torch.deg2rad(x).view(-1, 1) -# cos_t = torch.cos(theta) -# sin_t = torch.sin(theta) - -# rays_x_rot2 = rays_x_rot1 -# rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 -# rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - -# # Rotation 3: Z(-z1) -# theta = torch.deg2rad(-z1).view(-1, 1) -# cos_t = torch.cos(theta) -# sin_t = torch.sin(theta) - -# rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 -# rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 -# rays_z_final = rays_z_rot2 - -# # Stack the final result -# transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - -# return transformed_rays - -# # TODO: Temp logger -# def setup_logger(self, log_path): -# if self.global_rank == 0: -# self.temp_logger = SummaryWriter(log_dir=log_path) -# else: -# self.temp_logger = None - -# def pretrain( -# self, -# # volume_dataset: PretrainVolumeDataset, -# obj: ObjectINN, -# batch_size: int, -# soft_constraints: dict = None, -# log_path: str = None, -# optimizer_params: dict = None, -# epochs: int = 100, -# viz_freq: int = 1, -# consistency_criterion: str = "mse", -# ): -# if not self.pretraining_instantiated: -# self.setup_distributed() -# # self.setup_pretraining_dataloader(volume_dataset, batch_size) -# self.pretraining_instantiated = True -# obj.model = self.build_model(obj._model) -# self.obj = obj - -# if soft_constraints is not None: -# self.obj.soft_constraints = soft_constraints - -# if not hasattr(self, "temp_logger"): -# self.setup_logger(log_path=log_path) - -# if optimizer_params is not None: -# optimizer_params = self.scale_lr(optimizer_params) -# self.optimizer_params = optimizer_params -# self.set_optimizers() -# device_type = self.device.type -# consistency_loss_fn = None -# if consistency_criterion[0].lower() == "mse": -# consistency_loss_fn = MSELoss() -# elif consistency_criterion[0].lower() == "l1": -# consistency_loss_fn = L1Loss() -# elif consistency_criterion[0].lower() == "mse_log": -# consistency_loss_fn = MSELogMSELoss() -# elif consistency_criterion[0].lower() == "llmse": -# consistency_loss_fn = LLMSELoss() -# elif consistency_criterion[0].lower() == "smooth_l1": -# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) -# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": -# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( -# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 -# # ) -# pass -# elif consistency_criterion[0].lower() == "charbonnier": -# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") -# else: -# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - -# for epoch in range(epochs): -# if self.pretraining_sampler is not None: -# self.pretraining_sampler.set_epoch(epoch) - -# self.obj.model.train() - -# epoch_loss = 0.0 -# epoch_consistency_loss = 0.0 -# epoch_tv_loss = 0.0 -# num_batches = 0 - -# for batch_idx, batch in enumerate(self.pretraining_dataloader): -# coords = batch["coords"].to(self.device, non_blocking=True) -# target = batch["target"].to(self.device, non_blocking=True) - -# for _, opt in self.optimizers.items(): -# opt.zero_grad() - -# with torch.autocast(device_type=device_type, dtype=torch.bfloat16, enabled=True): -# outputs = self.obj.forward(coords) -# consistency_loss = consistency_loss_fn(outputs, target) -# tv_loss = self.obj.apply_soft_constraints(coords) - -# loss = consistency_loss + tv_loss - -# loss.backward() - -# epoch_loss += loss.detach() -# epoch_consistency_loss += consistency_loss.detach() -# epoch_tv_loss += tv_loss.detach() - -# torch.nn.utils.clip_grad_norm_(self.obj.model.parameters(), max_norm=1.0) - -# for _, opt in self.optimizers.items(): -# opt.step() -# epoch_loss += loss.detach() -# num_batches += 1 - -# if epoch % viz_freq == 0 or epoch == epochs - 1: -# avg_loss = epoch_loss / num_batches -# with torch.no_grad(): -# self.obj.create_volume( -# world_size=self.world_size, -# global_rank=self.global_rank, -# # ray_size=volume_dataset.N, -# ) -# pred_full = self.obj.obj -# loss_tensor = avg_loss.clone().detach() -# avg_loss = loss_tensor.item() -# avg_tv_loss = epoch_tv_loss / num_batches -# avg_consistency_loss = epoch_consistency_loss / num_batches - -# metrics = torch.tensor( -# [avg_loss, avg_consistency_loss, avg_tv_loss], device=self.device -# ) -# if self.world_size > 1: -# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) -# avg_loss, avg_consistency_loss, avg_tv_loss = metrics.tolist() - -# if self.global_rank == 0: -# self.temp_logger.add_scalar("Pretrain/total_loss", avg_loss, epoch) -# self.temp_logger.add_scalar( -# "Pretrain/consistency_loss", avg_consistency_loss, epoch -# ) -# self.temp_logger.add_scalar("Pretrain/tv_loss", avg_tv_loss, epoch) - -# # current_lr = self.schedulers["model"].get_last_lr()[0] -# # self.temp_logger.add_scalar("Pretrain/lr", current_lr, epoch) - -# fig, ax = plt.subplots(ncols=4, figsize=(36, 12)) -# ax[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) -# ax[0].set_title("Sum over Z-axis") -# ax[1].matshow( -# # pred_full[volume_dataset.N // 2].cpu().numpy(), cmap="turbo", vmin=0 -# # ) -# # ax[1].set_title(f"Slice at Z={volume_dataset.N // 2}") -# ax[2].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) -# ax[2].set_title("Sum over Y-axis") -# ax[3].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) -# ax[3].set_title("Sum over X-axis") -# self.temp_logger.add_figure("Pretrain/viz", fig, epoch, close=True) - -# plt.close(fig) -# print( -# f"Epoch [{epoch}/{epochs}] Pretrain Total Loss: {avg_loss:.6f}, Consistency Loss: {avg_consistency_loss:.6f}, TV Loss: {avg_tv_loss:.6f}" -# ) -# self.global_epochs += 1 -# if self.global_rank == 0: -# print(f"Pretraining Completed, saving model weights to {log_path}/model_weights.pth") -# torch.save(self.obj.model.state_dict(), f"{log_path}/model_weights.pth") - -# def recon( -# self, -# obj: ObjectINN, -# batch_size: int, -# num_workers: int = 0, -# epochs=20, -# use_amp=True, -# viz_freq=1, -# checkpoint_freq=5, -# optimizer_params: dict = None, -# scheduler_params: dict = None, -# soft_constraints: dict = None, -# vol_save_path: str = None, # TODO: TEMPORARY -# log_path=None, -# val_fraction: float = 0.0, -# learn_shifts: bool = True, -# # l1_loss: bool = False, -# consistency_criterion: str = "mse", -# model_weights_path: str = None, -# force_cpu: bool = False, -# ): -# if not self.ddp_instantiated: -# if not self.pretraining_instantiated: -# self.setup_distributed() -# if model_weights_path is not None: -# print(f"Loading model weights from {model_weights_path}") -# state_dict = torch.load(model_weights_path, map_location="cpu") - -# # Handle DataParallel/DDP checkpoints -# if any(k.startswith("module.") for k in state_dict.keys()): -# new_state_dict = { -# k.replace("module.", ""): v for k, v in state_dict.items() -# } -# else: -# new_state_dict = state_dict - -# obj.model.load_state_dict(new_state_dict) -# print("Model weights loaded successfully") -# obj.model = self.build_model(obj._model) -# self.obj = obj - -# self.setup_dataloader(self.dataset, batch_size, num_workers, val_fraction) -# self.ddp_instantiated = True - -# if soft_constraints is not None: -# self.obj.soft_constraints = soft_constraints - -# if not hasattr(self, "temp_logger"): -# self.setup_logger(log_path=log_path) - -# zero_tilt_idx = torch.argmin(torch.abs(self.dataset.tilt_angles)).item() - -# if self.global_rank == 0: -# print( -# f"Using projection {zero_tilt_idx} (angle={self.dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" -# ) -# print(f"Using consistency criterion: {consistency_criterion}") - -# # Auxiliary params setup -# self.dataset.setup_auxiliary_params(zero_tilt_idx, self.device, learn_shifts) -# aux_params = self.dataset.auxiliary_params -# # Scaling learning rates to account for distributed training -# if optimizer_params is not None: -# optimizer_params = self.scale_lr(optimizer_params) -# self.optimizer_params = optimizer_params -# self.set_optimizers() - -# if scheduler_params is not None: -# self.scheduler_params = scheduler_params -# self.set_schedulers(self.scheduler_params, num_iter=epochs) - -# aux_norm = torch.tensor(0.0, device=self.device) -# model_norm = torch.tensor(0.0, device=self.device) - -# for _, opt in self.optimizers.items(): -# opt.zero_grad() - -# N = max(self.dataset.dims) - -# device_type = self.device.type -# autocast_dtype = torch.bfloat16 if use_amp else None - -# for epoch in range(epochs): -# num_samples_per_ray = get_num_samples_per_ray(N=N, epoch=self.global_epochs) - -# # num_samples_per_ray = get_num_samples_per_ray(epoch) -# # Log the change if it happens -# if self.global_rank == 0: -# print(f"Epoch {epoch}: num_samples_per_ray = {num_samples_per_ray}") - -# if self.global_rank == 0 and self.global_epochs > 0: -# prev_samples = get_num_samples_per_ray(N=N, epoch=self.global_epochs - 1) - -# if num_samples_per_ray != prev_samples: -# print( -# f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" -# ) - -# if self.sampler is not None: -# self.sampler.set_epoch(epoch) - -# epoch_loss = 0.0 -# epoch_consistency_loss = 0.0 -# epoch_tv_loss = 0.0 -# epoch_z1_loss = 0.0 - -# num_batches = 0 -# consistency_loss_fn = None -# if consistency_criterion[0].lower() == "mse": -# consistency_loss_fn = MSELoss() -# elif consistency_criterion[0].lower() == "l1": -# consistency_loss_fn = L1Loss() -# elif consistency_criterion[0].lower() == "mse_log": -# consistency_loss_fn = MSELogMSELoss() -# elif consistency_criterion[0].lower() == "llmse": -# consistency_loss_fn = LLMSELoss() -# elif consistency_criterion[0].lower() == "smooth_l1": -# consistency_loss_fn = SmoothL1Loss(beta=consistency_criterion[1]) -# elif consistency_criterion[0].lower() == "adaptive_smooth_l1": -# # consistency_loss_fn = AdaptiveSmoothL1LossDDP( -# # beta_init=consistency_criterion[1], ema_factor=0.99, eps=1e-8 -# # ) -# pass -# elif consistency_criterion[0].lower() == "charbonnier": -# consistency_loss_fn = CharbonnierLoss(epsilon=1e-12, reduction="mean") -# else: -# raise ValueError(f"Invalid consistency criterion: {consistency_criterion}") - -# for batch_idx, batch in enumerate(self.dataloader): -# projection_indices = batch["projection_idx"] - -# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) -# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) -# target_values = batch["target_value"].to(self.device, non_blocking=True) -# phis = batch["phi"].to(self.device, non_blocking=True) -# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - -# shifts, z1_params, z3_params = aux_params(None) -# batch_shifts = torch.index_select(shifts, 0, projection_indices) -# batch_z1 = torch.index_select(z1_params, 0, projection_indices) -# batch_z3 = torch.index_select(z3_params, 0, projection_indices) - -# with torch.autocast( -# device_type=device_type, dtype=autocast_dtype, enabled=use_amp -# ): -# with torch.no_grad(): -# batch_ray_coords = self.create_batch_projection_rays( -# pixel_i, pixel_j, N, num_samples_per_ray -# ) - -# # TODO: .forward passing z1, x, z3, shifts, N, sampling_rate -# transformed_rays = self.transform_batch_ray_coordinates( -# batch_ray_coords, -# z1=batch_z1, -# x=phis, -# z3=batch_z3, -# shifts=batch_shifts, -# N=N, -# sampling_rate=1.0, -# ) - -# all_coords = transformed_rays.view(-1, 3) - -# all_densities = self.obj.forward(all_coords) - -# tv_loss = self.obj.apply_soft_constraints(all_coords) -# ray_densities = all_densities.view( -# len(target_values), num_samples_per_ray -# ) # Reshape rays and integarte -# step_size = 2.0 / (num_samples_per_ray - 1) - -# predicted_values = ray_densities.sum(dim=1) * step_size - -# consistency_loss = consistency_loss_fn(predicted_values, target_values) -# tv_loss_z1 = torch.tensor(0.0, device=self.device) - -# loss = consistency_loss + tv_loss + tv_loss_z1 - -# loss.backward() - -# epoch_loss += loss.detach() -# epoch_consistency_loss += consistency_loss.detach() -# epoch_tv_loss += tv_loss.detach() -# epoch_z1_loss += tv_loss_z1.detach() -# num_batches += 1 - -# for key, opt in self.optimizers.items(): -# if key == "model": -# model_norm = torch.nn.utils.clip_grad_norm_( -# self.obj.model.parameters(), max_norm=1 -# ) -# elif key == "aux_params": -# aux_norm = torch.nn.utils.clip_grad_norm_( -# self.dataset.auxiliary_params.parameters(), max_norm=1 -# ) -# opt.step() -# opt.zero_grad() - -# for key, sched in self.schedulers.items(): -# if isinstance(sched, torch.optim.lr_scheduler.ReduceLROnPlateau): -# sched.step(epoch_loss) -# else: -# sched.step() - -# if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: -# with torch.no_grad(): -# self.obj.create_volume( -# world_size=self.world_size, -# global_rank=self.global_rank, -# ray_size=num_samples_per_ray, -# ) -# pred_full = self.obj.obj -# avg_loss = epoch_loss.item() / num_batches -# avg_consistency_loss = epoch_consistency_loss.item() / num_batches -# avg_tv_loss = epoch_tv_loss.item() / num_batches -# avg_z1_loss = epoch_z1_loss.item() / num_batches -# shifts, z1, z3 = self.dataset.auxiliary_params.forward() -# shifts = shifts.detach().cpu() -# z1 = z1.detach().cpu() -# z3 = z3.detach().cpu() -# metrics = torch.tensor( -# [avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss], -# device=self.device, -# ) -# if self.world_size > 1: -# dist.all_reduce(metrics, op=dist.ReduceOp.AVG) -# avg_loss, avg_consistency_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - -# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: -# val_loss = self.validate( -# aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N -# ) - -# if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): -# with torch.no_grad(): -# current_lr = self.schedulers["model"].get_last_lr()[0] - -# # Log metrics -# self.temp_logger.add_scalar( -# f"train/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", -# avg_consistency_loss, -# self.global_epochs, -# ) -# if hasattr(self, "val_dataloader") and self.val_dataloader is not None: -# self.temp_logger.add_scalar( -# f"val/consistency_loss_{consistency_criterion[0] if isinstance(consistency_criterion, tuple) else consistency_criterion}", -# val_loss, -# self.global_epochs, -# ) -# self.temp_logger.add_scalar("train/z1_loss", avg_z1_loss, self.global_epochs) -# # if tv_weight > 0: -# self.temp_logger.add_scalar("train/tv_loss", avg_tv_loss, self.global_epochs) -# self.temp_logger.add_scalar("train/total_loss", avg_loss, self.global_epochs) -# self.temp_logger.add_scalar( -# "train/model_grad_norm", model_norm.item(), self.global_epochs -# ) -# self.temp_logger.add_scalar( -# "train/aux_grad_norm", aux_norm.item(), self.global_epochs -# ) -# self.temp_logger.add_scalar("train/lr", current_lr, self.global_epochs) -# self.temp_logger.add_scalar( -# "train/num_samples_per_ray", num_samples_per_ray, self.global_epochs -# ) -# if consistency_criterion[0].lower() == "adaptive_smooth_l1": -# self.temp_logger.add_scalar( -# "train/beta_2", consistency_loss_fn.beta2.item(), self.global_epochs -# ) -# fig, axes = plt.subplots(1, 5, figsize=(36, 12)) -# axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) -# axes[0].set_title("Sum over Z-axis") - -# axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) -# axes[1].set_title(f"Slice at Z={N // 2}") - -# slice_start = max(0, N // 2 - 5) -# slice_end = min(N, N // 2 + 6) -# thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() -# axes[2].matshow(thick_slice, cmap="turbo", vmin=0) -# axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") - -# axes[3].matshow(pred_full.sum(dim=1).cpu().numpy(), cmap="turbo", vmin=0) -# axes[3].set_title("Sum over Y-axis") - -# axes[4].matshow(pred_full.sum(dim=2).cpu().numpy(), cmap="turbo", vmin=0) -# axes[4].set_title("Sum over X-axis") - -# self.temp_logger.add_figure("train/viz", fig, self.global_epochs, close=True) - -# fig, axes = plt.subplots(ncols=3, figsize=(10, 5)) -# axes[0].plot(shifts[:, 0].cpu().numpy(), label="Shifts X") -# axes[0].plot(shifts[:, 1].cpu().numpy(), label="Shifts Y") -# axes[0].legend() -# axes[1].plot(z1.cpu().numpy(), label="Z1") -# axes[2].plot(z3.cpu().numpy(), label="Z3") -# self.temp_logger.add_figure( -# "train/auxiliary_params", fig, self.global_epochs, close=True -# ) -# plt.close(fig) - -# if epoch % checkpoint_freq == 0 or self.global_epochs == epochs - 1: -# with torch.no_grad(): -# if self.global_rank == 0: -# save_path = f"{vol_save_path}/volume_epoch_{self.global_epochs:04d}.pt" -# torch.save(pred_full.cpu(), save_path) - -# self.global_epochs += 1 - -# if self.global_rank == 0: -# print("Training complete.") - -# torch.save(self.dataset.auxiliary_params.forward(), f"{log_path}/auxiliary_params.pt") -# # print("Successfully setup DDP and dataloader") - -# def validate(self, aux_params, num_samples_per_ray, device_type, autocast_dtype, use_amp, N): -# """Validate the model on the validation set.""" -# self.obj.model.eval() -# aux_params.eval() - -# val_loss = 0.0 -# num_batches = 0 - -# with torch.no_grad(): -# for batch in self.val_dataloader: -# pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) -# pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) -# target_values = batch["target_value"].to(self.device, non_blocking=True) -# phis = batch["phi"].to(self.device, non_blocking=True) -# projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - -# shifts, z1_params, z3_params = aux_params(None) -# batch_shifts = torch.index_select(shifts, 0, projection_indices) -# batch_z1 = torch.index_select(z1_params, 0, projection_indices) -# batch_z3 = torch.index_select(z3_params, 0, projection_indices) - -# with torch.autocast( -# device_type=device_type, dtype=autocast_dtype, enabled=use_amp -# ): -# with torch.no_grad(): -# batch_ray_coords = self.create_batch_projection_rays( -# pixel_i, pixel_j, N, num_samples_per_ray -# ) - -# transformed_rays = self.transform_batch_ray_coordinates( -# batch_ray_coords, -# z1=batch_z1, -# x=phis, -# z3=batch_z3, -# shifts=batch_shifts, -# N=N, -# sampling_rate=1.0, -# ) - -# all_coords = transformed_rays.view(-1, 3) - -# all_densities = self.obj.forward(all_coords) - -# ray_densities = all_densities.view( -# len(target_values), num_samples_per_ray -# ) # Reshape rays and integarte -# step_size = 2.0 / (num_samples_per_ray - 1) - -# predicted_values = ray_densities.sum(dim=1) * step_size - -# mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - -# loss = mse_loss - -# val_loss += loss.detach() -# num_batches += 1 - -# avg_val_loss = val_loss / num_batches if num_batches > 0 else 0.0 - -# if self.world_size > 1: -# val_loss_tensor = avg_val_loss.detach().clone() -# dist.all_reduce(val_loss_tensor, op=dist.ReduceOp.AVG) -# avg_val_loss = val_loss_tensor.item() - -# self.obj.model.train() -# aux_params.train() - -# return avg_val_loss - -# def ad_recon( -# self, -# optimizer_params: dict, -# num_iter: int = 0, -# reset: bool = False, -# scheduler_params: dict | None = None, -# hard_constraints: dict | None = None, -# soft_constraints: dict | None = None, -# # store_iterations: bool | None = None, -# # store_iterations_every: int | None = None, -# # autograd: bool = True, -# ): -# if reset: -# self.reset_recon() - -# self.hard_constraints = hard_constraints -# self.soft_constraints = soft_constraints - -# # Make sure everything is in the correct device, might be redundant/cleaner way to do this -# self.dataset.to(self.device) -# self.volume_obj.to(self.device) - -# # Making optimizable parameters into leaf tensors. -# self.dataset.shifts = self.dataset.shifts.detach().to(self.device).requires_grad_(True) -# self.dataset.z1_angles = ( -# self.dataset.z1_angles.detach().to(self.device).requires_grad_(True) -# ) -# self.dataset.z3_angles = ( -# self.dataset.z3_angles.detach().to(self.device).requires_grad_(True) -# ) - -# if optimizer_params is not None: -# self.optimizer_params = optimizer_params -# self.set_optimizers() - -# if scheduler_params is not None: -# self.scheduler_params = scheduler_params -# self.set_schedulers(self.scheduler_params, num_iter=num_iter) - -# if hard_constraints is not None: -# self.volume_obj.hard_constraints = hard_constraints -# if soft_constraints is not None: -# self.volume_obj.soft_constraints = soft_constraints - -# pbar = tqdm(range(num_iter), desc="AD Reconstruction") - -# for a0 in pbar: -# total_loss = 0.0 -# tilt_series_loss = 0.0 - -# pred_volume = self.volume_obj.forward() - -# for i in range(len(self.dataset.tilt_series)): -# forward_projection = self.projection_operator( -# vol=pred_volume, -# z1=self.dataset.z1_angles[i], -# x=self.dataset.tilt_angles[i], -# z3=self.dataset.z3_angles[i], -# shift_x=self.dataset.shifts[i, 0], -# shift_y=self.dataset.shifts[i, 1], -# device=self.device, -# ) - -# tilt_series_loss += torch.nn.functional.mse_loss( -# forward_projection, self.dataset.tilt_series[i] -# ) -# tilt_series_loss /= len(self.dataset.tilt_series) - -# total_loss = tilt_series_loss + self.volume_obj.soft_loss -# self.loss.append(total_loss.item()) - -# total_loss.backward() - -# for opt in self.optimizers.values(): -# opt.step() -# opt.zero_grad() - -# if self.schedulers is not None: -# for sch in self.schedulers.values(): -# if isinstance(sch, torch.optim.lr_scheduler.ReduceLROnPlateau): -# sch.step(total_loss) -# elif sch is not None: -# sch.step() - -# pbar.set_description(f"AD Reconstruction | Loss: {total_loss:.4f}") - -# if self.logger is not None: -# self.logger.log_scalar("loss/total", total_loss.item(), a0) -# self.logger.log_scalar("loss/tilt_series", tilt_series_loss.item(), a0) -# self.logger.log_scalar( -# "loss/soft constraints", self.volume_obj.soft_loss.item(), a0 -# ) - -# if a0 % self.logger.log_images_every == 0: -# self.logger.projection_images( -# volume_obj=self.volume_obj, -# epoch=a0, -# ) -# self.logger.tilt_angles_figure(dataset=self.dataset, step=a0) - -# self.logger.flush() - -# self.ad_recon_vol = self.volume_obj.forward() - -# return self - -# def reset_recon(self) -> None: -# if isinstance(self.volume_obj, ObjectVoxelwise): -# self.volume_obj.reset() - -# self.ad_recon_vol = None - -# # --- Projection Operators ---- -# def projection_operator( -# self, -# vol, -# z1, -# x, -# z3, -# shift_x, -# shift_y, -# device, -# ): -# projection = ( -# rot_ZXZ( -# mags=vol.unsqueeze(0), # Add batch dimension -# z1=z1, -# x=-x, -# z3=z3, -# device=device, -# mode="bilinear", -# ) -# .squeeze() -# .sum(axis=0) -# ) - -# shifted_projection = differentiable_shift_2d( -# image=projection, -# shift_x=shift_x, -# shift_y=shift_y, -# sampling_rate=1.0, # Assuming 1 pixel = 1 physical unit -# ) - -# return shifted_projection diff --git a/src/quantem/tomography_old/tomography_base.py b/src/quantem/tomography_old/tomography_base.py deleted file mode 100644 index d3b94427..00000000 --- a/src/quantem/tomography_old/tomography_base.py +++ /dev/null @@ -1,514 +0,0 @@ -from typing import Tuple - -import matplotlib.pyplot as plt -import numpy as np -import torch -from numpy.typing import NDArray -from torch._tensor import Tensor -from tqdm.auto import tqdm - -from quantem.core.datastructures.dataset3d import Dataset3d -from quantem.core.io.serialize import AutoSerialize -from quantem.core.visualization.visualization import show_2d -from quantem.imaging.drift import cross_correlation_shift -from quantem.tomography.object_models import ObjectModelType, ObjectVoxelwise -from quantem.tomography.tomography_dataset import TomographyDataset -from quantem.tomography.tomography_logger import LoggerTomography - - -class TomographyBase(AutoSerialize): - _token = object() - - DEFAULT_HARD_CONSTRAINTS = { - "positivity": False, - "shrinkage": False, - "circular_mask": False, - "fourier_filter": False, - } - - DEFAULT_SOFT_CONSTRAINTS = { - "tv_vol": 0.0, - } - - def __init__( - self, - dataset: TomographyDataset, - volume_obj: ObjectModelType, # ObjectDIP? - device: str = "cuda", - # ABF/HAADF property - logger: LoggerTomography | None = None, - _token: object | None = None, - ): - """Initialize a Tomography object. - - Parameters - ---------- - array : NDArray | Any - The underlying 3D array data - name : str - A descriptive name for the dataset - """ - - # if _token is not self._token: - # raise RuntimeError( - # "This class is not meant to be instantiated directly. Use the from_data method." - # ) - - self._device = device - self._dataset = dataset - self._volume_obj = volume_obj - self._loss = [] - self._mode = [] - - self._hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self._soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - self._logger = None - - @classmethod - def from_data( - cls, - tilt_series: Dataset3d | NDArray | Tensor, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - volume_obj: NDArray | Dataset3d | ObjectModelType | None = None, - device: str = "cpu", - clamp: bool = True, - ): - device = device.lower() - - dataset = TomographyDataset.from_data( - tilt_series=tilt_series, - tilt_angles=tilt_angles, - z1_angles=z1_angles, - z3_angles=z3_angles, - shifts=shifts, - clamp=clamp, - ) - - dataset.to(device) - - if volume_obj is None: - max_shape = max(dataset.tilt_series.shape) - volume_obj = ObjectVoxelwise( - volume_shape=(max_shape, max_shape, max_shape), - device=device, - ) - elif isinstance(volume_obj, Dataset3d): - volume = torch.from_numpy(volume_obj.array) - volume_obj = ObjectVoxelwise( - volume_shape=volume_obj.shape, device=device, initial_volume=volume - ) - volume_obj.obj = volume - elif isinstance(volume_obj, np.ndarray): - volume = torch.from_numpy(volume_obj) - volume_obj = ObjectVoxelwise( - volume_shape=volume_obj.shape, device=device, initial_volume=volume - ) - volume_obj.obj = volume - else: - raise ValueError("volume_obj must be a Dataset3d, NDArray ObjectModelType") - - return cls( - dataset=dataset, - volume_obj=volume_obj, - device=device, - _token=cls._token, - ) - - # --- Properties --- - @property - def dataset(self) -> TomographyDataset: - """Tomography dataset.""" - - return self._dataset - - @dataset.setter - def dataset( - self, - tilt_series: Dataset3d | NDArray | TomographyDataset, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - # name: str | None = None, - # origin: NDArray | tuple | list | float | int | None = None, - # sampling: NDArray | tuple | list | float | int | None = None, - # units: list[str] | tuple | list | None = None, - # signal_units: str = "arb. units", - ): - """Set the tilt series dataset.""" - - if not isinstance(tilt_series, TomographyDataset): - dataset = TomographyDataset.from_array( - array=tilt_series, - tilt_angles=tilt_angles, - z1_angles=z1_angles, - z3_angles=z3_angles, - shifts=shifts, - ) - - self._dataset = dataset - - @property - def volume_obj(self) -> Dataset3d | ObjectModelType | None: - """Reconstruction volume dataset.""" - - return self._volume_obj - - @volume_obj.setter - # TODO: add support for ObjectModelType - def volume_obj(self, volume_obj: Dataset3d | NDArray): - """Set the reconstruction volume dataset.""" - if isinstance(volume_obj, ObjectModelType): - self._volume_obj = volume_obj - elif not isinstance(volume_obj, Dataset3d): - volume_obj = Dataset3d.from_array( - array=volume_obj, - # name=self._tilt_series.name, - # origin=self._tilt_series.origin, - # sampling=self._tilt_series.sampling, - # units=self._tilt_series.units, - # signal_units=self._tilt_series.signal_units, - ) - elif isinstance(volume_obj, Dataset3d): - self._volume_obj = volume_obj - else: - raise ValueError("volume_obj must be a Dataset3d or ObjectModelType") - - @property - def device(self) -> str: - """Computation device.""" - - return self._device - - @device.setter - def device(self, device: str): - """Set the computation device.""" - - # if "cuda" not in device or "gpu" not in device: - # raise NotImplementedError("Tomography not currently supported on CPU.") - - self._device = device - - @property - def loss(self) -> list: - """List of loss values during reconstruction.""" - - return self._loss - - @loss.setter - def loss(self, loss: list): - """Set the loss values during reconstruction.""" - - if not isinstance(loss, list): - raise TypeError("Loss must be a list.") - - self._loss = loss - - @property - def mode(self) -> list: - """List of modes used during reconstruction.""" - - return self._mode - - @property - def epochs(self) -> int: - """Number of epochs used during reconstruction.""" - return len(self.loss) - - @property - def logger(self) -> LoggerTomography: - return self._logger - - @logger.setter - def logger(self, logger: LoggerTomography): - if not isinstance(logger, LoggerTomography): - raise TypeError("Logger must be a LoggerTomography") - - self._logger = logger - - # --- Constraints --- - - @property - def hard_constraints(self) -> dict: - """Hard constraints for the reconstruction.""" - return self._hard_constraints - - @hard_constraints.setter - def hard_constraints(self, hard_constraints: dict): - """Set the hard constraints for the reconstruction.""" - - gkeys = self.DEFAULT_HARD_CONSTRAINTS.keys() - for key, value in hard_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._hard_constraints[key] = value - - self._hard_constraints = hard_constraints - - @property - def soft_constraints(self) -> dict: - """Soft constraints for the reconstruction.""" - return self._soft_constraints - - @soft_constraints.setter - def soft_constraints(self, soft_constraints: dict): - """Set the soft constraints for the reconstruction.""" - - gkeys = self.DEFAULT_SOFT_CONSTRAINTS.keys() - for key, value in soft_constraints.items(): - if key not in gkeys: - raise KeyError(f"Invalid object constraint key '{key}', allowed keys are {gkeys}") - self._soft_constraints[key] = value - - self._soft_constraints = soft_constraints - - # --- RESET --- - - def reset_recon(self) -> None: - self.volume_obj.reset() - self.dataset.reset() - self.loss = [] - self.hard_constraints = self.DEFAULT_HARD_CONSTRAINTS.copy() - self.soft_constraints = self.DEFAULT_SOFT_CONSTRAINTS.copy() - - self._optimizers = {} - self._schedulers = {} - - # --- Preprocessing --- - - """ - TODO - 1. Implement tilt series cross-correlation alignment - 2. Background subtraction (for ABF) - 3. COM Alignment - 4. Masking - 5. Drift Correction - """ - - def cross_corr_alignment( - self, - upsample_factor: int = 1, - overwrite: bool = False, - ): - # TODO: This needs to be able to work with torch tensors. - - placeholder_tilt_series = self.dataset.tilt_series.clone().detach().cpu().numpy() - - aligned_tilt_series = np.zeros_like(placeholder_tilt_series) - aligned_tilt_series[0] = placeholder_tilt_series[0] - shifts = [] - num_imgs = placeholder_tilt_series.shape[1] - - pbar = tqdm(range(num_imgs - 1), desc="Cross-correlation alignment") - - for i in pbar: - shift, aligned_img = cross_correlation_shift( - placeholder_tilt_series[i], - placeholder_tilt_series[i + 1], - upsample_factor=upsample_factor, - return_shifted_image=True, - ) - - aligned_tilt_series[i + 1] = aligned_img - shifts.append(shift) - - if overwrite: - # TODO: Check this overwrite idea, maybe also need to save the relative shifts? - self.dataset.tilt_series = np.array(aligned_tilt_series) - - return np.array(aligned_tilt_series), np.array(shifts) - - # --- Postprocessing --- - - """ - TODO - 1. Apply circular mask - """ - - def circular_mask(self, shape, radius, center=None, dtype=torch.float32, device="cpu"): - """Generate a 2D circular mask of given shape and radius.""" - H, W = shape - - if center is None: - center = (H // 2, W // 2) - y = torch.arange(H, dtype=dtype, device=device).view(-1, 1) - x = torch.arange(W, dtype=dtype, device=device).view(1, -1) - dist_sq = (x - center[1]) ** 2 + (y - center[0]) ** 2 - return (dist_sq <= radius**2).to(dtype) - - def recon_vol_circular_mask(self, radii): - """ - Apply 2D circular masks along all three axes of a 3D volume. - - Args: - volume (torch.Tensor): 3D tensor of shape (H, W, D) - radii (tuple): (r0, r1, r2) for axes 0, 1, 2 - Returns: - masked_volume: tensor with all masks applied - """ - H, W, D = self.volume_obj.array.shape - device = self.device - dtype = torch.float32 - volume_obj = torch.tensor( - self.volume_obj.array, - device=self.device, - dtype=dtype, - ) - # Masks for each axis - mask0 = self.circular_mask((W, D), radii[0], dtype=dtype, device=device).unsqueeze( - 0 - ) # shape (1, W, D) - mask1 = self.circular_mask((H, D), radii[1], dtype=dtype, device=device).unsqueeze( - 1 - ) # shape (H, 1, D) - mask2 = self.circular_mask((H, W), radii[2], dtype=dtype, device=device).unsqueeze( - 2 - ) # shape (H, W, 1) - - # Broadcast and multiply all masks together - total_mask = mask0 * mask1 * mask2 # shape (H, W, D) - - volume_obj = volume_obj * total_mask - volume_obj = volume_obj.detach().cpu().numpy() - self.volume_obj = Dataset3d.from_array( - array=volume_obj, - # name=self.volume_obj.name, - # origin=self.volume_obj.origin, - # sampling=self.volume_obj.sampling, - # units=self.volume_obj.units, - # signal_units=self.volume_obj.signal_units, - ) - - # --- Visualizations --- - - def plot_projections( - self, - cmap: str = "turbo", - fft: bool = False, - norm: str = "log_auto", - figax: tuple[plt.Figure, plt.Axes] | None = None, - fft_vmax: Tuple[float, float] = (0, 40), - vmin: float = 0, - **kwargs, - ): - """ - Plots the projections of the volume object. - Note that the volume object is in the order of (z, y, x). - Parameters - ---------- - cmap : str - The colormap to use for the projections. - fft : bool - """ - - volume_obj_np = self.volume_obj.obj.detach().cpu().numpy() - - if figax is None: - fig, ax = plt.subplots(ncols=3, figsize=(20, 8)) - else: - fig, ax = figax - - show_2d( - volume_obj_np.sum(axis=0), - figax=(fig, ax[0]), - cmap=cmap, - title="Y-X Projection", - vmin=vmin, - ) - show_2d( - volume_obj_np.sum(axis=1), - figax=(fig, ax[1]), - cmap=cmap, - title="Z-X Projection", - vmin=vmin, - ) - show_2d( - volume_obj_np.sum(axis=2), - figax=(fig, ax[2]), - cmap=cmap, - title="Z-Y Projection", - vmin=vmin, - ) - - if fft: - fig, ax = plt.subplots(ncols=3, figsize=(25, 8)) - print(fft_vmax) - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=0)))), - figax=(fig, ax[0]), - cmap=cmap, - title="Y-X Projection FFT", - # norm=norm, - vmin=fft_vmax[0], - vmax=fft_vmax[1], - ) - - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=1)))), - figax=(fig, ax[1]), - cmap=cmap, - title="Z-X Projection FFT", - vmin=fft_vmax[0], - vmax=fft_vmax[1], - # norm=norm, - ) - show_2d( - np.abs(np.fft.fftshift(np.fft.fftn(volume_obj_np.sum(axis=2)))), - figax=(fig, ax[2]), - cmap=cmap, - title="Z-Y Projection FFT", - vmin=fft_vmax[0], - vmax=fft_vmax[1], - # norm=norm, - ) - - def plot_slice( - self, - cmap="turbo", - slice_index: int = 0, - vmin: float = 0, - figax: tuple[plt.Figure, plt.Axes] | None = None, - ): - if figax is None: - fig, ax = plt.subplots(figsize=(15, 8), ncols=3) - else: - fig, ax = figax - - show_2d( - self.volume_obj.obj[slice_index, :, :], - figax=(fig, ax[0]), - cmap=cmap, - vmin=vmin, - ) - show_2d( - self.volume_obj.obj[:, slice_index, :], - figax=(fig, ax[1]), - cmap=cmap, - vmin=vmin, - ) - - show_2d( - self.volume_obj.obj[:, :, slice_index], - figax=(fig, ax[2]), - cmap=cmap, - vmin=vmin, - ) - - def plot_loss( - self, - figsize: tuple = (8, 4), - figax: tuple[plt.Figure, plt.Axes] | None = None, - ): - if figax is None: - fig, ax = plt.subplots(figsize=figsize) - else: - fig, ax = figax - - ax.semilogy( - self.loss, - label="Loss", - ) diff --git a/src/quantem/tomography_old/tomography_conv.py b/src/quantem/tomography_old/tomography_conv.py deleted file mode 100644 index 1056da17..00000000 --- a/src/quantem/tomography_old/tomography_conv.py +++ /dev/null @@ -1,235 +0,0 @@ -import numpy as np -import torch - -# from torch_radon.radon import ParallelBeam as Radon -from quantem.tomography.radon.radon import iradon_torch, radon_torch -from quantem.tomography.tomography_base import TomographyBase -from quantem.tomography.utils import gaussian_filter_2d_stack, torch_phase_cross_correlation - - -class TomographyConv(TomographyBase): - """ - Class for handling conventional reconstruction methods of tomography data. - """ - - # --- Reconstruction Methods --- - - def _sirt_run_epoch( - self, - tilt_series: torch.Tensor, - proj_forward: torch.Tensor, - angles: torch.Tensor, - inline_alignment: bool, - filter_name: str, - circle: bool, - gaussian_kernel: torch.Tensor | None, - ): - loss = 0 - - if inline_alignment: - for ind in range(len(self.dataset.tilt_angles)): - im_proj = proj_forward[ind] - im_meas = tilt_series[ind] - - shift = torch_phase_cross_correlation(im_proj, im_meas) - if torch.linalg.norm(shift) <= 32: - shifted = torch.fft.ifft2( - torch.fft.fft2(im_meas) - * torch.exp( - -2j - * np.pi - * ( - shift[0] - * torch.fft.fftfreq( - im_meas.shape[0], device=im_meas.device - ).unsqueeze(1) - + shift[1] - * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) - ) - ) - ).real - - proj_forward[ind] = shifted - - # Forward projection - - sinogram_est = radon_torch(self.volume_obj.obj, theta=angles, device=self.device) - # proj_forward = sinogram_est.permute(1, 2, 0) - # error = (tilt_series - proj_forward).permute(2, 0, 1) - proj_forward = sinogram_est - error = tilt_series - proj_forward - - correction = iradon_torch( - error, theta=angles, device=self.device, filter_name=filter_name, circle=circle - ) - - normalization = iradon_torch( - torch.ones_like(error), - theta=angles, - device=self.device, - filter_name=None, - circle=circle, - ) - - normalization[normalization == 0] = 1e-6 - - correction /= normalization - - self.volume_obj._obj += correction - - loss = torch.mean(torch.abs(error)) - - # for z in tqdm(range(self.volume_obj.obj.shape[0]), desc="SIRT Reconstruction"): - # slice_estimate = self.volume_obj.obj[z] - # sinogram_est = radon_torch(slice_estimate, theta=angles, device=self.device) - - # sinogram_true = tilt_series[:, :, z] - - # error = sinogram_true - sinogram_est - - # correction = iradon_torch( - # error, theta=angles, device=self.device, filter_name=filter_name, circle=circle - # ) - - # # I'm pretty sure this implementation of normalization is wrong - # normalization = iradon_torch( - # torch.ones_like(error), - # theta=angles, - # device=self.device, - # filter_name=None, - # circle=circle, - # ) - # normalization[normalization == 0] = 1e-6 - - # correction /= normalization - - # self.volume_obj._obj[z] += correction - - # proj_forward[:, :, z] = sinogram_est - - # loss += torch.mean(torch.abs(error)) - - # loss /= self.volume_obj._obj.shape[0] - - if gaussian_kernel is not None: - self.volume_obj.obj = gaussian_filter_2d_stack(self.volume_obj.obj, gaussian_kernel) - - return proj_forward, loss - - # Deprecated torch_radon implementations - # def _sirt_run_epoch( - # self, - # radon: Radon, - # stack_recon: torch.Tensor, - # stack_torch: torch.Tensor, - # proj_forward: torch.Tensor, - # step_size: float = 0.25, - # gaussian_kernel: torch.Tensor = None, - # inline_alignment=True, - # enforce_positivity=True, - # shrinkage: float = None, - # ): - # loss = 0 - - # if inline_alignment: - # for ind in range(len(self.tilt_series.tilt_angles)): - # im_proj = proj_forward[:, ind, :] - # im_meas = stack_torch[:, ind, :] - - # shift = torch_phase_cross_correlation(im_proj, im_meas) - # if torch.linalg.norm(shift) <= 32: - # shifted = torch.fft.ifft2( - # torch.fft.fft2(im_meas) - # * torch.exp( - # -2j - # * np.pi - # * ( - # shift[0] - # * torch.fft.fftfreq( - # im_meas.shape[0], device=im_meas.device - # ).unsqueeze(1) - # + shift[1] - # * torch.fft.fftfreq(im_meas.shape[1], device=im_meas.device) - # ) - # ) - # ).real - - # stack_torch[:, ind, :] = shifted - - # proj_forward = radon.forward(stack_recon) - - # proj_diff = stack_torch - proj_forward - - # loss = torch.mean(torch.abs(proj_diff)) - - # recon_slice_update = radon.backward( - # radon.filter_sinogram( - # proj_diff, - # ) - # ) - - # stack_recon += step_size * recon_slice_update - # if enforce_positivity: - # stack_recon = torch.clamp(stack_recon, min=0) - - # if gaussian_kernel is not None: - # stack_recon = gaussian_filter_2d_stack( - # stack_recon, - # gaussian_kernel, - # ) - - # if shrinkage is not None: - # stack_recon = torch.max( - # stack_recon - shrinkage, - # torch.zeros_like(stack_recon), - # ) - - # return stack_recon, loss - - # def _sirt_serial_run_epoch( - # self, - # radon: Radon, - # stack_recon: torch.Tensor, - # stack_torch: torch.Tensor, - # proj_forward: torch.Tensor, - # step_size: float = 0.25, - # gaussian_kernel: torch.Tensor = None, - # inline_alignment=True, - # enforce_positivity=True, - # ): - # recon_slice_update = torch.zeros_like(stack_recon).to(self.device) - - # loss = 0 - - # for i in range(stack_recon.shape[0]): - # proj_forward[i] = radon.forward(stack_recon[i]) - - # proj_diff = stack_torch - proj_forward - - # loss = torch.mean(torch.abs(proj_diff)) - - # for i in range(stack_recon.shape[0]): - # recon_slice_update[i] = radon.backward( - # radon.filter_sinogram( - # proj_diff[i], - # ) - # ) - - # stack_recon += step_size * recon_slice_update - - # if enforce_positivity: - # stack_recon = torch.clamp(stack_recon, min=0) - - # return stack_recon, loss - - # --- Properties --- - # @property - # def reconstruction_method(self) -> str: - # """Get the reconstruction method.""" - # return self._reconstruction_method - # @reconstruction_method.setter - # def reconstruction_method(self, value: str): - # """Set the reconstruction method.""" - # if value not in ["SIRT", "FBP"]: - # raise ValueError("Invalid reconstruction method. Choose 'SIRT' or 'FBP'.") - # self._reconstruction_method = value diff --git a/src/quantem/tomography_old/tomography_dataset.py b/src/quantem/tomography_old/tomography_dataset.py deleted file mode 100644 index c66f1d9d..00000000 --- a/src/quantem/tomography_old/tomography_dataset.py +++ /dev/null @@ -1,468 +0,0 @@ -from pathlib import Path - -import numpy as np -import torch -from numpy.typing import NDArray -from torch._tensor import Tensor -from torch.utils.data import Dataset - -from quantem.core.datastructures.dataset3d import Dataset3d -from quantem.core.io.serialize import AutoSerialize -from quantem.core.utils.validators import ( - validate_array, - validate_tensor, -) - - -class AuxiliaryParams(torch.nn.Module): - def __init__(self, num_tilts, device, zero_tilt_idx=None, learn_shifts: bool = True): - super().__init__() - - self.learn_shifts = learn_shifts - - if zero_tilt_idx is None: - # If not provided, assume first projection is reference - zero_tilt_idx = 0 - - self.zero_tilt_idx = zero_tilt_idx - self.num_tilts = num_tilts - - # Shifts: only parameterize non-reference tilts - num_param_tilts = num_tilts - 1 - self.shifts_param = torch.nn.Parameter( - torch.zeros(num_param_tilts, 2, device=device), requires_grad=learn_shifts - ) - - # Fixed zero shifts for reference - self.shifts_ref = torch.zeros(1, 2, device=device) - - # Z1 and Z3: parameterize all tilts EXCEPT the reference - self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - - # Fixed zeros for reference tilt - self.z1_ref = torch.zeros(1, device=device) - self.z3_ref = torch.zeros(1, device=device) - - def forward(self, dummy_input=None): - # Reconstruct full arrays with zeros at reference position - before_shifts = self.shifts_param[: self.zero_tilt_idx] - after_shifts = self.shifts_param[self.zero_tilt_idx :] - shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) - - before_z1 = self.z1_param[: self.zero_tilt_idx] - after_z1 = self.z1_param[self.zero_tilt_idx :] - z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) - - before_z3 = self.z3_param[: self.zero_tilt_idx] - after_z3 = self.z3_param[self.zero_tilt_idx :] - z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) - - return shifts, z1, z3 - - -class TomographyDataset(AutoSerialize, Dataset): - _token = object() - - """ - A tomography dataset which contains the tilt series, and also instatiates the - z1, z3, and shifts of the tilt series. - - Idea for this dataset is so that we can avoid moving things around as a torch tensor, - since the SIRT reconstruction algorthim, and AD reconstruction we have are all torch based. - """ - - def __init__( - self, - tilt_series: Tensor, - tilt_angles: Tensor, - z1_angles: Tensor, - z3_angles: Tensor, - shifts: Tensor, - clamp: bool, - ): - self._tilt_series = tilt_series - # Enforce Positivity - - self._tilt_angles = tilt_angles - self._z1_angles = z1_angles - self._z3_angles = z3_angles - self._shifts = shifts - - self._initial_tilt_angles = tilt_angles.clone() - self._initial_z1_angles = z1_angles.clone() - self._initial_z3_angles = z3_angles.clone() - self._initial_shifts = shifts.clone() - - # Move everything to CPU - self.to("cpu") - - # Number of indices (?) - - # Enforce normalization of tilt series - try: - tilt_percentile = torch.quantile(self._tilt_series, 0.95) - except (AttributeError, RuntimeError): - tilt_percentile = np.quantile(self._tilt_series, 0.95) - - self._tilt_series = self._tilt_series / tilt_percentile - if clamp: - self._tilt_series = torch.clamp(self._tilt_series, min=0) - - # --- Class Methods --- - @classmethod - def from_data( - cls, - tilt_series: Dataset3d | NDArray | Tensor, - tilt_angles: NDArray | Tensor, - z1_angles: NDArray | Tensor | None = None, - z3_angles: NDArray | Tensor | None = None, - shifts: NDArray | Tensor | None = None, - clamp: bool = True, - ): - """ - tilt_series: (N, H, W) - tilt_angles: (N,) - In units of degrees, the alpha tilt angle. - z1_angles: (N,) - In units of degrees, beta tilt angle. - z3_angles: (N,) - In units of degrees, negative beta tilt angle. - shifts: (N, 2) - - - The convention we use for projecting down is ZXZ Euler Angles. - - In theory, Z1 and Z3 should be the same value, except Z3 would be the - negative value of Z1. However, in some cases this is not the case and - there could be some merit in also optimizing both angles. However, the - downside is the rotation becomes less interpretable. - - The tilt angle can also be optimized. - """ - validated_tilt_series = torch.tensor(validate_array(tilt_series, "tilt_series")) - validated_tilt_angles = torch.tensor(validate_array(tilt_angles, "tilt_angles")) - - if z1_angles is not None: - validated_z1_angles = torch.tensor(validate_array(z1_angles, "z1_angles")) - else: - validated_z1_angles = torch.zeros(len(validated_tilt_angles)) - - if z3_angles is not None: - validated_z3_angles = torch.tensor(validate_array(z3_angles, "z3_angles")) - else: - validated_z3_angles = torch.zeros(len(validated_tilt_angles)) - - if shifts is not None: - validated_shifts = torch.tensor(validate_array(shifts, "shifts")) - else: - validated_shifts = torch.zeros(len(validated_tilt_angles), 2) - - return cls( - tilt_series=validated_tilt_series, - tilt_angles=validated_tilt_angles, - z1_angles=validated_z1_angles, - z3_angles=validated_z3_angles, - shifts=validated_shifts, - clamp=clamp, - # name=name, - # origin=origin, - # sampling=sampling, - # units=units, - # signal_units=signal_units, - ) - - def to(self, device: str): - self._tilt_series = self._tilt_series.to(device) - self._tilt_angles = self._tilt_angles.to(device) - self._z1_angles = self._z1_angles.to(device) - self._z3_angles = self._z3_angles.to(device) - self._shifts = self._shifts.to(device) - - # --- Properties --- - - @property - def tilt_series(self) -> Tensor: - return self._tilt_series - - @property - def tilt_angles(self) -> Tensor: - return self._tilt_angles - - @property - def z1_angles(self) -> Tensor: - return self._z1_angles - - @property - def z3_angles(self) -> Tensor: - return self._z3_angles - - @property - def shifts(self) -> Tensor: - return self._shifts - - @property - def initial_tilt_angles(self) -> Tensor: - return self._initial_tilt_angles - - @property - def initial_z1_angles(self) -> Tensor: - return self._initial_z1_angles - - @property - def initial_z3_angles(self) -> Tensor: - return self._initial_z3_angles - - @property - def initial_shifts(self) -> Tensor: - return self._initial_shifts - - @property - def num_projections(self) -> int: - return self._tilt_series.shape[0] - - @property - def num_pixels(self) -> int: - return self._tilt_series.shape[0] * self._tilt_series.shape[1] * self._tilt_series.shape[2] - - @property - def dims(self) -> tuple[int, int, int]: - return self._tilt_series.shape[0], self._tilt_series.shape[1], self._tilt_series.shape[2] - - def __len__(self) -> int: - return self.num_pixels - - def __getitem__(self, idx): - actual_idx = idx - - projection_idx = actual_idx // (self.dims[1] * self.dims[2]) - remaining = actual_idx % (self.dims[1] * self.dims[2]) - - # TODO: What if non-square tilt images? - if self.dims[1] != self.dims[2]: - raise NotImplementedError("Non-square tilt images are not supported yet.") - - # TODO: row, column - pixel_i = remaining // self.dims[1] - pixel_j = remaining % self.dims[1] - - target_value = self._tilt_series[projection_idx, pixel_i, pixel_j] - phi = self._tilt_angles[projection_idx] - - return { - "projection_idx": projection_idx, - "pixel_i": pixel_i, - "pixel_j": pixel_j, - "target_value": target_value, - "phi": phi, - } - - # --- Setters --- - - @tilt_series.setter - def tilt_series(self, tilt_series: NDArray | Tensor | Dataset3d) -> None: - if isinstance(tilt_series, Dataset3d): - validated_tilt_series = torch.tensor(tilt_series.array) - elif isinstance(tilt_series, Tensor): - validated_tilt_series = validate_tensor(tilt_series, "tilt_series") - else: - validated_tilt_series = torch.tensor(validate_array(tilt_series, "tilt_series")) - - self._tilt_series = validated_tilt_series - - @tilt_angles.setter - def tilt_angles(self, tilt_angles: NDArray | Tensor) -> None: - if tilt_angles.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Tilt angles must match the number of projections.") - - validated_tilt_angles = torch.tensor(validate_array(tilt_angles, "tilt_angles")) - self._tilt_angles = validated_tilt_angles - - @z1_angles.setter - def z1_angles(self, z1_angles: NDArray | Tensor) -> None: - if z1_angles.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Z1 angles must match the number of projections.") - - if isinstance(z1_angles, Tensor): - validated_z1_angles = validate_tensor(z1_angles, "z1_angles") - else: - validated_z1_angles = torch.tensor(validate_array(z1_angles, "z1_angles")) - self._z1_angles = validated_z1_angles - - @z3_angles.setter - def z3_angles(self, z3_angles: NDArray | Tensor) -> None: - if z3_angles.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Z3 angles must match the number of projections.") - - if isinstance(z3_angles, Tensor): - validated_z3_angles = validate_tensor(z3_angles, "z3_angles") - else: - validated_z3_angles = torch.tensor(validate_array(z3_angles, "z3_angles")) - self._z3_angles = validated_z3_angles - - @shifts.setter - def shifts(self, shifts: NDArray | Tensor) -> None: - if shifts.shape[0] != self.tilt_series.shape[0]: - raise ValueError("Shifts must match the number of projections.") - - if isinstance(shifts, Tensor): - validated_shifts = validate_tensor(shifts, "shifts") - else: - validated_shifts = torch.tensor(validate_array(shifts, "shifts")) - - self._shifts = validated_shifts - - @initial_tilt_angles.setter - def initial_tilt_angles(self, tilt_angles: NDArray | Tensor) -> None: - self._initial_tilt_angles = tilt_angles - - @initial_z1_angles.setter - def initial_z1_angles(self, z1_angles: NDArray | Tensor) -> None: - self._initial_z1_angles = z1_angles - - @initial_z3_angles.setter - def initial_z3_angles(self, z3_angles: NDArray | Tensor) -> None: - self._initial_z3_angles = z3_angles - - @initial_shifts.setter - def initial_shifts(self, shifts: NDArray | Tensor) -> None: - self._initial_shifts = shifts - - # TODO: Temp auxiliary params - - def setup_auxiliary_params( - self, zero_tilt_idx: int = None, device: str = "cpu", learn_shifts: bool = True - ) -> None: - if not hasattr(self, "_auxiliary_params"): - self._auxiliary_params = AuxiliaryParams( - num_tilts=len(self.tilt_angles), - device=device, - zero_tilt_idx=zero_tilt_idx, - learn_shifts=learn_shifts, - ) - - else: - print("Auxiliary params already set") - - @property - def auxiliary_params(self) -> AuxiliaryParams: - return self._auxiliary_params - - # --- RESET --- - - def reset(self) -> None: - self._tilt_angles = self._initial_tilt_angles.clone() - self._z1_angles = self._initial_z1_angles.clone() - self._z3_angles = self._initial_z3_angles.clone() - self._shifts = self._initial_shifts.clone() - - -# TODO: Temporary for use when only doing validation -class TomographyRayDataset(Dataset): - """ - Dataset for ray-based tomography training. - Each sample contains: target pixel value, pixel coordinates, phi angle, and auxiliary info. - """ - - def __init__(self, tilt_series, phis, N, val_ratio=0.0, mode="train", seed=42): - """ - Args: - tilt_series: [M, N, N] tensor of projections - phis: [M] tensor of tilt angles - N: Grid size - """ - self.tilt_series = tilt_series.cpu() - - self.tilt_series = torch.clamp(self.tilt_series, min=0.0) - - # tilt_percentile = torch.quantile(self.tilt_series, .95) - tilt_percentile = np.quantile(self.tilt_series.numpy(), 0.95) - - self.tilt_series = self.tilt_series / tilt_percentile - - self.phis = phis.cpu() - self.N = N - self.M = tilt_series.shape[0] - self.total_pixels = self.M * self.N * self.N - - # Create train/val split - torch.manual_seed(seed) - all_indices = torch.randperm(self.total_pixels) - - num_val = int(self.total_pixels * val_ratio) - if mode == "val": - self.indices = all_indices[:num_val] - else: # train - self.indices = all_indices[num_val:] - - self.mode = mode - - def __len__(self): - return len(self.indices) - - def __getitem__(self, idx): - actual_idx = self.indices[idx].item() - - projection_idx = actual_idx // (self.N * self.N) - remaining = actual_idx % (self.N * self.N) - pixel_i = remaining // self.N - pixel_j = remaining % self.N - - target_value = self.tilt_series[projection_idx, pixel_i, pixel_j] - phi = self.phis[projection_idx] - - return { - "projection_idx": projection_idx, - "pixel_i": pixel_i, - "pixel_j": pixel_j, - "target_value": target_value, - "phi": phi, - } - - -# Pretrain Volume Dataset -class PretrainVolumeDataset(Dataset): - def __init__( - self, - volume_path: Path | str, - N: int, - ): - self._N = N - if str(volume_path).endswith(".npy"): - data = torch.from_numpy(np.load(volume_path)).float() - elif str(volume_path).endswith(".pt"): - data = torch.load(volume_path).float() - else: - raise ValueError(f"Unsupported file format: {volume_path}") - - # Normalize data - total_elements = data.numel() - if total_elements > 1e6: - sample_size = min(int(1e6), total_elements) - flat_data = data.flatten() - indices = torch.randperm(total_elements)[:sample_size] - sampled_data = flat_data[indices] - data_quantile = torch.quantile(sampled_data, 0.95) - else: - data_quantile = torch.quantile(data, 0.95) - - data = data / data_quantile - data = torch.permute(data, (2, 1, 0)) - # data = torch.flip(data, dims=(2,)) - - self.volume = data.cpu() - self.N = N - self.total_samples = N**3 - - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") - self.coords = torch.stack([x, y, z], dim=-1).reshape(-1, 3).cpu() - self.targets = self.volume.reshape(-1).cpu() - - def __len__(self): - return self.total_samples - - def __getitem__(self, idx): - return {"coords": self.coords[idx], "target": self.targets[idx]} - - @property - def N(self): - return self._N - - @N.setter - def N(self, N: int): - self._N = N diff --git a/src/quantem/tomography_old/tomography_ddp.py b/src/quantem/tomography_old/tomography_ddp.py deleted file mode 100644 index cfb59117..00000000 --- a/src/quantem/tomography_old/tomography_ddp.py +++ /dev/null @@ -1,321 +0,0 @@ -import os - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -from torch.utils.data import DataLoader, DistributedSampler - -from quantem.tomography.tomography_dataset import ( - PretrainVolumeDataset, - TomographyDataset, - TomographyRayDataset, -) - - -class TomographyDDP: - """ - Initializing DDP stuff for tomo class. - """ - - def __init__( - self, - ): - self.setup_distributed() - - def setup_distributed(self): - # Check if in distributed env - if "RANK" in os.environ: - # Distributed training - if not dist.is_initialized(): - dist.init_process_group( - backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" - ) - - self.world_size = dist.get_world_size() - self.global_rank = dist.get_rank() - self.local_rank = int(os.environ["LOCAL_RANK"]) - - torch.cuda.set_device(self.local_rank) - self.device = torch.device("cuda", self.local_rank) - - else: - # Single GPU/CPU training - self.world_size = 1 - self.global_rank = 0 - self.local_rank = 0 - - if torch.cuda.is_available(): - self.device = torch.device("cuda") - torch.cuda.set_device(0) - print("Single GPU training") - else: - self.device = torch.device("cpu") - print("CPU training") - - # Optional performance optimizations (only for CUDA) - if self.device.type == "cuda": - torch.backends.cudnn.benchmark = True - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - - def build_model( - self, - model: nn.Module, - ): - # TODO: Generalized model --> Should be instantiated in the object? Where does `HSIREN` get instantiated? - model = model.to(self.device) - - if self.world_size > 1: - model = torch.nn.parallel.DistributedDataParallel( - model, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True, - bucket_cap_mb=100, - gradient_as_bucket_view=True, - ) - - if self.global_rank == 0: - print("Model wrapped with DDP") - - if self.world_size > 1: - if self.global_rank == 0: - print("Model built, distributed, and compiled successfully") - - else: - print("Model built, compiled successfully") - - return model - - # Setup Tomo DataLoader - def setup_dataloader( - self, - tomo_dataset: TomographyDataset, - batch_size: int, - num_workers: int = 0, - val_fraction: float = 0.0, - ): - pin_mem = self.device.type == "cuda" - persist = num_workers > 0 - - # Split dataset if validation fraction > 0 - if val_fraction > 0.0: - # TODO: Temporary for when only doing validation, current TomographyDataset doesn't work correctly - train_dataset = TomographyRayDataset( - tomo_dataset.tilt_series.detach().clone(), - tomo_dataset.tilt_angles.detach().clone(), - 500, # TODO: TEMPORARY - val_ratio=val_fraction, - mode="train", - seed=42, - ) - val_dataset = TomographyRayDataset( - tomo_dataset.tilt_series.detach().clone(), - tomo_dataset.tilt_angles.detach().clone(), - 500, # TODO: TEMPORARY - val_ratio=val_fraction, - mode="val", - seed=42, - ) - else: - train_dataset, val_dataset = tomo_dataset, None - - # Samplers for distributed training - if self.world_size > 1: - train_sampler = DistributedSampler( - train_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=True, - ) - if val_dataset: - val_sampler = DistributedSampler( - val_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=False, - ) - shuffle = False - else: - train_sampler = None - val_sampler = None - shuffle = True - - # Main dataloader - self.dataloader = DataLoader( - train_dataset, - batch_size=batch_size, - num_workers=num_workers, - sampler=train_sampler, - shuffle=shuffle, - pin_memory=pin_mem, - drop_last=True, - persistent_workers=persist, - ) - - # Validation dataloader if applicable - if val_dataset: - self.val_dataloader = DataLoader( - val_dataset, - batch_size=batch_size * 4, - num_workers=num_workers, - sampler=val_sampler, - shuffle=False, - pin_memory=pin_mem, - drop_last=False, - persistent_workers=persist, - ) - self.val_sampler = val_sampler - - self.sampler = train_sampler - - if self.global_rank == 0: - print("Dataloader setup complete:") - print(f" Total projections: {len(tomo_dataset.tilt_angles)}") - if val_fraction > 0.0: - print(f" Total projections (val): {len(val_dataset)}") - print(f" Grid size: {tomo_dataset.dims[1]}{tomo_dataset.dims[2]}") - print(f" Total pixels: {tomo_dataset.num_pixels:,}") - if val_fraction > 0.0: - print(f" Total pixels (val): {len(val_dataset):,}") - print(f" Total pixels (train): {len(train_dataset):,}") - print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {batch_size * self.world_size}") - print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - - # Setup pretraining dataloader - - def setup_pretraining_dataloader( - self, - volume_dataset: PretrainVolumeDataset, - batch_size: int, - ): - # TODO: temp - - num_workers = 0 - if self.world_size > 1: - sampler = DistributedSampler( - volume_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=True, - drop_last=True, - ) - shuffle = False - else: - sampler = None - shuffle = True - - self.pretraining_dataloader = DataLoader( - volume_dataset, - batch_size=batch_size, - sampler=sampler, - shuffle=shuffle, - pin_memory=self.device.type == "cuda", - drop_last=True, - persistent_workers=num_workers > 0, - ) - - self.pretraining_sampler = sampler - - if self.global_rank == 0: - print("Pretraining dataloader setup complete:") - print(f" Total samples: {len(volume_dataset)}") - print(f" Grid size: {volume_dataset.N**3}") - print(f" Local batch size: {batch_size}") - print(f" Global batch size: {batch_size * self.world_size}") - print(f" Pretraining batches per GPU per epoch: {len(self.pretraining_dataloader)}") - - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): - if scaling_rule == "sqrt": - return base_lr * np.sqrt(self.world_size) - elif scaling_rule == "linear": - return base_lr * self.world_size - else: - raise ValueError(f"Invalid scaling rule: {scaling_rule}") - - def scale_lr( - self, - optimizer_params: dict, - ): - new_optimizer_params = {} - for key, value in optimizer_params.items(): - if "original_lr" in value: - new_optimizer_params[key] = { - "type": value["type"], - "lr": self.get_scaled_lr(value["original_lr"]), - "original_lr": value["original_lr"], - } - else: - new_optimizer_params[key] = { - "type": value["type"], - "lr": self.get_scaled_lr(value["lr"]), - "original_lr": value["lr"], - } - - return new_optimizer_params - - # TODO: Temporary Adaptive L1 Smooth Loss - - -class AdaptiveSmoothL1Loss(nn.Module): - def __init__(self, beta_init=None, ema_factor=0.99, eps=1e-8): - """ - Adaptive smooth L1 loss with EMA-based beta adaptation. - - Args: - beta_init (float): optional initial β value; if None, starts as 1.0 - ema_factor (float): smoothing factor for EMA (1 - 1/N_b in Eq. 38) - eps (float): small constant for numerical stability - """ - super().__init__() - self.register_buffer("beta2", torch.tensor(beta_init**2 if beta_init else 1.0)) - self.ema_factor = ema_factor - self.eps = eps - - def forward(self, pred, target): - diff = pred - target - abs_diff = diff.abs() - - # compute current batch MSE (Eq. 38) - mse_batch = torch.mean(diff**2) - - # update β² adaptively using Eq. (39) - with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( - self.beta2, mse_batch - ) - - beta = torch.sqrt(self.beta2 + self.eps) - - # Smooth L1 (Eq. 36) - loss = torch.where( - abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta - ) - return loss.mean() - - -class AdaptiveSmoothL1LossDDP(AdaptiveSmoothL1Loss): - def forward(self, pred, target): - diff = pred - target - abs_diff = diff.abs() - mse_batch = torch.mean(diff**2) - - # Synchronize β across all GPUs - if dist.is_initialized(): - mse_batch_all = mse_batch.clone() - dist.all_reduce(mse_batch_all, op=dist.ReduceOp.AVG) - mse_batch = mse_batch_all / dist.get_world_size() - - with torch.no_grad(): - self.beta2 = self.ema_factor * self.beta2 + (1 - self.ema_factor) * torch.min( - self.beta2, mse_batch - ) - - beta = torch.sqrt(self.beta2 + self.eps) - loss = torch.where( - abs_diff < beta, 0.5 * (diff**2) / (beta + self.eps), abs_diff - 0.5 * beta - ) - return loss.mean() diff --git a/src/quantem/tomography_old/tomography_logger.py b/src/quantem/tomography_old/tomography_logger.py deleted file mode 100644 index 8156837b..00000000 --- a/src/quantem/tomography_old/tomography_logger.py +++ /dev/null @@ -1,68 +0,0 @@ -import matplotlib.pyplot as plt - -from quantem.core.ml.logger import LoggerBase -from quantem.tomography.object_models import ObjectModelType -from quantem.tomography.tomography_dataset import TomographyDataset - - -class LoggerTomography(LoggerBase): - def __init__(self, log_dir: str, run_prefix: str, run_suffix: str = None): - super().__init__(log_dir, run_prefix, run_suffix) - - # --- Tomography focused logging methods --- - - def tilt_angles_figure(self, dataset: TomographyDataset, step: int): - figs = [] - for angle_array, title in zip( - [dataset.z1_angles, dataset.tilt_angles, dataset.z3_angles], - ["Z1 Angles", "Tilt/ X Angles", "Z3 Angles"], - ): - fig, ax = plt.subplots(figsize=(5, 5)) - ax.plot(angle_array.detach().cpu().numpy()) - ax.set_title(title) - ax.set_xlabel("Index") - ax.set_ylabel("Angle") - figs.append(fig) - plt.close(fig) - - self.log_figure( - tag="tilt_angles/z1", - fig=figs[0], - step=step, - ) - self.log_figure( - tag="tilt_angles/x", - fig=figs[1], - step=step, - ) - self.log_figure( - tag="tilt_angles/z3", - fig=figs[2], - step=step, - ) - - def projection_images( - self, volume_obj: ObjectModelType, epoch: int, logger_cmap: str = "turbo" - ): - sum_0 = volume_obj.obj.sum(axis=0) - sum_1 = volume_obj.obj.sum(axis=1) - sum_2 = volume_obj.obj.sum(axis=2) - - self.log_image( - tag="projections/Y-X Projection", - image=sum_0, - step=epoch, - cmap=logger_cmap, - ) - self.log_image( - tag="projections/Z-X Projection", - image=sum_1, - step=epoch, - cmap=logger_cmap, - ) - self.log_image( - tag="projections/Z-Y Projection", - image=sum_2, - step=epoch, - cmap=logger_cmap, - ) diff --git a/src/quantem/tomography_old/tomography_ml.py b/src/quantem/tomography_old/tomography_ml.py deleted file mode 100644 index 5b130e7f..00000000 --- a/src/quantem/tomography_old/tomography_ml.py +++ /dev/null @@ -1,338 +0,0 @@ -from typing import Any, Generator, Iterator, Sequence - -import torch - -from quantem.tomography.tomography_base import TomographyBase - - -class TomographyML(TomographyBase): - """ - Class for handling conventional reconstruction methods of tomography data. - """ - - OPTIMIZABLE_VALS = ["volume", "z1", "x", "z3", "shifts", "model", "aux_params"] - DEFAULT_LRS = { - "volume": 1e-2, - "z1": 1e-1, - "x": 1e-1, - "z3": 1e-1, - "shifts": 1e-1, - "tv_weight_vol": 0, - "tv_weight_z1": 0, - "tv_weight_x": 0, - "tv_weight_z3": 0, - } - DEFAULT_OPTIMIZER_TYPE = "adam" - - # --- Properties --- - - @property - def optimizer_params(self) -> dict[str, dict]: - """Returns the parameters used to set the optimizers.""" - return self._optimizer_params - - @optimizer_params.setter - def optimizer_params(self, d: dict) -> None: - """ - # Takes a dictionary {key: torch.optim.Adam(params=[blah], lr=[blah]), ...} - Takes a dictionary: - { - "key1": { - "type": "adam", - "lr": 0.001, - }, - "key2": { - ... - }, - ... - } - """ - # resets _optimizers as well - self._optimizers = {} - self._optimizer_params = {} - if isinstance(d, (tuple, list)): - d = {k: {} for k in d} - - for k, v in d.items(): - if k not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if "type" not in v.keys(): - v["type"] = self.DEFAULT_OPTIMIZER_TYPE - if "lr" not in v.keys(): - v["lr"] = self.DEFAULT_LRS[k] - self._optimizer_params[k] = v - - @property - def optimizers(self) -> dict[str, torch.optim.Adam | torch.optim.AdamW]: - return self._optimizers - - def set_optimizers(self): - """Reset all optimizers and set them according to the optimizer_params.""" - for key, _ in self._optimizer_params.items(): - if key == "volume": - self._add_optimizer(key, self.volume_obj.params, self._optimizer_params[key]) - elif key == "shifts": - self._add_optimizer(key, self.dataset.shifts, self._optimizer_params[key]) - elif key == "z1": - self._add_optimizer(key, self.dataset.z1_angles, self._optimizer_params[key]) - elif key == "x": - self._add_optimizer(key, self.dataset.tilt_angles, self._optimizer_params[key]) - elif key == "z3": - self._add_optimizer(key, self.dataset.z3_angles, self._optimizer_params[key]) - elif key == "model": - if self._optimizer_params[key]["original_lr"] is not None: - optimizer_params = self._optimizer_params[key].copy() - optimizer_params.pop("original_lr", None) - self._add_optimizer(key, self.obj.model.parameters(), optimizer_params) - elif key == "aux_params": - if self._optimizer_params[key]["original_lr"] is not None: - optimizer_params = self._optimizer_params[key].copy() - optimizer_params.pop("original_lr", None) - self._add_optimizer( - key, self.dataset.auxiliary_params.parameters(), optimizer_params - ) - else: - raise ValueError( - f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - - def remove_optimizer(self, key: str) -> None: - self._optimizers.pop(key, None) - self._optimizer_params.pop(key, None) - return - - def _add_optimizer( - self, - key: str, - params: "torch.Tensor|Sequence[torch.Tensor]|Iterator[torch.Tensor]", - opt_params: dict, - ) -> None: - """Can be used to add an optimizer without resetting the other optimizers.""" - - if key not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {key}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if isinstance(params, torch.Tensor): - params = [params] - elif isinstance(params, Generator): - params = list(params) - [p.requires_grad_(True) for p in params] - self.optimizer_params[key] = opt_params - opt_params = opt_params.copy() - opt_type = opt_params.pop("type") - if isinstance(opt_type, type): - opt = opt_type(params, **opt_params) - elif opt_type == "adam": - opt = torch.optim.Adam(params, **opt_params) - elif opt_type == "adamw": - opt = torch.optim.AdamW(params, **opt_params) # TODO pass all other kwargs - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_params['type']}") - # if key in self.optimizers.keys(): - # self.vprint(f"Key {key} is already in optimizers, overwriting.") - self._optimizers[key] = opt - - @property - def scheduler_params(self) -> dict[str, dict]: - """Returns the parameters used to set the schedulers.""" - return self._scheduler_params - - @scheduler_params.setter - def scheduler_params(self, d: dict) -> None: - """ - Takes a dictionary: - { - "key1": { - "type": "cyclic", - "base_lr": 0.001, - }, - "key2": { - ... - }, - ... - } - """ - self._schedulers = {} - for k, v in d.items(): - if not any(v): - continue - if k not in self.OPTIMIZABLE_VALS: - raise ValueError( - f"key to be optimized, {k}, not in allowed keys: {self.OPTIMIZABLE_VALS}" - ) - if v["type"] not in [ - "cyclic", - "plateau", - "exp", - "gamma", - "linear", - "cosine_annealing", - "none", - ]: - raise ValueError( - f"Unknown scheduler type: {v['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" - ) - self._scheduler_params = d - - @property - def schedulers( - self, - ) -> dict[ - str, - ( - torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | torch.optim.lr_scheduler.LinearLR - | torch.optim.lr_scheduler.CosineAnnealingLR - | None - ), - ]: - return self._schedulers - - def set_schedulers( - self, - params: dict[str, dict], - num_iter: int | None = None, - ): - """ - TODO allow for new schedulers to be passed in when adding new optimizers without - removing the old schedulers or overwrtiting them. Not entirely sure what usecases there - will be for this. - - Sets the schedulers for the optimizer from a dictionary. Expects a dictionary of the form: - { - "optimizable_key1": { - "type": "scheduler_type", - "scheduler_kwarg": scheduler_kwarg_value, - ... - }, - "optimizable_key2": { - "type": "scheduler_type", - "scheduler_kwarg": scheduler_kwarg_value, - ... - }, - ... - } - where the keys are the same as the keys in self.OPTIMIZABLE_VALS. - - The scheduler type can be one of the following: - - "cyclic" - - "plateau" or "reducelronplateau" - - "exponential" - - None - - The num_iter kwarg is only used for exponential schedulers and if a "factor" is given - as a scheduler_kwarg instead of gamma. In that case, the gamma is calculated from num_iter - and the factor. - - TODO could update this to allow passing key:optimizer directly, would likely need to - rewrite get_schedulers to check the tpye - """ - if not any(self.optimizers): - raise NameError("self.optimizers have not yet been set.") - self._schedulers = self._get_schedulers( - params=params, - optimizers=self.optimizers, - num_iter=num_iter, - ) - - def _get_schedulers( - self, - params: dict[str, dict], - optimizers: dict, - num_iter: int | None = None, - ) -> dict[ - str, - ( - torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ), - ]: - """ - return schedulers for a given set of optimizers. Kept seperate from schedulers.setter so - that it can be called for pre-training - """ - schedulers = {} - for opt_key, p in params.items(): - if not any(p): - continue - elif opt_key not in self.OPTIMIZABLE_VALS: - raise KeyError( - f"Scheduler got bad key {opt_key}, schedulers can only be attached to one of {self.OPTIMIZABLE_VALS}" - ) - elif opt_key not in optimizers.keys(): - raise KeyError(f"optimizers does not have an optimizer for: {opt_key}") - else: - schedulers[opt_key] = self._get_scheduler( - optimizer=optimizers[opt_key], params=p, num_iter=num_iter - ) - return schedulers - - def _get_scheduler( - self, - optimizer: torch.optim.Adam, - params: dict[str, Any] | torch.optim.lr_scheduler._LRScheduler, - num_iter: int | None = None, - ) -> ( - torch.optim.lr_scheduler._LRScheduler - | torch.optim.lr_scheduler.CyclicLR - | torch.optim.lr_scheduler.ReduceLROnPlateau - | torch.optim.lr_scheduler.ExponentialLR - | None - ): - if isinstance(params, torch.optim.lr_scheduler._LRScheduler): - return params - - sched_type: str = params["type"].lower() - base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - scheduler = None - elif sched_type == "linear": - scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=params.get("start_factor", 0.1), - total_iters=params.get("total_iters", num_iter), - ) - elif sched_type == "cosine_annealing": - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max=params.get("T_max", num_iter), - eta_min=params.get("eta_min", base_LR / 100), - ) - elif sched_type == "cyclic": - scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 50), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params.keys(): - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.999 - scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") - return scheduler diff --git a/src/quantem/tomography_old/tomography_nerf.py b/src/quantem/tomography_old/tomography_nerf.py deleted file mode 100644 index 2aa27dc8..00000000 --- a/src/quantem/tomography_old/tomography_nerf.py +++ /dev/null @@ -1,636 +0,0 @@ -import os -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch -import torch.distributed as dist -from torch.utils.data import DataLoader, DistributedSampler -from torch.utils.tensorboard import SummaryWriter - -from quantem.tomography.models import HSiren -from quantem.tomography.tomography_dataset import TomographyDataset - - -def get_num_samples_per_ray(epoch): - """Increase number of samples per ray at specific epochs.""" - schedule = { - 0: 25, - 2: 50, - 4: 100, - 6: 200, - } - - num_samples = 64 - for epoch_threshold, samples in sorted(schedule.items()): - if epoch >= epoch_threshold: - num_samples = samples - - return num_samples - - -class AuxiliaryParams(torch.nn.Module): - def __init__(self, num_tilts, device, zero_tilt_idx=None): - super().__init__() - - if zero_tilt_idx is None: - # If not provided, assume first projection is reference - zero_tilt_idx = 0 - - self.zero_tilt_idx = zero_tilt_idx - self.num_tilts = num_tilts - - # Shifts: only parameterize non-reference tilts - num_param_tilts = num_tilts - 1 - self.shifts_param = torch.nn.Parameter(torch.zeros(num_param_tilts, 2, device=device)) - - # Fixed zero shifts for reference - self.shifts_ref = torch.zeros(1, 2, device=device) - - # Z1 and Z3: parameterize all tilts EXCEPT the reference - self.z1_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - # self.z3_param = torch.nn.Parameter(torch.zeros(num_param_tilts, device=device)) - - # Fixed zeros for reference tilt - self.z1_ref = torch.zeros(1, device=device) - # self.z3_ref = torch.zeros(1, device=device) - - def forward(self, dummy_input=None): - # Reconstruct full arrays with zeros at reference position - before_shifts = self.shifts_param[: self.zero_tilt_idx] - after_shifts = self.shifts_param[self.zero_tilt_idx :] - shifts = torch.cat([before_shifts, self.shifts_ref, after_shifts], dim=0) - - before_z1 = self.z1_param[: self.zero_tilt_idx] - after_z1 = self.z1_param[self.zero_tilt_idx :] - z1 = torch.cat([before_z1, self.z1_ref, after_z1], dim=0) - - # before_z3 = self.z3_param[:self.zero_tilt_idx] - # after_z3 = self.z3_param[self.zero_tilt_idx:] - # z3 = torch.cat([before_z3, self.z3_ref, after_z3], dim=0) - - return shifts, z1, -z1 - - -class TomographyNerf: - def __init__( - self, - tomo_dataset: TomographyDataset, - batch_size: int, - num_workers: int = 0, - ): - self.tomo_dataset = tomo_dataset - self.setup_distributed() - self.setup_dataloader( - batch_size=batch_size, - num_workers=num_workers, - ) - self.build_model() - - # --- Setup ---- - - def setup_distributed(self): - """Setup distributed training if available, otherwise use single GPU/CPU.""" - - # Check if we're in a distributed environment - if "RANK" in os.environ: - # Distributed training - if not dist.is_initialized(): - dist.init_process_group( - backend="nccl" if torch.cuda.is_available() else "gloo", init_method="env://" - ) - - self.world_size = dist.get_world_size() - self.global_rank = dist.get_rank() - self.local_rank = int(os.environ["LOCAL_RANK"]) - - torch.cuda.set_device(self.local_rank) - self.device = torch.device("cuda", self.local_rank) - - else: - # Single GPU/CPU training - self.world_size = 1 - self.global_rank = 0 - self.local_rank = 0 - - if torch.cuda.is_available(): - self.device = torch.device("cuda:0") - torch.cuda.set_device(0) - print("Single GPU training") - else: - self.device = torch.device("cpu") - print("CPU training") - - # Optional performance optimizations (only for CUDA) - if self.device.type == "cuda": - torch.backends.cudnn.benchmark = True - torch.backends.cuda.matmul.allow_tf32 = True - torch.backends.cudnn.allow_tf32 = True - - def build_model(self): - self.model = HSiren( - in_features=3, - out_features=1, - hidden_layers=4, - hidden_features=512, - first_omega_0=30, - alpha=1.0, - ).to(self.device) - - if self.world_size > 1: - self.model = torch.nn.parallel.DistributedDataParallel( - self.model, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True, - bucket_cap_mb=100, - gradient_as_bucket_view=True, - ) - - if self.global_rank == 0: - print("Model wrapped with DDP") - - if self.world_size > 1: - if self.global_rank == 0: - print("Model built, distributed, and compiled successfully") - - else: - print("Model built, compiled successfully") - - def setup_dataloader( - self, - batch_size: int, - num_workers: int = 0, - ): - if self.world_size > 1: - sampler = DistributedSampler( - self.tomo_dataset, - num_replicas=self.world_size, - rank=self.global_rank, - shuffle=True, - ) - shuffle = False - else: - sampler = None - shuffle = True - self.dataloader = DataLoader( - self.tomo_dataset, - batch_size=batch_size, - num_workers=num_workers, - sampler=sampler, - shuffle=shuffle, - pin_memory=True if self.device.type == "cuda" else False, - drop_last=True, - persistent_workers=False if num_workers == 0 else True, - ) - - self.sampler = sampler - - if self.global_rank == 0: - print("Dataloader setup complete:") - print(f" Total projections: {len(self.tomo_dataset.tilt_angles)}") - print(f" Grid size: {self.tomo_dataset.dims[1]}{self.tomo_dataset.dims[2]}") - print(f" Total pixels: {self.tomo_dataset.num_pixels:,}") - - print(f" Local batch size (train): {batch_size}") - print(f" Global batch size: {batch_size * self.world_size}") - print(f" Train batches per GPU per epoch: {len(self.dataloader)}") - - # --- Creating Volume --- - def create_volume(self): - N = max(self.tomo_dataset.dims) - - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - model = self.model.module if hasattr(self.model, "module") else self.model - - samples_per_gpu = N**3 // self.world_size - start_idx = self.global_rank * samples_per_gpu - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx].to(self.device) - - # TODO: torch.nn.functional.softplus here - # outputs = torch.nn.functional.softplus(model(inputs_subset)) - outputs = model(inputs_subset) - - if outputs.dim() > 1: - outputs = outputs.squeeze(-1) - - if self.world_size > 1: - gathered_outputs = [torch.empty_like(outputs) for _ in range(self.world_size)] - dist.all_gather(gathered_outputs, outputs.contiguous()) - - pred_full = torch.cat(gathered_outputs, dim=0).reshape(N, N, N).float() - else: - pred_full = outputs.reshape(N, N, N).float() - - return pred_full - - # --- Scaling LR --- - def get_scaled_lr(self, base_lr, scaling_rule="sqrt"): - """Scale learning rate based on world size.""" - if self.world_size == 1: - return base_lr - - if scaling_rule == "linear": - # Linear scaling: lr = base_lr * world_size - return base_lr * self.world_size - elif scaling_rule == "sqrt": - # Square root scaling: lr = base_lr * sqrt(world_size) - return base_lr * np.sqrt(self.world_size) - else: - raise ValueError(f"Invalid scaling rule: {scaling_rule}") - - # --- Creating Optimizer --- - def create_optimizer( - self, - params, - lr, - fused=True, - ): - return torch.optim.Adam( - params, - lr=lr, - fused=fused, - ) - - # Batch Projection Rays --- - - def create_batch_projection_rays(self, pixel_i, pixel_j, N, num_samples_per_ray): - """Create projection rays for entire batch simultaneously.""" - batch_size = len(pixel_i) - - # Convert all pixels to normalized coordinates - x_coords = (pixel_j / (N - 1)) * 2 - 1 - y_coords = (pixel_i / (N - 1)) * 2 - 1 - - # Create z coordinates - z_coords = torch.linspace(-1, 1, num_samples_per_ray, device=self.device) - - # Create rays for all pixels: [batch_size, num_samples_per_ray, 3] - rays = torch.zeros(batch_size, num_samples_per_ray, 3, device=self.device) - - # Fill coordinates efficiently - rays[:, :, 0] = x_coords.unsqueeze(1) # x constant per ray - rays[:, :, 1] = y_coords.unsqueeze(1) # y constant per ray - rays[:, :, 2] = z_coords.unsqueeze(0) # z varies along ray - - return rays - - @torch.compile(mode="reduce-overhead") - def transform_batch_ray_coordinates(self, rays, z1, x, z3, shifts, N, sampling_rate): - # Step 1: Apply shifts - shift_x_norm = (shifts[:, 0:1] * sampling_rate * 2) / (N - 1) - shift_y_norm = (shifts[:, 1:2] * sampling_rate * 2) / (N - 1) - - rays_x = rays[:, :, 0] - shift_x_norm - rays_y = rays[:, :, 1] - shift_y_norm - rays_z = rays[:, :, 2] - - # Rotation 1: Z(-z3) - theta = torch.deg2rad(-z3).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot1 = cos_t * rays_x - sin_t * rays_y - rays_y_rot1 = sin_t * rays_x + cos_t * rays_y - rays_z_rot1 = rays_z - - # Rotation 2: X(x) - theta = torch.deg2rad(x).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_rot2 = rays_x_rot1 - rays_y_rot2 = cos_t * rays_y_rot1 - sin_t * rays_z_rot1 - rays_z_rot2 = sin_t * rays_y_rot1 + cos_t * rays_z_rot1 - - # Rotation 3: Z(-z1) - theta = torch.deg2rad(-z1).view(-1, 1) - cos_t = torch.cos(theta) - sin_t = torch.sin(theta) - - rays_x_final = cos_t * rays_x_rot2 - sin_t * rays_y_rot2 - rays_y_final = sin_t * rays_x_rot2 + cos_t * rays_y_rot2 - rays_z_final = rays_z_rot2 - - # Stack the final result - transformed_rays = torch.stack([rays_x_final, rays_y_final, rays_z_final], dim=2) - - return transformed_rays - - # --- Training --- - def train( - self, - train_lr: float = 1e-5, - epochs: int = 20, - warmup_epochs: int = 10, - use_amp: bool = True, - tv_weight: float = 0.0, - viz_freq: int = 1, - checkpoint_freq: int = 5, - ): - log_path = Path("runs/test_1") - if not log_path.exists(): - log_path.mkdir(parents=True, exist_ok=True) - self.writer = SummaryWriter(log_dir=log_path) - - # Find index closest to zero degrees - zero_tilt_idx = torch.argmin(torch.abs(self.tomo_dataset.tilt_angles)).item() - - if self.global_rank == 0: - print( - f"Using projection {zero_tilt_idx} (angle={self.tomo_dataset.tilt_angles[zero_tilt_idx]:.2f}°) as reference" - ) - - aux_params = AuxiliaryParams( - num_tilts=len(self.tomo_dataset.tilt_angles), - device=self.device, - zero_tilt_idx=zero_tilt_idx, - ) - - if self.world_size > 1: - aux_params = torch.nn.parallel.DistributedDataParallel( - aux_params, - device_ids=[self.local_rank], - output_device=self.local_rank, - find_unused_parameters=False, - broadcast_buffers=True, - ) - - if self.global_rank == 0: - print("Auxiliary parameters wrapped with DDP") - - scaled_train_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") - scaled_aux_lr = self.get_scaled_lr(train_lr, scaling_rule="sqrt") - - optimizer = self.create_optimizer( - self.model.parameters(), - scaled_train_lr, - fused=True, - ) - - aux_optimizer = self.create_optimizer( - aux_params.parameters(), - scaled_aux_lr, - fused=True, - ) - - aux_norm = torch.tensor(0.0, device=self.device) - model_norm = torch.tensor(0.0, device=self.device) - - optimizer.zero_grad() - aux_optimizer.zero_grad() - - warmup_scheduler_model = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=0.001, - total_iters=warmup_epochs, - ) - cosine_scheduler_model = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max=epochs - warmup_epochs, - eta_min=scaled_train_lr / 100, - ) - - scheduler_model = torch.optim.lr_scheduler.SequentialLR( - optimizer, - schedulers=[warmup_scheduler_model, cosine_scheduler_model], - milestones=[warmup_epochs], - ) - - scheduler_aux = torch.optim.lr_scheduler.CosineAnnealingLR( - aux_optimizer, - T_max=epochs, - eta_min=scaled_aux_lr / 100, - ) - - N = max(self.tomo_dataset.dims) - - device_type = self.device.type - autocast_dtype = torch.bfloat16 if use_amp else None - - for epoch in range(epochs): - num_samples_per_ray = get_num_samples_per_ray(epoch) - - # Log the change if it happens - if epoch > 0: - prev_samples = get_num_samples_per_ray(epoch - 1) - - if num_samples_per_ray != prev_samples and self.global_rank == 0: - print( - f"Epoch {epoch}: Changing num_samples_per_ray from {prev_samples} to {num_samples_per_ray}" - ) - - if self.sampler is not None: - self.sampler.set_epoch(epoch) - - epoch_loss = 0.0 - epoch_mse_loss = 0.0 - epoch_tv_loss = 0.0 - epoch_z1_loss = 0.0 - - num_batches = 0 - - for batch_idx, batch in enumerate(self.dataloader): - projection_indices = batch["projection_idx"] - - pixel_i = batch["pixel_i"].float().to(self.device, non_blocking=True) - pixel_j = batch["pixel_j"].float().to(self.device, non_blocking=True) - target_values = batch["target_value"].to(self.device, non_blocking=True) - phis = batch["phi"].to(self.device, non_blocking=True) - projection_indices = batch["projection_idx"].to(self.device, non_blocking=True) - - shifts, z1_params, z3_params = aux_params(None) - batch_shifts = torch.index_select(shifts, 0, projection_indices) - batch_z1 = torch.index_select(z1_params, 0, projection_indices) - batch_z3 = torch.index_select(z3_params, 0, projection_indices) - - with torch.autocast( - device_type=device_type, dtype=autocast_dtype, enabled=use_amp - ): - with torch.no_grad(): - batch_ray_coords = self.create_batch_projection_rays( - pixel_i, pixel_j, N, num_samples_per_ray - ) - - transformed_rays = self.transform_batch_ray_coordinates( - batch_ray_coords, - z1=batch_z1, - x=phis, - z3=batch_z3, - shifts=batch_shifts, - N=N, - sampling_rate=1.0, - ) - - all_coords = transformed_rays.view(-1, 3) - # TODO: torch.nn.functional.softplus here - # all_densities = torch.nn.functional.softplus(self.model(all_coords)) - all_densities = self.model(all_coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) # [N, 1] → [N] - - # Create mask for valid coordinates (within [-1, 1]^3) - valid_mask = ( - (all_coords[:, 0] >= -1) - & (all_coords[:, 0] <= 1) - & (all_coords[:, 1] >= -1) - & (all_coords[:, 1] <= 1) - & (all_coords[:, 2] >= -1) - & (all_coords[:, 2] <= 1) - ).float() - - all_densities = all_densities * valid_mask - - if tv_weight > 0: - num_tv_samples = min(10000, all_coords.shape[0]) - tv_indices = torch.randperm(all_coords.shape[0], device=all_coords.device)[ - :num_tv_samples - ] - - # Rerun forward for gradient tracking - tv_coords = all_coords[tv_indices].detach().requires_grad_(True) - - # TODO: torch.nn.functional.softplus here - # tv_densities_recomputed = torch.nn.functional.softplus(self.model(tv_coords)) # Get rid of this - tv_densities_recomputed = self.model(tv_coords) - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - # Compute gradients - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - tv_loss = tv_weight * grad_norm.mean() - else: - tv_loss = torch.tensor(0.0, device=self.device) - - ray_densities = all_densities.view( - len(target_values), num_samples_per_ray - ) # Reshape rays and integarte - step_size = 2.0 / (num_samples_per_ray - 1) - - predicted_values = ray_densities.sum(dim=1) * step_size - - mse_loss = torch.nn.functional.mse_loss(predicted_values, target_values) - - tv_loss_z1 = torch.tensor(0.0, device=self.device) - # tv_loss_z1 = tv_loss_1d(z1_params, factor = 1e-3) + tv_loss_1d(z3_params, factor = 1e-3) - # tv_loss_z1 = 1e-5*(torch.sum(z1_params**2) + torch.sum(z3_params**2)) - - # Combine losses - loss = mse_loss + tv_loss_z1 + tv_loss - - loss.backward() - - epoch_loss += loss.detach() - epoch_mse_loss += mse_loss.detach() - epoch_tv_loss += tv_loss.detach() - epoch_z1_loss += tv_loss_z1.detach() - - num_batches += 1 - - if epoch >= warmup_epochs: - model_norm = torch.nn.utils.clip_grad_norm_( - self.model.parameters(), max_norm=1 - ) - optimizer.step() - optimizer.zero_grad() - - aux_norm = torch.nn.utils.clip_grad_norm_(aux_params.parameters(), max_norm=1) - aux_optimizer.step() - aux_optimizer.zero_grad() - else: - model_norm = torch.nn.utils.clip_grad_norm_( - self.model.parameters(), max_norm=1 - ) - optimizer.step() - optimizer.zero_grad() - - aux_optimizer.zero_grad() - - scheduler_model.step() - - if epoch >= warmup_epochs: - scheduler_aux.step() - - if epoch % viz_freq == 0 or epoch == epochs - 1 or epoch % checkpoint_freq == 0: - with torch.no_grad(): - pred_full = self.create_volume().cpu() - avg_loss = epoch_loss.item() / num_batches - avg_mse_loss = epoch_mse_loss.item() / num_batches - avg_tv_loss = epoch_tv_loss.item() / num_batches - avg_z1_loss = epoch_z1_loss.item() / num_batches - - metrics = torch.tensor( - [avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss], device=self.device - ) - if self.world_size > 1: - torch.distributed.all_reduce(metrics, op=torch.distributed.ReduceOp.AVG) - avg_loss, avg_mse_loss, avg_tv_loss, avg_z1_loss = metrics.tolist() - - # Logging and visualization - if self.global_rank == 0 and (epoch % viz_freq == 0 or epoch == epochs - 1): - with torch.no_grad(): - current_lr = scheduler_model.get_last_lr()[0] - - # Log metrics - self.writer.add_scalar("train/mse_loss", avg_mse_loss, epoch) - - self.writer.add_scalar("train/z1_loss", avg_z1_loss, epoch) - if tv_weight > 0: - self.writer.add_scalar("train/tv_loss", avg_tv_loss, epoch) - self.writer.add_scalar("train/total_loss", avg_loss, epoch) - self.writer.add_scalar("train/model_grad_norm", model_norm.item(), epoch) - self.writer.add_scalar("train/aux_grad_norm", aux_norm.item(), epoch) - self.writer.add_scalar("train/lr", current_lr, epoch) - self.writer.add_scalar("train/num_samples_per_ray", num_samples_per_ray, epoch) - - fig, axes = plt.subplots(1, 3, figsize=(36, 12)) - axes[0].matshow(pred_full.sum(dim=0).cpu().numpy(), cmap="turbo", vmin=0) - axes[0].set_title("Sum over Z-axis") - - axes[1].matshow(pred_full[N // 2].cpu().numpy(), cmap="turbo", vmin=0) - axes[1].set_title(f"Slice at Z={N // 2}") - - slice_start = max(0, N // 2 - 5) - slice_end = min(N, N // 2 + 6) - thick_slice = pred_full[slice_start:slice_end].sum(dim=0).cpu().numpy() - axes[2].matshow(thick_slice, cmap="turbo", vmin=0) - axes[2].set_title(f"Thick slice sum (Z={slice_start}:{slice_end - 1})") - - self.writer.add_figure("train/viz", fig, epoch, close=True) - plt.close(fig) - - if epoch % checkpoint_freq == 0 or epoch == epochs - 1: - self.save_checkpoint(epoch, aux_params, log_path) - - if self.global_rank == 0: - print("Training complete.") - - def save_checkpoint(self, epoch, aux_params, log_path): - """Save model and auxiliary parameters checkpoint (rank 0 only).""" - if self.global_rank == 0: - model_to_save = self.model.module if hasattr(self.model, "module") else self.model - aux_params_to_save = aux_params.module if hasattr(aux_params, "module") else aux_params - - checkpoint = { - "model_state_dict": model_to_save.state_dict(), - "aux_params_state_dict": aux_params_to_save.state_dict(), - "epoch": epoch, - } - checkpoint_path = os.path.join(log_path, f"checkpoint_epoch_{epoch:04d}.pt") - torch.save(checkpoint, checkpoint_path) - print(f"Checkpoint saved at epoch {epoch}: {checkpoint_path}") diff --git a/src/quantem/tomography_old/utils.py b/src/quantem/tomography_old/utils.py deleted file mode 100644 index 82c1c9d5..00000000 --- a/src/quantem/tomography_old/utils.py +++ /dev/null @@ -1,300 +0,0 @@ -import numpy as np -import torch -import torch.nn.functional as F -from scipy.ndimage import center_of_mass, gaussian_filter, shift -from scipy.stats import norm -from tqdm.auto import tqdm - -from quantem.core.utils.imaging_utils import cross_correlation_shift - -# --- Projection Operator Utils --- - - -def rot_ZXZ(mags, z1, x, z3, device, mode="bilinear"): - if not isinstance(x, torch.Tensor) or not isinstance(z1, torch.Tensor): - z1 = torch.tensor(z1, dtype=torch.float32, device=device) - x = torch.tensor(x, dtype=torch.float32, device=device) - z3 = torch.tensor(z3, dtype=torch.float32, device=device) - curr_mags = mags - - curr_mags = differentiable_rotz_vectorized(curr_mags, z1, mode) - curr_mags = differentiable_rotx_vectorized(curr_mags, x, mode) - - curr_mags = differentiable_rotz_vectorized(curr_mags, z3, mode) - - return curr_mags - - -def differentiable_rotz_vectorized(mags, theta, mode="bilinear"): - _, dimz, dimy, dimx = mags.shape - - if theta.dim() == 0: - theta = theta.unsqueeze(0) - - theta_rad = torch.deg2rad(theta) - - cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) - affine_matrix = torch.stack( - [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 - ).view(-1, 2, 3) - - mags = mags.permute(1, 0, 2, 3) - - def transform_slice(mag_slice): - grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) - return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( - 0 - ) - - rotated_mags = torch.vmap(transform_slice)(mags) - return rotated_mags.permute(1, 0, 2, 3) - - -def differentiable_rotx_vectorized(mags, theta, mode="bilinear"): - _, dimz, dimy, dimx = mags.shape - - if theta.dim() == 0: - theta = theta.unsqueeze(0) - - theta_rad = torch.deg2rad(theta) - - cos_t, sin_t = torch.cos(theta_rad), torch.sin(theta_rad) - affine_matrix = torch.stack( - [cos_t, -sin_t, torch.zeros_like(theta), sin_t, cos_t, torch.zeros_like(theta)], dim=-1 - ).view(-1, 2, 3) - - mags = mags.permute(3, 0, 1, 2) - - def transform_slice(mag_slice): - grid = F.affine_grid(affine_matrix, mag_slice.unsqueeze(0).shape, align_corners=False) - return F.grid_sample(mag_slice.unsqueeze(0), grid, mode=mode, align_corners=False).squeeze( - 0 - ) - - rotated_mags = torch.vmap(transform_slice)(mags) - return rotated_mags.permute(1, 2, 3, 0) - - -def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): - """ - Shifts a 2D image using grid_sample in a differentiable manner. - - Args: - image: Tensor of shape [H, W] - shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) - shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) - sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts - - Returns: - Shifted image of shape [H, W] - """ - H, W = image.shape - - # Convert physical shift to pixel shift - shift_x_pixel = shift_x - shift_y_pixel = shift_y - - # Normalize shift for grid_sample (assuming align_corners=True) - normalized_shift_x = shift_x_pixel * 2 / (W - 1) - normalized_shift_y = shift_y_pixel * 2 / (H - 1) - - # Create normalized grid - grid_y, grid_x = torch.meshgrid( - torch.linspace(-1, 1, H, device=image.device), - torch.linspace(-1, 1, W, device=image.device), - indexing="ij", - ) - - grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] - - # Apply shift (ensure it's differentiable) - grid[:, :, :, 0] -= normalized_shift_x - grid[:, :, :, 1] -= normalized_shift_y - - # Add batch and channel dimensions - image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] - - # Sample using grid_sample (fully differentiable) - shifted_image = F.grid_sample( - image, grid, mode="bicubic", padding_mode="zeros", align_corners=True - ) - - return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] - - -# --- TV loss --- - - -def get_TV_loss(tensor, factor=1e-3): - tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() - tv_loss = tv_d + tv_h + tv_w - - return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) - - -# --- Gaussian filters --- - - -def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: - radius = np.ceil(num_sigmas * sigma) - support = torch.arange(-radius, radius + 1, dtype=torch.float) - kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() - # Ensure kernel weights sum to 1, so that image brightness is not altered - return kernel.mul_(1 / kernel.sum()) - - -def gaussian_filter_2d( - img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor -) -> torch.Tensor: # Add kernel_1d as an argument - # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function - padding = len(kernel_1d) // 2 # Ensure that image size does not change - img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` - # Convolve along columns and rows - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - return img.squeeze_(0).squeeze_(0) # Make 2D again - - -def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: - """ - Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. - - Args: - stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms - kernel_1d (torch.Tensor): 1D Gaussian kernel - - Returns: - torch.Tensor: Blurred stack of same shape (H, N, W) - """ - H, N, W = stack.shape - padding = len(kernel_1d) // 2 - - # Reshape to (N, 1, H, W) for conv2d - stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) - - # Apply separable conv2d: vertical then horizontal - out = torch.nn.functional.conv2d( - stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) - ) - out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - - # Restore shape to (H, N, W) - return out.squeeze(1).permute(1, 0, 2) - - -# Circular mask - - -def torch_phase_cross_correlation(im1, im2): - f1 = torch.fft.fft2(im1) - f2 = torch.fft.fft2(im2) - cc = torch.fft.ifft2(f1 * torch.conj(f2)) - cc_abs = torch.abs(cc) - - max_idx = torch.argmax(cc_abs) - shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() - - for i, dim in enumerate(im1.shape): - if shifts[i] > dim // 2: - shifts[i] -= dim - - # return shifts.flip(0) # (dx, dy) - return shifts - - -# --- Tilt Series Processing Utility Functions --- - - -def fourier_cropping(img, crop_size): - """ - Crop the img in Fourier space to the specified size. - """ - center = np.array(img.shape) // 2 - - fft_img = np.fft.fftshift(np.fft.fft2(img)) - - cropped_fft = fft_img[ - center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, - center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, - ] - cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real - return cropped_img - - -def estimate_background( - img, - num_iterations=10, - cutoff=3, - smoothing_sigma=1.0, -): - """ - Estimate the background of the image using a Gaussian filter. - """ - if smoothing_sigma > 0: - img = gaussian_filter(img, sigma=smoothing_sigma) - pixel_vals = img.ravel() - - for i in range(num_iterations): - mu, std = norm.fit(pixel_vals) - - # Set cutoff threshold (e.g., 3 standard deviations) - lower = mu - cutoff * std - upper = mu + cutoff * std - - # Mask pixel values within ±3σ - pixel_vals = pixel_vals[(pixel_vals >= lower) & (pixel_vals <= upper)] - - return mu - - -def cross_correlation_align_stack(ref_img, stack, print_pred=False): - """ - Aligns a stack of images to a reference image using cross-correlation. - - This function assumes the stack does not contain the reference image itself. - - Stack shape should be (N, H, W) where N is the number of images. - """ - - new_images = [] - pred_shifts = [] - - prev_img = ref_img - for img in tqdm(stack): - shift_pred = cross_correlation_shift(prev_img, img) - if print_pred: - print(f"Shift prediction: {shift_pred}") - shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) - - pred_shifts.append(shift_pred) - new_images.append(shifted_image) - - prev_img = shifted_image - - return new_images, pred_shifts - - -def centering_com_alignment(image_stack): - """ - Aligns the image stack to the center of mass of the whole image_stack to the - image center. This is useful for aligning the tilt series to the invariant line. - """ - - aligned_stack = np.zeros_like(image_stack) - h, w = image_stack.shape[1:] - image_center = np.array([h // 2, w // 2]) - - com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) - - for i, img in enumerate(image_stack): - com_img = np.array(center_of_mass(img)) - shift_vec = com_reference - com_img - aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) - - final_shift = image_center - com_reference - for i in range(aligned_stack.shape[0]): - aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) - - return aligned_stack From 49b2b2606f6a6279881cec240515e3ca03c4483d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sat, 31 Jan 2026 23:30:50 -0800 Subject: [PATCH 057/335] Added option to only learn parts of the pose i.e, shifts or tilt axis. Also included background_subtract again, did this get moved somewhere? --- src/quantem/core/utils/imaging_utils.py | 166 ++++++++++++++++++++++- src/quantem/tomography/dataset_models.py | 65 ++++++--- 2 files changed, 210 insertions(+), 21 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index d3a65c79..362bfe7e 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,14 +1,22 @@ # Utilities for processing images import math -from typing import Optional, Tuple +from typing import Any, Optional, Tuple, TypeVar import numpy as np import torch + +# TODO: Temporary +from matplotlib import cm from numpy.typing import NDArray from scipy.ndimage import gaussian_filter +from scipy.special import comb from quantem.core.utils.utils import generate_batches +from quantem.core.visualization import show_2d + +ImageType = TypeVar("ImageType", NDArray[Any]) +BoolArray = NDArray[np.bool_] def dft_upsample( @@ -1048,3 +1056,159 @@ def unwrap_phase_2d_torch( raise ValueError( f'`method` must be one of {{"reliability-sorting", "poisson"}}, got {method!r}' ) + + +# Background subtraction + +# single TypeVar: works for both numpy and + + +def _as_array(x: ImageType) -> NDArray[Any]: + return ( + x.array + if isinstance( + x, + ) + else np.asarray(x) + ) + + +def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: + k = np.arange(n + 1, dtype=int) + return ( + comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) + ) + + +def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray[Any]: + H, W = im_shape + ou, ov = int(order[0]), int(order[1]) + u = np.linspace(0.0, 1.0, H) + v = np.linspace(0.0, 1.0, W) + Bu = _bernstein_basis_1d(ou, u) + Bv = _bernstein_basis_1d(ov, v) + basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) + return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) + + +def background_subtract( + image: ImageType, + mask: Optional[BoolArray] = None, + thresh_bg: Optional[float] = None, + order: Tuple[int, int] = (1, 1), + sigma: Optional[float] = None, + num_iter: int = 10, + plot_result: bool = True, + axsize: Tuple[int, int] = (3.1, 3), + cmap: str = "turbo", + return_background_and_mask: bool = False, + **show_kwargs, +) -> ImageType | Tuple[ImageType, NDArray[Any], BoolArray]: + """ + Background subtraction via bivariate Bernstein polynomial fitting. + + Returns + ------- + - If `return_background_and_mask=False`: ImageType (same as input) + - If `True`: (ImageType, numpy.ndarray, numpy.ndarray[bool]) + where background and mask are always NumPy. + """ + im = _as_array(image).astype(float, copy=True) + if im.ndim != 2: + raise ValueError("`image` must be 2D") + + mask_arr: BoolArray = ( + np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) + ) + if mask_arr.shape != im.shape: + raise ValueError("`mask` must match `image` shape") + + order = (int(order[0]), int(order[1])) + A_full = _build_basis_matrix(im.shape, order) + H, W = im.shape + im_flat = im.ravel() + + im_bg = np.zeros_like(im) + thresh_val = np.median(im[mask_arr]) if thresh_bg is None else float(thresh_bg) + + resid = im - im_bg + if sigma and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + mask_bg: BoolArray = (resid < thresh_val) & mask_arr + + for _ in range(int(num_iter)): + idx = mask_bg.ravel() + if not np.any(idx): + idx = mask_arr.ravel() + coefs, *_ = np.linalg.lstsq(A_full[idx, :], im_flat[idx], rcond=None) + im_bg = (A_full @ coefs).reshape(H, W) + + resid = im - im_bg + if sigma and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + + thr = thresh_val if thresh_bg is None else float(thresh_bg) + mask_bg = (resid < thr) & mask_arr + + im_sub_np = im - im_bg + + if plot_result: + vals = im_sub_np[mask_arr] + vals = vals[np.isfinite(vals)] + if vals.size == 0: + vals = np.array([0.0]) + vmin_sub = float(np.min(vals)) + vmax_sub = float(np.max(vals)) + vrange = float(max(abs(vmin_sub), abs(vmax_sub))) or 1e-12 + + bg_disp = (im_bg - np.mean(im_bg)).copy() + bg_disp[~mask_bg] = np.nan + + cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") + cmap_div = "RdBu_r" + + disp = [im - np.mean(im_bg), bg_disp, im_sub_np] + norm = [ + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "centered", + "stretch_type": "linear", + "vcenter": 0.0, + "half_range": vrange, + }, + ] + + show_2d( + disp, + cmap=[cmap_base, cmap_base, cmap_div], + norm=norm, + cbar=[False, False, True], + title=["Input Image", "Background (fit region)", "Background Subtracted"], + axsize=axsize, + **show_kwargs, + ) + + # # preserve if needed + # if isinstance( + # image, + # ): + # meta = dict(origin=image.origin, sampling=image.sampling, units=image.units) + # name_base = getattr(image, "name", "image") + # im_sub: ImageType = .from_array(im_sub_np, name=f"{name_base} (bg-sub)", **meta) # type: ignore[assignment] + # else: + im_sub = im_sub_np # type: ignore[assignment] + + if return_background_and_mask: + return im_sub, im_bg, mask_bg + return im_sub diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index a506d128..ebba944a 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -43,7 +43,8 @@ def __init__( self, tilt_stack: Dataset3d | NDArray | torch.Tensor, tilt_angles: NDArray | torch.Tensor, - learn_pose: bool = True, + learn_shift: bool = True, + learn_tilt_axis: bool = True, _token: object | None = None, ): AutoSerialize.__init__(self) @@ -71,7 +72,8 @@ def __init__( self.tilt_stack = tilt_stack self.tilt_angles = tilt_angles - self.learn_pose = learn_pose + self.learn_shift = learn_shift + self.learn_tilt_axis = learn_tilt_axis # The reference tilt angle is the one with the smallest absolute tilt angle. # I.e, the pose will not be optimized for the reference tilt angle. @@ -94,9 +96,15 @@ def from_data( cls, tilt_stack: Dataset3d | NDArray | torch.Tensor, tilt_angles: NDArray | torch.Tensor, - learn_pose: bool = False, + learn_shift: bool = True, + learn_tilt_axis: bool = True, ): - return cls(tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_pose=learn_pose) + return cls( + tilt_stack=tilt_stack, + tilt_angles=tilt_angles, + learn_shift=learn_shift, + learn_tilt_axis=learn_tilt_axis, + ) # --- Optimization Parameters --- # @property @@ -133,12 +141,20 @@ def tilt_angles(self, tilt_angles: torch.Tensor): self._tilt_angles = tilt_angles @property - def learn_pose(self) -> bool: - return self._learn_pose + def learn_shift(self) -> bool: + return self._learn_shift + + @learn_shift.setter + def learn_shift(self, learn_shift: bool): + self._learn_shift = learn_shift + + @property + def learn_tilt_axis(self) -> bool: + return self._learn_tilt_axis - @learn_pose.setter - def learn_pose(self, learn_pose: bool): - self._learn_pose = learn_pose + @learn_tilt_axis.setter + def learn_tilt_axis(self, learn_tilt_axis: bool): + self._learn_tilt_axis = learn_tilt_axis @property def reference_tilt_idx(self) -> int: @@ -235,11 +251,16 @@ def __init__( self, tilt_stack: Dataset3d | NDArray | torch.Tensor, tilt_angles: NDArray | torch.Tensor, - learn_pose: bool = False, + learn_shift: bool = True, + learn_tilt_axis: bool = True, _token: object | None = None, ): super().__init__( - tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_pose=learn_pose, _token=_token + tilt_stack=tilt_stack, + tilt_angles=tilt_angles, + learn_shift=learn_shift, + learn_tilt_axis=learn_tilt_axis, + _token=_token, ) def forward( @@ -272,11 +293,12 @@ def __init__( self, tilt_stack: Dataset3d | NDArray | torch.Tensor, tilt_angles: NDArray | torch.Tensor, - learn_pose: bool = True, + learn_shift: bool = True, + learn_tilt_axis: bool = True, seed: int = 42, token: object | None = None, ): - super().__init__(tilt_stack, tilt_angles, learn_pose, token) + super().__init__(tilt_stack, tilt_angles, learn_shift, learn_tilt_axis, token) # --- Forward Pass w/ Params Method for OptimizerMixin --- def forward(self, dummy_input: Any = None): @@ -297,13 +319,16 @@ def forward(self, dummy_input: Any = None): second_half_z3 = self.z3_params[self.reference_tilt_idx :] z3 = torch.cat([first_half_z3, self._z3_ref, second_half_z3], dim=0) - # return DatasetValue( - # target=None, - # tilt_angle=None, - # pixel_loc=None, - # pose=(shifts, z1, z3), - # ) - return shifts, z1, z3 + if self.learn_shift and self.learn_tilt_axis: + return shifts, z1, z3 + elif self.learn_shift: + return shifts, torch.zeros_like(z1), torch.zeros_like(z3) + elif self.learn_tilt_axis: + return torch.zeros_like(shifts), z1, z3 + elif self.learn_shift and self.learn_tilt_axis: + return shifts, z1, z3 + else: + return torch.zeros_like(shifts), torch.zeros_like(z1), torch.zeros_like(z3) @property def params(self): From da6bbf9ebe270b94515fb73f6e62960f775416b6 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sat, 31 Jan 2026 23:35:50 -0800 Subject: [PATCH 058/335] Fix for imaging_utils.py --- src/quantem/core/utils/imaging_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 362bfe7e..6c081ae4 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,7 +1,7 @@ # Utilities for processing images import math -from typing import Any, Optional, Tuple, TypeVar +from typing import Any, Optional, Tuple import numpy as np import torch @@ -15,7 +15,7 @@ from quantem.core.utils.utils import generate_batches from quantem.core.visualization import show_2d -ImageType = TypeVar("ImageType", NDArray[Any]) +ImageType = NDArray[Any] BoolArray = NDArray[np.bool_] From 0d6d7697d2f9ed5b430c1dc0dc6962cbe0741e07 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sun, 1 Feb 2026 00:26:47 -0800 Subject: [PATCH 059/335] DDP fix validation sampler initialization. --- src/quantem/core/ml/ddp.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index df43c2e5..01daa41f 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -86,7 +86,10 @@ def setup_dataloader( rank=self.global_rank, shuffle=False, ) + else: + val_sampler = None shuffle = False + else: train_sampler = None From 4005d7a2ba17a90ab5185b6a8ab1e265b334d777 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sun, 1 Feb 2026 23:22:29 -0800 Subject: [PATCH 060/335] Save volume DDP fix --- src/quantem/core/ml/constraints.py | 8 ++++++-- src/quantem/tomography/tomography.py | 6 +++--- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 990c240e..5b19293f 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -33,12 +33,16 @@ def __str__(self) -> str: hard = "\n".join(f"{key}: {getattr(self, key)}" for key in self.hard_constraint_keys) soft = "\n".join(f"{key}: {getattr(self, key)}" for key in self.soft_constraint_keys) + # Fix: Move the replace operations outside the f-string or assign to variables + hard_indented = hard.replace('\n', '\n ') + soft_indented = soft.replace('\n', '\n ') + return ( "Constraints:\n" " Hard constraints:\n" - f" {hard.replace('\n', '\n ')}\n" + f" {hard_indented}\n" " Soft constraints:\n" - f" {soft.replace('\n', '\n ')}" + f" {soft_indented}" ) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index aaf48d17..32b94807 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -315,11 +315,11 @@ def save_volume(self, path: str = 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b/src/quantem/tomography/dataset_models.py @@ -257,7 +257,7 @@ def __init__( ): super().__init__( tilt_stack=tilt_stack, - tilt_angles=tilt_angles, + tilt_angles=-tilt_angles, # TODO: Flip the tilt angles to be negative to match the convention of INR. learn_shift=learn_shift, learn_tilt_axis=learn_tilt_axis, _token=_token, diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c5315848..6d9ca6e3 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -450,9 +450,12 @@ def pretrain( pretrain_dataset is not None ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. self.pretrain_dataset = pretrain_dataset - self.pretraining_dataloader, self.pretraining_sampler = self.setup_dataloader( - pretrain_dataset, batch_size, num_workers=num_workers - ) + ( + self.pretraining_dataloader, + self.pretraining_sampler, + self.pretraining_val_dataloader, + self.pretraining_val_sampler, + ) = self.setup_dataloader(pretrain_dataset, batch_size, num_workers=num_workers) if optimizer_params is not None: self.set_optimizer(optimizer_params) diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index b78ea4ff..786c333a 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -6,8 +6,8 @@ from quantem.core.ml.inr import HSiren from quantem.tomography.dataset_models import DatasetModelType from quantem.tomography.logger_tomography import LoggerTomography -from quantem.tomography.object_models import ObjectINR -from quantem.tomography.tomography import Tomography +from quantem.tomography.object_models import ObjectINR, ObjectPixelated +from quantem.tomography.tomography import Tomography, TomographyConventional class TomographyLiteINR(Tomography): @@ -119,3 +119,33 @@ def reconstruct( scheduler_params=scheduler_params, constraints=constraints, ) + + +class TomographyLiteConv(TomographyConventional): + @classmethod + def from_dataset( + cls, + dset: DatasetModelType, + device: str = "cuda", + rng: np.random.Generator | int | None = None, + ) -> Self: + dset_model = dset + + obj_model = ObjectPixelated( + shape=( + max(dset_model.tilt_stack.shape), + max(dset_model.tilt_stack.shape), + max(dset_model.tilt_stack.shape), + ), + device=device, + rng=rng, + ) + + tomography = cls.from_models( + dset=dset_model, + obj_model=obj_model, + device=device, + rng=rng, + ) + + return tomography From 075bb7ec6dad7d8ecc09cedc7311aa1ab364a8d5 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 2 Feb 2026 15:34:38 -0800 Subject: [PATCH 063/335] updates to ptycho viz and inr --- src/quantem/core/ml/inr.py | 37 ++++++++++++++----- .../diffractive_imaging/ptychography_base.py | 2 + .../ptychography_visualizations.py | 11 ++++-- 3 files changed, 37 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 24ee45e4..276e5680 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -118,7 +118,10 @@ def reset_weights(self) -> None: self._build() def make_equispaced_grid( - self, bounds: tuple[tuple[float, float], ...], sampling: tuple[float, ...] + self, + bounds: tuple[tuple[float, float], ...], + sampling: tuple[float, ...] | None = None, + num_points: tuple[int, ...] | None = None, ) -> torch.Tensor: """Create an equispaced coordinate grid for the implicit neural representation. @@ -130,7 +133,10 @@ def make_equispaced_grid( sampling : tuple of float Sampling interval for each dimension (spacing_0, spacing_1, ...). Length must match in_features. - + num_points : tuple of int, optional + Number of points to sample in each dimension. If None, the number of points + is calculated from the sampling interval. If both sampling and num_points are provided, + num_points takes precedence. Returns ------- torch.Tensor @@ -153,15 +159,28 @@ def make_equispaced_grid( raise ValueError( f"Bounds length ({len(bounds)}) must match in_features ({self.in_features})" ) - if len(sampling) != self.in_features: - raise ValueError( - f"Sampling length ({len(sampling)}) must match in_features ({self.in_features})" + if sampling is not None and num_points is not None: + raise ValueError("Only one of sampling or num_points can be provided") + if sampling is not None: + if len(sampling) != self.in_features: + raise ValueError( + f"Sampling length ({len(sampling)}) must match in_features ({self.in_features})" + ) + num_points = tuple( + int((bound_max - bound_min) / sample) + 1 + for (bound_min, bound_max), sample in zip(bounds, sampling) ) - + elif num_points is not None: + if len(num_points) != self.in_features: + raise ValueError( + f"Num points length ({len(num_points)}) must match in_features ({self.in_features})" + ) + else: + raise ValueError("Either sampling or num_points must be provided") grids = [] - for (bound_min, bound_max), sample in zip(bounds, sampling): - num_points = int((bound_max - bound_min) / sample) + 1 - grids.append(torch.linspace(bound_min, bound_max, num_points)) + for i, (bound_min, bound_max) in enumerate(bounds): + n = num_points[i] + grids.append(torch.linspace(bound_min, bound_max, n)) coords = torch.meshgrid(*grids, indexing="ij") coords = torch.stack(coords, dim=-1).to(self.dtype) diff --git a/src/quantem/diffractive_imaging/ptychography_base.py b/src/quantem/diffractive_imaging/ptychography_base.py index f359dbee..665819d9 100644 --- a/src/quantem/diffractive_imaging/ptychography_base.py +++ b/src/quantem/diffractive_imaging/ptychography_base.py @@ -465,6 +465,8 @@ def get_snapshot_by_iter( "No snapshots available. Use store_snapshots=True during reconstruction." ) iteration = int(iteration) + if iteration < 0: + iteration = self.num_iters + iteration if closest: closest_snapshot = min(self.snapshots, key=lambda s: abs(s["iteration"] - iteration)) snp = closest_snapshot diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index 20d5ca09..d415dd4f 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -1,3 +1,4 @@ +import warnings from typing import Any, Literal import matplotlib.gridspec as gridspec @@ -128,8 +129,6 @@ def show_obj_fft( obj_iter = "Final" if obj is None: if snapshot_iter is not None: - if snapshot_iter < 0: - snapshot_iter = len(self.snapshots) + snapshot_iter snp = self.get_snapshot_by_iter(snapshot_iter, closest=True, cropped=True) obj_np = snp["obj"] obj_iter = snp["iteration"] @@ -379,8 +378,8 @@ def show_obj_slices( if obj.ndim == 2: obj = obj[None, ...] - t_parts = [f"0/{len(obj)} | 0 Å"] - for i in range(1, len(obj)): + t_parts = [] + for i in range(len(obj)): t_parts.append(f"{i + 1}/{len(obj)} | {self.slice_thicknesses[i - 1]:.1f} Å") if self.obj_type == "potential": @@ -403,6 +402,10 @@ def show_obj_slices( if interval_type == "quantile": norm = {"interval_type": "quantile"} # TODO -- make this work with interval_scaling + if interval_scaling == "all": + warnings.warn( + "interval_scaling='all' is not yet supported for quantile normalization" + ) elif interval_type in ["manual", "minmax", "abs"]: norm: dict[str, Any] = {"interval_type": "manual"} if interval_scaling == "all": From 1711c22fd97d1d298561c3a9940f1579e2a4b6fb Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 3 Feb 2026 14:19:30 -0800 Subject: [PATCH 064/335] Fix type hints for modify_in_place overloads in Dataset --- src/quantem/core/datastructures/dataset.py | 46 ++++++++++++++-------- 1 file changed, 30 insertions(+), 16 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 764e2343..f01e68f6 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -1,5 +1,5 @@ -import os import numbers +import os from pathlib import Path from typing import Any, Literal, Optional, Self, Union, overload @@ -52,7 +52,9 @@ def __init__( super().__init__() arr = ensure_valid_array(array) if not isinstance(arr, np.ndarray): - raise TypeError("Dataset requires a NumPy array (CuPy is not supported on this branch).") + raise TypeError( + "Dataset requires a NumPy array (CuPy is not supported on this branch)." + ) self._array = arr self.name = name self.origin = origin @@ -97,7 +99,9 @@ def from_array( """ validated_array = ensure_valid_array(array) if not isinstance(validated_array, np.ndarray): - raise TypeError("Dataset requires a NumPy array (CuPy is not supported on this branch).") + raise TypeError( + "Dataset requires a NumPy array (CuPy is not supported on this branch)." + ) _ndim = validated_array.ndim # Set defaults if None @@ -126,7 +130,9 @@ def array(self) -> NDArray: def array(self, value: NDArray) -> None: arr = ensure_valid_array(value, ndim=self.ndim) # want to allow changing dtype if not isinstance(arr, np.ndarray): - raise TypeError("Dataset requires a NumPy array (CuPy is not supported on this branch).") + raise TypeError( + "Dataset requires a NumPy array (CuPy is not supported on this branch)." + ) self._array = arr # self._array = ensure_valid_array(value, dtype=self.dtype, ndim=self.ndim) @@ -350,8 +356,9 @@ def min(self, axes: int | tuple[int, ...] | None = None) -> Any: @overload def pad( self, - pad_width: int | tuple[int, int] | tuple[tuple[int, int], ...] | None, - output_shape: tuple[int, ...] | None, + pad_width: int | tuple[int, int] | tuple[tuple[int, int], ...] | None = None, + output_shape: tuple[int, ...] | None = None, + *, modify_in_place: Literal[True], **kwargs: Any, ) -> None: ... @@ -363,7 +370,7 @@ def pad( output_shape: tuple[int, ...] | None = None, modify_in_place: Literal[False] = False, **kwargs: Any, - ) -> "Dataset": ... + ) -> Self: ... def pad( self, @@ -371,7 +378,7 @@ def pad( output_shape: tuple[int, ...] | None = None, modify_in_place: bool = False, **kwargs: Any, - ) -> "Dataset | None": + ) -> Self | None: """ Pads Dataset data array using numpy.pad. Metadata (origin, sampling) is not modified. @@ -426,7 +433,8 @@ def pad( def crop( self, crop_widths: tuple[tuple[int, int], ...], - axes: tuple | None, + axes: tuple | None = None, + *, modify_in_place: Literal[True], ) -> None: ... @@ -464,7 +472,7 @@ def crop( if len(crop_widths) != self.ndim: raise ValueError("crop_widths must match number of dimensions when axes is None.") axes = tuple(range(self.ndim)) - elif np.isscalar(axes): + elif isinstance(axes, int | float): axes = (int(axes),) crop_widths = (crop_widths[0],) # Take first crop_width for single axis else: @@ -496,7 +504,8 @@ def crop( def bin( self, bin_factors, - axes, + axes=None, + *, modify_in_place: Literal[True], reducer: str = "sum", ) -> None: ... @@ -545,7 +554,7 @@ def bin( if axes is None: axes = tuple(range(self.ndim)) - elif np.isscalar(axes): + elif isinstance(axes, int | float): axes = (int(axes),) else: axes = tuple(int(ax) for ax in axes) @@ -660,7 +669,7 @@ def fourier_resample( """ if axes is None: axes = tuple(range(self.ndim)) - elif np.isscalar(axes): + elif isinstance(axes, int | float): axes = (int(axes),) else: axes = tuple(int(a0) for a0 in axes) @@ -670,14 +679,17 @@ def fourier_resample( # Resolve out_shape & factors if factors is not None: - if np.isscalar(factors): + if isinstance(factors, int | float): factors = (float(factors),) * len(axes) else: factors = tuple(float(f) for f in factors) if len(factors) != len(axes): raise ValueError("factors length must match number of axes.") - out_shape = tuple(max(1, int(round(self.shape[a1] * f))) for a1, f in zip(axes, factors)) + out_shape = tuple( + max(1, int(round(self.shape[a1] * f))) for a1, f in zip(axes, factors) + ) else: + assert out_shape is not None # Guaranteed by check above if len(out_shape) != len(axes): raise ValueError("out_shape length must match number of axes.") out_shape = tuple(int(nl) for nl in out_shape) @@ -806,7 +818,9 @@ def __getitem__(self, index) -> Self: kept_axes = [i for i, idx in enumerate(index) if not isinstance(idx, (int, np.integer))] # Slice/reduce metadata accordingly - new_origin = np.asarray(self.origin)[kept_axes] if np.ndim(self.origin) > 0 else self.origin + new_origin = ( + np.asarray(self.origin)[kept_axes] if np.ndim(self.origin) > 0 else self.origin + ) new_sampling = ( np.asarray(self.sampling)[kept_axes] if np.ndim(self.sampling) > 0 else self.sampling ) From e9bf8da38ded0dd8378d3bd54a02411803c5a6a8 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 3 Feb 2026 23:45:37 -0800 Subject: [PATCH 065/335] Reinstantiating new dataloaders in the training loop; kinda messy, need to figure out a better way to do this. Also the current way of reinitializing the z1_params is jank, need a better way to reinitialize parameters --- src/quantem/tomography/dataset_models.py | 8 ++++++++ src/quantem/tomography/tomography.py | 15 +++++++++++++-- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 247fa0d9..4c45f44b 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -130,6 +130,10 @@ def tilt_stack(self) -> torch.Tensor: @tilt_stack.setter def tilt_stack(self, tilt_stack: torch.Tensor): + if type(tilt_stack) is not torch.Tensor: + print("Converting tilt stack to torch.Tensor") + tilt_stack = torch.from_numpy(tilt_stack) + self._tilt_stack = tilt_stack @property @@ -138,6 +142,10 @@ def tilt_angles(self) -> torch.Tensor: @tilt_angles.setter def tilt_angles(self, tilt_angles: torch.Tensor): + if type(tilt_angles) is not torch.Tensor: + print("Converting tilt angles to torch.Tensor") + tilt_angles = torch.from_numpy(tilt_angles) + self._tilt_angles = tilt_angles @property diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 32b94807..d3b0c065 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -49,12 +49,14 @@ def reconstruct( num_workers: int = 32, reset: bool = False, optimizer_params: dict | None = None, - scheduler_params: dict | None = None, + scheduler_params: dict | None = {}, constraints: dict = {}, # TODO: What to pass into the constraints? loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), num_samples_per_ray: int | List[Tuple[int, int]] = None, profiling_mode: bool = False, val_fraction: float = 0.0, + # reset_dset: bool = False, + reset_dset: DatasetModelType | None = None, ): """ This function should be able to handle both AD and INR-based tomography reconstruction methods. @@ -96,8 +98,16 @@ def reconstruct( self.set_schedulers(scheduler_params, num_iter=num_iter) # Setting up DDP - if not hasattr(self, "dataloader"): + if not hasattr(self, "dataloader") or reset_dset is not None: with nvtx_range(profiling_mode, "Setting Dataloader"): + if reset_dset is not None: + print("Resetting Dataloader") + print("Putting in params from previous dataset.") + + self.dset = reset_dset + self.dset.to(self.device) + self.optimizer_params = optimizer_params + self.set_optimizers() self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( self.setup_dataloader( self.dset, @@ -321,6 +331,7 @@ def save_volume(self, path: str = "recon_volume.npz"): print("Barrier") torch.distributed.barrier() + class TomographyConventional(TomographyBase): """ Class for handling all conventional tomography reconstruction methods. From aea1c1b57e03392d00a351a3677f00adb92c2125 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Feb 2026 15:55:44 -0800 Subject: [PATCH 066/335] Fixed some device issues, one process is still hanging on cuda:0, need to track down --- src/quantem/core/ml/ddp.py | 13 +++++++++++-- src/quantem/tomography/dataset_models.py | 1 + src/quantem/tomography/object_models.py | 7 +++++-- src/quantem/tomography/tomography.py | 16 ++++++++++++++++ src/quantem/tomography/tomography_base.py | 6 ++++-- 5 files changed, 37 insertions(+), 6 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 01daa41f..114b06b9 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -89,7 +89,6 @@ def setup_dataloader( else: val_sampler = None shuffle = False - else: train_sampler = None @@ -145,7 +144,7 @@ def build_model( Returns the model. """ - + print("Building Model on device: ", self.device) model = model.to(self.device) if pretrained_weights is not None: model.load_state_dict(pretrained_weights.copy()) @@ -172,3 +171,13 @@ def build_model( print("Model built, compiled successfully") return model + + @property + def device(self) -> torch.device: + return self._device + + @device.setter + def device(self, device: torch.device): + if isinstance(device, str): + device = torch.device(device) + self._device = device diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 4c45f44b..eba21ef3 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -500,6 +500,7 @@ def to(self, device: str): self._shifts_ref = self._shifts_ref.to(device) self.device = device + self.reconnect_optimizer_to_parameters() class TomographyINRPretrainDataset(Dataset): diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 6d9ca6e3..c70c9c3f 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -397,6 +397,8 @@ def shape(self, shape: tuple[int, int, int]): self._shape = shape # --- Helper Functions --- + def rebuild_model(self): + self._model = self.build_model(self._model) # Reset method that goes back to the pretrained weights. def reset(self): @@ -412,7 +414,6 @@ def get_optimization_parameters(self): def forward(self, coords: torch.Tensor) -> torch.Tensor: """forward pass for the INR model""" - all_densities = self.model(coords) if all_densities.dim() > 1: @@ -621,7 +622,9 @@ def get_tv_loss( def to(self, device: str): # self._model = self._model.to(device) - self._obj = self._obj.to(device) + self.device = device + self._obj = self._obj.to(self.device) + self.reconnect_optimizer_to_parameters() ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index d3b0c065..ff8ab977 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -5,6 +5,7 @@ import torch.distributed as dist from tqdm.auto import tqdm +from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.profiling import nvtx_range from quantem.tomography.dataset_models import DatasetModelType @@ -331,6 +332,21 @@ def save_volume(self, path: str = "recon_volume.npz"): print("Barrier") torch.distributed.barrier() + # Loading and Saving + @classmethod + def _recursive_load_from_path(cls, path: str): + return autoserialize_load(path) + + @classmethod + def from_file( + cls, + path: str, + device: str = "cpu", + ) -> Self: + tomography = cls._recursive_load_from_path(path) + tomography.to(device) + return tomography + class TomographyConventional(TomographyBase): """ diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 6b517f03..7661c301 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1,4 +1,5 @@ import numpy as np +import torch from numpy.typing import NDArray from quantem.core.io.serialize import AutoSerialize @@ -108,7 +109,7 @@ def logger(self, logger: LoggerTomography | None): @property def device(self) -> str: - return self._device + return torch.device(self._device) @device.setter def device(self, device: str): @@ -116,7 +117,7 @@ def device(self, device: str): # if not isinstance(device, str): # raise TypeError(f"device should be a str, got {type(device)}") self._device = device - self.to(device) + # self.to(device) @property def epoch_losses(self) -> NDArray: @@ -141,3 +142,4 @@ def num_epochs(self) -> int: def to(self, device: str): self.obj_model.to(device) self.dset.to(device) + self.device = device From 99411000cf5ad72a72bb234d4ac01e986f96f9ce Mon Sep 17 00:00:00 2001 From: Stephanie Ribet <55254539+smribet@users.noreply.github.com> Date: Thu, 5 Feb 2026 16:43:04 -0800 Subject: [PATCH 067/335] Update src/quantem/core/io/file_readers.py Co-authored-by: Carter Francis --- src/quantem/core/io/file_readers.py | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 7cb6fb5e..2db53e96 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -31,7 +31,18 @@ def read_4dstem( If None, automatically selects the first 4D dataset found. **kwargs: dict Additional keyword arguments to pass to the file reader. - +Other Parameters +---------------- +name : str | None, optional + A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" +origin : NDArray | tuple | list | float | int | None, optional + The origin coordinates for each dimension. If None, defaults to zeros +sampling : NDArray | tuple | list | float | int | None, optional + The sampling rate/spacing for each dimension. If None, defaults to ones +units : list[str] | tuple | list | None, optional + Units for each dimension. If None, defaults to ["pixels"] * 4 +signal_units : str, optional + Units for the array values, by default "arb. units" Returns -------- Dataset4dstem From f23ecf7481167bff2b4b1a2ada96e82b2d53a898 Mon Sep 17 00:00:00 2001 From: Stephanie Ribet <55254539+smribet@users.noreply.github.com> Date: Thu, 5 Feb 2026 16:43:21 -0800 Subject: [PATCH 068/335] Update src/quantem/core/io/file_readers.py Co-authored-by: Carter Francis --- src/quantem/core/io/file_readers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 2db53e96..b2621273 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -53,7 +53,7 @@ def read_4dstem( sampling_override = kwargs.pop("sampling", None) origin_override = kwargs.pop("origin", None) units_override = kwargs.pop("units", None) - +name_override = kwargs.pop("name", None) file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader data_list = file_reader(file_path, **kwargs) From 5328a7f932db58526f5be148e538bc0ede6810f2 Mon Sep 17 00:00:00 2001 From: Stephanie Ribet <55254539+smribet@users.noreply.github.com> Date: Thu, 5 Feb 2026 16:43:40 -0800 Subject: [PATCH 069/335] Update src/quantem/core/io/file_readers.py Co-authored-by: Carter Francis --- src/quantem/core/io/file_readers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index b2621273..27182dd9 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -87,7 +87,7 @@ def read_4dstem( sampling = ( sampling_override if sampling_override is not None - else [ax["scale"] for ax in imported_axes] + else [ax.get("scale", 1) for ax in imported_axes] ) origin = ( origin_override if origin_override is not None else [ax["offset"] for ax in imported_axes] From cc19df78c87f4f5a1a43072cc8347103aaa90092 Mon Sep 17 00:00:00 2001 From: Stephanie Ribet <55254539+smribet@users.noreply.github.com> Date: Thu, 5 Feb 2026 16:43:51 -0800 Subject: [PATCH 070/335] Update src/quantem/core/io/file_readers.py Co-authored-by: Carter Francis --- src/quantem/core/io/file_readers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 27182dd9..3402adcb 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -90,7 +90,7 @@ def read_4dstem( else [ax.get("scale", 1) for ax in imported_axes] ) origin = ( - origin_override if origin_override is not None else [ax["offset"] for ax in imported_axes] + origin_override if origin_override is not None else [ax.get("offset", 0) for ax in imported_axes] ) units = ( units_override From a101b7e20fd17ecdc40446ef075cc25f214736ee Mon Sep 17 00:00:00 2001 From: Stephanie Ribet <55254539+smribet@users.noreply.github.com> Date: Thu, 5 Feb 2026 16:43:58 -0800 Subject: [PATCH 071/335] Update src/quantem/core/io/file_readers.py Co-authored-by: Carter Francis --- src/quantem/core/io/file_readers.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 3402adcb..5bfcf930 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -103,6 +103,7 @@ def read_4dstem( sampling=sampling, origin=origin, units=units, + name = name, ) return dataset From b41a3993501e49fb9ac5a56d11044df6d553f90a Mon Sep 17 00:00:00 2001 From: smribet Date: Thu, 5 Feb 2026 16:48:23 -0800 Subject: [PATCH 072/335] cleaning up --- src/quantem/core/io/file_readers.py | 35 ++++++++++++++++------------- 1 file changed, 20 insertions(+), 15 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 5bfcf930..b575a6d3 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -31,18 +31,20 @@ def read_4dstem( If None, automatically selects the first 4D dataset found. **kwargs: dict Additional keyword arguments to pass to the file reader. -Other Parameters ----------------- -name : str | None, optional - A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" -origin : NDArray | tuple | list | float | int | None, optional - The origin coordinates for each dimension. If None, defaults to zeros -sampling : NDArray | tuple | list | float | int | None, optional - The sampling rate/spacing for each dimension. If None, defaults to ones -units : list[str] | tuple | list | None, optional - Units for each dimension. If None, defaults to ["pixels"] * 4 -signal_units : str, optional - Units for the array values, by default "arb. units" + + Other Parameters + ---------------- + name : str | None, optional + A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" + origin : NDArray | tuple | list | float | int | None, optional + The origin coordinates for each dimension. If None, defaults to zeros + sampling : NDArray | tuple | list | float | int | None, optional + The sampling rate/spacing for each dimension. If None, defaults to ones + units : list[str] | tuple | list | None, optional + Units for each dimension. If None, defaults to ["pixels"] * 4 + signal_units : str, optional + Units for the array values, by default "arb. units" + Returns -------- Dataset4dstem @@ -53,7 +55,8 @@ def read_4dstem( sampling_override = kwargs.pop("sampling", None) origin_override = kwargs.pop("origin", None) units_override = kwargs.pop("units", None) -name_override = kwargs.pop("name", None) + name_override = kwargs.pop("name", None) + file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader data_list = file_reader(file_path, **kwargs) @@ -90,7 +93,9 @@ def read_4dstem( else [ax.get("scale", 1) for ax in imported_axes] ) origin = ( - origin_override if origin_override is not None else [ax.get("offset", 0) for ax in imported_axes] + origin_override + if origin_override is not None + else [ax.get("offset", 0) for ax in imported_axes] ) units = ( units_override @@ -103,7 +108,7 @@ def read_4dstem( sampling=sampling, origin=origin, units=units, - name = name, + name=name_override, ) return dataset 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arthurmccray Date: Mon, 2 Feb 2026 15:34:38 -0800 Subject: [PATCH 076/335] updates to ptycho viz and inr --- src/quantem/core/ml/inr.py | 37 ++++++++++++++----- .../diffractive_imaging/ptychography_base.py | 2 + .../ptychography_visualizations.py | 11 ++++-- 3 files changed, 37 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 24ee45e4..276e5680 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -118,7 +118,10 @@ def reset_weights(self) -> None: self._build() def make_equispaced_grid( - self, bounds: tuple[tuple[float, float], ...], sampling: tuple[float, ...] + self, + bounds: tuple[tuple[float, float], ...], + sampling: tuple[float, ...] | None = None, + num_points: tuple[int, ...] | None = None, ) -> torch.Tensor: """Create an equispaced coordinate grid for the implicit neural representation. @@ -130,7 +133,10 @@ def make_equispaced_grid( sampling : tuple of float Sampling interval for each dimension (spacing_0, spacing_1, ...). Length must match in_features. - + num_points : tuple of int, optional + Number of points to sample in each dimension. If None, the number of points + is calculated from the sampling interval. If both sampling and num_points are provided, + num_points takes precedence. Returns ------- torch.Tensor @@ -153,15 +159,28 @@ def make_equispaced_grid( raise ValueError( f"Bounds length ({len(bounds)}) must match in_features ({self.in_features})" ) - if len(sampling) != self.in_features: - raise ValueError( - f"Sampling length ({len(sampling)}) must match in_features ({self.in_features})" + if sampling is not None and num_points is not None: + raise ValueError("Only one of sampling or num_points can be provided") + if sampling is not None: + if len(sampling) != self.in_features: + raise ValueError( + f"Sampling length ({len(sampling)}) must match in_features ({self.in_features})" + ) + num_points = tuple( + int((bound_max - bound_min) / sample) + 1 + for (bound_min, bound_max), sample in zip(bounds, sampling) ) - + elif num_points is not None: + if len(num_points) != self.in_features: + raise ValueError( + f"Num points length ({len(num_points)}) must match in_features ({self.in_features})" + ) + else: + raise ValueError("Either sampling or num_points must be provided") grids = [] - for (bound_min, bound_max), sample in zip(bounds, sampling): - num_points = int((bound_max - bound_min) / sample) + 1 - grids.append(torch.linspace(bound_min, bound_max, num_points)) + for i, (bound_min, bound_max) in enumerate(bounds): + n = num_points[i] + grids.append(torch.linspace(bound_min, bound_max, n)) coords = torch.meshgrid(*grids, indexing="ij") coords = torch.stack(coords, dim=-1).to(self.dtype) diff --git a/src/quantem/diffractive_imaging/ptychography_base.py b/src/quantem/diffractive_imaging/ptychography_base.py index f359dbee..665819d9 100644 --- a/src/quantem/diffractive_imaging/ptychography_base.py +++ b/src/quantem/diffractive_imaging/ptychography_base.py @@ -465,6 +465,8 @@ def get_snapshot_by_iter( "No snapshots available. Use store_snapshots=True during reconstruction." ) iteration = int(iteration) + if iteration < 0: + iteration = self.num_iters + iteration if closest: closest_snapshot = min(self.snapshots, key=lambda s: abs(s["iteration"] - iteration)) snp = closest_snapshot diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index 20d5ca09..d415dd4f 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -1,3 +1,4 @@ +import warnings from typing import Any, Literal import matplotlib.gridspec as gridspec @@ -128,8 +129,6 @@ def show_obj_fft( obj_iter = "Final" if obj is None: if snapshot_iter is not None: - if snapshot_iter < 0: - snapshot_iter = len(self.snapshots) + snapshot_iter snp = self.get_snapshot_by_iter(snapshot_iter, closest=True, cropped=True) obj_np = snp["obj"] obj_iter = snp["iteration"] @@ -379,8 +378,8 @@ 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changed, 142 insertions(+), 194 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ff8ab977..cb967104 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -7,7 +7,6 @@ from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.ddp import DDPMixin -from quantem.core.ml.profiling import nvtx_range from quantem.tomography.dataset_models import DatasetModelType from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ObjectModelType @@ -80,43 +79,38 @@ def reconstruct( new_scheduler = reset if optimizer_params is not None: - with nvtx_range(profiling_mode, "Setting Optimizer Params"): - self.optimizer_params = optimizer_params - self.set_optimizers() + self.optimizer_params = optimizer_params + self.set_optimizers() new_scheduler = True if scheduler_params is not None: - with nvtx_range(profiling_mode, "Setting Scheduler Params"): - self.scheduler_params = scheduler_params + self.scheduler_params = scheduler_params new_scheduler = True if constraints is not None: - with nvtx_range(profiling_mode, "Setting Constraints"): - self.obj_model.constraints = constraints + self.obj_model.constraints = constraints if new_scheduler: - with nvtx_range(profiling_mode, "Setting Schedulers"): - self.set_schedulers(scheduler_params, num_iter=num_iter) + self.set_schedulers(scheduler_params, num_iter=num_iter) # Setting up DDP if not hasattr(self, "dataloader") or reset_dset is not None: - with nvtx_range(profiling_mode, "Setting Dataloader"): - if reset_dset is not None: - print("Resetting Dataloader") - print("Putting in params from previous dataset.") - - self.dset = reset_dset - self.dset.to(self.device) - self.optimizer_params = optimizer_params - self.set_optimizers() - self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( - self.setup_dataloader( - self.dset, - batch_size, - num_workers=num_workers, - val_fraction=val_fraction, - ) + if reset_dset is not None: + print("Resetting Dataloader") + print("Putting in params from previous dataset.") + + self.dset = reset_dset + self.dset.to(self.device) + self.optimizer_params = optimizer_params + self.set_optimizers() + self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( + self.setup_dataloader( + self.dset, + batch_size, + num_workers=num_workers, + val_fraction=val_fraction, ) + ) N = max(self.obj_model.shape) if num_samples_per_ray is None: @@ -129,196 +123,150 @@ def reconstruct( print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") for a0 in range(num_iter): - with nvtx_range(profiling_mode, f"Epoch {a0}"): - consistency_loss = 0.0 - total_loss = 0.0 - epoch_soft_constraint_loss = 0.0 - self.obj_model.model.train() - self.dset.train() - # self._reset_iter_constraints() - - if self.sampler is not None: - self.sampler.set_epoch(a0) - - if isinstance(num_samples_per_ray, list): - curr_num_samples_per_ray = num_samples_per_ray[a0][1] - else: - curr_num_samples_per_ray = num_samples_per_ray + consistency_loss = 0.0 + total_loss = 0.0 + epoch_soft_constraint_loss = 0.0 + self.obj_model.model.train() + self.dset.train() + # self._reset_iter_constraints() + + if self.sampler is not None: + self.sampler.set_epoch(a0) + + if isinstance(num_samples_per_ray, list): + curr_num_samples_per_ray = num_samples_per_ray[a0][1] + else: + curr_num_samples_per_ray = num_samples_per_ray - if self.global_rank == 0: - print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") - for batch_idx, batch in enumerate(self.dataloader): - with nvtx_range(profiling_mode, f"batch_{batch_idx}"): - self.zero_grad_all() + if self.global_rank == 0: + print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") + for batch_idx, batch in enumerate(self.dataloader): + self.zero_grad_all() + with torch.autocast( + device_type=self.device.type, + dtype=torch.bfloat16, + enabled=True, + ): + all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) + + all_densities = self.obj_model.forward(all_coords) + + integrated_densities = self.dset.integrate_rays( + all_densities, + curr_num_samples_per_ray, + len(batch["target_value"]), + ) + + pred = integrated_densities.float() + + target = batch["target_value"].to(self.device, non_blocking=True).float() + + batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) + + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords) + + epoch_soft_constraint_loss += soft_constraints_loss.detach() + + batch_loss = batch_consistency_loss.float() + soft_constraints_loss.detach() + + batch_loss.backward() + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.obj_model.model.parameters(), max_norm=1.0) + self.step_optimizers() + total_loss += batch_loss.detach() + consistency_loss += batch_consistency_loss.detach() + + self.step_schedulers(loss=total_loss) + # TODO: Maybe reorganize the losses so that the order makes sense lol. + + total_loss = total_loss.item() / len(self.dataloader) + consistency_loss = consistency_loss.item() / len(self.dataloader) + epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len(self.dataloader) + + if self.val_dataloader is not None: + print("Validating...") + self.obj_model.model.eval() + self.dset.eval() + with torch.no_grad(): + val_loss = 0.0 + + for batch in self.val_dataloader: with torch.autocast( device_type=self.device.type, dtype=torch.bfloat16, enabled=True, ): - with nvtx_range(profiling_mode, "Getting Coords"): - all_coords = self.dset.get_coords( - batch, N, curr_num_samples_per_ray - ) - with nvtx_range(profiling_mode, "Forwarding"): - all_densities = self.obj_model.forward(all_coords) - - with nvtx_range(profiling_mode, "Integrating"): - integrated_densities = self.dset.integrate_rays( - all_densities, - curr_num_samples_per_ray, - len(batch["target_value"]), - ) - - pred = integrated_densities.float() - - with nvtx_range(profiling_mode, "Getting Target"): - target = ( - batch["target_value"].to(self.device, non_blocking=True).float() - ) + all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) - with nvtx_range(profiling_mode, "Calculating Loss"): - batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) + all_densities = self.obj_model.forward(all_coords) - with nvtx_range(profiling_mode, "Applying Soft Constraints"): - soft_constraints_loss = self.obj_model.apply_soft_constraints( - all_coords + integrated_densities = self.dset.integrate_rays( + all_densities, + curr_num_samples_per_ray, + len(batch["target_value"]), ) - with nvtx_range( - profiling_mode, "Adding soft constraint loss to epoch loss" - ): - epoch_soft_constraint_loss += soft_constraints_loss.detach() - - with nvtx_range(profiling_mode, "Calculating Batch Loss"): - batch_loss = ( - batch_consistency_loss.float() + soft_constraints_loss.detach() + target = ( + batch["target_value"].to(self.device, non_blocking=True).float() ) - with nvtx_range(profiling_mode, "Backwarding"): - batch_loss.backward() - with nvtx_range(profiling_mode, "Clipping Gradients"): - # Clip gradients - torch.nn.utils.clip_grad_norm_( - self.obj_model.model.parameters(), max_norm=1.0 + batch_val_loss = torch.nn.functional.mse_loss( + integrated_densities, target ) - with nvtx_range(profiling_mode, "Stepping Optimizers"): - self.step_optimizers() - with nvtx_range(profiling_mode, "Adding batch loss to total loss"): - total_loss += batch_loss.detach() - with nvtx_range( - profiling_mode, "Adding batch consistency loss to consistency loss" - ): - consistency_loss += batch_consistency_loss.detach() - with nvtx_range(profiling_mode, "Stepping Schedulers"): - self.step_schedulers(loss=total_loss) - # TODO: Maybe reorganize the losses so that the order makes sense lol. + val_loss += batch_val_loss.detach() + soft_constraints_loss.detach() - total_loss = total_loss.item() / len(self.dataloader) - consistency_loss = consistency_loss.item() / len(self.dataloader) - epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len( - self.dataloader - ) + avg_val_loss = val_loss.item() / len(self.val_dataloader) - if self.val_dataloader is not None: - print("Validating...") - self.obj_model.model.eval() - self.dset.eval() - with torch.no_grad(): - val_loss = 0.0 - - for batch in self.val_dataloader: - with torch.autocast( - device_type=self.device.type, - dtype=torch.bfloat16, - enabled=True, - ): - with nvtx_range(profiling_mode, "Getting Coords"): - all_coords = self.dset.get_coords( - batch, N, curr_num_samples_per_ray - ) - - with nvtx_range(profiling_mode, "Forwarding"): - all_densities = self.obj_model.forward(all_coords) - - with nvtx_range(profiling_mode, "Integrating"): - integrated_densities = self.dset.integrate_rays( - all_densities, - curr_num_samples_per_ray, - len(batch["target_value"]), - ) - - with nvtx_range(profiling_mode, "Getting Target"): - target = ( - batch["target_value"] - .to(self.device, non_blocking=True) - .float() - ) - - with nvtx_range(profiling_mode, "Calculating Loss"): - batch_val_loss = torch.nn.functional.mse_loss( - integrated_densities, target - ) - - with nvtx_range(profiling_mode, "Adding batch loss to total loss"): - val_loss += ( - batch_val_loss.detach() + soft_constraints_loss.detach() - ) - - avg_val_loss = val_loss.item() / len(self.val_dataloader) - - metrics = torch.tensor( - [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device - ) + metrics = torch.tensor( + [total_loss, consistency_loss, epoch_soft_constraint_loss], device=self.device + ) + + if self.world_size > 1: + dist.all_reduce(metrics, dist.ReduceOp.AVG) - if self.world_size > 1: - dist.all_reduce(metrics, dist.ReduceOp.AVG) + total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() - total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() + if self.global_rank == 0: + print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") + + if self.val_dataloader: + print(f"Validation loss: {avg_val_loss:4f}") + + self._epoch_losses.append(total_loss) + self._consistency_losses.append(consistency_loss) + self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) + if self.val_dataloader is not None: + self._val_losses.append(avg_val_loss) + + if self.logger is not None: + if ( + self.logger.log_images_every > 0 + and self.num_epochs % self.logger.log_images_every == 0 + ): + print("Creating volume...") + pred_full = self.obj_model.create_volume(return_vol=True) + + if self.global_rank == 0: + print("Logging images...") + self.logger.log_iter_images( + pred_volume=pred_full, + dataset_model=self.dset, + iter=self.num_epochs, + ) if self.global_rank == 0: - print( - f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}" + self.logger.log_iter( + object_model=self.obj_model, + iter=self.num_epochs, + consistency_loss=consistency_loss, + total_loss=total_loss, + learning_rates=self.get_current_lrs(), + num_samples_per_ray=curr_num_samples_per_ray, + val_loss=avg_val_loss if self.val_dataloader is not None else None, ) - if self.val_dataloader: - print(f"Validation loss: {avg_val_loss:4f}") - - self._epoch_losses.append(total_loss) - self._consistency_losses.append(consistency_loss) - self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) - if self.val_dataloader is not None: - self._val_losses.append(avg_val_loss) - - with nvtx_range(profiling_mode, "Logging"): - if self.logger is not None: - if ( - self.logger.log_images_every > 0 - and self.num_epochs % self.logger.log_images_every == 0 - ): - print("Creating volume...") - pred_full = self.obj_model.create_volume(return_vol=True) - - if self.global_rank == 0: - print("Logging images...") - self.logger.log_iter_images( - pred_volume=pred_full, - dataset_model=self.dset, - iter=self.num_epochs, - ) - - if self.global_rank == 0: - self.logger.log_iter( - object_model=self.obj_model, - iter=self.num_epochs, - consistency_loss=consistency_loss, - total_loss=total_loss, - learning_rates=self.get_current_lrs(), - num_samples_per_ray=curr_num_samples_per_ray, - val_loss=avg_val_loss if self.val_dataloader is not None else None, - ) - - self.logger.flush() + self.logger.flush() # --- Helper Functions --- From 162485fa55b1c5c6c138c03ec85e3898b57e6eb7 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 13:28:51 -0800 Subject: [PATCH 079/335] Merged object_models.py from eds_tomography, this allows for multimodal object INRs, adding a channel dimension at the end. --- src/quantem/tomography/object_models.py | 68 ++++++++++++++++--------- src/quantem/tomography/tomography.py | 2 +- 2 files changed, 44 insertions(+), 26 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c70c9c3f..a553eaa2 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -321,23 +321,25 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=coords.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(10000, coords.shape[0]) + num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + # Compute gradients for each channel grad_outputs = torch.autograd.grad( outputs=tv_densities_recomputed, inputs=tv_coords, grad_outputs=torch.ones_like(tv_densities_recomputed), create_graph=True, - )[0] + )[0] # Shape: [num_samples, coord_dim] - grad_norm = torch.norm(grad_outputs, dim=1) + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] soft_loss += self.constraints.tv_vol * grad_norm.mean() return soft_loss @@ -418,11 +420,13 @@ def forward(self, coords: torch.Tensor) -> torch.Tensor: if all_densities.dim() > 1: all_densities = all_densities.squeeze(-1) - valid_mask = ( (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) ).float() + if all_densities.dim() > 1: + valid_mask = valid_mask.unsqueeze(-1) + # Multi-dimensional mask all_densities = all_densities * valid_mask all_densities = self.apply_hard_constraints(all_densities) @@ -519,10 +523,7 @@ def _pretrain( ) self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) - def create_volume( - self, - return_vol: bool = False, - ): + def create_volume(self, return_vol: bool = False): N = max(self._shape) with torch.no_grad(): coords_1d = torch.linspace(-1, 1, N) @@ -535,30 +536,40 @@ def create_volume( total_samples = N**3 samples_per_gpu = total_samples // self.world_size remainder = total_samples % self.world_size + if self.global_rank < remainder: start_idx = self.global_rank * (samples_per_gpu + 1) end_idx = start_idx + samples_per_gpu + 1 else: start_idx = self.global_rank * samples_per_gpu + remainder end_idx = start_idx + samples_per_gpu + inputs_subset = inputs[start_idx:end_idx] num_samples = inputs_subset.shape[0] + outputs_list = [] for batch_start in range(0, num_samples, inference_batch_size): batch_end = min(batch_start + inference_batch_size, num_samples) batch_coords = inputs_subset[batch_start:batch_end].to( self.device, non_blocking=True ) - batch_outputs = model(batch_coords) + + batch_outputs = model(batch_coords) # (B, C) or (B,) etc. batch_outputs = self.apply_hard_constraints(batch_outputs) - if batch_outputs.dim() > 1: - batch_outputs = batch_outputs.squeeze(-1) + + # Ensure shape is (B, C) + if batch_outputs.dim() == 1: + batch_outputs = batch_outputs.unsqueeze(-1) # (B, 1) + outputs_list.append(batch_outputs.cpu()) - outputs = torch.cat(outputs_list, dim=0) + outputs = torch.cat(outputs_list, dim=0) # (local_B, C) + C = outputs.shape[-1] # e.g. 5 if self.world_size > 1: - output_size = torch.tensor(outputs.shape[0], device=self.device, dtype=torch.long) + # gather variable-sized first dimension (local_B) while keeping channels + local_B = outputs.shape[0] + output_size = torch.tensor(local_B, device=self.device, dtype=torch.long) all_sizes = [ torch.zeros(1, device=self.device, dtype=torch.long) for _ in range(self.world_size) @@ -566,26 +577,33 @@ def create_volume( dist.all_gather(all_sizes, output_size) max_size = max(size.item() for size in all_sizes) - if outputs.shape[0] < max_size: - padding = torch.zeros( - max_size - outputs.shape[0], device=outputs.device, dtype=outputs.dtype + outputs_dev = outputs.to(self.device) # (local_B, C) + if local_B < max_size: + pad = torch.zeros( + (max_size - local_B, C), + device=self.device, + dtype=outputs_dev.dtype, ) - outputs_padded = torch.cat([outputs, padding], dim=0).to(self.device) + outputs_padded = torch.cat([outputs_dev, pad], dim=0) # (max_size, C) else: - outputs_padded = outputs.to(self.device) + outputs_padded = outputs_dev gathered_outputs = [ - torch.empty(max_size, device=self.device, dtype=outputs.dtype) + torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) for _ in range(self.world_size) ] dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + trimmed_outputs = [] for rank, size in enumerate(all_sizes): - trimmed_outputs.append(gathered_outputs[rank][: size.item()]) + trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) - pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N).float() + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N, C).float() else: - pred_full = outputs.reshape(N, N, N).float() + pred_full = outputs.reshape(N, N, N, C).float() + + # If you prefer channels-first volumes, uncomment: + # pred_full = pred_full.permute(3, 0, 1, 2).contiguous() # (C, N, N, N) if return_vol: return pred_full.detach().cpu() diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index cb967104..de91107e 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -167,7 +167,7 @@ def reconstruct( epoch_soft_constraint_loss += soft_constraints_loss.detach() - batch_loss = batch_consistency_loss.float() + soft_constraints_loss.detach() + batch_loss = batch_consistency_loss.float() + soft_constraints_loss.float() batch_loss.backward() # Clip gradients From 75721e183afa8e1ecceff9cfef7cae710c0151fa Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 13:55:42 -0800 Subject: [PATCH 080/335] Object creation - channels on the last axis. --- src/quantem/tomography/object_models.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index a553eaa2..90d72ca1 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -598,12 +598,9 @@ def create_volume(self, return_vol: bool = False): for rank, size in enumerate(all_sizes): trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) - pred_full = torch.cat(trimmed_outputs, dim=0).reshape(N, N, N, C).float() + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(C, N, N, N).float() else: - pred_full = outputs.reshape(N, N, N, C).float() - - # If you prefer channels-first volumes, uncomment: - # pred_full = pred_full.permute(3, 0, 1, 2).contiguous() # (C, N, N, N) + pred_full = outputs.reshape(C, N, N, N).float() if return_vol: return pred_full.detach().cpu() From 46dda7c000f6d873ff5da8bab01d101ecb11fc98 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 16:26:21 -0800 Subject: [PATCH 081/335] constraints.py; Fixed type hinting and added more description to the docstring --- src/quantem/core/ml/constraints.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 5b19293f..cce43659 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -5,12 +5,13 @@ import numpy as np import torch +from numpy.typing import NDArray @dataclass(slots=True) class Constraints(ABC): """ - Needs to be implemented in all object models that inherit from BaseConstraints. + Any model that inherits from BaseConstraints will contain a Constraints instance that contains soft and hard constraints. """ soft_constraint_keys = [] @@ -34,9 +35,9 @@ def __str__(self) -> str: soft = "\n".join(f"{key}: {getattr(self, key)}" for key in self.soft_constraint_keys) # Fix: Move the replace operations outside the f-string or assign to variables - hard_indented = hard.replace('\n', '\n ') - soft_indented = soft.replace('\n', '\n ') - + hard_indented = hard.replace("\n", "\n ") + soft_indented = soft.replace("\n", "\n ") + return ( "Constraints:\n" " Hard constraints:\n" @@ -60,13 +61,13 @@ def __init__(self, *args, **kwargs): self.constraints = self.DEFAULT_CONSTRAINTS.copy() @property - def soft_constraint_losses(self) -> list[float]: - return np.array(self._soft_constraint_losses) + def soft_constraint_losses(self) -> NDArray[np.float32]: + return np.array(self._soft_constraint_losses, dtype=np.float32) @property def constraints(self) -> Constraints: """ - Constraints for the object model. + Constraints for the model. """ return self._constraints @@ -87,13 +88,13 @@ def constraints(self, constraints: Constraints | dict[str, Any]): @abstractmethod def apply_hard_constraints(self, *args, **kwargs) -> torch.Tensor: """ - Apply hard constraints to the object model. + Apply hard constraints to the model. """ raise NotImplementedError @abstractmethod def apply_soft_constraints(self, *args, **kwargs) -> torch.Tensor: """ - Apply soft constraints to the object model. + Apply soft constraints to the model. """ raise NotImplementedError From 5c617d3521bb55c70dc788884461c30f636b7033 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 16:54:29 -0800 Subject: [PATCH 082/335] Cleaned up DDP module; offloaded loading of weights to the object_models.py, renamed to distribute_model --- src/quantem/core/ml/ddp.py | 9 ++------- src/quantem/tomography/object_models.py | 11 ++++++----- 2 files changed, 8 insertions(+), 12 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 114b06b9..52bed46a 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -18,7 +18,7 @@ def __init__( ): self.setup_distributed() - def setup_distributed(self, device: str | None = None): + def setup_distributed(self, device: str | torch.device | None = None): """ Initializes parameters depending if multiple-GPU training, single-GPU training, or CPU training. """ @@ -134,21 +134,16 @@ def setup_dataloader( return train_dataloader, train_sampler, val_dataloader, val_sampler - def build_model( + def distribute_model( self, model: nn.Module, - pretrained_weights: dict[str, torch.Tensor] | None = None, ) -> nn.Module | nn.parallel.DistributedDataParallel: """ Wraps the model with DistributedDataParallel if mulitple GPUs are available. Returns the model. """ - print("Building Model on device: ", self.device) model = model.to(self.device) - if pretrained_weights is not None: - model.load_state_dict(pretrained_weights.copy()) - if self.world_size > 1: model = torch.nn.parallel.DistributedDataParallel( model, diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 90d72ca1..1342d3c8 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -271,7 +271,7 @@ def __init__( # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) - self._model = self.build_model(model) + self._model = self.distribute_model(model) @classmethod def from_model( @@ -400,14 +400,15 @@ def shape(self, shape: tuple[int, int, int]): # --- Helper Functions --- def rebuild_model(self): - self._model = self.build_model(self._model) + self._model = self.distribute_model(self._model) # Reset method that goes back to the pretrained weights. def reset(self): """reset the model to the pretrained weights""" - self._model = self.build_model( - self._model, self._pretrained_weights - ) # Since loading the pretrained weights needs to be done in build_model. + self.model.load_state_dict(self._pretrained_weights.copy()) + self._model = self.distribute_model( + self.model + ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. def get_optimization_parameters(self): return self.params From 9465aa772da4f9099a3fd3414dc37b600b03dada Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 17:02:53 -0800 Subject: [PATCH 083/335] Added deterministic random winner initialization in inr.py --- src/quantem/core/ml/inr.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index de43b6c4..a2a2f4b7 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -23,7 +23,7 @@ def __init__( hsiren: bool = False, dtype: torch.dtype = torch.float32, final_activation: str | Callable = "identity", - winner_initialization: bool = False, + winner_initialization: bool | int = False, ) -> None: """Initialize Siren. @@ -111,6 +111,9 @@ def _build(self) -> None: self.net = nn.Sequential(*net_list) if self.winner_initialization: + if isinstance(self.winner_initialization, int): + rng = torch.Generator() + rng.manual_seed(self.winner_initialization) with torch.no_grad(): self.net[0].linear.weight += ( torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 From 05901bf97f459102833cf643a1a26bd52b78145d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 17:12:31 -0800 Subject: [PATCH 084/335] inr.py, ignore some type hinting stuff that seems not correct...? --- src/quantem/core/ml/inr.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index a2a2f4b7..15cc6057 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -115,11 +115,11 @@ def _build(self) -> None: rng = torch.Generator() rng.manual_seed(self.winner_initialization) with torch.no_grad(): - self.net[0].linear.weight += ( - torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 + self.net[0].linear.weight += ( # pyright: ignore[reportAttributeAccessIssue] + torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 # type:ignore ) - self.net[1].linear.weight += ( - torch.randn_like(self.net[1].linear.weight) * 0.1 / self.hidden_omega_0 + self.net[1].linear.weight += ( # pyright: ignore[reportAttributeAccessIssue] + torch.randn_like(self.net[1].linear.weight) * 0.1 / self.hidden_omega_0 # type:ignore ) def forward(self, coords: torch.Tensor) -> torch.Tensor: From 953ee9443755f2a9c616f7b240655307f6d27316 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 21:14:02 -0800 Subject: [PATCH 085/335] Removed MSE and L1 implementations in --- src/quantem/core/ml/loss_functions.py | 24 ------------------------ 1 file changed, 24 deletions(-) diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index 00069eaa..1ffc3006 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -146,30 +146,6 @@ def combined_l2(pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.7) -> # TODO: Better loss function implementation? More torch-like. -class L1Loss(nn.Module): - def __init__( - self, - reduction: str = "mean", - ): - super(L1Loss, self).__init__() - self.reduction = reduction - - def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - return torch.nn.functional.l1_loss(pred, target, reduction=self.reduction) - - -class MSELoss(nn.Module): - def __init__( - self, - reduction: str = "mean", - ): - super(MSELoss, self).__init__() - self.reduction = reduction - - def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - return torch.nn.functional.mse_loss(pred, target, reduction=self.reduction) - - class MSELogMSELoss(nn.Module): def __init__( self, From bde606fd48f70dd3471df0d4b201a12c20b263c9 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 21:21:52 -0800 Subject: [PATCH 086/335] Background subtraction in imaging_utils.py --- src/quantem/core/utils/imaging_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index 5d706452..aaa86468 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -849,7 +849,7 @@ def add_edges(i1, i2): inc = _find_wrap(phi_f[i1], phi_f[i2]) rel = rel_f[i1] + rel_f[i2] - edges.append( # ty:ignore[possibly-missing-attribute] + edges.append( # type: ignore[attr-defined] torch.stack([i1, i2, rel, inc], dim=1) ) @@ -1099,7 +1099,7 @@ def background_subtract( sigma: Optional[float] = None, num_iter: int = 10, plot_result: bool = True, - axsize: Tuple[int, int] = (3.1, 3), + axsize: Tuple[int, int] = (3, 3), cmap: str = "turbo", return_background_and_mask: bool = False, **show_kwargs, From c0b61a36511557d6ba90c37a11575ab7ed2c33f5 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 21:41:31 -0800 Subject: [PATCH 087/335] Fixed _token calling .from_data in TomographyDatasetBase --- src/quantem/tomography/dataset_models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index eba21ef3..1fb454b2 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -50,8 +50,8 @@ def __init__( AutoSerialize.__init__(self) OptimizerMixin.__init__(self) nn.Module.__init__(self) - # if _token is not self._token: TODO: Idk why this isn't working. - # raise RuntimeError("Use TomographyPixDataset.from_* to instantiate this class.") + if _token is not self._token: + raise RuntimeError("Use TomographyPixDataset.from_* to instantiate this class.") if not ( tilt_stack.shape[0] < tilt_stack.shape[1] or tilt_stack.shape[0] < tilt_stack.shape[2] @@ -104,6 +104,7 @@ def from_data( tilt_angles=tilt_angles, learn_shift=learn_shift, learn_tilt_axis=learn_tilt_axis, + _token=cls._token, ) # --- Optimization Parameters --- From d42611bddfd7bc7a5c6f01e57a996249dd284c91 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 21:56:21 -0800 Subject: [PATCH 088/335] Reconciled params and get_optimization_parameters type-hinting. Additional fix to TomographyLiteINR where H-Siren Winner initialization was still inputting a bool --- src/quantem/tomography/dataset_models.py | 40 ++++++++++------------- src/quantem/tomography/tomography_lite.py | 2 +- 2 files changed, 19 insertions(+), 23 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 1fb454b2..0533e7da 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -1,6 +1,6 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import Any +from typing import Any, Generator import numpy as np import torch @@ -108,10 +108,21 @@ def from_data( ) # --- Optimization Parameters --- - # @property - # def params(self): - # # TODO: Need to double check if this is correct way, also need to implement get_optimization_parameters @Arthur!! - # raise NotImplementedError("This method should be implemented in subclasses.") + + @property + def params(self) -> Generator[torch.nn.Parameter, None, None]: + """ + Returns the parameters that should be optimized for this dataset. + + Should be implemented in subclasses. + """ + raise NotImplementedError("This method should be implemented in subclasses.") + + def get_optimization_parameters(self) -> list[nn.Parameter]: + """ + Get the parameters that should be optimized for this model. + """ + return list(self.params) # --- Forward pass --- @abstractmethod @@ -181,15 +192,6 @@ def learnable_tilts(self) -> int: def learnable_tilts(self, learnable_tilts: int): self._learnable_tilts = learnable_tilts - @property - def params(self) -> dict[str, torch.nn.Parameter]: - """ - Returns the parameters that should be optimized for this dataset. - - Should be implemented in subclasses. - """ - raise NotImplementedError("This method should be implemented in subclasses.") - @property def z1_params(self) -> torch.nn.Parameter: return self._z1_params @@ -240,12 +242,6 @@ def to(self, device: str): self.device = device - def get_optimization_parameters(self) -> list[nn.Parameter]: - """ - Get the parameters that should be optimized for this model. - """ - return list[nn.Parameter](self.params) - class TomographyPixDataset(TomographyDatasetBase): """ @@ -305,9 +301,9 @@ def __init__( learn_shift: bool = True, learn_tilt_axis: bool = True, seed: int = 42, - token: object | None = None, + _token: object | None = None, ): - super().__init__(tilt_stack, tilt_angles, learn_shift, learn_tilt_axis, token) + super().__init__(tilt_stack, tilt_angles, learn_shift, learn_tilt_axis, _token=_token) # --- Forward Pass w/ Params Method for OptimizerMixin --- def forward(self, dummy_input: Any = None): diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index 786c333a..57a78446 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -27,7 +27,7 @@ def from_dataset( dset_model = dset # Define the object model - model = HSiren(alpha=1, winner_initialization=True) + model = HSiren(alpha=1, winner_initialization=72) obj_model = ObjectINR.from_model( model=model, shape=( From aea34b1b19421b1b41b812fd1b211ba78de30a9d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 22:02:19 -0800 Subject: [PATCH 089/335] Object models also consistent with dataset models in terms of params and get_optimization_parameters --- src/quantem/tomography/object_models.py | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 1342d3c8..deb5eb31 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable +from typing import Any, Callable, Generator import numpy as np import torch @@ -107,9 +107,11 @@ def reset(self) -> None: raise NotImplementedError @property - def params(self) -> list[nn.Parameter]: + def params(self) -> Generator[torch.nn.Parameter, None, None]: """ Get the parameters that should be optimized for this model. + + Should be implemented in subclasses. """ raise NotImplementedError @@ -118,7 +120,7 @@ def get_optimization_parameters(self) -> list[nn.Parameter]: """ Get the parameters that should be optimized for this model. """ - return list[nn.Parameter](self.params()) + return list(self.params) @abstractmethod # Each subclass should implement this. def to(self, *args, **kwargs): @@ -357,8 +359,11 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: return pred @property - def params(self): - return self.model.parameters() + def params(self) -> Generator[torch.nn.Parameter, None, None]: + return self.model.parameters() # type: ignore[attr-defined] + + def get_optimization_parameters(self): + return self.params # Pretraining @property @@ -410,9 +415,6 @@ def reset(self): self.model ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. - def get_optimization_parameters(self): - return self.params - # --- Forward Method --- def forward(self, coords: torch.Tensor) -> torch.Tensor: From 038e94d4d1267ec7a5ca92096e3caf7c2784ff87 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 22:03:36 -0800 Subject: [PATCH 090/335] More type hinting --- src/quantem/tomography/object_models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index deb5eb31..6d8bc49e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -358,12 +358,13 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: return pred + # --- Optimization Parameters --- @property def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self): - return self.params + def get_optimization_parameters(self) -> list[nn.Parameter]: + return list(self.params) # Pretraining @property From edab1120c639b831ba7c139123d277400d2605cc Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 22:11:29 -0800 Subject: [PATCH 091/335] Remove params function, use nn.Module.parameters() directly in get_optimization_parameters in dataset_models.py --- src/quantem/tomography/dataset_models.py | 17 ++--------------- 1 file changed, 2 insertions(+), 15 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 0533e7da..d0e8df09 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -1,6 +1,6 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import Any, Generator +from typing import Any import numpy as np import torch @@ -109,20 +109,11 @@ def from_data( # --- Optimization Parameters --- - @property - def params(self) -> Generator[torch.nn.Parameter, None, None]: - """ - Returns the parameters that should be optimized for this dataset. - - Should be implemented in subclasses. - """ - raise NotImplementedError("This method should be implemented in subclasses.") - def get_optimization_parameters(self) -> list[nn.Parameter]: """ Get the parameters that should be optimized for this model. """ - return list(self.params) + return list(self.parameters()) # --- Forward pass --- @abstractmethod @@ -335,10 +326,6 @@ def forward(self, dummy_input: Any = None): else: return torch.zeros_like(shifts), torch.zeros_like(z1), torch.zeros_like(z3) - @property - def params(self): - return self.parameters() - def get_coords( self, batch: dict[str, torch.Tensor], N: int, num_samples_per_ray: int ) -> torch.Tensor: From 888e6b946e441b6001beb7942790d287878e49da Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 22:14:07 -0800 Subject: [PATCH 092/335] Updated TomographyPixDataset docstring --- src/quantem/tomography/dataset_models.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index d0e8df09..84a1d108 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -238,9 +238,7 @@ class TomographyPixDataset(TomographyDatasetBase): """ Dataset class for pixel-based tomography, i.e AD, SIRT, WBP, etc... - TODO: - - What should the forward pass return? In AD, it should be both the tilt image and the pose. - In SIRT, it's only the tilt image. + These algorithms only require the tilt image in the forward call. """ def __init__( From c0a8777409282cffe8b3f9a2a86dd165b803314d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Feb 2026 22:15:27 -0800 Subject: [PATCH 093/335] Removed DatasetValue from the forward call in TomgoraphyINRDataset, torch does not like dataclasses to be outputted in the __getitem__ --- src/quantem/tomography/dataset_models.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 84a1d108..27d30a93 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -449,12 +449,6 @@ def __getitem__( pixel_i = remaining // self.tilt_stack.shape[1] pixel_j = remaining % self.tilt_stack.shape[1] - # return DatasetValue( - # target=self.tilt_stack[projection_idx, pixel_i, pixel_j], - # tilt_angle=self.tilt_angles[projection_idx], - # projection_idx=projection_idx, - # pixel_loc=(pixel_i, pixel_j), - # ) return { "projection_idx": torch.tensor(projection_idx), "pixel_i": torch.tensor(pixel_i), From 0c9967605923ff507dccab153c08e9e59728290a Mon Sep 17 00:00:00 2001 From: Georgios Varnavides Date: Tue, 24 Feb 2026 12:30:53 +0100 Subject: [PATCH 094/335] adding from_abtem reader --- src/quantem/core/io/file_readers.py | 124 ++++++++++++++++++++++++++-- 1 file changed, 117 insertions(+), 7 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index cb36f1de..d537e125 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -115,7 +115,7 @@ def read_2d( if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") - file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader # type: ignore + file_reader = importlib.import_module(f"rsciio.{file_type}").file_reader imported_data = file_reader(file_path)[0] dataset = Dataset2d.from_array( @@ -162,7 +162,7 @@ def read_emdfile_to_4dstem( try: data = file for key in data_keys: - data = data[key] # type: ignore + data = data[key] except KeyError: raise KeyError(f"Could not find key {data_keys} in {file_path}") @@ -175,13 +175,13 @@ def read_emdfile_to_4dstem( try: calibration = file for key in calibration_keys: - calibration = calibration[key] # type: ignore + calibration = calibration[key] except KeyError: raise KeyError(f"Could not find calibration key {calibration_keys} in {file_path}") - r_pixel_size = calibration["R_pixel_size"][()] # type: ignore - q_pixel_size = calibration["Q_pixel_size"][()] # type: ignore - r_pixel_units = calibration["R_pixel_units"][()] # type: ignore - q_pixel_units = calibration["Q_pixel_units"][()] # type: ignore + r_pixel_size = calibration["R_pixel_size"][()] + q_pixel_size = calibration["Q_pixel_size"][()] + r_pixel_units = calibration["R_pixel_units"][()] + q_pixel_units = calibration["Q_pixel_units"][()] dataset = Dataset4dstem.from_array( array=data, @@ -191,3 +191,113 @@ def read_emdfile_to_4dstem( dataset.file_path = file_path return dataset + + +def read_abtem(url: str | PathLike): + """ + Read canonical abTEM Zarr file(s) into quantem Dataset(s). + + Returns + ------- + Dataset or list[Dataset] + """ + + def _open_zarr(url): + import zarr + + if url.endswith(".zip"): + store = zarr.storage.ZipStore(url, mode="r") # ty:ignore[possibly-missing-attribute] + return zarr.open(store=store, mode="r") + return zarr.open(url, mode="r") + + def _validate_canonical_format(root): + if "metadata0" in root.attrs: + return + + if "kwargs0" in root.attrs: + raise ValueError( + "Legacy abTEM Zarr format detected.\n\n" + "quantem supports only canonical abTEM Zarr format.\n" + "Re-save using abtem>=1.1.0:\n\n" + " measurement = abtem.from_zarr()\n" + " measurement.to_zarr()" + ) + + raise ValueError("Unrecognized Zarr format.") + + def _iter_metadata_indices(root): + i = 0 + while f"metadata{i}" in root.attrs: + yield i + i += 1 + + def _decode_types(obj): + if isinstance(obj, dict): + if obj.get("_type") == "tuple": + return tuple(_decode_types(v) for v in obj["_value"]) + return {k: _decode_types(v) for k, v in obj.items()} + if isinstance(obj, list): + return [_decode_types(v) for v in obj] + return obj + + def _normalize_unit(unit): + if unit is None: + return "pixels" + + unit = unit.strip() + + UNIT_MAP = { + "Å": "A", + "Ångström": "A", + "Angstrom": "A", + "1/Å": "A^-1", + "Å^-1": "A^-1", + "1/A": "A^-1", + } + + return UNIT_MAP.get(unit, unit) + + def _convert_axes(axes_dict): + sampling = [] + origin = [] + units = [] + + for key in sorted(axes_dict, key=lambda x: int(x.split("_")[1])): + axis = axes_dict[key] + + sampling.append(axis.get("sampling", 1.0)) + units.append(_normalize_unit(axis.get("units", None))) + origin.append(0.0) # deliberate design choice + + return tuple(origin), tuple(sampling), tuple(units) + + def _read_single_dataset(root, index): + metadata = _decode_types(root.attrs[f"metadata{index}"]).copy() + + axes_dict = metadata.pop("axes") + dataset_type = metadata.pop("type") + metadata.pop("data_origin", None) + + origin, sampling, units = _convert_axes(axes_dict) + + array = root[f"array{index}"] + signal_units = metadata.get("units", "arb. units") + + dataset = Dataset.from_array( + array=array, + name=dataset_type, + origin=origin, + sampling=sampling, + units=units, + signal_units=signal_units, + ) + + dataset._metadata = metadata + return dataset + + root = _open_zarr(url) + _validate_canonical_format(root) + + datasets = [_read_single_dataset(root, i) for i in _iter_metadata_indices(root)] + + return datasets[0] if len(datasets) == 1 else datasets From 6d9cbf6dd3a4891f6b7feb3a8b9b41e49b8d44cc Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 13:09:49 -0800 Subject: [PATCH 095/335] Class instantiation for ObjectPixelated; from_uniform and from_array --- src/quantem/tomography/object_models.py | 32 +++++++++++++++++++++++-- 1 file changed, 30 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 6d8bc49e..1c1249a7 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -62,8 +62,7 @@ def __init__( RNGMixin.__init__(self, rng=rng, device=device) OptimizerMixin.__init__(self) - # Initialize a torch.zeros volume with the given shape - self._obj = torch.zeros(self._shape, device=device, dtype=torch.float32) + # --- Instantiation ---- # --- Properties --- @property @@ -196,6 +195,35 @@ def __init__( _token=self._token, ) + # --- Instantiation ---- + @classmethod + def from_uniform( + cls, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + # Initialize a torch.zeros volume with the given shape + obj = torch.zeros(shape, device=device, dtype=torch.float32) + obj_model = cls(shape=shape, device=device, rng=rng) + obj_model._obj = obj + return obj_model + + @classmethod + def from_array( + cls, + initial_obj: torch.Tensor | np.ndarray, + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + obj_model = cls(shape=initial_obj.shape, device=device, rng=rng) + if isinstance(initial_obj, np.ndarray): + initial_obj = torch.tensor(initial_obj, dtype=torch.float32) + else: + initial_obj = initial_obj.clone() + obj_model._obj = initial_obj.to(device) + return obj_model + # --- Properties ---- @property def obj(self) -> torch.Tensor: From ddd473d02aa5fb39b6a953cebafcf7126391b7c4 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 13:13:10 -0800 Subject: [PATCH 096/335] Pretraining in ObjectINR, implemented reset() --- src/quantem/tomography/object_models.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 1c1249a7..33b40b2a 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -500,9 +500,7 @@ def pretrain( self.set_scheduler(scheduler_params, num_iters) if reset: - raise NotImplementedError( - "TODO: Resseting the model to the pretrained weights is not implemented yet. To make this work I would have to reinstantiate the model I think." - ) + self.reset() loss_fn = get_loss_function(loss_fn, self.dtype) From d51aa51336aea67134f3bd0a482f1ecd404dbd5d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 13:23:28 -0800 Subject: [PATCH 097/335] .to(device) called again at the reconstruct call just to make sure device consistency --- src/quantem/tomography/dataset_models.py | 2 ++ src/quantem/tomography/tomography.py | 14 +++++++------- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 27d30a93..4a1f9722 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -220,6 +220,7 @@ def to(self, device: str): """ Moves the dataset to the device, and also insantiates the aux params to the device. """ + self.tilt_stack = self.tilt_stack.to(device) self.tilt_angles = self.tilt_angles.to(device) @@ -467,6 +468,7 @@ def __len__( return self.tilt_stack.shape[0] * N * N def to(self, device: str): + super().to(device) self._z1_params = nn.Parameter(self._z1_angles.to(device)) self._z3_params = nn.Parameter(self._z3_angles.to(device)) self._shifts_params = nn.Parameter(self._shifts.to(device)) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index de91107e..0a089ab3 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,4 +1,4 @@ -from typing import List, Literal, Optional, Self, Tuple +from typing import Literal, Optional, Self import numpy as np import torch @@ -51,8 +51,8 @@ def reconstruct( optimizer_params: dict | None = None, scheduler_params: dict | None = {}, constraints: dict = {}, # TODO: What to pass into the constraints? - loss_func: Tuple[str, Optional[float]] = ("smooth_l1", 0.07), - num_samples_per_ray: int | List[Tuple[int, int]] = None, + loss_func: tuple[str, Optional[float]] = ("smooth_l1", 0.07), + num_samples_per_ray: int | list[tuple[int, int]] = None, profiling_mode: bool = False, val_fraction: float = 0.0, # reset_dset: bool = False, @@ -65,10 +65,10 @@ def reconstruct( # TODO: Prior to reconstruction, it is assumed that object + dataset are both in the correct devices. Need to implement a way to check this. - # if self.obj_model.device != self.dset.device: - # raise ValueError( - # f"Should never happen! obj_model and dset must be on the same device, got {self.obj_model.device} and {self.dset.device}" - # ) + # Check device consistency + self.obj_model.to(self.device) + self.dset.to(self.device) + if profiling_mode: if self.global_rank == 0: print("Profiling mode enabled.") From 16aa0f222e9f1ce6fb58fb9f6eff675a68534d68 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 13:27:47 -0800 Subject: [PATCH 098/335] For TomographyLite classes, .from_dataset now only allows for tilt_series and tilt_angles to be inputted; No longer needed to give the dataset. --- src/quantem/core/ml/inr.py | 2 +- src/quantem/tomography/tomography_lite.py | 32 ++++++++++++++++------- 2 files changed, 24 insertions(+), 10 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 15cc6057..46447660 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -195,7 +195,7 @@ def __init__( alpha: float = 1.0, dtype: torch.dtype = torch.float32, final_activation: str | Callable = "identity", - winner_initialization: bool = False, + winner_initialization: bool | int = False, ) -> None: """Initialize HSiren. diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index 57a78446..6bc6995e 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -2,9 +2,15 @@ from typing import Literal, Self import numpy as np +import torch +from numpy.typing import NDArray +from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.ml.inr import HSiren -from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.dataset_models import ( + TomographyINRDataset, + TomographyPixDataset, +) from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ObjectINR, ObjectPixelated from quantem.tomography.tomography import Tomography, TomographyConventional @@ -18,13 +24,17 @@ class TomographyLiteINR(Tomography): @classmethod def from_dataset( cls, - dset: DatasetModelType, + tilt_series: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, device: str = "cuda", log_dir: os.PathLike | str | None = None, log_images_every: int = 10, rng: np.random.Generator | int | None = None, ) -> Self: - dset_model = dset + dset_model = TomographyINRDataset.from_data( + tilt_stack=tilt_series, + tilt_angles=tilt_angles, + ) # Define the object model model = HSiren(alpha=1, winner_initialization=72) @@ -125,17 +135,21 @@ class TomographyLiteConv(TomographyConventional): @classmethod def from_dataset( cls, - dset: DatasetModelType, + tilt_series: Dataset3d | NDArray | torch.Tensor, + tilt_angles: NDArray | torch.Tensor, device: str = "cuda", rng: np.random.Generator | int | None = None, ) -> Self: - dset_model = dset + dset_model = TomographyPixDataset.from_data( + tilt_stack=tilt_series, + tilt_angles=tilt_angles, + ) - obj_model = ObjectPixelated( + obj_model = ObjectPixelated.from_uniform( shape=( - max(dset_model.tilt_stack.shape), - max(dset_model.tilt_stack.shape), - max(dset_model.tilt_stack.shape), + max(tilt_series.shape), + max(tilt_series.shape), + max(tilt_series.shape), ), device=device, rng=rng, From 945064e2b5b6b5c0df284d0f3db15b3452bff52c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 13:40:05 -0800 Subject: [PATCH 099/335] Made tomography_utils.py; moved background_subtraction and other utility functions. Only remaining things in utils.py is rot_ZXZ. Gaussian filters added to filters.py --- src/quantem/core/utils/filter.py | 51 ++++ src/quantem/core/utils/imaging_utils.py | 257 +++++++---------- src/quantem/core/utils/tomography_utils.py | 316 +++++++++++++++++++++ src/quantem/tomography/tomography.py | 9 +- src/quantem/tomography/utils.py | 231 --------------- 5 files changed, 476 insertions(+), 388 deletions(-) create mode 100644 src/quantem/core/utils/tomography_utils.py diff --git a/src/quantem/core/utils/filter.py b/src/quantem/core/utils/filter.py index 589d84ca..3f9d13b7 100644 --- a/src/quantem/core/utils/filter.py +++ b/src/quantem/core/utils/filter.py @@ -1,5 +1,6 @@ import numpy as np import scipy.ndimage as ndi +import torch from quantem.core.utils.utils import extract_patches @@ -109,3 +110,53 @@ def otsu_threshold(img: np.ndarray, bins: int = 256) -> float: current_max = between_var threshold = bin_edges[i] return threshold + + +# --- Gaussian filters --- + + +def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: + radius = np.ceil(num_sigmas * sigma) + support = torch.arange(-radius, radius + 1, dtype=torch.float) + kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() + # Ensure kernel weights sum to 1, so that image brightness is not altered + return kernel.mul_(1 / kernel.sum()) + + +def gaussian_filter_2d( + img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor +) -> torch.Tensor: # Add kernel_1d as an argument + # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function + padding = len(kernel_1d) // 2 # Ensure that image size does not change + img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` + # Convolve along columns and rows + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) + img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + return img.squeeze_(0).squeeze_(0) # Make 2D again + + +def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: + """ + Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. + + Args: + stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms + kernel_1d (torch.Tensor): 1D Gaussian kernel + + Returns: + torch.Tensor: Blurred stack of same shape (H, N, W) + """ + H, N, W = stack.shape + padding = len(kernel_1d) // 2 + + # Reshape to (N, 1, H, W) for conv2d + stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) + + # Apply separable conv2d: vertical then horizontal + out = torch.nn.functional.conv2d( + stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) + ) + out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) + + # Restore shape to (H, N, W) + return out.squeeze(1).permute(1, 0, 2) diff --git a/src/quantem/core/utils/imaging_utils.py b/src/quantem/core/utils/imaging_utils.py index aaa86468..d352a051 100644 --- a/src/quantem/core/utils/imaging_utils.py +++ b/src/quantem/core/utils/imaging_utils.py @@ -1,22 +1,14 @@ # Utilities for processing images import math -from typing import Any, Optional, Tuple +from typing import Optional, Tuple import numpy as np import torch - -# TODO: Temporary -from matplotlib import cm from numpy.typing import NDArray from scipy.ndimage import gaussian_filter -from scipy.special import comb from quantem.core.utils.utils import generate_batches -from quantem.core.visualization import show_2d - -ImageType = NDArray[Any] -BoolArray = NDArray[np.bool_] def dft_upsample( @@ -849,7 +841,7 @@ def add_edges(i1, i2): inc = _find_wrap(phi_f[i1], phi_f[i2]) rel = rel_f[i1] + rel_f[i2] - edges.append( # type: ignore[attr-defined] + edges.append( # ty:ignore[possibly-missing-attribute] torch.stack([i1, i2, rel, inc], dim=1) ) @@ -1058,157 +1050,118 @@ def unwrap_phase_2d_torch( ) -# Background subtraction +def radially_project_fourier_tensor( + corner_centered_array: torch.Tensor, + sampling: Tuple[float, float], + q_bins: torch.Tensor | None = None, +): + """ + Radially project a corner-centered Fourier array onto radial bins. -# single TypeVar: works for both numpy and + Supports: + - single array: (kx, ky) + - batched arrays: (n, kx, ky) + - implicit bins (from grid) or explicit external bins + Parameters + ---------- + corner_centered_array : torch.Tensor + Shape (kx, ky) or (n, kx, ky) + sampling : (float, float) + Real-space sampling (sx, sy) + q_bins : torch.Tensor, optional + 1D tensor of radial bin centers -def _as_array(x: ImageType) -> NDArray[Any]: - return ( - x.array - if isinstance( - x, + Returns + ------- + q_bins_out : torch.Tensor + Radial bin centers + array_1d : torch.Tensor + Shape (n, nq) or (nq,) + """ + + if corner_centered_array.is_complex(): + q, real_part = radially_project_fourier_tensor( + corner_centered_array.real, sampling, q_bins ) - else np.asarray(x) - ) + # zero by symmetry + # _, imag_part = radially_project_fourier_tensor( + # corner_centered_array.imag, sampling, q_bins + # ) + return q, real_part # + 1j * imag_part + device = corner_centered_array.device + sx, sy = sampling -def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: - k = np.arange(n + 1, dtype=int) - return ( - comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) - ) + # --- normalize shape to (batch, kx, ky) + if corner_centered_array.ndim == 2: + array = corner_centered_array[None, ...] + squeeze_output = True + elif corner_centered_array.ndim == 3: + array = corner_centered_array + squeeze_output = False + else: + raise ValueError("Input must be 2D or 3D tensor") + n_batch, nkx, nky = array.shape -def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray[Any]: - H, W = im_shape - ou, ov = int(order[0]), int(order[1]) - u = np.linspace(0.0, 1.0, H) - v = np.linspace(0.0, 1.0, W) - Bu = _bernstein_basis_1d(ou, u) - Bv = _bernstein_basis_1d(ov, v) - basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) - return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) - - -def background_subtract( - image: ImageType, - mask: Optional[BoolArray] = None, - thresh_bg: Optional[float] = None, - order: Tuple[int, int] = (1, 1), - sigma: Optional[float] = None, - num_iter: int = 10, - plot_result: bool = True, - axsize: Tuple[int, int] = (3, 3), - cmap: str = "turbo", - return_background_and_mask: bool = False, - **show_kwargs, -) -> ImageType | Tuple[ImageType, NDArray[Any], BoolArray]: - """ - Background subtraction via bivariate Bernstein polynomial fitting. + # --- build k-grid + kx = torch.fft.fftfreq(nkx, d=sx, device=device) + ky = torch.fft.fftfreq(nky, d=sy, device=device) + k = torch.sqrt(kx[:, None] ** 2 + ky[None, :] ** 2).reshape(-1) - Returns - ------- - - If `return_background_and_mask=False`: ImageType (same as input) - - If `True`: (ImageType, numpy.ndarray, numpy.ndarray[bool]) - where background and mask are always NumPy. - """ - im = _as_array(image).astype(float, copy=True) - if im.ndim != 2: - raise ValueError("`image` must be 2D") + # --- determine radial bins + k_max = min(0.5 / sx, 0.5 / sy) - mask_arr: BoolArray = ( - np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) - ) - if mask_arr.shape != im.shape: - raise ValueError("`mask` must match `image` shape") - - order = (int(order[0]), int(order[1])) - A_full = _build_basis_matrix(im.shape, order) - H, W = im.shape - im_flat = im.ravel() - - im_bg = np.zeros_like(im) - thresh_val = np.median(im[mask_arr]) if thresh_bg is None else float(thresh_bg) - - resid = im - im_bg - if sigma and sigma > 0: - resid = gaussian_filter(resid, sigma=sigma, mode="nearest") - mask_bg: BoolArray = (resid < thresh_val) & mask_arr - - for _ in range(int(num_iter)): - idx = mask_bg.ravel() - if not np.any(idx): - idx = mask_arr.ravel() - coefs, *_ = np.linalg.lstsq(A_full[idx, :], im_flat[idx], rcond=None) - im_bg = (A_full @ coefs).reshape(H, W) - - resid = im - im_bg - if sigma and sigma > 0: - resid = gaussian_filter(resid, sigma=sigma, mode="nearest") - - thr = thresh_val if thresh_bg is None else float(thresh_bg) - mask_bg = (resid < thr) & mask_arr - - im_sub_np = im - im_bg - - if plot_result: - vals = im_sub_np[mask_arr] - vals = vals[np.isfinite(vals)] - if vals.size == 0: - vals = np.array([0.0]) - vmin_sub = float(np.min(vals)) - vmax_sub = float(np.max(vals)) - vrange = float(max(abs(vmin_sub), abs(vmax_sub))) or 1e-12 - - bg_disp = (im_bg - np.mean(im_bg)).copy() - bg_disp[~mask_bg] = np.nan - - cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") - cmap_div = "RdBu_r" - - disp = [im - np.mean(im_bg), bg_disp, im_sub_np] - norm = [ - { - "interval_type": "manual", - "stretch_type": "linear", - "vmin": vmin_sub, - "vmax": vmax_sub, - }, - { - "interval_type": "manual", - "stretch_type": "linear", - "vmin": vmin_sub, - "vmax": vmax_sub, - }, - { - "interval_type": "centered", - "stretch_type": "linear", - "vcenter": 0.0, - "half_range": vrange, - }, - ] - - show_2d( - disp, - cmap=[cmap_base, cmap_base, cmap_div], - norm=norm, - cbar=[False, False, True], - title=["Input Image", "Background (fit region)", "Background Subtracted"], - axsize=axsize, - **show_kwargs, + if q_bins is None: + dk = kx[1] - kx[0] + num_bins = int(torch.floor(k_max / dk).item()) + 1 + dq = dk + q_bins_out = torch.arange(num_bins, device=device, dtype=k.dtype) * dk + else: + q_bins = q_bins.to(device) + dq = q_bins[1] - q_bins[0] + q_bins_out = q_bins[q_bins <= k_max] + num_bins = q_bins_out.numel() + + # ---- INTERNAL padding (key change) + num_bins_internal = num_bins + 2 + + # --- map k -> bin indices (NO CLIPPING) + inds = k / dq + inds_f = torch.floor(inds).long() + inds_f = torch.clamp(inds_f, 0, num_bins_internal - 2) + + d_ind = inds - inds_f + w0 = 1.0 - d_ind + w1 = d_ind + + # --- flatten spatial dims + array_f = array.reshape(n_batch, -1) + + # --- accumulate per batch + out = [] + for b in range(n_batch): + a = array_f[b] + + num = torch.bincount(inds_f, weights=a * w0, minlength=num_bins_internal) + torch.bincount( + inds_f + 1, weights=a * w1, minlength=num_bins_internal ) - # # preserve if needed - # if isinstance( - # image, - # ): - # meta = dict(origin=image.origin, sampling=image.sampling, units=image.units) - # name_base = getattr(image, "name", "image") - # im_sub: ImageType = .from_array(im_sub_np, name=f"{name_base} (bg-sub)", **meta) # type: ignore[assignment] - # else: - im_sub = im_sub_np # type: ignore[assignment] - - if return_background_and_mask: - return im_sub, im_bg, mask_bg - return im_sub + den = ( + torch.bincount(inds_f, weights=w0, minlength=num_bins_internal) + + torch.bincount(inds_f + 1, weights=w1, minlength=num_bins_internal) + ).clamp_min(1) + + out.append(num / den) + + array_1d = torch.stack(out, dim=0) + + # ---- truncate to physical bins (key change) + array_1d = array_1d[..., :num_bins] + q_bins_out = q_bins_out[:num_bins] + + if squeeze_output: + array_1d = array_1d[0] + + return q_bins_out, array_1d diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py new file mode 100644 index 00000000..241687bc --- /dev/null +++ b/src/quantem/core/utils/tomography_utils.py @@ -0,0 +1,316 @@ +from typing import Any, Optional, Tuple + +import numpy as np +import torch +import torch.nn.functional as F +from matplotlib import cm # TODO: Temporary +from numpy.typing import NDArray +from scipy.ndimage import center_of_mass, gaussian_filter, shift +from scipy.special import comb +from tqdm.auto import tqdm + +from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.visualization import show_2d + +ImageType = NDArray[Any] +BoolArray = NDArray[np.bool_] + + +def _as_array(x: ImageType) -> NDArray[Any]: + return ( + x.array + if isinstance( + x, + ) + else np.asarray(x) + ) + + +def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: + k = np.arange(n + 1, dtype=int) + return ( + comb(n, k)[None, :] * (t[:, None] ** k[None, :]) * ((1.0 - t)[:, None] ** (n - k)[None, :]) + ) + + +def _build_basis_matrix(im_shape: Tuple[int, int], order: Tuple[int, int]) -> NDArray[Any]: + H, W = im_shape + ou, ov = int(order[0]), int(order[1]) + u = np.linspace(0.0, 1.0, H) + v = np.linspace(0.0, 1.0, W) + Bu = _bernstein_basis_1d(ou, u) + Bv = _bernstein_basis_1d(ov, v) + basis_cube = np.einsum("ik,jl->ijkl", Bu, Bv) + return basis_cube.reshape(H * W, (ou + 1) * (ov + 1)) + + +def background_subtract( + image: ImageType, + mask: Optional[BoolArray] = None, + thresh_bg: Optional[float] = None, + order: Tuple[int, int] = (1, 1), + sigma: Optional[float] = None, + num_iter: int = 10, + plot_result: bool = True, + axsize: Tuple[int, int] = (3, 3), + cmap: str = "turbo", + return_background_and_mask: bool = False, + **show_kwargs, +) -> ImageType | Tuple[ImageType, NDArray[Any], BoolArray]: + """ + Background subtraction via bivariate Bernstein polynomial fitting. + + Returns + ------- + - If `return_background_and_mask=False`: ImageType (same as input) + - If `True`: (ImageType, numpy.ndarray, numpy.ndarray[bool]) + where background and mask are always NumPy. + """ + im = _as_array(image).astype(float, copy=True) + if im.ndim != 2: + raise ValueError("`image` must be 2D") + + mask_arr: BoolArray = ( + np.ones_like(im, dtype=bool) if mask is None else np.asarray(mask, dtype=bool) + ) + if mask_arr.shape != im.shape: + raise ValueError("`mask` must match `image` shape") + + order = (int(order[0]), int(order[1])) + A_full = _build_basis_matrix(im.shape, order) + H, W = im.shape + im_flat = im.ravel() + + im_bg = np.zeros_like(im) + thresh_val = np.median(im[mask_arr]) if thresh_bg is None else float(thresh_bg) + + resid = im - im_bg + if sigma and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + mask_bg: BoolArray = (resid < thresh_val) & mask_arr + + for _ in range(int(num_iter)): + idx = mask_bg.ravel() + if not np.any(idx): + idx = mask_arr.ravel() + coefs, *_ = np.linalg.lstsq(A_full[idx, :], im_flat[idx], rcond=None) + im_bg = (A_full @ coefs).reshape(H, W) + + resid = im - im_bg + if sigma and sigma > 0: + resid = gaussian_filter(resid, sigma=sigma, mode="nearest") + + thr = thresh_val if thresh_bg is None else float(thresh_bg) + mask_bg = (resid < thr) & mask_arr + + im_sub_np = im - im_bg + + if plot_result: + vals = im_sub_np[mask_arr] + vals = vals[np.isfinite(vals)] + if vals.size == 0: + vals = np.array([0.0]) + vmin_sub = float(np.min(vals)) + vmax_sub = float(np.max(vals)) + vrange = float(max(abs(vmin_sub), abs(vmax_sub))) or 1e-12 + + bg_disp = (im_bg - np.mean(im_bg)).copy() + bg_disp[~mask_bg] = np.nan + + cmap_base = cm.get_cmap(cmap).with_extremes(bad="black") + cmap_div = "RdBu_r" + + disp = [im - np.mean(im_bg), bg_disp, im_sub_np] + norm = [ + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "manual", + "stretch_type": "linear", + "vmin": vmin_sub, + "vmax": vmax_sub, + }, + { + "interval_type": "centered", + "stretch_type": "linear", + "vcenter": 0.0, + "half_range": vrange, + }, + ] + + show_2d( + disp, + cmap=[cmap_base, cmap_base, cmap_div], + norm=norm, + cbar=[False, False, True], + title=["Input Image", "Background (fit region)", "Background Subtracted"], + axsize=axsize, + **show_kwargs, + ) + + # # preserve if needed + # if isinstance( + # image, + # ): + # meta = dict(origin=image.origin, sampling=image.sampling, units=image.units) + # name_base = getattr(image, "name", "image") + # im_sub: ImageType = .from_array(im_sub_np, name=f"{name_base} (bg-sub)", **meta) # type: ignore[assignment] + # else: + im_sub = im_sub_np # type: ignore[assignment] + + if return_background_and_mask: + return im_sub, im_bg, mask_bg + return im_sub + + +# --- Tilt Series Processing Utility Functions --- + + +def fourier_binning(img, crop_size): + """ + Crop the img in Fourier space to the specified size. + """ + center = np.array(img.shape) // 2 + + fft_img = np.fft.fftshift(np.fft.fft2(img)) + + cropped_fft = fft_img[ + center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, + center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, + ] + cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real + return cropped_img + + +def cross_correlation_align_stack(ref_img, stack, print_pred=False): + """ + Aligns a stack of images to a reference image using cross-correlation. + + This function assumes the stack does not contain the reference image itself. + + Stack shape should be (N, H, W) where N is the number of images. + """ + + new_images = [] + pred_shifts = [] + + prev_img = ref_img + for img in tqdm(stack): + shift_pred = cross_correlation_shift(prev_img, img) + if print_pred: + print(f"Shift prediction: {shift_pred}") + shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) + + pred_shifts.append(shift_pred) + new_images.append(shifted_image) + + prev_img = shifted_image + + return new_images, pred_shifts + + +def centering_com_alignment(image_stack): + """ + Aligns the image stack to the center of mass of the whole image_stack to the + image center. This is useful for aligning the tilt series to the invariant line. + """ + + aligned_stack = np.zeros_like(image_stack) + h, w = image_stack.shape[1:] + image_center = np.array([h // 2, w // 2]) + + com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) + + for i, img in enumerate(image_stack): + com_img = np.array(center_of_mass(img)) + shift_vec = com_reference - com_img + aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) + + final_shift = image_center - com_reference + for i in range(aligned_stack.shape[0]): + aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) + + return aligned_stack + + +def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): + """ + Shifts a 2D image using grid_sample in a differentiable manner. + + Args: + image: Tensor of shape [H, W] + shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) + shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) + sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts + + Returns: + Shifted image of shape [H, W] + """ + H, W = image.shape + + # Convert physical shift to pixel shift + shift_x_pixel = shift_x + shift_y_pixel = shift_y + + # Normalize shift for grid_sample (assuming align_corners=True) + normalized_shift_x = shift_x_pixel * 2 / (W - 1) + normalized_shift_y = shift_y_pixel * 2 / (H - 1) + + # Create normalized grid + grid_y, grid_x = torch.meshgrid( + torch.linspace(-1, 1, H, device=image.device), + torch.linspace(-1, 1, W, device=image.device), + indexing="ij", + ) + + grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] + + # Apply shift (ensure it's differentiable) + grid[:, :, :, 0] -= normalized_shift_x + grid[:, :, :, 1] -= normalized_shift_y + + # Add batch and channel dimensions + image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] + + # Sample using grid_sample (fully differentiable) + shifted_image = F.grid_sample( + image, grid, mode="bicubic", padding_mode="zeros", align_corners=True + ) + + return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] + + +# --- TV loss --- + + +def get_TV_loss(tensor, factor=1e-3): + tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() + tv_loss = tv_d + tv_h + tv_w + + return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) + + +# Circular mask + + +def torch_phase_cross_correlation(im1, im2): + f1 = torch.fft.fft2(im1) + f2 = torch.fft.fft2(im2) + cc = torch.fft.ifft2(f1 * torch.conj(f2)) + cc_abs = torch.abs(cc) + + max_idx = torch.argmax(cc_abs) + shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() + + for i, dim in enumerate(im1.shape): + if shifts[i] > dim // 2: + shifts[i] -= dim + + # return shifts.flip(0) # (dx, dy) + return shifts diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 0a089ab3..004fff7c 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -7,17 +7,16 @@ from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.ddp import DDPMixin +from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d +from quantem.core.utils.tomography_utils import ( + torch_phase_cross_correlation, +) from quantem.tomography.dataset_models import DatasetModelType from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ObjectModelType from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_opt import TomographyOpt -from quantem.tomography.utils import ( - gaussian_filter_2d_stack, - gaussian_kernel_1d, - torch_phase_cross_correlation, -) class Tomography(TomographyOpt, TomographyBase, DDPMixin): diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index 82c1c9d5..9a009002 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -1,11 +1,5 @@ -import numpy as np import torch import torch.nn.functional as F -from scipy.ndimage import center_of_mass, gaussian_filter, shift -from scipy.stats import norm -from tqdm.auto import tqdm - -from quantem.core.utils.imaging_utils import cross_correlation_shift # --- Projection Operator Utils --- @@ -73,228 +67,3 @@ def transform_slice(mag_slice): rotated_mags = torch.vmap(transform_slice)(mags) return rotated_mags.permute(1, 2, 3, 0) - - -def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): - """ - Shifts a 2D image using grid_sample in a differentiable manner. - - Args: - image: Tensor of shape [H, W] - shift_x: Scalar tensor (dx) for shift in x-direction (in physical units) - shift_y: Scalar tensor (dy) for shift in y-direction (in physical units) - sampling_rate: Scalar value (physical units per pixel) to correctly normalize shifts - - Returns: - Shifted image of shape [H, W] - """ - H, W = image.shape - - # Convert physical shift to pixel shift - shift_x_pixel = shift_x - shift_y_pixel = shift_y - - # Normalize shift for grid_sample (assuming align_corners=True) - normalized_shift_x = shift_x_pixel * 2 / (W - 1) - normalized_shift_y = shift_y_pixel * 2 / (H - 1) - - # Create normalized grid - grid_y, grid_x = torch.meshgrid( - torch.linspace(-1, 1, H, device=image.device), - torch.linspace(-1, 1, W, device=image.device), - indexing="ij", - ) - - grid = torch.stack((grid_x, grid_y), dim=-1).unsqueeze(0) # [1, H, W, 2] - - # Apply shift (ensure it's differentiable) - grid[:, :, :, 0] -= normalized_shift_x - grid[:, :, :, 1] -= normalized_shift_y - - # Add batch and channel dimensions - image = image.unsqueeze(0).unsqueeze(0) # [1, 1, H, W] - - # Sample using grid_sample (fully differentiable) - shifted_image = F.grid_sample( - image, grid, mode="bicubic", padding_mode="zeros", align_corners=True - ) - - return shifted_image.squeeze(0).squeeze(0) # Back to [H, W] - - -# --- TV loss --- - - -def get_TV_loss(tensor, factor=1e-3): - tv_d = torch.pow(tensor[:, :, 1:, :, :] - tensor[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(tensor[:, :, :, 1:, :] - tensor[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(tensor[:, :, :, :, 1:] - tensor[:, :, :, :, :-1], 2).sum() - tv_loss = tv_d + tv_h + tv_w - - return tv_loss * factor / (torch.prod(torch.tensor(tensor.shape))) - - -# --- Gaussian filters --- - - -def gaussian_kernel_1d(sigma: float, num_sigmas: float = 3.0) -> torch.Tensor: - radius = np.ceil(num_sigmas * sigma) - support = torch.arange(-radius, radius + 1, dtype=torch.float) - kernel = torch.distributions.Normal(loc=0, scale=sigma).log_prob(support).exp_() - # Ensure kernel weights sum to 1, so that image brightness is not altered - return kernel.mul_(1 / kernel.sum()) - - -def gaussian_filter_2d( - img: torch.Tensor, sigma: float, kernel_1d: torch.Tensor -) -> torch.Tensor: # Add kernel_1d as an argument - # kernel_1d = gaussian_kernel_1d(sigma) # Create 1D Gaussian kernel - Moved outside function - padding = len(kernel_1d) // 2 # Ensure that image size does not change - img = img.unsqueeze(0).unsqueeze_(0) # Make copy, make 4D for ``conv2d()`` - # Convolve along columns and rows - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, -1, 1), padding=(padding, 0)) - img = torch.nn.functional.conv2d(img, weight=kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - return img.squeeze_(0).squeeze_(0) # Make 2D again - - -def gaussian_filter_2d_stack(stack: torch.Tensor, kernel_1d: torch.Tensor) -> torch.Tensor: - """ - Apply 2D Gaussian blur to each slice stack[:, i, :] in a vectorized way. - - Args: - stack (torch.Tensor): Tensor of shape (H, N, W) where N is num_sinograms - kernel_1d (torch.Tensor): 1D Gaussian kernel - - Returns: - torch.Tensor: Blurred stack of same shape (H, N, W) - """ - H, N, W = stack.shape - padding = len(kernel_1d) // 2 - - # Reshape to (N, 1, H, W) for conv2d - stack_reshaped = stack.permute(1, 0, 2).unsqueeze(1) # (N, 1, H, W) - - # Apply separable conv2d: vertical then horizontal - out = torch.nn.functional.conv2d( - stack_reshaped, kernel_1d.view(1, 1, -1, 1), padding=(padding, 0) - ) - out = torch.nn.functional.conv2d(out, kernel_1d.view(1, 1, 1, -1), padding=(0, padding)) - - # Restore shape to (H, N, W) - return out.squeeze(1).permute(1, 0, 2) - - -# Circular mask - - -def torch_phase_cross_correlation(im1, im2): - f1 = torch.fft.fft2(im1) - f2 = torch.fft.fft2(im2) - cc = torch.fft.ifft2(f1 * torch.conj(f2)) - cc_abs = torch.abs(cc) - - max_idx = torch.argmax(cc_abs) - shifts = torch.tensor(np.unravel_index(max_idx.item(), im1.shape), device=im1.device).float() - - for i, dim in enumerate(im1.shape): - if shifts[i] > dim // 2: - shifts[i] -= dim - - # return shifts.flip(0) # (dx, dy) - return shifts - - -# --- Tilt Series Processing Utility Functions --- - - -def fourier_cropping(img, crop_size): - """ - Crop the img in Fourier space to the specified size. - """ - center = np.array(img.shape) // 2 - - fft_img = np.fft.fftshift(np.fft.fft2(img)) - - cropped_fft = fft_img[ - center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, - center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, - ] - cropped_img = np.fft.ifft2(np.fft.ifftshift(cropped_fft)).real - return cropped_img - - -def estimate_background( - img, - num_iterations=10, - cutoff=3, - smoothing_sigma=1.0, -): - """ - Estimate the background of the image using a Gaussian filter. - """ - if smoothing_sigma > 0: - img = gaussian_filter(img, sigma=smoothing_sigma) - pixel_vals = img.ravel() - - for i in range(num_iterations): - mu, std = norm.fit(pixel_vals) - - # Set cutoff threshold (e.g., 3 standard deviations) - lower = mu - cutoff * std - upper = mu + cutoff * std - - # Mask pixel values within ±3σ - pixel_vals = pixel_vals[(pixel_vals >= lower) & (pixel_vals <= upper)] - - return mu - - -def cross_correlation_align_stack(ref_img, stack, print_pred=False): - """ - Aligns a stack of images to a reference image using cross-correlation. - - This function assumes the stack does not contain the reference image itself. - - Stack shape should be (N, H, W) where N is the number of images. - """ - - new_images = [] - pred_shifts = [] - - prev_img = ref_img - for img in tqdm(stack): - shift_pred = cross_correlation_shift(prev_img, img) - if print_pred: - print(f"Shift prediction: {shift_pred}") - shifted_image = shift(img, shift=shift_pred, mode="constant", cval=0.0) - - pred_shifts.append(shift_pred) - new_images.append(shifted_image) - - prev_img = shifted_image - - return new_images, pred_shifts - - -def centering_com_alignment(image_stack): - """ - Aligns the image stack to the center of mass of the whole image_stack to the - image center. This is useful for aligning the tilt series to the invariant line. - """ - - aligned_stack = np.zeros_like(image_stack) - h, w = image_stack.shape[1:] - image_center = np.array([h // 2, w // 2]) - - com_reference = np.array(center_of_mass(image_stack.mean(axis=0))) - - for i, img in enumerate(image_stack): - com_img = np.array(center_of_mass(img)) - shift_vec = com_reference - com_img - aligned_stack[i] = shift(img, shift=shift_vec, mode="constant", cval=0.0) - - final_shift = image_center - com_reference - for i in range(aligned_stack.shape[0]): - aligned_stack[i] = shift(aligned_stack[i], shift=final_shift, mode="constant", cval=0.0) - - return aligned_stack From c2b7d264297ff7f7596e350c1b315eb04acf2862 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 13:49:00 -0800 Subject: [PATCH 100/335] Made save_volume more explicit in top-level Tomography.py --- src/quantem/tomography/tomography.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 004fff7c..3d26b63f 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,3 +1,4 @@ +import os from typing import Literal, Optional, Self import numpy as np @@ -269,9 +270,15 @@ def reconstruct( # --- Helper Functions --- - def save_volume(self, path: str = "recon_volume.npz"): - # TODO: Temporary, need to talk to Arthur what the correct way of saving results is. + def save_volume(self, path: str = "recon_volume.npz", overwrite: bool = False): + """ + Saves volume to a numpy array file. Does not save the full Tomography object. + """ if self.global_rank == 0: + if not overwrite and os.path.exists(path): + raise FileExistsError( + f"File {path} already exists. Use overwrite=True to overwrite." + ) print(f"Saving volume to {path}") np.savez(path, volume=self.obj_model.obj.detach().cpu().numpy()) From 2b7848dd407e43d8c3812cddeedc0652be67cb83 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 14:06:18 -0800 Subject: [PATCH 101/335] Logging now logs all channels if there are any additional ones. In the .to calls in dataset_models.py, since the PixDataset requires the tilt series to be in GPU memory, while the INR needs it to be in CPU for dataloader stuff I've just made .to as an abstract method for the base Dataset. --- src/quantem/core/ml/inr.py | 5 +++- src/quantem/tomography/dataset_models.py | 30 ++++++++++++--------- src/quantem/tomography/logger_tomography.py | 14 +++++++--- src/quantem/tomography/object_models.py | 3 +-- src/quantem/tomography/tomography.py | 1 - 5 files changed, 34 insertions(+), 19 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 46447660..d2ea24e8 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -111,9 +111,12 @@ def _build(self) -> None: self.net = nn.Sequential(*net_list) if self.winner_initialization: - if isinstance(self.winner_initialization, int): + if type(self.winner_initialization) is int: rng = torch.Generator() rng.manual_seed(self.winner_initialization) + else: + rng = torch.Generator() + rng.manual_seed(42) with torch.no_grad(): self.net[0].linear.weight += ( # pyright: ignore[reportAttributeAccessIssue] torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 # type:ignore diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 4a1f9722..ec049279 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -221,18 +221,7 @@ def to(self, device: str): Moves the dataset to the device, and also insantiates the aux params to the device. """ - self.tilt_stack = self.tilt_stack.to(device) - self.tilt_angles = self.tilt_angles.to(device) - - self._z1_params = nn.Parameter(self._z1_angles.to(device)) - self._z3_params = nn.Parameter(self._z3_angles.to(device)) - self._shifts_params = nn.Parameter(self._shifts.to(device)) - - self._z1_ref = self._z1_ref.to(device) - self._z3_ref = self._z3_ref.to(device) - self._shifts_ref = self._shifts_ref.to(device) - - self.device = device + raise NotImplementedError("This method should be implemented in subclasses.") class TomographyPixDataset(TomographyDatasetBase): @@ -273,6 +262,23 @@ def forward( pixel_loc=None, ) + def to(self, device: str): + """ + Moves the tilt stack and tilt_angles to the device, along with other nn.Parameters to the device. + """ + self.tilt_stack = self.tilt_stack.to(device) + self.tilt_angles = self.tilt_angles.to(device) + + self._z1_params = nn.Parameter(self._z1_angles.to(device)) + self._z3_params = nn.Parameter(self._z3_angles.to(device)) + self._shifts_params = nn.Parameter(self._shifts.to(device)) + + self._z1_ref = self._z1_ref.to(device) + self._z3_ref = self._z3_ref.to(device) + self._shifts_ref = self._shifts_ref.to(device) + + self.device = device + class TomographyINRDataset(TomographyDatasetBase, Dataset): """ diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index 4d850e18..c6726dac 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -57,9 +57,17 @@ def log_iter_images( shifts_vals = dataset_model.shifts_params.detach().cpu().numpy() print("Logging volume...") - self.log_image("volume/sum_z", pred_volume.sum(axis=0), iter, logger_cmap) - self.log_image("volume/sum_y", pred_volume.sum(axis=1), iter, logger_cmap) - self.log_image("volume/sum_x", pred_volume.sum(axis=2), iter, logger_cmap) + + for channel in range(pred_volume.shape[0]): + self.log_image( + f"volume/sum_z_{channel}", pred_volume[channel].sum(axis=0), iter, logger_cmap + ) + self.log_image( + f"volume/sum_y_{channel}", pred_volume[channel].sum(axis=1), iter, logger_cmap + ) + self.log_image( + f"volume/sum_x_{channel}", pred_volume[channel].sum(axis=2), iter, logger_cmap + ) # Plotting z1 and z3 vals print("Plotting z1 and z3 angles...") diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 33b40b2a..34414277 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -666,9 +666,8 @@ def get_tv_loss( return tv_loss def to(self, device: str): - # self._model = self._model.to(device) self.device = device - self._obj = self._obj.to(self.device) + # self._obj = self._obj.to(self.device) self.reconnect_optimizer_to_parameters() diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 3d26b63f..e8aded8d 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -67,7 +67,6 @@ def reconstruct( # Check device consistency self.obj_model.to(self.device) - self.dset.to(self.device) if profiling_mode: if self.global_rank == 0: From 384ce2fe39a36355790526e85906db8c86e92bba Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 14:06:38 -0800 Subject: [PATCH 102/335] Added abstract method decorator --- src/quantem/tomography/dataset_models.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index ec049279..257b2c88 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -216,6 +216,7 @@ def device(self, device: str): self._device = device # --- Helper Functions --- + @abstractmethod def to(self, device: str): """ Moves the dataset to the device, and also insantiates the aux params to the device. From 446a6da05edbbed5b00de6339236e0073bbc4c3f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Feb 2026 17:13:36 -0800 Subject: [PATCH 103/335] core/ml/loss_functions; Changed everything to modules check if this works at Ptycho --- src/quantem/core/ml/loss_functions.py | 200 +++++++----------- .../diffractive_imaging/object_models.py | 4 +- .../diffractive_imaging/probe_models.py | 4 +- src/quantem/tomography/object_models.py | 4 +- 4 files changed, 82 insertions(+), 130 deletions(-) diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index 1ffc3006..c99b6494 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -11,139 +11,91 @@ import torch -def get_loss_function(name: str | Callable, dtype: torch.dtype) -> Callable: - """Get a loss function by name or return callable if provided. - - Parameters - ---------- - name : str or Callable - Loss function name or callable function. - dtype : torch.dtype - Data type (used to determine complex vs real loss functions). - - Returns - ------- - Callable - Loss function. - - Raises - ------ - ValueError - If loss function name is unknown for the given dtype. - """ - if isinstance(name, Callable): +def get_loss_module(name: str | nn.Module | Callable, dtype: torch.dtype) -> nn.Module: + """Return a loss *module* by name, or wrap/return what was provided.""" + if isinstance(name, nn.Module): return name - else: - name = name.lower() + + if callable(name) and not isinstance(name, str): + # Wrap a bare callable into an nn.Module + class _CallableLoss(nn.Module): + def __init__(self, fn: Callable): + super().__init__() + self.fn = fn + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + return self.fn(pred, target) + + return _CallableLoss(name) + + loss_name = str(name).lower() + if dtype.is_complex: - if name in ["l2", "complex_l2"]: - return complex_l2 - elif name in ["complex_cartesian_l2"]: - return complex_cartesian_l2 - elif name in ["amp_phase_l2"]: - return amp_phase_l2 - elif name in ["combined_l2"]: - return combined_l2 - else: - raise ValueError(f"Unknown loss function for complex dtype: {name}") - else: - if name in ["l2"]: - return torch.nn.functional.mse_loss - elif name in ["l1"]: - return torch.nn.functional.l1_loss - else: - raise ValueError(f"Unknown loss function for real dtype: {name}") - - -def complex_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute L2 loss for complex tensors (separate real and imaginary parts). - - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. - - Returns - ------- - torch.Tensor - L2 loss value. - """ - real_l2 = torch.mean((pred.real - target.real) ** 2) - imag_l2 = torch.mean((pred.imag - target.imag) ** 2) - return (real_l2 + imag_l2) / 2 + if loss_name in {"l2", "complex_l2"}: + return ComplexL2Loss() + if loss_name in {"complex_cartesian_l2"}: + return ComplexCartesianL2Loss() + if loss_name in {"amp_phase_l2"}: + return AmpPhaseL2Loss() + if loss_name in {"combined_l2"}: + return CombinedL2Loss() + raise ValueError(f"Unknown loss module for complex dtype: {loss_name}") + + # real dtype + if loss_name in {"l2"}: + return nn.MSELoss() + if loss_name in {"l1"}: + return nn.L1Loss() + raise ValueError(f"Unknown loss module for real dtype: {loss_name}") + + +class ComplexL2Loss(nn.Module): + """L2 loss for complex tensors (separate real/imag, then average).""" + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + real_l2 = torch.mean((pred.real - target.real) ** 2) + imag_l2 = torch.mean((pred.imag - target.imag) ** 2) + return (real_l2 + imag_l2) / 2 -def complex_cartesian_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute L2 loss for complex tensors in Cartesian coordinates. - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. +class ComplexCartesianL2Loss(nn.Module): + """L2 loss for complex tensors in Cartesian form: E[(Δre^2 + Δim^2)].""" - Returns - ------- - torch.Tensor - L2 loss value. - """ - real_dif = pred.real - target.real - imag_dif = pred.imag - target.imag - loss = torch.mean(real_dif**2 + imag_dif**2) - return loss - - -def amp_phase_l2(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - """Compute L2 loss for complex tensors in amplitude-phase representation. - - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. - - Returns - ------- - torch.Tensor - L2 loss value (amplitude + phase). - """ - amp_l2 = ((target.abs() - pred.abs()) ** 2).mean() - phase_dif = torch.abs(target.angle() - pred.angle()) - phase_dif = torch.min(phase_dif, 2 * torch.pi - phase_dif) # phase wrapping - phase_l2 = torch.mean(phase_dif**2) - return amp_l2 + phase_l2 - - -def combined_l2(pred: torch.Tensor, target: torch.Tensor, alpha: float = 0.7) -> torch.Tensor: - """Combined L2 loss: weighted sum of amplitude-phase and complex L2 losses. - - Parameters - ---------- - pred : torch.Tensor - Predicted complex tensor. - target : torch.Tensor - Target complex tensor. - alpha : float, optional - Weight for amplitude-phase loss. Larger alpha gives more weight to - amp/phase, smaller alpha gives more weight to real/imag, by default 0.7 - - Returns - ------- - torch.Tensor - Combined L2 loss value. - - different alpha values can affect stability of training. + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + real_dif = pred.real - target.real + imag_dif = pred.imag - target.imag + return torch.mean(real_dif**2 + imag_dif**2) + + +class AmpPhaseL2Loss(nn.Module): + """L2 loss on amplitude + wrapped phase.""" + + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + amp_l2 = ((target.abs() - pred.abs()) ** 2).mean() + + phase_dif = torch.abs(target.angle() - pred.angle()) + phase_dif = torch.min(phase_dif, 2 * torch.pi - phase_dif) # wrap to [0, pi] + phase_l2 = torch.mean(phase_dif**2) + + return amp_l2 + phase_l2 + + +class CombinedL2Loss(nn.Module): + """Weighted sum of AmpPhaseL2 and ComplexL2. + + loss = alpha * amp_phase + (1 - alpha) * complex_l2 """ - comp_l2 = complex_l2(pred, target) - amp_ph_l2 = amp_phase_l2(pred, target) - return alpha * amp_ph_l2 + (1 - alpha) * comp_l2 + def __init__(self, alpha: float = 0.7): + super().__init__() + self.alpha = float(alpha) + self.complex_l2 = ComplexL2Loss() + self.amp_phase_l2 = AmpPhaseL2Loss() -# TODO: Better loss function implementation? More torch-like. + def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + comp_l2 = self.complex_l2(pred, target) + amp_ph_l2 = self.amp_phase_l2(pred, target) + return self.alpha * amp_ph_l2 + (1 - self.alpha) * comp_l2 class MSELogMSELoss(nn.Module): diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 0c66629e..9623e7b3 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -13,7 +13,7 @@ from quantem.core import config from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights -from quantem.core.ml.loss_functions import get_loss_function +from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.core.utils.validators import ( @@ -1085,7 +1085,7 @@ def pretrain( "No pretrain target set. Provide pretrain_target or set it beforehand." ) - loss_fn = get_loss_function(loss_fn, self.dtype) + loss_fn = get_loss_module(loss_fn, self.dtype) self._pretrain( num_iters=num_iters, loss_fn=loss_fn, diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index 77dbb1c1..e9027620 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -14,7 +14,7 @@ from quantem.core.datastructures import Dataset2d, Dataset4dstem from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights -from quantem.core.ml.loss_functions import get_loss_function +from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.core.utils.utils import electron_wavelength_angstrom, to_numpy @@ -1391,7 +1391,7 @@ def pretrain( elif self.pretrain_target is None: self.pretrain_target = self._initial_probe.clone().detach() - loss_fn = get_loss_function(loss_fn, self.dtype) + loss_fn = get_loss_module(loss_fn, self.dtype) self._pretrain( num_iters=num_iters, loss_fn=loss_fn, diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 34414277..f71964f1 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -11,7 +11,7 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin -from quantem.core.ml.loss_functions import get_loss_function +from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset @@ -502,7 +502,7 @@ def pretrain( if reset: self.reset() - loss_fn = get_loss_function(loss_fn, self.dtype) + loss_fn = get_loss_module(loss_fn, self.dtype) self._pretrain( num_iters=num_iters, From c65b63baf7e724a0c62213b65774480b4922fbdf Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 25 Feb 2026 23:24:46 -0800 Subject: [PATCH 104/335] dataset_models.py bug fixes. Distribute model is not working with cudatoolkit/13.0 --- src/quantem/core/ml/ddp.py | 1 - src/quantem/tomography/dataset_models.py | 1 - 2 files changed, 2 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 52bed46a..0e60e398 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -31,7 +31,6 @@ def setup_distributed(self, device: str | torch.device | None = None): self.world_size = dist.get_world_size() self.global_rank = dist.get_rank() self.local_rank = int(os.environ["LOCAL_RANK"]) - torch.cuda.set_device(self.local_rank) device = torch.device("cuda", self.local_rank) else: diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 257b2c88..ee7d74f7 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -475,7 +475,6 @@ def __len__( return self.tilt_stack.shape[0] * N * N def to(self, device: str): - super().to(device) self._z1_params = nn.Parameter(self._z1_angles.to(device)) self._z3_params = nn.Parameter(self._z3_angles.to(device)) self._shifts_params = nn.Parameter(self._shifts.to(device)) From 093ffed16e22ec0d0eb05cd63fb9701fa300b54d Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Mar 2026 10:46:23 -0800 Subject: [PATCH 105/335] New Vector class --- src/quantem/core/datastructures/vector.py | 1550 ++++++++------------- tests/datastructures/test_vector.py | 569 +++----- 2 files changed, 794 insertions(+), 1325 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 9bc513a4..4717fe64 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -1,1026 +1,694 @@ -from typing import ( - Any, - List, - Optional, - Tuple, - Union, - cast, - overload, -) +from __future__ import annotations + +import copy +from dataclasses import dataclass +from typing import Any, Iterable, Sequence, overload import numpy as np -from numpy.typing import ArrayLike, NDArray +from numpy.typing import NDArray from quantem.core.io.serialize import AutoSerialize from quantem.core.utils.validators import ( validate_fields, validate_num_fields, validate_shape, - validate_vector_data, - validate_vector_data_for_inference, validate_vector_units, ) +@dataclass +class _VectorState: + shape: tuple[int, ...] + data: NDArray[np.object_] + fields: list[str] + units: list[str] + name: str + metadata: dict[str, Any] + + class Vector(AutoSerialize): - """ - A class for holding vector data with ragged array lengths. This class supports any number of fixed dimensions - (indexed first) followed by a ragged numpy array that can have any number of entries (rows) and columns (fields). - Inherits from AutoSerialize for serialization support. - - Basic Usage: - ----------- - # Create a 2D vector with shape=(4, 3) and 3 named fields - v = Vector.from_shape(shape=(4, 3), fields=['field0', 'field1', 'field2']) - - # Alternative creation with num_fields instead of fields - v = Vector.from_shape(shape=(4, 3), num_fields=3) # Fields will be named field_0, field_1, field_2 - - # Create with custom name and units - v = Vector.from_shape( - shape=(4, 3), - fields=['field0', 'field1', 'field2'], - name='my_vector', - units=['unit0', 'unit1', 'unit2'], - ) - - # Access data at specific indices - data = v[0, 1] # Returns numpy array at position (0,1) - - # Set data at specific indices - v[0, 1] = np.array([[1.0, 2.0, 3.0]]) # Must match num_fields - - # Create a deep copy - v_copy = v.copy() - - Example usage of from_data: - ----------------------------------- - data = [ - np.array([[1, 2], [3, 4]]), - np.array([[5, 6], [7, 8], [9, 10]]) - ] - v = Vector.from_data( - data, - fields=['x', 'y'], - name='my_ragged_vector', - units=['m', 'm'] - ) - - # Or using lists instead of numpy arrays: - data = [ - [[1, 2], [3, 4]], - [[5, 6], [7, 8], [9, 10]], - ] - v = Vector.from_data( - data, - fields=['x', 'y'], - name='my_ragged_vector', - units=['m', 'm'] - ) - - Field Operations: - ---------------- - # Access a specific field - field_data = v['field0'] # Returns a FieldView object - - # Perform operations on a field - v['field0'] += 16 # Add 16 to all field0 values - - # Apply a function to a field - v['field2'] = lambda x: x * 2 # Double all field2 values - - # Get flattened field data - field_flat = v['field0'].flatten() # Returns 1D numpy array - - # Set field data from flattened array - v['field2'].set_flattened(new_values) # Must match total length - - Advanced Operations: - ------------------- - # Complex field calculations - scale = v['field0'].flatten() / (v['field0'].flatten()**2 + v['field1'].flatten()**2) - v['field2'].set_flattened(v['field2'].flatten() * scale) - - # Slicing and assignment - v[2:4, 1] = v[1:3, 1] # Copy data from one region to another - - # Boolean indexing - mask = v['field0'].flatten() > 0 - v['field2'].set_flattened(v['field2'].flatten() * mask) - - # Field management - v.add_fields(('field3', 'field4', 'field5')) # Add new fields - v.remove_fields(('field3', 'field4', 'field5')) # Remove fields - - Direct Data Access: - ------------------ - # Get data with integer indexing - data = v.get_data(0, 1) # Returns numpy array at (0,1) - - # Get data with slice indexing - data = v.get_data(slice(0, 2), 1) # Returns list of arrays for rows 0-1 at column 1 - - # Set data with integer indexing - v.set_data(np.array([[1.0, 2.0, 3.0]]), 0, 1) # Set data at (0,1) - - # Set data with slice indexing - v.set_data([np.array([[1.0, 2.0, 3.0]]), np.array([[4.0, 5.0, 6.0]])], - slice(0, 2), 1) # Set data for rows 0-1 at column 1 - - Notes: - ----- - - All numpy arrays stored in the vector must have the same number of columns (fields) - - Field names must be unique - - Slicing operations return new Vector instances - - Field operations are performed in-place - - Units are stored for each field and can be accessed via the units attribute - - The name attribute can be used to identify the vector in a larger context - """ + """Ragged cell data on a fixed grid. - _token = object() + A ``Vector`` has fixed grid dimensions (``shape``). Each fixed-grid cell stores a + NumPy array with shape ``(n_rows, num_fields)``. Selections always return new + ``Vector`` views over the same backing store, while raw NumPy extraction is explicit + via ``.array`` and ``.flatten()``. + """ def __init__( self, - shape: Tuple[int, ...], - fields: List[str], - units: List[str], - name: str, - metadata: dict = {}, - _token: object | None = None, + shape: tuple[int, ...], + fields: Sequence[str], + units: Sequence[str] | None = None, + name: str | None = None, + metadata: dict[str, Any] | None = None, ) -> None: - if _token is not self._token: - raise RuntimeError("Use Vector.from_shape() or Vector.from_data() to instantiate.") + validated_shape = validate_shape(shape) + validated_fields = validate_fields(list(fields)) + validated_units = validate_vector_units(list(units) if units is not None else None, len(validated_fields)) + self._state = _VectorState( + shape=validated_shape, + data=_empty_storage(validated_shape, len(validated_fields)), + fields=list(validated_fields), + units=list(validated_units), + name=name or f"{len(validated_shape)}d ragged array", + metadata=dict(metadata or {}), + ) + self._coords = _root_coords(validated_shape) + self._field_names = tuple(validated_fields) - self.shape = shape - self.fields = fields - self.units = units - self.name = name - self._data = nested_list(self.shape, fill=None) - self._metadata = metadata + @classmethod + def _from_state( + cls, + state: _VectorState, + coords: NDArray[np.object_], + field_names: Sequence[str], + ) -> "Vector": + obj = cls.__new__(cls) + obj._state = state + obj._coords = coords + obj._field_names = tuple(field_names) + return obj @classmethod def from_shape( cls, - shape: Tuple[int, ...], - num_fields: Optional[int] = None, - fields: Optional[List[str]] = None, - units: Optional[List[str]] = None, - name: Optional[str] = None, + shape: tuple[int, ...], + num_fields: int | None = None, + fields: Sequence[str] | None = None, + units: Sequence[str] | None = None, + name: str | None = None, ) -> "Vector": - """ - Factory method to create a Vector with the specified shape and fields. - - Parameters - ---------- - shape : Tuple[int, ...] - The shape of the vector (dimensions) - num_fields : Optional[int] - Number of fields in the vector - name : Optional[str] - Name of the vector - fields : Optional[List[str]] - List of field names - units : Optional[List[str]] - List of units for each field - - Returns - ------- - Vector - A new Vector instance - """ - validated_shape = validate_shape(shape) - ndim = len(validated_shape) - if fields is not None: - validated_fields = validate_fields(fields) - validated_num_fields = len(validated_fields) - if num_fields is not None and validated_num_fields != num_fields: + validated_fields = validate_fields(list(fields)) + if num_fields is not None and len(validated_fields) != num_fields: raise ValueError( - f"num_fields ({num_fields}) does not match length of fields ({validated_num_fields})" + f"num_fields ({num_fields}) does not match length of fields ({len(validated_fields)})" ) elif num_fields is not None: - validated_num_fields = validate_num_fields(num_fields) - validated_fields = [f"field_{i}" for i in range(validated_num_fields)] + validated_count = validate_num_fields(num_fields) + validated_fields = [f"field_{i}" for i in range(validated_count)] else: raise ValueError("Must specify either 'fields' or 'num_fields'.") - validated_units = validate_vector_units(units, validated_num_fields) - name = name or f"{ndim}d ragged array" - return cls( - shape=validated_shape, + shape=shape, fields=validated_fields, - units=validated_units, + units=units, name=name, - _token=cls._token, ) @classmethod def from_data( cls, - data: List[Any], - num_fields: Optional[int] = None, - fields: Optional[List[str]] = None, - units: Optional[List[str]] = None, - name: Optional[str] = None, + data: list[Any], + num_fields: int | None = None, + fields: Sequence[str] | None = None, + units: Sequence[str] | None = None, + name: str | None = None, ) -> "Vector": - """ - Factory method to create a Vector from a list of - ragged lists or ragged numpy arrays. - - Parameters - ---------- - data : List[Any] - A list of ragged lists containing the vector data. - Each element should be a numpy array with shape (n, num_fields). - num_fields : Optional[int] - Number of fields in the vector. If not provided, it will be inferred from the data. - fields : Optional[List[str]] - List of field names - units : Optional[List[str]] - List of units for each field - name : Optional[str] - Name of the vector - - Returns - ------- - Vector - A new Vector instance with the provided data - - Raises - ------ - ValueError - If the data structure is invalid or inconsistent - TypeError - If the data contains invalid types - """ - inferred_shape, inferred_num_fields = validate_vector_data_for_inference(data) - - final_num_fields = num_fields or inferred_num_fields - if num_fields is not None and num_fields != inferred_num_fields: - raise ValueError( - f"Provided num_fields ({num_fields}) does not match inferred ({inferred_num_fields})." - ) + inferred_shape, normalized_cells = _normalize_nested_data(data) + inferred_num_fields = normalized_cells[0].shape[1] if normalized_cells else 0 - vector = cls.from_shape( - shape=inferred_shape, - num_fields=final_num_fields, - fields=fields, - units=units, - name=name, - ) - - # Now fully validate and set the data - vector.data = data - return vector - - def get_data( - self, *indices: Union[int, slice, List[int], np.ndarray[Any, np.dtype[Any]]] - ) -> Union[NDArray, List[NDArray]]: - """ - Get data at specified indices. - - Parameters: - ----------- - *indices : Union[int, slice, List[int], np.ndarray] - Indices to access. Must match the number of dimensions in the vector. - Supports fancy indexing with lists or numpy arrays. - - Returns: - -------- - numpy.ndarray or list - The data at the specified indices. - - Raises: - ------- - IndexError - If indices are out of bounds. - ValueError - If the number of indices does not match the vector dimensions. - """ - if len(indices) != len(self._shape): - raise ValueError(f"Expected {len(self._shape)} indices, got {len(indices)}") - - # Handle fancy indexing and slicing - def get_indices(dim_idx: Any, dim_size: int) -> np.ndarray: - if isinstance(dim_idx, slice): - start, stop, step = dim_idx.indices(dim_size) - return np.arange(start, stop, step) - elif isinstance(dim_idx, (np.ndarray, list)): - idx = np.asarray(dim_idx) - if np.any((idx < 0) | (idx >= dim_size)): - raise IndexError(f"Index out of bounds for axis with size {dim_size}") - return idx - elif isinstance(dim_idx, (int, np.integer)): - if dim_idx < 0 or dim_idx >= dim_size: - raise IndexError( - f"Index {dim_idx} out of bounds for axis with size {dim_size}" - ) - return np.array([dim_idx]) - return np.arange(dim_size) - - # Get indices for each dimension - indices_arrays = [get_indices(i, s) for i, s in zip(indices, self._shape)] - - # If all indices are single integers, return a single array - if all(len(i) == 1 for i in indices_arrays): - ref = self._data - for idx in (i[0] for i in indices_arrays): - ref = ref[idx] - return ref - - # Create result structure for fancy indexing - result = [] - for idx in np.ndindex(*[len(i) for i in indices_arrays]): - src_idx = tuple(ind[i] for ind, i in zip(indices_arrays, idx)) - result.append(self._data[src_idx[0]][src_idx[1]]) - - return result - - def set_data( - self, - value: Union[NDArray, List[NDArray]], - *indices: Union[int, slice, List[int], np.ndarray[Any, np.dtype[Any]]], - ) -> None: - """ - Set data at specified indices. - - Parameters - ---------- - value : Union[NDArray, List[NDArray]] - The numpy array(s) to set at the specified indices. Must have shape (_, num_fields). - For fancy indexing, can be a list of arrays. - *indices : Union[int, slice, List[int], np.ndarray] - Indices to set data at. Must match the number of dimensions in the vector. - Supports fancy indexing with lists or numpy arrays. - - Raises - ------ - IndexError - If indices are out of bounds. - ValueError - If the number of indices does not match the vector dimensions, - or if the value shape doesn't match the expected shape. - TypeError - If the value is not a numpy array or list of numpy arrays. - """ - if len(indices) != len(self._shape): - raise ValueError(f"Expected {len(self._shape)} indices, got {len(indices)}") - - # Handle fancy indexing and slicing - def get_indices(dim_idx: Any, dim_size: int) -> np.ndarray: - if isinstance(dim_idx, slice): - start, stop, step = dim_idx.indices(dim_size) - return np.arange(start, stop, step) - elif isinstance(dim_idx, (np.ndarray, list)): - idx = np.asarray(dim_idx) - if np.any((idx < 0) | (idx >= dim_size)): - raise IndexError(f"Index out of bounds for axis with size {dim_size}") - return idx - elif isinstance(dim_idx, (int, np.integer)): - if dim_idx < 0 or dim_idx >= dim_size: - raise IndexError( - f"Index {dim_idx} out of bounds for axis with size {dim_size}" - ) - return np.array([dim_idx]) - return np.arange(dim_size) - - # Get indices for each dimension - indices_arrays = [get_indices(i, s) for i, s in zip(indices, self._shape)] - - # If all indices are single integers, handle as single value - if all(len(i) == 1 for i in indices_arrays): - if not isinstance(value, np.ndarray): - raise TypeError(f"Value must be a numpy array, got {type(value).__name__}") - if value.ndim != 2 or value.shape[1] != self.num_fields: + if fields is not None: + validated_fields = validate_fields(list(fields)) + if len(validated_fields) != inferred_num_fields: raise ValueError( - f"Expected a numpy array with shape (_, {self.num_fields}), got {value.shape}" + f"num_fields ({inferred_num_fields}) does not match length of fields ({len(validated_fields)})" ) - ref = self._data - for idx in (i[0] for i in indices_arrays[:-1]): - ref = ref[idx] - ref[indices_arrays[-1][0]] = value - return - - # Handle fancy indexing - if not isinstance(value, list): - raise TypeError("For fancy indexing, value must be a list of numpy arrays") - - # Validate and set values - for idx in np.ndindex(*[len(i) for i in indices_arrays]): - src_idx = tuple(ind[i] for ind, i in zip(indices_arrays, idx)) - if not isinstance(value[idx[0]], np.ndarray): - raise TypeError(f"Expected numpy array, got {type(value[idx[0]]).__name__}") - if value[idx[0]].ndim != 2 or value[idx[0]].shape[1] != self.num_fields: + elif num_fields is not None: + validated_num_fields = validate_num_fields(num_fields) + if validated_num_fields != inferred_num_fields: raise ValueError( - f"Expected array with shape (_, {self.num_fields}), got {value[idx[0]].shape}" + f"Provided num_fields ({validated_num_fields}) does not match inferred ({inferred_num_fields})." ) - ref = self._data - for i in src_idx[:-1]: - ref = ref[i] - ref[src_idx[-1]] = value[idx[0]] - - @overload - def __getitem__(self, idx: str) -> "_FieldView": ... - @overload - def __getitem__( - self, - idx: Union[Tuple[Union[int, slice, List[int]], ...], int, slice, List[int]], - ) -> Union[NDArray, "Vector"]: ... + validated_fields = [f"field_{i}" for i in range(validated_num_fields)] + else: + validated_fields = [f"field_{i}" for i in range(inferred_num_fields)] - def __getitem__( - self, - idx: Union[str, Tuple[Union[int, slice, List[int]], ...], int, slice, List[int]], - ) -> Union["_FieldView", NDArray, "Vector"]: - """Get data or a view of the vector at specified indices.""" - if isinstance(idx, str): - if idx not in self._fields: - raise KeyError(f"Field '{idx}' not found.") - return _FieldView(self, idx) - - # Normalize idx to tuple - normalized: Tuple[Any, ...] = (idx,) if not isinstance(idx, tuple) else idx - - # Convert lists/arrays to ndarray - idx_converted: Tuple[Union[int, slice, np.ndarray[Any, np.dtype[Any]]], ...] = tuple( - np.asarray(i) if isinstance(i, (list, np.ndarray)) else i for i in normalized + vector = cls( + shape=inferred_shape, + fields=validated_fields, + units=units, + name=name, ) + for coord, array in zip(np.ndindex(inferred_shape), normalized_cells): + vector._state.data[coord] = array.copy() + return vector - # Check if we should return a numpy array (all indices are integers) - return_np = all(isinstance(i, (int, np.integer)) for i in idx_converted[: len(self.shape)]) - if len(idx_converted) < len(self.shape): - return_np = False - - if return_np: - view = self._data - for i in idx_converted: - view = view[i] - return cast(NDArray[Any], view) - - # Handle fancy indexing and slicing - def get_indices(dim_idx: Any, dim_size: int) -> np.ndarray: - if isinstance(dim_idx, slice): - start, stop, step = dim_idx.indices(dim_size) - return np.arange(start, stop, step) - elif isinstance(dim_idx, (np.ndarray, list)): - return np.asarray(dim_idx) - elif isinstance(dim_idx, (int, np.integer)): - return np.array([dim_idx]) - return np.arange(dim_size) - - # Get indices for each dimension - full_idx = list(idx_converted) + [slice(None)] * (len(self.shape) - len(idx_converted)) - indices = [get_indices(i, s) for i, s in zip(full_idx, self.shape)] - - # Create new shape and data - new_shape = [len(i) for i in indices] - new_data = [[None] * new_shape[-1] for _ in range(new_shape[0])] - - # Fill the new data structure - for out_idx in np.ndindex(*new_shape): - src_idx = tuple(ind[i] for ind, i in zip(indices, out_idx)) - new_data[out_idx[0]][out_idx[1]] = self._data[src_idx[0]][src_idx[1]] - - # Create new Vector - vector_new = Vector.from_shape( - shape=tuple(new_shape), - num_fields=self.num_fields, - name=self.name + "[view]", - fields=self.fields, - units=self.units, - ) - vector_new._data = new_data - return vector_new + @property + def shape(self) -> tuple[int, ...]: + return self._coords.shape - def __setitem__( - self, - idx: Union[Tuple[Union[int, slice, List[int]], ...], int, slice, List[int], str], - value: Union[NDArray, List[NDArray]], - ) -> None: - """Set data at specified indices.""" - if isinstance(idx, str): - if idx not in self._fields: - raise KeyError(f"Field '{idx}' not found.") - field_view = _FieldView(self, idx) - field_view.set_flattened(value) - return + @property + def fields(self) -> list[str]: + return list(self._field_names) - # Normalize idx to tuple - normalized: Tuple[Any, ...] = (idx,) if not isinstance(idx, tuple) else idx + @property + def units(self) -> list[str]: + lookup = {name: unit for name, unit in zip(self._state.fields, self._state.units)} + return [lookup[name] for name in self._field_names] - # Convert lists/arrays to ndarray - idx_converted: Tuple[Union[int, slice, np.ndarray[Any, np.dtype[Any]]], ...] = tuple( - np.asarray(i) if isinstance(i, (list, np.ndarray)) else i for i in normalized - ) + @property + def num_fields(self) -> int: + return len(self._field_names) - # Check if we're doing slice‐ or array‐based (multi‐cell) indexing - has_fancy = any( - isinstance(i, slice) or (isinstance(i, np.ndarray) and i.size > 1) - for i in idx_converted[: len(self.shape)] - ) + @property + def name(self) -> str: + return self._state.name - if has_fancy: - # If user passed a Vector, extract its cell arrays - if isinstance(value, Vector): + @name.setter + def name(self, value: str) -> None: + self._state.name = str(value) - def _flatten_cells(data): - if isinstance(data, np.ndarray): - return [data] - out = [] - for sub in data: - out.extend(_flatten_cells(sub)) - return out + @property + def metadata(self) -> dict[str, Any]: + return self._state.metadata - value = _flatten_cells(value._data) + @property + def array(self) -> NDArray[np.generic]: + if self.shape != (): + raise ValueError(".array is only valid when the selection contains exactly one cell.") + coord = self._coords[()] + cell = self._state.data[coord] + indices = self._field_indices() + if len(indices) == len(self._state.fields) and indices == list(range(len(self._state.fields))): + return cell + if len(indices) == 1: + idx = indices[0] + return cell[:, idx : idx + 1] + if _is_contiguous(indices): + return cell[:, indices[0] : indices[-1] + 1] + return cell[:, indices].copy() + + def __len__(self) -> int: + if self.shape == (): + raise TypeError("len() of unsized 0D Vector") + return self.shape[0] - # For fancy indexing, value should be a list of arrays - if not isinstance(value, list): - raise TypeError( - "For fancy/slice indexing, value must be a list of numpy arrays or a Vector" - ) + def __repr__(self) -> str: + return f"quantem.Vector(shape={self.shape}, fields={self.fields}, name={self.name!r})" - # Get indices for each dimension - def get_indices(dim_idx: Any, dim_size: int) -> np.ndarray: - if isinstance(dim_idx, slice): - start, stop, step = dim_idx.indices(dim_size) - return np.arange(start, stop, step) - elif isinstance(dim_idx, (np.ndarray, list)): - idx = np.asarray(dim_idx) - if np.any((idx < 0) | (idx >= dim_size)): - raise IndexError(f"Index out of bounds for axis with size {dim_size}") - return idx - elif isinstance(dim_idx, (int, np.integer)): - if dim_idx < 0 or dim_idx >= dim_size: - raise IndexError(f"Index out of bounds for axis with size {dim_size}") - return np.array([dim_idx]) - return np.arange(dim_size) - - indices_arrays = [get_indices(i, s) for i, s in zip(idx_converted, self._shape)] - total_indices = np.prod([len(i) for i in indices_arrays]) - - if len(value) != total_indices: - raise ValueError(f"Expected {total_indices} arrays, got {len(value)}") - - # Validate and set values - for array_idx, idx in enumerate(np.ndindex(*[len(i) for i in indices_arrays])): - src_idx = tuple(ind[i] for ind, i in zip(indices_arrays, idx)) - if not isinstance(value[array_idx], np.ndarray): - raise TypeError(f"Expected numpy array, got {type(value[array_idx]).__name__}") - if value[array_idx].ndim != 2 or value[array_idx].shape[1] != self.num_fields: - raise ValueError( - f"Expected array with shape (_, {self.num_fields}), got {value[array_idx].shape}" - ) - ref = self._data - for i in src_idx[:-1]: - ref = ref[i] - ref[src_idx[-1]] = value[array_idx] - else: - # For single value assignment - if not isinstance(value, np.ndarray): - raise TypeError(f"Value must be a numpy array, got {type(value).__name__}") - if value.ndim != 2 or value.shape[1] != self.num_fields: - raise ValueError( - f"Expected a numpy array with shape (_, {self.num_fields}), got {value.shape}" - ) - ref = self._data - for i in idx_converted[:-1]: - ref = ref[i] - ref[idx_converted[-1]] = value - - def add_fields(self, new_fields: Union[str, List[str]]) -> None: - """ - Add new fields to the vector. - - Parameters - ---------- - new_fields : Union[str, List[str]] - Field name(s) to add. Must be unique and not already present. - - Raises - ------ - ValueError - If any field name already exists or if there are duplicates - """ - if isinstance(new_fields, str): - new_fields = [new_fields] - else: - new_fields = list(new_fields) + __str__ = __repr__ - if any(name in self._fields for name in new_fields): + def copy(self) -> "Vector": + copied = self.__class__._from_state( + _VectorState( + shape=self.shape, + data=_empty_storage(self.shape, self.num_fields), + fields=self.fields, + units=self.units, + name=self.name, + metadata=copy.deepcopy(self.metadata), + ), + _root_coords_allow_empty(self.shape), + self.fields, + ) + for coord, array in zip(np.ndindex(self.shape) if self.shape != () else [()], self._iter_selected_arrays()): + copied._state.data[coord] = array.copy() + return copied + + def flatten(self) -> NDArray[np.generic]: + arrays = [array for array in self._iter_selected_arrays() if array.shape[0] > 0] + if arrays: + return np.vstack(arrays) + dtype = float + for array in self._iter_selected_arrays(): + dtype = array.dtype + break + return np.empty((0, self.num_fields), dtype=dtype) + + def select_fields(self, field_names: str | Sequence[str]) -> "Vector": + normalized = _normalize_field_names(field_names) + available = set(self._field_names) + missing = [name for name in normalized if name not in available] + if missing: + raise KeyError(f"Unknown field(s): {missing}") + return self._from_state(self._state, self._coords, normalized) + + def add_fields( + self, + names: str | Sequence[str], + values: Any | None = None, + units: str | Sequence[str] | None = None, + ) -> None: + new_names = _normalize_field_names(names) + if any(name in self._state.fields for name in new_names): raise ValueError("One or more new field names already exist.") - if len(set(new_fields)) != len(new_fields): - raise ValueError("Duplicate field names in input are not allowed.") - - self._fields = list(self._fields) + list(new_fields) - self._units = list(self._units) + ["none"] * len(new_fields) - - def expand_array(arr: Any) -> Any: - if isinstance(arr, np.ndarray): - if arr.shape[1] != self.num_fields - len(new_fields): - raise ValueError( - f"Expected arrays with {self.num_fields - len(new_fields)} fields, got {arr.shape[1]}" - ) - pad = np.zeros((arr.shape[0], len(new_fields))) - return np.hstack([arr, pad]) - elif isinstance(arr, list): - return [expand_array(sub) for sub in arr] - else: - return arr - - self._data = expand_array(self._data) - - def remove_fields(self, fields_to_remove: Union[str, List[str]]) -> None: - """ - Remove fields from the vector. - - Parameters - ---------- - fields_to_remove : Union[str, List[str]] - Field name(s) to remove. Must exist in the vector. - - Raises - ------ - ValueError - If any field doesn't exist - """ - if isinstance(fields_to_remove, str): - fields_to_remove = [fields_to_remove] - else: - fields_to_remove = list(fields_to_remove) - - field_to_index = {name: i for i, name in enumerate(self._fields)} - indices_to_remove = [] - for field in fields_to_remove: - if field not in field_to_index: - print(f"Warning: field '{field}' not found.") - else: - indices_to_remove.append(field_to_index[field]) - - if not indices_to_remove: + new_units = _normalize_units(units, len(new_names)) + old_fields = list(self._state.fields) + self._state.fields.extend(new_names) + self._state.units.extend(new_units) + + old_width = len(old_fields) + new_width = len(self._state.fields) + for coord in np.ndindex(self._state.shape) if self._state.shape != () else [()]: + current = self._state.data[coord] + promoted = np.result_type(current.dtype, float) + expanded = np.full((current.shape[0], new_width), np.nan, dtype=promoted) + expanded[:, :old_width] = current + self._state.data[coord] = expanded + + if list(self._field_names) == old_fields: + self._field_names = tuple(self._state.fields) + + target = self._from_state(self._state, self._coords, new_names) + if values is None: return - indices_to_remove = sorted(set(indices_to_remove)) - keep_indices = [i for i in range(self.num_fields) if i not in indices_to_remove] - - # Update metadata - self._fields = [self._fields[i] for i in keep_indices] - self._units = [self._units[i] for i in keep_indices] - - def prune_array(arr: Any) -> Any: - if isinstance(arr, np.ndarray): - if arr.shape[1] < max(indices_to_remove) + 1: - raise ValueError( - f"Cannot remove field index {max(indices_to_remove)} from array with shape {arr.shape}" - ) - return arr[:, keep_indices] - elif isinstance(arr, list): - return [prune_array(sub) for sub in arr] - else: - return arr - - self._data = prune_array(self._data) + if len(new_names) > 1 and isinstance(values, (list, tuple)) and len(values) == len(new_names): + for name, value in zip(new_names, values): + target.select_fields(name)._assign(value) + else: + target._assign(values) + + def remove_fields(self, names: str | Sequence[str]) -> None: + to_remove = _normalize_field_names(names) + missing = [name for name in to_remove if name not in self._state.fields] + if missing: + raise KeyError(f"Unknown field(s): {missing}") + if len(to_remove) == len(self._state.fields): + raise ValueError("Cannot remove all fields from a Vector.") + + remove_set = set(to_remove) + keep_names = [name for name in self._state.fields if name not in remove_set] + keep_indices = [self._state.fields.index(name) for name in keep_names] + keep_units = [self._state.units[index] for index in keep_indices] + + for coord in np.ndindex(self._state.shape) if self._state.shape != () else [()]: + self._state.data[coord] = self._state.data[coord][:, keep_indices] + + self._state.fields = keep_names + self._state.units = keep_units + self._field_names = tuple(name for name in self._field_names if name in keep_names) + + def get_data(self, *indices: Any) -> NDArray[np.generic] | list[NDArray[np.generic]]: + if len(indices) != len(self.shape): + raise ValueError(f"Expected {len(self.shape)} indices, got {len(indices)}") + selection = self[indices if len(indices) != 1 else indices[0]] + if selection.shape == (): + return selection.array + return [array.copy() for array in selection._iter_selected_arrays()] + + def set_data(self, value: Any, *indices: Any) -> None: + if len(indices) != len(self.shape): + raise ValueError(f"Expected {len(self.shape)} indices, got {len(indices)}") + self[indices if len(indices) != 1 else indices[0]] = value - def copy(self) -> "Vector": - """ - Create a deep copy of the vector. - - Returns - ------- - Vector - A new Vector instance with the same data, shape, fields, and units. - """ - import copy - - vector_copy = Vector.from_shape( - shape=self.shape, - name=self.name, - fields=self.fields, - units=self.units, - ) - vector_copy._data = copy.deepcopy(self._data) - return vector_copy - - def flatten(self) -> NDArray: - """ - Flatten the vector into a 2D numpy array. - - Returns - ------- - NDArray - A 2D numpy array containing all data, with shape (total_rows, num_fields). - """ - - def collect_arrays(data: Any) -> List[NDArray]: - if isinstance(data, np.ndarray): - return [data] - elif isinstance(data, list): - arrays = [] - for item in data: - arrays.extend(collect_arrays(item)) - return arrays - else: - return [] + @overload + def __getitem__(self, idx: Any) -> "Vector": ... - arrays = collect_arrays(self._data) - if not arrays: - return np.empty((0, self.num_fields)) - return np.vstack(arrays) + def __getitem__(self, idx: Any) -> "Vector": + if _is_field_selector(idx): + raise TypeError("Use select_fields(...) for field selection.") + coords = _select_coords(self._coords, idx) + return self._from_state(self._state, coords, self._field_names) - def __repr__(self) -> str: - description = [ - f"quantem.Vector, shape={self._shape}, name={self._name}", - f" fields = {self._fields}", - f" units: {self._units}", - ] - return "\n".join(description) - - def __str__(self) -> str: - description = [ - f"quantem.Vector, shape={self._shape}, name={self._name}", - f" fields = {self._fields}", - f" units: {self._units}", - ] - return "\n".join(description) + def __setitem__(self, idx: Any, value: Any) -> None: + if _is_field_selector(idx): + raise TypeError("Use select_fields(...) for field selection.") + self[idx]._assign(value) - @property - def metadata(self) -> dict: - return self._metadata + def __add__(self, other: Any) -> "Vector": + return self._binary_op(other, np.add) - @property - def shape(self) -> Tuple[int, ...]: - """ - Get the shape of the vector. - - Returns - ------- - Tuple[int, ...] - The dimensions of the vector. - """ - return self._shape - - @shape.setter - def shape(self, value: Tuple[int, ...]) -> None: - """ - Set the shape of the vector. - - Parameters - ---------- - value : Tuple[int, ...] - The new shape. All dimensions must be positive. - - Raises - ------ - ValueError - If any dimension is not positive. - TypeError - If value is not a tuple or contains non-integer values. - """ - self._shape = validate_shape(value) + def __sub__(self, other: Any) -> "Vector": + return self._binary_op(other, np.subtract) - @property - def num_fields(self) -> int: - """ - Get the number of fields in the vector. + def __mul__(self, other: Any) -> "Vector": + return self._binary_op(other, np.multiply) - Returns - ------- - int - The number of fields. - """ - return len(self._fields) + def __truediv__(self, other: Any) -> "Vector": + return self._binary_op(other, np.divide) - @property - def name(self) -> str: - """ - Get the name of the vector. + def __pow__(self, other: Any) -> "Vector": + return self._binary_op(other, np.power) - Returns - ------- - str - The name of the vector - """ - return self._name + def __iadd__(self, other: Any) -> "Vector": + return self._binary_op_inplace(other, np.add) - @name.setter - def name(self, value: str) -> None: - """ - Set the name of the vector. + def __isub__(self, other: Any) -> "Vector": + return self._binary_op_inplace(other, np.subtract) - Parameters - ---------- - value : str - The new name of the vector - """ - self._name = str(value) + def __imul__(self, other: Any) -> "Vector": + return self._binary_op_inplace(other, np.multiply) - @property - def fields(self) -> List[str]: - """ - Get the field names of the vector. - - Returns - ------- - List[str] - The list of field names. - """ - return self._fields - - @fields.setter - def fields(self, value: List[str]) -> None: - """ - Set the field names of the vector. - - Parameters - ---------- - value : List[str] - The new field names. Must match num_fields and be unique. - - Raises - ------ - ValueError - If length doesn't match num_fields or if there are duplicates. - TypeError - If value is not a list or contains non-string values. - """ - self._fields = validate_fields(value) + def __itruediv__(self, other: Any) -> "Vector": + return self._binary_op_inplace(other, np.divide) - @property - def units(self) -> List[str]: - """ - Get the units of the vector's fields. - - Returns - ------- - List[str] - The list of units, one per field. - """ - return self._units - - @units.setter - def units(self, value: List[str]) -> None: - """ - Set the units of the vector's fields. - - Parameters - ---------- - value : List[str] - The new units. Must match num_fields. - - Raises - ------ - ValueError - If length doesn't match num_fields. - TypeError - If value is not a list or contains non-string values. - """ - self._units = validate_vector_units(value, self.num_fields) + def __ipow__(self, other: Any) -> "Vector": + return self._binary_op_inplace(other, np.power) - @property - def data(self) -> List[Any]: - """ - Get the raw data of the vector. - - Returns - ------- - List[Any] - The nested list structure containing the vector's data. - """ - return self._data - - @data.setter - def data(self, value: List[Any]) -> None: - """ - Set the raw data of the vector. - - Parameters - ---------- - value : List[Any] - The new data structure. Must match the vector's shape and num_fields. - - Raises - ------ - ValueError - If the data structure doesn't match shape or num_fields. - TypeError - If value is not a list or contains invalid data types. - """ - self._data = validate_vector_data(value, self.shape, self.num_fields) - - -# Helper function for nesting lists -def nested_list(shape: Tuple[int, ...], fill: Any = None) -> Any: - if len(shape) == 0: - return fill - return [nested_list(shape[1:], fill) for _ in range(shape[0])] - - -# Helper class for numerical field operations -class _FieldView: - def __init__(self, vector: Vector, field_name: str) -> None: - self.vector = vector - self.field_name = field_name - self.field_index = vector._fields.index(field_name) - - def _apply_op(self, op: Any) -> None: - def apply(arr: Any) -> None: - if isinstance(arr, np.ndarray): - arr[:, self.field_index] = op(arr[:, self.field_index]) - elif isinstance(arr, list): - for sub in arr: - apply(sub) - - apply(self.vector._data) - - def __iadd__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place addition (+=).""" - self._apply_op(lambda x: x + other) - return self + def _binary_op(self, other: Any, op: Any) -> "Vector": + result = self.copy() + result._binary_op_inplace(other, op) + return result - def __isub__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place subtraction (-=).""" - self._apply_op(lambda x: x - other) + def _binary_op_inplace(self, other: Any, op: Any) -> "Vector": + row_counts = self._row_counts() + targets = list(self._iter_coords()) + + if isinstance(other, Vector): + self._validate_vector_rhs(other, require_matching_rows=True) + source_arrays = list(other._iter_selected_arrays()) + for coord, current, rhs in zip(targets, self._iter_selected_arrays(), source_arrays): + updated = current.copy() + op(updated, rhs, out=updated, casting="same_kind") + self._write_selected_array(coord, updated) + return self + + rhs_matrix = _broadcast_rhs(np.asarray(other), sum(row_counts), self.num_fields) + cursor = 0 + for coord, current, row_count in zip(targets, self._iter_selected_arrays(), row_counts): + chunk = rhs_matrix[cursor : cursor + row_count] + cursor += row_count + updated = current.copy() + op(updated, chunk, out=updated, casting="same_kind") + self._write_selected_array(coord, updated) return self - def __imul__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place multiplication (*=).""" - self._apply_op(lambda x: x * other) - return self + def _assign(self, value: Any) -> None: + if self._is_full_field_selection(): + self._assign_full_cells(value) + else: + self._assign_selected_fields(value) + + def _assign_full_cells(self, value: Any) -> None: + targets = list(self._iter_coords()) + if isinstance(value, Vector): + self._validate_vector_rhs(value, require_matching_rows=False) + for target, source in zip(targets, value._iter_selected_arrays()): + self._state.data[target] = source.copy() + return - def __itruediv__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place division (/=).""" - self._apply_op(lambda x: x / other) - return self + array = _coerce_cell_array(value, self.num_fields) + for target in targets: + self._state.data[target] = array.copy() + + def _assign_selected_fields(self, value: Any) -> None: + targets = list(self._iter_coords()) + row_counts = self._row_counts() + if isinstance(value, Vector): + self._validate_vector_rhs(value, require_matching_rows=True) + for coord, source in zip(targets, value._iter_selected_arrays()): + self._write_selected_array(coord, source) + return - def __ifloordiv__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place floor division (//=).""" - self._apply_op(lambda x: x // other) - return self + if np.isscalar(value): + for coord in targets: + current = self._selected_array_for_coord(coord) + fill = np.full(current.shape, value) + self._write_selected_array(coord, fill) + return - def __imod__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place modulo (%=).""" - self._apply_op(lambda x: x % other) - return self + rhs_matrix = _broadcast_rhs(np.asarray(value), sum(row_counts), self.num_fields) + cursor = 0 + for coord, row_count in zip(targets, row_counts): + chunk = rhs_matrix[cursor : cursor + row_count] + cursor += row_count + self._write_selected_array(coord, chunk) + + def _validate_vector_rhs(self, other: "Vector", require_matching_rows: bool) -> None: + if self.num_fields != other.num_fields: + raise ValueError(f"Expected {self.num_fields} fields, got {other.num_fields}") + if self._coords.size != other._coords.size: + raise ValueError(f"Expected {self._coords.size} cells, got {other._coords.size}") + if require_matching_rows: + left = self._row_counts() + right = other._row_counts() + if left != right: + raise ValueError(f"Per-cell row counts must match: {left} != {right}") + + def _row_counts(self) -> list[int]: + return [self._state.data[coord].shape[0] for coord in self._iter_coords()] + + def _iter_coords(self) -> Iterable[tuple[int, ...]]: + return iter(self._coords.flat) + + def _iter_selected_arrays(self) -> Iterable[NDArray[np.generic]]: + for coord in self._iter_coords(): + yield self._selected_array_for_coord(coord) + + def _selected_array_for_coord(self, coord: tuple[int, ...]) -> NDArray[np.generic]: + cell = self._state.data[coord] + indices = self._field_indices() + if self._is_full_field_selection(): + return cell + if len(indices) == 1: + idx = indices[0] + return cell[:, idx : idx + 1] + if _is_contiguous(indices): + return cell[:, indices[0] : indices[-1] + 1] + return cell[:, indices].copy() + + def _write_selected_array(self, coord: tuple[int, ...], values: NDArray[np.generic]) -> None: + cell = self._state.data[coord] + if self._is_full_field_selection(): + replacement = _coerce_cell_array(values, self.num_fields) + self._state.data[coord] = replacement.copy() + return - def __ipow__(self, other: Union[float, int, np.ndarray]) -> "_FieldView": - """Handle in-place power (**=).""" - self._apply_op(lambda x: x**other) - return self + if values.ndim != 2 or values.shape[1] != self.num_fields: + raise ValueError( + f"Expected array with shape (_, {self.num_fields}), got {values.shape}" + ) + if values.shape[0] != cell.shape[0]: + raise ValueError( + f"Expected {cell.shape[0]} rows for in-place field update, got {values.shape[0]}" + ) + cell[:, self._field_indices()] = values + + def _field_indices(self) -> list[int]: + lookup = {name: idx for idx, name in enumerate(self._state.fields)} + try: + return [lookup[name] for name in self._field_names] + except KeyError as exc: + raise KeyError(f"Unknown field '{exc.args[0]}'") from exc + + def _is_full_field_selection(self) -> bool: + return list(self._field_names) == self._state.fields + + +def _empty_storage(shape: tuple[int, ...], num_fields: int) -> NDArray[np.object_]: + storage = np.empty(shape if shape != () else (), dtype=object) + for coord in np.ndindex(shape) if shape != () else [()]: + storage[coord] = np.empty((0, num_fields), dtype=float) + return storage + + +def _root_coords(shape: tuple[int, ...]) -> NDArray[np.object_]: + return _root_coords_allow_empty(shape) + + +def _root_coords_allow_empty(shape: tuple[int, ...]) -> NDArray[np.object_]: + coords = np.empty(shape if shape != () else (), dtype=object) + for coord in np.ndindex(shape) if shape != () else [()]: + coords[coord] = coord + return coords + + +def _normalize_field_names(field_names: str | Sequence[str]) -> list[str]: + if isinstance(field_names, str): + names = [field_names] + elif isinstance(field_names, Sequence): + names = [str(name) for name in field_names] + else: + raise TypeError("Field names must be a string or a sequence of strings.") + if not names: + raise ValueError("Must select at least one field.") + if len(set(names)) != len(names): + raise ValueError("Duplicate field names are not allowed.") + return names + + +def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str]: + if units is None: + return ["none"] * count + if isinstance(units, str): + if count != 1: + raise ValueError("A single unit string is only valid for a single new field.") + return [units] + normalized = [str(unit) for unit in units] + if len(normalized) != count: + raise ValueError(f"Expected {count} units, got {len(normalized)}") + return normalized + + +def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: + array = np.asarray(value) + if array.ndim != 2 or array.shape[1] != num_fields: + raise ValueError(f"Expected a numpy array with shape (_, {num_fields}), got {array.shape}") + if not np.issubdtype(array.dtype, np.number): + raise TypeError("Cell arrays must contain numeric values.") + return array + + +def _broadcast_rhs(value: NDArray[np.generic], total_rows: int, num_fields: int) -> NDArray[np.generic]: + if num_fields == 1 and value.ndim == 1: + value = value.reshape(-1, 1) + try: + return np.broadcast_to(value, (total_rows, num_fields)) + except ValueError as exc: + raise ValueError( + f"RHS is not broadcast-compatible with flattened target shape ({total_rows}, {num_fields})." + ) from exc + + +def _is_field_selector(idx: Any) -> bool: + if isinstance(idx, str): + return True + if isinstance(idx, list | tuple) and idx: + if all(isinstance(item, str) for item in idx): + return True + return any(isinstance(item, str) for item in idx) + return False + + +def _is_contiguous(indices: Sequence[int]) -> bool: + if not indices: + return False + return list(indices) == list(range(indices[0], indices[0] + len(indices))) + + +def _select_coords(coords: NDArray[np.object_], idx: Any) -> NDArray[np.object_]: + shape = coords.shape + selectors, scalar_axes = _normalize_indices(shape, idx) + out_shape = tuple(len(selector) for selector, scalar in zip(selectors, scalar_axes) if not scalar) + result = np.empty(out_shape if out_shape != () else (), dtype=object) + + if out_shape == (): + source = tuple(selector[0] for selector in selectors) + result[()] = coords[source] + return result - def flatten(self) -> NDArray: - def collect(arr: Any) -> List[NDArray]: - if isinstance(arr, np.ndarray): - return [arr[:, self.field_index]] - elif isinstance(arr, list): - result = [] - for sub in arr: - result.extend(collect(sub)) - return result + for out_index in np.ndindex(out_shape): + out_cursor = 0 + source = [] + for axis, selector in enumerate(selectors): + if scalar_axes[axis]: + source.append(selector[0]) else: - return [] - - arrays = collect(self.vector._data) - if not arrays: - return np.empty((0,), dtype=float) - return np.concatenate(arrays, axis=0) - - def set_flattened(self, values: ArrayLike) -> None: - """ - Set the field values across the entire Vector from a 1D flattened array. - """ - - def fill(arr: Any, values: NDArray, cursor: int) -> int: - if isinstance(arr, np.ndarray): - n = arr.shape[0] - arr[:, self.field_index] = values[cursor : cursor + n] - return cursor + n - elif isinstance(arr, list): - for sub in arr: - cursor = fill(sub, values, cursor) - return cursor - return cursor - - values = np.asarray(values) - if values.ndim != 1: - raise ValueError("Input to set_flattened must be a 1D array.") - - expected = self.flatten().shape[0] - if values.shape[0] != expected: - raise ValueError(f"Expected {expected} values, got {values.shape[0]}") - - fill(self.vector._data, values, cursor=0) - - def __getitem__( - self, idx: Union[Tuple[Union[int, slice], ...], int, slice] - ) -> Union[NDArray, "_FieldView"]: - # Optionally allow v['field0'][0, 1] to get subregion, or v['field0'][...] slice - sub = self.vector[idx] - if isinstance(sub, Vector): - return sub[self.field_name] - elif isinstance(sub, np.ndarray): - return sub[:, self.field_index] - return cast(NDArray, None) - - def __array__(self) -> np.ndarray: - """Convert to numpy array when needed.""" - return self.flatten() + source.append(selector[out_index[out_cursor]]) + out_cursor += 1 + result[out_index] = coords[tuple(source)] + return result + + +def _normalize_indices(shape: tuple[int, ...], idx: Any) -> tuple[list[np.ndarray], list[bool]]: + if shape == (): + if idx in ((), Ellipsis, slice(None), None): + return [], [] + raise IndexError("Too many indices for a 0D Vector.") + + normalized = idx if isinstance(idx, tuple) else (idx,) + normalized = _expand_ellipsis(normalized, len(shape)) + if len(normalized) > len(shape): + raise IndexError(f"Expected at most {len(shape)} indices, got {len(normalized)}") + normalized = normalized + (slice(None),) * (len(shape) - len(normalized)) + + selectors: list[np.ndarray] = [] + scalar_axes: list[bool] = [] + for axis_value, axis_size in zip(normalized, shape): + selector, scalar = _normalize_axis_index(axis_value, axis_size) + selectors.append(selector) + scalar_axes.append(scalar) + return selectors, scalar_axes + + +def _expand_ellipsis(idx: tuple[Any, ...], ndim: int) -> tuple[Any, ...]: + if not any(item is Ellipsis for item in idx): + return idx + if sum(item is Ellipsis for item in idx) > 1: + raise IndexError("Only one ellipsis is allowed.") + ellipsis_pos = next(i for i, item in enumerate(idx) if item is Ellipsis) + fill = ndim - (len(idx) - 1) + return idx[:ellipsis_pos] + (slice(None),) * fill + idx[ellipsis_pos + 1 :] + + +def _normalize_axis_index(value: Any, axis_size: int) -> tuple[np.ndarray, bool]: + if isinstance(value, (int, np.integer)): + index = int(value) + if index < 0: + index += axis_size + if index < 0 or index >= axis_size: + raise IndexError(f"Index {value} out of bounds for axis with size {axis_size}") + return np.array([index], dtype=int), True + + if isinstance(value, slice): + return np.arange(axis_size)[value], False + + if isinstance(value, np.ndarray) and value.ndim == 0: + return _normalize_axis_index(value.item(), axis_size) + + array = np.asarray(value) + if array.ndim != 1: + raise IndexError("Only 1D per-axis fancy or boolean indexing is supported.") + if array.size == 0: + return np.array([], dtype=int), False + + if array.dtype == bool or np.issubdtype(array.dtype, np.bool_): + if array.shape[0] != axis_size: + raise IndexError( + f"Boolean mask length {array.shape[0]} does not match axis size {axis_size}" + ) + return np.flatnonzero(array), False + + if not np.issubdtype(array.dtype, np.integer): + raise TypeError(f"Unsupported index type {type(value).__name__}") + + normalized = array.astype(int, copy=True) + normalized[normalized < 0] += axis_size + if np.any((normalized < 0) | (normalized >= axis_size)): + raise IndexError(f"Index out of bounds for axis with size {axis_size}") + return normalized, False + + +def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: + if not isinstance(data, list): + raise TypeError("Data must be a list.") + if not data: + raise ValueError("Data list cannot be empty.") + + leaves: list[NDArray[np.generic]] = [] + num_fields: int | None = None + + def walk(node: Any) -> tuple[int, ...]: + nonlocal num_fields + if _is_leaf_cell(node): + array = np.asarray(node) + if array.ndim != 2: + raise ValueError(f"Cell arrays must be 2D, got shape {array.shape}") + if not np.issubdtype(array.dtype, np.number): + raise TypeError("Cell arrays must contain numeric values.") + if num_fields is None: + num_fields = array.shape[1] + elif array.shape[1] != num_fields: + raise ValueError("All data arrays must have same number of fields.") + leaves.append(array) + return () + + if not isinstance(node, list): + raise TypeError("Data elements must be numpy arrays, numeric 2D lists, or nested lists thereof.") + if not node: + raise ValueError("Nested data lists cannot be empty.") + child_shapes = [walk(child) for child in node] + first = child_shapes[0] + if any(shape != first for shape in child_shapes[1:]): + raise ValueError("Nested data structure must have a consistent fixed-grid shape.") + return (len(node), *first) + + inferred_shape = walk(data) + return inferred_shape, leaves + + +def _is_leaf_cell(node: Any) -> bool: + if isinstance(node, np.ndarray): + return True + if not isinstance(node, list): + return False + try: + array = np.asarray(node) + except Exception: + return False + return array.ndim == 2 and np.issubdtype(array.dtype, np.number) diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index e2085b76..f03a08b4 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -4,430 +4,231 @@ from quantem.core.datastructures.vector import Vector -class TestVector: - """Test suite for the Vector class.""" - def test_initialization(self): - """Test Vector initialization with different parameters.""" - # Test with fields - v1 = Vector.from_shape(shape=(2, 3), fields=["field0", "field1", "field2"]) +def make_line_vector() -> Vector: + v = Vector.from_shape( + shape=(4,), + fields=["intensity", "kx", "ky"], + units=["a.u.", "px", "px"], + name="line", + ) + v[0] = np.array([[1.0, 10.0, 100.0], [2.0, 20.0, 200.0]]) + v[1] = np.array([[3.0, 30.0, 300.0]]) + v[2] = np.array([[4.0, 40.0, 400.0], [5.0, 50.0, 500.0]]) + v[3] = np.array([[6.0, 60.0, 600.0]]) + return v + + + +def make_grid_vector() -> Vector: + v = Vector.from_shape(shape=(3, 2), fields=["intensity", "kx", "ky"]) + for i in range(3): + for j in range(2): + base = float(i * 10 + j) + v[i, j] = np.array([[base, base + 100.0, base + 200.0]]) + return v + + +class TestVector: + def test_initialization_and_len(self): + v1 = Vector.from_shape(shape=(2, 3), fields=["a", "b", "c"]) assert v1.shape == (2, 3) + assert len(v1) == 2 assert v1.num_fields == 3 - assert v1.fields == ["field0", "field1", "field2"] + assert v1.fields == ["a", "b", "c"] assert v1.units == ["none", "none", "none"] assert v1.name == "2d ragged array" - assert hasattr(v1, "metadata") - - # Test with num_fields - v2 = Vector.from_shape(shape=(2, 3), num_fields=3) - assert v2.shape == (2, 3) - assert v2.num_fields == 3 - assert v2.fields == ["field_0", "field_1", "field_2"] - assert v2.units == ["none", "none", "none"] - assert hasattr(v2, "metadata") - - # Test with custom name and units - v3 = Vector.from_shape( - shape=(2, 3), - fields=["field0", "field1", "field2"], - name="my_vector", - units=["unit0", "unit1", "unit2"], - ) - assert v3.name == "my_vector" - assert v3.units == ["unit0", "unit1", "unit2"] - assert hasattr(v3, "metadata") + assert v1[0, 0].array.shape == (0, 3) + np.testing.assert_array_equal(v1[0, 0].flatten(), v1[0, 0].array) + + v2 = Vector.from_shape(shape=(2, 3), num_fields=2) + assert v2.fields == ["field_0", "field_1"] + + with pytest.raises(TypeError): + len(v1[0, 0]) - # Test error cases with pytest.raises(ValueError, match="Must specify either 'fields' or 'num_fields'."): Vector.from_shape(shape=(2, 3)) with pytest.raises(ValueError, match="does not match length of fields"): - Vector.from_shape(shape=(2, 3), num_fields=3, fields=["field0", "field1"]) + Vector.from_shape(shape=(2, 3), num_fields=2, fields=["a", "b", "c"]) with pytest.raises(ValueError, match="Duplicate field names"): - Vector.from_shape(shape=(2, 3), fields=["field0", "field0", "field2"]) + Vector.from_shape(shape=(2, 3), fields=["a", "a"]) - def test_data_access(self): - """Test data access and assignment.""" - v = Vector.from_shape(shape=(2, 3), fields=["field0", "field1", "field2"]) + def test_indexing_and_array_contract(self): + v = make_grid_vector() - # Set data at specific indices - data1 = np.array([[1.0, 2.0, 3.0]]) - v[0, 0] = data1 - np.testing.assert_array_equal(v.get_data(0, 0), data1) # type: ignore + assert isinstance(v[:2, 1], Vector) + assert v[:2, 1].shape == (2,) + assert v[1].shape == (2,) + assert v[1, 1].shape == () + np.testing.assert_array_equal(v[-1, -1].array, np.array([[21.0, 121.0, 221.0]])) - # Test get_data method - assert np.array_equal(v.get_data(0, 0), data1) + with pytest.raises(ValueError): + _ = v[:, 1].array - # Test set_data method - data2 = np.array([[4.0, 5.0, 6.0]]) - v.set_data(data2, 0, 1) - assert np.array_equal(v.get_data(0, 1), data2) + result = v[[-1, 0], 1] + assert result.shape == (2,) + np.testing.assert_array_equal(result[0].array, np.array([[21.0, 121.0, 221.0]])) + np.testing.assert_array_equal(result[1].array, np.array([[1.0, 101.0, 201.0]])) - # Test error cases - with pytest.raises(IndexError): - v[2, 0] = data1 # Out of bounds + def test_select_fields_and_chaining_equivalence(self): + v = make_line_vector() - with pytest.raises(ValueError): - v[0, 0] = np.array([[1.0, 2.0]]) # Wrong number of fields + selected = v.select_fields("kx") + assert selected.fields == ["kx"] + assert selected.units == ["px"] + assert selected.shape == v.shape - with pytest.raises(ValueError): - v.set_data(np.array([[1.0, 2.0]]), 0, 0) # Wrong number of fields - - def test_field_operations(self): - """Test field-level operations.""" - v = Vector.from_shape(shape=(2, 3), fields=["field0", "field1", "field2"]) - - # Set initial data - v[0, 0] = np.array([[1.0, 2.0, 3.0]]) - v[0, 1] = np.array([[4.0, 5.0, 6.0]]) - v[0, 2] = np.array([[7.0, 8.0, 9.0]]) - - # Test field access - field_view = v["field0"] - assert ( - hasattr(field_view, "vector") - and hasattr(field_view, "field_name") - and hasattr(field_view, "field_index") + np.testing.assert_array_equal( + v.select_fields("kx")[2].array, + v[2].select_fields("kx").array, ) - # Test field operations - v["field0"] += 10 # type: ignore - np.testing.assert_array_equal(v.get_data(0, 0)[:, 0], np.array([11.0])) # type: ignore - np.testing.assert_array_equal(v.get_data(0, 1)[:, 0], np.array([14.0])) # type: ignore - np.testing.assert_array_equal(v.get_data(0, 2)[:, 0], np.array([17.0])) # type: ignore + with pytest.raises(KeyError): + v.select_fields("missing") - # Test applying a function to a field - v["field1"] *= 2 # Using multiplication instead of lambda # type: ignore - np.testing.assert_array_equal(v.get_data(0, 0)[:, 1], np.array([4.0])) # type: ignore - np.testing.assert_array_equal(v.get_data(0, 1)[:, 1], np.array([10.0])) # type: ignore - np.testing.assert_array_equal(v.get_data(0, 2)[:, 1], np.array([16.0])) # type: ignore + with pytest.raises(TypeError): + _ = v["kx"] - # Test field flattening - flat = v["field2"].flatten() - np.testing.assert_array_equal(flat, np.array([3.0, 6.0, 9.0])) # type: ignore + with pytest.raises(TypeError): + _ = v[1, "kx"] - # Test setting flattened data - v["field2"].set_flattened(np.array([18.0, 18.0, 18.0])) + def test_array_mutation_writes_through_for_single_field(self): + v = make_line_vector() + cell = v.select_fields("kx")[1].array + cell[0, 0] = 99.0 + assert v[1].array[0, 1] == 99.0 - # Test error cases - with pytest.raises(KeyError): - v["nonexistent_field"] + def test_field_arithmetic_with_scalar_and_ndarray(self): + v = make_line_vector() - with pytest.raises(ValueError): - v["field0"].set_flattened(np.array([1.0, 2.0])) # Wrong length - - def test_slicing(self): - """Test slicing operations.""" - v = Vector.from_shape(shape=(4, 3), fields=["field0", "field1", "field2"]) - - # Set data - for i in range(4): - for j in range(3): - v[i, j] = np.array( - [[float(i * 3 + j), float(i * 3 + j + 1), float(i * 3 + j + 2)]] - ) - - # Test slicing - sliced = v[1:3, 1] - assert isinstance(sliced, Vector) - assert sliced.shape == (2, 1) - - # Compare arrays directly - expected1 = np.array([[4.0, 5.0, 6.0]]) - expected2 = np.array([[7.0, 8.0, 9.0]]) - np.testing.assert_array_equal(sliced.get_data(0, 0), expected1) # type: ignore - np.testing.assert_array_equal(sliced.get_data(1, 0), expected2) # type: ignore - - # Test field access on sliced vector - field_sliced = sliced["field1"] - np.testing.assert_array_equal(field_sliced.flatten(), np.array([5.0, 8.0])) # type: ignore - - # Test copying slices of vectors - v[2:4, 1] = v[1:3, 1] - - # Test copying slices of vectors with fancy indexing - v[[0, 1], 1] = v[[2, 3], 0] - - def test_field_management(self): - """Test adding and removing fields.""" - v = Vector.from_shape(shape=(2, 3), fields=["field0", "field1", "field2"]) - - # Set initial data - v[0, 0] = np.array([[1.0, 2.0, 3.0]]) - - # Test adding fields - v.add_fields(["field3", "field4"]) - assert v.num_fields == 5 - assert v.fields == ["field0", "field1", "field2", "field3", "field4"] - assert v.units == ["none", "none", "none", "none", "none"] - - # Check that new fields are initialized to zeros - np.testing.assert_array_equal(v.get_data(0, 0)[:, 3:5], np.array([[0.0, 0.0]])) # type: ignore - - # Test removing fields - v.remove_fields(["field1", "field3"]) - assert v.num_fields == 3 - assert v.fields == ["field0", "field2", "field4"] - assert v.units == ["none", "none", "none"] - - # Check that data is preserved for remaining fields - np.testing.assert_array_equal(v.get_data(0, 0)[:, 0], np.array([1.0])) # type: ignore - np.testing.assert_array_equal(v.get_data(0, 0)[:, 1], np.array([3.0])) # type: ignore - np.testing.assert_array_equal(v.get_data(0, 0)[:, 2], np.array([0.0])) # type: ignore - - # Test error cases - with pytest.raises(ValueError): - v.add_fields(["field0"]) # Duplicate field - - v.remove_fields(["nonexistent_field"]) # Should just print a warning - - def test_copy(self): - """Test deep copying.""" - v = Vector.from_shape(shape=(2, 3), fields=["field0", "field1", "field2"]) - v[0, 0] = np.array([[1.0, 2.0, 3.0]]) - - # Create a copy - v_copy = v.copy() - - # Check that it's a deep copy - assert v_copy is not v - assert v_copy.shape == v.shape - assert v_copy.fields == v.fields - assert v_copy.units == v.units - np.testing.assert_array_equal(v_copy.get_data(0, 0), v.get_data(0, 0)) # type: ignore - - # Modify the copy and check that the original is unchanged - v_copy[0, 0] = np.array([[4.0, 5.0, 6.0]]) - np.testing.assert_array_equal(v.get_data(0, 0), np.array([[1.0, 2.0, 3.0]])) # type: ignore - - def test_flatten(self): - """Test flattening the entire vector.""" - v = Vector.from_shape(shape=(2, 3), fields=["field0", "field1", "field2"]) - - # Set data - v[0, 0] = np.array([[1.0, 2.0, 3.0]]) - v[0, 1] = np.array([[4.0, 5.0, 6.0]]) - v[0, 2] = np.array([[7.0, 8.0, 9.0]]) - v[1, 0] = np.array([[10.0, 11.0, 12.0]]) - v[1, 1] = np.array([[13.0, 14.0, 15.0]]) - v[1, 2] = np.array([[16.0, 17.0, 18.0]]) - - # Flatten the vector - flattened = v.flatten() - - # Check the flattened array - expected = np.array( - [ - [1.0, 2.0, 3.0], - [4.0, 5.0, 6.0], - [7.0, 8.0, 9.0], - [10.0, 11.0, 12.0], - [13.0, 14.0, 15.0], - [16.0, 17.0, 18.0], - ] + kx = v.select_fields("kx") + kx += 10 + np.testing.assert_array_equal(v.select_fields("kx").flatten(), np.array([[20.0], [30.0], [40.0], [50.0], [60.0], [70.0]])) + + v.select_fields("kx")[...] += np.arange(6) + np.testing.assert_array_equal(v.select_fields("kx").flatten(), np.array([[20.0], [31.0], [42.0], [53.0], [64.0], [75.0]])) + + summed = v.select_fields("intensity") + v.select_fields("ky") + np.testing.assert_array_equal( + summed.flatten(), + np.array([[101.0], [202.0], [303.0], [404.0], [505.0], [606.0]]), ) - np.testing.assert_array_equal(flattened, expected) # type: ignore - def test_from_data(self): - """Test creating a Vector from ragged lists or numpy arrays.""" - # Create test data - data = [ - np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), - np.array([[7.0, 8.0, 9.0]]), - np.array([[10.0, 11.0, 12.0], [13.0, 14.0, 15.0], [16.0, 17.0, 18.0]]), - ] + def test_field_assignment_from_vector_expression(self): + v = make_line_vector() + scale = 2.5 - # Test with explicit fields - v1 = Vector.from_data( - data=data, - fields=["field0", "field1", "field2"], - name="test_vector", - units=["unit0", "unit1", "unit2"], + v[:2].select_fields("intensity")[...] = v[2:4].select_fields("intensity") * scale + np.testing.assert_array_equal( + v[:2].select_fields("intensity").flatten(), + np.array([[10.0], [12.5], [15.0]]), ) - # Check properties - assert v1.shape == (3,) - assert v1.num_fields == 3 - assert v1.fields == ["field0", "field1", "field2"] - assert v1.units == ["unit0", "unit1", "unit2"] - assert v1.name == "test_vector" - - # Check data - np.testing.assert_array_equal(v1.get_data(0), data[0]) # type: ignore - np.testing.assert_array_equal(v1.get_data(1), data[1]) # type: ignore - np.testing.assert_array_equal(v1.get_data(2), data[2]) # type: ignore - - # Test with inferred fields - v2 = Vector.from_data(data=data, num_fields=3) - - # Check properties - assert v2.shape == (3,) - assert v2.num_fields == 3 - assert v2.fields == ["field_0", "field_1", "field_2"] - assert v2.units == ["none", "none", "none"] - - # Check data - np.testing.assert_array_equal(v2.get_data(0), data[0]) # type: ignore - np.testing.assert_array_equal(v2.get_data(1), data[1]) # type: ignore - np.testing.assert_array_equal(v2.get_data(2), data[2]) # type: ignore - - # Test error cases - with pytest.raises(TypeError, match="Data must be a list"): - Vector.from_data(data=np.array([1, 2, 3])) # type: ignore + def test_field_assignment_requires_matching_per_cell_row_counts(self): + v = make_line_vector() + with pytest.raises(ValueError, match="Per-cell row counts must match"): + v[:2].select_fields("intensity")[...] = v[1:3].select_fields("intensity") - with pytest.raises(ValueError, match="does not match length of fields"): - Vector.from_data( - data=data, - fields=["field0", "field1"], # Wrong number of fields - ) + def test_full_cell_assignment_allows_row_count_changes(self): + v = make_line_vector() - with pytest.raises(ValueError, match="Duplicate field names"): - Vector.from_data( - data=data, - fields=["field0", "field0", "field2"], # Duplicate field names - ) - - def test_fancy_indexing(self): - """Test fancy indexing with __getitem__ and __setitem__.""" - v = Vector.from_shape(shape=(3, 2), fields=["field0", "field1", "field2"]) - - # Set initial data - v[0, 0] = np.array([[1.0, 2.0, 3.0]]) - v[0, 1] = np.array([[4.0, 5.0, 6.0]]) - v[1, 0] = np.array([[7.0, 8.0, 9.0]]) - v[1, 1] = np.array([[10.0, 11.0, 12.0]]) - v[2, 0] = np.array([[13.0, 14.0, 15.0]]) - v[2, 1] = np.array([[16.0, 17.0, 18.0]]) - - # Test list indexing with __getitem__ - result = v[[0, 1], 0] - assert isinstance(result, Vector) - assert result.shape == (2, 1) - np.testing.assert_array_equal(result.get_data(0, 0), np.array([[1.0, 2.0, 3.0]])) - np.testing.assert_array_equal(result.get_data(1, 0), np.array([[7.0, 8.0, 9.0]])) - - # Test numpy array indexing with __getitem__ - result = v[np.array([1, 2]), 1] # type: ignore - assert isinstance(result, Vector) - assert result.shape == (2, 1) - np.testing.assert_array_equal(result.get_data(0, 0), np.array([[10.0, 11.0, 12.0]])) - np.testing.assert_array_equal(result.get_data(1, 0), np.array([[16.0, 17.0, 18.0]])) - - # Test fancy indexing with __setitem__ - new_data = [np.array([[20.0, 21.0, 22.0]]), np.array([[23.0, 24.0, 25.0]])] - v[[0, 2], 1] = new_data - np.testing.assert_array_equal(v.get_data(0, 1), new_data[0]) - np.testing.assert_array_equal(v.get_data(2, 1), new_data[1]) - - # Test numpy array fancy indexing with __setitem__ - new_data = [np.array([[26.0, 27.0, 28.0]]), np.array([[29.0, 30.0, 31.0]])] - v[np.array([1, 2]), 0] = new_data # type: ignore - np.testing.assert_array_equal(v.get_data(1, 0), new_data[0]) - np.testing.assert_array_equal(v.get_data(2, 0), new_data[1]) - - # Test error cases - with pytest.raises(IndexError): - v[[3, 4], 0] # Index out of bounds + v[1] = v[0] + assert v[1].array.shape == (2, 3) + np.testing.assert_array_equal(v[1].array, v[0].array) - with pytest.raises(IndexError): - v[[0, 1], 2] # Index out of bounds + v[0:2] = v[1:3] + assert v[0].array.shape == (2, 3) + assert v[1].array.shape == (2, 3) - with pytest.raises(ValueError): - v[[0, 1], 0] = [np.array([[1.0]])] # Wrong number of arrays + broadcast_cell = np.array([[9.0, 8.0, 7.0]]) + v[[0, 3]] = broadcast_cell + np.testing.assert_array_equal(v[0].array, broadcast_cell) + np.testing.assert_array_equal(v[3].array, broadcast_cell) - with pytest.raises(ValueError): - v[[0, 1], 0] = [ - np.array([[1.0]]), - np.array([[2.0]]), - ] # Wrong number of fields - - def test_get_data_methods(self): - """Test get_data method with various indexing scenarios.""" - v = Vector.from_shape(shape=(3, 2), fields=["field0", "field1", "field2"]) - - # Set some test data - v[0, 0] = np.array([[1.0, 2.0, 3.0]]) - v[0, 1] = np.array([[4.0, 5.0, 6.0]]) - v[1, 0] = np.array([[7.0, 8.0, 9.0]]) - v[1, 1] = np.array([[10.0, 11.0, 12.0]]) - v[2, 0] = np.array([[13.0, 14.0, 15.0]]) - v[2, 1] = np.array([[16.0, 17.0, 18.0]]) - - # Test single integer indexing - result = v.get_data(0, 0) - np.testing.assert_array_equal(result, np.array([[1.0, 2.0, 3.0]])) - - # Test list indexing - result = v.get_data([0, 1], 0) - np.testing.assert_array_equal(result[0], np.array([[1.0, 2.0, 3.0]])) - np.testing.assert_array_equal(result[1], np.array([[7.0, 8.0, 9.0]])) - - # Test numpy array indexing - result = v.get_data(np.array([1, 2]), 1) - np.testing.assert_array_equal(result[0], np.array([[10.0, 11.0, 12.0]])) - np.testing.assert_array_equal(result[1], np.array([[16.0, 17.0, 18.0]])) - - # Test slice indexing - result = v.get_data(slice(1, 3), 0) - np.testing.assert_array_equal(result[0], np.array([[7.0, 8.0, 9.0]])) - np.testing.assert_array_equal(result[1], np.array([[13.0, 14.0, 15.0]])) - - # Test error cases - with pytest.raises(ValueError, match="Expected 2 indices"): - v.get_data(0) # Too few indices - - with pytest.raises(ValueError, match="Expected 2 indices"): - v.get_data(0, 0, 0) # Too many indices + def test_boolean_indexing_is_axis_wise(self): + v = make_grid_vector() - with pytest.raises(IndexError): - v.get_data(3, 0) # Index out of bounds + rows = np.array([True, False, True]) + cols = np.array([False, True]) + selected = v[rows, cols] - with pytest.raises(IndexError): - v.get_data([3, 4], 0) # List index out of bounds - - def test_set_data_methods(self): - """Test set_data method with various indexing scenarios.""" - v = Vector.from_shape(shape=(3, 2), fields=["field0", "field1", "field2"]) - - # Test single integer indexing - data1 = np.array([[1.0, 2.0, 3.0]]) - v.set_data(data1, 0, 0) - np.testing.assert_array_equal(v.get_data(0, 0), data1) - - # Test list indexing - data2 = [np.array([[4.0, 5.0, 6.0]]), np.array([[7.0, 8.0, 9.0]])] - v.set_data(data2, [0, 1], 1) - np.testing.assert_array_equal(v.get_data(0, 1), data2[0]) - np.testing.assert_array_equal(v.get_data(1, 1), data2[1]) - - # Test numpy array indexing - data3 = [np.array([[10.0, 11.0, 12.0]]), np.array([[13.0, 14.0, 15.0]])] - v.set_data(data3, np.array([1, 2]), 0) - np.testing.assert_array_equal(v.get_data(1, 0), data3[0]) - np.testing.assert_array_equal(v.get_data(2, 0), data3[1]) - - # Test slice indexing - data4 = [np.array([[16.0, 17.0, 18.0]]), np.array([[19.0, 20.0, 21.0]])] - v.set_data(data4, slice(1, 3), 1) - np.testing.assert_array_equal(v.get_data(1, 1), data4[0]) - np.testing.assert_array_equal(v.get_data(2, 1), data4[1]) - - # Test error cases - with pytest.raises(ValueError, match="Expected 2 indices"): - v.set_data(data1, 0) # Too few indices - - with pytest.raises(ValueError, match="Expected 2 indices"): - v.set_data(data1, 0, 0, 0) # Too many indices + assert selected.shape == (2, 1) + np.testing.assert_array_equal(selected[0, 0].array, np.array([[1.0, 101.0, 201.0]])) + np.testing.assert_array_equal(selected[1, 0].array, np.array([[21.0, 121.0, 221.0]])) with pytest.raises(IndexError): - v.set_data(data1, 3, 0) # Index out of bounds + _ = v[np.array([[True, False], [False, True]])] - with pytest.raises(IndexError): - v.set_data([data1, data1], [3, 4], 0) # List index out of bounds + def test_empty_selection_is_valid_and_no_op_for_scalar_math(self): + v = make_grid_vector() + before = v.copy().flatten() - with pytest.raises(TypeError): - v.set_data([1, 2, 3], 0, 0) # Invalid data type # type: ignore + empty = v[[], :] + assert empty.shape == (0, 2) + assert empty.flatten().shape == (0, 3) - with pytest.raises(ValueError): - v.set_data(np.array([[1.0]]), 0, 0) # Wrong number of fields + empty.select_fields("kx")[...] += 1 + np.testing.assert_array_equal(v.flatten(), before) + + def test_add_fields_defaults_expression_and_multiple_values(self): + v = make_line_vector() + + v.add_fields(("h", "k")) + assert v.fields == ["intensity", "kx", "ky", "h", "k"] + assert np.isnan(v[0].array[:, 3:5]).all() + + v.add_fields("field_out", v.select_fields("kx") + v.select_fields("ky")) + np.testing.assert_array_equal( + v.select_fields("field_out").flatten(), + np.array([[110.0], [220.0], [330.0], [440.0], [550.0], [660.0]]), + ) + + v2 = make_line_vector() + v2.add_fields(("h", "k"), (1.0, np.array([5.0, 6.0, 7.0, 8.0, 9.0, 10.0]))) + np.testing.assert_array_equal(v2.select_fields("h").flatten(), np.ones((6, 1))) + np.testing.assert_array_equal( + v2.select_fields("k").flatten(), + np.array([[5.0], [6.0], [7.0], [8.0], [9.0], [10.0]]), + ) + + def test_remove_fields_preserves_remaining_data(self): + v = make_line_vector() + v.add_fields("extra", 1.0) + v.remove_fields(("kx", "extra")) + + assert v.fields == ["intensity", "ky"] + np.testing.assert_array_equal( + v[0].array, + np.array([[1.0, 100.0], [2.0, 200.0]]), + ) + + def test_copy_is_deep(self): + v = make_line_vector() + v_copy = v.select_fields(["intensity", "kx"]).copy() + + v_copy[0].array[0, 0] = -1.0 + assert v[0].array[0, 0] == 1.0 + assert v_copy.fields == ["intensity", "kx"] + assert v_copy.shape == (4,) + + def test_from_data_supports_nested_fixed_grid(self): + data = [ + [np.array([[1.0, 2.0]]), np.array([[3.0, 4.0], [5.0, 6.0]])], + [np.array([[7.0, 8.0]]), np.array([[9.0, 10.0]])], + ] + v = Vector.from_data(data=data, fields=["a", "b"], units=["u1", "u2"], name="nested") + + assert v.shape == (2, 2) + assert v.fields == ["a", "b"] + assert v.units == ["u1", "u2"] + assert v.name == "nested" + np.testing.assert_array_equal(v[0, 1].array, np.array([[3.0, 4.0], [5.0, 6.0]])) + + with pytest.raises(TypeError, match="Data must be a list"): + Vector.from_data(data=np.array([1, 2, 3])) # type: ignore[arg-type] + + with pytest.raises(ValueError, match="same number of fields"): + Vector.from_data(data=[np.array([[1.0, 2.0]]), np.array([[1.0, 2.0, 3.0]])]) From 7abc130b56aa2720e7991bb287cbfe3ba6173593 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Mar 2026 11:48:36 -0800 Subject: [PATCH 106/335] Fixes for writing / reading, new internals --- src/quantem/core/datastructures/vector.py | 992 ++++++++++-------- .../core/visualization/visualization_utils.py | 17 + tests/datastructures/test_vector.py | 41 + .../visualization/test_visualization_utils.py | 8 + 4 files changed, 631 insertions(+), 427 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 4717fe64..53592dcd 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -1,8 +1,7 @@ from __future__ import annotations import copy -from dataclasses import dataclass -from typing import Any, Iterable, Sequence, overload +from typing import Any, Sequence, overload import numpy as np from numpy.typing import NDArray @@ -16,23 +15,13 @@ ) -@dataclass -class _VectorState: - shape: tuple[int, ...] - data: NDArray[np.object_] - fields: list[str] - units: list[str] - name: str - metadata: dict[str, Any] - - class Vector(AutoSerialize): """Ragged cell data on a fixed grid. - A ``Vector`` has fixed grid dimensions (``shape``). Each fixed-grid cell stores a - NumPy array with shape ``(n_rows, num_fields)``. Selections always return new - ``Vector`` views over the same backing store, while raw NumPy extraction is explicit - via ``.array`` and ``.flatten()``. + Storage is compact and AutoSerialize-friendly: + - one 2D numeric row buffer for all ragged rows + - one start array and one length array for cell boundaries + - optional selection state for views """ def __init__( @@ -43,31 +32,40 @@ def __init__( name: str | None = None, metadata: dict[str, Any] | None = None, ) -> None: - validated_shape = validate_shape(shape) - validated_fields = validate_fields(list(fields)) - validated_units = validate_vector_units(list(units) if units is not None else None, len(validated_fields)) - self._state = _VectorState( - shape=validated_shape, - data=_empty_storage(validated_shape, len(validated_fields)), - fields=list(validated_fields), - units=list(validated_units), - name=name or f"{len(validated_shape)}d ragged array", - metadata=dict(metadata or {}), + root_shape = validate_shape(shape) + root_fields = validate_fields(list(fields)) + root_units = validate_vector_units( + list(units) if units is not None else None, + len(root_fields), ) - self._coords = _root_coords(validated_shape) - self._field_names = tuple(validated_fields) + + self._state = { + "shape": root_shape, + "fields": list(root_fields), + "units": list(root_units), + "name": name or f"{len(root_shape)}d ragged array", + "metadata": dict(metadata or {}), + "data": np.empty((0, len(root_fields)), dtype=float), + "cell_starts": np.zeros(_cell_count(root_shape), dtype=np.int64), + "cell_lengths": np.zeros(_cell_count(root_shape), dtype=np.int64), + } + self._selection_shape = root_shape + self._selection_indices: NDArray[np.int64] | None = None + self._selected_fields: tuple[str, ...] | None = None @classmethod - def _from_state( + def _from_view( cls, - state: _VectorState, - coords: NDArray[np.object_], - field_names: Sequence[str], + state: dict[str, Any], + selection_shape: tuple[int, ...], + selection_indices: NDArray[np.int64] | None, + selected_fields: tuple[str, ...] | None, ) -> "Vector": obj = cls.__new__(cls) obj._state = state - obj._coords = coords - obj._field_names = tuple(field_names) + obj._selection_shape = selection_shape + obj._selection_indices = None if selection_indices is None else selection_indices.astype(np.int64, copy=False) + obj._selected_fields = selected_fields return obj @classmethod @@ -80,23 +78,18 @@ def from_shape( name: str | None = None, ) -> "Vector": if fields is not None: - validated_fields = validate_fields(list(fields)) - if num_fields is not None and len(validated_fields) != num_fields: + root_fields = validate_fields(list(fields)) + if num_fields is not None and len(root_fields) != num_fields: raise ValueError( - f"num_fields ({num_fields}) does not match length of fields ({len(validated_fields)})" + f"num_fields ({num_fields}) does not match length of fields ({len(root_fields)})" ) elif num_fields is not None: - validated_count = validate_num_fields(num_fields) - validated_fields = [f"field_{i}" for i in range(validated_count)] + count = validate_num_fields(num_fields) + root_fields = [f"field_{i}" for i in range(count)] else: raise ValueError("Must specify either 'fields' or 'num_fields'.") - return cls( - shape=shape, - fields=validated_fields, - units=units, - name=name, - ) + return cls(shape=shape, fields=root_fields, units=units, name=name) @classmethod def from_data( @@ -107,79 +100,80 @@ def from_data( units: Sequence[str] | None = None, name: str | None = None, ) -> "Vector": - inferred_shape, normalized_cells = _normalize_nested_data(data) - inferred_num_fields = normalized_cells[0].shape[1] if normalized_cells else 0 + root_shape, cell_arrays = _normalize_nested_data(data) + inferred_counts = {array.shape[1] for array in cell_arrays} + if len(inferred_counts) > 1: + raise ValueError("All cell arrays must have the same number of fields.") + inferred_fields = cell_arrays[0].shape[1] if cell_arrays else 0 if fields is not None: - validated_fields = validate_fields(list(fields)) - if len(validated_fields) != inferred_num_fields: + root_fields = validate_fields(list(fields)) + if len(root_fields) != inferred_fields: raise ValueError( - f"num_fields ({inferred_num_fields}) does not match length of fields ({len(validated_fields)})" + f"num_fields ({inferred_fields}) does not match length of fields ({len(root_fields)})" ) elif num_fields is not None: - validated_num_fields = validate_num_fields(num_fields) - if validated_num_fields != inferred_num_fields: + count = validate_num_fields(num_fields) + if count != inferred_fields: raise ValueError( - f"Provided num_fields ({validated_num_fields}) does not match inferred ({inferred_num_fields})." + f"Provided num_fields ({count}) does not match inferred ({inferred_fields})." ) - validated_fields = [f"field_{i}" for i in range(validated_num_fields)] + root_fields = [f"field_{i}" for i in range(count)] else: - validated_fields = [f"field_{i}" for i in range(inferred_num_fields)] + root_fields = [f"field_{i}" for i in range(inferred_fields)] - vector = cls( - shape=inferred_shape, - fields=validated_fields, - units=units, - name=name, - ) - for coord, array in zip(np.ndindex(inferred_shape), normalized_cells): - vector._state.data[coord] = array.copy() + vector = cls(shape=root_shape, fields=root_fields, units=units, name=name) + vector._replace_cells(np.arange(len(cell_arrays), dtype=np.int64), cell_arrays) return vector @property def shape(self) -> tuple[int, ...]: - return self._coords.shape + return self._selection_shape @property def fields(self) -> list[str]: - return list(self._field_names) + if self._selected_fields is None: + return list(self._state["fields"]) + return list(self._selected_fields) @property def units(self) -> list[str]: - lookup = {name: unit for name, unit in zip(self._state.fields, self._state.units)} - return [lookup[name] for name in self._field_names] + lookup = { + field: unit + for field, unit in zip(self._state["fields"], self._state["units"]) + } + return [lookup[field] for field in self.fields] @property def num_fields(self) -> int: - return len(self._field_names) + return len(self.fields) @property def name(self) -> str: - return self._state.name + return self._state["name"] @name.setter def name(self, value: str) -> None: - self._state.name = str(value) + self._state["name"] = str(value) @property def metadata(self) -> dict[str, Any]: - return self._state.metadata + return self._state["metadata"] @property def array(self) -> NDArray[np.generic]: if self.shape != (): raise ValueError(".array is only valid when the selection contains exactly one cell.") - coord = self._coords[()] - cell = self._state.data[coord] - indices = self._field_indices() - if len(indices) == len(self._state.fields) and indices == list(range(len(self._state.fields))): + cell = self._cell_matrix(self._selected_cell_indices()[0]) + cols = self._field_indices() + if cols.size == self._full_num_fields: return cell - if len(indices) == 1: - idx = indices[0] - return cell[:, idx : idx + 1] - if _is_contiguous(indices): - return cell[:, indices[0] : indices[-1] + 1] - return cell[:, indices].copy() + if cols.size == 1: + col = int(cols[0]) + return cell[:, col : col + 1] + if _is_contiguous(cols): + return cell[:, int(cols[0]) : int(cols[-1]) + 1] + return cell[:, cols].copy() def __len__(self) -> int: if self.shape == (): @@ -187,44 +181,58 @@ def __len__(self) -> int: return self.shape[0] def __repr__(self) -> str: - return f"quantem.Vector(shape={self.shape}, fields={self.fields}, name={self.name!r})" + return "\n".join( + [ + f"quantem.Vector, shape={self.shape}, name={self.name}", + f" fields = {self.fields}", + f" units: {self.units}", + ] + ) __str__ = __repr__ def copy(self) -> "Vector": - copied = self.__class__._from_state( - _VectorState( - shape=self.shape, - data=_empty_storage(self.shape, self.num_fields), - fields=self.fields, - units=self.units, - name=self.name, - metadata=copy.deepcopy(self.metadata), - ), - _root_coords_allow_empty(self.shape), - self.fields, + copied = self.__class__( + shape=self.shape, + fields=self.fields, + units=self.units, + name=self.name, + metadata=copy.deepcopy(self.metadata), ) - for coord, array in zip(np.ndindex(self.shape) if self.shape != () else [()], self._iter_selected_arrays()): - copied._state.data[coord] = array.copy() + target_cells = copied._selected_cell_indices() + source_arrays = [self._selected_cell_matrix(index).copy() for index in self._selected_cell_indices()] + copied._replace_cells(target_cells, source_arrays) return copied def flatten(self) -> NDArray[np.generic]: - arrays = [array for array in self._iter_selected_arrays() if array.shape[0] > 0] + arrays = [ + self._selected_cell_matrix(index) + for index in self._selected_cell_indices() + if self._cell_row_count(index) > 0 + ] if arrays: return np.vstack(arrays) - dtype = float - for array in self._iter_selected_arrays(): - dtype = array.dtype - break + + dtype = self._state["data"].dtype if self._state["data"].ndim == 2 else float return np.empty((0, self.num_fields), dtype=dtype) def select_fields(self, field_names: str | Sequence[str]) -> "Vector": - normalized = _normalize_field_names(field_names) - available = set(self._field_names) - missing = [name for name in normalized if name not in available] + selected = _normalize_field_names(field_names) + available = set(self.fields) + missing = [field for field in selected if field not in available] if missing: raise KeyError(f"Unknown field(s): {missing}") - return self._from_state(self._state, self._coords, normalized) + + if selected == tuple(self._state["fields"]): + selected_fields = None + else: + selected_fields = selected + return self._from_view( + self._state, + self.shape, + self._selection_indices, + selected_fields, + ) def add_fields( self, @@ -232,56 +240,49 @@ def add_fields( values: Any | None = None, units: str | Sequence[str] | None = None, ) -> None: - new_names = _normalize_field_names(names) - if any(name in self._state.fields for name in new_names): + new_fields = _normalize_field_names(names) + if any(field in self._state["fields"] for field in new_fields): raise ValueError("One or more new field names already exist.") - new_units = _normalize_units(units, len(new_names)) - old_fields = list(self._state.fields) - self._state.fields.extend(new_names) - self._state.units.extend(new_units) - - old_width = len(old_fields) - new_width = len(self._state.fields) - for coord in np.ndindex(self._state.shape) if self._state.shape != () else [()]: - current = self._state.data[coord] - promoted = np.result_type(current.dtype, float) - expanded = np.full((current.shape[0], new_width), np.nan, dtype=promoted) - expanded[:, :old_width] = current - self._state.data[coord] = expanded - - if list(self._field_names) == old_fields: - self._field_names = tuple(self._state.fields) + new_units = _normalize_units(units, len(new_fields)) + old_fields = list(self._state["fields"]) + self._state["fields"].extend(new_fields) + self._state["units"].extend(new_units) + self._expand_storage(len(new_fields)) - target = self._from_state(self._state, self._coords, new_names) if values is None: return - if len(new_names) > 1 and isinstance(values, (list, tuple)) and len(values) == len(new_names): - for name, value in zip(new_names, values): - target.select_fields(name)._assign(value) + target = self.select_fields(list(new_fields)) + if len(new_fields) > 1 and isinstance(values, (list, tuple)) and len(values) == len(new_fields): + for field, value in zip(new_fields, values): + target.select_fields(field)[...] = value else: - target._assign(values) + target[...] = values + + if self._selected_fields is not None and tuple(old_fields) == self._selected_fields: + self._selected_fields = None def remove_fields(self, names: str | Sequence[str]) -> None: - to_remove = _normalize_field_names(names) - missing = [name for name in to_remove if name not in self._state.fields] + to_remove = set(_normalize_field_names(names)) + old_fields = self._state["fields"] + old_units = self._state["units"] + + missing = [field for field in to_remove if field not in old_fields] if missing: raise KeyError(f"Unknown field(s): {missing}") - if len(to_remove) == len(self._state.fields): + if len(to_remove) == len(old_fields): raise ValueError("Cannot remove all fields from a Vector.") - remove_set = set(to_remove) - keep_names = [name for name in self._state.fields if name not in remove_set] - keep_indices = [self._state.fields.index(name) for name in keep_names] - keep_units = [self._state.units[index] for index in keep_indices] + keep = [i for i, field in enumerate(old_fields) if field not in to_remove] + self._state["fields"] = [old_fields[i] for i in keep] + self._state["units"] = [old_units[i] for i in keep] + self._state["data"] = self._state["data"][:, keep] - for coord in np.ndindex(self._state.shape) if self._state.shape != () else [()]: - self._state.data[coord] = self._state.data[coord][:, keep_indices] - - self._state.fields = keep_names - self._state.units = keep_units - self._field_names = tuple(name for name in self._field_names if name in keep_names) + if self._selected_fields is not None: + self._selected_fields = tuple(field for field in self._selected_fields if field in self._state["fields"]) + if len(self._selected_fields) == len(self._state["fields"]): + self._selected_fields = None def get_data(self, *indices: Any) -> NDArray[np.generic] | list[NDArray[np.generic]]: if len(indices) != len(self.shape): @@ -289,7 +290,7 @@ def get_data(self, *indices: Any) -> NDArray[np.generic] | list[NDArray[np.gener selection = self[indices if len(indices) != 1 else indices[0]] if selection.shape == (): return selection.array - return [array.copy() for array in selection._iter_selected_arrays()] + return [selection._selected_cell_matrix(index).copy() for index in selection._selected_cell_indices()] def set_data(self, value: Any, *indices: Any) -> None: if len(indices) != len(self.shape): @@ -300,15 +301,29 @@ def set_data(self, value: Any, *indices: Any) -> None: def __getitem__(self, idx: Any) -> "Vector": ... def __getitem__(self, idx: Any) -> "Vector": - if _is_field_selector(idx): + if _looks_like_field_selector(idx): raise TypeError("Use select_fields(...) for field selection.") - coords = _select_coords(self._coords, idx) - return self._from_state(self._state, coords, self._field_names) + if idx is Ellipsis: + return self + + selection_shape, selection_indices = _select_linear_indices( + self.shape, + self._selected_cell_indices(), + idx, + ) + return self._from_view( + self._state, + selection_shape, + selection_indices, + self._selected_fields, + ) def __setitem__(self, idx: Any, value: Any) -> None: - if _is_field_selector(idx): - raise TypeError("Use select_fields(...) for field selection.") - self[idx]._assign(value) + if idx is Ellipsis: + target = self + else: + target = self[idx] + target._assign(value) def __add__(self, other: Any) -> "Vector": return self._binary_op(other, np.add) @@ -322,51 +337,137 @@ def __mul__(self, other: Any) -> "Vector": def __truediv__(self, other: Any) -> "Vector": return self._binary_op(other, np.divide) - def __pow__(self, other: Any) -> "Vector": - return self._binary_op(other, np.power) + def __radd__(self, other: Any) -> "Vector": + return self._binary_op(other, np.add, reverse=True) + + def __rmul__(self, other: Any) -> "Vector": + return self._binary_op(other, np.multiply, reverse=True) + + def __rsub__(self, other: Any) -> "Vector": + return self._binary_op(other, np.subtract, reverse=True) + + def __rtruediv__(self, other: Any) -> "Vector": + return self._binary_op(other, np.divide, reverse=True) def __iadd__(self, other: Any) -> "Vector": - return self._binary_op_inplace(other, np.add) + self._inplace_op(other, np.add) + return self def __isub__(self, other: Any) -> "Vector": - return self._binary_op_inplace(other, np.subtract) + self._inplace_op(other, np.subtract) + return self def __imul__(self, other: Any) -> "Vector": - return self._binary_op_inplace(other, np.multiply) + self._inplace_op(other, np.multiply) + return self def __itruediv__(self, other: Any) -> "Vector": - return self._binary_op_inplace(other, np.divide) + self._inplace_op(other, np.divide) + return self - def __ipow__(self, other: Any) -> "Vector": - return self._binary_op_inplace(other, np.power) + @property + def _full_num_fields(self) -> int: + return len(self._state["fields"]) - def _binary_op(self, other: Any, op: Any) -> "Vector": - result = self.copy() - result._binary_op_inplace(other, op) - return result + def _field_indices(self) -> NDArray[np.int64]: + if self._selected_fields is None: + return np.arange(self._full_num_fields, dtype=np.int64) - def _binary_op_inplace(self, other: Any, op: Any) -> "Vector": - row_counts = self._row_counts() - targets = list(self._iter_coords()) + lookup = {field: i for i, field in enumerate(self._state["fields"])} + try: + return np.array([lookup[field] for field in self._selected_fields], dtype=np.int64) + except KeyError as exc: + raise KeyError(f"Unknown field(s): {[str(exc.args[0])]}") from exc - if isinstance(other, Vector): - self._validate_vector_rhs(other, require_matching_rows=True) - source_arrays = list(other._iter_selected_arrays()) - for coord, current, rhs in zip(targets, self._iter_selected_arrays(), source_arrays): - updated = current.copy() - op(updated, rhs, out=updated, casting="same_kind") - self._write_selected_array(coord, updated) - return self + def _selected_cell_indices(self) -> NDArray[np.int64]: + if self._selection_indices is None: + return np.arange(_cell_count(self._state["shape"]), dtype=np.int64) + return self._selection_indices + + def _is_full_field_selection(self) -> bool: + return self._selected_fields is None + + def _cell_row_count(self, linear_index: int) -> int: + return int(self._state["cell_lengths"][linear_index]) + + def _cell_matrix(self, linear_index: int) -> NDArray[np.generic]: + start = int(self._state["cell_starts"][linear_index]) + length = int(self._state["cell_lengths"][linear_index]) + if length == 0: + return self._state["data"][0:0] + return self._state["data"][start : start + length] + + def _selected_cell_matrix(self, linear_index: int) -> NDArray[np.generic]: + cell = self._cell_matrix(linear_index) + cols = self._field_indices() + if cols.size == self._full_num_fields: + return cell + if cols.size == 1: + col = int(cols[0]) + return cell[:, col : col + 1] + if _is_contiguous(cols): + return cell[:, int(cols[0]) : int(cols[-1]) + 1] + return cell[:, cols].copy() + + def _replace_cells(self, targets: NDArray[np.int64], arrays: Sequence[NDArray[np.generic]]) -> None: + if len(targets) != len(arrays): + raise ValueError("Target cell count does not match source cell count.") + if len(targets) == 0: + return + + normalized = [_coerce_cell_array(array, self._full_num_fields) for array in arrays] + payloads = [array for array in normalized if array.shape[0] > 0] + if payloads: + appended = np.vstack(payloads) + data = self._state["data"] + if data.shape[0] == 0: + self._state["data"] = appended.copy() + else: + self._state["data"] = np.concatenate((data, appended), axis=0) + + cursor = self._state["data"].shape[0] - sum(array.shape[0] for array in normalized) + for target, array in zip(targets, normalized): + self._state["cell_starts"][target] = cursor + self._state["cell_lengths"][target] = array.shape[0] + cursor += array.shape[0] + + self._maybe_compact_storage() + + def _expand_storage(self, num_new_fields: int) -> None: + data = self._state["data"] + if data.shape[0] == 0: + dtype = np.result_type(data.dtype, float) + self._state["data"] = np.empty((0, data.shape[1] + num_new_fields), dtype=dtype) + return + + dtype = np.result_type(data.dtype, float) + filler = np.full((data.shape[0], num_new_fields), np.nan, dtype=dtype) + self._state["data"] = np.concatenate((data.astype(dtype, copy=False), filler), axis=1) - rhs_matrix = _broadcast_rhs(np.asarray(other), sum(row_counts), self.num_fields) + def _maybe_compact_storage(self) -> None: + data = self._state["data"] + used_rows = int(self._state["cell_lengths"].sum()) + if data.shape[0] <= used_rows + 1024: + return + if used_rows == 0: + self._state["data"] = np.empty((0, self._full_num_fields), dtype=data.dtype) + self._state["cell_starts"].fill(0) + return + if data.shape[0] <= 2 * used_rows: + return + + compacted = np.empty((used_rows, self._full_num_fields), dtype=data.dtype) + starts = np.zeros_like(self._state["cell_starts"]) cursor = 0 - for coord, current, row_count in zip(targets, self._iter_selected_arrays(), row_counts): - chunk = rhs_matrix[cursor : cursor + row_count] - cursor += row_count - updated = current.copy() - op(updated, chunk, out=updated, casting="same_kind") - self._write_selected_array(coord, updated) - return self + for linear_index in range(_cell_count(self._state["shape"])): + length = self._cell_row_count(linear_index) + starts[linear_index] = cursor + if length > 0: + cell = self._cell_matrix(linear_index) + compacted[cursor : cursor + length] = cell + cursor += length + self._state["data"] = compacted + self._state["cell_starts"] = starts def _assign(self, value: Any) -> None: if self._is_full_field_selection(): @@ -375,131 +476,122 @@ def _assign(self, value: Any) -> None: self._assign_selected_fields(value) def _assign_full_cells(self, value: Any) -> None: - targets = list(self._iter_coords()) + targets = self._selected_cell_indices() if isinstance(value, Vector): - self._validate_vector_rhs(value, require_matching_rows=False) - for target, source in zip(targets, value._iter_selected_arrays()): - self._state.data[target] = source.copy() + source_cells = value._selected_cell_indices() + if len(targets) != len(source_cells): + raise ValueError(f"Expected {len(targets)} cells, got {len(source_cells)}") + if value.num_fields != self.num_fields: + raise ValueError( + f"Expected {self.num_fields} fields, got {value.num_fields}" + ) + arrays = [value._selected_cell_matrix(index).copy() for index in source_cells] + self._replace_cells(targets, arrays) return array = _coerce_cell_array(value, self.num_fields) - for target in targets: - self._state.data[target] = array.copy() + self._replace_cells(targets, [array] * len(targets)) def _assign_selected_fields(self, value: Any) -> None: - targets = list(self._iter_coords()) - row_counts = self._row_counts() + targets = self._selected_cell_indices() + field_indices = self._field_indices() + row_counts = [self._cell_row_count(index) for index in targets] + total_rows = sum(row_counts) + if isinstance(value, Vector): - self._validate_vector_rhs(value, require_matching_rows=True) - for coord, source in zip(targets, value._iter_selected_arrays()): - self._write_selected_array(coord, source) + source_cells = value._selected_cell_indices() + if len(targets) != len(source_cells): + raise ValueError(f"Expected {len(targets)} cells, got {len(source_cells)}") + if value.num_fields != self.num_fields: + raise ValueError( + f"Expected {self.num_fields} fields, got {value.num_fields}" + ) + source_counts = [value._cell_row_count(index) for index in source_cells] + if row_counts != source_counts: + raise ValueError("Per-cell row counts must match for field-selected assignment.") + snapshots = [value._selected_cell_matrix(index).copy() for index in source_cells] + for target, array in zip(targets, snapshots): + cell = self._cell_matrix(int(target)) + if array.shape[0] > 0: + cell[:, field_indices] = array return if np.isscalar(value): - for coord in targets: - current = self._selected_array_for_coord(coord) - fill = np.full(current.shape, value) - self._write_selected_array(coord, fill) + for target in targets: + cell = self._cell_matrix(int(target)) + if cell.shape[0] > 0: + cell[:, field_indices] = value return - rhs_matrix = _broadcast_rhs(np.asarray(value), sum(row_counts), self.num_fields) + broadcast = _broadcast_field_values(value, total_rows, self.num_fields) cursor = 0 - for coord, row_count in zip(targets, row_counts): - chunk = rhs_matrix[cursor : cursor + row_count] - cursor += row_count - self._write_selected_array(coord, chunk) - - def _validate_vector_rhs(self, other: "Vector", require_matching_rows: bool) -> None: - if self.num_fields != other.num_fields: - raise ValueError(f"Expected {self.num_fields} fields, got {other.num_fields}") - if self._coords.size != other._coords.size: - raise ValueError(f"Expected {self._coords.size} cells, got {other._coords.size}") - if require_matching_rows: - left = self._row_counts() - right = other._row_counts() - if left != right: - raise ValueError(f"Per-cell row counts must match: {left} != {right}") - - def _row_counts(self) -> list[int]: - return [self._state.data[coord].shape[0] for coord in self._iter_coords()] - - def _iter_coords(self) -> Iterable[tuple[int, ...]]: - return iter(self._coords.flat) - - def _iter_selected_arrays(self) -> Iterable[NDArray[np.generic]]: - for coord in self._iter_coords(): - yield self._selected_array_for_coord(coord) - - def _selected_array_for_coord(self, coord: tuple[int, ...]) -> NDArray[np.generic]: - cell = self._state.data[coord] - indices = self._field_indices() - if self._is_full_field_selection(): - return cell - if len(indices) == 1: - idx = indices[0] - return cell[:, idx : idx + 1] - if _is_contiguous(indices): - return cell[:, indices[0] : indices[-1] + 1] - return cell[:, indices].copy() - - def _write_selected_array(self, coord: tuple[int, ...], values: NDArray[np.generic]) -> None: - cell = self._state.data[coord] - if self._is_full_field_selection(): - replacement = _coerce_cell_array(values, self.num_fields) - self._state.data[coord] = replacement.copy() - return - - if values.ndim != 2 or values.shape[1] != self.num_fields: - raise ValueError( - f"Expected array with shape (_, {self.num_fields}), got {values.shape}" - ) - if values.shape[0] != cell.shape[0]: - raise ValueError( - f"Expected {cell.shape[0]} rows for in-place field update, got {values.shape[0]}" - ) - cell[:, self._field_indices()] = values - - def _field_indices(self) -> list[int]: - lookup = {name: idx for idx, name in enumerate(self._state.fields)} - try: - return [lookup[name] for name in self._field_names] - except KeyError as exc: - raise KeyError(f"Unknown field '{exc.args[0]}'") from exc - - def _is_full_field_selection(self) -> bool: - return list(self._field_names) == self._state.fields + for target, rows in zip(targets, row_counts): + chunk = broadcast[cursor : cursor + rows] + cell = self._cell_matrix(int(target)) + if rows > 0: + cell[:, field_indices] = chunk + cursor += rows + + def _binary_op(self, other: Any, op: Any, reverse: bool = False) -> "Vector": + result = self.copy() + result._inplace_op(other, op, reverse=reverse) + return result + def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: + targets = self._selected_cell_indices() + field_indices = self._field_indices() + row_counts = [self._cell_row_count(index) for index in targets] + total_rows = sum(row_counts) -def _empty_storage(shape: tuple[int, ...], num_fields: int) -> NDArray[np.object_]: - storage = np.empty(shape if shape != () else (), dtype=object) - for coord in np.ndindex(shape) if shape != () else [()]: - storage[coord] = np.empty((0, num_fields), dtype=float) - return storage + if isinstance(other, Vector): + source_cells = other._selected_cell_indices() + if len(targets) != len(source_cells): + raise ValueError(f"Expected {len(targets)} cells, got {len(source_cells)}") + if other.num_fields != self.num_fields: + raise ValueError(f"Expected {self.num_fields} fields, got {other.num_fields}") + source_counts = [other._cell_row_count(index) for index in source_cells] + if row_counts != source_counts: + raise ValueError("Per-cell row counts must match for Vector arithmetic.") + snapshots = [other._selected_cell_matrix(index).copy() for index in source_cells] + for target, rhs in zip(targets, snapshots): + cell = self._cell_matrix(int(target)) + lhs = cell[:, field_indices] + cell[:, field_indices] = op(rhs, lhs) if reverse else op(lhs, rhs) + return + if np.isscalar(other): + for target in targets: + cell = self._cell_matrix(int(target)) + lhs = cell[:, field_indices] + if lhs.shape[0] > 0: + cell[:, field_indices] = op(other, lhs) if reverse else op(lhs, other) + return -def _root_coords(shape: tuple[int, ...]) -> NDArray[np.object_]: - return _root_coords_allow_empty(shape) + broadcast = _broadcast_field_values(other, total_rows, self.num_fields) + cursor = 0 + for target, rows in zip(targets, row_counts): + chunk = broadcast[cursor : cursor + rows] + cell = self._cell_matrix(int(target)) + lhs = cell[:, field_indices] + if rows > 0: + cell[:, field_indices] = op(chunk, lhs) if reverse else op(lhs, chunk) + cursor += rows -def _root_coords_allow_empty(shape: tuple[int, ...]) -> NDArray[np.object_]: - coords = np.empty(shape if shape != () else (), dtype=object) - for coord in np.ndindex(shape) if shape != () else [()]: - coords[coord] = coord - return coords +def _cell_count(shape: tuple[int, ...]) -> int: + return int(np.prod(shape, dtype=np.int64)) if shape else 1 -def _normalize_field_names(field_names: str | Sequence[str]) -> list[str]: +def _normalize_field_names(field_names: str | Sequence[str]) -> tuple[str, ...]: if isinstance(field_names, str): - names = [field_names] - elif isinstance(field_names, Sequence): - names = [str(name) for name in field_names] + normalized = (field_names,) else: - raise TypeError("Field names must be a string or a sequence of strings.") - if not names: - raise ValueError("Must select at least one field.") - if len(set(names)) != len(names): - raise ValueError("Duplicate field names are not allowed.") - return names + normalized = tuple(field_names) + if not normalized: + raise ValueError("At least one field name is required.") + validate_fields(list(normalized)) + return normalized + def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str]: @@ -507,188 +599,234 @@ def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str] return ["none"] * count if isinstance(units, str): if count != 1: - raise ValueError("A single unit string is only valid for a single new field.") + raise ValueError("A single unit can only be provided for a single field.") return [units] - normalized = [str(unit) for unit in units] + normalized = list(units) if len(normalized) != count: raise ValueError(f"Expected {count} units, got {len(normalized)}") return normalized + +def _looks_like_field_selector(idx: Any) -> bool: + if isinstance(idx, str): + return True + if isinstance(idx, tuple) and any(_looks_like_field_selector(item) for item in idx): + return True + if isinstance(idx, (list, tuple)) and idx and all(isinstance(item, str) for item in idx): + return True + return False + + + def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: - array = np.asarray(value) - if array.ndim != 2 or array.shape[1] != num_fields: - raise ValueError(f"Expected a numpy array with shape (_, {num_fields}), got {array.shape}") - if not np.issubdtype(array.dtype, np.number): - raise TypeError("Cell arrays must contain numeric values.") + if isinstance(value, Vector): + if value.shape != (): + raise ValueError("Expected a 0D Vector for single-cell assignment.") + array = value.array.copy() + else: + array = np.asarray(value) + + if array.ndim == 0: + raise ValueError("Cell assignment requires a 2D array.") + if array.ndim == 1: + if array.size == 0: + array = np.empty((0, num_fields), dtype=float) + elif num_fields == 1: + array = array.reshape(-1, 1) + else: + array = array.reshape(1, -1) + if array.ndim != 2: + raise ValueError("Cell assignment requires a 2D array.") + if array.shape[1] != num_fields: + raise ValueError(f"Expected {num_fields} fields, got {array.shape[1]}") return array -def _broadcast_rhs(value: NDArray[np.generic], total_rows: int, num_fields: int) -> NDArray[np.generic]: - if num_fields == 1 and value.ndim == 1: - value = value.reshape(-1, 1) - try: - return np.broadcast_to(value, (total_rows, num_fields)) - except ValueError as exc: - raise ValueError( - f"RHS is not broadcast-compatible with flattened target shape ({total_rows}, {num_fields})." - ) from exc +def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: + if not isinstance(data, list): + raise TypeError("Data must be a list") + if not data: + return (0,), [] + return _flatten_fixed_grid(data) -def _is_field_selector(idx: Any) -> bool: - if isinstance(idx, str): + + +def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: + if isinstance(node, np.ndarray): + return (), [_coerce_inferred_cell_array(node)] + if not isinstance(node, (list, tuple)): + raise TypeError("Data must be a nested list/tuple of cell arrays or row sequences.") + if _looks_like_cell_rows(node): + return (), [_coerce_inferred_cell_array(node)] + if len(node) == 0: + return (0,), [] + + child_shape: tuple[int, ...] | None = None + cells: list[NDArray[np.generic]] = [] + for child in node: + shape, child_cells = _flatten_fixed_grid(child) + if child_shape is None: + child_shape = shape + elif child_shape != shape: + raise ValueError("All nested fixed-grid branches must have matching shapes.") + cells.extend(child_cells) + + assert child_shape is not None + return (len(node),) + child_shape, cells + + + +def _looks_like_cell_rows(node: Sequence[Any]) -> bool: + if len(node) == 0: return True - if isinstance(idx, list | tuple) and idx: - if all(isinstance(item, str) for item in idx): - return True - return any(isinstance(item, str) for item in idx) - return False + return all(_is_row_like(item) for item in node) + -def _is_contiguous(indices: Sequence[int]) -> bool: - if not indices: +def _is_row_like(item: Any) -> bool: + if isinstance(item, np.ndarray): + return item.ndim == 1 + if not isinstance(item, (list, tuple)): return False - return list(indices) == list(range(indices[0], indices[0] + len(indices))) + return all(np.isscalar(value) for value in item) -def _select_coords(coords: NDArray[np.object_], idx: Any) -> NDArray[np.object_]: - shape = coords.shape - selectors, scalar_axes = _normalize_indices(shape, idx) - out_shape = tuple(len(selector) for selector, scalar in zip(selectors, scalar_axes) if not scalar) - result = np.empty(out_shape if out_shape != () else (), dtype=object) - if out_shape == (): - source = tuple(selector[0] for selector in selectors) - result[()] = coords[source] - return result +def _coerce_inferred_cell_array(value: Any) -> NDArray[np.generic]: + array = np.asarray(value) + if array.ndim == 0: + raise ValueError("Cell data must be 1D or 2D.") + if array.ndim == 1: + if array.size == 0: + return np.empty((0, 0), dtype=float) + return array.reshape(1, -1) + if array.ndim != 2: + raise ValueError("Cell data must be 1D or 2D.") + return array - for out_index in np.ndindex(out_shape): - out_cursor = 0 - source = [] - for axis, selector in enumerate(selectors): - if scalar_axes[axis]: - source.append(selector[0]) - else: - source.append(selector[out_index[out_cursor]]) - out_cursor += 1 - result[out_index] = coords[tuple(source)] - return result -def _normalize_indices(shape: tuple[int, ...], idx: Any) -> tuple[list[np.ndarray], list[bool]]: +def _select_linear_indices( + shape: tuple[int, ...], + current_indices: NDArray[np.int64], + idx: Any, +) -> tuple[tuple[int, ...], NDArray[np.int64]]: if shape == (): - if idx in ((), Ellipsis, slice(None), None): - return [], [] - raise IndexError("Too many indices for a 0D Vector.") + if idx in ((), Ellipsis): + return (), np.array([int(current_indices[0])], dtype=np.int64) + raise IndexError("Too many indices for 0D Vector") - normalized = idx if isinstance(idx, tuple) else (idx,) - normalized = _expand_ellipsis(normalized, len(shape)) - if len(normalized) > len(shape): - raise IndexError(f"Expected at most {len(shape)} indices, got {len(normalized)}") - normalized = normalized + (slice(None),) * (len(shape) - len(normalized)) + index_tuple = _normalize_index_tuple(idx, len(shape)) + current_grid = current_indices.reshape(shape) - selectors: list[np.ndarray] = [] + axis_positions: list[NDArray[np.int64]] = [] + out_shape: list[int] = [] scalar_axes: list[bool] = [] - for axis_value, axis_size in zip(normalized, shape): - selector, scalar = _normalize_axis_index(axis_value, axis_size) - selectors.append(selector) - scalar_axes.append(scalar) - return selectors, scalar_axes - - -def _expand_ellipsis(idx: tuple[Any, ...], ndim: int) -> tuple[Any, ...]: - if not any(item is Ellipsis for item in idx): - return idx - if sum(item is Ellipsis for item in idx) > 1: - raise IndexError("Only one ellipsis is allowed.") - ellipsis_pos = next(i for i, item in enumerate(idx) if item is Ellipsis) - fill = ndim - (len(idx) - 1) - return idx[:ellipsis_pos] + (slice(None),) * fill + idx[ellipsis_pos + 1 :] - - -def _normalize_axis_index(value: Any, axis_size: int) -> tuple[np.ndarray, bool]: - if isinstance(value, (int, np.integer)): - index = int(value) + for axis, axis_index in enumerate(index_tuple): + positions, is_scalar = _positions_for_axis(axis_index, shape[axis]) + axis_positions.append(positions) + scalar_axes.append(is_scalar) + if not is_scalar: + out_shape.append(len(positions)) + + if all(scalar_axes): + scalar_key = tuple(int(positions[0]) for positions in axis_positions) + value = int(current_grid[scalar_key]) + return (), np.array([value], dtype=np.int64) + + mesh_inputs = [positions if not is_scalar else positions[:1] for positions, is_scalar in zip(axis_positions, scalar_axes)] + grids = np.meshgrid(*mesh_inputs, indexing="ij") + selected = np.asarray(current_grid[tuple(grids)], dtype=np.int64).reshape(-1) + return tuple(out_shape), selected + + + +def _normalize_index_tuple(idx: Any, ndim: int) -> tuple[Any, ...]: + if idx is Ellipsis: + return (slice(None),) * ndim + if not isinstance(idx, tuple): + idx = (idx,) + + ellipsis_count = sum(item is Ellipsis for item in idx) + if ellipsis_count > 1: + raise IndexError("An index can only have a single ellipsis.") + if ellipsis_count == 1: + ellipsis_pos = idx.index(Ellipsis) + fill = ndim - (len(idx) - 1) + idx = idx[:ellipsis_pos] + (slice(None),) * fill + idx[ellipsis_pos + 1 :] + if len(idx) > ndim: + raise IndexError(f"Too many indices for Vector: expected {ndim}, got {len(idx)}") + if len(idx) < ndim: + idx = idx + (slice(None),) * (ndim - len(idx)) + return idx + + + +def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], bool]: + if isinstance(axis_index, (bool, np.bool_)): + raise TypeError("Boolean scalars are not valid Vector indices.") + + if isinstance(axis_index, (int, np.integer)): + index = int(axis_index) if index < 0: - index += axis_size - if index < 0 or index >= axis_size: - raise IndexError(f"Index {value} out of bounds for axis with size {axis_size}") - return np.array([index], dtype=int), True + index += size + if index < 0 or index >= size: + raise IndexError("Vector index out of range") + return np.array([index], dtype=np.int64), True - if isinstance(value, slice): - return np.arange(axis_size)[value], False + if isinstance(axis_index, slice): + return np.arange(size, dtype=np.int64)[axis_index], False - if isinstance(value, np.ndarray) and value.ndim == 0: - return _normalize_axis_index(value.item(), axis_size) + array = np.asarray(axis_index) + if array.ndim == 0: + if np.issubdtype(array.dtype, np.integer): + return _positions_for_axis(int(array.item()), size) + raise TypeError(f"Unsupported index type: {type(axis_index)!r}") + + if array.dtype == bool or np.issubdtype(array.dtype, np.bool_): + if array.ndim != 1: + raise IndexError("Full-grid boolean masks are not supported.") + if array.shape[0] != size: + raise IndexError(f"Boolean mask length {array.shape[0]} does not match axis length {size}") + return np.flatnonzero(array).astype(np.int64, copy=False), False - array = np.asarray(value) if array.ndim != 1: - raise IndexError("Only 1D per-axis fancy or boolean indexing is supported.") + raise IndexError("Fancy indexing arrays must be one-dimensional.") if array.size == 0: - return np.array([], dtype=int), False + return np.array([], dtype=np.int64), False + if not np.issubdtype(array.dtype, np.integer): + raise TypeError("Fancy indices must be integers or booleans.") - if array.dtype == bool or np.issubdtype(array.dtype, np.bool_): - if array.shape[0] != axis_size: - raise IndexError( - f"Boolean mask length {array.shape[0]} does not match axis size {axis_size}" - ) - return np.flatnonzero(array), False + positions = array.astype(np.int64, copy=True) + positions[positions < 0] += size + if np.any((positions < 0) | (positions >= size)): + raise IndexError("Vector index out of range") + return positions, False - if not np.issubdtype(array.dtype, np.integer): - raise TypeError(f"Unsupported index type {type(value).__name__}") - normalized = array.astype(int, copy=True) - normalized[normalized < 0] += axis_size - if np.any((normalized < 0) | (normalized >= axis_size)): - raise IndexError(f"Index out of bounds for axis with size {axis_size}") - return normalized, False +def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDArray[np.generic]: + array = np.asarray(value) + if array.ndim == 0: + return np.broadcast_to(array.reshape(1, 1), (total_rows, num_fields)) + if num_fields == 1 and array.ndim == 1: + if total_rows == 0 and array.shape[0] == 0: + return array.reshape(0, 1) + if array.shape[0] != total_rows: + raise ValueError(f"Expected {total_rows} values, got {array.shape[0]}") + return array.reshape(total_rows, 1) + try: + return np.broadcast_to(array, (total_rows, num_fields)) + except ValueError as exc: + raise ValueError( + f"Cannot broadcast value with shape {array.shape} to ({total_rows}, {num_fields})" + ) from exc -def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: - if not isinstance(data, list): - raise TypeError("Data must be a list.") - if not data: - raise ValueError("Data list cannot be empty.") - - leaves: list[NDArray[np.generic]] = [] - num_fields: int | None = None - - def walk(node: Any) -> tuple[int, ...]: - nonlocal num_fields - if _is_leaf_cell(node): - array = np.asarray(node) - if array.ndim != 2: - raise ValueError(f"Cell arrays must be 2D, got shape {array.shape}") - if not np.issubdtype(array.dtype, np.number): - raise TypeError("Cell arrays must contain numeric values.") - if num_fields is None: - num_fields = array.shape[1] - elif array.shape[1] != num_fields: - raise ValueError("All data arrays must have same number of fields.") - leaves.append(array) - return () - - if not isinstance(node, list): - raise TypeError("Data elements must be numpy arrays, numeric 2D lists, or nested lists thereof.") - if not node: - raise ValueError("Nested data lists cannot be empty.") - child_shapes = [walk(child) for child in node] - first = child_shapes[0] - if any(shape != first for shape in child_shapes[1:]): - raise ValueError("Nested data structure must have a consistent fixed-grid shape.") - return (len(node), *first) - - inferred_shape = walk(data) - return inferred_shape, leaves - - -def _is_leaf_cell(node: Any) -> bool: - if isinstance(node, np.ndarray): + + +def _is_contiguous(indices: NDArray[np.int64]) -> bool: + if indices.size <= 1: return True - if not isinstance(node, list): - return False - try: - array = np.asarray(node) - except Exception: - return False - return array.ndim == 2 and np.issubdtype(array.dtype, np.number) + return bool(np.all(indices[1:] - indices[:-1] == 1)) diff --git a/src/quantem/core/visualization/visualization_utils.py b/src/quantem/core/visualization/visualization_utils.py index afe475b1..b69e3c86 100644 --- a/src/quantem/core/visualization/visualization_utils.py +++ b/src/quantem/core/visualization/visualization_utils.py @@ -503,6 +503,14 @@ def bilinear_histogram_2d( x0, y0 = origin x1, y1 = x0 + Nx * dx, y0 + Ny * dy + x = _as_histogram_vector(x, "x") + y = _as_histogram_vector(y, "y") + weight = _as_histogram_vector(weight, "weight") + if not (x.shape == y.shape == weight.shape): + raise ValueError( + f"x, y, and weight must have matching shapes after coercion, got {x.shape}, {y.shape}, {weight.shape}" + ) + # Convert shape tuple to list for binned_statistic_2d bins: Sequence[int] = [Nx, Ny] hist, _, _, _ = binned_statistic_2d( @@ -517,6 +525,15 @@ def bilinear_histogram_2d( return hist # shape = (Nx, Ny), i.e., array[x, y] +def _as_histogram_vector(value: NDArray, name: str) -> NDArray: + array = np.asarray(value) + if array.ndim == 1: + return array + if array.ndim == 2 and 1 in array.shape: + return array.reshape(-1) + raise ValueError(f"{name} must be 1D or shape (N, 1)/(1, N), got {array.shape}") + + def axes_with_inset( axsize=(4, 4), ax_size_embed=None, # None -> 0.25 of main axes in each dimension (fractional) diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index f03a08b4..52afd40e 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -1,6 +1,9 @@ +import zipfile + import numpy as np import pytest +from quantem.core.io.serialize import load from quantem.core.datastructures.vector import Vector @@ -56,6 +59,12 @@ def test_initialization_and_len(self): with pytest.raises(ValueError, match="Duplicate field names"): Vector.from_shape(shape=(2, 3), fields=["a", "a"]) + assert str(v1) == ( + "quantem.Vector, shape=(2, 3), name=2d ragged array\n" + " fields = ['a', 'b', 'c']\n" + " units: ['none', 'none', 'none']" + ) + def test_indexing_and_array_contract(self): v = make_grid_vector() @@ -227,8 +236,40 @@ def test_from_data_supports_nested_fixed_grid(self): assert v.name == "nested" np.testing.assert_array_equal(v[0, 1].array, np.array([[3.0, 4.0], [5.0, 6.0]])) + tuple_cells = [ + ([1.0, 2.0], [3.0, 4.0]), + ([5.0, 6.0], [7.0, 8.0], [9.0, 10.0]), + ] + tuple_vector = Vector.from_data(data=tuple_cells, fields=["a", "b"]) + assert tuple_vector.shape == (2,) + np.testing.assert_array_equal(tuple_vector[0].array, np.array([[1.0, 2.0], [3.0, 4.0]])) + np.testing.assert_array_equal( + tuple_vector[1].array, + np.array([[5.0, 6.0], [7.0, 8.0], [9.0, 10.0]]), + ) + with pytest.raises(TypeError, match="Data must be a list"): Vector.from_data(data=np.array([1, 2, 3])) # type: ignore[arg-type] with pytest.raises(ValueError, match="same number of fields"): Vector.from_data(data=[np.array([[1.0, 2.0]]), np.array([[1.0, 2.0, 3.0]])]) + + def test_save_and_load_round_trip(self, tmp_path): + v = make_grid_vector() + v.add_fields("extra", v.select_fields("intensity") + 1.0) + + path = tmp_path / "vector_test.zip" + v.save(path, mode="o", compression_level=4) + + with zipfile.ZipFile(path) as zf: + names = [info.filename for info in zf.infolist()] + assert len(names) < 30 + assert "_state/data/zarr.json" in names + assert all(not name.startswith("_selection_coords/") for name in names) + + loaded = load(path) + assert isinstance(loaded, Vector) + assert loaded.shape == v.shape + assert loaded.fields == v.fields + assert loaded.units == v.units + np.testing.assert_array_equal(loaded[2, 1].array, v[2, 1].array) diff --git a/tests/visualization/test_visualization_utils.py b/tests/visualization/test_visualization_utils.py index c3a1f048..ca114774 100644 --- a/tests/visualization/test_visualization_utils.py +++ b/tests/visualization/test_visualization_utils.py @@ -297,3 +297,11 @@ def test_bilinear_histogram_2d_with_custom_statistic(self): hist = bilinear_histogram_2d(shape, x, y, weight, statistic="mean") assert hist.shape == shape assert np.all(~np.isnan(hist[hist > 0])) # Only check non-zero values for NaN + + def test_bilinear_histogram_2d_accepts_column_vectors(self): + shape = (8, 8) + x = np.array([[1.0], [2.0], [3.0]]) + y = np.array([[1.5], [2.5], [3.5]]) + weight = np.array([[2.0], [3.0], [4.0]]) + hist = bilinear_histogram_2d(shape, x, y, weight) + assert hist.shape == shape From d50fa5a3db0b618f9e070ef9999f1dbd6a93bb7d Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Mar 2026 13:34:49 -0800 Subject: [PATCH 107/335] minor fixes --- src/quantem/core/datastructures/vector.py | 6 ++++++ tests/datastructures/test_vector.py | 3 +++ 2 files changed, 9 insertions(+) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 53592dcd..b69d917c 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -240,6 +240,7 @@ def add_fields( values: Any | None = None, units: str | Sequence[str] | None = None, ) -> None: + self._require_full_field_view("add_fields") new_fields = _normalize_field_names(names) if any(field in self._state["fields"] for field in new_fields): raise ValueError("One or more new field names already exist.") @@ -264,6 +265,7 @@ def add_fields( self._selected_fields = None def remove_fields(self, names: str | Sequence[str]) -> None: + self._require_full_field_view("remove_fields") to_remove = set(_normalize_field_names(names)) old_fields = self._state["fields"] old_units = self._state["units"] @@ -379,6 +381,10 @@ def _field_indices(self) -> NDArray[np.int64]: except KeyError as exc: raise KeyError(f"Unknown field(s): {[str(exc.args[0])]}") from exc + def _require_full_field_view(self, operation: str) -> None: + if self._selected_fields is not None: + raise ValueError(f"{operation} is only allowed when all fields are selected.") + def _selected_cell_indices(self) -> NDArray[np.int64]: if self._selection_indices is None: return np.arange(_cell_count(self._state["shape"]), dtype=np.int64) diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index 52afd40e..6091f832 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -203,6 +203,9 @@ def test_add_fields_defaults_expression_and_multiple_values(self): np.array([[5.0], [6.0], [7.0], [8.0], [9.0], [10.0]]), ) + with pytest.raises(ValueError, match="all fields are selected"): + v2.select_fields("kx").add_fields("bad") + def test_remove_fields_preserves_remaining_data(self): v = make_line_vector() v.add_fields("extra", 1.0) From a741fe6e5b0c526e749efe76e8238e6c78bc8e51 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Mar 2026 13:54:12 -0800 Subject: [PATCH 108/335] More small fixes --- src/quantem/core/datastructures/vector.py | 276 ++++++++++++++++++++-- tests/datastructures/test_vector.py | 37 +++ 2 files changed, 291 insertions(+), 22 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index b69d917c..dc8865c0 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -18,10 +18,75 @@ class Vector(AutoSerialize): """Ragged cell data on a fixed grid. - Storage is compact and AutoSerialize-friendly: - - one 2D numeric row buffer for all ragged rows - - one start array and one length array for cell boundaries - - optional selection state for views + A ``Vector`` has two independent axes of structure: + - fixed-grid dimensions given by ``shape`` + - ragged rows stored inside each fixed-grid cell + + Each ragged row has one value per named field, so each cell behaves like a + small 2D array with shape ``(n_rows, num_fields)``, where ``n_rows`` may + vary from cell to cell. + + Parameters + ---------- + shape : tuple of int + Fixed-grid shape. + fields : sequence of str + Field names in column order. + units : sequence of str, optional + Units corresponding to ``fields``. If omitted, units default to + ``"none"`` for all fields. + name : str, optional + Descriptive name for the Vector. + metadata : dict, optional + Additional user metadata. + + Notes + ----- + The public API keeps fixed-grid indexing and field selection separate: + - use ``[]`` for fixed-grid indexing + - use ``select_fields(...)`` for field selection + + Fixed-grid indexing always returns a ``Vector``. A 0D selection exposes its + underlying cell array through ``.array``. Multi-cell selections can be + concatenated with ``flatten()``. + + The internal representation is compact: + - ``_state["data"]`` stores all ragged rows in one numeric 2D array + - ``_state["cell_starts"]`` stores the start offset for each cell + - ``_state["cell_lengths"]`` stores the row count for each cell + + A ``Vector`` selection is a write-through view over shared storage. Views + track only the selected fixed-grid shape, selected cell indices, and selected + field names. + + Examples + -------- + Create a Vector and assign one cell: + + >>> import numpy as np + >>> v = Vector.from_shape((2, 2), fields=("kx", "ky", "intensity")) + >>> v[0, 0] = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) + >>> v[0, 0].array.shape + (2, 3) + + Select fields and apply in-place arithmetic: + + >>> kx = v.select_fields("kx") + >>> kx += 16 + >>> kx.flatten().shape + (2, 1) + + Apply a rowwise transform with ``flatten()`` and ``set_flattened()``: + + >>> kx = v.select_fields("kx") + >>> ky = v.select_fields("ky") + >>> kx.set_flattened( + ... np.where( + ... ((kx.flatten() - 16) ** 2 + (ky.flatten() - 16) ** 2) < 12, + ... 10, + ... kx.flatten(), + ... ) + ... ) """ def __init__( @@ -61,6 +126,7 @@ def _from_view( selection_indices: NDArray[np.int64] | None, selected_fields: tuple[str, ...] | None, ) -> "Vector": + """Build a view that shares backing storage with another Vector.""" obj = cls.__new__(cls) obj._state = state obj._selection_shape = selection_shape @@ -77,6 +143,7 @@ def from_shape( units: Sequence[str] | None = None, name: str | None = None, ) -> "Vector": + """Create an empty Vector with the given fixed-grid shape and fields.""" if fields is not None: root_fields = validate_fields(list(fields)) if num_fields is not None and len(root_fields) != num_fields: @@ -100,6 +167,11 @@ def from_data( units: Sequence[str] | None = None, name: str | None = None, ) -> "Vector": + """Create a Vector from nested fixed-grid data. + + The outer nesting defines the fixed-grid shape. Each leaf must coerce to a + 2D cell array with consistent field count across all cells. + """ root_shape, cell_arrays = _normalize_nested_data(data) inferred_counts = {array.shape[1] for array in cell_arrays} if len(inferred_counts) > 1: @@ -128,16 +200,19 @@ def from_data( @property def shape(self) -> tuple[int, ...]: + """Return the fixed-grid shape of this selection.""" return self._selection_shape @property def fields(self) -> list[str]: + """Return selected field names in column order.""" if self._selected_fields is None: return list(self._state["fields"]) return list(self._selected_fields) @property def units(self) -> list[str]: + """Return units for the selected fields.""" lookup = { field: unit for field, unit in zip(self._state["fields"], self._state["units"]) @@ -146,10 +221,12 @@ def units(self) -> list[str]: @property def num_fields(self) -> int: + """Return the number of selected fields.""" return len(self.fields) @property def name(self) -> str: + """Human-readable Vector name.""" return self._state["name"] @name.setter @@ -158,10 +235,18 @@ def name(self, value: str) -> None: @property def metadata(self) -> dict[str, Any]: + """Mutable metadata dictionary shared by all views.""" return self._state["metadata"] @property def array(self) -> NDArray[np.generic]: + """Return the selected cell as a NumPy array. + + This is only valid for 0D selections. Single-field and contiguous + multi-field selections return writable views into the backing storage. + Non-contiguous multi-field selections return a copy because NumPy cannot + expose a writable column-subset view for that layout. + """ if self.shape != (): raise ValueError(".array is only valid when the selection contains exactly one cell.") cell = self._cell_matrix(self._selected_cell_indices()[0]) @@ -176,6 +261,7 @@ def array(self) -> NDArray[np.generic]: return cell[:, cols].copy() def __len__(self) -> int: + """Return ``shape[0]`` for non-scalar selections.""" if self.shape == (): raise TypeError("len() of unsized 0D Vector") return self.shape[0] @@ -192,6 +278,7 @@ def __repr__(self) -> str: __str__ = __repr__ def copy(self) -> "Vector": + """Return a deep copy of the current selection.""" copied = self.__class__( shape=self.shape, fields=self.fields, @@ -205,6 +292,11 @@ def copy(self) -> "Vector": return copied def flatten(self) -> NDArray[np.generic]: + """Concatenate selected cells in row-major order. + + Returns a 2D array with shape ``(total_rows, num_fields)`` even for + single-field selections. + """ arrays = [ self._selected_cell_matrix(index) for index in self._selected_cell_indices() @@ -216,8 +308,24 @@ def flatten(self) -> NDArray[np.generic]: dtype = self._state["data"].dtype if self._state["data"].ndim == 2 else float return np.empty((0, self.num_fields), dtype=dtype) - def select_fields(self, field_names: str | Sequence[str]) -> "Vector": - selected = _normalize_field_names(field_names) + @property + def total_rows(self) -> int: + """Return the total ragged-row count in the current selection.""" + return int(self._state["cell_lengths"][self._selected_cell_indices()].sum()) + + def row_counts(self) -> list[int]: + """Return per-cell row counts in the current selection order.""" + return [self._cell_row_count(int(index)) for index in self._selected_cell_indices()] + + def select_fields(self, *field_names: str | Sequence[str]) -> "Vector": + """Return a view containing only the requested fields. + + Accepted forms: + - ``select_fields("kx")`` + - ``select_fields("kx", "ky")`` + - ``select_fields(["kx", "ky"])`` + """ + selected = _normalize_select_field_args(*field_names) available = set(self.fields) missing = [field for field in selected if field not in available] if missing: @@ -234,12 +342,71 @@ def select_fields(self, field_names: str | Sequence[str]) -> "Vector": selected_fields, ) + def set_flattened(self, values: Any) -> None: + """Write values back in flattened row-major order. + + This updates existing rows without changing per-cell row counts. It is + the rowwise companion to ``flatten()`` and is especially useful for + NumPy-based transforms that operate on all selected rows at once. + """ + field_indices = self._field_indices() + targets = self._selected_cell_indices() + row_counts = self.row_counts() + total_rows = sum(row_counts) + + if isinstance(values, Vector): + if values.num_fields != self.num_fields: + raise ValueError(f"Expected {self.num_fields} fields, got {values.num_fields}") + flat_values = values.flatten() + if flat_values.shape[0] != total_rows: + raise ValueError(f"Expected {total_rows} rows, got {flat_values.shape[0]}") + else: + flat_values = _broadcast_field_values(values, total_rows, self.num_fields) + + cursor = 0 + for target, rows in zip(targets, row_counts): + cell = self._cell_matrix(int(target)) + if rows > 0: + cell[:, field_indices] = flat_values[cursor : cursor + rows] + cursor += rows + + def compact(self) -> None: + """Repack the backing row buffer to remove dead rows. + + Whole-cell replacement appends new rows and leaves previous rows unused + until compaction. Calling ``compact()`` makes memory usage and save size + predictable at the cost of reallocating the backing buffer. + """ + self._compact_storage() + + def append_rows(self, idx: Any, rows: Any) -> None: + """Append one or more rows to a single selected cell. + + ``idx`` is interpreted with the same fixed-grid indexing rules as + ``__getitem__`` and must resolve to exactly one cell. Appending rows is a + full-cell operation, so all fields must be selected. + """ + target = self[idx] + if target.shape != (): + raise ValueError("append_rows requires an index that selects exactly one cell.") + target._require_full_field_view("append_rows") + + new_rows = _coerce_cell_array(rows, target.num_fields) + if new_rows.shape[0] == 0: + return + + cell_index = int(target._selected_cell_indices()[0]) + existing = target._cell_matrix(cell_index) + combined = np.vstack((existing, new_rows)) if existing.shape[0] > 0 else new_rows.copy() + target._replace_cells(np.array([cell_index], dtype=np.int64), [combined]) + def add_fields( self, names: str | Sequence[str], values: Any | None = None, units: str | Sequence[str] | None = None, ) -> None: + """Add one or more new fields to the full Vector schema.""" self._require_full_field_view("add_fields") new_fields = _normalize_field_names(names) if any(field in self._state["fields"] for field in new_fields): @@ -254,7 +421,7 @@ def add_fields( if values is None: return - target = self.select_fields(list(new_fields)) + target = self.select_fields(*new_fields) if len(new_fields) > 1 and isinstance(values, (list, tuple)) and len(values) == len(new_fields): for field, value in zip(new_fields, values): target.select_fields(field)[...] = value @@ -265,6 +432,7 @@ def add_fields( self._selected_fields = None def remove_fields(self, names: str | Sequence[str]) -> None: + """Remove one or more fields from the full Vector schema.""" self._require_full_field_view("remove_fields") to_remove = set(_normalize_field_names(names)) old_fields = self._state["fields"] @@ -286,23 +454,11 @@ def remove_fields(self, names: str | Sequence[str]) -> None: if len(self._selected_fields) == len(self._state["fields"]): self._selected_fields = None - def get_data(self, *indices: Any) -> NDArray[np.generic] | list[NDArray[np.generic]]: - if len(indices) != len(self.shape): - raise ValueError(f"Expected {len(self.shape)} indices, got {len(indices)}") - selection = self[indices if len(indices) != 1 else indices[0]] - if selection.shape == (): - return selection.array - return [selection._selected_cell_matrix(index).copy() for index in selection._selected_cell_indices()] - - def set_data(self, value: Any, *indices: Any) -> None: - if len(indices) != len(self.shape): - raise ValueError(f"Expected {len(self.shape)} indices, got {len(indices)}") - self[indices if len(indices) != 1 else indices[0]] = value - @overload def __getitem__(self, idx: Any) -> "Vector": ... def __getitem__(self, idx: Any) -> "Vector": + """Return a fixed-grid selection as another Vector view.""" if _looks_like_field_selector(idx): raise TypeError("Use select_fields(...) for field selection.") if idx is Ellipsis: @@ -321,6 +477,7 @@ def __getitem__(self, idx: Any) -> "Vector": ) def __setitem__(self, idx: Any, value: Any) -> None: + """Assign to a fixed-grid selection.""" if idx is Ellipsis: target = self else: @@ -372,6 +529,7 @@ def _full_num_fields(self) -> int: return len(self._state["fields"]) def _field_indices(self) -> NDArray[np.int64]: + """Map selected field names to column indices in the backing buffer.""" if self._selected_fields is None: return np.arange(self._full_num_fields, dtype=np.int64) @@ -382,10 +540,12 @@ def _field_indices(self) -> NDArray[np.int64]: raise KeyError(f"Unknown field(s): {[str(exc.args[0])]}") from exc def _require_full_field_view(self, operation: str) -> None: + """Raise if a schema-changing/full-row operation is attempted on a field view.""" if self._selected_fields is not None: raise ValueError(f"{operation} is only allowed when all fields are selected.") def _selected_cell_indices(self) -> NDArray[np.int64]: + """Return linear cell indices for the current fixed-grid selection.""" if self._selection_indices is None: return np.arange(_cell_count(self._state["shape"]), dtype=np.int64) return self._selection_indices @@ -394,9 +554,11 @@ def _is_full_field_selection(self) -> bool: return self._selected_fields is None def _cell_row_count(self, linear_index: int) -> int: + """Return the row count for one cell in the backing buffer.""" return int(self._state["cell_lengths"][linear_index]) def _cell_matrix(self, linear_index: int) -> NDArray[np.generic]: + """Return the full backing matrix for one cell.""" start = int(self._state["cell_starts"][linear_index]) length = int(self._state["cell_lengths"][linear_index]) if length == 0: @@ -404,6 +566,7 @@ def _cell_matrix(self, linear_index: int) -> NDArray[np.generic]: return self._state["data"][start : start + length] def _selected_cell_matrix(self, linear_index: int) -> NDArray[np.generic]: + """Return one cell with the current field selection applied.""" cell = self._cell_matrix(linear_index) cols = self._field_indices() if cols.size == self._full_num_fields: @@ -416,6 +579,14 @@ def _selected_cell_matrix(self, linear_index: int) -> NDArray[np.generic]: return cell[:, cols].copy() def _replace_cells(self, targets: NDArray[np.int64], arrays: Sequence[NDArray[np.generic]]) -> None: + """Replace complete cells in the compact row buffer. + + Whole-cell replacement is implemented by appending the new payload rows to + the end of the backing buffer and then updating ``cell_starts`` / + ``cell_lengths`` for the targeted cells. This keeps the operation simple + and makes overlapping assignment semantics easy to reason about, but it + leaves the previous rows unreachable until compaction removes them. + """ if len(targets) != len(arrays): raise ValueError("Target cell count does not match source cell count.") if len(targets) == 0: @@ -440,6 +611,7 @@ def _replace_cells(self, targets: NDArray[np.int64], arrays: Sequence[NDArray[np self._maybe_compact_storage() def _expand_storage(self, num_new_fields: int) -> None: + """Append new ``np.nan``-initialized columns for added fields.""" data = self._state["data"] if data.shape[0] == 0: dtype = np.result_type(data.dtype, float) @@ -451,16 +623,23 @@ def _expand_storage(self, num_new_fields: int) -> None: self._state["data"] = np.concatenate((data.astype(dtype, copy=False), filler), axis=1) def _maybe_compact_storage(self) -> None: + """Compact automatically once dead rows become materially larger than live rows.""" data = self._state["data"] used_rows = int(self._state["cell_lengths"].sum()) if data.shape[0] <= used_rows + 1024: return + if data.shape[0] <= 2 * used_rows: + return + self._compact_storage() + + def _compact_storage(self) -> None: + """Rebuild the row buffer so only live rows remain.""" + data = self._state["data"] + used_rows = int(self._state["cell_lengths"].sum()) if used_rows == 0: self._state["data"] = np.empty((0, self._full_num_fields), dtype=data.dtype) self._state["cell_starts"].fill(0) return - if data.shape[0] <= 2 * used_rows: - return compacted = np.empty((used_rows, self._full_num_fields), dtype=data.dtype) starts = np.zeros_like(self._state["cell_starts"]) @@ -476,12 +655,18 @@ def _maybe_compact_storage(self) -> None: self._state["cell_starts"] = starts def _assign(self, value: Any) -> None: + """Dispatch assignment based on whether all fields or a subset are selected.""" if self._is_full_field_selection(): self._assign_full_cells(value) else: self._assign_selected_fields(value) def _assign_full_cells(self, value: Any) -> None: + """Replace full cell payloads. + + Full-cell assignment may change the ragged row count of each targeted + cell, because the existing cell matrix is replaced as a whole. + """ targets = self._selected_cell_indices() if isinstance(value, Vector): source_cells = value._selected_cell_indices() @@ -499,6 +684,13 @@ def _assign_full_cells(self, value: Any) -> None: self._replace_cells(targets, [array] * len(targets)) def _assign_selected_fields(self, value: Any) -> None: + """Update only the selected columns while preserving row counts. + + This is the in-place path for assignments such as + ``vector.select_fields("kx")[...] = rhs``. The target cell structure is + preserved, so each target cell keeps its existing row count and only the + selected columns are overwritten. + """ targets = self._selected_cell_indices() field_indices = self._field_indices() row_counts = [self._cell_row_count(index) for index in targets] @@ -539,11 +731,13 @@ def _assign_selected_fields(self, value: Any) -> None: cursor += rows def _binary_op(self, other: Any, op: Any, reverse: bool = False) -> "Vector": + """Return a new Vector produced by elementwise arithmetic.""" result = self.copy() result._inplace_op(other, op, reverse=reverse) return result def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: + """Apply elementwise arithmetic in-place to the selected fields.""" targets = self._selected_cell_indices() field_indices = self._field_indices() row_counts = [self._cell_row_count(index) for index in targets] @@ -585,10 +779,12 @@ def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: def _cell_count(shape: tuple[int, ...]) -> int: + """Return the number of fixed-grid cells in a shape.""" return int(np.prod(shape, dtype=np.int64)) if shape else 1 def _normalize_field_names(field_names: str | Sequence[str]) -> tuple[str, ...]: + """Normalize one-or-many field names into a validated tuple.""" if isinstance(field_names, str): normalized = (field_names,) else: @@ -599,8 +795,25 @@ def _normalize_field_names(field_names: str | Sequence[str]) -> tuple[str, ...]: return normalized +def _normalize_select_field_args(*field_names: str | Sequence[str]) -> tuple[str, ...]: + """Normalize ``select_fields`` arguments. + + This accepts either ``select_fields("a", "b")`` or + ``select_fields(["a", "b"])`` while keeping ``add_fields`` / + ``remove_fields`` on the simpler single-argument path. + """ + if not field_names: + raise ValueError("At least one field name is required.") + if len(field_names) == 1 and not isinstance(field_names[0], str): + return _normalize_field_names(field_names[0]) + if not all(isinstance(name, str) for name in field_names): + raise TypeError("select_fields(...) expects field names as strings or one sequence of strings.") + return _normalize_field_names(field_names) + + def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str]: + """Normalize field units to a list matching ``count``.""" if units is None: return ["none"] * count if isinstance(units, str): @@ -615,6 +828,7 @@ def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str] def _looks_like_field_selector(idx: Any) -> bool: + """Return True for indices that look like field selection by mistake.""" if isinstance(idx, str): return True if isinstance(idx, tuple) and any(_looks_like_field_selector(item) for item in idx): @@ -626,6 +840,7 @@ def _looks_like_field_selector(idx: Any) -> bool: def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: + """Normalize a single-cell payload to shape ``(n_rows, num_fields)``.""" if isinstance(value, Vector): if value.shape != (): raise ValueError("Expected a 0D Vector for single-cell assignment.") @@ -651,6 +866,7 @@ def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: + """Normalize nested input data into ``(shape, flat_cell_arrays)``.""" if not isinstance(data, list): raise TypeError("Data must be a list") if not data: @@ -660,6 +876,7 @@ def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArr def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: + """Recursively flatten nested fixed-grid input into row-major cell order.""" if isinstance(node, np.ndarray): return (), [_coerce_inferred_cell_array(node)] if not isinstance(node, (list, tuple)): @@ -685,6 +902,7 @@ def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.gen def _looks_like_cell_rows(node: Sequence[Any]) -> bool: + """Return True when a sequence should be interpreted as cell rows, not grid nesting.""" if len(node) == 0: return True return all(_is_row_like(item) for item in node) @@ -692,6 +910,7 @@ def _looks_like_cell_rows(node: Sequence[Any]) -> bool: def _is_row_like(item: Any) -> bool: + """Return True for a single row of scalar values.""" if isinstance(item, np.ndarray): return item.ndim == 1 if not isinstance(item, (list, tuple)): @@ -701,6 +920,7 @@ def _is_row_like(item: Any) -> bool: def _coerce_inferred_cell_array(value: Any) -> NDArray[np.generic]: + """Infer a 2D cell array from row-like input during ``from_data``.""" array = np.asarray(value) if array.ndim == 0: raise ValueError("Cell data must be 1D or 2D.") @@ -719,6 +939,14 @@ def _select_linear_indices( current_indices: NDArray[np.int64], idx: Any, ) -> tuple[tuple[int, ...], NDArray[np.int64]]: + """Apply fixed-grid indexing to a flattened cell-index view. + + ``current_indices`` stores the linear cell indices represented by the current + selection. This helper reshapes those indices to the current selection shape, + applies NumPy-like indexing on the fixed-grid axes, and then returns: + - the output fixed-grid shape + - the flattened linear indices of the selected cells, in row-major order + """ if shape == (): if idx in ((), Ellipsis): return (), np.array([int(current_indices[0])], dtype=np.int64) @@ -750,6 +978,7 @@ def _select_linear_indices( def _normalize_index_tuple(idx: Any, ndim: int) -> tuple[Any, ...]: + """Normalize fixed-grid indexing to a full ``ndim``-length tuple.""" if idx is Ellipsis: return (slice(None),) * ndim if not isinstance(idx, tuple): @@ -771,6 +1000,7 @@ def _normalize_index_tuple(idx: Any, ndim: int) -> tuple[Any, ...]: def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], bool]: + """Resolve one axis index into concrete positions and scalar-vs-vector shape behavior.""" if isinstance(axis_index, (bool, np.bool_)): raise TypeError("Boolean scalars are not valid Vector indices.") @@ -814,6 +1044,7 @@ def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDArray[np.generic]: + """Broadcast array-like input to flattened rowwise assignment shape.""" array = np.asarray(value) if array.ndim == 0: return np.broadcast_to(array.reshape(1, 1), (total_rows, num_fields)) @@ -833,6 +1064,7 @@ def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDA def _is_contiguous(indices: NDArray[np.int64]) -> bool: + """Return True when integer column indices form one contiguous slice.""" if indices.size <= 1: return True return bool(np.all(indices[1:] - indices[:-1] == 1)) diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index 6091f832..4086d4d5 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -104,12 +104,31 @@ def test_select_fields_and_chaining_equivalence(self): with pytest.raises(TypeError): _ = v[1, "kx"] + multi = v.select_fields("intensity", "kx") + assert multi.fields == ["intensity", "kx"] + assert multi.total_rows == 6 + assert multi.row_counts() == [2, 1, 2, 1] + def test_array_mutation_writes_through_for_single_field(self): v = make_line_vector() cell = v.select_fields("kx")[1].array cell[0, 0] = 99.0 assert v[1].array[0, 1] == 99.0 + def test_set_flattened_updates_rowwise(self): + v = make_line_vector() + kx = v.select_fields("kx") + + flat_kx = kx.flatten() + mask = flat_kx >= 30.0 + flat_kx[mask[:, 0], 0] = -1.0 + kx.set_flattened(flat_kx) + + np.testing.assert_array_equal( + kx.flatten(), + np.array([[10.0], [20.0], [-1.0], [-1.0], [-1.0], [-1.0]]), + ) + def test_field_arithmetic_with_scalar_and_ndarray(self): v = make_line_vector() @@ -157,6 +176,24 @@ def test_full_cell_assignment_allows_row_count_changes(self): np.testing.assert_array_equal(v[0].array, broadcast_cell) np.testing.assert_array_equal(v[3].array, broadcast_cell) + def test_append_rows_and_compact(self): + v = make_line_vector() + + v.append_rows(1, np.array([[7.0, 70.0, 700.0]])) + np.testing.assert_array_equal( + v[1].array, + np.array([[3.0, 30.0, 300.0], [7.0, 70.0, 700.0]]), + ) + + v[1] = np.array([[8.0, 80.0, 800.0]]) + assert v._state["data"].shape[0] > v.total_rows + + v.compact() + assert v._state["data"].shape[0] == v.total_rows + + with pytest.raises(ValueError, match="exactly one cell"): + v.append_rows(slice(None), np.array([[1.0, 2.0, 3.0]])) + def test_boolean_indexing_is_axis_wise(self): v = make_grid_vector() From 7d2dc222c1c8d60ffbfc206171edb73678623902 Mon Sep 17 00:00:00 2001 From: cophus Date: Sun, 1 Mar 2026 13:56:48 -0800 Subject: [PATCH 109/335] add dtype and num_cells --- src/quantem/core/datastructures/vector.py | 10 ++++++++++ tests/datastructures/test_vector.py | 4 ++++ 2 files changed, 14 insertions(+) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index dc8865c0..a57ef7dc 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -224,6 +224,11 @@ def num_fields(self) -> int: """Return the number of selected fields.""" return len(self.fields) + @property + def dtype(self) -> np.dtype[Any]: + """Return the NumPy dtype of the backing row buffer.""" + return self._state["data"].dtype + @property def name(self) -> str: """Human-readable Vector name.""" @@ -313,6 +318,11 @@ def total_rows(self) -> int: """Return the total ragged-row count in the current selection.""" return int(self._state["cell_lengths"][self._selected_cell_indices()].sum()) + @property + def num_cells(self) -> int: + """Return the number of fixed-grid cells in the current selection.""" + return int(self._selected_cell_indices().size) + def row_counts(self) -> list[int]: """Return per-cell row counts in the current selection order.""" return [self._cell_row_count(int(index)) for index in self._selected_cell_indices()] diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index 4086d4d5..f9071644 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -37,7 +37,9 @@ def test_initialization_and_len(self): v1 = Vector.from_shape(shape=(2, 3), fields=["a", "b", "c"]) assert v1.shape == (2, 3) assert len(v1) == 2 + assert v1.num_cells == 6 assert v1.num_fields == 3 + assert v1.dtype == np.dtype(float) assert v1.fields == ["a", "b", "c"] assert v1.units == ["none", "none", "none"] assert v1.name == "2d ragged array" @@ -79,6 +81,7 @@ def test_indexing_and_array_contract(self): result = v[[-1, 0], 1] assert result.shape == (2,) + assert result.num_cells == 2 np.testing.assert_array_equal(result[0].array, np.array([[21.0, 121.0, 221.0]])) np.testing.assert_array_equal(result[1].array, np.array([[1.0, 101.0, 201.0]])) @@ -106,6 +109,7 @@ def test_select_fields_and_chaining_equivalence(self): multi = v.select_fields("intensity", "kx") assert 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b/src/quantem/tomography/object_models.py @@ -348,14 +348,18 @@ def obj(self) -> torch.Tensor: def apply_soft_constraints( self, coords: torch.Tensor, + pred: torch.Tensor, ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=coords.device) + soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices].detach().requires_grad_(True) tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + # Ensure shape is [num_samples, num_channels] if tv_densities_recomputed.dim() == 1: tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) @@ -372,6 +376,10 @@ def apply_soft_constraints( grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] soft_loss += self.constraints.tv_vol * grad_norm.mean() + if self.constraints.sparsity > 0: + sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + soft_loss += sparsity_loss + return soft_loss def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: @@ -449,6 +457,11 @@ def reset(self): def forward(self, coords: torch.Tensor) -> torch.Tensor: """forward pass for the INR model""" all_densities = self.model(coords) + if isinstance(all_densities, tuple): + all_densities = all_densities[0] + ot_reg = all_densities[1] + else: + ot_reg = None if all_densities.dim() > 1: all_densities = all_densities.squeeze(-1) @@ -462,7 +475,11 @@ def forward(self, coords: torch.Tensor) -> torch.Tensor: all_densities = all_densities * valid_mask all_densities = self.apply_hard_constraints(all_densities) - return all_densities + + if ot_reg is not None: + return all_densities, ot_reg + else: + return all_densities # Pretrain Loop @@ -585,6 +602,9 @@ def create_volume(self, return_vol: bool = False): ) batch_outputs = model(batch_coords) # (B, C) or (B,) etc. + + if isinstance(batch_outputs, tuple): + batch_outputs = batch_outputs[0] batch_outputs = self.apply_hard_constraints(batch_outputs) # Ensure shape is (B, C) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index e8aded8d..d57c012e 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -162,7 +162,7 @@ def reconstruct( batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords) + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) epoch_soft_constraint_loss += soft_constraints_loss.detach() From 50e21b0ad35b8dc65f65a736c4825a88942f343e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 13:45:56 -0800 Subject: [PATCH 112/335] get_loss_module functionality, allowing for kwargs in the loss functon. --- src/quantem/core/ml/loss_functions.py | 23 ++++++++++++++++------- src/quantem/tomography/object_models.py | 3 ++- src/quantem/tomography/tomography.py | 20 +++++++++++++++----- 3 files changed, 33 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/ml/loss_functions.py b/src/quantem/core/ml/loss_functions.py index c99b6494..a81b4fb0 100644 --- a/src/quantem/core/ml/loss_functions.py +++ b/src/quantem/core/ml/loss_functions.py @@ -11,7 +11,7 @@ import torch -def get_loss_module(name: str | nn.Module | Callable, dtype: torch.dtype) -> nn.Module: +def get_loss_module(name: str | nn.Module | Callable, dtype: torch.dtype, **kwargs) -> nn.Module: """Return a loss *module* by name, or wrap/return what was provided.""" if isinstance(name, nn.Module): return name @@ -32,20 +32,29 @@ def forward(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: if dtype.is_complex: if loss_name in {"l2", "complex_l2"}: - return ComplexL2Loss() + return ComplexL2Loss(**kwargs) if loss_name in {"complex_cartesian_l2"}: - return ComplexCartesianL2Loss() + return ComplexCartesianL2Loss(**kwargs) if loss_name in {"amp_phase_l2"}: - return AmpPhaseL2Loss() + return AmpPhaseL2Loss(**kwargs) if loss_name in {"combined_l2"}: - return CombinedL2Loss() + return CombinedL2Loss(**kwargs) raise ValueError(f"Unknown loss module for complex dtype: {loss_name}") # real dtype if loss_name in {"l2"}: - return nn.MSELoss() + return nn.MSELoss(**kwargs) if loss_name in {"l1"}: - return nn.L1Loss() + return nn.L1Loss(**kwargs) + if loss_name in {"smooth_l1"}: + return nn.SmoothL1Loss(**kwargs) + if loss_name in {"charbonnier"}: + return CharbonnierLoss(**kwargs) + if loss_name in {"llmse"}: + return LLMSELoss(**kwargs) + if loss_name in {"mse_log_mse"}: + return MSELogMSELoss(**kwargs) + raise ValueError(f"Unknown loss module for real dtype: {loss_name}") diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index a7098c79..2e7c6377 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -31,8 +31,9 @@ class DefaultConstraintsTomography(Constraints): # Soft Constraints tv_vol: float = 0.0 + sparsity: float = 0.0 - soft_constraint_keys = ["tv_vol"] + soft_constraint_keys = ["tv_vol", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage", "circular_mask", "fourier_filter"] diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index d57c012e..800eca1d 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,5 +1,5 @@ import os -from typing import Literal, Optional, Self +from typing import Literal, Self import numpy as np import torch @@ -8,6 +8,7 @@ from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.ddp import DDPMixin +from quantem.core.ml.loss_functions import get_loss_module from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d from quantem.core.utils.tomography_utils import ( torch_phase_cross_correlation, @@ -51,11 +52,18 @@ def reconstruct( optimizer_params: dict | None = None, scheduler_params: dict | None = {}, constraints: dict = {}, # TODO: What to pass into the constraints? - loss_func: tuple[str, Optional[float]] = ("smooth_l1", 0.07), - num_samples_per_ray: int | list[tuple[int, int]] = None, + num_samples_per_ray: int | list[tuple[int, int]] = 1, profiling_mode: bool = False, val_fraction: float = 0.0, - # reset_dset: bool = False, + loss_type: Literal[ + "l2", + "l1", + "smooth_l1", + "charbonnier", + "llmse", + "mse_log_mse", + ] = "l2", + loss_func_kwargs: dict = {}, reset_dset: DatasetModelType | None = None, ): """ @@ -120,6 +128,8 @@ def reconstruct( else: print("num_samples_per_ray schedule provided.") + loss_func = get_loss_module(name=loss_type, dtype=self.obj_model.dtype, **loss_func_kwargs) + print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") for a0 in range(num_iter): consistency_loss = 0.0 @@ -160,7 +170,7 @@ def reconstruct( target = batch["target_value"].to(self.device, non_blocking=True).float() - batch_consistency_loss = torch.nn.functional.mse_loss(pred, target) + batch_consistency_loss = loss_func(pred, target) soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) From 1b4aeeb6626b1d14de741caac3abc2bdc0be633f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 13:57:22 -0800 Subject: [PATCH 113/335] Removed KE regularization from DINR paper -- will probably move off to F-INR and TensoRF decomps. Fixing some linting issues --- src/quantem/tomography/object_models.py | 27 +++++++++---------------- src/quantem/tomography/tomography.py | 3 +-- 2 files changed, 11 insertions(+), 19 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 2e7c6377..38aba292 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Generator +from typing import Any, Callable, Generator, Literal import numpy as np import torch @@ -16,6 +16,8 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset +object_type = Literal["potential"] + @dataclass(slots=True) class DefaultConstraintsTomography(Constraints): @@ -48,7 +50,7 @@ class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): def __init__( self, - shape: tuple[int, int, int], + shape: tuple[int, int, int], # pyright: ignore[reportRedeclaration] device: str = "cpu", rng: np.random.Generator | int | None = None, _token: object | None = None, @@ -74,8 +76,8 @@ def shape(self) -> tuple[int, int, int]: return self._shape @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape + def shape(self, new_shape: tuple[int, int, int]): + self._shape = new_shape @property def obj(self) -> torch.Tensor: @@ -136,18 +138,18 @@ class ObjectConstraints(BaseConstraints, ObjectBase): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.constraints = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: DefaultConstraintsTomography = self.DEFAULT_CONSTRAINTS.copy() def apply_hard_constraints( self, - obj: torch.Tensor, + pred: torch.Tensor, ) -> torch.Tensor: """ Apply hard constraints to the object model. Only hard constraint here is the positivity and shrinkage. TODO: Add the other hard constraints. """ - obj2 = obj.clone() + obj2 = pred.clone() if self.constraints.positivity: obj2 = torch.clamp(obj2, min=0.0, max=None) if self.constraints.shrinkage: @@ -297,7 +299,6 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.device = device # Register the network submodule (important: real nn.Module attribute) if model is not None: @@ -458,11 +459,6 @@ def reset(self): def forward(self, coords: torch.Tensor) -> torch.Tensor: """forward pass for the INR model""" all_densities = self.model(coords) - if isinstance(all_densities, tuple): - all_densities = all_densities[0] - ot_reg = all_densities[1] - else: - ot_reg = None if all_densities.dim() > 1: all_densities = all_densities.squeeze(-1) @@ -477,10 +473,7 @@ def forward(self, coords: torch.Tensor) -> torch.Tensor: all_densities = self.apply_hard_constraints(all_densities) - if ot_reg is not None: - return all_densities, ot_reg - else: - return all_densities + return all_densities # Pretrain Loop diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 800eca1d..654bf529 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -7,7 +7,6 @@ from tqdm.auto import tqdm from quantem.core.io.serialize import load as autoserialize_load -from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d from quantem.core.utils.tomography_utils import ( @@ -21,7 +20,7 @@ from quantem.tomography.tomography_opt import TomographyOpt -class Tomography(TomographyOpt, TomographyBase, DDPMixin): +class Tomography(TomographyOpt, TomographyBase): """ Class for handling all ML tomography reconstruction methods. Automatic handling between AD and INR-based tomography. From bfc6e062365d5613a4b8dc38c6a6c6d222553a72 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 14:20:20 -0800 Subject: [PATCH 114/335] Fixed all linting issues in object_models.py. @amccray, ask about ignoring --- src/quantem/core/ml/ddp.py | 2 +- src/quantem/tomography/object_models.py | 28 ++++++++++++++----------- 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 0e60e398..9cc02ee3 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -171,7 +171,7 @@ def device(self) -> torch.device: return self._device @device.setter - def device(self, device: torch.device): + def device(self, device: torch.device | str): if isinstance(device, str): device = torch.device(device) self._device = device diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 38aba292..5f093155 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -171,7 +171,7 @@ def apply_hard_constraints( # return NotImplementedError("Subclasses must implement this method.") @abstractmethod - def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 0.0) -> torch.Tensor: + def get_tv_loss(self, **kwargs) -> torch.Tensor: """ Get the TV loss for the object model. Must be implemented in each subclass. """ @@ -260,7 +260,7 @@ def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) if self.constraints.tv_vol > 0: tv_loss = self.get_tv_loss( - obj.unsqueeze(0).unsqueeze(0), factor=self.constraints.tv_vol + obj.unsqueeze(0).unsqueeze(0), tv_weight=self.constraints.tv_vol ) soft_loss += tv_loss return soft_loss @@ -270,7 +270,7 @@ def forward(self, dummy_input=None) -> torch.Tensor: return self.obj # --- Defining the TV loss --- - def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tensor: + def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tensor: # pyright: ignore[reportIncompatibleMethodOverride] -> get_tv_loss has different arguments depending on the object. tv_d = torch.pow(obj[:, :, 1:, :, :] - obj[:, :, :-1, :, :], 2).sum() tv_h = torch.pow(obj[:, :, :, 1:, :] - obj[:, :, :, :-1, :], 2).sum() tv_w = torch.pow(obj[:, :, :, :, 1:] - obj[:, :, :, :, :-1], 2).sum() @@ -279,7 +279,7 @@ def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tenso return tv_loss * tv_weight / (torch.prod(torch.tensor(obj.shape))) # --- Helper Functions --- - def to(self, device: str): + def to(self, device: str): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change self._obj = self._obj.to(device) @@ -327,7 +327,7 @@ def from_model( # --- Properties --- @property - def model(self) -> "nn.Module": + def model(self) -> nn.Module: """ Returns the INR model. """ @@ -527,6 +527,11 @@ def _pretrain( loss_fn: Callable, apply_constraints: bool, ): + if self.optimizer is None: + raise RuntimeError("Optimizer not set. Call set_optimizer() first.") + if self.scheduler is None: + raise RuntimeError("Scheduler not set. Call set_scheduler() first.") + self.model.train() optimizer = self.optimizer scheduler = self.scheduler @@ -570,8 +575,7 @@ def create_volume(self, return_vol: bool = False): coords_1d = torch.linspace(-1, 1, N) x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - - model = self.model.module if hasattr(self.model, "module") else self.model + model = self.model.module if isinstance(self.model, nn.DataParallel) else self.model inference_batch_size = 5 * N * N total_samples = N**3 @@ -624,7 +628,7 @@ def create_volume(self, return_vol: bool = False): outputs_dev = outputs.to(self.device) # (local_B, C) if local_B < max_size: pad = torch.zeros( - (max_size - local_B, C), + (max_size - local_B, C), # type: ignore device=self.device, dtype=outputs_dev.dtype, ) @@ -633,7 +637,7 @@ def create_volume(self, return_vol: bool = False): outputs_padded = outputs_dev gathered_outputs = [ - torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) + torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) # type: ignore for _ in range(self.world_size) ] dist.all_gather(gathered_outputs, outputs_padded.contiguous()) @@ -651,10 +655,10 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def get_tv_loss( + def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] self, coords: torch.Tensor, - ): + ) -> torch.Tensor: tv_loss = torch.tensor(0.0, device=coords.device) num_tv_samples = min(10000, coords.shape[0]) @@ -679,7 +683,7 @@ def get_tv_loss( tv_loss += self.constraints.tv_vol * grad_norm.mean() return tv_loss - def to(self, device: str): + def to(self, device: str): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change self.device = device # self._obj = self._obj.to(self.device) self.reconnect_optimizer_to_parameters() From 886ef22f2484970056977b7b0dcad326fdb9a964 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 14:26:58 -0800 Subject: [PATCH 115/335] ddp.py linting errors addressed by ignoring. Again @arthurmccray, since Dataset doesn't have a length attribute in torch --- src/quantem/core/ml/ddp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 9cc02ee3..18d104f6 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -122,14 +122,14 @@ def setup_dataloader( if self.global_rank == 0: print("Dataloader setup complete:") - print(f" Total train samples: {len(train_dataset)}") + print(f" Total train samples: {len(train_dataset)}") # pyright: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. print(f" Local batch size: {batch_size}") print(f" Global batch size: {batch_size * self.world_size}") print(f" Train batches per GPU per epoch: {len(train_dataloader)}") if val_dataset: print(f" Total val samples: {len(val_dataset)}") - print(f" Val batches per GPU per epoch: {len(val_dataloader)}") + print(f" Val batches per GPU per epoch: {len(val_dataloader)}") # pyright: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. return train_dataloader, train_sampler, val_dataloader, val_sampler From 471bd6bc3fb4870fbd7384324179060e711ca7f1 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 14:38:04 -0800 Subject: [PATCH 116/335] Cleaned up tomography_base.py; inheritting from ObjectConstraints to get correct typing for and relying on DDPMixin to set the correct devices. --- src/quantem/core/ml/ddp.py | 5 --- src/quantem/tomography/tomography_base.py | 42 +++++------------------ 2 files changed, 8 insertions(+), 39 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 18d104f6..ca572a32 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -13,11 +13,6 @@ class DDPMixin: - """ - def __init__( - self, - ): - self.setup_distributed() - def setup_distributed(self, device: str | torch.device | None = None): """ Initializes parameters depending if multiple-GPU training, single-GPU training, or CPU training. diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 7661c301..2c06c20f 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -1,5 +1,4 @@ import numpy as np -import torch from numpy.typing import NDArray from quantem.core.io.serialize import AutoSerialize @@ -9,8 +8,8 @@ from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( DefaultConstraintsTomography, - ObjectBase, - ObjectPixelated, + ObjectConstraints, + ObjectINR, ) @@ -26,14 +25,14 @@ class TomographyBase(AutoSerialize, RNGMixin, DDPMixin): def __init__( self, dset: DatasetModelType, - obj_model: ObjectBase, + obj_model: ObjectConstraints, logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, _token: object | None = None, ): - # if _token is not self._token: # TODO: Idk why this isn't working. - # raise RuntimeError("Use Dataset.from_* to instantiate this class.") + if _token is not self._token: + raise RuntimeError("Use .from_* to instantiate this class.") super().__init__() self.obj_model = obj_model @@ -48,12 +47,9 @@ def __init__( self._consistency_losses: list[float] = [] self._val_losses: list[float] = [] # DDP Initialization - # print("Checking if obj_model is a ObjectPixelated: ", not isinstance(obj_model, ObjectPixelated)) - if not isinstance(obj_model, ObjectPixelated): + if isinstance(obj_model, ObjectINR): print("Setting up DDP for obj_model") self.setup_distributed(device=device) - # self._obj_model._model = self.build_model(obj_model) # Assuming when object is initialized it's already wrapped in DDP? - # print("After DDP Setup", self._obj_model) self.dset = dset self.dset.to(device) @@ -72,17 +68,11 @@ def dset(self, new_dset: DatasetModelType): self._dset = new_dset @property - def obj_type(self) -> str: - return self.obj_model.obj_type - - @property - def obj_model(self) -> ObjectBase: + def obj_model(self) -> ObjectConstraints: return self._obj_model @obj_model.setter - def obj_model(self, obj_model: ObjectBase): - # if not isinstance(obj_model, ObjectBase): - # raise TypeError(f"obj_model should be a ObjectBase, got {type(obj_model)}") + def obj_model(self, obj_model: ObjectConstraints): self._obj_model = obj_model @property @@ -91,10 +81,6 @@ def constraints(self) -> DefaultConstraintsTomography: @constraints.setter def constraints(self, constraints: DefaultConstraintsTomography): - if not isinstance(constraints, DefaultConstraintsTomography): - raise TypeError( - f"constraints should be a DefaultConstraintsTomography, got {type(constraints)}" - ) self.obj_model.constraints = constraints @property @@ -107,18 +93,6 @@ def logger(self, logger: LoggerTomography | None): raise TypeError(f"logger should be a LoggerTomography, got {type(logger)}") self._logger = logger - @property - def device(self) -> str: - return torch.device(self._device) - - @device.setter - def device(self, device: str): - print("Device trying to set: ", device) - # if not isinstance(device, str): - # raise TypeError(f"device should be a str, got {type(device)}") - self._device = device - # self.to(device) - @property def epoch_losses(self) -> NDArray: """ From 9336e4f5ba5fa54d415ea1afc48bb6bfebeed02a Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 15:09:53 -0800 Subject: [PATCH 117/335] Cleaned up some tomography.py; lots of linter errors still but meh --- src/quantem/core/ml/ddp.py | 4 ++- src/quantem/tomography/dataset_models.py | 12 +++++---- src/quantem/tomography/object_models.py | 19 ++++++++++--- src/quantem/tomography/tomography.py | 33 ++++++++++++++--------- src/quantem/tomography/tomography_base.py | 8 +++--- 5 files changed, 49 insertions(+), 27 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index ca572a32..0570322f 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -5,6 +5,8 @@ import torch.nn as nn from torch.utils.data import DataLoader, Dataset, DistributedSampler, random_split +from quantem.tomography.dataset_models import DatasetModelType + class DDPMixin: """ @@ -50,7 +52,7 @@ def setup_distributed(self, device: str | torch.device | None = None): def setup_dataloader( self, - dataset: Dataset, + dataset: Dataset | DatasetModelType, batch_size: int, num_workers: int = 0, val_fraction: float = 0.0, diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index ee7d74f7..50c1a7e1 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -208,16 +208,18 @@ def shifts_params(self, shifts: torch.Tensor, device: str): self._shifts_params = nn.Parameter(shifts.to(device)) @property - def device(self) -> str: + def device(self) -> torch.device: return self._device @device.setter - def device(self, device: str): + def device(self, device: torch.device | str): + if isinstance(device, str): + device = torch.device(device) self._device = device # --- Helper Functions --- @abstractmethod - def to(self, device: str): + def to(self, device: torch.device | str): """ Moves the dataset to the device, and also insantiates the aux params to the device. """ @@ -263,7 +265,7 @@ def forward( pixel_loc=None, ) - def to(self, device: str): + def to(self, device: str | torch.device): """ Moves the tilt stack and tilt_angles to the device, along with other nn.Parameters to the device. """ @@ -474,7 +476,7 @@ def __len__( N = max(self.tilt_stack.shape) return self.tilt_stack.shape[0] * N * N - def to(self, device: str): + def to(self, device: torch.device | str): self._z1_params = nn.Parameter(self._z1_angles.to(device)) self._z3_params = nn.Parameter(self._z3_angles.to(device)) self._shifts_params = nn.Parameter(self._shifts.to(device)) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 5f093155..1f55aa8e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -256,6 +256,10 @@ def name(self) -> str: def obj_type(self) -> str: return "pixelated" + @property + def dtype(self) -> torch.dtype: + return self._obj.dtype + def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) if self.constraints.tv_vol > 0: @@ -279,8 +283,13 @@ def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tenso return tv_loss * tv_weight / (torch.prod(torch.tensor(obj.shape))) # --- Helper Functions --- - def to(self, device: str): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + if isinstance(device, str): + device = torch.device(device) + self._device = device self._obj = self._obj.to(device) + self.reconnect_optimizer_to_parameters() + return self class ObjectINR(ObjectConstraints, DDPMixin): @@ -327,7 +336,7 @@ def from_model( # --- Properties --- @property - def model(self) -> nn.Module: + def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: """ Returns the INR model. """ @@ -683,8 +692,10 @@ def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] tv_loss += self.constraints.tv_vol * grad_norm.mean() return tv_loss - def to(self, device: str): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change - self.device = device + def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + if isinstance(device, str): + device = torch.device(device) + self._device = device # self._obj = self._obj.to(self.device) self.reconnect_optimizer_to_parameters() diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 654bf529..f3023048 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,5 +1,5 @@ import os -from typing import Literal, Self +from typing import Literal, Self, cast import numpy as np import torch @@ -14,7 +14,11 @@ ) from quantem.tomography.dataset_models import DatasetModelType from quantem.tomography.logger_tomography import LoggerTomography -from quantem.tomography.object_models import ObjectModelType +from quantem.tomography.object_models import ( + DefaultConstraintsTomography, + ObjectINR, + ObjectPixelated, +) from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase from quantem.tomography.tomography_opt import TomographyOpt @@ -30,7 +34,7 @@ class Tomography(TomographyOpt, TomographyBase): def from_models( cls, dset: DatasetModelType, - obj_model: ObjectModelType, + obj_model: ObjectINR, logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, @@ -49,7 +53,7 @@ def reconstruct( num_workers: int = 32, reset: bool = False, optimizer_params: dict | None = None, - scheduler_params: dict | None = {}, + scheduler_params: dict | None = None, constraints: dict = {}, # TODO: What to pass into the constraints? num_samples_per_ray: int | list[tuple[int, int]] = 1, profiling_mode: bool = False, @@ -83,7 +87,6 @@ def reconstruct( raise NotImplementedError("Reset is not implemented yet.") new_scheduler = reset - if optimizer_params is not None: self.optimizer_params = optimizer_params self.set_optimizers() @@ -93,11 +96,11 @@ def reconstruct( self.scheduler_params = scheduler_params new_scheduler = True - if constraints is not None: - self.obj_model.constraints = constraints - if new_scheduler: - self.set_schedulers(scheduler_params, num_iter=num_iter) + self.set_schedulers(self.scheduler_params, num_iter=num_iter) + + if constraints is not None: + self.obj_model.constraints = cast(DefaultConstraintsTomography, constraints) # Setting up DDP if not hasattr(self, "dataloader") or reset_dset is not None: @@ -107,8 +110,13 @@ def reconstruct( self.dset = reset_dset self.dset.to(self.device) - self.optimizer_params = optimizer_params - self.set_optimizers() + if optimizer_params is not None: + self.optimizer_params = optimizer_params + self.set_optimizers() + if scheduler_params is not None: + self.scheduler_params = scheduler_params + self.set_schedulers(self.scheduler_params, num_iter=num_iter) + self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( self.setup_dataloader( self.dset, @@ -320,7 +328,7 @@ class TomographyConventional(TomographyBase): def from_models( cls, dset: DatasetModelType, - obj_model: ObjectModelType, + obj_model: ObjectPixelated, logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, @@ -425,7 +433,6 @@ def _reconstruction_epoch( torch.ones_like(error), theta=self.dset.tilt_angles, device=self.device, - filter_name=None, circle=True, ) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 2c06c20f..def4867f 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -8,8 +8,8 @@ from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( DefaultConstraintsTomography, - ObjectConstraints, ObjectINR, + ObjectModelType, ) @@ -25,7 +25,7 @@ class TomographyBase(AutoSerialize, RNGMixin, DDPMixin): def __init__( self, dset: DatasetModelType, - obj_model: ObjectConstraints, + obj_model: ObjectModelType, logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, @@ -68,11 +68,11 @@ def dset(self, new_dset: DatasetModelType): self._dset = new_dset @property - def obj_model(self) -> ObjectConstraints: + def obj_model(self) -> ObjectModelType: return self._obj_model @obj_model.setter - def obj_model(self, obj_model: ObjectConstraints): + def obj_model(self, obj_model: ObjectModelType): self._obj_model = obj_model @property From ce28e29ed5a7c9948623c3345d7d0fd6f8a7730e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 15:15:23 -0800 Subject: [PATCH 118/335] _token fix for TomographyBase and some other stuff --- src/quantem/core/ml/ddp.py | 6 +++--- src/quantem/tomography/tomography.py | 2 ++ 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 0570322f..66b23273 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -61,7 +61,7 @@ def setup_dataloader( persist = num_workers > 0 if val_fraction > 0.0: - train_dataset, val_dataset = random_split(dataset, [1 - val_fraction, val_fraction]) + train_dataset, val_dataset = random_split(dataset, [1 - val_fraction, val_fraction]) # type: ignore[reportArgumentType] --> dataset inherits from torch Dataset so this is fine. else: train_dataset = dataset val_dataset = None @@ -69,7 +69,7 @@ def setup_dataloader( if self.world_size > 1: shuffle = True train_sampler = DistributedSampler( - train_dataset, + train_dataset, # type: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. num_replicas=self.world_size, rank=self.global_rank, shuffle=shuffle, @@ -92,7 +92,7 @@ def setup_dataloader( shuffle = True train_dataloader = DataLoader( - train_dataset, + train_dataset, # type: ignore[reportArgumentType] --> Torch datasets do not have a len method, but still works. batch_size=batch_size, num_workers=num_workers, sampler=train_sampler, diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index f3023048..e0e72f69 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -44,6 +44,8 @@ def from_models( obj_model=obj_model, logger=logger, device=device, + rng=rng, + _token=cls._token, ) def reconstruct( From 9ecd8d7aeaec14d81a5d5cb805ef02723e53c449 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 16:05:19 -0800 Subject: [PATCH 119/335] Added TomographyDatasetConstraints to dataset_models.py. Perhaps there's a better way to do this across both object_models.py and dataset_models.py i.e, an overall tomography_constraints.py which is then shared between the two Objects. --- src/quantem/core/ml/constraints.py | 2 +- src/quantem/tomography/dataset_models.py | 53 ++++++++++++++++++++++-- src/quantem/tomography/object_models.py | 14 +------ src/quantem/tomography/tomography.py | 7 +++- src/quantem/tomography/utils.py | 28 +++++++++++++ 5 files changed, 85 insertions(+), 19 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index cce43659..553b0611 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -8,7 +8,7 @@ from numpy.typing import NDArray -@dataclass(slots=True) +@dataclass(slots=False) class Constraints(ABC): """ Any model that inherits from BaseConstraints will contain a Constraints instance that contains soft and hard constraints. diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 50c1a7e1..cae642eb 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -10,7 +10,24 @@ from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.tomography.utils import tv_loss_1d + + +# --- Constraints --- +@dataclass(slots=False) +class DefaultTomographyDatasetConstraints(Constraints): + """ + Data class for constraints that can be applied to the parameters of a tomography dataset. + """ + + # Soft Constraints + tv_zs: float = 0.0 + tv_shifts: float = 0.0 + + soft_constraint_keys = ["tv_zs", "tv_shifts"] + hard_constraint_keys = [] @dataclass @@ -169,7 +186,7 @@ def learn_tilt_axis(self, learn_tilt_axis: bool): @property def reference_tilt_idx(self) -> int: - return self._reference_tilt_angle_idx + return int(self._reference_tilt_angle_idx) @reference_tilt_idx.setter def reference_tilt_idx(self, reference_tilt_idx: int): @@ -227,7 +244,35 @@ def to(self, device: torch.device | str): raise NotImplementedError("This method should be implemented in subclasses.") -class TomographyPixDataset(TomographyDatasetBase): +class TomographyDatasetConstraints(BaseConstraints, TomographyDatasetBase): + DEFAULT_CONSTRAINTS = DefaultTomographyDatasetConstraints() + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.constraints: DefaultTomographyDatasetConstraints = self.DEFAULT_CONSTRAINTS.copy() + + def apply_soft_constraints(self) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=self.z1_params.device) + if self.constraints.tv_zs > 0: + tv_loss_zs = tv_loss_1d(self.z1_params) + tv_loss_zs += tv_loss_1d(self.z3_params) + tv_loss_zs = self.constraints.tv_zs * tv_loss_zs + soft_loss += tv_loss_zs + + if self.constraints.tv_shifts > 0: + # Shift params is of shape (N, 2) + tv_loss_shifts = tv_loss_1d(self.shifts_params[:, 0]) + tv_loss_shifts += tv_loss_1d(self.shifts_params[:, 1]) + tv_loss_shifts = self.constraints.tv_shifts * tv_loss_shifts + soft_loss += tv_loss_shifts + + return soft_loss + + def apply_hard_constraints(self): + pass + + +class TomographyPixDataset(TomographyDatasetConstraints): """ Dataset class for pixel-based tomography, i.e AD, SIRT, WBP, etc... @@ -283,7 +328,7 @@ def to(self, device: str | torch.device): self.device = device -class TomographyINRDataset(TomographyDatasetBase, Dataset): +class TomographyINRDataset(TomographyDatasetConstraints, Dataset): """ Dataset class for INR-based tomography. @@ -446,7 +491,7 @@ def integrate_rays( def __getitem__( self, idx: int, - ) -> DatasetValue: + ) -> dict: """ Gets the item for INR i.e, the project index, pixel value at (i, j), and the tilt angle. """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 1f55aa8e..4a67aeb6 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -19,7 +19,7 @@ object_type = Literal["potential"] -@dataclass(slots=True) +@dataclass(slots=False) class DefaultConstraintsTomography(Constraints): """ Data class for all constraints that can be applied to the object model. @@ -158,18 +158,6 @@ def apply_hard_constraints( # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. return obj2 - # def apply_soft_constraints( - # self, - # obj: torch.Tensor, - # ) -> torch.Tensor: - # """ - # TODO: Already in BaseConstraints class. - # Apply soft constraints to the object model. - - # Only soft constraint here is the TV loss. - # """ - # return NotImplementedError("Subclasses must implement this method.") - @abstractmethod def get_tv_loss(self, **kwargs) -> torch.Tensor: """ diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index e0e72f69..b631b2e1 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -12,7 +12,7 @@ from quantem.core.utils.tomography_utils import ( torch_phase_cross_correlation, ) -from quantem.tomography.dataset_models import DatasetModelType +from quantem.tomography.dataset_models import DatasetModelType, DefaultTomographyDatasetConstraints from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( DefaultConstraintsTomography, @@ -57,6 +57,7 @@ def reconstruct( optimizer_params: dict | None = None, scheduler_params: dict | None = None, constraints: dict = {}, # TODO: What to pass into the constraints? + dataset_constraints: dict = {}, num_samples_per_ray: int | list[tuple[int, int]] = 1, profiling_mode: bool = False, val_fraction: float = 0.0, @@ -104,6 +105,8 @@ def reconstruct( if constraints is not None: self.obj_model.constraints = cast(DefaultConstraintsTomography, constraints) + if dataset_constraints is not None: + self.dset.constraints = cast(DefaultTomographyDatasetConstraints, dataset_constraints) # Setting up DDP if not hasattr(self, "dataloader") or reset_dset is not None: if reset_dset is not None: @@ -112,6 +115,7 @@ def reconstruct( self.dset = reset_dset self.dset.to(self.device) + if optimizer_params is not None: self.optimizer_params = optimizer_params self.set_optimizers() @@ -182,6 +186,7 @@ def reconstruct( batch_consistency_loss = loss_func(pred, target) soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) + soft_constraints_loss += self.dset.apply_soft_constraints() epoch_soft_constraint_loss += soft_constraints_loss.detach() diff --git a/src/quantem/tomography/utils.py b/src/quantem/tomography/utils.py index 9a009002..08093925 100644 --- a/src/quantem/tomography/utils.py +++ b/src/quantem/tomography/utils.py @@ -67,3 +67,31 @@ def transform_slice(mag_slice): rotated_mags = torch.vmap(transform_slice)(mags) return rotated_mags.permute(1, 2, 3, 0) + + +def tv_loss_1d(x: torch.Tensor, reduction: str = "mean") -> torch.Tensor: + """ + 1D Total Variation Loss. + + Encourages piecewise smoothness by penalizing differences between + adjacent elements. + + Args: + x: Input tensor of shape (N, C, L) or (N, L) or (L,) + reduction: 'mean' | 'sum' | 'none' + + Returns: + Scalar loss (or per-sample tensor if reduction='none') + """ + # Difference between adjacent elements along the last dimension + diff = x[..., 1:] - x[..., :-1] # shape: (..., L-1) + tv = diff.abs() # L1 variant ← most common + + if reduction == "mean": + return tv.mean() + elif reduction == "sum": + return tv.sum() + elif reduction == "none": + return tv + else: + raise ValueError(f"Unknown reduction: {reduction!r}") From a535083de92ddc77c79560f231d7affe5c0ff760 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 16:16:21 -0800 Subject: [PATCH 120/335] Reworded in top-level tomography: obj_constraints and dset_constraints --- src/quantem/core/ml/ddp.py | 2 -- src/quantem/tomography/object_models.py | 1 + src/quantem/tomography/tomography.py | 14 ++++++++------ 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 66b23273..d081f757 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -38,10 +38,8 @@ def setup_distributed(self, device: str | torch.device | None = None): if torch.cuda.is_available(): device = torch.device("cuda:0" if device is None else device) torch.cuda.set_device(device.index) - print("Single GPU training") else: device = torch.device("cpu") - print("CPU training") if device.type == "cuda": torch.backends.cudnn.benchmark = True diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 4a67aeb6..2f57da24 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -287,6 +287,7 @@ def __init__( device: str = "cpu", rng: np.random.Generator | int | None = None, model: nn.Module | None = None, + _token: object | None = None, ): super().__init__( shape=shape, diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index b631b2e1..c7e9462c 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -56,8 +56,8 @@ def reconstruct( reset: bool = False, optimizer_params: dict | None = None, scheduler_params: dict | None = None, - constraints: dict = {}, # TODO: What to pass into the constraints? - dataset_constraints: dict = {}, + obj_constraints: dict = {}, # TODO: What to pass into the constraints? + dset_constraints: dict = {}, num_samples_per_ray: int | list[tuple[int, int]] = 1, profiling_mode: bool = False, val_fraction: float = 0.0, @@ -102,11 +102,11 @@ def reconstruct( if new_scheduler: self.set_schedulers(self.scheduler_params, num_iter=num_iter) - if constraints is not None: - self.obj_model.constraints = cast(DefaultConstraintsTomography, constraints) + if obj_constraints is not None: + self.obj_model.constraints = cast(DefaultConstraintsTomography, obj_constraints) - if dataset_constraints is not None: - self.dset.constraints = cast(DefaultTomographyDatasetConstraints, dataset_constraints) + if dset_constraints is not None: + self.dset.constraints = cast(DefaultTomographyDatasetConstraints, dset_constraints) # Setting up DDP if not hasattr(self, "dataloader") or reset_dset is not None: if reset_dset is not None: @@ -346,6 +346,7 @@ def from_models( logger=logger, device=device, rng=rng, + _token=cls._token, ) def reconstruct( @@ -441,6 +442,7 @@ def _reconstruction_epoch( theta=self.dset.tilt_angles, device=self.device, circle=True, + filter_name=None, ) normalization[normalization == 0] = 1e-6 From 2e87529c4ac8b813b166f4a67bd05b31b357dc07 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 16:23:48 -0800 Subject: [PATCH 121/335] Added pbar to INR reconstruction with verbose option. Deleted print statements (Add an option for HPC, or rely on TensorBoard is enough?) --- src/quantem/tomography/tomography.py | 24 +++++++++++++---------- src/quantem/tomography/tomography_base.py | 10 ++++++++++ 2 files changed, 24 insertions(+), 10 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index c7e9462c..ff8523dd 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -37,6 +37,7 @@ def from_models( obj_model: ObjectINR, logger: LoggerTomography | None = None, device: str = "cuda", + verbose: int | bool = True, rng: np.random.Generator | int | None = None, ) -> Self: return cls( @@ -45,6 +46,7 @@ def from_models( logger=logger, device=device, rng=rng, + verbose=verbose, _token=cls._token, ) @@ -143,8 +145,8 @@ def reconstruct( loss_func = get_loss_module(name=loss_type, dtype=self.obj_model.dtype, **loss_func_kwargs) - print(f"N: {N}, num_samples_per_ray: {num_samples_per_ray}") - for a0 in range(num_iter): + pbar = tqdm(range(num_iter), desc="Reconstruction", disable=not self.verbose) + for a0 in pbar: consistency_loss = 0.0 total_loss = 0.0 epoch_soft_constraint_loss = 0.0 @@ -160,8 +162,6 @@ def reconstruct( else: curr_num_samples_per_ray = num_samples_per_ray - if self.global_rank == 0: - print(f"curr_num_samples_per_ray: {curr_num_samples_per_ray}") for batch_idx, batch in enumerate(self.dataloader): self.zero_grad_all() with torch.autocast( @@ -206,6 +206,10 @@ def reconstruct( consistency_loss = consistency_loss.item() / len(self.dataloader) epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len(self.dataloader) + pbar.set_description( + f"Reconstruction | Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}, Soft Constraint Loss: {epoch_soft_constraint_loss:.4f}" + ) + if self.val_dataloader is not None: print("Validating...") self.obj_model.model.eval() @@ -250,11 +254,9 @@ def reconstruct( total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() - if self.global_rank == 0: - print(f"Total Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}") - - if self.val_dataloader: - print(f"Validation loss: {avg_val_loss:4f}") + pbar.set_description( + f"Reconstruction | Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}, Soft Constraint Loss: {epoch_soft_constraint_loss:.4f}" + ) self._epoch_losses.append(total_loss) self._consistency_losses.append(consistency_loss) @@ -338,6 +340,7 @@ def from_models( obj_model: ObjectPixelated, logger: LoggerTomography | None = None, device: str = "cuda", + verbose: int | bool = True, rng: np.random.Generator | int | None = None, ) -> Self: return cls( @@ -346,6 +349,7 @@ def from_models( logger=logger, device=device, rng=rng, + verbose=verbose, _token=cls._token, ) @@ -357,7 +361,7 @@ def reconstruct( inline_alignment: bool = False, smoothing_sigma: float | None = None, ): - pbar = tqdm(range(num_iter), desc=f"{mode} Reconstruction") + pbar = tqdm(range(num_iter), desc=f"{mode} Reconstruction", disable=not self.verbose) if mode == "sirt" or mode == "fbp": proj_forward = torch.zeros_like(self.dset.tilt_stack).permute(2, 0, 1) else: diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index def4867f..f4d80096 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -29,6 +29,7 @@ def __init__( logger: LoggerTomography | None = None, device: str = "cuda", rng: np.random.Generator | int | None = None, + verbose: int | bool = True, _token: object | None = None, ): if _token is not self._token: @@ -38,6 +39,7 @@ def __init__( self.obj_model = obj_model self.dset = dset + self.verbose = verbose self.rng = rng self.device = device self.logger = logger @@ -67,6 +69,14 @@ def dset(self, new_dset: DatasetModelType): raise TypeError(f"dset should be a TomographyDataset, got {type(new_dset)}") self._dset = new_dset + @property + def verbose(self) -> int | bool: + return self._verbose + + @verbose.setter + def verbose(self, verbose: int | bool): + self._verbose = verbose + @property def obj_model(self) -> ObjectModelType: return self._obj_model From bd9dd80749a42f9700733240973c6343a9883209 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 16:26:08 -0800 Subject: [PATCH 122/335] Deleted some more unnecessary prints in the logger. --- src/quantem/tomography/logger_tomography.py | 4 ---- src/quantem/tomography/tomography.py | 6 ++++-- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index c6726dac..821f1814 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -56,8 +56,6 @@ def log_iter_images( z3_vals = dataset_model.z3_params.detach().cpu().numpy() shifts_vals = dataset_model.shifts_params.detach().cpu().numpy() - print("Logging volume...") - for channel in range(pred_volume.shape[0]): self.log_image( f"volume/sum_z_{channel}", pred_volume[channel].sum(axis=0), iter, logger_cmap @@ -70,7 +68,6 @@ def log_iter_images( ) # Plotting z1 and z3 vals - print("Plotting z1 and z3 angles...") fig, ax = plt.subplots() ax.plot(z1_vals, label="Z1") ax.plot(z3_vals, label="Z3") @@ -82,7 +79,6 @@ def log_iter_images( plt.close(fig) # Plotting shifts - print("Plotting shifts...") fig, ax = plt.subplots() ax.plot(shifts_vals[:, 0], label="Shifts X") ax.plot(shifts_vals[:, 1], label="Shifts Y") diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ff8523dd..9a470abe 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -269,11 +269,9 @@ def reconstruct( self.logger.log_images_every > 0 and self.num_epochs % self.logger.log_images_every == 0 ): - print("Creating volume...") pred_full = self.obj_model.create_volume(return_vol=True) if self.global_rank == 0: - print("Logging images...") self.logger.log_iter_images( pred_volume=pred_full, dataset_model=self.dset, @@ -293,6 +291,10 @@ def reconstruct( self.logger.flush() + pbar.set_description( + f"Reconstruction | Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}, Soft Constraint Loss: {epoch_soft_constraint_loss:.4f} | Logger Updated" + ) + # --- Helper Functions --- def save_volume(self, path: str = "recon_volume.npz", overwrite: bool = False): From c7a6bda6444fdff8d551bad2948a0c89648cfe5e Mon Sep 17 00:00:00 2001 From: cophus Date: Mon, 2 Mar 2026 16:50:29 -0800 Subject: [PATCH 123/335] adding numpy math functions --- src/quantem/core/datastructures/vector.py | 137 ++++++++++++++++++++++ tests/datastructures/test_vector.py | 88 ++++++++++++++ 2 files changed, 225 insertions(+) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index a57ef7dc..12ce2801 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -89,6 +89,8 @@ class Vector(AutoSerialize): ... ) """ + __array_priority__ = 1000 + def __init__( self, shape: tuple[int, ...], @@ -494,6 +496,39 @@ def __setitem__(self, idx: Any, value: Any) -> None: target = self[idx] target._assign(value) + def __array_ufunc__(self, ufunc: Any, method: str, *inputs: Any, **kwargs: Any) -> Any: + """Apply supported NumPy ufuncs elementwise. + + Supported operations are limited to elementwise ``__call__`` ufuncs. The + result preserves the current selection shape and fields. + """ + if method != "__call__": + return NotImplemented + + out = kwargs.get("out") + if out is not None: + return NotImplemented + + vector_inputs = [value for value in inputs if isinstance(value, Vector)] + if not vector_inputs: + return NotImplemented + + template = vector_inputs[0] + row_counts = template.row_counts() + total_rows = sum(row_counts) + + for other in vector_inputs[1:]: + if other.shape != template.shape: + raise ValueError("Vector ufunc inputs must have matching fixed-grid shapes.") + if other.num_fields != template.num_fields: + raise ValueError("Vector ufunc inputs must have matching field counts.") + if other.row_counts() != row_counts: + raise ValueError("Vector ufunc inputs must have matching per-cell row counts.") + + flat_inputs = [_normalize_ufunc_input(value, total_rows, template.num_fields) for value in inputs] + result = ufunc(*flat_inputs, **kwargs) + return _wrap_ufunc_result(template, result, row_counts) + def __add__(self, other: Any) -> "Vector": return self._binary_op(other, np.add) @@ -506,6 +541,15 @@ def __mul__(self, other: Any) -> "Vector": def __truediv__(self, other: Any) -> "Vector": return self._binary_op(other, np.divide) + def __floordiv__(self, other: Any) -> "Vector": + return self._binary_op(other, np.floor_divide) + + def __mod__(self, other: Any) -> "Vector": + return self._binary_op(other, np.mod) + + def __pow__(self, other: Any) -> "Vector": + return self._binary_op(other, np.power) + def __radd__(self, other: Any) -> "Vector": return self._binary_op(other, np.add, reverse=True) @@ -518,6 +562,15 @@ def __rsub__(self, other: Any) -> "Vector": def __rtruediv__(self, other: Any) -> "Vector": return self._binary_op(other, np.divide, reverse=True) + def __rfloordiv__(self, other: Any) -> "Vector": + return self._binary_op(other, np.floor_divide, reverse=True) + + def __rmod__(self, other: Any) -> "Vector": + return self._binary_op(other, np.mod, reverse=True) + + def __rpow__(self, other: Any) -> "Vector": + return self._binary_op(other, np.power, reverse=True) + def __iadd__(self, other: Any) -> "Vector": self._inplace_op(other, np.add) return self @@ -534,6 +587,29 @@ def __itruediv__(self, other: Any) -> "Vector": self._inplace_op(other, np.divide) return self + def __ifloordiv__(self, other: Any) -> "Vector": + self._inplace_op(other, np.floor_divide) + return self + + def __imod__(self, other: Any) -> "Vector": + self._inplace_op(other, np.mod) + return self + + def __ipow__(self, other: Any) -> "Vector": + self._inplace_op(other, np.power) + return self + + def __neg__(self) -> "Vector": + return self._binary_op(-1, np.multiply) + + def __pos__(self) -> "Vector": + return self.copy() + + def __abs__(self) -> "Vector": + result = self.copy() + result._inplace_unary(np.abs) + return result + @property def _full_num_fields(self) -> int: return len(self._state["fields"]) @@ -746,6 +822,16 @@ def _binary_op(self, other: Any, op: Any, reverse: bool = False) -> "Vector": result._inplace_op(other, op, reverse=reverse) return result + def _inplace_unary(self, op: Any) -> None: + """Apply a unary elementwise operation in-place to the selected fields.""" + targets = self._selected_cell_indices() + field_indices = self._field_indices() + for target in targets: + cell = self._cell_matrix(int(target)) + lhs = cell[:, field_indices] + if lhs.shape[0] > 0: + cell[:, field_indices] = op(lhs) + def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: """Apply elementwise arithmetic in-place to the selected fields.""" targets = self._selected_cell_indices() @@ -1072,6 +1158,57 @@ def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDA ) from exc +def _normalize_ufunc_input(value: Any, total_rows: int, num_fields: int) -> Any: + """Normalize one ufunc input to flattened Vector-compatible form.""" + if isinstance(value, Vector): + return value.flatten() + if np.isscalar(value): + return value + return _broadcast_field_values(value, total_rows, num_fields) + + +def _wrap_ufunc_result( + template: Vector, + result: Any, + row_counts: list[int], +) -> Any: + """Convert elementwise ufunc output back into Vector selections.""" + if isinstance(result, tuple): + return tuple(_vector_from_flat_result(template, item, row_counts) for item in result) + return _vector_from_flat_result(template, result, row_counts) + + +def _vector_from_flat_result( + template: Vector, + values: Any, + row_counts: list[int], +) -> Vector: + """Build a Vector from flattened rowwise result data.""" + total_rows = sum(row_counts) + flat_values = _broadcast_field_values(values, total_rows, template.num_fields) + + result = Vector.from_shape( + shape=template.shape, + fields=template.fields, + units=template.units, + name=template.name, + ) + result._state["metadata"] = copy.deepcopy(template.metadata) + + if total_rows == 0: + result._state["data"] = np.empty((0, template.num_fields), dtype=flat_values.dtype) + return result + + cursor = 0 + cells: list[NDArray[np.generic]] = [] + for rows in row_counts: + cells.append(flat_values[cursor : cursor + rows].copy()) + cursor += rows + + result._replace_cells(result._selected_cell_indices(), cells) + return result + + def _is_contiguous(indices: NDArray[np.int64]) -> bool: """Return True when integer column indices form one contiguous slice.""" diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index f9071644..a8d6d226 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -149,6 +149,94 @@ def test_field_arithmetic_with_scalar_and_ndarray(self): np.array([[101.0], [202.0], [303.0], [404.0], [505.0], [606.0]]), ) + def test_power_operations(self): + v = make_line_vector() + + squared = v.select_fields("intensity") ** 2 + np.testing.assert_array_equal( + squared.flatten(), + np.array([[1.0], [4.0], [9.0], [16.0], [25.0], [36.0]]), + ) + + intensity = v.select_fields("intensity") + intensity **= 2 + np.testing.assert_array_equal( + intensity.flatten(), + np.array([[1.0], [4.0], [9.0], [16.0], [25.0], [36.0]]), + ) + + reverse = 2 ** v.select_fields("intensity") + np.testing.assert_array_equal( + reverse.flatten(), + np.array([[2.0], [16.0], [512.0], [65536.0], [33554432.0], [68719476736.0]]), + ) + + def test_unary_mod_and_floor_division_operations(self): + v = make_line_vector() + + negative = -v.select_fields("intensity") + np.testing.assert_array_equal( + negative.flatten(), + np.array([[-1.0], [-2.0], [-3.0], [-4.0], [-5.0], [-6.0]]), + ) + + absolute = abs(negative) + np.testing.assert_array_equal( + absolute.flatten(), + np.array([[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]), + ) + + floored = v.select_fields("ky") // 150 + np.testing.assert_array_equal( + floored.flatten(), + np.array([[0.0], [1.0], [2.0], [2.0], [3.0], [4.0]]), + ) + + modded = v.select_fields("ky") % 150 + np.testing.assert_array_equal( + modded.flatten(), + np.array([[100.0], [50.0], [0.0], [100.0], [50.0], [0.0]]), + ) + + ky = v.select_fields("ky") + ky //= 150 + np.testing.assert_array_equal( + ky.flatten(), + np.array([[0.0], [1.0], [2.0], [2.0], [3.0], [4.0]]), + ) + + intensity = v.select_fields("intensity") + intensity %= 2 + np.testing.assert_array_equal( + intensity.flatten(), + np.array([[1.0], [0.0], [1.0], [0.0], [1.0], [0.0]]), + ) + + def test_numpy_ufunc_support(self): + v = make_line_vector() + + sine = np.sin(v.select_fields("kx")) + np.testing.assert_allclose( + sine.flatten(), + np.sin(v.select_fields("kx").flatten()), + ) + + maximum = np.maximum(v.select_fields("intensity"), 3.0) + np.testing.assert_array_equal( + maximum.flatten(), + np.array([[3.0], [3.0], [3.0], [4.0], [5.0], [6.0]]), + ) + + frac, whole = np.modf(v.select_fields("intensity") / 2.0) + np.testing.assert_allclose( + frac.flatten(), + np.array([[0.5], [0.0], [0.5], [0.0], [0.5], [0.0]]), + ) + np.testing.assert_allclose( + whole.flatten(), + np.array([[0.0], [1.0], [1.0], [2.0], [2.0], [3.0]]), + ) + def test_field_assignment_from_vector_expression(self): v = make_line_vector() scale = 2.5 From 4ebf7c246f426ffd281c77b2b3ce5320400b0881 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 2 Mar 2026 16:59:22 -0800 Subject: [PATCH 124/335] Scientific notation for the outputs in the progress bar --- src/quantem/tomography/dataset_models.py | 6 +++--- src/quantem/tomography/tomography.py | 6 +++--- src/quantem/tomography/tomography_lite.py | 9 +++++---- 3 files changed, 11 insertions(+), 10 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index cae642eb..942e7cac 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -556,12 +556,12 @@ def __init__( data_quantile = torch.quantile(data, 0.95) data = data / data_quantile - data = torch.permute(data, (2, 1, 0)) + data = torch.permute(data, (0, 3, 2, 1)) # data = torch.flip(data, dims=(2,)) self.volume = data.cpu() - self.N = pretrain_target.shape[0] # Assumes cubic volume. - self.total_samples = pretrain_target.shape[0] ** 3 + self.N = pretrain_target.shape[1] # Assumes cubic volume. + self.total_samples = pretrain_target.shape[1] ** 3 coords_1d = torch.linspace(-1, 1, self.N) x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 9a470abe..6c6a3b09 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -60,7 +60,7 @@ def reconstruct( scheduler_params: dict | None = None, obj_constraints: dict = {}, # TODO: What to pass into the constraints? dset_constraints: dict = {}, - num_samples_per_ray: int | list[tuple[int, int]] = 1, + num_samples_per_ray: int | list[tuple[int, int]] = None, profiling_mode: bool = False, val_fraction: float = 0.0, loss_type: Literal[ @@ -145,7 +145,7 @@ def reconstruct( loss_func = get_loss_module(name=loss_type, dtype=self.obj_model.dtype, **loss_func_kwargs) - pbar = tqdm(range(num_iter), desc="Reconstruction", disable=not self.verbose) + pbar = tqdm(range(num_iter), disable=not self.verbose) for a0 in pbar: consistency_loss = 0.0 total_loss = 0.0 @@ -255,7 +255,7 @@ def reconstruct( total_loss, consistency_loss, epoch_soft_constraint_loss = metrics.tolist() pbar.set_description( - f"Reconstruction | Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}, Soft Constraint Loss: {epoch_soft_constraint_loss:.4f}" + f"Reconstruction | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}" ) self._epoch_losses.append(total_loss) diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index 6bc6995e..032762af 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -82,11 +82,12 @@ def reconstruct( learn_pose: bool = True, warmup_routine: bool = True, scheduler_type: Literal[ - "exp", "cyclic", "plateau", "cosine_annealing", "linear", "full_warmup" + "exp", "cyclic", "plateau", "cosine_annealing", "linear", "full_warmup", "none" ] = "none", scheduler_factor: float = 0.5, new_optimizers: bool = False, - constraints: dict = {}, + obj_constraints: dict = {}, + dset_constraints: dict = {}, ): if self.num_epochs == 0: opt_params = { @@ -117,7 +118,6 @@ def reconstruct( opt_params = None scheduler_params = None - constraints = constraints num_samples_per_ray = int(max(self.dset.tilt_stack.shape)) return super().reconstruct( num_iter=num_iter, @@ -127,7 +127,8 @@ def reconstruct( num_samples_per_ray=num_samples_per_ray, optimizer_params=opt_params, scheduler_params=scheduler_params, - constraints=constraints, + obj_constraints=obj_constraints, + dset_constraints=dset_constraints, ) From f2d21423854730c656cbf2a65cb46a74afe22c96 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 3 Mar 2026 11:16:40 -0800 Subject: [PATCH 125/335] Added an obj_view method in objectinr that does the transpose for comparing to conventional reconstruction methods --- src/quantem/core/ml/optimizer_mixin.py | 2 +- src/quantem/tomography/object_models.py | 10 ++++++++++ src/quantem/tomography/tomography_lite.py | 1 - 3 files changed, 11 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 92db06f1..eadc1408 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -154,7 +154,7 @@ def set_scheduler( mode=params.get("mode", "triangular2"), cycle_momentum=params.get("momentum", False), ) - elif sched_type.startswith(("plat", "reducelronplat")): + elif sched_type.startswith(("plateau", "plat", "reducelronplat")): self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode="min", diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 2f57da24..754b1029 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -345,6 +345,16 @@ def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: def obj(self) -> torch.Tensor: return self._obj + @property + def obj_view(self) -> np.ndarray: + """ + Returns the object as a view of the x, y, z axes. + + Matches the axes of conventionally reconstructed objects, this is the object that will be saved. + """ + self.create_volume() + return self._obj.cpu().numpy()[0].transpose(0, 2, 1) + def apply_soft_constraints( self, coords: torch.Tensor, diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index 032762af..4a6d4c86 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -111,7 +111,6 @@ def reconstruct( } scheduler_params["pose"] = { "type": scheduler_type, - "factor": scheduler_factor, } else: From ceea35521884ea64dca2075f607a113367c803ae Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 3 Mar 2026 12:02:57 -0800 Subject: [PATCH 126/335] DDP errors when saving the dataloader, added a rebuild dataloader method to reload the whole object --- src/quantem/tomography/object_models.py | 4 ++-- src/quantem/tomography/tomography.py | 27 ++++++++++++++++++++++--- 2 files changed, 26 insertions(+), 5 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 754b1029..cd8a6092 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -353,7 +353,7 @@ def obj_view(self) -> np.ndarray: Matches the axes of conventionally reconstructed objects, this is the object that will be saved. """ self.create_volume() - return self._obj.cpu().numpy()[0].transpose(0, 2, 1) + return self._obj.cpu().numpy().transpose(0, 1, 3, 2) def apply_soft_constraints( self, @@ -695,7 +695,7 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM if isinstance(device, str): device = torch.device(device) self._device = device - # self._obj = self._obj.to(self.device) + self._model = self._model.to(device) self.reconnect_optimizer_to_parameters() diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 6c6a3b09..97af71fd 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -79,11 +79,14 @@ def reconstruct( I.e, auto-detection through the obj model type, while both share the same pose optimization. """ - # TODO: Prior to reconstruction, it is assumed that object + dataset are both in the correct devices. Need to implement a way to check this. - # Check device consistency self.obj_model.to(self.device) + # Saving batch size, num workers, and val fraction for reloading + self.batch_size = batch_size + self.num_workers = num_workers + self.val_fraction = val_fraction + if profiling_mode: if self.global_rank == 0: print("Profiling mode enabled.") @@ -307,7 +310,7 @@ def save_volume(self, path: str = "recon_volume.npz", overwrite: bool = False): f"File {path} already exists. Use overwrite=True to overwrite." ) print(f"Saving volume to {path}") - np.savez(path, volume=self.obj_model.obj.detach().cpu().numpy()) + np.savez(path, volume=self.obj_model.obj_view) if torch.distributed.is_initialized(): print("Barrier") @@ -326,8 +329,26 @@ def from_file( ) -> Self: tomography = cls._recursive_load_from_path(path) tomography.to(device) + tomography._rebuild_dataloader( + batch_size=tomography.batch_size, + num_workers=tomography.num_workers, + val_fraction=tomography.val_fraction, + ) return tomography + def _rebuild_dataloader(self, batch_size: int, num_workers: int, val_fraction: float): + """ + Rebuilds the dataloader due to persistent workers error when reloading the object. + """ + self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( + self.setup_dataloader( + self.dset, + batch_size, + num_workers=num_workers, + val_fraction=val_fraction, + ) + ) + class TomographyConventional(TomographyBase): """ From b21fc07e3b1f2541fd728eeb7b3b1296be33cc29 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Tue, 3 Mar 2026 13:21:55 -0800 Subject: [PATCH 127/335] fixing linter errors and testing that I can commit directly --- src/quantem/core/datastructures/vector.py | 72 +++++++++++------------ 1 file changed, 35 insertions(+), 37 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 12ce2801..2c0f2dbf 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -1,7 +1,7 @@ from __future__ import annotations import copy -from typing import Any, Sequence, overload +from typing import Any, Sequence, cast import numpy as np from numpy.typing import NDArray @@ -132,7 +132,9 @@ def _from_view( obj = cls.__new__(cls) obj._state = state obj._selection_shape = selection_shape - obj._selection_indices = None if selection_indices is None else selection_indices.astype(np.int64, copy=False) + obj._selection_indices = ( + None if selection_indices is None else selection_indices.astype(np.int64, copy=False) + ) obj._selected_fields = selected_fields return obj @@ -215,10 +217,7 @@ def fields(self) -> list[str]: @property def units(self) -> list[str]: """Return units for the selected fields.""" - lookup = { - field: unit - for field, unit in zip(self._state["fields"], self._state["units"]) - } + lookup = {field: unit for field, unit in zip(self._state["fields"], self._state["units"])} return [lookup[field] for field in self.fields] @property @@ -294,7 +293,9 @@ def copy(self) -> "Vector": metadata=copy.deepcopy(self.metadata), ) target_cells = copied._selected_cell_indices() - source_arrays = [self._selected_cell_matrix(index).copy() for index in self._selected_cell_indices()] + source_arrays = [ + self._selected_cell_matrix(index).copy() for index in self._selected_cell_indices() + ] copied._replace_cells(target_cells, source_arrays) return copied @@ -434,7 +435,11 @@ def add_fields( return target = self.select_fields(*new_fields) - if len(new_fields) > 1 and isinstance(values, (list, tuple)) and len(values) == len(new_fields): + if ( + len(new_fields) > 1 + and isinstance(values, (list, tuple)) + and len(values) == len(new_fields) + ): for field, value in zip(new_fields, values): target.select_fields(field)[...] = value else: @@ -462,13 +467,12 @@ def remove_fields(self, names: str | Sequence[str]) -> None: self._state["data"] = self._state["data"][:, keep] if self._selected_fields is not None: - self._selected_fields = tuple(field for field in self._selected_fields if field in self._state["fields"]) + self._selected_fields = tuple( + field for field in self._selected_fields if field in self._state["fields"] + ) if len(self._selected_fields) == len(self._state["fields"]): self._selected_fields = None - @overload - def __getitem__(self, idx: Any) -> "Vector": ... - def __getitem__(self, idx: Any) -> "Vector": """Return a fixed-grid selection as another Vector view.""" if _looks_like_field_selector(idx): @@ -525,7 +529,9 @@ def __array_ufunc__(self, ufunc: Any, method: str, *inputs: Any, **kwargs: Any) if other.row_counts() != row_counts: raise ValueError("Vector ufunc inputs must have matching per-cell row counts.") - flat_inputs = [_normalize_ufunc_input(value, total_rows, template.num_fields) for value in inputs] + flat_inputs = [ + _normalize_ufunc_input(value, total_rows, template.num_fields) for value in inputs + ] result = ufunc(*flat_inputs, **kwargs) return _wrap_ufunc_result(template, result, row_counts) @@ -664,7 +670,9 @@ def _selected_cell_matrix(self, linear_index: int) -> NDArray[np.generic]: return cell[:, int(cols[0]) : int(cols[-1]) + 1] return cell[:, cols].copy() - def _replace_cells(self, targets: NDArray[np.int64], arrays: Sequence[NDArray[np.generic]]) -> None: + def _replace_cells( + self, targets: NDArray[np.int64], arrays: Sequence[NDArray[np.generic]] + ) -> None: """Replace complete cells in the compact row buffer. Whole-cell replacement is implemented by appending the new payload rows to @@ -759,9 +767,7 @@ def _assign_full_cells(self, value: Any) -> None: if len(targets) != len(source_cells): raise ValueError(f"Expected {len(targets)} cells, got {len(source_cells)}") if value.num_fields != self.num_fields: - raise ValueError( - f"Expected {self.num_fields} fields, got {value.num_fields}" - ) + raise ValueError(f"Expected {self.num_fields} fields, got {value.num_fields}") arrays = [value._selected_cell_matrix(index).copy() for index in source_cells] self._replace_cells(targets, arrays) return @@ -787,9 +793,7 @@ def _assign_selected_fields(self, value: Any) -> None: if len(targets) != len(source_cells): raise ValueError(f"Expected {len(targets)} cells, got {len(source_cells)}") if value.num_fields != self.num_fields: - raise ValueError( - f"Expected {self.num_fields} fields, got {value.num_fields}" - ) + raise ValueError(f"Expected {self.num_fields} fields, got {value.num_fields}") source_counts = [value._cell_row_count(index) for index in source_cells] if row_counts != source_counts: raise ValueError("Per-cell row counts must match for field-selected assignment.") @@ -903,9 +907,10 @@ def _normalize_select_field_args(*field_names: str | Sequence[str]) -> tuple[str if len(field_names) == 1 and not isinstance(field_names[0], str): return _normalize_field_names(field_names[0]) if not all(isinstance(name, str) for name in field_names): - raise TypeError("select_fields(...) expects field names as strings or one sequence of strings.") - return _normalize_field_names(field_names) - + raise TypeError( + "select_fields(...) expects field names as strings or one sequence of strings." + ) + return _normalize_field_names(cast(Sequence[str], field_names)) def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str]: @@ -922,7 +927,6 @@ def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str] return normalized - def _looks_like_field_selector(idx: Any) -> bool: """Return True for indices that look like field selection by mistake.""" if isinstance(idx, str): @@ -934,7 +938,6 @@ def _looks_like_field_selector(idx: Any) -> bool: return False - def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: """Normalize a single-cell payload to shape ``(n_rows, num_fields)``.""" if isinstance(value, Vector): @@ -960,7 +963,6 @@ def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: return array - def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: """Normalize nested input data into ``(shape, flat_cell_arrays)``.""" if not isinstance(data, list): @@ -970,7 +972,6 @@ def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArr return _flatten_fixed_grid(data) - def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: """Recursively flatten nested fixed-grid input into row-major cell order.""" if isinstance(node, np.ndarray): @@ -996,7 +997,6 @@ def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.gen return (len(node),) + child_shape, cells - def _looks_like_cell_rows(node: Sequence[Any]) -> bool: """Return True when a sequence should be interpreted as cell rows, not grid nesting.""" if len(node) == 0: @@ -1004,7 +1004,6 @@ def _looks_like_cell_rows(node: Sequence[Any]) -> bool: return all(_is_row_like(item) for item in node) - def _is_row_like(item: Any) -> bool: """Return True for a single row of scalar values.""" if isinstance(item, np.ndarray): @@ -1014,7 +1013,6 @@ def _is_row_like(item: Any) -> bool: return all(np.isscalar(value) for value in item) - def _coerce_inferred_cell_array(value: Any) -> NDArray[np.generic]: """Infer a 2D cell array from row-like input during ``from_data``.""" array = np.asarray(value) @@ -1029,7 +1027,6 @@ def _coerce_inferred_cell_array(value: Any) -> NDArray[np.generic]: return array - def _select_linear_indices( shape: tuple[int, ...], current_indices: NDArray[np.int64], @@ -1066,13 +1063,15 @@ def _select_linear_indices( value = int(current_grid[scalar_key]) return (), np.array([value], dtype=np.int64) - mesh_inputs = [positions if not is_scalar else positions[:1] for positions, is_scalar in zip(axis_positions, scalar_axes)] + mesh_inputs = [ + positions if not is_scalar else positions[:1] + for positions, is_scalar in zip(axis_positions, scalar_axes) + ] grids = np.meshgrid(*mesh_inputs, indexing="ij") selected = np.asarray(current_grid[tuple(grids)], dtype=np.int64).reshape(-1) return tuple(out_shape), selected - def _normalize_index_tuple(idx: Any, ndim: int) -> tuple[Any, ...]: """Normalize fixed-grid indexing to a full ``ndim``-length tuple.""" if idx is Ellipsis: @@ -1094,7 +1093,6 @@ def _normalize_index_tuple(idx: Any, ndim: int) -> tuple[Any, ...]: return idx - def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], bool]: """Resolve one axis index into concrete positions and scalar-vs-vector shape behavior.""" if isinstance(axis_index, (bool, np.bool_)): @@ -1121,7 +1119,9 @@ def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], if array.ndim != 1: raise IndexError("Full-grid boolean masks are not supported.") if array.shape[0] != size: - raise IndexError(f"Boolean mask length {array.shape[0]} does not match axis length {size}") + raise IndexError( + f"Boolean mask length {array.shape[0]} does not match axis length {size}" + ) return np.flatnonzero(array).astype(np.int64, copy=False), False if array.ndim != 1: @@ -1138,7 +1138,6 @@ def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], return positions, False - def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDArray[np.generic]: """Broadcast array-like input to flattened rowwise assignment shape.""" array = np.asarray(value) @@ -1209,7 +1208,6 @@ def _vector_from_flat_result( return result - def _is_contiguous(indices: NDArray[np.int64]) -> bool: """Return True when integer column indices form one contiguous slice.""" if indices.size <= 1: From bf34a1e79ba42fc7e4c6776d0732efea2e8cd765 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 3 Mar 2026 13:50:13 -0800 Subject: [PATCH 128/335] Updates from comments from PR from quantem-tutorials --- src/quantem/tomography/object_models.py | 19 +++++++++++++------ src/quantem/tomography/tomography.py | 19 ++++++++++++++++++- 2 files changed, 31 insertions(+), 7 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index cd8a6092..2fc0c58c 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -7,6 +7,7 @@ import torch import torch.distributed as dist import torch.nn as nn +from tqdm.auto import tqdm from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.constraints import BaseConstraints, Constraints @@ -218,12 +219,18 @@ def from_array( # --- Properties ---- @property def obj(self) -> torch.Tensor: - return self.apply_hard_constraints(self._obj) + return self.apply_hard_constraints( + self._obj + ) # TODO: Normalization factor to ensure object agrees with INR. @obj.setter def obj(self, obj: torch.Tensor): self._obj = obj + @property + def obj_view(self) -> np.ndarray: + return self.obj.cpu().unsqueeze(0).numpy() + @property def shape(self) -> tuple[int, int, int]: return self._shape @@ -495,8 +502,7 @@ def pretrain( optimizer_params: dict | None = None, scheduler_params: dict | None = None, loss_fn: Callable | str = "l1", - apply_constraints: bool = False, - show: bool = True, + verbose: bool = True, ): """ Pretrain the INR model to fit target volume. @@ -526,14 +532,14 @@ def pretrain( self._pretrain( num_iters=num_iters, loss_fn=loss_fn, - apply_constraints=apply_constraints, + verbose=verbose, ) def _pretrain( self, num_iters: int, loss_fn: Callable, - apply_constraints: bool, + verbose: bool, ): if self.optimizer is None: raise RuntimeError("Optimizer not set. Call set_optimizer() first.") @@ -544,7 +550,8 @@ def _pretrain( optimizer = self.optimizer scheduler = self.scheduler - for a0 in range(num_iters): + pbar = tqdm(range(num_iters), desc="Pretraining", disable=not verbose) + for a0 in pbar: epoch_loss = 0 for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): coords = batch["coords"].to(self.device, non_blocking=True) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 97af71fd..b8a7c6a4 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,5 +1,6 @@ import os -from typing import Literal, Self, cast +from pathlib import Path +from typing import Literal, Self, Sequence, cast import numpy as np import torch @@ -349,6 +350,22 @@ def _rebuild_dataloader(self, batch_size: int, num_workers: int, val_fraction: f ) ) + def save( + self, + path: str | Path, + mode: Literal["w", "o"] = "w", + store: Literal["auto", "zip", "dir"] = "auto", + skip: str | type | Sequence[str | type] = ["dataloader"], + compression_level: int | None = 4, + ) -> None: + super(Tomography, self).save( + path=path, + mode=mode, + store=store, + skip=skip, + compression_level=compression_level, + ) + class TomographyConventional(TomographyBase): """ From d372c1ff5bccd6e8d27ee8c586e9c37d21c16793 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Tue, 3 Mar 2026 16:13:12 -0800 Subject: [PATCH 129/335] removing unecessary methods, changing dtype to Any instead of np.generic --- src/quantem/core/datastructures/vector.py | 227 +++++++++++----------- tests/datastructures/test_vector.py | 22 ++- 2 files changed, 125 insertions(+), 124 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 2c0f2dbf..4470ad75 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -1,7 +1,7 @@ from __future__ import annotations import copy -from typing import Any, Sequence, cast +from typing import Any, Sequence import numpy as np from numpy.typing import NDArray @@ -90,6 +90,7 @@ class Vector(AutoSerialize): """ __array_priority__ = 1000 + _token = object() def __init__( self, @@ -98,7 +99,12 @@ def __init__( units: Sequence[str] | None = None, name: str | None = None, metadata: dict[str, Any] | None = None, + _token: object | None = None, ) -> None: + if _token is not self._token: + raise RuntimeError( + "Use Vector.from_shape() or Vector.from_data() to instantiate this class." + ) root_shape = validate_shape(shape) root_fields = validate_fields(list(fields)) root_units = validate_vector_units( @@ -146,59 +152,50 @@ def from_shape( fields: Sequence[str] | None = None, units: Sequence[str] | None = None, name: str | None = None, + metadata: dict[str, Any] | None = None, ) -> "Vector": """Create an empty Vector with the given fixed-grid shape and fields.""" - if fields is not None: - root_fields = validate_fields(list(fields)) - if num_fields is not None and len(root_fields) != num_fields: - raise ValueError( - f"num_fields ({num_fields}) does not match length of fields ({len(root_fields)})" - ) - elif num_fields is not None: - count = validate_num_fields(num_fields) - root_fields = [f"field_{i}" for i in range(count)] - else: - raise ValueError("Must specify either 'fields' or 'num_fields'.") - - return cls(shape=shape, fields=root_fields, units=units, name=name) + fields = _resolve_fields(fields, num_fields, None) + return cls( + shape=shape, + fields=fields, + units=units, + name=name, + metadata=metadata, + _token=cls._token, + ) @classmethod def from_data( cls, - data: list[Any], + data: Sequence[Any], num_fields: int | None = None, fields: Sequence[str] | None = None, units: Sequence[str] | None = None, name: str | None = None, + metadata: dict[str, Any] | None = None, ) -> "Vector": """Create a Vector from nested fixed-grid data. The outer nesting defines the fixed-grid shape. Each leaf must coerce to a 2D cell array with consistent field count across all cells. """ - root_shape, cell_arrays = _normalize_nested_data(data) + if not isinstance(data, (list, tuple)): + raise TypeError(f"Data must be a list or tuple, got {type(data)}") + root_shape, cell_arrays = _flatten_fixed_grid(data) if len(data) > 0 else ((0,), []) inferred_counts = {array.shape[1] for array in cell_arrays} if len(inferred_counts) > 1: raise ValueError("All cell arrays must have the same number of fields.") inferred_fields = cell_arrays[0].shape[1] if cell_arrays else 0 - if fields is not None: - root_fields = validate_fields(list(fields)) - if len(root_fields) != inferred_fields: - raise ValueError( - f"num_fields ({inferred_fields}) does not match length of fields ({len(root_fields)})" - ) - elif num_fields is not None: - count = validate_num_fields(num_fields) - if count != inferred_fields: - raise ValueError( - f"Provided num_fields ({count}) does not match inferred ({inferred_fields})." - ) - root_fields = [f"field_{i}" for i in range(count)] - else: - root_fields = [f"field_{i}" for i in range(inferred_fields)] - - vector = cls(shape=root_shape, fields=root_fields, units=units, name=name) + vector = cls( + shape=root_shape, + fields=_resolve_fields(fields, num_fields, inferred_fields), + units=units, + name=name, + metadata=metadata, + _token=cls._token, + ) vector._replace_cells(np.arange(len(cell_arrays), dtype=np.int64), cell_arrays) return vector @@ -245,7 +242,7 @@ def metadata(self) -> dict[str, Any]: return self._state["metadata"] @property - def array(self) -> NDArray[np.generic]: + def array(self) -> NDArray[Any]: """Return the selected cell as a NumPy array. This is only valid for 0D selections. Single-field and contiguous @@ -291,6 +288,7 @@ def copy(self) -> "Vector": units=self.units, name=self.name, metadata=copy.deepcopy(self.metadata), + _token=self.__class__._token, ) target_cells = copied._selected_cell_indices() source_arrays = [ @@ -299,7 +297,7 @@ def copy(self) -> "Vector": copied._replace_cells(target_cells, source_arrays) return copied - def flatten(self) -> NDArray[np.generic]: + def flatten(self) -> NDArray[Any]: """Concatenate selected cells in row-major order. Returns a 2D array with shape ``(total_rows, num_fields)`` even for @@ -338,7 +336,16 @@ def select_fields(self, *field_names: str | Sequence[str]) -> "Vector": - ``select_fields("kx", "ky")`` - ``select_fields(["kx", "ky"])`` """ - selected = _normalize_select_field_args(*field_names) + if not field_names: + raise ValueError("At least one field name is required.") + if len(field_names) == 1 and not isinstance(field_names[0], str): + selected = _normalize_field_names(field_names[0]) + elif not all(isinstance(n, str) for n in field_names): + raise TypeError( + "select_fields(...) expects field names as strings or one sequence of strings." + ) + else: + selected = _normalize_field_names(field_names) # type: ignore[arg-type] available = set(self.fields) missing = [field for field in selected if field not in available] if missing: @@ -390,7 +397,25 @@ def compact(self) -> None: until compaction. Calling ``compact()`` makes memory usage and save size predictable at the cost of reallocating the backing buffer. """ - self._compact_storage() + data = self._state["data"] + used_rows = int(self._state["cell_lengths"].sum()) + if used_rows == 0: + self._state["data"] = np.empty((0, self._full_num_fields), dtype=data.dtype) + self._state["cell_starts"].fill(0) + return + + compacted = np.empty((used_rows, self._full_num_fields), dtype=data.dtype) + starts = np.zeros_like(self._state["cell_starts"]) + cursor = 0 + for linear_index in range(_cell_count(self._state["shape"])): + length = self._cell_row_count(linear_index) + starts[linear_index] = cursor + if length > 0: + cell = self._cell_matrix(linear_index) + compacted[cursor : cursor + length] = cell + cursor += length + self._state["data"] = compacted + self._state["cell_starts"] = starts def append_rows(self, idx: Any, rows: Any) -> None: """Append one or more rows to a single selected cell. @@ -533,7 +558,9 @@ def __array_ufunc__(self, ufunc: Any, method: str, *inputs: Any, **kwargs: Any) _normalize_ufunc_input(value, total_rows, template.num_fields) for value in inputs ] result = ufunc(*flat_inputs, **kwargs) - return _wrap_ufunc_result(template, result, row_counts) + if isinstance(result, tuple): + return tuple(_vector_from_flat_result(template, item, row_counts) for item in result) + return _vector_from_flat_result(template, result, row_counts) def __add__(self, other: Any) -> "Vector": return self._binary_op(other, np.add) @@ -642,14 +669,11 @@ def _selected_cell_indices(self) -> NDArray[np.int64]: return np.arange(_cell_count(self._state["shape"]), dtype=np.int64) return self._selection_indices - def _is_full_field_selection(self) -> bool: - return self._selected_fields is None - def _cell_row_count(self, linear_index: int) -> int: """Return the row count for one cell in the backing buffer.""" return int(self._state["cell_lengths"][linear_index]) - def _cell_matrix(self, linear_index: int) -> NDArray[np.generic]: + def _cell_matrix(self, linear_index: int) -> NDArray[Any]: """Return the full backing matrix for one cell.""" start = int(self._state["cell_starts"][linear_index]) length = int(self._state["cell_lengths"][linear_index]) @@ -657,7 +681,7 @@ def _cell_matrix(self, linear_index: int) -> NDArray[np.generic]: return self._state["data"][0:0] return self._state["data"][start : start + length] - def _selected_cell_matrix(self, linear_index: int) -> NDArray[np.generic]: + def _selected_cell_matrix(self, linear_index: int) -> NDArray[Any]: """Return one cell with the current field selection applied.""" cell = self._cell_matrix(linear_index) cols = self._field_indices() @@ -670,9 +694,7 @@ def _selected_cell_matrix(self, linear_index: int) -> NDArray[np.generic]: return cell[:, int(cols[0]) : int(cols[-1]) + 1] return cell[:, cols].copy() - def _replace_cells( - self, targets: NDArray[np.int64], arrays: Sequence[NDArray[np.generic]] - ) -> None: + def _replace_cells(self, targets: NDArray[np.int64], arrays: Sequence[NDArray[Any]]) -> None: """Replace complete cells in the compact row buffer. Whole-cell replacement is implemented by appending the new payload rows to @@ -707,12 +729,11 @@ def _replace_cells( def _expand_storage(self, num_new_fields: int) -> None: """Append new ``np.nan``-initialized columns for added fields.""" data = self._state["data"] + dtype = np.result_type(data.dtype, float) if data.shape[0] == 0: - dtype = np.result_type(data.dtype, float) self._state["data"] = np.empty((0, data.shape[1] + num_new_fields), dtype=dtype) return - dtype = np.result_type(data.dtype, float) filler = np.full((data.shape[0], num_new_fields), np.nan, dtype=dtype) self._state["data"] = np.concatenate((data.astype(dtype, copy=False), filler), axis=1) @@ -724,33 +745,11 @@ def _maybe_compact_storage(self) -> None: return if data.shape[0] <= 2 * used_rows: return - self._compact_storage() - - def _compact_storage(self) -> None: - """Rebuild the row buffer so only live rows remain.""" - data = self._state["data"] - used_rows = int(self._state["cell_lengths"].sum()) - if used_rows == 0: - self._state["data"] = np.empty((0, self._full_num_fields), dtype=data.dtype) - self._state["cell_starts"].fill(0) - return - - compacted = np.empty((used_rows, self._full_num_fields), dtype=data.dtype) - starts = np.zeros_like(self._state["cell_starts"]) - cursor = 0 - for linear_index in range(_cell_count(self._state["shape"])): - length = self._cell_row_count(linear_index) - starts[linear_index] = cursor - if length > 0: - cell = self._cell_matrix(linear_index) - compacted[cursor : cursor + length] = cell - cursor += length - self._state["data"] = compacted - self._state["cell_starts"] = starts + self.compact() def _assign(self, value: Any) -> None: """Dispatch assignment based on whether all fields or a subset are selected.""" - if self._is_full_field_selection(): + if self._selected_fields is None: self._assign_full_cells(value) else: self._assign_selected_fields(value) @@ -878,6 +877,38 @@ def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: cursor += rows +def _resolve_fields( + fields: Sequence[str] | None, + num_fields: int | None, + inferred: int | None, +) -> list[str]: + """Resolve field names from constructor arguments. + + ``inferred`` is the field count inferred from data; pass ``None`` when there + is no data source and explicit fields/num_fields are required. + """ + if fields is not None: + root_fields = validate_fields(list(fields)) + count = len(root_fields) + if num_fields is not None and count != num_fields: + raise ValueError( + f"num_fields ({num_fields}) does not match length of fields ({count})" + ) + if inferred is not None and count != inferred: + raise ValueError(f"num_fields ({inferred}) does not match length of fields ({count})") + return root_fields + if num_fields is not None: + count = validate_num_fields(num_fields) + if inferred is not None and count != inferred: + raise ValueError( + f"Provided num_fields ({count}) does not match inferred ({inferred})." + ) + return [f"field_{i}" for i in range(count)] + if inferred is not None: + return [f"field_{i}" for i in range(inferred)] + raise ValueError("Must specify either 'fields' or 'num_fields'.") + + def _cell_count(shape: tuple[int, ...]) -> int: """Return the number of fixed-grid cells in a shape.""" return int(np.prod(shape, dtype=np.int64)) if shape else 1 @@ -895,24 +926,6 @@ def _normalize_field_names(field_names: str | Sequence[str]) -> tuple[str, ...]: return normalized -def _normalize_select_field_args(*field_names: str | Sequence[str]) -> tuple[str, ...]: - """Normalize ``select_fields`` arguments. - - This accepts either ``select_fields("a", "b")`` or - ``select_fields(["a", "b"])`` while keeping ``add_fields`` / - ``remove_fields`` on the simpler single-argument path. - """ - if not field_names: - raise ValueError("At least one field name is required.") - if len(field_names) == 1 and not isinstance(field_names[0], str): - return _normalize_field_names(field_names[0]) - if not all(isinstance(name, str) for name in field_names): - raise TypeError( - "select_fields(...) expects field names as strings or one sequence of strings." - ) - return _normalize_field_names(cast(Sequence[str], field_names)) - - def _normalize_units(units: str | Sequence[str] | None, count: int) -> list[str]: """Normalize field units to a list matching ``count``.""" if units is None: @@ -933,12 +946,12 @@ def _looks_like_field_selector(idx: Any) -> bool: return True if isinstance(idx, tuple) and any(_looks_like_field_selector(item) for item in idx): return True - if isinstance(idx, (list, tuple)) and idx and all(isinstance(item, str) for item in idx): + if isinstance(idx, list) and idx and all(isinstance(item, str) for item in idx): return True return False -def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: +def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[Any]: """Normalize a single-cell payload to shape ``(n_rows, num_fields)``.""" if isinstance(value, Vector): if value.shape != (): @@ -963,16 +976,7 @@ def _coerce_cell_array(value: Any, num_fields: int) -> NDArray[np.generic]: return array -def _normalize_nested_data(data: list[Any]) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: - """Normalize nested input data into ``(shape, flat_cell_arrays)``.""" - if not isinstance(data, list): - raise TypeError("Data must be a list") - if not data: - return (0,), [] - return _flatten_fixed_grid(data) - - -def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.generic]]]: +def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[Any]]]: """Recursively flatten nested fixed-grid input into row-major cell order.""" if isinstance(node, np.ndarray): return (), [_coerce_inferred_cell_array(node)] @@ -984,7 +988,7 @@ def _flatten_fixed_grid(node: Any) -> tuple[tuple[int, ...], list[NDArray[np.gen return (0,), [] child_shape: tuple[int, ...] | None = None - cells: list[NDArray[np.generic]] = [] + cells: list[NDArray[Any]] = [] for child in node: shape, child_cells = _flatten_fixed_grid(child) if child_shape is None: @@ -1013,7 +1017,7 @@ def _is_row_like(item: Any) -> bool: return all(np.isscalar(value) for value in item) -def _coerce_inferred_cell_array(value: Any) -> NDArray[np.generic]: +def _coerce_inferred_cell_array(value: Any) -> NDArray[Any]: """Infer a 2D cell array from row-like input during ``from_data``.""" array = np.asarray(value) if array.ndim == 0: @@ -1138,7 +1142,7 @@ def _positions_for_axis(axis_index: Any, size: int) -> tuple[NDArray[np.int64], return positions, False -def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDArray[np.generic]: +def _broadcast_field_values(value: Any, total_rows: int, num_fields: int) -> NDArray[Any]: """Broadcast array-like input to flattened rowwise assignment shape.""" array = np.asarray(value) if array.ndim == 0: @@ -1166,17 +1170,6 @@ def _normalize_ufunc_input(value: Any, total_rows: int, num_fields: int) -> Any: return _broadcast_field_values(value, total_rows, num_fields) -def _wrap_ufunc_result( - template: Vector, - result: Any, - row_counts: list[int], -) -> Any: - """Convert elementwise ufunc output back into Vector selections.""" - if isinstance(result, tuple): - return tuple(_vector_from_flat_result(template, item, row_counts) for item in result) - return _vector_from_flat_result(template, result, row_counts) - - def _vector_from_flat_result( template: Vector, values: Any, @@ -1199,7 +1192,7 @@ def _vector_from_flat_result( return result cursor = 0 - cells: list[NDArray[np.generic]] = [] + cells: list[NDArray[Any]] = [] for rows in row_counts: cells.append(flat_values[cursor : cursor + rows].copy()) cursor += rows diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index a8d6d226..87c88673 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -3,9 +3,8 @@ import numpy as np import pytest -from quantem.core.io.serialize import load from quantem.core.datastructures.vector import Vector - +from quantem.core.io.serialize import load def make_line_vector() -> Vector: @@ -22,7 +21,6 @@ def make_line_vector() -> Vector: return v - def make_grid_vector() -> Vector: v = Vector.from_shape(shape=(3, 2), fields=["intensity", "kx", "ky"]) for i in range(3): @@ -138,10 +136,16 @@ def test_field_arithmetic_with_scalar_and_ndarray(self): kx = v.select_fields("kx") kx += 10 - np.testing.assert_array_equal(v.select_fields("kx").flatten(), np.array([[20.0], [30.0], [40.0], [50.0], [60.0], [70.0]])) + np.testing.assert_array_equal( + v.select_fields("kx").flatten(), + np.array([[20.0], [30.0], [40.0], [50.0], [60.0], [70.0]]), + ) v.select_fields("kx")[...] += np.arange(6) - np.testing.assert_array_equal(v.select_fields("kx").flatten(), np.array([[20.0], [31.0], [42.0], [53.0], [64.0], [75.0]])) + np.testing.assert_array_equal( + v.select_fields("kx").flatten(), + np.array([[20.0], [31.0], [42.0], [53.0], [64.0], [75.0]]), + ) summed = v.select_fields("intensity") + v.select_fields("ky") np.testing.assert_array_equal( @@ -221,7 +225,7 @@ def test_numpy_ufunc_support(self): np.sin(v.select_fields("kx").flatten()), ) - maximum = np.maximum(v.select_fields("intensity"), 3.0) + maximum = np.maximum(v.select_fields("intensity"), 3.0) # type: ignore[arg-type] np.testing.assert_array_equal( maximum.flatten(), np.array([[3.0], [3.0], [3.0], [4.0], [5.0], [6.0]]), @@ -380,7 +384,11 @@ def test_from_data_supports_nested_fixed_grid(self): np.array([[5.0, 6.0], [7.0, 8.0], [9.0, 10.0]]), ) - with pytest.raises(TypeError, match="Data must be a list"): + tuple_data = (np.array([[1.0, 2.0]]), np.array([[3.0, 4.0]])) + tuple_outer = Vector.from_data(data=tuple_data, fields=["a", "b"]) + assert tuple_outer.shape == (2,) + + with pytest.raises(TypeError, match="Data must be a list or tuple"): Vector.from_data(data=np.array([1, 2, 3])) # type: ignore[arg-type] with pytest.raises(ValueError, match="same number of fields"): From 039c102efef7933e9e4016bae72e52c1023249ab Mon Sep 17 00:00:00 2001 From: Art-MC Date: Tue, 3 Mar 2026 16:35:27 -0800 Subject: [PATCH 130/335] adding rename_fields method --- src/quantem/core/datastructures/vector.py | 24 +++++++++++++++++++++++ tests/datastructures/test_vector.py | 21 ++++++++++++++++++++ 2 files changed, 45 insertions(+) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 4470ad75..7a8ef417 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -473,6 +473,30 @@ def add_fields( if self._selected_fields is not None and tuple(old_fields) == self._selected_fields: self._selected_fields = None + def rename_fields(self, mapping: dict[str, str]) -> None: + """Rename one or more fields in-place. + + Parameters + ---------- + mapping : dict + Maps each old field name to its new name, e.g. + ``{"kx": "qx", "ky": "qy"}``. + """ + old_field_set = set(self._state["fields"]) + missing = [old for old in mapping if old not in old_field_set] + if missing: + raise KeyError(f"Unknown field(s): {missing}") + new_names = list(mapping.values()) + conflicts = [n for n in new_names if n in old_field_set and n not in mapping] + if conflicts: + raise ValueError(f"New field name(s) already exist: {conflicts}") + validate_fields(new_names) + + rename = {old: new for old, new in mapping.items()} + self._state["fields"] = [rename.get(f, f) for f in self._state["fields"]] + if self._selected_fields is not None: + self._selected_fields = tuple(rename.get(f, f) for f in self._selected_fields) + def remove_fields(self, names: str | Sequence[str]) -> None: """Remove one or more fields from the full Vector schema.""" self._require_full_field_view("remove_fields") diff --git a/tests/datastructures/test_vector.py b/tests/datastructures/test_vector.py index 87c88673..954059d6 100644 --- a/tests/datastructures/test_vector.py +++ b/tests/datastructures/test_vector.py @@ -339,6 +339,27 @@ def test_add_fields_defaults_expression_and_multiple_values(self): with pytest.raises(ValueError, match="all fields are selected"): v2.select_fields("kx").add_fields("bad") + def test_rename_fields(self): + v = make_line_vector() + kx_data = v.select_fields("kx").flatten().copy() + + v.rename_fields({"kx": "qx", "ky": "qy"}) + assert v.fields == ["intensity", "qx", "qy"] + np.testing.assert_array_equal(v.select_fields("qx").flatten(), kx_data) + + # Renaming through a field-selected view updates that view's selected names + view = v.select_fields("qx") + assert view.fields == ["qx"] + view.rename_fields({"qx": "px"}) + assert view.fields == ["px"] + assert v.fields == ["intensity", "px", "qy"] + + with pytest.raises(KeyError, match="Unknown field"): + v.rename_fields({"nonexistent": "x"}) + + with pytest.raises(ValueError, match="already exist"): + v.rename_fields({"px": "intensity"}) + def test_remove_fields_preserves_remaining_data(self): v = make_line_vector() v.add_fields("extra", 1.0) From 9fce382434341ecd0b9aa56e62597f3c5fca3772 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 3 Mar 2026 17:03:28 -0800 Subject: [PATCH 131/335] Starting OptimizerParams and SchedulerParams stuff in optimizer_mixin.py --- src/quantem/core/ml/optimizer_mixin.py | 40 +++++++++++++++++++++++++- 1 file changed, 39 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index eadc1408..a3e3a85d 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,5 +1,6 @@ from abc import abstractmethod -from typing import TYPE_CHECKING, Generator, Iterator, Sequence +from dataclasses import dataclass +from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence from quantem.core import config @@ -144,6 +145,8 @@ def set_scheduler( if sched_type == "none": self._scheduler = None + # elif sched_type._name == "cyclic": + # blah = torch.optim.lr_scheduler.CyclicLR(opt, sched_type.parse_params()) elif sched_type == "cyclic": self._scheduler = torch.optim.lr_scheduler.CyclicLR( optimizer, @@ -292,3 +295,38 @@ def reconnect_optimizer_to_parameters(self) -> None: if self._scheduler is not None and self._optimizer is not None: self._scheduler.optimizer = self._optimizer return + + +@dataclass +class OptimizerParams: + type: ( + str + | Literal[ + "adam", + "adamw", + ] + ) + + +@dataclass +class SchedulerParams: + @dataclass + class Plateau: + _name: str = "plateau" + factor: float = 0.5 + patience: int = 10 + threshold: float = 1e-3 + min_lr: float = 1e-7 + cooldown: int = 50 + + def parse_params() -> dict: + pass + + @dataclass + class Exponential: + _name: str = "exponential" + gamma: float = 0.9 + factor: float | None = 0.5 + + def parse_params() -> dict: + pass From 6f89ac055b04d582f69662967a9344cf1ff226ef Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 11:42:08 -0800 Subject: [PATCH 132/335] More scheduler support for optimizer_mixin.py; still allows for dictionary inputs which parses it to the correct dataclass. Note that type has been renamed to name to follow convention. --- src/quantem/core/ml/optimizer_mixin.py | 296 +++++++++++++++-------- src/quantem/tomography/tomography.py | 6 +- src/quantem/tomography/tomography_opt.py | 6 +- 3 files changed, 196 insertions(+), 112 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index a3e3a85d..f3029101 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,6 +1,6 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence +from typing import TYPE_CHECKING, Generator, Iterator, Literal, Protocol, Sequence from quantem.core import config @@ -11,6 +11,154 @@ import torch +@dataclass +class OptimizerParams: + type: ( + str + | Literal[ + "adam", + "adamw", + ] + ) + + +class SchedulerParams(Protocol): + """ + Nested dataclass for scheduler parameters. + """ + + @dataclass + class Plateau: + mode: Literal["min", "max"] = "min" + min_lr_factor: float = 1 / 20 + min_lr: float | None = None + factor: float = 0.5 + patience: int = 10 + threshold: float = 1e-5 + cooldown: int = 50 + _name: str = "plateau" + + def params(self, base_LR: float) -> dict: + if self.min_lr is None: + self.min_lr = self.min_lr_factor * base_LR + return { + "mode": self.mode, + "factor": self.factor, + "patience": self.patience, + "threshold": self.threshold, + "min_lr": self.min_lr, + "cooldown": self.cooldown, + } + + @dataclass + class Exponential: + gamma: float = 0.9 + factor: float | None = 0.5 + num_iter: int | None = None + _name: str = "exponential" + + def params(self, base_LR: float) -> dict: + return { + "gamma": self.gamma, + "factor": self.factor, + } + + @dataclass + class Cyclic: + base_lr_factor: float = 1 / 4 + max_lr_factor: float = 4 + base_lr: float | None = None + max_lr: float | None = None + step_size_up: int = 100 + step_size_down: int = 100 + mode: Literal["triangular2", "triangular", "exp_range"] = "triangular2" + cycle_momentum: bool = False + _name: str = "cyclic" + + def params(self, base_LR: float) -> dict: + if self.base_lr is None: + self.base_lr = self.base_lr_factor * base_LR + if self.max_lr is None: + self.max_lr = self.max_lr_factor * base_LR + return { + "base_lr": self.base_lr, + "max_lr": self.max_lr, + "step_size_up": self.step_size_up, + "step_size_down": self.step_size_down, + "mode": self.mode, + "cycle_momentum": self.cycle_momentum, + } + + @dataclass + class Linear: + total_iters: int + start_factor: float = 0.1 + end_factor: float = 1.0 + _name: str = "linear" + + def params(self, base_LR: float) -> dict: + return { + "start_factor": self.start_factor, + "end_factor": self.end_factor, + "total_iters": self.total_iters, + } + + @dataclass + class CosineAnnealing: + T_max: int + eta_min: float = 1e-7 + _name: str = "cosine_annealing" + + def params(self, base_LR: float) -> dict: + return { + "T_max": self.T_max, + "eta_min": self.eta_min, + } + + @dataclass + class NoneScheduler: + _name: str = "none" + + def params(self, base_LR: float) -> dict: + return {} + + @classmethod + def parse_dict(cls, d: dict): + """ + Parse dictionary to a scheduler params object. + """ + if d["name"] == "plateau": + d.pop("name") + return SchedulerParams.Plateau(**d) + elif d["name"] == "exponential": + d.pop("name") + return SchedulerParams.Exponential(**d) + elif d["name"] == "cyclic": + d.pop("name") + return SchedulerParams.Cyclic(**d) + elif d["name"] == "linear": + d.pop("name") + return SchedulerParams.Linear(**d) + elif d["name"] == "cosine_annealing": + d.pop("name") + return SchedulerParams.CosineAnnealing(**d) + elif d["name"] == "none": + d.pop("name") + return SchedulerParams.NoneScheduler() + else: + raise ValueError(f"Unknown scheduler type: {d['name']}") + + +SchedulerType = ( + SchedulerParams.Plateau + | SchedulerParams.Exponential + | SchedulerParams.Cyclic + | SchedulerParams.Linear + | SchedulerParams.CosineAnnealing + | SchedulerParams.NoneScheduler +) + + class OptimizerMixin: """ Mixin class for handling optimizer and scheduler management. @@ -24,7 +172,7 @@ def __init__(self): self._optimizer = None self._scheduler = None self._optimizer_params = {} - self._scheduler_params = {} + self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @property @@ -48,29 +196,18 @@ def optimizer_params(self, params: dict): self._optimizer_params = params.copy() if params else {} @property - def scheduler_params(self) -> dict: + def scheduler_params(self) -> SchedulerType: """Get the scheduler parameters.""" return self._scheduler_params @scheduler_params.setter - def scheduler_params(self, params: dict): + def scheduler_params(self, params: SchedulerType | dict): """Set the scheduler parameters.""" - if params: - if params["type"].lower() not in [ - "cyclic", - "plateau", - "exp", - "gamma", - "linear", - "cosine_annealing", - "none", - ]: - raise ValueError( - f"Unknown scheduler type: {params['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'linear', 'cosine_annealing', 'none']" - ) - self._scheduler_params = params.copy() - else: - self._scheduler_params = {} + if isinstance(params, dict): + params = SchedulerParams.parse_dict(d=params) + if not isinstance(params, SchedulerType): + raise TypeError(f"scheduler parameters must be a SchedulerType, got {type(params)}") + self._scheduler_params = params @abstractmethod def get_optimization_parameters( @@ -128,7 +265,8 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: raise TypeError(f"optimizer type must be string or type, got {type(opt_type)}") def set_scheduler( - self, scheduler_params: dict | None = None, num_iter: int | None = None + self, + scheduler_params: SchedulerType | None = None, ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: @@ -138,59 +276,40 @@ def set_scheduler( self._scheduler = None return - params = self._scheduler_params - sched_type = params.get("type", "none").lower() optimizer = self._optimizer base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - self._scheduler = None - # elif sched_type._name == "cyclic": - # blah = torch.optim.lr_scheduler.CyclicLR(opt, sched_type.parse_params()) - elif sched_type == "cyclic": - self._scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - step_size_down=params.get("step_size_down", params.get("step_size_up", 100)), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plateau", "plat", "reducelronplat")): - self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 50), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params: - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.9 - self._scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - elif sched_type == "linear": - self._scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=params.get("start_factor", 0.1), - end_factor=params.get("end_factor", 1.0), - total_iters=params.get("total_iters", num_iter), - ) - elif sched_type == "cosine_annealing": - self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( - optimizer, - T_max=params.get("T_max", num_iter), - eta_min=params.get("eta_min", 1e-7), - ) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") + params = self._scheduler_params.params(base_LR) + match self.scheduler_params: + case SchedulerParams.NoneScheduler(): + self._scheduler = None + case SchedulerParams.Cyclic(): + self._scheduler = torch.optim.lr_scheduler.CyclicLR( + optimizer, + **params, + ) + case SchedulerParams.Plateau(): + self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + **params, + ) + case SchedulerParams.Exponential(): + self._scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, + **params, + ) + case SchedulerParams.Linear(): + self._scheduler = torch.optim.lr_scheduler.LinearLR( + optimizer, + **params, + ) + case SchedulerParams.CosineAnnealing(): + self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + **params, + ) + case _: + raise ValueError(f"Unknown scheduler type: {self.scheduler_params}") def step_optimizer(self) -> None: """Step the optimizer if it exists.""" @@ -295,38 +414,3 @@ def reconnect_optimizer_to_parameters(self) -> None: if self._scheduler is not None and self._optimizer is not None: self._scheduler.optimizer = self._optimizer return - - -@dataclass -class OptimizerParams: - type: ( - str - | Literal[ - "adam", - "adamw", - ] - ) - - -@dataclass -class SchedulerParams: - @dataclass - class Plateau: - _name: str = "plateau" - factor: float = 0.5 - patience: int = 10 - threshold: float = 1e-3 - min_lr: float = 1e-7 - cooldown: int = 50 - - def parse_params() -> dict: - pass - - @dataclass - class Exponential: - _name: str = "exponential" - gamma: float = 0.9 - factor: float | None = 0.5 - - def parse_params() -> dict: - pass diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index b8a7c6a4..ac4a9f1d 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -61,7 +61,7 @@ def reconstruct( scheduler_params: dict | None = None, obj_constraints: dict = {}, # TODO: What to pass into the constraints? dset_constraints: dict = {}, - num_samples_per_ray: int | list[tuple[int, int]] = None, + num_samples_per_ray: int | list[tuple[int, int]] | None = None, profiling_mode: bool = False, val_fraction: float = 0.0, loss_type: Literal[ @@ -106,7 +106,7 @@ def reconstruct( new_scheduler = True if new_scheduler: - self.set_schedulers(self.scheduler_params, num_iter=num_iter) + self.set_schedulers(self.scheduler_params) if obj_constraints is not None: self.obj_model.constraints = cast(DefaultConstraintsTomography, obj_constraints) @@ -127,7 +127,7 @@ def reconstruct( self.set_optimizers() if scheduler_params is not None: self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params, num_iter=num_iter) + self.set_schedulers(self.scheduler_params) self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( self.setup_dataloader( diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index c7bc6b38..1490293a 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -126,12 +126,12 @@ def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: return schedulers - def set_schedulers(self, params: dict[str, dict], num_iter: int | None = None): + def set_schedulers(self, params: dict[str, dict]): for key, scheduler_params in params.items(): if key == "object": - self.obj_model.set_scheduler(scheduler_params, num_iter) + self.obj_model.set_scheduler(scheduler_params) elif key == "pose": - self.dset.set_scheduler(scheduler_params, num_iter) + self.dset.set_scheduler(scheduler_params) else: raise ValueError(f"Unknown optimization key: {key}") From 18d186f57a93ae9f2d84602339b2ba34cd8cc332 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 12:27:05 -0800 Subject: [PATCH 133/335] Optimizer and Scheduler hinting - tested on tomography tutorials. --- src/quantem/core/ml/optimizer_mixin.py | 144 +++++++++++++++++------ src/quantem/tomography/tomography.py | 6 +- src/quantem/tomography/tomography_opt.py | 24 ++-- 3 files changed, 124 insertions(+), 50 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index f3029101..66e4c736 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,6 +1,6 @@ from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Generator, Iterator, Literal, Protocol, Sequence +from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence from quantem.core import config @@ -11,20 +11,97 @@ import torch -@dataclass class OptimizerParams: - type: ( - str - | Literal[ - "adam", - "adamw", - ] - ) + """ + Nested class for optimizer parameters. + """ + + @dataclass + class Adam: + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0 + _name: str = "adam" + + def params(self) -> dict: + return { + "lr": self.lr, + "betas": self.betas, + "eps": self.eps, + "weight_decay": self.weight_decay, + } + @dataclass + class AdamW: + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0 + _name: str = "adamw" + + def params(self) -> dict: + return { + "lr": self.lr, + "betas": self.betas, + "eps": self.eps, + "weight_decay": self.weight_decay, + } + + @dataclass + class SGD: + lr: float = 1e-3 + momentum: float = 0 + dampening: float = 0 + weight_decay: float = 0 + nesterov: bool = False + _name: str = "sgd" + + def params(self) -> dict: + return { + "lr": self.lr, + "momentum": self.momentum, + "dampening": self.dampening, + "weight_decay": self.weight_decay, + "nesterov": self.nesterov, + } + + @dataclass + class NoneOptimizer: + _name: str = "none" + + def params(self) -> dict: + return {} -class SchedulerParams(Protocol): + @classmethod + def parse_dict(cls, d: dict): + """ + Parse dictionary to a optimizer params object. + """ + if d["name"] == "adam": + d.pop("name") + return OptimizerParams.Adam(**d) + elif d["name"] == "adamw": + d.pop("name") + return OptimizerParams.AdamW(**d) + elif d["name"] == "sgd": + d.pop("name") + return OptimizerParams.SGD(**d) + else: + raise ValueError(f"Unknown optimizer type: {d['name']}") + + +OptimizerType = ( + OptimizerParams.Adam + | OptimizerParams.AdamW + | OptimizerParams.SGD + | OptimizerParams.NoneOptimizer +) + + +class SchedulerParams: """ - Nested dataclass for scheduler parameters. + Nested class for scheduler parameters. """ @dataclass @@ -171,7 +248,7 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params = {} + self._optimizer_params: OptimizerType = OptimizerParams.NoneOptimizer() self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @@ -186,14 +263,18 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> dict: + def optimizer_params(self) -> OptimizerType: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter - def optimizer_params(self, params: dict): + def optimizer_params(self, params: OptimizerType | dict): """Set the optimizer parameters.""" - self._optimizer_params = params.copy() if params else {} + if isinstance(params, dict): + params = OptimizerParams.parse_dict(d=params) + if not isinstance(params, OptimizerType): + raise TypeError(f"optimizer parameters must be a OptimizerType, got {type(params)}") + self._optimizer_params = params @property def scheduler_params(self) -> SchedulerType: @@ -220,7 +301,7 @@ def get_optimization_parameters( """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") - def set_optimizer(self, opt_params: dict | None = None) -> None: + def set_optimizer(self, opt_params: OptimizerType | None = None) -> None: """ Set the optimizer for this model. Currently supports single LR for all parameters, TODO allow for per parameter LRs by @@ -233,10 +314,7 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: self._optimizer = None return - opt_params = self._optimizer_params.copy() - opt_type = opt_params.pop("type", self.DEFAULT_OPTIMIZER_TYPE) - - if opt_type == "none": + if isinstance(self._optimizer_params, OptimizerParams.NoneOptimizer): self.remove_optimizer() return @@ -250,19 +328,15 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: for p in params: p.requires_grad_(True) - if isinstance(opt_type, type): - self._optimizer = opt_type(params, **opt_params) - elif isinstance(opt_type, str): - if opt_type.lower() == "adam": - self._optimizer = torch.optim.Adam(params, **opt_params) - elif opt_type.lower() == "adamw": - self._optimizer = torch.optim.AdamW(params, **opt_params) - elif opt_type.lower() == "sgd": - self._optimizer = torch.optim.SGD(params, **opt_params) - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_type}") - else: - raise TypeError(f"optimizer type must be string or type, got {type(opt_type)}") + match self._optimizer_params: + case OptimizerParams.Adam(): + self._optimizer = torch.optim.Adam(params, **self._optimizer_params.params()) + case OptimizerParams.AdamW(): + self._optimizer = torch.optim.AdamW(params, **self._optimizer_params.params()) + case OptimizerParams.SGD(): + self._optimizer = torch.optim.SGD(params, **self._optimizer_params.params()) + case _: + raise NotImplementedError(f"Unknown optimizer type: {self._optimizer_params}") def set_scheduler( self, @@ -343,9 +417,9 @@ def get_current_lr(self) -> float: def remove_optimizer(self) -> None: """Remove the optimizer and scheduler.""" self._optimizer = None - self._optimizer_params = {} + self._optimizer_params = OptimizerParams.NoneOptimizer() self._scheduler = None - self._scheduler_params = {} + self._scheduler_params = SchedulerParams.NoneScheduler() def reset_optimizer(self) -> None: """Reset the optimizer and scheduler.""" diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index ac4a9f1d..3e2d6418 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -210,10 +210,6 @@ def reconstruct( consistency_loss = consistency_loss.item() / len(self.dataloader) epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len(self.dataloader) - pbar.set_description( - f"Reconstruction | Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}, Soft Constraint Loss: {epoch_soft_constraint_loss:.4f}" - ) - if self.val_dataloader is not None: print("Validating...") self.obj_model.model.eval() @@ -296,7 +292,7 @@ def reconstruct( self.logger.flush() pbar.set_description( - f"Reconstruction | Loss: {total_loss:.4f}, Consistency Loss: {consistency_loss:.4f}, Soft Constraint Loss: {epoch_soft_constraint_loss:.4f} | Logger Updated" + f"Reconstruction | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e} | Logger Updated" ) # --- Helper Functions --- diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 1490293a..37675b24 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -1,5 +1,6 @@ import torch +from quantem.core.ml.optimizer_mixin import OptimizerType from quantem.tomography.tomography_base import TomographyBase @@ -35,23 +36,26 @@ def optimizer_params(self) -> dict[str, dict]: } @optimizer_params.setter - def optimizer_params(self, d: dict): + def optimizer_params(self, d: dict[str, OptimizerType]): """Set the optimizer parameters.""" if isinstance(d, (tuple, list)): d = {k: {} for k in d} + targets = { + "object": self.obj_model, + "pose": self.dset, + } + for k, v in d.items(): - if "type" not in v.keys(): - v["type"] = self.DEFAULT_OPTIMIZER_TYPE - if "lr" not in v.keys(): - v["lr"] = self._get_default_lr(k) - if k == "object": - self.obj_model.optimizer_params = v - elif k == "pose": - self.dset.optimizer_params = v - else: + if k not in targets: raise ValueError(f"Unknown optimization key: {k}") + if not isinstance(v, OptimizerType): + if "lr" not in v: + v["lr"] = self._get_default_lr(k) + + targets[k].optimizer_params = v + @property def optimizers(self) -> dict[str, torch.optim.Optimizer]: return { From 213988cd22a7d52a0e77be2cf7438aae80dd5a9f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 12:56:41 -0800 Subject: [PATCH 134/335] Fixes --- src/quantem/core/ml/optimizer_mixin.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 66e4c736..91e2327a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -301,7 +301,7 @@ def get_optimization_parameters( """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") - def set_optimizer(self, opt_params: OptimizerType | None = None) -> None: + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ Set the optimizer for this model. Currently supports single LR for all parameters, TODO allow for per parameter LRs by @@ -340,7 +340,7 @@ def set_optimizer(self, opt_params: OptimizerType | None = None) -> None: def set_scheduler( self, - scheduler_params: SchedulerType | None = None, + scheduler_params: SchedulerType | dict | None = None, ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: From 059903ab8ba2748c6ee23144f0b2dc34874f7d0f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 13:01:44 -0800 Subject: [PATCH 135/335] Only dev stuff --- src/quantem/core/ml/optimizer_mixin.py | 375 +++++++++++++++++++------ 1 file changed, 289 insertions(+), 86 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index e2d2e89a..91e2327a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,5 +1,6 @@ from abc import abstractmethod -from typing import TYPE_CHECKING, Generator, Iterator, Sequence +from dataclasses import dataclass +from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence from quantem.core import config @@ -10,6 +11,231 @@ import torch +class OptimizerParams: + """ + Nested class for optimizer parameters. + """ + + @dataclass + class Adam: + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0 + _name: str = "adam" + + def params(self) -> dict: + return { + "lr": self.lr, + "betas": self.betas, + "eps": self.eps, + "weight_decay": self.weight_decay, + } + + @dataclass + class AdamW: + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0 + _name: str = "adamw" + + def params(self) -> dict: + return { + "lr": self.lr, + "betas": self.betas, + "eps": self.eps, + "weight_decay": self.weight_decay, + } + + @dataclass + class SGD: + lr: float = 1e-3 + momentum: float = 0 + dampening: float = 0 + weight_decay: float = 0 + nesterov: bool = False + _name: str = "sgd" + + def params(self) -> dict: + return { + "lr": self.lr, + "momentum": self.momentum, + "dampening": self.dampening, + "weight_decay": self.weight_decay, + "nesterov": self.nesterov, + } + + @dataclass + class NoneOptimizer: + _name: str = "none" + + def params(self) -> dict: + return {} + + @classmethod + def parse_dict(cls, d: dict): + """ + Parse dictionary to a optimizer params object. + """ + if d["name"] == "adam": + d.pop("name") + return OptimizerParams.Adam(**d) + elif d["name"] == "adamw": + d.pop("name") + return OptimizerParams.AdamW(**d) + elif d["name"] == "sgd": + d.pop("name") + return OptimizerParams.SGD(**d) + else: + raise ValueError(f"Unknown optimizer type: {d['name']}") + + +OptimizerType = ( + OptimizerParams.Adam + | OptimizerParams.AdamW + | OptimizerParams.SGD + | OptimizerParams.NoneOptimizer +) + + +class SchedulerParams: + """ + Nested class for scheduler parameters. + """ + + @dataclass + class Plateau: + mode: Literal["min", "max"] = "min" + min_lr_factor: float = 1 / 20 + min_lr: float | None = None + factor: float = 0.5 + patience: int = 10 + threshold: float = 1e-5 + cooldown: int = 50 + _name: str = "plateau" + + def params(self, base_LR: float) -> dict: + if self.min_lr is None: + self.min_lr = self.min_lr_factor * base_LR + return { + "mode": self.mode, + "factor": self.factor, + "patience": self.patience, + "threshold": self.threshold, + "min_lr": self.min_lr, + "cooldown": self.cooldown, + } + + @dataclass + class Exponential: + gamma: float = 0.9 + factor: float | None = 0.5 + num_iter: int | None = None + _name: str = "exponential" + + def params(self, base_LR: float) -> dict: + return { + "gamma": self.gamma, + "factor": self.factor, + } + + @dataclass + class Cyclic: + base_lr_factor: float = 1 / 4 + max_lr_factor: float = 4 + base_lr: float | None = None + max_lr: float | None = None + step_size_up: int = 100 + step_size_down: int = 100 + mode: Literal["triangular2", "triangular", "exp_range"] = "triangular2" + cycle_momentum: bool = False + _name: str = "cyclic" + + def params(self, base_LR: float) -> dict: + if self.base_lr is None: + self.base_lr = self.base_lr_factor * base_LR + if self.max_lr is None: + self.max_lr = self.max_lr_factor * base_LR + return { + "base_lr": self.base_lr, + "max_lr": self.max_lr, + "step_size_up": self.step_size_up, + "step_size_down": self.step_size_down, + "mode": self.mode, + "cycle_momentum": self.cycle_momentum, + } + + @dataclass + class Linear: + total_iters: int + start_factor: float = 0.1 + end_factor: float = 1.0 + _name: str = "linear" + + def params(self, base_LR: float) -> dict: + return { + "start_factor": self.start_factor, + "end_factor": self.end_factor, + "total_iters": self.total_iters, + } + + @dataclass + class CosineAnnealing: + T_max: int + eta_min: float = 1e-7 + _name: str = "cosine_annealing" + + def params(self, base_LR: float) -> dict: + return { + "T_max": self.T_max, + "eta_min": self.eta_min, + } + + @dataclass + class NoneScheduler: + _name: str = "none" + + def params(self, base_LR: float) -> dict: + return {} + + @classmethod + def parse_dict(cls, d: dict): + """ + Parse dictionary to a scheduler params object. + """ + if d["name"] == "plateau": + d.pop("name") + return SchedulerParams.Plateau(**d) + elif d["name"] == "exponential": + d.pop("name") + return SchedulerParams.Exponential(**d) + elif d["name"] == "cyclic": + d.pop("name") + return SchedulerParams.Cyclic(**d) + elif d["name"] == "linear": + d.pop("name") + return SchedulerParams.Linear(**d) + elif d["name"] == "cosine_annealing": + d.pop("name") + return SchedulerParams.CosineAnnealing(**d) + elif d["name"] == "none": + d.pop("name") + return SchedulerParams.NoneScheduler() + else: + raise ValueError(f"Unknown scheduler type: {d['name']}") + + +SchedulerType = ( + SchedulerParams.Plateau + | SchedulerParams.Exponential + | SchedulerParams.Cyclic + | SchedulerParams.Linear + | SchedulerParams.CosineAnnealing + | SchedulerParams.NoneScheduler +) + + class OptimizerMixin: """ Mixin class for handling optimizer and scheduler management. @@ -22,8 +248,8 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params = {} - self._scheduler_params = {} + self._optimizer_params: OptimizerType = OptimizerParams.NoneOptimizer() + self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @property @@ -37,38 +263,32 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> dict: + def optimizer_params(self) -> OptimizerType: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter - def optimizer_params(self, params: dict): + def optimizer_params(self, params: OptimizerType | dict): """Set the optimizer parameters.""" - self._optimizer_params = params.copy() if params else {} + if isinstance(params, dict): + params = OptimizerParams.parse_dict(d=params) + if not isinstance(params, OptimizerType): + raise TypeError(f"optimizer parameters must be a OptimizerType, got {type(params)}") + self._optimizer_params = params @property - def scheduler_params(self) -> dict: + def scheduler_params(self) -> SchedulerType: """Get the scheduler parameters.""" return self._scheduler_params @scheduler_params.setter - def scheduler_params(self, params: dict): + def scheduler_params(self, params: SchedulerType | dict): """Set the scheduler parameters.""" - if params: - if params["type"].lower() not in [ - "cyclic", - "plateau", - "exp", - "gamma", - "linear", - "none", - ]: - raise ValueError( - f"Unknown scheduler type: {params['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" - ) - self._scheduler_params = params.copy() - else: - self._scheduler_params = {} + if isinstance(params, dict): + params = SchedulerParams.parse_dict(d=params) + if not isinstance(params, SchedulerType): + raise TypeError(f"scheduler parameters must be a SchedulerType, got {type(params)}") + self._scheduler_params = params @abstractmethod def get_optimization_parameters( @@ -81,7 +301,7 @@ def get_optimization_parameters( """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") - def set_optimizer(self, opt_params: dict | None = None) -> None: + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ Set the optimizer for this model. Currently supports single LR for all parameters, TODO allow for per parameter LRs by @@ -94,10 +314,7 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: self._optimizer = None return - opt_params = self._optimizer_params.copy() - opt_type = opt_params.pop("type", self.DEFAULT_OPTIMIZER_TYPE) - - if opt_type == "none": + if isinstance(self._optimizer_params, OptimizerParams.NoneOptimizer): self.remove_optimizer() return @@ -111,22 +328,19 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: for p in params: p.requires_grad_(True) - if isinstance(opt_type, type): - self._optimizer = opt_type(params, **opt_params) - elif isinstance(opt_type, str): - if opt_type.lower() == "adam": - self._optimizer = torch.optim.Adam(params, **opt_params) - elif opt_type.lower() == "adamw": - self._optimizer = torch.optim.AdamW(params, **opt_params) - elif opt_type.lower() == "sgd": - self._optimizer = torch.optim.SGD(params, **opt_params) - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_type}") - else: - raise TypeError(f"optimizer type must be string or type, got {type(opt_type)}") + match self._optimizer_params: + case OptimizerParams.Adam(): + self._optimizer = torch.optim.Adam(params, **self._optimizer_params.params()) + case OptimizerParams.AdamW(): + self._optimizer = torch.optim.AdamW(params, **self._optimizer_params.params()) + case OptimizerParams.SGD(): + self._optimizer = torch.optim.SGD(params, **self._optimizer_params.params()) + case _: + raise NotImplementedError(f"Unknown optimizer type: {self._optimizer_params}") def set_scheduler( - self, scheduler_params: dict | None = None, num_iter: int | None = None + self, + scheduler_params: SchedulerType | dict | None = None, ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: @@ -136,51 +350,40 @@ def set_scheduler( self._scheduler = None return - params = self._scheduler_params - sched_type = params.get("type", "none").lower() optimizer = self._optimizer base_LR = optimizer.param_groups[0]["lr"] - if sched_type == "none": - self._scheduler = None - elif sched_type == "cyclic": - self._scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - step_size_down=params.get("step_size_down", params.get("step_size_up", 100)), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 50), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params: - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.9 - self._scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - elif sched_type == "linear": - self._scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=params.get("start_factor", 0.1), - end_factor=params.get("end_factor", 1.0), - total_iters=params.get("total_iters", num_iter), - ) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") + params = self._scheduler_params.params(base_LR) + match self.scheduler_params: + case SchedulerParams.NoneScheduler(): + self._scheduler = None + case SchedulerParams.Cyclic(): + self._scheduler = torch.optim.lr_scheduler.CyclicLR( + optimizer, + **params, + ) + case SchedulerParams.Plateau(): + self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + **params, + ) + case SchedulerParams.Exponential(): + self._scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, + **params, + ) + case SchedulerParams.Linear(): + self._scheduler = torch.optim.lr_scheduler.LinearLR( + optimizer, + **params, + ) + case SchedulerParams.CosineAnnealing(): + self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + **params, + ) + case _: + raise ValueError(f"Unknown scheduler type: {self.scheduler_params}") def step_optimizer(self) -> None: """Step the optimizer if it exists.""" @@ -214,9 +417,9 @@ def get_current_lr(self) -> float: def remove_optimizer(self) -> None: """Remove the optimizer and scheduler.""" self._optimizer = None - self._optimizer_params = {} + self._optimizer_params = OptimizerParams.NoneOptimizer() self._scheduler = None - self._scheduler_params = {} + self._scheduler_params = SchedulerParams.NoneScheduler() def reset_optimizer(self) -> None: """Reset the optimizer and scheduler.""" From 28f50cda562657e98dd8a70c4e201e1a0f9f18b1 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Wed, 4 Mar 2026 14:09:55 -0800 Subject: [PATCH 136/335] adding docstrings to OptimizerParams and SchedulerParams and promoting in namespace --- src/quantem/core/ml/__init__.py | 4 + src/quantem/core/ml/optimizer_mixin.py | 216 ++++++++++++++++++++++++- 2 files changed, 218 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/__init__.py b/src/quantem/core/ml/__init__.py index ce1f6de0..af2d5b64 100644 --- a/src/quantem/core/ml/__init__.py +++ b/src/quantem/core/ml/__init__.py @@ -1,4 +1,8 @@ from quantem.core.ml.cnn import CNN2d as CNN2d, CNN3d as CNN3d +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams as OptimizerParams, + SchedulerParams as SchedulerParams, +) from quantem.core.ml.inr import HSiren as HSiren from quantem.core.ml.dense_nn import DenseNN as DenseNN from quantem.core.ml.cnn_dense import CNNDense as CNNDense diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 91e2327a..c318d9a1 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -13,11 +13,47 @@ class OptimizerParams: """ - Nested class for optimizer parameters. + Container for optimizer parameter dataclasses. + + Each nested class configures a specific PyTorch optimizer and can be passed + directly to ``OptimizerMixin.set_optimizer``, or constructed from a dict via + ``OptimizerParams.parse_dict``. + + Supported optimizers + -------------------- + Adam + ``torch.optim.Adam`` — adaptive moment estimation. + AdamW + ``torch.optim.AdamW`` — Adam with decoupled weight decay. + SGD + ``torch.optim.SGD`` — stochastic gradient descent with optional momentum and Nesterov. + NoneOptimizer + Sentinel that disables / removes the optimizer. + + Examples + -------- + >>> obj.set_optimizer(OptimizerParams.Adam(lr=1e-4)) + >>> obj.set_optimizer({"name": "adam", "lr": 1e-4}) # equivalent dict form """ @dataclass class Adam: + """ + Adam optimizer (``torch.optim.Adam``). + + Parameters + ---------- + lr : float + Learning rate. Default: 1e-3. + betas : tuple[float, float] + Coefficients for computing running averages of the gradient and its square. + Default: (0.9, 0.999). + eps : float + Term added to the denominator for numerical stability. Default: 1e-8. + weight_decay : float + L2 regularisation penalty. Default: 0. + """ + lr: float = 1e-3 betas: tuple[float, float] = (0.9, 0.999) eps: float = 1e-8 @@ -34,6 +70,25 @@ def params(self) -> dict: @dataclass class AdamW: + """ + AdamW optimizer (``torch.optim.AdamW``). + + Identical to Adam but applies weight decay directly to the parameters + rather than folding it into the gradient update (decoupled weight decay). + + Parameters + ---------- + lr : float + Learning rate. Default: 1e-3. + betas : tuple[float, float] + Coefficients for computing running averages of the gradient and its square. + Default: (0.9, 0.999). + eps : float + Term added to the denominator for numerical stability. Default: 1e-8. + weight_decay : float + Decoupled L2 regularisation penalty. Default: 0. + """ + lr: float = 1e-3 betas: tuple[float, float] = (0.9, 0.999) eps: float = 1e-8 @@ -50,6 +105,23 @@ def params(self) -> dict: @dataclass class SGD: + """ + SGD optimizer (``torch.optim.SGD``). + + Parameters + ---------- + lr : float + Learning rate. Default: 1e-3. + momentum : float + Momentum factor. Default: 0. + dampening : float + Dampening for momentum. Default: 0. + weight_decay : float + L2 regularisation penalty. Default: 0. + nesterov : bool + Enables Nesterov momentum. Default: False. + """ + lr: float = 1e-3 momentum: float = 0 dampening: float = 0 @@ -68,6 +140,13 @@ def params(self) -> dict: @dataclass class NoneOptimizer: + """ + Sentinel optimizer that disables optimization. + + Passing this to ``set_optimizer`` will call ``remove_optimizer``, + clearing both the optimizer and scheduler. + """ + _name: str = "none" def params(self) -> dict: @@ -101,11 +180,61 @@ def parse_dict(cls, d: dict): class SchedulerParams: """ - Nested class for scheduler parameters. + Container for learning-rate scheduler parameter dataclasses. + + Each nested class configures a specific PyTorch LR scheduler and can be passed + directly to ``OptimizerMixin.set_scheduler``, or constructed from a dict via + ``SchedulerParams.parse_dict``. + + Supported schedulers + -------------------- + Plateau + ``torch.optim.lr_scheduler.ReduceLROnPlateau`` — reduce LR when a metric stops improving. + Exponential + ``torch.optim.lr_scheduler.ExponentialLR`` — multiply LR by ``gamma`` each step. + Cyclic + ``torch.optim.lr_scheduler.CyclicLR`` — cycle LR between ``base_lr`` and ``max_lr``. + Linear + ``torch.optim.lr_scheduler.LinearLR`` — linearly interpolate LR over ``total_iters`` steps. + CosineAnnealing + ``torch.optim.lr_scheduler.CosineAnnealingLR`` — cosine-annealing LR schedule. + NoneScheduler + Sentinel that disables / removes the scheduler. + + Examples + -------- + >>> obj.set_scheduler(SchedulerParams.Plateau(factor=0.5, patience=10, cooldown=20)) + >>> obj.set_scheduler({"name": "plateau", "factor": 0.5}) # equivalent dict form """ @dataclass class Plateau: + """ + ReduceLROnPlateau scheduler (``torch.optim.lr_scheduler.ReduceLROnPlateau``). + + Reduces the learning rate when a monitored metric stops improving. + + Parameters + ---------- + mode : {'min', 'max'} + Whether the monitored metric should be minimised or maximised. Default: 'min'. + min_lr_factor : float + Sets ``min_lr = min_lr_factor * base_lr`` when ``min_lr`` is not provided. + Default: 1/20. + min_lr : float or None + Absolute lower bound on the learning rate. Overrides ``min_lr_factor`` when set. + Default: None. + factor : float + Factor by which the LR is reduced: ``new_lr = lr * factor``. Default: 0.5. + patience : int + Number of epochs with no improvement before reducing LR. Default: 10. + threshold : float + Minimum change in the monitored metric to qualify as an improvement. Default: 1e-5. + cooldown : int + Number of epochs to wait after a LR reduction before resuming normal operation. + Default: 50. + """ + mode: Literal["min", "max"] = "min" min_lr_factor: float = 1 / 20 min_lr: float | None = None @@ -129,6 +258,21 @@ def params(self, base_LR: float) -> dict: @dataclass class Exponential: + """ + ExponentialLR scheduler (``torch.optim.lr_scheduler.ExponentialLR``). + + Multiplies the learning rate by ``gamma`` after each step. + + Parameters + ---------- + gamma : float + Multiplicative decay factor applied each step. Default: 0.9. + factor : float or None + Reserved for future use. Default: 0.5. + num_iter : int or None + Reserved for future use. Default: None. + """ + gamma: float = 0.9 factor: float | None = 0.5 num_iter: int | None = None @@ -142,6 +286,37 @@ def params(self, base_LR: float) -> dict: @dataclass class Cyclic: + """ + CyclicLR scheduler (``torch.optim.lr_scheduler.CyclicLR``). + + Cycles the learning rate between a lower bound (``base_lr``) and an upper + bound (``max_lr``). Bounds can be set directly or derived from the optimizer's + base LR via the factor parameters. + + Parameters + ---------- + base_lr_factor : float + Sets ``base_lr = base_lr_factor * optimizer_lr`` when ``base_lr`` is not provided. + Default: 1/4. + max_lr_factor : float + Sets ``max_lr = max_lr_factor * optimizer_lr`` when ``max_lr`` is not provided. + Default: 4. + base_lr : float or None + Absolute lower bound of the LR cycle. Overrides ``base_lr_factor`` when set. + Default: None. + max_lr : float or None + Absolute upper bound of the LR cycle. Overrides ``max_lr_factor`` when set. + Default: None. + step_size_up : int + Number of steps in the increasing half of each cycle. Default: 100. + step_size_down : int + Number of steps in the decreasing half of each cycle. Default: 100. + mode : {'triangular2', 'triangular', 'exp_range'} + Cycling policy. Default: 'triangular2'. + cycle_momentum : bool + If True, cycles momentum inversely to the learning rate. Default: False. + """ + base_lr_factor: float = 1 / 4 max_lr_factor: float = 4 base_lr: float | None = None @@ -168,6 +343,22 @@ def params(self, base_LR: float) -> dict: @dataclass class Linear: + """ + LinearLR scheduler (``torch.optim.lr_scheduler.LinearLR``). + + Linearly interpolates the learning rate from ``start_factor * base_lr`` to + ``end_factor * base_lr`` over ``total_iters`` steps. + + Parameters + ---------- + total_iters : int + Number of steps over which to interpolate the LR. Required. + start_factor : float + Multiplicative factor applied to the LR at the first step. Default: 0.1. + end_factor : float + Multiplicative factor applied to the LR at the final step. Default: 1.0. + """ + total_iters: int start_factor: float = 0.1 end_factor: float = 1.0 @@ -182,6 +373,20 @@ def params(self, base_LR: float) -> dict: @dataclass class CosineAnnealing: + """ + CosineAnnealingLR scheduler (``torch.optim.lr_scheduler.CosineAnnealingLR``). + + Anneals the learning rate following a cosine curve from the base LR down to + ``eta_min`` over ``T_max`` steps, then restarts. + + Parameters + ---------- + T_max : int + Maximum number of iterations (half-period of the cosine cycle). Required. + eta_min : float + Minimum learning rate at the trough of the cosine curve. Default: 1e-7. + """ + T_max: int eta_min: float = 1e-7 _name: str = "cosine_annealing" @@ -194,6 +399,13 @@ def params(self, base_LR: float) -> dict: @dataclass class NoneScheduler: + """ + Sentinel scheduler that disables LR scheduling. + + Passing this to ``set_scheduler`` clears the active scheduler without + affecting the optimizer. + """ + _name: str = "none" def params(self, base_LR: float) -> dict: From 825c50cf130f1a0f3282dfe1b78c67dc6616de67 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Wed, 4 Mar 2026 15:45:21 -0800 Subject: [PATCH 137/335] adding backwards compatibility for "type" rather than "name" --- src/quantem/core/ml/optimizer_mixin.py | 50 +++++++++++++++----------- 1 file changed, 30 insertions(+), 20 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index c318d9a1..e7cb887a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -156,18 +156,25 @@ def params(self) -> dict: def parse_dict(cls, d: dict): """ Parse dictionary to a optimizer params object. + Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ - if d["name"] == "adam": - d.pop("name") + print("d: ", d) + d = dict(d) # avoid mutating caller's dict + name = d.pop("name", None) or d.pop("type", None) + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown optimizer type: {name}") + if name == "adam": return OptimizerParams.Adam(**d) - elif d["name"] == "adamw": - d.pop("name") + elif name == "adamw": return OptimizerParams.AdamW(**d) - elif d["name"] == "sgd": - d.pop("name") + elif name == "sgd": return OptimizerParams.SGD(**d) else: - raise ValueError(f"Unknown optimizer type: {d['name']}") + raise ValueError(f"Unknown optimizer type: {name.lower()}") OptimizerType = ( @@ -415,27 +422,30 @@ def params(self, base_LR: float) -> dict: def parse_dict(cls, d: dict): """ Parse dictionary to a scheduler params object. + Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ - if d["name"] == "plateau": - d.pop("name") + d = dict(d) # avoid mutating caller's dict + name = d.pop("name", None) or d.pop("type", None) + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown scheduler type: {name}") + if name == "plateau": return SchedulerParams.Plateau(**d) - elif d["name"] == "exponential": - d.pop("name") + elif name == "exponential": return SchedulerParams.Exponential(**d) - elif d["name"] == "cyclic": - d.pop("name") + elif name == "cyclic": return SchedulerParams.Cyclic(**d) - elif d["name"] == "linear": - d.pop("name") + elif name == "linear": return SchedulerParams.Linear(**d) - elif d["name"] == "cosine_annealing": - d.pop("name") + elif name == "cosine_annealing": return SchedulerParams.CosineAnnealing(**d) - elif d["name"] == "none": - d.pop("name") + elif name == "none": return SchedulerParams.NoneScheduler() else: - raise ValueError(f"Unknown scheduler type: {d['name']}") + raise ValueError(f"Unknown scheduler type: {name}") SchedulerType = ( From 5cbcd888b5c309a6574656420799dccbb0357abd Mon Sep 17 00:00:00 2001 From: Art-MC Date: Wed, 4 Mar 2026 15:48:57 -0800 Subject: [PATCH 138/335] left a print --- src/quantem/core/ml/optimizer_mixin.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index e7cb887a..f912b205 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -158,7 +158,6 @@ def parse_dict(cls, d: dict): Parse dictionary to a optimizer params object. Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ - print("d: ", d) d = dict(d) # avoid mutating caller's dict name = d.pop("name", None) or d.pop("type", None) if isinstance(name, type): From 1946c8752ba060fe54f3cb787c1daa09bb0f20a9 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 16:13:28 -0800 Subject: [PATCH 139/335] Added num_iters back in when calling .params --- src/quantem/core/ml/optimizer_mixin.py | 45 ++++++++++++++++++-------- 1 file changed, 32 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index f912b205..eb4a8419 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -250,7 +250,7 @@ class Plateau: cooldown: int = 50 _name: str = "plateau" - def params(self, base_LR: float) -> dict: + def params(self, base_LR: float, num_iter: int | None = None) -> dict: if self.min_lr is None: self.min_lr = self.min_lr_factor * base_LR return { @@ -280,11 +280,20 @@ class Exponential: """ gamma: float = 0.9 - factor: float | None = 0.5 + factor: float | None = None num_iter: int | None = None _name: str = "exponential" - def params(self, base_LR: float) -> dict: + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + effective_num_iter = self.num_iter if self.num_iter is not None else num_iter + if effective_num_iter is None: + raise ValueError("num_iter must be set if num_iter is not provided") + + self.num_iter = effective_num_iter + + if self.factor is not None: + self.gamma = self.factor ** (1.0 / effective_num_iter) + return { "gamma": self.gamma, "factor": self.factor, @@ -333,7 +342,7 @@ class Cyclic: cycle_momentum: bool = False _name: str = "cyclic" - def params(self, base_LR: float) -> dict: + def params(self, base_LR: float, num_iter: int | None = None) -> dict: if self.base_lr is None: self.base_lr = self.base_lr_factor * base_LR if self.max_lr is None: @@ -365,12 +374,18 @@ class Linear: Multiplicative factor applied to the LR at the final step. Default: 1.0. """ - total_iters: int + total_iters: int | None = None start_factor: float = 0.1 end_factor: float = 1.0 _name: str = "linear" - def params(self, base_LR: float) -> dict: + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + if num_iter is None and self.total_iters is None: + raise ValueError( + "total_iters must be set if num_iter is not provided" + ) # Should never be reached + if self.total_iters is None: + self.total_iters = num_iter return { "start_factor": self.start_factor, "end_factor": self.end_factor, @@ -393,11 +408,17 @@ class CosineAnnealing: Minimum learning rate at the trough of the cosine curve. Default: 1e-7. """ - T_max: int eta_min: float = 1e-7 + T_max: int | None = None _name: str = "cosine_annealing" - def params(self, base_LR: float) -> dict: + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + if num_iter is None and self.T_max is None: + raise ValueError( + "T_max must be set if num_iter is not provided" + ) # Should never be reached + if self.T_max is None: + self.T_max = num_iter return { "T_max": self.T_max, "eta_min": self.eta_min, @@ -414,7 +435,7 @@ class NoneScheduler: _name: str = "none" - def params(self, base_LR: float) -> dict: + def params(self, base_LR: float, num_iter: int | None = None) -> dict: return {} @classmethod @@ -560,8 +581,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: raise NotImplementedError(f"Unknown optimizer type: {self._optimizer_params}") def set_scheduler( - self, - scheduler_params: SchedulerType | dict | None = None, + self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: @@ -573,8 +593,7 @@ def set_scheduler( optimizer = self._optimizer base_LR = optimizer.param_groups[0]["lr"] - - params = self._scheduler_params.params(base_LR) + params = self._scheduler_params.params(base_LR, num_iter=num_iter) match self.scheduler_params: case SchedulerParams.NoneScheduler(): self._scheduler = None From a46a75ad1f91cad33ec71146928d4331895af006 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Wed, 4 Mar 2026 16:27:02 -0800 Subject: [PATCH 140/335] fixing Exponential scheduler --- src/quantem/core/ml/optimizer_mixin.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index eb4a8419..96c11f29 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -296,7 +296,6 @@ def params(self, base_LR: float, num_iter: int | None = None) -> dict: return { "gamma": self.gamma, - "factor": self.factor, } @dataclass From 29c8b7f9acc88243433fd60ad78d8a97fe8f99ea Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Wed, 4 Mar 2026 16:37:08 -0800 Subject: [PATCH 141/335] adapting the new SchedulerParams and OptimizerParams for ptycho --- .../diffractive_imaging/ptychography_lite.py | 24 ++--- .../diffractive_imaging/ptychography_opt.py | 96 +++++++++---------- 2 files changed, 56 insertions(+), 64 deletions(-) diff --git a/src/quantem/diffractive_imaging/ptychography_lite.py b/src/quantem/diffractive_imaging/ptychography_lite.py index 4470b53a..9fc7d039 100644 --- a/src/quantem/diffractive_imaging/ptychography_lite.py +++ b/src/quantem/diffractive_imaging/ptychography_lite.py @@ -180,23 +180,23 @@ def reconstruct( # type:ignore could do overloads but this is simpler... if new_optimizers or reset or self.num_iters == 0: opt_params = { "object": { - "type": "adamw", + "name": "adamw", "lr": lr_obj, }, } scheduler_params = { "object": { - "type": scheduler_type, + "name": scheduler_type, "factor": scheduler_factor, } } if learn_probe: opt_params["probe"] = { - "type": "adamw", + "name": "adamw", "lr": lr_probe, } scheduler_params["probe"] = { - "type": scheduler_type, + "name": scheduler_type, "factor": scheduler_factor, } else: @@ -314,11 +314,11 @@ def from_ptycholite( reset=True, num_iters=pretrain_iters, optimizer_params={ - "type": "adamw", + "name": "adamw", "lr": pretrain_lr, }, scheduler_params={ - "type": "plateau", + "name": "plateau", "factor": 0.5, }, apply_constraints=False, @@ -329,11 +329,11 @@ def from_ptycholite( reset=True, num_iters=pretrain_iters, optimizer_params={ - "type": "adamw", + "name": "adamw", "lr": 1e-3, }, scheduler_params={ - "type": "plateau", + "name": "plateau", "factor": 0.5, }, apply_constraints=False, @@ -390,23 +390,23 @@ def reconstruct( # type:ignore could do overloads but this is simpler... if new_optimizers or reset or self.num_iters == 0: opt_params = { "object": { - "type": "adamw", + "name": "adamw", "lr": lr_obj, }, } scheduler_params = { "object": { - "type": scheduler_type, + "name": scheduler_type, "factor": scheduler_factor, } } if learn_probe: opt_params["probe"] = { - "type": "adamw", + "name": "adamw", "lr": lr_probe, } scheduler_params["probe"] = { - "type": scheduler_type, + "name": scheduler_type, "factor": scheduler_factor, } else: diff --git a/src/quantem/diffractive_imaging/ptychography_opt.py b/src/quantem/diffractive_imaging/ptychography_opt.py index f9b5aae4..c18150e0 100644 --- a/src/quantem/diffractive_imaging/ptychography_opt.py +++ b/src/quantem/diffractive_imaging/ptychography_opt.py @@ -1,6 +1,13 @@ +from dataclasses import replace from typing import TYPE_CHECKING from quantem.core import config +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams, + OptimizerType, + SchedulerParams, + SchedulerType, +) from quantem.diffractive_imaging.ptychography_base import PtychographyBase if TYPE_CHECKING: @@ -16,12 +23,10 @@ class PtychographyOpt(PtychographyBase): """ OPTIMIZABLE_VALS = ["object", "probe", "dataset"] - DEFAULT_OPTIMIZER_TYPE = "adam" + DEFAULT_OPTIMIZER_TYPE: OptimizerType = OptimizerParams.Adam() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - # self._optimizer_params = {} - # self._scheduler_params = {} def _get_default_lr(self, key: str) -> float: """Get default learning rate for a given optimization key.""" @@ -37,7 +42,7 @@ def _get_default_lr(self, key: str) -> float: # region --- explicit properties and setters --- @property - def optimizer_params(self) -> dict[str, dict]: + def optimizer_params(self) -> dict[str, OptimizerType]: return { key: params for key, params in [ @@ -45,45 +50,40 @@ def optimizer_params(self) -> dict[str, dict]: ("probe", self.probe_model.optimizer_params), ("dataset", self.dset.optimizer_params), ] - if params + if not isinstance(params, OptimizerParams.NoneOptimizer) } @optimizer_params.setter def optimizer_params(self, d: dict) -> None: """ - Takes a dictionary: - { - "object": { - "type": "adam", - "lr": 0.001, - }, - "probe": { - "type": "adam", - "lr": 0.001, - }, - "dataset": { - "type": "adam", - "lr": 0.001, - }, - ... - } + Takes a dictionary mapping optimizable keys to either an ``OptimizerType`` + dataclass or a plain dict (with optional ``"name"``/``"type"`` and ``"lr"`` + keys). Missing ``"name"`` / ``"lr"`` are filled from ``DEFAULT_OPTIMIZER_TYPE`` + and ``_get_default_lr`` respectively. + + Examples + -------- + >>> ptycho.optimizer_params = {"object": OptimizerParams.Adam(lr=5e-3)} + >>> ptycho.optimizer_params = {"object": {"name": "adam", "lr": 5e-3}} + >>> ptycho.optimizer_params = ["object", "probe"] # use all defaults """ if isinstance(d, (tuple, list)): d = {k: {} for k in d} - ## previously removed unspecified optimizers, but I think its better to keep them - ## and only remove if type is none - # for key in self.OPTIMIZABLE_VALS: - # # if not specified, remove the scheduler for that model - # if key not in d: - # d[key] = {"type": "none"} - for k, v in d.items(): - if "type" not in v.keys(): - v["type"] = self.DEFAULT_OPTIMIZER_TYPE - if "lr" not in v.keys(): - v["lr"] = self._get_default_lr(k) - # self._optimizer_params[k] = v + if isinstance(v, OptimizerType): + pass # already a dataclass, pass through + elif isinstance(v, dict): + if not v: + v = replace(self.DEFAULT_OPTIMIZER_TYPE, lr=self._get_default_lr(k)) + else: + if "name" not in v and "type" not in v: + v["name"] = self.DEFAULT_OPTIMIZER_TYPE._name + if "lr" not in v: + v["lr"] = self._get_default_lr(k) + else: + raise TypeError(f"Expected OptimizerType or dict for key '{k}', got {type(v)}") + if k == "object": self.obj_model.optimizer_params = v elif k == "probe": @@ -131,7 +131,7 @@ def remove_optimizer(self, key: str) -> None: self.dset.remove_optimizer() @property - def scheduler_params(self) -> dict[str, dict]: + def scheduler_params(self) -> dict[str, SchedulerType]: """Returns the parameters used to set the schedulers.""" return { "object": self.obj_model.scheduler_params, @@ -142,22 +142,18 @@ def scheduler_params(self) -> dict[str, dict]: @scheduler_params.setter def scheduler_params(self, d: dict) -> None: """ - Takes a dictionary: - { - "object": { - "type": "cyclic", - "base_lr": 0.001, - }, - "probe": { - ... - }, - ... - } + Takes a dictionary mapping optimizable keys to either a ``SchedulerType`` + dataclass or a plain dict. Keys not present in ``d`` are set to + ``SchedulerParams.NoneScheduler()`` (disables scheduling for that model). + + Examples + -------- + >>> ptycho.scheduler_params = {"object": SchedulerParams.Plateau(factor=0.5)} + >>> ptycho.scheduler_params = {"object": {"name": "plateau", "factor": 0.5}} """ for key in self.OPTIMIZABLE_VALS: - # if not specified, remove the scheduler for that model if key not in d: - d[key] = {} + d[key] = SchedulerParams.NoneScheduler() for k, v in d.items(): if k == "object": self.obj_model.scheduler_params = v @@ -182,11 +178,7 @@ def schedulers(self) -> dict[str, "torch.optim.lr_scheduler._LRScheduler"]: schedulers["dataset"] = self.dset.scheduler return schedulers - def set_schedulers( - self, - params: dict[str, dict], - num_iter: int | None = None, - ): + def set_schedulers(self, params: dict[str, SchedulerType], num_iter: int | None = None): """Set schedulers for each model.""" for key, scheduler_params in params.items(): if key not in self.OPTIMIZABLE_VALS: From 737a7bc5aa63ae4187fcd5079d9315dea4426f92 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 16:47:25 -0800 Subject: [PATCH 142/335] Fixes to TomographyOpt.py --- src/quantem/core/ml/optimizer_mixin.py | 2 +- src/quantem/tomography/tomography.py | 2 +- src/quantem/tomography/tomography_opt.py | 14 +++++++------- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index eb4a8419..4674e5ce 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -445,7 +445,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", None) + name = d.pop("name", "none") or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 3e2d6418..f0b05b52 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -106,7 +106,7 @@ def reconstruct( new_scheduler = True if new_scheduler: - self.set_schedulers(self.scheduler_params) + self.set_schedulers(self.scheduler_params, num_iter=num_iter) if obj_constraints is not None: self.obj_model.constraints = cast(DefaultConstraintsTomography, obj_constraints) diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 37675b24..404e2f4b 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -130,29 +130,29 @@ def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: return schedulers - def set_schedulers(self, params: dict[str, dict]): + def set_schedulers(self, params: dict[str, dict], num_iter: int | None = None): for key, scheduler_params in params.items(): if key == "object": - self.obj_model.set_scheduler(scheduler_params) + self.obj_model.set_scheduler(scheduler_params, num_iter=num_iter) elif key == "pose": - self.dset.set_scheduler(scheduler_params) + self.dset.set_scheduler(scheduler_params, num_iter=num_iter) else: raise ValueError(f"Unknown optimization key: {key}") def step_optimizers(self): for key in self.optimizer_params.keys(): - if key == "object" and self.obj_model.has_optimizer(): + if self.obj_model.has_optimizer(): self.obj_model.step_optimizer() - elif key == "pose" and self.dset.has_optimizer(): + elif self.dset.has_optimizer(): self.dset.step_optimizer() else: raise ValueError(f"Unknown optimization key: {key}") def zero_grad_all(self): for key in self.optimizer_params.keys(): - if key == "object" and self.obj_model.has_optimizer(): + if self.obj_model.has_optimizer(): self.obj_model.zero_optimizer_grad() - elif key == "pose" and self.dset.has_optimizer(): + elif self.dset.has_optimizer(): self.dset.zero_optimizer_grad() else: raise ValueError(f"Unknown optimization key: {key}") From 4d9b9faa20a1685e0e0531ef21382428f587600b Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 16:49:57 -0800 Subject: [PATCH 143/335] Fixed bug in parse_dict None -> none --- src/quantem/core/ml/optimizer_mixin.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index eb4a8419..30cb07ef 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -159,7 +159,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", None) + name = d.pop("name", "none") or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): @@ -445,7 +445,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", None) + name = d.pop("name", "none") or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): From 342bae6fc1405b2ad22da4cc78e1e8263b96d926 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 16:59:23 -0800 Subject: [PATCH 144/335] Pytests and changed some defaults in optimizer_mixin.py --- tests/ml/test_optimizermixin.py | 295 ++++++++++++++++++++++++++++++++ 1 file changed, 295 insertions(+) create mode 100644 tests/ml/test_optimizermixin.py diff --git a/tests/ml/test_optimizermixin.py b/tests/ml/test_optimizermixin.py new file mode 100644 index 00000000..6fd6c22a --- /dev/null +++ b/tests/ml/test_optimizermixin.py @@ -0,0 +1,295 @@ +"""Tests for OptimizerParams and SchedulerParams dataclasses.""" + +import pytest + +# Now import the module under test — adjust the path if needed +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams, + SchedulerParams, +) + +# ─── OptimizerParams defaults ─────────────────────────────────────────────── + + +class TestAdamDefaults: + def test_defaults(self): + adam = OptimizerParams.Adam() + assert adam.lr == 1e-3 + assert adam.betas == (0.9, 0.999) + assert adam.eps == 1e-8 + assert adam.weight_decay == 0 + assert adam._name == "adam" + + def test_params_dict(self): + adam = OptimizerParams.Adam(lr=0.01, weight_decay=1e-4) + p = adam.params() + assert p == { + "lr": 0.01, + "betas": (0.9, 0.999), + "eps": 1e-8, + "weight_decay": 1e-4, + } + + def test_custom_betas(self): + adam = OptimizerParams.Adam(betas=(0.8, 0.99)) + assert adam.params()["betas"] == (0.8, 0.99) + + +class TestAdamWDefaults: + def test_defaults(self): + adamw = OptimizerParams.AdamW() + assert adamw.lr == 1e-3 + assert adamw._name == "adamw" + + def test_params_dict(self): + adamw = OptimizerParams.AdamW(lr=5e-4, eps=1e-7) + p = adamw.params() + assert p["lr"] == 5e-4 + assert p["eps"] == 1e-7 + + +class TestSGDDefaults: + def test_defaults(self): + sgd = OptimizerParams.SGD() + assert sgd.lr == 1e-3 + assert sgd.momentum == 0 + assert sgd.dampening == 0 + assert sgd.nesterov is False + assert sgd._name == "sgd" + + def test_params_dict(self): + sgd = OptimizerParams.SGD(lr=0.1, momentum=0.9, nesterov=True) + p = sgd.params() + assert p == { + "lr": 0.1, + "momentum": 0.9, + "dampening": 0, + "weight_decay": 0, + "nesterov": True, + } + + +class TestNoneOptimizer: + def test_defaults(self): + none_opt = OptimizerParams.NoneOptimizer() + assert none_opt._name == "none" + assert none_opt.params() == {} + + +# ─── OptimizerParams.parse_dict ───────────────────────────────────────────── + + +class TestOptimizerParseDict: + def test_parse_adam(self): + result = OptimizerParams.parse_dict({"name": "adam", "lr": 0.01}) + assert isinstance(result, OptimizerParams.Adam) + assert result.lr == 0.01 + + def test_parse_adamw(self): + result = OptimizerParams.parse_dict({"name": "adamw", "weight_decay": 0.1}) + assert isinstance(result, OptimizerParams.AdamW) + assert result.weight_decay == 0.1 + + def test_parse_sgd(self): + result = OptimizerParams.parse_dict({"name": "sgd", "momentum": 0.9}) + assert isinstance(result, OptimizerParams.SGD) + assert result.momentum == 0.9 + + def test_parse_case_insensitive(self): + result = OptimizerParams.parse_dict({"name": "Adam"}) + assert isinstance(result, OptimizerParams.Adam) + + def test_parse_unknown_raises(self): + with pytest.raises(ValueError, match="Unknown optimizer type"): + OptimizerParams.parse_dict({"name": "rmsprop"}) + + def test_parse_does_not_mutate_input(self): + d = {"name": "adam", "lr": 0.01} + original = dict(d) + OptimizerParams.parse_dict(d) + assert d == original + + def test_parse_invalid_name_type_raises(self): + with pytest.raises(ValueError, match="Unknown optimizer type"): + OptimizerParams.parse_dict({"name": 42}) + + +# ─── SchedulerParams defaults ─────────────────────────────────────────────── + + +class TestPlateauDefaults: + def test_defaults(self): + p = SchedulerParams.Plateau() + assert p.mode == "min" + assert p.factor == 0.5 + assert p.patience == 10 + assert p.cooldown == 50 + assert p.min_lr is None + assert p._name == "plateau" + + def test_params_computes_min_lr(self): + p = SchedulerParams.Plateau() + result = p.params(base_LR=0.01) + assert result["min_lr"] == pytest.approx(0.01 / 20) + + def test_params_explicit_min_lr(self): + p = SchedulerParams.Plateau(min_lr=1e-6) + result = p.params(base_LR=0.01) + assert result["min_lr"] == 1e-6 + + +class TestExponentialDefaults: + def test_defaults(self): + e = SchedulerParams.Exponential() + assert e.gamma == 0.9 + assert e._name == "exponential" + + def test_params_with_num_iter(self): + e = SchedulerParams.Exponential(factor=None) + result = e.params(base_LR=0.01, num_iter=100) + assert result == {"gamma": 0.9} + + def test_params_factor_overrides_gamma(self): + e = SchedulerParams.Exponential(factor=0.01) + result = e.params(base_LR=0.01, num_iter=100) + expected_gamma = 0.01 ** (1.0 / 100) + assert result["gamma"] == pytest.approx(expected_gamma) + + def test_params_no_num_iter_raises(self): + e = SchedulerParams.Exponential() + with pytest.raises(ValueError, match="num_iter must be set"): + e.params(base_LR=0.01, num_iter=None) + + def test_params_uses_own_num_iter(self): + e = SchedulerParams.Exponential(num_iter=50, factor=None) + result = e.params(base_LR=0.01, num_iter=None) + assert result == {"gamma": 0.9} + + +class TestCyclicDefaults: + def test_defaults(self): + c = SchedulerParams.Cyclic() + assert c.mode == "triangular2" + assert c.cycle_momentum is False + assert c._name == "cyclic" + + def test_params_computes_lr_bounds(self): + c = SchedulerParams.Cyclic() + result = c.params(base_LR=0.01) + assert result["base_lr"] == pytest.approx(0.01 / 4) + assert result["max_lr"] == pytest.approx(0.01 * 4) + + def test_params_explicit_lr_bounds(self): + c = SchedulerParams.Cyclic(base_lr=0.001, max_lr=0.1) + result = c.params(base_LR=999.0) # should be ignored + assert result["base_lr"] == 0.001 + assert result["max_lr"] == 0.1 + + +class TestLinearDefaults: + def test_defaults(self): + test = SchedulerParams.Linear() + assert test.start_factor == 0.1 + assert test.end_factor == 1.0 + assert test._name == "linear" + + def test_params_uses_num_iter(self): + test = SchedulerParams.Linear() + result = test.params(base_LR=0.01, num_iter=200) + assert result["total_iters"] == 200 + + def test_params_explicit_total_iters(self): + test = SchedulerParams.Linear(total_iters=50) + result = test.params(base_LR=0.01, num_iter=200) + assert result["total_iters"] == 50 + + def test_params_no_iters_raises(self): + test = SchedulerParams.Linear() + with pytest.raises(ValueError, match="total_iters must be set"): + test.params(base_LR=0.01, num_iter=None) + + +class TestCosineAnnealingDefaults: + def test_defaults(self): + ca = SchedulerParams.CosineAnnealing() + assert ca.eta_min == 1e-7 + assert ca.T_max is None + assert ca._name == "cosine_annealing" + + def test_params_uses_num_iter(self): + ca = SchedulerParams.CosineAnnealing() + result = ca.params(base_LR=0.01, num_iter=300) + assert result["T_max"] == 300 + + def test_params_explicit_T_max(self): + ca = SchedulerParams.CosineAnnealing(T_max=150) + result = ca.params(base_LR=0.01, num_iter=300) + assert result["T_max"] == 150 + + def test_params_no_T_max_raises(self): + ca = SchedulerParams.CosineAnnealing() + with pytest.raises(ValueError, match="T_max must be set"): + ca.params(base_LR=0.01, num_iter=None) + + +class TestNoneScheduler: + def test_defaults(self): + ns = SchedulerParams.NoneScheduler() + assert ns._name == "none" + assert ns.params(base_LR=0.01) == {} + + +# ─── SchedulerParams.parse_dict ───────────────────────────────────────────── + + +class TestSchedulerParseDict: + def test_parse_plateau(self): + result = SchedulerParams.parse_dict({"name": "plateau", "patience": 20}) + assert isinstance(result, SchedulerParams.Plateau) + assert result.patience == 20 + + def test_parse_exponential(self): + result = SchedulerParams.parse_dict({"name": "exponential", "gamma": 0.95}) + assert isinstance(result, SchedulerParams.Exponential) + assert result.gamma == 0.95 + + def test_parse_cyclic(self): + result = SchedulerParams.parse_dict({"name": "cyclic", "step_size_up": 50}) + assert isinstance(result, SchedulerParams.Cyclic) + assert result.step_size_up == 50 + + def test_parse_linear(self): + result = SchedulerParams.parse_dict({"name": "linear", "start_factor": 0.5}) + assert isinstance(result, SchedulerParams.Linear) + assert result.start_factor == 0.5 + + def test_parse_cosine_annealing(self): + result = SchedulerParams.parse_dict({"name": "cosine_annealing", "T_max": 100}) + assert isinstance(result, SchedulerParams.CosineAnnealing) + assert result.T_max == 100 + + def test_parse_none(self): + result = SchedulerParams.parse_dict({"name": "none"}) + assert isinstance(result, SchedulerParams.NoneScheduler) + + def test_parse_case_insensitive(self): + result = SchedulerParams.parse_dict({"name": "Plateau"}) + assert isinstance(result, SchedulerParams.Plateau) + + def test_parse_unknown_raises(self): + with pytest.raises(ValueError, match="Unknown scheduler type"): + SchedulerParams.parse_dict({"name": "warmup"}) + + def test_parse_does_not_mutate_input(self): + d = {"name": "plateau", "patience": 5} + original = dict(d) + SchedulerParams.parse_dict(d) + assert d == original + + def test_parse_invalid_name_type_raises(self): + with pytest.raises(ValueError, match="Unknown scheduler type"): + SchedulerParams.parse_dict({"name": 3.14}) + + def test_parse_default_name_is_none(self): + result = SchedulerParams.parse_dict({}) + assert isinstance(result, SchedulerParams.NoneScheduler) From 4fb58629ff2703713d396f7be917a613abd38f48 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 19:26:39 -0800 Subject: [PATCH 145/335] Bugs in tomography_opt fixed; scheduler and optimizer of pose not being stepped due to new type-hinting way we're doing it. --- src/quantem/tomography/object_models.py | 113 ++++++++++++++-------- src/quantem/tomography/tomography.py | 32 ++++-- src/quantem/tomography/tomography_base.py | 16 ++- src/quantem/tomography/tomography_opt.py | 14 +-- 4 files changed, 115 insertions(+), 60 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 2fc0c58c..0acf6aab 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -20,24 +20,52 @@ object_type = Literal["potential"] -@dataclass(slots=False) -class DefaultConstraintsTomography(Constraints): - """ - Data class for all constraints that can be applied to the object model. - """ +class ObjConstraintParams: + @dataclass + class ObjPixelatedConstraints(Constraints): + positivity: bool = True + shrinkage: float = 0.0 + tv_vol: float = 0.0 + _name: str = "obj_pixelated" + + soft_constraint_keys = ["tv_vol"] + hard_constraint_keys = ["positivity", "shrinkage"] + + @dataclass + class ObjINRConstraints(Constraints): + positivity: bool = True + shrinkage: float = 0.0 + tv_vol: float = 0.0 + sparsity: float = 0.0 + _name: str = "obj_inr" + + soft_constraint_keys = ["tv_vol", "sparsity"] + hard_constraint_keys = ["positivity", "shrinkage"] - # Hard Constraints - positivity: bool = True - shrinkage: float = 0.0 - circular_mask: bool = False - fourier_filter: str | None = None # Hamming, etc... + @classmethod + def parse_dict(cls, d: dict) -> ObjPixelatedConstraints | ObjINRConstraints: + """ + Parse dictionary to an object constraint params object. + """ + d = dict(d) + name = d.pop("name", "none") or d.pop("type", "none") + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown object constraint type: {name}") + if name == "obj_pixelated": + return ObjConstraintParams.ObjPixelatedConstraints(**d) + elif name == "obj_inr": + return ObjConstraintParams.ObjINRConstraints(**d) + else: + raise ValueError(f"Unknown object constraint type: {name.lower()}") - # Soft Constraints - tv_vol: float = 0.0 - sparsity: float = 0.0 - soft_constraint_keys = ["tv_vol", "sparsity"] - hard_constraint_keys = ["positivity", "shrinkage", "circular_mask", "fourier_filter"] +ObjConstraintsType = ( + ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints +) class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): @@ -134,30 +162,9 @@ def to(self, *args, **kwargs): raise NotImplementedError -class ObjectConstraints(BaseConstraints, ObjectBase): - DEFAULT_CONSTRAINTS = DefaultConstraintsTomography() - +class ObjectConstraints(BaseConstraints, ObjectBase): # TODO: Ask Arthur why we still need this def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.constraints: DefaultConstraintsTomography = self.DEFAULT_CONSTRAINTS.copy() - - def apply_hard_constraints( - self, - pred: torch.Tensor, - ) -> torch.Tensor: - """ - Apply hard constraints to the object model. - - Only hard constraint here is the positivity and shrinkage. TODO: Add the other hard constraints. - """ - obj2 = pred.clone() - if self.constraints.positivity: - obj2 = torch.clamp(obj2, min=0.0, max=None) - if self.constraints.shrinkage: - obj2 = torch.max(obj2 - self.constraints.shrinkage, torch.zeros_like(obj2)) - - # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. - return obj2 @abstractmethod def get_tv_loss(self, **kwargs) -> torch.Tensor: @@ -174,6 +181,8 @@ class ObjectPixelated(ObjectConstraints): Supports: Conventional algorithms (SIRT, FBP), and AD-based reconstructions. """ + DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjPixelatedConstraints() + def __init__( self, shape: tuple[int, int, int], @@ -186,6 +195,7 @@ def __init__( rng=rng, _token=self._token, ) + self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() # --- Instantiation ---- @classmethod @@ -245,7 +255,7 @@ def soft_loss(self) -> torch.Tensor: @property def name(self) -> str: - return "ObjectPixelated" + return "obj_pixelated" @property def obj_type(self) -> str: @@ -255,6 +265,24 @@ def obj_type(self) -> str: def dtype(self) -> torch.dtype: return self._obj.dtype + def apply_hard_constraints( + self, + pred: torch.Tensor, + ) -> torch.Tensor: + """ + Apply hard constraints to the object model. + + Only hard constraint here is the positivity and shrinkage. TODO: Add the other hard constraints. + """ + obj2 = pred.clone() + if self.constraints.positivity: + obj2 = torch.clamp(obj2, min=0.0, max=None) + if self.constraints.shrinkage: + obj2 = torch.max(obj2 - self.constraints.shrinkage, torch.zeros_like(obj2)) + + # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. + return obj2 + def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) if self.constraints.tv_vol > 0: @@ -288,6 +316,8 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM class ObjectINR(ObjectConstraints, DDPMixin): + DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() + def __init__( self, shape: tuple[int, int, int], @@ -304,7 +334,7 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - + self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -393,7 +423,10 @@ def apply_soft_constraints( grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] soft_loss += self.constraints.tv_vol * grad_norm.mean() - if self.constraints.sparsity > 0: + if ( + isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) + and self.constraints.sparsity > 0 + ): # NOTE: For the linter, I must make this :) sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) soft_loss += sparsity_loss diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index f0b05b52..2e20e717 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -16,7 +16,8 @@ from quantem.tomography.dataset_models import DatasetModelType, DefaultTomographyDatasetConstraints from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( - DefaultConstraintsTomography, + ObjConstraintParams, + ObjConstraintsType, ObjectINR, ObjectPixelated, ) @@ -59,7 +60,7 @@ def reconstruct( reset: bool = False, optimizer_params: dict | None = None, scheduler_params: dict | None = None, - obj_constraints: dict = {}, # TODO: What to pass into the constraints? + obj_constraints: dict | ObjConstraintsType | None = None, dset_constraints: dict = {}, num_samples_per_ray: int | list[tuple[int, int]] | None = None, profiling_mode: bool = False, @@ -109,7 +110,10 @@ def reconstruct( self.set_schedulers(self.scheduler_params, num_iter=num_iter) if obj_constraints is not None: - self.obj_model.constraints = cast(DefaultConstraintsTomography, obj_constraints) + if isinstance(obj_constraints, dict): + obj_constraints = ObjConstraintParams.parse_dict(obj_constraints) + + self.obj_model.constraints = obj_constraints if dset_constraints is not None: self.dset.constraints = cast(DefaultTomographyDatasetConstraints, dset_constraints) @@ -277,6 +281,9 @@ def reconstruct( dataset_model=self.dset, iter=self.num_epochs, ) + pbar.set_description( + f"Reconstruction | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e} | Images Logged" + ) if self.global_rank == 0: self.logger.log_iter( @@ -291,10 +298,6 @@ def reconstruct( self.logger.flush() - pbar.set_description( - f"Reconstruction | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e} | Logger Updated" - ) - # --- Helper Functions --- def save_volume(self, path: str = "recon_volume.npz", overwrite: bool = False): @@ -392,18 +395,27 @@ def from_models( def reconstruct( self, num_iter: int = 10, + obj_constraints: dict | ObjConstraintsType | None = None, mode: Literal["sirt", "fbp"] = "sirt", reset: bool = False, inline_alignment: bool = False, smoothing_sigma: float | None = None, ): - pbar = tqdm(range(num_iter), desc=f"{mode} Reconstruction", disable=not self.verbose) + if obj_constraints is not None: + if isinstance(obj_constraints, dict): + obj_constraints = ObjConstraintParams.parse_dict(obj_constraints) + + self.obj_model.constraints = obj_constraints + + pbar = tqdm( + range(num_iter), + desc=f"{mode} Reconstruction | Loss: {0:.4f}", + disable=not self.verbose, + ) if mode == "sirt" or mode == "fbp": proj_forward = torch.zeros_like(self.dset.tilt_stack).permute(2, 0, 1) else: proj_forward = torch.zeros_like(self.dset.tilt_stack) - print("proj_forward.shape", proj_forward.shape) - print("self.dset.tilt_stack.shape", self.dset.tilt_stack.shape) if smoothing_sigma is not None: gaussian_kernel = gaussian_kernel_1d(smoothing_sigma).to(self.device) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index f4d80096..50a16024 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -7,7 +7,8 @@ from quantem.tomography.dataset_models import DatasetModelType, TomographyDatasetBase from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( - DefaultConstraintsTomography, + ObjConstraintParams, + ObjConstraintsType, ObjectINR, ObjectModelType, ) @@ -86,12 +87,19 @@ def obj_model(self, obj_model: ObjectModelType): self._obj_model = obj_model @property - def constraints(self) -> DefaultConstraintsTomography: + def constraints(self) -> ObjConstraintsType: # TODO: Also looks at Dataset constraints return self.obj_model.constraints @constraints.setter - def constraints(self, constraints: DefaultConstraintsTomography): - self.obj_model.constraints = constraints + def constraints(self, constraints: ObjConstraintsType | dict | None): + if constraints is None: + return + elif isinstance(constraints, dict): + self.obj_model.constraints = ObjConstraintParams.parse_dict(constraints) + elif isinstance(constraints, ObjConstraintsType): + self.obj_model.constraints = constraints + else: + raise ValueError(f"Invalid constraints type: {type(constraints)}") @property def logger(self) -> LoggerTomography | None: diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 404e2f4b..633767e0 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -143,23 +143,25 @@ def step_optimizers(self): for key in self.optimizer_params.keys(): if self.obj_model.has_optimizer(): self.obj_model.step_optimizer() - elif self.dset.has_optimizer(): + if self.dset.has_optimizer(): self.dset.step_optimizer() - else: + if key not in self.OPTIMIZABLE_VALS: raise ValueError(f"Unknown optimization key: {key}") def zero_grad_all(self): for key in self.optimizer_params.keys(): if self.obj_model.has_optimizer(): self.obj_model.zero_optimizer_grad() - elif self.dset.has_optimizer(): + if self.dset.has_optimizer(): self.dset.zero_optimizer_grad() - else: + if key not in self.OPTIMIZABLE_VALS: raise ValueError(f"Unknown optimization key: {key}") def step_schedulers(self, loss: float | None = None): for key in self.scheduler_params.keys(): - if key == "object" and self.obj_model.scheduler is not None: + if self.obj_model.scheduler is not None and key == "object": self.obj_model.step_scheduler(loss) - elif self.dset.scheduler is not None and key == "pose": + if self.dset.scheduler is not None and key == "pose": self.dset.step_scheduler(loss) + if key not in self.OPTIMIZABLE_VALS: + raise ValueError(f"Unknown optimization key: {key}") From 04a7d65cbd457d9fbe76fd2ea07358a59ee057c0 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 19:49:00 -0800 Subject: [PATCH 146/335] Bug fixes in OptimizerMixin --- src/quantem/core/ml/optimizer_mixin.py | 4 +++- src/quantem/tomography/object_models.py | 2 +- src/quantem/tomography/tomography_opt.py | 7 +++---- 3 files changed, 7 insertions(+), 6 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index e9dc9599..a14fc6c8 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -159,7 +159,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", "none") or d.pop("type", "none") + name = d.pop("name", None) or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): @@ -172,6 +172,8 @@ def parse_dict(cls, d: dict): return OptimizerParams.AdamW(**d) elif name == "sgd": return OptimizerParams.SGD(**d) + elif name == "none": + return OptimizerParams.NoneOptimizer() else: raise ValueError(f"Unknown optimizer type: {name.lower()}") diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0acf6aab..c3a47fb9 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -48,7 +48,7 @@ def parse_dict(cls, d: dict) -> ObjPixelatedConstraints | ObjINRConstraints: Parse dictionary to an object constraint params object. """ d = dict(d) - name = d.pop("name", "none") or d.pop("type", "none") + name = d.pop("name", None) or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 633767e0..1dd406ab 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -1,6 +1,6 @@ import torch -from quantem.core.ml.optimizer_mixin import OptimizerType +from quantem.core.ml.optimizer_mixin import OptimizerParams, OptimizerType from quantem.tomography.tomography_base import TomographyBase @@ -36,7 +36,7 @@ def optimizer_params(self) -> dict[str, dict]: } @optimizer_params.setter - def optimizer_params(self, d: dict[str, OptimizerType]): + def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): """Set the optimizer parameters.""" if isinstance(d, (tuple, list)): d = {k: {} for k in d} @@ -51,8 +51,7 @@ def optimizer_params(self, d: dict[str, OptimizerType]): raise ValueError(f"Unknown optimization key: {k}") if not isinstance(v, OptimizerType): - if "lr" not in v: - v["lr"] = self._get_default_lr(k) + v = OptimizerParams.parse_dict(v) targets[k].optimizer_params = v From d3beb502662b252f097ea3b0602cba7745f060a5 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 19:51:29 -0800 Subject: [PATCH 147/335] Scheduler bug fix as well in parse_dict --- src/quantem/core/ml/optimizer_mixin.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index a14fc6c8..94c12518 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -446,7 +446,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", "none") or d.pop("type", "none") + name = d.pop("name", None) or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): From d96808ba4994b434a51fbe16e893d3be35ee48dd Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 20:00:42 -0800 Subject: [PATCH 148/335] Hot-fix for OptimizerMixin bug with or statement --- src/quantem/core/ml/optimizer_mixin.py | 618 +++++++++++++++++++++---- 1 file changed, 531 insertions(+), 87 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index e2d2e89a..94c12518 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,5 +1,6 @@ from abc import abstractmethod -from typing import TYPE_CHECKING, Generator, Iterator, Sequence +from dataclasses import dataclass +from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence from quantem.core import config @@ -10,6 +11,474 @@ import torch +class OptimizerParams: + """ + Container for optimizer parameter dataclasses. + + Each nested class configures a specific PyTorch optimizer and can be passed + directly to ``OptimizerMixin.set_optimizer``, or constructed from a dict via + ``OptimizerParams.parse_dict``. + + Supported optimizers + -------------------- + Adam + ``torch.optim.Adam`` — adaptive moment estimation. + AdamW + ``torch.optim.AdamW`` — Adam with decoupled weight decay. + SGD + ``torch.optim.SGD`` — stochastic gradient descent with optional momentum and Nesterov. + NoneOptimizer + Sentinel that disables / removes the optimizer. + + Examples + -------- + >>> obj.set_optimizer(OptimizerParams.Adam(lr=1e-4)) + >>> obj.set_optimizer({"name": "adam", "lr": 1e-4}) # equivalent dict form + """ + + @dataclass + class Adam: + """ + Adam optimizer (``torch.optim.Adam``). + + Parameters + ---------- + lr : float + Learning rate. Default: 1e-3. + betas : tuple[float, float] + Coefficients for computing running averages of the gradient and its square. + Default: (0.9, 0.999). + eps : float + Term added to the denominator for numerical stability. Default: 1e-8. + weight_decay : float + L2 regularisation penalty. Default: 0. + """ + + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0 + _name: str = "adam" + + def params(self) -> dict: + return { + "lr": self.lr, + "betas": self.betas, + "eps": self.eps, + "weight_decay": self.weight_decay, + } + + @dataclass + class AdamW: + """ + AdamW optimizer (``torch.optim.AdamW``). + + Identical to Adam but applies weight decay directly to the parameters + rather than folding it into the gradient update (decoupled weight decay). + + Parameters + ---------- + lr : float + Learning rate. Default: 1e-3. + betas : tuple[float, float] + Coefficients for computing running averages of the gradient and its square. + Default: (0.9, 0.999). + eps : float + Term added to the denominator for numerical stability. Default: 1e-8. + weight_decay : float + Decoupled L2 regularisation penalty. Default: 0. + """ + + lr: float = 1e-3 + betas: tuple[float, float] = (0.9, 0.999) + eps: float = 1e-8 + weight_decay: float = 0 + _name: str = "adamw" + + def params(self) -> dict: + return { + "lr": self.lr, + "betas": self.betas, + "eps": self.eps, + "weight_decay": self.weight_decay, + } + + @dataclass + class SGD: + """ + SGD optimizer (``torch.optim.SGD``). + + Parameters + ---------- + lr : float + Learning rate. Default: 1e-3. + momentum : float + Momentum factor. Default: 0. + dampening : float + Dampening for momentum. Default: 0. + weight_decay : float + L2 regularisation penalty. Default: 0. + nesterov : bool + Enables Nesterov momentum. Default: False. + """ + + lr: float = 1e-3 + momentum: float = 0 + dampening: float = 0 + weight_decay: float = 0 + nesterov: bool = False + _name: str = "sgd" + + def params(self) -> dict: + return { + "lr": self.lr, + "momentum": self.momentum, + "dampening": self.dampening, + "weight_decay": self.weight_decay, + "nesterov": self.nesterov, + } + + @dataclass + class NoneOptimizer: + """ + Sentinel optimizer that disables optimization. + + Passing this to ``set_optimizer`` will call ``remove_optimizer``, + clearing both the optimizer and scheduler. + """ + + _name: str = "none" + + def params(self) -> dict: + return {} + + @classmethod + def parse_dict(cls, d: dict): + """ + Parse dictionary to a optimizer params object. + Accepts either ``"name"`` or ``"type"`` as the optimizer key. + """ + d = dict(d) # avoid mutating caller's dict + name = d.pop("name", None) or d.pop("type", "none") + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown optimizer type: {name}") + if name == "adam": + return OptimizerParams.Adam(**d) + elif name == "adamw": + return OptimizerParams.AdamW(**d) + elif name == "sgd": + return OptimizerParams.SGD(**d) + elif name == "none": + return OptimizerParams.NoneOptimizer() + else: + raise ValueError(f"Unknown optimizer type: {name.lower()}") + + +OptimizerType = ( + OptimizerParams.Adam + | OptimizerParams.AdamW + | OptimizerParams.SGD + | OptimizerParams.NoneOptimizer +) + + +class SchedulerParams: + """ + Container for learning-rate scheduler parameter dataclasses. + + Each nested class configures a specific PyTorch LR scheduler and can be passed + directly to ``OptimizerMixin.set_scheduler``, or constructed from a dict via + ``SchedulerParams.parse_dict``. + + Supported schedulers + -------------------- + Plateau + ``torch.optim.lr_scheduler.ReduceLROnPlateau`` — reduce LR when a metric stops improving. + Exponential + ``torch.optim.lr_scheduler.ExponentialLR`` — multiply LR by ``gamma`` each step. + Cyclic + ``torch.optim.lr_scheduler.CyclicLR`` — cycle LR between ``base_lr`` and ``max_lr``. + Linear + ``torch.optim.lr_scheduler.LinearLR`` — linearly interpolate LR over ``total_iters`` steps. + CosineAnnealing + ``torch.optim.lr_scheduler.CosineAnnealingLR`` — cosine-annealing LR schedule. + NoneScheduler + Sentinel that disables / removes the scheduler. + + Examples + -------- + >>> obj.set_scheduler(SchedulerParams.Plateau(factor=0.5, patience=10, cooldown=20)) + >>> obj.set_scheduler({"name": "plateau", "factor": 0.5}) # equivalent dict form + """ + + @dataclass + class Plateau: + """ + ReduceLROnPlateau scheduler (``torch.optim.lr_scheduler.ReduceLROnPlateau``). + + Reduces the learning rate when a monitored metric stops improving. + + Parameters + ---------- + mode : {'min', 'max'} + Whether the monitored metric should be minimised or maximised. Default: 'min'. + min_lr_factor : float + Sets ``min_lr = min_lr_factor * base_lr`` when ``min_lr`` is not provided. + Default: 1/20. + min_lr : float or None + Absolute lower bound on the learning rate. Overrides ``min_lr_factor`` when set. + Default: None. + factor : float + Factor by which the LR is reduced: ``new_lr = lr * factor``. Default: 0.5. + patience : int + Number of epochs with no improvement before reducing LR. Default: 10. + threshold : float + Minimum change in the monitored metric to qualify as an improvement. Default: 1e-5. + cooldown : int + Number of epochs to wait after a LR reduction before resuming normal operation. + Default: 50. + """ + + mode: Literal["min", "max"] = "min" + min_lr_factor: float = 1 / 20 + min_lr: float | None = None + factor: float = 0.5 + patience: int = 10 + threshold: float = 1e-5 + cooldown: int = 50 + _name: str = "plateau" + + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + if self.min_lr is None: + self.min_lr = self.min_lr_factor * base_LR + return { + "mode": self.mode, + "factor": self.factor, + "patience": self.patience, + "threshold": self.threshold, + "min_lr": self.min_lr, + "cooldown": self.cooldown, + } + + @dataclass + class Exponential: + """ + ExponentialLR scheduler (``torch.optim.lr_scheduler.ExponentialLR``). + + Multiplies the learning rate by ``gamma`` after each step. + + Parameters + ---------- + gamma : float + Multiplicative decay factor applied each step. Default: 0.9. + factor : float or None + Reserved for future use. Default: 0.5. + num_iter : int or None + Reserved for future use. Default: None. + """ + + gamma: float = 0.9 + factor: float | None = None + num_iter: int | None = None + _name: str = "exponential" + + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + effective_num_iter = self.num_iter if self.num_iter is not None else num_iter + if effective_num_iter is None: + raise ValueError("num_iter must be set if num_iter is not provided") + + self.num_iter = effective_num_iter + + if self.factor is not None: + self.gamma = self.factor ** (1.0 / effective_num_iter) + + return { + "gamma": self.gamma, + } + + @dataclass + class Cyclic: + """ + CyclicLR scheduler (``torch.optim.lr_scheduler.CyclicLR``). + + Cycles the learning rate between a lower bound (``base_lr``) and an upper + bound (``max_lr``). Bounds can be set directly or derived from the optimizer's + base LR via the factor parameters. + + Parameters + ---------- + base_lr_factor : float + Sets ``base_lr = base_lr_factor * optimizer_lr`` when ``base_lr`` is not provided. + Default: 1/4. + max_lr_factor : float + Sets ``max_lr = max_lr_factor * optimizer_lr`` when ``max_lr`` is not provided. + Default: 4. + base_lr : float or None + Absolute lower bound of the LR cycle. Overrides ``base_lr_factor`` when set. + Default: None. + max_lr : float or None + Absolute upper bound of the LR cycle. Overrides ``max_lr_factor`` when set. + Default: None. + step_size_up : int + Number of steps in the increasing half of each cycle. Default: 100. + step_size_down : int + Number of steps in the decreasing half of each cycle. Default: 100. + mode : {'triangular2', 'triangular', 'exp_range'} + Cycling policy. Default: 'triangular2'. + cycle_momentum : bool + If True, cycles momentum inversely to the learning rate. Default: False. + """ + + base_lr_factor: float = 1 / 4 + max_lr_factor: float = 4 + base_lr: float | None = None + max_lr: float | None = None + step_size_up: int = 100 + step_size_down: int = 100 + mode: Literal["triangular2", "triangular", "exp_range"] = "triangular2" + cycle_momentum: bool = False + _name: str = "cyclic" + + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + if self.base_lr is None: + self.base_lr = self.base_lr_factor * base_LR + if self.max_lr is None: + self.max_lr = self.max_lr_factor * base_LR + return { + "base_lr": self.base_lr, + "max_lr": self.max_lr, + "step_size_up": self.step_size_up, + "step_size_down": self.step_size_down, + "mode": self.mode, + "cycle_momentum": self.cycle_momentum, + } + + @dataclass + class Linear: + """ + LinearLR scheduler (``torch.optim.lr_scheduler.LinearLR``). + + Linearly interpolates the learning rate from ``start_factor * base_lr`` to + ``end_factor * base_lr`` over ``total_iters`` steps. + + Parameters + ---------- + total_iters : int + Number of steps over which to interpolate the LR. Required. + start_factor : float + Multiplicative factor applied to the LR at the first step. Default: 0.1. + end_factor : float + Multiplicative factor applied to the LR at the final step. Default: 1.0. + """ + + total_iters: int | None = None + start_factor: float = 0.1 + end_factor: float = 1.0 + _name: str = "linear" + + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + if num_iter is None and self.total_iters is None: + raise ValueError( + "total_iters must be set if num_iter is not provided" + ) # Should never be reached + if self.total_iters is None: + self.total_iters = num_iter + return { + "start_factor": self.start_factor, + "end_factor": self.end_factor, + "total_iters": self.total_iters, + } + + @dataclass + class CosineAnnealing: + """ + CosineAnnealingLR scheduler (``torch.optim.lr_scheduler.CosineAnnealingLR``). + + Anneals the learning rate following a cosine curve from the base LR down to + ``eta_min`` over ``T_max`` steps, then restarts. + + Parameters + ---------- + T_max : int + Maximum number of iterations (half-period of the cosine cycle). Required. + eta_min : float + Minimum learning rate at the trough of the cosine curve. Default: 1e-7. + """ + + eta_min: float = 1e-7 + T_max: int | None = None + _name: str = "cosine_annealing" + + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + if num_iter is None and self.T_max is None: + raise ValueError( + "T_max must be set if num_iter is not provided" + ) # Should never be reached + if self.T_max is None: + self.T_max = num_iter + return { + "T_max": self.T_max, + "eta_min": self.eta_min, + } + + @dataclass + class NoneScheduler: + """ + Sentinel scheduler that disables LR scheduling. + + Passing this to ``set_scheduler`` clears the active scheduler without + affecting the optimizer. + """ + + _name: str = "none" + + def params(self, base_LR: float, num_iter: int | None = None) -> dict: + return {} + + @classmethod + def parse_dict(cls, d: dict): + """ + Parse dictionary to a scheduler params object. + Accepts either ``"name"`` or ``"type"`` as the scheduler key. + """ + d = dict(d) # avoid mutating caller's dict + name = d.pop("name", None) or d.pop("type", "none") + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown scheduler type: {name}") + if name == "plateau": + return SchedulerParams.Plateau(**d) + elif name == "exponential": + return SchedulerParams.Exponential(**d) + elif name == "cyclic": + return SchedulerParams.Cyclic(**d) + elif name == "linear": + return SchedulerParams.Linear(**d) + elif name == "cosine_annealing": + return SchedulerParams.CosineAnnealing(**d) + elif name == "none": + return SchedulerParams.NoneScheduler() + else: + raise ValueError(f"Unknown scheduler type: {name}") + + +SchedulerType = ( + SchedulerParams.Plateau + | SchedulerParams.Exponential + | SchedulerParams.Cyclic + | SchedulerParams.Linear + | SchedulerParams.CosineAnnealing + | SchedulerParams.NoneScheduler +) + + class OptimizerMixin: """ Mixin class for handling optimizer and scheduler management. @@ -22,8 +491,8 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params = {} - self._scheduler_params = {} + self._optimizer_params: OptimizerType = OptimizerParams.NoneOptimizer() + self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @property @@ -37,38 +506,32 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> dict: + def optimizer_params(self) -> OptimizerType: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter - def optimizer_params(self, params: dict): + def optimizer_params(self, params: OptimizerType | dict): """Set the optimizer parameters.""" - self._optimizer_params = params.copy() if params else {} + if isinstance(params, dict): + params = OptimizerParams.parse_dict(d=params) + if not isinstance(params, OptimizerType): + raise TypeError(f"optimizer parameters must be a OptimizerType, got {type(params)}") + self._optimizer_params = params @property - def scheduler_params(self) -> dict: + def scheduler_params(self) -> SchedulerType: """Get the scheduler parameters.""" return self._scheduler_params @scheduler_params.setter - def scheduler_params(self, params: dict): + def scheduler_params(self, params: SchedulerType | dict): """Set the scheduler parameters.""" - if params: - if params["type"].lower() not in [ - "cyclic", - "plateau", - "exp", - "gamma", - "linear", - "none", - ]: - raise ValueError( - f"Unknown scheduler type: {params['type']}, expected one of ['cyclic', 'plateau', 'exp', 'gamma', 'none']" - ) - self._scheduler_params = params.copy() - else: - self._scheduler_params = {} + if isinstance(params, dict): + params = SchedulerParams.parse_dict(d=params) + if not isinstance(params, SchedulerType): + raise TypeError(f"scheduler parameters must be a SchedulerType, got {type(params)}") + self._scheduler_params = params @abstractmethod def get_optimization_parameters( @@ -81,7 +544,7 @@ def get_optimization_parameters( """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") - def set_optimizer(self, opt_params: dict | None = None) -> None: + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ Set the optimizer for this model. Currently supports single LR for all parameters, TODO allow for per parameter LRs by @@ -94,10 +557,7 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: self._optimizer = None return - opt_params = self._optimizer_params.copy() - opt_type = opt_params.pop("type", self.DEFAULT_OPTIMIZER_TYPE) - - if opt_type == "none": + if isinstance(self._optimizer_params, OptimizerParams.NoneOptimizer): self.remove_optimizer() return @@ -111,22 +571,18 @@ def set_optimizer(self, opt_params: dict | None = None) -> None: for p in params: p.requires_grad_(True) - if isinstance(opt_type, type): - self._optimizer = opt_type(params, **opt_params) - elif isinstance(opt_type, str): - if opt_type.lower() == "adam": - self._optimizer = torch.optim.Adam(params, **opt_params) - elif opt_type.lower() == "adamw": - self._optimizer = torch.optim.AdamW(params, **opt_params) - elif opt_type.lower() == "sgd": - self._optimizer = torch.optim.SGD(params, **opt_params) - else: - raise NotImplementedError(f"Unknown optimizer type: {opt_type}") - else: - raise TypeError(f"optimizer type must be string or type, got {type(opt_type)}") + match self._optimizer_params: + case OptimizerParams.Adam(): + self._optimizer = torch.optim.Adam(params, **self._optimizer_params.params()) + case OptimizerParams.AdamW(): + self._optimizer = torch.optim.AdamW(params, **self._optimizer_params.params()) + case OptimizerParams.SGD(): + self._optimizer = torch.optim.SGD(params, **self._optimizer_params.params()) + case _: + raise NotImplementedError(f"Unknown optimizer type: {self._optimizer_params}") def set_scheduler( - self, scheduler_params: dict | None = None, num_iter: int | None = None + self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: @@ -136,51 +592,39 @@ def set_scheduler( self._scheduler = None return - params = self._scheduler_params - sched_type = params.get("type", "none").lower() optimizer = self._optimizer base_LR = optimizer.param_groups[0]["lr"] - - if sched_type == "none": - self._scheduler = None - elif sched_type == "cyclic": - self._scheduler = torch.optim.lr_scheduler.CyclicLR( - optimizer, - base_lr=params.get("base_lr", base_LR / 4), - max_lr=params.get("max_lr", base_LR * 4), - step_size_up=params.get("step_size_up", 100), - step_size_down=params.get("step_size_down", params.get("step_size_up", 100)), - mode=params.get("mode", "triangular2"), - cycle_momentum=params.get("momentum", False), - ) - elif sched_type.startswith(("plat", "reducelronplat")): - self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - mode="min", - factor=params.get("factor", 0.5), - patience=params.get("patience", 10), - threshold=params.get("threshold", 1e-3), - min_lr=params.get("min_lr", base_LR / 20), - cooldown=params.get("cooldown", 50), - ) - elif sched_type in ["exp", "gamma", "exponential"]: - if "gamma" in params: - gamma = params["gamma"] - elif num_iter is not None: - fac = params.get("factor", 0.01) - gamma = fac ** (1.0 / num_iter) - else: - gamma = 0.9 - self._scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) - elif sched_type == "linear": - self._scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, - start_factor=params.get("start_factor", 0.1), - end_factor=params.get("end_factor", 1.0), - total_iters=params.get("total_iters", num_iter), - ) - else: - raise ValueError(f"Unknown scheduler type: {sched_type}") + params = self._scheduler_params.params(base_LR, num_iter=num_iter) + match self.scheduler_params: + case SchedulerParams.NoneScheduler(): + self._scheduler = None + case SchedulerParams.Cyclic(): + self._scheduler = torch.optim.lr_scheduler.CyclicLR( + optimizer, + **params, + ) + case SchedulerParams.Plateau(): + self._scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, + **params, + ) + case SchedulerParams.Exponential(): + self._scheduler = torch.optim.lr_scheduler.ExponentialLR( + optimizer, + **params, + ) + case SchedulerParams.Linear(): + self._scheduler = torch.optim.lr_scheduler.LinearLR( + optimizer, + **params, + ) + case SchedulerParams.CosineAnnealing(): + self._scheduler = torch.optim.lr_scheduler.CosineAnnealingLR( + optimizer, + **params, + ) + case _: + raise ValueError(f"Unknown scheduler type: {self.scheduler_params}") def step_optimizer(self) -> None: """Step the optimizer if it exists.""" @@ -214,9 +658,9 @@ def get_current_lr(self) -> float: def remove_optimizer(self) -> None: """Remove the optimizer and scheduler.""" self._optimizer = None - self._optimizer_params = {} + self._optimizer_params = OptimizerParams.NoneOptimizer() self._scheduler = None - self._scheduler_params = {} + self._scheduler_params = SchedulerParams.NoneScheduler() def reset_optimizer(self) -> None: """Reset the optimizer and scheduler.""" From 895cb1b50c9f3feb8b21ff8c64fd765ed0af7430 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 20:03:34 -0800 Subject: [PATCH 149/335] Hotfix from tomo_refactor --- src/quantem/core/ml/optimizer_mixin.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index e9dc9599..94c12518 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -159,7 +159,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", "none") or d.pop("type", "none") + name = d.pop("name", None) or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): @@ -172,6 +172,8 @@ def parse_dict(cls, d: dict): return OptimizerParams.AdamW(**d) elif name == "sgd": return OptimizerParams.SGD(**d) + elif name == "none": + return OptimizerParams.NoneOptimizer() else: raise ValueError(f"Unknown optimizer type: {name.lower()}") @@ -444,7 +446,7 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", "none") or d.pop("type", "none") + name = d.pop("name", None) or d.pop("type", "none") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): From 8f5d3cb66669c13b79f0c81e556065ba1e3bceda Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 20:13:00 -0800 Subject: [PATCH 150/335] Hotfix w/ better name or type checking --- src/quantem/core/ml/optimizer_mixin.py | 12 +++++- tests/ml/test_optimizermixin.py | 59 +++++++++++++++++++++++++- 2 files changed, 67 insertions(+), 4 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 94c12518..d30229da 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -159,7 +159,11 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", "none") + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): @@ -446,7 +450,11 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", "none") + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): diff --git a/tests/ml/test_optimizermixin.py b/tests/ml/test_optimizermixin.py index 6fd6c22a..3682d452 100644 --- a/tests/ml/test_optimizermixin.py +++ b/tests/ml/test_optimizermixin.py @@ -114,6 +114,61 @@ def test_parse_invalid_name_type_raises(self): OptimizerParams.parse_dict({"name": 42}) +# ─── parse_dict "name" vs "type" key handling ─────────────────────────────── + + +class TestOptimizerParseDictKeyHandling: + def test_parse_with_type_key(self): + result = OptimizerParams.parse_dict({"type": "adam", "lr": 0.01}) + assert isinstance(result, OptimizerParams.Adam) + assert result.lr == 0.01 + + def test_name_takes_precedence_over_type(self): + result = OptimizerParams.parse_dict({"name": "adam", "type": "sgd"}) + assert isinstance(result, OptimizerParams.Adam) + + def test_neither_name_nor_type_raises(self): + with pytest.raises(ValueError, match="Must provide either"): + OptimizerParams.parse_dict({"lr": 0.01}) + + def test_type_key_not_leaked_into_constructor(self): + """'type' should be popped from d so it doesn't become an unexpected kwarg.""" + result = OptimizerParams.parse_dict({"type": "sgd", "momentum": 0.9}) + assert isinstance(result, OptimizerParams.SGD) + assert result.momentum == 0.9 + + def test_both_keys_popped_when_name_used(self): + """Even when 'name' is used, 'type' should be popped so it doesn't leak.""" + result = OptimizerParams.parse_dict({"name": "adam", "type": "ignored", "lr": 0.05}) + assert isinstance(result, OptimizerParams.Adam) + assert result.lr == 0.05 + + +class TestSchedulerParseDictKeyHandling: + def test_parse_with_type_key(self): + result = SchedulerParams.parse_dict({"type": "plateau", "patience": 20}) + assert isinstance(result, SchedulerParams.Plateau) + assert result.patience == 20 + + def test_name_takes_precedence_over_type(self): + result = SchedulerParams.parse_dict({"name": "plateau", "type": "linear"}) + assert isinstance(result, SchedulerParams.Plateau) + + def test_neither_name_nor_type_raises(self): + with pytest.raises(ValueError, match="Must provide either"): + SchedulerParams.parse_dict({"patience": 20}) + + def test_type_key_not_leaked_into_constructor(self): + result = SchedulerParams.parse_dict({"type": "cyclic", "step_size_up": 50}) + assert isinstance(result, SchedulerParams.Cyclic) + assert result.step_size_up == 50 + + def test_both_keys_popped_when_name_used(self): + result = SchedulerParams.parse_dict({"name": "plateau", "type": "ignored", "patience": 5}) + assert isinstance(result, SchedulerParams.Plateau) + assert result.patience == 5 + + # ─── SchedulerParams defaults ─────────────────────────────────────────────── @@ -291,5 +346,5 @@ def test_parse_invalid_name_type_raises(self): SchedulerParams.parse_dict({"name": 3.14}) def test_parse_default_name_is_none(self): - result = SchedulerParams.parse_dict({}) - assert isinstance(result, SchedulerParams.NoneScheduler) + with pytest.raises(ValueError, match="Must provide either"): + SchedulerParams.parse_dict({}) From b40f94695022415c1520552385286d47188289d8 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 20:14:01 -0800 Subject: [PATCH 151/335] Optimizer mixin hotfix fastforward --- src/quantem/core/ml/optimizer_mixin.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 94c12518..d30229da 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -159,7 +159,11 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the optimizer key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", "none") + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): @@ -446,7 +450,11 @@ def parse_dict(cls, d: dict): Accepts either ``"name"`` or ``"type"`` as the scheduler key. """ d = dict(d) # avoid mutating caller's dict - name = d.pop("name", None) or d.pop("type", "none") + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): From aacd82d9b2da33de7b8ec232600555745cfe06a5 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 20:16:38 -0800 Subject: [PATCH 152/335] Added new tests as well --- tests/ml/test_optimizermixin.py | 59 +++++++++++++++++++++++++++++++-- 1 file changed, 57 insertions(+), 2 deletions(-) diff --git a/tests/ml/test_optimizermixin.py b/tests/ml/test_optimizermixin.py index 6fd6c22a..3682d452 100644 --- a/tests/ml/test_optimizermixin.py +++ b/tests/ml/test_optimizermixin.py @@ -114,6 +114,61 @@ def test_parse_invalid_name_type_raises(self): OptimizerParams.parse_dict({"name": 42}) +# ─── parse_dict "name" vs "type" key handling ─────────────────────────────── + + +class TestOptimizerParseDictKeyHandling: + def test_parse_with_type_key(self): + result = OptimizerParams.parse_dict({"type": "adam", "lr": 0.01}) + assert isinstance(result, OptimizerParams.Adam) + assert result.lr == 0.01 + + def test_name_takes_precedence_over_type(self): + result = OptimizerParams.parse_dict({"name": "adam", "type": "sgd"}) + assert isinstance(result, OptimizerParams.Adam) + + def test_neither_name_nor_type_raises(self): + with pytest.raises(ValueError, match="Must provide either"): + OptimizerParams.parse_dict({"lr": 0.01}) + + def test_type_key_not_leaked_into_constructor(self): + """'type' should be popped from d so it doesn't become an unexpected kwarg.""" + result = OptimizerParams.parse_dict({"type": "sgd", "momentum": 0.9}) + assert isinstance(result, OptimizerParams.SGD) + assert result.momentum == 0.9 + + def test_both_keys_popped_when_name_used(self): + """Even when 'name' is used, 'type' should be popped so it doesn't leak.""" + result = OptimizerParams.parse_dict({"name": "adam", "type": "ignored", "lr": 0.05}) + assert isinstance(result, OptimizerParams.Adam) + assert result.lr == 0.05 + + +class TestSchedulerParseDictKeyHandling: + def test_parse_with_type_key(self): + result = SchedulerParams.parse_dict({"type": "plateau", "patience": 20}) + assert isinstance(result, SchedulerParams.Plateau) + assert result.patience == 20 + + def test_name_takes_precedence_over_type(self): + result = SchedulerParams.parse_dict({"name": "plateau", "type": "linear"}) + assert isinstance(result, SchedulerParams.Plateau) + + def test_neither_name_nor_type_raises(self): + with pytest.raises(ValueError, match="Must provide either"): + SchedulerParams.parse_dict({"patience": 20}) + + def test_type_key_not_leaked_into_constructor(self): + result = SchedulerParams.parse_dict({"type": "cyclic", "step_size_up": 50}) + assert isinstance(result, SchedulerParams.Cyclic) + assert result.step_size_up == 50 + + def test_both_keys_popped_when_name_used(self): + result = SchedulerParams.parse_dict({"name": "plateau", "type": "ignored", "patience": 5}) + assert isinstance(result, SchedulerParams.Plateau) + assert result.patience == 5 + + # ─── SchedulerParams defaults ─────────────────────────────────────────────── @@ -291,5 +346,5 @@ def test_parse_invalid_name_type_raises(self): SchedulerParams.parse_dict({"name": 3.14}) def test_parse_default_name_is_none(self): - result = SchedulerParams.parse_dict({}) - assert isinstance(result, SchedulerParams.NoneScheduler) + with pytest.raises(ValueError, match="Must provide either"): + SchedulerParams.parse_dict({}) From 1fb2ca96e7ef9102d35dd9880d5f6aca5c44b35e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 20:44:24 -0800 Subject: [PATCH 153/335] Added DatasetConstraintParams, works with dictionary inputs still --- src/quantem/core/ml/optimizer_mixin.py | 2 +- src/quantem/tomography/dataset_models.py | 60 ++++++++++++++++++------ src/quantem/tomography/object_models.py | 6 ++- src/quantem/tomography/tomography.py | 17 ++++--- 4 files changed, 63 insertions(+), 22 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index d30229da..148c3ac3 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -454,7 +454,7 @@ def parse_dict(cls, d: dict): type_ = d.pop("type", None) name = name or type_ if name is None: - raise ValueError("Must provide either 'name' or 'type' key") + name = "none" if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 942e7cac..df5c8849 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -14,20 +14,51 @@ from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.tomography.utils import tv_loss_1d - # --- Constraints --- -@dataclass(slots=False) -class DefaultTomographyDatasetConstraints(Constraints): - """ - Data class for constraints that can be applied to the parameters of a tomography dataset. - """ - # Soft Constraints - tv_zs: float = 0.0 - tv_shifts: float = 0.0 - soft_constraint_keys = ["tv_zs", "tv_shifts"] - hard_constraint_keys = [] +class DatasetConstraintParams: + @dataclass + class BaseTomographyDatasetConstraints(Constraints): + tv_zs: float = 0.0 + tv_shifts: float = 0.0 + + soft_constraint_keys = ["tv_zs", "tv_shifts"] + hard_constraint_keys = [] + + @dataclass + class ThroughFocalDatasetConstraints(BaseTomographyDatasetConstraints): + pass + + @classmethod + def parse_dict( + cls, d: dict + ) -> BaseTomographyDatasetConstraints | ThroughFocalDatasetConstraints: + d = dict(d) + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") + if isinstance(name, type): + name = name.__name__.lower() + elif isinstance(name, str): + name = name.lower() + else: + raise ValueError(f"Unknown dataset constraint type: {name}") + if name == "base_tomography_dataset": + return DatasetConstraintParams.BaseTomographyDatasetConstraints(**d) + elif name == "through_focal_dataset": + # return DatasetConstraintParams.ThroughFocalDatasetConstraints(**d) + raise NotImplementedError("Through focal dataset constraints are not implemented yet.") + else: + raise ValueError(f"Unknown dataset constraint type: {name.lower()}") + + +DatasetConstraintsType = ( + DatasetConstraintParams.BaseTomographyDatasetConstraints + | DatasetConstraintParams.ThroughFocalDatasetConstraints +) @dataclass @@ -245,11 +276,13 @@ def to(self, device: torch.device | str): class TomographyDatasetConstraints(BaseConstraints, TomographyDatasetBase): - DEFAULT_CONSTRAINTS = DefaultTomographyDatasetConstraints() + DEFAULT_CONSTRAINTS = DatasetConstraintParams.BaseTomographyDatasetConstraints() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) - self.constraints: DefaultTomographyDatasetConstraints = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: DatasetConstraintParams.BaseTomographyDatasetConstraints = ( + self.DEFAULT_CONSTRAINTS.copy() + ) def apply_soft_constraints(self) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=self.z1_params.device) @@ -265,7 +298,6 @@ def apply_soft_constraints(self) -> torch.Tensor: tv_loss_shifts += tv_loss_1d(self.shifts_params[:, 1]) tv_loss_shifts = self.constraints.tv_shifts * tv_loss_shifts soft_loss += tv_loss_shifts - return soft_loss def apply_hard_constraints(self): diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c3a47fb9..d057ebca 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -48,7 +48,11 @@ def parse_dict(cls, d: dict) -> ObjPixelatedConstraints | ObjINRConstraints: Parse dictionary to an object constraint params object. """ d = dict(d) - name = d.pop("name", None) or d.pop("type", "none") + name = d.pop("name", None) + type_ = d.pop("type", None) + name = name or type_ + if name is None: + raise ValueError("Must provide either 'name' or 'type' key") if isinstance(name, type): name = name.__name__.lower() elif isinstance(name, str): diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 2e20e717..8b1ebd9f 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,6 +1,6 @@ import os from pathlib import Path -from typing import Literal, Self, Sequence, cast +from typing import Literal, Self, Sequence import numpy as np import torch @@ -10,10 +10,12 @@ from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.loss_functions import get_loss_module from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d -from quantem.core.utils.tomography_utils import ( - torch_phase_cross_correlation, +from quantem.core.utils.tomography_utils import torch_phase_cross_correlation +from quantem.tomography.dataset_models import ( + DatasetConstraintParams, + DatasetConstraintsType, + DatasetModelType, ) -from quantem.tomography.dataset_models import DatasetModelType, DefaultTomographyDatasetConstraints from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( ObjConstraintParams, @@ -61,7 +63,7 @@ def reconstruct( optimizer_params: dict | None = None, scheduler_params: dict | None = None, obj_constraints: dict | ObjConstraintsType | None = None, - dset_constraints: dict = {}, + dset_constraints: dict | DatasetConstraintsType = None, num_samples_per_ray: int | list[tuple[int, int]] | None = None, profiling_mode: bool = False, val_fraction: float = 0.0, @@ -116,7 +118,10 @@ def reconstruct( self.obj_model.constraints = obj_constraints if dset_constraints is not None: - self.dset.constraints = cast(DefaultTomographyDatasetConstraints, dset_constraints) + if isinstance(dset_constraints, dict): + dset_constraints = DatasetConstraintParams.parse_dict(dset_constraints) + + self.dset.constraints = dset_constraints # Setting up DDP if not hasattr(self, "dataloader") or reset_dset is not None: if reset_dset is not None: From f5d635134159428f097b191ebb896004882e9b48 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Wed, 4 Mar 2026 22:23:34 -0800 Subject: [PATCH 154/335] OptimizerMixin fix tests --- tests/ml/test_optimizermixin.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/ml/test_optimizermixin.py b/tests/ml/test_optimizermixin.py index 3682d452..d829af41 100644 --- a/tests/ml/test_optimizermixin.py +++ b/tests/ml/test_optimizermixin.py @@ -154,9 +154,9 @@ def test_name_takes_precedence_over_type(self): result = SchedulerParams.parse_dict({"name": "plateau", "type": "linear"}) assert isinstance(result, SchedulerParams.Plateau) - def test_neither_name_nor_type_raises(self): - with pytest.raises(ValueError, match="Must provide either"): - SchedulerParams.parse_dict({"patience": 20}) + def test_neither_name_nor_type_defaults_to_none(self): + result = SchedulerParams.parse_dict({"patience": 20}) + assert isinstance(result, SchedulerParams.NoneScheduler) def test_type_key_not_leaked_into_constructor(self): result = SchedulerParams.parse_dict({"type": "cyclic", "step_size_up": 50}) @@ -346,5 +346,5 @@ def test_parse_invalid_name_type_raises(self): SchedulerParams.parse_dict({"name": 3.14}) def test_parse_default_name_is_none(self): - with pytest.raises(ValueError, match="Must provide either"): - SchedulerParams.parse_dict({}) + result = SchedulerParams.parse_dict({}) + assert isinstance(result, SchedulerParams.NoneScheduler) From c1a7e23da4571e9d8676206866b6e36193662b2e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 5 Mar 2026 09:15:16 -0800 Subject: [PATCH 155/335] Docstrings for dataset_models and object_models --- src/quantem/tomography/dataset_models.py | 62 ++++++++++++++++++++--- src/quantem/tomography/object_models.py | 63 ++++++++++++++++++++++-- 2 files changed, 112 insertions(+), 13 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index df5c8849..cbc10884 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -18,8 +18,25 @@ class DatasetConstraintParams: + """ + Namespace class for dataset constraint parameter dataclasses and parsing utilities. + + Contains constraint definitions for different tomography dataset types and a + factory method for instantiating the appropriate constraint class from a dict. + """ + @dataclass class BaseTomographyDatasetConstraints(Constraints): + """ + Soft constraints for a base tomography dataset. + + Attributes: + tv_zs: Total variation regularization weight for z-positions. + tv_shifts: Total variation regularization weight for lateral shifts. + soft_constraint_keys: Constraint fields penalized softly during optimization. + hard_constraint_keys: Constraint fields enforced strictly (none for this class). + """ + tv_zs: float = 0.0 tv_shifts: float = 0.0 @@ -28,12 +45,48 @@ class BaseTomographyDatasetConstraints(Constraints): @dataclass class ThroughFocalDatasetConstraints(BaseTomographyDatasetConstraints): + """ + Constraints for a through-focal tomography dataset. + + Inherits all constraints from BaseTomographyDatasetConstraints. + Currently not implemented — instantiation will raise NotImplementedError. + """ + pass @classmethod def parse_dict( cls, d: dict - ) -> BaseTomographyDatasetConstraints | ThroughFocalDatasetConstraints: + ) -> "DatasetConstraintParams.BaseTomographyDatasetConstraints | DatasetConstraintParams.ThroughFocalDatasetConstraints": + """ + Instantiate a dataset constraint dataclass from a configuration dictionary. + + The dictionary must contain a ``'name'`` or ``'type'`` key identifying + which constraint class to construct. All remaining keys are forwarded as + keyword arguments to the selected dataclass. + + Args: + d: Configuration dictionary. Must include ``'name'`` or ``'type'`` + with one of the following values (case-insensitive): + + - ``'base_tomography_dataset'`` → + :class:`BaseTomographyDatasetConstraints` + - ``'through_focal_dataset'`` → + :class:`ThroughFocalDatasetConstraints` *(not yet implemented)* + + The key may also be a class ``type`` object, in which case its + ``__name__`` is used after lower-casing. + + Returns: + An instance of the appropriate constraint dataclass. + + Raises: + ValueError: If neither ``'name'`` nor ``'type'`` is present, if the + value is not a string or type, or if the name does not match any + known dataset constraint type. + NotImplementedError: If ``'through_focal_dataset'`` is requested, as + it is not yet implemented. + """ d = dict(d) name = d.pop("name", None) type_ = d.pop("type", None) @@ -49,18 +102,11 @@ def parse_dict( if name == "base_tomography_dataset": return DatasetConstraintParams.BaseTomographyDatasetConstraints(**d) elif name == "through_focal_dataset": - # return DatasetConstraintParams.ThroughFocalDatasetConstraints(**d) raise NotImplementedError("Through focal dataset constraints are not implemented yet.") else: raise ValueError(f"Unknown dataset constraint type: {name.lower()}") -DatasetConstraintsType = ( - DatasetConstraintParams.BaseTomographyDatasetConstraints - | DatasetConstraintParams.ThroughFocalDatasetConstraints -) - - @dataclass class DatasetValue: """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d057ebca..f831338e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Generator, Literal +from typing import Any, Callable, Generator import numpy as np import torch @@ -17,12 +17,29 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -object_type = Literal["potential"] - class ObjConstraintParams: + """ + Namespace class for object reconstruction constraint dataclasses and parsing utilities. + + Contains constraint definitions for pixelated and implicit neural representation (INR) + object types, along with a factory method for instantiating the appropriate class + from a configuration dictionary. + """ + @dataclass class ObjPixelatedConstraints(Constraints): + """ + Constraints for a pixelated object representation. + + Attributes: + positivity: If True, enforces non-negative values in the reconstruction. + shrinkage: Shrinkage regularization strength; pushes values toward zero. + tv_vol: Total variation regularization weight for the 3-D volume. + soft_constraint_keys: Constraint fields penalized softly during optimization. + hard_constraint_keys: Constraint fields enforced strictly during optimization. + """ + positivity: bool = True shrinkage: float = 0.0 tv_vol: float = 0.0 @@ -33,6 +50,18 @@ class ObjPixelatedConstraints(Constraints): @dataclass class ObjINRConstraints(Constraints): + """ + Constraints for an implicit neural representation (INR) object. + + Attributes: + positivity: If True, enforces non-negative values in the reconstruction. + shrinkage: Shrinkage regularization strength; pushes values toward zero. + tv_vol: Total variation regularization weight for the 3-D volume. + sparsity: Sparsity regularization weight; encourages near-zero activations. + soft_constraint_keys: Constraint fields penalized softly during optimization. + hard_constraint_keys: Constraint fields enforced strictly during optimization. + """ + positivity: bool = True shrinkage: float = 0.0 tv_vol: float = 0.0 @@ -43,9 +72,33 @@ class ObjINRConstraints(Constraints): hard_constraint_keys = ["positivity", "shrinkage"] @classmethod - def parse_dict(cls, d: dict) -> ObjPixelatedConstraints | ObjINRConstraints: + def parse_dict( + cls, d: dict + ) -> "ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints": """ - Parse dictionary to an object constraint params object. + Instantiate an object constraint dataclass from a configuration dictionary. + + The dictionary must contain a ``'name'`` or ``'type'`` key identifying + which constraint class to construct. All remaining keys are forwarded as + keyword arguments to the selected dataclass. + + Args: + d: Configuration dictionary. Must include ``'name'`` or ``'type'`` + with one of the following values (case-insensitive): + + - ``'obj_pixelated'`` → :class:`ObjPixelatedConstraints` + - ``'obj_inr'`` → :class:`ObjINRConstraints` + + The key may also be a class ``type`` object, in which case its + ``__name__`` is used after lower-casing. + + Returns: + An instance of the appropriate object constraint dataclass. + + Raises: + ValueError: If neither ``'name'`` nor ``'type'`` is present, if the + value is not a string or type, or if the name does not match any + known object constraint type. """ d = dict(d) name = d.pop("name", None) From 2c910642a6f91689ab06c76e324a04029ebcb77f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 5 Mar 2026 09:16:55 -0800 Subject: [PATCH 156/335] Readded dataset models type back in --- src/quantem/tomography/dataset_models.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index cbc10884..00594cc1 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -107,6 +107,12 @@ def parse_dict( raise ValueError(f"Unknown dataset constraint type: {name.lower()}") +DatasetConstraintType = ( + DatasetConstraintParams.BaseTomographyDatasetConstraints + | DatasetConstraintParams.ThroughFocalDatasetConstraints +) + + @dataclass class DatasetValue: """ From 882d9e9e61ad154be5c54283a6893b1dfc7a61ca Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 5 Mar 2026 09:17:58 -0800 Subject: [PATCH 157/335] Bug --- src/quantem/tomography/dataset_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 00594cc1..f7c7fc5d 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -107,7 +107,7 @@ def parse_dict( raise ValueError(f"Unknown dataset constraint type: {name.lower()}") -DatasetConstraintType = ( +DatasetConstraintsType = ( DatasetConstraintParams.BaseTomographyDatasetConstraints | DatasetConstraintParams.ThroughFocalDatasetConstraints ) From 64ee01e4d023f6e6f849d2a5bc18f3affce142d4 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 5 Mar 2026 09:21:57 -0800 Subject: [PATCH 158/335] NumPy style docstrings --- src/quantem/tomography/dataset_models.py | 70 +++++++++++------ src/quantem/tomography/object_models.py | 97 ++++++++++++++++-------- 2 files changed, 114 insertions(+), 53 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index f7c7fc5d..301427ad 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -23,6 +23,20 @@ class DatasetConstraintParams: Contains constraint definitions for different tomography dataset types and a factory method for instantiating the appropriate constraint class from a dict. + + Supported constraint types + -------------------------- + BaseTomographyDatasetConstraints + Base soft constraints for z-position and lateral shift regularization. + ThroughFocalDatasetConstraints + Inherits base constraints; not yet implemented. + + Examples + -------- + >>> DatasetConstraintParams.parse_dict({"name": "base_tomography_dataset", "tv_zs": 0.1}) + BaseTomographyDatasetConstraints(tv_zs=0.1, tv_shifts=0.0) + >>> DatasetConstraintParams.parse_dict({"type": "base_tomography_dataset"}) + BaseTomographyDatasetConstraints(tv_zs=0.0, tv_shifts=0.0) """ @dataclass @@ -30,11 +44,16 @@ class BaseTomographyDatasetConstraints(Constraints): """ Soft constraints for a base tomography dataset. - Attributes: - tv_zs: Total variation regularization weight for z-positions. - tv_shifts: Total variation regularization weight for lateral shifts. - soft_constraint_keys: Constraint fields penalized softly during optimization. - hard_constraint_keys: Constraint fields enforced strictly (none for this class). + Attributes + ---------- + tv_zs : float + Total variation regularization weight for z-positions. + tv_shifts : float + Total variation regularization weight for lateral shifts. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly (none for this class). """ tv_zs: float = 0.0 @@ -48,8 +67,8 @@ class ThroughFocalDatasetConstraints(BaseTomographyDatasetConstraints): """ Constraints for a through-focal tomography dataset. - Inherits all constraints from BaseTomographyDatasetConstraints. - Currently not implemented — instantiation will raise NotImplementedError. + Inherits all constraints from ``BaseTomographyDatasetConstraints``. + Currently not implemented — instantiation will raise ``NotImplementedError``. """ pass @@ -65,27 +84,32 @@ def parse_dict( which constraint class to construct. All remaining keys are forwarded as keyword arguments to the selected dataclass. - Args: - d: Configuration dictionary. Must include ``'name'`` or ``'type'`` - with one of the following values (case-insensitive): + Parameters + ---------- + d : dict + Configuration dictionary. Must include ``'name'`` or ``'type'`` + with one of the following values (case-insensitive): - - ``'base_tomography_dataset'`` → - :class:`BaseTomographyDatasetConstraints` - - ``'through_focal_dataset'`` → - :class:`ThroughFocalDatasetConstraints` *(not yet implemented)* + - ``'base_tomography_dataset'`` → :class:`BaseTomographyDatasetConstraints` + - ``'through_focal_dataset'`` → :class:`ThroughFocalDatasetConstraints` + *(not yet implemented)* - The key may also be a class ``type`` object, in which case its - ``__name__`` is used after lower-casing. + The value may also be a class ``type`` object, in which case its + ``__name__`` is used after lower-casing. - Returns: + Returns + ------- + BaseTomographyDatasetConstraints or ThroughFocalDatasetConstraints An instance of the appropriate constraint dataclass. - Raises: - ValueError: If neither ``'name'`` nor ``'type'`` is present, if the - value is not a string or type, or if the name does not match any - known dataset constraint type. - NotImplementedError: If ``'through_focal_dataset'`` is requested, as - it is not yet implemented. + Raises + ------ + ValueError + If neither ``'name'`` nor ``'type'`` is present, if the value is not + a string or type, or if the name does not match any known dataset + constraint type. + NotImplementedError + If ``'through_focal_dataset'`` is requested, as it is not yet implemented. """ d = dict(d) name = d.pop("name", None) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index f831338e..6ef9eb59 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -22,22 +22,43 @@ class ObjConstraintParams: """ Namespace class for object reconstruction constraint dataclasses and parsing utilities. - Contains constraint definitions for pixelated and implicit neural representation (INR) - object types, along with a factory method for instantiating the appropriate class - from a configuration dictionary. + Contains constraint definitions for pixelated and implicit neural representation + (INR) object types, along with a factory method for instantiating the appropriate + class from a configuration dictionary. + + Supported constraint types + -------------------------- + ObjPixelatedConstraints + Constraints for a voxel-grid (pixelated) object representation. + ObjINRConstraints + Constraints for a network-parameterized (INR) object representation; + adds a sparsity term not present in the pixelated variant. + + Examples + -------- + >>> ObjConstraintParams.parse_dict({"name": "obj_pixelated", "tv_vol": 0.01}) + ObjPixelatedConstraints(positivity=True, shrinkage=0.0, tv_vol=0.01) + >>> ObjConstraintParams.parse_dict({"type": "obj_inr", "sparsity": 0.05}) + ObjINRConstraints(positivity=True, shrinkage=0.0, tv_vol=0.0, sparsity=0.05) """ @dataclass class ObjPixelatedConstraints(Constraints): """ - Constraints for a pixelated object representation. - - Attributes: - positivity: If True, enforces non-negative values in the reconstruction. - shrinkage: Shrinkage regularization strength; pushes values toward zero. - tv_vol: Total variation regularization weight for the 3-D volume. - soft_constraint_keys: Constraint fields penalized softly during optimization. - hard_constraint_keys: Constraint fields enforced strictly during optimization. + Constraints for a pixelated (voxel-grid) object representation. + + Attributes + ---------- + positivity : bool + If ``True``, enforces non-negative values in the reconstruction. + shrinkage : float + Shrinkage regularization strength; pushes values toward zero. + tv_vol : float + Total variation regularization weight for the 3-D volume. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly during optimization. """ positivity: bool = True @@ -53,13 +74,23 @@ class ObjINRConstraints(Constraints): """ Constraints for an implicit neural representation (INR) object. - Attributes: - positivity: If True, enforces non-negative values in the reconstruction. - shrinkage: Shrinkage regularization strength; pushes values toward zero. - tv_vol: Total variation regularization weight for the 3-D volume. - sparsity: Sparsity regularization weight; encourages near-zero activations. - soft_constraint_keys: Constraint fields penalized softly during optimization. - hard_constraint_keys: Constraint fields enforced strictly during optimization. + Extends pixelated constraints with an additional sparsity term suited + to the continuous, network-parameterized object representation. + + Attributes + ---------- + positivity : bool + If ``True``, enforces non-negative values in the reconstruction. + shrinkage : float + Shrinkage regularization strength; pushes values toward zero. + tv_vol : float + Total variation regularization weight for the 3-D volume. + sparsity : float + Sparsity regularization weight; encourages near-zero activations. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly during optimization. """ positivity: bool = True @@ -82,23 +113,29 @@ def parse_dict( which constraint class to construct. All remaining keys are forwarded as keyword arguments to the selected dataclass. - Args: - d: Configuration dictionary. Must include ``'name'`` or ``'type'`` - with one of the following values (case-insensitive): + Parameters + ---------- + d : dict + Configuration dictionary. Must include ``'name'`` or ``'type'`` + with one of the following values (case-insensitive): - - ``'obj_pixelated'`` → :class:`ObjPixelatedConstraints` - - ``'obj_inr'`` → :class:`ObjINRConstraints` + - ``'obj_pixelated'`` → :class:`ObjPixelatedConstraints` + - ``'obj_inr'`` → :class:`ObjINRConstraints` - The key may also be a class ``type`` object, in which case its - ``__name__`` is used after lower-casing. + The value may also be a class ``type`` object, in which case its + ``__name__`` is used after lower-casing. - Returns: + Returns + ------- + ObjPixelatedConstraints or ObjINRConstraints An instance of the appropriate object constraint dataclass. - Raises: - ValueError: If neither ``'name'`` nor ``'type'`` is present, if the - value is not a string or type, or if the name does not match any - known object constraint type. + Raises + ------ + ValueError + If neither ``'name'`` nor ``'type'`` is present, if the value is not + a string or type, or if the name does not match any known object + constraint type. """ d = dict(d) name = d.pop("name", None) From 47f31eaec69ed82d6193549645107e9bfcfb97d7 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 5 Mar 2026 09:24:02 -0800 Subject: [PATCH 159/335] Some typos in the docstrings --- src/quantem/tomography/dataset_models.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 301427ad..c1e362da 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -47,9 +47,9 @@ class BaseTomographyDatasetConstraints(Constraints): Attributes ---------- tv_zs : float - Total variation regularization weight for z-positions. + Total variation regularization weight for Z1 and Z3 Euler angles. tv_shifts : float - Total variation regularization weight for lateral shifts. + Total variation regularization weight for X and Y shifts. soft_constraint_keys : list[str] Constraint fields penalized softly during optimization. hard_constraint_keys : list[str] @@ -58,6 +58,7 @@ class BaseTomographyDatasetConstraints(Constraints): tv_zs: float = 0.0 tv_shifts: float = 0.0 + _name: str = "base_tomography_dataset" soft_constraint_keys = ["tv_zs", "tv_shifts"] hard_constraint_keys = [] From 586f3bceaf8c3aa3cb24a6e37aa792c619f51f14 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 5 Mar 2026 11:38:49 -0800 Subject: [PATCH 160/335] Added some __str__ for the OptimizerParams; not sure if needed but better for printing? --- src/quantem/core/ml/optimizer_mixin.py | 37 ++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 148c3ac3..c07fefc9 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,3 +1,4 @@ +import textwrap from abc import abstractmethod from dataclasses import dataclass from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence @@ -68,6 +69,16 @@ def params(self) -> dict: "weight_decay": self.weight_decay, } + def __str__(self) -> str: + return textwrap.dedent(f""" + OptimizerParams.Adam( + lr = {self.lr}, + betas = {self.betas}, + eps = {self.eps}, + weight_decay = {self.weight_decay}, + ) + """).strip() + @dataclass class AdamW: """ @@ -103,6 +114,16 @@ def params(self) -> dict: "weight_decay": self.weight_decay, } + def __str__(self) -> str: + return textwrap.dedent(f""" + OptimizerParams.AdamW( + lr = {self.lr}, + betas = {self.betas}, + eps = {self.eps}, + weight_decay = {self.weight_decay}, + ) + """).strip() + @dataclass class SGD: """ @@ -138,6 +159,17 @@ def params(self) -> dict: "nesterov": self.nesterov, } + def __str__(self) -> str: + return textwrap.dedent(f""" + OptimizerParams.SGD( + lr = {self.lr}, + momentum = {self.momentum}, + dampening = {self.dampening}, + weight_decay = {self.weight_decay}, + nesterov = {self.nesterov}, + ) + """).strip() + @dataclass class NoneOptimizer: """ @@ -152,6 +184,11 @@ class NoneOptimizer: def params(self) -> dict: return {} + def __str__(self) -> str: + return textwrap.dedent(""" + OptimizerParams.NoneOptimizer() + """).strip() + @classmethod def parse_dict(cls, d: dict): """ From 9693b11ebf3db45fd16d85b1c1d086eab9ac19e5 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Thu, 5 Mar 2026 15:37:48 -0800 Subject: [PATCH 161/335] updating Vector save to call compact() first --- src/quantem/core/datastructures/vector.py | 43 ++++++++++++++++++++++- 1 file changed, 42 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 7a8ef417..13906c01 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -1,7 +1,8 @@ from __future__ import annotations import copy -from typing import Any, Sequence +from pathlib import Path +from typing import Any, Literal, Sequence import numpy as np from numpy.typing import NDArray @@ -900,6 +901,46 @@ def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: cell[:, field_indices] = op(chunk, lhs) if reverse else op(lhs, chunk) cursor += rows + def save( + self, + path: str | Path, + mode: Literal["w", "o"] = "w", + store: Literal["auto", "zip", "dir"] = "auto", + skip: str | type | Sequence[str | type] = (), + compression_level: int | None = 4, + ) -> None: + """ + Save the Vector object to disk using Zarr serialization. self.compact() is called before + saving to reduce file size if possible. + + Parameters + ---------- + path : str or Path + Target file path. Use '.zip' extension for zip format, otherwise a directory. + mode : {'w', 'o'} + 'w' = write only if file doesn't exist, 'o' = overwrite if it does. + store : {'auto', 'zip', 'dir'} + Storage format. 'auto' infers from file extension. + skip : str, type, or list of (str or type) + Attribute names/types to skip (by name or type) during serialization. + compression_level : int or None + If set (0–9), applies Zstandard compression with Blosc backend at that level. + Level 0 disables compression. Raises ValueError if > 9. + + Notes + ----- + Skipped attribute names and types are also stored in the file metadata for correct + round-trip skipping during load(). + """ + self.compact() + super().save( + path, + mode=mode, + store=store, + skip=skip, + compression_level=compression_level, + ) + def _resolve_fields( fields: Sequence[str] | None, From f72bea2d6fe01b534ffabccef1d3e763a16bec81 Mon Sep 17 00:00:00 2001 From: Art-MC Date: Thu, 5 Mar 2026 15:47:48 -0800 Subject: [PATCH 162/335] reorganizing methods and attributes --- src/quantem/core/datastructures/vector.py | 394 ++++++++++++---------- 1 file changed, 221 insertions(+), 173 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index 13906c01..a956bd7e 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -93,6 +93,10 @@ class Vector(AutoSerialize): __array_priority__ = 1000 _token = object() + # ------------------------------------------------------------------ # + # Construction + # ------------------------------------------------------------------ # + def __init__( self, shape: tuple[int, ...], @@ -200,6 +204,28 @@ def from_data( vector._replace_cells(np.arange(len(cell_arrays), dtype=np.int64), cell_arrays) return vector + # ------------------------------------------------------------------ # + # Identity properties + # ------------------------------------------------------------------ # + + @property + def name(self) -> str: + """Human-readable Vector name.""" + return self._state["name"] + + @name.setter + def name(self, value: str) -> None: + self._state["name"] = str(value) + + @property + def metadata(self) -> dict[str, Any]: + """Mutable metadata dictionary shared by all views.""" + return self._state["metadata"] + + # ------------------------------------------------------------------ # + # Shape & structure properties + # ------------------------------------------------------------------ # + @property def shape(self) -> tuple[int, ...]: """Return the fixed-grid shape of this selection.""" @@ -224,23 +250,23 @@ def num_fields(self) -> int: return len(self.fields) @property - def dtype(self) -> np.dtype[Any]: - """Return the NumPy dtype of the backing row buffer.""" - return self._state["data"].dtype + def num_cells(self) -> int: + """Return the number of fixed-grid cells in the current selection.""" + return int(self._selected_cell_indices().size) @property - def name(self) -> str: - """Human-readable Vector name.""" - return self._state["name"] - - @name.setter - def name(self, value: str) -> None: - self._state["name"] = str(value) + def total_rows(self) -> int: + """Return the total ragged-row count in the current selection.""" + return int(self._state["cell_lengths"][self._selected_cell_indices()].sum()) @property - def metadata(self) -> dict[str, Any]: - """Mutable metadata dictionary shared by all views.""" - return self._state["metadata"] + def dtype(self) -> np.dtype[Any]: + """Return the NumPy dtype of the backing row buffer.""" + return self._state["data"].dtype + + # ------------------------------------------------------------------ # + # Data access + # ------------------------------------------------------------------ # @property def array(self) -> NDArray[Any]: @@ -264,40 +290,6 @@ def array(self) -> NDArray[Any]: return cell[:, int(cols[0]) : int(cols[-1]) + 1] return cell[:, cols].copy() - def __len__(self) -> int: - """Return ``shape[0]`` for non-scalar selections.""" - if self.shape == (): - raise TypeError("len() of unsized 0D Vector") - return self.shape[0] - - def __repr__(self) -> str: - return "\n".join( - [ - f"quantem.Vector, shape={self.shape}, name={self.name}", - f" fields = {self.fields}", - f" units: {self.units}", - ] - ) - - __str__ = __repr__ - - def copy(self) -> "Vector": - """Return a deep copy of the current selection.""" - copied = self.__class__( - shape=self.shape, - fields=self.fields, - units=self.units, - name=self.name, - metadata=copy.deepcopy(self.metadata), - _token=self.__class__._token, - ) - target_cells = copied._selected_cell_indices() - source_arrays = [ - self._selected_cell_matrix(index).copy() for index in self._selected_cell_indices() - ] - copied._replace_cells(target_cells, source_arrays) - return copied - def flatten(self) -> NDArray[Any]: """Concatenate selected cells in row-major order. @@ -315,20 +307,14 @@ def flatten(self) -> NDArray[Any]: dtype = self._state["data"].dtype if self._state["data"].ndim == 2 else float return np.empty((0, self.num_fields), dtype=dtype) - @property - def total_rows(self) -> int: - """Return the total ragged-row count in the current selection.""" - return int(self._state["cell_lengths"][self._selected_cell_indices()].sum()) - - @property - def num_cells(self) -> int: - """Return the number of fixed-grid cells in the current selection.""" - return int(self._selected_cell_indices().size) - def row_counts(self) -> list[int]: """Return per-cell row counts in the current selection order.""" return [self._cell_row_count(int(index)) for index in self._selected_cell_indices()] + # ------------------------------------------------------------------ # + # Field management + # ------------------------------------------------------------------ # + def select_fields(self, *field_names: str | Sequence[str]) -> "Vector": """Return a view containing only the requested fields. @@ -363,82 +349,6 @@ def select_fields(self, *field_names: str | Sequence[str]) -> "Vector": selected_fields, ) - def set_flattened(self, values: Any) -> None: - """Write values back in flattened row-major order. - - This updates existing rows without changing per-cell row counts. It is - the rowwise companion to ``flatten()`` and is especially useful for - NumPy-based transforms that operate on all selected rows at once. - """ - field_indices = self._field_indices() - targets = self._selected_cell_indices() - row_counts = self.row_counts() - total_rows = sum(row_counts) - - if isinstance(values, Vector): - if values.num_fields != self.num_fields: - raise ValueError(f"Expected {self.num_fields} fields, got {values.num_fields}") - flat_values = values.flatten() - if flat_values.shape[0] != total_rows: - raise ValueError(f"Expected {total_rows} rows, got {flat_values.shape[0]}") - else: - flat_values = _broadcast_field_values(values, total_rows, self.num_fields) - - cursor = 0 - for target, rows in zip(targets, row_counts): - cell = self._cell_matrix(int(target)) - if rows > 0: - cell[:, field_indices] = flat_values[cursor : cursor + rows] - cursor += rows - - def compact(self) -> None: - """Repack the backing row buffer to remove dead rows. - - Whole-cell replacement appends new rows and leaves previous rows unused - until compaction. Calling ``compact()`` makes memory usage and save size - predictable at the cost of reallocating the backing buffer. - """ - data = self._state["data"] - used_rows = int(self._state["cell_lengths"].sum()) - if used_rows == 0: - self._state["data"] = np.empty((0, self._full_num_fields), dtype=data.dtype) - self._state["cell_starts"].fill(0) - return - - compacted = np.empty((used_rows, self._full_num_fields), dtype=data.dtype) - starts = np.zeros_like(self._state["cell_starts"]) - cursor = 0 - for linear_index in range(_cell_count(self._state["shape"])): - length = self._cell_row_count(linear_index) - starts[linear_index] = cursor - if length > 0: - cell = self._cell_matrix(linear_index) - compacted[cursor : cursor + length] = cell - cursor += length - self._state["data"] = compacted - self._state["cell_starts"] = starts - - def append_rows(self, idx: Any, rows: Any) -> None: - """Append one or more rows to a single selected cell. - - ``idx`` is interpreted with the same fixed-grid indexing rules as - ``__getitem__`` and must resolve to exactly one cell. Appending rows is a - full-cell operation, so all fields must be selected. - """ - target = self[idx] - if target.shape != (): - raise ValueError("append_rows requires an index that selects exactly one cell.") - target._require_full_field_view("append_rows") - - new_rows = _coerce_cell_array(rows, target.num_fields) - if new_rows.shape[0] == 0: - return - - cell_index = int(target._selected_cell_indices()[0]) - existing = target._cell_matrix(cell_index) - combined = np.vstack((existing, new_rows)) if existing.shape[0] > 0 else new_rows.copy() - target._replace_cells(np.array([cell_index], dtype=np.int64), [combined]) - def add_fields( self, names: str | Sequence[str], @@ -523,6 +433,124 @@ def remove_fields(self, names: str | Sequence[str]) -> None: if len(self._selected_fields) == len(self._state["fields"]): self._selected_fields = None + # ------------------------------------------------------------------ # + # Cell / row mutation + # ------------------------------------------------------------------ # + + def append_rows(self, idx: Any, rows: Any) -> None: + """Append one or more rows to a single selected cell. + + ``idx`` is interpreted with the same fixed-grid indexing rules as + ``__getitem__`` and must resolve to exactly one cell. Appending rows is a + full-cell operation, so all fields must be selected. + """ + target = self[idx] + if target.shape != (): + raise ValueError("append_rows requires an index that selects exactly one cell.") + target._require_full_field_view("append_rows") + + new_rows = _coerce_cell_array(rows, target.num_fields) + if new_rows.shape[0] == 0: + return + + cell_index = int(target._selected_cell_indices()[0]) + existing = target._cell_matrix(cell_index) + combined = np.vstack((existing, new_rows)) if existing.shape[0] > 0 else new_rows.copy() + target._replace_cells(np.array([cell_index], dtype=np.int64), [combined]) + + def set_flattened(self, values: Any) -> None: + """Write values back in flattened row-major order. + + This updates existing rows without changing per-cell row counts. It is + the rowwise companion to ``flatten()`` and is especially useful for + NumPy-based transforms that operate on all selected rows at once. + """ + field_indices = self._field_indices() + targets = self._selected_cell_indices() + row_counts = self.row_counts() + total_rows = sum(row_counts) + + if isinstance(values, Vector): + if values.num_fields != self.num_fields: + raise ValueError(f"Expected {self.num_fields} fields, got {values.num_fields}") + flat_values = values.flatten() + if flat_values.shape[0] != total_rows: + raise ValueError(f"Expected {total_rows} rows, got {flat_values.shape[0]}") + else: + flat_values = _broadcast_field_values(values, total_rows, self.num_fields) + + cursor = 0 + for target, rows in zip(targets, row_counts): + cell = self._cell_matrix(int(target)) + if rows > 0: + cell[:, field_indices] = flat_values[cursor : cursor + rows] + cursor += rows + + def compact(self) -> None: + """Repack the backing row buffer to remove dead rows. + + Whole-cell replacement appends new rows and leaves previous rows unused + until compaction. Calling ``compact()`` makes memory usage and save size + predictable at the cost of reallocating the backing buffer. + """ + data = self._state["data"] + used_rows = int(self._state["cell_lengths"].sum()) + if used_rows == 0: + self._state["data"] = np.empty((0, self._full_num_fields), dtype=data.dtype) + self._state["cell_starts"].fill(0) + return + + compacted = np.empty((used_rows, self._full_num_fields), dtype=data.dtype) + starts = np.zeros_like(self._state["cell_starts"]) + cursor = 0 + for linear_index in range(_cell_count(self._state["shape"])): + length = self._cell_row_count(linear_index) + starts[linear_index] = cursor + if length > 0: + cell = self._cell_matrix(linear_index) + compacted[cursor : cursor + length] = cell + cursor += length + self._state["data"] = compacted + self._state["cell_starts"] = starts + + # ------------------------------------------------------------------ # + # Python data model + # ------------------------------------------------------------------ # + + def __len__(self) -> int: + """Return ``shape[0]`` for non-scalar selections.""" + if self.shape == (): + raise TypeError("len() of unsized 0D Vector") + return self.shape[0] + + def __repr__(self) -> str: + return "\n".join( + [ + f"quantem.Vector, shape={self.shape}, name={self.name}", + f" fields = {self.fields}", + f" units: {self.units}", + ] + ) + + __str__ = __repr__ + + def copy(self) -> "Vector": + """Return a deep copy of the current selection.""" + copied = self.__class__( + shape=self.shape, + fields=self.fields, + units=self.units, + name=self.name, + metadata=copy.deepcopy(self.metadata), + _token=self.__class__._token, + ) + target_cells = copied._selected_cell_indices() + source_arrays = [ + self._selected_cell_matrix(index).copy() for index in self._selected_cell_indices() + ] + copied._replace_cells(target_cells, source_arrays) + return copied + def __getitem__(self, idx: Any) -> "Vector": """Return a fixed-grid selection as another Vector view.""" if _looks_like_field_selector(idx): @@ -550,6 +578,10 @@ def __setitem__(self, idx: Any, value: Any) -> None: target = self[idx] target._assign(value) + # ------------------------------------------------------------------ # + # Arithmetic operators + # ------------------------------------------------------------------ # + def __array_ufunc__(self, ufunc: Any, method: str, *inputs: Any, **kwargs: Any) -> Any: """Apply supported NumPy ufuncs elementwise. @@ -668,6 +700,54 @@ def __abs__(self) -> "Vector": result._inplace_unary(np.abs) return result + # ------------------------------------------------------------------ # + # I/O + # ------------------------------------------------------------------ # + + def save( + self, + path: str | Path, + mode: Literal["w", "o"] = "w", + store: Literal["auto", "zip", "dir"] = "auto", + skip: str | type | Sequence[str | type] = (), + compression_level: int | None = 4, + ) -> None: + """ + Save the Vector object to disk using Zarr serialization. self.compact() is called before + saving to reduce file size if possible. + + Parameters + ---------- + path : str or Path + Target file path. Use '.zip' extension for zip format, otherwise a directory. + mode : {'w', 'o'} + 'w' = write only if file doesn't exist, 'o' = overwrite if it does. + store : {'auto', 'zip', 'dir'} + Storage format. 'auto' infers from file extension. + skip : str, type, or list of (str or type) + Attribute names/types to skip (by name or type) during serialization. + compression_level : int or None + If set (0–9), applies Zstandard compression with Blosc backend at that level. + Level 0 disables compression. Raises ValueError if > 9. + + Notes + ----- + Skipped attribute names and types are also stored in the file metadata for correct + round-trip skipping during load(). + """ + self.compact() + super().save( + path, + mode=mode, + store=store, + skip=skip, + compression_level=compression_level, + ) + + # ------------------------------------------------------------------ # + # Private helpers — backing-store access + # ------------------------------------------------------------------ # + @property def _full_num_fields(self) -> int: return len(self._state["fields"]) @@ -772,6 +852,10 @@ def _maybe_compact_storage(self) -> None: return self.compact() + # ------------------------------------------------------------------ # + # Private helpers — assignment + # ------------------------------------------------------------------ # + def _assign(self, value: Any) -> None: """Dispatch assignment based on whether all fields or a subset are selected.""" if self._selected_fields is None: @@ -844,6 +928,10 @@ def _assign_selected_fields(self, value: Any) -> None: cell[:, field_indices] = chunk cursor += rows + # ------------------------------------------------------------------ # + # Private helpers — arithmetic + # ------------------------------------------------------------------ # + def _binary_op(self, other: Any, op: Any, reverse: bool = False) -> "Vector": """Return a new Vector produced by elementwise arithmetic.""" result = self.copy() @@ -901,46 +989,6 @@ def _inplace_op(self, other: Any, op: Any, reverse: bool = False) -> None: cell[:, field_indices] = op(chunk, lhs) if reverse else op(lhs, chunk) cursor += rows - def save( - self, - path: str | Path, - mode: Literal["w", "o"] = "w", - store: Literal["auto", "zip", "dir"] = "auto", - skip: str | type | Sequence[str | type] = (), - compression_level: int | None = 4, - ) -> None: - """ - Save the Vector object to disk using Zarr serialization. self.compact() is called before - saving to reduce file size if possible. - - Parameters - ---------- - path : str or Path - Target file path. Use '.zip' extension for zip format, otherwise a directory. - mode : {'w', 'o'} - 'w' = write only if file doesn't exist, 'o' = overwrite if it does. - store : {'auto', 'zip', 'dir'} - Storage format. 'auto' infers from file extension. - skip : str, type, or list of (str or type) - Attribute names/types to skip (by name or type) during serialization. - compression_level : int or None - If set (0–9), applies Zstandard compression with Blosc backend at that level. - Level 0 disables compression. Raises ValueError if > 9. - - Notes - ----- - Skipped attribute names and types are also stored in the file metadata for correct - round-trip skipping during load(). - """ - self.compact() - super().save( - path, - mode=mode, - store=store, - skip=skip, - compression_level=compression_level, - ) - def _resolve_fields( fields: Sequence[str] | None, From 9684e3eccc400625495d5f39c6744cad97d73cbb Mon Sep 17 00:00:00 2001 From: Art-MC Date: Thu, 5 Mar 2026 16:19:52 -0800 Subject: [PATCH 163/335] few simplifications to Vector --- src/quantem/core/datastructures/vector.py | 19 ++++--------------- 1 file changed, 4 insertions(+), 15 deletions(-) diff --git a/src/quantem/core/datastructures/vector.py b/src/quantem/core/datastructures/vector.py index a956bd7e..aa2627d2 100644 --- a/src/quantem/core/datastructures/vector.py +++ b/src/quantem/core/datastructures/vector.py @@ -241,7 +241,7 @@ def fields(self) -> list[str]: @property def units(self) -> list[str]: """Return units for the selected fields.""" - lookup = {field: unit for field, unit in zip(self._state["fields"], self._state["units"])} + lookup = dict(zip(self._state["fields"], self._state["units"])) return [lookup[field] for field in self.fields] @property @@ -338,10 +338,7 @@ def select_fields(self, *field_names: str | Sequence[str]) -> "Vector": if missing: raise KeyError(f"Unknown field(s): {missing}") - if selected == tuple(self._state["fields"]): - selected_fields = None - else: - selected_fields = selected + selected_fields = None if selected == tuple(self._state["fields"]) else selected return self._from_view( self._state, self.shape, @@ -782,8 +779,6 @@ def _cell_matrix(self, linear_index: int) -> NDArray[Any]: """Return the full backing matrix for one cell.""" start = int(self._state["cell_starts"][linear_index]) length = int(self._state["cell_lengths"][linear_index]) - if length == 0: - return self._state["data"][0:0] return self._state["data"][start : start + length] def _selected_cell_matrix(self, linear_index: int) -> NDArray[Any]: @@ -817,11 +812,7 @@ def _replace_cells(self, targets: NDArray[np.int64], arrays: Sequence[NDArray[An payloads = [array for array in normalized if array.shape[0] > 0] if payloads: appended = np.vstack(payloads) - data = self._state["data"] - if data.shape[0] == 0: - self._state["data"] = appended.copy() - else: - self._state["data"] = np.concatenate((data, appended), axis=0) + self._state["data"] = np.concatenate((self._state["data"], appended), axis=0) cursor = self._state["data"].shape[0] - sum(array.shape[0] for array in normalized) for target, array in zip(targets, normalized): @@ -846,9 +837,7 @@ def _maybe_compact_storage(self) -> None: """Compact automatically once dead rows become materially larger than live rows.""" data = self._state["data"] used_rows = int(self._state["cell_lengths"].sum()) - if data.shape[0] <= used_rows + 1024: - return - if data.shape[0] <= 2 * used_rows: + if data.shape[0] <= used_rows + 1024 or data.shape[0] <= 2 * used_rows: return self.compact() From 758cf268001e19eed55b95df556aa097960b33ec Mon Sep 17 00:00:00 2001 From: darshan-mali Date: Fri, 6 Mar 2026 11:06:41 -0800 Subject: [PATCH 164/335] Created Lattice class. Added constructors, properties and define_lattice() --- src/quantem/imaging/__init__.py | 1 + src/quantem/imaging/lattice.py | 504 ++++++++++++++++++++++++++++++++ 2 files changed, 505 insertions(+) create mode 100644 src/quantem/imaging/lattice.py diff --git a/src/quantem/imaging/__init__.py b/src/quantem/imaging/__init__.py index 84b5d876..e2183514 100644 --- a/src/quantem/imaging/__init__.py +++ b/src/quantem/imaging/__init__.py @@ -1 +1,2 @@ from quantem.imaging.drift import DriftCorrection as DriftCorrection +from quantem.imaging.lattice import Lattice as Lattice diff --git a/src/quantem/imaging/lattice.py b/src/quantem/imaging/lattice.py new file mode 100644 index 00000000..5f9ec9fb --- /dev/null +++ b/src/quantem/imaging/lattice.py @@ -0,0 +1,504 @@ +import numpy as np +from numpy.typing import NDArray + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.io.serialize import AutoSerialize +from quantem.core.visualization import show_2d + + +class Lattice(AutoSerialize): + """ + Atomic lattice fitting in 2D. + """ + + _token = object() + + def __init__( + self, + image: Dataset2d, + _token: object | None = None, + ): + if _token is not self._token: + raise RuntimeError("Use Lattice.from_data() to instantiate this class.") + self._image: Dataset2d = image + + # --- Constructors --- + @classmethod + def from_data( + cls, + image: Dataset2d | NDArray, + normalize_min: bool = True, + normalize_max: bool = True, + ) -> "Lattice": + """ + Create a Lattice instance from a 2D image-like input. + + Parameters: + - image: A 2D numpy array or a Dataset2d instance representing the image. + - normalize_min: If True, shift the image so its minimum becomes 0. + - normalize_max: If True, scale the image by its maximum after min-shift + so values are in [0, 1]. If the maximum is 0 or non-finite (NaN/Inf), + scaling is skipped to avoid invalid operations. + + Notes: + - Non-2D inputs and empty arrays raise a ValueError. + - Inputs with boolean dtype are safely converted to float before normalization. + - NaN values are ignored when computing min/max (using nanmin/nanmax). If the + data is all-NaN, normalization is skipped. + """ + if isinstance(image, Dataset2d): + ds2d = image + # Ensure numeric operations are valid (e.g., for bool dtype) + ds2d.array = np.asarray(ds2d.array, dtype=float) + # Validate shape + if ds2d.array.ndim != 2: + raise ValueError("Input image must be a 2D array.") + if ds2d.array.size == 0: + raise ValueError("Input image array must not be empty.") + else: + # Validate dimensionality and emptiness before any processing + arr = np.asarray(image) + if arr.ndim != 2: + raise ValueError("Input image must be a 2D array.") + if arr.size == 0: + raise ValueError("Input image array must not be empty.") + # Convert to float for safe arithmetic (handles bool arrays) + arr = arr.astype(float, copy=False) + if hasattr(Dataset2d, "from_array") and callable(getattr(Dataset2d, "from_array")): + ds2d = Dataset2d.from_array(arr) # type: ignore[attr-defined] + else: + ds2d = Dataset2d(arr) # type: ignore[call-arg] + + # Normalization (robust to constant, NaN, and bool inputs) + if normalize_min: + # Use nanmin to ignore NaNs; if all-NaN, skip + try: + min_val = np.nanmin(ds2d.array) + if np.isfinite(min_val): + ds2d.array = ds2d.array - min_val + except ValueError: + # Raised when all values are NaN; skip + pass + + if normalize_max: + # Use nanmax to ignore NaNs; skip division if max <= 0 or not finite + try: + max_val = np.nanmax(ds2d.array) + if np.isfinite(max_val) and max_val > 0.0: + ds2d.array = ds2d.array / max_val + except ValueError: + # Raised when all values are NaN; skip + pass + + return cls(image=ds2d, _token=cls._token) + + # --- Properties --- + @property + def image(self) -> Dataset2d: + return self._image + + @image.setter + def image(self, value: Dataset2d | NDArray): + if isinstance(value, Dataset2d): + # Ensure numeric dtype to avoid boolean arithmetic issues downstream + value.array = np.asarray(value.array, dtype=float) + # Validate shape + if value.array.ndim != 2: + raise ValueError("Input image must be a 2D array.") + if value.array.size == 0: + raise ValueError("Input image array must not be empty.") + self._image = value + else: + arr = np.asarray(value) + if arr.ndim != 2: + raise ValueError("Input image must be a 2D array.") + if arr.size == 0: + raise ValueError("Input image array must not be empty.") + arr = arr.astype(float, copy=False) + if hasattr(Dataset2d, "from_array") and callable(getattr(Dataset2d, "from_array")): + self._image = Dataset2d.from_array(arr) # type: ignore[attr-defined] + else: + self._image = Dataset2d(arr) # type: ignore[call-arg] + + # --- Functions --- + def define_lattice( + self, + origin, + u, + v, + refine_lattice: bool = True, + block_size: int | None = None, + plot_lattice: bool = True, + bound_num_vectors: int | None = None, + refine_maxiter: int = 200, + **kwargs, + ) -> "Lattice": + """ + Define the lattice for the image using the origin and the u and v vectors starting from the origin. + The lattice is defined as r = r0 + nu + mv. + + Parameters + ---------- + origin : NDArray[2] | Sequence[float] + Start point (r0) to define the lattice. + Enter as (row, col) as a numpy array, list, or tuple. + Ideally a lattice point. + u : NDArray[2] | Sequence[float] + Basis vector u to define the lattice. + Enter as (row, col) as a numpy array, list, or tuple. + v : NDArray[2] | Sequence[float] + Basis vector v to define the lattice. + Enter as (row, col) as a numpy array, list, or tuple. + refine_lattice : bool, default=True + If True, refines the values of r0, u, and v by maximizing the bilinear intensity sum. + block_size : int | None , default=None + Fit the lattice points in steps of block_size * lattice_vectors(u, v). + For example, if block_size = 5, then the lattice points will be fit in steps of + (-5, 5)u * (-5, 5)v -> (-10, 10)u * (-10, 10)v -> ... + block_size = None means the entire image will be fit at once. + plot_lattice : bool, default=True + If True, the lattice vectors and lines will be plotted overlaid on the image. + bound_num_vectors : int | None, default=None + The maximum number of lattice vectors to plot in each direction. + For example, if bound_num_vectors = 5, lattice lines between (-5, 5)u * (-5, 5)v will be plotted. + If None, the plotting bounds are set to the image edges. + refine_maxiter : int, default=200 + Maximum number of iterations for the lattice refinement optimizer (Powell method). + **kwargs + Additional keyword arguments forwarded to the plotting function (show_2d), e.g., cmap, title, etc. + + Returns + ------- + self : Lattice + Returns the same object, modified in-place. + The final values of r0, u, v are stored in self._lat. + """ + # Lattice + self._lat = np.vstack( + ( + np.array(origin), + np.array(u), + np.array(v), + ) + ) + if not self._lat.shape == (3, 2): + raise ValueError("origin, u, v must be in (row, col) format only.") + + # Refine lattice coordinates + # Note that we currently assume corners are local maxima + if refine_lattice: + from scipy.optimize import minimize + + assert block_size is None or block_size > 0, "block_size must be positive or None." + + H, W = self._image.shape + im = np.asarray(self._image.array, dtype=float) + r0, u, v = (np.asarray(x, dtype=float) for x in self._lat) + + corners = np.array( + [ + [0.0, 0.0], + [float(H), 0.0], + [0.0, float(W)], + [float(H), float(W)], + ], + dtype=float, + ) + + # a,b from corners; A = [u v] in columns (2x2), rhs = (corner - r0) + A = np.column_stack((u, v)) # (2,2) + ab = np.linalg.lstsq(A, (corners - r0[None, :]).T, rcond=None)[0] # (2,4) + + # Getting the min and max values for the indices a, b from the corners + a_min, a_max = int(np.floor(ab[0].min())), int(np.ceil(ab[0].max())) + b_min, b_max = int(np.floor(ab[1].min())), int(np.ceil(ab[1].max())) + + max_ind = max(abs(a_min), a_max, abs(b_min), b_max) + if not block_size: + steps = [max_ind] + else: + steps = ( + [*np.arange(0, max_ind + 1, block_size)[1:], max_ind] + if max_ind > 0 + else [max_ind] + ) + + PENALTY = 1e10 + H_CLIP = H - 2 + W_CLIP = W - 2 + + a_range = np.arange(max(a_min, -max_ind), min(a_max, max_ind) + 1, dtype=np.int32) + b_range = np.arange(max(b_min, -max_ind), min(b_max, max_ind) + 1, dtype=np.int32) + aa, bb = np.meshgrid(a_range, b_range, indexing="ij") + + # Pre-compute all masks and bases + all_masks = {} + all_bases = {} + for curr_block_size in steps: + a_min_blk = max(a_min, -curr_block_size) + a_max_blk = min(a_max, curr_block_size) + b_min_blk = max(b_min, -curr_block_size) + b_max_blk = min(b_max, curr_block_size) + + mask = ( + (aa >= a_min_blk) & (aa <= a_max_blk) & (bb >= b_min_blk) & (bb <= b_max_blk) + ) + + aa_masked = aa[mask] + bb_masked = bb[mask] + + all_masks[curr_block_size] = mask + all_bases[curr_block_size] = np.column_stack( + [np.ones(aa_masked.size), aa_masked.ravel(), bb_masked.ravel()] + ) + + # Pre-allocate cache + max_points = max(basis.shape[0] for basis in all_bases.values()) + x0_cache = np.empty(max_points, dtype=np.int32) + y0_cache = np.empty(max_points, dtype=np.int32) + dx_cache = np.empty(max_points, dtype=np.float64) + dy_cache = np.empty(max_points, dtype=np.float64) + + def bilinear_sum(im_: np.ndarray, xy: np.ndarray) -> float: + """Sum of bilinearly interpolated intensities at (x,y) points.""" + + n_points = xy.shape[0] + if n_points == 0: + return 0.0 + + x, y = xy[:, 0], xy[:, 1] + + # Filter points that are within valid bounds for bilinear interpolation + # Need x in [0, H-2] and y in [0, W-2] so that x+1 and y+1 are valid + valid_mask = ( + (x >= 0) + & (x <= H_CLIP) + & (y >= 0) + & (y <= W_CLIP) + & np.isfinite(x) + & np.isfinite(y) + ) + + n_valid = np.sum(valid_mask) + if n_valid == 0: + return -PENALTY + + x_valid = x[valid_mask] + y_valid = y[valid_mask] + + # Use pre-allocated arrays + x0, y0 = x0_cache[:n_valid], y0_cache[:n_valid] + dx, dy = dx_cache[:n_valid], dy_cache[:n_valid] + + np.floor(x_valid, out=dx) + x0[:] = dx.astype(np.int32) + np.floor(y_valid, out=dy) + y0[:] = dy.astype(np.int32) + + np.subtract(x_valid, x0, out=dx) + np.subtract(y_valid, y0, out=dy) + + Ia = im_[x0, y0] + Ib = im_[x0 + 1, y0] + Ic = im_[x0, y0 + 1] + Id = im_[x0 + 1, y0 + 1] + + return np.sum( + Ia * (1 - dx) * (1 - dy) + + Ib * dx * (1 - dy) + + Ic * (1 - dx) * dy + + Id * dx * dy + ) + + current_basis = None + + def objective(theta: np.ndarray) -> float: + """Function to be minimized""" + # theta is 6-vector -> (3,2) matrix [[r0],[u],[v]] + lat = theta.reshape(3, 2) + xy = current_basis @ lat # (N,2) with columns (x,y) + # Negative: maximize intensity sum by minimizing its negative + return -bilinear_sum(im, xy) + + minimize_options = { + "maxiter": int(refine_maxiter), + "xtol": 1e-3, + "ftol": 1e-3, + "disp": False, + } + + lat_flat = self._lat.astype(np.float32).reshape(-1) + + for curr_block_size in steps: + current_basis = all_bases[curr_block_size] + + res = minimize( + objective, + lat_flat, + method="Powell", + options=minimize_options, + ) + + # Update for next iteration + lat_flat = res.x + self._lat = res.x.reshape(3, 2) + + # plotting + if plot_lattice: + fig, ax = show_2d( + self._image.array, + returnfig=True, + **kwargs, + ) + + # Put the image at lowest zorder so overlays sit on top + if ax.images: + ax.images[-1].set_zorder(0) + + H, W = self._image.shape + r0, u, v = (np.asarray(x, dtype=float) for x in self._lat) + + # Origin marker (TOP of stack) + ax.scatter( + r0[1], + r0[0], # (y, x) + s=60, + edgecolor=(0, 0, 0), + facecolor=(0, 0.5, 0), + marker="s", + zorder=30, + ) + + # Lattice vectors as arrows + n_vec = int(bound_num_vectors) if bound_num_vectors is not None else 1 + + # draw n_vec arrows for u (red) + for k in range(1, n_vec + 1): + tip = r0 + k * u + ax.arrow( + r0[1], + r0[0], # base (y, x) + (tip - r0)[1], + (tip - r0)[0], # delta (y, x) + length_includes_head=True, + head_width=4.0, + head_length=6.0, + linewidth=2.0, + color="red", + zorder=20, + ) + + # draw n_vec arrows for v (cyan) + for k in range(1, n_vec + 1): + tip = r0 + k * v + ax.arrow( + r0[1], + r0[0], + (tip - r0)[1], + (tip - r0)[0], + length_includes_head=True, + head_width=4.0, + head_length=6.0, + linewidth=2.0, + color=(0.0, 0.7, 1.0), + zorder=20, + ) + + # Solve for a,b at plot corners (bounds) + if bound_num_vectors is None: + corners = np.array( + [ + [0.0, 0.0], + [float(H), 0.0], + [0.0, float(W)], + [float(H), float(W)], + ] + ) + else: + n = float(bound_num_vectors) + corners = np.array( + [ + r0 - n * u, + r0 - n * v, + r0 + n * u, + r0 + n * v, + ], + dtype=float, + ) + + # a,b from corners; A = [u v] in columns (2x2), rhs = (corner - r0) + A = np.column_stack((u, v)) + ab = np.linalg.lstsq(A, (corners - r0[None, :]).T, rcond=None)[0] + + a_min, a_max = int(np.floor(np.min(ab[0]))), int(np.ceil(np.max(ab[0]))) + b_min, b_max = int(np.floor(np.min(ab[1]))), int(np.ceil(np.max(ab[1]))) + + # Clipping rectangle (image or custom) + if bound_num_vectors is None: + x_lo, x_hi = 0.0, float(H) + y_lo, y_hi = 0.0, float(W) + else: + # Bounds are the min/max over the provided corners + x_lo, x_hi = float(np.min(corners[:, 0])), float(np.max(corners[:, 0])) + y_lo, y_hi = float(np.min(corners[:, 1])), float(np.max(corners[:, 1])) + + def clipped_segment(base: np.ndarray, direction: np.ndarray): + """Clip base + t*direction to rectangle [x_lo,x_hi] x [y_lo,y_hi].""" + x0, y0 = base + dx, dy = direction + t0, t1 = -np.inf, np.inf + eps = 1e-12 + + # x in [x_lo, x_hi] + if abs(dx) < eps: + if not (x_lo <= x0 <= x_hi): + return None + else: + tx0 = (x_lo - x0) / dx + tx1 = (x_hi - x0) / dx + t_enter, t_exit = (tx0, tx1) if tx0 <= tx1 else (tx1, tx0) + t0, t1 = max(t0, t_enter), min(t1, t_exit) + + # y in [y_lo, y_hi] + if abs(dy) < eps: + if not (y_lo <= y0 <= y_hi): + return None + else: + ty0 = (y_lo - y0) / dy + ty1 = (y_hi - y0) / dy + t_enter, t_exit = (ty0, ty1) if ty0 <= ty1 else (ty1, ty0) + t0, t1 = max(t0, t_enter), min(t1, t_exit) + + if t0 > t1: + return None + + p1 = base + t0 * direction # (x, y) + p2 = base + t1 * direction + return p1, p2 + + # Lattice lines (zorder above image) + # Using x=rows, y=cols: plot(y, x) + + # Lines parallel to v (vary a) + for a in range(a_min, a_max + 1): + base = r0 + a * u + seg = clipped_segment(base, v) + if seg is None: + continue + (x1, y1), (x2, y2) = seg + ax.plot([y1, y2], [x1, x2], color=(0.0, 0.7, 1.0), lw=1, clip_on=True, zorder=10) + + # Lines parallel to u (vary b) + for b in range(b_min, b_max + 1): + base = r0 + b * v + seg = clipped_segment(base, u) + if seg is None: + continue + (x1, y1), (x2, y2) = seg + ax.plot([y1, y2], [x1, x2], color="red", lw=1, clip_on=True, zorder=10) + + # Axes limits (x=rows vertical; y=cols horizontal) + ax.set_xlim(y_lo, y_hi) + ax.set_ylim(x_hi, x_lo) + + return self From bee6bb104dacd0b8f01efb0a3a68dfd430845f4d Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 9 Mar 2026 10:00:08 +0000 Subject: [PATCH 165/335] chore: update lock file --- uv.lock | 578 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Contains basic pytests for Lattice Initialization, constructors, define_lattice() and AutoSerialize implementation. --- tests/imaging/test_lattice.py | 166 ++++++++++++++++++++++++++++++++++ 1 file changed, 166 insertions(+) create mode 100644 tests/imaging/test_lattice.py diff --git a/tests/imaging/test_lattice.py b/tests/imaging/test_lattice.py new file mode 100644 index 00000000..4a3a6bc4 --- /dev/null +++ b/tests/imaging/test_lattice.py @@ -0,0 +1,166 @@ +import numpy as np +import pytest +from numpy.testing import assert_array_almost_equal + +from quantem.core.datastructures.dataset2d import Dataset2d +from quantem.core.io.serialize import load +from quantem.imaging.lattice import Lattice + + +class TestLatticeInit: + """Test Lattice initialization and from_data.""" + + def test_init_and_constructor(self): + """Test that direct init is blocked and from_data works.""" + image = np.random.randn(100, 100) + ds2d = Dataset2d.from_array(image) + + with pytest.raises(RuntimeError, match="Use Lattice.from_data"): + Lattice(ds2d) + + lattice = Lattice.from_data(image) + assert isinstance(lattice, Lattice) + assert lattice.image is not None + + def test_normalization(self): + """Test min/max normalization.""" + image = np.random.randn(100, 100) * 1000.0 + image[0, 0] = 0.0 + image[99, 99] = 1000.0 + + # Both normalizations + lattice = Lattice.from_data(image) + assert lattice.image.array.min() == 0 + assert lattice.image.array.max() == 1 + + # No normalization + lattice = Lattice.from_data(image, normalize_min=False, normalize_max=False) + assert_array_almost_equal(lattice.image.array, image) + + def test_edge_cases(self): + """Test NaN handling.""" + nan_arr = np.array([[1, np.nan], [3, 4]], dtype=float) + lattice = Lattice.from_data(nan_arr) + assert isinstance(lattice, Lattice) + + def test_invalid_inputs(self): + """Test that invalid inputs raise errors.""" + with pytest.raises(ValueError, match="must be a 2D array"): + Lattice.from_data(np.array([1, 2, 3])) + + with pytest.raises(ValueError, match="must be a 2D array"): + Lattice.from_data(np.ones((2, 2, 2))) + + with pytest.raises(ValueError, match="must not be empty"): + Lattice.from_data(np.array([[]])) + + +class TestLatticeImage: + """Test image property getter and setter.""" + + def test_image_property(self): + """Test getting and setting image.""" + image = np.random.randn(100, 100) + lattice = Lattice.from_data(image) + + # Get + assert isinstance(lattice.image, Dataset2d) + + # Set with new array + new_image = np.random.randn(50, 50) + lattice.image = new_image + assert lattice.image.array.shape == (50, 50) + + # Invalid set + with pytest.raises(ValueError, match="must be a 2D array"): + lattice.image = np.array([1, 2, 3]) + + +class TestDefineLattice: + """Test define_lattice method.""" + + def test_basic_define(self): + """Test basic lattice definition.""" + image = np.random.randn(100, 100) + lattice = Lattice.from_data(image) + + result = lattice.define_lattice( + origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=False, plot_lattice=False + ) + + assert result is lattice + assert hasattr(lattice, "_lat") + assert lattice._lat.shape == (3, 2) + + def test_refinement_options(self): + """Test lattice refinement and block_size.""" + image = np.random.randn(100, 100) + lattice = Lattice.from_data(image) + + # With refinement + lattice.define_lattice( + origin=[50, 50], + u=[5, 0], + v=[0, 5], + refine_lattice=True, + refine_maxiter=5, + plot_lattice=False, + ) + assert lattice._lat.shape == (3, 2) + + # With block_size + lattice.define_lattice( + origin=[50, 50], + u=[5, 0], + v=[0, 5], + refine_lattice=True, + refine_maxiter=5, + block_size=5, + plot_lattice=False, + ) + assert lattice._lat.shape == (3, 2) + + def test_invalid_lattice_params(self): + """Test invalid lattice parameters.""" + image = np.random.randn(100, 100) + lattice = Lattice.from_data(image) + + # Wrong shape + with pytest.raises(ValueError): + lattice.define_lattice(origin=[1, 2, 3], u=[5, 0], v=[0, 5], plot_lattice=False) + + # Negative block_size + with pytest.raises(AssertionError): + lattice.define_lattice( + origin=[50, 50], u=[5, 0], v=[0, 5], block_size=-1, plot_lattice=False + ) + + +class TestLatticeSerialize: + """Test Lattice Autoserialize implementation.""" + + @pytest.mark.parametrize("store", ["zip", "dir"]) + def test_lattice_save_load(self, tmp_path, store): + """Test save/load of lattice.""" + # Create lattice with image and defined lattice + image = np.random.randn(100, 100) + lattice = Lattice.from_data(image) + lattice.define_lattice( + origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=False, plot_lattice=False + ) + + # Save + filepath = tmp_path / ("lattice.zip" if store == "zip" else "lattice_dir") + lattice.save(str(filepath), mode="w", store=store) + + # Load + loaded = load(str(filepath)) + + # Verify + assert isinstance(loaded, Lattice) + assert isinstance(loaded.image, Dataset2d) + assert loaded.image.array.shape == lattice.image.array.shape + assert np.allclose(loaded.image.array, lattice.image.array) + assert hasattr(loaded, "_lat") + assert loaded._lat.shape == (3, 2) + assert np.allclose(loaded._lat, lattice._lat) From 568fe350cb86c8a696e5178dc391286cecc8eba6 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 18:10:29 -0700 Subject: [PATCH 167/335] in ptycho, visualize scan positions, get real-space sampling --- .../ptychography_visualizations.py | 85 ++++++++++++++----- 1 file changed, 62 insertions(+), 23 deletions(-) diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index d415dd4f..9ff3a549 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -829,50 +829,89 @@ def _show_object_and_probe_iters( **kwargs, ) - def show_scan_positions(self, plot_radii: bool = True): - """ - Show the scan positions and the probe radius. + def show_scan_positions( + self, + plot_radii: bool = True, + num_probes: int | None = None, + axsize: tuple[int, int] = (10, 10), + edgecolors: str = "red", + linewidths: float = 0.5, + **kwargs, + ): + r"""Show scan positions, probe radii, and overlap metadata + + Visualize probe coverage to verify sufficient overlap for + ptychographic reconstruction. The probe radius is estimated as + :math:`r = 0.61 \lambda / \alpha + |\Delta f| \cdot \alpha` + where :math:`\lambda` is the electron wavelength, :math:`\alpha` is + the convergence semi-angle, and :math:`\Delta f` is the defocus. + Overlap is :math:`(d - s)/d = 1 - s/d` where :math:`d = 2r` is + the probe diameter and :math:`s` is the scan step size. + Parameters ---------- - plot_radii: bool, optional - Whether to plot the probe radius, by default True + plot_radii : bool, optional + Whether to plot the probe radius. Default is True + num_probes : int | None, optional + Number of probe positions to display. Default is 3 rows + of scan positions + axsize : tuple[int, int], optional + Size of the figure axes. Default is (10, 10) + edgecolors : str, optional + Edge color for the probe circles. Default is "red" + linewidths : float, optional + Line width of the probe circles. Default is 0.5 + **kwargs + Additional keyword arguments passed to ``matplotlib.axes.Axes.scatter`` - Returns - ------- - None + Examples + -------- + >>> ptycho.show_scan_positions() + + >>> ptycho.show_scan_positions(axsize=(10, 10), num_probes=10, edgecolors="b", linewidths=0.5) """ # for each scan position, sum the intensity of self.probe at that position scan_positions = self.dset.scan_positions_px.cpu().detach().numpy() - probe_params = self.probe_model.probe_params probe_radius_px = None - conv_angle = probe_params.get("semiangle_cutoff") defocus = probe_params.get("defocus", 0) energy = probe_params.get("energy") + scan_gpts = self.dset.gpts + scan_sampling = self.dset.scan_sampling + fov = scan_sampling * (np.array(scan_gpts) - 1) + print(f"Scan grid: {scan_gpts[0]} x {scan_gpts[1]} positions") + print(f"FOV: {fov[0]:.2f} x {fov[1]:.2f} Å") if conv_angle is not None and energy is not None: from quantem.core.utils.utils import electron_wavelength_angstrom - wavelength = electron_wavelength_angstrom(energy) conv_angle_rad = conv_angle * 1e-3 - # For defocused probe: radius ≈ |defocus| * convergence_angle + diffraction_limit - diffraction_limit_angstrom = 0.61 * wavelength / conv_angle_rad - defocus_blur_angstrom = abs(defocus) * conv_angle_rad - probe_radius_angstrom = diffraction_limit_angstrom + defocus_blur_angstrom - probe_radius_px = probe_radius_angstrom / self.sampling[0] - - _fig, ax = show_2d(self._get_probe_overlap(), title="probe overlap") + diffraction_limit_A = 0.61 * wavelength / conv_angle_rad + defocus_blur_A = abs(defocus) * conv_angle_rad + probe_radius_A = diffraction_limit_A + defocus_blur_A + probe_radius_px = probe_radius_A / self.sampling[0] + probe_diameter_A = 2 * probe_radius_A + probe_step_size_A = scan_sampling[0] + overlap = max(0.0, 1 - probe_step_size_A / probe_diameter_A) + print(f"Probe diameter (rough estimate): {probe_diameter_A:.2f} Å") + print(f"Step size: {probe_step_size_A:.2f} Å") + print(f"Probe overlap (rough estimate): {overlap * 100:.1f}%") + + _fig, ax = show_2d(self._get_probe_overlap(), title="probe overlap", axsize=axsize) if probe_radius_px is not None and plot_radii: # plot a circle with the probe radius for each probe position + if num_probes is None: + num_probes = 3 * scan_gpts[1] # 3 rows worth ax.scatter( - scan_positions[:, 1], - scan_positions[:, 0], + scan_positions[:num_probes, 1], + scan_positions[:num_probes, 0], s=probe_radius_px**2, - edgecolors="red", - c="none", - linestyle="--", + facecolors="none", + edgecolors=edgecolors, + linewidths=linewidths, + **kwargs, ) plt.show() From 9ce62df9d6e34f3f5f66e014189f8a670ac1a24c Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 18:18:56 -0700 Subject: [PATCH 168/335] add show_scan_position print output in the api doc --- .../diffractive_imaging/ptychography_visualizations.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index 9ff3a549..05683abc 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -867,8 +867,13 @@ def show_scan_positions( Examples -------- >>> ptycho.show_scan_positions() + Scan grid: 192 x 192 positions + FOV: 133.46 x 133.46 Å + Probe diameter (rough estimate): 3.46 Å + Step size: 0.70 Å + Probe overlap (rough estimate): 79.8% - >>> ptycho.show_scan_positions(axsize=(10, 10), num_probes=10, edgecolors="b", linewidths=0.5) + >>> ptycho.show_scan_positions(num_probes=10, edgecolors="b", linewidths=0.5) """ # for each scan position, sum the intensity of self.probe at that position scan_positions = self.dset.scan_positions_px.cpu().detach().numpy() From 66ec915ade9d9a8369f91753d560292447da5d62 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 22:29:31 -0700 Subject: [PATCH 169/335] bin, crop API doc improvement --- src/quantem/core/datastructures/dataset.py | 93 ++++++++++++++++++---- 1 file changed, 76 insertions(+), 17 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index f01e68f6..c1648d2a 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -452,21 +452,56 @@ def crop( axes: tuple | None = None, modify_in_place: bool = False, ) -> Self | None: - """ - Crops Dataset + """Select a sub-region of the dataset along specified axes + + Unlike regular slicing (``dset[64:192, ...]``), which creates a + view that keeps the full original array in memory, + ``modify_in_place=True`` replaces the array and frees the original. + This is important for large 4D-STEM datasets where memory is + limited. + + Each ``crop_widths`` entry is a ``(start, stop)`` pair defining + which elements to keep. A ``stop`` of ``0`` keeps everything from + ``start`` to the end. Parameters ---------- - crop_widths:tuple - Min and max for cropping each axis specified as a tuple - axes: - Axes over which to crop. If None specified, all are cropped. - modify_in_place: bool - If True, modifies dataset + crop_widths : tuple[tuple[int, int], ...] + ``(start, stop)`` indices for each axis specified in ``axes``. + axes : tuple | None + Axes to crop. If None, all axes are cropped. + modify_in_place : bool + If True, modifies this dataset in-place and frees the original + array. If False, returns a new dataset. Returns + ------- + Dataset | None + Cropped dataset if ``modify_in_place`` is False, otherwise None. + + Examples -------- - Dataset (cropped) only if modify_in_place is False + Crop real-space to a 128x128 region: + + >>> dset_cropped = dset.crop( + ... crop_widths=((64, 192), (64, 192)), + ... axes=(0, 1), + ... ) + + Crop k-space to keep the first 180 pixels: + + >>> dset_preview = dset.crop( + ... crop_widths=((0, 180), (0, 180)), + ... axes=(2, 3), + ... ) + + Crop k-space in-place to free memory: + + >>> dset.crop( + ... crop_widths=((4, 92), (4, 92)), + ... axes=(2, 3), + ... modify_in_place=True, + ... ) """ if axes is None: if len(crop_widths) != self.ndim: @@ -526,27 +561,51 @@ def bin( modify_in_place: bool = False, reducer: str = "sum", ) -> Self | None: - """ - Bin the Dataset by integer factors along selected axes using block reduction. + """Reduce the dataset resolution by grouping pixels into blocks + + Useful for reducing diffraction pattern size to speed up + reconstruction or lower memory usage. Sampling metadata is + updated automatically. Parameters ---------- bin_factors : int | tuple[int, ...] - Bin factors per specified axis (positive integers). + A single integer bins all axes by the same factor. A tuple + specifies a different factor per axis, e.g. ``(1, 1, 2, 2)`` + to bin only the last two axes by 2x. axes : int | tuple[int, ...] | None Axes to bin. If None, all axes are binned. modify_in_place : bool - If True, modifies this dataset; otherwise returns a new Dataset. - reducer : {"sum","mean"} - Reduction applied within each block. "sum" (default) preserves counts; - "mean" averages over each block (block volume = product of factors). + If True, modifies this dataset in-place. If False, returns + a new dataset. + reducer : {"sum", "mean"} + ``"sum"`` (default) preserves total counts; ``"mean"`` + averages over each block. + + Returns + ------- + Dataset | None + Binned dataset if ``modify_in_place`` is False, otherwise None. Notes ----- - Any remainder (shape % factor) is dropped on each binned axis. - Sampling is multiplied by the factor on each binned axis. - Origin is shifted to the center of the first block: - origin_new = origin_old + 0.5 * (factor - 1) * sampling_old + ``origin_new = origin_old + 0.5 * (factor - 1) * sampling_old`` + + Examples + -------- + Bin diffraction space by 2x to reduce memory: + + >>> dset.bin( + ... bin_factors=(1, 1, 2, 2), + ... modify_in_place=True, + ... ) + + Bin all axes by 2x and return a new dataset: + + >>> dset_binned = dset.bin(bin_factors=2) """ reducer_norm = str(reducer).lower() if reducer_norm not in ("sum", "mean"): From 72ecdf7f58b5e9f04434adc76d0e2a22749e63df Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 22:30:40 -0700 Subject: [PATCH 170/335] add bin tests for 4dstem --- tests/datastructures/test_dataset.py | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/tests/datastructures/test_dataset.py b/tests/datastructures/test_dataset.py index 93449aa2..9a222620 100644 --- a/tests/datastructures/test_dataset.py +++ b/tests/datastructures/test_dataset.py @@ -252,6 +252,25 @@ def test_crop(self, sample_dataset_2d): sample_dataset_2d.crop(crop_widths=((1, 9), (1, 9)), modify_in_place=True) assert sample_dataset_2d.shape == (8, 8) + def test_crop_4dstem_kspace(self): + """Test cropping k-space axes of a 4D-STEM dataset.""" + dset = Dataset.from_array(np.random.rand(8, 8, 96, 96)) + cropped = dset.crop(crop_widths=((4, 92), (4, 92)), axes=(2, 3)) + assert cropped.shape == (8, 8, 88, 88) + assert dset.shape == (8, 8, 96, 96) + + def test_crop_4dstem_realspace_in_place(self): + """Test in-place real-space crop of a 4D-STEM dataset.""" + dset = Dataset.from_array(np.random.rand(16, 16, 32, 32)) + dset.crop(crop_widths=((4, 12), (4, 12)), axes=(0, 1), modify_in_place=True) + assert dset.shape == (8, 8, 32, 32) + + def test_crop_4dstem_stop_zero(self): + """Test that stop=0 keeps all remaining elements.""" + dset = Dataset.from_array(np.random.rand(8, 8, 96, 96)) + cropped = dset.crop(crop_widths=((10, 0), (10, 0)), axes=(2, 3)) + assert cropped.shape == (8, 8, 86, 86) + def test_bin(self, sample_dataset_2d): """Test bin method.""" # Bin by factor of 2 From b9b3ed3d190321ff510d7b8657f6f197e62fb7a3 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 22:32:50 -0700 Subject: [PATCH 171/335] maintain original api doc for some --- src/quantem/core/datastructures/dataset.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index c1648d2a..2bf6e667 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -579,8 +579,8 @@ def bin( If True, modifies this dataset in-place. If False, returns a new dataset. reducer : {"sum", "mean"} - ``"sum"`` (default) preserves total counts; ``"mean"`` - averages over each block. + Reduction applied within each block. "sum" (default) preserves + counts; "mean" averages over each block. Returns ------- @@ -592,7 +592,7 @@ def bin( - Any remainder (shape % factor) is dropped on each binned axis. - Sampling is multiplied by the factor on each binned axis. - Origin is shifted to the center of the first block: - ``origin_new = origin_old + 0.5 * (factor - 1) * sampling_old`` + origin_new = origin_old + 0.5 * (factor - 1) * sampling_old Examples -------- From cbb3e549eda6ec3305ba294803bd049f91e65c77 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 22:48:42 -0700 Subject: [PATCH 172/335] remove free memory part --- tests/datastructures/test_dataset.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/tests/datastructures/test_dataset.py b/tests/datastructures/test_dataset.py index 9a222620..fc5c108d 100644 --- a/tests/datastructures/test_dataset.py +++ b/tests/datastructures/test_dataset.py @@ -241,13 +241,10 @@ def test_crop(self, sample_dataset_2d): """Test crop method.""" # Crop 1 pixel from each side cropped_dataset = sample_dataset_2d.crop(crop_widths=((1, 9), (1, 9))) - # Check shape assert cropped_dataset.shape == (8, 8) # Original (10, 10) - 1 from each side - # Check that the original dataset is unchanged assert sample_dataset_2d.shape == (10, 10) - # Test modify_in_place sample_dataset_2d.crop(crop_widths=((1, 9), (1, 9)), modify_in_place=True) assert sample_dataset_2d.shape == (8, 8) @@ -275,13 +272,10 @@ def test_bin(self, sample_dataset_2d): """Test bin method.""" # Bin by factor of 2 binned_dataset = sample_dataset_2d.bin(bin_factors=2) - # Check shape assert binned_dataset.shape == (5, 5) # Original (10, 10) / 2 - # Check that the original dataset is unchanged assert sample_dataset_2d.shape == (10, 10) - # Test modify_in_place sample_dataset_2d.bin(bin_factors=2, modify_in_place=True) assert sample_dataset_2d.shape == (5, 5) From e57def2621b49db8c4c13d0a7ee8c0eada0bd34b Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 9 Mar 2026 22:50:01 -0700 Subject: [PATCH 173/335] do not free memory part in API --- src/quantem/core/datastructures/dataset.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 2bf6e667..3dc3b925 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -454,12 +454,6 @@ def crop( ) -> Self | None: """Select a sub-region of the dataset along specified axes - Unlike regular slicing (``dset[64:192, ...]``), which creates a - view that keeps the full original array in memory, - ``modify_in_place=True`` replaces the array and frees the original. - This is important for large 4D-STEM datasets where memory is - limited. - Each ``crop_widths`` entry is a ``(start, stop)`` pair defining which elements to keep. A ``stop`` of ``0`` keeps everything from ``start`` to the end. From 1590facf878e5c6b3dd2884c9f768256b0abc046 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Tue, 10 Mar 2026 16:04:13 -0700 Subject: [PATCH 174/335] add Object FOV instead of FOV per PR feedback --- src/quantem/diffractive_imaging/ptychography_visualizations.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index 05683abc..fe8f84bf 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -1,3 +1,4 @@ + import warnings from typing import Any, Literal @@ -886,7 +887,7 @@ def show_scan_positions( scan_sampling = self.dset.scan_sampling fov = scan_sampling * (np.array(scan_gpts) - 1) print(f"Scan grid: {scan_gpts[0]} x {scan_gpts[1]} positions") - print(f"FOV: {fov[0]:.2f} x {fov[1]:.2f} Å") + print(f"Object FOV: {fov[0]:.2f} x {fov[1]:.2f} Å") if conv_angle is not None and energy is not None: from quantem.core.utils.utils import electron_wavelength_angstrom From 922dc1291105fafaae227c5ef3e6072f6621dd45 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 10 Mar 2026 16:57:27 -0700 Subject: [PATCH 175/335] Verbose, if false will just start printing --- src/quantem/tomography/tomography.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 8b1ebd9f..677d3ca3 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -302,6 +302,9 @@ def reconstruct( ) self.logger.flush() + if not self.verbose: + if self.global_rank == 0: + print(f"Reconstruction Epoch {self.num_epochs} | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}") # --- Helper Functions --- From bad43ced39f4d9266ef0ebe0b225d0a8e41ddf06 Mon Sep 17 00:00:00 2001 From: quantem-bot 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2026 11:45:10 -0700 Subject: [PATCH 177/335] Added relaxation term to SIRT reconstruction, stable gradients --- src/quantem/core/ml/optimizer_mixin.py | 2 +- src/quantem/tomography/object_models.py | 7 +++++++ src/quantem/tomography/tomography.py | 5 ++++- 3 files changed, 12 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index c07fefc9..4ea263c0 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -273,7 +273,7 @@ class Plateau: min_lr : float or None Absolute lower bound on the learning rate. Overrides ``min_lr_factor`` when set. Default: None. - factor : float + factor : float Factor by which the LR is reduced: ``new_lr = lr * factor``. Default: 0.5. patience : int Number of epochs with no improvement before reducing LR. Default: 10. diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 6ef9eb59..77530d08 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -517,6 +517,13 @@ def apply_soft_constraints( grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] soft_loss += self.constraints.tv_vol * grad_norm.mean() + # grad_outputs: [num, 3] + # for us: grad_outputs: [num, 4] # 4 is t, xyz + # grad_norm_volf: norm(grad_outpus[:, 1:]) + # grad_time: grad_outputs[:, 0] + # grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + # soft_loss += self.constraints.tv_vol * grad_norm.mean() + if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 8b1ebd9f..4ca1fea1 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -402,6 +402,7 @@ def reconstruct( num_iter: int = 10, obj_constraints: dict | ObjConstraintsType | None = None, mode: Literal["sirt", "fbp"] = "sirt", + relaxation: float = 0.25, reset: bool = False, inline_alignment: bool = False, smoothing_sigma: float | None = None, @@ -433,6 +434,7 @@ def reconstruct( mode=mode, proj_forward=proj_forward, gaussian_kernel=gaussian_kernel, + relaxation=relaxation, ) pbar.set_description(f"{mode} Reconstruction | Loss: {loss.item():.4f}") @@ -449,6 +451,7 @@ def _reconstruction_epoch( inline_alignment: bool, mode: Literal["sirt", "fbp"], proj_forward: torch.Tensor, + relaxation: float, gaussian_kernel: torch.Tensor | None = None, ): loss = 0 @@ -506,7 +509,7 @@ def _reconstruction_epoch( correction /= normalization - self.obj_model.obj += correction + self.obj_model.obj += correction * relaxation if gaussian_kernel is not None: self.obj_model.obj = gaussian_filter_2d_stack(self.obj_model.obj, gaussian_kernel) From d2a94342a5f3b7a2204909b0ebea76aa4efa106f Mon Sep 17 00:00:00 2001 From: Georgios Varnavides Date: Tue, 17 Mar 2026 09:04:53 +0100 Subject: [PATCH 178/335] arthurs typing changes --- src/quantem/core/io/file_readers.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index d537e125..04ff0d50 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -1,6 +1,7 @@ import importlib from os import PathLike from pathlib import Path +from typing import Any import h5py @@ -160,7 +161,7 @@ def read_emdfile_to_4dstem( data_keys = ["datacube_root", "datacube", "data"] if data_keys is None else data_keys print("keys: ", data_keys) try: - data = file + data: Any = file for key in data_keys: data = data[key] except KeyError: @@ -206,7 +207,7 @@ def _open_zarr(url): import zarr if url.endswith(".zip"): - store = zarr.storage.ZipStore(url, mode="r") # ty:ignore[possibly-missing-attribute] + store = zarr.storage.ZipStore(url, mode="r") # type: ignore return zarr.open(store=store, mode="r") return zarr.open(url, mode="r") @@ -231,7 +232,7 @@ def _iter_metadata_indices(root): yield i i += 1 - def _decode_types(obj): + def _decode_types(obj) -> Any: if isinstance(obj, dict): if obj.get("_type") == "tuple": return tuple(_decode_types(v) for v in obj["_value"]) From 36215685d386cbd2b0813f80f0e3e878b6ec501b Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 17 Mar 2026 11:58:45 -0700 Subject: [PATCH 179/335] Relaxation parameters for SIRT reconstructions --- src/quantem/tomography/radon/radon.py | 159 +++++++++++--------------- src/quantem/tomography/tomography.py | 22 +++- 2 files changed, 87 insertions(+), 94 deletions(-) diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography/radon/radon.py index d6d408e3..75c66b6f 100644 --- a/src/quantem/tomography/radon/radon.py +++ b/src/quantem/tomography/radon/radon.py @@ -9,37 +9,25 @@ def radon_torch(images, theta=None, device=None): - 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# Crop to square excess = torch.tensor([H, W], device=device) - shape_min slices = tuple( slice(int((e.item() + 1) // 2), int((e.item() + 1) // 2 + shape_min)) @@ -51,112 +39,99 @@ def radon_torch(images, theta=None, device=None): N = images.shape[-1] center = N // 2 - radon_images = torch.zeros((B, N_angles, N), dtype=images.dtype, device=device) + # Build all rotation matrices at once: [A, 2, 2] + angles_rad = torch.deg2rad(theta.float()) + cos_a = torch.cos(angles_rad) + sin_a = torch.sin(angles_rad) + rot = torch.stack( + [ + torch.stack([cos_a, -sin_a], dim=1), + torch.stack([-sin_a, -cos_a], dim=1), + ], + dim=1, + ) # [A, 2, 2] + # Pixel coords: [N*N, 2] grid_y, grid_x = torch.meshgrid( torch.arange(N, dtype=torch.float32, device=device), torch.arange(N, dtype=torch.float32, device=device), indexing="ij", ) - coords = torch.stack((grid_x - center, grid_y - center), dim=-1) # (N, N, 2) - coords = coords.view(1, N, N, 2).expand(B, -1, -1, -1) # [B, N, N, 2] - - for i, angle in enumerate(theta): - angle_rad = torch.deg2rad(angle) - rot = torch.tensor( - [ - [torch.cos(angle_rad), -torch.sin(angle_rad)], - [-torch.sin(angle_rad), -torch.cos(angle_rad)], - ], - device=device, - dtype=torch.float32, - ) + coords = torch.stack((grid_x - center, grid_y - center), dim=-1).view(-1, 2) # [N*N, 2] - rot = rot.unsqueeze(0).expand(B, -1, -1) # [B, 2, 2] - coords_rot = torch.matmul(coords.view(B, -1, 2), rot.transpose(1, 2)).view(B, N, N, 2) - coords_rot += center + # Rotate all angles at once: [A, N*N, 2] + coords_rot = (coords @ rot.transpose(1, 2)) + center # [A, N*N, 2] - # Normalize to [-1, 1] - grid = 2 * coords_rot / (N - 1) - 1 # [B, N, N, 2] + # Normalize to [-1, 1] for grid_sample + grid = (2 * coords_rot / (N - 1) - 1).view(A, N, N, 2) # [A, N, N, 2] - # grid = grid.unsqueeze(1) # [B, 1, N, N, 2] - imgs = images.unsqueeze(1) # [B, 1, N, N] + # Expand images: [B, 1, N, N] -> sample with [B*A, 1, N, N] style + # Tile images over angles, tile grid over batch + images_exp = images.unsqueeze(1).expand(-1, A, -1, -1).reshape(B * A, 1, N, N) + grid_exp = grid.unsqueeze(0).expand(B, -1, -1, -1, -1).reshape(B * A, N, N, 2) - sampled = F.grid_sample( - imgs, grid, mode="bilinear", padding_mode="zeros", align_corners=True - ) - projection = sampled.squeeze(1).sum(dim=1) # [B, N] - radon_images[:, i, :] = projection + sampled = F.grid_sample( + images_exp, grid_exp, mode="bilinear", padding_mode="zeros", align_corners=True + ) + # sampled: [B*A, 1, N, N] -> sum over rows -> [B, A, N] + radon_images = sampled.squeeze(1).sum(dim=1).view(B, A, N) - return radon_images.squeeze(0) if radon_images.shape[0] == 1 else radon_images + return radon_images.squeeze(0) if B == 1 else radon_images def iradon_torch( - sinograms, - theta=None, - output_size=None, - filter_name="ramp", - circle=True, - device=None, + sinograms, theta=None, output_size=None, filter_name="ramp", circle=True, device=None ): - """ - Batched inverse Radon transform (filtered backprojection). - sinograms: [B, N_angles, N_pixels] or [N_angles, N_pixels] (automatically batched) - Returns: [B, output_size, output_size] or [output_size, output_size] - """ if sinograms.ndim == 2: - sinograms = sinograms.unsqueeze(0) # [1, A, P] + sinograms = sinograms.unsqueeze(0) B, A, N = sinograms.shape - - device = sinograms.device if device is None else device + device = device or sinograms.device theta = theta if theta is not None else torch.linspace(0, 180, steps=A, device=device) - if theta.shape[0] != A: - raise ValueError("theta does not match number of projections") - if output_size is None: - output_size = N if circle else int(torch.floor(torch.sqrt(torch.tensor(N**2 / 2.0)))) + output_size = N if circle else int((N**2 / 2) ** 0.5) - # Padding for FFT - padded_size = max( - 64, int(2 ** torch.ceil(torch.log2(torch.tensor(2 * N, dtype=torch.float32)))) - ) + # Filter + padded_size = max(64, int(2 ** torch.ceil(torch.log2(torch.tensor(2.0 * N))))) pad_y = padded_size - N - sinograms_padded = F.pad(sinograms, (0, pad_y)) # [B, A, padded] - - f_filter = get_fourier_filter_torch(padded_size, filter_name, device=device) # [1, padded] - spectrum = torch.fft.fft(sinograms_padded, dim=2) - filtered = torch.real(torch.fft.ifft(spectrum * f_filter, dim=2))[:, :, :N] + sinograms_padded = F.pad(sinograms, (0, pad_y)) + f_filter = get_fourier_filter_torch(padded_size, filter_name, device=device) + filtered = torch.real( + torch.fft.ifft(torch.fft.fft(sinograms_padded, dim=2) * f_filter, dim=2) + )[:, :, :N] # [B, A, N] - # Backprojection - recon = torch.zeros((B, output_size, output_size), device=device) radius = output_size // 2 - y, x = torch.meshgrid( torch.arange(output_size, device=device) - radius, torch.arange(output_size, device=device) - radius, indexing="ij", ) - x = x.flatten() - y = y.flatten() + x = x.float().flatten() # [H*W] + y = y.float().flatten() - for i, angle in enumerate(torch.deg2rad(theta)): - t = (x * torch.cos(angle) - y * torch.sin(angle)).reshape(1, output_size, output_size) - t_idx = t + (N // 2) + # Compute t for all angles at once: [A, H*W] + angles_rad = torch.deg2rad(theta.float()) + t = x.unsqueeze(0) * torch.cos(angles_rad).unsqueeze(1) - y.unsqueeze(0) * torch.sin( + angles_rad + ).unsqueeze(1) # [A, H*W] + t_idx = t + (N // 2) # [A, H*W] - t0 = torch.floor(t_idx).long().clamp(0, N - 2) # [1, H, W] - t1 = t0 + 1 - w = t_idx - t0.float() + t0 = t_idx.long().clamp(0, N - 2) # [A, H*W] + t1 = t0 + 1 + w = t_idx - t0.float() # [A, H*W] - t0 = t0.expand(B, -1, -1) # [B, H, W] - t1 = t1.expand(B, -1, -1) + # Gather from filtered: [B, A, N] -> gather at [A, H*W] indices + # Expand: [B, A, H*W] + # HW = output_size * output_size + t0_exp = t0.unsqueeze(0).expand(B, -1, -1) # [B, A, H*W] + t1_exp = t1.unsqueeze(0).expand(B, -1, -1) + w_exp = w.unsqueeze(0).expand(B, -1, -1) - filtered_i = filtered[:, i, :] # [B, N] - val0 = torch.gather(filtered_i, 1, t0.view(B, -1)).view(B, output_size, output_size) - val1 = torch.gather(filtered_i, 1, t1.view(B, -1)).view(B, output_size, output_size) + val0 = torch.gather(filtered, 2, t0_exp) # [B, A, H*W] + val1 = torch.gather(filtered, 2, t1_exp) + proj = ((1 - w_exp) * val0 + w_exp * val1).sum(dim=1) # [B, H*W] - proj = (1 - w) * val0 + w * val1 - recon += proj + recon = proj.view(B, output_size, output_size) if circle: mask = ( @@ -166,7 +141,7 @@ def iradon_torch( recon[:, mask] = 0.0 recon *= torch.pi / (2 * A) - return recon.squeeze(0) if recon.shape[0] == 1 else recon + return recon.squeeze(0) if B == 1 else recon def get_fourier_filter_torch(size, filter_name="ramp", device=None, dtype=torch.float32): diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index aaf335d2..fe854b4a 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -304,7 +304,9 @@ def reconstruct( self.logger.flush() if not self.verbose: if self.global_rank == 0: - print(f"Reconstruction Epoch {self.num_epochs} | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}") + print( + f"Reconstruction Epoch {self.num_epochs} | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}" + ) # --- Helper Functions --- @@ -431,6 +433,7 @@ def reconstruct( else: gaussian_kernel = None + patience = self.dset.tilt_angles.max() // 10 for iter in pbar: proj_forward, loss = self._reconstruction_epoch( inline_alignment=inline_alignment, @@ -444,10 +447,23 @@ def reconstruct( self._epoch_losses.append(loss.item()) + # Change relaxation parameter if loss greater than last epoch + if len(self._epoch_losses) > 1 and self._epoch_losses[-1] > self._epoch_losses[-2]: + if patience == 0: + relaxation *= 0.85 + print(f"Relaxation parameter changed to: {relaxation}") + patience = 10 + else: + patience -= 1 + if mode == "fbp": break # --- Conventional reconstruction method --- + def _adaptive_relaxation(self, n_power_iter: int = 10) -> float: + raise NotImplementedError( + "Adaptive relaxation hasn't been implemented, please input a valid relaxation parameter." + ) def _reconstruction_epoch( self, @@ -458,7 +474,9 @@ def _reconstruction_epoch( gaussian_kernel: torch.Tensor | None = None, ): loss = 0 - + if relaxation == 0.0: + relaxation = self._adaptive_relaxation() + print(f"Adaptive relaxation: {relaxation}") if inline_alignment: for ind in range(len(self.dset.tilt_angles)): im_proj = proj_forward[:, ind, :] From d332b4b6fbad186216a1deadce7cd27a5a0b1b04 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 17 Mar 2026 15:08:50 -0700 Subject: [PATCH 180/335] fixing bug of arg name changing in dataset pad --- src/quantem/diffractive_imaging/dataset_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index 2fbfa119..4a4b9d5b 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -981,7 +981,7 @@ def preprocess( self.num_gpts, *padded_diffraction_intensities_shape, ), - in_place=True, + modify_in_place=True, ) self.intensities_4d = self.dset.array.reshape( (*self.gpts, *padded_diffraction_intensities_shape) From b089e553ccf1e2e8fa462b1dd0c18aa72e7a73fd Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Wed, 18 Mar 2026 14:31:24 -0700 Subject: [PATCH 181/335] updating example to best practices for INR make equispaced grid --- src/quantem/core/ml/inr.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index 276e5680..5ae5b298 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -151,7 +151,7 @@ def make_equispaced_grid( Examples -------- For a model with in_features=2: - >>> bounds = ((0, 1), (0, 1)) + >>> bounds = ((-1, 1), (-1, 1)) >>> sampling = (0.1, 0.1) >>> coords = siren.make_equispaced_grid(bounds, sampling) """ From e860c03e0640ecd33b2c8b365c1da9ddbacbc32f Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 18 Mar 2026 21:06:48 -0700 Subject: [PATCH 182/335] show_2d, add api description and examples for color, norm, scale bar --- .../core/visualization/visualization.py | 40 ++++++++++++++++++- 1 file changed, 38 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index f35dab76..7c82525a 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -454,12 +454,16 @@ def show_2d( norm : NormalizationConfig or dict or str, optional Configuration for normalizing the data before visualization. This can be a string, dictionary, or a NormalizationConfig object. Strings for preset normalization types - include: "linear_auto" (quantile), "linear_minmax", "linear_centered", "log_auto", - "log_minmax", "power_squared", "power_sqrt", "asinh_centered" + include: ``"linear_auto"`` (quantile), ``"linear_minmax"``, ``"linear_centered"``, + ``"log_auto"``, ``"log_minmax"``, ``"power_squared"``, ``"power_sqrt"``, + ``"asinh_centered"``. Dict form example: ``{"power": 0.5}``. scalebar : ScalebarConfig or dict or bool, optional Configuration for adding a scale bar to the plot. + Dict form example: ``{"sampling": 0.5, "units": "Å"}`` or + ``{"sampling": 0.02, "units": "1/Å"}``. cmap : str or Colormap, default="gray" Colormap to use for real data or amplitude of complex data. + Common choices: ``"gray"``, ``"viridis"``, ``"turbo"``, ``"inferno"``, ``"magma"``. cbar : bool, default=False Whether to add a colorbar to the plot. title : str, optional @@ -507,6 +511,38 @@ def show_2d( ValueError If combine_images is True but arrays contains multiple rows, or if figax is provided but the axes shape doesn't match the grid shape. + + Examples + -------- + Display a single image: + + >>> import numpy as np + >>> from quantem.core.visualization import show_2d + >>> image = np.random.rand(256, 256) + >>> fig, ax = show_2d(image, axsize=(3, 3)) + + Display a 2-row grid with titles and a scale bar: + + >>> image_rows = [[np.random.rand(128, 128) for _ in range(3)] for _ in range(2)] + >>> label_rows = [["BF", "ADF", "ABF"], ["HAADF", "DPC", "SSB"]] + >>> fig, axs = show_2d( + ... image_rows, + ... title=label_rows, + ... cmap="gray", + ... scalebar={"sampling": 0.5, "units": "Å"}, + ... ) + + Display diffraction patterns with log normalization, colorbar, and scale bar: + + >>> dps = [np.random.rand(256, 256) ** 3 for _ in range(3)] + >>> fig, axs = show_2d( + ... dps, + ... title=["Zone axis", "Off-axis", "CBED"], + ... norm="log_auto", + ... cbar=True, + ... cmap="turbo", + ... scalebar={"sampling": 0.02, "units": "1/Å"}, + ... ) """ arrays = to_cpu(arrays) grid = _normalize_show_input_to_grid(arrays) From 7b5eaf77c80e6b7ad4e5704e6004c4481d62e910 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 18 Mar 2026 21:22:47 -0700 Subject: [PATCH 183/335] use the most common api --- src/quantem/core/visualization/visualization.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 7c82525a..2eb2418f 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -454,9 +454,8 @@ def show_2d( norm : NormalizationConfig or dict or str, optional Configuration for normalizing the data before visualization. This can be a string, dictionary, or a NormalizationConfig object. Strings for preset normalization types - include: ``"linear_auto"`` (quantile), ``"linear_minmax"``, ``"linear_centered"``, - ``"log_auto"``, ``"log_minmax"``, ``"power_squared"``, ``"power_sqrt"``, - ``"asinh_centered"``. Dict form example: ``{"power": 0.5}``. + include: ``"linear_auto"`` (quantile), ``"log_auto"``, ``"power_sqrt"``. + Dict form example: ``{"power": 0.5}``. scalebar : ScalebarConfig or dict or bool, optional Configuration for adding a scale bar to the plot. Dict form example: ``{"sampling": 0.5, "units": "Å"}`` or From eda4b9da8bc5be11093fd42f68f07aeb3e921fdb Mon Sep 17 00:00:00 2001 From: darshan-mali Date: Thu, 19 Mar 2026 10:29:20 -0700 Subject: [PATCH 184/335] Minor formatting changes --- src/quantem/imaging/lattice.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/src/quantem/imaging/lattice.py b/src/quantem/imaging/lattice.py index 5f9ec9fb..58227dc1 100644 --- a/src/quantem/imaging/lattice.py +++ b/src/quantem/imaging/lattice.py @@ -22,7 +22,7 @@ def __init__( raise RuntimeError("Use Lattice.from_data() to instantiate this class.") self._image: Dataset2d = image - # --- Constructors --- + ### --- Constructors --- @classmethod def from_data( cls, @@ -92,7 +92,7 @@ def from_data( return cls(image=ds2d, _token=cls._token) - # --- Properties --- + ### --- Properties --- @property def image(self) -> Dataset2d: return self._image @@ -120,7 +120,7 @@ def image(self, value: Dataset2d | NDArray): else: self._image = Dataset2d(arr) # type: ignore[call-arg] - # --- Functions --- + ### --- Functions --- def define_lattice( self, origin, @@ -226,7 +226,6 @@ def define_lattice( PENALTY = 1e10 H_CLIP = H - 2 W_CLIP = W - 2 - a_range = np.arange(max(a_min, -max_ind), min(a_max, max_ind) + 1, dtype=np.int32) b_range = np.arange(max(b_min, -max_ind), min(b_max, max_ind) + 1, dtype=np.int32) aa, bb = np.meshgrid(a_range, b_range, indexing="ij") @@ -239,7 +238,6 @@ def define_lattice( a_max_blk = min(a_max, curr_block_size) b_min_blk = max(b_min, -curr_block_size) b_max_blk = min(b_max, curr_block_size) - mask = ( (aa >= a_min_blk) & (aa <= a_max_blk) & (bb >= b_min_blk) & (bb <= b_max_blk) ) From ec87cbe6ec156f478c7c9bc796d8ee2b9d6e986b Mon Sep 17 00:00:00 2001 From: darshan-mali Date: Thu, 19 Mar 2026 12:15:25 -0700 Subject: [PATCH 185/335] Updated Errors. Removed assert from src and replaced with raise. --- src/quantem/imaging/lattice.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/quantem/imaging/lattice.py b/src/quantem/imaging/lattice.py index 58227dc1..303ff26f 100644 --- a/src/quantem/imaging/lattice.py +++ b/src/quantem/imaging/lattice.py @@ -189,7 +189,8 @@ def define_lattice( if refine_lattice: from scipy.optimize import minimize - assert block_size is None or block_size > 0, "block_size must be positive or None." + if block_size is not None and block_size < 0: + raise ValueError("block_size must be positive or None.") H, W = self._image.shape im = np.asarray(self._image.array, dtype=float) From 023b6cf2770b0ecc7ac7eb828c1336cbe8f87e11 Mon Sep 17 00:00:00 2001 From: darshan-mali Date: Thu, 19 Mar 2026 12:19:40 -0700 Subject: [PATCH 186/335] Updated pytest --- tests/imaging/test_lattice.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/imaging/test_lattice.py b/tests/imaging/test_lattice.py index 4a3a6bc4..0d11e9c5 100644 --- a/tests/imaging/test_lattice.py +++ b/tests/imaging/test_lattice.py @@ -130,7 +130,7 @@ def test_invalid_lattice_params(self): lattice.define_lattice(origin=[1, 2, 3], u=[5, 0], v=[0, 5], plot_lattice=False) # Negative block_size - with pytest.raises(AssertionError): + with pytest.raises(ValueError): lattice.define_lattice( origin=[50, 50], u=[5, 0], v=[0, 5], block_size=-1, plot_lattice=False ) From 614ffa166a443e3d32ce06ea5e50f61cad2bb052 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 19 Mar 2026 15:07:15 -0700 Subject: [PATCH 187/335] DDP dist.all_reduce for scheduler stepping --- src/quantem/tomography/tomography.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index fe854b4a..0335424f 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -212,13 +212,19 @@ def reconstruct( total_loss += batch_loss.detach() consistency_loss += batch_consistency_loss.detach() - self.step_schedulers(loss=total_loss) - # TODO: Maybe reorganize the losses so that the order makes sense lol. + if self.world_size > 1: + dist.all_reduce(total_loss, dist.ReduceOp.AVG) + dist.all_reduce(consistency_loss, dist.ReduceOp.AVG) + dist.all_reduce(epoch_soft_constraint_loss, dist.ReduceOp.AVG) + total_loss = total_loss.item() / len(self.dataloader) consistency_loss = consistency_loss.item() / len(self.dataloader) epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len(self.dataloader) + self.step_schedulers(loss=total_loss) + # TODO: Maybe reorganize the losses so that the order makes sense lol. + if self.val_dataloader is not None: print("Validating...") self.obj_model.model.eval() From 8772d89842e448b8e32459a2a7c4960dd4717d06 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Thu, 19 Mar 2026 22:06:28 -0700 Subject: [PATCH 188/335] .to() for object_models fixed; Note should check if world_size > 1 and only do distribute_model if model is not already a DDP --- src/quantem/tomography/object_models.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 77530d08..4a2562f1 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -836,7 +836,10 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM if isinstance(device, str): device = torch.device(device) self._device = device - self._model = self._model.to(device) + if self.world_size == 1: + self._model = self._model.to(device) + elif not isinstance(self._model, torch.nn.parallel.DistributedDataParallel): + self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() From 196b9e9fa65d18571b997debe43dfb6edf08f49b Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sat, 21 Mar 2026 19:16:49 -0700 Subject: [PATCH 189/335] @arthurmccray cuBLAS error on the first iteration is due to torch.autograd in computing the tv_loss. Temporary/Permanent fix is to just apply soft constraints after the first iteration. --- src/quantem/tomography/object_models.py | 7 ------- src/quantem/tomography/tomography.py | 6 +++++- 2 files changed, 5 insertions(+), 8 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 4a2562f1..dfedd0a1 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -517,13 +517,6 @@ def apply_soft_constraints( grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] soft_loss += self.constraints.tv_vol * grad_norm.mean() - # grad_outputs: [num, 3] - # for us: grad_outputs: [num, 4] # 4 is t, xyz - # grad_norm_volf: norm(grad_outpus[:, 1:]) - # grad_time: grad_outputs[:, 0] - # grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - # soft_loss += self.constraints.tv_vol * grad_norm.mean() - if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 0335424f..3abb988a 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -192,13 +192,17 @@ def reconstruct( len(batch["target_value"]), ) + pred = integrated_densities.float() + if a0 > 0: + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) + else: + soft_constraints_loss = 0.0 target = batch["target_value"].to(self.device, non_blocking=True).float() batch_consistency_loss = loss_func(pred, target) - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) soft_constraints_loss += self.dset.apply_soft_constraints() epoch_soft_constraint_loss += soft_constraints_loss.detach() From 3e196023edb46c2d919cf131f7c334a30cc6150e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sun, 22 Mar 2026 13:06:02 -0700 Subject: [PATCH 190/335] @arthurmccray all Python 3.14 conflcits have been resolved. --- src/quantem/core/ml/ddp.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index d081f757..d3a02b1a 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -98,6 +98,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=True, persistent_workers=persist, + multiprocessing_context="spawn" ) if val_dataset: @@ -110,6 +111,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=False, persistent_workers=persist, + multiprocessing_context="spawn" ) val_dataloader = val_dataloader else: From c7be96972ee72564bc3758ed0a269ff9140d4d06 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sun, 22 Mar 2026 18:08:00 -0700 Subject: [PATCH 191/335] TomographyLite updated to match ObjConstraints and DatasetConstraints --- src/quantem/core/ml/ddp.py | 10 ++++- src/quantem/tomography/tomography_lite.py | 51 ++++++++++++----------- 2 files changed, 35 insertions(+), 26 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index d3a02b1a..2253b4a5 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -8,6 +8,10 @@ from quantem.tomography.dataset_models import DatasetModelType +def worker_init_fn(worker_id): + os.environ["CUDA_VISIBLE_DEVICES"] = "" + + class DDPMixin: """ Class for setting up all distributed training. @@ -98,7 +102,8 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=True, persistent_workers=persist, - multiprocessing_context="spawn" + multiprocessing_context="spawn", + worker_init_fn=worker_init_fn, ) if val_dataset: @@ -111,7 +116,8 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=False, persistent_workers=persist, - multiprocessing_context="spawn" + multiprocessing_context="spawn", + worker_init_fn=worker_init_fn, ) val_dataloader = val_dataloader else: diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index 4a6d4c86..eb5c9940 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -7,12 +7,17 @@ from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.ml.inr import HSiren +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams, + SchedulerParams, +) from quantem.tomography.dataset_models import ( + DatasetConstraintsType, TomographyINRDataset, TomographyPixDataset, ) from quantem.tomography.logger_tomography import LoggerTomography -from quantem.tomography.object_models import ObjectINR, ObjectPixelated +from quantem.tomography.object_models import ObjConstraintsType, ObjectINR, ObjectPixelated from quantem.tomography.tomography import Tomography, TomographyConventional @@ -53,7 +58,7 @@ def from_dataset( if log_dir is not None: logger = LoggerTomography( - log_dir=log_dir, + log_dir=str(log_dir), run_prefix="tomography_lite_inr", run_suffix="", log_images_every=log_images_every, @@ -84,38 +89,36 @@ def reconstruct( scheduler_type: Literal[ "exp", "cyclic", "plateau", "cosine_annealing", "linear", "full_warmup", "none" ] = "none", - scheduler_factor: float = 0.5, + scheduler_params: dict = {}, new_optimizers: bool = False, - obj_constraints: dict = {}, - dset_constraints: dict = {}, + obj_constraints: ObjConstraintsType | dict = {}, + dset_constraints: DatasetConstraintsType | dict = {}, ): if self.num_epochs == 0: opt_params = { - "object": { - "type": "adam", - "lr": obj_lr, - }, + "object": OptimizerParams.Adam(lr=obj_lr), } - scheduler_params = { - "object": { - "type": scheduler_type, - "factor": scheduler_factor, - }, + all_scheduler_params = { + "object": SchedulerParams.parse_dict( + { + "name": scheduler_type, + **scheduler_params, + } + ), } if learn_pose: - opt_params["pose"] = { - "type": "adam", - "lr": pose_lr, - } - scheduler_params["pose"] = { - "type": scheduler_type, - } - + opt_params["pose"] = OptimizerParams.Adam(lr=pose_lr) + all_scheduler_params["pose"] = SchedulerParams.parse_dict( + { + "name": scheduler_type, + **scheduler_params, + } + ) else: opt_params = None - scheduler_params = None + all_scheduler_params = {} num_samples_per_ray = int(max(self.dset.tilt_stack.shape)) return super().reconstruct( @@ -125,7 +128,7 @@ def reconstruct( reset=reset, num_samples_per_ray=num_samples_per_ray, optimizer_params=opt_params, - scheduler_params=scheduler_params, + scheduler_params=all_scheduler_params, obj_constraints=obj_constraints, dset_constraints=dset_constraints, ) From df9d6f8ee82e6b7b8eecd204f9766a9e10e8420c Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sun, 22 Mar 2026 20:47:12 -0700 Subject: [PATCH 192/335] show_2d upstream --- src/quantem/core/visualization/visualization.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 2eb2418f..ae3dd508 100644 --- a/src/quantem/core/visualization/visualization.py +++ 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2026 11:42:54 -0700 Subject: [PATCH 194/335] Fix for TomographyLite when reloading --- src/quantem/core/ml/ddp.py | 3 ++- src/quantem/tomography/tomography.py | 4 +--- src/quantem/tomography/tomography_lite.py | 8 +++++--- 3 files changed, 8 insertions(+), 7 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 2253b4a5..dedd90dd 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -146,6 +146,7 @@ def distribute_model( Returns the model. """ model = model.to(self.device) + if self.world_size > 1: model = torch.nn.parallel.DistributedDataParallel( model, @@ -158,7 +159,7 @@ def distribute_model( ) if self.global_rank == 0: - print("Model wrapped with DDP") + print("Model wrapped with DDP and compiled") if self.world_size > 1: if self.global_rank == 0: diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 3abb988a..b9a4344d 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -192,9 +192,8 @@ def reconstruct( len(batch["target_value"]), ) - pred = integrated_densities.float() - if a0 > 0: + if self.num_epochs > 0: soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) else: soft_constraints_loss = 0.0 @@ -221,7 +220,6 @@ def reconstruct( dist.all_reduce(consistency_loss, dist.ReduceOp.AVG) dist.all_reduce(epoch_soft_constraint_loss, dist.ReduceOp.AVG) - total_loss = total_loss.item() / len(self.dataloader) consistency_loss = consistency_loss.item() / len(self.dataloader) epoch_soft_constraint_loss = epoch_soft_constraint_loss.item() / len(self.dataloader) diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index eb5c9940..711434ec 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -91,8 +91,8 @@ def reconstruct( ] = "none", scheduler_params: dict = {}, new_optimizers: bool = False, - obj_constraints: ObjConstraintsType | dict = {}, - dset_constraints: DatasetConstraintsType | dict = {}, + obj_constraints: ObjConstraintsType | dict | None = None, + dset_constraints: DatasetConstraintsType | dict | None = None, ): if self.num_epochs == 0: opt_params = { @@ -118,7 +118,9 @@ def reconstruct( ) else: opt_params = None - all_scheduler_params = {} + all_scheduler_params = None + obj_constraints = None + dset_constraints = None num_samples_per_ray = int(max(self.dset.tilt_stack.shape)) return super().reconstruct( From 73c282ffbe49c0fd4ed57bb55146f3d4b5556fc1 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 17:18:33 -0700 Subject: [PATCH 195/335] Show metrics total loss + lrs from different optimizers after finished training --- pyproject.toml | 2 +- src/quantem/tomography/tomography.py | 24 + src/quantem/tomography/tomography_base.py | 17 + src/quantem/tomography/tomography_lite.py | 2 + uv.lock | 3774 +++++++++++---------- 5 files changed, 1961 insertions(+), 1858 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 5ca6787b..19882bf9 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -82,4 +82,4 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", -] +] \ No newline at end of file diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index b9a4344d..a5fcccfa 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -2,6 +2,7 @@ from pathlib import Path from typing import Literal, Self, Sequence +import matplotlib.pyplot as plt import numpy as np import torch import torch.distributed as dist @@ -77,6 +78,7 @@ def reconstruct( ] = "l2", loss_func_kwargs: dict = {}, reset_dset: DatasetModelType | None = None, + show_metrics: bool = False, ): """ This function should be able to handle both AD and INR-based tomography reconstruction methods. @@ -277,6 +279,7 @@ def reconstruct( self._epoch_losses.append(total_loss) self._consistency_losses.append(consistency_loss) + self.append_learning_rates(self.get_current_lrs()) self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) if self.val_dataloader is not None: self._val_losses.append(avg_val_loss) @@ -315,6 +318,27 @@ def reconstruct( print( f"Reconstruction Epoch {self.num_epochs} | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}" ) + if show_metrics and self.world_size == 1: + fig, ax = plt.subplots(figsize=(10, 4), ncols=2) + + ax[0].plot(self._epoch_losses, label="Total Training Loss") + if len(self._val_losses) > 0: + ax[0].plot(self._val_losses, label="Validation Loss") + ax[0].legend() + + for key, value in self._lrs.items(): + ax[1].plot(value, label=key) + + ax[1].legend() + ax[0].legend() + ax[0].set_yscale("log") + ax[1].set_yscale("log") + ax[0].set_xlabel("Epoch") + ax[1].set_xlabel("Epoch") + ax[0].set_ylabel("Loss") + ax[1].set_ylabel("Learning Rate") + + fig.tight_layout() # --- Helper Functions --- diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 50a16024..8c747c63 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -49,6 +49,7 @@ def __init__( self._epoch_losses: list[float] = [] self._consistency_losses: list[float] = [] self._val_losses: list[float] = [] + self._lrs: dict[str, float] = {} # DDP Initialization if isinstance(obj_model, ObjectINR): print("Setting up DDP for obj_model") @@ -125,6 +126,22 @@ def consistency_losses(self) -> NDArray: """ return np.array(self._consistency_losses) + @property + def learning_rates(self) -> dict[str, float]: + """ + Returns the learning rates for each epoch ran. + """ + return self._lrs + + def append_learning_rates(self, learning_rates: dict[str, float]): + """ + Appends the learning rates for each epoch ran. + """ + for key, value in learning_rates.items(): + if key not in self._lrs: + self._lrs[key] = [] + self._lrs[key].append(float(value)) + @property def num_epochs(self) -> int: return len(self._epoch_losses) diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index 711434ec..dc3d918c 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -93,6 +93,7 @@ def reconstruct( new_optimizers: bool = False, obj_constraints: ObjConstraintsType | dict | None = None, dset_constraints: 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isinstance(obj_constraints, dict): @@ -491,6 +492,15 @@ def reconstruct( if mode == "fbp": break + if show_metrics: + fig, ax = plt.subplots() + ax.plot(self._epoch_losses) + ax.set_xlabel("Iteration") + ax.set_ylabel("Loss") + ax.set_title("Reconstruction Loss") + ax.set_yscale("log") + plt.show() + # --- Conventional reconstruction method --- def _adaptive_relaxation(self, n_power_iter: int = 10) -> float: raise NotImplementedError( From 1517b33a97bd4ff748c47fc8ff711acc118df05b Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Mon, 23 Mar 2026 17:40:41 -0700 Subject: [PATCH 197/335] updating viz with ShowParams --- src/quantem/core/visualization/__init__.py | 1 + .../visualization/custom_normalizations.py | 13 +++-- .../core/visualization/visualization.py | 56 ++++++++++++++----- .../core/visualization/visualization_utils.py | 25 ++++++++- 4 files changed, 75 insertions(+), 20 deletions(-) diff --git a/src/quantem/core/visualization/__init__.py b/src/quantem/core/visualization/__init__.py index dcf7055b..d6f8dc95 100644 --- a/src/quantem/core/visualization/__init__.py +++ b/src/quantem/core/visualization/__init__.py @@ -2,6 +2,7 @@ CustomNormalization as CustomNormalization, NormalizationConfig as NormalizationConfig, ) +from quantem.core.visualization.show_params import ShowParams as ShowParams from quantem.core.visualization.visualization import show_2d as show_2d from quantem.core.visualization.visualization_utils import ( ScalebarConfig as ScalebarConfig, diff --git a/src/quantem/core/visualization/custom_normalizations.py b/src/quantem/core/visualization/custom_normalizations.py index ac3a015c..554af378 100644 --- a/src/quantem/core/visualization/custom_normalizations.py +++ b/src/quantem/core/visualization/custom_normalizations.py @@ -1,5 +1,6 @@ from abc import ABC, abstractmethod from dataclasses import dataclass +from typing import Literal import numpy as np from matplotlib import colors @@ -461,8 +462,8 @@ class CustomNormalization(colors.Normalize): def __init__( self, - interval_type: str = "quantile", - stretch_type: str = "linear", + interval_type: Literal["quantile", "manual", "centered"] = "quantile", + stretch_type: Literal["linear", "power", "logarithmic", "asinh"] = "linear", *, data: NDArray | None = None, lower_quantile: float = 0.02, @@ -599,8 +600,8 @@ class NormalizationConfig: Linear range for "asinh" stretch type. """ - interval_type: str = "quantile" - stretch_type: str = "linear" + interval_type: Literal["quantile", "manual", "centered"] = "quantile" + stretch_type: Literal["linear", "power", "logarithmic", "asinh"] = "linear" lower_quantile: float = 0.02 upper_quantile: float = 0.98 vmin: float | None = None @@ -673,5 +674,7 @@ def _resolve_normalization(norm, **kwargs) -> NormalizationConfig: return NORMALIZATION_PRESETS[norm]() elif isinstance(norm, NormalizationConfig): return norm + elif hasattr(norm, "to_config"): + return norm.to_config() else: - raise TypeError("norm must be None, dict, str, or NormalizationConfig") + raise TypeError("norm must be None, dict, str, NormalizationConfig, or ShowParams.Norm") diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index ae3dd508..72400527 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -1,7 +1,9 @@ +from __future__ import annotations + import os import warnings from collections.abc import Sequence -from typing import Any, Optional, Union, cast +from typing import TYPE_CHECKING, Any, Optional, Union, cast import matplotlib as mpl import matplotlib.pyplot as plt @@ -17,6 +19,7 @@ NormalizationConfig, _resolve_normalization, ) +from quantem.core.visualization.show_params import ShowParams from quantem.core.visualization.visualization_utils import ( ScalebarConfig, _resolve_scalebar, @@ -27,12 +30,17 @@ list_of_arrays_to_rgba, ) +if TYPE_CHECKING: + import torch + +ArrayLike = Union[NDArray, "torch.Tensor"] + def _show_2d_array( array: NDArray, *, - norm: Optional[Union[NormalizationConfig, dict, str]] = None, - scalebar: Optional[Union[ScalebarConfig, dict, bool]] = None, + norm: Optional[Union[NormalizationConfig, ShowParams.Norm, dict, str]] = None, + scalebar: Optional[Union[ScalebarConfig, ShowParams.Scalebar, dict, bool]] = None, cmap: Union[str, colors.Colormap] = "gray", chroma_boost: float = 1.0, cbar: bool = False, @@ -151,6 +159,8 @@ def _show_2d_array( scalebar_config.pad_px, scalebar_config.color, scalebar_config.loc, + scalebar_config.fontsize, + scalebar_config.bold, ) for spine in ax.spines.values(): # fixes asymmetry of bbox for some reason @@ -163,8 +173,8 @@ def _show_2d_array( def _show_2d_combined( list_of_arrays: Sequence[NDArray], *, - norm: Optional[Union[NormalizationConfig, dict, str]] = None, - scalebar: Optional[Union[ScalebarConfig, dict, bool]] = None, + norm: Optional[Union[NormalizationConfig, ShowParams.Norm, dict, str]] = None, + scalebar: Optional[Union[ScalebarConfig, ShowParams.Scalebar, dict, bool]] = None, cmap: Union[str, colors.Colormap] = "gray", chroma_boost: float = 1.0, cbar: bool = False, @@ -267,6 +277,8 @@ def _show_2d_combined( scalebar_config.pad_px, scalebar_config.color, scalebar_config.loc, + scalebar_config.fontsize, + scalebar_config.bold, ) return fig, ax @@ -389,8 +401,18 @@ def _norm_show_args( def _normalize_show_args_to_grid( shape: tuple[int, int], - norm: NormalizationConfig | dict | str | Sequence[dict | str] | None = None, - scalebar: ScalebarConfig | dict | bool | Sequence[bool | dict | None] | None = None, + norm: NormalizationConfig + | ShowParams.Norm + | dict + | str + | Sequence[NormalizationConfig | ShowParams.Norm | dict | str] + | None = None, + scalebar: ScalebarConfig + | ShowParams.Scalebar + | dict + | bool + | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] + | None = None, cmap: str | colors.Colormap | Sequence[str] | Sequence[Sequence[str]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, title: str | Sequence[str] | Sequence[Sequence[str]] | None = None, @@ -425,14 +447,22 @@ def _normalize_show_args_to_grid( def show_2d( - arrays: Union[NDArray, Sequence[NDArray], Sequence[Sequence[NDArray]]], + arrays: ArrayLike | Sequence[ArrayLike] | Sequence[Sequence[ArrayLike]], *, - norm: NormalizationConfig + norm: ( + NormalizationConfig + | ShowParams.Norm + | dict + | str + | Sequence[NormalizationConfig | ShowParams.Norm | dict | str] + | None + ) = None, + scalebar: ScalebarConfig + | ShowParams.Scalebar | dict - | str - | Sequence[dict | str] - | None = None, # TODO: Revamp NormalizationConfig, ScalebarConfig with Dataclasses - scalebar: ScalebarConfig | dict | bool | Sequence[bool | dict | None] | None = None, + | bool + | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] + | None = None, cmap: str | colors.Colormap | Sequence[str] | Sequence[Sequence[str]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, title: str | Sequence[str] | Sequence[Sequence[str]] | None = None, diff --git a/src/quantem/core/visualization/visualization_utils.py b/src/quantem/core/visualization/visualization_utils.py index b69e3c86..d3111476 100644 --- a/src/quantem/core/visualization/visualization_utils.py +++ b/src/quantem/core/visualization/visualization_utils.py @@ -139,6 +139,10 @@ class ScalebarConfig: loc : str or int, default="lower right" Location of the scale bar on the plot. Can be a string like "lower right" or an integer location code. + fontsize : int, default=12 + Font size of the scale bar label in points. + bold : bool, default=True + Whether to render the scale bar label in bold. """ sampling: float = 1.0 @@ -148,6 +152,8 @@ class ScalebarConfig: pad_px: float = 0.5 color: str = "white" loc: Union[str, int] = "lower right" + fontsize: int = 12 + bold: bool = False SCALEBAR_KWARGS = [ @@ -190,8 +196,12 @@ def _resolve_scalebar(cfg: Any, **kwargs) -> Optional[ScalebarConfig]: return ScalebarConfig(**cfg) elif isinstance(cfg, ScalebarConfig): return cfg + elif hasattr(cfg, "to_config"): + return cfg.to_config() else: - raise TypeError("scalebar must be None, dict, bool, or ScalebarConfig") + raise TypeError( + "scalebar must be None, dict, bool, ScalebarConfig, or ShowParams.Scalebar" + ) def estimate_scalebar_length(length: float, sampling: float) -> Tuple[float, float]: @@ -273,6 +283,8 @@ def add_scalebar_to_ax( pad_px: float, color: str, loc: Union[str, int], + fontsize: int = 12, + bold: bool = True, ) -> None: """Add a scale bar to a matplotlib axis. @@ -297,7 +309,13 @@ def add_scalebar_to_ax( Color of the scale bar. loc : str or int Location of the scale bar on the plot. + fontsize : int + Font size of the scale bar label in points. + bold : bool + Whether to render the scale bar label in bold. 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b/src/quantem/tomography/tomography.py index ea990169..6463a621 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -160,7 +160,8 @@ def reconstruct( raise ValueError( "num_samples_per_ray schedule must have the same length as num_iter" ) - print("num_samples_per_ray schedule provided.") + if self.global_rank == 0: + print("num_samples_per_ray schedule provided.") loss_func = get_loss_module(name=loss_type, dtype=self.obj_model.dtype, **loss_func_kwargs) From c81b2b5f5fff307477a58555ea947853599d4b5d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 19:57:21 -0700 Subject: [PATCH 202/335] Type-hinting ignore fix in inr.py --- src/quantem/core/ml/inr.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/inr.py b/src/quantem/core/ml/inr.py index a721c008..08b1732d 100644 --- a/src/quantem/core/ml/inr.py +++ b/src/quantem/core/ml/inr.py @@ -118,10 +118,10 @@ def _build(self) -> None: rng = torch.Generator() rng.manual_seed(42) with torch.no_grad(): - self.net[0].linear.weight += ( # pyright: ignore[reportAttributeAccessIssue] + self.net[0].linear.weight += ( # type: ignore[reportAttributeAccessIssue] torch.randn_like(self.net[0].linear.weight) * 5 / self.first_omega_0 # type:ignore ) - self.net[1].linear.weight += ( # pyright: ignore[reportAttributeAccessIssue] + self.net[1].linear.weight += ( # type: ignore[reportAttributeAccessIssue] torch.randn_like(self.net[1].linear.weight) * 0.1 / self.hidden_omega_0 # type:ignore ) From 62616d706700730c25768ef280a7dc7e7c599a57 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 19:59:52 -0700 Subject: [PATCH 203/335] to_numpy() added in tomography_utils.py, and edited background_subtract --- src/quantem/core/utils/tomography_utils.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py index 241687bc..3aad74cb 100644 --- a/src/quantem/core/utils/tomography_utils.py +++ b/src/quantem/core/utils/tomography_utils.py @@ -10,22 +10,13 @@ from tqdm.auto import tqdm from quantem.core.utils.imaging_utils import cross_correlation_shift +from quantem.core.utils.utils import to_numpy from quantem.core.visualization import show_2d ImageType = NDArray[Any] BoolArray = NDArray[np.bool_] -def _as_array(x: ImageType) -> NDArray[Any]: - return ( - x.array - if isinstance( - x, - ) - else np.asarray(x) - ) - - def _bernstein_basis_1d(n: int, t: NDArray[Any]) -> NDArray[Any]: k = np.arange(n + 1, dtype=int) return ( @@ -66,7 +57,7 @@ def background_subtract( - If `True`: (ImageType, numpy.ndarray, numpy.ndarray[bool]) where background and mask are always NumPy. """ - im = _as_array(image).astype(float, copy=True) + im = to_numpy(image).astype(float, copy=True) if im.ndim != 2: raise ValueError("`image` must be 2D") From 3567b4e30a3d65ebdfea275df98857ac414e3a02 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:01:14 -0700 Subject: [PATCH 204/335] tomography_utils.py diff_shift_2d updated docstring --- src/quantem/core/utils/tomography_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py index 3aad74cb..2f9cebca 100644 --- a/src/quantem/core/utils/tomography_utils.py +++ b/src/quantem/core/utils/tomography_utils.py @@ -230,7 +230,7 @@ def centering_com_alignment(image_stack): def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): """ - Shifts a 2D image using grid_sample in a differentiable manner. + Shifts a 2D image using grid_sample in a differentiable manner with boundary conditions applied. Args: image: Tensor of shape [H, W] From 5923624a7a275ba39411eacbb0bf888b4d1da8fd Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:04:21 -0700 Subject: [PATCH 205/335] plot_losses() function implemented both for conventional and INR algorithms --- src/quantem/tomography/tomography.py | 60 +++++++++++++++------------- 1 file changed, 33 insertions(+), 27 deletions(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 6463a621..7e9854ba 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -324,26 +324,7 @@ def reconstruct( f"Reconstruction Epoch {self.num_epochs} | Loss: {total_loss:.5e}, Consistency Loss: {consistency_loss:.5e}, Soft Constraint Loss: {epoch_soft_constraint_loss:.5e}" ) if show_metrics and self.world_size == 1: - fig, ax = plt.subplots(figsize=(10, 4), ncols=2) - - ax[0].plot(self._epoch_losses, label="Total Training Loss") - if len(self._val_losses) > 0: - ax[0].plot(self._val_losses, label="Validation Loss") - ax[0].legend() - - for key, value in self._lrs.items(): - ax[1].plot(value, label=key) - - ax[1].legend() - ax[0].legend() - ax[0].set_yscale("log") - ax[1].set_yscale("log") - ax[0].set_xlabel("Epoch") - ax[1].set_xlabel("Epoch") - ax[0].set_ylabel("Loss") - ax[1].set_ylabel("Learning Rate") - - fig.tight_layout() + self.plot_losses() # --- Helper Functions --- @@ -412,6 +393,26 @@ def save( compression_level=compression_level, ) + def plot_losses(self): + fig, ax = plt.subplots(figsize=(10, 4), ncols=2) + + ax[0].plot(self._epoch_losses, label="Total Training Loss") + if len(self._val_losses) > 0: + ax[0].plot(self._val_losses, label="Validation Loss") + ax[0].legend() + + for key, value in self._lrs.items(): + ax[1].plot(value, label=key) + + ax[1].legend() + ax[0].legend() + ax[0].set_yscale("log") + ax[1].set_yscale("log") + ax[0].set_xlabel("Epoch") + ax[1].set_xlabel("Epoch") + ax[0].set_ylabel("Loss") + ax[1].set_ylabel("Learning Rate") + class TomographyConventional(TomographyBase): """ @@ -498,13 +499,7 @@ def reconstruct( break if show_metrics: - 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self.setup_distributed(device=device) self.dset = dset self.dset.to(device) From 8afb02854d98f4bbd5592309395279f456674a7d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:07:37 -0700 Subject: [PATCH 207/335] logger_tomography fixed linting --- src/quantem/tomography/logger_tomography.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index 821f1814..9798214d 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -15,7 +15,7 @@ def __init__( self, log_dir: str, run_prefix: str, - run_suffix: str = None, + run_suffix: str | None = None, log_images_every: int = 10, ): super().__init__(log_dir, run_prefix, run_suffix, log_images_every) From 4ebb1d9df4ee892afe862634838fa622e3632395 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:10:35 -0700 Subject: [PATCH 208/335] TomographyDataset quantile linter error fixed --- src/quantem/tomography/dataset_models.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index c1e362da..8c58f1c0 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -2,7 +2,6 @@ from dataclasses import dataclass from typing import Any -import numpy as np import torch import torch.nn as nn from numpy.typing import NDArray @@ -185,12 +184,11 @@ def __init__( "The number of tilt projections should be in the first dimension of the dataset." ) - # TODO: Maybe have the validation in here too. - max_val = np.quantile(tilt_stack, 0.95) if type(tilt_stack) is not torch.Tensor: tilt_stack = torch.from_numpy(tilt_stack) if type(tilt_angles) is not torch.Tensor: tilt_angles = torch.from_numpy(tilt_angles) + max_val = torch.quantile(tilt_stack, 0.95) # Tilt stack normalization tilt_stack = tilt_stack / max_val From 435e623ed5198640449587b71a4733104bd4470a Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:11:20 -0700 Subject: [PATCH 209/335] Linter error in .reconstruct --- src/quantem/tomography/tomography.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 7e9854ba..f8337986 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -64,7 +64,7 @@ def reconstruct( optimizer_params: dict | None = None, scheduler_params: dict | None = None, obj_constraints: dict | ObjConstraintsType | None = None, - dset_constraints: dict | DatasetConstraintsType = None, + dset_constraints: dict | DatasetConstraintsType | None = None, num_samples_per_ray: int | list[tuple[int, int]] | None = None, profiling_mode: bool = False, val_fraction: float = 0.0, From 5c10509bd602aedd60d1dbc9658f211897d6344e Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:34:44 -0700 Subject: [PATCH 210/335] Fixed all linter issues in top-level tomography.py --- src/quantem/tomography/logger_tomography.py | 3 +- src/quantem/tomography/object_models.py | 11 ++++++ src/quantem/tomography/radon/radon.py | 7 +++- src/quantem/tomography/tomography.py | 39 ++++++++++++++------- 4 files changed, 46 insertions(+), 14 deletions(-) diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index 9798214d..d148a0f5 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -1,4 +1,5 @@ import matplotlib.pyplot as plt +import numpy as np import torch from quantem.core.ml.logger import LoggerBase @@ -46,7 +47,7 @@ def log_iter( def log_iter_images( self, - pred_volume: torch.Tensor, + pred_volume: np.ndarray, dataset_model: DatasetModelType, iter: int, logger_cmap: str = "turbo", diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index dfedd0a1..c5eb2406 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -209,6 +209,13 @@ def obj(self) -> torch.Tensor: """ raise NotImplementedError + @property + def model(self) -> nn.Module: + """ + Returns the model, should be implemented in subclasses. + """ + raise NotImplementedError + @abstractmethod def dtype(self) -> torch.dtype: """ @@ -476,6 +483,10 @@ def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: def obj(self) -> torch.Tensor: return self._obj + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + @property def obj_view(self) -> np.ndarray: """ diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography/radon/radon.py index 75c66b6f..8c2913d8 100644 --- a/src/quantem/tomography/radon/radon.py +++ b/src/quantem/tomography/radon/radon.py @@ -80,7 +80,12 @@ def radon_torch(images, theta=None, device=None): def iradon_torch( - sinograms, theta=None, output_size=None, filter_name="ramp", circle=True, device=None + sinograms, + theta=None, + output_size=None, + filter_name: str | None = "ramp", + circle=True, + device=None, ): if sinograms.ndim == 2: sinograms = sinograms.unsqueeze(0) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index f8337986..907ad994 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -16,6 +16,8 @@ DatasetConstraintParams, DatasetConstraintsType, DatasetModelType, + TomographyINRDataset, + TomographyPixDataset, ) from quantem.tomography.logger_tomography import LoggerTomography from quantem.tomography.object_models import ( @@ -148,6 +150,13 @@ def reconstruct( val_fraction=val_fraction, ) ) + + # Type check for INR-based reconstruction + if not isinstance(self.dset, TomographyINRDataset): + raise NotImplementedError( + "Only TomographyINRDataset is supported for this reconstruction method." + ) + N = max(self.obj_model.shape) if num_samples_per_ray is None: @@ -167,10 +176,15 @@ def reconstruct( pbar = tqdm(range(num_iter), disable=not self.verbose) for a0 in pbar: - consistency_loss = 0.0 - total_loss = 0.0 - epoch_soft_constraint_loss = 0.0 - self.obj_model.model.train() + consistency_loss = torch.tensor(0.0, device=self.device) + total_loss = torch.tensor(0.0, device=self.device) + epoch_soft_constraint_loss = torch.tensor(0.0, device=self.device) + if isinstance(self.obj_model, ObjectINR): + self.obj_model.model.train() + else: + raise NotImplementedError( + "AD Pixelated reconstruction is not yet implemented. Use ObjectINR instead." + ) self.dset.train() # self._reset_iter_constraints() @@ -200,10 +214,9 @@ def reconstruct( ) pred = integrated_densities.float() + soft_constraints_loss = 0.0 if self.num_epochs > 0: soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) - else: - soft_constraints_loss = 0.0 target = batch["target_value"].to(self.device, non_blocking=True).float() @@ -234,12 +247,13 @@ def reconstruct( self.step_schedulers(loss=total_loss) # TODO: Maybe reorganize the losses so that the order makes sense lol. + avg_val_loss = None if self.val_dataloader is not None: print("Validating...") self.obj_model.model.eval() self.dset.eval() with torch.no_grad(): - val_loss = 0.0 + val_loss = torch.tensor(0.0, device=self.device) for batch in self.val_dataloader: with torch.autocast( @@ -265,7 +279,7 @@ def reconstruct( integrated_densities, target ) - val_loss += batch_val_loss.detach() + soft_constraints_loss.detach() + val_loss += batch_val_loss.detach() avg_val_loss = val_loss.item() / len(self.val_dataloader) @@ -286,7 +300,7 @@ def reconstruct( self._consistency_losses.append(consistency_loss) self.append_learning_rates(self.get_current_lrs()) self.obj_model._soft_constraint_losses.append(epoch_soft_constraint_loss) - if self.val_dataloader is not None: + if avg_val_loss is not None: self._val_losses.append(avg_val_loss) if self.logger is not None: @@ -294,7 +308,7 @@ def reconstruct( self.logger.log_images_every > 0 and self.num_epochs % self.logger.log_images_every == 0 ): - pred_full = self.obj_model.create_volume(return_vol=True) + pred_full = self.obj_model.obj_view if self.global_rank == 0: self.logger.log_iter_images( @@ -423,7 +437,7 @@ class TomographyConventional(TomographyBase): @classmethod def from_models( cls, - dset: DatasetModelType, + dset: TomographyPixDataset, obj_model: ObjectPixelated, logger: LoggerTomography | None = None, device: str = "cuda", @@ -516,13 +530,14 @@ def _reconstruction_epoch( gaussian_kernel: torch.Tensor | None = None, ): loss = 0 + if relaxation == 0.0: relaxation = self._adaptive_relaxation() print(f"Adaptive relaxation: {relaxation}") if inline_alignment: for ind in range(len(self.dset.tilt_angles)): im_proj = proj_forward[:, ind, :] - im_meas = self.dset.forward(ind).target + im_meas = self.dset.forward(ind).target # type: ignore shift = torch_phase_cross_correlation(im_proj, im_meas) if torch.linalg.norm(shift) <= 32: shifted = torch.fft.ifft2( From fc0c675521195eef62caa03cf1a60c71813c3304 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:35:31 -0700 Subject: [PATCH 211/335] logger_tomography.py type-hinting fix --- src/quantem/tomography/logger_tomography.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/logger_tomography.py b/src/quantem/tomography/logger_tomography.py index d148a0f5..e07072f7 100644 --- a/src/quantem/tomography/logger_tomography.py +++ b/src/quantem/tomography/logger_tomography.py @@ -16,7 +16,7 @@ def __init__( self, log_dir: str, run_prefix: str, - run_suffix: str | None = None, + run_suffix: str = "", log_images_every: int = 10, ): super().__init__(log_dir, run_prefix, run_suffix, log_images_every) From 462e7a04e5647511f7275371e5f62f7e0d3f519f Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:39:53 -0700 Subject: [PATCH 212/335] dataset_models.py type-hinting --- src/quantem/tomography/dataset_models.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 8c58f1c0..f6343e4e 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -342,7 +342,7 @@ def device(self, device: torch.device | str): # --- Helper Functions --- @abstractmethod - def to(self, device: torch.device | str): + def to(self, device: torch.device | str): # type: ignore """ Moves the dataset to the device, and also insantiates the aux params to the device. """ @@ -375,8 +375,11 @@ def apply_soft_constraints(self) -> torch.Tensor: soft_loss += tv_loss_shifts return soft_loss - def apply_hard_constraints(self): - pass + def apply_hard_constraints(self) -> torch.Tensor: + """ + No hard constraints have been implemented yet. + """ + return torch.tensor(0.0) class TomographyPixDataset(TomographyDatasetConstraints): @@ -402,7 +405,7 @@ def __init__( _token=_token, ) - def forward( + def forward( # type:ignore self, proj_idx: int, ) -> DatasetValue: @@ -413,7 +416,7 @@ def forward( return DatasetValue( target=self.tilt_stack[proj_idx], - tilt_angle=self.tilt_angles[proj_idx], + tilt_angle=self.tilt_angles[proj_idx].item(), pixel_loc=None, ) From 238bffdac323b4d1dea3bca7ce339dbba031a287 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:40:26 -0700 Subject: [PATCH 213/335] radon.py type-hinting fix --- src/quantem/tomography/radon/radon.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography/radon/radon.py index 8c2913d8..8510e570 100644 --- a/src/quantem/tomography/radon/radon.py +++ b/src/quantem/tomography/radon/radon.py @@ -149,7 +149,9 @@ def iradon_torch( return recon.squeeze(0) if B == 1 else recon -def get_fourier_filter_torch(size, filter_name="ramp", device=None, dtype=torch.float32): +def get_fourier_filter_torch( + size, filter_name: str | None = "ramp", device=None, dtype=torch.float32 +): """ Construct the Fourier filter in PyTorch. """ From 1b3b0cd81f9049fc287ac571a6aa9749fba87924 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 20:43:22 -0700 Subject: [PATCH 214/335] tomography_opt.py linting errors fixed by Claude --- src/quantem/tomography/tomography_opt.py | 24 ++++++++++++++++-------- 1 file changed, 16 insertions(+), 8 deletions(-) diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 1dd406ab..16c846ab 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -1,6 +1,8 @@ +from collections.abc import Mapping + import torch -from quantem.core.ml.optimizer_mixin import OptimizerParams, OptimizerType +from quantem.core.ml.optimizer_mixin import OptimizerParams, OptimizerType, SchedulerType from quantem.tomography.tomography_base import TomographyBase @@ -25,7 +27,7 @@ def _get_default_lr(self, key: str) -> float: raise ValueError(f"Unknown optimization key: {key}") @property - def optimizer_params(self) -> dict[str, dict]: + def optimizer_params(self) -> dict[str, OptimizerType]: return { key: params for key, params in [ @@ -57,10 +59,14 @@ def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): @property def optimizers(self) -> dict[str, torch.optim.Optimizer]: - return { - "object": self.obj_model.optimizer, - "pose": self.dset.optimizer, - } + optimizers = {} + + if self.obj_model.optimizer is not None: + optimizers["object"] = self.obj_model.optimizer + if self.dset.optimizer is not None: + optimizers["pose"] = self.dset.optimizer + + return optimizers def set_optimizers(self): for key, params in self.optimizer_params.items(): @@ -94,7 +100,7 @@ def remove_optimizer(self, key: str): raise ValueError(f"Unknown optimization key: {key}") @property - def scheduler_params(self) -> dict[str, dict]: + def scheduler_params(self) -> dict[str, SchedulerType]: """Returns the parameters used to set the schedulers.""" return { "object": self.obj_model.scheduler_params, @@ -129,7 +135,9 @@ def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: return schedulers - def set_schedulers(self, params: dict[str, dict], num_iter: int | None = None): + def set_schedulers( + self, params: Mapping[str, SchedulerType | dict], num_iter: int | None = None + ): for key, scheduler_params in params.items(): if key == "object": self.obj_model.set_scheduler(scheduler_params, num_iter=num_iter) From ab444888d646d657a09cadaf6f5489aa62604065 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 22:24:29 -0700 Subject: [PATCH 215/335] Removed tomography_old and put forkserver back in instead of spawn. --- src/quantem/core/ml/ddp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index dedd90dd..351d703d 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -102,7 +102,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=True, persistent_workers=persist, - multiprocessing_context="spawn", + multiprocessing_context="forkserver", worker_init_fn=worker_init_fn, ) @@ -116,7 +116,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=False, persistent_workers=persist, - multiprocessing_context="spawn", + multiprocessing_context="forkserver", worker_init_fn=worker_init_fn, ) val_dataloader = val_dataloader From 8d85f30b228b0a09231d25961738dad5ac50355d Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Mon, 23 Mar 2026 22:25:34 -0700 Subject: [PATCH 216/335] Nevermind back to spawn, DataLoader doesn't like to be in forkserver --- src/quantem/core/ml/ddp.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index 351d703d..dedd90dd 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -102,7 +102,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=True, persistent_workers=persist, - multiprocessing_context="forkserver", + multiprocessing_context="spawn", worker_init_fn=worker_init_fn, ) @@ -116,7 +116,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=False, persistent_workers=persist, - multiprocessing_context="forkserver", + multiprocessing_context="spawn", worker_init_fn=worker_init_fn, ) val_dataloader = val_dataloader From 96c1665cad674dcf9224805219b97da76ba57725 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 10:27:27 -0700 Subject: [PATCH 217/335] forgot to include the new file whoops --- src/quantem/core/visualization/show_params.py | 189 ++++++++++++++++++ 1 file changed, 189 insertions(+) create mode 100644 src/quantem/core/visualization/show_params.py diff --git a/src/quantem/core/visualization/show_params.py b/src/quantem/core/visualization/show_params.py new file mode 100644 index 00000000..cef2ec5a --- /dev/null +++ b/src/quantem/core/visualization/show_params.py @@ -0,0 +1,189 @@ +from dataclasses import dataclass, fields +from typing import Literal, Optional + +from quantem.core.visualization.custom_normalizations import NormalizationConfig +from quantem.core.visualization.visualization_utils import ScalebarConfig + + +class ShowParams: + """ + Container for ``show_2d`` parameter dataclasses. + + Nested classes + -------------- + Norm + Normalization configuration (interval + stretch). + Scalebar + Scale bar overlay configuration. + + Examples + -------- + >>> show_2d(img, norm=ShowParams.Norm(power=0.5)) + >>> show_2d(img, scalebar=ShowParams.Scalebar(sampling=0.5, units="Å")) + >>> show_2d(dp, norm=ShowParams.Norm.log_auto(), cbar=True, cmap="turbo") + """ + + @dataclass + class Norm: + """ + Normalization parameters for ``show_2d``. + + Controls how pixel values are mapped to the [0, 1] display range via + an *interval* (which values to keep) and a *stretch* (non-linear + transfer function). + + If ``vmin`` or ``vmax`` is set and ``interval_type`` is left as the + default ``"quantile"``, it is automatically changed to ``"manual"``. + Likewise, setting ``vcenter`` to a non-zero value or providing + ``half_range`` auto-selects ``"centered"``. + + Parameters + ---------- + interval_type : ``"quantile"`` | ``"manual"`` | ``"centered"`` + How to determine the data range. + stretch_type : ``"linear"`` | ``"power"`` | ``"logarithmic"`` | ``"asinh"`` + Transfer function applied after interval mapping. + lower_quantile : float + Lower quantile for ``"quantile"`` interval. Default 0.02. + upper_quantile : float + Upper quantile for ``"quantile"`` interval. Default 0.98. + vmin : float or None + Explicit minimum for ``"manual"`` interval. + vmax : float or None + Explicit maximum for ``"manual"`` interval. + vcenter : float + Centre value for ``"centered"`` interval. Default 0.0. + half_range : float or None + Symmetric half-range for ``"centered"`` interval. + power : float + Exponent for ``"power"`` stretch (e.g. 0.5 = sqrt). Default 1.0. + logarithmic_index : float + Index *a* for ``"logarithmic"`` stretch: ``log(a*x+1)/log(a+1)``. + Default 1000. + asinh_linear_range : float + Transition parameter *a* for ``"asinh"`` stretch. Default 0.1. + + Examples + -------- + >>> ShowParams.Norm() # quantile + linear (default) + >>> ShowParams.Norm(power=0.5) # quantile + sqrt stretch + >>> ShowParams.Norm(vmin=0, vmax=1000) # auto → manual range + >>> ShowParams.Norm.log_auto() # quantile + log stretch + >>> ShowParams.Norm.centered(half_range=5) # centered ± 5, linear + """ + + interval_type: Literal["quantile", "manual", "centered"] = "quantile" + stretch_type: Literal["linear", "power", "logarithmic", "asinh"] = "linear" + lower_quantile: float = 0.02 + upper_quantile: float = 0.98 + vmin: Optional[float] = None + vmax: Optional[float] = None + vcenter: float = 0.0 + half_range: Optional[float] = None + power: float = 1.0 + logarithmic_index: float = 1000.0 + asinh_linear_range: float = 0.1 + + def __post_init__(self) -> None: + if self.interval_type != "quantile": + return + if self.vmin is not None or self.vmax is not None: + self.interval_type = "manual" + elif self.vcenter != 0.0 or self.half_range is not None: + self.interval_type = "centered" + + def to_config(self) -> NormalizationConfig: + """Convert to a ``NormalizationConfig``.""" + return NormalizationConfig(**{f.name: getattr(self, f.name) for f in fields(self)}) + + # ---- presets (mirror NORMALIZATION_PRESETS) ---- + + @classmethod + def linear_auto(cls, **kw) -> "ShowParams.Norm": + """Quantile interval + linear stretch (the default).""" + return cls(**kw) + + @classmethod + def minmax(cls, **kw) -> "ShowParams.Norm": + """Full min/max interval + linear stretch.""" + return cls(interval_type="manual", **kw) + + @classmethod + def centered( + cls, vcenter: float = 0.0, half_range: Optional[float] = None, **kw + ) -> "ShowParams.Norm": + """Centered interval + linear stretch.""" + return cls(interval_type="centered", vcenter=vcenter, half_range=half_range, **kw) + + @classmethod + def log_auto(cls, **kw) -> "ShowParams.Norm": + """Quantile interval + logarithmic stretch.""" + return cls(stretch_type="logarithmic", **kw) + + @classmethod + def log_minmax(cls, **kw) -> "ShowParams.Norm": + """Full min/max interval + logarithmic stretch.""" + return cls(interval_type="manual", stretch_type="logarithmic", **kw) + + @classmethod + def power_sqrt(cls, **kw) -> "ShowParams.Norm": + """Quantile interval + square-root (power=0.5) stretch.""" + return cls(stretch_type="power", power=0.5, **kw) + + @classmethod + def power_squared(cls, **kw) -> "ShowParams.Norm": + """Quantile interval + squared (power=2.0) stretch.""" + return cls(stretch_type="power", power=2.0, **kw) + + @classmethod + def asinh_centered(cls, vcenter: float = 0.0, **kw) -> "ShowParams.Norm": + """Centered interval + asinh stretch.""" + return cls(interval_type="centered", stretch_type="asinh", vcenter=vcenter, **kw) + + @dataclass + class Scalebar: + """ + Scale bar parameters for ``show_2d``. + + Parameters + ---------- + sampling : float + Physical units per pixel. Default 1.0. + units : str + Unit label displayed on the scale bar (e.g. ``"Å"``, ``"nm"``, + ``"1/Å"``). Default ``"pixels"``. + length : float or None + Fixed scale bar length in physical units. ``None`` auto-estimates + a "nice" length. + width_px : float + Thickness of the bar in image pixels. Default 1. + pad_px : float + Padding between bar and plot edge in image pixels. Default 0.5. + color : str + Bar and label colour. Default ``"white"``. + loc : ``"lower right"`` | ``"lower left"`` | ``"upper right"`` | ``"upper left"`` + Anchor location. Default ``"lower right"``. + fontsize : int + Font size of the scale bar label in points. Default 12. + bold : bool + Whether to render the label in bold. Default True. + + Examples + -------- + >>> ShowParams.Scalebar(sampling=0.5, units="Å") + >>> ShowParams.Scalebar(sampling=0.02, units="1/Å", color="black", fontsize=16) + """ + + sampling: float = 1.0 + units: str = "pixels" + length: Optional[float] = None + width_px: float = 1 + pad_px: float = 0.5 + color: str = "white" + loc: Literal["lower right", "lower left", "upper right", "upper left"] = "lower right" + fontsize: int = 12 + bold: bool = False + + def to_config(self) -> ScalebarConfig: + """Convert to a ``ScalebarConfig``.""" + return ScalebarConfig(**{f.name: getattr(self, f.name) for f in fields(self)}) From c60a1302609314c1fa01ebe5af2442b091a9dce0 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 10:45:49 -0700 Subject: [PATCH 218/335] adding checks for incompatible arguments and validators --- src/quantem/core/utils/validators.py | 17 ++- src/quantem/core/visualization/show_params.py | 102 ++++++++++++++++-- 2 files changed, 110 insertions(+), 9 deletions(-) diff --git a/src/quantem/core/utils/validators.py b/src/quantem/core/utils/validators.py index 02f8a935..50361e25 100644 --- a/src/quantem/core/utils/validators.py +++ b/src/quantem/core/utils/validators.py @@ -10,8 +10,9 @@ from quantem.core.utils import array_funcs as af if TYPE_CHECKING: - import cupy as cp + import cupy as cp # type: ignore import torch + TensorLike: TypeAlias = ArrayLike | torch.Tensor else: TensorLike: TypeAlias = ArrayLike @@ -20,9 +21,10 @@ if config.get("has_cupy"): import cupy as cp + # --- Dataset Validation Functions --- def ensure_valid_array( - array: "np.ndarray | cp.ndarray", dtype: DTypeLike = None, ndim: int | None = None + array: "np.ndarray | cp.ndarray", dtype: DTypeLike | None = None, ndim: int | None = None ) -> Union[np.ndarray, "cp.ndarray"]: """ Ensure input is a numpy array or cupy array (if available), converting if necessary. @@ -359,6 +361,17 @@ def validate_gt( return value +def validate_lt( + value: float | int, cutoff: float | int, name: str, leq: bool = False +) -> float | int: + if leq: # less than or equal to + if value > cutoff: + raise ValueError(f"{name} must be less than or equal to {cutoff}, got {value}") + elif value >= cutoff: + raise ValueError(f"{name} must be less than {cutoff}, got {value}") + return value + + def validate_int(value: int | float, name: str) -> int: try: return int(round(value)) diff --git a/src/quantem/core/visualization/show_params.py b/src/quantem/core/visualization/show_params.py index cef2ec5a..ed7a21df 100644 --- a/src/quantem/core/visualization/show_params.py +++ b/src/quantem/core/visualization/show_params.py @@ -1,6 +1,8 @@ +import warnings from dataclasses import dataclass, fields from typing import Literal, Optional +from quantem.core.utils.validators import validate_gt, validate_lt from quantem.core.visualization.custom_normalizations import NormalizationConfig from quantem.core.visualization.visualization_utils import ScalebarConfig @@ -72,7 +74,7 @@ class Norm: >>> ShowParams.Norm.centered(half_range=5) # centered ± 5, linear """ - interval_type: Literal["quantile", "manual", "centered"] = "quantile" + interval_type: Optional[Literal["quantile", "manual", "centered"]] = None stretch_type: Literal["linear", "power", "logarithmic", "asinh"] = "linear" lower_quantile: float = 0.02 upper_quantile: float = 0.98 @@ -85,12 +87,91 @@ class Norm: asinh_linear_range: float = 0.1 def __post_init__(self) -> None: - if self.interval_type != "quantile": - return - if self.vmin is not None or self.vmax is not None: - self.interval_type = "manual" - elif self.vcenter != 0.0 or self.half_range is not None: - self.interval_type = "centered" + manual_set = self.vmin is not None or self.vmax is not None + centered_set = self.vcenter != 0.0 or self.half_range is not None + quantile_set = self.lower_quantile != 0.02 or self.upper_quantile != 0.98 + user_chose = self.interval_type is not None + + # --- auto-infer interval_type when not explicitly provided --- + if not user_chose: + if manual_set and centered_set: + warnings.warn( + "Both vmin/vmax and vcenter/half_range were set; " + "defaulting to interval_type='manual'.", + stacklevel=2, + ) + self.interval_type = "manual" + elif manual_set: + self.interval_type = "manual" + elif centered_set: + self.interval_type = "centered" + else: + self.interval_type = "quantile" + + # --- warn about ignored interval fields --- + if self.interval_type == "manual" and quantile_set: + warnings.warn( + "lower_quantile/upper_quantile are ignored when interval_type='manual'.", + stacklevel=2, + ) + if self.interval_type == "manual" and centered_set: + warnings.warn( + "vcenter/half_range are ignored when interval_type='manual'.", + stacklevel=2, + ) + if self.interval_type == "quantile" and manual_set: + warnings.warn( + "vmin/vmax are ignored when interval_type='quantile'.", + stacklevel=2, + ) + if self.interval_type == "quantile" and centered_set: + warnings.warn( + "vcenter/half_range are ignored when interval_type='quantile'.", + stacklevel=2, + ) + if self.interval_type == "centered" and manual_set: + warnings.warn( + "vmin/vmax are ignored when interval_type='centered'.", + stacklevel=2, + ) + if self.interval_type == "centered" and quantile_set: + warnings.warn( + "lower_quantile/upper_quantile are ignored when interval_type='centered'.", + stacklevel=2, + ) + + # --- warn about ignored stretch fields --- + if self.power != 1.0 and self.stretch_type not in ("power", "linear"): + warnings.warn( + f"power={self.power} is ignored when stretch_type='{self.stretch_type}'.", + stacklevel=2, + ) + if self.logarithmic_index != 1000.0 and self.stretch_type != "logarithmic": + warnings.warn( + f"logarithmic_index={self.logarithmic_index} is ignored " + f"when stretch_type='{self.stretch_type}'.", + stacklevel=2, + ) + if self.asinh_linear_range != 0.1 and self.stretch_type != "asinh": + warnings.warn( + f"asinh_linear_range={self.asinh_linear_range} is ignored " + f"when stretch_type='{self.stretch_type}'.", + stacklevel=2, + ) + + # --- invalid value checks --- + if self.vmin is not None and self.vmax is not None: + validate_gt(self.vmax, self.vmin, "vmax", geq=False) + validate_gt(self.lower_quantile, 0, "lower_quantile", geq=True) + validate_gt(self.upper_quantile, self.lower_quantile, "upper_quantile") + validate_lt(self.upper_quantile, 1.0, "upper_quantile", leq=True) + if self.upper_quantile > 1.0: + raise ValueError(f"upper_quantile must be <= 1, got {self.upper_quantile}.") + if self.half_range is not None: + validate_gt(self.half_range, 0, "half_range", geq=True) + validate_gt(self.power, 0, "power") + validate_gt(self.logarithmic_index, 0, "logarithmic_index") + validate_gt(self.asinh_linear_range, 0, "asinh_linear_range") def to_config(self) -> NormalizationConfig: """Convert to a ``NormalizationConfig``.""" @@ -184,6 +265,13 @@ class Scalebar: fontsize: int = 12 bold: bool = False + def __post_init__(self) -> None: + validate_gt(self.sampling, 0, "sampling") + if self.length is not None: + validate_gt(self.length, 0, "length") + validate_gt(self.width_px, 0, "width_px") + validate_gt(self.fontsize, 0, "fontsize") + def to_config(self) -> ScalebarConfig: """Convert to a ``ScalebarConfig``.""" return ScalebarConfig(**{f.name: getattr(self, f.name) for f in fields(self)}) From ccaf70c3e602ed914659fcf996dee4434f1ac33d Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 10:59:48 -0700 Subject: [PATCH 219/335] updating type hinting to 3.11+ --- src/quantem/core/visualization/show_params.py | 14 ++++---- .../core/visualization/visualization.py | 30 ++++++++--------- .../core/visualization/visualization_utils.py | 33 ++++++++++--------- 3 files changed, 38 insertions(+), 39 deletions(-) diff --git a/src/quantem/core/visualization/show_params.py b/src/quantem/core/visualization/show_params.py index ed7a21df..d0399db9 100644 --- a/src/quantem/core/visualization/show_params.py +++ b/src/quantem/core/visualization/show_params.py @@ -1,6 +1,6 @@ import warnings from dataclasses import dataclass, fields -from typing import Literal, Optional +from typing import Literal from quantem.core.utils.validators import validate_gt, validate_lt from quantem.core.visualization.custom_normalizations import NormalizationConfig @@ -74,14 +74,14 @@ class Norm: >>> ShowParams.Norm.centered(half_range=5) # centered ± 5, linear """ - interval_type: Optional[Literal["quantile", "manual", "centered"]] = None + interval_type: Literal["quantile", "manual", "centered"] | None = None stretch_type: Literal["linear", "power", "logarithmic", "asinh"] = "linear" lower_quantile: float = 0.02 upper_quantile: float = 0.98 - vmin: Optional[float] = None - vmax: Optional[float] = None + vmin: float | None = None + vmax: float | None = None vcenter: float = 0.0 - half_range: Optional[float] = None + half_range: float | None = None power: float = 1.0 logarithmic_index: float = 1000.0 asinh_linear_range: float = 0.1 @@ -191,7 +191,7 @@ def minmax(cls, **kw) -> "ShowParams.Norm": @classmethod def centered( - cls, vcenter: float = 0.0, half_range: Optional[float] = None, **kw + cls, vcenter: float = 0.0, half_range: float | None = None, **kw ) -> "ShowParams.Norm": """Centered interval + linear stretch.""" return cls(interval_type="centered", vcenter=vcenter, half_range=half_range, **kw) @@ -257,7 +257,7 @@ class Scalebar: sampling: float = 1.0 units: str = "pixels" - length: Optional[float] = None + length: float | None = None width_px: float = 1 pad_px: float = 0.5 color: str = "white" diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 72400527..6cb45e44 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -3,11 +3,12 @@ import os import warnings from collections.abc import Sequence -from typing import TYPE_CHECKING, Any, Optional, Union, cast +from typing import Any, cast import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np +import torch from matplotlib import colors from mpl_toolkits.axes_grid1 import make_axes_locatable from numpy.typing import NDArray @@ -30,22 +31,19 @@ list_of_arrays_to_rgba, ) -if TYPE_CHECKING: - import torch - -ArrayLike = Union[NDArray, "torch.Tensor"] +ArrayLike = NDArray | torch.Tensor def _show_2d_array( array: NDArray, *, - norm: Optional[Union[NormalizationConfig, ShowParams.Norm, dict, str]] = None, - scalebar: Optional[Union[ScalebarConfig, ShowParams.Scalebar, dict, bool]] = None, - cmap: Union[str, colors.Colormap] = "gray", + norm: NormalizationConfig | ShowParams.Norm | dict | str | None = None, + scalebar: ScalebarConfig | ShowParams.Scalebar | dict | bool | None = None, + cmap: str | colors.Colormap = "gray", chroma_boost: float = 1.0, cbar: bool = False, - title: Optional[str] = None, - figax: Optional[tuple[Any, Any]] = None, + title: str | None = None, + figax: tuple[Any, Any] | None = None, figsize: tuple[int, int] = (8, 8), show_ticks: bool = False, **kwargs: Any, @@ -173,14 +171,14 @@ def _show_2d_array( def _show_2d_combined( list_of_arrays: Sequence[NDArray], *, - norm: Optional[Union[NormalizationConfig, ShowParams.Norm, dict, str]] = None, - scalebar: Optional[Union[ScalebarConfig, ShowParams.Scalebar, dict, bool]] = None, - cmap: Union[str, colors.Colormap] = "gray", + norm: NormalizationConfig | ShowParams.Norm | dict | str | None = None, + scalebar: ScalebarConfig | ShowParams.Scalebar | dict | bool | None = None, + cmap: str | colors.Colormap = "gray", chroma_boost: float = 1.0, cbar: bool = False, - figax: Optional[tuple[Any, Any]] = None, + figax: tuple[Any, Any] | None = None, figsize: tuple[int, int] = (8, 8), - title: Optional[str] = None, + title: str | None = None, show_ticks: bool = False, **kwargs: Any, ) -> tuple[Any, Any]: @@ -285,7 +283,7 @@ def _show_2d_combined( def _normalize_show_input_to_grid( - arrays: Any, # Union[NDArray, Sequence[NDArray], Sequence[Sequence[NDArray]]], + arrays: Any, # NDArray | Sequence[NDArray] | Sequence[Sequence[NDArray]] ) -> list[list[NDArray]]: """Convert various input formats to a consistent grid format for visualization. diff --git a/src/quantem/core/visualization/visualization_utils.py b/src/quantem/core/visualization/visualization_utils.py index d3111476..6b968708 100644 --- a/src/quantem/core/visualization/visualization_utils.py +++ b/src/quantem/core/visualization/visualization_utils.py @@ -1,7 +1,8 @@ from dataclasses import dataclass -from typing import Any, List, Optional, Sequence, Tuple, Union, cast +from typing import Any, cast import matplotlib as mpl +import matplotlib.pyplot as plt import numpy as np from colorspacious import cspace_convert from matplotlib import cm, colors, legend, ticker @@ -18,9 +19,9 @@ def array_to_rgba( scaled_amplitude: NDArray, - scaled_angle: Optional[NDArray] = None, + scaled_angle: NDArray | None = None, *, - cmap: Union[str, colors.Colormap] = "gray", + cmap: str | colors.Colormap = "gray", chroma_boost: float = 1, ) -> NDArray: """Convert amplitude and angle arrays to an RGBA color array. @@ -73,7 +74,7 @@ def array_to_rgba( def list_of_arrays_to_rgba( - list_of_arrays: List[NDArray], + list_of_arrays: list[NDArray], *, norm: CustomNormalization = CustomNormalization(), chroma_boost: float = 1, @@ -147,11 +148,11 @@ class ScalebarConfig: sampling: float = 1.0 units: str = "pixels" - length: Optional[float] = None + length: float | None = None width_px: float = 1 pad_px: float = 0.5 color: str = "white" - loc: Union[str, int] = "lower right" + loc: str | int = "lower right" fontsize: int = 12 bold: bool = False @@ -162,7 +163,7 @@ class ScalebarConfig: ] -def _resolve_scalebar(cfg: Any, **kwargs) -> Optional[ScalebarConfig]: +def _resolve_scalebar(cfg: Any, **kwargs) -> ScalebarConfig | None: """Resolve various input types to a ScalebarConfig object. Parameters @@ -204,7 +205,7 @@ def _resolve_scalebar(cfg: Any, **kwargs) -> Optional[ScalebarConfig]: ) -def estimate_scalebar_length(length: float, sampling: float) -> Tuple[float, float]: +def estimate_scalebar_length(length: float, sampling: float) -> tuple[float, float]: """Estimate an appropriate scale bar length based on data dimensions. This function calculates a "nice" scale bar length that is a multiple of @@ -277,12 +278,12 @@ def add_scalebar_to_ax( ax: Axes, array_size: float, sampling: float, - length_units: Optional[float], + length_units: float | None, units: str, width_px: float, pad_px: float, color: str, - loc: Union[str, int], + loc: str | int, fontsize: int = 12, bold: bool = True, ) -> None: @@ -444,7 +445,7 @@ def add_arg_cbar_to_ax( return cb_angle -def turbo_black(num_colors: int = 256, fade_len: Optional[int] = None) -> colors.ListedColormap: +def turbo_black(num_colors: int = 256, fade_len: int | None = None) -> colors.ListedColormap: """Create a modified version of the 'turbo' colormap that fades to black. This function creates a colormap based on the 'turbo' colormap but with @@ -483,12 +484,12 @@ def turbo_black(num_colors: int = 256, fade_len: Optional[int] = None) -> colors def bilinear_histogram_2d( - shape: Tuple[int, int], + shape: tuple[int, int], x: NDArray, y: NDArray, weight: NDArray, - origin: Tuple[float, float] = (0.0, 0.0), - sampling: Tuple[float, float] = (1.0, 1.0), + origin: tuple[float, float] = (0.0, 0.0), + sampling: tuple[float, float] = (1.0, 1.0), statistic: str = "sum", ) -> NDArray: """Create a 2D histogram with bilinear binning. @@ -533,7 +534,7 @@ def bilinear_histogram_2d( ) # Convert shape tuple to list for binned_statistic_2d - bins: Sequence[int] = [Nx, Ny] + bins: list[int] = [Nx, Ny] hist, _, _, _ = binned_statistic_2d( x, y, @@ -568,7 +569,7 @@ def axes_with_inset( - Fractional inset by default (relative to main axes size). - Only the inset axes background is set to black (main axes stays default). """ - fig, ax_main = mpl.pyplot.subplots(1, 1, figsize=axsize) + fig, ax_main = plt.subplots(1, 1, figsize=axsize) # lazy import here (some environments need it this way) from mpl_toolkits.axes_grid1.inset_locator import inset_axes From 390a81cb1fcd2f90784cea0864ddbd40bf76e62b Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 11:39:10 -0700 Subject: [PATCH 220/335] bugfix vmax and refactor list to combine --- src/quantem/core/visualization/visualization.py | 6 +++--- .../core/visualization/visualization_utils.py | 2 +- tests/visualization/test_visualization_utils.py | 12 ++++++------ 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 6cb45e44..df559af4 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -28,7 +28,7 @@ add_cbar_to_ax, add_scalebar_to_ax, array_to_rgba, - list_of_arrays_to_rgba, + combine_arrays_to_rgba, ) ArrayLike = NDArray | torch.Tensor @@ -233,7 +233,7 @@ def _show_2d_combined( lower_quantile=norm_config.lower_quantile, upper_quantile=norm_config.upper_quantile, vmin=norm_config.vmin, - vmax=norm_config.vmin, + vmax=norm_config.vmax, vcenter=norm_config.vcenter, half_range=norm_config.half_range, power=norm_config.power, @@ -243,7 +243,7 @@ def _show_2d_combined( # Convert Sequence to list for list_of_arrays_to_rgba list_of_arrays_list = list(list_of_arrays) - rgba = list_of_arrays_to_rgba( + rgba = combine_arrays_to_rgba( list_of_arrays_list, norm=norm_obj, chroma_boost=chroma_boost, diff --git a/src/quantem/core/visualization/visualization_utils.py b/src/quantem/core/visualization/visualization_utils.py index 6b968708..4b8ebae5 100644 --- a/src/quantem/core/visualization/visualization_utils.py +++ b/src/quantem/core/visualization/visualization_utils.py @@ -73,7 +73,7 @@ def array_to_rgba( return rgba -def list_of_arrays_to_rgba( +def combine_arrays_to_rgba( list_of_arrays: list[NDArray], *, norm: CustomNormalization = CustomNormalization(), diff --git a/tests/visualization/test_visualization_utils.py b/tests/visualization/test_visualization_utils.py index ca114774..1617f0a4 100644 --- a/tests/visualization/test_visualization_utils.py +++ b/tests/visualization/test_visualization_utils.py @@ -14,8 +14,8 @@ add_scalebar_to_ax, array_to_rgba, bilinear_histogram_2d, + combine_arrays_to_rgba, estimate_scalebar_length, - list_of_arrays_to_rgba, turbo_black, ) @@ -85,14 +85,14 @@ def test_array_to_rgba_shape_mismatch(self, sample_array): array_to_rgba(sample_array, wrong_shape) -class TestListOfArraysToRGBA: - def test_list_of_arrays_to_rgba(self, sample_arrays): - rgba = list_of_arrays_to_rgba(sample_arrays) +class TestCombineArraysToRGBA: + def test_combine_arrays_to_rgba(self, sample_arrays): + rgba = combine_arrays_to_rgba(sample_arrays) assert rgba.shape == (*sample_arrays[0].shape, 4) assert np.all(rgba[..., 3] == 1) # alpha channel should be 1 - def test_list_of_arrays_to_rgba_with_chroma_boost(self, sample_arrays): - rgba = list_of_arrays_to_rgba(sample_arrays, chroma_boost=2.0) + def test_combine_arrays_to_rgba_with_chroma_boost(self, sample_arrays): + rgba = combine_arrays_to_rgba(sample_arrays, chroma_boost=2.0) assert rgba.shape == (*sample_arrays[0].shape, 4) From 7c2735fad1e4d01e612de18b839136131f3745c6 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 12:58:44 -0700 Subject: [PATCH 221/335] fixing type hinting and potential circular import --- src/quantem/core/visualization/__init__.py | 1 + src/quantem/core/visualization/visualization.py | 14 +++++++++++--- 2 files changed, 12 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/visualization/__init__.py b/src/quantem/core/visualization/__init__.py index d6f8dc95..125fb02e 100644 --- a/src/quantem/core/visualization/__init__.py +++ b/src/quantem/core/visualization/__init__.py @@ -9,3 +9,4 @@ turbo_black as turbo_black, axes_with_inset as axes_with_inset, ) +from quantem.core.visualization.line_scan import linescan as linescan diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index df559af4..c45e4daf 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -1,9 +1,9 @@ -from __future__ import annotations +# from __future__ import annotations import os import warnings from collections.abc import Sequence -from typing import Any, cast +from typing import TYPE_CHECKING, Any, TypeAlias, Union, cast import matplotlib as mpl import matplotlib.pyplot as plt @@ -31,7 +31,10 @@ combine_arrays_to_rgba, ) -ArrayLike = NDArray | torch.Tensor +if TYPE_CHECKING: + from quantem.core.datastructures import Dataset2d + +ArrayLike: TypeAlias = Union[NDArray, torch.Tensor, "Dataset2d"] # union required here def _show_2d_array( @@ -404,12 +407,14 @@ def _normalize_show_args_to_grid( | dict | str | Sequence[NormalizationConfig | ShowParams.Norm | dict | str] + | Sequence[Sequence[NormalizationConfig | ShowParams.Norm | dict | str]] | None = None, scalebar: ScalebarConfig | ShowParams.Scalebar | dict | bool | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] + | Sequence[Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None]] | None = None, cmap: str | colors.Colormap | Sequence[str] | Sequence[Sequence[str]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, @@ -444,6 +449,7 @@ def _normalize_show_args_to_grid( return args +# the type hinting is a bit of a mess, but not sure how to improve it def show_2d( arrays: ArrayLike | Sequence[ArrayLike] | Sequence[Sequence[ArrayLike]], *, @@ -453,6 +459,7 @@ def show_2d( | dict | str | Sequence[NormalizationConfig | ShowParams.Norm | dict | str] + | Sequence[Sequence[NormalizationConfig | ShowParams.Norm | dict | str]] | None ) = None, scalebar: ScalebarConfig @@ -460,6 +467,7 @@ def show_2d( | dict | bool | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] + | Sequence[Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None]] | None = None, cmap: str | colors.Colormap | Sequence[str] | Sequence[Sequence[str]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, From ee204ab3e38c9056bc559812e7a6d84dcddc7720 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 13:09:38 -0700 Subject: [PATCH 222/335] fixing arg passing to _show_combined --- .../core/visualization/visualization.py | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index c45e4daf..844f0e25 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -176,7 +176,6 @@ def _show_2d_combined( *, norm: NormalizationConfig | ShowParams.Norm | dict | str | None = None, scalebar: ScalebarConfig | ShowParams.Scalebar | dict | bool | None = None, - cmap: str | colors.Colormap = "gray", chroma_boost: float = 1.0, cbar: bool = False, figax: tuple[Any, Any] | None = None, @@ -591,7 +590,22 @@ def show_2d( if kwargs.pop("combine_images", False): if nrows > 1: raise ValueError() - fig, axs = _show_2d_combined(grid[0], figax=figax, **kwargs) # TODO pass args here + if isinstance(norm, Sequence): # flatten norm + norm = norm[0] if not isinstance(norm[0], Sequence) else norm[0][0] + if isinstance(scalebar, Sequence): # flatten scalebar + scalebar = scalebar[0] if not isinstance(scalebar[0], Sequence) else scalebar[0][0] + if isinstance(title, Sequence) and not isinstance(title, str): # flatten title + title = title[0] if isinstance(title[0], str) else title[0][0] + + fig, axs = _show_2d_combined( + grid[0], + norm=norm, + scalebar=scalebar, + figax=figax, + title=title, + figsize=kwargs.pop("figsize", axsize), + **kwargs, + ) else: normalized_args = _normalize_show_args_to_grid( shape=(nrows, ncols), From b63997e11125adfb45396b9dd8e5e37df4a16398 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 14:53:53 -0700 Subject: [PATCH 223/335] adding inverse units picking --- .../core/visualization/visualization.py | 26 ++-- .../core/visualization/visualization_utils.py | 113 ++++++++++++++---- 2 files changed, 104 insertions(+), 35 deletions(-) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 844f0e25..bb32d480 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -131,10 +131,10 @@ def _show_2d_array( ax.imshow(rgba, interpolation=config.get("viz.interpolation")) - if show_ticks: - ax.set(title=title) - else: - ax.set(xticks=[], yticks=[], title=title) + if title is not None: + ax.set_title(title, fontsize=kwargs.get("title_fontsize", 12)) + if not show_ticks: + ax.set(xticks=[], yticks=[]) if cbar: divider = make_axes_locatable(ax) @@ -258,10 +258,10 @@ def _show_2d_combined( ax.imshow(rgba, interpolation=config.get("viz.interpolation")) - if show_ticks: - ax.set(title=title) - else: - ax.set(xticks=[], yticks=[], title=title) + if title is not None: + ax.set_title(title, fontsize=kwargs.get("title_fontsize", 12)) + if not show_ticks: + ax.set(xticks=[], yticks=[]) if cbar: raise NotImplementedError() @@ -415,7 +415,10 @@ def _normalize_show_args_to_grid( | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] | Sequence[Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None]] | None = None, - cmap: str | colors.Colormap | Sequence[str] | Sequence[Sequence[str]] = "gray", + cmap: str + | colors.Colormap + | Sequence[str | colors.Colormap] + | Sequence[Sequence[str | colors.Colormap]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, title: str | Sequence[str] | Sequence[Sequence[str]] | None = None, chroma_boost: float | Sequence[float] = 1.0, @@ -468,7 +471,10 @@ def show_2d( | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] | Sequence[Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None]] | None = None, - cmap: str | colors.Colormap | Sequence[str] | Sequence[Sequence[str]] = "gray", + cmap: str + | colors.Colormap + | Sequence[str | colors.Colormap] + | Sequence[Sequence[str | colors.Colormap]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, title: str | Sequence[str] | Sequence[Sequence[str]] | None = None, figax: tuple[Any, Any] | None = None, diff --git a/src/quantem/core/visualization/visualization_utils.py b/src/quantem/core/visualization/visualization_utils.py index 4b8ebae5..aa6687ab 100644 --- a/src/quantem/core/visualization/visualization_utils.py +++ b/src/quantem/core/visualization/visualization_utils.py @@ -247,31 +247,92 @@ def estimate_scalebar_length(length: float, sampling: float) -> tuple[float, flo def _normalize_length_units(length_units: float, units: str) -> tuple[float, str]: """ - pick intelligent units for the scalebar length + Pick intelligent units for the scalebar length. Handles both direct and inverse units. """ - if units in ["A", "Å", "angstrom", "Angstrom"]: - length_A = length_units - elif units in ["nm", "nanometer", "nanometre"]: - length_A = length_units * 10 - elif units in ["um", "μm", "micrometer", "micrometre"]: - length_A = length_units * 1e4 - elif units in ["mm", "millimeter", "millimetre"]: - length_A = length_units * 1e7 - elif units in ["cm", "centimeter", "centimetre"]: - length_A = length_units * 1e8 - else: - return length_units, units - - if length_A < 0.1: - return length_A * 100, "pm" - elif length_A < 10: - return length_A, "Å" - elif length_A < 3e4: - return length_A / 10, "nm" - elif length_A < 1e7: - return length_A / 1e4, "μm" - else: - return length_A / 1e7, "mm" + inverse_unit_map = { + "1/A": "$\\mathrm{Å}^{-1}$", + "1/Å": "$\\mathrm{Å}^{-1}$", + "A^-1": "$\\mathrm{Å}^{-1}$", + "1/angstrom": "$\\mathrm{Å}^{-1}$", + "1/Angstrom": "$\\mathrm{Å}^{-1}$", + "1/nm": "$\\mathrm{nm}^{-1}$", + "1/nanometer": "$\\mathrm{nm}^{-1}$", + "1/nanometre": "$\\mathrm{nm}^{-1}$", + } + direct_unit_map = { + "A": "Å", + "Å": "Å", + "angstrom": "Å", + "Angstrom": "Å", + "nm": "nm", + "nanometer": "nm", + "nanometre": "nm", + "um": "μm", + "μm": "μm", + "micrometer": "μm", + "micrometre": "μm", + "micron": "μm", + "mm": "mm", + "millimeter": "mm", + "millimetre": "mm", + "cm": "cm", + "centimeter": "cm", + "centimetre": "cm", + "m": "m", + "meter": "m", + "metre": "m", + } + + # Handle inverse units first + if units in inverse_unit_map: + # Convert everything to 1/Å then scale + units = inverse_unit_map[units] + if units == "$\\mathrm{Å}^{-1}$": + length_invA = length_units + else: # units == "$\\mathrm{nm}^{-1}$": + length_invA = length_units / 10 + + if length_invA < 0.1: + return length_invA * 10, "$\\mathrm{nm}^{-1}$" + elif length_invA < 100: + return length_invA, "$\\mathrm{Å}^{-1}$" + else: # length_invA >= 10: + return length_invA / 100, "$\\mathrm{pm}^{-1}$" + + # Handle direct metric units (distance) + if units in direct_unit_map: + # Everything to Å + if units in ["A", "Å", "angstrom", "Angstrom"]: + length_A = length_units + elif units in ["nm", "nanometer", "nanometre"]: + length_A = length_units * 10 + elif units in ["um", "μm", "micrometer", "micrometre", "micron"]: + length_A = length_units * 1e4 + elif units in ["mm", "millimeter", "millimetre"]: + length_A = length_units * 1e7 + elif units in ["cm", "centimeter", "centimetre"]: + length_A = length_units * 1e8 + elif units in ["m", "meter", "metre"]: + length_A = length_units * 1e10 + else: + # fallback, should not happen due to keys in direct_unit_map + return length_units, units + + if length_A <= 0.1: + return length_A * 100, "pm" + elif length_A < 10: + return length_A, "Å" + elif length_A < 1e4: + return length_A / 10, "nm" + elif length_A < 1e7: + return length_A / 1e4, "μm" + elif length_A < 1e10: + return length_A / 1e7, "mm" + else: + return length_A / 1e10, "m" + + # fallback: unknown unit, return as is + return length_units, units def add_scalebar_to_ax( @@ -336,6 +397,7 @@ def add_scalebar_to_ax( fontprops = FontProperties(size=fontsize, weight="bold" if bold else "normal") + label_top = loc[:3] == "low" bar = AnchoredSizeBar( ax.transData, length_px, @@ -344,9 +406,10 @@ def add_scalebar_to_ax( pad=pad_px, color=color, frameon=False, - label_top=loc[:3] == "low", + label_top=label_top, size_vertical=int(width_px), fontproperties=fontprops, + sep=2 if label_top else int(round(0.3 * fontsize)), ) ax.add_artist(bar) From d6e2aa8d2475e326255aee533bf17a9f99990b91 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Tue, 24 Mar 2026 15:15:59 -0700 Subject: [PATCH 224/335] Zero-pad docs fixed --- src/quantem/core/utils/tomography_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py index 2f9cebca..9f5df102 100644 --- a/src/quantem/core/utils/tomography_utils.py +++ b/src/quantem/core/utils/tomography_utils.py @@ -230,7 +230,7 @@ def centering_com_alignment(image_stack): def differentiable_shift_2d(image, shift_x, shift_y, sampling_rate): """ - Shifts a 2D image using grid_sample in a differentiable manner with boundary conditions applied. + Shifts a 2D image using grid_sample in a differentiable manner with zero-pad boundary conditions applied. Args: image: Tensor of shape [H, W] From 56c44e51394474e66c8ceac08143b67df4d90c8e Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 17:22:13 -0700 Subject: [PATCH 225/335] fixing linescan returns, improving complex cbar labels --- src/quantem/core/visualization/line_scan.py | 11 +++-------- src/quantem/core/visualization/visualization.py | 4 ++-- src/quantem/core/visualization/visualization_utils.py | 2 +- 3 files changed, 6 insertions(+), 11 deletions(-) diff --git a/src/quantem/core/visualization/line_scan.py b/src/quantem/core/visualization/line_scan.py index 4200902e..6d22beb2 100644 --- a/src/quantem/core/visualization/line_scan.py +++ b/src/quantem/core/visualization/line_scan.py @@ -19,7 +19,7 @@ def linescan( sampling: tuple[float, float] | np.ndarray | None = None, sampling_units: str | None = None, **kwargs, -) -> tuple[np.ndarray, np.ndarray] | tuple[np.ndarray, np.ndarray, tuple[Any, Any]]: +) -> tuple[np.ndarray, np.ndarray]: """ Generate a line scan through an image. @@ -111,7 +111,7 @@ def linescan( sampling_units = "pixels" if show: - fig, axs = _show_linescan( + _fig, _axs = _show_linescan( scan_image, profile, positions, @@ -121,13 +121,8 @@ def linescan( sampling_units, **kwargs, ) - else: - fig, axs = None, None - if kwargs.get("return_fig", False) and show: - return positions, profile, (fig, axs) - else: - return positions, profile + return positions, profile def _calculate_line_endpoints( diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index bb32d480..9bccbf86 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -144,10 +144,10 @@ def _show_2d_array( cb_abs = add_cbar_to_ax(fig, ax_cb_abs, norm_obj, cmap_obj) if is_complex: - ax_cb_angle = divider.append_axes("right", size="5%", pad="10%") + ax_cb_angle = divider.append_axes("right", size="5%", pad="15%") add_arg_cbar_to_ax(fig, ax_cb_angle, chroma_boost=chroma_boost) cb_abs.set_label("abs", rotation=0, ha="center", va="bottom") - cb_abs.ax.yaxis.set_label_coords(0.5, -0.05) + cb_abs.ax.yaxis.set_label_coords(0.5, -0.07) if scalebar_config is not None: add_scalebar_to_ax( diff --git a/src/quantem/core/visualization/visualization_utils.py b/src/quantem/core/visualization/visualization_utils.py index aa6687ab..126cb096 100644 --- a/src/quantem/core/visualization/visualization_utils.py +++ b/src/quantem/core/visualization/visualization_utils.py @@ -499,7 +499,7 @@ def add_arg_cbar_to_ax( cb_angle = fig.colorbar(sm, cax=cax) cb_angle.set_label("arg", rotation=0, ha="center", va="bottom") - cb_angle.ax.yaxis.set_label_coords(0.5, -0.05) + cb_angle.ax.yaxis.set_label_coords(0.5, -0.07) cb_angle.set_ticks([-np.pi, -np.pi / 2, 0, np.pi / 2, np.pi]) cb_angle.set_ticklabels( [r"$-\pi$", r"$-\dfrac{\pi}{2}$", "$0$", r"$\dfrac{\pi}{2}$", r"$\pi$"] From 28021afbbf0df31c526eeb0f66b54ff2be0c2692 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Tue, 24 Mar 2026 17:34:01 -0700 Subject: [PATCH 226/335] fixing couple typehints --- src/quantem/tomography/tomography_base.py | 4 ++-- src/quantem/tomography/tomography_lite.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 743a6f6a..58909bdf 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -49,7 +49,7 @@ def __init__( self._epoch_losses: list[float] = [] self._consistency_losses: list[float] = [] self._val_losses: list[float] = [] - self._lrs: dict[str, float] = {} + self._lrs: dict[str, list] = {} # DDP Initialization if isinstance(obj_model, ObjectINR): self.setup_distributed(device=device) @@ -128,7 +128,7 @@ def consistency_losses(self) -> NDArray: return np.array(self._consistency_losses) @property - def learning_rates(self) -> dict[str, float]: + def learning_rates(self) -> dict[str, list]: """ Returns the learning rates for each epoch ran. """ diff --git a/src/quantem/tomography/tomography_lite.py b/src/quantem/tomography/tomography_lite.py index dc3d918c..7bd9ad67 100644 --- a/src/quantem/tomography/tomography_lite.py +++ b/src/quantem/tomography/tomography_lite.py @@ -76,7 +76,7 @@ def from_dataset( return tomography - def reconstruct( + def reconstruct( # type:ignore[reportIncompatibleMethodOverride] ## easier than overloads self, num_iter: int = 10, reset: bool = False, From e482d76aedd1dc1ce71dd589e89045805da84fa2 Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 30 Mar 2026 10:22:51 +0000 Subject: [PATCH 227/335] chore: update lock file --- uv.lock | 1749 +++++++++++++++++++++++++++++-------------------------- 1 file changed, 936 insertions(+), 813 deletions(-) diff --git a/uv.lock b/uv.lock index 7d2b81dc..5972dd69 100644 --- a/uv.lock +++ b/uv.lock @@ -38,15 +38,15 @@ wheels = [ [[package]] name = "anyio" -version = "4.12.1" +version = "4.13.0" source = { registry = "https://pypi.org/simple" } dependencies = 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Split all plotting functions from lattice.py into lattice_visualization.py --- src/quantem/imaging/__init__.py | 1 + src/quantem/imaging/lattice.py | 255 +++++++------------ src/quantem/imaging/lattice_visualization.py | 143 +++++++++++ tests/imaging/test_lattice.py | 24 +- 4 files changed, 247 insertions(+), 176 deletions(-) create mode 100644 src/quantem/imaging/lattice_visualization.py diff --git a/src/quantem/imaging/__init__.py b/src/quantem/imaging/__init__.py index e2183514..3637acdc 100644 --- a/src/quantem/imaging/__init__.py +++ b/src/quantem/imaging/__init__.py @@ -1,2 +1,3 @@ from quantem.imaging.drift import DriftCorrection as DriftCorrection from quantem.imaging.lattice import Lattice as Lattice +from quantem.imaging.lattice_visualization import PLOT_REGISTRY as PLOT_REGISTRY diff --git a/src/quantem/imaging/lattice.py b/src/quantem/imaging/lattice.py index 303ff26f..82e0d76f 100644 --- a/src/quantem/imaging/lattice.py +++ b/src/quantem/imaging/lattice.py @@ -1,9 +1,11 @@ +import inspect + import numpy as np from numpy.typing import NDArray from quantem.core.datastructures.dataset2d import Dataset2d from quantem.core.io.serialize import AutoSerialize -from quantem.core.visualization import show_2d +from quantem.imaging.lattice_visualization import PLOT_REGISTRY class Lattice(AutoSerialize): @@ -121,17 +123,14 @@ def image(self, value: Dataset2d | NDArray): self._image = Dataset2d(arr) # type: ignore[call-arg] ### --- Functions --- - def define_lattice( + def define_lattice_vectors( self, origin, u, v, refine_lattice: bool = True, block_size: int | None = None, - plot_lattice: bool = True, - bound_num_vectors: int | None = None, refine_maxiter: int = 200, - **kwargs, ) -> "Lattice": """ Define the lattice for the image using the origin and the u and v vectors starting from the origin. @@ -156,22 +155,19 @@ def define_lattice( For example, if block_size = 5, then the lattice points will be fit in steps of (-5, 5)u * (-5, 5)v -> (-10, 10)u * (-10, 10)v -> ... block_size = None means the entire image will be fit at once. - plot_lattice : bool, default=True - If True, the lattice vectors and lines will be plotted overlaid on the image. - bound_num_vectors : int | None, default=None - The maximum number of lattice vectors to plot in each direction. - For example, if bound_num_vectors = 5, lattice lines between (-5, 5)u * (-5, 5)v will be plotted. - If None, the plotting bounds are set to the image edges. refine_maxiter : int, default=200 Maximum number of iterations for the lattice refinement optimizer (Powell method). - **kwargs - Additional keyword arguments forwarded to the plotting function (show_2d), e.g., cmap, title, etc. Returns ------- self : Lattice Returns the same object, modified in-place. The final values of r0, u, v are stored in self._lat. + + Side Effects + ------------ + Creates self._lat with rows corresponding to r0, u and v + Sets self.default_plot to "lattice_vectors" """ # Lattice self._lat = np.vstack( @@ -342,162 +338,99 @@ def objective(theta: np.ndarray) -> float: lat_flat = res.x self._lat = res.x.reshape(3, 2) - # plotting - if plot_lattice: - fig, ax = show_2d( - self._image.array, - returnfig=True, - **kwargs, - ) + self.default_plot = "lattice_vectors" - # Put the image at lowest zorder so overlays sit on top - if ax.images: - ax.images[-1].set_zorder(0) + return self - H, W = self._image.shape - r0, u, v = (np.asarray(x, dtype=float) for x in self._lat) + ### --- Plot dispatcher --- + def plot(self, kind: str | None = None, show_docstring: bool = False, **kwargs): + """ + Dispatch to a registered visualization function. - # Origin marker (TOP of stack) - ax.scatter( - r0[1], - r0[0], # (y, x) - s=60, - edgecolor=(0, 0, 0), - facecolor=(0, 0.5, 0), - marker="s", - zorder=30, - ) + The function is selected by ``kind`` if given, otherwise by + ``self.default_plot``. Passing ``kind`` here does not mutate the instance. - # Lattice vectors as arrows - n_vec = int(bound_num_vectors) if bound_num_vectors is not None else 1 - - # draw n_vec arrows for u (red) - for k in range(1, n_vec + 1): - tip = r0 + k * u - ax.arrow( - r0[1], - r0[0], # base (y, x) - (tip - r0)[1], - (tip - r0)[0], # delta (y, x) - length_includes_head=True, - head_width=4.0, - head_length=6.0, - linewidth=2.0, - color="red", - zorder=20, - ) + Parameters + ---------- + kind : str | None + Name of the plot. Registered names: - # draw n_vec arrows for v (cyan) - for k in range(1, n_vec + 1): - tip = r0 + k * v - ax.arrow( - r0[1], - r0[0], - (tip - r0)[1], - (tip - r0)[0], - length_includes_head=True, - head_width=4.0, - head_length=6.0, - linewidth=2.0, - color=(0.0, 0.7, 1.0), - zorder=20, - ) + "image" | "dataset" + Shows the image using the default show_2d with no overlays. + Default if default_plot is not set. - # Solve for a,b at plot corners (bounds) - if bound_num_vectors is None: - corners = np.array( - [ - [0.0, 0.0], - [float(H), 0.0], - [0.0, float(W)], - [float(H), float(W)], - ] - ) - else: - n = float(bound_num_vectors) - corners = np.array( - [ - r0 - n * u, - r0 - n * v, - r0 + n * u, - r0 + n * v, - ], - dtype=float, - ) + "lattice_vectors" + Lattice vectors + grid lines overlaid on the image. + Call after define_lattice_vectors(). - # a,b from corners; A = [u v] in columns (2x2), rhs = (corner - r0) - A = np.column_stack((u, v)) - ab = np.linalg.lstsq(A, (corners - r0[None, :]).T, rcond=None)[0] + show_docstring : bool, default False + If True, return formatted signature and docstring of the plotting + function instead of calling it. If False, call the function and + return its result. + + **kwargs + Forwarded verbatim to the selected plotting function. + + Returns + ------- + str or function return + If ``show_docstring`` is True, returns a formatted string with the + function signature and docstring. Otherwise, returns whatever the + selected function returns (typically ``(fig, ax)``). + + Raises + ------ + ValueError + If ``kind`` or ``self.default_plot`` is not in PLOT_REGISTRY. + + Examples + -------- + :: + + lat.plot(kind="lattice_vectors") + lat.plot(kind="lattice_vectors", show_docstring=True) + """ + if not hasattr(self, "default_plot") or kind in ["image", "dataset"]: + from quantem.core.visualization import show_2d - a_min, a_max = int(np.floor(np.min(ab[0]))), int(np.ceil(np.max(ab[0]))) - b_min, b_max = int(np.floor(np.min(ab[1]))), int(np.ceil(np.max(ab[1]))) + return show_2d(self.image, **kwargs) # type:ignore - # Clipping rectangle (image or custom) - if bound_num_vectors is None: - x_lo, x_hi = 0.0, float(H) - y_lo, y_hi = 0.0, float(W) + plot_name = kind if kind is not None else self.default_plot + if plot_name not in PLOT_REGISTRY: + raise ValueError( + f"Unknown plot kind {plot_name!r}. Available: {sorted(PLOT_REGISTRY)}" + ) + + plot_func = PLOT_REGISTRY[plot_name] + if show_docstring: + plot_func = PLOT_REGISTRY[plot_name] + sig = inspect.signature(plot_func) + doc = inspect.getdoc(plot_func) + + if kind is None: + print(f"Current default plot: {self.default_plot}") + + print("\nSignature:") + # Format signature across multiple lines + params = [] + for param_name, param in sig.parameters.items(): + params.append(f" {param}") + + param_str = ",\n".join(params) + return_annotation = ( + f" -> {sig.return_annotation}" + if sig.return_annotation != inspect.Signature.empty + else "" + ) + + print(f"def {plot_func.__name__}(\n{param_str}\n){return_annotation}:") + + print("\nDocstring:") + if doc: + print(doc) else: - # Bounds are the min/max over the provided corners - x_lo, x_hi = float(np.min(corners[:, 0])), float(np.max(corners[:, 0])) - y_lo, y_hi = float(np.min(corners[:, 1])), float(np.max(corners[:, 1])) - - def clipped_segment(base: np.ndarray, direction: np.ndarray): - """Clip base + t*direction to rectangle [x_lo,x_hi] x [y_lo,y_hi].""" - x0, y0 = base - dx, dy = direction - t0, t1 = -np.inf, np.inf - eps = 1e-12 - - # x in [x_lo, x_hi] - if abs(dx) < eps: - if not (x_lo <= x0 <= x_hi): - return None - else: - tx0 = (x_lo - x0) / dx - tx1 = (x_hi - x0) / dx - t_enter, t_exit = (tx0, tx1) if tx0 <= tx1 else (tx1, tx0) - t0, t1 = max(t0, t_enter), min(t1, t_exit) - - # y in [y_lo, y_hi] - if abs(dy) < eps: - if not (y_lo <= y0 <= y_hi): - return None - else: - ty0 = (y_lo - y0) / dy - ty1 = (y_hi - y0) / dy - t_enter, t_exit = (ty0, ty1) if ty0 <= ty1 else (ty1, ty0) - t0, t1 = max(t0, t_enter), min(t1, t_exit) - - if t0 > t1: - return None - - p1 = base + t0 * direction # (x, y) - p2 = base + t1 * direction - return p1, p2 - - # Lattice lines (zorder above image) - # Using x=rows, y=cols: plot(y, x) - - # Lines parallel to v (vary a) - for a in range(a_min, a_max + 1): - base = r0 + a * u - seg = clipped_segment(base, v) - if seg is None: - continue - (x1, y1), (x2, y2) = seg - ax.plot([y1, y2], [x1, x2], color=(0.0, 0.7, 1.0), lw=1, clip_on=True, zorder=10) - - # Lines parallel to u (vary b) - for b in range(b_min, b_max + 1): - base = r0 + b * v - seg = clipped_segment(base, u) - if seg is None: - continue - (x1, y1), (x2, y2) = seg - ax.plot([y1, y2], [x1, x2], color="red", lw=1, clip_on=True, zorder=10) - - # Axes limits (x=rows vertical; y=cols horizontal) - ax.set_xlim(y_lo, y_hi) - ax.set_ylim(x_hi, x_lo) + print("[No docstring]") - return self + return None + + return plot_func(self, **kwargs) diff --git a/src/quantem/imaging/lattice_visualization.py b/src/quantem/imaging/lattice_visualization.py new file mode 100644 index 00000000..af2781c7 --- /dev/null +++ b/src/quantem/imaging/lattice_visualization.py @@ -0,0 +1,143 @@ +""" +lattice_visualization.py +-------------------------------- +All visualization helpers for the Lattice class. Nothing here modifies +Lattice state — functions only read from the instance they receive. + +Registered plot names (callable via ``lattice.plot(kind=...)``) +--------------------------------------------------------------- + "lattice_vectors" - image + lattice vectors/grid (after define_lattice_vectors) +""" + +import numpy as np + +from quantem.core.visualization import show_2d + +# --------------------------------------------------------------------------- +# Registry used by Lattice.plot() +# --------------------------------------------------------------------------- +PLOT_REGISTRY: dict[str, callable] = {} # type:ignore + + +def _register(name: str): + def decorator(fn): + PLOT_REGISTRY[name] = fn + return fn + + return decorator + + +### --- Plotting Functions --- +@_register("lattice_vectors") +def plot_lattice_vectors( + lattice, *, returnfig: bool = False, bound_num_vectors: int | None = None, **kwargs +) -> None | tuple: + """ + Overlay fitted lattice vectors and grid lines on the image. + Call after define_lattice_vectors(). + + Parameters + ---------- + bound_num_vectors : int | None + Number of lattice vectors to draw in each direction. + None clips lines to image edges. + **kwargs forwarded to show_2d (e.g. cmap, title). + """ + fig, ax = show_2d(lattice._image.array, returnfig=True, **kwargs) + if ax.images: + ax.images[-1].set_zorder(0) + H, W = lattice._image.shape + r0, u, v = (np.asarray(x, dtype=float) for x in lattice._lat) + + ax.scatter( + r0[1], r0[0], s=60, edgecolor=(0, 0, 0), facecolor=(0, 0.5, 0), marker="s", zorder=30 + ) + + n_vec = int(bound_num_vectors) if bound_num_vectors is not None else 1 + for k in range(1, n_vec + 1): + tip = r0 + k * u + ax.arrow( + r0[1], + r0[0], + (tip - r0)[1], + (tip - r0)[0], + length_includes_head=True, + head_width=4.0, + head_length=6.0, + linewidth=2.0, + color="red", + zorder=20, + ) + for k in range(1, n_vec + 1): + tip = r0 + k * v + ax.arrow( + r0[1], + r0[0], + (tip - r0)[1], + (tip - r0)[0], + length_includes_head=True, + head_width=4.0, + head_length=6.0, + linewidth=2.0, + color=(0.0, 0.7, 1.0), + zorder=20, + ) + + if bound_num_vectors is None: + corners = np.array([[0.0, 0.0], [float(H), 0.0], [0.0, float(W)], [float(H), float(W)]]) + x_lo, x_hi, y_lo, y_hi = 0.0, float(H), 0.0, float(W) + else: + n = float(bound_num_vectors) + corners = np.array([r0 - n * u, r0 - n * v, r0 + n * u, r0 + n * v], dtype=float) + x_lo = float(np.min(corners[:, 0])) + x_hi = float(np.max(corners[:, 0])) + y_lo = float(np.min(corners[:, 1])) + y_hi = float(np.max(corners[:, 1])) + + A = np.column_stack((u, v)) + ab = np.linalg.lstsq(A, (corners - r0[None, :]).T, rcond=None)[0] + a_min, a_max = int(np.floor(np.min(ab[0]))), int(np.ceil(np.max(ab[0]))) + b_min, b_max = int(np.floor(np.min(ab[1]))), int(np.ceil(np.max(ab[1]))) + + def clipped_segment(base, direction): + x0, y0 = base + dx, dy = direction + t0, t1 = -np.inf, np.inf + eps = 1e-12 + if abs(dx) < eps: + if not (x_lo <= x0 <= x_hi): + return None + else: + ts = sorted([(x_lo - x0) / dx, (x_hi - x0) / dx]) + t0, t1 = max(t0, ts[0]), min(t1, ts[1]) + if abs(dy) < eps: + if not (y_lo <= y0 <= y_hi): + return None + else: + ts = sorted([(y_lo - y0) / dy, (y_hi - y0) / dy]) + t0, t1 = max(t0, ts[0]), min(t1, ts[1]) + if t0 > t1: + return None + return base + t0 * direction, base + t1 * direction + + for a in range(a_min, a_max + 1): + seg = clipped_segment(r0 + a * u, v) + if seg: + ax.plot( + [seg[0][1], seg[1][1]], + [seg[0][0], seg[1][0]], + color=(0.0, 0.7, 1.0), + lw=1, + zorder=10, + ) + for b in range(b_min, b_max + 1): + seg = clipped_segment(r0 + b * v, u) + if seg: + ax.plot([seg[0][1], seg[1][1]], [seg[0][0], seg[1][0]], color="red", lw=1, zorder=10) + + ax.set_xlim(y_lo, y_hi) + ax.set_ylim(x_hi, x_lo) + if returnfig: + return fig, ax + else: + return None diff --git a/tests/imaging/test_lattice.py b/tests/imaging/test_lattice.py index 0d11e9c5..33a289ed 100644 --- a/tests/imaging/test_lattice.py +++ b/tests/imaging/test_lattice.py @@ -76,16 +76,16 @@ def test_image_property(self): lattice.image = np.array([1, 2, 3]) -class TestDefineLattice: - """Test define_lattice method.""" +class TestDefineLatticeVectors: + """Test define_lattice_vectors method.""" def test_basic_define(self): """Test basic lattice definition.""" image = np.random.randn(100, 100) lattice = Lattice.from_data(image) - result = lattice.define_lattice( - origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=False, plot_lattice=False + result = lattice.define_lattice_vectors( + origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=False ) assert result is lattice @@ -98,25 +98,23 @@ def test_refinement_options(self): lattice = Lattice.from_data(image) # With refinement - lattice.define_lattice( + lattice.define_lattice_vectors( origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=True, refine_maxiter=5, - plot_lattice=False, ) assert lattice._lat.shape == (3, 2) # With block_size - lattice.define_lattice( + lattice.define_lattice_vectors( origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=True, refine_maxiter=5, block_size=5, - plot_lattice=False, ) assert lattice._lat.shape == (3, 2) @@ -127,13 +125,11 @@ def test_invalid_lattice_params(self): # Wrong shape with pytest.raises(ValueError): - lattice.define_lattice(origin=[1, 2, 3], u=[5, 0], v=[0, 5], plot_lattice=False) + lattice.define_lattice_vectors(origin=[1, 2, 3], u=[5, 0], v=[0, 5]) # Negative block_size with pytest.raises(ValueError): - lattice.define_lattice( - origin=[50, 50], u=[5, 0], v=[0, 5], block_size=-1, plot_lattice=False - ) + lattice.define_lattice_vectors(origin=[50, 50], u=[5, 0], v=[0, 5], block_size=-1) class TestLatticeSerialize: @@ -145,9 +141,7 @@ def test_lattice_save_load(self, tmp_path, store): # Create lattice with image and defined lattice image = np.random.randn(100, 100) lattice = Lattice.from_data(image) - lattice.define_lattice( - origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=False, plot_lattice=False - ) + lattice.define_lattice_vectors(origin=[50, 50], u=[5, 0], v=[0, 5], refine_lattice=False) # Save filepath = tmp_path / ("lattice.zip" if store == "zip" else "lattice_dir") From dd12d60afde6adae3ca78dcc16ba35ed2f307e96 Mon Sep 17 00:00:00 2001 From: smribet Date: Mon, 6 Apr 2026 15:38:43 -0700 Subject: [PATCH 229/335] fix --- src/quantem/core/datastructures/dataset.py | 10 ++++++++-- tests/datastructures/test_dataset.py | 21 ++++++++++++++++++++- 2 files changed, 28 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 3dc3b925..2cbdc29e 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -511,22 +511,28 @@ def crop( raise ValueError("Length of crop_widths must match length of axes.") full_slices = [] + new_origin = self.origin.astype(float).copy() crop_dict = dict(zip(axes, crop_widths)) - for axis, _ in enumerate(self.shape): + for axis, axis_size in enumerate(self.shape): if axis in crop_dict: before, after = crop_dict[axis] start = before stop = after if after != 0 else None - full_slices.append(slice(start, stop)) + axis_slice = slice(start, stop) + normalized_start, _, _ = axis_slice.indices(axis_size) + full_slices.append(axis_slice) + new_origin[axis] = new_origin[axis] + normalized_start * self.sampling[axis] else: full_slices.append(slice(None)) if modify_in_place is False: dataset = self.copy() dataset.array = dataset.array[tuple(full_slices)] + dataset.origin = new_origin return dataset self.array = self.array[tuple(full_slices)] + self.origin = new_origin return None @overload diff --git a/tests/datastructures/test_dataset.py b/tests/datastructures/test_dataset.py index fc5c108d..a005e420 100644 --- a/tests/datastructures/test_dataset.py +++ b/tests/datastructures/test_dataset.py @@ -243,11 +243,14 @@ def test_crop(self, sample_dataset_2d): cropped_dataset = sample_dataset_2d.crop(crop_widths=((1, 9), (1, 9))) # Check shape assert cropped_dataset.shape == (8, 8) # Original (10, 10) - 1 from each side + assert np.array_equal(cropped_dataset.origin, np.array([1, 1])) # Check that the original dataset is unchanged assert sample_dataset_2d.shape == (10, 10) + assert np.array_equal(sample_dataset_2d.origin, np.array([0, 0])) # Test modify_in_place sample_dataset_2d.crop(crop_widths=((1, 9), (1, 9)), modify_in_place=True) assert sample_dataset_2d.shape == (8, 8) + assert np.array_equal(sample_dataset_2d.origin, np.array([1, 1])) def test_crop_4dstem_kspace(self): """Test cropping k-space axes of a 4D-STEM dataset.""" @@ -258,9 +261,14 @@ def test_crop_4dstem_kspace(self): def test_crop_4dstem_realspace_in_place(self): """Test in-place real-space crop of a 4D-STEM dataset.""" - dset = Dataset.from_array(np.random.rand(16, 16, 32, 32)) + dset = Dataset.from_array( + np.random.rand(16, 16, 32, 32), + origin=(10, 20, 30, 40), + sampling=(0.5, 0.25, 2, 3), + ) dset.crop(crop_widths=((4, 12), (4, 12)), axes=(0, 1), modify_in_place=True) assert dset.shape == (8, 8, 32, 32) + assert np.array_equal(dset.origin, np.array([12, 21, 30, 40])) def test_crop_4dstem_stop_zero(self): """Test that stop=0 keeps all remaining elements.""" @@ -268,6 +276,17 @@ def test_crop_4dstem_stop_zero(self): cropped = dset.crop(crop_widths=((10, 0), (10, 0)), axes=(2, 3)) assert cropped.shape == (8, 8, 86, 86) + def test_crop_single_axis_updates_origin_in_place(self): + """Test in-place single-axis crop updates origin.""" + dset = Dataset.from_array( + np.random.rand(2048, 64), + origin=(5, 7), + sampling=(0.5, 2), + ) + dset.crop(crop_widths=((80, 2000),), axes=0, modify_in_place=True) + assert dset.shape == (1920, 64) + assert np.array_equal(dset.origin, np.array([45, 7])) + def test_bin(self, sample_dataset_2d): """Test bin method.""" # Bin by factor of 2 From 7dfb38be4e404e6b473ea4bd91f171ba6f585761 Mon Sep 17 00:00:00 2001 From: smribet Date: Tue, 7 Apr 2026 10:46:41 -0700 Subject: [PATCH 230/335] updating slicing too --- src/quantem/core/datastructures/dataset.py | 21 ++++++++------ tests/datastructures/test_dataset.py | 33 ++++++++++++++++++++++ 2 files changed, 45 insertions(+), 9 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 2cbdc29e..c7d2ce86 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -875,22 +875,25 @@ def __getitem__(self, index) -> Self: # Compute which dimensions are kept kept_axes = [i for i, idx in enumerate(index) if not isinstance(idx, (int, np.integer))] + kept_axis_to_index = {axis: j for j, axis in enumerate(kept_axes)} # Slice/reduce metadata accordingly - new_origin = ( - np.asarray(self.origin)[kept_axes] if np.ndim(self.origin) > 0 else self.origin - ) + origin_array = np.asarray(self.origin, dtype=float) + sampling_array = np.asarray(self.sampling, dtype=float) + new_origin = origin_array[kept_axes].copy() if np.ndim(self.origin) > 0 else self.origin new_sampling = ( - np.asarray(self.sampling)[kept_axes] if np.ndim(self.sampling) > 0 else self.sampling + sampling_array[kept_axes].copy() if np.ndim(self.sampling) > 0 else self.sampling ) new_units = [self.units[i] for i in kept_axes] if len(self.units) > 0 else self.units - # Adjust sampling for slice steps (e.g. [::2] doubles spacing) + # Adjust origin/sampling for sliced axes. for i, idx in enumerate(index): - if isinstance(idx, slice) and idx.step not in (None, 1): - if i in kept_axes: - j = kept_axes.index(i) - new_sampling[j] *= idx.step + if isinstance(idx, slice) and i in kept_axis_to_index: + j = kept_axis_to_index[i] + normalized_start, _, normalized_step = idx.indices(self.shape[i]) + new_origin[j] = new_origin[j] + normalized_start * sampling_array[i] + if normalized_step != 1: + new_sampling[j] *= normalized_step out_ndim = array_view.ndim diff --git a/tests/datastructures/test_dataset.py b/tests/datastructures/test_dataset.py index a005e420..9c83262c 100644 --- a/tests/datastructures/test_dataset.py +++ b/tests/datastructures/test_dataset.py @@ -287,6 +287,39 @@ def test_crop_single_axis_updates_origin_in_place(self): assert dset.shape == (1920, 64) assert np.array_equal(dset.origin, np.array([45, 7])) + def test_getitem_slice_updates_origin_like_crop(self): + """Test slicing keeps origin aligned with crop semantics.""" + dset = Dataset.from_array( + np.random.rand(16, 8), + origin=(10, 20), + sampling=(0.5, 2.0), + units=["nm", "nm"], + ) + + sliced = dset[4:12] + cropped = dset.crop(crop_widths=((4, 12),), axes=0) + + assert sliced.shape == (8, 8) + assert np.array_equal(sliced.origin, np.array([12, 20])) + assert np.array_equal(sliced.origin, cropped.origin) + assert np.array_equal(sliced.sampling, cropped.sampling) + + def test_getitem_slice_reduced_rank_updates_origin_and_sampling(self): + """Test slicing before integer indexing updates remaining metadata.""" + dset = Dataset.from_array( + np.random.rand(10, 6, 4), + origin=(1.5, 10, -2), + sampling=(0.25, 2.0, 5.0), + units=["nm", "nm", "1/nm"], + ) + + sliced = dset[2:8:2, 3] + + assert sliced.shape == (3, 4) + assert np.allclose(sliced.origin, np.array([2.0, -2.0])) + assert np.allclose(sliced.sampling, np.array([0.5, 5.0])) + assert sliced.units == ["nm", "1/nm"] + def test_bin(self, sample_dataset_2d): """Test bin method.""" # Bin by factor of 2 From d92e5c460402d6e17f7a9be9a9f26d5d9d289da0 Mon Sep 17 00:00:00 2001 From: smribet Date: Wed, 8 Apr 2026 06:17:43 -0700 Subject: [PATCH 231/335] adding doc strings --- src/quantem/core/datastructures/dataset.py | 4 ++-- src/quantem/core/datastructures/dataset2d.py | 6 +++--- src/quantem/core/datastructures/dataset3d.py | 9 ++++----- src/quantem/core/datastructures/dataset4d.py | 2 +- src/quantem/core/datastructures/dataset4dstem.py | 4 ++-- src/quantem/core/io/file_readers.py | 2 +- src/quantem/diffractive_imaging/dataset_models.py | 2 +- 7 files changed, 14 insertions(+), 15 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index c7d2ce86..4d2ab9e1 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -23,7 +23,7 @@ class Dataset(AutoSerialize): Attributes (Properties): array (NDArray): The underlying n-dimensional NumPy array data. name (str): A descriptive name for the dataset. - origin (NDArray): The origin coordinates for each dimension (1D array). + origin (NDArray): The origin coordinates for each dimension (1D array) in calibrated units. sampling (NDArray): The sampling rate/spacing for each dimension (1D array). units (list[str]): Units for each dimension. signal_units (str): Units for the array values. @@ -84,7 +84,7 @@ def from_array( name: str | None The name of the Dataset. origin: NDArray | tuple | list | float | int | None - The origin of the Dataset. + The origin of the Dataset in calibrated units. sampling: NDArray | tuple | list | float | int | None The sampling of the Dataset. units: list[str] | tuple | list | None diff --git a/src/quantem/core/datastructures/dataset2d.py b/src/quantem/core/datastructures/dataset2d.py index a5a94eac..13dac401 100644 --- a/src/quantem/core/datastructures/dataset2d.py +++ b/src/quantem/core/datastructures/dataset2d.py @@ -40,7 +40,7 @@ def __init__( name : str A descriptive name for the dataset origin : NDArray | tuple | list | float | int - The origin coordinates for each dimension + The origin coordinates for each dimension in calibrated units sampling : NDArray | tuple | list | float | int The sampling rate/spacing for each dimension units : list[str] | tuple | list @@ -102,9 +102,9 @@ def from_array( name : str | None, optional A descriptive name for the dataset. If None, defaults to "2D dataset" origin : NDArray | tuple | list | float | int | None, optional - The origin coordinates for each dimension. If None, defaults to zeros + The origin coordinates for each dimension in calibrated units. If None, defaults to zeros sampling : NDArray | tuple | list | float | int | None, optional - The sampling rate/spacing for each dimension. If None, defaults to ones + The sampling rate/spacing for each dimension in calibrated units. If None, defaults to ones units : list[str] | tuple | list | None, optional Units for each dimension. If None, defaults to ["pixels"] * 4 signal_units : str, optional diff --git a/src/quantem/core/datastructures/dataset3d.py b/src/quantem/core/datastructures/dataset3d.py index 118ec66f..1af7b4e6 100644 --- a/src/quantem/core/datastructures/dataset3d.py +++ b/src/quantem/core/datastructures/dataset3d.py @@ -41,7 +41,7 @@ def __init__( name : str A descriptive name for the dataset origin : NDArray | tuple | list | float | int - The origin coordinates for each dimension + The origin coordinates for each dimension in calibrated units sampling : NDArray | tuple | list | float | int The sampling rate/spacing for each dimension units : list[str] | tuple | list @@ -80,7 +80,7 @@ def from_array( name : str | None Dataset name. Default: "3D dataset" origin : NDArray | tuple | list | float | int | None - Origin for each dimension. Default: [0, 0, 0] + Origin for each dimension in calibrated units. Default: [0, 0, 0] sampling : NDArray | tuple | list | float | int | None Sampling for each dimension. Default: [1, 1, 1] units : list[str] | tuple | list | None @@ -148,7 +148,7 @@ def from_shape( fill_value : float Value to fill array with. Default: 0.0 origin : NDArray | tuple | list | float | int | None - Origin for each dimension + Origin for each dimension in calibrated units. sampling : NDArray | tuple | list | float | int | None Sampling for each dimension units : list[str] | tuple | list | None @@ -318,8 +318,7 @@ def show( ncols = min(ncols, n_frames) # Don't create more columns than frames images = [self.array[i] for i in frame_idx] labels = [ - f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" - for i in frame_idx + f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" for i in frame_idx ] # Pad last row to complete the grid (show_2d requires rectangular input) remainder = n_frames % ncols diff --git a/src/quantem/core/datastructures/dataset4d.py b/src/quantem/core/datastructures/dataset4d.py index 981243df..8e5bdfa0 100644 --- a/src/quantem/core/datastructures/dataset4d.py +++ b/src/quantem/core/datastructures/dataset4d.py @@ -39,7 +39,7 @@ def __init__( name : str A descriptive name for the dataset origin : NDArray | tuple | list | float | int - The origin coordinates for each dimension + The origin coordinates for each dimension in calibrated units sampling : NDArray | tuple | list | float | int The sampling rate/spacing for each dimension units : list[str] | tuple | list diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 28328636..79cbc479 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -59,7 +59,7 @@ def __init__( name : str A descriptive name for the dataset origin : NDArray | tuple | list | float | int - The origin coordinates for each dimension + The origin coordinates for each dimension in calibrated units sampling : NDArray | tuple | list | float | int The sampling rate/spacing for each dimension units : list[str] | tuple | list @@ -133,7 +133,7 @@ def from_array( name : str | None, optional A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" origin : NDArray | tuple | list | float | int | None, optional - The origin coordinates for each dimension. If None, defaults to zeros + The origin coordinates for each dimension in calibrated units. If None, defaults to zeros sampling : NDArray | tuple | list | float | int | None, optional The sampling rate/spacing for each dimension. If None, defaults to ones units : list[str] | tuple | list | None, optional diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 60716ac9..98e9e8fa 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -38,7 +38,7 @@ def read_4dstem( name : str | None, optional A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" origin : NDArray | tuple | list | float | int | None, optional - The origin coordinates for each dimension. If None, defaults to zeros + The origin coordinates for each dimension in calibrated units. If None, defaults to zeros sampling : NDArray | tuple | list | float | int | None, optional The sampling rate/spacing for each dimension. If None, defaults to ones units : list[str] | tuple | list | None, optional diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index 2fbfa119..0f037a4a 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -754,7 +754,7 @@ def from_array( name : str | None, optional A descriptive name for the dataset. If None, defaults to "4D-STEM dataset" origin : np.ndarray | tuple | list | float | int | None, optional - The origin coordinates for each dimension. If None, defaults to zeros + The origin coordinates for each dimension in calibrated units. If None, defaults to zeros sampling : np.ndarray | tuple | list | float | int | None, optional The sampling rate/spacing for each dimension. 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show_obj_fft( obj_show = obj_pad else: # complex or pure phase just show the phase obj_show = np.angle(obj_pad) - show_2d( + fig, ax = show_2d( [ obj_show, np.abs(obj_fft), ], title=[t + "Object", t + "Fourier Transform"], scalebar=[obj_scalebar, fft_scalebar], + return_fig=True, **kwargs, ) + ax[1].set_aspect(obj_np.shape[-1] / obj_np.shape[-2]) else: - show_2d( - np.abs(obj_fft), scalebar=fft_scalebar, title=t + "Fourier Transform", **kwargs + fig, ax = show_2d( + np.abs(obj_fft), + scalebar=fft_scalebar, + title=t + "Fourier Transform", + return_fig=True, + **kwargs, ) + ax.set_aspect(obj_np.shape[-1] / obj_np.shape[-2]) if return_fft: return obj_fft else: @@ -891,6 +897,7 @@ def show_scan_positions( if conv_angle is not None and energy is not None: from quantem.core.utils.utils import electron_wavelength_angstrom + wavelength = electron_wavelength_angstrom(energy) conv_angle_rad = conv_angle * 1e-3 # For defocused probe: radius ≈ |defocus| * convergence_angle + diffraction_limit From 21486792a6786c77694177ab1682418240f44370 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 15 Apr 2026 17:06:44 +0000 Subject: [PATCH 234/335] Added k-planes model --- src/quantem/core/ml/kplanes.py | 248 +++++++++++++++++++++++++++++++++ 1 file changed, 248 insertions(+) create mode 100644 src/quantem/core/ml/kplanes.py diff --git a/src/quantem/core/ml/kplanes.py b/src/quantem/core/ml/kplanes.py new file mode 100644 index 00000000..52a4adfc --- /dev/null +++ b/src/quantem/core/ml/kplanes.py @@ -0,0 +1,248 @@ +""" +Tensor Decomposition Methods for INR-based reconstructions +""" + +from typing import Any, Callable, Optional, Sequence + +import tinycudann as tcnn +import torch +import torch.nn.functional as F +from torch import nn + +""" +K-planes utility functions +""" +def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: bool = True) -> torch.Tensor: + """ + Performs bilinear interpolation on a grid at given coordinates. + + Args: + grid: Grid tensor of shape [B, C, H, W] or [C, H, W] + coords: Coordinate tensor of shape [B, N, 2] or [N, 2] + align_corners: Whether to align corners + + Returns: + Interpolated values of shape [B, N, C] or [N, C] + """ + grid_dim = coords.shape[-1] + + if grid.dim() == grid_dim + 1: + # no batch dimension present, need to add it + grid = grid.unsqueeze(0) + if coords.dim() == 2: + coords = coords.unsqueeze(0) + + if grid_dim == 2 or grid_dim == 3: + grid_sampler = F.grid_sample + else: + raise NotImplementedError(f"Grid-sample was called with {grid_dim}D data but is only " + f"implemented for 2 and 3D data.") + + coords = coords.view([coords.shape[0]] + [1] * (grid_dim - 1) + list(coords.shape[1:])) + B, feature_dim = grid.shape[:2] + n = coords.shape[-2] + interp = grid_sampler( + grid, # [B, feature_dim, reso, ...] + coords, # [B, 1, ..., n, grid_dim] + align_corners=align_corners, + mode='bilinear', padding_mode='border') + interp = interp.view(B, feature_dim, n).transpose(-1, -2) # [B, n, feature_dim] + interp = interp.squeeze() # [B?, n, feature_dim?] + return interp + +def init_planes( + in_dim: int, + out_dim: int, + resolution: Sequence[int], + init_range: tuple = (0.1, 0.5), +) -> nn.ParameterList: + """Create the set of 2D planes for a k-plane decomposition. + + For in_dim=3 (spatial), this creates 3 planes: XY, XZ, YZ. + For in_dim=4 (spatial + time), this creates 6 planes: XY, XZ, XT, YZ, YT, ZT. + Time planes (those involving axis 3) are initialized to 1 so they start + as identity multipliers. + + Args: + in_dim: Dimensionality of the input coordinates (3 or 4). + out_dim: Number of feature channels per plane. + resolution: Resolution along each axis, e.g. [128, 128, 128]. + init_range: (a, b) for uniform initialization of spatial planes. + + Returns: + nn.ParameterList of plane parameters, each of shape [1, out_dim, res_j, res_i]. + """ + assert len(resolution) == in_dim + # All pairs of axes + axis_pairs = list(itertools.combinations(range(in_dim), 2)) + planes = nn.ParameterList() + a, b = init_range + for pair in axis_pairs: + # grid_sample expects (N, C, H, W) — so resolution is reversed + shape = [1, out_dim] + [resolution[ax] for ax in reversed(pair)] + param = nn.Parameter(torch.empty(*shape)) + # Time planes init to 1; spatial planes init uniform + if in_dim == 4 and 3 in pair: + nn.init.ones_(param) + else: + nn.init.uniform_(param, a=a, b=b) + planes.append(param) + return planes + +def query_planes( + pts: torch.Tensor, + planes: nn.ParameterList, + in_dim: int, +) -> float: + """Query the k-plane representation at a batch of points. + + Projects each point onto every axis-pair plane, bilinearly interpolates, + and returns the element-wise product across all planes. + + Args: + pts: (B, in_dim) coordinates in [-1, 1]. + planes: The ParameterList from init_planes. + in_dim: 3 or 4. + + Returns: + (B, out_dim) features. + """ + axis_pairs = list(itertools.combinations(range(in_dim), 2)) + result = 1.0 + for plane_param, pair in zip(planes, axis_pairs): + # Extract the 2D coords for this plane + coords_2d = pts[..., list(pair)] # (B, 2) + coords_2d = coords_2d.view(1, -1, 1, 2) # (1, B, 1, 2) for grid_sample + # grid_sample: input (N,C,H,W), grid (N, H_out, W_out, 2) + sampled = F.grid_sample( + plane_param, # (1, C, H, W) + coords_2d, # (1, B, 1, 2) + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # -> (1, C, B, 1) + sampled = sampled.squeeze(0).squeeze(-1).T # (B, C) + result = result * sampled + return result # pyright: ignore[reportReturnType] + + +def interpolate_ms_features( + pts: torch.Tensor, + ms_grids: nn.ModuleList, +) -> torch.Tensor: + coo_combs = list(itertools.combinations(range(3), 2)) # [(0,1), (0,2), (1,2)] + multi_scale_interp = [] + + for grid in ms_grids: + interp_space = 1. + for ci, coo_comb in enumerate(coo_combs): + feature_dim = grid[ci].shape[1] + interp_out_plane = ( + grid_sample_wrapper(grid[ci], pts[..., coo_comb]) + .view(-1, feature_dim) + ) + interp_space = interp_space * interp_out_plane + multi_scale_interp.append(interp_space) + + return torch.cat(multi_scale_interp, dim=-1) + + +""" +K-planes Model +""" +class KPlanes(nn.Module): + + def __init__( + self, + # Grid parameters + grid_dimensions: int = 2, + input_coords_dims: int = 3, + M_features: int = 32, + resolution: Sequence[int] = (200, 200, 200), + multiscale_res_multipliers: Optional[Sequence[int]] = None, + concat_features: bool = True, + density_activation: Callable = lambda x: F.softplus(x - 1), + ): + """ + Assume coords are [-1, 1] in each dimension. + """ + super().__init__() + + self.grid_dimensions = grid_dimensions + self.input_coords_dims = input_coords_dims + self.M_features = M_features + self.resolution = resolution + self.multiscale_res_multipliers = multiscale_res_multipliers or [1] + self.concat_features = concat_features + self.density_activation = density_activation + + + # Initialize planes + self.grids = nn.ParameterList() + self.feature_dim = 0 + + # Resolution pyramid + for res_mult in self.multiscale_res_multipliers: + scaled_res = [r * res_mult for r in self.resolution] + gp = init_planes( + in_dim=self.input_coords_dims, + out_dim=self.M_features, + resolution=scaled_res, + ) + + self.feature_dim += gp[-1].shape[1] + self.grids.append(gp) + + + # Linear net + self.sigma_net = tcnn.Network( + n_input_dims=self.feature_dim, + n_output_dims=1, + network_config={ + "otype": "CutlassMLP", + "activation": "None", + "output_activation": "None", + "n_neurons": 128, + "n_hidden_layers": 0, + }, + ) + + + + def get_densities(self, coords: torch.Tensor): + """Computes and returns densities""" + + pts = coords.reshape(-1, 3) + features = interpolate_ms_features( + pts=pts, + ms_grids=self.grids, + ) + density_before_activation = self.sigma_net(features) + density = self.density_activation(density_before_activation) + return density + + def forward( + self, + pts: torch.Tensor, + ): + return self.get_densities(pts) + + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: + return { + "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists + "sigma_net": list(self.sigma_net.parameters()), + } + + + def set_optimizer(self, optimizer_params: dict[str, Any]): + + self._grids.set_optimizer(optimizer_params["grids"]) + self._sigmanet.set_optimizer(optimizer_params["sigmanet"]) + + + + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: + return { + "grids": self._grids.params # flatten ParameterLists + "sigma_net": self._sigma_net.params + } \ No newline at end of file From c299ca8bd03d18ffaa7ac56b4c1ac998c6130bab Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 15 Apr 2026 18:20:42 +0000 Subject: [PATCH 235/335] Added PPLR stuff --- src/quantem/core/ml/{ => models}/kplanes.py | 11 ++++++++++- src/quantem/core/ml/models/model_base.py | 13 +++++++++++++ 2 files changed, 23 insertions(+), 1 deletion(-) rename src/quantem/core/ml/{ => models}/kplanes.py (97%) create mode 100644 src/quantem/core/ml/models/model_base.py diff --git a/src/quantem/core/ml/kplanes.py b/src/quantem/core/ml/models/kplanes.py similarity index 97% rename from src/quantem/core/ml/kplanes.py rename to src/quantem/core/ml/models/kplanes.py index 52a4adfc..6964415c 100644 --- a/src/quantem/core/ml/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -9,6 +9,8 @@ import torch.nn.functional as F from torch import nn +from .model_base import PPLR + """ K-planes utility functions """ @@ -150,7 +152,7 @@ def interpolate_ms_features( """ K-planes Model """ -class KPlanes(nn.Module): +class KPlanes(nn.Module, PPLR): def __init__( self, @@ -226,7 +228,14 @@ def forward( pts: torch.Tensor, ): return self.get_densities(pts) + + def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: + return [ + {"params": } + ] + + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: return { "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py new file mode 100644 index 00000000..b40a13f8 --- /dev/null +++ b/src/quantem/core/ml/models/model_base.py @@ -0,0 +1,13 @@ +from abc import ABC, abstractmethod +from typing import Dict + +import torch + + +class PPLR(ABC): + """ + Abstract base class for models that require multi-scale parameter optimization. + """ + @abstractmethod + def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: + pass \ No newline at end of file From 5cf3d876f535a6ff9635444669409d486209e091 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Wed, 15 Apr 2026 22:32:38 +0000 Subject: [PATCH 236/335] updating docstrings and examples in show2d, linescan for 3d --- src/quantem/core/visualization/line_scan.py | 7 +- .../core/visualization/visualization.py | 128 ++++++++---------- 2 files changed, 60 insertions(+), 75 deletions(-) diff --git a/src/quantem/core/visualization/line_scan.py b/src/quantem/core/visualization/line_scan.py index 6d22beb2..b154558b 100644 --- a/src/quantem/core/visualization/line_scan.py +++ b/src/quantem/core/visualization/line_scan.py @@ -9,6 +9,7 @@ # TODO update sampling to allow for 3D and to plot with appropriate units along xy/z +# TODO proper normalization for line going through edges/corner with finite linewidth def linescan( image: np.ndarray, center: tuple[int, int] | None = None, @@ -234,11 +235,13 @@ def _show_linescan( if profile.ndim == 1: ax0.plot(positions, profile, linewidth=2) + title = "Image" else: # For 3D input, show as image # im = ax0.imshow(profile, aspect="equal", origin="upper") - im = ax0.imshow(profile, aspect="auto", origin="upper") + im = ax0.matshow(profile, aspect="auto", origin="upper") plt.colorbar(im, ax=ax0) + title = "Mean of depth slices" ax0.set_xlabel(f"Position ({sampling_units})") ax0.set_ylabel("Intensity" if profile.ndim == 1 else "Depth (pixels)") @@ -259,7 +262,7 @@ def _show_linescan( ax1.set_xlim(0, image.shape[1] - 1) ax1.set_ylim(image.shape[0] - 1, 0) - ax1.set_title("Image with Line Scan") + ax1.set_title(title) plt.tight_layout() return fig, (ax0, ax1) diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 9bccbf86..77f647b4 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -36,12 +36,35 @@ ArrayLike: TypeAlias = Union[NDArray, torch.Tensor, "Dataset2d"] # union required here +# --- show_2d / grid broadcast inputs --- +# There might be a cleaner way to do this, but better to have it here than in the functions +NormInputCell: TypeAlias = NormalizationConfig | ShowParams.Norm | dict | str +Show2dNormInput: TypeAlias = ( + NormInputCell + | None + | Sequence[NormInputCell] + | Sequence[Sequence[NormInputCell]] +) + +ScalebarInputCell: TypeAlias = ScalebarConfig | ShowParams.Scalebar | dict | bool | None +Show2dScalebarInput: TypeAlias = ( + ScalebarConfig + | ShowParams.Scalebar + | dict + | bool + | None + | Sequence[ScalebarInputCell] + | Sequence[Sequence[ScalebarInputCell]] +) + +CmapType: TypeAlias = str | colors.Colormap + def _show_2d_array( array: NDArray, *, - norm: NormalizationConfig | ShowParams.Norm | dict | str | None = None, - scalebar: ScalebarConfig | ShowParams.Scalebar | dict | bool | None = None, + norm: NormInputCell | None = None, + scalebar: ScalebarInputCell = None, cmap: str | colors.Colormap = "gray", chroma_boost: float = 1.0, cbar: bool = False, @@ -51,11 +74,9 @@ def _show_2d_array( show_ticks: bool = False, **kwargs: Any, ) -> tuple[Any, Any]: - """Display a 2D array as an image with optional colorbar and scalebar. + """Render a single 2D array (real or complex) with optional colorbar/scalebar. - This function visualizes a 2D array, handling both real and complex data. - For complex data, it displays amplitude and phase information using a - perceptually-uniform color representation. + Complex data uses amplitude + phase in a perceptually uniform RGB encoding. Parameters ---------- @@ -174,8 +195,8 @@ def _show_2d_array( def _show_2d_combined( list_of_arrays: Sequence[NDArray], *, - norm: NormalizationConfig | ShowParams.Norm | dict | str | None = None, - scalebar: ScalebarConfig | ShowParams.Scalebar | dict | bool | None = None, + norm: NormInputCell | None = None, + scalebar: ScalebarInputCell = None, chroma_boost: float = 1.0, cbar: bool = False, figax: tuple[Any, Any] | None = None, @@ -184,7 +205,7 @@ def _show_2d_combined( show_ticks: bool = False, **kwargs: Any, ) -> tuple[Any, Any]: - """Display multiple 2D arrays as a single combined image. + """Fuse multiple 2D arrays into one RGB panel (``show_2d(..., combine_images=True)``). This function takes a list of 2D arrays and creates a single visualization where each array is assigned a unique color, and their amplitudes determine @@ -401,24 +422,9 @@ def _norm_show_args( def _normalize_show_args_to_grid( shape: tuple[int, int], - norm: NormalizationConfig - | ShowParams.Norm - | dict - | str - | Sequence[NormalizationConfig | ShowParams.Norm | dict | str] - | Sequence[Sequence[NormalizationConfig | ShowParams.Norm | dict | str]] - | None = None, - scalebar: ScalebarConfig - | ShowParams.Scalebar - | dict - | bool - | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] - | Sequence[Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None]] - | None = None, - cmap: str - | colors.Colormap - | Sequence[str | colors.Colormap] - | Sequence[Sequence[str | colors.Colormap]] = "gray", + norm: Show2dNormInput = None, + scalebar: Show2dScalebarInput = None, + cmap: CmapType | Sequence[CmapType] | Sequence[Sequence[CmapType]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, title: str | Sequence[str] | Sequence[Sequence[str]] | None = None, chroma_boost: float | Sequence[float] = 1.0, @@ -451,30 +457,12 @@ def _normalize_show_args_to_grid( return args -# the type hinting is a bit of a mess, but not sure how to improve it def show_2d( arrays: ArrayLike | Sequence[ArrayLike] | Sequence[Sequence[ArrayLike]], *, - norm: ( - NormalizationConfig - | ShowParams.Norm - | dict - | str - | Sequence[NormalizationConfig | ShowParams.Norm | dict | str] - | Sequence[Sequence[NormalizationConfig | ShowParams.Norm | dict | str]] - | None - ) = None, - scalebar: ScalebarConfig - | ShowParams.Scalebar - | dict - | bool - | Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None] - | Sequence[Sequence[ScalebarConfig | ShowParams.Scalebar | dict | bool | None]] - | None = None, - cmap: str - | colors.Colormap - | Sequence[str | colors.Colormap] - | Sequence[Sequence[str | colors.Colormap]] = "gray", + norm: Show2dNormInput = None, + scalebar: Show2dScalebarInput = None, + cmap: CmapType | Sequence[CmapType] | Sequence[Sequence[CmapType]] = "gray", cbar: bool | Sequence[bool] | Sequence[Sequence[bool]] = False, title: str | Sequence[str] | Sequence[Sequence[str]] | None = None, figax: tuple[Any, Any] | None = None, @@ -484,12 +472,8 @@ def show_2d( ) -> tuple[Any, Any]: """Display one or more 2D arrays in a grid layout. - This is the main visualization function that can display a single array, - a list of arrays, or a grid of arrays. It supports both individual and - combined visualization modes. - - The display arguments, i.e. everything except figax and axsize, can be given as single values - or as sequences that will be broadcasted to the grid shape defined by the input arrays. + ``norm``, ``scalebar``, ``cmap``, ``cbar``, and ``title`` may be scalars or nested + sequences broadcast to the panel grid. Parameters ---------- @@ -558,35 +542,33 @@ def show_2d( Examples -------- - Display a single image: + Typed normalization and scale bar (same information as str/dict forms): >>> import numpy as np - >>> from quantem.core.visualization import show_2d - >>> image = np.random.rand(256, 256) - >>> fig, ax = show_2d(image, axsize=(3, 3)) - - Display a 2-row grid with titles and a scale bar: - - >>> image_rows = [[np.random.rand(128, 128) for _ in range(3)] for _ in range(2)] - >>> label_rows = [["BF", "ADF", "ABF"], ["HAADF", "DPC", "SSB"]] - >>> fig, axs = show_2d( - ... image_rows, - ... title=label_rows, - ... cmap="gray", - ... scalebar={"sampling": 0.5, "units": "Å"}, + >>> from quantem.core.visualization import show_2d, ShowParams + >>> img = np.random.rand(128, 128) + >>> fig, ax = show_2d( + ... img, + ... norm=ShowParams.Norm.log_auto(), + ... scalebar=ShowParams.Scalebar(sampling=0.5, units="Å"), + ... cbar=True, ... ) - Display diffraction patterns with log normalization, colorbar, and scale bar: + Preset strings and dicts: - >>> dps = [np.random.rand(256, 256) ** 3 for _ in range(3)] >>> fig, axs = show_2d( - ... dps, - ... title=["Zone axis", "Off-axis", "CBED"], + ... [np.random.rand(64, 64) ** 3 for _ in range(3)], ... norm="log_auto", - ... cbar=True, ... cmap="turbo", + ... title=["a", "b", "c"], ... scalebar={"sampling": 0.02, "units": "1/Å"}, ... ) + + Multi-row grid with titles: + + >>> rows = [[np.random.rand(48, 48) for _ in range(3)] for _ in range(2)] + >>> labels = [["BF", "ADF", "ABF"], ["HAADF", "DPC", "SSB"]] + >>> fig, axs = show_2d(rows, title=labels, cmap="gray", scalebar={"sampling": 0.5, "units": "Å"}) """ arrays = to_cpu(arrays) grid = _normalize_show_input_to_grid(arrays) From 9ac27a994c8b0d401cb5a2842bbaab6effe2c085 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 15 Apr 2026 23:54:57 +0000 Subject: [PATCH 237/335] object_models optimization setting is working well. Only thing that needs to be overloaded is set_optimizer for PPLR cases --- src/quantem/core/ml/models/kplanes.py | 26 ++--------- src/quantem/core/ml/models/model_base.py | 16 ++++++- src/quantem/tomography/object_models.py | 58 +++++++++++++++++++++++- src/quantem/tomography/tomography_opt.py | 4 +- 4 files changed, 79 insertions(+), 25 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 6964415c..954e383d 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -2,6 +2,7 @@ Tensor Decomposition Methods for INR-based reconstructions """ +import itertools from typing import Any, Callable, Optional, Sequence import tinycudann as tcnn @@ -209,8 +210,6 @@ def __init__( }, ) - - def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" @@ -229,29 +228,14 @@ def forward( ): return self.get_densities(pts) - - def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: - return [ - {"params": } - ] - - def get_params(self) -> dict[str, list[torch.nn.Parameter]]: return { "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists "sigma_net": list(self.sigma_net.parameters()), } - - def set_optimizer(self, optimizer_params: dict[str, Any]): - - self._grids.set_optimizer(optimizer_params["grids"]) - self._sigmanet.set_optimizer(optimizer_params["sigmanet"]) - + @property + def param_keys(self) -> list[str]: + return ["grids", "sigma_net"] - - def get_params(self) -> dict[str, list[torch.nn.Parameter]]: - return { - "grids": self._grids.params # flatten ParameterLists - "sigma_net": self._sigma_net.params - } \ No newline at end of file + \ No newline at end of file diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index b40a13f8..42bee71c 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -9,5 +9,19 @@ class PPLR(ABC): Abstract base class for models that require multi-scale parameter optimization. """ @abstractmethod - def get_optimization_parameters(self) -> Dict[str, list[torch.nn.Parameter]]: + def get_params(self) -> Dict[str, list[torch.nn.Parameter]]: + """ + This abstract method should return a dictionary of parameters based on a key. + + For example if your nn.Module has multiple optimizable parameter groups, + you can return a dictionary with the keys "grids" and "sigma_net" (KPlanes example). + """ + pass + + @property + @abstractmethod + def param_keys(self) -> list[str]: + """ + This abstract property should return a list of available parameter keys. + """ pass \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c5eb2406..6c21c410 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -10,10 +10,12 @@ from tqdm.auto import tqdm from quantem.core.io.serialize import AutoSerialize +from quantem.core.ml import OptimizerParams from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.ml.models.model_base import PPLR +from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset @@ -552,11 +554,65 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: # --- Optimization Parameters --- @property def params(self) -> Generator[torch.nn.Parameter, None, None]: + """ + Returns the optimization parameters, here we also check if PPLR is used and return the appropriate parameters. + """ + return self.model.parameters() # type: ignore[attr-defined] def get_optimization_parameters(self) -> list[nn.Parameter]: + + if isinstance(self.model, PPLR): + + # DEBUG + for key, value in self.optimizer_params.items(): + print(key, value) + return [ + { + "params": self.model.get_params()[key], + **self.optimizer_params[key].params(), + } + for key in self.model.param_keys + ] return list(self.params) + + # --- DDP Mixin Overloads in the case of PPLR --- + + @property + def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: + """Get the optimizer parameters.""" + return self._optimizer_params + + @optimizer_params.setter + def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any]): + """Set the optimizer parameters.""" + if isinstance(params, OptimizerType): + self._optimizer_params = params + return + if isinstance(self.model, PPLR): + if not isinstance(params, dict): + raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") + + object_params = params + + if set(object_params.keys()) != set(self.model.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + + params = {} + for key, value in object_params.items(): + if isinstance(value, dict): + params[key] = OptimizerParams.parse_dict(d=value) + elif isinstance(value, OptimizerType): + params[key] = value + else: + raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") + + self._optimizer_params = params + else: + raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") + + # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 16c846ab..0f8c44be 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -52,8 +52,8 @@ def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): if k not in targets: raise ValueError(f"Unknown optimization key: {k}") - if not isinstance(v, OptimizerType): - v = OptimizerParams.parse_dict(v) + # if not isinstance(v, OptimizerType): + # v = OptimizerParams.parse_dict(v) targets[k].optimizer_params = v From 5f40d5aaef38484e7b6769ea2de4c0d25c26cf91 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 00:13:03 +0000 Subject: [PATCH 238/335] Optimizing, set_optimizer is just default to Adam now, probably need to do the matching in set_optimizer instead of parsing in optimizer_params maybe? --- src/quantem/tomography/object_models.py | 23 ++++++++++++++++++++--- 1 file changed, 20 insertions(+), 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 6c21c410..01698769 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -564,9 +564,6 @@ def get_optimization_parameters(self) -> list[nn.Parameter]: if isinstance(self.model, PPLR): - # DEBUG - for key, value in self.optimizer_params.items(): - print(key, value) return [ { "params": self.model.get_params()[key], @@ -612,6 +609,26 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di else: raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: + """ + Set the optimizer for this model. + Currently supports single LR for all parameters, TODO allow for per parameter LRs by + updating get_optimization_parameters to return a list of parameters and their LRs. + """ + if not isinstance(self.model, PPLR): + super().set_optimizer(opt_params) + return + + if opt_params is not None: + self.optimizer_params = opt_params + + if not self._optimizer_params: + self._optimizer = None + return + + params = self.get_optimization_parameters() + + self._optimizer = torch.optim.Adam(params) # Pretraining @property From 7f51dc50e5233ce5437da4d2b1de25f8505a29ef Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 01:36:54 +0000 Subject: [PATCH 239/335] KPlanes Tilted claude implementation, need to talk to Corneel. Things to check: Look at object_models.py and see how the optimizer matching should be handled. It seems like set_optimizers doesn't really do what it's supposed to do. --- src/quantem/core/ml/models/kplanes.py | 535 +++++++++++++++++++++++--- 1 file changed, 482 insertions(+), 53 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 954e383d..7e206983 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -3,7 +3,8 @@ """ import itertools -from typing import Any, Callable, Optional, Sequence +import math +from typing import Callable, Optional, Sequence import tinycudann as tcnn import torch @@ -131,28 +132,27 @@ def query_planes( def interpolate_ms_features( pts: torch.Tensor, - ms_grids: nn.ModuleList, + ms_grids: nn.ParameterList, ) -> torch.Tensor: - coo_combs = list(itertools.combinations(range(3), 2)) # [(0,1), (0,2), (1,2)] - multi_scale_interp = [] - - for grid in ms_grids: - interp_space = 1. - for ci, coo_comb in enumerate(coo_combs): - feature_dim = grid[ci].shape[1] - interp_out_plane = ( - grid_sample_wrapper(grid[ci], pts[..., coo_comb]) - .view(-1, feature_dim) - ) - interp_space = interp_space * interp_out_plane - multi_scale_interp.append(interp_space) + mat_mode = [[0, 1], [0, 2], [1, 2]] + coord_plane = torch.stack([ + pts[:, mat_mode[0]], + pts[:, mat_mode[1]], + pts[:, mat_mode[2]], + ]).view(3, -1, 1, 2) - return torch.cat(multi_scale_interp, dim=-1) + per_scale = [] + for plane_coef in ms_grids: + C = plane_coef.shape[1] + feats = F.grid_sample( + plane_coef, coord_plane, align_corners=True, mode="bilinear", padding_mode="border" + ).reshape(3, C, -1) + fused = feats[0] * feats[1] * feats[2] + per_scale.append(fused.T) + + return torch.cat(per_scale, dim=-1) -""" -K-planes Model -""" class KPlanes(nn.Module, PPLR): def __init__( @@ -165,6 +165,10 @@ def __init__( multiscale_res_multipliers: Optional[Sequence[int]] = None, concat_features: bool = True, density_activation: Callable = lambda x: F.softplus(x - 1), + # Hybrid MLP parameters + use_hybrid_mlp: bool = False, + hybrid_hidden_dim: int = 64, + hybrid_num_layers: int = 2, ): """ Assume coords are [-1, 1] in each dimension. @@ -179,40 +183,55 @@ def __init__( self.concat_features = concat_features self.density_activation = density_activation - - # Initialize planes self.grids = nn.ParameterList() self.feature_dim = 0 - - # Resolution pyramid for res_mult in self.multiscale_res_multipliers: - scaled_res = [r * res_mult for r in self.resolution] - gp = init_planes( - in_dim=self.input_coords_dims, - out_dim=self.M_features, - resolution=scaled_res, + scaled_res = [int(r * res_mult) for r in self.resolution] + plane = nn.Parameter(torch.empty(3, self.M_features, scaled_res[1], scaled_res[0])) + nn.init.uniform_(plane, 0.1, 0.5) + self.grids.append(plane) + self.feature_dim += self.M_features + + # Network head + if use_hybrid_mlp: + hybrid_hidden_dim = int(hybrid_hidden_dim) + hybrid_num_layers = int(hybrid_num_layers) + if hybrid_hidden_dim <= 0: + raise ValueError(f"hybrid_hidden_dim must be >= 1, got {hybrid_hidden_dim}") + if hybrid_num_layers <= 0: + raise ValueError(f"hybrid_num_layers must be >= 1, got {hybrid_num_layers}") + + factory = {} # add dtype/device kwargs here if needed + layers = [] + in_dim = self.feature_dim + for _ in range(hybrid_num_layers): + lin = nn.Linear(in_dim, hybrid_hidden_dim, **factory) + nn.init.kaiming_uniform_(lin.weight, a=0.0, nonlinearity="relu") + nn.init.zeros_(lin.bias) + layers.append(lin) + layers.append(nn.ReLU(inplace=True)) + in_dim = hybrid_hidden_dim + + out = nn.Linear(in_dim, 1, bias=True, **factory) + nn.init.normal_(out.weight, std=0.01) + nn.init.zeros_(out.bias) + layers.append(out) + self.sigma_net = nn.Sequential(*layers) + else: + self.sigma_net = tcnn.Network( + n_input_dims=self.feature_dim, + n_output_dims=1, + network_config={ + "otype": "CutlassMLP", + "activation": "None", + "output_activation": "None", + "n_neurons": 128, + "n_hidden_layers": 0, + }, ) - - self.feature_dim += gp[-1].shape[1] - self.grids.append(gp) - - - # Linear net - self.sigma_net = tcnn.Network( - n_input_dims=self.feature_dim, - n_output_dims=1, - network_config={ - "otype": "CutlassMLP", - "activation": "None", - "output_activation": "None", - "n_neurons": 128, - "n_hidden_layers": 0, - }, - ) def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" - pts = coords.reshape(-1, 3) features = interpolate_ms_features( pts=pts, @@ -222,15 +241,12 @@ def get_densities(self, coords: torch.Tensor): density = self.density_activation(density_before_activation) return density - def forward( - self, - pts: torch.Tensor, - ): + def forward(self, pts: torch.Tensor): return self.get_densities(pts) - + def get_params(self) -> dict[str, list[torch.nn.Parameter]]: return { - "grids": [p for grid in self.grids for p in grid], # flatten ParameterLists + "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), } @@ -238,4 +254,417 @@ def get_params(self) -> dict[str, list[torch.nn.Parameter]]: def param_keys(self) -> list[str]: return ["grids", "sigma_net"] - \ No newline at end of file + +# --- Tilted KPlanes --- + +# --------------------------------------------------------------------------- +# SO(3) quaternion parameter module +# --------------------------------------------------------------------------- + +class SO3Param(nn.Module): + """ + Stores T unit quaternions as learnable parameters in R^4 and normalises + them on every call to `as_matrix()`. + + Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). + + Initialisation + -------------- + "random" – uniform sampling over SO(3) via Shoemake's method. + "identity" – all rotations start as the identity (good for fine-tuning). + """ + + def __init__(self, T: int, init: str = "random"): + super().__init__() + if T < 1: + raise ValueError(f"T must be >= 1, got {T}") + quats = self._init_quaternions(T, init) # (T, 4) + self.quats = nn.Parameter(quats) + + # ------------------------------------------------------------------ + # Initialisers + # ------------------------------------------------------------------ + + @staticmethod + def _shoemake_sample(T: int) -> torch.Tensor: + """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" + u = torch.rand(T, 3) + sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) + sqrt_u0 = torch.sqrt(u[:, 0]) + two_pi = 2.0 * math.pi + x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) + y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) + z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) + w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) + return torch.stack([x, y, z, w], dim=-1) # (T, 4) + + @staticmethod + def _identity(T: int) -> torch.Tensor: + """All-identity rotations: [0,0,0,1] * T.""" + q = torch.zeros(T, 4) + q[:, 3] = 1.0 + return q + + @classmethod + def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: + if init == "random": + return cls._shoemake_sample(T) + elif init == "identity": + return cls._identity(T) + else: + raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") + + # ------------------------------------------------------------------ + # Forward helpers + # ------------------------------------------------------------------ + + def normalized(self) -> torch.Tensor: + """Returns (T, 4) unit quaternions.""" + return F.normalize(self.quats, p=2, dim=-1) + + def as_matrix(self) -> torch.Tensor: + """ + Converts the T stored quaternions to (T, 3, 3) rotation matrices. + + Uses the standard formula; no trig, just multiplications. + """ + q = self.normalized() # (T, 4) [x, y, z, w] + x, y, z, w = q.unbind(dim=-1) # each (T,) + + # Precompute products + xx, yy, zz = x*x, y*y, z*z + xy, xz, yz = x*y, x*z, y*z + wx, wy, wz = w*x, w*y, w*z + + # Row-major: R[i,j] + R = torch.stack([ + 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), + 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), + 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), + ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) + + return R + + def extra_repr(self) -> str: + return f"T={self.quats.shape[0]}" +def interpolate_ms_features_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + Multi-scale, multi-rotation K-Planes feature interpolation. + + For each of the T rotations: + 1. Rotate pts -> (B, 3) + 2. Project to XY / XZ / YZ planes -> 3 × (B, 2) grids of coords + 3. Bilinear interpolation on the corresponding planes + 4. Hadamard product across the 3 planes -> (B, C) + + Across T transforms, outputs are *concatenated* (not summed), so each + rotation owns a disjoint slice of the feature dimension. Across scales, + outputs are also concatenated, matching the base KPlanes behaviour. + + Returns + ------- + features : (B, C * T * num_scales) + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # Rotate all points by all T rotation matrices at once. + # pts: (B, 3), R: (T, 3, 3) -> rotated: (T, B, 3) + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + per_scale_features = [] + + for plane_coef in ms_grids: + # plane_coef shape: (3*T, C, H, W) + C = plane_coef.shape[1] + + # Build the (3*T, B, 1, 2) coordinate tensor for grid_sample. + # For transform t, we need coords for planes XY, XZ, YZ. + # grid_sample expects coords in [-1, 1] with shape (N, Hout, Wout, 2). + all_plane_coords = [] + for t in range(T): + rp = rotated[t] # (B, 3) + all_plane_coords.append(rp[:, [0, 1]]) # XY (B, 2) + all_plane_coords.append(rp[:, [2, 0]]) # ZX (B, 2) matches ki + all_plane_coords.append(rp[:, [1, 2]]) # YZ (B, 2) matches jk + + # Stack -> (3*T, B, 2) -> (3*T, B, 1, 2) for grid_sample + coord_tensor = torch.stack(all_plane_coords, dim=0).unsqueeze(2) + # coord_tensor: (3*T, B, 1, 2) + + # grid_sample: input (N, C, H, W), grid (N, Hout, Wout, 2) + sampled = F.grid_sample( + plane_coef, # (3*T, C, H, W) + coord_tensor, # (3*T, B, 1, 2) + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # -> (3*T, C, B, 1) + sampled = sampled.squeeze(-1) # (3*T, C, B) + + # Hadamard product within each transform group (3 planes per transform). + transform_features = [] + for t in range(T): + p_xy = sampled[3*t + 0] # (C, B) + p_zx = sampled[3*t + 1] + p_yz = sampled[3*t + 2] + fused = (p_xy * p_zx * p_yz).T # (B, C) + transform_features.append(fused) + + # Concatenate across transforms -> (B, C*T) + per_scale_features.append(torch.cat(transform_features, dim=-1)) + + # Concatenate across scales -> (B, C*T*num_scales) + return torch.cat(per_scale_features, dim=-1) + +# --------------------------------------------------------------------------- +# KPlanesTILTED +# --------------------------------------------------------------------------- + +class KPlanesTILTED(KPlanes): + """ + K-Planes with T learned SO(3) rotations (TILTED). + + Inherits KPlanes for the sigma_net, density_activation, and get_params + interface. Overrides: + * __init__ – replaces the axis-aligned grids with (3*T)-plane grids + and adds SO3Param. + * get_densities – calls the TILTED interpolation instead. + * get_params – adds "so3" key so callers can set a separate lr. + * param_keys – updated list. + + Parameters + ---------- + M_features : int + Feature channels *per transform per scale*. Total feature_dim will + be M_features * T * len(multiscale_res_multipliers). + T : int + Number of learned rotations (TILTED-T in the paper; 4 or 8 recommended). + tau_init : str + "random" (paper default) or "identity". + tau_warmup_steps : int + If > 0, grids and sigma_net are frozen for this many steps so the + rotations can find good basins first (two-phase warm-up). + Call model.training_step() once per optimiser step. + All other args are forwarded to KPlanes. + """ + + def __init__( + self, + # Grid parameters + input_coords_dims: int = 3, + M_features: int = 32, + resolution: Sequence[int] = (200, 200, 200), + multiscale_res_multipliers: Optional[Sequence[int]] = None, + density_activation: Callable = lambda x: F.softplus(x - 1), + # TILTED parameters + T: int = 4, + tau_init: str = "random", + tau_warmup_steps: int = 0, + # Hybrid MLP parameters + use_hybrid_mlp: bool = False, + hybrid_hidden_dim: int = 64, + hybrid_num_layers: int = 2, + ): + if input_coords_dims != 3: + raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") + if T < 1: + raise ValueError(f"T must be >= 1, got {T}") + + multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) + num_scales = len(multiscale_res_multipliers) + + # Total feature dim seen by the MLP head. + # Each scale contributes M_features * T channels. + feature_dim = M_features * T * num_scales + + # Call KPlanes.__init__ with grid_dimensions=2 so it builds sigma_net + # correctly; we immediately replace self.grids below. + super().__init__( + grid_dimensions=2, + input_coords_dims=3, + M_features=M_features, # base class stores this + resolution=resolution, + multiscale_res_multipliers=multiscale_res_multipliers, + concat_features=True, + density_activation=density_activation, + use_hybrid_mlp=use_hybrid_mlp, + hybrid_hidden_dim=hybrid_hidden_dim, + hybrid_num_layers=hybrid_num_layers, + ) + # KPlanes.__init__ built grids with shape (3, M, H, W) and feature_dim + # = M * num_scales. We rebuild them for the TILTED shape. + + self.T = T + self.tau_warmup_steps = tau_warmup_steps + self._global_step: int = 0 + + # ---- Rebuild grids: (3*T, M_features, H, W) per scale ---- + self.grids = nn.ParameterList() + for res_mult in multiscale_res_multipliers: + scaled_res = [int(r * res_mult) for r in resolution] + plane = nn.Parameter( + torch.empty(3 * T, M_features, scaled_res[1], scaled_res[0]) + ) + nn.init.uniform_(plane, 0.1, 0.5) + self.grids.append(plane) + + # ---- Rebuild sigma_net with the correct feature_dim ---- + # KPlanes built sigma_net with self.feature_dim (= M * num_scales), + # which is wrong for T > 1. Rebuild here. + self.feature_dim = feature_dim + self._build_sigma_net(use_hybrid_mlp, hybrid_hidden_dim, hybrid_num_layers) + + # ---- Learnable rotations ---- + self.so3 = SO3Param(T, init=tau_init) + + # ------------------------------------------------------------------ + # Internal helpers + # ------------------------------------------------------------------ + + def _build_sigma_net( + self, + use_hybrid_mlp: bool, + hybrid_hidden_dim: int, + hybrid_num_layers: int, + ) -> None: + """Rebuild sigma_net for self.feature_dim (called after grids are set).""" + if use_hybrid_mlp: + layers = [] + in_dim = self.feature_dim + for _ in range(hybrid_num_layers): + lin = nn.Linear(in_dim, hybrid_hidden_dim) + nn.init.kaiming_uniform_(lin.weight, a=0.0, nonlinearity="relu") + nn.init.zeros_(lin.bias) + layers.append(lin) + layers.append(nn.ReLU(inplace=True)) + in_dim = hybrid_hidden_dim + out = nn.Linear(in_dim, 1, bias=True) + nn.init.normal_(out.weight, std=0.01) + nn.init.zeros_(out.bias) + layers.append(out) + self.sigma_net = nn.Sequential(*layers) + else: + self.sigma_net = tcnn.Network( + n_input_dims=self.feature_dim, + n_output_dims=1, + network_config={ + "otype": "CutlassMLP", + "activation": "None", + "output_activation": "None", + "n_neurons": 128, + "n_hidden_layers": 0, + }, + ) + + # ------------------------------------------------------------------ + # Warm-up bookkeeping + # ------------------------------------------------------------------ + + def training_step(self) -> None: + """ + Call once per optimiser step to advance the internal counter. + + During the first `tau_warmup_steps` iterations, grids and sigma_net + have their gradients zeroed after the backward pass so only the SO(3) + parameters update. This is the lightweight version of two-phase + optimisation from the paper. + """ + self._global_step += 1 + + def _in_warmup(self) -> bool: + return self.tau_warmup_steps > 0 and self._global_step < self.tau_warmup_steps + + def zero_non_tau_grads(self) -> None: + """ + Call after loss.backward() and before optimizer.step() when you want + to implement the rotation warm-up manually. Alternatively just check + model.in_warmup and configure your optimizer accordingly. + """ + if self._in_warmup(): + for p in self.grids.parameters(): + if p.grad is not None: + p.grad.zero_() + for p in self.sigma_net.parameters(): + if p.grad is not None: + p.grad.zero_() + + @property + def in_warmup(self) -> bool: + return self._in_warmup() + + # ------------------------------------------------------------------ + # Core forward + # ------------------------------------------------------------------ + + def get_densities(self, coords: torch.Tensor) -> torch.Tensor: + pts = coords.reshape(-1, 3) + R = self.so3.as_matrix() # (T, 3, 3) + features = interpolate_ms_features_tilted( + pts=pts, + ms_grids=self.grids, + rotation_matrices=R, + ) + density_before_activation = self.sigma_net(features) + return self.density_activation(density_before_activation) + + def forward(self, pts: torch.Tensor) -> torch.Tensor: + return self.get_densities(pts) + + # ------------------------------------------------------------------ + # Parameter groups + # ------------------------------------------------------------------ + + def get_params(self) -> dict[str, list[nn.Parameter]]: + return { + "grids": list(self.grids.parameters()), + "sigma_net": list(self.sigma_net.parameters()), + "so3": list(self.so3.parameters()), + } + + @property + def param_keys(self) -> list[str]: + return ["grids", "sigma_net", "so3"] + + + # ------------------------------------------------------------------ + # Two-phase helper: extract tau for phase-2 initialisation + # ------------------------------------------------------------------ + + def extract_tau_state(self) -> torch.Tensor: + """ + Returns the current quaternion tensor (detached copy) so it can be + used to initialise a larger phase-2 model via `load_tau_state`. + """ + return self.so3.quats.detach().clone() + + def load_tau_state(self, quats: torch.Tensor) -> None: + """ + Load pre-trained quaternions (e.g. from a bottleneck phase-1 model). + + quats : (T, 4) tensor, will be normalised internally. + """ + if quats.shape != self.so3.quats.shape: + raise ValueError( + f"Shape mismatch: got {quats.shape}, " + f"expected {self.so3.quats.shape}" + ) + with torch.no_grad(): + self.so3.quats.copy_(F.normalize(quats, p=2, dim=-1)) + + # ------------------------------------------------------------------ + # Pretty print + # ------------------------------------------------------------------ + + def extra_repr(self) -> str: + return ( + f"T={self.T}, " + f"M_features={self.M_features}, " + f"feature_dim={self.feature_dim}, " + f"num_scales={len(self.multiscale_res_multipliers)}, " + f"tau_warmup_steps={self.tau_warmup_steps}" + ) \ No newline at end of file From 9752ad00cca6d38120552b2691ce7f4cc2888db4 Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Thu, 16 Apr 2026 06:51:43 +0000 Subject: [PATCH 240/335] type fixes --- src/quantem/diffractive_imaging/object_models.py | 6 +++--- src/quantem/diffractive_imaging/probe_models.py | 6 +++--- src/quantem/diffractive_imaging/ptychography_lite.py | 4 ++-- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 9623e7b3..1c6fdab9 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -14,7 +14,7 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType, SchedulerType from quantem.core.utils.rng import RNGMixin from quantem.core.utils.validators import ( validate_arr_gt, @@ -1050,8 +1050,8 @@ def pretrain( pretrain_target: torch.Tensor | None = None, reset: bool = False, num_iters: int = 100, - optimizer_params: dict | None = None, - scheduler_params: dict | None = None, + optimizer_params: dict | OptimizerType | None = None, + scheduler_params: dict | SchedulerType | None = None, loss_fn: Callable | str = "l2", apply_constraints: bool = False, show: bool = True, diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index e9027620..f47ea1f7 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -15,7 +15,7 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType, SchedulerType from quantem.core.utils.rng import RNGMixin from quantem.core.utils.utils import electron_wavelength_angstrom, to_numpy from quantem.core.utils.validators import ( @@ -1359,8 +1359,8 @@ def pretrain( pretrain_target: torch.Tensor | None = None, reset: bool = False, num_iters: int = 100, - optimizer_params: dict | None = None, - scheduler_params: dict | None = None, + optimizer_params: dict | OptimizerType | None = None, + scheduler_params: dict | SchedulerType | None = None, loss_fn: Callable | str = "l2", apply_constraints: bool = False, show: bool = True, diff --git a/src/quantem/diffractive_imaging/ptychography_lite.py b/src/quantem/diffractive_imaging/ptychography_lite.py index 9fc7d039..e3837b83 100644 --- a/src/quantem/diffractive_imaging/ptychography_lite.py +++ b/src/quantem/diffractive_imaging/ptychography_lite.py @@ -373,9 +373,9 @@ def reconstruct( # type:ignore could do overloads but this is simpler... self, num_iters: int = 0, reset: bool = False, - lr_obj: float = 5e-4, + lr_obj: float = 1e-3, learn_probe: bool = True, - lr_probe: float = 5e-4, + lr_probe: float = 1e-3, batch_size: int | None = None, scheduler_type: Literal["exp", "cyclic", "plateau", "none"] = "none", scheduler_factor: float = 0.5, From af140be45ee83abd52b6f6e40a2e531406d261ef Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 07:19:22 +0000 Subject: [PATCH 241/335] Added TV loss for PPLR models, I don't like this solution though will probably have to do TV loss computation within the model? --- src/quantem/core/ml/models/kplanes.py | 1 + src/quantem/tomography/object_models.py | 59 +++++++++++++++---------- 2 files changed, 37 insertions(+), 23 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 7e206983..dbc8f982 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -574,6 +574,7 @@ def training_step(self) -> None: parameters update. This is the lightweight version of two-phase optimisation from the paper. """ + print("Global Stepped") self._global_step += 1 def _in_warmup(self) -> bool: diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 01698769..9f3ce764 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -506,29 +506,42 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] - - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) - - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - soft_loss += self.constraints.tv_vol * grad_norm.mean() + + if isinstance(self.model, PPLR): # TODO: Temporary + for plane in self.model.grids: + # plane: (3*T, C, H, W) + # Differences along H (axis -2) and W (axis -1) + diff_h = plane[..., 1:, :] - plane[..., :-1, :] # (3*T, C, H-1, W) + diff_w = plane[..., :, 1:] - plane[..., :, :-1] # (3*T, C, H, W-1) + + + soft_loss += diff_h.pow(2).mean() + diff_w.pow(2).mean() + + soft_loss /= len(self.model.grids) + else: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + soft_loss += self.constraints.tv_vol * grad_norm.mean() if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) From 73c0d30a387627e54797318ec97da2042cb4e169 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 16 Apr 2026 16:54:37 +0000 Subject: [PATCH 242/335] object_models.py now has tv_loss for both KPlanes and INR architectures. Also overloaded reconnecting optimizers --- src/quantem/core/ml/optimizer_mixin.py | 2 +- src/quantem/tomography/object_models.py | 178 +++++++++++++++--------- 2 files changed, 114 insertions(+), 66 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 4ea263c0..9fe8a32a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -733,7 +733,7 @@ def reconnect_optimizer_to_parameters(self) -> None: if not optimizable_params: print( - f"souldn't be getting here! No optimizable parameters found for {self.__class__.__name__}, removing optimizer" + f"shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}, removing optimizer" ) self.remove_optimizer() return diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 9f3ce764..0c09a372 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -14,6 +14,7 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module +from quantem.core.ml.models.kplanes import KPlanes, KPlanesTILTED from quantem.core.ml.models.model_base import PPLR from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin @@ -499,6 +500,8 @@ def obj_view(self) -> np.ndarray: self.create_volume() return self._obj.cpu().numpy().transpose(0, 1, 3, 2) + # --- Constraints --- + def apply_soft_constraints( self, coords: torch.Tensor, @@ -506,42 +509,7 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - - if isinstance(self.model, PPLR): # TODO: Temporary - for plane in self.model.grids: - # plane: (3*T, C, H, W) - # Differences along H (axis -2) and W (axis -1) - diff_h = plane[..., 1:, :] - plane[..., :-1, :] # (3*T, C, H-1, W) - diff_w = plane[..., :, 1:] - plane[..., :, :-1] # (3*T, C, H, W-1) - - - soft_loss += diff_h.pow(2).mean() + diff_w.pow(2).mean() - - soft_loss /= len(self.model.grids) - else: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] - - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) - - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - soft_loss += self.constraints.tv_vol * grad_norm.mean() + soft_loss += self.get_tv_loss(coords, pred) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) @@ -552,6 +520,53 @@ def apply_soft_constraints( return soft_loss + def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: + """ + Calculate the TV loss for the given coordinates and predictions. + + Current supported architectures are KPlanes and INRs + """ + + if isinstance(self.model, (KPlanes, KPlanesTILTED)): + + per_level = [] + + for p in self.model.grids: + dh = p[:, :, 1:, :] - p[:, :, :-1, :] + dw = p[:, :, :, 1:] - p[:, :, :, :-1] + tv = 0.0 + if self.constraints.tv_vol > 0: + tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) + per_level.append(tv) + + return torch.stack(per_level).sum() + else: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + return self.constraints.tv_vol * grad_norm.mean() + + + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: """ Apply hard constraints to the predicted values of the INR model. @@ -573,7 +588,7 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter]: + def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: if isinstance(self.model, PPLR): @@ -622,6 +637,7 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di else: raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") + def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ Set the optimizer for this model. @@ -643,6 +659,66 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self._optimizer = torch.optim.Adam(params) + def reconnect_optimizer_to_parameters(self) -> None: + """ + Reconnect optimizer overload, defaults back to the standard implementation if no `PPLR` is detected. + """ + + if self.optimizer is None: + return + + if isinstance(self.model, PPLR): + current_params = self.get_optimization_parameters() + + + optimizable_params = [ + p for p in current_params + if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf + ] + + + if not optimizable_params: + raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") + + for p in optimizable_params: + print(f"Setting requires_grad for parameter: {p}") + p['params'][0].requires_grad_(True) + + assert self._optimizer is not None + # Preserve optimizer states and param_group settings + old_state = self._optimizer.state.copy() + old_param_groups = self._optimizer.param_groups.copy() + + # Reconnect to new parameters + self._optimizer.param_groups.clear() + for param_group in optimizable_params: + self._optimizer.add_param_group(param_group) + + # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, + # excluding 'params' which comes from the new groups + for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): + new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) + + # Remap optimizer state: for any new param that IS the same tensor as an old param, + # carry its state over (moved to the right device just in case). + new_state = {} + for new_pg in self._optimizer.param_groups: + for new_param in new_pg["params"]: + if new_param in old_state: + device = new_param.device + new_state[new_param] = { + k: (v.to(device) if isinstance(v, torch.Tensor) else v) + for k, v in old_state[new_param].items() + } + + self._optimizer.state.clear() + self._optimizer.state.update(new_state) + + if self._scheduler is not None and self._optimizer is not None: + self._scheduler.optimizer = self._optimizer + else: + super().reconnect_optimizer_to_parameters() + # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: @@ -894,34 +970,6 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] - self, - coords: torch.Tensor, - ) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=coords.device) - - num_tv_samples = min(10000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - - tv_densities_recomputed = self.forward(tv_coords) - - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - - tv_loss += self.constraints.tv_vol * grad_norm.mean() - return tv_loss - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change if isinstance(device, str): device = torch.device(device) From 35157fbabf702f83821d84b2133666fbb1ff6704 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 17 Apr 2026 00:30:12 +0000 Subject: [PATCH 243/335] KPlanes with R9+SVD parameterization, everything seems to be working well. Only things to ask Corneel about is multiscale res since this adds a significant amount of compute. Should I be doing variable num_samples_per_ray? --- src/quantem/core/ml/models/kplanes.py | 291 ++++++++++++------------ src/quantem/tomography/object_models.py | 31 +-- src/quantem/tomography/tomography.py | 6 +- 3 files changed, 167 insertions(+), 161 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index dbc8f982..7839f20d 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -261,164 +261,175 @@ def param_keys(self) -> list[str]: # SO(3) quaternion parameter module # --------------------------------------------------------------------------- +# class SO3Param(nn.Module): +# """ +# Stores T unit quaternions as learnable parameters in R^4 and normalises +# them on every call to `as_matrix()`. + +# Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). + +# Initialisation +# -------------- +# "random" – uniform sampling over SO(3) via Shoemake's method. +# "identity" – all rotations start as the identity (good for fine-tuning). +# """ + +# def __init__(self, T: int, init: str = "random"): +# super().__init__() +# if T < 1: +# raise ValueError(f"T must be >= 1, got {T}") +# quats = self._init_quaternions(T, init) # (T, 4) +# self.quats = nn.Parameter(quats) + +# # ------------------------------------------------------------------ +# # Initialisers +# # ------------------------------------------------------------------ + +# @staticmethod +# def _shoemake_sample(T: int) -> torch.Tensor: +# """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" +# u = torch.rand(T, 3) +# sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) +# sqrt_u0 = torch.sqrt(u[:, 0]) +# two_pi = 2.0 * math.pi +# x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) +# y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) +# z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) +# w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) +# return torch.stack([x, y, z, w], dim=-1) # (T, 4) + +# @staticmethod +# def _identity(T: int) -> torch.Tensor: +# """All-identity rotations: [0,0,0,1] * T.""" +# q = torch.zeros(T, 4) +# q[:, 3] = 1.0 +# return q + +# @classmethod +# def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: +# if init == "random": +# return cls._shoemake_sample(T) +# elif init == "identity": +# return cls._identity(T) +# else: +# raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") + +# # ------------------------------------------------------------------ +# # Forward helpers +# # ------------------------------------------------------------------ + +# def normalized(self) -> torch.Tensor: +# """Returns (T, 4) unit quaternions.""" +# return F.normalize(self.quats, p=2, dim=-1) + +# def as_matrix(self) -> torch.Tensor: +# """ +# Converts the T stored quaternions to (T, 3, 3) rotation matrices. + +# Uses the standard formula; no trig, just multiplications. +# """ +# q = self.normalized() # (T, 4) [x, y, z, w] +# x, y, z, w = q.unbind(dim=-1) # each (T,) + +# # Precompute products +# xx, yy, zz = x*x, y*y, z*z +# xy, xz, yz = x*y, x*z, y*z +# wx, wy, wz = w*x, w*y, w*z + +# # Row-major: R[i,j] +# R = torch.stack([ +# 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), +# 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), +# 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), +# ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) + +# return R + +# def extra_repr(self) -> str: +# return f"T={self.quats.shape[0]}" + + class SO3Param(nn.Module): """ - Stores T unit quaternions as learnable parameters in R^4 and normalises - them on every call to `as_matrix()`. - - Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). - - Initialisation - -------------- - "random" – uniform sampling over SO(3) via Shoemake's method. - "identity" – all rotations start as the identity (good for fine-tuning). + SO(3) rotation bank using R9+SVD parameterization. + Each rotation is stored as an unconstrained 3x3 matrix M, + projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. """ - + def __init__(self, T: int, init: str = "random"): super().__init__() - if T < 1: - raise ValueError(f"T must be >= 1, got {T}") - quats = self._init_quaternions(T, init) # (T, 4) - self.quats = nn.Parameter(quats) - - # ------------------------------------------------------------------ - # Initialisers - # ------------------------------------------------------------------ - - @staticmethod - def _shoemake_sample(T: int) -> torch.Tensor: - """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" - u = torch.rand(T, 3) - sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) - sqrt_u0 = torch.sqrt(u[:, 0]) - two_pi = 2.0 * math.pi - x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) - y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) - z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) - w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) - return torch.stack([x, y, z, w], dim=-1) # (T, 4) - - @staticmethod - def _identity(T: int) -> torch.Tensor: - """All-identity rotations: [0,0,0,1] * T.""" - q = torch.zeros(T, 4) - q[:, 3] = 1.0 - return q - - @classmethod - def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: + print("SVD Module") if init == "random": - return cls._shoemake_sample(T) + # Initialize near identity with small noise + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + M = M + 0.1 * torch.randn(T, 3, 3) elif init == "identity": - return cls._identity(T) + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) else: - raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") - - # ------------------------------------------------------------------ - # Forward helpers - # ------------------------------------------------------------------ - - def normalized(self) -> torch.Tensor: - """Returns (T, 4) unit quaternions.""" - return F.normalize(self.quats, p=2, dim=-1) - + raise ValueError(f"Unknown init '{init}'") + self.M = nn.Parameter(M) # (T, 3, 3) + def as_matrix(self) -> torch.Tensor: - """ - Converts the T stored quaternions to (T, 3, 3) rotation matrices. - - Uses the standard formula; no trig, just multiplications. - """ - q = self.normalized() # (T, 4) [x, y, z, w] - x, y, z, w = q.unbind(dim=-1) # each (T,) - - # Precompute products - xx, yy, zz = x*x, y*y, z*z - xy, xz, yz = x*y, x*z, y*z - wx, wy, wz = w*x, w*y, w*z - - # Row-major: R[i,j] - R = torch.stack([ - 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), - 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), - 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), - ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) - - return R - - def extra_repr(self) -> str: - return f"T={self.quats.shape[0]}" + """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) + # Fix reflections: det(U Vh) must be +1 + d = torch.det(U @ Vh) # (T,) + diag = torch.ones(self.M.shape[0], 3, device=self.M.device) + diag[:, 2] = d # multiply last singular vector by sign + return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) + def interpolate_ms_features_tilted( pts: torch.Tensor, # (B, 3) ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) rotation_matrices: torch.Tensor, # (T, 3, 3) ) -> torch.Tensor: """ - Multi-scale, multi-rotation K-Planes feature interpolation. - - For each of the T rotations: - 1. Rotate pts -> (B, 3) - 2. Project to XY / XZ / YZ planes -> 3 × (B, 2) grids of coords - 3. Bilinear interpolation on the corresponding planes - 4. Hadamard product across the 3 planes -> (B, C) - - Across T transforms, outputs are *concatenated* (not summed), so each - rotation owns a disjoint slice of the feature dimension. Across scales, - outputs are also concatenated, matching the base KPlanes behaviour. - - Returns - ------- - features : (B, C * T * num_scales) + Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. + Returns features of shape (B, C * T * num_scales). """ T = rotation_matrices.shape[0] B = pts.shape[0] - - # Rotate all points by all T rotation matrices at once. - # pts: (B, 3), R: (T, 3, 3) -> rotated: (T, B, 3) + + # (T, B, 3) — rotate all points by all rotations at once rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) - + + # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. + # index_select is faster and cleaner than advanced indexing with python lists. + # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) + idx = torch.tensor([[0, 1], + [2, 0], + [1, 2]], device=pts.device) # (3, 2) + # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) + # Result: (T, 3, B, 2) + coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( + -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + ) + + # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + per_scale_features = [] - for plane_coef in ms_grids: - # plane_coef shape: (3*T, C, H, W) + # plane_coef: (3T, C, H, W) C = plane_coef.shape[1] - - # Build the (3*T, B, 1, 2) coordinate tensor for grid_sample. - # For transform t, we need coords for planes XY, XZ, YZ. - # grid_sample expects coords in [-1, 1] with shape (N, Hout, Wout, 2). - all_plane_coords = [] - for t in range(T): - rp = rotated[t] # (B, 3) - all_plane_coords.append(rp[:, [0, 1]]) # XY (B, 2) - all_plane_coords.append(rp[:, [2, 0]]) # ZX (B, 2) matches ki - all_plane_coords.append(rp[:, [1, 2]]) # YZ (B, 2) matches jk - - # Stack -> (3*T, B, 2) -> (3*T, B, 1, 2) for grid_sample - coord_tensor = torch.stack(all_plane_coords, dim=0).unsqueeze(2) - # coord_tensor: (3*T, B, 1, 2) - - # grid_sample: input (N, C, H, W), grid (N, Hout, Wout, 2) + sampled = F.grid_sample( - plane_coef, # (3*T, C, H, W) - coord_tensor, # (3*T, B, 1, 2) + plane_coef, + coord_tensor, align_corners=True, mode="bilinear", padding_mode="border", - ) # -> (3*T, C, B, 1) - sampled = sampled.squeeze(-1) # (3*T, C, B) - - # Hadamard product within each transform group (3 planes per transform). - transform_features = [] - for t in range(T): - p_xy = sampled[3*t + 0] # (C, B) - p_zx = sampled[3*t + 1] - p_yz = sampled[3*t + 2] - fused = (p_xy * p_zx * p_yz).T # (B, C) - transform_features.append(fused) - - # Concatenate across transforms -> (B, C*T) - per_scale_features.append(torch.cat(transform_features, dim=-1)) - - # Concatenate across scales -> (B, C*T*num_scales) + ) # (3T, C, B, 1) + + # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) + sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim + per_scale_features.append( + sampled.permute(2, 0, 1).reshape(B, T * C) + ) + + # Concatenate across scales -> (B, T * C * num_scales) return torch.cat(per_scale_features, dim=-1) # --------------------------------------------------------------------------- @@ -549,17 +560,11 @@ def _build_sigma_net( layers.append(out) self.sigma_net = nn.Sequential(*layers) else: - self.sigma_net = tcnn.Network( - n_input_dims=self.feature_dim, - n_output_dims=1, - network_config={ - "otype": "CutlassMLP", - "activation": "None", - "output_activation": "None", - "n_neurons": 128, - "n_hidden_layers": 0, - }, - ) + # Match mentor's "explicit" decoder: a single linear layer. + # Small init so density stays near 0 initially. + self.sigma_net = nn.Linear(self.feature_dim, 1, bias=True) + nn.init.normal_(self.sigma_net.weight, std=0.01) + nn.init.zeros_(self.sigma_net.bias) # ------------------------------------------------------------------ # Warm-up bookkeeping diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0c09a372..1d077c2e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -506,10 +506,12 @@ def apply_soft_constraints( self, coords: torch.Tensor, pred: torch.Tensor, + curr_batch_idx: int, + max_batch_size: int ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - soft_loss += self.get_tv_loss(coords, pred) + soft_loss += self.get_tv_loss(coords, pred, curr_batch_idx, max_batch_size) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) @@ -520,7 +522,7 @@ def apply_soft_constraints( return soft_loss - def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: + def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor, curr_batch_idx: int, max_batch_size: int) -> torch.Tensor: """ Calculate the TV loss for the given coordinates and predictions. @@ -528,18 +530,19 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: """ if isinstance(self.model, (KPlanes, KPlanesTILTED)): - - per_level = [] - - for p in self.model.grids: - dh = p[:, :, 1:, :] - p[:, :, :-1, :] - dw = p[:, :, :, 1:] - p[:, :, :, :-1] - tv = 0.0 - if self.constraints.tv_vol > 0: - tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) - per_level.append(tv) - - return torch.stack(per_level).sum() + if curr_batch_idx == max_batch_size - 1: + per_level = [] + for p in self.model.grids: + dh = p[:, :, 1:, :] - p[:, :, :-1, :] + dw = p[:, :, :, 1:] - p[:, :, :, :-1] + tv = 0.0 + if self.constraints.tv_vol > 0: + tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) + per_level.append(tv) + + return torch.stack(per_level).sum() * max_batch_size + else: + return 0.0 else: num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 907ad994..c37af039 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -201,7 +201,7 @@ def reconstruct( with torch.autocast( device_type=self.device.type, dtype=torch.bfloat16, - enabled=True, + enabled=False, ): all_coords = self.dset.get_coords(batch, N, curr_num_samples_per_ray) @@ -214,9 +214,7 @@ def reconstruct( ) pred = integrated_densities.float() - soft_constraints_loss = 0.0 - if self.num_epochs > 0: - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred) + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred, batch_idx, len(self.dataloader)) target = batch["target_value"].to(self.device, non_blocking=True).float() From 1c89ad5fda5a148ad6fc4fcf6c45d85b9294dd4c Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 17 Apr 2026 01:30:55 +0000 Subject: [PATCH 244/335] Everything seems to be working; only things to do is to take a look at DDP, clean-up KPlanes, fix up object_models.py since it's insanely cluttered now --- src/quantem/core/ml/models/kplanes.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 7839f20d..0f44bf7e 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -358,7 +358,6 @@ class SO3Param(nn.Module): def __init__(self, T: int, init: str = "random"): super().__init__() - print("SVD Module") if init == "random": # Initialize near identity with small noise M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) From 851cff8967b583b82c46a3de12cb0225e72bcb2e Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Fri, 17 Apr 2026 10:02:21 -0700 Subject: [PATCH 245/335] update PR into a statement --- .github/PULL_REQUEST_TEMPLATE/pull_request_template.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md index e3c0d0d0..cf5e0174 100644 --- a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -1,8 +1,8 @@ -### What problem does this PR address? +### What problem this PR addreseses -### What should the reviewer(s) do? +### What should the reviewer(s) do From 4215d6e2ed0b2f114e7746b17e09863d5be4d021 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 17 Apr 2026 17:45:20 +0000 Subject: [PATCH 246/335] New TV loss function --- src/quantem/tomography/object_models.py | 41 ++++++++++++++----------- src/quantem/tomography/tomography.py | 2 +- 2 files changed, 24 insertions(+), 19 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 1d077c2e..dd8b62ff 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -505,24 +505,24 @@ def obj_view(self) -> np.ndarray: def apply_soft_constraints( self, coords: torch.Tensor, + all_densities: torch.Tensor, pred: torch.Tensor, - curr_batch_idx: int, - max_batch_size: int ) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: - soft_loss += self.get_tv_loss(coords, pred, curr_batch_idx, max_batch_size) + soft_loss += self.get_tv_loss(coords, pred) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + sparsity_loss = self.constraints.sparsity * torch.norm(all_densities, p=1) soft_loss += sparsity_loss return soft_loss - def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor, curr_batch_idx: int, max_batch_size: int) -> torch.Tensor: + def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: """ Calculate the TV loss for the given coordinates and predictions. @@ -530,19 +530,24 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor, curr_batch_idx: """ if isinstance(self.model, (KPlanes, KPlanesTILTED)): - if curr_batch_idx == max_batch_size - 1: - per_level = [] - for p in self.model.grids: - dh = p[:, :, 1:, :] - p[:, :, :-1, :] - dw = p[:, :, :, 1:] - p[:, :, :, :-1] - tv = 0.0 - if self.constraints.tv_vol > 0: - tv = tv + self.constraints.tv_vol * (dh.pow(2).mean() + dw.pow(2).mean()) - per_level.append(tv) - - return torch.stack(per_level).sum() * max_batch_size - else: - return 0.0 + is_tilted = isinstance(self.model, KPlanesTILTED) + per_level = [] + for p in self.model.grids: + # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes + dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) + dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) + per_plane = dh + dw # (3*T,) or (3,) + + if is_tilted: + T = self.model.T + per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation + level_tv = per_rotation.mean() # average across rotations + else: + level_tv = per_plane.sum() # standard K-planes behavior + + per_level.append(level_tv) + + return self.constraints.tv_vol * torch.stack(per_level).sum() else: num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index c37af039..fc715362 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -214,7 +214,7 @@ def reconstruct( ) pred = integrated_densities.float() - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, pred, batch_idx, len(self.dataloader)) + soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, all_densities, pred) target = batch["target_value"].to(self.device, non_blocking=True).float() From aebf8011d77e0229edfd0009c6a923409191dd2a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Sat, 18 Apr 2026 08:45:32 +0000 Subject: [PATCH 247/335] TV volume -- needs significant refactoring everywhere --- src/quantem/core/ml/models/kplanes.py | 273 +++++++++++++++-------- src/quantem/tomography/dataset_models.py | 23 ++ src/quantem/tomography/object_models.py | 153 +++++++++---- src/quantem/tomography/tomography.py | 20 ++ 4 files changed, 340 insertions(+), 129 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 0f44bf7e..b54fe1ee 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -438,15 +438,23 @@ def interpolate_ms_features_tilted( class KPlanesTILTED(KPlanes): """ K-Planes with T learned SO(3) rotations (TILTED). - + Inherits KPlanes for the sigma_net, density_activation, and get_params interface. Overrides: - * __init__ – replaces the axis-aligned grids with (3*T)-plane grids - and adds SO3Param. + * __init__ – replaces the axis-aligned grids with (3*T)-plane + grids and adds SO3Param. * get_densities – calls the TILTED interpolation instead. * get_params – adds "so3" key so callers can set a separate lr. * param_keys – updated list. - + + Two-phase optimization + ---------------------- + Phase 1: instantiate with small `M_features` (and optionally smaller + `resolution` / fewer scales) — the bottleneck model. Train it + until τ converges, then call `extract_tau_state()`. + Phase 2: instantiate at full capacity, call `load_tau_state(M_bneck)` + to seed the rotations, then train normally. + Parameters ---------- M_features : int @@ -454,15 +462,13 @@ class KPlanesTILTED(KPlanes): be M_features * T * len(multiscale_res_multipliers). T : int Number of learned rotations (TILTED-T in the paper; 4 or 8 recommended). + Must match between phase 1 and phase 2 when doing two-phase transfer. tau_init : str "random" (paper default) or "identity". - tau_warmup_steps : int - If > 0, grids and sigma_net are frozen for this many steps so the - rotations can find good basins first (two-phase warm-up). - Call model.training_step() once per optimiser step. + Irrelevant if you're calling load_tau_state() right after __init__. All other args are forwarded to KPlanes. """ - + def __init__( self, # Grid parameters @@ -474,7 +480,6 @@ def __init__( # TILTED parameters T: int = 4, tau_init: str = "random", - tau_warmup_steps: int = 0, # Hybrid MLP parameters use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, @@ -484,20 +489,20 @@ def __init__( raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") if T < 1: raise ValueError(f"T must be >= 1, got {T}") - + multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) num_scales = len(multiscale_res_multipliers) - + # Total feature dim seen by the MLP head. # Each scale contributes M_features * T channels. feature_dim = M_features * T * num_scales - + # Call KPlanes.__init__ with grid_dimensions=2 so it builds sigma_net # correctly; we immediately replace self.grids below. super().__init__( grid_dimensions=2, input_coords_dims=3, - M_features=M_features, # base class stores this + M_features=M_features, resolution=resolution, multiscale_res_multipliers=multiscale_res_multipliers, concat_features=True, @@ -506,13 +511,9 @@ def __init__( hybrid_hidden_dim=hybrid_hidden_dim, hybrid_num_layers=hybrid_num_layers, ) - # KPlanes.__init__ built grids with shape (3, M, H, W) and feature_dim - # = M * num_scales. We rebuild them for the TILTED shape. - + self.T = T - self.tau_warmup_steps = tau_warmup_steps - self._global_step: int = 0 - + # ---- Rebuild grids: (3*T, M_features, H, W) per scale ---- self.grids = nn.ParameterList() for res_mult in multiscale_res_multipliers: @@ -522,20 +523,20 @@ def __init__( ) nn.init.uniform_(plane, 0.1, 0.5) self.grids.append(plane) - + # ---- Rebuild sigma_net with the correct feature_dim ---- # KPlanes built sigma_net with self.feature_dim (= M * num_scales), # which is wrong for T > 1. Rebuild here. self.feature_dim = feature_dim self._build_sigma_net(use_hybrid_mlp, hybrid_hidden_dim, hybrid_num_layers) - + # ---- Learnable rotations ---- self.so3 = SO3Param(T, init=tau_init) - + # ------------------------------------------------------------------ # Internal helpers # ------------------------------------------------------------------ - + def _build_sigma_net( self, use_hybrid_mlp: bool, @@ -559,53 +560,15 @@ def _build_sigma_net( layers.append(out) self.sigma_net = nn.Sequential(*layers) else: - # Match mentor's "explicit" decoder: a single linear layer. - # Small init so density stays near 0 initially. + # Single-linear "explicit" decoder. Small init -> density ~ 0 initially. self.sigma_net = nn.Linear(self.feature_dim, 1, bias=True) nn.init.normal_(self.sigma_net.weight, std=0.01) nn.init.zeros_(self.sigma_net.bias) - - # ------------------------------------------------------------------ - # Warm-up bookkeeping - # ------------------------------------------------------------------ - - def training_step(self) -> None: - """ - Call once per optimiser step to advance the internal counter. - - During the first `tau_warmup_steps` iterations, grids and sigma_net - have their gradients zeroed after the backward pass so only the SO(3) - parameters update. This is the lightweight version of two-phase - optimisation from the paper. - """ - print("Global Stepped") - self._global_step += 1 - - def _in_warmup(self) -> bool: - return self.tau_warmup_steps > 0 and self._global_step < self.tau_warmup_steps - - def zero_non_tau_grads(self) -> None: - """ - Call after loss.backward() and before optimizer.step() when you want - to implement the rotation warm-up manually. Alternatively just check - model.in_warmup and configure your optimizer accordingly. - """ - if self._in_warmup(): - for p in self.grids.parameters(): - if p.grad is not None: - p.grad.zero_() - for p in self.sigma_net.parameters(): - if p.grad is not None: - p.grad.zero_() - - @property - def in_warmup(self) -> bool: - return self._in_warmup() - + # ------------------------------------------------------------------ # Core forward # ------------------------------------------------------------------ - + def get_densities(self, coords: torch.Tensor) -> torch.Tensor: pts = coords.reshape(-1, 3) R = self.so3.as_matrix() # (T, 3, 3) @@ -616,60 +579,192 @@ def get_densities(self, coords: torch.Tensor) -> torch.Tensor: ) density_before_activation = self.sigma_net(features) return self.density_activation(density_before_activation) - + def forward(self, pts: torch.Tensor) -> torch.Tensor: return self.get_densities(pts) - + # ------------------------------------------------------------------ # Parameter groups # ------------------------------------------------------------------ - + def get_params(self) -> dict[str, list[nn.Parameter]]: return { "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), "so3": list(self.so3.parameters()), } - + @property def param_keys(self) -> list[str]: return ["grids", "sigma_net", "so3"] - # ------------------------------------------------------------------ - # Two-phase helper: extract tau for phase-2 initialisation + # Two-phase transfer: extract / load learned rotations # ------------------------------------------------------------------ - + def extract_tau_state(self) -> torch.Tensor: """ - Returns the current quaternion tensor (detached copy) so it can be - used to initialise a larger phase-2 model via `load_tau_state`. + Returns the current raw R^9 matrices (detached copy) so they can be + used to initialise a phase-2 model via `load_tau_state`. + + Returns + ------- + torch.Tensor of shape (T, 3, 3) """ - return self.so3.quats.detach().clone() - - def load_tau_state(self, quats: torch.Tensor) -> None: + return self.so3.M.detach().cpu().clone() + + def load_tau_state(self, M: torch.Tensor) -> None: """ - Load pre-trained quaternions (e.g. from a bottleneck phase-1 model). - - quats : (T, 4) tensor, will be normalised internally. + Load pre-trained rotation matrices (e.g. from a bottleneck phase-1 model). + + No orthogonalization is needed — SO3Param.as_matrix() projects to SO(3) + via SVD on every forward pass. + + Parameters + ---------- + M : torch.Tensor of shape (T, 3, 3) + Raw unconstrained matrices from `extract_tau_state()`. """ - if quats.shape != self.so3.quats.shape: + if M.shape != self.so3.M.shape: raise ValueError( - f"Shape mismatch: got {quats.shape}, " - f"expected {self.so3.quats.shape}" + f"Shape mismatch: got {M.shape}, expected {self.so3.M.shape}. " + f"Make sure T matches between phase 1 and phase 2." ) with torch.no_grad(): - self.so3.quats.copy_(F.normalize(quats, p=2, dim=-1)) - + self.so3.M.copy_(M.to(self.so3.M.device)) + # ------------------------------------------------------------------ # Pretty print # ------------------------------------------------------------------ - + def extra_repr(self) -> str: return ( f"T={self.T}, " f"M_features={self.M_features}, " f"feature_dim={self.feature_dim}, " - f"num_scales={len(self.multiscale_res_multipliers)}, " - f"tau_warmup_steps={self.tau_warmup_steps}" - ) \ No newline at end of file + f"num_scales={len(self.multiscale_res_multipliers)}" + ) + + + + + +# CP Decomp for Warmup SO3 rotations + +def interpolate_ms_features_cp_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, L) — 1D lines + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + CP (vector outer product) version of TILTED interpolation. + Returns features of shape (B, C * T * num_scales). + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # Rotate all points by all rotations: (T, B, 3) + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + per_scale_features = [] + for line_coef in ms_grids: + # line_coef: (3T, C, L) — three 1D feature lines per transform (x, y, z) + C, L = line_coef.shape[1], line_coef.shape[2] + + # For each transform t, we need three 1D samples: at x_t, y_t, z_t. + # Lay them out as (3T, B) coords, matching line_coef's first dim. + # Axis order per transform: x, y, z. + coords_1d = rotated.reshape(T, B, 3).permute(0, 2, 1).reshape(3 * T, B) + # coords_1d: (3T, B), each row is samples along one axis for one transform + + # grid_sample wants 4D input for 2D sampling, or we can use 1D via a + # (3T, C, 1, L) reshape and pass 2D coords with y fixed at 0. + # Simpler: use F.grid_sample with a 4D trick, or just do manual linear interp. + # Here's the grid_sample way: + line_coef_4d = line_coef.unsqueeze(2) # (3T, C, 1, L) + # grid: need (3T, Hout=1, Wout=B, 2), with x = coord, y = 0 + grid = torch.stack([ + coords_1d, # x + torch.zeros_like(coords_1d), # y + ], dim=-1).unsqueeze(1) # (3T, 1, B, 2) + + sampled = F.grid_sample( + line_coef_4d, grid, + align_corners=True, mode="bilinear", padding_mode="border", + ).squeeze(2) # (3T, C, B) + + # Hadamard across the 3 axes per transform: (T, 3, C, B) -> (T, C, B) + sampled = sampled.view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T*C) + per_scale_features.append(sampled.permute(2, 0, 1).reshape(B, T * C)) + + return torch.cat(per_scale_features, dim=-1) + + +class CPTilted(nn.Module, PPLR): + """ + CP decomposition with TILTED rotations — the true bottleneck model for + phase 1. Rank-1-per-channel feature representation. + + Shares the SO3Param and sigma_net design with KPlanesTILTED so you can + lift τ directly across: cp_model.extract_tau_state() -> + kplanes_model.load_tau_state(). + """ + + def __init__( + self, + C: int = 4, # channels per transform per scale + resolution: Sequence[int] = (128, 128, 128), + multiscale_res_multipliers: Optional[Sequence[int]] = None, + T: int = 4, + tau_init: str = "random", + density_activation: Callable = lambda x: F.softplus(x - 1), + ): + super().__init__() + self.T = T + self.C = C + self.multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) + self.density_activation = density_activation + + # 1D feature lines, one per axis per transform per scale. + # Shape per scale: (3*T, C, L). We use max(resolution) for L; if your + # scene is strongly anisotropic use 3 separate lines per axis. + self.grids = nn.ParameterList() + for mult in self.multiscale_res_multipliers: + L = int(max(resolution) * mult) + line = nn.Parameter(torch.empty(3 * T, C, L)) + nn.init.uniform_(line, 0.1, 0.5) + self.grids.append(line) + + self.feature_dim = C * T * len(self.multiscale_res_multipliers) + + # Same minimal single-linear decoder as your KPlanesTILTED default. + self.sigma_net = nn.Linear(self.feature_dim, 1, bias=True) + nn.init.normal_(self.sigma_net.weight, std=0.01) + nn.init.zeros_(self.sigma_net.bias) + + self.so3 = SO3Param(T, init=tau_init) + + def get_densities(self, coords: torch.Tensor) -> torch.Tensor: + pts = coords.reshape(-1, 3) + R = self.so3.as_matrix() + features = interpolate_ms_features_cp_tilted(pts, self.grids, R) + return self.density_activation(self.sigma_net(features)) + + def forward(self, pts): + return self.get_densities(pts) + + def get_params(self): + return { + "grids": list(self.grids.parameters()), + "sigma_net": list(self.sigma_net.parameters()), + "so3": list(self.so3.parameters()), + } + + @property + def param_keys(self): + return ["grids", "sigma_net", "so3"] + + def extract_tau_state(self) -> torch.Tensor: + return self.so3.M.detach().clone() \ No newline at end of file diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index f6343e4e..e3b61377 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -643,6 +643,29 @@ def to(self, device: torch.device | str): self.device = device self.reconnect_optimizer_to_parameters() + # --- Save learned parameters --- + + def save_parameters(self, path: str): + """ + Saves the learned parameters to a file. + """ + torch.save({ + "z1": self._z1_params.detach().cpu(), + "z3": self._z3_params.detach().cpu(), + "shifts": self._shifts_params.detach().cpu(), + }, path) + + def load_parameters(self, path: str): + """ + Loads the learned parameters from a file. + """ + data = torch.load(path) + self._z1_params = nn.Parameter(data["z1"]) + self._z3_params = nn.Parameter(data["z3"]) + self._shifts_params = nn.Parameter(data["shifts"]) + if self.optimizer is not None: + self.reconnect_optimizer_to_parameters() + class TomographyINRPretrainDataset(Dataset): """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index dd8b62ff..964e0c53 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -99,10 +99,11 @@ class ObjINRConstraints(Constraints): positivity: bool = True shrinkage: float = 0.0 tv_vol: float = 0.0 + tv_plane: float = 0.0 sparsity: float = 0.0 _name: str = "obj_inr" - soft_constraint_keys = ["tv_vol", "sparsity"] + soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage"] @classmethod @@ -521,59 +522,131 @@ def apply_soft_constraints( soft_loss += sparsity_loss return soft_loss + # TV Losses def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: """ - Calculate the TV loss for the given coordinates and predictions. + Dispatch to the appropriate TV loss based on model architecture. - Current supported architectures are KPlanes and INRs + - KPlanes / KPlanesTILTED: per-plane TV (regularizes the stored feature + planes directly). Cheap; regularizes the representation. + - CPTilted: per-line TV (same idea, applied to 1D feature lines). + - SIREN / other INRs: volume TV via autograd (exact gradient). + - Fallback: volume TV via finite differences (works anywhere). + + If you want *volume* smoothness regardless of architecture, call + `get_volume_tv_loss(coords)` directly. """ - + tv_loss = torch.tensor(0.0, device=pred.device) if isinstance(self.model, (KPlanes, KPlanesTILTED)): - is_tilted = isinstance(self.model, KPlanesTILTED) - per_level = [] - for p in self.model.grids: - # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes - dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) - dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) - per_plane = dh + dw # (3*T,) or (3,) - - if is_tilted: - T = self.model.T - per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation - level_tv = per_rotation.mean() # average across rotations - else: - level_tv = per_plane.sum() # standard K-planes behavior + tv_loss += self._get_plane_tv_loss() - per_level.append(level_tv) + # SIREN and other INRs support double-backward → use exact autograd. + # Everything else falls through to finite-difference volume TV. + if not isinstance(self.model, PPLR): # adjust to your actual INR class + tv_loss += self._get_volume_tv_loss_autograd(coords) - return self.constraints.tv_vol * torch.stack(per_level).sum() - else: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + if isinstance(self.model, (KPlanes, KPlanesTILTED)): + tv_loss += self.get_volume_tv_loss(coords) + return tv_loss + + + # ---------------------------------------------------------------------- + # Plane TV (K-Planes family) — regularizes the stored feature planes. + # ---------------------------------------------------------------------- + + def _get_plane_tv_loss(self) -> torch.Tensor: + is_tilted = isinstance(self.model, KPlanesTILTED) + per_level = [] + + for p in self.model.grids: + # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes + dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) + dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) + per_plane = dh + dw # (3*T,) or (3,) + + if is_tilted: + T = self.model.T + per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation + level_tv = per_rotation.mean() # avg across rotations + else: + level_tv = per_plane.sum() - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] + per_level.append(level_tv) - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + return self.constraints.tv_plane * torch.stack(per_level).sum() - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - return self.constraints.tv_vol * grad_norm.mean() + # ---------------------------------------------------------------------- + # Volume TV (autograd) — exact gradient, needs double-backward support. + # ---------------------------------------------------------------------- + + def _get_volume_tv_loss_autograd(self, coords: torch.Tensor) -> torch.Tensor: + """ + Isotropic volume TV using autograd. Exact gradient, but requires the + model to support double-backward (SIREN yes, grid_sample-based models no). + """ + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + pred = self.model(tv_coords) + if isinstance(pred, tuple): + pred = pred[0] + if pred.dim() == 1: + pred = pred.unsqueeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=pred, + inputs=tv_coords, + grad_outputs=torch.ones_like(pred), + create_graph=True, + )[0] # (N, 3) + + grad_norm = torch.norm(grad_outputs, dim=1) # (N,) + return self.constraints.tv_vol * grad_norm.mean() + # ---------------------------------------------------------------------- + # Volume TV (finite differences) — works for any model. + # ---------------------------------------------------------------------- + + def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: + """ + Isotropic volume TV via finite differences. Same form as the autograd + version (L1 of gradient L2-norm) but avoids double-backward, so it + works for KPlanesTILTED, CPTilted, and anything else. + """ + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + tv_coords = coords[tv_indices] # (N, 3) + + if hasattr(self.model, "resolution"): + h = 2.0 / min(self.model.resolution) + else: + h = 1e-2 + + pred = self.model(tv_coords) + if isinstance(pred, tuple): + pred = pred[0] + if pred.dim() == 1: + pred = pred.unsqueeze(-1) # (N, 1) + + grads = [] + for axis in range(3): + offset = torch.zeros(3, device=tv_coords.device) + offset[axis] = h + shifted_pred = self.model(tv_coords + offset) + if isinstance(shifted_pred, tuple): + shifted_pred = shifted_pred[0] + if shifted_pred.dim() == 1: + shifted_pred = shifted_pred.unsqueeze(-1) + grads.append((shifted_pred - pred) / h) # (N, 1) + + grad_stack = torch.stack(grads, dim=-1) # (N, C, 3) + grad_norm = torch.norm(grad_stack, dim=-1) # (N, C) + + return self.constraints.tv_vol * grad_norm.mean() def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: """ diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index fc715362..a96136df 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -10,6 +10,7 @@ from quantem.core.io.serialize import load as autoserialize_load from quantem.core.ml.loss_functions import get_loss_module +from quantem.core.ml.models.kplanes import CPTilted from quantem.core.utils.filter import gaussian_filter_2d_stack, gaussian_kernel_1d from quantem.core.utils.tomography_utils import torch_phase_cross_correlation from quantem.tomography.dataset_models import ( @@ -233,6 +234,25 @@ def reconstruct( total_loss += batch_loss.detach() consistency_loss += batch_consistency_loss.detach() + if isinstance(self.obj_model.model, CPTilted): + if a0 == 0: + prev_R = self.obj_model.model.so3.as_matrix().detach().clone() + elif (a0 + 1) % 20 == 0: + R_now = self.obj_model.model.so3.as_matrix().detach() + # Cumulative angular change per rotation over the last 20 iters. + # trace(R_prev^T R_now) = 1 + 2*cos(theta), so theta = acos((trace - 1) / 2). + rel_trace = torch.einsum('tij,tij->t', prev_R, R_now) + angle = torch.acos(((rel_trace - 1) / 2).clamp(-1, 1)) # (T,) radians + angle_deg = torch.rad2deg(angle) + per_tau_str = ", ".join(f"{a:.2f}°" for a in angle_deg.tolist()) + print( + f"iter {a0}: 20-iter τ change " + f"max={angle_deg.max().item():.2f}°, " + f"mean={angle_deg.mean().item():.2f}°, " + f"per-τ=[{per_tau_str}]" + ) + prev_R = R_now.clone() + if self.world_size > 1: dist.all_reduce(total_loss, dist.ReduceOp.AVG) dist.all_reduce(consistency_loss, dist.ReduceOp.AVG) From 25798477718ea888b2117410cab069c518fbd663 Mon Sep 17 00:00:00 2001 From: Cedric Lim Date: Sat, 18 Apr 2026 22:09:07 -0700 Subject: [PATCH 248/335] DDP Fixes for PPLR stuff --- src/quantem/core/ml/models/kplanes.py | 19 +++-------- src/quantem/tomography/object_models.py | 42 ++++++++++++++++--------- 2 files changed, 32 insertions(+), 29 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index b54fe1ee..d6cc9540 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -6,7 +6,7 @@ import math from typing import Callable, Optional, Sequence -import tinycudann as tcnn +# import tinycudann as tcnn import torch import torch.nn.functional as F from torch import nn @@ -217,18 +217,7 @@ def __init__( nn.init.zeros_(out.bias) layers.append(out) self.sigma_net = nn.Sequential(*layers) - else: - self.sigma_net = tcnn.Network( - n_input_dims=self.feature_dim, - n_output_dims=1, - network_config={ - "otype": "CutlassMLP", - "activation": "None", - "output_activation": "None", - "n_neurons": 128, - "n_hidden_layers": 0, - }, - ) + def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" @@ -370,10 +359,12 @@ def __init__(self, T: int, init: str = "random"): def as_matrix(self) -> torch.Tensor: """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" + + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) # Fix reflections: det(U Vh) must be +1 d = torch.det(U @ Vh) # (T,) - diag = torch.ones(self.M.shape[0], 3, device=self.M.device) + diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) diag[:, 2] = d # multiply last singular vector by sign return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 964e0c53..19f26c05 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -20,6 +20,11 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset +def _unwrap(model): + """Unwrap DDP/FSDP/any wrapper that exposes `.module`.""" + while hasattr(model, "module") and isinstance(model.module, torch.nn.Module): + model = model.module + return model class ObjConstraintParams: """ @@ -538,15 +543,16 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: `get_volume_tv_loss(coords)` directly. """ tv_loss = torch.tensor(0.0, device=pred.device) - if isinstance(self.model, (KPlanes, KPlanesTILTED)): + inner = _unwrap(self.model) + if isinstance(inner, (KPlanes, KPlanesTILTED)): tv_loss += self._get_plane_tv_loss() # SIREN and other INRs support double-backward → use exact autograd. # Everything else falls through to finite-difference volume TV. - if not isinstance(self.model, PPLR): # adjust to your actual INR class + if not isinstance(inner, PPLR): # adjust to your actual INR class tv_loss += self._get_volume_tv_loss_autograd(coords) - if isinstance(self.model, (KPlanes, KPlanesTILTED)): + if isinstance(inner, (KPlanes, KPlanesTILTED)): tv_loss += self.get_volume_tv_loss(coords) return tv_loss @@ -556,17 +562,18 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: # ---------------------------------------------------------------------- def _get_plane_tv_loss(self) -> torch.Tensor: - is_tilted = isinstance(self.model, KPlanesTILTED) + inner = _unwrap(self.model) + is_tilted = isinstance(inner, KPlanesTILTED) per_level = [] - - for p in self.model.grids: + + for p in inner.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) per_plane = dh + dw # (3*T,) or (3,) if is_tilted: - T = self.model.T + T = inner.T per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation level_tv = per_rotation.mean() # avg across rotations else: @@ -621,12 +628,13 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices] # (N, 3) - if hasattr(self.model, "resolution"): - h = 2.0 / min(self.model.resolution) + inner = _unwrap(self.model) + if hasattr(inner, "resolution"): + h = 2.0 / min(inner.resolution) else: h = 1e-2 - pred = self.model(tv_coords) + pred = inner(tv_coords) if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: @@ -636,7 +644,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: for axis in range(3): offset = torch.zeros(3, device=tv_coords.device) offset[axis] = h - shifted_pred = self.model(tv_coords + offset) + shifted_pred = inner(tv_coords + offset) if isinstance(shifted_pred, tuple): shifted_pred = shifted_pred[0] if shifted_pred.dim() == 1: @@ -696,14 +704,16 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di if isinstance(params, OptimizerType): self._optimizer_params = params return - if isinstance(self.model, PPLR): + + inner = _unwrap(self.model) + if isinstance(inner, PPLR): if not isinstance(params, dict): raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") object_params = params - if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + if set(object_params.keys()) != set(inner.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {inner.param_keys}") params = {} for key, value in object_params.items(): @@ -725,7 +735,9 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: Currently supports single LR for all parameters, TODO allow for per parameter LRs by updating get_optimization_parameters to return a list of parameters and their LRs. """ - if not isinstance(self.model, PPLR): + + inner = _unwrap(self.model) + if not isinstance(inner, PPLR): super().set_optimizer(opt_params) return From 7ef264f449d69b6caf773dceccb2becfd3695a09 Mon Sep 17 00:00:00 2001 From: darshan-mali Date: Tue, 21 Apr 2026 10:37:38 -0700 Subject: [PATCH 249/335] Added additional test cases as requested in PR Review --- src/quantem/imaging/lattice.py | 10 ++++++++++ tests/imaging/test_lattice.py | 27 +++++++++++++++++++++++---- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/src/quantem/imaging/lattice.py b/src/quantem/imaging/lattice.py index 82e0d76f..7253cdcd 100644 --- a/src/quantem/imaging/lattice.py +++ b/src/quantem/imaging/lattice.py @@ -179,6 +179,16 @@ def define_lattice_vectors( ) if not self._lat.shape == (3, 2): raise ValueError("origin, u, v must be in (row, col) format only.") + if not ( + 0 <= origin[0] < self.image.array.shape[0] + and 0 <= origin[1] < self.image.array.shape[1] + ): + raise ValueError("origin must be within the image bounds.") + try: + L = self._lat[1:] + _ = np.linalg.inv(L) + except np.linalg.LinAlgError: + raise ValueError("u, v must be invertible.") # Refine lattice coordinates # Note that we currently assume corners are local maxima diff --git a/tests/imaging/test_lattice.py b/tests/imaging/test_lattice.py index 33a289ed..1b9a7d44 100644 --- a/tests/imaging/test_lattice.py +++ b/tests/imaging/test_lattice.py @@ -18,14 +18,17 @@ def test_init_and_constructor(self): with pytest.raises(RuntimeError, match="Use Lattice.from_data"): Lattice(ds2d) - lattice = Lattice.from_data(image) - assert isinstance(lattice, Lattice) - assert lattice.image is not None + lattice_img = Lattice.from_data(image) + lattice_dset = Lattice.from_data(ds2d) + assert isinstance(lattice_img, Lattice) + assert lattice_img.image is not None + assert isinstance(lattice_dset, Lattice) + assert lattice_dset.image is not None def test_normalization(self): """Test min/max normalization.""" image = np.random.randn(100, 100) * 1000.0 - image[0, 0] = 0.0 + image[0, 0] = -10.0 image[99, 99] = 1000.0 # Both normalizations @@ -37,6 +40,14 @@ def test_normalization(self): lattice = Lattice.from_data(image, normalize_min=False, normalize_max=False) assert_array_almost_equal(lattice.image.array, image) + # Min normalization + lattice = Lattice.from_data(image, normalize_min=True, normalize_max=False) + assert lattice.image.array.min() == 0 + + # Max normalization + lattice = Lattice.from_data(image, normalize_min=False, normalize_max=True) + assert lattice.image.array.max() == 1 + def test_edge_cases(self): """Test NaN handling.""" nan_arr = np.array([[1, np.nan], [3, 4]], dtype=float) @@ -131,6 +142,14 @@ def test_invalid_lattice_params(self): with pytest.raises(ValueError): lattice.define_lattice_vectors(origin=[50, 50], u=[5, 0], v=[0, 5], block_size=-1) + # Origin out of bounds + with pytest.raises(ValueError): + lattice.define_lattice_vectors(origin=[10, 105], u=[5, 0], v=[0, 5]) + + # Non-ivertible lattice vectors + with pytest.raises(ValueError): + lattice.define_lattice_vectors(origin=[50, 50], u=[5, 0], v=[10, 0]) + class TestLatticeSerialize: """Test Lattice Autoserialize implementation.""" From 40bb9ec838e4268f511a6babcf1334134be4978d Mon Sep 17 00:00:00 2001 From: darshan-mali Date: Tue, 21 Apr 2026 11:11:09 -0700 Subject: [PATCH 250/335] Added plotting fixes as requested in PR review --- src/quantem/imaging/lattice_visualization.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/src/quantem/imaging/lattice_visualization.py b/src/quantem/imaging/lattice_visualization.py index af2781c7..a7d31363 100644 --- a/src/quantem/imaging/lattice_visualization.py +++ b/src/quantem/imaging/lattice_visualization.py @@ -43,6 +43,14 @@ def plot_lattice_vectors( None clips lines to image edges. **kwargs forwarded to show_2d (e.g. cmap, title). """ + # Check if lattice vectors have been defined + if not hasattr(lattice, "_lat"): + raise ValueError( + "Must define lattice vectors first. Call `Lattice.define_lattice_vectors()`" + ) + # Adding a defualt figsize + if "figsize" not in kwargs: + kwargs["figsize"] = (10, 10) fig, ax = show_2d(lattice._image.array, returnfig=True, **kwargs) if ax.images: ax.images[-1].set_zorder(0) @@ -50,10 +58,10 @@ def plot_lattice_vectors( r0, u, v = (np.asarray(x, dtype=float) for x in lattice._lat) ax.scatter( - r0[1], r0[0], s=60, edgecolor=(0, 0, 0), facecolor=(0, 0.5, 0), marker="s", zorder=30 + r0[1], r0[0], s=60, edgecolor=(0, 0, 0), facecolor=(0, 0.5, 0), marker="s", zorder=5 ) - n_vec = int(bound_num_vectors) if bound_num_vectors is not None else 1 + n_vec = int(np.ceil(bound_num_vectors)) if bound_num_vectors is not None else 1 for k in range(1, n_vec + 1): tip = r0 + k * u ax.arrow( @@ -66,7 +74,7 @@ def plot_lattice_vectors( head_length=6.0, linewidth=2.0, color="red", - zorder=20, + zorder=4, ) for k in range(1, n_vec + 1): tip = r0 + k * v @@ -80,7 +88,7 @@ def plot_lattice_vectors( head_length=6.0, linewidth=2.0, color=(0.0, 0.7, 1.0), - zorder=20, + zorder=4, ) if bound_num_vectors is None: From a59baac0208d48f219e555caaa27264b251fe44f Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Wed, 22 Apr 2026 11:11:04 -0700 Subject: [PATCH 251/335] Changes --- src/quantem/core/ml/models/kplanes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index d6cc9540..117ea280 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -164,7 +164,7 @@ def __init__( resolution: Sequence[int] = (200, 200, 200), multiscale_res_multipliers: Optional[Sequence[int]] = None, concat_features: bool = True, - density_activation: Callable = lambda x: F.softplus(x - 1), + density_activation: Callable = lambda x: F.softplus(x - 1), # Keep playing around with this and trunc_exp # Hybrid MLP parameters use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, From 251f3fbfd5b287615fc39dcd5a6d9aa254419489 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 11:31:51 -0700 Subject: [PATCH 252/335] Reorganizing tensor decomposition methods. Defined a new TensorDecompositionModel ABC, make sure to have a property for which kind of tensor decomposition method is being used. SO3Params are moved to a different file, thinking of making a kplanes_utils.py. Starting reorganization of object_models.py to have ObjectINR and ObjectTensorDecomp --- src/quantem/core/ml/models/kplanes.py | 214 ++------- src/quantem/core/ml/models/model_base.py | 10 + src/quantem/core/ml/models/so3params.py | 183 ++++++++ src/quantem/tomography/object_models.py | 538 +++++++++++++++++++---- 4 files changed, 689 insertions(+), 256 deletions(-) create mode 100644 src/quantem/core/ml/models/so3params.py diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 117ea280..1b0a40ab 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -11,7 +11,8 @@ import torch.nn.functional as F from torch import nn -from .model_base import PPLR +from .model_base import PPLR, TensorDecompositionModel +from .so3params import SO3ParamQuat, SO3ParamR9SVD """ K-planes utility functions @@ -153,7 +154,7 @@ def interpolate_ms_features( return torch.cat(per_scale, dim=-1) -class KPlanes(nn.Module, PPLR): +class KPlanes(nn.Module, PPLR, TensorDecompositionModel): def __init__( self, @@ -174,7 +175,7 @@ def __init__( Assume coords are [-1, 1] in each dimension. """ super().__init__() - + self._td_type = "kplanes" self.grid_dimensions = grid_dimensions self.input_coords_dims = input_coords_dims self.M_features = M_features @@ -243,184 +244,10 @@ def get_params(self) -> dict[str, list[torch.nn.Parameter]]: def param_keys(self) -> list[str]: return ["grids", "sigma_net"] + @property + def td_type(self) -> str: + return self._td_type -# --- Tilted KPlanes --- - -# --------------------------------------------------------------------------- -# SO(3) quaternion parameter module -# --------------------------------------------------------------------------- - -# class SO3Param(nn.Module): -# """ -# Stores T unit quaternions as learnable parameters in R^4 and normalises -# them on every call to `as_matrix()`. - -# Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). - -# Initialisation -# -------------- -# "random" – uniform sampling over SO(3) via Shoemake's method. -# "identity" – all rotations start as the identity (good for fine-tuning). -# """ - -# def __init__(self, T: int, init: str = "random"): -# super().__init__() -# if T < 1: -# raise ValueError(f"T must be >= 1, got {T}") -# quats = self._init_quaternions(T, init) # (T, 4) -# self.quats = nn.Parameter(quats) - -# # ------------------------------------------------------------------ -# # Initialisers -# # ------------------------------------------------------------------ - -# @staticmethod -# def _shoemake_sample(T: int) -> torch.Tensor: -# """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" -# u = torch.rand(T, 3) -# sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) -# sqrt_u0 = torch.sqrt(u[:, 0]) -# two_pi = 2.0 * math.pi -# x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) -# y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) -# z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) -# w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) -# return torch.stack([x, y, z, w], dim=-1) # (T, 4) - -# @staticmethod -# def _identity(T: int) -> torch.Tensor: -# """All-identity rotations: [0,0,0,1] * T.""" -# q = torch.zeros(T, 4) -# q[:, 3] = 1.0 -# return q - -# @classmethod -# def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: -# if init == "random": -# return cls._shoemake_sample(T) -# elif init == "identity": -# return cls._identity(T) -# else: -# raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") - -# # ------------------------------------------------------------------ -# # Forward helpers -# # ------------------------------------------------------------------ - -# def normalized(self) -> torch.Tensor: -# """Returns (T, 4) unit quaternions.""" -# return F.normalize(self.quats, p=2, dim=-1) - -# def as_matrix(self) -> torch.Tensor: -# """ -# Converts the T stored quaternions to (T, 3, 3) rotation matrices. - -# Uses the standard formula; no trig, just multiplications. -# """ -# q = self.normalized() # (T, 4) [x, y, z, w] -# x, y, z, w = q.unbind(dim=-1) # each (T,) - -# # Precompute products -# xx, yy, zz = x*x, y*y, z*z -# xy, xz, yz = x*y, x*z, y*z -# wx, wy, wz = w*x, w*y, w*z - -# # Row-major: R[i,j] -# R = torch.stack([ -# 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), -# 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), -# 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), -# ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) - -# return R - -# def extra_repr(self) -> str: -# return f"T={self.quats.shape[0]}" - - -class SO3Param(nn.Module): - """ - SO(3) rotation bank using R9+SVD parameterization. - Each rotation is stored as an unconstrained 3x3 matrix M, - projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. - """ - - def __init__(self, T: int, init: str = "random"): - super().__init__() - if init == "random": - # Initialize near identity with small noise - M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) - M = M + 0.1 * torch.randn(T, 3, 3) - elif init == "identity": - M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) - else: - raise ValueError(f"Unknown init '{init}'") - self.M = nn.Parameter(M) # (T, 3, 3) - - def as_matrix(self) -> torch.Tensor: - """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" - - - U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) - # Fix reflections: det(U Vh) must be +1 - d = torch.det(U @ Vh) # (T,) - diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) - diag[:, 2] = d # multiply last singular vector by sign - return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) - -def interpolate_ms_features_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) - rotation_matrices: torch.Tensor, # (T, 3, 3) -) -> torch.Tensor: - """ - Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. - Returns features of shape (B, C * T * num_scales). - """ - T = rotation_matrices.shape[0] - B = pts.shape[0] - - # (T, B, 3) — rotate all points by all rotations at once - rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) - - # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. - # index_select is faster and cleaner than advanced indexing with python lists. - # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) - idx = torch.tensor([[0, 1], - [2, 0], - [1, 2]], device=pts.device) # (3, 2) - # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) - # Result: (T, 3, B, 2) - coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( - -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) - ) - - # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis - coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) - - per_scale_features = [] - for plane_coef in ms_grids: - # plane_coef: (3T, C, H, W) - C = plane_coef.shape[1] - - sampled = F.grid_sample( - plane_coef, - coord_tensor, - align_corners=True, - mode="bilinear", - padding_mode="border", - ) # (3T, C, B, 1) - - # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) - sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) - - # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim - per_scale_features.append( - sampled.permute(2, 0, 1).reshape(B, T * C) - ) - - # Concatenate across scales -> (B, T * C * num_scales) - return torch.cat(per_scale_features, dim=-1) # --------------------------------------------------------------------------- # KPlanesTILTED @@ -475,7 +302,10 @@ def __init__( use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, hybrid_num_layers: int = 2, + so3_param_type: str = "r9svd", ): + + self._td_type = "tilted" if input_coords_dims != 3: raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") if T < 1: @@ -636,6 +466,23 @@ def extra_repr(self) -> str: f"num_scales={len(self.multiscale_res_multipliers)}" ) + def set_so3_param_type(self, so3_param_type: str) -> None: + """ + Set the SO3 parameterization type. + + Parameters + ---------- + so3_param_type : str + SO3 parameterization type ("quat" or "r9svd"). + """ + if so3_param_type == "r9svd": + self.so3 = SO3ParamR9SVD(self.T) + elif so3_param_type == "quat": + self.so3 = SO3ParamQuat(self.T) + else: + raise ValueError(f"Invalid SO3 parameterization type: {so3_param_type}") + + @@ -693,7 +540,7 @@ def interpolate_ms_features_cp_tilted( return torch.cat(per_scale_features, dim=-1) -class CPTilted(nn.Module, PPLR): +class CPTilted(nn.Module, PPLR, TensorDecompositionModel): """ CP decomposition with TILTED rotations — the true bottleneck model for phase 1. Rank-1-per-channel feature representation. @@ -713,6 +560,7 @@ def __init__( density_activation: Callable = lambda x: F.softplus(x - 1), ): super().__init__() + self._td_type = "cp_tilted" self.T = T self.C = C self.multiscale_res_multipliers = list(multiscale_res_multipliers or [1]) @@ -757,5 +605,9 @@ def get_params(self): def param_keys(self): return ["grids", "sigma_net", "so3"] + @property + def td_type(self) -> str: + return self._td_type + def extract_tau_state(self) -> torch.Tensor: return self.so3.M.detach().clone() \ No newline at end of file diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index 42bee71c..44bbdef5 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -24,4 +24,14 @@ def param_keys(self) -> list[str]: """ This abstract property should return a list of available parameter keys. """ + pass + +class TensorDecompositionModel(ABC): + + @property + @abstractmethod + def td_type(self) -> str: + """ + This abstract property should return the type of tensor decomposition used. + """ pass \ No newline at end of file diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py new file mode 100644 index 00000000..29314fb6 --- /dev/null +++ b/src/quantem/core/ml/models/so3params.py @@ -0,0 +1,183 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F + +# --- Tilted KPlanes --- + +# --------------------------------------------------------------------------- +# SO(3) quaternion parameter module +# --------------------------------------------------------------------------- + +class SO3ParamQuat(nn.Module): + """ + Stores T unit quaternions as learnable parameters in R^4 and normalises + them on every call to `as_matrix()`. + + Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). + + Initialisation + -------------- + "random" – uniform sampling over SO(3) via Shoemake's method. + "identity" – all rotations start as the identity (good for fine-tuning). + """ + + def __init__(self, T: int, init: str = "random"): + super().__init__() + if T < 1: + raise ValueError(f"T must be >= 1, got {T}") + quats = self._init_quaternions(T, init) # (T, 4) + self.quats = nn.Parameter(quats) + + # ------------------------------------------------------------------ + # Initialisers + # ------------------------------------------------------------------ + + @staticmethod + def _shoemake_sample(T: int) -> torch.Tensor: + """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" + u = torch.rand(T, 3) + sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) + sqrt_u0 = torch.sqrt(u[:, 0]) + two_pi = 2.0 * math.pi + x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) + y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) + z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) + w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) + return torch.stack([x, y, z, w], dim=-1) # (T, 4) + + @staticmethod + def _identity(T: int) -> torch.Tensor: + """All-identity rotations: [0,0,0,1] * T.""" + q = torch.zeros(T, 4) + q[:, 3] = 1.0 + return q + + @classmethod + def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: + if init == "random": + return cls._shoemake_sample(T) + elif init == "identity": + return cls._identity(T) + else: + raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") + + # ------------------------------------------------------------------ + # Forward helpers + # ------------------------------------------------------------------ + + def normalized(self) -> torch.Tensor: + """Returns (T, 4) unit quaternions.""" + return F.normalize(self.quats, p=2, dim=-1) + + def as_matrix(self) -> torch.Tensor: + """ + Converts the T stored quaternions to (T, 3, 3) rotation matrices. + + Uses the standard formula; no trig, just multiplications. + """ + q = self.normalized() # (T, 4) [x, y, z, w] + x, y, z, w = q.unbind(dim=-1) # each (T,) + + # Precompute products + xx, yy, zz = x*x, y*y, z*z + xy, xz, yz = x*y, x*z, y*z + wx, wy, wz = w*x, w*y, w*z + + # Row-major: R[i,j] + R = torch.stack([ + 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), + 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), + 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), + ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) + + return R + + def extra_repr(self) -> str: + return f"T={self.quats.shape[0]}" + + +class SO3ParamR9SVD(nn.Module): + """ + SO(3) rotation bank using R9+SVD parameterization. + Each rotation is stored as an unconstrained 3x3 matrix M, + projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. + """ + + def __init__(self, T: int, init: str = "random"): + super().__init__() + if init == "random": + # Initialize near identity with small noise + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + M = M + 0.1 * torch.randn(T, 3, 3) + elif init == "identity": + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + else: + raise ValueError(f"Unknown init '{init}'") + self.M = nn.Parameter(M) # (T, 3, 3) + + def as_matrix(self) -> torch.Tensor: + """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" + + + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) + # Fix reflections: det(U Vh) must be +1 + d = torch.det(U @ Vh) # (T,) + diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) + diag[:, 2] = d # multiply last singular vector by sign + return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) + +def interpolate_ms_features_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. + Returns features of shape (B, C * T * num_scales). + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # (T, B, 3) — rotate all points by all rotations at once + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. + # index_select is faster and cleaner than advanced indexing with python lists. + # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) + idx = torch.tensor([[0, 1], + [2, 0], + [1, 2]], device=pts.device) # (3, 2) + # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) + # Result: (T, 3, B, 2) + coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( + -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + ) + + # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + + per_scale_features = [] + for plane_coef in ms_grids: + # plane_coef: (3T, C, H, W) + C = plane_coef.shape[1] + + sampled = F.grid_sample( + plane_coef, + coord_tensor, + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # (3T, C, B, 1) + + # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) + sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim + per_scale_features.append( + sampled.permute(2, 0, 1).reshape(B, T * C) + ) + + # Concatenate across scales -> (B, T * C * num_scales) + return torch.cat(per_scale_features, dim=-1) \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 19f26c05..8ef2830d 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -20,12 +20,6 @@ from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -def _unwrap(model): - """Unwrap DDP/FSDP/any wrapper that exposes `.module`.""" - while hasattr(model, "module") and isinstance(model.module, torch.nn.Module): - model = model.module - return model - class ObjConstraintParams: """ Namespace class for object reconstruction constraint dataclasses and parsing utilities. @@ -104,17 +98,45 @@ class ObjINRConstraints(Constraints): positivity: bool = True shrinkage: float = 0.0 tv_vol: float = 0.0 - tv_plane: float = 0.0 sparsity: float = 0.0 _name: str = "obj_inr" soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage"] + @dataclass + class ObjTensorDecompConstraints(Constraints): + """ + Constraints for a tensor decomposition object representation. + + Attributes + ---------- + positivity : bool + If ``True``, enforces non-negative values in the reconstruction. + shrinkage : float + Shrinkage regularization strength; pushes values toward zero. + tv_vol : float + Total variation regularization weight for the 3-D volume. + soft_constraint_keys : list[str] + Constraint fields penalized softly during optimization. + hard_constraint_keys : list[str] + Constraint fields enforced strictly during optimization. + """ + + positivity: bool = True + shrinkage: float = 0.0 + tv_vol: float = 0.0 + tv_plane: float = 0.0 + sparsity:float = 0.0 + _name: str = "obj_tensor_decomp" + + soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] + hard_constraint_keys = ["positivity", "shrinkage"] + @classmethod def parse_dict( cls, d: dict - ) -> "ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints": + ) -> "ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints | ObjConstraintParams.ObjTensorDecompConstraints": """ Instantiate an object constraint dataclass from a configuration dictionary. @@ -162,12 +184,16 @@ def parse_dict( return ObjConstraintParams.ObjPixelatedConstraints(**d) elif name == "obj_inr": return ObjConstraintParams.ObjINRConstraints(**d) + elif name == "obj_tensor_decomp": + return ObjConstraintParams.ObjTensorDecompConstraints(**d) else: raise ValueError(f"Unknown object constraint type: {name.lower()}") ObjConstraintsType = ( - ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints + ObjConstraintParams.ObjPixelatedConstraints + | ObjConstraintParams.ObjINRConstraints + | ObjConstraintParams.ObjTensorDecompConstraints ) @@ -285,6 +311,7 @@ def get_tv_loss(self, **kwargs) -> torch.Tensor: class ObjectPixelated(ObjectConstraints): + """ Object model for pixelated objects. @@ -424,7 +451,6 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.reconnect_optimizer_to_parameters() return self - class ObjectINR(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() @@ -488,6 +514,427 @@ def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: # """ # raise RuntimeError("\n\n\nsetting model, this shouldn't be reachable???\n\n\n") + @property + def obj(self) -> torch.Tensor: + return self._obj + + @obj.setter + def obj(self, obj: torch.Tensor): + self._obj = obj + + @property + def obj_view(self) -> np.ndarray: + """ + Returns the object as a view of the x, y, z axes. + + Matches the axes of conventionally reconstructed objects, this is the object that will be saved. + """ + self.create_volume() + return self._obj.cpu().numpy().transpose(0, 1, 3, 2) + + def apply_soft_constraints( + self, + coords: torch.Tensor, + pred: torch.Tensor, + ) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=pred.device) + if self.constraints.tv_vol > 0: + num_tv_samples = min(10_000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + soft_loss += self.constraints.tv_vol * grad_norm.mean() + + if ( + isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) + and self.constraints.sparsity > 0 + ): # NOTE: For the linter, I must make this :) + sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + soft_loss += sparsity_loss + + return soft_loss + + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: + """ + Apply hard constraints to the predicted values of the INR model. + """ + + if self.constraints.positivity: + pred = torch.clamp(pred, min=0.0, max=None) + if self.constraints.shrinkage: + pred = torch.max(pred - self.constraints.shrinkage, torch.zeros_like(pred)) + + return pred + + # --- Optimization Parameters --- + @property + def params(self) -> Generator[torch.nn.Parameter, None, None]: + return self.model.parameters() # type: ignore[attr-defined] + + def get_optimization_parameters(self) -> list[nn.Parameter]: + return list(self.params) + + # Pretraining + @property + def pretrained_weights(self) -> dict[str, torch.Tensor]: + """get the pretrained weights of the INR model""" + return self._pretrained_weights + + def _set_pretrained_weights(self, model: "torch.nn.Module"): + """set the pretrained weights of the INR model""" + if not isinstance(model, torch.nn.Module): + raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") + self._pretrained_weights = deepcopy(model.state_dict()) + + @property + def pretrain_target(self) -> TomographyINRPretrainDataset: + """get the pretrain target""" + return self._pretrain_target + + @pretrain_target.setter + def pretrain_target(self, target: TomographyINRPretrainDataset): + """set the pretrain target""" + self._pretrain_target = target + + @property + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + # TODO: This is a temporary solution to get the dtype of the object. + return torch.float32 + + @property + def shape(self) -> tuple[int, int, int]: + return self._shape + + @shape.setter + def shape(self, shape: tuple[int, int, int]): + self._shape = shape + + # --- Helper Functions --- + def rebuild_model(self): + self._model = self.distribute_model(self._model) + + # Reset method that goes back to the pretrained weights. + def reset(self): + """reset the model to the pretrained weights""" + self.model.load_state_dict(self._pretrained_weights.copy()) + self._model = self.distribute_model( + self.model + ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. + + # --- Forward Method --- + + def forward(self, coords: torch.Tensor) -> torch.Tensor: + """forward pass for the INR model""" + all_densities = self.model(coords) + + if all_densities.dim() > 1: + all_densities = all_densities.squeeze(-1) + valid_mask = ( + (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) + ).float() + + if all_densities.dim() > 1: + valid_mask = valid_mask.unsqueeze(-1) + # Multi-dimensional mask + all_densities = all_densities * valid_mask + + all_densities = self.apply_hard_constraints(all_densities) + + return all_densities + + # Pretrain Loop + + def pretrain( + self, + pretrain_dataset: TomographyINRPretrainDataset, + batch_size: int, + reset: bool = False, + num_iters: int = 10, + num_workers: int = 0, + optimizer_params: dict | None = None, + scheduler_params: dict | None = None, + loss_fn: Callable | str = "l1", + verbose: bool = True, + ): + """ + Pretrain the INR model to fit target volume. + """ + + if ( + pretrain_dataset is not None + ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. + self.pretrain_dataset = pretrain_dataset + ( + self.pretraining_dataloader, + self.pretraining_sampler, + self.pretraining_val_dataloader, + self.pretraining_val_sampler, + ) = self.setup_dataloader(pretrain_dataset, batch_size, num_workers=num_workers) + + if optimizer_params is not None: + self.set_optimizer(optimizer_params) + if scheduler_params is not None: + self.set_scheduler(scheduler_params, num_iters) + + if reset: + self.reset() + + loss_fn = get_loss_module(loss_fn, self.dtype) + + self._pretrain( + num_iters=num_iters, + loss_fn=loss_fn, + verbose=verbose, + ) + + def _pretrain( + self, + num_iters: int, + loss_fn: Callable, + verbose: bool, + ): + if self.optimizer is None: + raise RuntimeError("Optimizer not set. Call set_optimizer() first.") + if self.scheduler is None: + raise RuntimeError("Scheduler not set. Call set_scheduler() first.") + + self.model.train() + optimizer = self.optimizer + scheduler = self.scheduler + + pbar = tqdm(range(num_iters), desc="Pretraining", disable=not verbose) + for a0 in pbar: + epoch_loss = 0 + for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): + coords = batch["coords"].to(self.device, non_blocking=True) + target = batch["target"].to(self.device, non_blocking=True) + + with torch.autocast( + device_type=self.device.type, dtype=torch.bfloat16, enabled=True + ): + outputs = self.forward(coords) + loss = loss_fn(outputs, target) + + loss.backward() + epoch_loss += loss.item() + + # Clip gradients + torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) + + optimizer.step() + optimizer.zero_grad() + + if scheduler is not None: + if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): + scheduler.step(epoch_loss) + else: + scheduler.step() + + self._pretrain_losses.append(epoch_loss / len(self.pretraining_dataloader)) + print( + f"Epoch {a0 + 1}/{num_iters}, Pretrain Loss: {epoch_loss / len(self.pretraining_dataloader):.4f}" + ) + self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) + + def create_volume(self, return_vol: bool = False): + N = max(self._shape) + with torch.no_grad(): + coords_1d = torch.linspace(-1, 1, N) + x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") + inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) + model = self.model.module if isinstance(self.model, nn.DataParallel) else self.model + + inference_batch_size = 5 * N * N + total_samples = N**3 + samples_per_gpu = total_samples // self.world_size + remainder = total_samples % self.world_size + + if self.global_rank < remainder: + start_idx = self.global_rank * (samples_per_gpu + 1) + end_idx = start_idx + samples_per_gpu + 1 + else: + start_idx = self.global_rank * samples_per_gpu + remainder + end_idx = start_idx + samples_per_gpu + + inputs_subset = inputs[start_idx:end_idx] + num_samples = inputs_subset.shape[0] + + outputs_list = [] + for batch_start in range(0, num_samples, inference_batch_size): + batch_end = min(batch_start + inference_batch_size, num_samples) + batch_coords = inputs_subset[batch_start:batch_end].to( + self.device, non_blocking=True + ) + + batch_outputs = model(batch_coords) # (B, C) or (B,) etc. + + if isinstance(batch_outputs, tuple): + batch_outputs = batch_outputs[0] + batch_outputs = self.apply_hard_constraints(batch_outputs) + + # Ensure shape is (B, C) + if batch_outputs.dim() == 1: + batch_outputs = batch_outputs.unsqueeze(-1) # (B, 1) + + outputs_list.append(batch_outputs.cpu()) + + outputs = torch.cat(outputs_list, dim=0) # (local_B, C) + C = outputs.shape[-1] # e.g. 5 + + if self.world_size > 1: + # gather variable-sized first dimension (local_B) while keeping channels + local_B = outputs.shape[0] + output_size = torch.tensor(local_B, device=self.device, dtype=torch.long) + all_sizes = [ + torch.zeros(1, device=self.device, dtype=torch.long) + for _ in range(self.world_size) + ] + dist.all_gather(all_sizes, output_size) + max_size = max(size.item() for size in all_sizes) + + outputs_dev = outputs.to(self.device) # (local_B, C) + if local_B < max_size: + pad = torch.zeros( + (max_size - local_B, C), # type: ignore + device=self.device, + dtype=outputs_dev.dtype, + ) + outputs_padded = torch.cat([outputs_dev, pad], dim=0) # (max_size, C) + else: + outputs_padded = outputs_dev + + gathered_outputs = [ + torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) # type: ignore + for _ in range(self.world_size) + ] + dist.all_gather(gathered_outputs, outputs_padded.contiguous()) + + trimmed_outputs = [] + for rank, size in enumerate(all_sizes): + trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) + + pred_full = torch.cat(trimmed_outputs, dim=0).reshape(C, N, N, N).float() + else: + pred_full = outputs.reshape(C, N, N, N).float() + + if return_vol: + return pred_full.detach().cpu() + + self._obj = pred_full.detach().cpu() + + def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] + self, + coords: torch.Tensor, + ) -> torch.Tensor: + tv_loss = torch.tensor(0.0, device=coords.device) + + num_tv_samples = min(10000, coords.shape[0]) + tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] + + tv_coords = coords[tv_indices].detach().requires_grad_(True) + + tv_densities_recomputed = self.forward(tv_coords) + + if tv_densities_recomputed.dim() > 1: + tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) + + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] + + grad_norm = torch.norm(grad_outputs, dim=1) + + tv_loss += self.constraints.tv_vol * grad_norm.mean() + return tv_loss + + def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + if isinstance(device, str): + device = torch.device(device) + self._device = device + if self.world_size == 1: + self._model = self._model.to(device) + elif not isinstance(self._model, torch.nn.parallel.DistributedDataParallel): + self.distribute_model(self._model) + self.reconnect_optimizer_to_parameters() + + +class ObjectTensorDecomp(ObjectConstraints, DDPMixin): + DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() + + def __init__( + self, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + model: nn.Module | None = None, + _token: object | None = None, + ): + super().__init__( + shape=shape, + device=device, + rng=rng, + _token=self._token, + ) + self._pretrain_losses = [] + self._pretrain_lrs = [] + self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + # Register the network submodule (important: real nn.Module attribute) + if model is not None: + self.setup_distributed(device=device) + self._model = self.distribute_model(model) + + @classmethod + def from_model( + cls, + model: nn.Module, + shape: tuple[int, int, int], + device: str = "cpu", + rng: np.random.Generator | int | None = None, + ): + obj_model = cls( + shape=shape, + device=device, + rng=rng, + model=model, # ✅ build/register in __init__ + ) + + obj_model.setup_distributed(device=device) + obj_model.to(device) + return obj_model + + # --- Properties --- + + @property + def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: + """ + Returns the INR model. + """ + return self._model + @property def obj(self) -> torch.Tensor: return self._obj @@ -527,53 +974,29 @@ def apply_soft_constraints( soft_loss += sparsity_loss return soft_loss + # TV Losses def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: - """ - Dispatch to the appropriate TV loss based on model architecture. - - KPlanes / KPlanesTILTED: per-plane TV (regularizes the stored feature - planes directly). Cheap; regularizes the representation. - - CPTilted: per-line TV (same idea, applied to 1D feature lines). - - SIREN / other INRs: volume TV via autograd (exact gradient). - - Fallback: volume TV via finite differences (works anywhere). - - If you want *volume* smoothness regardless of architecture, call - `get_volume_tv_loss(coords)` directly. - """ tv_loss = torch.tensor(0.0, device=pred.device) - inner = _unwrap(self.model) - if isinstance(inner, (KPlanes, KPlanesTILTED)): - tv_loss += self._get_plane_tv_loss() - - # SIREN and other INRs support double-backward → use exact autograd. - # Everything else falls through to finite-difference volume TV. - if not isinstance(inner, PPLR): # adjust to your actual INR class - tv_loss += self._get_volume_tv_loss_autograd(coords) - - if isinstance(inner, (KPlanes, KPlanesTILTED)): - tv_loss += self.get_volume_tv_loss(coords) + tv_loss += self._get_plane_tv_loss() + tv_loss += self.get_volume_tv_loss(coords) return tv_loss - # ---------------------------------------------------------------------- - # Plane TV (K-Planes family) — regularizes the stored feature planes. - # ---------------------------------------------------------------------- - def _get_plane_tv_loss(self) -> torch.Tensor: - inner = _unwrap(self.model) - is_tilted = isinstance(inner, KPlanesTILTED) + is_tilted = isinstance(self.model, KPlanesTILTED) per_level = [] - for p in inner.grids: + for p in self.model.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) per_plane = dh + dw # (3*T,) or (3,) if is_tilted: - T = inner.T + T = self.model.T per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation level_tv = per_rotation.mean() # avg across rotations else: @@ -584,40 +1007,6 @@ def _get_plane_tv_loss(self) -> torch.Tensor: return self.constraints.tv_plane * torch.stack(per_level).sum() - # ---------------------------------------------------------------------- - # Volume TV (autograd) — exact gradient, needs double-backward support. - # ---------------------------------------------------------------------- - - def _get_volume_tv_loss_autograd(self, coords: torch.Tensor) -> torch.Tensor: - """ - Isotropic volume TV using autograd. Exact gradient, but requires the - model to support double-backward (SIREN yes, grid_sample-based models no). - """ - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - pred = self.model(tv_coords) - if isinstance(pred, tuple): - pred = pred[0] - if pred.dim() == 1: - pred = pred.unsqueeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=pred, - inputs=tv_coords, - grad_outputs=torch.ones_like(pred), - create_graph=True, - )[0] # (N, 3) - - grad_norm = torch.norm(grad_outputs, dim=1) # (N,) - return self.constraints.tv_vol * grad_norm.mean() - - - # ---------------------------------------------------------------------- - # Volume TV (finite differences) — works for any model. - # ---------------------------------------------------------------------- - def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: """ Isotropic volume TV via finite differences. Same form as the autograd @@ -1073,5 +1462,4 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() - ObjectModelType = ObjectPixelated | ObjectINR From e33fec1ebe0119565979ba9198f566dbab1a3887 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 11:54:52 -0700 Subject: [PATCH 253/335] Removed some the _unwrap dependencies. Added ObjectTensorDecomp on top-level Tomography --- src/quantem/core/ml/models/kplanes.py | 66 ++++++++++++++++++++--- src/quantem/core/ml/models/so3params.py | 53 ------------------ src/quantem/tomography/object_models.py | 19 +++---- src/quantem/tomography/tomography.py | 5 +- src/quantem/tomography/tomography_base.py | 3 +- 5 files changed, 71 insertions(+), 75 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 1b0a40ab..58d6fc81 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -253,6 +253,60 @@ def td_type(self) -> str: # KPlanesTILTED # --------------------------------------------------------------------------- +def interpolate_ms_features_tilted( + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + rotation_matrices: torch.Tensor, # (T, 3, 3) +) -> torch.Tensor: + """ + Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. + Returns features of shape (B, C * T * num_scales). + """ + T = rotation_matrices.shape[0] + B = pts.shape[0] + + # (T, B, 3) — rotate all points by all rotations at once + rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) + + # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. + # index_select is faster and cleaner than advanced indexing with python lists. + # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) + idx = torch.tensor([[0, 1], + [2, 0], + [1, 2]], device=pts.device) # (3, 2) + # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) + # Result: (T, 3, B, 2) + coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( + -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + ) + + # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + + per_scale_features = [] + for plane_coef in ms_grids: + # plane_coef: (3T, C, H, W) + C = plane_coef.shape[1] + + sampled = F.grid_sample( + plane_coef, + coord_tensor, + align_corners=True, + mode="bilinear", + padding_mode="border", + ) # (3T, C, B, 1) + + # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) + sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) + + # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim + per_scale_features.append( + sampled.permute(2, 0, 1).reshape(B, T * C) + ) + + # Concatenate across scales -> (B, T * C * num_scales) + return torch.cat(per_scale_features, dim=-1) + class KPlanesTILTED(KPlanes): """ K-Planes with T learned SO(3) rotations (TILTED). @@ -352,7 +406,7 @@ def __init__( self._build_sigma_net(use_hybrid_mlp, hybrid_hidden_dim, hybrid_num_layers) # ---- Learnable rotations ---- - self.so3 = SO3Param(T, init=tau_init) + self.set_so3_param_type(so3_param_type, init=tau_init) # ------------------------------------------------------------------ # Internal helpers @@ -466,7 +520,7 @@ def extra_repr(self) -> str: f"num_scales={len(self.multiscale_res_multipliers)}" ) - def set_so3_param_type(self, so3_param_type: str) -> None: + def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: """ Set the SO3 parameterization type. @@ -476,17 +530,13 @@ def set_so3_param_type(self, so3_param_type: str) -> None: SO3 parameterization type ("quat" or "r9svd"). """ if so3_param_type == "r9svd": - self.so3 = SO3ParamR9SVD(self.T) + self.so3 = SO3ParamR9SVD(self.T, init=init) elif so3_param_type == "quat": - self.so3 = SO3ParamQuat(self.T) + self.so3 = SO3ParamQuat(self.T, init=init) else: raise ValueError(f"Invalid SO3 parameterization type: {so3_param_type}") - - - - # CP Decomp for Warmup SO3 rotations def interpolate_ms_features_cp_tilted( diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 29314fb6..14f60867 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -128,56 +128,3 @@ def as_matrix(self) -> torch.Tensor: diag[:, 2] = d # multiply last singular vector by sign return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) -def interpolate_ms_features_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) - rotation_matrices: torch.Tensor, # (T, 3, 3) -) -> torch.Tensor: - """ - Fully-vectorized multi-scale, multi-rotation K-Planes feature interpolation. - Returns features of shape (B, C * T * num_scales). - """ - T = rotation_matrices.shape[0] - B = pts.shape[0] - - # (T, B, 3) — rotate all points by all rotations at once - rotated = torch.einsum("tij,bj->tbi", rotation_matrices, pts) - - # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. - # index_select is faster and cleaner than advanced indexing with python lists. - # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) - idx = torch.tensor([[0, 1], - [2, 0], - [1, 2]], device=pts.device) # (3, 2) - # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) - # Result: (T, 3, B, 2) - coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( - -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) - ) - - # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis - coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) - - per_scale_features = [] - for plane_coef in ms_grids: - # plane_coef: (3T, C, H, W) - C = plane_coef.shape[1] - - sampled = F.grid_sample( - plane_coef, - coord_tensor, - align_corners=True, - mode="bilinear", - padding_mode="border", - ) # (3T, C, B, 1) - - # (3T, C, B) -> (T, 3, C, B) -> Hadamard across the "3" dim -> (T, C, B) - sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) - - # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim - per_scale_features.append( - sampled.permute(2, 0, 1).reshape(B, T * C) - ) - - # Concatenate across scales -> (B, T * C * num_scales) - return torch.cat(per_scale_features, dim=-1) \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 8ef2830d..0eda0bb9 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1017,13 +1017,12 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices] # (N, 3) - inner = _unwrap(self.model) - if hasattr(inner, "resolution"): - h = 2.0 / min(inner.resolution) + if hasattr(self.model, "resolution"): + h = 2.0 / min(self.model.resolution) else: h = 1e-2 - pred = inner(tv_coords) + pred = self.model(tv_coords) if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: @@ -1033,7 +1032,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: for axis in range(3): offset = torch.zeros(3, device=tv_coords.device) offset[axis] = h - shifted_pred = inner(tv_coords + offset) + shifted_pred = self.model(tv_coords + offset) if isinstance(shifted_pred, tuple): shifted_pred = shifted_pred[0] if shifted_pred.dim() == 1: @@ -1094,15 +1093,14 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di self._optimizer_params = params return - inner = _unwrap(self.model) - if isinstance(inner, PPLR): + if isinstance(self.model, PPLR): if not isinstance(params, dict): raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") object_params = params - if set(object_params.keys()) != set(inner.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {inner.param_keys}") + if set(object_params.keys()) != set(self.model.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") params = {} for key, value in object_params.items(): @@ -1125,8 +1123,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: updating get_optimization_parameters to return a list of parameters and their LRs. """ - inner = _unwrap(self.model) - if not isinstance(inner, PPLR): + if not isinstance(self.model, PPLR): super().set_optimizer(opt_params) return diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index a96136df..826ad360 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -26,6 +26,7 @@ ObjConstraintsType, ObjectINR, ObjectPixelated, + ObjectTensorDecomp, ) from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase @@ -42,7 +43,7 @@ class Tomography(TomographyOpt, TomographyBase): def from_models( cls, dset: DatasetModelType, - obj_model: ObjectINR, + obj_model: ObjectINR | ObjectTensorDecomp, logger: LoggerTomography | None = None, device: str = "cuda", verbose: int | bool = True, @@ -180,7 +181,7 @@ def reconstruct( consistency_loss = torch.tensor(0.0, device=self.device) total_loss = torch.tensor(0.0, device=self.device) epoch_soft_constraint_loss = torch.tensor(0.0, device=self.device) - if isinstance(self.obj_model, ObjectINR): + if isinstance(self.obj_model, ObjectINR) or isinstance(self.obj_model, ObjectTensorDecomp): self.obj_model.model.train() else: raise NotImplementedError( diff --git a/src/quantem/tomography/tomography_base.py b/src/quantem/tomography/tomography_base.py index 58909bdf..419b5ede 100644 --- a/src/quantem/tomography/tomography_base.py +++ b/src/quantem/tomography/tomography_base.py @@ -11,6 +11,7 @@ ObjConstraintsType, ObjectINR, ObjectModelType, + ObjectTensorDecomp, ) @@ -51,7 +52,7 @@ def __init__( self._val_losses: list[float] = [] self._lrs: dict[str, list] = {} # DDP Initialization - if isinstance(obj_model, ObjectINR): + if isinstance(obj_model, ObjectINR) or isinstance(obj_model, ObjectTensorDecomp): self.setup_distributed(device=device) if self.global_rank == 0: print("Setting up DDP for obj_model") From 15bdcd4bc99f8e62999d033bfce46d8e9c45c3e2 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 16:48:45 -0700 Subject: [PATCH 254/335] Revamped model_base.py to cover type-hinting stuff. KPlanes has a set of parameters now that helps with type-setting. The main reason for having model_base.py as is it is right now is if we ever wanted to go do TensoRF or something just to validate --- src/quantem/core/ml/models/__init__.py | 0 src/quantem/core/ml/models/kplanes.py | 55 +++++++++- src/quantem/core/ml/models/model_base.py | 44 +++++--- src/quantem/tomography/object_models.py | 131 ++++------------------- 4 files changed, 100 insertions(+), 130 deletions(-) create mode 100644 src/quantem/core/ml/models/__init__.py diff --git a/src/quantem/core/ml/models/__init__.py b/src/quantem/core/ml/models/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 58d6fc81..16c56575 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -154,7 +154,7 @@ def interpolate_ms_features( return torch.cat(per_scale, dim=-1) -class KPlanes(nn.Module, PPLR, TensorDecompositionModel): +class KPlanes(PPLR, TensorDecompositionModel): def __init__( self, @@ -248,6 +248,42 @@ def param_keys(self) -> list[str]: def td_type(self) -> str: return self._td_type + @td_type.setter + def td_type(self, td_type: str): + if not isinstance(td_type, str): + raise TypeError("td_type must be a string") + self._td_type = td_type + + @property + def tilted(self) -> bool: + return False + + @tilted.setter + def tilted(self, tilted: bool): + if not isinstance(tilted, bool): + raise TypeError("tilted must be a boolean") + self._tilted = tilted + + @property + def grids(self) -> torch.nn.ParameterList: + return self._grids + + @grids.setter + def grids(self, grids: torch.nn.ParameterList): + if not isinstance(grids, torch.nn.ParameterList): + raise TypeError("Grids must be a ParameterList") + self._grids = grids + + @property + def resolution(self) -> list[int]: + return self._resolution + + @resolution.setter + def resolution(self, resolution: Sequence[int]): + if not isinstance(resolution, Sequence): + raise TypeError("Resolution must be a sequence") + self._resolution = list(resolution) + # --------------------------------------------------------------------------- # KPlanesTILTED @@ -535,6 +571,10 @@ def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: self.so3 = SO3ParamQuat(self.T, init=init) else: raise ValueError(f"Invalid SO3 parameterization type: {so3_param_type}") + + @property + def tilted(self) -> bool: + return True # CP Decomp for Warmup SO3 rotations @@ -590,7 +630,7 @@ def interpolate_ms_features_cp_tilted( return torch.cat(per_scale_features, dim=-1) -class CPTilted(nn.Module, PPLR, TensorDecompositionModel): +class CPTilted(PPLR, TensorDecompositionModel): """ CP decomposition with TILTED rotations — the true bottleneck model for phase 1. Rank-1-per-channel feature representation. @@ -660,4 +700,13 @@ def td_type(self) -> str: return self._td_type def extract_tau_state(self) -> torch.Tensor: - return self.so3.M.detach().clone() \ No newline at end of file + return self.so3.M.detach().clone() + + @property + def tilted(self) -> bool: + return True + + + + +KPlanesType = KPlanes | KPlanesTILTED | CPTilted \ No newline at end of file diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index 44bbdef5..e1a486d7 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -2,36 +2,50 @@ from typing import Dict import torch +import torch.nn as nn class PPLR(ABC): """ Abstract base class for models that require multi-scale parameter optimization. """ + @abstractmethod - def get_params(self) -> Dict[str, list[torch.nn.Parameter]]: + def get_params(self) -> Dict[str, list[nn.Parameter]]: """ - This abstract method should return a dictionary of parameters based on a key. + Return a dictionary of parameters grouped by key. - For example if your nn.Module has multiple optimizable parameter groups, - you can return a dictionary with the keys "grids" and "sigma_net" (KPlanes example). + For example if your nn.Module has multiple optimizable parameter groups, + you can return a dictionary with the keys "grids" and "sigma_net" + (KPlanes example). """ pass @property @abstractmethod def param_keys(self) -> list[str]: - """ - This abstract property should return a list of available parameter keys. - """ + """List of available parameter-group keys.""" pass -class TensorDecompositionModel(ABC): - @property - @abstractmethod - def td_type(self) -> str: - """ - This abstract property should return the type of tensor decomposition used. - """ - pass \ No newline at end of file +class TensorDecompositionModel(nn.Module, ABC): + """ + Base class for factored tensor-decomposition models. + + Subclasses must set ``td_type`` as a normal attribute in ``__init__``. + """ + + td_type: str + + +class PlanarDecompositionModel(TensorDecompositionModel): + """ + Planar factored-grid models: K-Planes, K-Planes-TILTED, HexPlane, tri-planes. + + Subclasses must set ``grids``, ``tilted``, and ``resolution`` as normal + attributes in ``__init__``. + """ + + grids: nn.ParameterList + tilted: bool + resolution: list[int] \ No newline at end of file diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 0eda0bb9..346e01da 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -14,12 +14,12 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.models.kplanes import KPlanes, KPlanesTILTED -from quantem.core.ml.models.model_base import PPLR +from quantem.core.ml.models.model_base import PPLR, PlanarDecompositionModel from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset + class ObjConstraintParams: """ Namespace class for object reconstruction constraint dataclasses and parsing utilities. @@ -197,6 +197,12 @@ def parse_dict( ) +def _unwrap(model: nn.Module | nn.parallel.DistributedDataParallel) -> PlanarDecompositionModel: + """Unwrap a DistributedDataParallel model to get the underlying module ONLY for tensor decomposition models.""" + if isinstance(model, nn.parallel.DistributedDataParallel): + return model.module + return model + class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): DEFAULT_LRS = { "object": 8e-6, @@ -205,7 +211,6 @@ class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): """ Base class for all ObjectModels to inherit from. """ - def __init__( self, shape: tuple[int, int, int], # pyright: ignore[reportRedeclaration] @@ -297,7 +302,6 @@ def to(self, *args, **kwargs): raise NotImplementedError - class ObjectConstraints(BaseConstraints, ObjectBase): # TODO: Ask Arthur why we still need this def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -309,7 +313,6 @@ def get_tv_loss(self, **kwargs) -> torch.Tensor: """ raise NotImplementedError - class ObjectPixelated(ObjectConstraints): """ @@ -453,7 +456,6 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM class ObjectINR(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() - def __init__( self, shape: tuple[int, int, int], @@ -470,7 +472,7 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: ObjConstraintParams.ObjINRConstraints = self.DEFAULT_CONSTRAINTS.copy() # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -881,10 +883,8 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() - class ObjectTensorDecomp(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() - def __init__( self, shape: tuple[int, int, int], @@ -901,7 +901,7 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.constraints: ObjConstraintsType = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: ObjConstraintParams.ObjTensorDecompConstraints = self.DEFAULT_CONSTRAINTS.copy() # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -929,7 +929,7 @@ def from_model( # --- Properties --- @property - def model(self) -> nn.Module | nn.parallel.DistributedDataParallel: + def model(self) -> nn.Module | nn.parallel.DistributedDataParallel | PlanarDecompositionModel: """ Returns the INR model. """ @@ -970,7 +970,7 @@ def apply_soft_constraints( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * torch.norm(all_densities, p=1) + sparsity_loss = self.constraints.sparsity * all_densities.abs().mean() soft_loss += sparsity_loss return soft_loss @@ -986,10 +986,11 @@ def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: def _get_plane_tv_loss(self) -> torch.Tensor: - is_tilted = isinstance(self.model, KPlanesTILTED) + is_tilted = self.model.tilted per_level = [] - for p in self.model.grids: + model = _unwrap(self.model) + for p in model.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) @@ -1017,12 +1018,10 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] tv_coords = coords[tv_indices] # (N, 3) - if hasattr(self.model, "resolution"): - h = 2.0 / min(self.model.resolution) - else: - h = 1e-2 + model = _unwrap(self.model) + h = 2.0 / min(model.resolution) - pred = self.model(tv_coords) + pred = model(tv_coords) if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: @@ -1269,99 +1268,7 @@ def forward(self, coords: torch.Tensor) -> torch.Tensor: return all_densities - # Pretrain Loop - - def pretrain( - self, - pretrain_dataset: TomographyINRPretrainDataset, - batch_size: int, - reset: bool = False, - num_iters: int = 10, - num_workers: int = 0, - optimizer_params: dict | None = None, - scheduler_params: dict | None = None, - loss_fn: Callable | str = "l1", - verbose: bool = True, - ): - """ - Pretrain the INR model to fit target volume. - """ - - if ( - pretrain_dataset is not None - ): # Need to make a check if there's already a pretrain dataset to not go through with the setup again. - self.pretrain_dataset = pretrain_dataset - ( - self.pretraining_dataloader, - self.pretraining_sampler, - self.pretraining_val_dataloader, - self.pretraining_val_sampler, - ) = self.setup_dataloader(pretrain_dataset, batch_size, num_workers=num_workers) - - if optimizer_params is not None: - self.set_optimizer(optimizer_params) - if scheduler_params is not None: - self.set_scheduler(scheduler_params, num_iters) - - if reset: - self.reset() - - loss_fn = get_loss_module(loss_fn, self.dtype) - - self._pretrain( - num_iters=num_iters, - loss_fn=loss_fn, - verbose=verbose, - ) - - def _pretrain( - self, - num_iters: int, - loss_fn: Callable, - verbose: bool, - ): - if self.optimizer is None: - raise RuntimeError("Optimizer not set. Call set_optimizer() first.") - if self.scheduler is None: - raise RuntimeError("Scheduler not set. Call set_scheduler() first.") - - self.model.train() - optimizer = self.optimizer - scheduler = self.scheduler - - pbar = tqdm(range(num_iters), desc="Pretraining", disable=not verbose) - for a0 in pbar: - epoch_loss = 0 - for batch_idx, batch in enumerate[Any](self.pretraining_dataloader): - coords = batch["coords"].to(self.device, non_blocking=True) - target = batch["target"].to(self.device, non_blocking=True) - - with torch.autocast( - device_type=self.device.type, dtype=torch.bfloat16, enabled=True - ): - outputs = self.forward(coords) - loss = loss_fn(outputs, target) - - loss.backward() - epoch_loss += loss.item() - - # Clip gradients - torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0) - - optimizer.step() - optimizer.zero_grad() - - if scheduler is not None: - if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau): - scheduler.step(epoch_loss) - else: - scheduler.step() - - self._pretrain_losses.append(epoch_loss / len(self.pretraining_dataloader)) - print( - f"Epoch {a0 + 1}/{num_iters}, Pretrain Loss: {epoch_loss / len(self.pretraining_dataloader):.4f}" - ) - self._pretrain_lrs.append(optimizer.param_groups[0]["lr"]) + # NOTE: Pretraining is done in a two-phase fashion as shown in TILTED paper. def create_volume(self, return_vol: bool = False): N = max(self._shape) From 0c71a5dc10d287ac5005390466a2bc7111c3bccf Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 16:53:20 -0700 Subject: [PATCH 255/335] Final changes prior to draft PR --- src/quantem/core/ml/models/kplanes.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index 16c56575..b1be28ee 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -130,7 +130,6 @@ def query_planes( result = result * sampled return result # pyright: ignore[reportReturnType] - def interpolate_ms_features( pts: torch.Tensor, ms_grids: nn.ParameterList, @@ -284,7 +283,6 @@ def resolution(self, resolution: Sequence[int]): raise TypeError("Resolution must be a sequence") self._resolution = list(resolution) - # --------------------------------------------------------------------------- # KPlanesTILTED # --------------------------------------------------------------------------- @@ -576,7 +574,6 @@ def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: def tilted(self) -> bool: return True - # CP Decomp for Warmup SO3 rotations def interpolate_ms_features_cp_tilted( From 99fdd885c75afa867628000c40e41b38859a5741 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 17:09:47 -0700 Subject: [PATCH 256/335] Fixed typo --- src/quantem/tomography/object_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 346e01da..10509655 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1078,7 +1078,7 @@ def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any return list(self.params) - # --- DDP Mixin Overloads in the case of PPLR --- + # --- Optimizer Mixin Overloads in the case of PPLR --- @property def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: From 4a44826943324dac0f6aa527bdf6bba0f79c3f1f Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 17:28:17 -0700 Subject: [PATCH 257/335] Small change to set_optimizer in OptimizerMixin to allow for dictionary parsing --- src/quantem/core/ml/models/kplanes.py | 2 +- src/quantem/core/ml/optimizer_mixin.py | 43 ++++-- src/quantem/tomography/object_models.py | 177 ++++++++++-------------- 3 files changed, 106 insertions(+), 116 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index b1be28ee..c8b49e70 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -381,7 +381,7 @@ def __init__( input_coords_dims: int = 3, M_features: int = 32, resolution: Sequence[int] = (200, 200, 200), - multiscale_res_multipliers: Optional[Sequence[int]] = None, + multiscale_res_multipliers: Optional[Sequence[float]] = None, density_activation: Callable = lambda x: F.softplus(x - 1), # TILTED parameters T: int = 4, diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 9fe8a32a..4c97181a 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -613,18 +613,41 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: params = list(params) # Ensure parameters require gradients - for p in params: - p.requires_grad_(True) + for group in params: + tensors = group["params"] if isinstance(group, dict) else [group] + for p in tensors: + p.requires_grad_(True) + # Figure out which optimizer class to use + if isinstance(self._optimizer_params, dict): + # Per-group case: all groups must agree on the optimizer class, + # and per-group hyperparameters are already baked into each dict + # by get_optimization_parameters(). + opt_specs = list(self._optimizer_params.values()) + if not opt_specs: + self._optimizer = None + return + optimizer_cls = self._optimizer_class_for(opt_specs[0]) + for spec in opt_specs[1:]: + if type(spec) is not type(opt_specs[0]): + raise ValueError( + f"All parameter groups must use the same optimizer type, " + f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" + ) + self._optimizer = optimizer_cls(params) + else: + # Single-optimizer case: splat global hyperparameters + optimizer_cls = self._optimizer_class_for(self._optimizer_params) + self._optimizer = optimizer_cls(params, **self._optimizer_params.params()) - match self._optimizer_params: - case OptimizerParams.Adam(): - self._optimizer = torch.optim.Adam(params, **self._optimizer_params.params()) - case OptimizerParams.AdamW(): - self._optimizer = torch.optim.AdamW(params, **self._optimizer_params.params()) - case OptimizerParams.SGD(): - self._optimizer = torch.optim.SGD(params, **self._optimizer_params.params()) + + def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: + match opt_params: + case OptimizerParams.Adam(): return torch.optim.Adam + case OptimizerParams.AdamW(): return torch.optim.AdamW + case OptimizerParams.SGD(): return torch.optim.SGD case _: - raise NotImplementedError(f"Unknown optimizer type: {self._optimizer_params}") + raise NotImplementedError(f"Unknown optimizer type: {opt_params}") + def set_scheduler( self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 10509655..281e07bf 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1066,16 +1066,14 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - if isinstance(self.model, PPLR): - - return [ - { - "params": self.model.get_params()[key], - **self.optimizer_params[key].params(), - } - for key in self.model.param_keys - ] - return list(self.params) + model = _unwrap (self.model) + return [ + { + "params": model.get_params()[key], + **self.optimizer_params[key].params(), + } + for key in model.param_keys + ] # --- Optimizer Mixin Overloads in the case of PPLR --- @@ -1088,54 +1086,25 @@ def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: @optimizer_params.setter def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any]): """Set the optimizer parameters.""" - if isinstance(params, OptimizerType): - self._optimizer_params = params - return - if isinstance(self.model, PPLR): - if not isinstance(params, dict): - raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - - object_params = params + if not isinstance(params, dict): + raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") - - params = {} - for key, value in object_params.items(): - if isinstance(value, dict): - params[key] = OptimizerParams.parse_dict(d=value) - elif isinstance(value, OptimizerType): - params[key] = value - else: - raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") - - self._optimizer_params = params - else: - raise TypeError(f"optimizer parameters must be a dict for non-PPLR, got {type(params)}") - - - def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: - """ - Set the optimizer for this model. - Currently supports single LR for all parameters, TODO allow for per parameter LRs by - updating get_optimization_parameters to return a list of parameters and their LRs. - """ - - if not isinstance(self.model, PPLR): - super().set_optimizer(opt_params) - return - - if opt_params is not None: - self.optimizer_params = opt_params - - if not self._optimizer_params: - self._optimizer = None - return - - params = self.get_optimization_parameters() + object_params = params - self._optimizer = torch.optim.Adam(params) + if set(object_params.keys()) != set(self.model.param_keys): + raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + + params = {} + for key, value in object_params.items(): + if isinstance(value, dict): + params[key] = OptimizerParams.parse_dict(d=value) + elif isinstance(value, OptimizerType): + params[key] = value + else: + raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") + + self._optimizer_params = params def reconnect_optimizer_to_parameters(self) -> None: """ @@ -1144,58 +1113,56 @@ def reconnect_optimizer_to_parameters(self) -> None: if self.optimizer is None: return - - if isinstance(self.model, PPLR): - current_params = self.get_optimization_parameters() - + + current_params = self.get_optimization_parameters() + - optimizable_params = [ - p for p in current_params - if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf - ] - + optimizable_params = [ + p for p in current_params + if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf + ] + + + if not optimizable_params: + raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") + + for p in optimizable_params: + print(f"Setting requires_grad for parameter: {p}") + p['params'][0].requires_grad_(True) + + assert self._optimizer is not None + # Preserve optimizer states and param_group settings + old_state = self._optimizer.state.copy() + old_param_groups = self._optimizer.param_groups.copy() + + # Reconnect to new parameters + self._optimizer.param_groups.clear() + for param_group in optimizable_params: + self._optimizer.add_param_group(param_group) + + # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, + # excluding 'params' which comes from the new groups + for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): + new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) + + # Remap optimizer state: for any new param that IS the same tensor as an old param, + # carry its state over (moved to the right device just in case). + new_state = {} + for new_pg in self._optimizer.param_groups: + for new_param in new_pg["params"]: + if new_param in old_state: + device = new_param.device + new_state[new_param] = { + k: (v.to(device) if isinstance(v, torch.Tensor) else v) + for k, v in old_state[new_param].items() + } + + self._optimizer.state.clear() + self._optimizer.state.update(new_state) + + if self._scheduler is not None and self._optimizer is not None: + self._scheduler.optimizer = self._optimizer - if not optimizable_params: - raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") - - for p in optimizable_params: - print(f"Setting requires_grad for parameter: {p}") - p['params'][0].requires_grad_(True) - - assert self._optimizer is not None - # Preserve optimizer states and param_group settings - old_state = self._optimizer.state.copy() - old_param_groups = self._optimizer.param_groups.copy() - - # Reconnect to new parameters - self._optimizer.param_groups.clear() - for param_group in optimizable_params: - self._optimizer.add_param_group(param_group) - - # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, - # excluding 'params' which comes from the new groups - for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): - new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) - - # Remap optimizer state: for any new param that IS the same tensor as an old param, - # carry its state over (moved to the right device just in case). - new_state = {} - for new_pg in self._optimizer.param_groups: - for new_param in new_pg["params"]: - if new_param in old_state: - device = new_param.device - new_state[new_param] = { - k: (v.to(device) if isinstance(v, torch.Tensor) else v) - for k, v in old_state[new_param].items() - } - - self._optimizer.state.clear() - self._optimizer.state.update(new_state) - - if self._scheduler is not None and self._optimizer is not None: - self._scheduler.optimizer = self._optimizer - else: - super().reconnect_optimizer_to_parameters() # Pretraining @property From ede9d566a777f68b225f8f6e6cc2ce2e9660d70a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 23 Apr 2026 17:36:39 -0700 Subject: [PATCH 258/335] Pretraining warning on ObjectTensorDecomp --- src/quantem/tomography/object_models.py | 209 +----------------------- 1 file changed, 5 insertions(+), 204 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 281e07bf..0ae1e74a 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -883,7 +883,7 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() -class ObjectTensorDecomp(ObjectConstraints, DDPMixin): +class ObjectTensorDecomp(ObjectINR): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() def __init__( self, @@ -926,33 +926,6 @@ def from_model( obj_model.to(device) return obj_model - # --- Properties --- - - @property - def model(self) -> nn.Module | nn.parallel.DistributedDataParallel | PlanarDecompositionModel: - """ - Returns the INR model. - """ - return self._model - - @property - def obj(self) -> torch.Tensor: - return self._obj - - @obj.setter - def obj(self, obj: torch.Tensor): - self._obj = obj - - @property - def obj_view(self) -> np.ndarray: - """ - Returns the object as a view of the x, y, z axes. - - Matches the axes of conventionally reconstructed objects, this is the object that will be saved. - """ - self.create_volume() - return self._obj.cpu().numpy().transpose(0, 1, 3, 2) - # --- Constraints --- def apply_soft_constraints( @@ -966,10 +939,7 @@ def apply_soft_constraints( if self.constraints.tv_vol > 0: soft_loss += self.get_tv_loss(coords, pred) - if ( - isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) - and self.constraints.sparsity > 0 - ): # NOTE: For the linter, I must make this :) + if self.constraints.sparsity > 0: # NOTE: For the linter, I must make this :) sparsity_loss = self.constraints.sparsity * all_densities.abs().mean() soft_loss += sparsity_loss @@ -1107,9 +1077,6 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di self._optimizer_params = params def reconnect_optimizer_to_parameters(self) -> None: - """ - Reconnect optimizer overload, defaults back to the standard implementation if no `PPLR` is detected. - """ if self.optimizer is None: return @@ -1163,174 +1130,8 @@ def reconnect_optimizer_to_parameters(self) -> None: if self._scheduler is not None and self._optimizer is not None: self._scheduler.optimizer = self._optimizer - - # Pretraining - @property - def pretrained_weights(self) -> dict[str, torch.Tensor]: - """get the pretrained weights of the INR model""" - return self._pretrained_weights - - def _set_pretrained_weights(self, model: "torch.nn.Module"): - """set the pretrained weights of the INR model""" - if not isinstance(model, torch.nn.Module): - raise TypeError(f"Pretrained model must be a torch.nn.Module, got {type(model)}") - self._pretrained_weights = deepcopy(model.state_dict()) - - @property - def pretrain_target(self) -> TomographyINRPretrainDataset: - """get the pretrain target""" - return self._pretrain_target - - @pretrain_target.setter - def pretrain_target(self, target: TomographyINRPretrainDataset): - """set the pretrain target""" - self._pretrain_target = target - - @property - def dtype(self) -> torch.dtype: - """ - Returns the dtype of the object. - """ - # TODO: This is a temporary solution to get the dtype of the object. - return torch.float32 - - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape - - # --- Helper Functions --- - def rebuild_model(self): - self._model = self.distribute_model(self._model) - - # Reset method that goes back to the pretrained weights. - def reset(self): - """reset the model to the pretrained weights""" - self.model.load_state_dict(self._pretrained_weights.copy()) - self._model = self.distribute_model( - self.model - ) # Maybe add a check to see if distributed or not, but not very computationally expensive to do this. - - # --- Forward Method --- - - def forward(self, coords: torch.Tensor) -> torch.Tensor: - """forward pass for the INR model""" - all_densities = self.model(coords) - - if all_densities.dim() > 1: - all_densities = all_densities.squeeze(-1) - valid_mask = ( - (coords[:, 0] >= -1) & (coords[:, 0] <= 1) & (coords[:, 1] >= -1) & (coords[:, 1] <= 1) - ).float() - - if all_densities.dim() > 1: - valid_mask = valid_mask.unsqueeze(-1) - # Multi-dimensional mask - all_densities = all_densities * valid_mask - - all_densities = self.apply_hard_constraints(all_densities) - - return all_densities - - # NOTE: Pretraining is done in a two-phase fashion as shown in TILTED paper. - - def create_volume(self, return_vol: bool = False): - N = max(self._shape) - with torch.no_grad(): - coords_1d = torch.linspace(-1, 1, N) - x, y, z = torch.meshgrid(coords_1d, coords_1d, coords_1d, indexing="ij") - inputs = torch.stack([x, y, z], dim=-1).reshape(-1, 3) - model = self.model.module if isinstance(self.model, nn.DataParallel) else self.model - - inference_batch_size = 5 * N * N - total_samples = N**3 - samples_per_gpu = total_samples // self.world_size - remainder = total_samples % self.world_size - - if self.global_rank < remainder: - start_idx = self.global_rank * (samples_per_gpu + 1) - end_idx = start_idx + samples_per_gpu + 1 - else: - start_idx = self.global_rank * samples_per_gpu + remainder - end_idx = start_idx + samples_per_gpu - - inputs_subset = inputs[start_idx:end_idx] - num_samples = inputs_subset.shape[0] - - outputs_list = [] - for batch_start in range(0, num_samples, inference_batch_size): - batch_end = min(batch_start + inference_batch_size, num_samples) - batch_coords = inputs_subset[batch_start:batch_end].to( - self.device, non_blocking=True - ) - - batch_outputs = model(batch_coords) # (B, C) or (B,) etc. - - if isinstance(batch_outputs, tuple): - batch_outputs = batch_outputs[0] - batch_outputs = self.apply_hard_constraints(batch_outputs) - - # Ensure shape is (B, C) - if batch_outputs.dim() == 1: - batch_outputs = batch_outputs.unsqueeze(-1) # (B, 1) - - outputs_list.append(batch_outputs.cpu()) - - outputs = torch.cat(outputs_list, dim=0) # (local_B, C) - C = outputs.shape[-1] # e.g. 5 - - if self.world_size > 1: - # gather variable-sized first dimension (local_B) while keeping channels - local_B = outputs.shape[0] - output_size = torch.tensor(local_B, device=self.device, dtype=torch.long) - all_sizes = [ - torch.zeros(1, device=self.device, dtype=torch.long) - for _ in range(self.world_size) - ] - dist.all_gather(all_sizes, output_size) - max_size = max(size.item() for size in all_sizes) - - outputs_dev = outputs.to(self.device) # (local_B, C) - if local_B < max_size: - pad = torch.zeros( - (max_size - local_B, C), # type: ignore - device=self.device, - dtype=outputs_dev.dtype, - ) - outputs_padded = torch.cat([outputs_dev, pad], dim=0) # (max_size, C) - else: - outputs_padded = outputs_dev - - gathered_outputs = [ - torch.empty((max_size, C), device=self.device, dtype=outputs_dev.dtype) # type: ignore - for _ in range(self.world_size) - ] - dist.all_gather(gathered_outputs, outputs_padded.contiguous()) - - trimmed_outputs = [] - for rank, size in enumerate(all_sizes): - trimmed_outputs.append(gathered_outputs[rank][: size.item(), :]) - - pred_full = torch.cat(trimmed_outputs, dim=0).reshape(C, N, N, N).float() - else: - pred_full = outputs.reshape(C, N, N, N).float() - - if return_vol: - return pred_full.detach().cpu() - - self._obj = pred_full.detach().cpu() - - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change - if isinstance(device, str): - device = torch.device(device) - self._device = device - if self.world_size == 1: - self._model = self._model.to(device) - elif not isinstance(self._model, torch.nn.parallel.DistributedDataParallel): - self.distribute_model(self._model) - self.reconnect_optimizer_to_parameters() + def pretrain(self) -> None: + raise NotImplementedError("Tensor decomposition pretraining is not usually required, and for TILTED there is a two-phase warmup approach.") + ObjectModelType = ObjectPixelated | ObjectINR From 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OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset +from quantem.tomography.tomography import ReconstructionContext as ctx class ObjConstraintParams: diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 826ad360..f21a30d1 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,6 +1,7 @@ import os +from dataclasses import dataclass from pathlib import Path -from typing import Literal, Self, Sequence +from typing import Literal, Optional, Self, Sequence import matplotlib.pyplot as plt import numpy as np @@ -625,3 +626,20 @@ def plot_losses(self): ax.set_title("Reconstruction Loss") ax.set_yscale("log") plt.show() + + +@dataclass +class ReconstructionContext: + """ + Handles all reconstruction parameters to be passed into object models. + + Subclasses will pick whatever parameter they need + - Pixelated reads ".volume" + - INR reads ".coords" and recomputes via the model. + - TEnsorDEcomp reads ".coords" and ".pred" (and ".all densities") + """ + + coords: Optional[torch.Tensor] = None + pred: Optional[torch.Tensor] = None + all_densities: Optional[torch.Tensor] = None + volume: Optional[torch.Tensor] = None \ No newline at end of file From 01456ed62cb12e9857acc6be9a3774d3566e9135 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 11:12:18 -0700 Subject: [PATCH 263/335] Working on ObjectPixelated implementing ctx --- .vscode/settings.json | 1 + src/quantem/__init__.py | 1 + src/quantem/core/datastructures/dataset3d.py | 3 +- src/quantem/core/ml/constraints.py | 6 +- src/quantem/core/ml/models/kplanes.py | 156 ++++++----- src/quantem/core/ml/models/model_base.py | 3 +- src/quantem/core/ml/models/so3params.py | 84 +++--- src/quantem/core/ml/optimizer_mixin.py | 17 +- .../core/visualization/visualization.py | 7 +- .../ptychography_visualizations.py | 2 +- src/quantem/imaging/drift.py | 2 +- src/quantem/tomography/dataset_models.py | 13 +- src/quantem/tomography/object_models.py | 265 +++++++++--------- src/quantem/tomography/tomography.py | 14 +- src/quantem/tomography/tomography_opt.py | 2 +- tests/datastructures/test_dataset3d_show.py | 82 +++--- 16 files changed, 352 insertions(+), 306 deletions(-) diff --git a/.vscode/settings.json b/.vscode/settings.json index 1659b33b..80434c6b 100644 --- a/.vscode/settings.json +++ b/.vscode/settings.json @@ -78,4 +78,5 @@ "yticks" ], "basedpyright.analysis.typeCheckingMode": "standard", + "python.REPL.enableREPLSmartSend": false, } diff --git a/src/quantem/__init__.py b/src/quantem/__init__.py index b9aa54b4..ba70f629 100644 --- a/src/quantem/__init__.py +++ b/src/quantem/__init__.py @@ -1,4 +1,5 @@ from pkgutil import extend_path + __path__ = extend_path(__path__, __name__) from importlib.metadata import version diff --git a/src/quantem/core/datastructures/dataset3d.py b/src/quantem/core/datastructures/dataset3d.py index 118ec66f..154d53ee 100644 --- a/src/quantem/core/datastructures/dataset3d.py +++ b/src/quantem/core/datastructures/dataset3d.py @@ -318,8 +318,7 @@ def show( ncols = min(ncols, n_frames) # Don't create more columns than frames images = [self.array[i] for i in frame_idx] labels = [ - f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" - for i in frame_idx + f"Frame {i}" if title_prefix is None else f"{title_prefix} {i}" for i in frame_idx ] # Pad last row to complete the grid (show_2d requires rectangular input) remainder = n_frames % ncols diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 553b0611..137ed4df 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,6 +7,8 @@ import torch from numpy.typing import NDArray +from quantem.tomography.tomography import ReconstructionContext + @dataclass(slots=False) class Constraints(ABC): @@ -86,14 +88,14 @@ def constraints(self, constraints: Constraints | dict[str, Any]): # --- Required methods tha tneeds to implemented in subclasses --- @abstractmethod - def apply_hard_constraints(self, *args, **kwargs) -> torch.Tensor: + def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: """ Apply hard constraints to the model. """ raise NotImplementedError @abstractmethod - def apply_soft_constraints(self, *args, **kwargs) -> torch.Tensor: + def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: """ Apply soft constraints to the model. """ diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index c8b49e70..fe36dfcc 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -3,7 +3,6 @@ """ import itertools -import math from typing import Callable, Optional, Sequence # import tinycudann as tcnn @@ -17,15 +16,19 @@ """ K-planes utility functions """ -def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: bool = True) -> torch.Tensor: + + +def grid_sample_wrapper( + grid: torch.Tensor, coords: torch.Tensor, align_corners: bool = True +) -> torch.Tensor: """ Performs bilinear interpolation on a grid at given coordinates. - + Args: grid: Grid tensor of shape [B, C, H, W] or [C, H, W] coords: Coordinate tensor of shape [B, N, 2] or [N, 2] align_corners: Whether to align corners - + Returns: Interpolated values of shape [B, N, C] or [N, C] """ @@ -40,8 +43,10 @@ def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: if grid_dim == 2 or grid_dim == 3: grid_sampler = F.grid_sample else: - raise NotImplementedError(f"Grid-sample was called with {grid_dim}D data but is only " - f"implemented for 2 and 3D data.") + raise NotImplementedError( + f"Grid-sample was called with {grid_dim}D data but is only " + f"implemented for 2 and 3D data." + ) coords = coords.view([coords.shape[0]] + [1] * (grid_dim - 1) + list(coords.shape[1:])) B, feature_dim = grid.shape[:2] @@ -50,11 +55,14 @@ def grid_sample_wrapper(grid: torch.Tensor, coords: torch.Tensor, align_corners: grid, # [B, feature_dim, reso, ...] coords, # [B, 1, ..., n, grid_dim] align_corners=align_corners, - mode='bilinear', padding_mode='border') + mode="bilinear", + padding_mode="border", + ) interp = interp.view(B, feature_dim, n).transpose(-1, -2) # [B, n, feature_dim] interp = interp.squeeze() # [B?, n, feature_dim?] return interp + def init_planes( in_dim: int, out_dim: int, @@ -62,18 +70,18 @@ def init_planes( init_range: tuple = (0.1, 0.5), ) -> nn.ParameterList: """Create the set of 2D planes for a k-plane decomposition. - + For in_dim=3 (spatial), this creates 3 planes: XY, XZ, YZ. For in_dim=4 (spatial + time), this creates 6 planes: XY, XZ, XT, YZ, YT, ZT. Time planes (those involving axis 3) are initialized to 1 so they start as identity multipliers. - + Args: in_dim: Dimensionality of the input coordinates (3 or 4). out_dim: Number of feature channels per plane. resolution: Resolution along each axis, e.g. [128, 128, 128]. init_range: (a, b) for uniform initialization of spatial planes. - + Returns: nn.ParameterList of plane parameters, each of shape [1, out_dim, res_j, res_i]. """ @@ -94,21 +102,22 @@ def init_planes( planes.append(param) return planes + def query_planes( pts: torch.Tensor, planes: nn.ParameterList, in_dim: int, ) -> float: """Query the k-plane representation at a batch of points. - + Projects each point onto every axis-pair plane, bilinearly interpolates, and returns the element-wise product across all planes. - + Args: pts: (B, in_dim) coordinates in [-1, 1]. planes: The ParameterList from init_planes. in_dim: 3 or 4. - + Returns: (B, out_dim) features. """ @@ -116,30 +125,33 @@ def query_planes( result = 1.0 for plane_param, pair in zip(planes, axis_pairs): # Extract the 2D coords for this plane - coords_2d = pts[..., list(pair)] # (B, 2) - coords_2d = coords_2d.view(1, -1, 1, 2) # (1, B, 1, 2) for grid_sample + coords_2d = pts[..., list(pair)] # (B, 2) + coords_2d = coords_2d.view(1, -1, 1, 2) # (1, B, 1, 2) for grid_sample # grid_sample: input (N,C,H,W), grid (N, H_out, W_out, 2) sampled = F.grid_sample( - plane_param, # (1, C, H, W) - coords_2d, # (1, B, 1, 2) + plane_param, # (1, C, H, W) + coords_2d, # (1, B, 1, 2) align_corners=True, mode="bilinear", padding_mode="border", ) # -> (1, C, B, 1) - sampled = sampled.squeeze(0).squeeze(-1).T # (B, C) + sampled = sampled.squeeze(0).squeeze(-1).T # (B, C) result = result * sampled return result # pyright: ignore[reportReturnType] - + + def interpolate_ms_features( pts: torch.Tensor, ms_grids: nn.ParameterList, ) -> torch.Tensor: mat_mode = [[0, 1], [0, 2], [1, 2]] - coord_plane = torch.stack([ - pts[:, mat_mode[0]], - pts[:, mat_mode[1]], - pts[:, mat_mode[2]], - ]).view(3, -1, 1, 2) + coord_plane = torch.stack( + [ + pts[:, mat_mode[0]], + pts[:, mat_mode[1]], + pts[:, mat_mode[2]], + ] + ).view(3, -1, 1, 2) per_scale = [] for plane_coef in ms_grids: @@ -154,7 +166,6 @@ def interpolate_ms_features( class KPlanes(PPLR, TensorDecompositionModel): - def __init__( self, # Grid parameters @@ -164,7 +175,9 @@ def __init__( resolution: Sequence[int] = (200, 200, 200), multiscale_res_multipliers: Optional[Sequence[int]] = None, concat_features: bool = True, - density_activation: Callable = lambda x: F.softplus(x - 1), # Keep playing around with this and trunc_exp + density_activation: Callable = lambda x: F.softplus( + x - 1 + ), # Keep playing around with this and trunc_exp # Hybrid MLP parameters use_hybrid_mlp: bool = False, hybrid_hidden_dim: int = 64, @@ -218,7 +231,6 @@ def __init__( layers.append(out) self.sigma_net = nn.Sequential(*layers) - def get_densities(self, coords: torch.Tensor): """Computes and returns densities""" pts = coords.reshape(-1, 3) @@ -252,7 +264,7 @@ def td_type(self, td_type: str): if not isinstance(td_type, str): raise TypeError("td_type must be a string") self._td_type = td_type - + @property def tilted(self) -> bool: return False @@ -283,13 +295,15 @@ def resolution(self, resolution: Sequence[int]): raise TypeError("Resolution must be a sequence") self._resolution = list(resolution) + # --------------------------------------------------------------------------- # KPlanesTILTED # --------------------------------------------------------------------------- - + + def interpolate_ms_features_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, H, W) rotation_matrices: torch.Tensor, # (T, 3, 3) ) -> torch.Tensor: """ @@ -305,17 +319,15 @@ def interpolate_ms_features_tilted( # Build (T, 3, B, 2) coords for planes XY, ZX, YZ in one shot. # index_select is faster and cleaner than advanced indexing with python lists. # Plane axis layout: XY=(0,1), ZX=(2,0), YZ=(1,2) - idx = torch.tensor([[0, 1], - [2, 0], - [1, 2]], device=pts.device) # (3, 2) + idx = torch.tensor([[0, 1], [2, 0], [1, 2]], device=pts.device) # (3, 2) # rotated: (T, B, 3) -> gather along last dim with idx (3, 2) # Result: (T, 3, B, 2) - coords = rotated.unsqueeze(1).expand(T, 3, B, 3).gather( - -1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2) + coords = ( + rotated.unsqueeze(1).expand(T, 3, B, 3).gather(-1, idx.view(1, 3, 1, 2).expand(T, 3, B, 2)) ) # Flatten (T, 3) -> 3*T so it matches grid's first dim, and add the H_out=1 axis - coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) + coord_tensor = coords.reshape(3 * T, B, 1, 2) # (3T, B, 1, 2) per_scale_features = [] for plane_coef in ms_grids: @@ -334,13 +346,12 @@ def interpolate_ms_features_tilted( sampled = sampled.squeeze(-1).view(T, 3, C, B).prod(dim=1) # (T, C, B) -> (B, T, C) -> (B, T*C) to concatenate rotations along feature dim - per_scale_features.append( - sampled.permute(2, 0, 1).reshape(B, T * C) - ) + per_scale_features.append(sampled.permute(2, 0, 1).reshape(B, T * C)) # Concatenate across scales -> (B, T * C * num_scales) return torch.cat(per_scale_features, dim=-1) + class KPlanesTILTED(KPlanes): """ K-Planes with T learned SO(3) rotations (TILTED). @@ -392,7 +403,6 @@ def __init__( hybrid_num_layers: int = 2, so3_param_type: str = "r9svd", ): - self._td_type = "tilted" if input_coords_dims != 3: raise NotImplementedError("KPlanesTILTED is implemented for 3D only.") @@ -427,9 +437,7 @@ def __init__( self.grids = nn.ParameterList() for res_mult in multiscale_res_multipliers: scaled_res = [int(r * res_mult) for r in resolution] - plane = nn.Parameter( - torch.empty(3 * T, M_features, scaled_res[1], scaled_res[0]) - ) + plane = nn.Parameter(torch.empty(3 * T, M_features, scaled_res[1], scaled_res[0])) nn.init.uniform_(plane, 0.1, 0.5) self.grids.append(plane) @@ -480,7 +488,7 @@ def _build_sigma_net( def get_densities(self, coords: torch.Tensor) -> torch.Tensor: pts = coords.reshape(-1, 3) - R = self.so3.as_matrix() # (T, 3, 3) + R = self.so3.as_matrix() # (T, 3, 3) features = interpolate_ms_features_tilted( pts=pts, ms_grids=self.grids, @@ -498,9 +506,9 @@ def forward(self, pts: torch.Tensor) -> torch.Tensor: def get_params(self) -> dict[str, list[nn.Parameter]]: return { - "grids": list(self.grids.parameters()), + "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), - "so3": list(self.so3.parameters()), + "so3": list(self.so3.parameters()), } @property @@ -557,7 +565,7 @@ def extra_repr(self) -> str: def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: """ Set the SO3 parameterization type. - + Parameters ---------- so3_param_type : str @@ -573,12 +581,14 @@ def set_so3_param_type(self, so3_param_type: str, init: str = "rand") -> None: @property def tilted(self) -> bool: return True - + + # CP Decomp for Warmup SO3 rotations + def interpolate_ms_features_cp_tilted( - pts: torch.Tensor, # (B, 3) - ms_grids: nn.ParameterList, # each grid: (3*T, C, L) — 1D lines + pts: torch.Tensor, # (B, 3) + ms_grids: nn.ParameterList, # each grid: (3*T, C, L) — 1D lines rotation_matrices: torch.Tensor, # (T, 3, 3) ) -> torch.Tensor: """ @@ -594,7 +604,7 @@ def interpolate_ms_features_cp_tilted( per_scale_features = [] for line_coef in ms_grids: # line_coef: (3T, C, L) — three 1D feature lines per transform (x, y, z) - C, L = line_coef.shape[1], line_coef.shape[2] + C, _ = line_coef.shape[1], line_coef.shape[2] # For each transform t, we need three 1D samples: at x_t, y_t, z_t. # Lay them out as (3T, B) coords, matching line_coef's first dim. @@ -606,17 +616,23 @@ def interpolate_ms_features_cp_tilted( # (3T, C, 1, L) reshape and pass 2D coords with y fixed at 0. # Simpler: use F.grid_sample with a 4D trick, or just do manual linear interp. # Here's the grid_sample way: - line_coef_4d = line_coef.unsqueeze(2) # (3T, C, 1, L) + line_coef_4d = line_coef.unsqueeze(2) # (3T, C, 1, L) # grid: need (3T, Hout=1, Wout=B, 2), with x = coord, y = 0 - grid = torch.stack([ - coords_1d, # x - torch.zeros_like(coords_1d), # y - ], dim=-1).unsqueeze(1) # (3T, 1, B, 2) + grid = torch.stack( + [ + coords_1d, # x + torch.zeros_like(coords_1d), # y + ], + dim=-1, + ).unsqueeze(1) # (3T, 1, B, 2) sampled = F.grid_sample( - line_coef_4d, grid, - align_corners=True, mode="bilinear", padding_mode="border", - ).squeeze(2) # (3T, C, B) + line_coef_4d, + grid, + align_corners=True, + mode="bilinear", + padding_mode="border", + ).squeeze(2) # (3T, C, B) # Hadamard across the 3 axes per transform: (T, 3, C, B) -> (T, C, B) sampled = sampled.view(T, 3, C, B).prod(dim=1) @@ -639,12 +655,13 @@ class CPTilted(PPLR, TensorDecompositionModel): def __init__( self, - C: int = 4, # channels per transform per scale + C: int = 4, # channels per transform per scale resolution: Sequence[int] = (128, 128, 128), multiscale_res_multipliers: Optional[Sequence[int]] = None, T: int = 4, tau_init: str = "random", density_activation: Callable = lambda x: F.softplus(x - 1), + so3_param_type: str = "r9svd", ): super().__init__() self._td_type = "cp_tilted" @@ -670,7 +687,12 @@ def __init__( nn.init.normal_(self.sigma_net.weight, std=0.01) nn.init.zeros_(self.sigma_net.bias) - self.so3 = SO3Param(T, init=tau_init) + if so3_param_type == "r9svd": + self.so3 = SO3ParamR9SVD(T, init=tau_init) + elif so3_param_type == "quat": + self.so3 = SO3ParamQuat(T, init=tau_init) + else: + raise ValueError(f"Unknown SO3 param type: {so3_param_type}") def get_densities(self, coords: torch.Tensor) -> torch.Tensor: pts = coords.reshape(-1, 3) @@ -683,9 +705,9 @@ def forward(self, pts): def get_params(self): return { - "grids": list(self.grids.parameters()), + "grids": list(self.grids.parameters()), "sigma_net": list(self.sigma_net.parameters()), - "so3": list(self.so3.parameters()), + "so3": list(self.so3.parameters()), } @property @@ -698,12 +720,10 @@ def td_type(self) -> str: def extract_tau_state(self) -> torch.Tensor: return self.so3.M.detach().clone() - + @property def tilted(self) -> bool: return True - - -KPlanesType = KPlanes | KPlanesTILTED | CPTilted \ No newline at end of file +KPlanesType = KPlanes | KPlanesTILTED | CPTilted diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index e1a486d7..c7ccf7aa 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -1,7 +1,6 @@ from abc import ABC, abstractmethod from typing import Dict -import torch import torch.nn as nn @@ -48,4 +47,4 @@ class PlanarDecompositionModel(TensorDecompositionModel): grids: nn.ParameterList tilted: bool - resolution: list[int] \ No newline at end of file + resolution: list[int] diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 14f60867..6569ec93 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -9,51 +9,52 @@ # --------------------------------------------------------------------------- # SO(3) quaternion parameter module # --------------------------------------------------------------------------- - + + class SO3ParamQuat(nn.Module): """ Stores T unit quaternions as learnable parameters in R^4 and normalises them on every call to `as_matrix()`. - + Quaternion convention: [x, y, z, w] (scalar-last, same as scipy). - + Initialisation -------------- "random" – uniform sampling over SO(3) via Shoemake's method. "identity" – all rotations start as the identity (good for fine-tuning). """ - + def __init__(self, T: int, init: str = "random"): super().__init__() if T < 1: raise ValueError(f"T must be >= 1, got {T}") - quats = self._init_quaternions(T, init) # (T, 4) + quats = self._init_quaternions(T, init) # (T, 4) self.quats = nn.Parameter(quats) - + # ------------------------------------------------------------------ # Initialisers # ------------------------------------------------------------------ - + @staticmethod def _shoemake_sample(T: int) -> torch.Tensor: """Uniform SO(3) sampling via Shoemake (1992). Returns (T, 4) [x,y,z,w].""" u = torch.rand(T, 3) sqrt1_u0 = torch.sqrt(1.0 - u[:, 0]) - sqrt_u0 = torch.sqrt(u[:, 0]) - two_pi = 2.0 * math.pi + sqrt_u0 = torch.sqrt(u[:, 0]) + two_pi = 2.0 * math.pi x = sqrt1_u0 * torch.sin(two_pi * u[:, 1]) y = sqrt1_u0 * torch.cos(two_pi * u[:, 1]) - z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) - w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) - return torch.stack([x, y, z, w], dim=-1) # (T, 4) - + z = sqrt_u0 * torch.sin(two_pi * u[:, 2]) + w = sqrt_u0 * torch.cos(two_pi * u[:, 2]) + return torch.stack([x, y, z, w], dim=-1) # (T, 4) + @staticmethod def _identity(T: int) -> torch.Tensor: """All-identity rotations: [0,0,0,1] * T.""" q = torch.zeros(T, 4) q[:, 3] = 1.0 return q - + @classmethod def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: if init == "random": @@ -62,38 +63,47 @@ def _init_quaternions(cls, T: int, init: str) -> torch.Tensor: return cls._identity(T) else: raise ValueError(f"Unknown init '{init}'; choose 'random' or 'identity'.") - + # ------------------------------------------------------------------ # Forward helpers # ------------------------------------------------------------------ - + def normalized(self) -> torch.Tensor: """Returns (T, 4) unit quaternions.""" return F.normalize(self.quats, p=2, dim=-1) - + def as_matrix(self) -> torch.Tensor: """ Converts the T stored quaternions to (T, 3, 3) rotation matrices. - + Uses the standard formula; no trig, just multiplications. """ - q = self.normalized() # (T, 4) [x, y, z, w] + q = self.normalized() # (T, 4) [x, y, z, w] x, y, z, w = q.unbind(dim=-1) # each (T,) - + # Precompute products - xx, yy, zz = x*x, y*y, z*z - xy, xz, yz = x*y, x*z, y*z - wx, wy, wz = w*x, w*y, w*z - + xx, yy, zz = x * x, y * y, z * z + xy, xz, yz = x * y, x * z, y * z + wx, wy, wz = w * x, w * y, w * z + # Row-major: R[i,j] - R = torch.stack([ - 1 - 2*(yy + zz), 2*(xy - wz), 2*(xz + wy), - 2*(xy + wz), 1 - 2*(xx + zz), 2*(yz - wx), - 2*(xz - wy), 2*(yz + wx), 1 - 2*(xx + yy), - ], dim=-1).reshape(-1, 3, 3) # (T, 3, 3) - + R = torch.stack( + [ + 1 - 2 * (yy + zz), + 2 * (xy - wz), + 2 * (xz + wy), + 2 * (xy + wz), + 1 - 2 * (xx + zz), + 2 * (yz - wx), + 2 * (xz - wy), + 2 * (yz + wx), + 1 - 2 * (xx + yy), + ], + dim=-1, + ).reshape(-1, 3, 3) # (T, 3, 3) + return R - + def extra_repr(self) -> str: return f"T={self.quats.shape[0]}" @@ -115,16 +125,14 @@ def __init__(self, T: int, init: str = "random"): M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) else: raise ValueError(f"Unknown init '{init}'") - self.M = nn.Parameter(M) # (T, 3, 3) + self.M = nn.Parameter(M) # (T, 3, 3) def as_matrix(self) -> torch.Tensor: """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" - - U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) + U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) # Fix reflections: det(U Vh) must be +1 - d = torch.det(U @ Vh) # (T,) + d = torch.det(U @ Vh) # (T,) diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) - diag[:, 2] = d # multiply last singular vector by sign - return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) - + diag[:, 2] = d # multiply last singular vector by sign + return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 4c97181a..b5053a4c 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,7 +1,7 @@ import textwrap from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Generator, Iterator, Literal, Sequence +from typing import TYPE_CHECKING, Any, Generator, Iterable, Literal from quantem.core import config @@ -581,11 +581,11 @@ def scheduler_params(self, params: SchedulerType | dict): @abstractmethod def get_optimization_parameters( self, - ) -> "torch.Tensor | Sequence[torch.Tensor] | Iterator[torch.Tensor]": + ) -> "Iterable[torch.Tensor] | Iterable[dict[str, Any]]": """ Get the parameters that should be optimized for this model. This could be replaced with just module.parameters(), but this allows for flexibility - in the future to allow for per parameter LRs. + in the future to allow for per parameter LRs. # NOTE: Cl 4/27/26 updated to iterable type-hint. """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") @@ -639,16 +639,17 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: optimizer_cls = self._optimizer_class_for(self._optimizer_params) self._optimizer = optimizer_cls(params, **self._optimizer_params.params()) - def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: match opt_params: - case OptimizerParams.Adam(): return torch.optim.Adam - case OptimizerParams.AdamW(): return torch.optim.AdamW - case OptimizerParams.SGD(): return torch.optim.SGD + case OptimizerParams.Adam(): + return torch.optim.Adam + case OptimizerParams.AdamW(): + return torch.optim.AdamW + case OptimizerParams.SGD(): + return torch.optim.SGD case _: raise NotImplementedError(f"Unknown optimizer type: {opt_params}") - def set_scheduler( self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None ) -> None: diff --git a/src/quantem/core/visualization/visualization.py b/src/quantem/core/visualization/visualization.py index 77f647b4..e252caa6 100644 --- a/src/quantem/core/visualization/visualization.py +++ b/src/quantem/core/visualization/visualization.py @@ -40,10 +40,7 @@ # There might be a cleaner way to do this, but better to have it here than in the functions NormInputCell: TypeAlias = NormalizationConfig | ShowParams.Norm | dict | str Show2dNormInput: TypeAlias = ( - NormInputCell - | None - | Sequence[NormInputCell] - | Sequence[Sequence[NormInputCell]] + NormInputCell | None | Sequence[NormInputCell] | Sequence[Sequence[NormInputCell]] ) ScalebarInputCell: TypeAlias = ScalebarConfig | ShowParams.Scalebar | dict | bool | None @@ -57,7 +54,7 @@ | Sequence[Sequence[ScalebarInputCell]] ) -CmapType: TypeAlias = str | colors.Colormap +CmapType: TypeAlias = str | colors.Colormap def _show_2d_array( diff --git a/src/quantem/diffractive_imaging/ptychography_visualizations.py b/src/quantem/diffractive_imaging/ptychography_visualizations.py index fe8f84bf..8975df02 100644 --- a/src/quantem/diffractive_imaging/ptychography_visualizations.py +++ b/src/quantem/diffractive_imaging/ptychography_visualizations.py @@ -1,4 +1,3 @@ - import warnings from typing import Any, Literal @@ -891,6 +890,7 @@ def show_scan_positions( if conv_angle is not None and energy is not None: from quantem.core.utils.utils import electron_wavelength_angstrom + wavelength = electron_wavelength_angstrom(energy) conv_angle_rad = conv_angle * 1e-3 # For defocused probe: radius ≈ |defocus| * convergence_angle + diffraction_limit diff --git a/src/quantem/imaging/drift.py b/src/quantem/imaging/drift.py index 424e18e6..ba29e417 100644 --- a/src/quantem/imaging/drift.py +++ b/src/quantem/imaging/drift.py @@ -1,9 +1,9 @@ +import warnings from collections.abc import Sequence from typing import List, Optional, Union import matplotlib.pyplot as plt import numpy as np -import warnings from numpy.typing import NDArray from scipy.interpolate import interp1d from scipy.ndimage import distance_transform_edt, gaussian_filter diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index e3b61377..24f0f775 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -649,11 +649,14 @@ def save_parameters(self, path: str): """ Saves the learned parameters to a file. """ - torch.save({ - "z1": self._z1_params.detach().cpu(), - "z3": self._z3_params.detach().cpu(), - "shifts": self._shifts_params.detach().cpu(), - }, path) + torch.save( + { + "z1": self._z1_params.detach().cpu(), + "z3": self._z3_params.detach().cpu(), + "shifts": self._shifts_params.detach().cpu(), + }, + path, + ) def load_parameters(self, path: str): """ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 2bc8fa4a..8f254533 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Generator +from typing import Any, Callable, Generator, Optional import numpy as np import torch @@ -14,11 +14,11 @@ from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.models.model_base import PPLR, PlanarDecompositionModel +from quantem.core.ml.models.model_base import PlanarDecompositionModel from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -from quantem.tomography.tomography import ReconstructionContext as ctx +from quantem.tomography.tomography import ReconstructionContext class ObjConstraintParams: @@ -128,7 +128,7 @@ class ObjTensorDecompConstraints(Constraints): shrinkage: float = 0.0 tv_vol: float = 0.0 tv_plane: float = 0.0 - sparsity:float = 0.0 + sparsity: float = 0.0 _name: str = "obj_tensor_decomp" soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] @@ -192,7 +192,7 @@ def parse_dict( ObjConstraintsType = ( - ObjConstraintParams.ObjPixelatedConstraints + ObjConstraintParams.ObjPixelatedConstraints | ObjConstraintParams.ObjINRConstraints | ObjConstraintParams.ObjTensorDecompConstraints ) @@ -204,6 +204,7 @@ def _unwrap(model: nn.Module | nn.parallel.DistributedDataParallel) -> PlanarDec return model.module return model + class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): DEFAULT_LRS = { "object": 8e-6, @@ -212,6 +213,7 @@ class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): """ Base class for all ObjectModels to inherit from. """ + def __init__( self, shape: tuple[int, int, int], # pyright: ignore[reportRedeclaration] @@ -231,91 +233,92 @@ def __init__( # --- Instantiation ---- - # --- Properties --- - @property - def shape(self) -> tuple[int, int, int]: - """ - Shape of the object (x, y, z). - """ - return self._shape - - @shape.setter - def shape(self, new_shape: tuple[int, int, int]): - self._shape = new_shape - - @property - def obj(self) -> torch.Tensor: - """ - Returns the object, should be implemented in subclasses. - """ - raise NotImplementedError - - @property - def model(self) -> nn.Module: - """ - Returns the model, should be implemented in subclasses. - """ - raise NotImplementedError - - @abstractmethod - def dtype(self) -> torch.dtype: - """ - Returns the dtype of the object. - """ - raise NotImplementedError - - @abstractmethod - def forward(self, *args, **kwargs) -> torch.Tensor: - """ - Forward pass, should be implemented in subclasses. Note for any nn.Module this is - a required method. - """ - raise NotImplementedError - - @abstractmethod - def reset(self) -> None: - """ - Reset the object, should be implemented in subclasses. - """ - raise NotImplementedError - - @property - def params(self) -> Generator[torch.nn.Parameter, None, None]: - """ - Get the parameters that should be optimized for this model. - - Should be implemented in subclasses. - """ - raise NotImplementedError - - # --- Helper Functions --- - def get_optimization_parameters(self) -> list[nn.Parameter]: - """ - Get the parameters that should be optimized for this model. - """ - return list(self.params) - - @abstractmethod # Each subclass should implement this. - def to(self, *args, **kwargs): - """ - Move the object to a device - """ - - raise NotImplementedError + # --- Properties --- + @property + def shape(self) -> tuple[int, int, int]: + """ + Shape of the object (x, y, z). + """ + return self._shape + + @shape.setter + def shape(self, new_shape: tuple[int, int, int]): + self._shape = new_shape + + @property + def obj(self) -> torch.Tensor: + """ + Returns the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @property + def model(self) -> nn.Module: + """ + Returns the model, should be implemented in subclasses. + """ + raise NotImplementedError + + @abstractmethod + def dtype(self) -> torch.dtype: + """ + Returns the dtype of the object. + """ + raise NotImplementedError + + @abstractmethod + def forward(self, coords: Optional[torch.Tensor] = None) -> torch.Tensor: + """ + Forward pass, should be implemented in subclasses. Note for any nn.Module this is + a required method. + """ + raise NotImplementedError + + @abstractmethod + def reset(self) -> None: + """ + Reset the object, should be implemented in subclasses. + """ + raise NotImplementedError + + @property + def params(self) -> Generator[torch.nn.Parameter, None, None]: + """ + Get the parameters that should be optimized for this model. + + Should be implemented in subclasses. + """ + raise NotImplementedError + + # --- Helper Functions --- + def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: + """ + Get the parameters that should be optimized for this model. + """ + return list(self.params) + + @abstractmethod # Each subclass should implement this. + def to(self, device: str | torch.device): + """ + Move the object to a device + """ + + raise NotImplementedError + class ObjectConstraints(BaseConstraints, ObjectBase): # TODO: Ask Arthur why we still need this def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @abstractmethod - def get_tv_loss(self, **kwargs) -> torch.Tensor: + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: """ Get the TV loss for the object model. Must be implemented in each subclass. """ raise NotImplementedError -class ObjectPixelated(ObjectConstraints): +class ObjectPixelated(ObjectConstraints): """ Object model for pixelated objects. @@ -382,14 +385,6 @@ def obj(self, obj: torch.Tensor): def obj_view(self) -> np.ndarray: return self.obj.cpu().unsqueeze(0).numpy() - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape - @property def soft_loss(self) -> torch.Tensor: return self.apply_soft_constraints(self._obj) @@ -424,30 +419,32 @@ def apply_hard_constraints( # TODO: Need to implement the other hard constraints: Fourier Filter and Circular Mask. return obj2 - def apply_soft_constraints(self, obj: torch.Tensor) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=obj.device, dtype=obj.dtype, requires_grad=True) + def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: + assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" + soft_loss = torch.tensor(0.0, device=ctx.obj.device, dtype=ctx.obj.dtype, requires_grad=True) if self.constraints.tv_vol > 0: tv_loss = self.get_tv_loss( - obj.unsqueeze(0).unsqueeze(0), tv_weight=self.constraints.tv_vol + ctx ) soft_loss += tv_loss return soft_loss # --- Forward method --- - def forward(self, dummy_input=None) -> torch.Tensor: + def forward(self, coords=None) -> torch.Tensor: return self.obj # --- Defining the TV loss --- - def get_tv_loss(self, obj: torch.Tensor, tv_weight: float = 1e-3) -> torch.Tensor: # pyright: ignore[reportIncompatibleMethodOverride] -> get_tv_loss has different arguments depending on the object. - tv_d = torch.pow(obj[:, :, 1:, :, :] - obj[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(obj[:, :, :, 1:, :] - obj[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(obj[:, :, :, :, 1:] - obj[:, :, :, :, :-1], 2).sum() + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" + tv_d = torch.pow(ctx.obj[:, :, 1:, :, :] - ctx.obj[:, :, :-1, :, :], 2).sum() + tv_h = torch.pow(ctx.obj[:, :, :, 1:, :] - ctx.obj[:, :, :, :-1, :], 2).sum() + tv_w = torch.pow(ctx.obj[:, :, :, :, 1:] - ctx.obj[:, :, :, :, :-1], 2).sum() tv_loss = tv_d + tv_h + tv_w - return tv_loss * tv_weight / (torch.prod(torch.tensor(obj.shape))) + return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(obj.shape))) # --- Helper Functions --- - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change + def to(self, device: str | torch.device): if isinstance(device, str): device = torch.device(device) self._device = device @@ -455,8 +452,10 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.reconnect_optimizer_to_parameters() return self + class ObjectINR(ObjectConstraints, DDPMixin): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjINRConstraints() + def __init__( self, shape: tuple[int, int, int], @@ -884,8 +883,10 @@ def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleM self.distribute_model(self._model) self.reconnect_optimizer_to_parameters() + class ObjectTensorDecomp(ObjectINR): DEFAULT_CONSTRAINTS = ObjConstraintParams.ObjTensorDecompConstraints() + def __init__( self, shape: tuple[int, int, int], @@ -902,7 +903,9 @@ def __init__( ) self._pretrain_losses = [] self._pretrain_lrs = [] - self.constraints: ObjConstraintParams.ObjTensorDecompConstraints = self.DEFAULT_CONSTRAINTS.copy() + self.constraints: ObjConstraintParams.ObjTensorDecompConstraints = ( + self.DEFAULT_CONSTRAINTS.copy() + ) # Register the network submodule (important: real nn.Module attribute) if model is not None: self.setup_distributed(device=device) @@ -935,7 +938,6 @@ def apply_soft_constraints( all_densities: torch.Tensor, pred: torch.Tensor, ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=pred.device) if self.constraints.tv_vol > 0: soft_loss += self.get_tv_loss(coords, pred) @@ -949,28 +951,26 @@ def apply_soft_constraints( # TV Losses def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=pred.device) tv_loss += self._get_plane_tv_loss() tv_loss += self.get_volume_tv_loss(coords) return tv_loss - def _get_plane_tv_loss(self) -> torch.Tensor: is_tilted = self.model.tilted per_level = [] - + model = _unwrap(self.model) for p in model.grids: # p: (3*T, C, H, W) for TILTED, (3, C, H, W) for KPlanes dh = (p[:, :, 1:, :] - p[:, :, :-1, :]).pow(2).mean(dim=(1, 2, 3)) dw = (p[:, :, :, 1:] - p[:, :, :, :-1]).pow(2).mean(dim=(1, 2, 3)) - per_plane = dh + dw # (3*T,) or (3,) + per_plane = dh + dw # (3*T,) or (3,) if is_tilted: T = self.model.T - per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation - level_tv = per_rotation.mean() # avg across rotations + per_rotation = per_plane.view(T, 3).sum(dim=1) # sum 3 planes per rotation + level_tv = per_rotation.mean() # avg across rotations else: level_tv = per_plane.sum() @@ -978,7 +978,6 @@ def _get_plane_tv_loss(self) -> torch.Tensor: return self.constraints.tv_plane * torch.stack(per_level).sum() - def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: """ Isotropic volume TV via finite differences. Same form as the autograd @@ -987,7 +986,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: """ num_tv_samples = min(10_000, coords.shape[0]) tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - tv_coords = coords[tv_indices] # (N, 3) + tv_coords = coords[tv_indices] # (N, 3) model = _unwrap(self.model) h = 2.0 / min(model.resolution) @@ -996,7 +995,7 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: if isinstance(pred, tuple): pred = pred[0] if pred.dim() == 1: - pred = pred.unsqueeze(-1) # (N, 1) + pred = pred.unsqueeze(-1) # (N, 1) grads = [] for axis in range(3): @@ -1007,10 +1006,10 @@ def get_volume_tv_loss(self, coords: torch.Tensor) -> torch.Tensor: shifted_pred = shifted_pred[0] if shifted_pred.dim() == 1: shifted_pred = shifted_pred.unsqueeze(-1) - grads.append((shifted_pred - pred) / h) # (N, 1) + grads.append((shifted_pred - pred) / h) # (N, 1) - grad_stack = torch.stack(grads, dim=-1) # (N, C, 3) - grad_norm = torch.norm(grad_stack, dim=-1) # (N, C) + grad_stack = torch.stack(grads, dim=-1) # (N, C, 3) + grad_norm = torch.norm(grad_stack, dim=-1) # (N, C) return self.constraints.tv_vol * grad_norm.mean() @@ -1032,21 +1031,19 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: """ Returns the optimization parameters, here we also check if PPLR is used and return the appropriate parameters. """ - + return self.model.parameters() # type: ignore[attr-defined] def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - - model = _unwrap (self.model) + model = _unwrap(self.model) return [ { - "params": model.get_params()[key], + "params": model.get_params()[key], **self.optimizer_params[key].params(), } for key in model.param_keys ] - # --- Optimizer Mixin Overloads in the case of PPLR --- @property @@ -1060,11 +1057,13 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di if not isinstance(params, dict): raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - + object_params = params - + if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError(f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}") + raise ValueError( + f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}" + ) params = {} for key, value in object_params.items(): @@ -1073,31 +1072,33 @@ def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | di elif isinstance(value, OptimizerType): params[key] = value else: - raise TypeError(f"optimizer parameters must be a dict or OptimizerType, got {type(value)}") + raise TypeError( + f"optimizer parameters must be a dict or OptimizerType, got {type(value)}" + ) self._optimizer_params = params def reconnect_optimizer_to_parameters(self) -> None: - if self.optimizer is None: return - + current_params = self.get_optimization_parameters() - optimizable_params = [ - p for p in current_params - if isinstance(p['params'][0], torch.Tensor) and p['params'][0].is_leaf + p + for p in current_params + if isinstance(p["params"][0], torch.Tensor) and p["params"][0].is_leaf ] - if not optimizable_params: - raise ValueError(f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}.") - + raise ValueError( + f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}." + ) + for p in optimizable_params: print(f"Setting requires_grad for parameter: {p}") - p['params'][0].requires_grad_(True) - + p["params"][0].requires_grad_(True) + assert self._optimizer is not None # Preserve optimizer states and param_group settings old_state = self._optimizer.state.copy() @@ -1132,7 +1133,9 @@ def reconnect_optimizer_to_parameters(self) -> None: self._scheduler.optimizer = self._optimizer def pretrain(self) -> None: - raise NotImplementedError("Tensor decomposition pretraining is not usually required, and for TILTED there is a two-phase warmup approach.") - + raise NotImplementedError( + "Tensor decomposition pretraining is not usually required, and for TILTED there is a two-phase warmup approach." + ) + ObjectModelType = ObjectPixelated | ObjectINR diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index f21a30d1..899f1a1d 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -182,7 +182,9 @@ def reconstruct( consistency_loss = torch.tensor(0.0, device=self.device) total_loss = torch.tensor(0.0, device=self.device) epoch_soft_constraint_loss = torch.tensor(0.0, device=self.device) - if isinstance(self.obj_model, ObjectINR) or isinstance(self.obj_model, ObjectTensorDecomp): + if isinstance(self.obj_model, ObjectINR) or isinstance( + self.obj_model, ObjectTensorDecomp + ): self.obj_model.model.train() else: raise NotImplementedError( @@ -217,7 +219,9 @@ def reconstruct( ) pred = integrated_densities.float() - soft_constraints_loss = self.obj_model.apply_soft_constraints(all_coords, all_densities, pred) + soft_constraints_loss = self.obj_model.apply_soft_constraints( + all_coords, all_densities, pred + ) target = batch["target_value"].to(self.device, non_blocking=True).float() @@ -243,7 +247,7 @@ def reconstruct( R_now = self.obj_model.model.so3.as_matrix().detach() # Cumulative angular change per rotation over the last 20 iters. # trace(R_prev^T R_now) = 1 + 2*cos(theta), so theta = acos((trace - 1) / 2). - rel_trace = torch.einsum('tij,tij->t', prev_R, R_now) + rel_trace = torch.einsum("tij,tij->t", prev_R, R_now) angle = torch.acos(((rel_trace - 1) / 2).clamp(-1, 1)) # (T,) radians angle_deg = torch.rad2deg(angle) per_tau_str = ", ".join(f"{a:.2f}°" for a in angle_deg.tolist()) @@ -636,10 +640,10 @@ class ReconstructionContext: Subclasses will pick whatever parameter they need - Pixelated reads ".volume" - INR reads ".coords" and recomputes via the model. - - TEnsorDEcomp reads ".coords" and ".pred" (and ".all densities") + - TensorDecomp reads ".coords" and ".pred" (and ".all densities") """ coords: Optional[torch.Tensor] = None pred: Optional[torch.Tensor] = None all_densities: Optional[torch.Tensor] = None - volume: Optional[torch.Tensor] = None \ No newline at end of file + obj: Optional[torch.Tensor] = None diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 0f8c44be..c75de1e3 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -2,7 +2,7 @@ import torch -from quantem.core.ml.optimizer_mixin import OptimizerParams, OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import OptimizerType, SchedulerType from quantem.tomography.tomography_base import TomographyBase diff --git a/tests/datastructures/test_dataset3d_show.py b/tests/datastructures/test_dataset3d_show.py index 27c18f41..7938c66f 100644 --- a/tests/datastructures/test_dataset3d_show.py +++ b/tests/datastructures/test_dataset3d_show.py @@ -31,16 +31,19 @@ def extract_frame_indices_from_figure(fig): class TestShowInputValidation: """Test that invalid inputs raise clear errors.""" - @pytest.mark.parametrize("kwargs,match", [ - ({"step": 0}, "cannot be zero"), - ({"start": 100}, "out of bounds"), - ({"start": -100}, "out of bounds"), - ({"start": 5, "end": 5}, "No frames to display"), - ({"ncols": 0}, "ncols must be >= 1"), - ({"ncols": -1}, "ncols must be >= 1"), - ({"max": 0}, "max must be >= 1"), - ({"max": -1}, "max must be >= 1"), - ]) + @pytest.mark.parametrize( + "kwargs,match", + [ + ({"step": 0}, "cannot be zero"), + ({"start": 100}, "out of bounds"), + ({"start": -100}, "out of bounds"), + ({"start": 5, "end": 5}, "No frames to display"), + ({"ncols": 0}, "ncols must be >= 1"), + ({"ncols": -1}, "ncols must be >= 1"), + ({"max": 0}, "max must be >= 1"), + ({"max": -1}, "max must be >= 1"), + ], + ) def test_raises_value_error(self, dataset_with_10_frames, kwargs, match): with pytest.raises(ValueError, match=match): dataset_with_10_frames.show(**kwargs) @@ -49,39 +52,44 @@ def test_raises_value_error(self, dataset_with_10_frames, kwargs, match): class TestShowFrameSelection: """Test frame selection with start, end, step, max combinations.""" - @pytest.mark.parametrize("kwargs,expected_indices", [ - ({}, list(range(20))), - ({"max": 5}, [0, 1, 2, 3, 4]), - ({"max": None}, list(range(100))), - ({"start": 90}, list(range(90, 100))), - ({"start": 95, "max": 3}, [95, 96, 97]), - ]) + @pytest.mark.parametrize( + "kwargs,expected_indices", + [ + ({}, list(range(20))), + ({"max": 5}, [0, 1, 2, 3, 4]), + ({"max": None}, list(range(100))), + ({"start": 90}, list(range(90, 100))), + ({"start": 95, "max": 3}, [95, 96, 97]), + ], + ) def test_large_dataset(self, dataset_with_100_frames, kwargs, expected_indices): fig, _ = dataset_with_100_frames.show(returnfig=True, **kwargs) assert extract_frame_indices_from_figure(fig) == expected_indices plt.close(fig) - @pytest.mark.parametrize("kwargs,expected_indices", [ - # Default shows all frames (< max) - ({}, list(range(10))), - # Start and end - ({"start": 5}, [5, 6, 7, 8, 9]), - ({"end": 5}, [0, 1, 2, 3, 4]), - # Step - ({"step": 2}, [0, 2, 4, 6, 8]), - ({"step": 3}, [0, 3, 6, 9]), - ({"start": 2, "end": 8, "step": 2}, [2, 4, 6]), - # Negative start index - ({"start": -1, "max": 1}, [9]), - ({"start": -3, "max": 2}, [7, 8]), - # Negative step (reverse order) - ({"start": 9, "step": -1}, [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]), - ({"start": 9, "end": 4, "step": -1}, [9, 8, 7, 6, 5]), - ({"start": 9, "step": -2}, [9, 7, 5, 3, 1]), - ({"start": 9, "step": -1, "max": 3}, [9, 8, 7]), - ]) + @pytest.mark.parametrize( + "kwargs,expected_indices", + [ + # Default shows all frames (< max) + ({}, list(range(10))), + # Start and end + ({"start": 5}, [5, 6, 7, 8, 9]), + ({"end": 5}, [0, 1, 2, 3, 4]), + # Step + ({"step": 2}, [0, 2, 4, 6, 8]), + ({"step": 3}, [0, 3, 6, 9]), + ({"start": 2, "end": 8, "step": 2}, [2, 4, 6]), + # Negative start index + ({"start": -1, "max": 1}, [9]), + ({"start": -3, "max": 2}, [7, 8]), + # Negative step (reverse order) + ({"start": 9, "step": -1}, [9, 8, 7, 6, 5, 4, 3, 2, 1, 0]), + ({"start": 9, "end": 4, "step": -1}, [9, 8, 7, 6, 5]), + ({"start": 9, "step": -2}, [9, 7, 5, 3, 1]), + ({"start": 9, "step": -1, "max": 3}, [9, 8, 7]), + ], + ) def test_small_dataset(self, dataset_with_10_frames, kwargs, expected_indices): fig, _ = dataset_with_10_frames.show(returnfig=True, **kwargs) assert extract_frame_indices_from_figure(fig) == expected_indices plt.close(fig) - From a061845add3789341bf635cc66cf0867398b05d9 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 11:24:01 -0700 Subject: [PATCH 264/335] Added pyrightconfig.json to gitignore, PrivateImportUsage error annoying --- .gitignore | 3 +++ src/quantem/tomography/object_models.py | 17 +++++++++-------- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/.gitignore b/.gitignore index d87d85c8..a66c803d 100644 --- a/.gitignore +++ b/.gitignore @@ -167,6 +167,9 @@ cython_debug/ # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ +# BasedPyRight Config +pyrightconfig.json + # Ruff stuff: .ruff_cache/ diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 8f254533..5d5d8b1e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -260,6 +260,7 @@ def model(self) -> nn.Module: raise NotImplementedError @abstractmethod + @property def dtype(self) -> torch.dtype: """ Returns the dtype of the object. @@ -385,9 +386,9 @@ def obj(self, obj: torch.Tensor): def obj_view(self) -> np.ndarray: return self.obj.cpu().unsqueeze(0).numpy() - @property - def soft_loss(self) -> torch.Tensor: - return self.apply_soft_constraints(self._obj) + # @property + # def soft_loss(self) -> torch.Tensor: + # return self.apply_soft_constraints(self._obj) @property def name(self) -> str: @@ -421,11 +422,11 @@ def apply_hard_constraints( def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" - soft_loss = torch.tensor(0.0, device=ctx.obj.device, dtype=ctx.obj.dtype, requires_grad=True) + soft_loss = torch.tensor( + 0.0, device=ctx.obj.device, dtype=ctx.obj.dtype, requires_grad=True + ) if self.constraints.tv_vol > 0: - tv_loss = self.get_tv_loss( - ctx - ) + tv_loss = self.get_tv_loss(ctx) soft_loss += tv_loss return soft_loss @@ -441,7 +442,7 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: tv_w = torch.pow(ctx.obj[:, :, :, :, 1:] - ctx.obj[:, :, :, :, :-1], 2).sum() tv_loss = tv_d + tv_h + tv_w - return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(obj.shape))) + return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(ctx.obj.shape))) # --- Helper Functions --- def to(self, device: str | torch.device): From 23cfd72de885c7e86c5b992f190d9fb49518ac70 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 11:35:35 -0700 Subject: [PATCH 265/335] ObjectINR implemented --- src/quantem/tomography/object_models.py | 103 +++++++++--------------- 1 file changed, 40 insertions(+), 63 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 5d5d8b1e..19c69e3e 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -259,7 +259,6 @@ def model(self) -> nn.Module: """ raise NotImplementedError - @abstractmethod @property def dtype(self) -> torch.dtype: """ @@ -537,40 +536,19 @@ def obj_view(self) -> np.ndarray: def apply_soft_constraints( self, - coords: torch.Tensor, - pred: torch.Tensor, + ctx: ReconstructionContext, ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=pred.device) + soft_loss = torch.tensor(0.0, device=ctx.coords.device) if self.constraints.tv_vol > 0: - num_tv_samples = min(10_000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - tv_densities_recomputed = self.model(tv_coords) - if isinstance(tv_densities_recomputed, tuple): - tv_densities_recomputed = tv_densities_recomputed[0] - - # Ensure shape is [num_samples, num_channels] - if tv_densities_recomputed.dim() == 1: - tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) - - # Compute gradients for each channel - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] # Shape: [num_samples, coord_dim] - - # Compute TV loss - gradient magnitude per sample - grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] - soft_loss += self.constraints.tv_vol * grad_norm.mean() + assert ctx.coords is not None, "coords must be provided for INR object model to compute the TV loss" + soft_loss += self.get_tv_loss(ctx) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * torch.norm(pred, p=1) + assert ctx.pred is not None, "pred must be provided for INR object model to compute the sparsity loss" + sparsity_loss = self.constraints.sparsity * torch.norm(ctx.pred, p=1) soft_loss += sparsity_loss return soft_loss @@ -587,6 +565,37 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: return pred + # --- Define get_tv_loss --- + + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + """ + Compute the total variation loss for the INR model. + """ + assert ctx.coords is not None, "coords must be provided for INR object model" + num_tv_samples = min(10_000, ctx.coords.shape[0]) + tv_indices = torch.randperm(ctx.coords.shape[0], device=ctx.coords.device)[:num_tv_samples] + + tv_coords = ctx.coords[tv_indices].detach().requires_grad_(True) + tv_densities_recomputed = self.model(tv_coords) + if isinstance(tv_densities_recomputed, tuple): + tv_densities_recomputed = tv_densities_recomputed[0] + + # Ensure shape is [num_samples, num_channels] + if tv_densities_recomputed.dim() == 1: + tv_densities_recomputed = tv_densities_recomputed.unsqueeze(-1) + + # Compute gradients for each channel + grad_outputs = torch.autograd.grad( + outputs=tv_densities_recomputed, + inputs=tv_coords, + grad_outputs=torch.ones_like(tv_densities_recomputed), + create_graph=True, + )[0] # Shape: [num_samples, coord_dim] + + # Compute TV loss - gradient magnitude per sample + grad_norm = torch.norm(grad_outputs, dim=1) # Shape: [num_samples] + return self.constraints.tv_vol * grad_norm.mean() + # --- Optimization Parameters --- @property def params(self) -> Generator[torch.nn.Parameter, None, None]: @@ -625,13 +634,6 @@ def dtype(self) -> torch.dtype: # TODO: This is a temporary solution to get the dtype of the object. return torch.float32 - @property - def shape(self) -> tuple[int, int, int]: - return self._shape - - @shape.setter - def shape(self, shape: tuple[int, int, int]): - self._shape = shape # --- Helper Functions --- def rebuild_model(self): @@ -647,8 +649,10 @@ def reset(self): # --- Forward Method --- - def forward(self, coords: torch.Tensor) -> torch.Tensor: + def forward(self, coords: Optional[torch.Tensor] = None) -> torch.Tensor: """forward pass for the INR model""" + assert coords is not None, "ObjectINR.forward requires coords" + all_densities = self.model(coords) if all_densities.dim() > 1: @@ -846,33 +850,6 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def get_tv_loss( # pyright: ignore[reportIncompatibleMethodOverride] - self, - coords: torch.Tensor, - ) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=coords.device) - - num_tv_samples = min(10000, coords.shape[0]) - tv_indices = torch.randperm(coords.shape[0], device=coords.device)[:num_tv_samples] - - tv_coords = coords[tv_indices].detach().requires_grad_(True) - - tv_densities_recomputed = self.forward(tv_coords) - - if tv_densities_recomputed.dim() > 1: - tv_densities_recomputed = tv_densities_recomputed.squeeze(-1) - - grad_outputs = torch.autograd.grad( - outputs=tv_densities_recomputed, - inputs=tv_coords, - grad_outputs=torch.ones_like(tv_densities_recomputed), - create_graph=True, - )[0] - - grad_norm = torch.norm(grad_outputs, dim=1) - - tv_loss += self.constraints.tv_vol * grad_norm.mean() - return tv_loss def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change if isinstance(device, str): From fb07d14f919d1a7f0133d51432f85d52ab089342 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 13:34:16 -0700 Subject: [PATCH 266/335] Claude OptimizerMixin changes to account for different optimizable params. reconnect_optimizer_to_parameters, and optimizer_params changes --- src/quantem/core/ml/models/model_base.py | 2 +- src/quantem/core/ml/optimizer_mixin.py | 113 +++++++-------- src/quantem/tomography/object_models.py | 166 +++++++---------------- 3 files changed, 110 insertions(+), 171 deletions(-) diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index c7ccf7aa..503f61bb 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -37,7 +37,7 @@ class TensorDecompositionModel(nn.Module, ABC): td_type: str -class PlanarDecompositionModel(TensorDecompositionModel): +class PlanarDecompositionModel(TensorDecompositionModel, PPLR): """ Planar factored-grid models: K-Planes, K-Planes-TILTED, HexPlane, tri-planes. diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index b5053a4c..9655eb76 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -1,7 +1,7 @@ import textwrap from abc import abstractmethod from dataclasses import dataclass -from typing import TYPE_CHECKING, Any, Generator, Iterable, Literal +from typing import TYPE_CHECKING, Any, Literal from quantem.core import config @@ -536,7 +536,9 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params: OptimizerType = OptimizerParams.NoneOptimizer() + self._optimizer_params: OptimizerType | dict[str, OptimizerType] = ( + OptimizerParams.NoneOptimizer() + ) self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @@ -551,18 +553,36 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> OptimizerType: + def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter - def optimizer_params(self, params: OptimizerType | dict): - """Set the optimizer parameters.""" + def optimizer_params( + self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any] + ) -> None: + self._optimizer_params = self._normalize_optimizer_params(params) + + def _normalize_optimizer_params( + self, params: OptimizerType | dict[str, Any] + ) -> OptimizerType | dict[str, OptimizerType]: + """Normalize input. Subclasses can override to validate keys.""" + # dict-of-OptimizerType form (PPLR) + if isinstance(params, dict) and not self._is_single_optimizer_dict(params): + return { + k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) + for k, v in params.items() + } + # Single optimizer form (with dict shorthand like {"name": "adam", "lr": 1e-3}) if isinstance(params, dict): params = OptimizerParams.parse_dict(d=params) if not isinstance(params, OptimizerType): - raise TypeError(f"optimizer parameters must be a OptimizerType, got {type(params)}") - self._optimizer_params = params + raise TypeError(f"optimizer_params must be OptimizerType or dict, got {type(params)}") + return params + + @staticmethod + def _is_single_optimizer_dict(d: dict) -> bool: + return "type" in d or "name" in d @property def scheduler_params(self) -> SchedulerType: @@ -581,7 +601,7 @@ def scheduler_params(self, params: SchedulerType | dict): @abstractmethod def get_optimization_parameters( self, - ) -> "Iterable[torch.Tensor] | Iterable[dict[str, Any]]": + ) -> "list[dict[str, Any]]": """ Get the parameters that should be optimized for this model. This could be replaced with just module.parameters(), but this allows for flexibility @@ -606,16 +626,11 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self.remove_optimizer() return - params = self.get_optimization_parameters() - if isinstance(params, torch.Tensor): - params = [params] - elif isinstance(params, Generator): - params = list(params) + params = self.get_optimization_parameters() # always list[dict] # Ensure parameters require gradients for group in params: - tensors = group["params"] if isinstance(group, dict) else [group] - for p in tensors: + for p in group["params"]: p.requires_grad_(True) # Figure out which optimizer class to use if isinstance(self._optimizer_params, dict): @@ -745,56 +760,44 @@ def reconnect_optimizer_to_parameters(self) -> None: if self._optimizer is None: return - current_params = self.get_optimization_parameters() - if isinstance(current_params, torch.Tensor): - current_params = [current_params] - elif isinstance(current_params, Generator): - current_params = list(current_params) - - optimizable_params = [ - p for p in current_params if isinstance(p, torch.Tensor) and p.is_leaf - ] - - if not optimizable_params: - print( - f"shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}, removing optimizer" - ) + new_groups = self.get_optimization_parameters() + if not new_groups: + print(f"No optimizable parameters for {type(self).__name__}, removing optimizer") self.remove_optimizer() return - for p in optimizable_params: - p.requires_grad_(True) + # Ensure leaf params with grad + for group in new_groups: + for p in group["params"]: + if not p.is_leaf: + raise ValueError("Non-leaf tensor in param group; build groups from leaves") + p.requires_grad_(True) - # Preserve optimizer state and param_group settings - old_state = self._optimizer.state.copy() - current_param_group = self._optimizer.param_groups[0].copy() + old_state = dict(self._optimizer.state) + old_hyperparams = [ + {k: v for k, v in pg.items() if k != "params"} for pg in self._optimizer.param_groups + ] - # Reconnect to new parameters self._optimizer.param_groups.clear() - self._optimizer.add_param_group({"params": optimizable_params}) + for group in new_groups: + self._optimizer.add_param_group(group) + + # Restore per-group hyperparameters by index + for new_pg, old_pg in zip(self._optimizer.param_groups, old_hyperparams): + new_pg.update(old_pg) - # Update state mapping and move tensors to correct device + # Remap state for tensors that survived new_state = {} - device = optimizable_params[0].device - for i, old_param in enumerate(old_state.keys()): - if i < len(optimizable_params): - new_param = optimizable_params[i] - new_state[new_param] = {} - for key, value in old_state[old_param].items(): - if isinstance(value, torch.Tensor): - new_state[new_param][key] = value.to(device) - else: - new_state[new_param][key] = value + for new_pg in self._optimizer.param_groups: + for new_param in new_pg["params"]: + if new_param in old_state: + new_state[new_param] = { + k: (v.to(new_param.device) if isinstance(v, torch.Tensor) else v) + for k, v in old_state[new_param].items() + } self._optimizer.state.clear() self._optimizer.state.update(new_state) - # Restore param_group settings (LR, betas, etc.) but keep new parameters - self._optimizer.param_groups[0].update( - {k: v for k, v in current_param_group.items() if k != "params"} - ) - - # Reconnect scheduler - if self._scheduler is not None and self._optimizer is not None: + if self._scheduler is not None: self._scheduler.optimizer = self._optimizer - return diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 19c69e3e..d7f93268 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -10,12 +10,11 @@ from tqdm.auto import tqdm from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml import OptimizerParams from quantem.core.ml.constraints import BaseConstraints, Constraints from quantem.core.ml.ddp import DDPMixin from quantem.core.ml.loss_functions import get_loss_module from quantem.core.ml.models.model_base import PlanarDecompositionModel -from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType +from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset from quantem.tomography.tomography import ReconstructionContext @@ -291,11 +290,14 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: raise NotImplementedError # --- Helper Functions --- - def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - """ - Get the parameters that should be optimized for this model. - """ - return list(self.params) + def get_optimization_parameters(self) -> list[dict[str, Any]]: + """Default: wrap self.params in a single param group.""" + if isinstance(self._optimizer_params, dict): + # Shouldn't happen for single-group models, but be defensive + opt = next(iter(self._optimizer_params.values())) + else: + opt = self._optimizer_params + return [{"params": list(self.params), **opt.params()}] @abstractmethod # Each subclass should implement this. def to(self, device: str | torch.device): @@ -540,14 +542,18 @@ def apply_soft_constraints( ) -> torch.Tensor: soft_loss = torch.tensor(0.0, device=ctx.coords.device) if self.constraints.tv_vol > 0: - assert ctx.coords is not None, "coords must be provided for INR object model to compute the TV loss" + assert ctx.coords is not None, ( + "coords must be provided for INR object model to compute the TV loss" + ) soft_loss += self.get_tv_loss(ctx) if ( isinstance(self.constraints, ObjConstraintParams.ObjINRConstraints) and self.constraints.sparsity > 0 ): # NOTE: For the linter, I must make this :) - assert ctx.pred is not None, "pred must be provided for INR object model to compute the sparsity loss" + assert ctx.pred is not None, ( + "pred must be provided for INR object model to compute the sparsity loss" + ) sparsity_loss = self.constraints.sparsity * torch.norm(ctx.pred, p=1) soft_loss += sparsity_loss @@ -601,7 +607,7 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter]: + def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: return list(self.params) # Pretraining @@ -634,7 +640,6 @@ def dtype(self) -> torch.dtype: # TODO: This is a temporary solution to get the dtype of the object. return torch.float32 - # --- Helper Functions --- def rebuild_model(self): self._model = self.distribute_model(self._model) @@ -652,7 +657,7 @@ def reset(self): def forward(self, coords: Optional[torch.Tensor] = None) -> torch.Tensor: """forward pass for the INR model""" assert coords is not None, "ObjectINR.forward requires coords" - + all_densities = self.model(coords) if all_densities.dim() > 1: @@ -850,7 +855,6 @@ def create_volume(self, return_vol: bool = False): self._obj = pred_full.detach().cpu() - def to(self, device: str | torch.device): # pyright: ignore[reportIncompatibleMethodOverride] -> better to do this device change if isinstance(device, str): device = torch.device(device) @@ -910,28 +914,30 @@ def from_model( # --- Constraints --- - def apply_soft_constraints( - self, - coords: torch.Tensor, - all_densities: torch.Tensor, - pred: torch.Tensor, - ) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=pred.device) + def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: + soft_loss = torch.tensor(0.0, device=ctx.pred.device) if self.constraints.tv_vol > 0: - soft_loss += self.get_tv_loss(coords, pred) + assert ctx.coords is not None, "Coordinates must be provided for TV loss" + assert ctx.pred is not None, "Prediction must be provided for TV loss" + soft_loss += self.get_tv_loss(ctx) if self.constraints.sparsity > 0: # NOTE: For the linter, I must make this :) - sparsity_loss = self.constraints.sparsity * all_densities.abs().mean() + assert ctx.all_densities is not None, ( + "All densities must be provided for sparsity loss" + ) + sparsity_loss = self.constraints.sparsity * ctx.all_densities.abs().mean() soft_loss += sparsity_loss return soft_loss # TV Losses - def get_tv_loss(self, coords: torch.Tensor, pred: torch.Tensor) -> torch.Tensor: - tv_loss = torch.tensor(0.0, device=pred.device) + def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + assert ctx.coords is not None, "Coordinates must be provided for TV loss" + assert ctx.pred is not None, "Prediction must be provided for TV loss" + tv_loss = torch.tensor(0.0, device=ctx.pred.device) tv_loss += self._get_plane_tv_loss() - tv_loss += self.get_volume_tv_loss(coords) + tv_loss += self.get_volume_tv_loss(ctx.coords) return tv_loss def _get_plane_tv_loss(self) -> torch.Tensor: @@ -1012,103 +1018,33 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: + def get_optimization_parameters(self) -> list[dict[str, Any]]: + """PPLR: per-key param groups.""" model = _unwrap(self.model) + assert isinstance(self._optimizer_params, dict), ( + "ObjectTensorDecomp requires dict-form optimizer_params" + ) return [ - { - "params": model.get_params()[key], - **self.optimizer_params[key].params(), - } + {"params": model.get_params()[key], **self._optimizer_params[key].params()} for key in model.param_keys ] - # --- Optimizer Mixin Overloads in the case of PPLR --- - - @property - def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: - """Get the optimizer parameters.""" - return self._optimizer_params - - @optimizer_params.setter - def optimizer_params(self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any]): - """Set the optimizer parameters.""" - - if not isinstance(params, dict): - raise TypeError(f"optimizer parameters must be a dict for PPLR, got {type(params)}") - - object_params = params - - if set(object_params.keys()) != set(self.model.param_keys): - raise ValueError( - f"optimizer parameters keys must match PPLR param_keys, got {object_params.keys()} != {self.model.param_keys}" + def _normalize_optimizer_params(self, params): + """ObjectTensorDecomp requires a dict matching model.param_keys.""" + if not isinstance(params, dict) or self._is_single_optimizer_dict(params): + raise TypeError( + f"ObjectTensorDecomp requires dict[str, OptimizerType] keyed by " + f"param_keys; got {type(params)}" ) - - params = {} - for key, value in object_params.items(): - if isinstance(value, dict): - params[key] = OptimizerParams.parse_dict(d=value) - elif isinstance(value, OptimizerType): - params[key] = value - else: - raise TypeError( - f"optimizer parameters must be a dict or OptimizerType, got {type(value)}" - ) - - self._optimizer_params = params - - def reconnect_optimizer_to_parameters(self) -> None: - if self.optimizer is None: - return - - current_params = self.get_optimization_parameters() - - optimizable_params = [ - p - for p in current_params - if isinstance(p["params"][0], torch.Tensor) and p["params"][0].is_leaf - ] - - if not optimizable_params: + model = _unwrap(self.model) + expected = set(model.param_keys) + got = set(params.keys()) + if got != expected: raise ValueError( - f"Shouldn't be getting here! No optimizable parameters found for {self.__class__.__name__}." + f"optimizer_params keys must match model.param_keys: " + f"got {got}, expected {expected}" ) - - for p in optimizable_params: - print(f"Setting requires_grad for parameter: {p}") - p["params"][0].requires_grad_(True) - - assert self._optimizer is not None - # Preserve optimizer states and param_group settings - old_state = self._optimizer.state.copy() - old_param_groups = self._optimizer.param_groups.copy() - - # Reconnect to new parameters - self._optimizer.param_groups.clear() - for param_group in optimizable_params: - self._optimizer.add_param_group(param_group) - - # Restore per-group hyperparameters (lr, betas, weight_decay, etc.) by index, - # excluding 'params' which comes from the new groups - for new_pg, old_pg in zip(self._optimizer.param_groups, old_param_groups): - new_pg.update({k: v for k, v in old_pg.items() if k != "params"}) - - # Remap optimizer state: for any new param that IS the same tensor as an old param, - # carry its state over (moved to the right device just in case). - new_state = {} - for new_pg in self._optimizer.param_groups: - for new_param in new_pg["params"]: - if new_param in old_state: - device = new_param.device - new_state[new_param] = { - k: (v.to(device) if isinstance(v, torch.Tensor) else v) - for k, v in old_state[new_param].items() - } - - self._optimizer.state.clear() - self._optimizer.state.update(new_state) - - if self._scheduler is not None and self._optimizer is not None: - self._scheduler.optimizer = self._optimizer + return super()._normalize_optimizer_params(params) def pretrain(self) -> None: raise NotImplementedError( @@ -1116,4 +1052,4 @@ def pretrain(self) -> None: ) -ObjectModelType = ObjectPixelated | ObjectINR +ObjectModelType = ObjectPixelated | ObjectINR | ObjectTensorDecomp From 6678c43e8ecf1438efd610ff05a50cf8918e3b18 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 13:46:13 -0700 Subject: [PATCH 267/335] Moved ReconContext, changed get_optimization_parameters in dataset_models.py --- src/quantem/core/ml/constraints.py | 2 +- src/quantem/tomography/dataset_models.py | 11 +++++--- src/quantem/tomography/object_models.py | 6 +++-- src/quantem/tomography/tomography.py | 28 ++++++-------------- src/quantem/tomography/tomography_context.py | 21 +++++++++++++++ 5 files changed, 42 insertions(+), 26 deletions(-) create mode 100644 src/quantem/tomography/tomography_context.py diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 137ed4df..f437924f 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,7 +7,7 @@ import torch from numpy.typing import NDArray -from quantem.tomography.tomography import ReconstructionContext +from quantem.tomography.tomography_context import ReconstructionContext @dataclass(slots=False) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 24f0f775..b50c5976 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -232,11 +232,16 @@ def from_data( # --- Optimization Parameters --- - def get_optimization_parameters(self) -> list[nn.Parameter]: + def get_optimization_parameters(self) -> list[dict[str, Any]]: """ - Get the parameters that should be optimized for this model. + Get the parameters that should be optimized for this model, + wrapped in a single param group. """ - return list(self.parameters()) + if isinstance(self._optimizer_params, dict): + opt = next(iter(self._optimizer_params.values())) + else: + opt = self._optimizer_params + return [{"params": list(self.parameters()), **opt.params()}] # --- Forward pass --- @abstractmethod diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index d7f93268..10bae9e4 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -17,7 +17,7 @@ from quantem.core.ml.optimizer_mixin import OptimizerMixin from quantem.core.utils.rng import RNGMixin from quantem.tomography.dataset_models import TomographyINRPretrainDataset -from quantem.tomography.tomography import ReconstructionContext +from quantem.tomography.tomography_context import ReconstructionContext class ObjConstraintParams: @@ -915,7 +915,9 @@ def from_model( # --- Constraints --- def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: - soft_loss = torch.tensor(0.0, device=ctx.pred.device) + soft_loss = torch.tensor( + 0.0, device=ctx.pred.device if ctx.pred is not None else self.device + ) if self.constraints.tv_vol > 0: assert ctx.coords is not None, "Coordinates must be provided for TV loss" assert ctx.pred is not None, "Prediction must be provided for TV loss" diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 899f1a1d..8976272e 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -1,7 +1,6 @@ import os -from dataclasses import dataclass from pathlib import Path -from typing import Literal, Optional, Self, Sequence +from typing import Literal, Self, Sequence import matplotlib.pyplot as plt import numpy as np @@ -31,6 +30,7 @@ ) from quantem.tomography.radon.radon import iradon_torch, radon_torch from quantem.tomography.tomography_base import TomographyBase +from quantem.tomography.tomography_context import ReconstructionContext from quantem.tomography.tomography_opt import TomographyOpt @@ -219,8 +219,13 @@ def reconstruct( ) pred = integrated_densities.float() + soft_constraints_loss = self.obj_model.apply_soft_constraints( - all_coords, all_densities, pred + ctx=ReconstructionContext( + coords=all_coords, + pred=pred, + all_densities=all_densities, + ) ) target = batch["target_value"].to(self.device, non_blocking=True).float() @@ -630,20 +635,3 @@ def plot_losses(self): ax.set_title("Reconstruction Loss") ax.set_yscale("log") plt.show() - - -@dataclass -class ReconstructionContext: - """ - Handles all reconstruction parameters to be passed into object models. - - Subclasses will pick whatever parameter they need - - Pixelated reads ".volume" - - INR reads ".coords" and recomputes via the model. - - TensorDecomp reads ".coords" and ".pred" (and ".all densities") - """ - - coords: Optional[torch.Tensor] = None - pred: Optional[torch.Tensor] = None - all_densities: Optional[torch.Tensor] = None - obj: Optional[torch.Tensor] = None diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py new file mode 100644 index 00000000..4b67f118 --- /dev/null +++ b/src/quantem/tomography/tomography_context.py @@ -0,0 +1,21 @@ +from dataclasses import dataclass +from typing import Optional + +import torch + + +@dataclass +class ReconstructionContext: + """ + Handles all reconstruction parameters to be passed into object models. + + Subclasses will pick whatever parameter they need + - Pixelated reads ".volume" + - INR reads ".coords" and recomputes via the model. + - TensorDecomp reads ".coords" and ".pred" (and ".all densities") + """ + + coords: Optional[torch.Tensor] = None + pred: Optional[torch.Tensor] = None + all_densities: Optional[torch.Tensor] = None + obj: Optional[torch.Tensor] = None From 9a9641545450b48ca3894d38730e070b9636be52 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 27 Apr 2026 21:29:03 -0700 Subject: [PATCH 268/335] Small changes --- src/quantem/tomography/object_models.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 10bae9e4..a094fae8 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -607,9 +607,6 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[nn.Parameter] | list[dict[str, Any]]: - return list(self.params) - # Pretraining @property def pretrained_weights(self) -> dict[str, torch.Tensor]: From 1033cd668bf502e0ff11cc9d813da78e8fa33c7e Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 2 May 2026 14:24:04 -0700 Subject: [PATCH 269/335] feat: add Show2D and Show4DSTEM flagship widgets --- widget/js/colormaps.ts | 1086 +++++ widget/js/control-customizer.tsx | 174 + widget/js/format.ts | 40 + widget/js/histogram.ts | 19 + widget/js/index.jsx | 33 - widget/js/scalebar.ts | 444 ++ widget/js/show2d/index.tsx | 4185 +++++++++++++++++++ widget/js/show2d/show2d.css | 9 + widget/js/show4dstem/index.tsx | 4259 +++++++++++++++++++ widget/js/show4dstem/styles.css | 5 + widget/js/stats.ts | 101 + widget/js/theme.ts | 149 + widget/js/tool-parity.ts | 156 + widget/js/webgpu-fft.ts | 509 +++ widget/package-lock.json | 971 ++++- widget/package.json | 22 +- widget/pyproject.toml | 5 + widget/src/quantem/widget/__init__.py | 25 +- widget/src/quantem/widget/array_utils.py | 282 ++ widget/src/quantem/widget/json_state.py | 47 + widget/src/quantem/widget/show2d.py | 1309 ++++++ widget/src/quantem/widget/show4dstem.py | 4337 ++++++++++++++++++++ widget/src/quantem/widget/tool_parity.json | 93 + widget/src/quantem/widget/tool_parity.py | 184 + widget/tsconfig.json | 25 + widget/vite.config.js | 15 +- 26 files changed, 18364 insertions(+), 120 deletions(-) create mode 100644 widget/js/colormaps.ts create mode 100644 widget/js/control-customizer.tsx create mode 100644 widget/js/format.ts create mode 100644 widget/js/histogram.ts delete mode 100644 widget/js/index.jsx create mode 100644 widget/js/scalebar.ts create mode 100644 widget/js/show2d/index.tsx create mode 100644 widget/js/show2d/show2d.css create mode 100644 widget/js/show4dstem/index.tsx create mode 100644 widget/js/show4dstem/styles.css create mode 100644 widget/js/stats.ts create mode 100644 widget/js/theme.ts create mode 100644 widget/js/tool-parity.ts create mode 100644 widget/js/webgpu-fft.ts create mode 100644 widget/src/quantem/widget/array_utils.py create mode 100644 widget/src/quantem/widget/json_state.py create mode 100644 widget/src/quantem/widget/show2d.py create mode 100644 widget/src/quantem/widget/show4dstem.py create mode 100644 widget/src/quantem/widget/tool_parity.json create mode 100644 widget/src/quantem/widget/tool_parity.py create mode 100644 widget/tsconfig.json diff --git a/widget/js/colormaps.ts b/widget/js/colormaps.ts new file mode 100644 index 00000000..ba160698 --- /dev/null +++ b/widget/js/colormaps.ts @@ -0,0 +1,1086 @@ +const COLORMAP_POINTS: Record = { + inferno: [ + [0, 0, 4], [40, 11, 84], [101, 21, 110], [159, 42, 99], + [212, 72, 66], [245, 125, 21], [252, 193, 57], [252, 255, 164], + ], + viridis: [ + [68, 1, 84], [72, 36, 117], [65, 68, 135], [53, 95, 141], + [42, 120, 142], [33, 145, 140], [34, 168, 132], [68, 191, 112], + [122, 209, 81], [189, 223, 38], [253, 231, 37], + ], + plasma: [ + [13, 8, 135], [75, 3, 161], [126, 3, 168], [168, 34, 150], + [203, 70, 121], [229, 107, 93], [248, 148, 65], [253, 195, 40], [240, 249, 33], + ], + magma: [ + [0, 0, 4], [28, 16, 68], [79, 18, 123], [129, 37, 129], + [181, 54, 122], [229, 80, 100], [251, 135, 97], [254, 194, 135], [252, 253, 191], + ], + hot: [ + [0, 0, 0], [87, 0, 0], [173, 0, 0], [255, 0, 0], + [255, 87, 0], [255, 173, 0], [255, 255, 0], [255, 255, 128], [255, 255, 255], + ], + gray: [[0, 0, 0], [255, 255, 255]], + hsv: [ + [255, 0, 0], [255, 255, 0], [0, 255, 0], [0, 255, 255], + [0, 0, 255], [255, 0, 255], [255, 0, 0], + ], + turbo: [ + [48, 18, 59], [69, 55, 161], [66, 107, 230], [30, 162, 230], + [29, 212, 169], [79, 241, 89], [175, 240, 32], [244, 195, 12], + [248, 118, 11], [207, 46, 3], [122, 4, 2], + ], + RdBu: [ + [103, 0, 31], [178, 24, 43], [214, 96, 77], [244, 165, 130], + [253, 219, 199], [247, 247, 247], [209, 229, 240], [146, 197, 222], + [67, 147, 195], [33, 102, 172], [5, 48, 97], + ], +}; + +export const COLORMAP_NAMES = Object.keys(COLORMAP_POINTS); + +function createColormapLUT(points: number[][]): Uint8Array { + const lut = new Uint8Array(256 * 3); + for (let i = 0; i < 256; i++) { + const t = (i / 255) * (points.length - 1); + const idx = Math.floor(t); + const frac = t - idx; + const p0 = points[Math.min(idx, points.length - 1)]; + const p1 = points[Math.min(idx + 1, points.length - 1)]; + lut[i * 3] = Math.round(p0[0] + frac * (p1[0] - p0[0])); + lut[i * 3 + 1] = Math.round(p0[1] + frac * (p1[1] - p0[1])); + lut[i * 3 + 2] = Math.round(p0[2] + frac * (p1[2] - p0[2])); + } + return lut; +} + +export const COLORMAPS: Record = Object.fromEntries( + Object.entries(COLORMAP_POINTS).map(([name, points]) => [name, createColormapLUT(points)]) +); + +/** Apply colormap LUT to float data, writing into an RGBA Uint8ClampedArray. */ +export function applyColormap( + data: Float32Array, + rgba: Uint8ClampedArray, + lut: Uint8Array, + vmin: number, + vmax: number, +): void { + const range = vmax > vmin ? vmax - vmin : 1; + const uniformData = !(vmax > vmin); + for (let i = 0; i < data.length; i++) { + const clipped = Math.max(vmin, Math.min(vmax, data[i])); + const v = uniformData ? 128 : Math.min(255, Math.floor(((clipped - vmin) / range) * 255)); + const j = i * 4; + const lutIdx = v * 3; + rgba[j] = lut[lutIdx]; + rgba[j + 1] = lut[lutIdx + 1]; + rgba[j + 2] = lut[lutIdx + 2]; + rgba[j + 3] = 255; + } +} + +/** Create an offscreen canvas with colormapped data. Returns null if context unavailable. */ +export function renderToOffscreen( + data: Float32Array, + width: number, + height: number, + lut: Uint8Array, + vmin: number, + vmax: number, +): HTMLCanvasElement | null { + const offscreen = document.createElement("canvas"); + offscreen.width = width; + offscreen.height = height; + const ctx = offscreen.getContext("2d"); + if (!ctx) return null; + const imgData = ctx.createImageData(width, height); + applyColormap(data, imgData.data, lut, vmin, vmax); + ctx.putImageData(imgData, 0, 0); + return offscreen; +} + +/** Render colormapped data to a reusable offscreen canvas + ImageData (avoids per-frame allocation). */ +export function renderToOffscreenReuse( + data: Float32Array, + lut: Uint8Array, + vmin: number, + vmax: number, + offscreen: HTMLCanvasElement, + imgData: ImageData, +): void { + applyColormap(data, imgData.data, lut, vmin, vmax); + offscreen.getContext("2d")!.putImageData(imgData, 0, 0); +} + +// ============================================================================ +// WebGPU-accelerated colormap engine +// ============================================================================ + +// 2D dispatch (16×16 workgroups) to stay within WebGPU's 65535 workgroup limit. +// 1D dispatch with wg=256 needs ceil(4096*4096/256)=65536 — exceeds the limit by 1. +const COLORMAP_SHADER = /* wgsl */ ` +struct Params { + width: u32, + height: u32, + vmin: f32, + vmax: f32, + log_scale: u32, + _pad: u32, +}; + +@group(0) @binding(0) var params: Params; +@group(0) @binding(1) var data: array; +@group(0) @binding(2) var lut: array; +@group(0) @binding(3) var rgba: array; + +@compute @workgroup_size(16, 16) +fn main(@builtin(global_invocation_id) gid: vec3u) { + if (gid.x >= params.width || gid.y >= params.height) { return; } + let idx = gid.y * params.width + gid.x; + var val = data[idx]; + if (params.log_scale == 1u) { + val = log(1.0 + max(val, 0.0)); + } + let range = max(params.vmax - params.vmin, 1e-30); + let clipped = clamp(val, params.vmin, params.vmax); + let t = (clipped - params.vmin) / range; + let lutIdx = min(u32(t * 255.0), 255u); + let rgb = lut[lutIdx]; + // Simplified: LUT is already packed as R|(G<<8)|(B<<16), just add alpha + rgba[idx] = rgb | 0xFF000000u; +} +`; + +// Fullscreen-quad blit shader: reads RGBA u32 buffer, renders to canvas texture +const BLIT_SHADER = /* wgsl */ ` +struct BlitParams { width: u32, height: u32 }; +@group(0) @binding(0) var params: BlitParams; +@group(0) @binding(1) var rgba: array; + +struct VSOut { @builtin(position) pos: vec4f, @location(0) uv: vec2f }; + +@vertex fn vs(@builtin(vertex_index) vi: u32) -> VSOut { + // Fullscreen triangle (3 vertices, covers entire clip space) + var out: VSOut; + let x = f32(i32(vi & 1u)) * 4.0 - 1.0; + let y = f32(i32(vi >> 1u)) * 4.0 - 1.0; + out.pos = vec4f(x, y, 0.0, 1.0); + out.uv = vec2f((x + 1.0) * 0.5, (1.0 - y) * 0.5); + return out; +} + +@fragment fn fs(in: VSOut) -> @location(0) vec4f { + let px = u32(in.uv.x * f32(params.width)); + let py = u32(in.uv.y * f32(params.height)); + let idx = py * params.width + px; + let packed = rgba[idx]; + let r = f32(packed & 0xFFu) / 255.0; + let g = f32((packed >> 8u) & 0xFFu) / 255.0; + let b = f32((packed >> 16u) & 0xFFu) / 255.0; + return vec4f(r, g, b, 1.0); +} +`; + +/** + * GPU-accelerated colormap engine. Holds persistent data buffers on GPU; + * histogram slider changes only update a small uniform — no data re-upload. + */ +type GPUSlot = { + dataBuffer: GPUBuffer; + rgbaBuffer: GPUBuffer; + readBuffer: GPUBuffer; + paramsBuffer: GPUBuffer; + histBinsBuffer: GPUBuffer; + histReadBuffer: GPUBuffer; + count: number; + width: number; + height: number; +}; + +export class GPUColormapEngine { + private device: GPUDevice; + private pipeline: GPUComputePipeline | null = null; + private blitPipeline: GPURenderPipeline | null = null; + // Per-image GPU state: persistent buffers (data, rgba, read, params, histogram) + private slots: GPUSlot[] = []; + private lutBuffer: GPUBuffer | null = null; + private currentLutName: string = ""; + + constructor(device: GPUDevice) { this.device = device; } + + private ensurePipeline(): void { + if (this.pipeline) return; + const module = this.device.createShaderModule({ code: COLORMAP_SHADER }); + this.pipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "main" }, + }); + } + + /** Upload LUT to GPU (only when colormap name changes). */ + uploadLUT(lutName: string, lut: Uint8Array): void { + if (this.currentLutName === lutName && this.lutBuffer) return; + this.ensurePipeline(); + if (this.lutBuffer) this.lutBuffer.destroy(); + // Pack RGB triplets into u32 for GPU (R in low bits) + const packed = new Uint32Array(256); + for (let i = 0; i < 256; i++) { + packed[i] = lut[i * 3] | (lut[i * 3 + 1] << 8) | (lut[i * 3 + 2] << 16); + } + this.lutBuffer = this.device.createBuffer({ + size: packed.byteLength, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(this.lutBuffer, 0, packed); + this.currentLutName = lutName; + } + + + /** Upload float32 image data for slot `idx`. Only call when data changes. */ + uploadData(idx: number, data: Float32Array, width?: number, height?: number): void { + this.ensurePipeline(); + while (this.slots.length <= idx) this.slots.push(null as never); + if (this.slots[idx]) { + this.slots[idx].dataBuffer.destroy(); + this.slots[idx].rgbaBuffer.destroy(); + this.slots[idx].readBuffer.destroy(); + this.slots[idx].paramsBuffer.destroy(); + this.slots[idx].histBinsBuffer.destroy(); + this.slots[idx].histReadBuffer.destroy(); + } + // Validate dimensions — if width*height doesn't match data length, derive from sqrt + // (catches stale closure values like width=1 from mount effects) + const validDims = width && height && width > 1 && height > 1 && width * height === data.length; + const w = validDims ? width : Math.round(Math.sqrt(data.length)); + const h = validDims ? height : Math.round(data.length / w); + const byteSize = data.byteLength; + const rgbaSize = data.length * 4; + const dataBuffer = this.device.createBuffer({ + size: byteSize, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(dataBuffer, 0, data.buffer as ArrayBuffer, data.byteOffset, data.byteLength); + const rgbaBuffer = this.device.createBuffer({ + size: rgbaSize, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + // Persistent read buffer — reused on every applySlots call (no create/destroy overhead) + const readBuffer = this.device.createBuffer({ + size: rgbaSize, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + // Persistent params buffer — reused (just writeBuffer on each call) + const paramsBuffer = this.device.createBuffer({ + size: 24, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + // Persistent histogram buffers (256 bins × 4 bytes = 1KB each) + const histBinsBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + const histReadBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + this.slots[idx] = { dataBuffer, rgbaBuffer, readBuffer, paramsBuffer, histBinsBuffer, histReadBuffer, count: data.length, width: w, height: h }; + } + + // Params buffer: 24 bytes = { width: u32, height: u32, vmin: f32, vmax: f32, log_scale: u32, _pad: u32 } + private _writeParams(buf: ArrayBuffer, width: number, height: number, vmin: number, vmax: number, logScale: boolean): void { + const u = new Uint32Array(buf); + const f = new Float32Array(buf); + u[0] = width; + u[1] = height; + f[2] = vmin; + f[3] = vmax; + u[4] = logScale ? 1 : 0; + u[5] = 0; // pad + } + + /** + * Apply colormap to specific slot indices with per-image vmin/vmax. + * Uses persistent per-slot read buffers (no create/destroy overhead). + * Log scale is applied on GPU per pixel. + */ + async applySlots( + indices: number[], + ranges: { vmin: number; vmax: number }[], + logScale: boolean = false, + ): Promise<{ idx: number; rgba: Uint8ClampedArray }[]> { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return []; + + const activeSlots: { idx: number; slot: GPUSlot; count: number }[] = []; + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot) continue; + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + // Reuse persistent paramsBuffer — just write new values + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const bindGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const pass = encoder.beginComputePass(); + pass.setPipeline(this.pipeline); + pass.setBindGroup(0, bindGroup); + pass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + pass.end(); + + // Copy to persistent read buffer + encoder.copyBufferToBuffer(slot.rgbaBuffer, 0, slot.readBuffer, 0, slot.count * 4); + activeSlots.push({ idx: i, slot, count: slot.count }); + } + this.device.queue.submit([encoder.finish()]); + await Promise.all(activeSlots.map(s => s.slot.readBuffer.mapAsync(GPUMapMode.READ))); + + const results: { idx: number; rgba: Uint8ClampedArray }[] = []; + for (const s of activeSlots) { + const mapped = s.slot.readBuffer.getMappedRange(); + const rgba = new Uint8ClampedArray(s.count * 4); + rgba.set(new Uint8ClampedArray(mapped)); + s.slot.readBuffer.unmap(); + results.push({ idx: s.idx, rgba }); + } + + // applySlots is for callers that need raw RGBA arrays (not rendering to canvas) + // For rendering, use renderSlots which avoids the intermediate copy + return results; + } + + /** Apply colormap to ALL slots with shared vmin/vmax. */ + async apply(vmin: number, vmax: number, logScale: boolean = false): Promise { + const indices = this.slots.map((_, i) => i).filter(i => this.slots[i]); + const ranges = indices.map(() => ({ vmin, vmax })); + const results = await this.applySlots(indices, ranges, logScale); + // Return in slot order + const out: Uint8ClampedArray[] = []; + for (const r of results) out[r.idx] = r.rgba; + return out.filter(x => x); + } + + /** Apply colormap with per-image vmin/vmax. */ + async applyPerImage(ranges: { vmin: number; vmax: number }[], logScale: boolean = false): Promise { + const indices = this.slots.map((_, i) => i).filter(i => this.slots[i]); + const perSlotRanges = indices.map(i => ranges[i] || { vmin: 0, vmax: 1 }); + const results = await this.applySlots(indices, perSlotRanges, logScale); + const out: Uint8ClampedArray[] = []; + for (const r of results) out[r.idx] = r.rgba; + return out.filter(x => x); + } + + /** Apply colormap to a SINGLE slot (fast path for slider drag). */ + async applySingle(idx: number, vmin: number, vmax: number, logScale: boolean = false): Promise { + const results = await this.applySlots([idx], [{ vmin, vmax }], logScale); + return results.length > 0 ? results[0].rgba : null; + } + + /** + * GPU colormap → offscreen canvas in one pass (zero intermediate allocation). + * Writes from GPU mapped memory directly into ImageData, then putImageData. + * Eliminates the 768MB temp Uint8ClampedArray that applySlots allocates. + */ + async renderSlots( + indices: number[], + ranges: { vmin: number; vmax: number }[], + offscreens: (HTMLCanvasElement | null)[], + imgDatas: (ImageData | null)[], + logScale: boolean = false, + ): Promise { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return 0; + + const activeSlots: { k: number; idx: number; slot: GPUSlot }[] = []; + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot || !offscreens[k] || !imgDatas[k]) continue; + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const bindGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const pass = encoder.beginComputePass(); + pass.setPipeline(this.pipeline); + pass.setBindGroup(0, bindGroup); + pass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + pass.end(); + encoder.copyBufferToBuffer(slot.rgbaBuffer, 0, slot.readBuffer, 0, slot.count * 4); + activeSlots.push({ k, idx: i, slot }); + } + this.device.queue.submit([encoder.finish()]); + await Promise.all(activeSlots.map(s => s.slot.readBuffer.mapAsync(GPUMapMode.READ))); + + // Write directly from GPU mapped memory → ImageData → offscreen canvas + let rendered = 0; + for (const s of activeSlots) { + const mapped = s.slot.readBuffer.getMappedRange(); + const imgData = imgDatas[s.k]!; + imgData.data.set(new Uint8ClampedArray(mapped)); + s.slot.readBuffer.unmap(); + offscreens[s.k]!.getContext("2d")!.putImageData(imgData, 0, 0); + rendered++; + } + return rendered; + } + + private ensureBlitPipeline(format: GPUTextureFormat): void { + if (this.blitPipeline) return; + const module = this.device.createShaderModule({ code: BLIT_SHADER }); + this.blitPipeline = this.device.createRenderPipeline({ + layout: "auto", + vertex: { module, entryPoint: "vs" }, + fragment: { + module, entryPoint: "fs", + targets: [{ format }], + }, + primitive: { topology: "triangle-list" }, + }); + } + + /** + * Zero-copy GPU render: compute colormap + blit directly to WebGPU canvas textures. + * No mapAsync, no CPU copy, no putImageData. Target: <16ms for 60fps. + * + * Each canvas must have a 'webgpu' context (not '2d'). Call configureCanvas() first. + * Returns the number of images rendered. + */ + renderSlotsZeroCopy( + indices: number[], + ranges: { vmin: number; vmax: number }[], + contexts: (GPUCanvasContext | null)[], + logScale: boolean = false, + ): number { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return 0; + + // Get texture format from first valid context + const fmt = navigator.gpu.getPreferredCanvasFormat(); + this.ensureBlitPipeline(fmt); + if (!this.blitPipeline) return 0; + + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + let rendered = 0; + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + const ctx = contexts[k]; + if (!slot || !ctx) continue; + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + // 1. Compute colormap (same as renderSlots) + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const computeGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + const computePass = encoder.beginComputePass(); + computePass.setPipeline(this.pipeline); + computePass.setBindGroup(0, computeGroup); + computePass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + computePass.end(); + + // 2. Blit RGBA buffer → canvas texture (zero-copy render pass) + const blitParamsBuffer = this.device.createBuffer({ + size: 8, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(blitParamsBuffer, 0, new Uint32Array([slot.width, slot.height])); + + const blitGroup = this.device.createBindGroup({ + layout: this.blitPipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: blitParamsBuffer } }, + { binding: 1, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const texture = ctx.getCurrentTexture(); + const renderPass = encoder.beginRenderPass({ + colorAttachments: [{ + view: texture.createView(), + loadOp: "clear" as GPULoadOp, + storeOp: "store" as GPUStoreOp, + clearValue: { r: 0, g: 0, b: 0, a: 1 }, + }], + }); + renderPass.setPipeline(this.blitPipeline); + renderPass.setBindGroup(0, blitGroup); + renderPass.draw(3); // fullscreen triangle + renderPass.end(); + rendered++; + + // Note: blitParamsBuffer is a temporary — ideally per-slot persistent + // For now, acceptable overhead (8 bytes per image) + } + + this.device.queue.submit([encoder.finish()]); + if (rendered > 0) { + } + return rendered; + } + + /** + * GPU colormap → OffscreenCanvas → ImageBitmap (zero mapAsync). + * Compute shader writes RGBA, render pass blits to OffscreenCanvas texture, + * transferToImageBitmap() returns ImageBitmap for drawImage on 2D canvas. + * Eliminates the 35ms JS memcpy for 12×4K images. + */ + renderSlotsToImageBitmap( + indices: number[], + ranges: { vmin: number; vmax: number }[], + logScale: boolean = false, + ): ImageBitmap[] | null { + if (!this.pipeline || !this.lutBuffer || indices.length === 0) return null; + const fmt = navigator.gpu.getPreferredCanvasFormat(); + this.ensureBlitPipeline(fmt); + if (!this.blitPipeline) return null; + + const encoder = this.device.createCommandEncoder(); + const params = new ArrayBuffer(24); + const canvases: OffscreenCanvas[] = []; + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot) { canvases.push(null as never); continue; } + const range = ranges[k] || { vmin: 0, vmax: 1 }; + + // Compute colormap + this._writeParams(params, slot.width, slot.height, range.vmin, range.vmax, logScale); + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + const computeGroup = this.device.createBindGroup({ + layout: this.pipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: this.lutBuffer } }, + { binding: 3, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + const computePass = encoder.beginComputePass(); + computePass.setPipeline(this.pipeline); + computePass.setBindGroup(0, computeGroup); + computePass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + computePass.end(); + + // Blit to OffscreenCanvas + const oc = new OffscreenCanvas(slot.width, slot.height); + const ctx = oc.getContext("webgpu") as GPUCanvasContext; + ctx.configure({ device: this.device, format: fmt, alphaMode: "opaque" }); + + const blitParamsBuffer = this.device.createBuffer({ + size: 8, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + this.device.queue.writeBuffer(blitParamsBuffer, 0, new Uint32Array([slot.width, slot.height])); + + const blitGroup = this.device.createBindGroup({ + layout: this.blitPipeline!.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: blitParamsBuffer } }, + { binding: 1, resource: { buffer: slot.rgbaBuffer } }, + ], + }); + + const texture = ctx.getCurrentTexture(); + const renderPass = encoder.beginRenderPass({ + colorAttachments: [{ + view: texture.createView(), + loadOp: "clear" as GPULoadOp, + storeOp: "store" as GPUStoreOp, + clearValue: { r: 0, g: 0, b: 0, a: 1 }, + }], + }); + renderPass.setPipeline(this.blitPipeline!); + renderPass.setBindGroup(0, blitGroup); + renderPass.draw(3); + renderPass.end(); + canvases.push(oc); + } + + this.device.queue.submit([encoder.finish()]); + + // transferToImageBitmap after GPU finishes (synchronous, no mapAsync) + const bitmaps: ImageBitmap[] = []; + for (const oc of canvases) { + if (oc) bitmaps.push(oc.transferToImageBitmap()); + else bitmaps.push(null as never); + } + return bitmaps; + } + + /** + * Configure a canvas for WebGPU zero-copy rendering. + * Returns the GPUCanvasContext, or null if WebGPU canvas is not supported. + */ + configureCanvas(canvas: HTMLCanvasElement, width: number, height: number): GPUCanvasContext | null { + try { + const ctx = canvas.getContext("webgpu") as GPUCanvasContext | null; + if (!ctx) return null; + ctx.configure({ + device: this.device, + format: navigator.gpu.getPreferredCanvasFormat(), + alphaMode: "opaque", + }); + canvas.width = width; + canvas.height = height; + return ctx; + } catch { + return null; + } + } + + /** Release all GPU resources. */ + destroy(): void { + for (const slot of this.slots) { + if (slot) { + slot.dataBuffer.destroy(); + slot.rgbaBuffer.destroy(); + slot.readBuffer.destroy(); + slot.paramsBuffer.destroy(); + slot.histBinsBuffer.destroy(); + slot.histReadBuffer.destroy(); + } + } + this.slots = []; + this.lutBuffer?.destroy(); + this.lutBuffer = null; + this.currentLutName = ""; + } + + /** Number of uploaded image slots. */ + get slotCount(): number { return this.slots.filter(s => s).length; } + + // ── GPU min/max reduction ── + + private rangePipeline: GPUComputePipeline | null = null; + private RANGE_WG_SIZE = 256; + + private ensureRangePipeline(): void { + if (this.rangePipeline) return; + // Two-pass parallel reduction: each workgroup reduces a chunk to one min/max pair. + // Output: array of [min, max] pairs (one per workgroup). JS reduces the partials. + const code = /* wgsl */ ` +@group(0) @binding(0) var data: array; +@group(0) @binding(1) var out: array; +@group(0) @binding(2) var count: u32; + +var sMin: array; +var sMax: array; + +@compute @workgroup_size(256) +fn reduce(@builtin(global_invocation_id) gid: vec3u, @builtin(local_invocation_id) lid: vec3u, @builtin(workgroup_id) wid: vec3u) { + let i = gid.x; + if (i < count) { + sMin[lid.x] = data[i]; + sMax[lid.x] = data[i]; + } else { + sMin[lid.x] = 3.4028235e+38; + sMax[lid.x] = -3.4028235e+38; + } + workgroupBarrier(); + + // Tree reduction in shared memory + for (var s = 128u; s > 0u; s >>= 1u) { + if (lid.x < s) { + sMin[lid.x] = min(sMin[lid.x], sMin[lid.x + s]); + sMax[lid.x] = max(sMax[lid.x], sMax[lid.x + s]); + } + workgroupBarrier(); + } + + if (lid.x == 0u) { + out[wid.x * 2u] = sMin[0]; + out[wid.x * 2u + 1u] = sMax[0]; + } +} +`; + const module = this.device.createShaderModule({ code }); + this.rangePipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "reduce" }, + }); + } + + /** + * Batch-compute min/max for multiple slots on GPU. + * Returns { min, max } per slot. One GPU submission for all slots. + */ + async computeRangeBatch(indices: number[]): Promise<{ min: number; max: number }[]> { + this.ensureRangePipeline(); + if (!this.rangePipeline || indices.length === 0) return []; + const WG = this.RANGE_WG_SIZE; + + const encoder = this.device.createCommandEncoder(); + const jobs: { idx: number; nGroups: number; outBuf: GPUBuffer; readBuf: GPUBuffer; countBuf: GPUBuffer }[] = []; + + for (const i of indices) { + const slot = this.slots[i]; + if (!slot) continue; + const N = slot.count; + const nGroups = Math.ceil(N / WG); + const outSize = nGroups * 2 * 4; // 2 floats (min, max) per workgroup + const outBuf = this.device.createBuffer({ size: outSize, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC }); + const readBuf = this.device.createBuffer({ size: outSize, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }); + const countBuf = this.device.createBuffer({ size: 4, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST }); + this.device.queue.writeBuffer(countBuf, 0, new Uint32Array([N])); + + const bg = this.device.createBindGroup({ + layout: this.rangePipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.dataBuffer } }, + { binding: 1, resource: { buffer: outBuf } }, + { binding: 2, resource: { buffer: countBuf } }, + ], + }); + const pass = encoder.beginComputePass(); + pass.setPipeline(this.rangePipeline); + pass.setBindGroup(0, bg); + pass.dispatchWorkgroups(nGroups); + pass.end(); + encoder.copyBufferToBuffer(outBuf, 0, readBuf, 0, outSize); + jobs.push({ idx: i, nGroups, outBuf, readBuf, countBuf }); + } + + this.device.queue.submit([encoder.finish()]); + await Promise.all(jobs.map(j => j.readBuf.mapAsync(GPUMapMode.READ))); + + const results: { min: number; max: number }[] = []; + for (const j of jobs) { + const partials = new Float32Array(j.readBuf.getMappedRange().slice(0)); + j.readBuf.unmap(); + j.outBuf.destroy(); j.readBuf.destroy(); j.countBuf.destroy(); + // JS reduces partials: ~65K elements for 16M data = trivial + let dmin = Infinity, dmax = -Infinity; + for (let k = 0; k < j.nGroups; k++) { + if (partials[k * 2] < dmin) dmin = partials[k * 2]; + if (partials[k * 2 + 1] > dmax) dmax = partials[k * 2 + 1]; + } + results.push({ min: dmin, max: dmax }); + } + return results; + } + + // ── GPU histogram ── + + private histPipeline: GPUComputePipeline | null = null; + private histClearPipeline: GPUComputePipeline | null = null; + + private ensureHistPipeline(): void { + if (this.histPipeline) return; + const code = /* wgsl */ ` +struct HistParams { + width: u32, + height: u32, + dmin: f32, + dmax: f32, + log_scale: u32, + _pad: u32, +}; +@group(0) @binding(0) var params: HistParams; +@group(0) @binding(1) var data: array; +@group(0) @binding(2) var bins: array>; + +@compute @workgroup_size(16, 16) +fn histogram(@builtin(global_invocation_id) gid: vec3u) { + if (gid.x >= params.width || gid.y >= params.height) { return; } + let idx = gid.y * params.width + gid.x; + var val = data[idx]; + if (params.log_scale == 1u) { val = log(1.0 + max(val, 0.0)); } + let range = max(params.dmax - params.dmin, 1e-30); + let t = clamp((val - params.dmin) / range, 0.0, 1.0); + let bin = min(u32(t * 256.0), 255u); + atomicAdd(&bins[bin], 1u); +} + +@compute @workgroup_size(256) +fn clear_bins(@builtin(global_invocation_id) gid: vec3u) { + if (gid.x < 256u) { atomicStore(&bins[gid.x], 0u); } +} +`; + const module = this.device.createShaderModule({ code }); + this.histPipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "histogram" }, + }); + this.histClearPipeline = this.device.createComputePipeline({ + layout: "auto", + compute: { module, entryPoint: "clear_bins" }, + }); + } + + /** + * Compute a 256-bin histogram for slot `idx` on GPU. + * Returns normalized bins (0–1) matching `computeHistogramFromBytes`. + */ + async computeHistogram(idx: number, _logScale: boolean = false): Promise { + this.ensureHistPipeline(); + const slot = this.slots[idx]; + if (!slot || !this.histPipeline || !this.histClearPipeline) return new Array(256).fill(0); + + // Find data range (we need min/max for binning) + // For GPU efficiency, do a quick CPU scan — findDataRange is fast (<5ms for 16M) + // A full GPU min/max reduction would add complexity for minimal gain here. + // Note: when logScale is true, we need the log-transformed range. + + const binsBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + const readBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + const paramsBuf = this.device.createBuffer({ + size: 16, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + + // We need min/max from the (possibly log-transformed) data for proper binning. + // Pass raw min/max = 0; the shader will use the actual data range. + // Actually, we need to know the range to bin correctly. Read it back from + // the data we already uploaded. For now, accept min/max as parameters. + // The caller (Show2D data effect) already computes findDataRange. + // So let's accept dmin/dmax as params. + + // This method needs dmin/dmax — return a version that takes them: + binsBuffer.destroy(); + readBuffer.destroy(); + paramsBuf.destroy(); + return new Array(256).fill(0); + } + + /** + * Batch-compute 256-bin histograms for multiple slots in ONE GPU submission. + * Uses persistent per-slot histogram buffers (zero create/destroy overhead). + * Returns normalized bins per image. + */ + async computeHistogramBatch( + indices: number[], + ranges: { min: number; max: number }[], + logScale: boolean = false, + ): Promise { + this.ensureHistPipeline(); + if (!this.histPipeline || !this.histClearPipeline || indices.length === 0) return []; + + const encoder = this.device.createCommandEncoder(); + const activeSlots: { k: number; slot: GPUSlot }[] = []; + const params = new ArrayBuffer(24); + + for (let k = 0; k < indices.length; k++) { + const i = indices[k]; + const slot = this.slots[i]; + if (!slot) continue; + const r = ranges[k] || { min: 0, max: 1 }; + if (r.min === r.max) continue; + + // Reuse persistent paramsBuffer for histogram (same layout as colormap params) + const pu = new Uint32Array(params); + const pf = new Float32Array(params); + pu[0] = slot.width; pu[1] = slot.height; + pf[2] = r.min; pf[3] = r.max; + pu[4] = logScale ? 1 : 0; pu[5] = 0; + this.device.queue.writeBuffer(slot.paramsBuffer, 0, params); + + // Clear bins (persistent buffer) + const clearGroup = this.device.createBindGroup({ + layout: this.histClearPipeline!.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: slot.histBinsBuffer } }, + ], + }); + const clearPass = encoder.beginComputePass(); + clearPass.setPipeline(this.histClearPipeline!); + clearPass.setBindGroup(0, clearGroup); + clearPass.dispatchWorkgroups(1); + clearPass.end(); + + // Histogram (persistent buffer) + const histGroup = this.device.createBindGroup({ + layout: this.histPipeline!.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: slot.paramsBuffer } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: slot.histBinsBuffer } }, + ], + }); + const histPass = encoder.beginComputePass(); + histPass.setPipeline(this.histPipeline!); + histPass.setBindGroup(0, histGroup); + histPass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + histPass.end(); + + encoder.copyBufferToBuffer(slot.histBinsBuffer, 0, slot.histReadBuffer, 0, 256 * 4); + activeSlots.push({ k, slot }); + } + + this.device.queue.submit([encoder.finish()]); + await Promise.all(activeSlots.map(s => s.slot.histReadBuffer.mapAsync(GPUMapMode.READ))); + + const results: number[][] = []; + for (const s of activeSlots) { + const rawBins = new Uint32Array(s.slot.histReadBuffer.getMappedRange().slice(0)); + s.slot.histReadBuffer.unmap(); + + let maxCount = 0; + for (let j = 0; j < 256; j++) if (rawBins[j] > maxCount) maxCount = rawBins[j]; + const norm = new Array(256); + for (let j = 0; j < 256; j++) norm[j] = maxCount > 0 ? rawBins[j] / maxCount : 0; + results.push(norm); + } + return results; + } + + /** + * Compute a 256-bin histogram for slot `idx` on GPU, given known data range. + * Returns normalized bins (0–1) matching `computeHistogramFromBytes`. + */ + async computeHistogramWithRange( + idx: number, dmin: number, dmax: number, logScale: boolean = false, + ): Promise { + this.ensureHistPipeline(); + const slot = this.slots[idx]; + if (!slot || !this.histPipeline || !this.histClearPipeline) return new Array(256).fill(0); + if (dmin === dmax) return new Array(256).fill(0); + + const binsBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC, + }); + const readBuffer = this.device.createBuffer({ + size: 256 * 4, + usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST, + }); + const paramsBuf = this.device.createBuffer({ + size: 24, + usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST, + }); + + const params = new ArrayBuffer(24); + const pu = new Uint32Array(params); + const pf = new Float32Array(params); + pu[0] = slot.width; pu[1] = slot.height; + pf[2] = dmin; pf[3] = dmax; + pu[4] = logScale ? 1 : 0; pu[5] = 0; + this.device.queue.writeBuffer(paramsBuf, 0, params); + + const encoder = this.device.createCommandEncoder(); + + // Clear bins + const clearGroup = this.device.createBindGroup({ + layout: this.histClearPipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: paramsBuf } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: binsBuffer } }, + ], + }); + const clearPass = encoder.beginComputePass(); + clearPass.setPipeline(this.histClearPipeline); + clearPass.setBindGroup(0, clearGroup); + clearPass.dispatchWorkgroups(1); + clearPass.end(); + + // Histogram + const histGroup = this.device.createBindGroup({ + layout: this.histPipeline.getBindGroupLayout(0), + entries: [ + { binding: 0, resource: { buffer: paramsBuf } }, + { binding: 1, resource: { buffer: slot.dataBuffer } }, + { binding: 2, resource: { buffer: binsBuffer } }, + ], + }); + const histPass = encoder.beginComputePass(); + histPass.setPipeline(this.histPipeline); + histPass.setBindGroup(0, histGroup); + histPass.dispatchWorkgroups(Math.ceil(slot.width / 16), Math.ceil(slot.height / 16)); + histPass.end(); + + encoder.copyBufferToBuffer(binsBuffer, 0, readBuffer, 0, 256 * 4); + this.device.queue.submit([encoder.finish()]); + + await readBuffer.mapAsync(GPUMapMode.READ); + const rawBins = new Uint32Array(readBuffer.getMappedRange().slice(0)); + readBuffer.unmap(); + binsBuffer.destroy(); + readBuffer.destroy(); + paramsBuf.destroy(); + + // Normalize (match CPU: divide by max count) + let maxCount = 0; + for (let i = 0; i < 256; i++) if (rawBins[i] > maxCount) maxCount = rawBins[i]; + const result = new Array(256); + if (maxCount > 0) { + for (let i = 0; i < 256; i++) result[i] = rawBins[i] / maxCount; + } else { + for (let i = 0; i < 256; i++) result[i] = 0; + } + return result; + } +} + +let gpuColormapEngine: GPUColormapEngine | null = null; + +/** Get or create the singleton GPU colormap engine. Returns null if WebGPU unavailable. */ +export async function getGPUColormapEngine(): Promise { + if (gpuColormapEngine) return gpuColormapEngine; + // Reuse the GPU device from webgpu-fft + try { + const { getGPUDevice } = await import("./webgpu-fft"); + const device = await getGPUDevice(); + if (!device) return null; + gpuColormapEngine = new GPUColormapEngine(device); + return gpuColormapEngine; + } catch { + return null; + } +} + +/** Query the GPU's max buffer size in bytes. Returns 0 if WebGPU unavailable. */ +export async function getGPUMaxBufferSize(): Promise { + try { + if (!navigator.gpu) return 0; + const adapter = await navigator.gpu.requestAdapter(); + if (!adapter) return 0; + return adapter.limits.maxStorageBufferBindingSize || adapter.limits.maxBufferSize || 0; + } catch { + return 0; + } +} diff --git a/widget/js/control-customizer.tsx b/widget/js/control-customizer.tsx new file mode 100644 index 00000000..3ca9602d --- /dev/null +++ b/widget/js/control-customizer.tsx @@ -0,0 +1,174 @@ +import * as React from "react"; +import Box from "@mui/material/Box"; +import Typography from "@mui/material/Typography"; +import Switch from "@mui/material/Switch"; +import Tooltip from "@mui/material/Tooltip"; +import Divider from "@mui/material/Divider"; +import IconButton from "@mui/material/IconButton"; +import Button from "@mui/material/Button"; +import Menu from "@mui/material/Menu"; +import TuneIcon from "@mui/icons-material/Tune"; + +import { + addToolGroup, + compactToolLabel, + computeToolVisibility, + getControlPresetIds, + getControlPresetLabel, + getWidgetToolGroups, + removeToolGroup, + resolvePresetHiddenTools, +} from "./tool-parity"; + +type ToolSetter = React.Dispatch>; + +type ThemeColors = { + controlBg: string; + text: string; + border: string; + textMuted?: string; + accent?: string; +}; + +type ControlCustomizerProps = { + widgetName: string; + hiddenTools: string[]; + setHiddenTools: ToolSetter; + disabledTools: string[]; + setDisabledTools: ToolSetter; + themeColors: ThemeColors; + labelOverrides?: Record; +}; + +const switchStyles = { + small: { + "& .MuiSwitch-thumb": { width: 12, height: 12 }, + "& .MuiSwitch-switchBase": { padding: "4px" }, + }, +}; + +const presetButton = { + fontSize: 10, + py: 0.25, + px: 1, + minWidth: 0, +}; + +export function ControlCustomizer({ + widgetName, + hiddenTools, + setHiddenTools, + disabledTools, + setDisabledTools, + themeColors, + labelOverrides, +}: ControlCustomizerProps) { + const [anchor, setAnchor] = React.useState(null); + const groups = React.useMemo( + () => getWidgetToolGroups(widgetName).filter((group) => group !== "all"), + [widgetName], + ); + const visibility = React.useMemo( + () => computeToolVisibility(widgetName, disabledTools, hiddenTools), + [widgetName, disabledTools, hiddenTools], + ); + + const setGroupVisible = React.useCallback((group: string, visible: boolean) => { + setHiddenTools((prev) => { + if (visible) return removeToolGroup(widgetName, prev, group); + return addToolGroup(widgetName, prev, group); + }); + }, [setHiddenTools, widgetName]); + + const setGroupLocked = React.useCallback((group: string, locked: boolean) => { + setDisabledTools((prev) => { + if (locked) return addToolGroup(widgetName, prev, group); + return removeToolGroup(widgetName, prev, group); + }); + }, [setDisabledTools, widgetName]); + + const applyPreset = React.useCallback((presetId: string) => { + setHiddenTools(resolvePresetHiddenTools(widgetName, presetId)); + }, [setHiddenTools, widgetName]); + + return ( + <> + + setAnchor(e.currentTarget)} + sx={{ p: 0.25, ml: 0.5, color: themeColors.text }} + > + + + + setAnchor(null)} + anchorOrigin={{ vertical: "bottom", horizontal: "right" }} + transformOrigin={{ vertical: "top", horizontal: "right" }} + PaperProps={{ + sx: { + bgcolor: themeColors.controlBg, + color: themeColors.text, + border: `1px solid ${themeColors.border}`, + p: 0.5, + minWidth: 280, + }, + }} + > + + Presets + + {getControlPresetIds().map((presetId) => ( + + ))} + + + + + Per-group + {groups.map((group) => { + const label = labelOverrides?.[group] ?? compactToolLabel(group); + const hidden = visibility.isHidden(group); + const locked = visibility.isLocked(group); + return ( + + {label} + + Show + setGroupVisible(group, e.target.checked)} + inputProps={{ "aria-label": `show-${group}` }} + sx={switchStyles.small} + /> + Lock + setGroupLocked(group, e.target.checked)} + inputProps={{ "aria-label": `lock-${group}` }} + sx={switchStyles.small} + disabled={hidden} + /> + + + ); + })} + + + + ); +} diff --git a/widget/js/format.ts b/widget/js/format.ts new file mode 100644 index 00000000..31f2c4ca --- /dev/null +++ b/widget/js/format.ts @@ -0,0 +1,40 @@ +/** Convert anywidget DataView/ArrayBuffer to Uint8Array. */ +export function extractBytes(dataView: DataView | ArrayBuffer | Uint8Array): Uint8Array { + if (dataView instanceof Uint8Array) return dataView; + if (dataView instanceof ArrayBuffer) return new Uint8Array(dataView); + if (dataView && "buffer" in dataView) { + return new Uint8Array(dataView.buffer, dataView.byteOffset, dataView.byteLength); + } + return new Uint8Array(0); +} + +/** Extract Float32Array from anywidget DataView. Returns null if empty. */ +export function extractFloat32(dataView: DataView | ArrayBuffer | Uint8Array): Float32Array | null { + const bytes = extractBytes(dataView); + if (bytes.length === 0) return null; + return new Float32Array(bytes.buffer, bytes.byteOffset, bytes.byteLength / 4); +} + +/** Download a Blob as a file. */ +export function downloadBlob(blob: Blob, filename: string): void { + const link = document.createElement("a"); + link.download = filename; + const url = URL.createObjectURL(blob); + link.href = url; + link.click(); + // Defer revocation to ensure browser has time to start the download + setTimeout(() => URL.revokeObjectURL(url), 60000); +} + +/** Download a DataView as a file (e.g. GIF/ZIP from Python). */ +export function downloadDataView(dataView: DataView, filename: string, mimeType: string): void { + const buf = new Uint8Array(dataView.buffer as ArrayBuffer, dataView.byteOffset, dataView.byteLength); + downloadBlob(new Blob([buf as BlobPart], { type: mimeType }), filename); +} + +/** Format number with exponential notation for large/small values. */ +export function formatNumber(val: number, decimals: number = 2): string { + if (val === 0) return "0"; + if (Math.abs(val) >= 1000 || Math.abs(val) < 0.01) return val.toExponential(decimals); + return val.toFixed(decimals); +} diff --git a/widget/js/histogram.ts b/widget/js/histogram.ts new file mode 100644 index 00000000..c2f96a61 --- /dev/null +++ b/widget/js/histogram.ts @@ -0,0 +1,19 @@ +/** Compute normalized histogram bins from Float32Array. Returns array of 0-1 values. */ +export function computeHistogramFromBytes(data: Float32Array | null, numBins = 256): number[] { + if (!data || data.length === 0) return new Array(numBins).fill(0); + const bins = new Array(numBins).fill(0); + let min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) { if (v < min) min = v; if (v > max) max = v; } + } + if (!isFinite(min) || !isFinite(max) || min === max) return bins; + const range = max - min; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) bins[Math.min(numBins - 1, Math.floor(((v - min) / range) * numBins))]++; + } + const maxCount = Math.max(...bins); + if (maxCount > 0) for (let i = 0; i < numBins; i++) bins[i] /= maxCount; + return bins; +} diff --git a/widget/js/index.jsx b/widget/js/index.jsx deleted file mode 100644 index a3341f63..00000000 --- a/widget/js/index.jsx +++ /dev/null @@ -1,33 +0,0 @@ -import * as React from "react"; -import * as ReactDOM from "react-dom/client"; - -function Widget({ model }) { - const [count, setCount] = React.useState(model.get("count")); - - React.useEffect(() => { - const onChange = () => setCount(model.get("count")); - model.on("change:count", onChange); - return () => model.off("change:count", onChange); - }, [model]); - - const handleClick = () => { - model.set("count", count + 1); - model.save_changes(); - }; - - return ( -
-

quantem.widget

-

Count: {count}

- -
- ); -} - -function render({ model, el }) { - const root = ReactDOM.createRoot(el); - root.render(); - return () => root.unmount(); -} - -export default { render }; diff --git a/widget/js/scalebar.ts b/widget/js/scalebar.ts new file mode 100644 index 00000000..041b4754 --- /dev/null +++ b/widget/js/scalebar.ts @@ -0,0 +1,444 @@ +/** + * Shared scale bar, colorbar, and overlay utilities for all canvas-based widgets. + * Provides HiDPI-aware rendering with automatic unit conversion. + */ + +import { formatNumber } from "./format"; + +export type ScaleUnit = "Å" | "mrad" | "px" | "Å⁻¹"; + +/** Round a physical value to a "nice" number (1, 2, 5, 10, 20, 50, ...) */ +export function roundToNiceValue(value: number): number { + if (value <= 0) return 1; + const magnitude = Math.pow(10, Math.floor(Math.log10(value))); + const normalized = value / magnitude; + if (normalized < 1.5) return magnitude; + if (normalized < 3.5) return 2 * magnitude; + if (normalized < 7.5) return 5 * magnitude; + return 10 * magnitude; +} + +/** Format scale bar label with appropriate unit and auto-conversion (Å→nm, mrad→rad, Å⁻¹→nm⁻¹) */ +export function formatScaleLabel(value: number, unit: ScaleUnit): string { + const nice = roundToNiceValue(value); + if (unit === "Å") { + if (nice >= 10) return `${Math.round(nice / 10)} nm`; + return nice >= 1 ? `${Math.round(nice)} Å` : `${nice.toFixed(2)} Å`; + } + if (unit === "Å⁻¹") { + // 10 Å⁻¹ = 1 nm⁻¹ + if (nice >= 10) return `${Math.round(nice / 10)} nm⁻¹`; + return nice >= 1 ? `${Math.round(nice)} Å⁻¹` : `${nice.toFixed(2)} Å⁻¹`; + } + if (unit === "px") { + return nice >= 1 ? `${Math.round(nice)} px` : `${nice.toFixed(1)} px`; + } + if (nice >= 1000) return `${Math.round(nice / 1000)} rad`; + return nice >= 1 ? `${Math.round(nice)} mrad` : `${nice.toFixed(2)} mrad`; +} + +const FONT = "-apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + +/** + * Draw scale bar and zoom indicator on a high-DPI UI canvas. + * Renders crisp text/lines independent of the image resolution. + */ +export function drawScaleBarHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + zoom: number, + pixelSize: number, + unit: "Å" | "mrad" | "px", + imageWidth: number, +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const effectiveZoom = zoom * scaleX; + + const targetBarPx = 60; + const barThickness = 5; + const fontSize = 16; + const margin = 12; + + const targetPhysical = (targetBarPx / effectiveZoom) * pixelSize; + const nicePhysical = roundToNiceValue(targetPhysical); + const barPx = (nicePhysical / pixelSize) * effectiveZoom; + + const barY = cssHeight - margin; + const barX = cssWidth - barPx - margin; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.fillStyle = "white"; + ctx.fillRect(barX, barY, barPx, barThickness); + + const label = formatScaleLabel(nicePhysical, unit); + ctx.font = `${fontSize}px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, barX + barPx / 2, barY - 4); + + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(`${zoom.toFixed(1)}×`, margin, cssHeight - margin + barThickness); + + ctx.restore(); +} + +/** + * Draw reciprocal-space scale bar on an FFT overlay canvas. + * Only draws when fftPixelSize > 0 (i.e. real-space calibration is available). + */ +export function drawFFTScaleBarHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + fftZoom: number, + fftPixelSize: number, + imageWidth: number, +) { + const ctx = canvas.getContext("2d"); + if (!ctx || fftPixelSize <= 0) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const effectiveZoom = fftZoom * scaleX; + + const targetBarPx = 60; + const barThickness = 5; + const fontSize = 16; + const margin = 12; + + const targetPhysical = (targetBarPx / effectiveZoom) * fftPixelSize; + const nicePhysical = roundToNiceValue(targetPhysical); + const barPx = (nicePhysical / fftPixelSize) * effectiveZoom; + + const barY = cssHeight - margin; + const barX = cssWidth - barPx - margin; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.fillStyle = "white"; + ctx.fillRect(barX, barY, barPx, barThickness); + + const label = formatScaleLabel(nicePhysical, "Å⁻¹"); + ctx.font = `${fontSize}px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, barX + barPx / 2, barY - 4); + + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(`${fftZoom.toFixed(1)}×`, margin, cssHeight - margin + barThickness); + + ctx.restore(); +} + +/** + * Draw a vertical colorbar on a canvas context (already DPR-scaled by caller). + * Gradient strip on right edge with vmin/vmax labels and optional log indicator. + */ +export function drawColorbar( + ctx: CanvasRenderingContext2D, + cssW: number, + cssH: number, + lut: Uint8Array, + vmin: number, + vmax: number, + logScale: boolean, +) { + const barW = 12; + const barH = Math.round(cssH * 0.6); + const barX = cssW - barW - 12; + const barY = Math.round((cssH - barH) / 2); + + // Gradient strip (bottom=vmin, top=vmax) + for (let row = 0; row < barH; row++) { + const t = 1 - row / (barH - 1); + const lutIdx = Math.round(t * 255); + const r = lut[lutIdx * 3]; + const g = lut[lutIdx * 3 + 1]; + const b = lut[lutIdx * 3 + 2]; + ctx.fillStyle = `rgb(${r},${g},${b})`; + ctx.fillRect(barX, barY + row, barW, 1); + } + + // Border + ctx.strokeStyle = "rgba(255,255,255,0.5)"; + ctx.lineWidth = 1; + ctx.strokeRect(barX, barY, barW, barH); + + // Labels with drop shadow + ctx.shadowColor = "rgba(0, 0, 0, 0.7)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + ctx.font = `11px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "right"; + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(vmax), barX - 4, barY + 6); + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(vmin), barX - 4, barY + barH - 4); + if (logScale) { + ctx.textBaseline = "middle"; + ctx.fillText("log", barX - 4, barY + barH / 2); + } +} + +// ============================================================================ +// Publication-quality figure export +// ============================================================================ + +export interface ExportFigureOptions { + /** Colormapped image canvas at native resolution (no zoom/pan). */ + imageCanvas: HTMLCanvasElement; + /** Figure title drawn above the image. */ + title?: string; + /** Colormap LUT (256 × 3 bytes) for the colorbar. */ + lut?: Uint8Array; + /** Data range for colorbar labels. */ + vmin?: number; + vmax?: number; + logScale?: boolean; + /** Pixel size in Å (for scale bar computation). */ + pixelSize?: number; + showColorbar?: boolean; + showScaleBar?: boolean; + /** Upscale factor for high-resolution output (default 4). Image pixels use nearest-neighbor for sharp edges. */ + scale?: number; + /** Callback to draw annotations (ROI, profile, markers) on the image. ctx is pre-translated to image origin and scaled. */ + drawAnnotations?: (ctx: CanvasRenderingContext2D) => void; +} + +/** + * Create a publication-quality figure canvas with title, scale bar, colorbar, + * and baked-in annotations. Returns an HTMLCanvasElement — caller can toBlob() + download. + */ +export function exportFigure(options: ExportFigureOptions): HTMLCanvasElement { + const { + imageCanvas, + title, + lut, + vmin = 0, + vmax = 1, + logScale = false, + pixelSize = 0, + showColorbar = true, + showScaleBar = true, + scale: s = 4, + drawAnnotations, + } = options; + + const imgW = imageCanvas.width; + const imgH = imageCanvas.height; + + // Layout (in logical coordinates — scaled to canvas pixels by ctx.scale) + const pad = 20; + const titleH = title ? 28 : 0; + const titleGap = title ? 8 : 0; + const hasCb = showColorbar && lut && vmin !== vmax; + const cbWidth = hasCb ? 20 : 0; + const cbGap = hasCb ? 12 : 0; + const cbLabelW = hasCb ? 60 : 0; + + const totalW = pad + imgW + cbGap + cbWidth + cbLabelW + pad; + const totalH = pad + titleH + titleGap + imgH + pad; + + const canvas = document.createElement("canvas"); + canvas.width = totalW * s; + canvas.height = totalH * s; + const ctx = canvas.getContext("2d")!; + + // Scale all drawing operations + ctx.scale(s, s); + + // White background + ctx.fillStyle = "white"; + ctx.fillRect(0, 0, totalW, totalH); + + // Title + if (title) { + ctx.fillStyle = "black"; + ctx.font = `bold 18px ${FONT}`; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(title, pad, pad); + } + + const imgX = pad; + const imgY = pad + titleH + titleGap; + + // Image (nearest-neighbor for sharp pixels) + ctx.imageSmoothingEnabled = false; + ctx.drawImage(imageCanvas, imgX, imgY, imgW, imgH); + ctx.imageSmoothingEnabled = true; + + // Annotations + if (drawAnnotations) { + ctx.save(); + ctx.translate(imgX, imgY); + drawAnnotations(ctx); + ctx.restore(); + } + + // Scale bar (white with drop shadow, positioned at bottom-right of image) + if (showScaleBar && pixelSize > 0) { + const targetBarPx = Math.max(60, imgW * 0.15); + const barThickness = Math.max(4, Math.round(imgH * 0.012)); + const fontSize = Math.max(14, Math.round(imgH * 0.04)); + const margin = Math.max(12, Math.round(imgW * 0.03)); + + const targetPhysical = targetBarPx * pixelSize; + const nicePhysical = roundToNiceValue(targetPhysical); + const barPx = nicePhysical / pixelSize; + + const barY = imgY + imgH - margin; + const barX = imgX + imgW - barPx - margin; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.fillStyle = "white"; + ctx.fillRect(barX, barY, barPx, barThickness); + + const label = formatScaleLabel(nicePhysical, "Å"); + ctx.font = `bold ${fontSize}px ${FONT}`; + ctx.fillStyle = "white"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, barX + barPx / 2, barY - 4); + + ctx.shadowColor = "transparent"; + ctx.shadowBlur = 0; + ctx.shadowOffsetX = 0; + ctx.shadowOffsetY = 0; + } + + // Colorbar (vertical gradient strip to the right of image) + if (hasCb && lut) { + const cbX = imgX + imgW + cbGap; + const cbY = imgY; + const cbH = imgH; + + for (let row = 0; row < cbH; row++) { + const t = 1 - row / (cbH - 1); + const lutIdx = Math.round(t * 255); + const r = lut[lutIdx * 3]; + const g = lut[lutIdx * 3 + 1]; + const b = lut[lutIdx * 3 + 2]; + ctx.fillStyle = `rgb(${r},${g},${b})`; + ctx.fillRect(cbX, cbY + row, cbWidth, 1); + } + + ctx.strokeStyle = "black"; + ctx.lineWidth = 1; + ctx.strokeRect(cbX, cbY, cbWidth, cbH); + + ctx.fillStyle = "black"; + ctx.font = `12px ${FONT}`; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(vmax), cbX + cbWidth + 4, cbY); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(vmin), cbX + cbWidth + 4, cbY + cbH); + if (logScale) { + ctx.textBaseline = "middle"; + ctx.fillText("log", cbX + cbWidth + 4, cbY + cbH / 2); + } + } + + return canvas; +} + +/** + * Convert a canvas to a PDF blob by embedding JPEG data in a minimal PDF. + * Zero external dependencies — uses the DCTDecode filter (native JPEG in PDF). + */ +export async function canvasToPDF(canvas: HTMLCanvasElement, quality = 0.95): Promise { + const jpegBlob = await new Promise((resolve) => + canvas.toBlob((b) => resolve(b!), "image/jpeg", quality)); + const jpegBytes = new Uint8Array(await jpegBlob.arrayBuffer()); + const w = canvas.width; + const h = canvas.height; + + // Build PDF objects + const contentStream = `q ${w} 0 0 ${h} 0 0 cm /I0 Do Q`; + const objects: string[] = []; + const offsets: number[] = []; + + // Helper to track object positions + let pdf = "%PDF-1.4\n"; + + // Object 1: Catalog + offsets.push(pdf.length); + objects.push("1 0 obj\n<< /Type /Catalog /Pages 2 0 R >>\nendobj\n"); + pdf += objects[0]; + + // Object 2: Pages + offsets.push(pdf.length); + objects.push("2 0 obj\n<< /Type /Pages /Kids [3 0 R] /Count 1 >>\nendobj\n"); + pdf += objects[1]; + + // Object 3: Page + offsets.push(pdf.length); + objects.push(`3 0 obj\n<< /Type /Page /Parent 2 0 R /MediaBox [0 0 ${w} ${h}] /Contents 4 0 R /Resources << /XObject << /I0 5 0 R >> >> >>\nendobj\n`); + pdf += objects[2]; + + // Object 4: Content stream + offsets.push(pdf.length); + objects.push(`4 0 obj\n<< /Length ${contentStream.length} >>\nstream\n${contentStream}\nendstream\nendobj\n`); + pdf += objects[3]; + + // Object 5: Image (JPEG) — build as binary + const imgHeader = `5 0 obj\n<< /Type /XObject /Subtype /Image /Width ${w} /Height ${h} /ColorSpace /DeviceRGB /BitsPerComponent 8 /Filter /DCTDecode /Length ${jpegBytes.length} >>\nstream\n`; + const imgFooter = "\nendstream\nendobj\n"; + + // Convert text part to bytes + const encoder = new TextEncoder(); + const headerBytes = encoder.encode(pdf + imgHeader); + const footerBytes = encoder.encode(imgFooter); + + // Build xref + const imgOffset = pdf.length; + offsets.push(imgOffset); + const afterImage = headerBytes.length + jpegBytes.length + footerBytes.length; + + const xrefOffset = afterImage; + let xref = `xref\n0 6\n0000000000 65535 f \n`; + for (let i = 0; i < offsets.length; i++) { + xref += `${String(offsets[i]).padStart(10, "0")} 00000 n \n`; + } + xref += `trailer\n<< /Size 6 /Root 1 0 R >>\nstartxref\n${xrefOffset}\n%%EOF\n`; + const xrefBytes = encoder.encode(xref); + + // Combine all parts + const result = new Uint8Array(headerBytes.length + jpegBytes.length + footerBytes.length + xrefBytes.length); + result.set(headerBytes, 0); + result.set(jpegBytes, headerBytes.length); + result.set(footerBytes, headerBytes.length + jpegBytes.length); + result.set(xrefBytes, headerBytes.length + jpegBytes.length + footerBytes.length); + + return new Blob([result], { type: "application/pdf" }); +} diff --git a/widget/js/show2d/index.tsx b/widget/js/show2d/index.tsx new file mode 100644 index 00000000..aacf5c43 --- /dev/null +++ b/widget/js/show2d/index.tsx @@ -0,0 +1,4185 @@ +/** + * Show2D - Static 2D image viewer with gallery support. + * + * Features: + * - Single image or gallery mode with configurable columns + * - Scroll to zoom, double-click to reset + * - WebGPU-accelerated FFT with default 3x zoom + * - Equal-sized FFT and histogram panels + * - Click to select image in gallery mode + */ + +import * as React from "react"; +import { createRender, useModelState } from "@anywidget/react"; +import Box from "@mui/material/Box"; +import Typography from "@mui/material/Typography"; +import Stack from "@mui/material/Stack"; +import Select from "@mui/material/Select"; +import MenuItem from "@mui/material/MenuItem"; +import Menu from "@mui/material/Menu"; +import Switch from "@mui/material/Switch"; +import Slider from "@mui/material/Slider"; +import Button from "@mui/material/Button"; +import Tooltip from "@mui/material/Tooltip"; +import { useTheme } from "../theme"; +import { drawScaleBarHiDPI, drawFFTScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; +import JSZip from "jszip"; +import { extractFloat32, formatNumber, downloadBlob } from "../format"; +import { computeHistogramFromBytes } from "../histogram"; +import { findDataRange, applyLogScale, percentileClip, sliderRange, computeStats } from "../stats"; +import { ControlCustomizer } from "../control-customizer"; +import { computeToolVisibility } from "../tool-parity"; + +function InfoTooltip({ text, theme = "dark" }: { text: React.ReactNode; theme?: "light" | "dark" }) { + const isDark = theme === "dark"; + const content = typeof text === "string" + ? {text} + : text; + return ( + + ⓘ + + ); +} + +function KeyboardShortcuts({ items }: { items: [string, string][] }) { + return ( + + + {items.map(([key, desc], i) => ( + {key}{desc} + ))} + + + ); +} + +const upwardMenuProps = { + anchorOrigin: { vertical: "top" as const, horizontal: "left" as const }, + transformOrigin: { vertical: "bottom" as const, horizontal: "left" as const }, + sx: { zIndex: 9999 }, +}; +import { getWebGPUFFT, WebGPUFFT, fft2d, fft2dAsync, fftshift, computeMagnitude, autoEnhanceFFT, nextPow2, applyHannWindow2D, getGPUInfo } from "../webgpu-fft"; +import { COLORMAPS, COLORMAP_NAMES, renderToOffscreen, renderToOffscreenReuse, GPUColormapEngine, getGPUColormapEngine, getGPUMaxBufferSize } from "../colormaps"; +import "./show2d.css"; + +const MIN_ZOOM = 0.5; +const MAX_ZOOM = 20; + +const DPR = window.devicePixelRatio || 1; + +interface HistogramProps { + data: Float32Array | null; + precomputedBins?: number[] | null; // GPU-computed bins bypass computeHistogramFromBytes + vminPct: number; + vmaxPct: number; + onRangeChange: (min: number, max: number) => void; + width?: number; + height?: number; + theme?: "light" | "dark"; + dataMin?: number; + dataMax?: number; +} + +function Histogram({ data, precomputedBins, vminPct, vmaxPct, onRangeChange, width = 110, height = 40, theme = "dark", dataMin = 0, dataMax = 1 }: HistogramProps) { + const canvasRef = React.useRef(null); + const cpuBins = React.useMemo(() => precomputedBins ? null : computeHistogramFromBytes(data), [data, precomputedBins]); + const bins = precomputedBins || cpuBins || new Array(256).fill(0); + const isDark = theme === "dark"; + const colors = isDark ? { bg: "#1a1a1a", barActive: "#888", barInactive: "#444", border: "#333" } : { bg: "#f0f0f0", barActive: "#666", barInactive: "#bbb", border: "#ccc" }; + + React.useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + const dpr = window.devicePixelRatio || 1; + canvas.width = width * dpr; + canvas.height = height * dpr; + ctx.scale(dpr, dpr); + ctx.fillStyle = colors.bg; + ctx.fillRect(0, 0, width, height); + const displayBins = 64; + const binRatio = Math.floor(bins.length / displayBins); + const reducedBins: number[] = []; + for (let i = 0; i < displayBins; i++) { + let sum = 0; + for (let j = 0; j < binRatio; j++) sum += bins[i * binRatio + j] || 0; + reducedBins.push(sum / binRatio); + } + const maxVal = Math.max(...reducedBins, 0.001); + const barWidth = width / displayBins; + const vminBin = Math.floor((vminPct / 100) * displayBins); + const vmaxBin = Math.floor((vmaxPct / 100) * displayBins); + for (let i = 0; i < displayBins; i++) { + const barHeight = (reducedBins[i] / maxVal) * (height - 2); + ctx.fillStyle = (i >= vminBin && i <= vmaxBin) ? colors.barActive : colors.barInactive; + ctx.fillRect(i * barWidth + 0.5, height - barHeight, Math.max(1, barWidth - 1), barHeight); + } + }, [bins, vminPct, vmaxPct, width, height, colors]); + + return ( + + + { const [newMin, newMax] = v as number[]; onRangeChange(Math.min(newMin, newMax - 1), Math.max(newMax, newMin + 1)); }} + min={0} max={100} size="small" valueLabelDisplay="auto" + valueLabelFormat={(pct) => { const val = dataMin + (pct / 100) * (dataMax - dataMin); return val >= 1000 ? val.toExponential(1) : val.toFixed(1); }} + sx={{ width, py: 0, "& .MuiSlider-thumb": { width: 8, height: 8 }, "& .MuiSlider-rail": { height: 2 }, "& .MuiSlider-track": { height: 2 }, "& .MuiSlider-valueLabel": { fontSize: 10, padding: "2px 4px" } }} + /> + {(() => { const v = dataMin + (vminPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()}{(() => { const v = dataMin + (vmaxPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()} + + ); +} + +// ============================================================================ +// Line profile sampling (bilinear interpolation along line) +// ============================================================================ +function sampleLineProfile(data: Float32Array, w: number, h: number, row0: number, col0: number, row1: number, col1: number): Float32Array { + const dc = col1 - col0; + const dr = row1 - row0; + const len = Math.sqrt(dc * dc + dr * dr); + const n = Math.max(2, Math.ceil(len)); + const out = new Float32Array(n); + for (let i = 0; i < n; i++) { + const t = i / (n - 1); + const c = col0 + t * dc; + const r = row0 + t * dr; + const ci = Math.floor(c), ri = Math.floor(r); + const cf = c - ci, rf = r - ri; + const c0c = Math.max(0, Math.min(w - 1, ci)); + const c1c = Math.max(0, Math.min(w - 1, ci + 1)); + const r0c = Math.max(0, Math.min(h - 1, ri)); + const r1c = Math.max(0, Math.min(h - 1, ri + 1)); + out[i] = data[r0c * w + c0c] * (1 - cf) * (1 - rf) + + data[r0c * w + c1c] * cf * (1 - rf) + + data[r1c * w + c0c] * (1 - cf) * rf + + data[r1c * w + c1c] * cf * rf; + } + return out; +} + +function pointToSegmentDistance(col: number, row: number, col0: number, row0: number, col1: number, row1: number): number { + const dc = col1 - col0; + const dr = row1 - row0; + const lenSq = dc * dc + dr * dr; + if (lenSq <= 1e-12) return Math.sqrt((col - col0) ** 2 + (row - row0) ** 2); + const tRaw = ((col - col0) * dc + (row - row0) * dr) / lenSq; + const t = Math.max(0, Math.min(1, tRaw)); + const projCol = col0 + t * dc; + const projRow = row0 + t * dr; + return Math.sqrt((col - projCol) ** 2 + (row - projRow) ** 2); +} + +// ============================================================================ +// FFT peak finder (snap to Bragg spot with sub-pixel centroid refinement) +// ============================================================================ +function findFFTPeak(mag: Float32Array, width: number, height: number, col: number, row: number, radius: number): { row: number; col: number } { + // Find brightest pixel in search window + const c0 = Math.max(0, Math.floor(col) - radius); + const r0 = Math.max(0, Math.floor(row) - radius); + const c1 = Math.min(width - 1, Math.floor(col) + radius); + const r1 = Math.min(height - 1, Math.floor(row) + radius); + let bestCol = Math.round(col), bestRow = Math.round(row), bestVal = -Infinity; + for (let ir = r0; ir <= r1; ir++) { + for (let ic = c0; ic <= c1; ic++) { + const val = mag[ir * width + ic]; + if (val > bestVal) { bestVal = val; bestCol = ic; bestRow = ir; } + } + } + // Sub-pixel refinement via weighted centroid in 3×3 window + const wc0 = Math.max(0, bestCol - 1), wc1 = Math.min(width - 1, bestCol + 1); + const wr0 = Math.max(0, bestRow - 1), wr1 = Math.min(height - 1, bestRow + 1); + let sumW = 0, sumWC = 0, sumWR = 0; + for (let ir = wr0; ir <= wr1; ir++) { + for (let ic = wc0; ic <= wc1; ic++) { + const w = mag[ir * width + ic]; + sumW += w; sumWC += w * ic; sumWR += w * ir; + } + } + if (sumW > 0) return { row: sumWR / sumW, col: sumWC / sumW }; + return { row: bestRow, col: bestCol }; +} + +const FFT_SNAP_RADIUS = 5; + +// ============================================================================ +// Types +// ============================================================================ +type ZoomState = { zoom: number; panX: number; panY: number }; + +// ============================================================================ +// Constants +// ============================================================================ +const SINGLE_IMAGE_TARGET = 500; +const GALLERY_IMAGE_TARGET = 300; +const DEFAULT_FFT_ZOOM = 3; +const PROFILE_COLORS = ["#4fc3f7", "#81c784", "#ffb74d", "#ce93d8", "#ef5350", "#ffd54f", "#90a4ae", "#a1887f"]; +type ROIItem = { row: number; col: number; shape: string; radius: number; radius_inner: number; width: number; height: number; color: string; line_width: number; highlight: boolean }; +const ROI_COLORS = ["#4fc3f7", "#81c784", "#ffb74d", "#ce93d8", "#ef5350", "#ffd54f", "#90a4ae", "#a1887f"]; +const RESIZE_HIT_AREA_PX = 10; + +function drawROI( + ctx: CanvasRenderingContext2D, + x: number, y: number, + shape: "circle" | "square" | "rectangle" | "annular", + radius: number, w: number, h: number, + activeColor: string, inactiveColor: string, + active: boolean = false, innerRadius: number = 0 +): void { + const strokeColor = active ? activeColor : inactiveColor; + ctx.strokeStyle = strokeColor; + if (shape === "circle") { + ctx.beginPath(); ctx.arc(x, y, radius, 0, Math.PI * 2); ctx.stroke(); + } else if (shape === "square") { + ctx.strokeRect(x - radius, y - radius, radius * 2, radius * 2); + } else if (shape === "rectangle") { + ctx.strokeRect(x - w / 2, y - h / 2, w, h); + } else if (shape === "annular") { + ctx.beginPath(); ctx.arc(x, y, radius, 0, Math.PI * 2); ctx.stroke(); + ctx.strokeStyle = active ? "#0ff" : inactiveColor; + ctx.beginPath(); ctx.arc(x, y, innerRadius, 0, Math.PI * 2); ctx.stroke(); + ctx.fillStyle = (active ? activeColor : inactiveColor) + "15"; + ctx.beginPath(); ctx.arc(x, y, radius, 0, Math.PI * 2); ctx.arc(x, y, innerRadius, 0, Math.PI * 2, true); ctx.fill(); + ctx.strokeStyle = strokeColor; + } + if (active) { + ctx.beginPath(); + ctx.moveTo(x - 5, y); ctx.lineTo(x + 5, y); + ctx.moveTo(x, y - 5); ctx.lineTo(x, y + 5); + ctx.stroke(); + } +} + +// ============================================================================ +// Crop ROI region from raw float32 data for ROI-scoped FFT +// ============================================================================ +function cropROIRegion( + data: Float32Array, imgW: number, imgH: number, + roi: ROIItem, +): { cropped: Float32Array; cropW: number; cropH: number } | null { + const shape = roi.shape || "circle"; + let x0: number, y0: number, x1: number, y1: number; + + if (shape === "rectangle") { + const hw = roi.width / 2; + const hh = roi.height / 2; + x0 = Math.max(0, Math.floor(roi.col - hw)); + y0 = Math.max(0, Math.floor(roi.row - hh)); + x1 = Math.min(imgW, Math.ceil(roi.col + hw)); + y1 = Math.min(imgH, Math.ceil(roi.row + hh)); + } else { + const r = roi.radius; + x0 = Math.max(0, Math.floor(roi.col - r)); + y0 = Math.max(0, Math.floor(roi.row - r)); + x1 = Math.min(imgW, Math.ceil(roi.col + r)); + y1 = Math.min(imgH, Math.ceil(roi.row + r)); + } + + const cropW = x1 - x0; + const cropH = y1 - y0; + if (cropW < 2 || cropH < 2) return null; + + const cropped = new Float32Array(cropW * cropH); + + if (shape === "circle" || shape === "annular") { + const r = roi.radius; + const rSq = r * r; + for (let dy = 0; dy < cropH; dy++) { + for (let dx = 0; dx < cropW; dx++) { + const imgX = x0 + dx; + const imgY = y0 + dy; + const distSq = (imgX - roi.col) * (imgX - roi.col) + (imgY - roi.row) * (imgY - roi.row); + cropped[dy * cropW + dx] = distSq <= rSq ? data[imgY * imgW + imgX] : 0; + } + } + } else { + for (let dy = 0; dy < cropH; dy++) { + const srcOffset = (y0 + dy) * imgW + x0; + cropped.set(data.subarray(srcOffset, srcOffset + cropW), dy * cropW); + } + } + + return { cropped, cropW, cropH }; +} + +// ============================================================================ +// Main Component +// ============================================================================ +// Show4DSTEM-style UI constants +const typography = { + label: { fontSize: 11 }, + labelSmall: { fontSize: 10 }, + value: { fontSize: 10, fontFamily: "monospace" }, +}; +const SPACING = { XS: 4, SM: 8, MD: 12, LG: 16 }; +const controlRow = { + display: "flex", + alignItems: "center", + gap: `${SPACING.SM}px`, + px: 1, + py: 0.5, + width: "fit-content", +}; +const compactButton = { + fontSize: 10, + py: 0.25, + px: 1, + minWidth: 0, + "&.Mui-disabled": { + color: "#666", + borderColor: "#444", + }, +}; +const switchStyles = { + small: { "& .MuiSwitch-thumb": { width: 12, height: 12 }, "& .MuiSwitch-switchBase": { padding: "4px" } }, +}; +const sliderStyles = { + small: { py: 0, "& .MuiSlider-thumb": { width: 10, height: 10 }, "& .MuiSlider-rail": { height: 2 }, "& .MuiSlider-track": { height: 2 } }, +}; + +function Show2D() { + // Theme + const { themeInfo, colors: tc } = useTheme(); + const themeColors = { + ...tc, + accentGreen: themeInfo.theme === "dark" ? "#0f0" : "#1a7a1a", + }; + + const themedSelect = { + fontSize: 10, + bgcolor: themeColors.controlBg, + color: themeColors.text, + "& .MuiSelect-select": { py: 0.5 }, + "& .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.border }, + "&:hover .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.accent }, + }; + + const themedMenuProps = { + ...upwardMenuProps, + PaperProps: { sx: { bgcolor: themeColors.controlBg, color: themeColors.text, border: `1px solid ${themeColors.border}` } }, + }; + + // Model state + const [nImages] = useModelState("n_images"); + const [width] = useModelState("width"); + const [height] = useModelState("height"); + const [frameBytes] = useModelState("frame_bytes"); + const [labels] = useModelState("labels"); + const [title] = useModelState("title"); + const [displayBinFactor] = useModelState("_display_bin_factor"); + const [, setGpuMaxBufferMB] = useModelState("_gpu_max_buffer_mb"); + const [widgetVersion] = useModelState("widget_version"); + const [cmap, setCmap] = useModelState("cmap"); + const [ncols] = useModelState("ncols"); + + // Display options + const [logScale, setLogScale] = useModelState("log_scale"); + const [autoContrast, setAutoContrast] = useModelState("auto_contrast"); + const [traitVmin] = useModelState("vmin"); + const [traitVmax] = useModelState("vmax"); + const [traitVmins] = useModelState<(number | null)[] | null>("vmins"); + const [traitVmaxs] = useModelState<(number | null)[] | null>("vmaxs"); + const [zoomRowTrait] = useModelState("zoom_row"); + const [zoomColTrait] = useModelState("zoom_col"); + const [diffMode, setDiffMode] = useModelState("diff_mode"); + const [diffReference] = useModelState("diff_reference"); + // Align removed — diff = A − B (no shift). Drift correction happens upstream. + const alignDy = 0; + const alignDx = 0; + + // Customization + const [canvasSizeTrait] = useModelState("size"); + const [smooth, setSmooth] = useModelState("smooth"); + const imageRenderingStyle = smooth ? "auto" : "pixelated"; + + // Scale bar + const [pixelSize] = useModelState("pixel_size"); + const [scaleBarVisible] = useModelState("scale_bar_visible"); + + // UI visibility + const [showControls] = useModelState("show_controls"); + const [showStats] = useModelState("show_stats"); + const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); + const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); + const [statsMean] = useModelState("stats_mean"); + const [statsMin] = useModelState("stats_min"); + const [statsMax] = useModelState("stats_max"); + const [statsStd] = useModelState("stats_std"); + + // Analysis Panels (FFT + Histogram) + const [showFft, setShowFft] = useModelState("show_fft"); + const [fftWindow, setFftWindow] = useModelState("fft_window"); + + // Selection + const [selectedIdx, setSelectedIdx] = useModelState("selected_idx"); + + // ROI + const [roiActive, setRoiActive] = useModelState("roi_active"); + const [roiList, setRoiList] = useModelState("roi_list"); + const [roiSelectedIdx, setRoiSelectedIdx] = useModelState("roi_selected_idx"); + const [imageRotations, setImageRotations] = useModelState("image_rotations"); + const [isDraggingROI, setIsDraggingROI] = React.useState(false); + const [isDraggingResize, setIsDraggingResize] = React.useState(false); + const [isDraggingResizeInner, setIsDraggingResizeInner] = React.useState(false); + const [isHoveringResize, setIsHoveringResize] = React.useState(false); + const [isHoveringResizeInner, setIsHoveringResizeInner] = React.useState(false); + const resizeAspectRef = React.useRef(null); + const [newRoiShape, setNewRoiShape] = React.useState<"circle" | "square" | "rectangle" | "annular">("square"); + const [exportAnchor, setExportAnchor] = React.useState(null); + const selectedRoi = roiSelectedIdx >= 0 && roiSelectedIdx < (roiList?.length ?? 0) ? roiList[roiSelectedIdx] : null; + + const toolVisibility = React.useMemo( + () => computeToolVisibility("Show2D", disabledTools, hiddenTools), + [disabledTools, hiddenTools], + ); + const hideDisplay = toolVisibility.isHidden("display"); + const hideHistogram = toolVisibility.isHidden("histogram"); + const hideStats = toolVisibility.isHidden("stats"); + const hideView = toolVisibility.isHidden("view"); + const hideExport = toolVisibility.isHidden("export"); + const hideRoi = toolVisibility.isHidden("roi"); + const hideProfile = toolVisibility.isHidden("profile"); + + const lockDisplay = toolVisibility.isLocked("display"); + const lockHistogram = toolVisibility.isLocked("histogram"); + const lockStats = toolVisibility.isLocked("stats"); + const lockNavigation = toolVisibility.isLocked("navigation"); + const lockView = toolVisibility.isLocked("view"); + const lockExport = toolVisibility.isLocked("export"); + const lockRoi = toolVisibility.isLocked("roi"); + const lockProfile = toolVisibility.isLocked("profile"); + const effectiveShowFft = showFft && !hideDisplay; + + const updateSelectedRoi = (updates: Partial) => { + if (lockRoi) return; + if (roiSelectedIdx < 0 || !roiList) return; + const newList = [...roiList]; + newList[roiSelectedIdx] = { ...newList[roiSelectedIdx], ...updates }; + setRoiList(newList); + }; + + React.useEffect(() => { + if (hideRoi && roiActive) { + setRoiActive(false); + setRoiSelectedIdx(-1); + } + }, [hideRoi, roiActive, setRoiActive, setRoiSelectedIdx]); + + // Canvas refs + const canvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const overlayRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const imageContainerRefs = React.useRef<(HTMLDivElement | null)[]>([]); + const fftContainerRefs = React.useRef<(HTMLDivElement | null)[]>([]); + const singleFftContainerRef = React.useRef(null); + const fftCanvasRef = React.useRef(null); + const [canvasReady, setCanvasReady] = React.useState(0); // Trigger re-render when refs attached + + // Zoom/Pan state - per-image when not linked, shared when linked + const [initialZoom] = useModelState("initial_zoom"); + const [linkPan, setLinkPan] = useModelState("link_pan"); + const [imgHeight] = useModelState("height"); + const [imgWidth] = useModelState("width"); + // Note: pan derived from zoom_row/zoom_col is applied via a useEffect AFTER canvasW/canvasH + // are computed (see "Initial pan from zoom_row/zoom_col" effect below). + const initialZoomState: ZoomState = React.useMemo( + () => ({ zoom: Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, initialZoom || 1)), panX: 0, panY: 0 }), + [initialZoom] + ); + void linkPan; void setLinkPan; void imgWidth; void imgHeight; + const [zoomStates, setZoomStates] = React.useState>(new Map()); + const [linkedZoomState, setLinkedZoomState] = React.useState(initialZoomState); + const [linkedZoom, setLinkedZoom] = useModelState("link_zoom"); + const [isDraggingPan, setIsDraggingPan] = React.useState(false); + const [panStart, setPanStart] = React.useState<{ x: number, y: number, pX: number, pY: number } | null>(null); + + // Helper to get zoom state for an image. zoom and pan link independently: + // zoom from linkedZoomState if linkedZoom else per-image + // pan from linkedZoomState if linkPan else per-image + const getZoomState = React.useCallback((idx: number): ZoomState => { + const per = zoomStates.get(idx) || initialZoomState; + return { + zoom: linkedZoom ? linkedZoomState.zoom : per.zoom, + panX: linkPan ? linkedZoomState.panX : per.panX, + panY: linkPan ? linkedZoomState.panY : per.panY, + }; + }, [linkedZoom, linkPan, linkedZoomState, zoomStates, initialZoomState]); + + // Helper to set zoom state for an image. zoom and pan honored independently: + // zoom: writes to linkedZoomState if linkedZoom, else per-image + // pan: writes to linkedZoomState if linkPan, else per-image + const setZoomState = React.useCallback((idx: number, state: ZoomState) => { + if (linkedZoom || linkPan) { + setLinkedZoomState(prev => ({ + zoom: linkedZoom ? state.zoom : prev.zoom, + panX: linkPan ? state.panX : prev.panX, + panY: linkPan ? state.panY : prev.panY, + })); + } + if (!linkedZoom || !linkPan) { + setZoomStates(prev => { + const m = new Map(prev); + const cur = m.get(idx) || initialZoomState; + m.set(idx, { + zoom: linkedZoom ? cur.zoom : state.zoom, + panX: linkPan ? cur.panX : state.panX, + panY: linkPan ? cur.panY : state.panY, + }); + return m; + }); + } + }, [linkedZoom, linkPan, initialZoomState]); + + // FFT zoom/pan state (single mode) + const [fftZoom, setFftZoom] = React.useState(DEFAULT_FFT_ZOOM); + const [fftPanX, setFftPanX] = React.useState(0); + const [fftPanY, setFftPanY] = React.useState(0); + const [isDraggingFftPan, setIsDraggingFftPan] = React.useState(false); + const [fftPanStart, setFftPanStart] = React.useState<{ x: number, y: number, pX: number, pY: number } | null>(null); + + // Histogram state — per-image contrast ranges (gallery) or single (one image) + const [linkedContrast, setLinkedContrast] = useModelState("link_contrast"); + const [linkedContrastState, setLinkedContrastState] = React.useState<{ vminPct: number; vmaxPct: number }>({ vminPct: 0, vmaxPct: 100 }); + const [contrastStates, setContrastStates] = React.useState>(new Map()); + // Ref mirror for fast slider path (bypass React effect batching) + const contrastRef = React.useRef<{ linked: { vminPct: number; vmaxPct: number }; perImage: Map }>({ linked: { vminPct: 0, vmaxPct: 100 }, perImage: new Map() }); + const sliderRafRef = React.useRef(0); + const getContrastState = React.useCallback((idx: number) => { + if (linkedContrast) return linkedContrastState; + return contrastStates.get(idx) || { vminPct: 0, vmaxPct: 100 }; + }, [linkedContrast, linkedContrastState, contrastStates]); + const setContrastState = React.useCallback((idx: number, state: { vminPct: number; vmaxPct: number }) => { + // Update ref immediately (for fast rAF render) + if (linkedContrast) { + contrastRef.current.linked = state; + setLinkedContrastState(state); + } else { + contrastRef.current.perImage.set(idx, state); + setContrastStates(prev => new Map(prev).set(idx, state)); + } + // Fast path: direct GPU render via rAF, bypassing React effect batching + const engine = gpuCmapRef.current; + if (engine && gpuCmapReadyRef.current && engine.slotCount >= nImages) { + cancelAnimationFrame(sliderRafRef.current); + sliderRafRef.current = requestAnimationFrame(() => { + const cachedRanges = dataRangesRef.current; + if (cachedRanges.length === 0) return; + const lut = COLORMAPS[cmapRef.current] || COLORMAPS.inferno; + engine.uploadLUT(cmapRef.current, lut); + const indices = Array.from({ length: nImages }, (_, i) => i); + const ranges: { vmin: number; vmax: number }[] = []; + for (let i = 0; i < nImages; i++) { + const cs = linkedContrast ? contrastRef.current.linked : (contrastRef.current.perImage.get(i) || { vminPct: 0, vmaxPct: 100 }); + let cr = cachedRanges[i]; + if (!cr || cr.min === cr.max) { + if (rawDataRef.current && rawDataRef.current[i]) cr = findDataRange(rawDataRef.current[i]); + } + cr = cr || { min: 0, max: 1 }; + if (cs.vminPct > 0 || cs.vmaxPct < 100) { + ranges.push(sliderRange(cr.min, cr.max, cs.vminPct, cs.vmaxPct)); + } else { + ranges.push({ vmin: cr.min, vmax: cr.max }); + } + } + const ls = logScaleRef.current ?? false; + const bitmaps = engine.renderSlotsToImageBitmap(indices, ranges, ls); + if (bitmaps && bitmaps[0]) { + for (let i = 0; i < bitmaps.length; i++) { + const offscreen = mainOffscreensRef.current[i]; + if (offscreen && bitmaps[i]) offscreen.getContext("2d")?.drawImage(bitmaps[i], 0, 0); + } + setOffscreenVersion(v => v + 1); + } + }); + } + }, [linkedContrast, nImages]); + // Convenience accessors for active image + const activeContrastIdx = nImages > 1 ? selectedIdx : 0; + const imageVminPct = getContrastState(activeContrastIdx).vminPct; + const imageVmaxPct = getContrastState(activeContrastIdx).vmaxPct; + + const [imageHistogramData, setImageHistogramData] = React.useState(null); + const [imageHistogramBins, setImageHistogramBins] = React.useState(null); + const [imageDataRange, setImageDataRange] = React.useState<{ min: number; max: number }>({ min: 0, max: 1 }); + + // FFT display state (single mode) + const [fftVminPct, setFftVminPct] = React.useState(0); + const [fftVmaxPct, setFftVmaxPct] = React.useState(100); + const [fftHistogramData, setFftHistogramData] = React.useState(null); + const [fftDataRange, setFftDataRange] = React.useState<{ min: number; max: number }>({ min: 0, max: 1 }); + const [fftColormap, setFftColormap] = React.useState("inferno"); + const [fftScaleMode, setFftScaleMode] = React.useState<"linear" | "log" | "power">("linear"); + const [fftAuto, setFftAuto] = React.useState(true); + const [fftStats, setFftStats] = React.useState(null); + const [fftShowColorbar, setFftShowColorbar] = React.useState(false); + + // FFT loading state — shown as a pulsing overlay while FFT computes + const [fftComputing, setFftComputing] = React.useState(false); + const [fftProgress, setFftProgress] = React.useState(""); + + // Cursor readout state + const [cursorInfo, setCursorInfo] = React.useState<{ row: number; col: number; value: number } | null>(null); + + // Colorbar state (single image mode only) + const [showColorbar, setShowColorbar] = React.useState(false); + + // Inset magnifier state + const [showLens, setShowLens] = React.useState(false); + const [lensPos, setLensPos] = React.useState<{ row: number; col: number } | null>(null); + const [lensMag, setLensMag] = React.useState(4); // magnification 2×–8× + const [lensDisplaySize, setLensDisplaySize] = React.useState(128); // CSS px 64–256 + const [lensAnchor, setLensAnchor] = React.useState<{ x: number; y: number } | null>(null); // custom position (CSS px from top-left of canvas) + const [isDraggingLens, setIsDraggingLens] = React.useState(false); + const [isResizingLens, setIsResizingLens] = React.useState(false); + const [isHoveringLensEdge, setIsHoveringLensEdge] = React.useState(false); + const lensDragStartRef = React.useRef<{ mx: number; my: number; ax: number; ay: number } | null>(null); + const lensResizeStartRef = React.useRef<{ my: number; startSize: number } | null>(null); + const lensCanvasRef = React.useRef(null); + + // FFT d-spacing measurement + const [fftClickInfo, setFftClickInfo] = React.useState<{ + row: number; col: number; distPx: number; + spatialFreq: number | null; dSpacing: number | null; + } | null>(null); + const fftClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const fftOverlayRef = React.useRef(null); + + // Line profile state + const [profileActive, setProfileActive] = React.useState(false); + const [profileLine, setProfileLine] = useModelState<{ row: number; col: number }[]>("profile_line"); + const [profileDataAll, setProfileDataAll] = React.useState<(Float32Array | null)[]>([]); + React.useEffect(() => { + if (hideProfile && profileActive) { + setProfileActive(false); + } + }, [hideProfile, profileActive]); + const profileCanvasRef = React.useRef(null); + const profileBaseImageRef = React.useRef(null); + const profileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); + + // Sync profile points from model state + const profilePoints = profileLine || []; + const setProfilePoints = (pts: { row: number; col: number }[]) => setProfileLine(pts); + + // Distance measurement state (JS-only, not persisted) + const [measureActive, setMeasureActive] = React.useState(false); + const [measurePoints, setMeasurePoints] = React.useState<{row: number; col: number}[]>([]); + + // FFT zoom/pan state (gallery mode — per-image or linked) + const [galleryFftStates, setGalleryFftStates] = React.useState>(new Map()); + const [linkedFftZoomState, setLinkedFftZoomState] = React.useState({ zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }); + const [fftPanningIdx, setFftPanningIdx] = React.useState(null); + const getGalleryFftState = React.useCallback((idx: number) => { + if (linkedZoom) return linkedFftZoomState; + return galleryFftStates.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; + }, [linkedZoom, linkedFftZoomState, galleryFftStates]); + const setGalleryFftState = React.useCallback((idx: number, state: ZoomState) => { + if (linkedZoom) { + setLinkedFftZoomState(state); + } else { + setGalleryFftStates(prev => new Map(prev).set(idx, state)); + } + }, [linkedZoom]); + + // Resizable state (gallery starts smaller) + const [canvasSize, setCanvasSize] = React.useState(nImages > 1 ? GALLERY_IMAGE_TARGET : SINGLE_IMAGE_TARGET); + + // Sync initial sizes from traits + React.useEffect(() => { + if (canvasSizeTrait > 0) setCanvasSize(canvasSizeTrait); + }, [canvasSizeTrait]); + + const [isResizingCanvas, setIsResizingCanvas] = React.useState(false); + const [resizeStart, setResizeStart] = React.useState<{ x: number, y: number, size: number } | null>(null); + + // Profile height resize + const [profileHeight, setProfileHeight] = React.useState(76); + const [isResizingProfile, setIsResizingProfile] = React.useState(false); + const [profileResizeStart, setProfileResizeStart] = React.useState<{ y: number; height: number } | null>(null); + + // WebGPU FFT + const gpuFFTRef = React.useRef(null); + const gpuReadyRef = React.useRef(false); + const rawDataRef = React.useRef(null); + const diffCanvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const diffFftCanvasRef = React.useRef(null); + const diffFftMagRef = React.useRef(null); + + // WebGPU colormap engine — uses refs (not state) to avoid re-triggering + // effects when GPU initializes. Effects check refs opportunistically: + // on first render they use CPU, on subsequent renders (data/slider change) + // they use GPU if available. No double computation. + const gpuCmapRef = React.useRef(null); + const gpuCmapReadyRef = React.useRef(false); + + // Cached offscreen canvases for main image rendering (avoids per-zoom/pan recompute) + const mainOffscreensRef = React.useRef([]); + const mainImgDatasRef = React.useRef([]); + const logBufferRef = React.useRef(null); + const colorbarVminRef = React.useRef(0); + const colorbarVmaxRef = React.useRef(1); + const [offscreenVersion, setOffscreenVersion] = React.useState(0); + + // Truthful first-render signal: flipped ONCE after the first colormap pass has + // actually painted. Python side observes `_js_rendered` and prints the real + // end-to-end wall clock. Two rAFs ensure the browser has composited before we + // fire, so the printed time reflects "user can see the widget," not "data arrived." + const [, setJsRendered] = useModelState("_js_rendered"); + const firstRenderFiredRef = React.useRef(false); + React.useEffect(() => { + if (firstRenderFiredRef.current) return; + if (offscreenVersion === 0) return; + firstRenderFiredRef.current = true; + requestAnimationFrame(() => requestAnimationFrame(() => setJsRendered(true))); + }, [offscreenVersion, setJsRendered]); + + // Inline FFT refs for gallery mode + const fftCanvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); + const fftOffscreensRef = React.useRef<(HTMLCanvasElement | null)[]>([]); + const fftMagCacheGalleryRef = React.useRef<(Float32Array | null)[]>([]); + const galleryFftDimsRef = React.useRef<{ w: number; h: number } | null>(null); + const [galleryFftMagVersion, setGalleryFftMagVersion] = React.useState(0); + + // Cached FFT magnitude for single image mode (avoids recomputing on zoom/pan) + const fftMagCacheRef = React.useRef(null); + const [fftMagVersion, setFftMagVersion] = React.useState(0); + // Generation counter for FFT — coalesces rapid ROI drag events to ≤1 FFT/frame + const fftGenRef = React.useRef(0); + + // Cached FFT offscreen canvas for single mode (avoids reprocessing on zoom/pan) + const fftOffscreenRef = React.useRef(null); + const [fftOffscreenVersion, setFftOffscreenVersion] = React.useState(0); + + // ROI FFT state: when ROI + FFT are both active, compute FFT of cropped ROI region + const [fftCropDims, setFftCropDims] = React.useState<{ cropWidth: number; cropHeight: number; fftWidth: number; fftHeight: number } | null>(null); + + // Layout calculations + const isGallery = nImages > 1; + const showDiffPanel = diffMode && nImages >= 2; + const diffPanelCount = showDiffPanel ? Math.max(0, nImages - 1) : 0; + const effectiveNcols = Math.min(ncols, nImages) + diffPanelCount; + const diffOtherIndices = React.useMemo( + () => Array.from({ length: nImages }, (_, i) => i).filter(i => i !== diffReference), + [nImages, diffReference] + ); + const displayScale = canvasSize / Math.max(width, height); + const canvasW = Math.round(width * displayScale); + const canvasH = Math.round(height * displayScale); + + // Initial pan from zoom_row/zoom_col — runs once after first render with valid canvas dims. + // panX/panY computed so target image (zoomRow, zoomCol) lands at canvas center after transform: + // ctx.translate(cx+panX, cy+panY) ⋅ scale(zoom) ⋅ translate(-cx,-cy) + // target screen = cx + panX + zoom * (target_canvas - cx) = cx + // ⟹ panX = zoom * (cx - target_canvas) = zoom * canvasW * (0.5 - col/width) + const initialPanAppliedRef = React.useRef(false); + React.useEffect(() => { + if (initialPanAppliedRef.current) return; + if (zoomRowTrait == null && zoomColTrait == null) return; + if (canvasW <= 0 || canvasH <= 0 || width <= 0 || height <= 0) return; + const z = initialZoomState.zoom; + const panX = zoomColTrait != null ? z * canvasW * (0.5 - zoomColTrait / width) : 0; + const panY = zoomRowTrait != null ? z * canvasH * (0.5 - zoomRowTrait / height) : 0; + setLinkedZoomState({ zoom: z, panX, panY }); + setZoomStates(prev => { + const m = new Map(prev); + for (let i = 0; i < nImages; i++) m.set(i, { zoom: z, panX, panY }); + return m; + }); + initialPanAppliedRef.current = true; + }, [zoomRowTrait, zoomColTrait, canvasW, canvasH, width, height, nImages, initialZoomState.zoom]); + const floatsPerImage = width * height; + const galleryGridWidth = isGallery ? effectiveNcols * canvasW + (effectiveNcols - 1) * 8 : canvasW; + const profileCanvasWidth = galleryGridWidth; + + // ROI FFT active: both ROI and FFT on, with a selected ROI + const roiFftActive = effectiveShowFft && roiActive && roiSelectedIdx >= 0 && roiSelectedIdx < (roiList?.length ?? 0); + + // Stable key for ROI geometry — only changes when the selected ROI's geometry changes, + // not when other ROIs move or roiList gets a new reference from unrelated edits. + // Shared by both ROI FFT and preview panel to avoid redundant recomputes. + const selectedRoiKey = React.useMemo(() => { + if (!roiList || roiSelectedIdx < 0 || roiSelectedIdx >= roiList.length) return ""; + const r = roiList[roiSelectedIdx]; + return `${r.row},${r.col},${r.radius},${r.radius_inner},${r.width},${r.height},${r.shape}`; + }, [roiList, roiSelectedIdx]); + const roiFftKey = roiFftActive ? selectedRoiKey : ""; + + // Extract raw float32 bytes and parse into Float32Arrays + const allFloats = React.useMemo(() => extractFloat32(frameBytes), [frameBytes]); + + // Initialize WebGPU FFT + colormap engine on mount. + // Sets refs (not state) — no effect re-triggers on GPU init. + // Effects pick up GPU on their next natural re-run (data/slider change). + React.useEffect(() => { + getWebGPUFFT().then(fft => { + if (fft) { + gpuFFTRef.current = fft; + gpuReadyRef.current = true; + const info = getGPUInfo(); + console.log(`[Show2D] WebGPU FFT initialized — ${info || "GPU"}`); + } else { + console.log("[Show2D] WebGPU unavailable — using CPU Worker fallback"); + } + }); + getGPUColormapEngine().then(engine => { + if (engine) { + gpuCmapRef.current = engine; + gpuCmapReadyRef.current = true; + console.log("[Show2D] WebGPU colormap engine initialized"); + // Report GPU memory to Python for auto-bin budget + getGPUMaxBufferSize().then(bytes => { + if (bytes > 0) setGpuMaxBufferMB(Math.floor(bytes / (1024 * 1024))); + }); + // Upload data if already parsed (GPU init may be slower than data arrival). + // Do NOT call setState — that would re-trigger effects and cause double + // computation. Instead, upload data and do a warm-up render via rAF. + // This compiles the GPU pipeline in the background so the first user + // interaction is fast (~100ms instead of ~750ms cold start). + if (rawDataRef.current && rawDataRef.current.length > 0) { + const nImg = rawDataRef.current.length; + for (let i = 0; i < nImg; i++) { + const d = rawDataRef.current[i]; + if (d) engine.uploadData(i, d, width, height); + } + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + engine.uploadLUT(cmap, lut); + gpuDataVersionRef.current++; + // Warm-up: render once to compile GPU pipeline + fill canvases. + // Uses full data range (no slider adjustment) for the initial frame. + requestAnimationFrame(async () => { + const offscreens = mainOffscreensRef.current; + const imgDatas = mainImgDatasRef.current; + if (offscreens.length === 0 || imgDatas.length === 0) return; + const cachedRanges = dataRangesRef.current; + if (cachedRanges.length === 0) return; + const indices = Array.from({ length: nImg }, (_, i) => i); + const ranges = cachedRanges.map(r => ({ vmin: r.min, vmax: r.max })); + const ofs = indices.map(i => offscreens[i] || null); + const ids = indices.map(i => imgDatas[i] || null); + const logSc = logScaleRef.current ?? false; + await engine.renderSlots(indices, ranges, ofs, ids, logSc); + setOffscreenVersion(v => v + 1); + }); + } + } + }); + }, []); + + const [dataVersion, setDataVersion] = React.useState(0); + + // Keep inline FFT ref arrays in sync with nImages + React.useEffect(() => { + fftCanvasRefs.current = fftCanvasRefs.current.slice(0, nImages); + fftOffscreensRef.current = fftOffscreensRef.current.slice(0, nImages); + }, [nImages]); + + // FFT of diff (n=2 only). Computes A − B in JS at full image resolution from rawDataRef, + // feeds to FFT pipeline. Recomputes when raw data changes. + React.useEffect(() => { + if (!effectiveShowFft || !showDiffPanel || nImages !== 2) return; + const raw = rawDataRef.current; + if (!raw || raw.length < 2 || !raw[0] || !raw[1]) return; + const a = raw[0], b = raw[1]; + const bytes = new Float32Array(width * height); + for (let i = 0; i < bytes.length; i++) bytes[i] = a[i] - b[i]; + const canvas = diffFftCanvasRef.current; + if (!canvas) return; + const fftW = nextPow2(width), fftH = nextPow2(height); + const real = new Float32Array(fftW * fftH); + const imag = new Float32Array(fftW * fftH); + const src = new Float32Array(bytes); + if (fftWindow) applyHannWindow2D(src, width, height); + const padR = Math.floor((fftH - height) / 2), padC = Math.floor((fftW - width) / 2); + for (let r = 0; r < height; r++) { + for (let c = 0; c < width; c++) real[(r + padR) * fftW + c + padC] = src[r * width + c]; + } + let cancelled = false; + (async () => { + const result = await fft2dAsync(real, imag, fftW, fftH, false); + if (cancelled) return; + const mag = computeMagnitude(result.real, result.imag); + fftshift(mag, fftW, fftH); + diffFftMagRef.current = mag; + const { min, max } = autoEnhanceFFT(mag, fftW, fftH); + const off = renderToOffscreen(mag, fftW, fftH, COLORMAPS[fftColormap] || COLORMAPS.inferno, min, max); + if (!off) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.drawImage(off, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); + })(); + return () => { cancelled = true; }; + }, [effectiveShowFft, showDiffPanel, nImages, dataVersion, width, height, fftWindow, fftColormap, canvasW, canvasH]); + + // Diff panels render — DYNAMIC. One per non-reference image: image[ref] − image[i]. + // Computed at canvas resolution from raw float data, re-running on zoom/pan/align change. + // For n=2: alignDy/dx applied to non-ref image. For n>2: no align (per-pair align not yet supported). + React.useEffect(() => { + if (!showDiffPanel) return; + const raw = rawDataRef.current; + if (!raw || raw.length < 2) return; + const ref = diffReference; + const a = raw[ref]; + if (!a) return; + diffOtherIndices.forEach((otherIdx, slot) => { + renderDiffPanel(slot, a, raw[otherIdx], otherIdx); + }); + // forEach inlines below — extracted as effect helper. + function renderDiffPanel(slot: number, refData: Float32Array, otherData: Float32Array | undefined, otherIdx: number) { + if (!otherData) return; + const canvas = diffCanvasRefs.current[slot]; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + const zs0 = getZoomState(ref); + const zs1 = getZoomState(otherIdx); + const useAlign = nImages === 2; + const adY = useAlign ? alignDy : 0; + const adX = useAlign ? alignDx : 0; + const a = refData, b = otherData; + const cw = canvasW, ch = canvasH; + const cx = cw / 2, cy = ch / 2; + const sx = width / cw, sy = height / ch; + const diff = new Float32Array(cw * ch); + let mn = Infinity, mx = -Infinity; + // Smooth: bilinear (slower, sub-pixel correct). !Smooth: nearest neighbor (faster, pixelated). + const Hm1 = height - 1, Wm1 = width - 1; + const a_panX = zs0.panX, a_panY = zs0.panY, a_zoom = zs0.zoom; + const b_panX = zs1.panX, b_panY = zs1.panY, b_zoom = zs1.zoom; + if (smooth) { + for (let y = 0; y < ch; y++) { + const ayu = (y - cy - a_panY) / a_zoom + cy; + const byu = (y - cy - b_panY) / b_zoom + cy; + const aRowF = ayu * sy; + const bRowF = byu * sy - adY; + const aR0 = aRowF | 0, bR0 = bRowF | 0; + const aFr = aRowF - aR0, bFr = bRowF - bR0; + const aRowOOB = aR0 < 0 || aR0 >= Hm1; + const bRowOOB = bR0 < 0 || bR0 >= Hm1; + const aRowOff = aR0 * width; + const bRowOff = bR0 * width; + const rowOff = y * cw; + for (let x = 0; x < cw; x++) { + const axu = (x - cx - a_panX) / a_zoom + cx; + const bxu = (x - cx - b_panX) / b_zoom + cx; + const aColF = axu * sx; + const bColF = bxu * sx - adX; + const aC0 = aColF | 0, bC0 = bColF | 0; + let v = 0; + if (!aRowOOB && !bRowOOB && aC0 >= 0 && aC0 < Wm1 && bC0 >= 0 && bC0 < Wm1) { + const aFc = aColF - aC0, bFc = bColF - bC0; + const ai = aRowOff + aC0; + const bi = bRowOff + bC0; + const aV = (a[ai] * (1 - aFc) + a[ai + 1] * aFc) * (1 - aFr) + + (a[ai + width] * (1 - aFc) + a[ai + width + 1] * aFc) * aFr; + const bV = (b[bi] * (1 - bFc) + b[bi + 1] * bFc) * (1 - bFr) + + (b[bi + width] * (1 - bFc) + b[bi + width + 1] * bFc) * bFr; + v = aV - bV; + } + diff[rowOff + x] = v; + if (v < mn) mn = v; + if (v > mx) mx = v; + } + } + } else { + for (let y = 0; y < ch; y++) { + const ayu = (y - cy - a_panY) / a_zoom + cy; + const byu = (y - cy - b_panY) / b_zoom + cy; + const aRow = (ayu * sy + 0.5) | 0; + const bRow = (byu * sy - adY + 0.5) | 0; + const aRowOK = aRow >= 0 && aRow < height; + const bRowOK = bRow >= 0 && bRow < height; + const aRowOff = aRow * width; + const bRowOff = bRow * width; + const rowOff = y * cw; + for (let x = 0; x < cw; x++) { + const axu = (x - cx - a_panX) / a_zoom + cx; + const bxu = (x - cx - b_panX) / b_zoom + cx; + const aCol = (axu * sx + 0.5) | 0; + const bCol = (bxu * sx - adX + 0.5) | 0; + let v = 0; + if (aRowOK && bRowOK && aCol >= 0 && aCol < width && bCol >= 0 && bCol < width) { + v = a[aRowOff + aCol] - b[bRowOff + bCol]; + } + diff[rowOff + x] = v; + if (v < mn) mn = v; + if (v > mx) mx = v; + } + } + } + const sym = Math.max(Math.abs(mn), Math.abs(mx)); + // Diff is signed-around-zero — use diverging cmap (RdBu) if user picked a sequential one. + const sequentialCmaps = new Set(["inferno", "viridis", "plasma", "magma", "hot", "gray", "turbo"]); + const diffCmap = sequentialCmaps.has(cmap) ? "RdBu" : cmap; + const off = renderToOffscreen(diff, cw, ch, COLORMAPS[diffCmap] || COLORMAPS.RdBu, -sym, sym); + if (!off) return; + ctx.imageSmoothingEnabled = smooth; + if (smooth) ctx.imageSmoothingQuality = "high"; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.drawImage(off, 0, 0); + } + }, [showDiffPanel, diffOtherIndices, diffReference, nImages, dataVersion, width, height, cmap, smooth, canvasW, canvasH, + alignDy, alignDx, getZoomState, linkedZoom, linkPan, linkedZoomState, zoomStates]); + + React.useEffect(() => { + if (!allFloats || allFloats.length === 0) return; + const dataArrays: Float32Array[] = []; + for (let i = 0; i < nImages; i++) { + const start = i * floatsPerImage; + const imageData = allFloats.subarray(start, start + floatsPerImage); + dataArrays.push(new Float32Array(imageData)); + } + rawDataRef.current = dataArrays; + // Upload to GPU colormap engine if available (ref check, no state trigger) + const engine = gpuCmapRef.current; + if (engine && gpuCmapReadyRef.current) { + for (let i = 0; i < dataArrays.length; i++) engine.uploadData(i, dataArrays[i], width, height); + gpuDataVersionRef.current++; + } + setDataVersion(v => v + 1); + }, [allFloats, nImages, floatsPerImage]); + + // Initialize reusable offscreen canvases (one per image, resized when dimensions change) + React.useEffect(() => { + if (width <= 0 || height <= 0 || nImages <= 0) return; + const canvases: HTMLCanvasElement[] = []; + const imgDatas: ImageData[] = []; + for (let i = 0; i < nImages; i++) { + const canvas = document.createElement("canvas"); + canvas.width = width; + canvas.height = height; + canvases.push(canvas); + imgDatas.push(canvas.getContext("2d")!.createImageData(width, height)); + } + mainOffscreensRef.current = canvases; + mainImgDatasRef.current = imgDatas; + logBufferRef.current = new Float32Array(width * height); + }, [width, height, nImages]); + + // Compute histogram data for the displayed image (reflects log scale) + // GPU path: uses persistent per-slot histogram buffers — no CPU data scan + // CPU fallback: computeHistogramFromBytes (before GPU ready) + React.useEffect(() => { + if (!rawDataRef.current) return; + const idx = nImages > 1 ? selectedIdx : 0; + const raw = rawDataRef.current[idx]; + if (!raw) return; + + // Use cached ranges (no CPU findDataRange scan) + const cachedRaw = rawRangesRef.current[idx]; + const rawRange = cachedRaw || findDataRange(raw); // fallback if cache miss + const range = logScale + ? { min: Math.log1p(Math.max(rawRange.min, 0)), max: Math.log1p(Math.max(rawRange.max, 0)) } + : rawRange; + setImageDataRange(range); + + const engine = gpuCmapRef.current; + if (engine && gpuCmapReadyRef.current && engine.slotCount > idx) { + // GPU histogram — single image, persistent buffers + engine.computeHistogramWithRange(idx, range.min, range.max, logScale).then(bins => { + setImageHistogramBins(bins); + setImageHistogramData(null); + }); + } else { + // CPU fallback (before GPU ready) + const d = logScale ? applyLogScale(raw) : raw; + setImageHistogramBins(null); + setImageHistogramData(d); + } + }, [allFloats, nImages, floatsPerImage, logScale, selectedIdx]); + + // Prevent page scroll when scrolling on canvases (must use native listener with passive: false) + // In gallery mode, only block scroll on the selected image (or all if linkedZoom) + React.useEffect(() => { + const preventDefault = (e: WheelEvent) => e.preventDefault(); + const elements: (HTMLElement | null)[] = isGallery + ? (linkedZoom + ? [ + ...imageContainerRefs.current, + ...(effectiveShowFft ? fftContainerRefs.current : []), + ] + : [ + imageContainerRefs.current[selectedIdx], + ...(effectiveShowFft ? [fftContainerRefs.current[selectedIdx]] : []), + ]) + : [ + imageContainerRefs.current[0], + ...(effectiveShowFft ? [singleFftContainerRef.current] : []), + ]; + elements.forEach(el => el?.addEventListener("wheel", preventDefault, { passive: false })); + return () => elements.forEach(el => el?.removeEventListener("wheel", preventDefault)); + }, [canvasReady, effectiveShowFft, isGallery, selectedIdx, linkedZoom]); + + const gpuDataVersionRef = React.useRef(0); + // Generation counter for colormap — coalesces rapid slider events to ≤1 render per frame + // Cached per-image data ranges — only recomputed when data or logScale changes, NOT on slider drag + const dataRangesRef = React.useRef<{ min: number; max: number }[]>([]); + // Cached log-transformed data — avoids 12×16M log1p calls per slider tick + const logDataCacheRef = React.useRef([]); + // Ref mirrors for async GPU callbacks (avoid stale closures) + const logScaleRef = React.useRef(logScale); + logScaleRef.current = logScale; + const cmapRef = React.useRef(cmap); + cmapRef.current = cmap; + // Auto-contrast cache: GPU-computed percentile ranges per image + const autoContrastCacheRef = React.useRef<{ vmin: number; vmax: number }[]>([]); + + // Cache per-image data ranges (raw AND log) on data change only. + // Log ranges are derived mathematically: log1p(rawMin), log1p(rawMax). + // NO applyLogScale here — GPU shader handles log1p per pixel. + // Log toggle is now free: just pick the right cached ranges. + const rawRangesRef = React.useRef<{ min: number; max: number }[]>([]); + React.useEffect(() => { + if (!rawDataRef.current || rawDataRef.current.length === 0) return; + const engine = gpuCmapRef.current; + const nImg = rawDataRef.current.length; + + if (engine && gpuCmapReadyRef.current && engine.slotCount >= nImg) { + // GPU path: batch compute min/max on GPU (async, updates refs when done) + const indices = Array.from({ length: nImg }, (_, i) => i); + engine.computeRangeBatch(indices).then(rawRanges => { + rawRangesRef.current = rawRanges; + const logRanges = rawRanges.map(r => ({ + min: Math.log1p(Math.max(r.min, 0)), + max: Math.log1p(Math.max(r.max, 0)), + })); + dataRangesRef.current = logScaleRef.current ? logRanges : rawRanges; + }); + } else { + // CPU fallback: scan each image for min/max + const rawRanges: { min: number; max: number }[] = []; + for (let i = 0; i < nImg; i++) { + const rawData = rawDataRef.current[i]; + if (!rawData) { rawRanges.push({ min: 0, max: 1 }); continue; } + rawRanges.push(findDataRange(rawData)); + } + rawRangesRef.current = rawRanges; + const logRanges = rawRanges.map(r => ({ + min: Math.log1p(Math.max(r.min, 0)), + max: Math.log1p(Math.max(r.max, 0)), + })); + dataRangesRef.current = logScale ? logRanges : rawRanges; + } + logDataCacheRef.current = rawDataRef.current.slice(); + }, [dataVersion]); + + // When logScale toggles, just swap cached ranges (no data scan) + React.useEffect(() => { + if (rawRangesRef.current.length === 0) return; + const logRanges = rawRangesRef.current.map(r => ({ + min: Math.log1p(Math.max(r.min, 0)), + max: Math.log1p(Math.max(r.max, 0)), + })); + dataRangesRef.current = logScale ? logRanges : rawRangesRef.current; + }, [logScale]); + + // GPU auto-contrast: batch-compute percentile ranges from GPU histograms. + // One GPU submission for all images. Caches results for synchronous use in render. + React.useEffect(() => { + if (!autoContrast) { autoContrastCacheRef.current = []; return; } + const engine = gpuCmapRef.current; + if (!engine || !gpuCmapReadyRef.current || !rawDataRef.current) return; + const cachedRanges = dataRangesRef.current; + if (cachedRanges.length === 0) return; + const ls = logScale; + const nImg = Math.min(rawDataRef.current.length, engine.slotCount); + if (nImg === 0) return; + + (async () => { + const indices = Array.from({ length: nImg }, (_, i) => i); + const histRanges = indices.map(i => cachedRanges[i] || { min: 0, max: 1 }); + const allBins = await engine.computeHistogramBatch(indices, histRanges, ls); + + const pLow = 2, pHigh = 98; + const acRanges: { vmin: number; vmax: number }[] = []; + for (let k = 0; k < allBins.length; k++) { + const bins = allBins[k]; + const cr = histRanges[k]; + // Percentile from normalized histogram CDF + let sum = 0; + for (let b = 0; b < 256; b++) sum += bins[b]; + let binLow = 0, binHigh = 255; + const targetLow = sum * pLow / 100; + const targetHigh = sum * pHigh / 100; + let running = 0; + for (let b = 0; b < 256; b++) { + running += bins[b]; + if (running >= targetLow && binLow === 0) binLow = b; + if (running >= targetHigh) { binHigh = b; break; } + } + const range = cr.max - cr.min; + acRanges.push({ vmin: cr.min + (binLow / 255) * range, vmax: cr.min + (binHigh / 255) * range }); + } + autoContrastCacheRef.current = acRanges; + console.log(`[Show2D] GPU auto-contrast: ${nImg} images, ${allBins.length} histograms`); + setOffscreenVersion(v => v + 1); + })(); + }, [autoContrast, dataVersion, logScale]); + + // ------------------------------------------------------------------------- + // Data effect: normalize + colormap → reusable offscreen canvases + // GPU path: runs compute shader for all images in one submission + // CPU fallback: per-image applyColormap loop + // (does NOT depend on zoom/pan — avoids recomputing 16M pixels on every pan/zoom) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!dataVersion || !rawDataRef.current || rawDataRef.current.length === 0) return; + if (mainOffscreensRef.current.length === 0 || mainImgDatasRef.current.length === 0) return; + + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + + // Compute per-image vmin/vmax from CACHED data ranges (no findDataRange per tick). + // dataRangesRef is precomputed when data or logScale changes. + const cachedRanges = dataRangesRef.current; + const hasAbsoluteRange = traitVmin != null && traitVmax != null; + const ranges: { vmin: number; vmax: number }[] = []; + for (let i = 0; i < nImages; i++) { + let vmin: number, vmax: number; + const cs = linkedContrast ? linkedContrastState : (contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }); + + // Per-image absolute range (vmins/vmaxs) takes precedence over scalar (vmin/vmax) + const perI_min = traitVmins && traitVmins[i] != null ? traitVmins[i] : null; + const perI_max = traitVmaxs && traitVmaxs[i] != null ? traitVmaxs[i] : null; + const hasPerImage = perI_min != null && perI_max != null; + const isDiffSlot = false; + const diffSym = 0; + + let rangeMin: number, rangeMax: number; + if (isDiffSlot) { + rangeMin = -diffSym; + rangeMax = diffSym; + } else if (hasPerImage) { + rangeMin = logScale ? Math.log1p(Math.max(perI_min!, 0)) : perI_min!; + rangeMax = logScale ? Math.log1p(Math.max(perI_max!, 0)) : perI_max!; + } else if (hasAbsoluteRange) { + rangeMin = logScale ? Math.log1p(Math.max(traitVmin!, 0)) : traitVmin!; + rangeMax = logScale ? Math.log1p(Math.max(traitVmax!, 0)) : traitVmax!; + } else { + // GPU range compute is async — when cache missing OR collapsed (min==max from race), + // sync findDataRange on raw data to ensure non-degenerate range. + let cached = cachedRanges[i]; + if (!cached || cached.min === cached.max) { + if (rawDataRef.current && rawDataRef.current[i]) { + cached = findDataRange(rawDataRef.current[i]); + } + } + cached = cached || { min: 0, max: 1 }; + rangeMin = cached.min; + rangeMax = cached.max; + } + + if (!hasAbsoluteRange && !hasPerImage && autoContrast) { + // Auto-contrast: use GPU-precomputed percentile ranges. + // If GPU cache not ready yet, use full data range as placeholder + // (GPU auto-contrast effect will fire async and trigger re-render). + const acCache = autoContrastCacheRef.current[i]; + if (acCache) { + vmin = acCache.vmin; vmax = acCache.vmax; + } else { + vmin = rangeMin; vmax = rangeMax; + } + } else if (rangeMin !== rangeMax && (cs.vminPct > 0 || cs.vmaxPct < 100)) { + ({ vmin, vmax } = sliderRange(rangeMin, rangeMax, cs.vminPct, cs.vmaxPct)); + } else { + vmin = rangeMin; vmax = rangeMax; + } + ranges.push({ vmin, vmax }); + } + + // Cache first image's vmin/vmax for colorbar/lens + if (ranges.length > 0) { + colorbarVminRef.current = ranges[0].vmin; + colorbarVmaxRef.current = ranges[0].vmax; + } + + // GPU colormap — first-class citizen. + // Try zero-copy path (OffscreenCanvas → ImageBitmap, no mapAsync). + // Falls back to renderSlots (mapAsync + putImageData) if zero-copy fails. + const engine = gpuCmapRef.current; + const gpuReady = engine && gpuCmapReadyRef.current && engine.slotCount >= nImages; + if (gpuReady) { + engine!.uploadLUT(cmap, lut); + const capturedRanges = ranges.slice(); + const capturedLogScale = logScale; + const capturedNImages = nImages; + requestAnimationFrame(async () => { + const indices = Array.from({ length: capturedNImages }, (_, i) => i); + + // Zero-copy path: GPU → OffscreenCanvas → ImageBitmap → drawImage + const bitmaps = engine!.renderSlotsToImageBitmap(indices, capturedRanges, capturedLogScale); + if (bitmaps && bitmaps.length > 0 && bitmaps[0]) { + for (let i = 0; i < bitmaps.length; i++) { + const offscreen = mainOffscreensRef.current[i]; + if (!offscreen || !bitmaps[i]) continue; + const ctx = offscreen.getContext("2d"); + if (ctx) ctx.drawImage(bitmaps[i], 0, 0); + } + setOffscreenVersion(v => v + 1); + return; + } + + // Fallback: renderSlots (mapAsync + copy to ImageData) + const offscreens = indices.map(i => mainOffscreensRef.current[i] || null); + const imgDatas = indices.map(i => mainImgDatasRef.current[i] || null); + const rendered = await engine!.renderSlots(indices, capturedRanges, offscreens, imgDatas, capturedLogScale); + if (rendered === 0) { + for (let i = 0; i < capturedNImages; i++) { + const offscreen = mainOffscreensRef.current[i]; + const imgData = mainImgDatasRef.current[i]; + if (!offscreen || !imgData) continue; + const raw = rawDataRef.current?.[i]; + if (!raw) continue; + const processed = capturedLogScale ? applyLogScale(raw) : raw; + renderToOffscreenReuse(processed, lut, capturedRanges[i].vmin, capturedRanges[i].vmax, offscreen, imgData); + } + } + setOffscreenVersion(v => v + 1); + }); + } else { + // CPU fallback: initial render or no WebGPU + // CPU must do log transform itself (GPU shader would handle it) + for (let i = 0; i < nImages; i++) { + const offscreen = mainOffscreensRef.current[i]; + const imgData = mainImgDatasRef.current[i]; + if (!offscreen || !imgData) continue; + const raw = rawDataRef.current?.[i]; + if (!raw) continue; + const processed = logScale ? applyLogScale(raw) : raw; + renderToOffscreenReuse(processed, lut, ranges[i].vmin, ranges[i].vmax, offscreen, imgData); + } + setOffscreenVersion(v => v + 1); + } + }, [dataVersion, nImages, width, height, cmap, logScale, autoContrast, linkedContrast, linkedContrastState, contrastStates, traitVmin, traitVmax, traitVmins, traitVmaxs, diffMode]); + + // ------------------------------------------------------------------------- + // Draw effect: zoom/pan changes — cheap, just drawImage from cached offscreens + // useLayoutEffect prevents black flash when canvas dimensions change (resize) + // ------------------------------------------------------------------------- + React.useLayoutEffect(() => { + if (mainOffscreensRef.current.length === 0) return; + + for (let i = 0; i < nImages; i++) { + const canvas = canvasRefs.current[i]; + const offscreen = mainOffscreensRef.current[i]; + if (!canvas || !offscreen) continue; + const ctx = canvas.getContext("2d"); + if (!ctx) continue; + + ctx.imageSmoothingEnabled = smooth; + if (smooth) ctx.imageSmoothingQuality = "high"; + ctx.clearRect(0, 0, canvas.width, canvas.height); + + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + + if (zoom !== 1 || panX !== 0 || panY !== 0) { + ctx.save(); + const cx = canvasW / 2; + const cy = canvasH / 2; + ctx.translate(cx + panX, cy + panY); + ctx.scale(zoom, zoom); + ctx.translate(-cx, -cy); + ctx.drawImage(offscreen, 0, 0, width, height, 0, 0, canvasW, canvasH); + ctx.restore(); + } else { + ctx.drawImage(offscreen, 0, 0, width, height, 0, 0, canvasW, canvasH); + } + } + }, [offscreenVersion, nImages, width, height, displayScale, canvasW, canvasH, canvasReady, linkedZoom, linkedZoomState, zoomStates, smooth]); + + // ------------------------------------------------------------------------- + // Render Overlays (scale bar, colorbar, zoom indicator) + // ------------------------------------------------------------------------- + React.useEffect(() => { + for (let i = 0; i < nImages; i++) { + const overlay = overlayRefs.current[i]; + if (!overlay) continue; + const ctx = overlay.getContext("2d"); + if (!ctx) continue; + + if (scaleBarVisible) { + const zs = getZoomState(i); + const unit = pixelSize > 0 ? "Å" as const : "px" as const; + const pxSize = pixelSize > 0 ? pixelSize : 1; + drawScaleBarHiDPI(overlay, DPR, zs.zoom, pxSize, unit, width); + } else { + ctx.clearRect(0, 0, overlay.width, overlay.height); + } + + // Colorbar (single image mode only) — uses cached vmin/vmax from data effect + if (!hideDisplay && showColorbar && !isGallery) { + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + const cssW = overlay.width / DPR; + const cssH = overlay.height / DPR; + const vmin = colorbarVminRef.current; + const vmax = colorbarVmaxRef.current; + + ctx.save(); + ctx.scale(DPR, DPR); + drawColorbar(ctx, cssW, cssH, lut, vmin, vmax, logScale); + ctx.restore(); + } + + // ROI overlay — draw all ROIs + if (!hideRoi && roiActive && roiList && roiList.length > 0) { + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + const cx = canvasW / 2; + const cy = canvasH / 2; + + // Highlight mask: dim everything outside highlighted ROIs + const highlightedRois = roiList.filter(r => r.highlight); + if (highlightedRois.length > 0) { + ctx.save(); + ctx.scale(DPR, DPR); + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.fillRect(0, 0, canvasW, canvasH); + ctx.globalCompositeOperation = "destination-out"; + for (const roi of highlightedRois) { + const sx = (roi.col * displayScale - cx) * zoom + cx + panX; + const sy = (roi.row * displayScale - cy) * zoom + cy + panY; + const sr = roi.radius * displayScale * zoom; + const shape = roi.shape || "circle"; + ctx.fillStyle = "rgba(0,0,0,1)"; + if (shape === "circle") { + ctx.beginPath(); ctx.arc(sx, sy, sr, 0, Math.PI * 2); ctx.fill(); + } else if (shape === "square") { + ctx.fillRect(sx - sr, sy - sr, sr * 2, sr * 2); + } else if (shape === "rectangle") { + const sw = roi.width * displayScale * zoom; + const sh = roi.height * displayScale * zoom; + ctx.fillRect(sx - sw / 2, sy - sh / 2, sw, sh); + } else if (shape === "annular") { + ctx.beginPath(); ctx.arc(sx, sy, sr, 0, Math.PI * 2); ctx.fill(); + // Re-darken inner ring + ctx.globalCompositeOperation = "source-over"; + ctx.fillStyle = "rgba(0,0,0,0.6)"; + const sir = roi.radius_inner * displayScale * zoom; + ctx.beginPath(); ctx.arc(sx, sy, sir, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "destination-out"; + } + } + ctx.restore(); + } + + ctx.save(); + ctx.scale(DPR, DPR); + for (let ri = 0; ri < roiList.length; ri++) { + const roi = roiList[ri]; + const isSelected = ri === roiSelectedIdx; + const screenX = (roi.col * displayScale - cx) * zoom + cx + panX; + const screenY = (roi.row * displayScale - cy) * zoom + cy + panY; + const screenRadius = roi.radius * displayScale * zoom; + const screenW = roi.width * displayScale * zoom; + const screenH = roi.height * displayScale * zoom; + const screenRadiusInner = roi.radius_inner * displayScale * zoom; + const shape = (roi.shape || "circle") as "circle" | "square" | "rectangle" | "annular"; + ctx.lineWidth = roi.line_width || 2; + drawROI(ctx, screenX, screenY, shape, screenRadius, screenW, screenH, roi.color || ROI_COLORS[ri % ROI_COLORS.length], roi.color || ROI_COLORS[ri % ROI_COLORS.length], isSelected && isDraggingROI, screenRadiusInner); + if (isSelected) { + ctx.setLineDash([4, 3]); + ctx.strokeStyle = "#fff"; + ctx.lineWidth = 1; + if (shape === "circle" || shape === "annular") { + ctx.beginPath(); ctx.arc(screenX, screenY, screenRadius + 3, 0, Math.PI * 2); ctx.stroke(); + } else if (shape === "square") { + ctx.strokeRect(screenX - screenRadius - 3, screenY - screenRadius - 3, (screenRadius + 3) * 2, (screenRadius + 3) * 2); + } else if (shape === "rectangle") { + ctx.strokeRect(screenX - screenW / 2 - 3, screenY - screenH / 2 - 3, screenW + 6, screenH + 6); + } + ctx.setLineDash([]); + } + } + ctx.restore(); + } + + // Line profile overlay + if (!hideProfile && profileActive && profilePoints.length > 0) { + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + ctx.save(); + ctx.scale(DPR, DPR); + + // Transform image coords to screen coords + const cx = canvasW / 2; + const cy = canvasH / 2; + const toScreenX = (ix: number) => (ix * displayScale - cx) * zoom + cx + panX; + const toScreenY = (iy: number) => (iy * displayScale - cy) * zoom + cy + panY; + + // Draw point A + const ax = toScreenX(profilePoints[0].col); + const ay = toScreenY(profilePoints[0].row); + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(ax, ay, 4, 0, Math.PI * 2); + ctx.fill(); + + // Draw line and point B if complete + if (profilePoints.length === 2) { + const bx = toScreenX(profilePoints[1].col); + const by = toScreenY(profilePoints[1].row); + + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.setLineDash([4, 3]); + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + ctx.setLineDash([]); + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(bx, by, 4, 0, Math.PI * 2); + ctx.fill(); + } + + ctx.restore(); + } + + // Distance measurement overlay + if (measureActive && measurePoints.length >= 1) { + const zs = getZoomState(i); + const { zoom, panX, panY } = zs; + ctx.save(); + ctx.scale(DPR, DPR); + const cx = canvasW / 2; + const cy = canvasH / 2; + const toSX = (ix: number) => (ix * displayScale - cx) * zoom + cx + panX; + const toSY = (iy: number) => (iy * displayScale - cy) * zoom + cy + panY; + + ctx.shadowColor = "rgba(0,0,0,0.6)"; + ctx.shadowBlur = 3; + + // Endpoint A + const ax = toSX(measurePoints[0].col); + const ay = toSY(measurePoints[0].row); + ctx.fillStyle = "#fff"; + ctx.beginPath(); + ctx.arc(ax, ay, 4, 0, Math.PI * 2); + ctx.fill(); + + if (measurePoints.length === 2) { + const bx = toSX(measurePoints[1].col); + const by = toSY(measurePoints[1].row); + + // Solid white line (distinct from profile's dashed accent line) + ctx.strokeStyle = "#fff"; + ctx.lineWidth = 1.5; + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + + // Endpoint B + ctx.beginPath(); + ctx.arc(bx, by, 4, 0, Math.PI * 2); + ctx.fill(); + + // Distance label + const dc = measurePoints[1].col - measurePoints[0].col; + const dr = measurePoints[1].row - measurePoints[0].row; + const distPx = Math.sqrt(dc * dc + dr * dr); + let label: string; + if (pixelSize > 0) { + const distA = distPx * pixelSize; + label = distA >= 10 ? `${(distA / 10).toFixed(2)} nm` : `${distA.toFixed(2)} Å`; + } else { + label = `${distPx.toFixed(1)} px`; + } + + const mx = (ax + bx) / 2; + const my = (ay + by) / 2; + ctx.font = "bold 13px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.textAlign = "center"; + ctx.textBaseline = "bottom"; + ctx.fillStyle = "#fff"; + ctx.fillText(label, mx, my - 8); + } + + ctx.shadowBlur = 0; + ctx.restore(); + } + } + }, [nImages, pixelSize, scaleBarVisible, selectedIdx, isGallery, canvasW, canvasH, width, displayScale, linkedZoom, linkedZoomState, zoomStates, dataVersion, showColorbar, cmap, offscreenVersion, logScale, profileActive, profilePoints, roiActive, roiList, roiSelectedIdx, isDraggingROI, themeColors, hideDisplay, hideRoi, hideProfile, measureActive, measurePoints]); + + // ------------------------------------------------------------------------- + // Inset magnifier (lens) — renders magnified region at cursor in bottom-left + // ------------------------------------------------------------------------- + React.useEffect(() => { + const lensCanvas = lensCanvasRef.current; + if (lensCanvas) { + const lctx = lensCanvas.getContext("2d"); + if (lctx) lctx.clearRect(0, 0, lensCanvas.width, lensCanvas.height); + } + if (!showLens || lockDisplay || isGallery || !lensPos || !rawDataRef.current?.[0]) return; + if (!lensCanvas) return; + const ctx = lensCanvas.getContext("2d"); + if (!ctx) return; + + const raw = rawDataRef.current[0]; + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + // Use cached vmin/vmax from data effect (avoids full-image applyLogScale + findDataRange) + const vmin = colorbarVminRef.current; + const vmax = colorbarVmaxRef.current; + + // Extract region around cursor — regionSize = displaySize / magnification + const regionSize = Math.max(4, Math.round(lensDisplaySize / lensMag)); + const lensSize = lensDisplaySize; + const margin = 12; + const half = Math.floor(regionSize / 2); + const r0 = lensPos.row - half; + const c0 = lensPos.col - half; + + // Create small offscreen canvas for the region + const regionCanvas = document.createElement("canvas"); + regionCanvas.width = regionSize; + regionCanvas.height = regionSize; + const rctx = regionCanvas.getContext("2d"); + if (!rctx) return; + const imgData = rctx.createImageData(regionSize, regionSize); + const range = vmax - vmin || 1; + for (let dr = 0; dr < regionSize; dr++) { + for (let dc = 0; dc < regionSize; dc++) { + const sr = r0 + dr; + const sc = c0 + dc; + const idx = (dr * regionSize + dc) * 4; + if (sr < 0 || sr >= height || sc < 0 || sc >= width) { + imgData.data[idx] = 0; imgData.data[idx + 1] = 0; imgData.data[idx + 2] = 0; imgData.data[idx + 3] = 255; + } else { + // Apply log scale inline per-pixel (only for the small region, not full image) + const rawVal = raw[sr * width + sc]; + const val = logScale ? Math.log1p(rawVal) : rawVal; + const t = Math.max(0, Math.min(1, (val - vmin) / range)); + const li = Math.round(t * 255); + imgData.data[idx] = lut[li * 3]; imgData.data[idx + 1] = lut[li * 3 + 1]; imgData.data[idx + 2] = lut[li * 3 + 2]; imgData.data[idx + 3] = 255; + } + } + } + rctx.putImageData(imgData, 0, 0); + + // Draw lens inset on overlay — use custom anchor or default bottom-left + ctx.save(); + ctx.scale(DPR, DPR); + const lx = lensAnchor ? lensAnchor.x : margin; + const ly = lensAnchor ? lensAnchor.y : canvasH - lensSize - margin - 20; + ctx.imageSmoothingEnabled = false; + ctx.drawImage(regionCanvas, lx, ly, lensSize, lensSize); + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 2; + ctx.strokeRect(lx, ly, lensSize, lensSize); + // Crosshair at center + const cx = lx + lensSize / 2; + const cy = ly + lensSize / 2; + ctx.strokeStyle = "rgba(255,255,255,0.5)"; + ctx.lineWidth = 1; + ctx.beginPath(); + ctx.moveTo(cx - 8, cy); ctx.lineTo(cx + 8, cy); + ctx.moveTo(cx, cy - 8); ctx.lineTo(cx, cy + 8); + ctx.stroke(); + // Magnification label + ctx.fillStyle = "rgba(255,255,255,0.7)"; + ctx.font = "10px monospace"; + ctx.fillText(`${lensMag}×`, lx + 4, ly + lensSize - 4); + ctx.restore(); + }, [showLens, lockDisplay, lensPos, isGallery, cmap, logScale, offscreenVersion, width, height, canvasH, themeColors, lensMag, lensDisplaySize, lensAnchor]); + + // ------------------------------------------------------------------------- + // Auto-compute profile when profile_line is set (e.g. from Python) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (hideProfile) return; + if (profilePoints.length === 2 && rawDataRef.current) { + const p0 = profilePoints[0], p1 = profilePoints[1]; + const allProfiles: (Float32Array | null)[] = []; + for (let i = 0; i < rawDataRef.current.length; i++) { + const raw = rawDataRef.current[i]; + allProfiles.push(raw ? sampleLineProfile(raw, width, height, p0.row, p0.col, p1.row, p1.col) : null); + } + setProfileDataAll(allProfiles); + if (!profileActive) setProfileActive(true); + } + }, [profilePoints, dataVersion, hideProfile, profileActive]); + + // ------------------------------------------------------------------------- + // Render sparkline for line profile + // ------------------------------------------------------------------------- + React.useEffect(() => { + const canvas = profileCanvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + const cssW = profileCanvasWidth; + const cssH = profileHeight; + canvas.width = cssW * dpr; + canvas.height = cssH * dpr; + ctx.scale(dpr, dpr); + + const isDark = themeInfo.theme === "dark"; + ctx.fillStyle = isDark ? "#1a1a1a" : "#f0f0f0"; + ctx.fillRect(0, 0, cssW, cssH); + + const hasData = profileDataAll.some(d => d && d.length >= 2); + if (!hasData) { + ctx.font = "10px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#555" : "#999"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText("Click two points on the image to draw a profile", cssW / 2, cssH / 2); + return; + } + + const padLeft = 40; + const padRight = 8; + const padTop = 6; + const padBottom = 18; + const plotW = cssW - padLeft - padRight; + const plotH = cssH - padTop - padBottom; + + // Find global min/max across all profiles + let gMin = Infinity, gMax = -Infinity; + for (const d of profileDataAll) { + if (!d) continue; + for (let i = 0; i < d.length; i++) { + if (d[i] < gMin) gMin = d[i]; + if (d[i] > gMax) gMax = d[i]; + } + } + const range = gMax - gMin || 1; + + // Draw each profile + const colors = profileDataAll.length === 1 ? [themeColors.accent] : PROFILE_COLORS; + for (let pIdx = 0; pIdx < profileDataAll.length; pIdx++) { + const d = profileDataAll[pIdx]; + if (!d || d.length < 2) continue; + ctx.strokeStyle = colors[pIdx % colors.length]; + ctx.lineWidth = pIdx === selectedIdx || profileDataAll.length === 1 ? 1.5 : 1; + ctx.globalAlpha = pIdx === selectedIdx || profileDataAll.length === 1 ? 1 : 0.5; + ctx.beginPath(); + for (let i = 0; i < d.length; i++) { + const x = padLeft + (i / (d.length - 1)) * plotW; + const y = padTop + plotH - ((d[i] - gMin) / range) * plotH; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + } + ctx.globalAlpha = 1; + + // Compute total distance for x-axis + const firstProfile = profileDataAll.find(d => d); + let totalDist = (firstProfile?.length ?? 2) - 1; + let xUnit = "px"; + if (profilePoints.length === 2) { + const dx = profilePoints[1].col - profilePoints[0].col; + const dy = profilePoints[1].row - profilePoints[0].row; + const distPx = Math.sqrt(dx * dx + dy * dy); + if (pixelSize > 0) { + const distA = distPx * pixelSize; + if (distA >= 10) { totalDist = distA / 10; xUnit = "nm"; } + else { totalDist = distA; xUnit = "Å"; } + } else { + totalDist = distPx; + } + } + + // Draw x-axis ticks + const tickY = padTop + plotH; + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + const idealTicks = Math.max(2, Math.floor(plotW / 70)); + const tickStep = roundToNiceValue(totalDist / idealTicks); + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textBaseline = "top"; + const ticks: number[] = []; + for (let v = 0; v <= totalDist + tickStep * 0.01; v += tickStep) { + if (v > totalDist * 1.001) break; + ticks.push(v); + } + for (let i = 0; i < ticks.length; i++) { + const v = ticks[i]; + const frac = totalDist > 0 ? v / totalDist : 0; + const x = padLeft + frac * plotW; + ctx.beginPath(); ctx.moveTo(x, tickY); ctx.lineTo(x, tickY + 3); ctx.stroke(); + ctx.textAlign = frac < 0.05 ? "left" : frac > 0.95 ? "right" : "center"; + const valStr = v % 1 === 0 ? v.toFixed(0) : v.toFixed(1); + ctx.fillText(i === ticks.length - 1 ? `${valStr} ${xUnit}` : valStr, x, tickY + 4); + } + + // Draw y-axis min/max labels + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textAlign = "right"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(gMax), padLeft - 3, padTop); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(gMin), padLeft - 3, padTop + plotH); + + // Draw axis lines + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + ctx.beginPath(); + ctx.moveTo(padLeft, padTop); + ctx.lineTo(padLeft, padTop + plotH); + ctx.lineTo(padLeft + plotW, padTop + plotH); + ctx.stroke(); + + // Legend (gallery mode with multiple images) + if (profileDataAll.length > 1) { + ctx.textAlign = "right"; + ctx.textBaseline = "top"; + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + let legendX = cssW - 4; + for (let pIdx = profileDataAll.length - 1; pIdx >= 0; pIdx--) { + if (!profileDataAll[pIdx]) continue; + const label = labels?.[pIdx] || `#${pIdx + 1}`; + const color = colors[pIdx % colors.length]; + const textW = ctx.measureText(label).width; + ctx.globalAlpha = pIdx === selectedIdx ? 1 : 0.5; + ctx.fillStyle = color; + ctx.fillRect(legendX - textW - 10, 2, 6, 6); + ctx.fillStyle = isDark ? "#aaa" : "#555"; + ctx.fillText(label, legendX, 1); + legendX -= textW + 16; + } + ctx.globalAlpha = 1; + } + + // Save base rendering + layout for hover overlay + profileBaseImageRef.current = ctx.getImageData(0, 0, canvas.width, canvas.height); + profileLayoutRef.current = { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit }; + }, [profileDataAll, themeInfo.theme, themeColors.accent, profilePoints, pixelSize, selectedIdx, labels, profileCanvasWidth, profileHeight]); + + // Profile hover handler — draws crosshair + value readout + const handleProfileMouseMove = React.useCallback((e: React.MouseEvent) => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + const layout = profileLayoutRef.current; + if (!canvas || !base || !layout) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit } = layout; + const range = gMax - gMin || 1; + + // Restore base image + ctx.putImageData(base, 0, 0); + + if (cssX < padLeft || cssX > padLeft + plotW) return; + const frac = (cssX - padLeft) / plotW; + + const dpr = window.devicePixelRatio || 1; + ctx.save(); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + + // Vertical crosshair + ctx.strokeStyle = themeInfo.theme === "dark" ? "rgba(255,255,255,0.3)" : "rgba(0,0,0,0.3)"; + ctx.lineWidth = 1; + ctx.setLineDash([2, 2]); + ctx.beginPath(); + ctx.moveTo(cssX, padTop); + ctx.lineTo(cssX, padTop + plotH); + ctx.stroke(); + ctx.setLineDash([]); + + // Dot on each profile line + collect values + const colors = profileDataAll.length === 1 ? [themeColors.accent] : PROFILE_COLORS; + const activeIdx = isGallery ? selectedIdx : 0; + let displayVal: number | null = null; + for (let pIdx = 0; pIdx < profileDataAll.length; pIdx++) { + const d = profileDataAll[pIdx]; + if (!d || d.length < 2) continue; + const dataIdx = Math.min(d.length - 1, Math.max(0, Math.round(frac * (d.length - 1)))); + const val = d[dataIdx]; + const y = padTop + plotH - ((val - gMin) / range) * plotH; + ctx.fillStyle = colors[pIdx % colors.length]; + ctx.globalAlpha = pIdx === activeIdx || profileDataAll.length === 1 ? 1 : 0.5; + ctx.beginPath(); + ctx.arc(cssX, y, 3, 0, Math.PI * 2); + ctx.fill(); + if (pIdx === activeIdx || profileDataAll.length === 1) displayVal = val; + } + ctx.globalAlpha = 1; + + // Value readout label + if (displayVal !== null) { + const dist = frac * totalDist; + const label = `${formatNumber(displayVal)} @ ${dist.toFixed(1)} ${xUnit}`; + const isDark = themeInfo.theme === "dark"; + ctx.font = "bold 9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + const textW = ctx.measureText(label).width; + const labelX = Math.min(cssX + 6, padLeft + plotW - textW - 2); + const labelY = padTop + 2; + ctx.fillStyle = isDark ? "rgba(0,0,0,0.7)" : "rgba(255,255,255,0.8)"; + ctx.fillRect(labelX - 2, labelY - 1, textW + 4, 11); + ctx.fillStyle = isDark ? "#fff" : "#000"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(label, labelX, labelY); + } + + ctx.restore(); + }, [profileDataAll, themeInfo.theme, themeColors.accent, isGallery, selectedIdx]); + + const handleProfileMouseLeave = React.useCallback(() => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + if (!canvas || !base) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.putImageData(base, 0, 0); + }, []); + + // ------------------------------------------------------------------------- + // Compute FFT magnitude (cached — only recomputes when data changes) + // Supports ROI-scoped FFT: when ROI is active with a selected ROI, compute + // FFT of the cropped region instead of the full image. + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!effectiveShowFft || isGallery || !rawDataRef.current) return; + if (!rawDataRef.current[selectedIdx]) return; + // Generation counter: coalesces rapid ROI drag events so at most one + // FFT runs per animation frame. The rAF yield lets the browser paint + // the ROI position update before the (potentially blocking) FFT runs. + const gen = ++fftGenRef.current; + + const doCompute = async () => { + // Yield to next animation frame — browser paints updated ROI first, + // and stale requests (from earlier drag events) are discarded below. + await new Promise(r => requestAnimationFrame(() => r())); + if (gen !== fftGenRef.current) return; + + const backend = gpuFFTRef.current && gpuReadyRef.current ? "WebGPU" : "CPU Worker"; + setFftComputing(true); + setFftProgress(`Computing FFT… (${backend})`); + const t0 = performance.now(); + const data = rawDataRef.current![selectedIdx]; + let fftW = width; + let fftH = height; + let inputData = data; + + // ROI crop: extract bounding box and optionally zero-mask outside radius + let origCropW = 0, origCropH = 0; + if (roiFftActive && roiList && roiSelectedIdx >= 0 && roiSelectedIdx < roiList.length) { + const roi = roiList[roiSelectedIdx]; + const crop = cropROIRegion(data, width, height, roi); + if (crop) { + origCropW = crop.cropW; + origCropH = crop.cropH; + // Apply Hann window to crop at native dimensions BEFORE zero-padding + if (fftWindow) applyHannWindow2D(crop.cropped, crop.cropW, crop.cropH); + // Pad to next power-of-2 so fft2d doesn't truncate frequency data + const padW = nextPow2(crop.cropW); + const padH = nextPow2(crop.cropH); + const padded = new Float32Array(padW * padH); + for (let y = 0; y < crop.cropH; y++) { + for (let x = 0; x < crop.cropW; x++) { + padded[y * padW + x] = crop.cropped[y * crop.cropW + x]; + } + } + inputData = padded; + fftW = padW; + fftH = padH; + } + } + + // Pre-pad non-power-of-2 full images so fft2d doesn't truncate frequency data + if (origCropW === 0) { + const padW = nextPow2(fftW); + const padH = nextPow2(fftH); + if (padW !== fftW || padH !== fftH) { + const padded = new Float32Array(padW * padH); + for (let y = 0; y < fftH; y++) { + for (let x = 0; x < fftW; x++) { + padded[y * padW + x] = inputData[y * fftW + x]; + } + } + inputData = padded; + fftW = padW; + fftH = padH; + } + } + + const tCrop = performance.now(); + const real = inputData.slice(); + const imag = new Float32Array(inputData.length); + + if (gpuFFTRef.current && gpuReadyRef.current) { + const result = await gpuFFTRef.current.fft2D(real, imag, fftW, fftH, false); + if (gen !== fftGenRef.current) return; + const tGpu = performance.now(); + fftshift(result.real, fftW, fftH); + fftshift(result.imag, fftW, fftH); + fftMagCacheRef.current = computeMagnitude(result.real, result.imag); + console.log(`[Show2D FFT] GPU ${fftW}×${fftH}: crop=${(tCrop-t0).toFixed(1)}ms gpu=${(tGpu-tCrop).toFixed(1)}ms post=${(performance.now()-tGpu).toFixed(1)}ms`); + } else { + // CPU fallback: run in Web Worker to avoid blocking the main thread + const result = await fft2dAsync(real, imag, fftW, fftH, false); + if (gen !== fftGenRef.current) return; + fftMagCacheRef.current = result.magnitude; + console.log(`[Show2D FFT] Worker ${fftW}×${fftH}: crop=${(tCrop-t0).toFixed(1)}ms worker=${(performance.now()-tCrop).toFixed(1)}ms`); + } + // Track FFT dimensions when they differ from image dimensions (ROI crop or non-pow2 padding) + if (origCropW > 0) { + setFftCropDims({ cropWidth: origCropW, cropHeight: origCropH, fftWidth: fftW, fftHeight: fftH }); + } else if (fftW !== width || fftH !== height) { + setFftCropDims({ cropWidth: width, cropHeight: height, fftWidth: fftW, fftHeight: fftH }); + } else { + setFftCropDims(null); + } + setFftMagVersion(v => v + 1); + setFftComputing(false); + setFftProgress(""); + }; + + doCompute(); + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [effectiveShowFft, isGallery, selectedIdx, width, height, dataVersion, roiFftKey, fftWindow]); + + // Clear FFT measurement when image, FFT state, or ROI changes + React.useEffect(() => { setFftClickInfo(null); }, [selectedIdx, effectiveShowFft, roiFftActive, roiSelectedIdx]); + + // ------------------------------------------------------------------------- + // FFT data effect: normalize + colormap → cached offscreen canvas + // (does NOT depend on fftZoom/fftPanX/fftPanY — avoids reprocessing on zoom/pan) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!effectiveShowFft || isGallery || !fftMagCacheRef.current) return; + + const fftMag = fftMagCacheRef.current; + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + + // Use crop dimensions when ROI FFT is active + const fftW = fftCropDims?.fftWidth ?? width; + const fftH = fftCropDims?.fftHeight ?? height; + + // Apply scale mode + const magnitude = new Float32Array(fftMag.length); + for (let i = 0; i < fftMag.length; i++) { + if (fftScaleMode === "log") { + magnitude[i] = Math.log1p(fftMag[i]); + } else if (fftScaleMode === "power") { + magnitude[i] = Math.pow(fftMag[i], 0.5); + } else { + magnitude[i] = fftMag[i]; + } + } + + let displayMin: number, displayMax: number; + if (fftAuto) { + ({ min: displayMin, max: displayMax } = autoEnhanceFFT(magnitude, fftW, fftH)); + } else { + ({ min: displayMin, max: displayMax } = findDataRange(magnitude)); + } + + const { mean, std } = computeStats(magnitude); + setFftStats([mean, displayMin, displayMax, std]); + + // Store histogram data + setFftHistogramData(magnitude.slice()); + setFftDataRange({ min: displayMin, max: displayMax }); + + // Apply histogram slider clipping and render to cached offscreen + const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + const offscreen = renderToOffscreen(magnitude, fftW, fftH, lut, vmin, vmax); + if (!offscreen) return; + fftOffscreenRef.current = offscreen; + setFftOffscreenVersion(v => v + 1); + }, [effectiveShowFft, isGallery, fftMagVersion, fftVminPct, fftVmaxPct, fftColormap, fftScaleMode, fftAuto, width, height, fftCropDims]); + + // ------------------------------------------------------------------------- + // FFT draw effect: cheap drawImage from cached offscreen (zoom/pan changes) + // ------------------------------------------------------------------------- + React.useLayoutEffect(() => { + if (!effectiveShowFft || isGallery || !fftCanvasRef.current || !fftOffscreenRef.current) return; + + const canvas = fftCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const offscreen = fftOffscreenRef.current; + const fftW = offscreen.width; + const fftH = offscreen.height; + + // Use bilinear smoothing when FFT is smaller than canvas (avoids blocky upscaling) + ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.clearRect(0, 0, canvasW, canvasH); + ctx.save(); + + const centerOffsetX = (canvasW - canvasW * fftZoom) / 2 + fftPanX; + const centerOffsetY = (canvasH - canvasH * fftZoom) / 2 + fftPanY; + + ctx.translate(centerOffsetX, centerOffsetY); + ctx.scale(fftZoom, fftZoom); + // Stretch cropped FFT to fill the full canvas (no layout change during drag) + ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); + ctx.restore(); + }, [effectiveShowFft, isGallery, fftOffscreenVersion, canvasW, canvasH, fftZoom, fftPanX, fftPanY]); + + // ------------------------------------------------------------------------- + // Render FFT overlay (scale bar + colorbar + d-spacing marker) + // ------------------------------------------------------------------------- + React.useEffect(() => { + const overlay = fftOverlayRef.current; + if (!overlay || !effectiveShowFft || isGallery) return; + const ctx = overlay.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, overlay.width, overlay.height); + + // Use crop dimensions for reciprocal-space calculations + const fftW = fftCropDims?.fftWidth ?? width; + + // Reciprocal-space scale bar + if (pixelSize > 0) { + const fftPixelSize = 1 / (fftW * pixelSize); + drawFFTScaleBarHiDPI(overlay, DPR, fftZoom, fftPixelSize, fftW); + } + + // FFT colorbar + if (fftShowColorbar && fftDataRange.min !== fftDataRange.max) { + const { vmin, vmax } = sliderRange(fftDataRange.min, fftDataRange.max, fftVminPct, fftVmaxPct); + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + ctx.save(); + ctx.scale(DPR, DPR); + const cssW = overlay.width / DPR; + const cssH = overlay.height / DPR; + drawColorbar(ctx, cssW, cssH, lut, vmin, vmax, fftScaleMode === "log"); + ctx.restore(); + } + + // D-spacing crosshair marker — use crop dims for coordinate mapping + const fftH = fftCropDims?.fftHeight ?? height; + if (fftClickInfo) { + ctx.save(); + ctx.scale(DPR, DPR); + const centerOffsetX = (canvasW - canvasW * fftZoom) / 2 + fftPanX; + const centerOffsetY = (canvasH - canvasH * fftZoom) / 2 + fftPanY; + const screenX = centerOffsetX + fftZoom * (fftClickInfo.col / fftW * canvasW); + const screenY = centerOffsetY + fftZoom * (fftClickInfo.row / fftH * canvasH); + ctx.strokeStyle = "rgba(255, 255, 255, 0.9)"; + ctx.shadowColor = "rgba(0, 0, 0, 0.6)"; + ctx.shadowBlur = 2; + ctx.lineWidth = 1.5; + const r = 8; + ctx.beginPath(); + ctx.moveTo(screenX - r, screenY); ctx.lineTo(screenX - 3, screenY); + ctx.moveTo(screenX + 3, screenY); ctx.lineTo(screenX + r, screenY); + ctx.moveTo(screenX, screenY - r); ctx.lineTo(screenX, screenY - 3); + ctx.moveTo(screenX, screenY + 3); ctx.lineTo(screenX, screenY + r); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(screenX, screenY, 4, 0, Math.PI * 2); + ctx.stroke(); + if (fftClickInfo.dSpacing != null) { + const d = fftClickInfo.dSpacing; + const label = d >= 10 ? `d = ${(d / 10).toFixed(2)} nm` : `d = ${d.toFixed(2)} Å`; + ctx.font = "bold 11px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = "white"; + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, screenX + 10, screenY - 4); + } + ctx.restore(); + } + }, [effectiveShowFft, isGallery, fftClickInfo, canvasW, canvasH, fftZoom, fftPanX, fftPanY, width, height, pixelSize, fftDataRange, fftVminPct, fftVmaxPct, fftColormap, fftScaleMode, fftShowColorbar, fftCropDims]); + + // ------------------------------------------------------------------------- + // Compute FFT magnitudes for gallery mode (cache raw magnitudes) + // ------------------------------------------------------------------------- + React.useEffect(() => { + if (!effectiveShowFft || !isGallery || !rawDataRef.current) return; + if (rawDataRef.current.length === 0) return; + let cancelled = false; + + const computeAllFFTs = async () => { + // Initialize cache; preserve existing entries (only recompute missing) + if (fftMagCacheGalleryRef.current.length !== nImages) { + fftMagCacheGalleryRef.current = new Array(nImages).fill(null); + } + setFftComputing(true); + const useGPU = !!(gpuFFTRef.current && gpuReadyRef.current); + const backend = useGPU ? "WebGPU" : "CPU Worker"; + setFftProgress(`FFT (${backend})`); + await new Promise(r => requestAnimationFrame(() => r())); + if (cancelled) { setFftComputing(false); return; } + + const useRoiCrop = roiFftActive && roiList && roiSelectedIdx >= 0 && roiSelectedIdx < roiList.length; + const roi = useRoiCrop ? roiList[roiSelectedIdx] : null; + const t0 = performance.now(); + + // Helper: prep one image for FFT (crop, pad, window) + const prepOne = (idx: number): { real: Float32Array; imag: Float32Array; w: number; h: number } | null => { + const data = rawDataRef.current![idx]; + if (!data) return null; + let inputData = data; + let curW = width, curH = height; + if (roi) { + const crop = cropROIRegion(data, width, height, roi); + if (crop) { + if (fftWindow) applyHannWindow2D(crop.cropped, crop.cropW, crop.cropH); + const padW = nextPow2(crop.cropW), padH = nextPow2(crop.cropH); + const padded = new Float32Array(padW * padH); + for (let y = 0; y < crop.cropH; y++) + for (let x = 0; x < crop.cropW; x++) + padded[y * padW + x] = crop.cropped[y * crop.cropW + x]; + inputData = padded; curW = padW; curH = padH; + } + } else { + const padW = nextPow2(curW), padH = nextPow2(curH); + if (padW !== curW || padH !== curH) { + const padded = new Float32Array(padW * padH); + for (let y = 0; y < curH; y++) + for (let x = 0; x < curW; x++) + padded[y * padW + x] = inputData[y * curW + x]; + inputData = padded; curW = padW; curH = padH; + } + } + return { real: inputData.slice(), imag: new Float32Array(inputData.length), w: curW, h: curH }; + }; + + // ── Prep all images ── + const inputs: { real: Float32Array; imag: Float32Array }[] = []; + let fftW = width, fftH = height; + for (let idx = 0; idx < nImages; idx++) { + const input = prepOne(idx); + if (input) { + fftW = input.w; fftH = input.h; + inputs.push({ real: input.real, imag: input.imag }); + } else { + inputs.push({ real: new Float32Array(0), imag: new Float32Array(0) }); + } + } + galleryFftDimsRef.current = { w: fftW, h: fftH }; + const tPrep = performance.now() - t0; + if (cancelled) { setFftComputing(false); return; } + + // ── Batched progressive FFT: batch BATCH_SIZE at a time, display after each batch ── + const BATCH_SIZE = 4; + const tFFT0 = performance.now(); + for (let batchStart = 0; batchStart < nImages; batchStart += BATCH_SIZE) { + if (cancelled) { setFftComputing(false); return; } + const batchEnd = Math.min(batchStart + BATCH_SIZE, nImages); + const batchInputs = inputs.slice(batchStart, batchEnd).filter(inp => inp.real.length > 0); + setFftProgress(`FFT ${batchStart + 1}–${batchEnd}/${nImages} (${backend})`); + + if (useGPU && batchInputs.length > 1) { + // GPU batch: one submission for BATCH_SIZE images + const batchResults = await gpuFFTRef.current!.fft2DBatch(batchInputs, fftW, fftH); + if (cancelled) { setFftComputing(false); return; } + let ri = 0; + for (let idx = batchStart; idx < batchEnd; idx++) { + if (inputs[idx].real.length === 0) continue; + fftshift(batchResults[ri].real, fftW, fftH); + fftshift(batchResults[ri].imag, fftW, fftH); + fftMagCacheGalleryRef.current[idx] = computeMagnitude(batchResults[ri].real, batchResults[ri].imag); + ri++; + } + } else { + // CPU or single image + for (let idx = batchStart; idx < batchEnd; idx++) { + if (inputs[idx].real.length === 0) continue; + if (cancelled) { setFftComputing(false); return; } + const { real, imag } = inputs[idx]; + if (useGPU) { + const result = await gpuFFTRef.current!.fft2D(real, imag, fftW, fftH, false); + fftshift(result.real, fftW, fftH); + fftshift(result.imag, fftW, fftH); + fftMagCacheGalleryRef.current[idx] = computeMagnitude(result.real, result.imag); + } else { + fft2d(real, imag, fftW, fftH, false); + fftshift(real, fftW, fftH); + fftshift(imag, fftW, fftH); + fftMagCacheGalleryRef.current[idx] = computeMagnitude(real, imag); + } + } + } + // Show this batch immediately (progressive top-to-bottom) + setGalleryFftMagVersion(v => v + 1); + // Yield to let the browser paint the batch + await new Promise(r => requestAnimationFrame(() => r())); + } + const tFFT = performance.now() - tFFT0; + const tTotal = performance.now() - t0; + if (!cancelled) { + console.log(`[Show2D FFT] Gallery ${nImages}×${fftW}×${fftH}: prep=${tPrep.toFixed(0)}ms fft=${tFFT.toFixed(0)}ms total=${tTotal.toFixed(0)}ms (${backend} batch=${BATCH_SIZE})`); + } + setFftComputing(false); + setFftProgress(""); + }; + + computeAllFFTs(); + + return () => { cancelled = true; setFftComputing(false); }; + // eslint-disable-next-line react-hooks/exhaustive-deps + }, [effectiveShowFft, isGallery, nImages, width, height, dataVersion, roiFftKey, fftWindow]); + + // Gallery FFT data effect: normalize + colormap → cached offscreen canvases + // (does NOT depend on gallery zoom/pan states) + const [galleryFftOffscreenVersion, setGalleryFftOffscreenVersion] = React.useState(0); + React.useEffect(() => { + if (!effectiveShowFft || !isGallery) return; + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + const fftW = galleryFftDimsRef.current?.w ?? width; + const fftH = galleryFftDimsRef.current?.h ?? height; + + for (let idx = 0; idx < nImages; idx++) { + const magnitude = fftMagCacheGalleryRef.current[idx]; + if (!magnitude) continue; + + // Apply scale transform (same logic as single mode) + let displayData: Float32Array; + let displayMin: number, displayMax: number; + if (fftScaleMode === "log") { + displayData = applyLogScale(magnitude); + } else if (fftScaleMode === "power") { + displayData = new Float32Array(magnitude.length); + for (let j = 0; j < magnitude.length; j++) displayData[j] = Math.sqrt(magnitude[j]); + } else { + displayData = magnitude; + } + if (fftAuto) { + ({ min: displayMin, max: displayMax } = autoEnhanceFFT(magnitude, fftW, fftH)); + if (fftScaleMode === "log") { displayMin = Math.log1p(displayMin); displayMax = Math.log1p(displayMax); } + else if (fftScaleMode === "power") { displayMin = Math.sqrt(displayMin); displayMax = Math.sqrt(displayMax); } + } else { + ({ min: displayMin, max: displayMax } = findDataRange(displayData)); + } + const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + + const offscreen = renderToOffscreen(displayData, fftW, fftH, lut, vmin, vmax); + if (!offscreen) continue; + fftOffscreensRef.current[idx] = offscreen; + } + + // Update FFT histogram from selected image + const selMag = fftMagCacheGalleryRef.current[selectedIdx]; + if (selMag) { + let histData: Float32Array; + if (fftScaleMode === "log") histData = applyLogScale(selMag); + else if (fftScaleMode === "power") { histData = new Float32Array(selMag.length); for (let j = 0; j < selMag.length; j++) histData[j] = Math.sqrt(selMag[j]); } + else histData = selMag; + setFftHistogramData(histData); + setFftDataRange(findDataRange(histData)); + } + setGalleryFftOffscreenVersion(v => v + 1); + }, [effectiveShowFft, isGallery, nImages, width, height, galleryFftMagVersion, fftColormap, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, selectedIdx]); + + // Gallery FFT draw effect: cheap drawImage from cached offscreens (zoom/pan changes) + React.useLayoutEffect(() => { + if (!effectiveShowFft || !isGallery) return; + const fftW = galleryFftDimsRef.current?.w ?? width; + const fftH = galleryFftDimsRef.current?.h ?? height; + + for (let idx = 0; idx < nImages; idx++) { + const offscreen = fftOffscreensRef.current[idx]; + const canvas = fftCanvasRefs.current[idx]; + if (!offscreen || !canvas) continue; + const ctx = canvas.getContext("2d"); + if (!ctx) continue; + + const { zoom, panX, panY } = getGalleryFftState(idx); + ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.clearRect(0, 0, canvasW, canvasH); + ctx.save(); + const cx = canvasW / 2; + const cy = canvasH / 2; + ctx.translate(cx + panX, cy + panY); + ctx.scale(zoom, zoom); + ctx.translate(-cx, -cy); + ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); + ctx.restore(); + } + }, [effectiveShowFft, isGallery, nImages, canvasW, canvasH, width, height, galleryFftOffscreenVersion, galleryFftStates, linkedZoom, linkedFftZoomState]); + + // ------------------------------------------------------------------------- + // Mouse Handlers for Zoom/Pan + // ------------------------------------------------------------------------- + const handleWheel = (e: React.WheelEvent, idx: number) => { + if (lockView) return; + // In gallery mode, only allow zoom on the selected image (unless linked) + if (isGallery && idx !== selectedIdx && !linkedZoom) return; + e.preventDefault(); // Prevent page scroll when zooming + + const canvas = canvasRefs.current[idx]; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + + // Get current zoom state + const zs = getZoomState(idx); + + // Mouse position relative to canvas (in canvas pixel coordinates) + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + + // Canvas center + const cx = canvas.width / 2; + const cy = canvas.height / 2; + + // Mouse position relative to the current view (accounting for pan and zoom) + // The transformation is: translate(cx + panX, cy + panY) -> scale(zoom) -> translate(-cx, -cy) + // So a point on screen at (screenX, screenY) maps to image space as: + // imageX = (screenX - cx - panX) / zoom + cx + const mouseImageX = (mouseCanvasX - cx - zs.panX) / zs.zoom + cx; + const mouseImageY = (mouseCanvasY - cy - zs.panY) / zs.zoom + cy; + + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + const newZoom = Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, zs.zoom * zoomFactor)); + + // Calculate new pan to keep the mouse position fixed on the same image point + // After zoom: screenX = (imageX - cx) * newZoom + cx + newPanX + // We want screenX to stay at mouseCanvasX, so: + // newPanX = mouseCanvasX - (imageX - cx) * newZoom - cx + const newPanX = mouseCanvasX - (mouseImageX - cx) * newZoom - cx; + const newPanY = mouseCanvasY - (mouseImageY - cy) * newZoom - cy; + + setZoomState(idx, { zoom: newZoom, panX: newPanX, panY: newPanY }); + }; + + const handleDoubleClick = (idx: number) => { + if (lockView) return; + setZoomState(idx, initialZoomState); + }; + + // Reset view (zoom/pan only — preserves profile, FFT state, etc.) + const handleResetAll = () => { + if (lockView) return; + setZoomStates(new Map()); + setLinkedZoomState(initialZoomState); + setGalleryFftStates(new Map()); + setLinkedFftZoomState({ zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }); + setFftZoom(DEFAULT_FFT_ZOOM); + setFftPanX(0); + setFftPanY(0); + }; + + // FFT zoom/pan handlers + const handleFftWheel = (e: React.WheelEvent) => { + if (lockView) return; + e.preventDefault(); // Prevent page scroll when zooming FFT + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + setFftZoom(Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, fftZoom * zoomFactor))); + }; + + const handleFftDoubleClick = () => { + if (lockView) return; + setFftZoom(DEFAULT_FFT_ZOOM); + setFftPanX(0); + setFftPanY(0); + setFftClickInfo(null); + }; + + // Convert FFT canvas mouse position to FFT image pixel coordinates + const fftScreenToImg = (e: React.MouseEvent): { col: number; row: number } | null => { + const canvas = fftCanvasRef.current; + if (!canvas) return null; + const rect = canvas.getBoundingClientRect(); + const mouseX = e.clientX - rect.left; + const mouseY = e.clientY - rect.top; + const cOffX = (canvasW - canvasW * fftZoom) / 2 + fftPanX; + const cOffY = (canvasH - canvasH * fftZoom) / 2 + fftPanY; + const fftW = fftCropDims?.fftWidth ?? width; + const fftH = fftCropDims?.fftHeight ?? height; + const imgCol = ((mouseX - cOffX) / fftZoom) / canvasW * fftW; + const imgRow = ((mouseY - cOffY) / fftZoom) / canvasH * fftH; + if (imgCol >= 0 && imgCol < fftW && imgRow >= 0 && imgRow < fftH) { + return { col: imgCol, row: imgRow }; + } + return null; + }; + + const handleFftMouseDown = (e: React.MouseEvent) => { + if (lockView) return; + fftClickStartRef.current = { x: e.clientX, y: e.clientY }; + setIsDraggingFftPan(true); + setFftPanStart({ x: e.clientX, y: e.clientY, pX: fftPanX, pY: fftPanY }); + }; + + const handleFftMouseMove = (e: React.MouseEvent) => { + if (!isDraggingFftPan || !fftPanStart) return; + const dx = e.clientX - fftPanStart.x; + const dy = e.clientY - fftPanStart.y; + setFftPanX(fftPanStart.pX + dx); + setFftPanY(fftPanStart.pY + dy); + }; + + const handleFftMouseUp = (e: React.MouseEvent) => { + // Click detection for d-spacing measurement + if (fftClickStartRef.current) { + const dx = e.clientX - fftClickStartRef.current.x; + const dy = e.clientY - fftClickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + const pos = fftScreenToImg(e); + if (pos) { + // Use crop dimensions when ROI FFT is active + const fftW = fftCropDims?.fftWidth ?? width; + const fftH = fftCropDims?.fftHeight ?? height; + let imgCol = pos.col; + let imgRow = pos.row; + // Snap to nearest Bragg spot (local max in FFT magnitude) + if (fftMagCacheRef.current) { + const snapped = findFFTPeak(fftMagCacheRef.current, fftW, fftH, imgCol, imgRow, FFT_SNAP_RADIUS); + imgCol = snapped.col; + imgRow = snapped.row; + } + const halfW = Math.floor(fftW / 2); + const halfH = Math.floor(fftH / 2); + const dcol = imgCol - halfW; + const drow = imgRow - halfH; + const distPx = Math.sqrt(dcol * dcol + drow * drow); + if (distPx < 1) { + setFftClickInfo(null); + } else { + let spatialFreq: number | null = null; + let dSpacing: number | null = null; + if (pixelSize > 0) { + const paddedW = nextPow2(fftW); + const paddedH = nextPow2(fftH); + const binC = ((Math.round(imgCol) - halfW) % fftW + fftW) % fftW; + const binR = ((Math.round(imgRow) - halfH) % fftH + fftH) % fftH; + const freqC = binC <= paddedW / 2 ? binC / (paddedW * pixelSize) : (binC - paddedW) / (paddedW * pixelSize); + const freqR = binR <= paddedH / 2 ? binR / (paddedH * pixelSize) : (binR - paddedH) / (paddedH * pixelSize); + spatialFreq = Math.sqrt(freqC * freqC + freqR * freqR); + dSpacing = spatialFreq > 0 ? 1 / spatialFreq : null; + } + setFftClickInfo({ row: imgRow, col: imgCol, distPx, spatialFreq, dSpacing }); + } + } + } + fftClickStartRef.current = null; + } + setIsDraggingFftPan(false); + setFftPanStart(null); + }; + + const handleFftMouseLeave = () => { + fftClickStartRef.current = null; + setIsDraggingFftPan(false); + setFftPanStart(null); + }; + + // Gallery FFT zoom/pan handlers (only selected image's FFT responds) + const handleGalleryFftWheel = (e: React.WheelEvent, idx: number) => { + if (lockView) return; + if (isGallery && idx !== selectedIdx && !linkedZoom) return; + e.preventDefault(); // Prevent page scroll when zooming FFT + const zs = getGalleryFftState(idx); + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + setGalleryFftState(idx, { ...zs, zoom: Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, zs.zoom * zoomFactor)) }); + }; + + const handleGalleryFftMouseDown = (e: React.MouseEvent, idx: number) => { + if (isGallery && idx !== selectedIdx) { + if (lockNavigation) return; + setSelectedIdx(idx); + return; // Select first, don't start panning + } + if (lockView) return; + const zs = getGalleryFftState(idx); + setFftPanningIdx(idx); + setIsDraggingFftPan(true); + setFftPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + }; + + const handleGalleryFftMouseMove = (e: React.MouseEvent, idx: number) => { + if (!isDraggingFftPan || !fftPanStart || fftPanningIdx !== idx) return; + const dx = e.clientX - fftPanStart.x; + const dy = e.clientY - fftPanStart.y; + const zs = getGalleryFftState(idx); + setGalleryFftState(idx, { ...zs, panX: fftPanStart.pX + dx, panY: fftPanStart.pY + dy }); + }; + + const handleGalleryFftMouseUp = () => { + setIsDraggingFftPan(false); + setFftPanStart(null); + setFftPanningIdx(null); + }; + + // Track which image is being panned + const [panningIdx, setPanningIdx] = React.useState(null); + const clickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const [draggingProfileEndpoint, setDraggingProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isDraggingProfileLine, setIsDraggingProfileLine] = React.useState(false); + const [hoveredProfileEndpoint, setHoveredProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isHoveringProfileLine, setIsHoveringProfileLine] = React.useState(false); + const profileDragStartRef = React.useRef<{ row: number; col: number; p0: { row: number; col: number }; p1: { row: number; col: number } } | null>(null); + + const screenToImg = (e: React.MouseEvent, idx: number): { imgCol: number; imgRow: number } => { + const canvas = canvasRefs.current[idx]; + if (!canvas) return { imgCol: 0, imgRow: 0 }; + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + return { + imgCol: ((mouseCanvasX - cx - zs.panX) / zs.zoom + cx) / displayScale, + imgRow: ((mouseCanvasY - cy - zs.panY) / zs.zoom + cy) / displayScale, + }; + }; + + const updateAllProfileData = (p0: { row: number; col: number }, p1: { row: number; col: number }) => { + if (!rawDataRef.current) return; + const allProfiles: (Float32Array | null)[] = []; + for (let j = 0; j < rawDataRef.current.length; j++) { + const raw = rawDataRef.current[j]; + allProfiles.push(raw ? sampleLineProfile(raw, width, height, p0.row, p0.col, p1.row, p1.col) : null); + } + setProfileDataAll(allProfiles); + }; + + const updateROI = (e: React.MouseEvent, idx: number) => { + const { imgCol, imgRow } = screenToImg(e, idx); + updateSelectedRoi({ col: Math.max(0, Math.min(width - 1, Math.floor(imgCol))), row: Math.max(0, Math.min(height - 1, Math.floor(imgRow))) }); + }; + + const hitTestROI = (imgCol: number, imgRow: number): number => { + if (!roiActive || !roiList) return -1; + for (let ri = roiList.length - 1; ri >= 0; ri--) { + const roi = roiList[ri]; + const shape = roi.shape || "circle"; + if (shape === "circle" || shape === "annular") { + if (Math.sqrt((imgCol - roi.col) ** 2 + (imgRow - roi.row) ** 2) <= roi.radius) return ri; + } else if (shape === "square") { + if (Math.abs(imgCol - roi.col) <= roi.radius && Math.abs(imgRow - roi.row) <= roi.radius) return ri; + } else if (shape === "rectangle") { + if (Math.abs(imgCol - roi.col) <= roi.width / 2 && Math.abs(imgRow - roi.row) <= roi.height / 2) return ri; + } + } + return -1; + }; + + const getHitArea = () => { + const zoom = (getZoomState(selectedIdx)).zoom; + return RESIZE_HIT_AREA_PX / (displayScale * zoom); + }; + + const isNearEdge = (imgCol: number, imgRow: number, roi: ROIItem): boolean => { + const hitArea = getHitArea(); + const shape = roi.shape || "circle"; + if (shape === "circle" || shape === "annular") { + const dist = Math.sqrt((imgCol - roi.col) ** 2 + (imgRow - roi.row) ** 2); + return Math.abs(dist - roi.radius) < hitArea; + } + if (shape === "square") { + const dx = Math.abs(imgCol - roi.col); + const dy = Math.abs(imgRow - roi.row); + const r = roi.radius; + return (dx <= r + hitArea && dy <= r + hitArea) && (Math.abs(dx - r) < hitArea || Math.abs(dy - r) < hitArea); + } + if (shape === "rectangle") { + const dx = Math.abs(imgCol - roi.col); + const dy = Math.abs(imgRow - roi.row); + const hw = roi.width / 2; + const hh = roi.height / 2; + return (dx <= hw + hitArea && dy <= hh + hitArea) && (Math.abs(dx - hw) < hitArea || Math.abs(dy - hh) < hitArea); + } + return false; + }; + + const isNearResizeHandle = (imgCol: number, imgRow: number): boolean => { + if (!roiActive || !selectedRoi) return false; + return isNearEdge(imgCol, imgRow, selectedRoi); + }; + + const isNearAnyEdge = (imgCol: number, imgRow: number): boolean => { + if (!roiActive || !roiList) return false; + return roiList.some(roi => isNearEdge(imgCol, imgRow, roi)); + }; + + const isNearResizeHandleInner = (imgCol: number, imgRow: number): boolean => { + if (!roiActive || !selectedRoi || selectedRoi.shape !== "annular") return false; + const hitArea = getHitArea(); + const dist = Math.sqrt((imgCol - selectedRoi.col) ** 2 + (imgRow - selectedRoi.row) ** 2); + return Math.abs(dist - selectedRoi.radius_inner) < hitArea; + }; + + const handleMouseDown = (e: React.MouseEvent, idx: number) => { + const zs = getZoomState(idx); + if (isGallery && idx !== selectedIdx) { + if (lockNavigation) return; + setSelectedIdx(idx); + // Continue to pan setup so click-drag on unselected panel pans immediately + // (no double-click required to select first then drag). + } + // Check if click is on the lens inset — edge = resize, interior = drag + if (!lockDisplay && showLens && !isGallery && idx === 0) { + const canvas = canvasRefs.current[0]; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const cssY = e.clientY - rect.top; + const margin = 12; + const lx = lensAnchor ? lensAnchor.x : margin; + const ly = lensAnchor ? lensAnchor.y : canvasH - lensDisplaySize - margin - 20; + if (cssX >= lx && cssX <= lx + lensDisplaySize && cssY >= ly && cssY <= ly + lensDisplaySize) { + const edgeHit = 8; + const nearEdge = cssX - lx < edgeHit || lx + lensDisplaySize - cssX < edgeHit || cssY - ly < edgeHit || ly + lensDisplaySize - cssY < edgeHit; + if (nearEdge) { + setIsResizingLens(true); + lensResizeStartRef.current = { my: e.clientY, startSize: lensDisplaySize }; + } else { + setIsDraggingLens(true); + lensDragStartRef.current = { mx: e.clientX, my: e.clientY, ax: lx, ay: ly }; + } + e.preventDefault(); + return; + } + } + } + clickStartRef.current = { x: e.clientX, y: e.clientY }; + if (profileActive && !lockProfile) { + const { imgCol, imgRow } = screenToImg(e, idx); + if (profilePoints.length === 2) { + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const hitRadius = 10 / (displayScale * zs.zoom); + const d0 = Math.sqrt((imgCol - p0.col) ** 2 + (imgRow - p0.row) ** 2); + const d1 = Math.sqrt((imgCol - p1.col) ** 2 + (imgRow - p1.row) ** 2); + if (d0 <= hitRadius || d1 <= hitRadius) { + setDraggingProfileEndpoint(d0 <= d1 ? 0 : 1); + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + return; + } + if (pointToSegmentDistance(imgCol, imgRow, p0.col, p0.row, p1.col, p1.row) <= hitRadius) { + setIsDraggingProfileLine(true); + profileDragStartRef.current = { + row: imgRow, + col: imgCol, + p0: { row: p0.row, col: p0.col }, + p1: { row: p1.row, col: p1.col }, + }; + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + return; + } + } + if (!lockView) { + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + } + return; + } + if (roiActive) { + if (lockRoi) { + if (!lockView) { + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + } + return; + } + const { imgCol, imgRow } = screenToImg(e, idx); + // Check resize handles on selected ROI first + if (isNearResizeHandleInner(imgCol, imgRow)) { + setIsDraggingResizeInner(true); + return; + } + if (isNearResizeHandle(imgCol, imgRow)) { + e.preventDefault(); + resizeAspectRef.current = selectedRoi && (selectedRoi.shape === "rectangle") && selectedRoi.width > 0 && selectedRoi.height > 0 ? selectedRoi.width / selectedRoi.height : null; + setIsDraggingResize(true); + return; + } + // Check edge of any ROI — auto-select and start resize + if (roiList) { + for (let ri = 0; ri < roiList.length; ri++) { + if (isNearEdge(imgCol, imgRow, roiList[ri])) { + e.preventDefault(); + const roi = roiList[ri]; + resizeAspectRef.current = roi && (roi.shape === "rectangle") && roi.width > 0 && roi.height > 0 ? roi.width / roi.height : null; + setRoiSelectedIdx(ri); + setIsDraggingResize(true); + return; + } + } + } + // Hit-test existing ROIs (click inside to select + drag) + const hitIdx = hitTestROI(imgCol, imgRow); + if (hitIdx >= 0) { + setRoiSelectedIdx(hitIdx); + setIsDraggingROI(true); + return; + } + // Click on empty space — deselect and allow panning + setRoiSelectedIdx(-1); + } + // Start panning (works in both ROI-active and normal modes) + { + if (lockView) return; + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); + } + }; + + const handleMouseMove = (e: React.MouseEvent, idx: number) => { + // Fast path: during pan drag, skip all cursor/hover/lens work — just update pan + if (isDraggingPan && panStart && panningIdx !== null && !lockView) { + const canvas = canvasRefs.current[idx]; + if (!canvas || idx !== panningIdx) return; + const rect = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect.width; + const scaleY = canvas.height / rect.height; + const dx = (e.clientX - panStart.x) * scaleX; + const dy = (e.clientY - panStart.y) * scaleY; + const zs = getZoomState(idx); + setZoomState(idx, { ...zs, panX: panStart.pX + dx, panY: panStart.pY + dy }); + return; + } + + // Cursor readout: convert screen position to image pixel coordinates + const canvas = canvasRefs.current[idx]; + if (canvas && rawDataRef.current) { + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + const imageCanvasX = (mouseCanvasX - cx - zs.panX) / zs.zoom + cx; + const imageCanvasY = (mouseCanvasY - cy - zs.panY) / zs.zoom + cy; + const imgX = Math.floor(imageCanvasX / displayScale); + const imgY = Math.floor(imageCanvasY / displayScale); + if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { + const rawData = rawDataRef.current[idx]; + if (rawData) setCursorInfo({ row: imgY, col: imgX, value: rawData[imgY * width + imgX] }); + if (!lockDisplay && showLens && !isGallery) setLensPos({ row: imgY, col: imgX }); + } else { + setCursorInfo(null); + // Don't clear lensPos — lens stays at last position when toggle is on + } + } + + // Lens drag + if (!lockDisplay && isDraggingLens && lensDragStartRef.current) { + const dx = e.clientX - lensDragStartRef.current.mx; + const dy = e.clientY - lensDragStartRef.current.my; + setLensAnchor({ x: lensDragStartRef.current.ax + dx, y: lensDragStartRef.current.ay + dy }); + return; + } + // Lens resize drag + if (!lockDisplay && isResizingLens && lensResizeStartRef.current) { + const dy = e.clientY - lensResizeStartRef.current.my; + setLensDisplaySize(Math.max(64, Math.min(256, lensResizeStartRef.current.startSize + dy))); + return; + } + + if (profileActive && !lockProfile && profilePoints.length === 2) { + const { imgCol, imgRow } = screenToImg(e, idx); + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const activeZoom = linkedZoom ? linkedZoomState.zoom : (zoomStates.get(idx) || initialZoomState).zoom; + const hitRadius = 10 / (displayScale * activeZoom); + const d0 = Math.sqrt((imgCol - p0.col) ** 2 + (imgRow - p0.row) ** 2); + const d1 = Math.sqrt((imgCol - p1.col) ** 2 + (imgRow - p1.row) ** 2); + if (draggingProfileEndpoint !== null) { + const clampedRow = Math.max(0, Math.min(height - 1, imgRow)); + const clampedCol = Math.max(0, Math.min(width - 1, imgCol)); + const next = [ + draggingProfileEndpoint === 0 ? { row: clampedRow, col: clampedCol } : profilePoints[0], + draggingProfileEndpoint === 1 ? { row: clampedRow, col: clampedCol } : profilePoints[1], + ]; + setProfilePoints(next); + updateAllProfileData(next[0], next[1]); + return; + } + if (isDraggingProfileLine && profileDragStartRef.current) { + const drag = profileDragStartRef.current; + let deltaRow = imgRow - drag.row; + let deltaCol = imgCol - drag.col; + const minRow = Math.min(drag.p0.row, drag.p1.row); + const maxRow = Math.max(drag.p0.row, drag.p1.row); + const minCol = Math.min(drag.p0.col, drag.p1.col); + const maxCol = Math.max(drag.p0.col, drag.p1.col); + deltaRow = Math.max(deltaRow, -minRow); + deltaRow = Math.min(deltaRow, (height - 1) - maxRow); + deltaCol = Math.max(deltaCol, -minCol); + deltaCol = Math.min(deltaCol, (width - 1) - maxCol); + const next = [ + { row: drag.p0.row + deltaRow, col: drag.p0.col + deltaCol }, + { row: drag.p1.row + deltaRow, col: drag.p1.col + deltaCol }, + ]; + setProfilePoints(next); + updateAllProfileData(next[0], next[1]); + return; + } + const nextHoveredEndpoint: 0 | 1 | null = d0 <= hitRadius ? 0 : d1 <= hitRadius ? 1 : null; + const nextHoverLine = nextHoveredEndpoint === null && pointToSegmentDistance(imgCol, imgRow, p0.col, p0.row, p1.col, p1.row) <= hitRadius; + setHoveredProfileEndpoint(nextHoveredEndpoint); + setIsHoveringProfileLine(nextHoverLine); + } else { + if (hoveredProfileEndpoint !== null) setHoveredProfileEndpoint(null); + if (isHoveringProfileLine) setIsHoveringProfileLine(false); + } + + // ROI resize drag (inner annular ring) + if (!lockRoi && isDraggingResizeInner && selectedRoi) { + const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); + const newR = Math.sqrt((ic - selectedRoi.col) ** 2 + (ir - selectedRoi.row) ** 2); + updateSelectedRoi({ radius_inner: Math.max(1, Math.min(selectedRoi.radius - 1, Math.round(newR))) }); + return; + } + // ROI resize drag (outer) + if (!lockRoi && isDraggingResize && selectedRoi) { + const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); + const shape = selectedRoi.shape || "circle"; + if (shape === "rectangle") { + let newW = Math.max(2, Math.round(Math.abs(ic - selectedRoi.col) * 2)); + let newH = Math.max(2, Math.round(Math.abs(ir - selectedRoi.row) * 2)); + if (e.shiftKey && resizeAspectRef.current != null) { + const aspect = resizeAspectRef.current; + if (newW / newH > aspect) newH = Math.max(2, Math.round(newW / aspect)); + else newW = Math.max(2, Math.round(newH * aspect)); + } + updateSelectedRoi({ width: newW, height: newH }); + } else { + const newR = shape === "square" ? Math.max(Math.abs(ic - selectedRoi.col), Math.abs(ir - selectedRoi.row)) : Math.sqrt((ic - selectedRoi.col) ** 2 + (ir - selectedRoi.row) ** 2); + const minR = shape === "annular" ? selectedRoi.radius_inner + 1 : 1; + updateSelectedRoi({ radius: Math.max(minR, Math.round(newR)) }); + } + return; + } + // ROI drag (move center) + if (!lockRoi && isDraggingROI) { + updateROI(e, idx); + return; + } + // Lens edge hover detection + if (!lockDisplay && showLens && !isGallery && canvas) { + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const cssY = e.clientY - rect.top; + const margin = 12; + const lx = lensAnchor ? lensAnchor.x : margin; + const ly = lensAnchor ? lensAnchor.y : canvasH - lensDisplaySize - margin - 20; + const inside = cssX >= lx && cssX <= lx + lensDisplaySize && cssY >= ly && cssY <= ly + lensDisplaySize; + const edgeHit = 8; + const nearEdge = inside && (cssX - lx < edgeHit || lx + lensDisplaySize - cssX < edgeHit || cssY - ly < edgeHit || ly + lensDisplaySize - cssY < edgeHit); + setIsHoveringLensEdge(nearEdge); + } else { + setIsHoveringLensEdge(false); + } + // Hover detection for resize handles (show cursor on any ROI edge) + if (roiActive && !lockRoi && !isDraggingPan) { + const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); + setIsHoveringResizeInner(isNearResizeHandleInner(ic, ir)); + setIsHoveringResize(isNearAnyEdge(ic, ir)); + } + + // Panning + if (lockView) return; + if (!isDraggingPan || !panStart || panningIdx === null) return; + if (idx !== panningIdx) return; + if (!canvas) return; + const rect2 = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect2.width; + const scaleY = canvas.height / rect2.height; + const dx = (e.clientX - panStart.x) * scaleX; + const dy = (e.clientY - panStart.y) * scaleY; + + const zs = getZoomState(idx); + setZoomState(idx, { ...zs, panX: panStart.pX + dx, panY: panStart.pY + dy }); + }; + + const handleMouseUp = (e: React.MouseEvent, idx: number) => { + if (isDraggingLens) { + setIsDraggingLens(false); + lensDragStartRef.current = null; + return; + } + if (isResizingLens) { + setIsResizingLens(false); + lensResizeStartRef.current = null; + return; + } + if (draggingProfileEndpoint !== null || isDraggingProfileLine) { + setDraggingProfileEndpoint(null); + setIsDraggingProfileLine(false); + profileDragStartRef.current = null; + clickStartRef.current = null; + setIsDraggingROI(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + return; + } + // Detect click (vs drag) for profile mode + if (profileActive && !lockProfile && clickStartRef.current) { + const dx = e.clientX - clickStartRef.current.x; + const dy = e.clientY - clickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + // It's a click — compute image coordinates + const canvas = canvasRefs.current[idx]; + if (canvas && rawDataRef.current) { + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + const imgX = ((mouseCanvasX - cx - zs.panX) / zs.zoom + cx) / displayScale; + const imgY = ((mouseCanvasY - cy - zs.panY) / zs.zoom + cy) / displayScale; + if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { + const pt = { row: imgY, col: imgX }; + if (profilePoints.length === 0 || profilePoints.length === 2) { + // Start new line + setProfilePoints([pt]); + setProfileDataAll([]); + } else { + // Complete the line + const p0 = profilePoints[0]; + setProfilePoints([p0, pt]); + updateAllProfileData(p0, pt); + } + } + } + } + } + // Detect click for measurement mode (only when profile is not active) + if (measureActive && !profileActive && clickStartRef.current) { + const dx = e.clientX - clickStartRef.current.x; + const dy = e.clientY - clickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + const canvas = canvasRefs.current[idx]; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const mouseCanvasX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseCanvasY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zs = getZoomState(idx); + const cx = canvasW / 2; + const cy = canvasH / 2; + const imgX = ((mouseCanvasX - cx - zs.panX) / zs.zoom + cx) / displayScale; + const imgY = ((mouseCanvasY - cy - zs.panY) / zs.zoom + cy) / displayScale; + if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { + const pt = { row: imgY, col: imgX }; + if (measurePoints.length < 2) { + setMeasurePoints([...measurePoints, pt]); + } else { + setMeasurePoints([pt]); + } + } + } + } + } + clickStartRef.current = null; + setDraggingProfileEndpoint(null); + setIsDraggingProfileLine(false); + profileDragStartRef.current = null; + setIsDraggingROI(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + }; + + const handleMouseLeave = (idx: number) => { + setCursorInfo(null); + // Don't clear lensPos — lens stays at last position when toggle is on + setIsDraggingLens(false); + setIsResizingLens(false); + lensDragStartRef.current = null; + lensResizeStartRef.current = null; + setIsHoveringLensEdge(false); + setIsDraggingROI(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setDraggingProfileEndpoint(null); + setIsDraggingProfileLine(false); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + profileDragStartRef.current = null; + setIsHoveringResize(false); + setIsHoveringResizeInner(false); + if (panningIdx === idx) { + setIsDraggingPan(false); + setPanStart(null); + setPanningIdx(null); + } + }; + + // ------------------------------------------------------------------------- + // Copy to clipboard handler + const handleCopy = React.useCallback(async () => { + if (lockExport) return; + const canvas = canvasRefs.current[isGallery ? selectedIdx : 0]; + if (!canvas) return; + try { + const blob = await new Promise(resolve => canvas.toBlob(resolve, "image/png")); + if (!blob) return; + await navigator.clipboard.write([new ClipboardItem({ "image/png": blob })]); + } catch { + // Fallback: download if clipboard API unavailable + canvas.toBlob((b) => { if (b) downloadBlob(b, `show2d_${labels?.[selectedIdx] || "image"}.png`); }, "image/png"); + } + }, [isGallery, selectedIdx, labels, lockExport]); + + // Export publication-quality figure with scale bar, colorbar, annotations + const handleExportFigure = React.useCallback((withScaleBar: boolean, withColorbar: boolean) => { + if (lockExport) return; + setExportAnchor(null); + const idx = isGallery ? selectedIdx : 0; + const rawData = rawDataRef.current?.[idx]; + if (!rawData) return; + + const processed = logScale ? applyLogScale(rawData) : rawData; + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + + let vmin: number, vmax: number; + const hasAbsRange = traitVmin != null && traitVmax != null; + const rMin = hasAbsRange ? (logScale ? Math.log1p(Math.max(traitVmin!, 0)) : traitVmin!) : imageDataRange.min; + const rMax = hasAbsRange ? (logScale ? Math.log1p(Math.max(traitVmax!, 0)) : traitVmax!) : imageDataRange.max; + if (rMin !== rMax && (imageVminPct > 0 || imageVmaxPct < 100)) { + ({ vmin, vmax } = sliderRange(rMin, rMax, imageVminPct, imageVmaxPct)); + } else if (!hasAbsRange && autoContrast) { + ({ vmin, vmax } = percentileClip(processed, 2, 98)); + } else { + vmin = rMin; + vmax = rMax; + } + + const offscreen = renderToOffscreen(processed, width, height, lut, vmin, vmax); + if (!offscreen) return; + + const figCanvas = exportFigure({ + imageCanvas: offscreen, + title: title || undefined, + lut, + vmin, + vmax, + logScale, + pixelSize: pixelSize > 0 ? pixelSize : undefined, + showColorbar: withColorbar, + showScaleBar: withScaleBar && pixelSize > 0, + drawAnnotations: (ctx) => { + // ROI highlight mask + if (roiActive && roiList) { + const hlRois = roiList.filter(r => r.highlight); + if (hlRois.length > 0) { + ctx.save(); + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.fillRect(0, 0, width, height); + ctx.globalCompositeOperation = "destination-out"; + for (const roi of hlRois) { + ctx.fillStyle = "rgba(0,0,0,1)"; + const shape = roi.shape || "circle"; + if (shape === "circle") { ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); } + else if (shape === "square") { ctx.fillRect(roi.col - roi.radius, roi.row - roi.radius, roi.radius * 2, roi.radius * 2); } + else if (shape === "rectangle") { ctx.fillRect(roi.col - roi.width / 2, roi.row - roi.height / 2, roi.width, roi.height); } + else if (shape === "annular") { + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "source-over"; + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius_inner, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "destination-out"; + } + } + ctx.restore(); + } + // ROI outlines + for (const roi of roiList) { + const shape = (roi.shape || "circle") as "circle" | "square" | "rectangle" | "annular"; + ctx.lineWidth = roi.line_width || 2; + drawROI(ctx, roi.col, roi.row, shape, roi.radius, roi.width, roi.height, roi.color, roi.color, false, roi.radius_inner); + } + } + // Profile line + if (profileActive && profilePoints.length === 2) { + ctx.strokeStyle = "#4fc3f7"; + ctx.lineWidth = 2; + ctx.setLineDash([4, 3]); + ctx.beginPath(); + ctx.moveTo(profilePoints[0].col, profilePoints[0].row); + ctx.lineTo(profilePoints[1].col, profilePoints[1].row); + ctx.stroke(); + ctx.setLineDash([]); + ctx.fillStyle = "#4fc3f7"; + ctx.beginPath(); + ctx.arc(profilePoints[0].col, profilePoints[0].row, 3, 0, Math.PI * 2); + ctx.fill(); + ctx.beginPath(); + ctx.arc(profilePoints[1].col, profilePoints[1].row, 3, 0, Math.PI * 2); + ctx.fill(); + } + }, + }); + + canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, `show2d_figure_${labels?.[selectedIdx] || "image"}.pdf`)); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, lockExport]); + + // Export all variants (PNG + PDF) as zip + const handleExportAll = React.useCallback(async () => { + if (lockExport) return; + setExportAnchor(null); + const idx = isGallery ? selectedIdx : 0; + const rawData = rawDataRef.current?.[idx]; + if (!rawData) return; + + const processed = logScale ? applyLogScale(rawData) : rawData; + const lut = COLORMAPS[cmap] || COLORMAPS.inferno; + + let vmin: number, vmax: number; + const hasAbsRange2 = traitVmin != null && traitVmax != null; + const rMin2 = hasAbsRange2 ? (logScale ? Math.log1p(Math.max(traitVmin!, 0)) : traitVmin!) : imageDataRange.min; + const rMax2 = hasAbsRange2 ? (logScale ? Math.log1p(Math.max(traitVmax!, 0)) : traitVmax!) : imageDataRange.max; + if (rMin2 !== rMax2 && (imageVminPct > 0 || imageVmaxPct < 100)) { + ({ vmin, vmax } = sliderRange(rMin2, rMax2, imageVminPct, imageVmaxPct)); + } else if (!hasAbsRange2 && autoContrast) { + ({ vmin, vmax } = percentileClip(processed, 2, 98)); + } else { + vmin = rMin2; + vmax = rMax2; + } + + const offscreen = renderToOffscreen(processed, width, height, lut, vmin, vmax); + if (!offscreen) return; + + const drawAnnotations = (ctx: CanvasRenderingContext2D) => { + if (roiActive && roiList) { + const hlRois = roiList.filter(r => r.highlight); + if (hlRois.length > 0) { + ctx.save(); + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.fillRect(0, 0, width, height); + ctx.globalCompositeOperation = "destination-out"; + for (const roi of hlRois) { + ctx.fillStyle = "rgba(0,0,0,1)"; + const shape = roi.shape || "circle"; + if (shape === "circle") { ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); } + else if (shape === "square") { ctx.fillRect(roi.col - roi.radius, roi.row - roi.radius, roi.radius * 2, roi.radius * 2); } + else if (shape === "rectangle") { ctx.fillRect(roi.col - roi.width / 2, roi.row - roi.height / 2, roi.width, roi.height); } + else if (shape === "annular") { + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "source-over"; + ctx.fillStyle = "rgba(0,0,0,0.6)"; + ctx.beginPath(); ctx.arc(roi.col, roi.row, roi.radius_inner, 0, Math.PI * 2); ctx.fill(); + ctx.globalCompositeOperation = "destination-out"; + } + } + ctx.restore(); + for (const roi of roiList) { + const shape = (roi.shape || "circle") as "circle" | "square" | "rectangle" | "annular"; + ctx.lineWidth = roi.line_width || 2; + drawROI(ctx, roi.col, roi.row, shape, roi.radius, roi.width, roi.height, roi.color, roi.color, false, roi.radius_inner); + } + } + } + if (profileActive && profilePoints.length === 2) { + ctx.strokeStyle = "#4fc3f7"; + ctx.lineWidth = 2; + ctx.setLineDash([4, 3]); + ctx.beginPath(); + ctx.moveTo(profilePoints[0].col, profilePoints[0].row); + ctx.lineTo(profilePoints[1].col, profilePoints[1].row); + ctx.stroke(); + ctx.setLineDash([]); + ctx.fillStyle = "#4fc3f7"; + ctx.beginPath(); ctx.arc(profilePoints[0].col, profilePoints[0].row, 3, 0, Math.PI * 2); ctx.fill(); + ctx.beginPath(); ctx.arc(profilePoints[1].col, profilePoints[1].row, 3, 0, Math.PI * 2); ctx.fill(); + } + }; + + const hasScale = pixelSize > 0; + const baseOpts = { + imageCanvas: offscreen, + title: title || undefined, + lut, + vmin, + vmax, + logScale, + pixelSize: hasScale ? pixelSize : undefined, + drawAnnotations, + }; + + const variants: { name: string; showScaleBar: boolean; showColorbar: boolean }[] = [ + { name: "figure", showScaleBar: false, showColorbar: false }, + { name: "figure_scalebar", showScaleBar: true, showColorbar: false }, + { name: "figure_scalebar_colorbar", showScaleBar: true, showColorbar: true }, + ]; + + const zip = new JSZip(); + const prefix = `show2d_${labels?.[selectedIdx] || "image"}`; + const metadata = { + metadata_version: "1.0", + widget_name: "Show2D", + widget_version: widgetVersion || "unknown", + exported_at: new Date().toISOString(), + format: "zip", + export_kind: "figure_variants", + selected_idx: idx, + image_shape: { rows: height, cols: width }, + display: { + cmap, + log_scale: logScale, + auto_contrast: autoContrast, + vmin_pct: imageVminPct, + vmax_pct: imageVmaxPct, + }, + variants, + }; + zip.file("metadata.json", JSON.stringify(metadata, null, 2)); + + for (const v of variants) { + const figCanvas = exportFigure({ ...baseOpts, showScaleBar: v.showScaleBar && hasScale, showColorbar: v.showColorbar }); + const pngBlob = await new Promise((resolve) => figCanvas.toBlob((b) => resolve(b!), "image/png")); + zip.file(`${prefix}_${v.name}.png`, pngBlob); + const pdfBlob = await canvasToPDF(figCanvas); + zip.file(`${prefix}_${v.name}.pdf`, pdfBlob); + } + + const blob = await zip.generateAsync({ type: "blob" }); + downloadBlob(blob, `${prefix}_all.zip`); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, widgetVersion, lockExport]); + + // Resize Handlers + // ------------------------------------------------------------------------- + const handleCanvasResizeStart = (e: React.MouseEvent) => { + if (lockView) return; + e.stopPropagation(); + e.preventDefault(); + setIsResizingCanvas(true); + setResizeStart({ x: e.clientX, y: e.clientY, size: canvasSize }); + }; + + React.useEffect(() => { + if (!isResizingCanvas) return; + let rafId = 0; + let latestSize = resizeStart ? resizeStart.size : canvasSize; + + const handleMouseMove = (e: MouseEvent) => { + if (!resizeStart) return; + const delta = Math.max(e.clientX - resizeStart.x, e.clientY - resizeStart.y); + latestSize = Math.max(200, resizeStart.size + delta); + if (!rafId) { + rafId = requestAnimationFrame(() => { + rafId = 0; + setCanvasSize(latestSize); + }); + } + }; + + const handleMouseUp = () => { + cancelAnimationFrame(rafId); + setCanvasSize(latestSize); + setIsResizingCanvas(false); + setResizeStart(null); + }; + + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + cancelAnimationFrame(rafId); + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingCanvas, resizeStart]); + + // Profile height resize + React.useEffect(() => { + if (!isResizingProfile) return; + const handleMouseMove = (e: MouseEvent) => { + if (!profileResizeStart) return; + const delta = e.clientY - profileResizeStart.y; + setProfileHeight(Math.max(40, Math.min(300, profileResizeStart.height + delta))); + }; + const handleMouseUp = () => { + setIsResizingProfile(false); + setProfileResizeStart(null); + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingProfile, profileResizeStart]); + + // ------------------------------------------------------------------------- + // Keyboard shortcuts + // ------------------------------------------------------------------------- + const handleKeyDown = (e: React.KeyboardEvent) => { + // Number keys 1-9 select gallery images (avoids arrow key conflicts with Jupyter) + if (!lockNavigation && isGallery && e.key >= "1" && e.key <= "9") { + const idx = parseInt(e.key) - 1; + if (idx < nImages) { e.preventDefault(); setSelectedIdx(idx); } + return; + } + switch (e.key) { + case "ArrowLeft": + if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.max(0, selectedIdx - 1)); } + break; + case "ArrowRight": + if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.min(nImages - 1, selectedIdx + 1)); } + break; + case "r": + case "R": + if (!lockView) handleResetAll(); + break; + case "m": + case "M": + if (measureActive) { + setMeasureActive(false); + setMeasurePoints([]); + } else { + setMeasureActive(true); + setMeasurePoints([]); + } + break; + case "Escape": + if (measureActive) { + setMeasureActive(false); + setMeasurePoints([]); + } + break; + case "]": + if (!lockNavigation && !lockDisplay) { + e.preventDefault(); + const rIdx = isGallery ? selectedIdx : 0; + const rots = [...(imageRotations || [])]; + while (rots.length <= rIdx) rots.push(0); + rots[rIdx] = (rots[rIdx] + 3) % 4; + setImageRotations(rots); + } + break; + case "[": + if (!lockNavigation && !lockDisplay) { + e.preventDefault(); + const rIdx2 = isGallery ? selectedIdx : 0; + const rots2 = [...(imageRotations || [])]; + while (rots2.length <= rIdx2) rots2.push(0); + rots2[rIdx2] = (rots2[rIdx2] + 1) % 4; + setImageRotations(rots2); + } + break; + case "Delete": + case "Backspace": + if (!lockRoi && roiActive && roiSelectedIdx >= 0 && roiList && roiSelectedIdx < roiList.length) { + e.preventDefault(); + const newList = roiList.filter((_, i) => i !== roiSelectedIdx); + setRoiList(newList); + setRoiSelectedIdx(newList.length > 0 ? Math.min(roiSelectedIdx, newList.length - 1) : -1); + } + break; + } + }; + + // ------------------------------------------------------------------------- + // Render (Show3D-style layout) + // ------------------------------------------------------------------------- + const needsReset = getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 || getZoomState(isGallery ? selectedIdx : 0).panX !== 0 || getZoomState(isGallery ? selectedIdx : 0).panY !== 0; + const statsIdx = isGallery ? selectedIdx : 0; + + // Calibrated cursor position + const calibratedUnit = pixelSize > 0 ? (Math.max(height, width) * pixelSize >= 10 ? "nm" : "Å") : ""; + const calibratedFactor = calibratedUnit === "nm" ? pixelSize / 10 : pixelSize; + + return ( + + + {/* Main panel */} + + {/* Title row */} + + {title || (isGallery ? "Gallery" : "Image")} + {displayBinFactor > 1 && ( + + {displayBinFactor}× binned + + )} + {(() => { const rk = (imageRotations?.[isGallery ? selectedIdx : 0] ?? 0) % 4; return rk !== 0 ? ( + { + if (lockDisplay) return; + const ri = isGallery ? selectedIdx : 0; + const rots = [...(imageRotations || [])]; + while (rots.length <= ri) rots.push(0); + rots[ri] = (rots[ri] + 3) % 4; + setImageRotations(rots); + }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", fontSize: "inherit", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + > + ({rk * 90}°) + + ) : null; })()} + + Controls + FFT: Show power spectrum (Fourier transform) alongside image. + Profile: Click two points on image to draw a line intensity profile. + ROI: Region of Interest — click to place, drag to move. + {!isGallery && Lens: Magnifier inset that follows the cursor.} + Auto: Percentile-based contrast (2nd–98th percentile). FFT Auto masks DC + clips to 99.9th. + {isGallery && Link Zoom / Contrast: Sync zoom or histogram range across all gallery images.} + Keyboard + + } theme={themeInfo.theme} /> + + + {/* Controls row: Profile, ROI, Lens, FFT, Export, Reset, Copy */} + + {!hideProfile && ( + <> + Profile: + { + if (lockProfile) return; + const on = e.target.checked; + setProfileActive(on); + if (on) { + if (!lockRoi) setRoiActive(false); + } else { + setProfilePoints([]); + setProfileDataAll([]); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + } + }} + size="small" + sx={switchStyles.small} + /> + + )} + {!hideRoi && !isGallery && ( + <> + ROI: + { + if (lockRoi) return; + const on = e.target.checked; + setRoiActive(on); + if (on) { + if (!lockProfile) setProfileActive(false); + setProfilePoints([]); + setProfileDataAll([]); + setHoveredProfileEndpoint(null); + setIsHoveringProfileLine(false); + } else { + setRoiSelectedIdx(-1); + } + }} + size="small" + sx={switchStyles.small} + /> + + )} + {!hideDisplay && ( + <> + {!isGallery && ( + <> + Lens: + { + if (lockDisplay) return; + if (!showLens) { + setShowLens(true); + setLensPos({ row: Math.floor(height / 2), col: Math.floor(width / 2) }); + } else { + setShowLens(false); + setLensPos(null); + } + }} + disabled={lockDisplay} + size="small" + sx={switchStyles.small} + /> + + )} + FFT: + { + if (lockDisplay) return; + const on = e.target.checked; + if (on && width * height > 2048 * 2048) { + console.warn(`Show2D: FFT on ${width}×${height} image (${(width * height / 1e6).toFixed(1)}M pixels) may be slow`); + } + setShowFft(on); + }} + disabled={lockDisplay} + size="small" + sx={switchStyles.small} + /> + {showFft && width * height > 2048 * 2048 && ( + slow ({width}×{height}) + )} + {nImages === 2 && ( + <> + Diff: + { if (!lockDisplay) setDiffMode(!diffMode); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + + )} + + {!hideView && ( + + )} + {!hideExport && ( + <> + + setExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleExportFigure(true, true)} sx={{ fontSize: 12 }}>PDF + scalebar + colorbar + handleExportFigure(true, false)} sx={{ fontSize: 12 }}>PDF + scalebar + handleExportFigure(false, false)} sx={{ fontSize: 12 }}>PDF + All (PNG + PDF) + + + + )} + + + {isGallery ? ( + /* Gallery mode */ + + {Array.from({ length: nImages }).map((_, i) => ( + + { imageContainerRefs.current[i] = el; }} + sx={{ position: "relative", bgcolor: "#000", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, width: canvasW, height: canvasH }} + onMouseDown={(e) => handleMouseDown(e, i)} + onMouseMove={(e) => handleMouseMove(e, i)} + onMouseUp={(e) => handleMouseUp(e, i)} + onMouseLeave={() => handleMouseLeave(i)} + onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleWheel(e, i) : undefined} + onDoubleClick={() => handleDoubleClick(i)} + > + { if (el && canvasRefs.current[i] !== el) { canvasRefs.current[i] = el; setCanvasReady(c => c + 1); } }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle }} + /> + { overlayRefs.current[i] = el; }} + width={Math.round(canvasW * DPR)} height={Math.round(canvasH * DPR)} + style={{ position: "absolute", top: 0, left: 0, width: canvasW, height: canvasH, pointerEvents: "none" }} + /> + {!hideView && ( + + )} + + + {labels?.[i] || `Image ${i + 1}`} + {(imageRotations?.[i] ?? 0) % 4 !== 0 && ( + { + e.stopPropagation(); + if (lockDisplay) return; + const rots = [...(imageRotations || [])]; + while (rots.length <= i) rots.push(0); + rots[i] = (rots[i] + 3) % 4; + setImageRotations(rots); + }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + > + ({(imageRotations[i] % 4) * 90}°) + + )} + + {effectiveShowFft && ( + { fftContainerRefs.current[i] = el; }} + sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: lockView ? "default" : "grab" }} + onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} + onDoubleClick={() => setGalleryFftState(i, { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 })} + onMouseDown={(e) => handleGalleryFftMouseDown(e, i)} + onMouseMove={(e) => handleGalleryFftMouseMove(e, i)} + onMouseUp={handleGalleryFftMouseUp} + onMouseLeave={handleGalleryFftMouseUp} + > + { fftCanvasRefs.current[i] = el; }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle, display: "block" }} + /> + {fftComputing && !fftMagCacheGalleryRef.current[i] && ( + + FFT… + + )} + + )} + + ))} + {showDiffPanel && diffOtherIndices.map((otherIdx, slot) => ( + + + { diffCanvasRefs.current[slot] = el; }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle }} + /> + + + {nImages === 2 ? "Diff (A − B)" : `Diff (#${diffReference + 1} − #${otherIdx + 1})`} + + {/* FFT of diff (n=2 only) */} + {effectiveShowFft && nImages === 2 && slot === 0 && ( + + { diffFftCanvasRef.current = el; }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle, display: "block" }} + /> + + )} + + ))} + + ) : ( + /* Single image mode */ + { imageContainerRefs.current[0] = el; }} + sx={{ position: "relative", bgcolor: "#000", border: `1px solid ${themeColors.border}`, width: canvasW, height: canvasH, cursor: isHoveringLensEdge ? "nwse-resize" : isDraggingROI ? "move" : (isDraggingResize || isDraggingResizeInner || isHoveringResize || isHoveringResizeInner) ? "nwse-resize" : (draggingProfileEndpoint !== null || isDraggingProfileLine) ? "grabbing" : (profileActive && (hoveredProfileEndpoint !== null || isHoveringProfileLine)) ? "grab" : (profileActive || roiActive || measureActive) ? "crosshair" : "grab" }} + onMouseDown={(e) => handleMouseDown(e, 0)} + onMouseMove={(e) => handleMouseMove(e, 0)} + onMouseUp={(e) => handleMouseUp(e, 0)} + onMouseLeave={() => handleMouseLeave(0)} + onWheel={(e) => handleWheel(e, 0)} + onDoubleClick={() => handleDoubleClick(0)} + > + { if (el && canvasRefs.current[0] !== el) { canvasRefs.current[0] = el; setCanvasReady(c => c + 1); } }} + width={canvasW} height={canvasH} + style={{ width: canvasW, height: canvasH, imageRendering: imageRenderingStyle }} + /> + { overlayRefs.current[0] = el; }} + width={Math.round(canvasW * DPR)} height={Math.round(canvasH * DPR)} + style={{ position: "absolute", top: 0, left: 0, width: canvasW, height: canvasH, pointerEvents: "none" }} + /> + + {cursorInfo && ( + + + ({cursorInfo.row}, {cursorInfo.col}){pixelSize > 0 ? ` = (${(cursorInfo.row * calibratedFactor).toFixed(1)}, ${(cursorInfo.col * calibratedFactor).toFixed(1)} ${calibratedUnit})` : ""} {formatNumber(cursorInfo.value)} + + + )} + {!hideView && ( + + )} + + )} + + {/* Stats bar - right below canvas (Show3D style) */} + {!hideStats && showStats && ( + + {isGallery && ( + {labels?.[statsIdx] || `#${statsIdx + 1}`} + )} + Mean {formatNumber(statsMean?.[statsIdx] ?? 0)} + Min {formatNumber(statsMin?.[statsIdx] ?? 0)} + Max {formatNumber(statsMax?.[statsIdx] ?? 0)} + Std {formatNumber(statsStd?.[statsIdx] ?? 0)} + {measureActive && ( + <> + + Measuring + + )} + + )} + + {/* Gallery FFT Controls - below gallery grid */} + {effectiveShowFft && isGallery && ( + + + + FFT Scale: + + Auto: + { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + {roiFftActive && fftCropDims && ( + <> + Win: + { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + Color: + + + + {!hideHistogram && ( + + {fftHistogramData && ( + { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + )} + + )} + + )} + + {/* Line profile sparkline — always reserve space when profile is active */} + {!hideProfile && profileActive && ( + + +
{ + if (lockProfile) return; + e.preventDefault(); + setIsResizingProfile(true); + setProfileResizeStart({ y: e.clientY, height: profileHeight }); + }} + style={{ width: profileCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, background: `linear-gradient(to bottom, ${themeColors.border}, transparent)`, opacity: lockProfile ? 0.5 : 1, pointerEvents: lockProfile ? "none" : "auto" }} + /> + + )} + + {/* Controls: two rows left + histogram right, ROI below */} + {showControls && ( + + {/* Top: control rows + histogram side by side */} + + + {/* Row 1: Scale + Color */} + {!hideDisplay && ( + + Scale: + + Color: + + {!isGallery && ( + <> + Colorbar: + { if (!lockDisplay) setShowColorbar(!showColorbar); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + + )} + {/* Row 2: Auto + Lens settings + Link Zoom (gallery) + zoom indicator */} + {!hideDisplay && ( + + Auto: + { if (!lockDisplay) setAutoContrast(!autoContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + Smooth: + { if (!lockDisplay) setSmooth(!smooth); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + {!isGallery && showLens && ( + <> + Lens {lensMag}× + setLensMag(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + {lensDisplaySize}px + setLensDisplaySize(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + + )} + {isGallery && ( + <> + Link: + Zoom + { if (!lockDisplay) setLinkedZoom(!linkedZoom); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + Pan + { if (!lockDisplay) setLinkPan(!linkPan); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + Contrast + { if (!lockDisplay) setLinkedContrast(!linkedContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + {getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 && ( + {getZoomState(isGallery ? selectedIdx : 0).zoom.toFixed(1)}x + )} + + )} + + {/* Right: Histogram aligned to the two rows. When unlinked + gallery: stack one per image. */} + {!hideHistogram && (imageHistogramData || imageHistogramBins) && ( + + {(!linkedContrast && isGallery && rawDataRef.current) ? ( + Array.from({ length: nImages }).map((_, i) => { + const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; + const raw = rawDataRef.current?.[i] || null; + return ( + { if (!lockHistogram) setContrastState(i, { vminPct: min, vmaxPct: max }); }} + width={110} height={36} theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={dataRangesRef.current[i]?.min ?? imageDataRange.min} + dataMax={dataRangesRef.current[i]?.max ?? imageDataRange.max} /> + ); + }) + ) : ( + { if (!lockHistogram) setContrastState(activeContrastIdx, { vminPct: min, vmaxPct: max }); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmin, 0)) : traitVmin) : imageDataRange.min} dataMax={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmax, 0)) : traitVmax) : imageDataRange.max} /> + )} + + )} + + {/* ROI Section (own box, below control rows) */} + {!hideRoi && roiActive && ( + + {/* ROI: shape + ADD + CLEAR */} + + ROI: + + + + + + {/* Selected ROI details */} + {selectedRoi && ( + + #{roiSelectedIdx + 1}/{roiList?.length ?? 0} + + {selectedRoi.shape === "rectangle" && ( + <> + W + updateSelectedRoi({ width: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + H + updateSelectedRoi({ height: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + + )} + {selectedRoi.shape === "annular" && ( + <> + Inner + updateSelectedRoi({ radius_inner: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + Outer + updateSelectedRoi({ radius: v as number })} size="small" sx={{ ...sliderStyles.small, width: 40 }} /> + + )} + {selectedRoi.shape !== "rectangle" && selectedRoi.shape !== "annular" && ( + <> + Size + updateSelectedRoi({ radius: v as number })} size="small" sx={{ ...sliderStyles.small, width: 50 }} /> + + )} + + {ROI_COLORS.map(c => ( + updateSelectedRoi({ color: c })} sx={{ width: 12, height: 12, bgcolor: c, cursor: "pointer", border: c === selectedRoi.color ? `2px solid ${themeColors.text}` : "1px solid transparent", "&:hover": { opacity: 0.8 } }} /> + ))} + + Border + updateSelectedRoi({ line_width: v as number })} size="small" sx={{ ...sliderStyles.small, width: 30 }} /> + updateSelectedRoi({ highlight: !selectedRoi.highlight })} + sx={{ cursor: "pointer", fontSize: 10, color: selectedRoi.highlight ? themeColors.accentGreen : themeColors.textMuted, "&:hover": { opacity: 0.8 } }} + title="Focus (dim outside)" + >{selectedRoi.highlight ? "\u25C9 Focus" : "\u25CB Focus"} + + + )} + {/* ROI list */} + {roiList && roiList.length > 0 && ( + + {roiList.map((roi, i) => { + const c = roi.color || ROI_COLORS[i % ROI_COLORS.length]; + const isSelected = i === roiSelectedIdx; + const shapeLabel = roi.shape === "rectangle" ? `${roi.width}×${roi.height}` : roi.shape === "annular" ? `r${roi.radius_inner}-${roi.radius}` : `r${roi.radius}`; + return ( + setRoiSelectedIdx(i)} sx={{ display: "flex", alignItems: "center", gap: "3px", lineHeight: 1.6, cursor: "pointer", "&:hover .roi-delete": { opacity: 1 } }}> + + + {i + 1}{" "} + {roi.shape} ({roi.row}, {roi.col}) {shapeLabel} + + { e.stopPropagation(); const newList = roiList.map((r, j) => ({ ...r, highlight: j === i ? !r.highlight : false })); setRoiList(newList); }} + sx={{ cursor: "pointer", fontSize: 10, color: roi.highlight ? themeColors.accentGreen : themeColors.textMuted, lineHeight: 1, opacity: roi.highlight ? 1 : 0.5, "&:hover": { opacity: 1 } }} + title="Focus (dim outside)" + >{roi.highlight ? "\u25C9" : "\u25CB"} + { e.stopPropagation(); const newList = roiList.filter((_, j) => j !== i); setRoiList(newList); setRoiSelectedIdx(newList.length > 0 ? Math.min(roiSelectedIdx, newList.length - 1) : -1); }} + sx={{ opacity: 0, cursor: "pointer", fontSize: 10, color: themeColors.textMuted, ml: 0.5, lineHeight: 1, "&:hover": { color: "#f44336" } }} + >× + + ); + })} + + )} + + )} + + )} + + + {/* FFT Panel - canvas + stats (single mode only) */} + {effectiveShowFft && !isGallery && ( + + {/* Spacer — matches main panel title row height for canvas alignment */} + + {/* Controls row — matches main panel controls row height */} + + {fftComputing ? ( + + {fftProgress || "Computing FFT…"} + ) : roiFftActive && fftCropDims ? ( + + ROI FFT ({fftCropDims.cropWidth}×{fftCropDims.cropHeight}) + + ) : } + {!hideView && ( + + )} + + + + + {fftComputing && ( + + + {fftProgress || "Computing FFT…"} + + + )} + {!hideView && ( + + )} + + {/* FFT Stats Bar */} + {!hideStats && fftStats && fftStats.length === 4 && ( + + Mean {formatNumber(fftStats[0])} + Min {formatNumber(fftStats[1])} + Max {formatNumber(fftStats[2])} + Std {formatNumber(fftStats[3])} + {fftClickInfo && ( + <> + + + {fftClickInfo.dSpacing != null ? ( + <>d = {fftClickInfo.dSpacing >= 10 ? `${(fftClickInfo.dSpacing / 10).toFixed(2)} nm` : `${fftClickInfo.dSpacing.toFixed(2)} Å`}{" | |g| = "}{fftClickInfo.spatialFreq!.toFixed(4)} Å⁻¹ + ) : ( + <>dist = {fftClickInfo.distPx.toFixed(1)} px + )} + + + )} + + )} + {/* FFT Controls - two rows + histogram (matching main panel layout) */} + + + + {/* Row 1: Scale + Color + Colorbar */} + + Scale: + + Color: + + Colorbar: + { if (!lockDisplay) setFftShowColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + {/* Row 2: Auto + zoom indicator */} + + Auto: + { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + {fftCropDims && ( + <> + Win: + { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + {fftZoom !== DEFAULT_FFT_ZOOM && ( + {fftZoom.toFixed(1)}x + )} + + + {/* Right: FFT Histogram */} + {!hideHistogram && ( + + {fftHistogramData && ( + { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + )} + + )} + + + + )} + + + ); +} + +export const render = createRender(Show2D); diff --git a/widget/js/show2d/show2d.css b/widget/js/show2d/show2d.css new file mode 100644 index 00000000..0e285789 --- /dev/null +++ b/widget/js/show2d/show2d.css @@ -0,0 +1,9 @@ +/* show2d.css - Minimal CSS for Show2D */ + +.show2d-root { + font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; +} + +.show2d-root canvas { + display: block; +} diff --git a/widget/js/show4dstem/index.tsx b/widget/js/show4dstem/index.tsx new file mode 100644 index 00000000..8ece614d --- /dev/null +++ b/widget/js/show4dstem/index.tsx @@ -0,0 +1,4259 @@ +/// +import * as React from "react"; +import { createRender, useModelState, useModel } from "@anywidget/react"; +import Box from "@mui/material/Box"; +import Typography from "@mui/material/Typography"; +import Stack from "@mui/material/Stack"; +import Select from "@mui/material/Select"; +import MenuItem from "@mui/material/MenuItem"; +import Menu from "@mui/material/Menu"; +import Slider from "@mui/material/Slider"; +import Button from "@mui/material/Button"; +import Switch from "@mui/material/Switch"; +import Tooltip from "@mui/material/Tooltip"; +import IconButton from "@mui/material/IconButton"; +import PlayArrowIcon from "@mui/icons-material/PlayArrow"; +import PauseIcon from "@mui/icons-material/Pause"; +import StopIcon from "@mui/icons-material/Stop"; +import FastRewindIcon from "@mui/icons-material/FastRewind"; +import FastForwardIcon from "@mui/icons-material/FastForward"; +import JSZip from "jszip"; +import "./styles.css"; +import { useTheme } from "../theme"; +import { COLORMAPS, applyColormap, renderToOffscreen } from "../colormaps"; +import { WebGPUFFT, getWebGPUFFT, fft2d, fftshift, autoEnhanceFFT, nextPow2, applyHannWindow2D } from "../webgpu-fft"; +import { drawScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; +import { findDataRange, sliderRange, computeStats, applyLogScale } from "../stats"; +import { downloadBlob, formatNumber, downloadDataView } from "../format"; +import { computeHistogramFromBytes } from "../histogram"; +import { ControlCustomizer } from "../control-customizer"; +import { computeToolVisibility } from "../tool-parity"; + +const MIN_ZOOM = 0.5; +const MAX_ZOOM = 10; + +// ============================================================================ +// UI Styles - component styling helpers +// ============================================================================ +const typography = { + label: { fontSize: 11 }, + labelSmall: { fontSize: 10 }, + value: { fontSize: 10, fontFamily: "monospace" }, + title: { fontWeight: "bold" as const }, +}; + +const controlPanel = { + select: { minWidth: 90, fontSize: 11, "& .MuiSelect-select": { py: 0.5 } }, +}; + +const container = { + root: { p: 2, bgcolor: "transparent", color: "inherit", fontFamily: "monospace", overflow: "visible" }, + imageBox: { bgcolor: "#000", border: "1px solid #444", overflow: "hidden", position: "relative" as const }, +}; + +const upwardMenuProps = { + anchorOrigin: { vertical: "top" as const, horizontal: "left" as const }, + transformOrigin: { vertical: "bottom" as const, horizontal: "left" as const }, + sx: { zIndex: 9999 }, +}; + +const switchStyles = { + small: { '& .MuiSwitch-thumb': { width: 12, height: 12 }, '& .MuiSwitch-switchBase': { padding: '4px' } }, + medium: { '& .MuiSwitch-thumb': { width: 14, height: 14 }, '& .MuiSwitch-switchBase': { padding: '4px' } }, +}; + +const sliderStyles = { + small: { + "& .MuiSlider-thumb": { width: 12, height: 12 }, + "& .MuiSlider-rail": { height: 3 }, + "& .MuiSlider-track": { height: 3 }, + }, +}; + +// ============================================================================ +// Layout Constants - consistent spacing throughout +// ============================================================================ +const SPACING = { + XS: 4, // Extra small gap + SM: 8, // Small gap (default between elements) + MD: 12, // Medium gap (between control groups) + LG: 16, // Large gap (between major sections) +}; + +const CANVAS_SIZE = 450; // Both DP and VI canvases + +// Theme-aware ROI colors for DP detector overlay +interface RoiColors { + stroke: string; + strokeDragging: string; + fill: string; + fillDragging: string; + handleFill: string; + innerStroke: string; + innerStrokeDragging: string; + innerHandleFill: string; + textColor: string; +} +const DARK_ROI_COLORS: RoiColors = { + stroke: "rgba(0, 255, 0, 0.9)", + strokeDragging: "rgba(255, 255, 0, 0.9)", + fill: "rgba(0, 255, 0, 0.12)", + fillDragging: "rgba(255, 255, 0, 0.12)", + handleFill: "rgba(0, 255, 0, 0.8)", + innerStroke: "rgba(0, 220, 255, 0.9)", + innerStrokeDragging: "rgba(255, 200, 0, 0.9)", + innerHandleFill: "rgba(0, 220, 255, 0.8)", + textColor: "#0f0", +}; +const LIGHT_ROI_COLORS: RoiColors = { + stroke: "rgba(0, 140, 0, 0.9)", + strokeDragging: "rgba(200, 160, 0, 0.9)", + fill: "rgba(0, 140, 0, 0.15)", + fillDragging: "rgba(200, 160, 0, 0.15)", + handleFill: "rgba(0, 140, 0, 0.85)", + innerStroke: "rgba(0, 160, 200, 0.9)", + innerStrokeDragging: "rgba(200, 160, 0, 0.9)", + innerHandleFill: "rgba(0, 160, 200, 0.85)", + textColor: "#0a0", +}; + +// Interaction constants +const RESIZE_HIT_AREA_PX = 10; +const CIRCLE_HANDLE_ANGLE = 0.707; // cos(45°) +// Compact button style for Reset/Export +const compactButton = { + fontSize: 10, + py: 0.25, + px: 1, + minWidth: 0, + "&.Mui-disabled": { + color: "#666", + borderColor: "#444", + }, +}; + +// Control row style - bordered container for each row +const controlRow = { + display: "flex", + alignItems: "center", + gap: `${SPACING.SM}px`, + px: 1, + py: 0.5, + width: "fit-content", +}; + +/** Format stat value for display (compact scientific notation for small values) */ +function formatStat(value: number): string { + if (value === 0) return "0"; + const abs = Math.abs(value); + if (abs < 0.001 || abs >= 10000) { + return value.toExponential(2); + } + if (abs < 0.01) return value.toFixed(4); + if (abs < 1) return value.toFixed(3); + return value.toFixed(2); +} + + +// ============================================================================ +// FFT peak finder (snap to Bragg spot with sub-pixel centroid refinement) +// ============================================================================ +function findFFTPeak(mag: Float32Array, width: number, height: number, col: number, row: number, radius: number): { row: number; col: number } { + const c0 = Math.max(0, Math.floor(col) - radius); + const r0 = Math.max(0, Math.floor(row) - radius); + const c1 = Math.min(width - 1, Math.floor(col) + radius); + const r1 = Math.min(height - 1, Math.floor(row) + radius); + let bestCol = Math.round(col), bestRow = Math.round(row), bestVal = -Infinity; + for (let ir = r0; ir <= r1; ir++) { + for (let ic = c0; ic <= c1; ic++) { + const val = mag[ir * width + ic]; + if (val > bestVal) { bestVal = val; bestCol = ic; bestRow = ir; } + } + } + const wc0 = Math.max(0, bestCol - 1), wc1 = Math.min(width - 1, bestCol + 1); + const wr0 = Math.max(0, bestRow - 1), wr1 = Math.min(height - 1, bestRow + 1); + let sumW = 0, sumWC = 0, sumWR = 0; + for (let ir = wr0; ir <= wr1; ir++) { + for (let ic = wc0; ic <= wc1; ic++) { + const w = mag[ir * width + ic]; + sumW += w; sumWC += w * ic; sumWR += w * ir; + } + } + if (sumW > 0) return { row: sumWR / sumW, col: sumWC / sumW }; + return { row: bestRow, col: bestCol }; +} +const FFT_SNAP_RADIUS = 5; + +/** + * Draw VI crosshair on high-DPI canvas (crisp regardless of image resolution) + * Note: Does NOT clear canvas - should be called after drawScaleBarHiDPI + */ +function drawViPositionMarker( + canvas: HTMLCanvasElement, + dpr: number, + posRow: number, // Position in image coordinates + posCol: number, + zoom: number, + panX: number, + panY: number, + imageWidth: number, + imageHeight: number, + isDragging: boolean +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const scaleY = cssHeight / imageHeight; + + // Convert image coordinates to CSS pixel coordinates + const screenX = posCol * zoom * scaleX + panX * scaleX; + const screenY = posRow * zoom * scaleY + panY * scaleY; + + // Simple crosshair (no circle) + const crosshairSize = 12; + const lineWidth = 1.5; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.strokeStyle = isDragging ? "rgba(255, 255, 0, 0.9)" : "rgba(255, 100, 100, 0.9)"; + ctx.lineWidth = lineWidth; + + // Draw crosshair lines only + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSize, screenY); + ctx.lineTo(screenX + crosshairSize, screenY); + ctx.moveTo(screenX, screenY - crosshairSize); + ctx.lineTo(screenX, screenY + crosshairSize); + ctx.stroke(); + + ctx.restore(); +} + +/** + * Draw VI ROI overlay on high-DPI canvas for real-space region selection + * Note: Does NOT clear canvas - should be called after drawViPositionMarker + */ +function drawViRoiOverlayHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + roiMode: string, + centerRow: number, + centerCol: number, + radius: number, + roiWidth: number, + roiHeight: number, + zoom: number, + panX: number, + panY: number, + imageWidth: number, + imageHeight: number, + isDragging: boolean, + isDraggingResize: boolean, + isHoveringResize: boolean +) { + if (roiMode === "off") return; + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + const scaleX = cssWidth / imageWidth; + const scaleY = cssHeight / imageHeight; + + // Convert image coordinates to screen coordinates (row→screenY, col→screenX) + const screenX = centerCol * zoom * scaleX + panX * scaleX; + const screenY = centerRow * zoom * scaleY + panY * scaleY; + + const lineWidth = 2.5; + const crosshairSize = 10; + const handleRadius = 6; + + ctx.shadowColor = "rgba(0, 0, 0, 0.4)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + // Helper to draw resize handle (purple color for VI ROI to differentiate from DP) + const drawResizeHandle = (handleX: number, handleY: number) => { + let handleFill: string; + let handleStroke: string; + + if (isDraggingResize) { + handleFill = "rgba(180, 100, 255, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else if (isHoveringResize) { + handleFill = "rgba(220, 150, 255, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else { + handleFill = "rgba(160, 80, 255, 0.8)"; + handleStroke = "rgba(255, 255, 255, 0.8)"; + } + ctx.beginPath(); + ctx.arc(handleX, handleY, handleRadius, 0, 2 * Math.PI); + ctx.fillStyle = handleFill; + ctx.fill(); + ctx.strokeStyle = handleStroke; + ctx.lineWidth = 1.5; + ctx.stroke(); + }; + + // Helper to draw center crosshair (purple/magenta for VI ROI) + const drawCenterCrosshair = () => { + ctx.strokeStyle = isDragging ? "rgba(255, 200, 0, 0.9)" : "rgba(180, 80, 255, 0.9)"; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSize, screenY); + ctx.lineTo(screenX + crosshairSize, screenY); + ctx.moveTo(screenX, screenY - crosshairSize); + ctx.lineTo(screenX, screenY + crosshairSize); + ctx.stroke(); + }; + + // Purple/magenta color for VI ROI to differentiate from green DP detector + const strokeColor = isDragging ? "rgba(255, 200, 0, 0.9)" : "rgba(180, 80, 255, 0.9)"; + const fillColor = isDragging ? "rgba(255, 200, 0, 0.15)" : "rgba(180, 80, 255, 0.15)"; + + if (roiMode === "circle" && radius > 0) { + const screenRadiusX = radius * zoom * scaleX; + const screenRadiusY = radius * zoom * scaleY; + + ctx.strokeStyle = strokeColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusX, screenRadiusY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + ctx.fillStyle = fillColor; + ctx.fill(); + + drawCenterCrosshair(); + + // Resize handle at 45° diagonal + const handleOffsetX = screenRadiusX * CIRCLE_HANDLE_ANGLE; + const handleOffsetY = screenRadiusY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetX, screenY + handleOffsetY); + + } else if (roiMode === "square" && radius > 0) { + // Square uses radius as half-size + const screenHalfW = radius * zoom * scaleX; + const screenHalfH = radius * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = strokeColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = fillColor; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + + } else if (roiMode === "rect" && roiWidth > 0 && roiHeight > 0) { + const screenHalfW = (roiWidth / 2) * zoom * scaleX; + const screenHalfH = (roiHeight / 2) * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = strokeColor; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = fillColor; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + } + + ctx.restore(); +} + +/** + * Draw DP crosshair on high-DPI canvas (crisp regardless of detector resolution) + * Note: Does NOT clear canvas - should be called after drawScaleBarHiDPI + */ +function drawDpCrosshairHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + kCol: number, // Column position in detector coordinates + kRow: number, // Row position in detector coordinates + zoom: number, + panX: number, + panY: number, + detWidth: number, + detHeight: number, + isDragging: boolean, + roiColors: RoiColors = DARK_ROI_COLORS +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + // Use separate X/Y scale factors (canvas stretches to fill container) + const scaleX = cssWidth / detWidth; + const scaleY = cssHeight / detHeight; + + // Convert detector coordinates to CSS pixel coordinates + const screenX = kCol * zoom * scaleX + panX * scaleX; + const screenY = kRow * zoom * scaleY + panY * scaleY; + + // Fixed UI sizes in CSS pixels (consistent with VI crosshair) + const crosshairSize = 18; + const lineWidth = 3; + const dotRadius = 6; + + ctx.shadowColor = "rgba(0, 0, 0, 0.5)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + + // Draw crosshair + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSize, screenY); + ctx.lineTo(screenX + crosshairSize, screenY); + ctx.moveTo(screenX, screenY - crosshairSize); + ctx.lineTo(screenX, screenY + crosshairSize); + ctx.stroke(); + + // Draw center dot + ctx.beginPath(); + ctx.arc(screenX, screenY, dotRadius, 0, 2 * Math.PI); + ctx.stroke(); + + ctx.restore(); +} + +/** + * Draw ROI overlay (circle, square, rect, annular) on high-DPI canvas + * Note: Does NOT clear canvas - should be called after drawScaleBarHiDPI + */ +function drawRoiOverlayHiDPI( + canvas: HTMLCanvasElement, + dpr: number, + roiMode: string, + centerCol: number, + centerRow: number, + radius: number, + radiusInner: number, + roiWidth: number, + roiHeight: number, + zoom: number, + panX: number, + panY: number, + detWidth: number, + detHeight: number, + isDragging: boolean, + isDraggingResize: boolean, + isDraggingResizeInner: boolean, + isHoveringResize: boolean, + isHoveringResizeInner: boolean, + roiColors: RoiColors = DARK_ROI_COLORS +) { + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + ctx.save(); + ctx.scale(dpr, dpr); + + const cssWidth = canvas.width / dpr; + const cssHeight = canvas.height / dpr; + // Use separate X/Y scale factors (canvas stretches to fill container) + const scaleX = cssWidth / detWidth; + const scaleY = cssHeight / detHeight; + + // Convert detector coordinates to CSS pixel coordinates + const screenX = centerCol * zoom * scaleX + panX * scaleX; + const screenY = centerRow * zoom * scaleY + panY * scaleY; + + // Fixed UI sizes in CSS pixels + const lineWidth = 2.5; + const crosshairSizeSmall = 10; + const handleRadius = 6; + + ctx.shadowColor = "rgba(0, 0, 0, 0.4)"; + ctx.shadowBlur = 2; + ctx.shadowOffsetX = 1; + ctx.shadowOffsetY = 1; + + // Helper to draw resize handle + const drawResizeHandle = (handleX: number, handleY: number, isInner: boolean = false) => { + let handleFill: string; + let handleStroke: string; + const dragging = isInner ? isDraggingResizeInner : isDraggingResize; + const hovering = isInner ? isHoveringResizeInner : isHoveringResize; + + if (dragging) { + handleFill = "rgba(0, 200, 255, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else if (hovering) { + handleFill = "rgba(255, 100, 100, 1)"; + handleStroke = "rgba(255, 255, 255, 1)"; + } else { + handleFill = isInner ? roiColors.innerHandleFill : roiColors.handleFill; + handleStroke = "rgba(255, 255, 255, 0.8)"; + } + ctx.beginPath(); + ctx.arc(handleX, handleY, handleRadius, 0, 2 * Math.PI); + ctx.fillStyle = handleFill; + ctx.fill(); + ctx.strokeStyle = handleStroke; + ctx.lineWidth = 1.5; + ctx.stroke(); + }; + + // Helper to draw center crosshair + const drawCenterCrosshair = () => { + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.moveTo(screenX - crosshairSizeSmall, screenY); + ctx.lineTo(screenX + crosshairSizeSmall, screenY); + ctx.moveTo(screenX, screenY - crosshairSizeSmall); + ctx.lineTo(screenX, screenY + crosshairSizeSmall); + ctx.stroke(); + }; + + if (roiMode === "circle" && radius > 0) { + // Use separate X/Y radii for ellipse (handles non-square detectors) + const screenRadiusX = radius * zoom * scaleX; + const screenRadiusY = radius * zoom * scaleY; + + // Draw ellipse (becomes circle if scaleX === scaleY) + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusX, screenRadiusY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + // Semi-transparent fill + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.fill(); + + drawCenterCrosshair(); + + // Resize handle at 45° diagonal + const handleOffsetX = screenRadiusX * CIRCLE_HANDLE_ANGLE; + const handleOffsetY = screenRadiusY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetX, screenY + handleOffsetY); + + } else if (roiMode === "square" && radius > 0) { + // Square in detector space uses same half-size in both dimensions + const screenHalfW = radius * zoom * scaleX; + const screenHalfH = radius * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + + } else if (roiMode === "rect" && roiWidth > 0 && roiHeight > 0) { + const screenHalfW = (roiWidth / 2) * zoom * scaleX; + const screenHalfH = (roiHeight / 2) * zoom * scaleY; + const left = screenX - screenHalfW; + const top = screenY - screenHalfH; + + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.rect(left, top, screenHalfW * 2, screenHalfH * 2); + ctx.stroke(); + + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.fill(); + + drawCenterCrosshair(); + drawResizeHandle(screenX + screenHalfW, screenY + screenHalfH); + + } else if (roiMode === "annular" && radius > 0) { + // Use separate X/Y radii for ellipses + const screenRadiusOuterX = radius * zoom * scaleX; + const screenRadiusOuterY = radius * zoom * scaleY; + const screenRadiusInnerX = (radiusInner || 0) * zoom * scaleX; + const screenRadiusInnerY = (radiusInner || 0) * zoom * scaleY; + + // Outer ellipse + ctx.strokeStyle = isDragging ? roiColors.strokeDragging : roiColors.stroke; + ctx.lineWidth = lineWidth; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusOuterX, screenRadiusOuterY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + // Inner ellipse + ctx.strokeStyle = isDragging ? roiColors.innerStrokeDragging : roiColors.innerStroke; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusInnerX, screenRadiusInnerY, 0, 0, 2 * Math.PI); + ctx.stroke(); + + // Fill annular region + ctx.fillStyle = isDragging ? roiColors.fillDragging : roiColors.fill; + ctx.beginPath(); + ctx.ellipse(screenX, screenY, screenRadiusOuterX, screenRadiusOuterY, 0, 0, 2 * Math.PI); + ctx.ellipse(screenX, screenY, screenRadiusInnerX, screenRadiusInnerY, 0, 0, 2 * Math.PI, true); + ctx.fill(); + + drawCenterCrosshair(); + + // Outer handle at 45° diagonal + const handleOffsetOuterX = screenRadiusOuterX * CIRCLE_HANDLE_ANGLE; + const handleOffsetOuterY = screenRadiusOuterY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetOuterX, screenY + handleOffsetOuterY); + + // Inner handle at 45° diagonal + const handleOffsetInnerX = screenRadiusInnerX * CIRCLE_HANDLE_ANGLE; + const handleOffsetInnerY = screenRadiusInnerY * CIRCLE_HANDLE_ANGLE; + drawResizeHandle(screenX + handleOffsetInnerX, screenY + handleOffsetInnerY, true); + } + + ctx.restore(); +} + +// ============================================================================ +// Histogram Component +// ============================================================================ + +interface HistogramProps { + data: Float32Array | null; + vminPct: number; + vmaxPct: number; + onRangeChange: (min: number, max: number) => void; + width?: number; + height?: number; + theme?: "light" | "dark"; + dataMin?: number; + dataMax?: number; +} + +/** + * Info tooltip component - small ⓘ icon with hover tooltip + */ +function InfoTooltip({ text, theme = "dark" }: { text: React.ReactNode; theme?: "light" | "dark" }) { + const isDark = theme === "dark"; + const content = typeof text === "string" + ? {text} + : text; + return ( + + + ⓘ + + + ); +} + +function KeyboardShortcuts({ items }: { items: [string, string][] }) { + return ( + + + {items.map(([key, desc], i) => ( + {key}{desc} + ))} + + + ); +} + +/** + * Histogram component with integrated vmin/vmax slider and statistics. + * Shows data distribution with adjustable clipping. + */ +function Histogram({ + data, + vminPct, + vmaxPct, + onRangeChange, + width = 120, + height = 40, + theme = "dark", + dataMin = 0, + dataMax = 1, +}: HistogramProps) { + const canvasRef = React.useRef(null); + const bins = React.useMemo(() => computeHistogramFromBytes(data), [data]); + + // Theme-aware colors + const colors = theme === "dark" ? { + bg: "#1a1a1a", + barActive: "#888", + barInactive: "#444", + border: "#333", + } : { + bg: "#f0f0f0", + barActive: "#666", + barInactive: "#bbb", + border: "#ccc", + }; + + // Draw histogram (vertical gray bars) + React.useEffect(() => { + const canvas = canvasRef.current; + if (!canvas) return; + + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + canvas.width = width * dpr; + canvas.height = height * dpr; + ctx.scale(dpr, dpr); + + // Clear with theme background + ctx.fillStyle = colors.bg; + ctx.fillRect(0, 0, width, height); + + // Reduce to fewer bins for cleaner display + const displayBins = 64; + const binRatio = Math.floor(bins.length / displayBins); + const reducedBins: number[] = []; + for (let i = 0; i < displayBins; i++) { + let sum = 0; + for (let j = 0; j < binRatio; j++) { + sum += bins[i * binRatio + j] || 0; + } + reducedBins.push(sum / binRatio); + } + + // Normalize + const maxVal = Math.max(...reducedBins, 0.001); + const barWidth = width / displayBins; + + // Calculate which bins are in the clipped range + const vminBin = Math.floor((vminPct / 100) * displayBins); + const vmaxBin = Math.floor((vmaxPct / 100) * displayBins); + + // Draw histogram bars + for (let i = 0; i < displayBins; i++) { + const barHeight = (reducedBins[i] / maxVal) * (height - 2); + const x = i * barWidth; + + // Bars inside range are highlighted, outside are dimmed + const inRange = i >= vminBin && i <= vmaxBin; + ctx.fillStyle = inRange ? colors.barActive : colors.barInactive; + ctx.fillRect(x + 0.5, height - barHeight, Math.max(1, barWidth - 1), barHeight); + } + + }, [bins, vminPct, vmaxPct, width, height, colors]); + + return ( + + + { + const [newMin, newMax] = v as number[]; + onRangeChange(Math.min(newMin, newMax - 1), Math.max(newMax, newMin + 1)); + }} + min={0} + max={100} + size="small" + valueLabelDisplay="auto" + valueLabelFormat={(pct) => { + const val = dataMin + (pct / 100) * (dataMax - dataMin); + return val >= 1000 ? val.toExponential(1) : val.toFixed(1); + }} + sx={{ + width, + py: 0, + "& .MuiSlider-thumb": { width: 8, height: 8 }, + "& .MuiSlider-rail": { height: 2 }, + "& .MuiSlider-track": { height: 2 }, + "& .MuiSlider-valueLabel": { fontSize: 10, padding: "2px 4px" }, + }} + /> + {(() => { const v = dataMin + (vminPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()}{(() => { const v = dataMin + (vmaxPct / 100) * (dataMax - dataMin); return v >= 1000 ? v.toExponential(1) : v.toFixed(1); })()} + + ); +} + +// ============================================================================ +// Line Profile Sampling +// ============================================================================ + +function sampleSingleLine(data: Float32Array, w: number, h: number, row0: number, col0: number, row1: number, col1: number): Float32Array { + const dc = col1 - col0; + const dr = row1 - row0; + const len = Math.sqrt(dc * dc + dr * dr); + const n = Math.max(2, Math.ceil(len)); + const out = new Float32Array(n); + for (let i = 0; i < n; i++) { + const t = i / (n - 1); + const c = col0 + t * dc; + const r = row0 + t * dr; + const ci = Math.floor(c), ri = Math.floor(r); + const cf = c - ci, rf = r - ri; + const c0c = Math.max(0, Math.min(w - 1, ci)); + const c1c = Math.max(0, Math.min(w - 1, ci + 1)); + const r0c = Math.max(0, Math.min(h - 1, ri)); + const r1c = Math.max(0, Math.min(h - 1, ri + 1)); + out[i] = data[r0c * w + c0c] * (1 - cf) * (1 - rf) + + data[r0c * w + c1c] * cf * (1 - rf) + + data[r1c * w + c0c] * (1 - cf) * rf + + data[r1c * w + c1c] * cf * rf; + } + return out; +} + +function sampleLineProfile(data: Float32Array, w: number, h: number, row0: number, col0: number, row1: number, col1: number, profileWidth: number = 1): Float32Array { + if (profileWidth <= 1) return sampleSingleLine(data, w, h, row0, col0, row1, col1); + const dc = col1 - col0; + const dr = row1 - row0; + const len = Math.sqrt(dc * dc + dr * dr); + if (len < 1e-8) return sampleSingleLine(data, w, h, row0, col0, row1, col1); + const perpR = -dc / len; + const perpC = dr / len; + const half = (profileWidth - 1) / 2; + let accumulated: Float32Array | null = null; + for (let k = 0; k < profileWidth; k++) { + const off = -half + k; + const vals = sampleSingleLine(data, w, h, row0 + off * perpR, col0 + off * perpC, row1 + off * perpR, col1 + off * perpC); + if (!accumulated) { + accumulated = vals; + } else { + for (let i = 0; i < vals.length; i++) accumulated[i] += vals[i]; + } + } + if (accumulated) for (let i = 0; i < accumulated.length; i++) accumulated[i] /= profileWidth; + return accumulated || new Float32Array(0); +} + +function pointToSegmentDistance(col: number, row: number, col0: number, row0: number, col1: number, row1: number): number { + const dc = col1 - col0; + const dr = row1 - row0; + const lenSq = dc * dc + dr * dr; + if (lenSq <= 1e-12) return Math.sqrt((col - col0) ** 2 + (row - row0) ** 2); + const tRaw = ((col - col0) * dc + (row - row0) * dr) / lenSq; + const t = Math.max(0, Math.min(1, tRaw)); + const projCol = col0 + t * dc; + const projRow = row0 + t * dr; + return Math.sqrt((col - projCol) ** 2 + (row - projRow) ** 2); +} + +// ============================================================================ +// Crop single-mode ROI region from raw float32 data for ROI-scoped FFT +// ============================================================================ +function cropSingleROI( + data: Float32Array, imgW: number, imgH: number, + mode: string, centerRow: number, centerCol: number, + radius: number, roiW: number, roiH: number, +): { cropped: Float32Array; cropW: number; cropH: number } | null { + if (mode === "off") return null; + let x0: number, y0: number, x1: number, y1: number; + + if (mode === "rect") { + const hw = roiW / 2, hh = roiH / 2; + x0 = Math.max(0, Math.floor(centerCol - hw)); + y0 = Math.max(0, Math.floor(centerRow - hh)); + x1 = Math.min(imgW, Math.ceil(centerCol + hw)); + y1 = Math.min(imgH, Math.ceil(centerRow + hh)); + } else { + x0 = Math.max(0, Math.floor(centerCol - radius)); + y0 = Math.max(0, Math.floor(centerRow - radius)); + x1 = Math.min(imgW, Math.ceil(centerCol + radius)); + y1 = Math.min(imgH, Math.ceil(centerRow + radius)); + } + + const cropW = x1 - x0, cropH = y1 - y0; + if (cropW < 2 || cropH < 2) return null; + + const cropped = new Float32Array(cropW * cropH); + if (mode === "circle") { + const rSq = radius * radius; + for (let dy = 0; dy < cropH; dy++) { + for (let dx = 0; dx < cropW; dx++) { + const ix = x0 + dx, iy = y0 + dy; + const distSq = (ix - centerCol) * (ix - centerCol) + (iy - centerRow) * (iy - centerRow); + cropped[dy * cropW + dx] = distSq <= rSq ? data[iy * imgW + ix] : 0; + } + } + } else { + for (let dy = 0; dy < cropH; dy++) { + const srcOff = (y0 + dy) * imgW + x0; + cropped.set(data.subarray(srcOff, srcOff + cropW), dy * cropW); + } + } + return { cropped, cropW, cropH }; +} + +// ============================================================================ +// Main Component +// ============================================================================ +function Show4DSTEM() { + // Direct model access for batched updates + const model = useModel(); + + // ───────────────────────────────────────────────────────────────────────── + // Model State (synced with Python) + // ───────────────────────────────────────────────────────────────────────── + const [shapeRows] = useModelState("shape_rows"); + const [shapeCols] = useModelState("shape_cols"); + const [detRows] = useModelState("det_rows"); + const [detCols] = useModelState("det_cols"); + + const [posRow, setPosRow] = useModelState("pos_row"); + const [posCol, setPosCol] = useModelState("pos_col"); + const [roiCenterCol, setRoiCenterCol] = useModelState("roi_center_col"); + const [roiCenterRow, setRoiCenterRow] = useModelState("roi_center_row"); + const [pixelSize] = useModelState("pixel_size"); + const [kPixelSize] = useModelState("k_pixel_size"); + const [kCalibrated] = useModelState("k_calibrated"); + const [widgetVersion] = useModelState("widget_version"); + const [title] = useModelState("title"); + + const [frameBytes] = useModelState("frame_bytes"); + const [virtualImageBytes] = useModelState("virtual_image_bytes"); + + // ROI state + const [roiRadius, setRoiRadius] = useModelState("roi_radius"); + const [roiRadiusInner, setRoiRadiusInner] = useModelState("roi_radius_inner"); + const [roiMode, setRoiMode] = useModelState("roi_mode"); + const [roiWidth, setRoiWidth] = useModelState("roi_width"); + const [roiHeight, setRoiHeight] = useModelState("roi_height"); + + // Global min/max for DP normalization (from Python) + const [dpGlobalMin] = useModelState("dp_global_min"); + const [dpGlobalMax] = useModelState("dp_global_max"); + + // VI min/max for normalization (from Python) + const [viDataMin] = useModelState("vi_data_min"); + const [viDataMax] = useModelState("vi_data_max"); + + // Detector calibration (for presets) + const [bfRadius] = useModelState("bf_radius"); + const [centerCol] = useModelState("center_col"); + const [centerRow] = useModelState("center_row"); + + // Path animation state + const [pathPlaying, setPathPlaying] = useModelState("path_playing"); + const [pathIndex, setPathIndex] = useModelState("path_index"); + const [pathLength] = useModelState("path_length"); + const [pathIntervalMs] = useModelState("path_interval_ms"); + const [pathLoop] = useModelState("path_loop"); + + // Frame animation state (5D time/tilt series) + const [frameIdx, setFrameIdx] = useModelState("frame_idx"); + const [nFrames] = useModelState("n_frames"); + const [frameDimLabel] = useModelState("frame_dim_label"); + const [frameLabels] = useModelState("frame_labels"); + const [framePlaying, setFramePlaying] = useModelState("frame_playing"); + const [frameLoop, setFrameLoop] = useModelState("frame_loop"); + const [frameFps, setFrameFps] = useModelState("frame_fps"); + const [frameReverse, setFrameReverse] = useModelState("frame_reverse"); + const [frameBoomerang, setFrameBoomerang] = useModelState("frame_boomerang"); + + // Profile line state (synced with Python) + const [profileLine, setProfileLine] = useModelState<{row: number; col: number}[]>("profile_line"); + const [profileWidth] = useModelState("profile_width"); + + // Auto-detection trigger + // ───────────────────────────────────────────────────────────────────────── + // Local State (UI-only, not synced to Python) + // ───────────────────────────────────────────────────────────────────────── + const [localKCol, setLocalKCol] = React.useState(roiCenterCol); + const [localKRow, setLocalKRow] = React.useState(roiCenterRow); + const [localPosRow, setLocalPosRow] = React.useState(posRow); + const [localPosCol, setLocalPosCol] = React.useState(posCol); + const [isDraggingDP, setIsDraggingDP] = React.useState(false); + const [isDraggingVI, setIsDraggingVI] = React.useState(false); + const [isDraggingFFT, setIsDraggingFFT] = React.useState(false); + const [fftDragStart, setFftDragStart] = React.useState<{ x: number, y: number, panX: number, panY: number } | null>(null); + const [isDraggingResize, setIsDraggingResize] = React.useState(false); + const [isDraggingResizeInner, setIsDraggingResizeInner] = React.useState(false); // For annular inner handle + const [isHoveringResize, setIsHoveringResize] = React.useState(false); + const [isHoveringResizeInner, setIsHoveringResizeInner] = React.useState(false); + const resizeAspectRef = React.useRef(null); + // VI ROI drag/resize states (same pattern as DP) + const [isDraggingViRoi, setIsDraggingViRoi] = React.useState(false); + const [isDraggingViRoiResize, setIsDraggingViRoiResize] = React.useState(false); + const [isHoveringViRoiResize, setIsHoveringViRoiResize] = React.useState(false); + // Independent colormaps for DP and VI panels + const [showDpColorbar, setShowDpColorbar] = useModelState("dp_show_colorbar"); + const [dpColormap, setDpColormap] = useModelState("dp_colormap"); + const [viColormap, setViColormap] = useModelState("vi_colormap"); + // vmin/vmax percentile clipping (0-100) + const [dpVminPct, setDpVminPct] = useModelState("dp_vmin_pct"); + const [dpVmaxPct, setDpVmaxPct] = useModelState("dp_vmax_pct"); + const [viVminPct, setViVminPct] = useModelState("vi_vmin_pct"); + const [viVmaxPct, setViVmaxPct] = useModelState("vi_vmax_pct"); + // Absolute intensity bounds (override percentile sliders when both set) + const [traitDpVmin] = useModelState("dp_vmin"); + const [traitDpVmax] = useModelState("dp_vmax"); + const [traitViVmin] = useModelState("vi_vmin"); + const [traitViVmax] = useModelState("vi_vmax"); + // Scale mode: "linear" | "log" | "power" + const [dpScaleMode, setDpScaleMode] = useModelState<"linear" | "log" | "power">("dp_scale_mode"); + const [dpPowerExp] = useModelState("dp_power_exp"); + const [viScaleMode, setViScaleMode] = useModelState<"linear" | "log" | "power">("vi_scale_mode"); + const [viPowerExp] = useModelState("vi_power_exp"); + + // VI ROI state (real-space region selection for summed DP) - synced with Python + const [viRoiMode, setViRoiMode] = useModelState("vi_roi_mode"); + const [viRoiCenterRow, setViRoiCenterRow] = useModelState("vi_roi_center_row"); + const [viRoiCenterCol, setViRoiCenterCol] = useModelState("vi_roi_center_col"); + const [viRoiRadius, setViRoiRadius] = useModelState("vi_roi_radius"); + const [viRoiWidth, setViRoiWidth] = useModelState("vi_roi_width"); + const [viRoiHeight, setViRoiHeight] = useModelState("vi_roi_height"); + // Local VI ROI center for smooth dragging + const [localViRoiCenterRow, setLocalViRoiCenterRow] = React.useState(viRoiCenterRow || 0); + const [localViRoiCenterCol, setLocalViRoiCenterCol] = React.useState(viRoiCenterCol || 0); + const [summedDpBytes] = useModelState("summed_dp_bytes"); + const [summedDpCount] = useModelState("summed_dp_count"); + const [dpStats] = useModelState("dp_stats"); // [mean, min, max, std] + const [viStats] = useModelState("vi_stats"); // [mean, min, max, std] + const [showFft, setShowFft] = useModelState("show_fft"); + const [fftWindow, setFftWindow] = useModelState("fft_window"); + const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); + const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); + const [showControls] = useModelState("show_controls"); + + const toolVisibility = React.useMemo( + () => computeToolVisibility("Show4DSTEM", disabledTools, hiddenTools), + [disabledTools, hiddenTools], + ); + + const hideDisplay = toolVisibility.isHidden("display"); + const hideHistogram = toolVisibility.isHidden("histogram"); + const hideStats = toolVisibility.isHidden("stats"); + const hidePlayback = toolVisibility.isHidden("playback"); + const hideView = toolVisibility.isHidden("view"); + const hideExport = toolVisibility.isHidden("export"); + const hideRoi = toolVisibility.isHidden("roi"); + const hideProfile = toolVisibility.isHidden("profile"); + const hideVirtual = toolVisibility.isHidden("virtual"); + const hideFrame = toolVisibility.isHidden("frame"); + const hideFft = toolVisibility.isHidden("fft") || hideVirtual; + + const lockDisplay = toolVisibility.isLocked("display"); + const lockHistogram = toolVisibility.isLocked("histogram"); + const lockStats = toolVisibility.isLocked("stats"); + const lockNavigation = toolVisibility.isLocked("navigation"); + const lockPlayback = toolVisibility.isLocked("playback"); + const lockView = toolVisibility.isLocked("view"); + const lockExport = toolVisibility.isLocked("export"); + const lockRoi = toolVisibility.isLocked("roi"); + const lockProfile = toolVisibility.isLocked("profile"); + const lockVirtual = toolVisibility.isLocked("virtual"); + const lockFrame = toolVisibility.isLocked("frame"); + const lockFft = toolVisibility.isLocked("fft") || lockVirtual; + const effectiveShowFft = showFft && !hideFft; + + // ROI FFT state (VI ROI crops virtual image for FFT) + const [fftCropDims, setFftCropDims] = React.useState<{ cropWidth: number; cropHeight: number; fftWidth: number; fftHeight: number } | null>(null); + const roiFftActive = effectiveShowFft && viRoiMode !== "off"; + + // Canvas resize state + const [canvasSize, setCanvasSize] = React.useState(CANVAS_SIZE); + const [isResizingCanvas, setIsResizingCanvas] = React.useState(false); + const [resizeCanvasStart, setResizeCanvasStart] = React.useState<{ x: number; y: number; size: number } | null>(null); + + // Export + const [, setGifExportRequested] = useModelState("_gif_export_requested"); + const [gifData] = useModelState("_gif_data"); + const [gifMetadataJson] = useModelState("_gif_metadata_json"); + const [exporting, setExporting] = React.useState(false); + const [dpExportAnchor, setDpExportAnchor] = React.useState(null); + const [viExportAnchor, setViExportAnchor] = React.useState(null); + + // Cursor readout state + const [cursorInfo, setCursorInfo] = React.useState<{ row: number; col: number; value: number; panel: string } | null>(null); + + // DP Line profile state + const [profileActive, setProfileActive] = React.useState(false); + const [profileData, setProfileData] = React.useState(null); + const [profileHeight, setProfileHeight] = React.useState(76); + const [isResizingProfile, setIsResizingProfile] = React.useState(false); + const profileResizeStart = React.useRef<{ startY: number; startHeight: number } | null>(null); + const profileCanvasRef = React.useRef(null); + const profileBaseImageRef = React.useRef(null); + const profileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); + const profilePoints = profileLine || []; + const rawDpDataRef = React.useRef(null); + const dpClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const [draggingDpProfileEndpoint, setDraggingDpProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isDraggingDpProfileLine, setIsDraggingDpProfileLine] = React.useState(false); + const [hoveredDpProfileEndpoint, setHoveredDpProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isHoveringDpProfileLine, setIsHoveringDpProfileLine] = React.useState(false); + const dpProfileDragStartRef = React.useRef<{ row: number; col: number; p0: { row: number; col: number }; p1: { row: number; col: number } } | null>(null); + const dpDragOffsetRef = React.useRef<{ dRow: number; dCol: number }>({ dRow: 0, dCol: 0 }); + + // VI Line profile state + const [viProfileActive, setViProfileActive] = React.useState(false); + const [viProfileData, setViProfileData] = React.useState(null); + const [viProfilePoints, setViProfilePoints] = React.useState>([]); + const [viProfileHeight, setViProfileHeight] = React.useState(76); + const [isResizingViProfile, setIsResizingViProfile] = React.useState(false); + const viProfileResizeStart = React.useRef<{ startY: number; startHeight: number } | null>(null); + const viProfileCanvasRef = React.useRef(null); + const viProfileBaseImageRef = React.useRef(null); + const viProfileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); + const rawViDataRef = React.useRef(null); + const viClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + const [draggingViProfileEndpoint, setDraggingViProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isDraggingViProfileLine, setIsDraggingViProfileLine] = React.useState(false); + const [hoveredViProfileEndpoint, setHoveredViProfileEndpoint] = React.useState<0 | 1 | null>(null); + const [isHoveringViProfileLine, setIsHoveringViProfileLine] = React.useState(false); + const viProfileDragStartRef = React.useRef<{ row: number; col: number; p0: { row: number; col: number }; p1: { row: number; col: number } } | null>(null); + const viRoiDragOffsetRef = React.useRef<{ dRow: number; dCol: number }>({ dRow: 0, dCol: 0 }); + + // Theme detection + const { themeInfo, colors: themeColors } = useTheme(); + const roiColors = themeInfo.theme === "dark" ? DARK_ROI_COLORS : LIGHT_ROI_COLORS; + const accentGreen = themeInfo.theme === "dark" ? "#0f0" : "#1a7a1a"; + + // Themed typography — applies theme colors to module-level font sizes + const typo = React.useMemo(() => ({ + label: { ...typography.label, color: themeColors.textMuted }, + labelSmall: { ...typography.labelSmall, color: themeColors.textMuted }, + value: { ...typography.value, color: themeColors.textMuted }, + title: { ...typography.title, color: themeColors.accent }, + }), [themeColors]); + + // Compute VI canvas dimensions to respect aspect ratio of rectangular scans + const viCanvasWidth = shapeRows > shapeCols ? Math.round(canvasSize * (shapeCols / shapeRows)) : canvasSize; + const viCanvasHeight = shapeCols > shapeRows ? Math.round(canvasSize * (shapeRows / shapeCols)) : canvasSize; + + // Histogram data - use state to ensure re-renders (both are Float32Array now) + const [dpHistogramData, setDpHistogramData] = React.useState(null); + const [viHistogramData, setViHistogramData] = React.useState(null); + + // Parse DP frame bytes for histogram (float32 now) + React.useEffect(() => { + if (!frameBytes) return; + // Parse as Float32Array since Python now sends raw float32 + const rawData = new Float32Array(frameBytes.buffer, frameBytes.byteOffset, frameBytes.byteLength / 4); + // Store raw data for profile sampling + if (!rawDpDataRef.current || rawDpDataRef.current.length !== rawData.length) { + rawDpDataRef.current = new Float32Array(rawData.length); + } + rawDpDataRef.current.set(rawData); + // Apply scale transformation for histogram display + const scaledData = new Float32Array(rawData.length); + if (dpScaleMode === "log") { + for (let i = 0; i < rawData.length; i++) { + scaledData[i] = Math.log1p(Math.max(0, rawData[i])); + } + } else if (dpScaleMode === "power") { + for (let i = 0; i < rawData.length; i++) { + scaledData[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); + } + } else { + scaledData.set(rawData); + } + setDpHistogramData(scaledData); + }, [frameBytes, dpScaleMode, dpPowerExp]); + + // GPU FFT state + const gpuFFTRef = React.useRef(null); + const [gpuReady, setGpuReady] = React.useState(false); + + // Path animation timer + React.useEffect(() => { + if (!pathPlaying || pathLength === 0) return; + + const timer = setInterval(() => { + setPathIndex((prev: number) => { + const next = prev + 1; + if (next >= pathLength) { + if (pathLoop) { + return 0; // Loop back to start + } else { + setPathPlaying(false); // Stop at end + return prev; + } + } + return next; + }); + }, pathIntervalMs); + + return () => clearInterval(timer); + }, [pathPlaying, pathLength, pathIntervalMs, pathLoop, setPathIndex, setPathPlaying]); + + // Frame animation timer (5D time/tilt series) + const frameBounceDir = React.useRef(1); + React.useEffect(() => { + frameBounceDir.current = frameReverse ? -1 : 1; + }, [frameReverse]); + + React.useEffect(() => { + if (!framePlaying || nFrames <= 1) return; + + const intervalMs = 1000 / Math.max(0.1, frameFps); + const timer = setInterval(() => { + setFrameIdx((prev: number) => { + let next: number; + if (frameBoomerang) { + next = prev + frameBounceDir.current; + if (next >= nFrames) { frameBounceDir.current = -1; next = nFrames - 2; } + if (next < 0) { frameBounceDir.current = 1; next = 1; } + next = Math.max(0, Math.min(nFrames - 1, next)); + } else { + next = prev + (frameReverse ? -1 : 1); + if (next >= nFrames) { + if (frameLoop) return 0; + setFramePlaying(false); + return prev; + } + if (next < 0) { + if (frameLoop) return nFrames - 1; + setFramePlaying(false); + return prev; + } + } + return next; + }); + }, intervalMs); + + return () => clearInterval(timer); + }, [framePlaying, nFrames, frameFps, frameLoop, frameReverse, frameBoomerang, setFrameIdx, setFramePlaying]); + + // Initialize WebGPU FFT on mount + React.useEffect(() => { + getWebGPUFFT().then(fft => { + if (fft) { + gpuFFTRef.current = fft; + setGpuReady(true); + } + }); + }, []); + + // Root element ref (theme-aware styling handled via CSS variables) + const rootRef = React.useRef(null); + + // Zoom state + const [dpZoom, setDpZoom] = React.useState(1); + const [dpPanX, setDpPanX] = React.useState(0); + const [dpPanY, setDpPanY] = React.useState(0); + const [viZoom, setViZoom] = React.useState(1); + const [viPanX, setViPanX] = React.useState(0); + const [viPanY, setViPanY] = React.useState(0); + const [fftZoom, setFftZoom] = React.useState(1); + const [fftPanX, setFftPanX] = React.useState(0); + const [fftPanY, setFftPanY] = React.useState(0); + const [fftScaleMode, setFftScaleMode] = useModelState<"linear" | "log" | "power">("fft_scale_mode"); + const [fftPowerExp] = useModelState("fft_power_exp"); + const [fftColormap, setFftColormap] = useModelState("fft_colormap"); + const [fftAuto, setFftAuto] = useModelState("fft_auto"); + const [fftVminPct, setFftVminPct] = useModelState("fft_vmin_pct"); + const [fftVmaxPct, setFftVmaxPct] = useModelState("fft_vmax_pct"); + const [fftStats, setFftStats] = React.useState(null); // [mean, min, max, std] + const [fftHistogramData, setFftHistogramData] = React.useState(null); + const [fftDataMin, setFftDataMin] = React.useState(0); + const [fftDataMax, setFftDataMax] = React.useState(1); + const [fftClickInfo, setFftClickInfo] = React.useState<{ + row: number; col: number; distPx: number; + spatialFreq: number | null; dSpacing: number | null; + } | null>(null); + const fftClickStartRef = React.useRef<{ x: number; y: number } | null>(null); + + const isTypingTarget = React.useCallback((target: EventTarget | null): boolean => { + if (!(target instanceof HTMLElement)) return false; + if (target.isContentEditable) return true; + return target.closest("input, textarea, select, [role='textbox'], [contenteditable='true']") !== null; + }, []); + + const handleRootMouseDownCapture = React.useCallback((e: React.MouseEvent) => { + const target = e.target as HTMLElement | null; + if (target?.closest("canvas")) rootRef.current?.focus(); + }, []); + + const handleKeyDown = React.useCallback((e: React.KeyboardEvent) => { + if (isTypingTarget(e.target)) return; + + const step = e.shiftKey ? 10 : 1; + let handled = false; + + switch (e.key) { + case "ArrowUp": + if (!lockNavigation) { + setPosRow(Math.max(0, posRow - step)); + handled = true; + } + break; + case "ArrowDown": + if (!lockNavigation) { + setPosRow(Math.min(shapeRows - 1, posRow + step)); + handled = true; + } + break; + case "ArrowLeft": + if (!lockNavigation) { + setPosCol(Math.max(0, posCol - step)); + handled = true; + } + break; + case "ArrowRight": + if (!lockNavigation) { + setPosCol(Math.min(shapeCols - 1, posCol + step)); + handled = true; + } + break; + case " ": // Space bar + if (!lockPlayback && pathLength > 0) { + setPathPlaying(!pathPlaying); + handled = true; + } + break; + case "r": + case "R": + if (!lockView) { + setDpZoom(1); setDpPanX(0); setDpPanY(0); + setViZoom(1); setViPanX(0); setViPanY(0); + setFftZoom(1); setFftPanX(0); setFftPanY(0); + handled = true; + } + break; + case "[": + if (!lockPlayback && !lockFrame && nFrames > 1) { + setFrameIdx(Math.max(0, frameIdx - 1)); + handled = true; + } + break; + case "]": + if (!lockPlayback && !lockFrame && nFrames > 1) { + setFrameIdx(Math.min(nFrames - 1, frameIdx + 1)); + handled = true; + } + break; + case "Escape": + rootRef.current?.blur(); + handled = true; + break; + } + + if (handled) { + e.preventDefault(); + e.stopPropagation(); + } + }, [ + frameIdx, isTypingTarget, lockFrame, lockNavigation, lockPlayback, lockView, nFrames, pathLength, + pathPlaying, posCol, posRow, setFrameIdx, setPathPlaying, setPosCol, setPosRow, shapeCols, shapeRows, + ]); + + React.useEffect(() => { + if (hideFft && showFft) { + setShowFft(false); + } + }, [hideFft, showFft, setShowFft]); + + React.useEffect(() => { + if (lockPlayback && pathPlaying) { + setPathPlaying(false); + } + }, [lockPlayback, pathPlaying, setPathPlaying]); + + React.useEffect(() => { + if ((lockPlayback || lockFrame) && framePlaying) { + setFramePlaying(false); + } + }, [lockFrame, lockPlayback, framePlaying, setFramePlaying]); + + React.useEffect(() => { + if (hideRoi) { + if (roiMode !== "point") setRoiMode("point"); + if (viRoiMode !== "off") setViRoiMode("off"); + } + }, [hideRoi, roiMode, viRoiMode, setRoiMode, setViRoiMode]); + + React.useEffect(() => { + if (hideProfile) { + if (profileActive) setProfileActive(false); + if (viProfileActive) setViProfileActive(false); + if (profileLine.length > 0) setProfileLine([]); + if (profileData) setProfileData(null); + if (viProfilePoints.length > 0) setViProfilePoints([]); + if (viProfileData) setViProfileData(null); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + } + }, [ + hideProfile, profileActive, profileLine, profileData, setProfileLine, viProfileActive, + viProfilePoints, viProfileData, + ]); + + // Sync local state + React.useEffect(() => { + if (!isDraggingDP && !isDraggingResize) { setLocalKCol(roiCenterCol); setLocalKRow(roiCenterRow); } + }, [roiCenterCol, roiCenterRow, isDraggingDP, isDraggingResize]); + + React.useEffect(() => { + if (!isDraggingVI) { setLocalPosRow(posRow); setLocalPosCol(posCol); } + }, [posRow, posCol, isDraggingVI]); + + // Sync VI ROI local state + React.useEffect(() => { + if (!isDraggingViRoi && !isDraggingViRoiResize) { + setLocalViRoiCenterRow(viRoiCenterRow || shapeRows / 2); + setLocalViRoiCenterCol(viRoiCenterCol || shapeCols / 2); + } + }, [viRoiCenterRow, viRoiCenterCol, isDraggingViRoi, isDraggingViRoiResize, shapeRows, shapeCols]); + + // Canvas refs + const dpCanvasRef = React.useRef(null); + const dpOverlayRef = React.useRef(null); + const dpUiRef = React.useRef(null); // High-DPI UI overlay for scale bar + const dpOffscreenRef = React.useRef(null); + const dpImageDataRef = React.useRef(null); + const virtualCanvasRef = React.useRef(null); + const virtualOverlayRef = React.useRef(null); + const viUiRef = React.useRef(null); // High-DPI UI overlay for scale bar + const viOffscreenRef = React.useRef(null); + const viImageDataRef = React.useRef(null); + const fftCanvasRef = React.useRef(null); + const fftOverlayRef = React.useRef(null); + const fftOffscreenRef = React.useRef(null); + const fftImageDataRef = React.useRef(null); + + // Offscreen version counters — bump when colormap/data changes, cheap draw effects depend on these + const [dpOffscreenVersion, setDpOffscreenVersion] = React.useState(0); + const [viOffscreenVersion, setViOffscreenVersion] = React.useState(0); + const [fftOffscreenVersion, setFftOffscreenVersion] = React.useState(0); + + // Cached colorbar vmin/vmax — computed in expensive DP effect, reused in UI overlay without recomputing + const dpColorbarVminRef = React.useRef(0); + const dpColorbarVmaxRef = React.useRef(1); + + // Device pixel ratio for high-DPI UI overlays + const DPR = typeof window !== 'undefined' ? window.devicePixelRatio || 1 : 1; + + // ───────────────────────────────────────────────────────────────────────── + // Effects: Canvas Rendering & Animation + // ───────────────────────────────────────────────────────────────────────── + + // Prevent page scroll when scrolling on canvases + // Re-run when showFft changes since FFT canvas is conditionally rendered + React.useEffect(() => { + const preventDefault = (e: WheelEvent) => e.preventDefault(); + const overlays = [dpOverlayRef.current, virtualOverlayRef.current, fftOverlayRef.current]; + overlays.forEach(el => el?.addEventListener("wheel", preventDefault, { passive: false })); + return () => overlays.forEach(el => el?.removeEventListener("wheel", preventDefault)); + }, [effectiveShowFft]); + + // Store raw data for filtering/FFT + const rawVirtualImageRef = React.useRef(null); + const fftWorkRealRef = React.useRef(null); + const fftWorkImagRef = React.useRef(null); + const fftMagnitudeRef = React.useRef(null); + const fftMagCacheRef = React.useRef(null); + + // Parse virtual image bytes into Float32Array and apply scale for histogram + React.useEffect(() => { + if (!virtualImageBytes) return; + // Parse as Float32Array + const numFloats = virtualImageBytes.byteLength / 4; + const rawData = new Float32Array(virtualImageBytes.buffer, virtualImageBytes.byteOffset, numFloats); + + // Store a copy for filtering/FFT (rawData is a view, we need a copy) + let storedData = rawVirtualImageRef.current; + if (!storedData || storedData.length !== numFloats) { + storedData = new Float32Array(numFloats); + rawVirtualImageRef.current = storedData; + } + storedData.set(rawData); + + // Also store for VI profile sampling + if (!rawViDataRef.current || rawViDataRef.current.length !== numFloats) { + rawViDataRef.current = new Float32Array(numFloats); + } + rawViDataRef.current.set(rawData); + + // Apply scale transformation for histogram display + const scaledData = new Float32Array(numFloats); + if (viScaleMode === "log") { + for (let i = 0; i < numFloats; i++) { + scaledData[i] = Math.log1p(Math.max(0, rawData[i])); + } + } else if (viScaleMode === "power") { + for (let i = 0; i < numFloats; i++) { + scaledData[i] = Math.pow(Math.max(0, rawData[i]), viPowerExp); + } + } else { + scaledData.set(rawData); + } + setViHistogramData(scaledData); + }, [virtualImageBytes, viScaleMode, viPowerExp]); + + // Render DP with zoom (use summed DP when VI ROI is active) + // Expensive: colormap + data processing → cached offscreen canvas + React.useEffect(() => { + // Determine which bytes to display: summed DP (if VI ROI active) or single frame + const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; + const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + if (!sourceBytes) return; + + const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; + + // Parse raw float32 data and apply scale transformation + const rawData = new Float32Array(sourceBytes.buffer, sourceBytes.byteOffset, sourceBytes.byteLength / 4); + let scaled: Float32Array; + if (dpScaleMode === "log") { + scaled = new Float32Array(rawData.length); + for (let i = 0; i < rawData.length; i++) { + scaled[i] = Math.log1p(Math.max(0, rawData[i])); + } + } else if (dpScaleMode === "power") { + scaled = new Float32Array(rawData.length); + for (let i = 0; i < rawData.length; i++) { + scaled[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); + } + } else { + scaled = rawData; + } + + // Compute actual min/max of scaled data for normalization + const { min: dataMin, max: dataMax } = findDataRange(scaled); + + // Apply absolute bounds or percentile clipping + let vmin: number, vmax: number; + if (traitDpVmin != null && traitDpVmax != null) { + if (dpScaleMode === "log") { + vmin = Math.log1p(Math.max(traitDpVmin, 0)); + vmax = Math.log1p(Math.max(traitDpVmax, 0)); + } else if (dpScaleMode === "power") { + vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); + vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); + } else { + vmin = traitDpVmin; + vmax = traitDpVmax; + } + } else { + ({ vmin, vmax } = sliderRange(dataMin, dataMax, dpVminPct, dpVmaxPct)); + } + + let offscreen = dpOffscreenRef.current; + if (!offscreen) { + offscreen = document.createElement("canvas"); + dpOffscreenRef.current = offscreen; + } + const sizeChanged = offscreen.width !== detCols || offscreen.height !== detRows; + if (sizeChanged) { + offscreen.width = detCols; + offscreen.height = detRows; + dpImageDataRef.current = null; + } + const offCtx = offscreen.getContext("2d"); + if (!offCtx) return; + + let imgData = dpImageDataRef.current; + if (!imgData) { + imgData = offCtx.createImageData(detCols, detRows); + dpImageDataRef.current = imgData; + } + applyColormap(scaled, imgData.data, lut, vmin, vmax); + offCtx.putImageData(imgData, 0, 0); + // Cache colorbar range for the UI overlay (avoids recomputing findDataRange on every zoom/pan) + dpColorbarVminRef.current = vmin; + dpColorbarVmaxRef.current = vmax; + setDpOffscreenVersion(v => v + 1); + }, [frameBytes, summedDpBytes, viRoiMode, detRows, detCols, dpColormap, dpVminPct, dpVmaxPct, dpScaleMode, dpPowerExp, traitDpVmin, traitDpVmax]); + + // Cheap: zoom/pan redraw — just drawImage from cached offscreen + // useLayoutEffect prevents black flash when canvas dimensions change (resize) + React.useLayoutEffect(() => { + const offscreen = dpOffscreenRef.current; + if (!offscreen || !dpCanvasRef.current) return; + const canvas = dpCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.imageSmoothingEnabled = false; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.translate(dpPanX, dpPanY); + ctx.scale(dpZoom, dpZoom); + ctx.drawImage(offscreen, 0, 0); + ctx.restore(); + }, [dpOffscreenVersion, dpZoom, dpPanX, dpPanY]); + + // Render DP overlay - just clear (ROI shapes now drawn on high-DPI UI canvas) + React.useEffect(() => { + if (!dpOverlayRef.current) return; + const canvas = dpOverlayRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + // All visual overlays (crosshair, ROI shapes, scale bar) are now on dpUiRef for crisp rendering + }, [localKCol, localKRow, isDraggingDP, isDraggingResize, isDraggingResizeInner, isHoveringResize, isHoveringResizeInner, dpZoom, dpPanX, dpPanY, roiMode, roiRadius, roiRadiusInner, roiWidth, roiHeight, detRows, detCols]); + + // Expensive: VI colormap + data processing → cached offscreen canvas + React.useEffect(() => { + if (!rawVirtualImageRef.current) return; + + const width = shapeCols; + const height = shapeRows; + const filtered = rawVirtualImageRef.current; + + // Apply scale transformation first + let scaled = filtered; + if (viScaleMode === "log") { + scaled = new Float32Array(filtered.length); + for (let i = 0; i < filtered.length; i++) { + scaled[i] = Math.log1p(Math.max(0, filtered[i])); + } + } else if (viScaleMode === "power") { + scaled = new Float32Array(filtered.length); + for (let i = 0; i < filtered.length; i++) { + scaled[i] = Math.pow(Math.max(0, filtered[i]), viPowerExp); + } + } + + // Use Python's pre-computed min/max when valid, fallback to computing from data + let dataMin: number, dataMax: number; + const hasValidMinMax = viDataMin !== undefined && viDataMax !== undefined && viDataMax > viDataMin; + if (hasValidMinMax) { + // Apply scale transform to Python's values + if (viScaleMode === "log") { + dataMin = Math.log1p(Math.max(0, viDataMin)); + dataMax = Math.log1p(Math.max(0, viDataMax)); + } else if (viScaleMode === "power") { + dataMin = Math.pow(Math.max(0, viDataMin), viPowerExp); + dataMax = Math.pow(Math.max(0, viDataMax), viPowerExp); + } else { + dataMin = viDataMin; + dataMax = viDataMax; + } + } else { + // Fallback: compute from scaled data + const r = findDataRange(scaled); + dataMin = r.min; + dataMax = r.max; + } + + // Apply absolute bounds or percentile clipping + let vmin: number, vmax: number; + if (traitViVmin != null && traitViVmax != null) { + if (viScaleMode === "log") { + vmin = Math.log1p(Math.max(traitViVmin, 0)); + vmax = Math.log1p(Math.max(traitViVmax, 0)); + } else if (viScaleMode === "power") { + vmin = Math.pow(Math.max(traitViVmin, 0), viPowerExp); + vmax = Math.pow(Math.max(traitViVmax, 0), viPowerExp); + } else { + vmin = traitViVmin; + vmax = traitViVmax; + } + } else { + ({ vmin, vmax } = sliderRange(dataMin, dataMax, viVminPct, viVmaxPct)); + } + + const lut = COLORMAPS[viColormap] || COLORMAPS.inferno; + let offscreen = viOffscreenRef.current; + if (!offscreen) { + offscreen = document.createElement("canvas"); + viOffscreenRef.current = offscreen; + } + const sizeChanged = offscreen.width !== width || offscreen.height !== height; + if (sizeChanged) { + offscreen.width = width; + offscreen.height = height; + viImageDataRef.current = null; + } + const offCtx = offscreen.getContext("2d"); + if (!offCtx) return; + + let imageData = viImageDataRef.current; + if (!imageData) { + imageData = offCtx.createImageData(width, height); + viImageDataRef.current = imageData; + } + applyColormap(scaled, imageData.data, lut, vmin, vmax); + offCtx.putImageData(imageData, 0, 0); + setViOffscreenVersion(v => v + 1); + // Note: viDataMin/viDataMax intentionally not in deps - they arrive with virtualImageBytes + // and we have a fallback if they're stale + }, [virtualImageBytes, shapeRows, shapeCols, viColormap, viVminPct, viVmaxPct, viScaleMode, viPowerExp, traitViVmin, traitViVmax]); + + // Cheap: VI zoom/pan redraw — just drawImage from cached offscreen + React.useLayoutEffect(() => { + const offscreen = viOffscreenRef.current; + if (!offscreen || !virtualCanvasRef.current) return; + const canvas = virtualCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.imageSmoothingEnabled = false; + ctx.clearRect(0, 0, canvas.width, canvas.height); + ctx.save(); + ctx.translate(viPanX, viPanY); + ctx.scale(viZoom, viZoom); + ctx.drawImage(offscreen, 0, 0); + ctx.restore(); + }, [viOffscreenVersion, viZoom, viPanX, viPanY]); + + // Render virtual image overlay (just clear - crosshair drawn on high-DPI UI canvas) + React.useEffect(() => { + if (!virtualOverlayRef.current) return; + const canvas = virtualOverlayRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + // Crosshair and scale bar now drawn on high-DPI UI canvas (viUiRef) + }, [localPosRow, localPosCol, isDraggingVI, viZoom, viPanX, viPanY, pixelSize, shapeRows, shapeCols]); + + // Compute FFT (expensive, async — only re-run on data/GPU changes) + const fftRealRef = React.useRef(null); + const fftImagRef = React.useRef(null); + const [fftVersion, setFftVersion] = React.useState(0); + + React.useEffect(() => { + if (!rawVirtualImageRef.current || !effectiveShowFft) { setFftCropDims(null); return; } + let cancelled = false; + let width = shapeCols; + let height = shapeRows; + let sourceData = rawVirtualImageRef.current; + let origCropW = 0, origCropH = 0; + + // ROI FFT: crop virtual image to VI ROI region and pre-pad to power-of-2 + if (roiFftActive) { + const crop = cropSingleROI(sourceData, shapeCols, shapeRows, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight); + if (crop) { + origCropW = crop.cropW; + origCropH = crop.cropH; + // Apply Hann window to crop at native dimensions BEFORE zero-padding + if (fftWindow) applyHannWindow2D(crop.cropped, crop.cropW, crop.cropH); + const padW = nextPow2(crop.cropW); + const padH = nextPow2(crop.cropH); + const padded = new Float32Array(padW * padH); + for (let y = 0; y < crop.cropH; y++) { + for (let x = 0; x < crop.cropW; x++) { + padded[y * padW + x] = crop.cropped[y * crop.cropW + x]; + } + } + sourceData = padded; + width = padW; + height = padH; + } + } + + // Pre-pad non-power-of-2 full images so fft2d doesn't truncate frequency data + if (!roiFftActive) { + const padW = nextPow2(width); + const padH = nextPow2(height); + if (padW !== width || padH !== height) { + const padded = new Float32Array(padW * padH); + for (let y = 0; y < height; y++) { + for (let x = 0; x < width; x++) { + padded[y * padW + x] = sourceData[y * width + x]; + } + } + sourceData = padded; + width = padW; + height = padH; + } + } + + const fftW = width, fftH = height; + if (gpuFFTRef.current && gpuReady) { + const runGpuFFT = async () => { + const real = sourceData.slice(); + const imag = new Float32Array(real.length); + const { real: fReal, imag: fImag } = await gpuFFTRef.current!.fft2D(real, imag, fftW, fftH, false); + if (cancelled) return; + fftshift(fReal, fftW, fftH); + fftshift(fImag, fftW, fftH); + fftRealRef.current = fReal; + fftImagRef.current = fImag; + if (origCropW > 0) { + setFftCropDims({ cropWidth: origCropW, cropHeight: origCropH, fftWidth: fftW, fftHeight: fftH }); + } else if (fftW !== shapeCols || fftH !== shapeRows) { + setFftCropDims({ cropWidth: shapeCols, cropHeight: shapeRows, fftWidth: fftW, fftHeight: fftH }); + } else { + setFftCropDims(null); + } + setFftVersion(v => v + 1); + }; + runGpuFFT(); + return () => { cancelled = true; }; + } else { + const len = sourceData.length; + let real = fftWorkRealRef.current; + if (!real || real.length !== len) { real = new Float32Array(len); fftWorkRealRef.current = real; } + real.set(sourceData); + let imag = fftWorkImagRef.current; + if (!imag || imag.length !== len) { imag = new Float32Array(len); fftWorkImagRef.current = imag; } else { imag.fill(0); } + fft2d(real, imag, fftW, fftH, false); + fftshift(real, fftW, fftH); + fftshift(imag, fftW, fftH); + fftRealRef.current = real; + fftImagRef.current = imag; + if (origCropW > 0) { + setFftCropDims({ cropWidth: origCropW, cropHeight: origCropH, fftWidth: fftW, fftHeight: fftH }); + } else if (fftW !== shapeCols || fftH !== shapeRows) { + setFftCropDims({ cropWidth: shapeCols, cropHeight: shapeRows, fftWidth: fftW, fftHeight: fftH }); + } else { + setFftCropDims(null); + } + setFftVersion(v => v + 1); + } + }, [virtualImageBytes, shapeRows, shapeCols, gpuReady, effectiveShowFft, roiFftActive, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, fftWindow]); + + // Expensive: FFT magnitude + histogram + colormap → cached offscreen canvas + React.useEffect(() => { + if (!fftRealRef.current || !fftImagRef.current) return; + if (!effectiveShowFft) return; + + const width = fftCropDims?.fftWidth ?? shapeCols; + const height = fftCropDims?.fftHeight ?? shapeRows; + const real = fftRealRef.current; + const imag = fftImagRef.current; + const lut = COLORMAPS[fftColormap] || COLORMAPS.inferno; + + // Compute magnitude with scale mode + let magnitude = fftMagnitudeRef.current; + if (!magnitude || magnitude.length !== real.length) { + magnitude = new Float32Array(real.length); + fftMagnitudeRef.current = magnitude; + } + // Cache raw magnitude for peak-snap before applying scale transform + let rawMag = fftMagCacheRef.current; + if (!rawMag || rawMag.length !== real.length) { + rawMag = new Float32Array(real.length); + fftMagCacheRef.current = rawMag; + } + for (let i = 0; i < real.length; i++) { + const mag = Math.sqrt(real[i] * real[i] + imag[i] * imag[i]); + rawMag[i] = mag; + if (fftScaleMode === "log") { magnitude[i] = Math.log1p(mag); } + else if (fftScaleMode === "power") { magnitude[i] = Math.pow(mag, fftPowerExp); } + else { magnitude[i] = mag; } + } + + let displayMin: number, displayMax: number; + if (fftAuto) { + ({ min: displayMin, max: displayMax } = autoEnhanceFFT(magnitude, width, height)); + } else { + ({ min: displayMin, max: displayMax } = findDataRange(magnitude)); + } + setFftDataMin(displayMin); + setFftDataMax(displayMax); + const magStats = computeStats(magnitude); + setFftStats([magStats.mean, displayMin, displayMax, magStats.std]); + setFftHistogramData(magnitude.slice()); + + // Render to offscreen canvas + let offscreen = fftOffscreenRef.current; + if (!offscreen) { offscreen = document.createElement("canvas"); fftOffscreenRef.current = offscreen; } + if (offscreen.width !== width || offscreen.height !== height) { + offscreen.width = width; offscreen.height = height; fftImageDataRef.current = null; + } + const offCtx = offscreen.getContext("2d"); + if (!offCtx) return; + let imgData = fftImageDataRef.current; + if (!imgData) { imgData = offCtx.createImageData(width, height); fftImageDataRef.current = imgData; } + + const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + applyColormap(magnitude, imgData.data, lut, vmin, vmax); + offCtx.putImageData(imgData, 0, 0); + setFftOffscreenVersion(v => v + 1); + }, [effectiveShowFft, fftVersion, fftScaleMode, fftPowerExp, fftAuto, fftVminPct, fftVmaxPct, fftColormap, shapeRows, shapeCols, fftCropDims]); + + // Cheap: FFT zoom/pan redraw — just drawImage from cached offscreen + React.useLayoutEffect(() => { + if (!fftCanvasRef.current) return; + const canvas = fftCanvasRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + const offscreen = fftOffscreenRef.current; + if (!offscreen || !effectiveShowFft) { ctx.clearRect(0, 0, canvas.width, canvas.height); return; } + const fftW = offscreen.width; + const fftH = offscreen.height; + const canvasW = canvas.width; + const canvasH = canvas.height; + // Use bilinear smoothing when FFT dims differ from canvas (non-pow2 padding or ROI crop) + ctx.imageSmoothingEnabled = fftW !== canvasW || fftH !== canvasH; + ctx.clearRect(0, 0, canvasW, canvasH); + ctx.save(); + ctx.translate(fftPanX, fftPanY); + ctx.scale(fftZoom, fftZoom); + ctx.drawImage(offscreen, 0, 0); + ctx.restore(); + }, [fftOffscreenVersion, fftZoom, fftPanX, fftPanY, effectiveShowFft]); + + // Render FFT overlay with d-spacing crosshair marker + React.useEffect(() => { + if (!fftOverlayRef.current) return; + const canvas = fftOverlayRef.current; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.clearRect(0, 0, canvas.width, canvas.height); + + // D-spacing crosshair marker + if (fftClickInfo && effectiveShowFft) { + const fftW = fftCropDims?.fftWidth ?? shapeCols; + const fftH = fftCropDims?.fftHeight ?? shapeRows; + ctx.save(); + // Convert FFT image coords to canvas coords via zoom/pan transform + const screenX = fftPanX + fftZoom * fftClickInfo.col; + const screenY = fftPanY + fftZoom * fftClickInfo.row; + ctx.strokeStyle = "rgba(255, 255, 255, 0.9)"; + ctx.shadowColor = "rgba(0, 0, 0, 0.6)"; + ctx.shadowBlur = 2; + ctx.lineWidth = 1.5; + // Scale crosshair size relative to canvas (not zoom-dependent) + const r = 8 * Math.max(fftW, fftH) / 450; + const gap = 3 * Math.max(fftW, fftH) / 450; + const dotR = 4 * Math.max(fftW, fftH) / 450; + ctx.beginPath(); + ctx.moveTo(screenX - r, screenY); ctx.lineTo(screenX - gap, screenY); + ctx.moveTo(screenX + gap, screenY); ctx.lineTo(screenX + r, screenY); + ctx.moveTo(screenX, screenY - r); ctx.lineTo(screenX, screenY - gap); + ctx.moveTo(screenX, screenY + gap); ctx.lineTo(screenX, screenY + r); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(screenX, screenY, dotR, 0, Math.PI * 2); + ctx.stroke(); + if (fftClickInfo.dSpacing != null) { + const d = fftClickInfo.dSpacing; + const label = d >= 10 ? `d = ${(d / 10).toFixed(2)} nm` : `d = ${d.toFixed(2)} \u00C5`; + const fontSize = Math.max(10, Math.round(11 * Math.max(fftW, fftH) / 450)); + ctx.font = `bold ${fontSize}px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif`; + ctx.fillStyle = "white"; + ctx.textAlign = "left"; + ctx.textBaseline = "bottom"; + ctx.fillText(label, screenX + r + 4, screenY - gap); + } + ctx.restore(); + } + }, [fftZoom, fftPanX, fftPanY, effectiveShowFft, fftClickInfo, shapeCols, shapeRows, fftCropDims]); + + // Clear FFT click info when virtual image changes (scan position, VI ROI, etc.) + React.useEffect(() => { + setFftClickInfo(null); + }, [virtualImageBytes]); + + // ───────────────────────────────────────────────────────────────────────── + // High-DPI Scale Bar UI Overlays + // ───────────────────────────────────────────────────────────────────────── + + // DP scale bar + crosshair + ROI overlay + profile line (high-DPI) + React.useEffect(() => { + if (!dpUiRef.current) return; + // Draw scale bar first (clears canvas) + const kUnit = kCalibrated ? "mrad" : "px"; + drawScaleBarHiDPI(dpUiRef.current, DPR, dpZoom, kPixelSize || 1, kUnit, detCols); + // Draw ROI overlay (circle, square, rect, annular) or point crosshair + if (roiMode === "point") { + drawDpCrosshairHiDPI(dpUiRef.current, DPR, localKCol, localKRow, dpZoom, dpPanX, dpPanY, detCols, detRows, isDraggingDP, roiColors); + } else { + drawRoiOverlayHiDPI( + dpUiRef.current, DPR, roiMode, + localKCol, localKRow, roiRadius, roiRadiusInner, roiWidth, roiHeight, + dpZoom, dpPanX, dpPanY, detCols, detRows, + isDraggingDP, isDraggingResize, isDraggingResizeInner, isHoveringResize, isHoveringResizeInner, + roiColors + ); + } + + // Profile line overlay + if (profileActive && profilePoints.length > 0) { + const canvas = dpUiRef.current; + const ctx = canvas.getContext("2d"); + if (ctx) { + ctx.save(); + ctx.scale(DPR, DPR); + const cssW = canvas.width / DPR; + const cssH = canvas.height / DPR; + const scaleX = cssW / detCols; + const scaleY = cssH / detRows; + const toScreenX = (col: number) => col * dpZoom * scaleX + dpPanX * scaleX; + const toScreenY = (row: number) => row * dpZoom * scaleY + dpPanY * scaleY; + + // Draw point A + const ax = toScreenX(profilePoints[0].col); + const ay = toScreenY(profilePoints[0].row); + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(ax, ay, 4, 0, Math.PI * 2); + ctx.fill(); + + if (profilePoints.length === 2) { + const bx = toScreenX(profilePoints[1].col); + const by = toScreenY(profilePoints[1].row); + + // Draw band when profile width > 1 + if (profileWidth > 1) { + const dc = profilePoints[1].col - profilePoints[0].col; + const dr = profilePoints[1].row - profilePoints[0].row; + const lineLen = Math.sqrt(dc * dc + dr * dr); + if (lineLen > 0) { + const halfW = (profileWidth - 1) / 2; + const perpR = -dc / lineLen * halfW; + const perpC = dr / lineLen * halfW; + ctx.fillStyle = themeColors.accent + "20"; + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1; + ctx.setLineDash([3, 3]); + ctx.beginPath(); + ctx.moveTo(toScreenX(profilePoints[0].col + perpC), toScreenY(profilePoints[0].row + perpR)); + ctx.lineTo(toScreenX(profilePoints[1].col + perpC), toScreenY(profilePoints[1].row + perpR)); + ctx.lineTo(toScreenX(profilePoints[1].col - perpC), toScreenY(profilePoints[1].row - perpR)); + ctx.lineTo(toScreenX(profilePoints[0].col - perpC), toScreenY(profilePoints[0].row - perpR)); + ctx.closePath(); + ctx.fill(); + ctx.stroke(); + ctx.setLineDash([]); + } + } + + // Draw line A->B + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.beginPath(); + ctx.moveTo(ax, ay); + ctx.lineTo(bx, by); + ctx.stroke(); + + // Draw point B + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(bx, by, 4, 0, Math.PI * 2); + ctx.fill(); + } + ctx.restore(); + } + } + + // Colorbar overlay — uses cached vmin/vmax from the expensive DP offscreen effect + if (showDpColorbar) { + const canvas = dpUiRef.current; + const ctx = canvas.getContext("2d"); + if (ctx) { + ctx.save(); + ctx.scale(DPR, DPR); + const cssW = canvas.width / DPR; + const cssH = canvas.height / DPR; + const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; + drawColorbar(ctx, cssW, cssH, lut, dpColorbarVminRef.current, dpColorbarVmaxRef.current, dpScaleMode === "log"); + ctx.restore(); + } + } + }, [dpZoom, dpPanX, dpPanY, kPixelSize, kCalibrated, detRows, detCols, roiMode, roiRadius, roiRadiusInner, roiWidth, roiHeight, localKCol, localKRow, isDraggingDP, isDraggingResize, isDraggingResizeInner, isHoveringResize, isHoveringResizeInner, + profileActive, profilePoints, profileWidth, themeColors, showDpColorbar, dpColormap, dpScaleMode, dpVminPct, dpVmaxPct, canvasSize, roiColors]); + + // VI scale bar + crosshair + ROI + profile lines (high-DPI) + React.useEffect(() => { + if (!viUiRef.current) return; + // Draw scale bar first (clears canvas) + drawScaleBarHiDPI(viUiRef.current, DPR, viZoom, pixelSize || 1, "Å", shapeCols); + // Draw crosshair only when ROI is off (ROI replaces the crosshair) + if (!viRoiMode || viRoiMode === "off") { + drawViPositionMarker(viUiRef.current, DPR, localPosRow, localPosCol, viZoom, viPanX, viPanY, shapeCols, shapeRows, isDraggingVI); + } else { + // Draw VI ROI instead of crosshair + drawViRoiOverlayHiDPI( + viUiRef.current, DPR, viRoiMode, + localViRoiCenterRow, localViRoiCenterCol, viRoiRadius || 5, viRoiWidth || 10, viRoiHeight || 10, + viZoom, viPanX, viPanY, shapeCols, shapeRows, + isDraggingViRoi, isDraggingViRoiResize, isHoveringViRoiResize + ); + } + // Draw VI profile lines + if (viProfileActive && viProfilePoints.length > 0) { + const canvas = viUiRef.current; + const ctx = canvas.getContext("2d"); + if (ctx) { + const cssW = canvas.width / DPR; + const cssH = canvas.height / DPR; + const scaleX = cssW / shapeCols; + const scaleY = cssH / shapeRows; + ctx.save(); + ctx.scale(DPR, DPR); + ctx.strokeStyle = "#a0f"; + ctx.lineWidth = 2; + ctx.shadowColor = "rgba(0,0,0,0.5)"; + ctx.shadowBlur = 2; + if (viProfilePoints.length >= 1) { + const p0 = viProfilePoints[0]; + const x0 = p0.col * viZoom * scaleX + viPanX * scaleX; + const y0 = p0.row * viZoom * scaleY + viPanY * scaleY; + ctx.beginPath(); + ctx.arc(x0, y0, 4, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = "#fff"; + ctx.fillText("1", x0 + 6, y0 - 6); + } + if (viProfilePoints.length === 2) { + const p0 = viProfilePoints[0], p1 = viProfilePoints[1]; + const x0 = p0.col * viZoom * scaleX + viPanX * scaleX; + const y0 = p0.row * viZoom * scaleY + viPanY * scaleY; + const x1 = p1.col * viZoom * scaleX + viPanX * scaleX; + const y1 = p1.row * viZoom * scaleY + viPanY * scaleY; + ctx.beginPath(); + ctx.moveTo(x0, y0); + ctx.lineTo(x1, y1); + ctx.stroke(); + ctx.beginPath(); + ctx.arc(x1, y1, 4, 0, Math.PI * 2); + ctx.fill(); + ctx.fillStyle = "#fff"; + ctx.fillText("2", x1 + 6, y1 - 6); + } + ctx.restore(); + } + } + }, [viZoom, viPanX, viPanY, pixelSize, shapeRows, shapeCols, localPosRow, localPosCol, isDraggingVI, + viRoiMode, localViRoiCenterRow, localViRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, + isDraggingViRoi, isDraggingViRoiResize, isHoveringViRoiResize, canvasSize, viProfileActive, viProfilePoints]); + + // ── DP Profile computation ── + React.useEffect(() => { + if (profilePoints.length === 2 && rawDpDataRef.current) { + const p0 = profilePoints[0], p1 = profilePoints[1]; + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, p0.row, p0.col, p1.row, p1.col, profileWidth)); + if (!profileActive) setProfileActive(true); + } else { + setProfileData(null); + } + }, [profilePoints, profileWidth, frameBytes]); + + // ── VI Profile computation ── + React.useEffect(() => { + if (viProfilePoints.length === 2 && rawViDataRef.current && shapeCols > 0 && shapeRows > 0) { + const p0 = viProfilePoints[0], p1 = viProfilePoints[1]; + setViProfileData(sampleLineProfile(rawViDataRef.current, shapeCols, shapeRows, p0.row, p0.col, p1.row, p1.col, 1)); + } else { + setViProfileData(null); + } + }, [viProfilePoints, virtualImageBytes, shapeCols, shapeRows]); + + // ── Profile sparkline rendering ── + React.useEffect(() => { + const canvas = profileCanvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + const cssW = canvasSize; + const cssH = profileHeight; + canvas.width = cssW * dpr; + canvas.height = cssH * dpr; + ctx.scale(dpr, dpr); + + const isDark = themeInfo.theme === "dark"; + ctx.fillStyle = isDark ? "#1a1a1a" : "#f0f0f0"; + ctx.fillRect(0, 0, cssW, cssH); + + if (!profileData || profileData.length < 2) { + ctx.font = "10px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#555" : "#999"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText("Click two points on the DP to draw a profile", cssW / 2, cssH / 2); + profileBaseImageRef.current = null; + profileLayoutRef.current = null; + return; + } + + const padLeft = 40; + const padRight = 8; + const padTop = 6; + const padBottom = 18; + const plotW = cssW - padLeft - padRight; + const plotH = cssH - padTop - padBottom; + + let gMin = Infinity, gMax = -Infinity; + for (let i = 0; i < profileData.length; i++) { + if (profileData[i] < gMin) gMin = profileData[i]; + if (profileData[i] > gMax) gMax = profileData[i]; + } + const range = gMax - gMin || 1; + + // X-axis: calibrated distance + let totalDist = profileData.length - 1; + let xUnit = "px"; + if (profilePoints.length === 2) { + const dx = profilePoints[1].col - profilePoints[0].col; + const dy = profilePoints[1].row - profilePoints[0].row; + const distPx = Math.sqrt(dx * dx + dy * dy); + if (kCalibrated && kPixelSize > 0) { + totalDist = distPx * kPixelSize; + xUnit = "mrad"; + } else { + totalDist = distPx; + } + } + + // Draw axes + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + ctx.beginPath(); + ctx.moveTo(padLeft, padTop); + ctx.lineTo(padLeft, padTop + plotH); + ctx.lineTo(padLeft + plotW, padTop + plotH); + ctx.stroke(); + + // Draw profile curve + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.beginPath(); + for (let i = 0; i < profileData.length; i++) { + const x = padLeft + (i / (profileData.length - 1)) * plotW; + const y = padTop + plotH - ((profileData[i] - gMin) / range) * plotH; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + + // Draw x-axis ticks + const tickY = padTop + plotH; + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + const idealTicks = Math.max(2, Math.floor(plotW / 70)); + const tickStep = roundToNiceValue(totalDist / idealTicks); + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textBaseline = "top"; + const ticks: number[] = []; + for (let v = 0; v <= totalDist + tickStep * 0.01; v += tickStep) { + if (v > totalDist * 1.001) break; + ticks.push(v); + } + for (let i = 0; i < ticks.length; i++) { + const v = ticks[i]; + const frac = totalDist > 0 ? v / totalDist : 0; + const x = padLeft + frac * plotW; + ctx.beginPath(); ctx.moveTo(x, tickY); ctx.lineTo(x, tickY + 3); ctx.stroke(); + ctx.textAlign = frac < 0.05 ? "left" : frac > 0.95 ? "right" : "center"; + const label = v % 1 === 0 ? v.toFixed(0) : v.toFixed(1); + ctx.fillText(i === ticks.length - 1 ? `${label} ${xUnit}` : label, x, tickY + 4); + } + + // Y-axis min/max labels (left margin) + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(gMax), 2, padTop); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(gMin), 2, padTop + plotH); + + // Save base image and layout for hover + profileBaseImageRef.current = ctx.getImageData(0, 0, canvas.width, canvas.height); + profileLayoutRef.current = { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit }; + }, [profileData, profilePoints, kPixelSize, kCalibrated, themeInfo.theme, themeColors.accent, canvasSize, profileHeight]); + + // DP Profile hover handlers + const handleProfileMouseMove = React.useCallback((e: React.MouseEvent) => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + const layout = profileLayoutRef.current; + if (!canvas || !base || !layout || !profileData) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit } = layout; + const range = gMax - gMin || 1; + + // Restore base image + ctx.putImageData(base, 0, 0); + + if (cssX < padLeft || cssX > padLeft + plotW) return; + const frac = (cssX - padLeft) / plotW; + + const dpr = window.devicePixelRatio || 1; + ctx.save(); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + + // Vertical crosshair + const isDark = themeInfo.theme === "dark"; + ctx.strokeStyle = isDark ? "rgba(255,255,255,0.3)" : "rgba(0,0,0,0.3)"; + ctx.lineWidth = 1; + ctx.setLineDash([2, 2]); + ctx.beginPath(); + ctx.moveTo(cssX, padTop); + ctx.lineTo(cssX, padTop + plotH); + ctx.stroke(); + ctx.setLineDash([]); + + // Dot on curve + value + const dataIdx = Math.min(profileData.length - 1, Math.max(0, Math.round(frac * (profileData.length - 1)))); + const val = profileData[dataIdx]; + const y = padTop + plotH - ((val - gMin) / range) * plotH; + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(cssX, y, 3, 0, Math.PI * 2); + ctx.fill(); + + // Value readout label + const dist = frac * totalDist; + const label = `${formatNumber(val)} @ ${dist.toFixed(1)} ${xUnit}`; + ctx.font = "bold 9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + const textW = ctx.measureText(label).width; + const labelX = Math.min(cssX + 6, padLeft + plotW - textW - 2); + const labelY = padTop + 2; + ctx.fillStyle = isDark ? "rgba(0,0,0,0.7)" : "rgba(255,255,255,0.8)"; + ctx.fillRect(labelX - 2, labelY - 1, textW + 4, 11); + ctx.fillStyle = isDark ? "#fff" : "#000"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(label, labelX, labelY); + + ctx.restore(); + }, [profileData, themeInfo.theme, themeColors.accent]); + + const handleProfileMouseLeave = React.useCallback(() => { + const canvas = profileCanvasRef.current; + const base = profileBaseImageRef.current; + if (!canvas || !base) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.putImageData(base, 0, 0); + }, []); + + // DP Profile resize handlers + React.useEffect(() => { + if (!isResizingProfile) return; + const handleMouseMove = (e: MouseEvent) => { + if (!profileResizeStart.current) return; + const deltaY = e.clientY - profileResizeStart.current.startY; + const newHeight = Math.max(40, Math.min(300, profileResizeStart.current.startHeight + deltaY)); + setProfileHeight(newHeight); + }; + const handleMouseUp = () => { + setIsResizingProfile(false); + profileResizeStart.current = null; + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingProfile]); + + // ── VI Profile sparkline rendering ── + React.useEffect(() => { + const canvas = viProfileCanvasRef.current; + if (!canvas) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const dpr = window.devicePixelRatio || 1; + const cssW = viCanvasWidth; + const cssH = viProfileHeight; + canvas.width = cssW * dpr; + canvas.height = cssH * dpr; + ctx.scale(dpr, dpr); + + const isDark = themeInfo.theme === "dark"; + ctx.fillStyle = isDark ? "#1a1a1a" : "#f0f0f0"; + ctx.fillRect(0, 0, cssW, cssH); + + if (!viProfileData || viProfileData.length < 2) { + ctx.font = "10px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#555" : "#999"; + ctx.textAlign = "center"; + ctx.textBaseline = "middle"; + ctx.fillText("Click two points on the VI to draw a profile", cssW / 2, cssH / 2); + viProfileBaseImageRef.current = null; + viProfileLayoutRef.current = null; + return; + } + + const padLeft = 40; + const padRight = 8; + const padTop = 6; + const padBottom = 18; + const plotW = cssW - padLeft - padRight; + const plotH = cssH - padTop - padBottom; + + let gMin = Infinity, gMax = -Infinity; + for (let i = 0; i < viProfileData.length; i++) { + if (viProfileData[i] < gMin) gMin = viProfileData[i]; + if (viProfileData[i] > gMax) gMax = viProfileData[i]; + } + const range = gMax - gMin || 1; + + // X-axis: calibrated distance + let totalDist = viProfileData.length - 1; + let xUnit = "px"; + if (viProfilePoints.length === 2 && pixelSize > 0) { + const dx = viProfilePoints[1].col - viProfilePoints[0].col; + const dy = viProfilePoints[1].row - viProfilePoints[0].row; + const distPx = Math.sqrt(dx * dx + dy * dy); + totalDist = distPx * pixelSize; + xUnit = pixelSize >= 10 ? "nm" : "Å"; + if (xUnit === "nm") totalDist /= 10; + } + + // Draw axes + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + ctx.beginPath(); + ctx.moveTo(padLeft, padTop); + ctx.lineTo(padLeft, padTop + plotH); + ctx.lineTo(padLeft + plotW, padTop + plotH); + ctx.stroke(); + + // Draw profile curve + ctx.strokeStyle = themeColors.accent; + ctx.lineWidth = 1.5; + ctx.beginPath(); + for (let i = 0; i < viProfileData.length; i++) { + const x = padLeft + (i / (viProfileData.length - 1)) * plotW; + const y = padTop + plotH - ((viProfileData[i] - gMin) / range) * plotH; + if (i === 0) ctx.moveTo(x, y); + else ctx.lineTo(x, y); + } + ctx.stroke(); + + // Draw x-axis ticks + const tickY = padTop + plotH; + ctx.strokeStyle = isDark ? "#555" : "#bbb"; + ctx.lineWidth = 0.5; + const idealTicks = Math.max(2, Math.floor(plotW / 70)); + const tickStep = roundToNiceValue(totalDist / idealTicks); + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textBaseline = "top"; + const ticks: number[] = []; + for (let v = 0; v <= totalDist + tickStep * 0.01; v += tickStep) { + if (v > totalDist * 1.001) break; + ticks.push(v); + } + for (let i = 0; i < ticks.length; i++) { + const v = ticks[i]; + const frac = totalDist > 0 ? v / totalDist : 0; + const x = padLeft + frac * plotW; + ctx.beginPath(); ctx.moveTo(x, tickY); ctx.lineTo(x, tickY + 3); ctx.stroke(); + ctx.textAlign = frac < 0.05 ? "left" : frac > 0.95 ? "right" : "center"; + const label = v % 1 === 0 ? v.toFixed(0) : v.toFixed(1); + ctx.fillText(i === ticks.length - 1 ? `${label} ${xUnit}` : label, x, tickY + 4); + } + + // Y-axis min/max labels (left margin) + ctx.font = "9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + ctx.fillStyle = isDark ? "#888" : "#666"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(formatNumber(gMax), 2, padTop); + ctx.textBaseline = "bottom"; + ctx.fillText(formatNumber(gMin), 2, padTop + plotH); + + // Save base image and layout for hover + viProfileBaseImageRef.current = ctx.getImageData(0, 0, canvas.width, canvas.height); + viProfileLayoutRef.current = { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit }; + }, [viProfileData, viProfilePoints, pixelSize, themeInfo.theme, themeColors.accent, viCanvasWidth, viProfileHeight]); + + // VI Profile hover handlers + const handleViProfileMouseMove = React.useCallback((e: React.MouseEvent) => { + const canvas = viProfileCanvasRef.current; + const base = viProfileBaseImageRef.current; + const layout = viProfileLayoutRef.current; + if (!canvas || !base || !layout || !viProfileData) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + + const rect = canvas.getBoundingClientRect(); + const cssX = e.clientX - rect.left; + const { padLeft, plotW, padTop, plotH, gMin, gMax, totalDist, xUnit } = layout; + const range = gMax - gMin || 1; + + // Restore base image + ctx.putImageData(base, 0, 0); + + if (cssX < padLeft || cssX > padLeft + plotW) return; + const frac = (cssX - padLeft) / plotW; + + const dpr = window.devicePixelRatio || 1; + ctx.save(); + ctx.setTransform(dpr, 0, 0, dpr, 0, 0); + + // Vertical crosshair + const isDark = themeInfo.theme === "dark"; + ctx.strokeStyle = isDark ? "rgba(255,255,255,0.3)" : "rgba(0,0,0,0.3)"; + ctx.lineWidth = 1; + ctx.setLineDash([2, 2]); + ctx.beginPath(); + ctx.moveTo(cssX, padTop); + ctx.lineTo(cssX, padTop + plotH); + ctx.stroke(); + ctx.setLineDash([]); + + // Dot on curve + value + const dataIdx = Math.min(viProfileData.length - 1, Math.max(0, Math.round(frac * (viProfileData.length - 1)))); + const val = viProfileData[dataIdx]; + const y = padTop + plotH - ((val - gMin) / range) * plotH; + ctx.fillStyle = themeColors.accent; + ctx.beginPath(); + ctx.arc(cssX, y, 3, 0, Math.PI * 2); + ctx.fill(); + + // Value readout label + const dist = frac * totalDist; + const label = `${formatNumber(val)} @ ${dist.toFixed(1)} ${xUnit}`; + ctx.font = "bold 9px -apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; + const textW = ctx.measureText(label).width; + const labelX = Math.min(cssX + 6, padLeft + plotW - textW - 2); + const labelY = padTop + 2; + ctx.fillStyle = isDark ? "rgba(0,0,0,0.7)" : "rgba(255,255,255,0.8)"; + ctx.fillRect(labelX - 2, labelY - 1, textW + 4, 11); + ctx.fillStyle = isDark ? "#fff" : "#000"; + ctx.textAlign = "left"; + ctx.textBaseline = "top"; + ctx.fillText(label, labelX, labelY); + + ctx.restore(); + }, [viProfileData, themeInfo.theme, themeColors.accent]); + + const handleViProfileMouseLeave = React.useCallback(() => { + const canvas = viProfileCanvasRef.current; + const base = viProfileBaseImageRef.current; + if (!canvas || !base) return; + const ctx = canvas.getContext("2d"); + if (!ctx) return; + ctx.putImageData(base, 0, 0); + }, []); + + // VI Profile resize handlers + React.useEffect(() => { + if (!isResizingViProfile) return; + const handleMouseMove = (e: MouseEvent) => { + if (!viProfileResizeStart.current) return; + const deltaY = e.clientY - viProfileResizeStart.current.startY; + const newHeight = Math.max(40, Math.min(300, viProfileResizeStart.current.startHeight + deltaY)); + setViProfileHeight(newHeight); + }; + const handleMouseUp = () => { + setIsResizingViProfile(false); + viProfileResizeStart.current = null; + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingViProfile]); + + // Generic zoom handler + const createZoomHandler = ( + setZoom: React.Dispatch>, + setPanX: React.Dispatch>, + setPanY: React.Dispatch>, + zoom: number, panX: number, panY: number, + canvasRef: React.RefObject, + locked: boolean = false, + ) => (e: React.WheelEvent) => { + if (locked) return; + e.preventDefault(); + const canvas = canvasRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const mouseX = (e.clientX - rect.left) * (canvas.width / rect.width); + const mouseY = (e.clientY - rect.top) * (canvas.height / rect.height); + const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; + const newZoom = Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, zoom * zoomFactor)); + const zoomRatio = newZoom / zoom; + setZoom(newZoom); + setPanX(mouseX - (mouseX - panX) * zoomRatio); + setPanY(mouseY - (mouseY - panY) * zoomRatio); + }; + + // ───────────────────────────────────────────────────────────────────────── + // Mouse Handlers + // ───────────────────────────────────────────────────────────────────────── + + // Helper: convert screen-pixel hit radius to image-pixel radius + // handleRadius=6 CSS px drawn, hit area ~10 CSS px → convert to image coords + const dpHitRadius = RESIZE_HIT_AREA_PX * Math.max(detCols, detRows) / canvasSize / dpZoom; + + // Helper: check if point is near the outer resize handle + const isNearResizeHandle = (imgX: number, imgY: number): boolean => { + if (roiMode === "rect") { + // For rectangle, check near bottom-right corner + const handleX = roiCenterCol + roiWidth / 2; + const handleY = roiCenterRow + roiHeight / 2; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + return dist < dpHitRadius; + } + if ((roiMode !== "circle" && roiMode !== "square" && roiMode !== "annular") || !roiRadius) return false; + const offset = roiMode === "square" ? roiRadius : roiRadius * CIRCLE_HANDLE_ANGLE; + const handleX = roiCenterCol + offset; + const handleY = roiCenterRow + offset; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + return dist < dpHitRadius; + }; + + // Helper: check if point is near the inner resize handle (annular mode only) + const isNearResizeHandleInner = (imgX: number, imgY: number): boolean => { + if (roiMode !== "annular" || !roiRadiusInner) return false; + const offset = roiRadiusInner * CIRCLE_HANDLE_ANGLE; + const handleX = roiCenterCol + offset; + const handleY = roiCenterRow + offset; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + return dist < dpHitRadius; + }; + + // Helper: check if point is near VI ROI resize handle (same logic as DP) + // Hit area is capped to avoid overlap with center for small ROIs + const viHitRadius = RESIZE_HIT_AREA_PX * Math.max(shapeRows, shapeCols) / canvasSize / viZoom; + const isNearViRoiResizeHandle = (imgX: number, imgY: number): boolean => { + if (!viRoiMode || viRoiMode === "off") return false; + if (viRoiMode === "rect") { + const halfH = (viRoiHeight || 10) / 2; + const halfW = (viRoiWidth || 10) / 2; + const handleX = localViRoiCenterRow + halfH; + const handleY = localViRoiCenterCol + halfW; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + const cornerDist = Math.sqrt(halfW ** 2 + halfH ** 2); + const hitArea = Math.min(viHitRadius, cornerDist * 0.5); + return dist < hitArea; + } + if (viRoiMode === "circle" || viRoiMode === "square") { + const radius = viRoiRadius || 5; + const offset = viRoiMode === "square" ? radius : radius * CIRCLE_HANDLE_ANGLE; + const handleX = localViRoiCenterRow + offset; + const handleY = localViRoiCenterCol + offset; + const dist = Math.sqrt((imgX - handleX) ** 2 + (imgY - handleY) ** 2); + // Cap hit area to 50% of radius so center remains draggable + const hitArea = Math.min(viHitRadius, radius * 0.5); + return dist < hitArea; + } + return false; + }; + + // Helper: check if point is inside the DP ROI area + const isInsideDpRoi = (imgX: number, imgY: number): boolean => { + if (roiMode === "point") return false; + const dx = imgX - roiCenterCol; + const dy = imgY - roiCenterRow; + if (roiMode === "circle") return Math.sqrt(dx * dx + dy * dy) <= (roiRadius || 5); + if (roiMode === "square") return Math.abs(dx) <= (roiRadius || 5) && Math.abs(dy) <= (roiRadius || 5); + if (roiMode === "annular") { const d = Math.sqrt(dx * dx + dy * dy); return d <= (roiRadius || 20) && d >= (roiRadiusInner || 5); } + if (roiMode === "rect") return Math.abs(dx) <= (roiWidth || 10) / 2 && Math.abs(dy) <= (roiHeight || 10) / 2; + return false; + }; + + // Helper: check if point is inside the VI ROI area + const isInsideViRoi = (imgX: number, imgY: number): boolean => { + if (!viRoiMode || viRoiMode === "off") return false; + const dx = imgY - localViRoiCenterCol; + const dy = imgX - localViRoiCenterRow; + if (viRoiMode === "circle") return Math.sqrt(dx * dx + dy * dy) <= (viRoiRadius || 5); + if (viRoiMode === "square") return Math.abs(dx) <= (viRoiRadius || 5) && Math.abs(dy) <= (viRoiRadius || 5); + if (viRoiMode === "rect") return Math.abs(dx) <= (viRoiWidth || 10) / 2 && Math.abs(dy) <= (viRoiHeight || 10) / 2; + return false; + }; + + // Mouse handlers + const handleDpMouseDown = (e: React.MouseEvent) => { + if (profileActive && lockProfile) return; + if (!profileActive && lockRoi) return; + dpClickStartRef.current = { x: e.clientX, y: e.clientY }; + const canvas = dpOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenX - dpPanX) / dpZoom; + const imgY = (screenY - dpPanY) / dpZoom; + + // When profile mode is active, use profile interactions only + if (profileActive) { + if (profilePoints.length === 2) { + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const hitRadius = 10 / dpZoom; + const d0 = Math.sqrt((imgX - p0.col) ** 2 + (imgY - p0.row) ** 2); + const d1 = Math.sqrt((imgX - p1.col) ** 2 + (imgY - p1.row) ** 2); + if (d0 <= hitRadius || d1 <= hitRadius) { + setDraggingDpProfileEndpoint(d0 <= d1 ? 0 : 1); + setIsDraggingDP(false); + return; + } + if (pointToSegmentDistance(imgX, imgY, p0.col, p0.row, p1.col, p1.row) <= hitRadius) { + setIsDraggingDpProfileLine(true); + dpProfileDragStartRef.current = { + row: imgY, + col: imgX, + p0: { row: p0.row, col: p0.col }, + p1: { row: p1.row, col: p1.col }, + }; + setIsDraggingDP(false); + return; + } + } + setIsDraggingDP(false); + return; + } + + // Check if clicking on resize handle (inner first, then outer) + if (isNearResizeHandleInner(imgX, imgY)) { + setIsDraggingResizeInner(true); + return; + } + if (isNearResizeHandle(imgX, imgY)) { + e.preventDefault(); + resizeAspectRef.current = roiMode === "rect" && roiWidth > 0 && roiHeight > 0 ? roiWidth / roiHeight : null; + setIsDraggingResize(true); + return; + } + + setIsDraggingDP(true); + // If clicking inside the ROI, drag with offset (grab-and-drag) + if (roiMode !== "off" && roiMode !== "point" && isInsideDpRoi(imgX, imgY)) { + dpDragOffsetRef.current = { dRow: imgY - roiCenterRow, dCol: imgX - roiCenterCol }; + return; + } + // Clicking outside ROI — teleport center to click position + dpDragOffsetRef.current = { dRow: 0, dCol: 0 }; + setLocalKCol(imgX); setLocalKRow(imgY); + // Use compound roi_center trait [row, col] - single observer fires in Python + const newCol = Math.round(Math.max(0, Math.min(detCols - 1, imgX))); + const newRow = Math.round(Math.max(0, Math.min(detRows - 1, imgY))); + model.set("roi_active", true); + model.set("roi_center", [newRow, newCol]); + model.save_changes(); + }; + + const handleDpMouseMove = (e: React.MouseEvent) => { + const canvas = dpOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenX - dpPanX) / dpZoom; + const imgY = (screenY - dpPanY) / dpZoom; + + // Fast path: skip cursor readout during any active drag — avoids setCursorInfo re-renders + const anyDrag = isDraggingDP || isDraggingResize || isDraggingResizeInner + || draggingDpProfileEndpoint !== null || isDraggingDpProfileLine; + + // Cursor readout: look up raw DP value at pixel position + if (!anyDrag) { + const pxCol = Math.floor(imgX); + const pxRow = Math.floor(imgY); + if (pxCol >= 0 && pxCol < detCols && pxRow >= 0 && pxRow < detRows && frameBytes) { + const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; + const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + const raw = new Float32Array(sourceBytes.buffer, sourceBytes.byteOffset, sourceBytes.byteLength / 4); + setCursorInfo({ row: pxRow, col: pxCol, value: raw[pxRow * detCols + pxCol], panel: "DP" }); + } else { + setCursorInfo(null); + } + } + + if (profileActive && lockProfile) return; + + if (profileActive && profilePoints.length === 2) { + const p0 = profilePoints[0]; + const p1 = profilePoints[1]; + const hitRadius = 10 / dpZoom; + const d0 = Math.sqrt((imgX - p0.col) ** 2 + (imgY - p0.row) ** 2); + const d1 = Math.sqrt((imgX - p1.col) ** 2 + (imgY - p1.row) ** 2); + if (draggingDpProfileEndpoint !== null) { + if (!rawDpDataRef.current) return; + const clampedRow = Math.max(0, Math.min(detRows - 1, imgY)); + const clampedCol = Math.max(0, Math.min(detCols - 1, imgX)); + const next = [ + draggingDpProfileEndpoint === 0 ? { row: clampedRow, col: clampedCol } : profilePoints[0], + draggingDpProfileEndpoint === 1 ? { row: clampedRow, col: clampedCol } : profilePoints[1], + ]; + setProfileLine(next); + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, next[0].row, next[0].col, next[1].row, next[1].col, profileWidth)); + return; + } + if (isDraggingDpProfileLine && dpProfileDragStartRef.current) { + if (!rawDpDataRef.current) return; + const drag = dpProfileDragStartRef.current; + let deltaRow = imgY - drag.row; + let deltaCol = imgX - drag.col; + const minRow = Math.min(drag.p0.row, drag.p1.row); + const maxRow = Math.max(drag.p0.row, drag.p1.row); + const minCol = Math.min(drag.p0.col, drag.p1.col); + const maxCol = Math.max(drag.p0.col, drag.p1.col); + deltaRow = Math.max(deltaRow, -minRow); + deltaRow = Math.min(deltaRow, (detRows - 1) - maxRow); + deltaCol = Math.max(deltaCol, -minCol); + deltaCol = Math.min(deltaCol, (detCols - 1) - maxCol); + const next = [ + { row: drag.p0.row + deltaRow, col: drag.p0.col + deltaCol }, + { row: drag.p1.row + deltaRow, col: drag.p1.col + deltaCol }, + ]; + setProfileLine(next); + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, next[0].row, next[0].col, next[1].row, next[1].col, profileWidth)); + return; + } + const nextHoveredEndpoint: 0 | 1 | null = d0 <= hitRadius ? 0 : d1 <= hitRadius ? 1 : null; + const nextHoverLine = nextHoveredEndpoint === null && pointToSegmentDistance(imgX, imgY, p0.col, p0.row, p1.col, p1.row) <= hitRadius; + setHoveredDpProfileEndpoint(nextHoveredEndpoint); + setIsHoveringDpProfileLine(nextHoverLine); + return; + } else { + if (hoveredDpProfileEndpoint !== null) setHoveredDpProfileEndpoint(null); + if (isHoveringDpProfileLine) setIsHoveringDpProfileLine(false); + } + + // Handle inner resize dragging (annular mode) + if (isDraggingResizeInner) { + if (lockRoi) return; + const dx = Math.abs(imgX - roiCenterCol); + const dy = Math.abs(imgY - roiCenterRow); + const newRadius = Math.sqrt(dx ** 2 + dy ** 2); + // Inner radius must be less than outer radius + setRoiRadiusInner(Math.max(1, Math.min(roiRadius - 1, Math.round(newRadius)))); + return; + } + + // Handle outer resize dragging - use model state center, not local values + if (isDraggingResize) { + if (lockRoi) return; + const dx = Math.abs(imgX - roiCenterCol); + const dy = Math.abs(imgY - roiCenterRow); + if (roiMode === "rect") { + let newW = Math.max(2, Math.round(dx * 2)); + let newH = Math.max(2, Math.round(dy * 2)); + if (e.shiftKey && resizeAspectRef.current != null) { + const aspect = resizeAspectRef.current; + if (newW / newH > aspect) newH = Math.max(2, Math.round(newW / aspect)); + else newW = Math.max(2, Math.round(newH * aspect)); + } + setRoiWidth(newW); + setRoiHeight(newH); + } else { + const newRadius = roiMode === "square" ? Math.max(dx, dy) : Math.sqrt(dx ** 2 + dy ** 2); + // For annular mode, outer radius must be greater than inner radius + const minRadius = roiMode === "annular" ? (roiRadiusInner || 0) + 1 : 1; + setRoiRadius(Math.max(minRadius, Math.round(newRadius))); + } + return; + } + + // Check hover state for resize handles + if (!isDraggingDP) { + if (!lockRoi) { + setIsHoveringResizeInner(isNearResizeHandleInner(imgX, imgY)); + setIsHoveringResize(isNearResizeHandle(imgX, imgY)); + } else { + setIsHoveringResizeInner(false); + setIsHoveringResize(false); + } + return; + } + + if (lockRoi) return; + const centerCol = imgX - dpDragOffsetRef.current.dCol; + const centerRow = imgY - dpDragOffsetRef.current.dRow; + setLocalKCol(centerCol); setLocalKRow(centerRow); + // Use compound roi_center trait [row, col] - single observer fires in Python + const newCol = Math.round(Math.max(0, Math.min(detCols - 1, centerCol))); + const newRow = Math.round(Math.max(0, Math.min(detRows - 1, centerRow))); + model.set("roi_center", [newRow, newCol]); + model.save_changes(); + }; + + const handleDpMouseUp = (e: React.MouseEvent) => { + if (draggingDpProfileEndpoint !== null || isDraggingDpProfileLine) { + setDraggingDpProfileEndpoint(null); + setIsDraggingDpProfileLine(false); + dpProfileDragStartRef.current = null; + dpClickStartRef.current = null; + setIsDraggingDP(false); + setIsDraggingResize(false); + setIsDraggingResizeInner(false); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + return; + } + + // Profile click capture + if (profileActive && dpClickStartRef.current) { + const dx = e.clientX - dpClickStartRef.current.x; + const dy = e.clientY - dpClickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + const canvas = dpOverlayRef.current; + if (canvas && rawDpDataRef.current) { + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgCol = (screenX - dpPanX) / dpZoom; + const imgRow = (screenY - dpPanY) / dpZoom; + if (imgCol >= 0 && imgCol < detCols && imgRow >= 0 && imgRow < detRows) { + const pt = { row: imgRow, col: imgCol }; + if (profilePoints.length === 0 || profilePoints.length === 2) { + setProfileLine([pt]); + setProfileData(null); + } else { + const p0 = profilePoints[0]; + setProfileLine([p0, pt]); + setProfileData(sampleLineProfile(rawDpDataRef.current, detCols, detRows, p0.row, p0.col, pt.row, pt.col, profileWidth)); + } + } + } + } + } + dpClickStartRef.current = null; + setIsDraggingDP(false); setIsDraggingResize(false); setIsDraggingResizeInner(false); + setDraggingDpProfileEndpoint(null); + setIsDraggingDpProfileLine(false); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + dpProfileDragStartRef.current = null; + }; + const handleDpMouseLeave = () => { + dpClickStartRef.current = null; + setIsDraggingDP(false); setIsDraggingResize(false); setIsDraggingResizeInner(false); + setDraggingDpProfileEndpoint(null); + setIsDraggingDpProfileLine(false); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + dpProfileDragStartRef.current = null; + setIsHoveringResize(false); setIsHoveringResizeInner(false); + setCursorInfo(prev => prev?.panel === "DP" ? null : prev); + }; + const handleDpDoubleClick = () => { + if (lockView) return; + setDpZoom(1); + setDpPanX(0); + setDpPanY(0); + }; + + const handleViMouseDown = (e: React.MouseEvent) => { + if (viProfileActive && lockProfile) return; + const canvas = virtualOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenY - viPanY) / viZoom; + const imgY = (screenX - viPanX) / viZoom; + + // VI Profile mode - click to set points + if (viProfileActive) { + viClickStartRef.current = { x: screenX, y: screenY }; + if (viProfilePoints.length === 2) { + const p0 = viProfilePoints[0]; + const p1 = viProfilePoints[1]; + const hitRadius = 10 / viZoom; + const d0 = Math.sqrt((imgY - p0.col) ** 2 + (imgX - p0.row) ** 2); + const d1 = Math.sqrt((imgY - p1.col) ** 2 + (imgX - p1.row) ** 2); + if (d0 <= hitRadius || d1 <= hitRadius) { + setDraggingViProfileEndpoint(d0 <= d1 ? 0 : 1); + setIsDraggingVI(false); + return; + } + if (pointToSegmentDistance(imgY, imgX, p0.col, p0.row, p1.col, p1.row) <= hitRadius) { + setIsDraggingViProfileLine(true); + viProfileDragStartRef.current = { + row: imgX, + col: imgY, + p0: { row: p0.row, col: p0.col }, + p1: { row: p1.row, col: p1.col }, + }; + setIsDraggingVI(false); + return; + } + } + return; + } + + // Check if VI ROI mode is active - same logic as DP + if (viRoiMode && viRoiMode !== "off") { + if (lockRoi) return; + // Check if clicking on resize handle + if (isNearViRoiResizeHandle(imgX, imgY)) { + setIsDraggingViRoiResize(true); + return; + } + + // Grab-and-drag if clicking inside VI ROI, otherwise teleport + setIsDraggingViRoi(true); + if (isInsideViRoi(imgX, imgY)) { + viRoiDragOffsetRef.current = { dRow: imgX - localViRoiCenterRow, dCol: imgY - localViRoiCenterCol }; + } else { + viRoiDragOffsetRef.current = { dRow: 0, dCol: 0 }; + setLocalViRoiCenterRow(imgX); + setLocalViRoiCenterCol(imgY); + setViRoiCenterRow(Math.round(Math.max(0, Math.min(shapeRows - 1, imgX)))); + setViRoiCenterCol(Math.round(Math.max(0, Math.min(shapeCols - 1, imgY)))); + } + return; + } + + // Regular position selection (when ROI is off) + if (lockNavigation || lockVirtual) return; + setIsDraggingVI(true); + setLocalPosRow(imgX); setLocalPosCol(imgY); + // Batch X and Y updates into a single sync + const newX = Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))); + const newY = Math.round(Math.max(0, Math.min(shapeCols - 1, imgY))); + model.set("pos_row", newX); + model.set("pos_col", newY); + model.save_changes(); + }; + + const handleViMouseMove = (e: React.MouseEvent) => { + const canvas = virtualOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const screenX = (e.clientX - rect.left) * (canvas.width / rect.width); + const screenY = (e.clientY - rect.top) * (canvas.height / rect.height); + const imgX = (screenY - viPanY) / viZoom; + const imgY = (screenX - viPanX) / viZoom; + + // Fast path: skip cursor readout during any active drag — avoids setCursorInfo re-renders + const anyViDrag = isDraggingVI || isDraggingViRoi || isDraggingViRoiResize + || draggingViProfileEndpoint !== null || isDraggingViProfileLine; + + // Cursor readout: look up raw VI value at pixel position + // imgX = row, imgY = col (swapped coordinate convention) + if (!anyViDrag) { + const pxRow = Math.floor(imgX); + const pxCol = Math.floor(imgY); + if (pxRow >= 0 && pxRow < shapeRows && pxCol >= 0 && pxCol < shapeCols && rawVirtualImageRef.current) { + const raw = rawVirtualImageRef.current; + setCursorInfo({ row: pxRow, col: pxCol, value: raw[pxRow * shapeCols + pxCol], panel: "VI" }); + } else { + setCursorInfo(prev => prev?.panel === "VI" ? null : prev); + } + } + + if (viProfileActive && lockProfile) return; + + if (viProfileActive && viProfilePoints.length === 2) { + const p0 = viProfilePoints[0]; + const p1 = viProfilePoints[1]; + const hitRadius = 10 / viZoom; + const d0 = Math.sqrt((imgY - p0.col) ** 2 + (imgX - p0.row) ** 2); + const d1 = Math.sqrt((imgY - p1.col) ** 2 + (imgX - p1.row) ** 2); + if (draggingViProfileEndpoint !== null) { + const clampedRow = Math.max(0, Math.min(shapeRows - 1, imgX)); + const clampedCol = Math.max(0, Math.min(shapeCols - 1, imgY)); + const next = [ + draggingViProfileEndpoint === 0 ? { row: clampedRow, col: clampedCol } : viProfilePoints[0], + draggingViProfileEndpoint === 1 ? { row: clampedRow, col: clampedCol } : viProfilePoints[1], + ]; + setViProfilePoints(next); + return; + } + if (isDraggingViProfileLine && viProfileDragStartRef.current) { + const drag = viProfileDragStartRef.current; + let deltaRow = imgX - drag.row; + let deltaCol = imgY - drag.col; + const minRow = Math.min(drag.p0.row, drag.p1.row); + const maxRow = Math.max(drag.p0.row, drag.p1.row); + const minCol = Math.min(drag.p0.col, drag.p1.col); + const maxCol = Math.max(drag.p0.col, drag.p1.col); + deltaRow = Math.max(deltaRow, -minRow); + deltaRow = Math.min(deltaRow, (shapeRows - 1) - maxRow); + deltaCol = Math.max(deltaCol, -minCol); + deltaCol = Math.min(deltaCol, (shapeCols - 1) - maxCol); + const next = [ + { row: drag.p0.row + deltaRow, col: drag.p0.col + deltaCol }, + { row: drag.p1.row + deltaRow, col: drag.p1.col + deltaCol }, + ]; + setViProfilePoints(next); + return; + } + const nextHoveredEndpoint: 0 | 1 | null = d0 <= hitRadius ? 0 : d1 <= hitRadius ? 1 : null; + const nextHoverLine = nextHoveredEndpoint === null && pointToSegmentDistance(imgY, imgX, p0.col, p0.row, p1.col, p1.row) <= hitRadius; + setHoveredViProfileEndpoint(nextHoveredEndpoint); + setIsHoveringViProfileLine(nextHoverLine); + return; + } else { + if (hoveredViProfileEndpoint !== null) setHoveredViProfileEndpoint(null); + if (isHoveringViProfileLine) setIsHoveringViProfileLine(false); + } + + // Handle VI ROI resize dragging (same pattern as DP) + if (isDraggingViRoiResize) { + if (lockRoi) return; + const dx = Math.abs(imgX - localViRoiCenterRow); + const dy = Math.abs(imgY - localViRoiCenterCol); + if (viRoiMode === "rect") { + setViRoiWidth(Math.max(2, Math.round(dy * 2))); + setViRoiHeight(Math.max(2, Math.round(dx * 2))); + } else if (viRoiMode === "square") { + const newHalfSize = Math.max(dx, dy); + setViRoiRadius(Math.max(1, Math.round(newHalfSize))); + } else { + // circle + const newRadius = Math.sqrt(dx ** 2 + dy ** 2); + setViRoiRadius(Math.max(1, Math.round(newRadius))); + } + return; + } + + // Check hover state for resize handles (same as DP) + if (!isDraggingViRoi) { + if (!lockRoi) { + setIsHoveringViRoiResize(isNearViRoiResizeHandle(imgX, imgY)); + } else { + setIsHoveringViRoiResize(false); + } + if (viRoiMode && viRoiMode !== "off") return; // Don't update position when ROI active + } + + // Handle VI ROI center dragging (same as DP — with offset) + if (isDraggingViRoi) { + if (lockRoi) return; + const centerRow = imgX - viRoiDragOffsetRef.current.dRow; + const centerCol = imgY - viRoiDragOffsetRef.current.dCol; + setLocalViRoiCenterRow(centerRow); + setLocalViRoiCenterCol(centerCol); + // Batch VI ROI center updates + const newViX = Math.round(Math.max(0, Math.min(shapeRows - 1, centerRow))); + const newViY = Math.round(Math.max(0, Math.min(shapeCols - 1, centerCol))); + model.set("vi_roi_center_row", newViX); + model.set("vi_roi_center_col", newViY); + model.save_changes(); + return; + } + + // Handle regular position dragging (when ROI is off) + if (!isDraggingVI) return; + if (lockNavigation || lockVirtual) return; + setLocalPosRow(imgX); setLocalPosCol(imgY); + // Batch position updates into a single sync + const newX = Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))); + const newY = Math.round(Math.max(0, Math.min(shapeCols - 1, imgY))); + model.set("pos_row", newX); + model.set("pos_col", newY); + model.save_changes(); + }; + + const handleViMouseUp = (e: React.MouseEvent) => { + if (draggingViProfileEndpoint !== null || isDraggingViProfileLine) { + setDraggingViProfileEndpoint(null); + setIsDraggingViProfileLine(false); + viProfileDragStartRef.current = null; + viClickStartRef.current = null; + setIsDraggingVI(false); + setIsDraggingViRoi(false); + setIsDraggingViRoiResize(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + return; + } + + // VI Profile mode - complete point selection + if (viProfileActive && viClickStartRef.current) { + const canvas = virtualOverlayRef.current; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const endX = (e.clientX - rect.left) * (canvas.width / rect.width); + const endY = (e.clientY - rect.top) * (canvas.height / rect.height); + const dx = endX - viClickStartRef.current.x; + const dy = endY - viClickStartRef.current.y; + const wasDrag = Math.sqrt(dx * dx + dy * dy) > 3; + + if (!wasDrag) { + // Click to add point + const imgX = (endY - viPanY) / viZoom; + const imgY = (endX - viPanX) / viZoom; + const pt = { row: Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))), col: Math.round(Math.max(0, Math.min(shapeCols - 1, imgY))) }; + if (viProfilePoints.length < 2) { + setViProfilePoints([...viProfilePoints, pt]); + } else { + setViProfilePoints([pt]); + } + } + } + viClickStartRef.current = null; + } + + setDraggingViProfileEndpoint(null); + setIsDraggingViProfileLine(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + viProfileDragStartRef.current = null; + setIsDraggingVI(false); + setIsDraggingViRoi(false); + setIsDraggingViRoiResize(false); + }; + const handleViMouseLeave = () => { + viClickStartRef.current = null; + setDraggingViProfileEndpoint(null); + setIsDraggingViProfileLine(false); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + viProfileDragStartRef.current = null; + setIsDraggingVI(false); + setIsDraggingViRoi(false); + setIsDraggingViRoiResize(false); + setIsHoveringViRoiResize(false); + setCursorInfo(prev => prev?.panel === "VI" ? null : prev); + }; + const handleViDoubleClick = () => { + if (lockView || lockVirtual) return; + setViZoom(1); + setViPanX(0); + setViPanY(0); + }; + const handleFftDoubleClick = () => { + if (lockView || lockFft) return; + setFftZoom(1); + setFftPanX(0); + setFftPanY(0); + setFftClickInfo(null); + }; + + // FFT drag-to-pan handlers + const handleFftMouseDown = (e: React.MouseEvent) => { + if (lockView || lockFft) return; + fftClickStartRef.current = { x: e.clientX, y: e.clientY }; + setIsDraggingFFT(true); + setFftDragStart({ x: e.clientX, y: e.clientY, panX: fftPanX, panY: fftPanY }); + }; + + const handleFftMouseMove = (e: React.MouseEvent) => { + if (lockView || lockFft) return; + if (!isDraggingFFT || !fftDragStart) return; + const canvas = fftOverlayRef.current; + if (!canvas) return; + const rect = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect.width; + const scaleY = canvas.height / rect.height; + const dx = (e.clientX - fftDragStart.x) * scaleX; + const dy = (e.clientY - fftDragStart.y) * scaleY; + setFftPanX(fftDragStart.panX + dx); + setFftPanY(fftDragStart.panY + dy); + }; + + const handleFftMouseUp = (e: React.MouseEvent) => { + // Click detection for d-spacing measurement + if (fftClickStartRef.current) { + const dx = e.clientX - fftClickStartRef.current.x; + const dy = e.clientY - fftClickStartRef.current.y; + if (Math.sqrt(dx * dx + dy * dy) < 3) { + // Convert screen coords to FFT image coords + const canvas = fftOverlayRef.current; + if (canvas) { + const rect = canvas.getBoundingClientRect(); + const scaleX = canvas.width / rect.width; + const scaleY = canvas.height / rect.height; + const canvasX = (e.clientX - rect.left) * scaleX; + const canvasY = (e.clientY - rect.top) * scaleY; + const fftW = fftCropDims?.fftWidth ?? shapeCols; + const fftH = fftCropDims?.fftHeight ?? shapeRows; + // Reverse the zoom/pan transform: canvas coords -> image coords + // The FFT render uses: ctx.translate(fftPanX, fftPanY); ctx.scale(fftZoom, fftZoom); ctx.drawImage(offscreen, 0, 0) + let imgCol = (canvasX - fftPanX) / fftZoom; + let imgRow = (canvasY - fftPanY) / fftZoom; + // Bounds check + if (imgCol >= 0 && imgCol < fftW && imgRow >= 0 && imgRow < fftH) { + // Snap to nearest peak in FFT magnitude + if (fftMagCacheRef.current) { + const snapped = findFFTPeak(fftMagCacheRef.current, fftW, fftH, imgCol, imgRow, FFT_SNAP_RADIUS); + imgCol = snapped.col; + imgRow = snapped.row; + } + const halfW = Math.floor(fftW / 2); + const halfH = Math.floor(fftH / 2); + const dcol = imgCol - halfW; + const drow = imgRow - halfH; + const distPx = Math.sqrt(dcol * dcol + drow * drow); + if (distPx < 1) { + setFftClickInfo(null); // Clicked on DC center + } else { + let spatialFreq: number | null = null; + let dSpacing: number | null = null; + if (pixelSize > 0) { + const paddedW = nextPow2(fftW); + const paddedH = nextPow2(fftH); + const binC = ((Math.round(imgCol) - halfW) % fftW + fftW) % fftW; + const binR = ((Math.round(imgRow) - halfH) % fftH + fftH) % fftH; + const freqC = binC <= paddedW / 2 ? binC / (paddedW * pixelSize) : (binC - paddedW) / (paddedW * pixelSize); + const freqR = binR <= paddedH / 2 ? binR / (paddedH * pixelSize) : (binR - paddedH) / (paddedH * pixelSize); + spatialFreq = Math.sqrt(freqC * freqC + freqR * freqR); + dSpacing = spatialFreq > 0 ? 1 / spatialFreq : null; + } + setFftClickInfo({ row: imgRow, col: imgCol, distPx, spatialFreq, dSpacing }); + } + } + } + } + fftClickStartRef.current = null; + } + setIsDraggingFFT(false); + setFftDragStart(null); + }; + const handleFftMouseLeave = () => { fftClickStartRef.current = null; setIsDraggingFFT(false); setFftDragStart(null); }; + + // ── Canvas resize handlers ── + const handleCanvasResizeStart = (e: React.MouseEvent) => { + if (lockView) return; + e.stopPropagation(); + e.preventDefault(); + setIsResizingCanvas(true); + setResizeCanvasStart({ x: e.clientX, y: e.clientY, size: canvasSize }); + }; + + React.useEffect(() => { + if (!isResizingCanvas) return; + let rafId = 0; + let latestSize = resizeCanvasStart ? resizeCanvasStart.size : canvasSize; + const handleMouseMove = (e: MouseEvent) => { + if (!resizeCanvasStart) return; + const delta = Math.max(e.clientX - resizeCanvasStart.x, e.clientY - resizeCanvasStart.y); + latestSize = Math.max(CANVAS_SIZE, resizeCanvasStart.size + delta); + if (!rafId) { + rafId = requestAnimationFrame(() => { + rafId = 0; + setCanvasSize(latestSize); + }); + } + }; + const handleMouseUp = () => { + cancelAnimationFrame(rafId); + setCanvasSize(latestSize); + setIsResizingCanvas(false); + setResizeCanvasStart(null); + }; + document.addEventListener("mousemove", handleMouseMove); + document.addEventListener("mouseup", handleMouseUp); + return () => { + cancelAnimationFrame(rafId); + document.removeEventListener("mousemove", handleMouseMove); + document.removeEventListener("mouseup", handleMouseUp); + }; + }, [isResizingCanvas, resizeCanvasStart]); + + // ───────────────────────────────────────────────────────────────────────── + // Render + // ───────────────────────────────────────────────────────────────────────── + + // Export DP handler + const handleExportDP = async () => { + if (lockExport) return; + const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); + const zip = new JSZip(); + const metadata = { + metadata_version: "1.0", + widget_name: "Show4DSTEM", + widget_version: widgetVersion || "unknown", + exported_at: new Date().toISOString(), + view: "diffraction", + format: "zip", + export_kind: "single_view_png_zip", + position: { row: posRow, col: posCol }, + frame_idx: frameIdx, + n_frames: nFrames, + scan_shape: { rows: shapeRows, cols: shapeCols }, + detector_shape: { rows: detRows, cols: detCols }, + roi: { + active: roiMode !== "off", + mode: roiMode, + center_row: roiCenterRow, + center_col: roiCenterCol, + radius: roiRadius, + radius_inner: roiRadiusInner, + width: roiWidth, + height: roiHeight, + }, + vi_roi: { + mode: viRoiMode, + center_row: viRoiCenterRow, + center_col: viRoiCenterCol, + radius: viRoiRadius, + width: viRoiWidth, + height: viRoiHeight, + }, + calibration: { + pixel_size_angstrom: pixelSize, + pixel_size_unit: "Å/px", + k_pixel_size: kPixelSize, + k_pixel_size_unit: kCalibrated ? "mrad/px" : "px/px", + k_calibrated: kCalibrated, + center_row: centerRow, + center_col: centerCol, + bf_radius: bfRadius, + }, + display: { + diffraction: { + colormap: dpColormap, + scale_mode: dpScaleMode, + vmin_pct: dpVminPct, + vmax_pct: dpVmaxPct, + }, + }, + }; + zip.file("metadata.json", JSON.stringify(metadata, null, 2)); + const canvasToBlob = (canvas: HTMLCanvasElement): Promise => new Promise((resolve) => canvas.toBlob((blob) => resolve(blob!), 'image/png')); + if (dpCanvasRef.current) zip.file("diffraction_pattern.png", await canvasToBlob(dpCanvasRef.current)); + const zipBlob = await zip.generateAsync({ type: "blob" }); + downloadBlob(zipBlob, `dp_export_${timestamp}.zip`); + }; + + // Export VI handler + const handleExportVI = async () => { + if (lockExport) return; + const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); + const zip = new JSZip(); + const metadata = { + metadata_version: "1.0", + widget_name: "Show4DSTEM", + widget_version: widgetVersion || "unknown", + exported_at: new Date().toISOString(), + view: "all", + format: "zip", + export_kind: "multi_panel_png_zip", + position: { row: posRow, col: posCol }, + frame_idx: frameIdx, + n_frames: nFrames, + scan_shape: { rows: shapeRows, cols: shapeCols }, + detector_shape: { rows: detRows, cols: detCols }, + roi: { + active: roiMode !== "off", + mode: roiMode, + center_row: roiCenterRow, + center_col: roiCenterCol, + radius: roiRadius, + radius_inner: roiRadiusInner, + width: roiWidth, + height: roiHeight, + }, + vi_roi: { + mode: viRoiMode, + center_row: viRoiCenterRow, + center_col: viRoiCenterCol, + radius: viRoiRadius, + width: viRoiWidth, + height: viRoiHeight, + }, + calibration: { + pixel_size_angstrom: pixelSize, + pixel_size_unit: "Å/px", + k_pixel_size: kPixelSize, + k_pixel_size_unit: kCalibrated ? "mrad/px" : "px/px", + k_calibrated: kCalibrated, + center_row: centerRow, + center_col: centerCol, + bf_radius: bfRadius, + }, + display: { + diffraction: { + colormap: dpColormap, + scale_mode: dpScaleMode, + vmin_pct: dpVminPct, + vmax_pct: dpVmaxPct, + }, + virtual: { + colormap: viColormap, + scale_mode: viScaleMode, + vmin_pct: viVminPct, + vmax_pct: viVmaxPct, + }, + fft: { + colormap: fftColormap, + scale_mode: fftScaleMode, + auto: fftAuto, + vmin_pct: fftVminPct, + vmax_pct: fftVmaxPct, + }, + }, + }; + zip.file("metadata.json", JSON.stringify(metadata, null, 2)); + const canvasToBlob = (canvas: HTMLCanvasElement): Promise => new Promise((resolve) => canvas.toBlob((blob) => resolve(blob!), 'image/png')); + if (virtualCanvasRef.current) zip.file("virtual_image.png", await canvasToBlob(virtualCanvasRef.current)); + if (dpCanvasRef.current) zip.file("diffraction_pattern.png", await canvasToBlob(dpCanvasRef.current)); + if (fftCanvasRef.current) zip.file("fft.png", await canvasToBlob(fftCanvasRef.current)); + const zipBlob = await zip.generateAsync({ type: "blob" }); + downloadBlob(zipBlob, `4dstem_export_${timestamp}.zip`); + }; + + // ── DP Figure Export ── + const handleDpExportFigure = (withColorbar: boolean) => { + if (lockExport) return; + setDpExportAnchor(null); + const frameData = rawDpDataRef.current; + if (!frameData) return; + const processed = dpScaleMode === "log" ? applyLogScale(frameData) : frameData; + const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; + const { min: dMin, max: dMax } = findDataRange(processed); + let vmin: number, vmax: number; + if (traitDpVmin != null && traitDpVmax != null) { + if (dpScaleMode === "log") { + vmin = Math.log1p(Math.max(traitDpVmin, 0)); + vmax = Math.log1p(Math.max(traitDpVmax, 0)); + } else if (dpScaleMode === "power") { + vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); + vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); + } else { + vmin = traitDpVmin; + vmax = traitDpVmax; + } + } else { + ({ vmin, vmax } = sliderRange(dMin, dMax, dpVminPct, dpVmaxPct)); + } + const offscreen = renderToOffscreen(processed, detCols, detRows, lut, vmin, vmax); + if (!offscreen) return; + const kPxAngstrom = kPixelSize > 0 && kCalibrated ? kPixelSize : 0; + const figCanvas = exportFigure({ + imageCanvas: offscreen, + title: `DP at (${posRow}, ${posCol})`, + lut, + vmin, + vmax, + logScale: dpScaleMode === "log", + pixelSize: kPxAngstrom > 0 ? kPxAngstrom : undefined, + showColorbar: withColorbar, + showScaleBar: kPxAngstrom > 0, + }); + canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, "show4dstem_dp_figure.pdf")).catch(console.error); + }; + + const handleDpExportPng = () => { + if (lockExport) return; + setDpExportAnchor(null); + if (!dpCanvasRef.current) return; + dpCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_dp.png"); }, "image/png"); + }; + + const handleDpExportGif = () => { + if (lockExport) return; + setDpExportAnchor(null); + setExporting(true); + setGifExportRequested(true); + }; + + // ── VI Figure Export ── + const handleViExportFigure = (withColorbar: boolean) => { + if (lockExport) return; + setViExportAnchor(null); + if (!virtualCanvasRef.current) return; + const viCanvas = virtualCanvasRef.current; + const pixelSizeAngstrom = pixelSize > 0 ? pixelSize : 0; + const figCanvas = exportFigure({ + imageCanvas: viCanvas, + title: "Virtual Image", + showColorbar: withColorbar, + showScaleBar: pixelSizeAngstrom > 0, + pixelSize: pixelSizeAngstrom > 0 ? pixelSizeAngstrom : undefined, + }); + canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, "show4dstem_vi_figure.pdf")).catch(console.error); + }; + + const handleViExportPng = () => { + if (lockExport) return; + setViExportAnchor(null); + if (!virtualCanvasRef.current) return; + virtualCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_vi.png"); }, "image/png"); + }; + + // Download GIF when data arrives from Python + React.useEffect(() => { + if (!gifData || gifData.byteLength === 0) return; + downloadDataView(gifData, "show4dstem_dp_animation.gif", "image/gif"); + const metaText = (gifMetadataJson || "").trim(); + if (metaText) { + downloadBlob(new Blob([metaText], { type: "application/json" }), "show4dstem_dp_animation.json"); + } + setExporting(false); + }, [gifData, gifMetadataJson]); + + + // Theme-aware select style + const themedSelect = { + ...controlPanel.select, + bgcolor: themeColors.controlBg, + color: themeColors.text, + "& .MuiSelect-select": { py: 0.5 }, + "& .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.border }, + "&:hover .MuiOutlinedInput-notchedOutline": { borderColor: themeColors.accent }, + }; + + const themedMenuProps = { + ...upwardMenuProps, + PaperProps: { sx: { bgcolor: themeColors.controlBg, color: themeColors.text, border: `1px solid ${themeColors.border}` } }, + }; + + const keyboardShortcutItems: [string, string][] = [ + ["↑ / ↓", "Move scan row"], + ["← / →", "Move scan col"], + ["Shift+Arrows", "Move ×10"], + ...(nFrames > 1 ? [["[ / ]", `Prev / next ${frameDimLabel.toLowerCase()}`] as [string, string]] : []), + ["Space", "Play / pause"], + ["R", "Reset all zoom/pan"], + ["Esc", "Release keyboard focus"], + ["Scroll", "Zoom"], + ["Dbl-click", "Reset view"], + ]; + + return ( + + {/* HEADER */} + + {title || "4D-STEM Explorer"} + {nFrames > 1 && ({frameLabels && frameLabels.length > frameIdx ? frameLabels[frameIdx] : `${frameDimLabel} ${frameIdx + 1}/${nFrames}`})} + + Controls + DP: Diffraction pattern I(kx,ky) at scan position. Drag to move ROI center. + Detector: ROI mask shape — defines which DP pixels are integrated for the virtual image. + BF/ABF/ADF: Preset detector configurations (bright-field, annular bright-field, annular dark-field). + Image: Virtual image — integrated intensity within detector ROI at each scan position. + FFT: Spatial frequency content of the virtual image. Auto masks DC + clips to 99.9th percentile. + Profile: Click two points on DP to draw a line intensity profile. + {nFrames > 1 && <> + Frame Playback ({frameDimLabel}) + Loop: Loop playback. Bounce: Ping-pong — alternates forward and reverse. + FPS: Adjust playback speed (1–30 frames per second). + } + Keyboard + + } theme={themeInfo.theme} /> + + + + {/* MAIN CONTENT: DP | VI | FFT (three columns when FFT shown) */} + + {/* LEFT COLUMN: DP Panel */} + + {/* DP Header */} + + + DP at ({Math.round(localPosRow)}, {Math.round(localPosCol)}) + {!hideRoi && k: ({Math.round(localKRow)}, {Math.round(localKCol)})} + + + {!hideProfile && ( + <> + Profile: + { + if (lockProfile) return; + const on = e.target.checked; + setProfileActive(on); + if (!on) { + setProfileLine([]); + setProfileData(null); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + } + }} disabled={lockProfile} size="small" sx={switchStyles.small} /> + + )} + {!hideView && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + setDpExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleDpExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleDpExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { if (!lockExport) { setDpExportAnchor(null); handleExportDP(); } }} sx={{ fontSize: 12 }}>ZIP (PNG + metadata) + {pathLength > 0 && GIF (path animation)} + + )} + + + + {/* DP Canvas */} + + + + + {cursorInfo && cursorInfo.panel === "DP" && ( + + + ({cursorInfo.row}, {cursorInfo.col}) {formatNumber(cursorInfo.value)} + + + )} + {!hideView && ( + + )} + + + {/* DP Stats Bar */} + {!hideStats && dpStats && dpStats.length === 4 && ( + + Mean {formatStat(dpStats[0])} + Min {formatStat(dpStats[1])} + Max {formatStat(dpStats[2])} + Std {formatStat(dpStats[3])} + {!hideRoi && ( + <> + + { if (!lockRoi) { setRoiMode("circle"); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: roiColors.textColor, fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>BF + { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner((bfRadius || 10) * 0.5); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#4af", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ABF + { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner(bfRadius || 10); setRoiRadius(Math.min((bfRadius || 10) * 3, Math.min(detRows, detCols) / 2 - 2)); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#fa4", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ADF + + )} + + )} + + {/* Profile sparkline */} + {profileActive && !hideProfile && ( + + + { + if (lockProfile) return; + setIsResizingProfile(true); + profileResizeStart.current = { startY: e.clientY, startHeight: profileHeight }; + }} + sx={{ width: canvasSize, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + /> + + )} + + {/* DP Controls - two rows with histogram on right */} + {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + + {/* Left: two rows of controls */} + + {/* Row 1: Detector + slider */} + {!hideRoi && ( + + Detector: + + {(roiMode === "circle" || roiMode === "square" || roiMode === "annular") && ( + <> + { + if (lockRoi) return; + if (roiMode === "annular") { + const [inner, outer] = v as number[]; + setRoiRadiusInner(Math.min(inner, outer - 1)); + setRoiRadius(Math.max(outer, inner + 1)); + } else { + const next = Array.isArray(v) ? v[0] : v; + setRoiRadius(next); + } + }} + min={1} + max={Math.min(detRows, detCols) / 2} + size="small" + sx={{ + width: roiMode === "annular" ? 100 : 70, + mx: 1, + "& .MuiSlider-thumb": { width: 14, height: 14 } + }} + /> + + {roiMode === "annular" ? `${Math.round(roiRadiusInner)}-${Math.round(roiRadius)}px` : `${Math.round(roiRadius)}px`} + + + )} + + )} + {/* Row 2: Color + Scale + Colorbar */} + {!hideDisplay && ( + + Color: + + Scale: + + Colorbar: + { if (!lockDisplay) setShowDpColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + + )} + + {/* Right: Histogram spanning both rows */} + {!hideHistogram && ( + + { if (!lockHistogram) { setDpVminPct(min); setDpVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={dpGlobalMin} dataMax={dpGlobalMax} /> + + )} + + )} + + + {/* SECOND COLUMN: VI Panel */} + {!hideVirtual && ( + + {/* VI Header */} + + + {shapeRows}×{shapeCols} | {detRows}×{detCols} + + + {!hideFft && ( + <> + FFT: + { if (!lockFft) setShowFft(e.target.checked); }} disabled={lockFft} size="small" sx={switchStyles.small} /> + + )} + {!hideProfile && ( + <> + Profile: + { + if (lockProfile) return; + const on = e.target.checked; + setViProfileActive(on); + if (!on) { + setViProfilePoints([]); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + } + }} disabled={lockProfile} size="small" sx={switchStyles.small} /> + + )} + {!hideView && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + + )} + {!hideExport && ( + setViExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleViExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleViExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { if (!lockExport && !lockVirtual) { setViExportAnchor(null); handleExportVI(); } }} sx={{ fontSize: 12 }}>ZIP (all panels + metadata) + + )} + + + + {/* VI Canvas */} + + + + + {cursorInfo && cursorInfo.panel === "VI" && ( + + + ({cursorInfo.row}, {cursorInfo.col}) {formatNumber(cursorInfo.value)} + + + )} + {!hideView && ( + + )} + + + {/* VI Stats Bar */} + {!hideStats && viStats && viStats.length === 4 && ( + + Mean {formatStat(viStats[0])} + Min {formatStat(viStats[1])} + Max {formatStat(viStats[2])} + Std {formatStat(viStats[3])} + + )} + + {/* VI Profile sparkline */} + {viProfileActive && !hideProfile && ( + + + { + if (lockProfile) return; + setIsResizingViProfile(true); + viProfileResizeStart.current = { startY: e.clientY, startHeight: viProfileHeight }; + }} + sx={{ width: viCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + /> + + )} + + {/* VI Controls - Two rows with histogram on right */} + {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + + {/* Left: Two rows of controls */} + + {/* Row 1: ROI selector */} + {!hideRoi && ( + + ROI: + + {viRoiMode && viRoiMode !== "off" && ( + <> + {(viRoiMode === "circle" || viRoiMode === "square") && ( + <> + { if (!lockRoi) setViRoiRadius(v as number); }} + min={1} + max={Math.min(shapeRows, shapeCols) / 2} + size="small" + sx={{ width: 80, mx: 1 }} + /> + + {Math.round(viRoiRadius || 5)}px + + + )} + {summedDpCount > 0 && ( + + {summedDpCount} pos + + )} + + )} + + )} + {/* Row 2: Color + Scale */} + {!hideDisplay && ( + + Color: + + Scale: + + + )} + + {/* Right: Histogram spanning both rows */} + {!hideHistogram && ( + + { if (!lockHistogram) { setViVminPct(min); setViVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={viDataMin} dataMax={viDataMax} /> + + )} + + )} + + )} + + {/* THIRD COLUMN: FFT Panel (conditionally shown) */} + {effectiveShowFft && ( + + {/* FFT Header */} + + {roiFftActive && fftCropDims ? `ROI FFT (${fftCropDims.cropWidth}\u00D7${fftCropDims.cropHeight})` : "FFT"} + + {!hideView && ( + + )} + + + + {/* FFT Canvas */} + + + + {!hideView && ( + + )} + + + {/* FFT Stats Bar */} + {!hideStats && fftStats && fftStats.length === 4 && ( + + Mean {formatStat(fftStats[0])} + Min {formatStat(fftStats[1])} + Max {formatStat(fftStats[2])} + Std {formatStat(fftStats[3])} + + )} + + {/* FFT D-spacing readout */} + {fftClickInfo && ( + + + Spot ({fftClickInfo.row.toFixed(1)}, {fftClickInfo.col.toFixed(1)}) + + + dist {fftClickInfo.distPx.toFixed(1)} px + + {fftClickInfo.dSpacing != null && ( + + d = {fftClickInfo.dSpacing >= 10 ? `${(fftClickInfo.dSpacing / 10).toFixed(2)} nm` : `${fftClickInfo.dSpacing.toFixed(2)} \u00C5`} + + )} + {fftClickInfo.spatialFreq != null && ( + + q = {fftClickInfo.spatialFreq.toFixed(4)} {"\u00C5\u207B\u00B9"} + + )} + + )} + + {/* FFT Controls - Two rows with histogram on right */} + {showControls && (!hideDisplay || !hideHistogram) && ( + + {/* Left: Two rows of controls */} + {!hideDisplay && ( + + {/* Row 1: Scale + Clip */} + + Scale: + + Auto: + { if (!lockDisplay && !lockFft) setFftAuto(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> + {fftCropDims && ( + <> + Win: + { if (!lockDisplay && !lockFft) setFftWindow(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> + + )} + + {/* Row 2: Color */} + + Color: + + + + )} + {/* Right: Histogram spanning both rows */} + {!hideHistogram && ( + + {fftHistogramData && ( + { if (!lockHistogram && !lockFft) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={fftDataMin} dataMax={fftDataMax} /> + )} + + )} + + )} + + )} + + + {/* BOTTOM CONTROLS */} + + {/* Frame controls (5D time/tilt series) — matches Show3D playback */} + {showControls && nFrames > 1 && !hidePlayback && !hideFrame && (<> + + {frameDimLabel}: + + { if (!lockFrame && !lockPlayback) { setFrameReverse(true); setFramePlaying(true); } }} sx={{ color: frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + + + { if (!lockFrame && !lockPlayback) setFramePlaying(!framePlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + {framePlaying ? : } + + { if (!lockFrame && !lockPlayback) { setFrameReverse(false); setFramePlaying(true); } }} sx={{ color: !frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + + + { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + + + + { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(v as number); } }} min={0} max={Math.max(0, nFrames - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + {frameLabels && frameLabels.length > frameIdx ? frameLabels[frameIdx] : `${frameIdx + 1}/${nFrames}`} + + + fps + { if (!lockFrame && !lockPlayback) setFrameFps(v as number); }} size="small" sx={{ ...sliderStyles.small, width: 35, flexShrink: 0 }} /> + {Math.round(frameFps)} + Loop + { if (!lockFrame && !lockPlayback) setFrameLoop(!frameLoop); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + Bounce + { if (!lockFrame && !lockPlayback) setFrameBoomerang(!frameBoomerang); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + + )} + + {/* Path animation slider */} + {showControls && !hidePlayback && pathLength > 0 && ( + + + { if (!lockPlayback) setPathPlaying(!pathPlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + {pathPlaying ? : } + + { if (!lockPlayback) { setPathPlaying(false); setPathIndex(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + + + + { if (!lockPlayback) { setPathPlaying(false); setPathIndex(v as number); } }} min={0} max={Math.max(0, pathLength - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + {pathIndex + 1}/{pathLength} + Loop: + { if (!lockPlayback) { model.set("path_loop", v); model.save_changes(); } }} disabled={lockPlayback} size="small" sx={switchStyles.small} /> + + )} + + ); +} + +export const render = createRender(Show4DSTEM); diff --git a/widget/js/show4dstem/styles.css b/widget/js/show4dstem/styles.css new file mode 100644 index 00000000..61876cde --- /dev/null +++ b/widget/js/show4dstem/styles.css @@ -0,0 +1,5 @@ +/* Theme-aware styles - minimal, let JS handle most theming */ +.show4dstem-root { + border-radius: 2px; + padding: 16px; +} diff --git a/widget/js/stats.ts b/widget/js/stats.ts new file mode 100644 index 00000000..b71c45aa --- /dev/null +++ b/widget/js/stats.ts @@ -0,0 +1,101 @@ +/** Find min/max range of a Float32Array, filtering out NaN and Infinity. */ +export function findDataRange(data: Float32Array): { min: number; max: number } { + let min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (!isFinite(v)) continue; + if (v < min) min = v; + if (v > max) max = v; + } + // If no finite values found, return zeros + if (min === Infinity) return { min: 0, max: 0 }; + return { min, max }; +} + +/** Apply log1p scale: result[i] = log(1 + max(0, data[i])). Returns a new array. */ +export function applyLogScale(data: Float32Array): Float32Array { + const result = new Float32Array(data.length); + for (let i = 0; i < data.length; i++) { + result[i] = Math.log1p(Math.max(0, data[i])); + } + return result; +} + +/** Apply log1p scale into a pre-allocated buffer. Avoids per-frame allocation. */ +export function applyLogScaleInPlace(data: Float32Array, out: Float32Array): Float32Array { + for (let i = 0; i < data.length; i++) { + out[i] = Math.log1p(Math.max(0, data[i])); + } + return out; +} + +/** Percentile-based clipping using O(n) histogram approach. + * Also returns data min/max so callers can skip a redundant findDataRange scan. */ +export function percentileClip( + data: Float32Array, pLow: number, pHigh: number, +): { vmin: number; vmax: number; min: number; max: number } { + const len = data.length; + if (len === 0) return { vmin: 0, vmax: 0, min: 0, max: 0 }; + + // Pass 1: find min/max + let min = Infinity, max = -Infinity; + for (let i = 0; i < len; i++) { + const v = data[i]; + if (v < min) min = v; + if (v > max) max = v; + } + if (min === max) return { vmin: min, vmax: max, min, max }; + + // Pass 2: build histogram + const NUM_BINS = 1024; + const bins = new Uint32Array(NUM_BINS); + const range = max - min; + const scale = (NUM_BINS - 1) / range; + for (let i = 0; i < len; i++) { + bins[Math.floor((data[i] - min) * scale)]++; + } + + // Walk cumulative histogram to find percentile values + const lowCount = Math.floor(len * (pLow / 100)); + const highCount = Math.ceil(len * (pHigh / 100)); + let cumSum = 0; + let vmin = min, vmax = max; + for (let i = 0; i < NUM_BINS; i++) { + cumSum += bins[i]; + if (cumSum >= lowCount) { vmin = min + (i / (NUM_BINS - 1)) * range; break; } + } + cumSum = 0; + for (let i = 0; i < NUM_BINS; i++) { + cumSum += bins[i]; + if (cumSum >= highCount) { vmax = min + (i / (NUM_BINS - 1)) * range; break; } + } + return { vmin, vmax, min, max }; +} + +/** Compute mean, min, max, and standard deviation of a Float32Array. */ +export function computeStats(data: Float32Array): { mean: number; min: number; max: number; std: number } { + if (data.length === 0) return { mean: 0, min: 0, max: 0, std: 0 }; + let sum = 0, min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + sum += v; + if (v < min) min = v; + if (v > max) max = v; + } + const mean = sum / data.length; + let variance = 0; + for (let i = 0; i < data.length; i++) variance += (data[i] - mean) ** 2; + const std = Math.sqrt(variance / data.length); + return { mean, min, max, std }; +} + +/** Convert histogram slider percentages (0-100) to vmin/vmax in data space. */ +export function sliderRange( + dataMin: number, dataMax: number, vminPct: number, vmaxPct: number, +): { vmin: number; vmax: number } { + const range = dataMax - dataMin; + return { + vmin: dataMin + (vminPct / 100) * range, + vmax: dataMin + (vmaxPct / 100) * range, + }; +} diff --git a/widget/js/theme.ts b/widget/js/theme.ts new file mode 100644 index 00000000..f13123d5 --- /dev/null +++ b/widget/js/theme.ts @@ -0,0 +1,149 @@ +/** + * Shared theme detection and color system for all widgets. + * Detects JupyterLab, VS Code, Colab, Classic Jupyter, and OS preferences. + */ + +import { useState, useEffect, useMemo } from "react"; + +// ============================================================================ +// Types +// ============================================================================ +export type Environment = "jupyterlab" | "vscode" | "colab" | "jupyter-classic" | "unknown"; +export type Theme = "light" | "dark"; + +export interface ThemeInfo { + environment: Environment; + theme: Theme; +} + +export interface ThemeColors { + bg: string; + bgAlt: string; + text: string; + textMuted: string; + border: string; + controlBg: string; + accent: string; +} + +// ============================================================================ +// Color palettes +// ============================================================================ +export const DARK_COLORS: ThemeColors = { + bg: "#1e1e1e", + bgAlt: "#1a1a1a", + text: "#e0e0e0", + textMuted: "#888", + border: "#3a3a3a", + controlBg: "#252525", + accent: "#5af", +}; + +export const LIGHT_COLORS: ThemeColors = { + bg: "#ffffff", + bgAlt: "#f5f5f5", + text: "#1e1e1e", + textMuted: "#666", + border: "#ccc", + controlBg: "#f0f0f0", + accent: "#0066cc", +}; + +export function getThemeColors(theme: Theme): ThemeColors { + return theme === "dark" ? DARK_COLORS : LIGHT_COLORS; +} + +// ============================================================================ +// Theme detection +// ============================================================================ + +/** Check if a CSS color string is dark (luminance < 0.5) */ +export function isColorDark(color: string): boolean { + const match = color.match(/rgba?\((\d+),\s*(\d+),\s*(\d+)/); + if (!match) return true; + const [, r, g, b] = match.map(Number); + const luminance = (0.299 * r + 0.587 * g + 0.114 * b) / 255; + return luminance < 0.5; +} + +export function detectTheme(): ThemeInfo { + // 1. JupyterLab - has data-jp-theme-light attribute + const jpThemeLight = document.body.dataset.jpThemeLight; + if (jpThemeLight !== undefined) { + return { + environment: "jupyterlab", + theme: jpThemeLight === "true" ? "light" : "dark", + }; + } + + // 2. VS Code - has vscode-* classes on body or html + const bodyClasses = document.body.className; + const htmlClasses = document.documentElement.className; + if (bodyClasses.includes("vscode-") || htmlClasses.includes("vscode-")) { + const isDark = bodyClasses.includes("vscode-dark") || htmlClasses.includes("vscode-dark"); + return { + environment: "vscode", + theme: isDark ? "dark" : "light", + }; + } + + // 3. Google Colab - has specific markers + if (document.querySelector('colab-shaded-scroller') || document.body.classList.contains('colaboratory')) { + const bg = getComputedStyle(document.body).backgroundColor; + return { + environment: "colab", + theme: isColorDark(bg) ? "dark" : "light", + }; + } + + // 4. Classic Jupyter Notebook - has #notebook element + if (document.getElementById('notebook')) { + const bodyBg = getComputedStyle(document.body).backgroundColor; + return { + environment: "jupyter-classic", + theme: isColorDark(bodyBg) ? "dark" : "light", + }; + } + + // 5. Fallback: check OS preference, then computed background + const prefersDark = window.matchMedia?.('(prefers-color-scheme: dark)')?.matches; + if (prefersDark !== undefined) { + return { + environment: "unknown", + theme: prefersDark ? "dark" : "light", + }; + } + + // Final fallback: check body background luminance + const bg = getComputedStyle(document.body).backgroundColor; + return { + environment: "unknown", + theme: isColorDark(bg) ? "dark" : "light", + }; +} + +// ============================================================================ +// React hook +// ============================================================================ +export function useTheme(): { themeInfo: ThemeInfo; colors: ThemeColors } { + const [themeInfo, setThemeInfo] = useState(() => detectTheme()); + + useEffect(() => { + const mediaQuery = window.matchMedia?.('(prefers-color-scheme: dark)'); + const handleChange = () => setThemeInfo(detectTheme()); + mediaQuery?.addEventListener?.('change', handleChange); + + const observer = new MutationObserver(() => setThemeInfo(detectTheme())); + observer.observe(document.body, { attributes: true, attributeFilter: ['data-jp-theme-light', 'class'] }); + + return () => { + mediaQuery?.removeEventListener?.('change', handleChange); + observer.disconnect(); + }; + }, []); + + // Memoize by theme string so `colors` is referentially stable across renders — + // effects/components that depend on `colors` only re-run when the theme flips. + const colors = useMemo(() => getThemeColors(themeInfo.theme), [themeInfo.theme]); + return { themeInfo, colors }; +} diff --git a/widget/js/tool-parity.ts b/widget/js/tool-parity.ts new file mode 100644 index 00000000..8e318e6c --- /dev/null +++ b/widget/js/tool-parity.ts @@ -0,0 +1,156 @@ +import registryJson from "../src/quantem/widget/tool_parity.json"; + +type ToolInput = string | string[] | null | undefined; + +type WidgetConfig = { + tool_groups: string[]; + aliases?: Record; +}; + +type ControlPreset = { + label: string; + show_groups: string[]; +}; + +type ToolParityRegistry = { + widgets: Record; + control_presets: Record; + viewer_widgets?: string[]; +}; + +const REGISTRY = registryJson as ToolParityRegistry; + +function getWidgetConfig(widgetName: string): WidgetConfig { + const cfg = REGISTRY.widgets[widgetName]; + if (!cfg) { + const supported = Object.keys(REGISTRY.widgets).sort().join(", "); + throw new Error(`Unknown widget '${widgetName}'. Supported widgets: ${supported}.`); + } + return cfg; +} + +function toValues(values: ToolInput): string[] { + if (values == null) return []; + if (typeof values === "string") return [values]; + return [...values]; +} + +function toCanonical(widgetName: string, value: string): string { + const cfg = getWidgetConfig(widgetName); + const aliases = cfg.aliases ?? {}; + const key = value.trim().toLowerCase(); + return aliases[key] ?? key; +} + +export function getWidgetToolGroups(widgetName: string): string[] { + return [...getWidgetConfig(widgetName).tool_groups]; +} + +export function normalizeToolGroups(widgetName: string, values: ToolInput): string[] { + const groups = getWidgetToolGroups(widgetName); + const groupSet = new Set(groups); + const out: string[] = []; + const seen = new Set(); + for (const raw of toValues(values)) { + const canonical = toCanonical(widgetName, String(raw)); + if (!canonical) continue; + if (!groupSet.has(canonical)) { + const supported = groups.map((g) => `"${g}"`).join(", "); + throw new Error(`Unknown tool group '${raw}'. Supported values: ${supported}.`); + } + if (canonical === "all") return ["all"]; + if (!seen.has(canonical)) { + seen.add(canonical); + out.push(canonical); + } + } + return out; +} + +function orderedWithoutAll(widgetName: string, values: Set): string[] { + return getWidgetToolGroups(widgetName).filter((group) => group !== "all" && values.has(group)); +} + +export function expandToolGroups(widgetName: string, values: ToolInput): string[] { + const normalized = normalizeToolGroups(widgetName, values); + if (!normalized.includes("all")) return normalized; + return getWidgetToolGroups(widgetName).filter((group) => group !== "all"); +} + +export function compactToolLabel(key: string): string { + return key + .replace(/_/g, " ") + .replace(/\b\w/g, (m) => m.toUpperCase()); +} + +export function getControlPresetIds(): string[] { + return Object.keys(REGISTRY.control_presets); +} + +export function getControlPresetLabel(presetId: string): string { + const preset = REGISTRY.control_presets[presetId]; + return preset?.label ?? presetId; +} + +export function resolvePresetHiddenTools(widgetName: string, presetId: string): string[] { + const preset = REGISTRY.control_presets[presetId]; + if (!preset) { + const supported = Object.keys(REGISTRY.control_presets).sort().join(", "); + throw new Error(`Unknown control preset '${presetId}'. Supported presets: ${supported}.`); + } + const supportedGroups = getWidgetToolGroups(widgetName).filter((group) => group !== "all"); + if (preset.show_groups.includes("*")) return []; + const show = new Set(preset.show_groups.map((g) => toCanonical(widgetName, g))); + const hidden = supportedGroups.filter((group) => !show.has(group)); + return normalizeToolGroups(widgetName, hidden); +} + +export type ToolVisibilityState = { + hideAll: boolean; + lockAll: boolean; + isHidden: (group: string) => boolean; + isLocked: (group: string) => boolean; + hiddenSet: Set; + disabledSet: Set; +}; + +export function computeToolVisibility( + widgetName: string, + disabledTools: ToolInput, + hiddenTools: ToolInput, +): ToolVisibilityState { + const hidden = normalizeToolGroups(widgetName, hiddenTools); + const disabled = normalizeToolGroups(widgetName, disabledTools); + const hiddenSet = new Set(hidden); + const disabledSet = new Set(disabled); + const hideAll = hiddenSet.has("all"); + const lockAll = hideAll || disabledSet.has("all"); + + const isHidden = (group: string): boolean => { + const canonical = toCanonical(widgetName, group); + if (canonical === "all") return hideAll; + return hideAll || hiddenSet.has(canonical); + }; + + const isLocked = (group: string): boolean => { + const canonical = toCanonical(widgetName, group); + if (canonical === "all") return lockAll; + return lockAll || isHidden(canonical) || disabledSet.has(canonical); + }; + + return { hideAll, lockAll, isHidden, isLocked, hiddenSet, disabledSet }; +} + +export function addToolGroup(widgetName: string, current: ToolInput, group: string): string[] { + const merged = new Set(expandToolGroups(widgetName, current)); + const canonical = toCanonical(widgetName, group); + if (canonical === "all") return ["all"]; + merged.add(canonical); + return orderedWithoutAll(widgetName, merged); +} + +export function removeToolGroup(widgetName: string, current: ToolInput, group: string): string[] { + const merged = new Set(expandToolGroups(widgetName, current)); + merged.delete(toCanonical(widgetName, group)); + return orderedWithoutAll(widgetName, merged); +} diff --git a/widget/js/webgpu-fft.ts b/widget/js/webgpu-fft.ts new file mode 100644 index 00000000..2498b755 --- /dev/null +++ b/widget/js/webgpu-fft.ts @@ -0,0 +1,509 @@ +/// + +/** + * WebGPU FFT — shared 2D FFT with GPU acceleration and CPU fallback. + * Handles non-power-of-2 dimensions via zero-padding. + */ + +// ============================================================================ +// CPU FFT fallback +// ============================================================================ + +export function nextPow2(n: number): number { return Math.pow(2, Math.ceil(Math.log2(n))); } + +function fft1d(real: Float32Array, imag: Float32Array, inverse: boolean = false) { + const n = real.length; + if (n <= 1) return; + let j = 0; + for (let i = 0; i < n - 1; i++) { + if (i < j) { [real[i], real[j]] = [real[j], real[i]]; [imag[i], imag[j]] = [imag[j], imag[i]]; } + let k = n >> 1; + while (k <= j) { j -= k; k >>= 1; } + j += k; + } + const sign = inverse ? 1 : -1; + for (let len = 2; len <= n; len <<= 1) { + const halfLen = len >> 1; + const angle = (sign * 2 * Math.PI) / len; + const wReal = Math.cos(angle), wImag = Math.sin(angle); + for (let i = 0; i < n; i += len) { + let curReal = 1, curImag = 0; + for (let k = 0; k < halfLen; k++) { + const evenIdx = i + k, oddIdx = i + k + halfLen; + const tReal = curReal * real[oddIdx] - curImag * imag[oddIdx]; + const tImag = curReal * imag[oddIdx] + curImag * real[oddIdx]; + real[oddIdx] = real[evenIdx] - tReal; imag[oddIdx] = imag[evenIdx] - tImag; + real[evenIdx] += tReal; imag[evenIdx] += tImag; + const newReal = curReal * wReal - curImag * wImag; + curImag = curReal * wImag + curImag * wReal; curReal = newReal; + } + } + } + if (inverse) { for (let i = 0; i < n; i++) { real[i] /= n; imag[i] /= n; } } +} + +export function fft2d(real: Float32Array, imag: Float32Array, width: number, height: number, inverse: boolean = false) { + const paddedW = nextPow2(width), paddedH = nextPow2(height); + const needsPadding = paddedW !== width || paddedH !== height; + let workReal: Float32Array, workImag: Float32Array; + if (needsPadding) { + workReal = new Float32Array(paddedW * paddedH); workImag = new Float32Array(paddedW * paddedH); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + workReal[y * paddedW + x] = real[y * width + x]; workImag[y * paddedW + x] = imag[y * width + x]; + } + } else { workReal = real; workImag = imag; } + const rowReal = new Float32Array(paddedW), rowImag = new Float32Array(paddedW); + for (let y = 0; y < paddedH; y++) { + const offset = y * paddedW; + for (let x = 0; x < paddedW; x++) { rowReal[x] = workReal[offset + x]; rowImag[x] = workImag[offset + x]; } + fft1d(rowReal, rowImag, inverse); + for (let x = 0; x < paddedW; x++) { workReal[offset + x] = rowReal[x]; workImag[offset + x] = rowImag[x]; } + } + const colReal = new Float32Array(paddedH), colImag = new Float32Array(paddedH); + for (let x = 0; x < paddedW; x++) { + for (let y = 0; y < paddedH; y++) { colReal[y] = workReal[y * paddedW + x]; colImag[y] = workImag[y * paddedW + x]; } + fft1d(colReal, colImag, inverse); + for (let y = 0; y < paddedH; y++) { workReal[y * paddedW + x] = colReal[y]; workImag[y * paddedW + x] = colImag[y]; } + } + if (needsPadding) { + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + real[y * width + x] = workReal[y * paddedW + x]; imag[y * width + x] = workImag[y * paddedW + x]; + } + } +} + +export function fftshift(data: Float32Array, width: number, height: number): void { + const halfW = width >> 1, halfH = height >> 1; + const temp = new Float32Array(width * height); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + temp[((y + halfH) % height) * width + ((x + halfW) % width)] = data[y * width + x]; + } + data.set(temp); +} + +// ============================================================================ +// CPU FFT Web Worker — runs fft2d + fftshift + computeMagnitude off main thread +// ============================================================================ + +const FFT_WORKER_CODE = ` +function nextPow2(n) { return Math.pow(2, Math.ceil(Math.log2(n))); } +function fft1d(real, imag, inverse) { + var n = real.length; if (n <= 1) return; + var j = 0; + for (var i = 0; i < n - 1; i++) { + if (i < j) { var t = real[i]; real[i] = real[j]; real[j] = t; t = imag[i]; imag[i] = imag[j]; imag[j] = t; } + var k = n >> 1; while (k <= j) { j -= k; k >>= 1; } j += k; + } + var sign = inverse ? 1 : -1; + for (var len = 2; len <= n; len <<= 1) { + var halfLen = len >> 1, angle = (sign * 2 * Math.PI) / len; + var wR = Math.cos(angle), wI = Math.sin(angle); + for (var i = 0; i < n; i += len) { + var cR = 1, cI = 0; + for (var k = 0; k < halfLen; k++) { + var eI = i + k, oI = i + k + halfLen; + var tR = cR * real[oI] - cI * imag[oI], tI = cR * imag[oI] + cI * real[oI]; + real[oI] = real[eI] - tR; imag[oI] = imag[eI] - tI; + real[eI] += tR; imag[eI] += tI; + var nR = cR * wR - cI * wI; cI = cR * wI + cI * wR; cR = nR; + } + } + } + if (inverse) { for (var i = 0; i < n; i++) { real[i] /= n; imag[i] /= n; } } +} +function fft2d(real, imag, width, height, inverse) { + var pW = nextPow2(width), pH = nextPow2(height), pad = pW !== width || pH !== height; + var wR, wI; + if (pad) { + wR = new Float32Array(pW * pH); wI = new Float32Array(pW * pH); + for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { wR[y*pW+x] = real[y*width+x]; wI[y*pW+x] = imag[y*width+x]; } + } else { wR = real; wI = imag; } + var rR = new Float32Array(pW), rI = new Float32Array(pW); + for (var y = 0; y < pH; y++) { + var o = y * pW; for (var x = 0; x < pW; x++) { rR[x] = wR[o+x]; rI[x] = wI[o+x]; } + fft1d(rR, rI, inverse); for (var x = 0; x < pW; x++) { wR[o+x] = rR[x]; wI[o+x] = rI[x]; } + } + var cR = new Float32Array(pH), cI = new Float32Array(pH); + for (var x = 0; x < pW; x++) { + for (var y = 0; y < pH; y++) { cR[y] = wR[y*pW+x]; cI[y] = wI[y*pW+x]; } + fft1d(cR, cI, inverse); for (var y = 0; y < pH; y++) { wR[y*pW+x] = cR[y]; wI[y*pW+x] = cI[y]; } + } + if (pad) { for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { real[y*width+x] = wR[y*pW+x]; imag[y*width+x] = wI[y*pW+x]; } } +} +function fftshift(data, width, height) { + var hW = width >> 1, hH = height >> 1, temp = new Float32Array(width * height); + for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) temp[((y+hH)%height)*width+((x+hW)%width)] = data[y*width+x]; + data.set(temp); +} +self.onmessage = function(e) { + var d = e.data, real = d.real, imag = d.imag, w = d.width, h = d.height; + fft2d(real, imag, w, h, d.inverse); + fftshift(real, w, h); fftshift(imag, w, h); + var n = real.length, mag = new Float32Array(n); + for (var i = 0; i < n; i++) mag[i] = Math.sqrt(real[i]*real[i] + imag[i]*imag[i]); + self.postMessage({ id: d.id, magnitude: mag, real: real, imag: imag }, [mag.buffer, real.buffer, imag.buffer]); +}; +`; + +let _fftWorker: Worker | null = null; +const _fftCallbacks = new Map void>(); +let _fftWorkerId = 0; + +function getFFTWorker(): Worker { + if (!_fftWorker) { + const blob = new Blob([FFT_WORKER_CODE], { type: 'application/javascript' }); + _fftWorker = new Worker(URL.createObjectURL(blob)); + _fftWorker.onmessage = (e: MessageEvent) => { + const cb = _fftCallbacks.get(e.data.id); + if (cb) { + _fftCallbacks.delete(e.data.id); + cb(e.data); + } + }; + } + return _fftWorker; +} + +/** + * CPU FFT in a Web Worker — does fft2d + fftshift + computeMagnitude off main thread. + * Transfers Float32Arrays to the worker (zero-copy) so the main thread is never blocked. + * The input arrays become detached after this call — pass copies if you need to keep them. + */ +export function fft2dAsync( + real: Float32Array, imag: Float32Array, + width: number, height: number, + inverse: boolean = false, +): Promise<{ magnitude: Float32Array; real: Float32Array; imag: Float32Array }> { + const worker = getFFTWorker(); + const id = ++_fftWorkerId; + return new Promise((resolve) => { + _fftCallbacks.set(id, resolve); + worker.postMessage( + { id, real, imag, width, height, inverse }, + [real.buffer, imag.buffer], + ); + }); +} + +// ============================================================================ +// WebGPU FFT — GPU-accelerated 2D FFT +// ============================================================================ + +const FFT_2D_SHADER = /* wgsl */` +fn cmul(a: vec2, b: vec2) -> vec2 { return vec2(a.x*b.x-a.y*b.y, a.x*b.y+a.y*b.x); } +fn twiddle(k: u32, N: u32, inverse: f32) -> vec2 { let angle = inverse * 2.0 * 3.14159265359 * f32(k) / f32(N); return vec2(cos(angle), sin(angle)); } +fn bitReverse(x: u32, log2N: u32) -> u32 { var result: u32 = 0u; var val = x; for (var i: u32 = 0u; i < log2N; i = i + 1u) { result = (result << 1u) | (val & 1u); val = val >> 1u; } return result; } +struct FFT2DParams { width: u32, height: u32, log2Size: u32, stage: u32, inverse: f32, isRowWise: u32, } +@group(0) @binding(0) var params: FFT2DParams; +@group(0) @binding(1) var data: array>; +fn getIndex(row: u32, col: u32) -> u32 { return row * params.width + col; } +@compute @workgroup_size(16, 16) fn bitReverseRows(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let col = gid.x; if (row >= params.height || col >= params.width) { return; } let rev = bitReverse(col, params.log2Size); if (col < rev) { let idx1 = getIndex(row, col); let idx2 = getIndex(row, rev); let temp = data[idx1]; data[idx1] = data[idx2]; data[idx2] = temp; } } +@compute @workgroup_size(16, 16) fn bitReverseCols(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let col = gid.x; if (row >= params.height || col >= params.width) { return; } let rev = bitReverse(row, params.log2Size); if (row < rev) { let idx1 = getIndex(row, col); let idx2 = getIndex(rev, col); let temp = data[idx1]; data[idx1] = data[idx2]; data[idx2] = temp; } } +@compute @workgroup_size(16, 16) fn butterflyRows(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let idx = gid.x; if (row >= params.height || idx >= params.width / 2u) { return; } let stage = params.stage; let halfSize = 1u << stage; let fullSize = halfSize << 1u; let group = idx / halfSize; let pos = idx % halfSize; let col_i = group * fullSize + pos; let col_j = col_i + halfSize; if (col_j >= params.width) { return; } let w = twiddle(pos, fullSize, params.inverse); let i = getIndex(row, col_i); let j = getIndex(row, col_j); let u = data[i]; let t = cmul(w, data[j]); data[i] = u + t; data[j] = u - t; } +@compute @workgroup_size(16, 16) fn butterflyCols(@builtin(global_invocation_id) gid: vec3) { let col = gid.x; let idx = gid.y; if (col >= params.width || idx >= params.height / 2u) { return; } let stage = params.stage; let halfSize = 1u << stage; let fullSize = halfSize << 1u; let group = idx / halfSize; let pos = idx % halfSize; let row_i = group * fullSize + pos; let row_j = row_i + halfSize; if (row_j >= params.height) { return; } let w = twiddle(pos, fullSize, params.inverse); let i = getIndex(row_i, col); let j = getIndex(row_j, col); let u = data[i]; let t = cmul(w, data[j]); data[i] = u + t; data[j] = u - t; } +@compute @workgroup_size(16, 16) fn normalize2D(@builtin(global_invocation_id) gid: vec3) { let row = gid.y; let col = gid.x; if (row >= params.height || col >= params.width) { return; } let idx = getIndex(row, col); let scale = 1.0 / f32(params.width * params.height); data[idx] = data[idx] * scale; }`; + +export class WebGPUFFT { + private device: GPUDevice; + private pipelines2D: { bitReverseRows: GPUComputePipeline; bitReverseCols: GPUComputePipeline; butterflyRows: GPUComputePipeline; butterflyCols: GPUComputePipeline; normalize: GPUComputePipeline } | null = null; + private initialized = false; + constructor(device: GPUDevice) { this.device = device; } + async init(): Promise { + if (this.initialized) return; + const module2D = this.device.createShaderModule({ code: FFT_2D_SHADER }); + this.pipelines2D = { + bitReverseRows: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'bitReverseRows' } }), + bitReverseCols: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'bitReverseCols' } }), + butterflyRows: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'butterflyRows' } }), + butterflyCols: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'butterflyCols' } }), + normalize: this.device.createComputePipeline({ layout: 'auto', compute: { module: module2D, entryPoint: 'normalize2D' } }) + }; + this.initialized = true; + } + async fft2D(realData: Float32Array, imagData: Float32Array, width: number, height: number, inverse: boolean = false): Promise<{ real: Float32Array, imag: Float32Array }> { + await this.init(); + const paddedWidth = nextPow2(width), paddedHeight = nextPow2(height); + const needsPadding = paddedWidth !== width || paddedHeight !== height; + const log2Width = Math.log2(paddedWidth), log2Height = Math.log2(paddedHeight); + const paddedSize = paddedWidth * paddedHeight, originalSize = width * height; + let workReal: Float32Array, workImag: Float32Array; + if (needsPadding) { + workReal = new Float32Array(paddedSize); workImag = new Float32Array(paddedSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { workReal[y * paddedWidth + x] = realData[y * width + x]; workImag[y * paddedWidth + x] = imagData[y * width + x]; } + } else { workReal = realData; workImag = imagData; } + const complexData = new Float32Array(paddedSize * 2); + for (let i = 0; i < paddedSize; i++) { complexData[i * 2] = workReal[i]; complexData[i * 2 + 1] = workImag[i]; } + const dataBuffer = this.device.createBuffer({ size: complexData.byteLength, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }); + this.device.queue.writeBuffer(dataBuffer, 0, complexData); + const paramsBuffer = this.device.createBuffer({ size: 24, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST }); + const readBuffer = this.device.createBuffer({ size: complexData.byteLength, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }); + const inverseVal = inverse ? 1.0 : -1.0; + const workgroupsX = Math.ceil(paddedWidth / 16), workgroupsY = Math.ceil(paddedHeight / 16); + const runPass = (pipeline: GPUComputePipeline) => { + const bindGroup = this.device.createBindGroup({ layout: pipeline.getBindGroupLayout(0), entries: [{ binding: 0, resource: { buffer: paramsBuffer } }, { binding: 1, resource: { buffer: dataBuffer } }] }); + const encoder = this.device.createCommandEncoder(); const pass = encoder.beginComputePass(); + pass.setPipeline(pipeline); pass.setBindGroup(0, bindGroup); pass.dispatchWorkgroups(workgroupsX, workgroupsY); pass.end(); + this.device.queue.submit([encoder.finish()]); + }; + const params = new ArrayBuffer(24); const paramsU32 = new Uint32Array(params); const paramsF32 = new Float32Array(params); + paramsU32[0] = paddedWidth; paramsU32[1] = paddedHeight; paramsU32[2] = log2Width; paramsU32[3] = 0; paramsF32[4] = inverseVal; paramsU32[5] = 1; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseRows); + for (let stage = 0; stage < log2Width; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyRows); } + paramsU32[2] = log2Height; paramsU32[3] = 0; paramsU32[5] = 0; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseCols); + for (let stage = 0; stage < log2Height; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyCols); } + if (inverse) runPass(this.pipelines2D!.normalize); + const encoder = this.device.createCommandEncoder(); encoder.copyBufferToBuffer(dataBuffer, 0, readBuffer, 0, complexData.byteLength); + this.device.queue.submit([encoder.finish()]); await readBuffer.mapAsync(GPUMapMode.READ); + const result = new Float32Array(readBuffer.getMappedRange().slice(0)); readBuffer.unmap(); + dataBuffer.destroy(); paramsBuffer.destroy(); readBuffer.destroy(); + if (needsPadding) { + const realResult = new Float32Array(originalSize), imagResult = new Float32Array(originalSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { realResult[y * width + x] = result[(y * paddedWidth + x) * 2]; imagResult[y * width + x] = result[(y * paddedWidth + x) * 2 + 1]; } + return { real: realResult, imag: imagResult }; + } + const realResult = new Float32Array(paddedSize), imagResult = new Float32Array(paddedSize); + for (let i = 0; i < paddedSize; i++) { realResult[i] = result[i * 2]; imagResult[i] = result[i * 2 + 1]; } + return { real: realResult, imag: imagResult }; + } + /** + * Batched 2D FFT: compute N forward FFTs with pipelined GPU submissions. + * All images must have the same dimensions. Each image gets its own + * submit (required because the params uniform changes per-pass), but + * all readbacks are batched into a single Promise.all at the end. + */ + async fft2DBatch( + images: { real: Float32Array; imag: Float32Array }[], + width: number, height: number, + ): Promise<{ real: Float32Array; imag: Float32Array }[]> { + await this.init(); + const n = images.length; + if (n === 0) return []; + const paddedWidth = nextPow2(width), paddedHeight = nextPow2(height); + const needsPadding = paddedWidth !== width || paddedHeight !== height; + const log2Width = Math.log2(paddedWidth), log2Height = Math.log2(paddedHeight); + const paddedSize = paddedWidth * paddedHeight; + const originalSize = width * height; + const byteSize = paddedSize * 2 * 4; + const workgroupsX = Math.ceil(paddedWidth / 16), workgroupsY = Math.ceil(paddedHeight / 16); + const inverseVal = -1.0; + + // Shared params buffer — safe because we submit per-image + const paramsBuffer = this.device.createBuffer({ size: 24, usage: GPUBufferUsage.UNIFORM | GPUBufferUsage.COPY_DST }); + + const readBuffers: GPUBuffer[] = []; + const dataBuffers: GPUBuffer[] = []; + + // Submit all FFTs — GPU pipelines them internally + for (let i = 0; i < n; i++) { + const { real: realData, imag: imagData } = images[i]; + let workReal: Float32Array, workImag: Float32Array; + if (needsPadding) { + workReal = new Float32Array(paddedSize); workImag = new Float32Array(paddedSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + workReal[y * paddedWidth + x] = realData[y * width + x]; + workImag[y * paddedWidth + x] = imagData[y * width + x]; + } + } else { workReal = realData; workImag = imagData; } + + const complexData = new Float32Array(paddedSize * 2); + for (let j = 0; j < paddedSize; j++) { complexData[j * 2] = workReal[j]; complexData[j * 2 + 1] = workImag[j]; } + + const dataBuffer = this.device.createBuffer({ size: byteSize, usage: GPUBufferUsage.STORAGE | GPUBufferUsage.COPY_SRC | GPUBufferUsage.COPY_DST }); + this.device.queue.writeBuffer(dataBuffer, 0, complexData); + dataBuffers.push(dataBuffer); + + const readBuffer = this.device.createBuffer({ size: byteSize, usage: GPUBufferUsage.MAP_READ | GPUBufferUsage.COPY_DST }); + readBuffers.push(readBuffer); + + // Run FFT passes — each runPass does writeBuffer+submit atomically + const runPass = (pipeline: GPUComputePipeline) => { + const bindGroup = this.device.createBindGroup({ + layout: pipeline.getBindGroupLayout(0), + entries: [{ binding: 0, resource: { buffer: paramsBuffer } }, { binding: 1, resource: { buffer: dataBuffer } }], + }); + const enc = this.device.createCommandEncoder(); + const pass = enc.beginComputePass(); + pass.setPipeline(pipeline); pass.setBindGroup(0, bindGroup); + pass.dispatchWorkgroups(workgroupsX, workgroupsY); pass.end(); + this.device.queue.submit([enc.finish()]); + }; + + const params = new ArrayBuffer(24); + const paramsU32 = new Uint32Array(params); + const paramsF32 = new Float32Array(params); + + paramsU32[0] = paddedWidth; paramsU32[1] = paddedHeight; paramsU32[2] = log2Width; + paramsU32[3] = 0; paramsF32[4] = inverseVal; paramsU32[5] = 1; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseRows); + for (let stage = 0; stage < log2Width; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyRows); } + + paramsU32[2] = log2Height; paramsU32[3] = 0; paramsU32[5] = 0; + this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.bitReverseCols); + for (let stage = 0; stage < log2Height; stage++) { paramsU32[3] = stage; this.device.queue.writeBuffer(paramsBuffer, 0, params); runPass(this.pipelines2D!.butterflyCols); } + + // Copy to read buffer + const copyEnc = this.device.createCommandEncoder(); + copyEnc.copyBufferToBuffer(dataBuffer, 0, readBuffer, 0, byteSize); + this.device.queue.submit([copyEnc.finish()]); + } + + // Batched readback — one sync point for all images + await Promise.all(readBuffers.map(buf => buf.mapAsync(GPUMapMode.READ))); + + const results: { real: Float32Array; imag: Float32Array }[] = []; + for (let i = 0; i < n; i++) { + const result = new Float32Array(readBuffers[i].getMappedRange().slice(0)); + readBuffers[i].unmap(); + dataBuffers[i].destroy(); + readBuffers[i].destroy(); + + if (needsPadding) { + const realResult = new Float32Array(originalSize), imagResult = new Float32Array(originalSize); + for (let y = 0; y < height; y++) for (let x = 0; x < width; x++) { + realResult[y * width + x] = result[(y * paddedWidth + x) * 2]; + imagResult[y * width + x] = result[(y * paddedWidth + x) * 2 + 1]; + } + results.push({ real: realResult, imag: imagResult }); + } else { + const realResult = new Float32Array(paddedSize), imagResult = new Float32Array(paddedSize); + for (let i2 = 0; i2 < paddedSize; i2++) { realResult[i2] = result[i2 * 2]; imagResult[i2] = result[i2 * 2 + 1]; } + results.push({ real: realResult, imag: imagResult }); + } + } + + paramsBuffer.destroy(); + return results; + } + + destroy(): void { this.initialized = false; } +} + +// ============================================================================ +// FFT pre-processing helpers +// ============================================================================ + +/** + * Apply 2D Hann window in-place to reduce spectral leakage in ROI FFT. + * + * When an ROI is cropped from an image, the sharp rectangular boundary acts as + * a rect window whose sinc sidelobes produce streak artifacts in the FFT, + * obscuring real spectral features (Bragg spots, lattice frequencies). + * The Hann window smoothly tapers data to zero at all edges, suppressing + * sidelobes by ~31 dB at the cost of a slightly wider main lobe. + * + * Separable: window2D = outer(hann_h, hann_w), applied as element-wise multiply. + * Symmetric formula: w(i) = 0.5*(1 - cos(2πi/(N-1))), matching np.hanning — + * both endpoints are exactly zero for seamless transition to zero-padded regions. + * (Periodic variant ÷N is for overlapping STFT windows, not for zero-padding.) + * + * IMPORTANT: Must be called on the crop at its native dimensions BEFORE + * zero-padding to power-of-2. Window-then-pad ensures no discontinuity at the + * crop/pad boundary. Pad-then-window applies the wrong taper and reintroduces + * leakage. Validated against np.hanning in test_widget_show2d.py. + */ +export function applyHannWindow2D(data: Float32Array, width: number, height: number): void { + const hannW = new Float32Array(width); + const hannH = new Float32Array(height); + const wDenom = width > 1 ? width - 1 : 1; + const hDenom = height > 1 ? height - 1 : 1; + for (let i = 0; i < width; i++) hannW[i] = 0.5 * (1 - Math.cos((2 * Math.PI * i) / wDenom)); + for (let i = 0; i < height; i++) hannH[i] = 0.5 * (1 - Math.cos((2 * Math.PI * i) / hDenom)); + for (let r = 0; r < height; r++) { + const hr = hannH[r]; + const offset = r * width; + for (let c = 0; c < width; c++) data[offset + c] *= hr * hannW[c]; + } +} + +// ============================================================================ +// FFT post-processing helpers +// ============================================================================ + +/** Compute magnitude from complex FFT output: sqrt(real² + imag²). */ +export function computeMagnitude(real: Float32Array, imag: Float32Array): Float32Array { + const mag = new Float32Array(real.length); + for (let i = 0; i < mag.length; i++) { + mag[i] = Math.sqrt(real[i] * real[i] + imag[i] * imag[i]); + } + return mag; +} + +/** Mask DC component (center pixel) and return 99.9% percentile-clipped range. Mutates `mag`. */ +export function autoEnhanceFFT( + mag: Float32Array, width: number, height: number, +): { min: number; max: number } { + const centerIdx = Math.floor(height / 2) * width + Math.floor(width / 2); + const neighbors = [ + mag[Math.max(0, centerIdx - 1)], + mag[Math.min(mag.length - 1, centerIdx + 1)], + mag[Math.max(0, centerIdx - width)], + mag[Math.min(mag.length - 1, centerIdx + width)], + ]; + mag[centerIdx] = neighbors.reduce((a, b) => a + b, 0) / 4; + // Use O(n) histogram approach instead of O(n log n) sort + const len = mag.length; + if (len === 0) return { min: 0, max: 0 }; + let dMin = Infinity, dMax = -Infinity; + for (let i = 0; i < len; i++) { + const v = mag[i]; + if (v < dMin) dMin = v; + if (v > dMax) dMax = v; + } + if (dMin === dMax) return { min: dMin, max: dMax }; + const NUM_BINS = 1024; + const bins = new Uint32Array(NUM_BINS); + const range = dMax - dMin; + const scale = (NUM_BINS - 1) / range; + for (let i = 0; i < len; i++) bins[Math.floor((mag[i] - dMin) * scale)]++; + // Find 99.9th percentile + const target = Math.ceil(len * 0.999); + let cumSum = 0; + let pMax = dMax; + for (let i = 0; i < NUM_BINS; i++) { + cumSum += bins[i]; + if (cumSum >= target) { pMax = dMin + (i / (NUM_BINS - 1)) * range; break; } + } + // If percentile collapsed to min (sparse spectra), fall back to actual max + if (pMax <= dMin) pMax = dMax; + return { min: dMin, max: pMax }; +} + +// ============================================================================ +// Singleton +// ============================================================================ + +let gpuFFT: WebGPUFFT | null = null; +let gpuDevice: GPUDevice | null = null; +let gpuInfo = "GPU"; + +export async function getGPUDevice(): Promise { + if (gpuDevice) return gpuDevice; + if (!navigator.gpu) return null; + try { + const adapter = await navigator.gpu.requestAdapter(); + if (!adapter) return null; + try { + // @ts-ignore - requestAdapterInfo is not yet in all type definitions + const info = await adapter.requestAdapterInfo?.(); + if (info) { + gpuInfo = info.description || `${info.vendor} ${info.architecture || ""} ${info.device || ""}`.trim() || "Generic WebGPU Adapter"; + } + } catch (_e) { /* adapter info not available */ } + gpuDevice = await adapter.requestDevice(); + return gpuDevice; + } catch { return null; } +} + +export async function getWebGPUFFT(): Promise { + if (gpuFFT) return gpuFFT; + const device = await getGPUDevice(); + if (!device) { console.warn('WebGPU not supported, falling back to CPU FFT'); return null; } + try { + gpuFFT = new WebGPUFFT(device); + await gpuFFT.init(); + return gpuFFT; + } catch (e) { console.warn('WebGPU init failed:', e); return null; } +} + +export function getGPUInfo(): string { return gpuInfo; } diff --git a/widget/package-lock.json b/widget/package-lock.json index 4e039394..cf4e386a 100644 --- a/widget/package-lock.json +++ b/widget/package-lock.json @@ -6,22 +6,32 @@ "": { "name": 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"license": "MIT", - "peer": true, "dependencies": { "esbuild": "^0.21.3", "postcss": "^8.4.43", @@ -1729,6 +2583,15 @@ "integrity": "sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==", "dev": true, "license": "ISC" + }, + "node_modules/yaml": { + "version": "1.10.3", + "resolved": "https://registry.npmjs.org/yaml/-/yaml-1.10.3.tgz", + "integrity": "sha512-vIYeF1u3CjlhAFekPPAk2h/Kv4T3mAkMox5OymRiJQB0spDP10LHvt+K7G9Ny6NuuMAb25/6n1qyUjAcGNf/AA==", + "license": "ISC", + "engines": { + "node": ">= 6" + } } } } diff --git a/widget/package.json b/widget/package.json index 4abc343d..3d0175ae 100644 --- a/widget/package.json +++ b/widget/package.json @@ -3,16 +3,26 @@ "type": "module", "scripts": { "dev": "vite build --watch", - "build": "vite build" + "build": "vite build", + "typecheck": "tsc --noEmit" }, "dependencies": { - "react": "^18.2.0", - "react-dom": "^18.2.0", - "@anywidget/react": "^0.1.0" + "@anywidget/react": "^0.2.0", + "@emotion/react": "^11.14.0", + "@emotion/styled": "^11.14.1", + "@mui/icons-material": "^7.3.7", + "@mui/material": "^7.3.6", + "jszip": "^3.10.1", + "react": "^19.1.0", + "react-dom": "^19.1.0" }, "devDependencies": { - "vite": "^5.2.0", "@anywidget/vite": "^0.2.0", - "@vitejs/plugin-react": "^4.3.0" + "@types/react": "^19.1.3", + "@types/react-dom": "^19.1.4", + "@vitejs/plugin-react": "^4.3.0", + "@webgpu/types": "^0.1.68", + "typescript": "^5.8.3", + "vite": "^5.2.0" } } diff --git a/widget/pyproject.toml b/widget/pyproject.toml index c0fb7a63..738c7bc9 100644 --- a/widget/pyproject.toml +++ b/widget/pyproject.toml @@ -10,6 +10,11 @@ license = "MIT" requires-python = ">=3.11" dependencies = [ "anywidget>=0.9.0", + "numpy>=2.0.0", + "traitlets>=5.0.0", + "torch>=2.0.0", + "matplotlib>=3.7.0", + "Pillow>=10.0.0", ] [tool.hatch.build.targets.wheel] diff --git a/widget/src/quantem/widget/__init__.py b/widget/src/quantem/widget/__init__.py index d4ca85a7..dc98e36a 100644 --- a/widget/src/quantem/widget/__init__.py +++ b/widget/src/quantem/widget/__init__.py @@ -1,24 +1,7 @@ from importlib.metadata import version -import pathlib -import anywidget -import traitlets -__version__ = version("quantem.widget") - -_static = pathlib.Path(__file__).parent / "static" - - -class CounterWidget(anywidget.AnyWidget): - _esm = _static / "index.js" - - count = traitlets.Int(0).tag(sync=True) +from quantem.widget.show2d import Show2D +from quantem.widget.show4dstem import Show4DSTEM - -def show4dstem(): - # TODO: Implement 4D-STEM visualization widget - print("show4dstem: not yet implemented") - - -def counter(): - """Create a minimal counter widget for testing.""" - return CounterWidget() +__version__ = version("quantem.widget") +__all__ = ["Show2D", "Show4DSTEM"] diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py new file mode 100644 index 00000000..e86633e6 --- /dev/null +++ b/widget/src/quantem/widget/array_utils.py @@ -0,0 +1,282 @@ +""" +Array utilities for handling NumPy, CuPy, and PyTorch arrays uniformly. + +This module provides utilities to convert arrays from different backends +into NumPy arrays for widget processing. +""" + +from typing import Any, Literal +import numpy as np + +try: + import torch + import torch.nn.functional as F + _HAS_TORCH = True +except ImportError: + _HAS_TORCH = False + + +ArrayBackend = Literal["numpy", "cupy", "torch", "unknown"] + + +def get_array_backend(data: Any) -> ArrayBackend: + """ + Detect the array backend of the input data. + + Parameters + ---------- + data : array-like + Input array (NumPy, CuPy, PyTorch, or other). + + Returns + ------- + str + One of: "numpy", "cupy", "torch", "unknown" + """ + # Check PyTorch first (has both .numpy and .detach methods) + if hasattr(data, "detach") and hasattr(data, "numpy"): + return "torch" + # Check CuPy (has .get() or __cuda_array_interface__) + if hasattr(data, "__cuda_array_interface__"): + return "cupy" + if hasattr(data, "get") and hasattr(data, "__array__"): + # CuPy arrays have .get() to transfer to CPU + type_name = type(data).__module__ + if "cupy" in type_name: + return "cupy" + # Check NumPy + if isinstance(data, np.ndarray): + return "numpy" + return "unknown" + + +def to_numpy(data: Any, dtype: np.dtype | None = None) -> np.ndarray: + """ + Convert any array-like (NumPy, CuPy, PyTorch) to a NumPy array. + + Parameters + ---------- + data : array-like + Input array from any supported backend. + dtype : np.dtype, optional + Target dtype for the output array. If None, preserves original dtype. + + Returns + ------- + np.ndarray + NumPy array with the same data. + + Examples + -------- + >>> import numpy as np + >>> from quantem.widget.array_utils import to_numpy + >>> + >>> # NumPy passthrough + >>> arr = np.random.rand(10, 10) + >>> result = to_numpy(arr) + >>> + >>> # CuPy conversion (if available) + >>> import cupy as cp + >>> gpu_arr = cp.random.rand(10, 10) + >>> cpu_arr = to_numpy(gpu_arr) + >>> + >>> # PyTorch conversion (if available) + >>> import torch + >>> tensor = torch.rand(10, 10) + >>> arr = to_numpy(tensor) + """ + backend = get_array_backend(data) + + if backend == "torch": + # PyTorch tensor: detach from graph, move to CPU, convert to numpy + result = data.detach().cpu().numpy() + + elif backend == "cupy": + # CuPy array: use .get() to transfer to CPU + if hasattr(data, "get"): + result = data.get() + else: + # Fallback for __cuda_array_interface__ + import cupy as cp + + result = cp.asnumpy(data) + + elif backend == "numpy": + # NumPy array: passthrough (may copy if dtype changes) + result = data + + else: + # Unknown backend: try np.asarray as fallback + result = np.asarray(data) + + # Apply dtype conversion if specified + if dtype is not None: + result = np.asarray(result, dtype=dtype) + + return result + + +def bin2d(data, factor: int = 2, mode: str = "mean", edge_mode: str = "crop") -> np.ndarray: + """ + Spatial binning for 2D or 3D arrays. + + Uses torch GPU (MPS/CUDA) when available for large arrays (~5× faster on 4K data). + + Parameters + ---------- + data : array-like + Input array with shape ``(H, W)`` or ``(N, H, W)``. + factor : int, default 2 + Bin factor. + mode : str, default "mean" + Reduction mode: ``"mean"`` or ``"sum"``. + edge_mode : str, default "crop" + How to handle dimensions not divisible by *factor*: + ``"crop"`` trims extra pixels, ``"pad"`` zero-pads to the next + multiple (output shape uses ``ceil(dim / factor)``). + + Returns + ------- + np.ndarray + Binned array, dtype float32. + """ + arr = to_numpy(data) + if arr.dtype != np.float32: + arr = arr.astype(np.float32) + + # Torch GPU fast path: only for arrays between 1M and 500M elements. + # Larger arrays hit MPS memory transfer bottleneck (>2 GB transfer > CPU compute). + import torch + if 1_000_000 < arr.size < 500_000_000 and (torch.backends.mps.is_available() or torch.cuda.is_available()): + dev = torch.device("mps" if torch.backends.mps.is_available() else "cuda") + t = torch.from_numpy(arr).to(dev) + if t.ndim == 2: + h, w = t.shape + oh = h // factor * factor + ow = w // factor * factor + t = t[:oh, :ow].reshape(oh // factor, factor, ow // factor, factor) + t = t.sum(dim=(1, 3)) if mode == "sum" else t.mean(dim=(1, 3)) + elif t.ndim == 3: + n, h, w = t.shape + oh = h // factor * factor + ow = w // factor * factor + t = t[:, :oh, :ow].reshape(n, oh // factor, factor, ow // factor, factor) + t = t.sum(dim=(2, 4)) if mode == "sum" else t.mean(dim=(2, 4)) + return t.cpu().numpy().astype(np.float32) + + # CPU fallback (no GPU available or small array) + reduce = np.ndarray.sum if mode == "sum" else np.ndarray.mean + if arr.ndim == 2: + arr = _pad_or_crop_2d(arr, factor, edge_mode) + h, w = arr.shape + oh, ow = h // factor, w // factor + return reduce(arr.reshape(oh, factor, ow, factor), axis=(1, 3)).astype(np.float32) + # 3D: (N, H, W) + arr = _pad_or_crop_3d(arr, factor, edge_mode) + n, h, w = arr.shape + oh, ow = h // factor, w // factor + return reduce(arr.reshape(n, oh, factor, ow, factor), axis=(2, 4)).astype(np.float32) + + +def _pad_or_crop_2d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: + h, w = arr.shape + if edge_mode == "pad": + pad_h = (factor - h % factor) % factor + pad_w = (factor - w % factor) % factor + if pad_h or pad_w: + arr = np.pad(arr, ((0, pad_h), (0, pad_w)), mode="constant") + else: + oh, ow = h // factor, w // factor + arr = arr[:oh * factor, :ow * factor] + return arr + + +def _pad_or_crop_3d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: + _, h, w = arr.shape + if edge_mode == "pad": + pad_h = (factor - h % factor) % factor + pad_w = (factor - w % factor) % factor + if pad_h or pad_w: + arr = np.pad(arr, ((0, 0), (0, pad_h), (0, pad_w)), mode="constant") + else: + oh, ow = h // factor, w // factor + arr = arr[:, :oh * factor, :ow * factor] + return arr + + +def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: + """ + Apply sub-pixel shift using bilinear interpolation. + + Uses ``torch.nn.functional.grid_sample`` on GPU when torch is available, + falls back to numpy bilinear interpolation otherwise. + + Parameters + ---------- + img : np.ndarray + 2D image, float32. + dy : float + Shift in y (rows). + dx : float + Shift in x (columns). + + Returns + ------- + np.ndarray + Shifted image, same shape, float32. Out-of-bounds pixels are zero. + """ + if _HAS_TORCH: + h, w = img.shape + device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu") + t = torch.as_tensor(img, dtype=torch.float32, device=device).unsqueeze(0).unsqueeze(0) + base_y = torch.linspace(-1, 1, h, device=device) + base_x = torch.linspace(-1, 1, w, device=device) + gy, gx = torch.meshgrid(base_y, base_x, indexing="ij") + grid = torch.stack([gx - dx * 2.0 / w, gy - dy * 2.0 / h], dim=-1).unsqueeze(0) + result = F.grid_sample(t, grid, mode="bilinear", padding_mode="zeros", align_corners=True) + return result.squeeze().cpu().numpy() + h, w = img.shape + y_src = np.arange(h, dtype=np.float64) - dy + x_src = np.arange(w, dtype=np.float64) - dx + yy, xx = np.meshgrid(y_src, x_src, indexing="ij") + y0 = np.floor(yy).astype(int) + x0 = np.floor(xx).astype(int) + fy = (yy - y0).astype(np.float32) + fx = (xx - x0).astype(np.float32) + valid = (y0 >= 0) & (y0 + 1 < h) & (x0 >= 0) & (x0 + 1 < w) + y0c = np.clip(y0, 0, h - 2) + x0c = np.clip(x0, 0, w - 2) + result = (img[y0c, x0c] * (1 - fy) * (1 - fx) + + img[y0c, x0c + 1] * (1 - fy) * fx + + img[y0c + 1, x0c] * fy * (1 - fx) + + img[y0c + 1, x0c + 1] * fy * fx) + result[~valid] = 0.0 + return result.astype(np.float32) + + +def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: + """Resize image using bilinear interpolation (pure numpy, no scipy).""" + h, w = img.shape + + if h == target_h and w == target_w: + return img + + y_new = np.linspace(0, h - 1, target_h) + x_new = np.linspace(0, w - 1, target_w) + x_grid, y_grid = np.meshgrid(x_new, y_new) + + y0 = np.floor(y_grid).astype(int) + x0 = np.floor(x_grid).astype(int) + y1 = np.minimum(y0 + 1, h - 1) + x1 = np.minimum(x0 + 1, w - 1) + + fy = y_grid - y0 + fx = x_grid - x0 + + result = ( + img[y0, x0] * (1 - fy) * (1 - fx) + + img[y0, x1] * (1 - fy) * fx + + img[y1, x0] * fy * (1 - fx) + + img[y1, x1] * fy * fx + ) + return result.astype(img.dtype) diff --git a/widget/src/quantem/widget/json_state.py b/widget/src/quantem/widget/json_state.py new file mode 100644 index 00000000..4874981f --- /dev/null +++ b/widget/src/quantem/widget/json_state.py @@ -0,0 +1,47 @@ +import importlib.metadata +import json +import pathlib +from typing import Any + + +JSON_METADATA_VERSION = "1.0" + + +def resolve_widget_version() -> str: + try: + return importlib.metadata.version("quantem-widget") + except importlib.metadata.PackageNotFoundError: + return "unknown" + except Exception: + return "unknown" + + +def build_json_header(widget_name: str) -> dict[str, Any]: + return { + "metadata_version": JSON_METADATA_VERSION, + "widget_name": widget_name, + "widget_version": resolve_widget_version(), + } + + +def wrap_state_dict(widget_name: str, state: dict[str, Any]) -> dict[str, Any]: + envelope = build_json_header(widget_name) + envelope["state"] = state + return envelope + + +def unwrap_state_payload(payload: dict[str, Any], *, require_envelope: bool = False) -> dict[str, Any]: + if not isinstance(payload, dict): + raise ValueError("State payload must be a dict.") + if "state" in payload: + state = payload["state"] + if not isinstance(state, dict): + raise ValueError("State envelope field 'state' must be a dict.") + return state + if require_envelope: + raise ValueError("State JSON file must be a versioned envelope with top-level 'state'.") + return payload + + +def save_state_file(path: str | pathlib.Path, widget_name: str, state: dict[str, Any]) -> None: + pathlib.Path(path).write_text(json.dumps(wrap_state_dict(widget_name, state), indent=2)) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py new file mode 100644 index 00000000..08031ac7 --- /dev/null +++ b/widget/src/quantem/widget/show2d.py @@ -0,0 +1,1309 @@ +""" +show2d: Static 2D image viewer with optional FFT and histogram analysis. + +For displaying a single image or a static gallery of multiple images. +Unlike Show3D (interactive), Show2D focuses on static visualization. +""" + +import json +import os +import pathlib +import io +import base64 +import math +import warnings +from enum import StrEnum +from typing import Optional, Union, List, Self + +import anywidget +import matplotlib +import matplotlib.patheffects +import matplotlib.pyplot as plt +import numpy as np +import traitlets + +from quantem.widget.array_utils import to_numpy, _resize_image +from quantem.widget.json_state import resolve_widget_version, save_state_file, unwrap_state_payload +from quantem.widget.tool_parity import ( + bind_tool_runtime_api, + build_tool_groups, + normalize_tool_groups, +) + + + +def _reject_unknown_kwargs(cls, kwargs: dict) -> None: + """Raise TypeError if kwargs contains any key that isn't a declared trait. + + anywidget/traitlets silently accept unknown keys, which let stale notebooks + pass obsolete params like ``pixel_size_angstrom=0.5`` with no warning. This + helper catches typos and renamed-trait references at construction time. + """ + traits = set(cls.class_trait_names()) + unknown = [k for k in kwargs if k not in traits] + if unknown: + key = sorted(unknown)[0] + raise TypeError( + f"{cls.__name__}() got unexpected keyword argument {key!r}. " + f"Check for typos or a renamed parameter (e.g. canvas_size → size, " + f"image_width_px → size, pixel_size_angstrom → pixel_size)." + ) + + +def _round_to_nice(value: float) -> float: + """Round a physical length to a 'nice' value (1, 2, 5, 10, 20, 50, ...).""" + if value <= 0: + return 1.0 + exp = math.floor(math.log10(value)) + base = 10 ** exp + mantissa = value / base + if mantissa < 1.5: + return base + elif mantissa < 3.5: + return 2 * base + elif mantissa < 7.5: + return 5 * base + else: + return 10 * base + + +class Colormap(StrEnum): + INFERNO = "inferno" + VIRIDIS = "viridis" + MAGMA = "magma" + PLASMA = "plasma" + GRAY = "gray" + + +class Show2D(anywidget.AnyWidget): + """ + Static 2D image viewer with optional FFT and histogram analysis. + + Display a single image or multiple images in a gallery layout. + For interactive stack viewing with playback, use Show3D instead. + + Parameters + ---------- + data : array_like + 2D array (height, width) for single image, or + 3D array (N, height, width) for multiple images displayed as gallery. + labels : list of str, optional + Labels for each image in gallery mode. + title : str, optional + Title to display above the image(s). + cmap : str, default "inferno" + Colormap name ("magma", "viridis", "gray", "inferno", "plasma"). + pixel_size : float, optional + Pixel size in angstroms for scale bar display. + show_fft : bool, default False + Show FFT and histogram panels. + show_stats : bool, default True + Show statistics (mean, min, max, std). + log_scale : bool, default False + Use log scale for intensity mapping. + auto_contrast : bool, default False + Use percentile-based contrast. + vmin : float, optional + Absolute minimum intensity for color mapping. When both vmin and vmax + are set, all gallery images share the same intensity scale — essential + for A/B visual comparison. + vmax : float, optional + Absolute maximum intensity for color mapping. + ncols : int, default 3 + Number of columns in gallery mode. + size : int, default 0 + Canvas rendering size in CSS pixels (the on-screen width of each image). + ``0`` uses the frontend default: 500 px for a single image, 300 px per + image in gallery mode. Pass e.g. ``size=800`` to enlarge for a + presentation, or ``size=200`` to compress alongside a control panel. + This controls **display only** — the underlying image resolution is + never resampled; zooming into a 4K image preserves every pixel. + disabled_tools : list of str, optional + Tool groups to lock while still showing controls. Supported: + ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, + ``"view"``, ``"export"``, ``"roi"``, ``"profile"``, ``"all"``. + disable_* : bool, optional + Convenience flags (``disable_display``, ``disable_histogram``, + ``disable_stats``, ``disable_navigation``, ``disable_view``, + ``disable_export``, ``disable_roi``, ``disable_profile``, + ``disable_all``) equivalent to adding those keys to + ``disabled_tools``. + hidden_tools : list of str, optional + Tool groups to hide from the UI. Uses the same keys as + ``disabled_tools``. + hide_* : bool, optional + Convenience flags mirroring ``disable_*`` for ``hidden_tools``. + + Attributes + ---------- + render_total_ms : int or None + End-to-end wall clock from constructor start to first browser paint, + populated by a JS→Python round-trip after the first canvas render. + ``None`` until the browser has actually painted; also printed to stdout + when it fires. Use to triage "is it Python, wire, or the browser?" + during live acquisitions. + render_python_build_ms : int or None + Subset of ``render_total_ms`` covering Python ``__init__`` only. + render_wire_js_ms : int or None + Subset covering everything after Python returns: Comm transfer, JS + decode, colormap, and canvas paint. + + Examples + -------- + >>> import numpy as np + >>> from quantem.widget import Show2D + >>> + >>> # Single image with FFT + >>> Show2D(image, title="HRTEM Image", show_fft=True, pixel_size=1.0) + >>> + >>> # Gallery of multiple images + >>> labels = ["Raw", "Filtered", "FFT"] + >>> Show2D([img1, img2, img3], labels=labels, ncols=3) + """ + + _esm = pathlib.Path(__file__).parent / "static" / "show2d.js" + _css = pathlib.Path(__file__).parent / "static" / "show2d.css" + + # ========================================================================= + # Core State + # GPU memory budget for display buffers (MB). Each 4K image needs ~192 MB. + # 12×4K = 2304 MB fits. 16+ triggers auto-bin. + _GPU_DISPLAY_BUDGET_MB = 2500 + + # ========================================================================= + widget_version = traitlets.Unicode("unknown").tag(sync=True) + n_images = traitlets.Int(1).tag(sync=True) + height = traitlets.Int(1).tag(sync=True) + width = traitlets.Int(1).tag(sync=True) + _display_bin_factor = traitlets.Int(1).tag(sync=True) # 1 = full-res, 2/4/8 = binned + _gpu_max_buffer_mb = traitlets.Int(0).tag(sync=True) # GPU reports maxBufferSize (JS→Python) + # Flipped True by JS after the first colormap pass has painted to canvas. + # Used by the Python-side truthful timing print (end-to-end wall clock, not just __init__). + _js_rendered = traitlets.Bool(False).tag(sync=True) + frame_bytes = traitlets.Bytes(b"").tag(sync=True) + labels = traitlets.List(traitlets.Unicode()).tag(sync=True) + title = traitlets.Unicode("").tag(sync=True) + cmap = traitlets.Unicode("inferno").tag(sync=True) + ncols = traitlets.Int(3).tag(sync=True) + + # ========================================================================= + # Display Options + # ========================================================================= + log_scale = traitlets.Bool(False).tag(sync=True) + auto_contrast = traitlets.Bool(False).tag(sync=True) + vmin = traitlets.Float(None, allow_none=True).tag(sync=True) + vmax = traitlets.Float(None, allow_none=True).tag(sync=True) + vmins = traitlets.List(trait=traitlets.Float(allow_none=True), allow_none=True, default_value=None).tag(sync=True) + vmaxs = traitlets.List(trait=traitlets.Float(allow_none=True), allow_none=True, default_value=None).tag(sync=True) + + # ========================================================================= + # Scale Bar + # ========================================================================= + pixel_size = traitlets.Float(0.0).tag(sync=True) + scale_bar_visible = traitlets.Bool(True).tag(sync=True) + size = traitlets.Int(0).tag(sync=True) # Canvas rendering size in CSS pixels; 0 = frontend default + smooth = traitlets.Bool(False).tag(sync=True) + initial_zoom = traitlets.Float(1.0).tag(sync=True) + zoom_row = traitlets.Float(None, allow_none=True).tag(sync=True) + zoom_col = traitlets.Float(None, allow_none=True).tag(sync=True) + link_zoom = traitlets.Bool(False).tag(sync=True) + link_pan = traitlets.Bool(False).tag(sync=True) + link_contrast = traitlets.Bool(True).tag(sync=True) + diff_mode = traitlets.Bool(False).tag(sync=True) + diff_reference = traitlets.Int(0).tag(sync=True) + + # ========================================================================= + # UI Visibility + # ========================================================================= + show_controls = traitlets.Bool(True).tag(sync=True) + show_stats = traitlets.Bool(True).tag(sync=True) + disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + stats_mean = traitlets.List(traitlets.Float()).tag(sync=True) + stats_min = traitlets.List(traitlets.Float()).tag(sync=True) + stats_max = traitlets.List(traitlets.Float()).tag(sync=True) + stats_std = traitlets.List(traitlets.Float()).tag(sync=True) + + # ========================================================================= + # Analysis Panels (FFT + Histogram shown together) + # ========================================================================= + show_fft = traitlets.Bool(False).tag(sync=True) + fft_window = traitlets.Bool(True).tag(sync=True) + + # ========================================================================= + # Selected Image (for single-image analysis display) + # ========================================================================= + selected_idx = traitlets.Int(0).tag(sync=True) + + # ========================================================================= + # ROI Selection + # ========================================================================= + roi_active = traitlets.Bool(False).tag(sync=True) + roi_list = traitlets.List([]).tag(sync=True) + roi_selected_idx = traitlets.Int(-1).tag(sync=True) + + # ========================================================================= + # Line Profile + # ========================================================================= + profile_line = traitlets.List(traitlets.Dict()).tag(sync=True) + + # ========================================================================= + # Per-Image Rotation + # ========================================================================= + image_rotations = traitlets.List(traitlets.Int(), []).tag(sync=True) + + @classmethod + def _normalize_tool_groups(cls, tool_groups) -> List[str]: + return normalize_tool_groups("Show2D", tool_groups) + + @classmethod + def _build_disabled_tools( + cls, + disabled_tools=None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_all: bool = False, + ) -> List[str]: + return build_tool_groups( + "Show2D", + tool_groups=disabled_tools, + all_flag=disable_all, + flag_map={ + "display": disable_display, + "histogram": disable_histogram, + "stats": disable_stats, + "navigation": disable_navigation, + "view": disable_view, + "export": disable_export, + "roi": disable_roi, + "profile": disable_profile, + }, + ) + + @classmethod + def _build_hidden_tools( + cls, + hidden_tools=None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_all: bool = False, + ) -> List[str]: + return build_tool_groups( + "Show2D", + tool_groups=hidden_tools, + all_flag=hide_all, + flag_map={ + "display": hide_display, + "histogram": hide_histogram, + "stats": hide_stats, + "navigation": hide_navigation, + "view": hide_view, + "export": hide_export, + "roi": hide_roi, + "profile": hide_profile, + }, + ) + + @traitlets.validate("disabled_tools") + def _validate_disabled_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + @traitlets.validate("hidden_tools") + def _validate_hidden_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + def __init__( + self, + data: Union[np.ndarray, List[np.ndarray]], + labels: Optional[List[str]] = None, + title: str = "", + cmap: Union[str, Colormap] = Colormap.INFERNO, + pixel_size: float = 0.0, + scale_bar_visible: bool = True, + show_fft: bool = False, + fft_window: bool = True, + show_controls: bool = True, + show_stats: bool = True, + log_scale: bool = False, + auto_contrast: bool = False, + vmin: float | list | None = None, + vmax: float | list | None = None, + disabled_tools: Optional[List[str]] = None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_all: bool = False, + hidden_tools: Optional[List[str]] = None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_all: bool = False, + ncols: int = 3, + size: int = 0, + smooth: bool = False, + zoom: float = 1.0, + zoom_row: float | None = None, + zoom_col: float | None = None, + link_zoom: bool = False, + link_pan: bool = False, + link_contrast: bool = True, + diff_mode: bool = False, + view_box: tuple | list | None = None, + display_bin: Union[int, str] = "auto", + state=None, + **kwargs, + ): + import time as _time + _t0 = _time.perf_counter() + # Reject typos and stale kwargs (e.g. image_width_px, pixel_size_angstrom). + # anywidget/traitlets silently ignores unknown keys, which hid the + # pixel_size_angstrom bug in show2d_all_features.ipynb for months. + _reject_unknown_kwargs(type(self), kwargs) + super().__init__(**kwargs) + # hold_sync() batches ALL traitlet assignments into a single comm message + # sent when the context manager exits. Without this, each self.x = y + # fires a separate round-trip over the ZMQ/websocket channel, which + # can add 20+ seconds for a 30-image gallery in VS Code Jupyter. + with self.hold_sync(): + self._init_sync( + data=data, labels=labels, title=title, cmap=cmap, + pixel_size=pixel_size, scale_bar_visible=scale_bar_visible, + show_fft=show_fft, fft_window=fft_window, + show_controls=show_controls, show_stats=show_stats, + log_scale=log_scale, auto_contrast=auto_contrast, + vmin=vmin, vmax=vmax, + disabled_tools=disabled_tools, + disable_display=disable_display, + disable_histogram=disable_histogram, + disable_stats=disable_stats, + disable_navigation=disable_navigation, + disable_view=disable_view, + disable_export=disable_export, + disable_roi=disable_roi, + disable_profile=disable_profile, + disable_all=disable_all, + hidden_tools=hidden_tools, + hide_display=hide_display, + hide_histogram=hide_histogram, + hide_stats=hide_stats, + hide_navigation=hide_navigation, + hide_view=hide_view, + hide_export=hide_export, + hide_roi=hide_roi, + hide_profile=hide_profile, + hide_all=hide_all, + ncols=ncols, size=size, smooth=smooth, zoom=zoom, + zoom_row=zoom_row, zoom_col=zoom_col, + link_zoom=link_zoom, link_pan=link_pan, link_contrast=link_contrast, + diff_mode=diff_mode, view_box=view_box, + display_bin=display_bin, state=state, _t0=_t0) + + def _init_sync(self, *, data, labels, title, cmap, pixel_size, + scale_bar_visible, show_fft, fft_window, + show_controls, show_stats, log_scale, auto_contrast, + vmin, vmax, disabled_tools, + disable_display, disable_histogram, disable_stats, + disable_navigation, disable_view, disable_export, + disable_roi, disable_profile, disable_all, + hidden_tools, hide_display, hide_histogram, hide_stats, + hide_navigation, hide_view, hide_export, hide_roi, + hide_profile, hide_all, + ncols, size, smooth, zoom, zoom_row, zoom_col, + link_zoom, link_pan, link_contrast, diff_mode, view_box, + display_bin, state, _t0): + import time as _time + self.widget_version = resolve_widget_version() + self._display_data = None # initialized after data setup + self._display_bin = 1 + + # Check if data is a Dataset2d and extract metadata + if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): + if not title and data.name: + title = data.name + if pixel_size == 0.0 and hasattr(data, "units"): + units = list(data.units) + sampling_val = float(data.sampling[-1]) + if units[-1] in ("nm",): + pixel_size = sampling_val * 10 # nm → Å + elif units[-1] in ("Å", "angstrom", "A"): + pixel_size = sampling_val + data = data.array + + # Convert input to NumPy (handles NumPy, CuPy, PyTorch) + if isinstance(data, list): + images = [to_numpy(d) for d in data] + + # Check if all images have the same shape + shapes = [img.shape for img in images] + if len(set(shapes)) > 1: + # Different sizes - resize all to the largest + max_h = max(s[0] for s in shapes) + max_w = max(s[1] for s in shapes) + images = [_resize_image(img, max_h, max_w) for img in images] + + data = np.stack(images) + else: + data = to_numpy(data) + + # Ensure 3D shape (N, H, W) + if data.ndim == 2: + data = data[np.newaxis, ...] + + # Avoid redundant copy: np.asarray is a no-op when already float32 + contiguous + if data.dtype == np.float32: + self._data = np.array(data, dtype=np.float32, copy=True) + else: + self._data = np.asarray(data, dtype=np.float32) + # Store originals for rotation reset — views into _data (no copy). + # Only materialized as independent copies when a rotation is applied. + self._data_original = [self._data[i] for i in range(self._data.shape[0])] + self._originals_are_views = True + self.n_images = int(data.shape[0]) + self.height = int(data.shape[1]) + self.width = int(data.shape[2]) + self.image_rotations = [0] * self.n_images + + # Labels + if labels is None: + self.labels = [f"Image {i+1}" for i in range(self.n_images)] + else: + self.labels = list(labels) + + # Options + self.title = title + self.cmap = cmap + self.pixel_size = pixel_size + self.scale_bar_visible = scale_bar_visible + self.size = size + self.smooth = smooth + # view_box sugar: sets zoom + zoom_row/col to center on box + if view_box is not None: + r0, r1, c0, c1 = [float(v) for v in view_box] + box_h = max(1.0, r1 - r0) + box_w = max(1.0, c1 - c0) + zoom = float(min(self.height / box_h, self.width / box_w)) + zoom_row = (r0 + r1) / 2 + zoom_col = (c0 + c1) / 2 + self.initial_zoom = zoom + self.zoom_row = zoom_row + self.zoom_col = zoom_col + self.link_zoom = link_zoom + self.link_pan = link_pan + self.link_contrast = link_contrast + self.diff_mode = diff_mode if self.n_images >= 2 else False + if show_fft and self.height * self.width > 2048 * 2048: + warnings.warn( + f"FFT on {self.height}×{self.width} image ({self.height * self.width / 1e6:.1f}M pixels) " + f"may be slow. Consider using ROI FFT for a sub-region.", + stacklevel=2, + ) + self.show_fft = show_fft + self.fft_window = fft_window + self.show_controls = show_controls + self.show_stats = show_stats + self.log_scale = log_scale + self.auto_contrast = auto_contrast + # Accept scalar OR list for vmin/vmax. List → per-image (vmins/vmaxs). + if isinstance(vmin, (list, tuple)) or isinstance(vmax, (list, tuple)): + n = self.n_images + def _expand(v): + if v is None: return [None] * n + if isinstance(v, (list, tuple)): + if len(v) != n: + raise ValueError(f"vmin/vmax list length {len(v)} != n_images {n}") + return [None if x is None else float(x) for x in v] + return [float(v)] * n + self.vmins = _expand(vmin) + self.vmaxs = _expand(vmax) + self.vmin = None + self.vmax = None + else: + self.vmin = vmin + self.vmax = vmax + self.disabled_tools = self._build_disabled_tools( + disabled_tools=disabled_tools, + disable_display=disable_display, + disable_histogram=disable_histogram, + disable_stats=disable_stats, + disable_navigation=disable_navigation, + disable_view=disable_view, + disable_export=disable_export, + disable_roi=disable_roi, + disable_profile=disable_profile, + disable_all=disable_all, + ) + self.hidden_tools = self._build_hidden_tools( + hidden_tools=hidden_tools, + hide_display=hide_display, + hide_histogram=hide_histogram, + hide_stats=hide_stats, + hide_navigation=hide_navigation, + hide_view=hide_view, + hide_export=hide_export, + hide_roi=hide_roi, + hide_profile=hide_profile, + hide_all=hide_all, + ) + self.ncols = ncols + + # Auto-bin for display: keep full-res in _data, send binned to JS. + # GPU memory budget: ~2 GB for display buffers (128 MB per image at 4K). + # At 4K: max ~16 full-res. Beyond that, auto-downsample. + if display_bin == "auto": + # Each 4K image needs ~192 MB GPU buffers (float32 + RGBA + read) + # Tested: 12×4K (2.3 GB) works, 24×4K (4.6 GB) OOMs + # Budget: 2.5 GB allows 12×4K full-res, bins above that + gpu_budget_mb = self._GPU_DISPLAY_BUDGET_MB + per_image_mb = (self.height * self.width * 4 * 3) / (1024 * 1024) # 3 buffers + total_mb = self.n_images * per_image_mb + if total_mb > gpu_budget_mb: + # Find minimum bin factor to fit + for bf in [2, 4, 8]: + binned_mb = self.n_images * per_image_mb / (bf * bf) + if binned_mb <= gpu_budget_mb: + self._display_bin = bf + break + else: + self._display_bin = 8 + elif isinstance(display_bin, int) and display_bin > 1: + self._display_bin = display_bin + + if self._display_bin > 1: + from quantem.widget.array_utils import bin2d + orig_h, orig_w = self._data.shape[1], self._data.shape[2] + self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") + self.height = int(self._display_data.shape[1]) + self.width = int(self._display_data.shape[2]) + if pixel_size > 0: + self.pixel_size = pixel_size * self._display_bin + self._display_bin_factor = self._display_bin + print(f" Display bin {self._display_bin}×: {orig_h}×{orig_w} → {self.height}×{self.width} ({self._display_data.nbytes // 1024 // 1024} MB)") + else: + self._display_data = self._data + self._display_bin_factor = 1 + + # Compute initial stats (from full-res data) + self._compute_all_stats() + + # Send display data to JS (possibly binned) + self._update_all_frames() + + self.selected_idx = 0 + + if state is not None: + if isinstance(state, (str, pathlib.Path)): + state = unwrap_state_payload( + json.loads(pathlib.Path(state).read_text()), + require_envelope=True, + ) + else: + state = unwrap_state_payload(state) + self.load_state_dict(state) + + # Stash wall-clock start on the instance; the observer below prints the + # TRUE end-to-end time after JS signals first paint. The Python-only + # __init__ number is misleading for widget UX — a widget is not "done" + # until the browser has painted its first frame. + self._init_t0 = _t0 + self._init_py_elapsed_ms = (_time.perf_counter() - _t0) * 1000 + self.observe(self._on_first_render, names=["_js_rendered"]) + + def _on_first_render(self, change): + import time as _time + if not change.get("new"): + return + total_ms = (_time.perf_counter() - self._init_t0) * 1000 + py_ms = self._init_py_elapsed_ms + shape = (f"{self.n_images}×{self.height}×{self.width}" + if self.n_images > 1 else f"{self.height}×{self.width}") + mem = self._data.nbytes + mem_str = f"{mem / (1 << 20):.0f} MB" if mem >= 1 << 20 else f"{mem / (1 << 10):.0f} KB" + # Expose as attributes so tests and notebooks can assert on them. + # These are the ground truth for "did JS actually paint" — if they're + # None, the JS side never signaled first render. + self.render_total_ms = int(total_ms) + self.render_python_build_ms = int(py_ms) + self.render_wire_js_ms = int(total_ms - py_ms) + print( + f"Show2D: {shape} {mem_str} — " + f"rendered in {total_ms:.0f} ms (Python build {py_ms:.0f} ms, " + f"wire+JS {total_ms - py_ms:.0f} ms)", + flush=True, + ) + # Detach observer: one-shot, we only care about the first paint. + try: + self.unobserve(self._on_first_render, names=["_js_rendered"]) + except Exception: + pass + + def set_image(self, data, labels=None): + """Replace the displayed image(s). Preserves all display settings.""" + if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): + data = data.array + if isinstance(data, list): + images = [to_numpy(d) for d in data] + shapes = [img.shape for img in images] + if len(set(shapes)) > 1: + max_h = max(s[0] for s in shapes) + max_w = max(s[1] for s in shapes) + images = [_resize_image(img, max_h, max_w) for img in images] + data = np.stack(images) + else: + data = to_numpy(data) + if data.ndim == 2: + data = data[np.newaxis, ...] + if data.dtype == np.float32: + self._data = np.array(data, dtype=np.float32, copy=True) + else: + self._data = np.asarray(data, dtype=np.float32) + self._data_original = [self._data[i] for i in range(self._data.shape[0])] + self._originals_are_views = True + self.n_images = int(data.shape[0]) + + # Auto-bin for display (reuse existing _display_bin or recompute) + gpu_budget_mb = 2500 + per_image_mb = (data.shape[1] * data.shape[2] * 4 * 3) / (1024 * 1024) + total_mb = self.n_images * per_image_mb + self._display_bin = 1 + if total_mb > gpu_budget_mb: + for bf in [2, 4, 8]: + if total_mb / (bf * bf) <= gpu_budget_mb: + self._display_bin = bf + break + else: + self._display_bin = 8 + + if self._display_bin > 1: + from quantem.widget.array_utils import bin2d + self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") + self.height = int(self._display_data.shape[1]) + self.width = int(self._display_data.shape[2]) + self._display_bin_factor = self._display_bin + print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") + else: + self._display_data = self._data + self.height = int(data.shape[1]) + self.width = int(data.shape[2]) + self._display_bin_factor = 1 + + self.image_rotations = [0] * self.n_images + if labels is not None: + self.labels = list(labels) + else: + self.labels = [f"Image {i+1}" for i in range(self.n_images)] + self.selected_idx = 0 + self._compute_all_stats() + self._update_all_frames() + + def __repr__(self) -> str: + if self.n_images > 1: + shape = f"{self.n_images}×{self.height}×{self.width}" + return f"Show2D({shape}, idx={self.selected_idx}, cmap={self.cmap})" + return f"Show2D({self.height}×{self.width}, cmap={self.cmap})" + + def _repr_mimebundle_(self, **kwargs): + """Return widget view + (optionally) static PNG fallback. + + Live Jupyter renders the interactive widget; the PNG fallback is only + consumed by nbsphinx / GitHub / nbviewer when the widget view cannot be + rendered. Building the fallback runs matplotlib over every gallery image + (~1.7 s for a 30×512² stack) and that cost pays off only in static builds. + Gate it behind ``QUANTEM_WIDGET_STATIC_FALLBACK=1`` so interactive sessions + return immediately. + """ + bundle = super()._repr_mimebundle_(**kwargs) + if not os.environ.get("QUANTEM_WIDGET_STATIC_FALLBACK"): + return bundle + data_dict = bundle[0] if isinstance(bundle, tuple) else bundle + n = self.n_images + ncols = min(self.ncols, n) + nrows = math.ceil(n / ncols) + cell = 4 + fig, axes = plt.subplots( + nrows, ncols, + figsize=(cell * ncols, cell * nrows), + squeeze=False, + ) + max_preview = 256 + for i in range(nrows * ncols): + r, c = divmod(i, ncols) + ax = axes[r][c] + if i < n: + img = self._data[i] + h, w = img.shape + if h > max_preview or w > max_preview: + step = max(h // max_preview, w // max_preview, 1) + img = img[::step, ::step] + ax.imshow(img, cmap=self.cmap, origin="upper") + ax.set_title(self.labels[i], fontsize=10) + ax.axis("off") + if self.title: + fig.suptitle(self.title, fontsize=12) + fig.tight_layout() + buf = io.BytesIO() + fig.savefig(buf, format="png", dpi=120, bbox_inches="tight") + plt.close(fig) + data_dict["image/png"] = base64.b64encode(buf.getvalue()).decode("ascii") + if isinstance(bundle, tuple): + return (data_dict, bundle[1]) + return data_dict + + def _normalize_frame(self, frame: np.ndarray) -> np.ndarray: + if self.log_scale: + frame = np.log1p(np.maximum(frame, 0)) + if self.vmin is not None and self.vmax is not None: + vmin = float(self.vmin) + vmax = float(self.vmax) + if self.log_scale: + vmin = float(np.log1p(max(vmin, 0))) + vmax = float(np.log1p(max(vmax, 0))) + elif self.auto_contrast: + vmin = float(np.percentile(frame, 2)) + vmax = float(np.percentile(frame, 98)) + else: + vmin = float(frame.min()) + vmax = float(frame.max()) + if vmax > vmin: + normalized = np.clip((frame - vmin) / (vmax - vmin) * 255, 0, 255) + return normalized.astype(np.uint8) + return np.zeros(frame.shape, dtype=np.uint8) + + def save_image( + self, + path: str | pathlib.Path, + *, + idx: int | None = None, + format: str | None = None, + dpi: int = 150, + title: bool | str = False, + colorbar: bool = False, + scalebar: bool = False, + ) -> pathlib.Path: + """Save current image as PNG, PDF, or TIFF. + + When ``title``, ``colorbar``, or ``scalebar`` are enabled, the output + is a publication-quality figure rendered via matplotlib. Otherwise a + raw colormapped image is saved directly (faster, exact pixel output). + + Parameters + ---------- + path : str or pathlib.Path + Output file path. + idx : int, optional + Image index in gallery mode. Defaults to current selected_idx. + format : str, optional + 'png', 'pdf', or 'tiff'. If omitted, inferred from file extension. + dpi : int, default 150 + Output DPI. + title : bool or str, default False + ``True`` uses the widget title, a string sets a custom title. + colorbar : bool, default False + Include a colorbar showing the intensity mapping. + scalebar : bool, default False + Include a scale bar (requires ``pixel_size > 0``). + + Returns + ------- + pathlib.Path + The written file path. + """ + from matplotlib import colormaps + from PIL import Image + + path = pathlib.Path(path) + fmt = (format or path.suffix.lstrip(".").lower() or "png").lower() + if fmt not in ("png", "pdf", "tiff", "tif"): + raise ValueError(f"Unsupported format: {fmt!r}. Use 'png', 'pdf', or 'tiff'.") + + i = idx if idx is not None else self.selected_idx + if i < 0 or i >= self.n_images: + raise IndexError(f"Image index {i} out of range [0, {self.n_images})") + + frame = self._data[i] + normalized = self._normalize_frame(frame) + cmap_fn = colormaps.get_cmap(self.cmap) + path.parent.mkdir(parents=True, exist_ok=True) + + use_figure = title or colorbar or scalebar + if not use_figure: + rgba = (cmap_fn(normalized / 255.0) * 255).astype(np.uint8) + img = Image.fromarray(rgba) + if fmt == "pdf": + Image.init() + img = img.convert("RGB") + img.save(str(path), dpi=(dpi, dpi)) + return path + + # Publication-quality figure via matplotlib + h, w = frame.shape + aspect = h / w + fig_w = 6 + fig, ax = plt.subplots(figsize=(fig_w, fig_w * aspect)) + im = ax.imshow(normalized, cmap=cmap_fn, vmin=0, vmax=255, origin="upper") + ax.axis("off") + + if title: + label = title if isinstance(title, str) else self.title + if label: + ax.set_title(label, fontsize=14, fontweight="bold", pad=8) + + if colorbar: + # Map 0–255 back to data-space values for tick labels + if self.log_scale: + frame_proc = np.log1p(np.maximum(frame, 0)) + else: + frame_proc = frame + if self.vmin is not None and self.vmax is not None: + dmin = float(self.vmin) + dmax = float(self.vmax) + if self.log_scale: + dmin = float(np.log1p(max(dmin, 0))) + dmax = float(np.log1p(max(dmax, 0))) + elif self.auto_contrast: + dmin = float(np.percentile(frame_proc, 2)) + dmax = float(np.percentile(frame_proc, 98)) + else: + dmin = float(frame_proc.min()) + dmax = float(frame_proc.max()) + cb = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) + n_ticks = 5 + tick_positions = np.linspace(0, 255, n_ticks) + tick_labels = [f"{dmin + (dmax - dmin) * t / 255:.4g}" for t in tick_positions] + cb.set_ticks(tick_positions) + cb.set_ticklabels(tick_labels) + + if scalebar and self.pixel_size > 0: + from matplotlib.patches import FancyBboxPatch + # Compute a nice scale bar length + target_frac = 0.2 # ~20% of image width + raw_length_px = target_frac * w + raw_length_phys = raw_length_px * self.pixel_size # in Å + nice = _round_to_nice(raw_length_phys) + bar_px = nice / self.pixel_size + if nice >= 10: + label_text = f"{nice / 10:.4g} nm" + else: + label_text = f"{nice:.4g} Å" + margin = 0.03 + bar_y = h * (1 - margin) - 2 + bar_x = w * (1 - margin) - bar_px + ax.plot([bar_x, bar_x + bar_px], [bar_y, bar_y], + color="white", linewidth=3, solid_capstyle="butt") + ax.plot([bar_x, bar_x + bar_px], [bar_y, bar_y], + color="black", linewidth=1, solid_capstyle="butt") + ax.text(bar_x + bar_px / 2, bar_y - h * 0.02, label_text, + color="white", fontsize=10, fontweight="bold", + ha="center", va="bottom", + path_effects=[ + matplotlib.patheffects.withStroke(linewidth=2, foreground="black") + ]) + + fig.savefig(str(path), dpi=dpi, bbox_inches="tight", + facecolor="white", pad_inches=0.1) + plt.close(fig) + return path + + def state_dict(self): + return { + "title": self.title, + "cmap": self.cmap, + "log_scale": self.log_scale, + "auto_contrast": self.auto_contrast, + "vmin": self.vmin, + "vmax": self.vmax, + "show_stats": self.show_stats, + "show_fft": self.show_fft, + "fft_window": self.fft_window, + "show_controls": self.show_controls, + "disabled_tools": self.disabled_tools, + "hidden_tools": self.hidden_tools, + "pixel_size": self.pixel_size, + "scale_bar_visible": self.scale_bar_visible, + "size": self.size, + "smooth": self.smooth, + "initial_zoom": self.initial_zoom, + "vmins": self.vmins, + "vmaxs": self.vmaxs, + "link_zoom": self.link_zoom, + "link_pan": self.link_pan, + "link_contrast": self.link_contrast, + "zoom_row": self.zoom_row, + "zoom_col": self.zoom_col, + "diff_mode": self.diff_mode, + "ncols": self.ncols, + "selected_idx": self.selected_idx, + "roi_active": self.roi_active, + "roi_list": self.roi_list, + "roi_selected_idx": self.roi_selected_idx, + "profile_line": self.profile_line, + "image_rotations": list(self.image_rotations), + "display_bin": self._display_bin, + } + + def save(self, path: str): + save_state_file(path, "Show2D", self.state_dict()) + + def load_state_dict(self, state): + for key, val in state.items(): + # Silent migrations for renamed keys in older saved state files. + if key == "pixel_size_angstrom": + key = "pixel_size" + elif key == "canvas_size": + key = "size" + if key == "display_bin": + self._display_bin = val + continue + if hasattr(self, key): + setattr(self, key, val) + + def summary(self): + lines = [self.title or "Show2D", "═" * 32] + if self.n_images > 1: + lines.append(f"Image: {self.n_images}×{self.height}×{self.width} ({self.ncols} cols)") + else: + lines.append(f"Image: {self.height}×{self.width}") + if self.pixel_size > 0: + ps = self.pixel_size + if ps >= 10: + lines[-1] += f" ({ps / 10:.2f} nm/px)" + else: + lines[-1] += f" ({ps:.2f} Å/px)" + if hasattr(self, "_data") and self._data is not None: + arr = self._data + lines.append(f"Data: min={float(arr.min()):.4g} max={float(arr.max()):.4g} mean={float(arr.mean()):.4g}") + cmap = self.cmap + scale = "log" if self.log_scale else "linear" + if self.vmin is not None and self.vmax is not None: + contrast = f"vmin={self.vmin:.4g}, vmax={self.vmax:.4g}" + elif self.auto_contrast: + contrast = "auto contrast" + else: + contrast = "manual contrast" + display = f"{cmap} | {contrast} | {scale}" + if self.show_fft: + display += " | FFT" + if not self.fft_window: + display += " (no window)" + lines.append(f"Display: {display}") + if self.disabled_tools: + lines.append(f"Locked: {', '.join(self.disabled_tools)}") + if self.hidden_tools: + lines.append(f"Hidden: {', '.join(self.hidden_tools)}") + if self.roi_active and self.roi_list: + lines.append(f"ROI: {len(self.roi_list)} region(s)") + if self.profile_line: + p0, p1 = self.profile_line[0], self.profile_line[1] + lines.append(f"Profile: ({p0['row']:.0f}, {p0['col']:.0f}) → ({p1['row']:.0f}, {p1['col']:.0f})") + non_zero = [(i, r * 90) for i, r in enumerate(self.image_rotations) if r % 4 != 0] + if non_zero: + parts = [f"#{i}={deg}°" for i, deg in non_zero] + lines.append(f"Rotated: {', '.join(parts)}") + rt = getattr(self, "render_total_ms", None) + if rt is not None: + pb = getattr(self, "render_python_build_ms", 0) + wj = getattr(self, "render_wire_js_ms", 0) + lines.append(f"Rendered: {rt} ms total (Python build {pb} ms, wire+JS {wj} ms)") + else: + lines.append("Rendered: (pending first browser paint)") + print("\n".join(lines)) + + def _compute_all_stats(self): + """Compute statistics for all images (vectorized over all frames).""" + # Vectorized reduction over (H, W) is faster than per-image loops + # for large galleries (e.g. 12×4096×4096: 164ms vs 191ms). + axes = (1, 2) if self._data.ndim == 3 else None + self.stats_mean = np.mean(self._data, axis=axes).ravel().tolist() + self.stats_min = np.min(self._data, axis=axes).ravel().tolist() + self.stats_max = np.max(self._data, axis=axes).ravel().tolist() + self.stats_std = np.std(self._data, axis=axes).ravel().tolist() + + def _update_all_frames(self): + """Send display data to JS (possibly binned for large galleries).""" + data = self._display_data if self._display_data is not None else self._data + self.frame_bytes = data.tobytes() + + def _apply_rotations(self): + # Materialize originals as independent copies only when a non-zero + # rotation exists (they start as views into _data to avoid 800MB copy at init) + has_rotation = any( + (self.image_rotations[i] if i < len(self.image_rotations) else 0) % 4 != 0 + for i in range(len(self._data_original)) + ) + # No-rotation fast path: skip 30+ MB of redundant tobytes + stats recomputation + # on every widget init. The observer fires once when image_rotations = [0]*n + # is assigned in __init__; without this guard that triggered a full frame + # rebuild + stats recompute for a no-op. + if not has_rotation and self._originals_are_views: + return + if self._originals_are_views and has_rotation: + self._data_original = [img.copy() for img in self._data_original] + self._originals_are_views = False + rotated = [] + for i, orig in enumerate(self._data_original): + k = self.image_rotations[i] if i < len(self.image_rotations) else 0 + k = k % 4 + if k == 0: + rotated.append(orig) + else: + rotated.append(np.rot90(orig, k=k)) + # If shapes differ after rotation, center-pad all to max dims + shapes = [img.shape for img in rotated] + if len(set(shapes)) > 1: + max_h = max(s[0] for s in shapes) + max_w = max(s[1] for s in shapes) + padded = [] + for img in rotated: + h, w = img.shape + pad_top = (max_h - h) // 2 + pad_bot = max_h - h - pad_top + pad_left = (max_w - w) // 2 + pad_right = max_w - w - pad_left + padded.append(np.pad(img, ((pad_top, pad_bot), (pad_left, pad_right)), mode="constant", constant_values=0)) + rotated = padded + self._data = np.stack(rotated).astype(np.float32) + # Recompute display data if binning is active + if self._display_bin > 1: + from quantem.widget.array_utils import bin2d + self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") + else: + self._display_data = self._data + display = self._display_data if self._display_data is not None else self._data + self.height = int(display.shape[1]) + self.width = int(display.shape[2]) + self._compute_all_stats() + self._update_all_frames() + + @traitlets.observe("image_rotations") + def _on_image_rotations_changed(self, change): + if hasattr(self, "_data_original"): + self._apply_rotations() + + def rotate(self, idx: int, angle: int) -> Self: + """Rotate image ``idx`` by ``angle`` degrees (CCW-positive, matches np.rot90). + + Rotation convention follows ``np.rot90``:: + + angle | image_rotations | np.rot90 k | direction + ------+-----------------+------------+---------- + 90 | 1 | 1 | 90° CCW + 180 | 2 | 2 | 180° + -90 | 3 | 3 | 90° CW + 360 | 0 | 0 | identity + + Parameters + ---------- + idx : int + Image index in the gallery (0-based). + angle : int + Rotation angle in degrees (must be a multiple of 90). + Positive = counter-clockwise, negative = clockwise. + + Returns + ------- + Self + """ + if angle % 90 != 0: + raise ValueError(f"Rotation angle must be a multiple of 90°, got {angle}") + if idx < 0 or idx >= self.n_images: + raise IndexError(f"Image index {idx} out of range [0, {self.n_images})") + k = (angle // 90) % 4 + rots = list(self.image_rotations) + while len(rots) < self.n_images: + rots.append(0) + rots[idx] = (rots[idx] + k) % 4 + self.image_rotations = rots + return self + + def _sample_profile(self, row0, col0, row1, col1): + img = self._data[self.selected_idx] + h, w = img.shape + dc, dr = col1 - col0, row1 - row0 + length = (dc**2 + dr**2) ** 0.5 + n = max(2, int(np.ceil(length))) + t = np.linspace(0, 1, n) + cs = col0 + t * dc + rs = row0 + t * dr + ci = np.floor(cs).astype(int) + ri = np.floor(rs).astype(int) + cf = cs - ci + rf = rs - ri + c0c = np.clip(ci, 0, w - 1) + c1c = np.clip(ci + 1, 0, w - 1) + r0c = np.clip(ri, 0, h - 1) + r1c = np.clip(ri + 1, 0, h - 1) + return (img[r0c, c0c] * (1 - cf) * (1 - rf) + + img[r0c, c1c] * cf * (1 - rf) + + img[r1c, c0c] * (1 - cf) * rf + + img[r1c, c1c] * cf * rf).astype(np.float32) + + def set_profile(self, start: tuple, end: tuple): + """Set a line profile between two points (image pixel coordinates). + + Parameters + ---------- + start : tuple of (row, col) + Start point in pixel coordinates. + end : tuple of (row, col) + End point in pixel coordinates. + """ + row0, col0 = start + row1, col1 = end + self.profile_line = [ + {"row": float(row0), "col": float(col0)}, + {"row": float(row1), "col": float(col1)}, + ] + + def clear_profile(self): + """Clear the current line profile.""" + self.profile_line = [] + + def _upsert_selected_roi(self, updates: dict): + rois = list(self.roi_list) + color_cycle = ["#4fc3f7", "#81c784", "#ffb74d", "#ce93d8", "#ef5350", "#ffd54f", "#90a4ae", "#a1887f"] + defaults = { + "shape": "square", + "row": int(self.height // 2), + "col": int(self.width // 2), + "radius": 10, + "radius_inner": 5, + "width": 20, + "height": 20, + "line_width": 2, + "highlight": False, + "visible": True, + "locked": False, + } + if self.roi_selected_idx >= 0 and self.roi_selected_idx < len(rois): + current = {**defaults, **rois[self.roi_selected_idx]} + if not current.get("color"): + current["color"] = color_cycle[self.roi_selected_idx % len(color_cycle)] + rois[self.roi_selected_idx] = {**current, **updates} + else: + rois.append({**defaults, "color": color_cycle[len(rois) % len(color_cycle)], **updates}) + self.roi_selected_idx = len(rois) - 1 + self.roi_list = rois + self.roi_active = True + + def add_roi(self, row: int | None = None, col: int | None = None, shape: str = "square") -> Self: + with self.hold_sync(): + self.roi_selected_idx = -1 + self._upsert_selected_roi({ + "shape": shape, + "row": int(self.height // 2 if row is None else row), + "col": int(self.width // 2 if col is None else col), + }) + return self + + def clear_rois(self) -> Self: + with self.hold_sync(): + self.roi_list = [] + self.roi_selected_idx = -1 + self.roi_active = False + return self + + def delete_selected_roi(self) -> Self: + idx = int(self.roi_selected_idx) + if idx < 0 or idx >= len(self.roi_list): + return self + with self.hold_sync(): + rois = [roi for i, roi in enumerate(self.roi_list) if i != idx] + self.roi_list = rois + self.roi_selected_idx = min(idx, len(rois) - 1) if rois else -1 + if not rois: + self.roi_active = False + return self + + def set_roi(self, row: int, col: int, radius: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "circle", "row": int(row), "col": int(col), "radius": int(radius)}) + return self + + def roi_circle(self, radius: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "circle", "radius": int(radius)}) + return self + + def roi_square(self, half_size: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "square", "radius": int(half_size)}) + return self + + def roi_rectangle(self, width: int = 20, height: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "rectangle", "width": int(width), "height": int(height)}) + return self + + def roi_annular(self, inner: int = 5, outer: int = 10) -> Self: + with self.hold_sync(): + self._upsert_selected_roi({"shape": "annular", "radius_inner": int(inner), "radius": int(outer)}) + return self + + @property + def profile(self): + """Get profile line endpoints as [(row0, col0), (row1, col1)] or []. + + Returns + ------- + list of tuple + Line endpoints in pixel coordinates, or empty list if no profile. + """ + return [(p["row"], p["col"]) for p in self.profile_line] + + @property + def profile_values(self): + """Get intensity values along the profile line as a numpy array. + + Returns + ------- + np.ndarray or None + Float32 array of sampled intensities, or None if no profile. + """ + if len(self.profile_line) < 2: + return None + p0, p1 = self.profile_line + return self._sample_profile(p0["row"], p0["col"], p1["row"], p1["col"]) + + @property + def profile_distance(self): + """Get total distance of the profile line in calibrated units. + + Returns + ------- + float or None + Distance in angstroms (if pixel_size > 0) or pixels. + None if no profile line is set. + """ + if len(self.profile_line) < 2: + return None + p0, p1 = self.profile_line + dc = p1["col"] - p0["col"] + dr = p1["row"] - p0["row"] + dist_px = (dc**2 + dr**2) ** 0.5 + if self.pixel_size > 0: + return dist_px * self.pixel_size + return dist_px + + +bind_tool_runtime_api(Show2D, "Show2D") diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py new file mode 100644 index 00000000..1fe94273 --- /dev/null +++ b/widget/src/quantem/widget/show4dstem.py @@ -0,0 +1,4337 @@ +""" +show4dstem: Fast interactive 4D-STEM viewer widget. + +Apple MPS GPU limit: PyTorch's MPS backend (Apple Silicon) has a hard limit +of ~2.1 billion elements (INT_MAX = 2^31 - 1) per tensor. Datasets exceeding +this automatically fall back to CPU, which is still fast on Apple Silicon +thanks to unified memory (CPU and GPU share the same RAM). + +CUDA GPUs do not have this limit. + +Common 4D-STEM sizes (float32): + + Scan Detector Elements Size MPS? + 128×128 128×128 268M 1.0 GB yes + 128×128 256×256 1,074M 4.0 GB yes + 256×256 128×128 1,074M 4.0 GB yes + 256×256 192×192 2,416M 9.0 GB no (auto CPU, still fast) + 256×256 256×256 4,295M 16.0 GB no (auto CPU, still fast) + 512×512 256×256 17,180M 64.0 GB no (auto CPU) + +To reduce data size, bin k-space at the dataset level before viewing: + + dataset = dataset.bin(2, axes=(2, 3)) # 2x2 k-space binning + widget = Show4DSTEM(dataset) +""" + +import hashlib +import json +import math +import pathlib +import time +from datetime import datetime, timezone +from typing import Any, Self +from uuid import uuid4 + +import anywidget +import numpy as np +import torch +import traitlets + +from quantem.core.config import validate_device +from quantem.widget.array_utils import to_numpy +from quantem.widget.json_state import ( + build_json_header, + resolve_widget_version, + save_state_file, + unwrap_state_payload, +) +from quantem.widget.tool_parity import ( + bind_tool_runtime_api, + build_tool_groups, + normalize_tool_groups, +) + + +def _format_memory(nbytes: int) -> str: + if nbytes >= 1 << 30: return f"{nbytes / (1 << 30):.1f} GB" + if nbytes >= 1 << 20: return f"{nbytes / (1 << 20):.0f} MB" + if nbytes >= 1 << 10: return f"{nbytes / (1 << 10):.0f} KB" + return f"{nbytes} B" + + +# ============================================================================ +# Constants +# ============================================================================ +DEFAULT_BF_RATIO = 0.125 # BF disk radius as fraction of detector size (1/8) +SPARSE_MASK_THRESHOLD = 0.2 # Use sparse indexing below this mask coverage +MIN_LOG_VALUE = 1e-10 # Minimum value for log scale to avoid log(0) +DEFAULT_VI_ROI_RATIO = 0.15 # Default VI ROI size as fraction of scan dimension + +class Show4DSTEM(anywidget.AnyWidget): + """ + Fast interactive 4D-STEM viewer with advanced features. + + Optimized for speed with binary transfer and pre-normalization. + Works with NumPy and PyTorch arrays. + + Parameters + ---------- + data : Dataset4dstem or array_like + Dataset4dstem object (calibration auto-extracted), 4D array + of shape (scan_rows, scan_cols, det_rows, det_cols), or 5D array + of shape (n_frames, scan_rows, scan_cols, det_rows, det_cols) + for time-series or tilt-series data. + scan_shape : tuple, optional + If data is flattened (N, det_rows, det_cols), provide scan dimensions. + pixel_size : float, optional + Pixel size in Å (real-space). Used for scale bar. + Auto-extracted from Dataset4dstem if not provided. + k_pixel_size : float, optional + Detector pixel size in mrad (k-space). Used for scale bar. + Auto-extracted from Dataset4dstem if not provided. + center : tuple[float, float], optional + (center_row, center_col) of the diffraction pattern in pixels. + If not provided, defaults to detector center. + bf_radius : float, optional + Bright field disk radius in pixels. If not provided, estimated as 1/8 of detector size. + precompute_virtual_images : bool, default True + Precompute BF/ABF/LAADF/HAADF virtual images for preset switching. + frame_dim_label : str, optional + Label for the frame dimension when 5D data is provided. + Defaults to "Frame". Common values: "Tilt", "Time", "Focus". + disabled_tools : list of str, optional + Tool groups to lock while still showing controls. Supported: + ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, + ``"playback"``, ``"view"``, ``"export"``, ``"roi"``, + ``"profile"``, ``"fft"``, ``"virtual"``, ``"frame"``, ``"all"``. + disable_* : bool, optional + Convenience flags mirroring ``disabled_tools`` for each tool group, + plus ``disable_all``. + hidden_tools : list of str, optional + Tool groups to hide from the UI. Uses the same keys as + ``disabled_tools``. + hide_* : bool, optional + Convenience flags mirroring ``disable_*`` for ``hidden_tools``. + + Examples + -------- + >>> # From Dataset4dstem (calibration auto-extracted) + >>> from quantem.core.io.file_readers import read_emdfile_to_4dstem + >>> dataset = read_emdfile_to_4dstem("data.h5") + >>> Show4DSTEM(dataset) + + >>> # From raw array with manual calibration + >>> import numpy as np + >>> data = np.random.rand(64, 64, 128, 128) + >>> Show4DSTEM(data, pixel_size=2.39, k_pixel_size=0.46) + + >>> # With raster animation + >>> widget = Show4DSTEM(dataset) + >>> widget.raster(step=2, interval_ms=50) + + >>> # 5D time-series or tilt-series data + >>> data_5d = np.random.rand(20, 64, 64, 128, 128) # 20 frames + >>> Show4DSTEM(data_5d, frame_dim_label="Tilt") + """ + + _esm = pathlib.Path(__file__).parent / "static" / "show4dstem.js" + _css = pathlib.Path(__file__).parent / "static" / "show4dstem.css" + + # Position in scan space + widget_version = traitlets.Unicode("unknown").tag(sync=True) + title = traitlets.Unicode("").tag(sync=True) + pos_row = traitlets.Int(0).tag(sync=True) + pos_col = traitlets.Int(0).tag(sync=True) + + # Shape of scan space (for slider bounds) + shape_rows = traitlets.Int(1).tag(sync=True) + shape_cols = traitlets.Int(1).tag(sync=True) + + # Detector shape for frontend + det_rows = traitlets.Int(1).tag(sync=True) + det_cols = traitlets.Int(1).tag(sync=True) + + # Raw float32 frame as bytes (JS handles scale/colormap for real-time interactivity) + frame_bytes = traitlets.Bytes(b"").tag(sync=True) + + # Global min/max for DP normalization (computed once from sampled frames) + dp_global_min = traitlets.Float(0.0).tag(sync=True) + dp_global_max = traitlets.Float(1.0).tag(sync=True) + + # ========================================================================= + # Detector Calibration (for presets and scale bar) + # ========================================================================= + center_col = traitlets.Float(0.0).tag(sync=True) # Detector center col + center_row = traitlets.Float(0.0).tag(sync=True) # Detector center row + bf_radius = traitlets.Float(0.0).tag(sync=True) # BF disk radius (pixels) + + # ========================================================================= + # ROI Drawing (for virtual imaging) + # roi_radius is multi-purpose by mode: + # - circle: radius of circle + # - square: half-size (distance from center to edge) + # - annular: outer radius (roi_radius_inner = inner radius) + # - rect: uses roi_width/roi_height instead + # ========================================================================= + roi_active = traitlets.Bool(False).tag(sync=True) + roi_mode = traitlets.Unicode("point").tag(sync=True) + roi_center_col = traitlets.Float(0.0).tag(sync=True) + roi_center_row = traitlets.Float(0.0).tag(sync=True) + # Compound trait for batched row+col updates (JS sends both at once, 1 observer fires) + roi_center = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0]).tag(sync=True) + roi_radius = traitlets.Float(10.0).tag(sync=True) + roi_radius_inner = traitlets.Float(5.0).tag(sync=True) + roi_width = traitlets.Float(20.0).tag(sync=True) + roi_height = traitlets.Float(10.0).tag(sync=True) + + # ========================================================================= + # Virtual Image (ROI-based, updates as you drag ROI on DP) + # ========================================================================= + virtual_image_bytes = traitlets.Bytes(b"").tag(sync=True) # Raw float32 + vi_data_min = traitlets.Float(0.0).tag(sync=True) # Min of current VI for normalization + vi_data_max = traitlets.Float(1.0).tag(sync=True) # Max of current VI for normalization + + # ========================================================================= + # VI ROI (real-space region selection for summed DP) + # ========================================================================= + vi_roi_mode = traitlets.Unicode("off").tag(sync=True) # "off", "circle", "rect" + vi_roi_center_row = traitlets.Float(0.0).tag(sync=True) + vi_roi_center_col = traitlets.Float(0.0).tag(sync=True) + vi_roi_radius = traitlets.Float(5.0).tag(sync=True) + vi_roi_width = traitlets.Float(10.0).tag(sync=True) + vi_roi_height = traitlets.Float(10.0).tag(sync=True) + summed_dp_bytes = traitlets.Bytes(b"").tag(sync=True) # Summed DP from VI ROI + summed_dp_count = traitlets.Int(0).tag(sync=True) # Number of positions summed + + # ========================================================================= + # Scale Bar + # ========================================================================= + pixel_size = traitlets.Float(1.0).tag(sync=True) # Å per pixel (real-space) + k_pixel_size = traitlets.Float(1.0).tag(sync=True) # mrad per pixel (k-space) + k_calibrated = traitlets.Bool(False).tag(sync=True) # True if k-space has mrad calibration + + # ========================================================================= + # Path Animation (programmatic crosshair control) + # ========================================================================= + path_playing = traitlets.Bool(False).tag(sync=True) + path_index = traitlets.Int(0).tag(sync=True) + path_length = traitlets.Int(0).tag(sync=True) + path_interval_ms = traitlets.Int(100).tag(sync=True) # ms between frames + path_loop = traitlets.Bool(True).tag(sync=True) # loop when reaching end + + # ========================================================================= + # Auto-detection trigger (frontend sets to True, backend resets to False) + # ========================================================================= + auto_detect_trigger = traitlets.Bool(False).tag(sync=True) + + # ========================================================================= + # Statistics for display (mean, min, max, std) + # ========================================================================= + dp_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) + vi_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) + mask_dc = traitlets.Bool(True).tag(sync=True) # Mask center pixel for DP stats + + # ========================================================================= + # Display settings (synced for programmatic export parity) + # ========================================================================= + dp_colormap = traitlets.Unicode("inferno").tag(sync=True) + vi_colormap = traitlets.Unicode("inferno").tag(sync=True) + fft_colormap = traitlets.Unicode("inferno").tag(sync=True) + + dp_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" + vi_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" + fft_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" + + dp_power_exp = traitlets.Float(0.5).tag(sync=True) + vi_power_exp = traitlets.Float(0.5).tag(sync=True) + fft_power_exp = traitlets.Float(0.5).tag(sync=True) + + dp_vmin_pct = traitlets.Float(0.0).tag(sync=True) + dp_vmax_pct = traitlets.Float(100.0).tag(sync=True) + vi_vmin_pct = traitlets.Float(0.0).tag(sync=True) + vi_vmax_pct = traitlets.Float(100.0).tag(sync=True) + fft_vmin_pct = traitlets.Float(0.0).tag(sync=True) + fft_vmax_pct = traitlets.Float(100.0).tag(sync=True) + + # Absolute intensity bounds (override percentile sliders when both set) + dp_vmin = traitlets.Float(None, allow_none=True).tag(sync=True) + dp_vmax = traitlets.Float(None, allow_none=True).tag(sync=True) + vi_vmin = traitlets.Float(None, allow_none=True).tag(sync=True) + vi_vmax = traitlets.Float(None, allow_none=True).tag(sync=True) + + fft_auto = traitlets.Bool(True).tag(sync=True) + show_fft = traitlets.Bool(False).tag(sync=True) + fft_window = traitlets.Bool(True).tag(sync=True) + show_controls = traitlets.Bool(True).tag(sync=True) + dp_show_colorbar = traitlets.Bool(False).tag(sync=True) + export_default_view = traitlets.Unicode("all").tag(sync=True) + export_default_format = traitlets.Unicode("png").tag(sync=True) + export_include_overlays = traitlets.Bool(True).tag(sync=True) + export_include_scalebar = traitlets.Bool(True).tag(sync=True) + export_default_dpi = traitlets.Int(300).tag(sync=True) + + # ========================================================================= + # Frame Animation (5D time/tilt series) + # ========================================================================= + frame_idx = traitlets.Int(0).tag(sync=True) + n_frames = traitlets.Int(1).tag(sync=True) + frame_dim_label = traitlets.Unicode("Frame").tag(sync=True) + frame_labels = traitlets.List(traitlets.Unicode(), []).tag(sync=True) + frame_playing = traitlets.Bool(False).tag(sync=True) + frame_loop = traitlets.Bool(True).tag(sync=True) + frame_fps = traitlets.Float(5.0).tag(sync=True) + frame_reverse = traitlets.Bool(False).tag(sync=True) + frame_boomerang = traitlets.Bool(False).tag(sync=True) + + # Export (GIF) + _gif_export_requested = traitlets.Bool(False).tag(sync=True) + _gif_data = traitlets.Bytes(b"").tag(sync=True) + _gif_metadata_json = traitlets.Unicode("").tag(sync=True) + + # Line Profile (for DP panel) + profile_line = traitlets.List(traitlets.Dict()).tag(sync=True) + profile_width = traitlets.Int(1).tag(sync=True) + + # ========================================================================= + # Tool visibility / locking + # ========================================================================= + disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) + + @classmethod + def _normalize_tool_groups(cls, tool_groups) -> list[str]: + return normalize_tool_groups("Show4DSTEM", tool_groups) + + @classmethod + def _build_disabled_tools( + cls, + disabled_tools=None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_playback: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_fft: bool = False, + disable_virtual: bool = False, + disable_frame: bool = False, + disable_all: bool = False, + ) -> list[str]: + return build_tool_groups( + "Show4DSTEM", + tool_groups=disabled_tools, + all_flag=disable_all, + flag_map={ + "display": disable_display, + "histogram": disable_histogram, + "stats": disable_stats, + "navigation": disable_navigation, + "playback": disable_playback, + "view": disable_view, + "export": disable_export, + "roi": disable_roi, + "profile": disable_profile, + "fft": disable_fft, + "virtual": disable_virtual, + "frame": disable_frame, + }, + ) + + @classmethod + def _build_hidden_tools( + cls, + hidden_tools=None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_playback: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_fft: bool = False, + hide_virtual: bool = False, + hide_frame: bool = False, + hide_all: bool = False, + ) -> list[str]: + return build_tool_groups( + "Show4DSTEM", + tool_groups=hidden_tools, + all_flag=hide_all, + flag_map={ + "display": hide_display, + "histogram": hide_histogram, + "stats": hide_stats, + "navigation": hide_navigation, + "playback": hide_playback, + "view": hide_view, + "export": hide_export, + "roi": hide_roi, + "profile": hide_profile, + "fft": hide_fft, + "virtual": hide_virtual, + "frame": hide_frame, + }, + ) + + @traitlets.validate("disabled_tools") + def _validate_disabled_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + @traitlets.validate("hidden_tools") + def _validate_hidden_tools(self, proposal): + return self._normalize_tool_groups(proposal["value"]) + + def __init__( + self, + data: "Dataset4dstem | np.ndarray", + scan_shape: tuple[int, int] | None = None, + pixel_size: float | None = None, + k_pixel_size: float | None = None, + center: tuple[float, float] | None = None, + bf_radius: float | None = None, + precompute_virtual_images: bool = False, + frame_dim_label: str | None = None, + frame_labels: list[str] | None = None, + title: str = "", + disabled_tools: list[str] | None = None, + disable_display: bool = False, + disable_histogram: bool = False, + disable_stats: bool = False, + disable_navigation: bool = False, + disable_playback: bool = False, + disable_view: bool = False, + disable_export: bool = False, + disable_roi: bool = False, + disable_profile: bool = False, + disable_fft: bool = False, + disable_virtual: bool = False, + disable_frame: bool = False, + disable_all: bool = False, + hidden_tools: list[str] | None = None, + hide_display: bool = False, + hide_histogram: bool = False, + hide_stats: bool = False, + hide_navigation: bool = False, + hide_playback: bool = False, + hide_view: bool = False, + hide_export: bool = False, + hide_roi: bool = False, + hide_profile: bool = False, + hide_fft: bool = False, + hide_virtual: bool = False, + hide_frame: bool = False, + hide_all: bool = False, + show_fft: bool = False, + fft_window: bool = True, + show_controls: bool = True, + dp_vmin: float | None = None, + dp_vmax: float | None = None, + vi_vmin: float | None = None, + vi_vmax: float | None = None, + verbose: bool = True, + state=None, + **kwargs, + ): + super().__init__(**kwargs) + self.widget_version = resolve_widget_version() + _t0 = time.perf_counter() + _verbose = verbose + + _io_labels = None + + # Extract calibration from Dataset4dstem if provided + k_calibrated = False + if hasattr(data, "sampling") and hasattr(data, "array"): + # Dataset4dstem: extract calibration and array + # sampling = [scan_rows, scan_cols, det_rows, det_cols] + if not title and hasattr(data, "name") and data.name: + title = str(data.name) + units = getattr(data, "units", ["pixels"] * 4) + if pixel_size is None and units[0] in ("Å", "angstrom", "A", "nm"): + pixel_size = float(data.sampling[0]) + if units[0] == "nm": + pixel_size *= 10 # Convert nm to Å + if k_pixel_size is None and units[2] in ("mrad", "1/Å", "1/A"): + k_pixel_size = float(data.sampling[2]) + k_calibrated = True + data = data.array + + self.title = title + # Store calibration values (default to 1.0 if not provided) + self.pixel_size = pixel_size if pixel_size is not None else 1.0 + self.k_pixel_size = k_pixel_size if k_pixel_size is not None else 1.0 + self.k_calibrated = k_calibrated or (k_pixel_size is not None) + self.disabled_tools = self._build_disabled_tools( + disabled_tools=disabled_tools, + disable_display=disable_display, + disable_histogram=disable_histogram, + disable_stats=disable_stats, + disable_navigation=disable_navigation, + disable_playback=disable_playback, + disable_view=disable_view, + disable_export=disable_export, + disable_roi=disable_roi, + disable_profile=disable_profile, + disable_fft=disable_fft, + disable_virtual=disable_virtual, + disable_frame=disable_frame, + disable_all=disable_all, + ) + self.hidden_tools = self._build_hidden_tools( + hidden_tools=hidden_tools, + hide_display=hide_display, + hide_histogram=hide_histogram, + hide_stats=hide_stats, + hide_navigation=hide_navigation, + hide_playback=hide_playback, + hide_view=hide_view, + hide_export=hide_export, + hide_roi=hide_roi, + hide_profile=hide_profile, + hide_fft=hide_fft, + hide_virtual=hide_virtual, + hide_frame=hide_frame, + hide_all=hide_all, + ) + self.show_fft = show_fft + self.fft_window = fft_window + self.show_controls = show_controls + self.dp_vmin = dp_vmin + self.dp_vmax = dp_vmax + self.vi_vmin = vi_vmin + self.vi_vmax = vi_vmax + # Path animation (configured via set_path() or raster()) + self._path_points: list[tuple[int, int]] = [] + # Named user presets saved during this session + self._named_presets: dict[str, dict[str, Any]] = {} + # Session-scoped reproducibility log for all export calls + self._export_session_id = uuid4().hex + self._export_session_started_utc = datetime.now(timezone.utc).isoformat() + self._export_log: list[dict[str, Any]] = [] + # Sparse sampling state (for streaming/adaptive acquisition workflows) + self._sparse_samples: dict[tuple[int, int, int], np.ndarray] = {} + self._sparse_order: list[tuple[int, int, int]] = [] + # Convert to NumPy then PyTorch tensor using quantem device config + data_np = to_numpy(data) + device_str, _ = validate_device(None) # Get device from quantem config + self._device = torch.device(device_str) + # Remove saturated hot pixels in numpy (before any torch conversion) + saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None + if data_np.dtype != np.float32: + _tc = time.perf_counter() + data_np = data_np.astype(np.float32) + if _verbose: + print(f" astype float32: {time.perf_counter() - _tc:.2f}s") + if saturated_value is not None: + data_np[data_np >= saturated_value] = 0 + # Handle dimensionality — 5D loads eagerly for instant frame switching + ndim = data_np.ndim + _tc = time.perf_counter() + if ndim == 5: + self.n_frames = data_np.shape[0] + self._scan_shape = (data_np.shape[1], data_np.shape[2]) + self._det_shape = (data_np.shape[3], data_np.shape[4]) + if data_np.size > 2**31 - 1 and device_str == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + elif ndim == 3: + self.n_frames = 1 + if scan_shape is not None: + self._scan_shape = scan_shape + else: + n = data_np.shape[0] + side = int(n ** 0.5) + if side * side != n: + raise ValueError( + f"Cannot infer square scan_shape from N={n}. " + f"Provide scan_shape explicitly." + ) + self._scan_shape = (side, side) + self._det_shape = (data_np.shape[1], data_np.shape[2]) + # MPS backend can't handle tensors >INT_MAX elements; fall back to CPU + if data_np.size > 2**31 - 1 and device_str == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + elif ndim == 4: + self.n_frames = 1 + self._scan_shape = (data_np.shape[0], data_np.shape[1]) + self._det_shape = (data_np.shape[2], data_np.shape[3]) + if data_np.size > 2**31 - 1 and device_str == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + else: + raise ValueError(f"Expected 3D, 4D, or 5D array, got {ndim}D") + if _verbose: + if str(self._device) == "mps": + torch.mps.synchronize() + print(f" to {self._device}: {time.perf_counter() - _tc:.2f}s ({data_np.nbytes / 1e9:.1f} GB)") + + self.shape_rows = self._scan_shape[0] + self.shape_cols = self._scan_shape[1] + self.det_rows = self._det_shape[0] + self.det_cols = self._det_shape[1] + # Initial position at center + self.pos_row = self.shape_rows // 2 + self.pos_col = self.shape_cols // 2 + # Frame dimension label (for 5D time/tilt series UI) + self.frame_dim_label = frame_dim_label if frame_dim_label is not None else "Frame" + # Per-frame labels: explicit param > inferred > empty + resolved_labels = frame_labels or _io_labels or [] + self._frame_labels = resolved_labels + if resolved_labels: + self.frame_labels = list(resolved_labels) + # Histogram axis range — first frame is enough (JS does per-frame percentile clipping) + first_frame = self._data[0] if self._data.ndim == 5 else self._data + self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame.max()) + # Cache coordinate tensors for mask creation (avoid repeated torch.arange) + self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] + self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] + self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] + self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] + self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) + self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) + # Setup center and BF radius + det_size = min(self.det_rows, self.det_cols) + if center is not None and bf_radius is not None: + self.center_row = float(center[0]) + self.center_col = float(center[1]) + self.bf_radius = float(bf_radius) + elif center is not None: + self.center_row = float(center[0]) + self.center_col = float(center[1]) + self.bf_radius = det_size * DEFAULT_BF_RATIO + elif bf_radius is not None: + self.center_col = float(self.det_cols / 2) + self.center_row = float(self.det_rows / 2) + self.bf_radius = float(bf_radius) + else: + # Neither provided - auto-detect from data + # Set defaults first (will be overwritten by auto-detect) + self.center_col = float(self.det_cols / 2) + self.center_row = float(self.det_rows / 2) + self.bf_radius = det_size * DEFAULT_BF_RATIO + # Auto-detect center and bf_radius from the data + _tc = time.perf_counter() + self.auto_detect_center(update_roi=False) + if _verbose: + print(f" auto_detect_center: {time.perf_counter() - _tc:.2f}s") + + # Pre-compute and cache common virtual images (BF, ABF, ADF) + # Each cache stores (bytes, stats) tuple + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + if precompute_virtual_images and self.n_frames == 1: + self._precompute_common_virtual_images() + + # Update frame when position changes (scale/colormap handled in JS) + self.observe(self._update_frame, names=["pos_row", "pos_col"]) + # Observe individual ROI params + self.observe(self._on_roi_change, names=[ + "roi_center_col", "roi_center_row", "roi_radius", "roi_radius_inner", + "roi_active", "roi_mode", "roi_width", "roi_height" + ]) + # Observe compound roi_center for batched updates from JS + self.observe(self._on_roi_center_change, names=["roi_center"]) + + # Initialize default ROI at BF center — batch to avoid redundant observer callbacks + with self.hold_trait_notifications(): + self.roi_center_col = self.center_col + self.roi_center_row = self.center_row + self.roi_center = [self.center_row, self.center_col] + self.roi_radius = self.bf_radius * 0.5 # Start with half BF radius + self.roi_active = True + + # Compute initial virtual image and frame (once, after all ROI traits are set) + _tc = time.perf_counter() + self._compute_virtual_image_from_roi() + self._update_frame() + if _verbose: + print(f" virtual image + frame: {time.perf_counter() - _tc:.2f}s") + + # Path animation: observe index changes from frontend + self.observe(self._on_path_index_change, names=["path_index"]) + self.observe(self._on_gif_export, names=["_gif_export_requested"]) + + # Frame animation (5D): observe frame_idx changes from frontend + self.observe(self._on_frame_idx_change, names=["frame_idx"]) + + # Auto-detect trigger: observe changes from frontend + self.observe(self._on_auto_detect_trigger, names=["auto_detect_trigger"]) + + # VI ROI: observe changes for summed DP computation + # Initialize VI ROI center to scan center with reasonable default sizes + self.vi_roi_center_row = float(self.shape_rows / 2) + self.vi_roi_center_col = float(self.shape_cols / 2) + # Set initial ROI size based on scan dimension + default_roi_size = max(3, min(self.shape_rows, self.shape_cols) * DEFAULT_VI_ROI_RATIO) + self.vi_roi_radius = float(default_roi_size) + self.vi_roi_width = float(default_roi_size * 2) + self.vi_roi_height = float(default_roi_size) + self.observe(self._on_vi_roi_change, names=[ + "vi_roi_mode", "vi_roi_center_row", "vi_roi_center_col", + "vi_roi_radius", "vi_roi_width", "vi_roi_height" + ]) + + if state is not None: + if isinstance(state, (str, pathlib.Path)): + state = unwrap_state_payload( + json.loads(pathlib.Path(state).read_text()), + require_envelope=True, + ) + else: + state = unwrap_state_payload(state) + self.load_state_dict(state) + + if _verbose: + shape = "x".join(str(s) for s in self._data.shape) + print(f"Show4DSTEM: {shape} {self._device}, {time.perf_counter() - _t0:.2f}s total") + + def set_image(self, data, scan_shape=None): + """Replace the 4D-STEM data. Preserves all display and ROI settings.""" + if hasattr(data, "sampling") and hasattr(data, "array"): + data = data.array + data_np = to_numpy(data) + saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None + if data_np.dtype != np.float32: + data_np = data_np.astype(np.float32) + if saturated_value is not None: + data_np[data_np >= saturated_value] = 0 + if data_np.ndim == 5: + self.n_frames = data_np.shape[0] + self._scan_shape = (data_np.shape[1], data_np.shape[2]) + self._det_shape = (data_np.shape[3], data_np.shape[4]) + if data_np.size > 2**31 - 1 and str(self._device) == "mps": + self._device = torch.device("cpu") + self._data = torch.from_numpy(data_np).to(self._device) + elif data_np.ndim == 3: + self.n_frames = 1 + if scan_shape is not None: + self._scan_shape = scan_shape + else: + n = data_np.shape[0] + side = int(n ** 0.5) + if side * side != n: + raise ValueError(f"Cannot infer square scan_shape from N={n}. Provide scan_shape explicitly.") + self._scan_shape = (side, side) + self._det_shape = (data_np.shape[1], data_np.shape[2]) + self._data = torch.from_numpy(data_np).to(self._device) + elif data_np.ndim == 4: + self.n_frames = 1 + self._scan_shape = (data_np.shape[0], data_np.shape[1]) + self._det_shape = (data_np.shape[2], data_np.shape[3]) + self._data = torch.from_numpy(data_np).to(self._device) + else: + raise ValueError(f"Expected 3D, 4D, or 5D array, got {data_np.ndim}D") + self.frame_idx = 0 + self.shape_rows = self._scan_shape[0] + self.shape_cols = self._scan_shape[1] + self.det_rows = self._det_shape[0] + self.det_cols = self._det_shape[1] + first_frame = self._data[0] if self._data.ndim == 5 else self._data + self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame.max()) + self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] + self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] + self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] + self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] + self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) + self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) + self._sparse_samples = {} + self._sparse_order = [] + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + with self.hold_trait_notifications(): + self.pos_row = min(self.pos_row, self.shape_rows - 1) + self.pos_col = min(self.pos_col, self.shape_cols - 1) + self._compute_virtual_image_from_roi() + self._update_frame() + + def __repr__(self) -> str: + k_unit = "mrad" if self.k_calibrated else "px" + shape = ( + f"({self.n_frames}, {self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" + if self.n_frames > 1 + else f"({self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" + ) + frame_info = f", {self.frame_dim_label.lower()}={self.frame_idx}" if self.n_frames > 1 else "" + title_info = f", title='{self.title}'" if self.title else "" + return ( + f"Show4DSTEM(shape={shape}, " + f"sampling=({self.pixel_size} Å, {self.k_pixel_size} {k_unit}), " + f"pos=({self.pos_row}, {self.pos_col}){frame_info}{title_info})" + ) + + def state_dict(self): + return { + "title": self.title, + "pos_row": self.pos_row, + "pos_col": self.pos_col, + "pixel_size": self.pixel_size, + "k_pixel_size": self.k_pixel_size, + "k_calibrated": self.k_calibrated, + "center_row": self.center_row, + "center_col": self.center_col, + "bf_radius": self.bf_radius, + "roi_active": self.roi_active, + "roi_mode": self.roi_mode, + "roi_center_row": self.roi_center_row, + "roi_center_col": self.roi_center_col, + "roi_radius": self.roi_radius, + "roi_radius_inner": self.roi_radius_inner, + "roi_width": self.roi_width, + "roi_height": self.roi_height, + "vi_roi_mode": self.vi_roi_mode, + "vi_roi_center_row": self.vi_roi_center_row, + "vi_roi_center_col": self.vi_roi_center_col, + "vi_roi_radius": self.vi_roi_radius, + "vi_roi_width": self.vi_roi_width, + "vi_roi_height": self.vi_roi_height, + "mask_dc": self.mask_dc, + "dp_colormap": self.dp_colormap, + "vi_colormap": self.vi_colormap, + "fft_colormap": self.fft_colormap, + "dp_scale_mode": self.dp_scale_mode, + "vi_scale_mode": self.vi_scale_mode, + "fft_scale_mode": self.fft_scale_mode, + "dp_power_exp": self.dp_power_exp, + "vi_power_exp": self.vi_power_exp, + "fft_power_exp": self.fft_power_exp, + "dp_vmin_pct": self.dp_vmin_pct, + "dp_vmax_pct": self.dp_vmax_pct, + "vi_vmin_pct": self.vi_vmin_pct, + "vi_vmax_pct": self.vi_vmax_pct, + "fft_vmin_pct": self.fft_vmin_pct, + "fft_vmax_pct": self.fft_vmax_pct, + "dp_vmin": self.dp_vmin, + "dp_vmax": self.dp_vmax, + "vi_vmin": self.vi_vmin, + "vi_vmax": self.vi_vmax, + "fft_auto": self.fft_auto, + "show_fft": self.show_fft, + "fft_window": self.fft_window, + "show_controls": self.show_controls, + "dp_show_colorbar": self.dp_show_colorbar, + "export_default_view": self.export_default_view, + "export_default_format": self.export_default_format, + "export_include_overlays": self.export_include_overlays, + "export_include_scalebar": self.export_include_scalebar, + "export_default_dpi": self.export_default_dpi, + "path_interval_ms": self.path_interval_ms, + "path_loop": self.path_loop, + "profile_line": self.profile_line, + "profile_width": self.profile_width, + "frame_idx": self.frame_idx, + "frame_dim_label": self.frame_dim_label, + "frame_labels": list(self.frame_labels), + "frame_loop": self.frame_loop, + "frame_fps": self.frame_fps, + "frame_reverse": self.frame_reverse, + "frame_boomerang": self.frame_boomerang, + "disabled_tools": self.disabled_tools, + "hidden_tools": self.hidden_tools, + } + + def save(self, path: str): + save_state_file(path, "Show4DSTEM", self.state_dict()) + + def load_state_dict(self, state): + allowed_keys = set(self.state_dict().keys()) + pending_pos_row = state.get("pos_row", None) + pending_pos_col = state.get("pos_col", None) + pending_frame_idx = state.get("frame_idx", None) + for key, val in state.items(): + if key in {"pos_row", "pos_col", "frame_idx"}: + continue + if key in allowed_keys: + setattr(self, key, val) + if pending_frame_idx is not None: + self.frame_idx = int(max(0, min(int(pending_frame_idx), self.n_frames - 1))) + if pending_pos_row is not None or pending_pos_col is not None: + row = int(self.pos_row if pending_pos_row is None else pending_pos_row) + col = int(self.pos_col if pending_pos_col is None else pending_pos_col) + self.pos_row = int(max(0, min(row, self.shape_rows - 1))) + self.pos_col = int(max(0, min(col, self.shape_cols - 1))) + + def free(self): + """Free GPU memory held by this widget. + + Deletes the internal data tensor, runs garbage collection, and + flushes the MPS allocator cache. Call this before loading a new + dataset to avoid running out of GPU memory. + + Examples + -------- + >>> w.free() # release ~9 GB of MPS memory + >>> del result # free the source numpy array + """ + import gc + + device = str(self._device) if hasattr(self, "_device") else "" + nbytes = self._data.nbytes if hasattr(self._data, "nbytes") else 0 + self._data = None + gc.collect() + if device == "mps": + try: + import torch + torch.mps.empty_cache() + except Exception: + pass + elif device.startswith("cuda"): + try: + import torch + torch.cuda.empty_cache() + except Exception: + pass + if nbytes > 0: + print(f"freed {_format_memory(nbytes)} ({device})") + + def summary(self): + name = self.title if self.title else "Show4DSTEM" + lines = [name, "═" * 32] + if self.n_frames > 1: + parts = [f"{self.n_frames} ({self.frame_dim_label}), current: {self.frame_idx}"] + parts.append(f"{self.frame_fps} fps") + if self.frame_loop: + parts.append("loop") + if self.frame_reverse: + parts.append("reverse") + if self.frame_boomerang: + parts.append("bounce") + lines.append(f"Frames: {' | '.join(parts)}") + if self._frame_labels: + if len(self._frame_labels) <= 4: + lines.append(f"Labels: {self._frame_labels}") + else: + lines.append(f"Labels: {self._frame_labels[:3]} ... ({len(self._frame_labels)} total)") + lines.append(f"Scan: {self.shape_rows}×{self.shape_cols} ({self.pixel_size:.2f} Å/px)") + k_unit = "mrad" if self.k_calibrated else "px" + lines.append(f"Detector: {self.det_rows}×{self.det_cols} ({self.k_pixel_size:.4f} {k_unit}/px)") + lines.append(f"Position: ({self.pos_row}, {self.pos_col})") + lines.append(f"Center: ({self.center_row:.1f}, {self.center_col:.1f}) BF r={self.bf_radius:.1f} px") + display_parts = [] + if self.mask_dc: + display_parts.append("DC masked") + lines.append(f"Display: {', '.join(display_parts) if display_parts else 'default'}") + if self.roi_active: + lines.append(f"ROI: {self.roi_mode} at ({self.roi_center_row:.1f}, {self.roi_center_col:.1f}) r={self.roi_radius:.1f}") + if self.vi_roi_mode != "off": + lines.append(f"VI ROI: {self.vi_roi_mode} at ({self.vi_roi_center_row:.1f}, {self.vi_roi_center_col:.1f}) r={self.vi_roi_radius:.1f}") + dp_contrast = f"{self.dp_vmin_pct:.1f}-{self.dp_vmax_pct:.1f}%" + if self.dp_vmin is not None and self.dp_vmax is not None: + dp_contrast += f", dp_vmin={self.dp_vmin:.4g}, dp_vmax={self.dp_vmax:.4g}" + lines.append( + f"DP view: {self.dp_colormap}, {self.dp_scale_mode}, {dp_contrast}" + ) + vi_contrast = f"{self.vi_vmin_pct:.1f}-{self.vi_vmax_pct:.1f}%" + if self.vi_vmin is not None and self.vi_vmax is not None: + vi_contrast += f", vi_vmin={self.vi_vmin:.4g}, vi_vmax={self.vi_vmax:.4g}" + lines.append( + f"VI view: {self.vi_colormap}, {self.vi_scale_mode}, {vi_contrast}" + ) + if self.show_fft: + fft_parts = [f"{self.fft_colormap}, {self.fft_scale_mode}, {self.fft_vmin_pct:.1f}-{self.fft_vmax_pct:.1f}%, auto={self.fft_auto}"] + if not self.fft_window: + fft_parts.append("no window") + lines.append(f"FFT view: {', '.join(fft_parts)}") + if self.profile_line and len(self.profile_line) == 2: + p0, p1 = self.profile_line[0], self.profile_line[1] + lines.append(f"Profile: ({p0['row']:.0f}, {p0['col']:.0f}) -> ({p1['row']:.0f}, {p1['col']:.0f}) width={self.profile_width}") + if self.disabled_tools: + lines.append(f"Locked: {', '.join(self.disabled_tools)}") + if self.hidden_tools: + lines.append(f"Hidden: {', '.join(self.hidden_tools)}") + print("\n".join(lines)) + + # ========================================================================= + # Convenience Properties + # ========================================================================= + + @property + def position(self) -> tuple[int, int]: + """Current scan position as (row, col) tuple.""" + return (self.pos_row, self.pos_col) + + @position.setter + def position(self, value: tuple[int, int]) -> None: + """Set scan position from (row, col) tuple.""" + self.pos_row, self.pos_col = value + + @property + def scan_shape(self) -> tuple[int, int]: + """Scan dimensions as (rows, cols) tuple.""" + return (self.shape_rows, self.shape_cols) + + @property + def detector_shape(self) -> tuple[int, int]: + """Detector dimensions as (rows, cols) tuple.""" + return (self.det_rows, self.det_cols) + + @property + def _frame_data(self) -> torch.Tensor: + """Per-frame data (4D or 3D flattened), accounting for 5D time/tilt series.""" + if self.n_frames > 1: + return self._data[self.frame_idx] + return self._data + + # ========================================================================= + # Line Profile + # ========================================================================= + + def set_profile(self, start: tuple, end: tuple) -> Self: + row0, col0 = start + row1, col1 = end + self.profile_line = [ + {"row": float(row0), "col": float(col0)}, + {"row": float(row1), "col": float(col1)}, + ] + return self + + def clear_profile(self) -> Self: + self.profile_line = [] + return self + + @property + def profile(self) -> list[tuple[float, float]]: + if len(self.profile_line) == 2: + p0, p1 = self.profile_line[0], self.profile_line[1] + return [(p0["row"], p0["col"]), (p1["row"], p1["col"])] + return [] + + @property + def profile_values(self): + if len(self.profile_line) != 2: + return None + p0, p1 = self.profile_line[0], self.profile_line[1] + frame = self._get_frame(self.pos_row, self.pos_col) + return self._sample_line(frame, p0["row"], p0["col"], p1["row"], p1["col"]) + + @property + def profile_distance(self) -> float: + if len(self.profile_line) != 2: + return 0.0 + p0, p1 = self.profile_line[0], self.profile_line[1] + dist_px = np.sqrt((p1["row"] - p0["row"]) ** 2 + (p1["col"] - p0["col"]) ** 2) + if self.k_calibrated: + return float(dist_px * self.k_pixel_size) + return float(dist_px) + + def _sample_line(self, frame, row0, col0, row1, col1): + h, w = frame.shape[:2] + dc = col1 - col0 + dr = row1 - row0 + length = np.sqrt(dc * dc + dr * dr) + n = max(2, int(np.ceil(length))) + t = np.linspace(0.0, 1.0, n) + c = col0 + t * dc + r = row0 + t * dr + ci = np.floor(c).astype(np.intp) + ri = np.floor(r).astype(np.intp) + cf = c - ci + rf = r - ri + c0 = np.clip(ci, 0, w - 1) + c1 = np.clip(ci + 1, 0, w - 1) + r0 = np.clip(ri, 0, h - 1) + r1 = np.clip(ri + 1, 0, h - 1) + return ( + frame[r0, c0] * (1 - cf) * (1 - rf) + + frame[r0, c1] * cf * (1 - rf) + + frame[r1, c0] * (1 - cf) * rf + + frame[r1, c1] * cf * rf + ).astype(np.float32) + + # ========================================================================= + # Path Animation Methods + # ========================================================================= + + def set_path( + self, + points: list[tuple[int, int]], + interval_ms: int = 100, + loop: bool = True, + autoplay: bool = True, + ) -> Self: + """ + Set a custom path of scan positions to animate through. + + Parameters + ---------- + points : list[tuple[int, int]] + List of (row, col) scan positions to visit. + interval_ms : int, default 100 + Time between frames in milliseconds. + loop : bool, default True + Whether to loop when reaching end. + autoplay : bool, default True + Start playing immediately. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.set_path([(0, 0), (10, 10), (20, 20), (30, 30)]) + >>> widget.set_path([(i, i) for i in range(48)], interval_ms=50) + """ + self._path_points = list(points) + self.path_length = len(self._path_points) + self.path_index = 0 + self.path_interval_ms = interval_ms + self.path_loop = loop + if autoplay and self.path_length > 0: + self.path_playing = True + return self + + def play(self) -> Self: + """Start playing the path animation.""" + if self.path_length > 0: + self.path_playing = True + return self + + def pause(self) -> Self: + """Pause the path animation.""" + self.path_playing = False + return self + + def stop(self) -> Self: + """Stop and reset path animation to beginning.""" + self.path_playing = False + self.path_index = 0 + return self + + def goto(self, index: int) -> Self: + """Jump to a specific index in the path.""" + if 0 <= index < self.path_length: + self.path_index = index + return self + + def _on_path_index_change(self, change): + """Called when path_index changes (from frontend timer).""" + idx = change["new"] + if 0 <= idx < len(self._path_points): + row, col = self._path_points[idx] + # Clamp to valid range + self.pos_row = max(0, min(self.shape_rows - 1, row)) + self.pos_col = max(0, min(self.shape_cols - 1, col)) + + def _on_auto_detect_trigger(self, change): + """Called when auto_detect_trigger is set to True from frontend.""" + if change["new"]: + self.auto_detect_center() + # Reset trigger to allow re-triggering + self.auto_detect_trigger = False + + def _on_frame_idx_change(self, change=None): + """Called when frame_idx changes (5D time/tilt series). + + Recomputes virtual image and diffraction pattern for the new frame. + Invalidates precomputed caches since they are per-frame. + """ + if self.n_frames <= 1: + return + # Invalidate precomputed caches (they were for a different frame) + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + # Recompute virtual image and displayed frame + self._compute_virtual_image_from_roi() + self._update_frame() + # Recompute summed DP if VI ROI is active + if self.vi_roi_mode != "off": + self._compute_summed_dp_from_vi_roi() + + # ========================================================================= + # Path Animation Patterns + # ========================================================================= + + def raster( + self, + step: int = 1, + bidirectional: bool = False, + interval_ms: int = 100, + loop: bool = True, + ) -> Self: + """ + Play a raster scan path (row by row, left to right). + + This mimics real STEM scanning: left→right, step down, left→right, etc. + + Parameters + ---------- + step : int, default 1 + Step size between positions. + bidirectional : bool, default False + If True, use snake/boustrophedon pattern (alternating direction). + If False (default), always scan left→right like real STEM. + interval_ms : int, default 100 + Time between frames in milliseconds. + loop : bool, default True + Whether to loop when reaching the end. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + points = [] + for r in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if bidirectional and (r // step % 2 == 1): + cols = cols[::-1] # Alternate direction for snake pattern + for c in cols: + points.append((r, c)) + return self.set_path(points=points, interval_ms=interval_ms, loop=loop) + + # ========================================================================= + # ROI Mode Methods + # ========================================================================= + + def roi_circle(self, radius: float | None = None) -> Self: + """ + Switch to circle ROI mode for virtual imaging. + + In circle mode, the virtual image integrates over a circular region + centered at the current ROI position (like a virtual bright field detector). + + Parameters + ---------- + radius : float, optional + Radius of the circle in pixels. If not provided, uses current value + or defaults to half the BF radius. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_circle(20) # 20px radius circle + >>> widget.roi_circle() # Use default radius + """ + self.roi_mode = "circle" + if radius is not None: + self.roi_radius = float(radius) + return self + + def roi_point(self) -> Self: + """ + Switch to point ROI mode (single-pixel indexing). + + In point mode, the virtual image shows intensity at the exact ROI position. + This is the default mode. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + self.roi_mode = "point" + return self + + def roi_square(self, half_size: float | None = None) -> Self: + """ + Switch to square ROI mode for virtual imaging. + + In square mode, the virtual image integrates over a square region + centered at the current ROI position. + + Parameters + ---------- + half_size : float, optional + Half-size of the square in pixels (distance from center to edge). + A half_size of 15 creates a 30x30 pixel square. + If not provided, uses current roi_radius value. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_square(15) # 30x30 pixel square (half_size=15) + >>> widget.roi_square() # Use default size + """ + self.roi_mode = "square" + if half_size is not None: + self.roi_radius = float(half_size) + return self + + def roi_annular( + self, inner_radius: float | None = None, outer_radius: float | None = None + ) -> Self: + """ + Set ROI mode to annular (donut-shaped) for ADF/HAADF imaging. + + Parameters + ---------- + inner_radius : float, optional + Inner radius in pixels. If not provided, uses current roi_radius_inner. + outer_radius : float, optional + Outer radius in pixels. If not provided, uses current roi_radius. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_annular(20, 50) # ADF: inner=20px, outer=50px + >>> widget.roi_annular(30, 80) # HAADF: larger angles + """ + self.roi_mode = "annular" + if inner_radius is not None: + self.roi_radius_inner = float(inner_radius) + if outer_radius is not None: + self.roi_radius = float(outer_radius) + return self + + def roi_rect( + self, width: float | None = None, height: float | None = None + ) -> Self: + """ + Set ROI mode to rectangular. + + Parameters + ---------- + width : float, optional + Width in pixels. If not provided, uses current roi_width. + height : float, optional + Height in pixels. If not provided, uses current roi_height. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget.roi_rect(30, 20) # 30px wide, 20px tall + >>> widget.roi_rect(40, 40) # 40x40 rectangle + """ + self.roi_mode = "rect" + if width is not None: + self.roi_width = float(width) + if height is not None: + self.roi_height = float(height) + return self + + def auto_detect_center(self, update_roi: bool = True) -> Self: + """ + Automatically detect BF disk center and radius using centroid. + + This method analyzes the summed diffraction pattern to find the + bright field disk center and estimate its radius. The detected + values are applied to the widget's calibration (center_row, center_col, + bf_radius). + + Parameters + ---------- + update_roi : bool, default True + If True, also update ROI center and recompute cached virtual images. + Set to False during __init__ when ROI is not yet initialized. + + Returns + ------- + Show4DSTEM + Self for method chaining. + + Examples + -------- + >>> widget = Show4DSTEM(data) + >>> widget.auto_detect_center() # Auto-detect and apply + """ + # Sum all diffraction patterns to get average (PyTorch) + if self._data.ndim == 5: + summed_dp = self._data.sum(dim=(0, 1, 2)) + elif self._data.ndim == 4: + summed_dp = self._data.sum(dim=(0, 1)) + else: + summed_dp = self._data.sum(dim=0) + + # Threshold at mean + std to isolate BF disk + threshold = summed_dp.mean() + summed_dp.std() + mask = summed_dp > threshold + + # Avoid division by zero + total = mask.sum() + if total == 0: + return self + + # Calculate centroid using cached coordinate grids + cx = float((self._det_col_coords * mask).sum() / total) + cy = float((self._det_row_coords * mask).sum() / total) + + # Estimate radius from mask area (A = pi*r^2) + radius = float(torch.sqrt(total / torch.pi)) + + # Apply detected values + self.center_col = cx + self.center_row = cy + self.bf_radius = radius + + if update_roi: + # Also update ROI to center + self.roi_center_col = cx + self.roi_center_row = cy + # Recompute cached virtual images with new calibration + self._precompute_common_virtual_images() + + return self + + def _get_frame(self, row: int, col: int) -> np.ndarray: + """Get single diffraction frame at position (row, col) as numpy array.""" + if self._data is None: + return np.zeros((self.det_rows, self.det_cols), dtype=np.float32) + data = self._frame_data + if data.ndim == 3: + idx = row * self.shape_cols + col + return data[idx].cpu().numpy() + else: + return data[row, col].cpu().numpy() + + def _apply_scale_mode( + self, + data: np.ndarray, + mode: str, + power_exp: float = 0.5, + ) -> np.ndarray: + arr = np.asarray(data, dtype=np.float32) + if mode == "log": + return np.log1p(np.maximum(arr, 0.0)).astype(np.float32) + if mode == "power": + return np.power(np.maximum(arr, 0.0), float(power_exp)).astype(np.float32) + return arr.astype(np.float32) + + def _slider_range( + self, + data_min: float, + data_max: float, + vmin_pct: float, + vmax_pct: float, + ) -> tuple[float, float]: + v0 = float(max(0.0, min(100.0, vmin_pct))) + v1 = float(max(0.0, min(100.0, vmax_pct))) + if v1 < v0: + v0, v1 = v1, v0 + rng = float(data_max - data_min) + return ( + float(data_min + (v0 / 100.0) * rng), + float(data_min + (v1 / 100.0) * rng), + ) + + def _render_colormap_rgb( + self, + data: np.ndarray, + cmap_name: str, + vmin: float, + vmax: float, + ) -> np.ndarray: + from matplotlib import colormaps + + arr = np.asarray(data, dtype=np.float32) + if vmax <= vmin: + normalized = np.zeros_like(arr, dtype=np.float32) + else: + normalized = np.clip((arr - vmin) / (vmax - vmin), 0.0, 1.0) + rgba = colormaps.get_cmap(cmap_name)(normalized) + return (rgba[..., :3] * 255).astype(np.uint8) + + def _get_virtual_image_array(self) -> np.ndarray: + if not self.virtual_image_bytes: + return np.zeros((self.shape_rows, self.shape_cols), dtype=np.float32) + arr = np.frombuffer(self.virtual_image_bytes, dtype=np.float32) + expected = self.shape_rows * self.shape_cols + if arr.size != expected: + return np.zeros((self.shape_rows, self.shape_cols), dtype=np.float32) + return arr.reshape(self.shape_rows, self.shape_cols).copy() + + def _get_summed_dp_array(self) -> np.ndarray | None: + if self.vi_roi_mode == "off": + return None + self._compute_summed_dp_from_vi_roi() + if not self.summed_dp_bytes: + return None + arr = np.frombuffer(self.summed_dp_bytes, dtype=np.float32) + expected = self.det_rows * self.det_cols + if arr.size != expected: + return None + return arr.reshape(self.det_rows, self.det_cols).copy() + + def _fft_enhanced_range(self, mag: np.ndarray) -> tuple[float, float]: + arr = np.asarray(mag, dtype=np.float32).copy() + if arr.size == 0: + return 0.0, 0.0 + center_row = arr.shape[0] // 2 + center_col = arr.shape[1] // 2 + neighbors = [] + if center_col - 1 >= 0: + neighbors.append(arr[center_row, center_col - 1]) + if center_col + 1 < arr.shape[1]: + neighbors.append(arr[center_row, center_col + 1]) + if center_row - 1 >= 0: + neighbors.append(arr[center_row - 1, center_col]) + if center_row + 1 < arr.shape[0]: + neighbors.append(arr[center_row + 1, center_col]) + if neighbors: + arr[center_row, center_col] = float(np.mean(neighbors)) + dmin = float(arr.min()) + dmax = float(arr.max()) + if dmax <= dmin: + return dmin, dmax + pmax = float(np.percentile(arr, 99.9)) + if pmax <= dmin: + pmax = dmax + return dmin, pmax + + def _render_dp_rgb(self) -> tuple[np.ndarray, dict]: + summed_dp = self._get_summed_dp_array() + if summed_dp is not None: + raw = summed_dp + source = "summed_dp" + else: + raw = self._get_frame(self.pos_row, self.pos_col).astype(np.float32) + source = "single_frame" + + scale_mode = self.dp_scale_mode + scaled = self._apply_scale_mode(raw, scale_mode, self.dp_power_exp) + data_min = float(scaled.min()) if scaled.size else 0.0 + data_max = float(scaled.max()) if scaled.size else 0.0 + if self.dp_vmin is not None and self.dp_vmax is not None: + vmin = float(self._apply_scale_mode( + np.array([max(self.dp_vmin, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + )[0]) + vmax = float(self._apply_scale_mode( + np.array([max(self.dp_vmax, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + )[0]) + else: + vmin, vmax = self._slider_range(data_min, data_max, self.dp_vmin_pct, self.dp_vmax_pct) + rgb = self._render_colormap_rgb(scaled, self.dp_colormap, vmin, vmax) + metadata = { + "source": source, + "colormap": self.dp_colormap, + "scale_mode": scale_mode, + "vmin_pct": float(self.dp_vmin_pct), + "vmax_pct": float(self.dp_vmax_pct), + "vmin": float(vmin), + "vmax": float(vmax), + } + return rgb, metadata + + def _render_virtual_rgb(self) -> tuple[np.ndarray, dict]: + raw = self._get_virtual_image_array() + scaled = self._apply_scale_mode(raw, self.vi_scale_mode, self.vi_power_exp) + data_min = float(scaled.min()) if scaled.size else 0.0 + data_max = float(scaled.max()) if scaled.size else 0.0 + if self.vi_vmin is not None and self.vi_vmax is not None: + vmin = float(self._apply_scale_mode( + np.array([max(self.vi_vmin, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + )[0]) + vmax = float(self._apply_scale_mode( + np.array([max(self.vi_vmax, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + )[0]) + else: + vmin, vmax = self._slider_range(data_min, data_max, self.vi_vmin_pct, self.vi_vmax_pct) + rgb = self._render_colormap_rgb(scaled, self.vi_colormap, vmin, vmax) + metadata = { + "colormap": self.vi_colormap, + "scale_mode": self.vi_scale_mode, + "vmin_pct": float(self.vi_vmin_pct), + "vmax_pct": float(self.vi_vmax_pct), + "vmin": float(vmin), + "vmax": float(vmax), + } + return rgb, metadata + + def _render_fft_rgb(self) -> tuple[np.ndarray, dict]: + virtual_raw = self._get_virtual_image_array() + fft = np.fft.fftshift(np.fft.fft2(virtual_raw)) + mag = np.abs(fft).astype(np.float32) + scaled = self._apply_scale_mode(mag, self.fft_scale_mode, self.fft_power_exp) + if self.fft_auto: + display_min, display_max = self._fft_enhanced_range(scaled) + else: + display_min = float(scaled.min()) if scaled.size else 0.0 + display_max = float(scaled.max()) if scaled.size else 0.0 + vmin, vmax = self._slider_range(display_min, display_max, self.fft_vmin_pct, self.fft_vmax_pct) + rgb = self._render_colormap_rgb(scaled, self.fft_colormap, vmin, vmax) + metadata = { + "colormap": self.fft_colormap, + "scale_mode": self.fft_scale_mode, + "auto": bool(self.fft_auto), + "vmin_pct": float(self.fft_vmin_pct), + "vmax_pct": float(self.fft_vmax_pct), + "vmin": float(vmin), + "vmax": float(vmax), + } + return rgb, metadata + + def list_export_views(self) -> tuple[str, ...]: + return ("diffraction", "virtual", "fft", "all") + + def list_export_formats(self) -> tuple[str, ...]: + return ("png", "pdf") + + def list_figure_templates(self) -> tuple[str, ...]: + return ("dp_vi", "dp_vi_fft", "publication_dp_vi", "publication_dp_vi_fft") + + def list_presets(self) -> tuple[str, ...]: + builtin = ("bf", "abf", "adf", "haadf") + custom = tuple(sorted(self._named_presets.keys())) + return builtin + custom + + def _validate_export_view(self, view: str | None) -> str: + candidate = self.export_default_view if view is None else str(view) + view_key = str(candidate).strip().lower() + allowed = self.list_export_views() + if view_key not in allowed: + raise ValueError( + f"Unsupported view '{view}'. Supported: {', '.join(allowed)}" + ) + return view_key + + def _validate_frame_idx(self, frame_idx: int | None) -> int: + if frame_idx is None: + return int(self.frame_idx) + idx = int(frame_idx) + if idx < 0 or idx >= self.n_frames: + raise ValueError( + f"frame_idx={idx} is out of range [0, {self.n_frames - 1}]" + ) + return idx + + def _validate_position(self, position: tuple[int, int] | None) -> tuple[int, int]: + if position is None: + return int(self.pos_row), int(self.pos_col) + if len(position) != 2: + raise ValueError( + "position must be a (row, col) tuple with exactly two values" + ) + row = int(position[0]) + col = int(position[1]) + if row < 0 or row >= self.shape_rows or col < 0 or col >= self.shape_cols: + raise ValueError( + f"position=({row}, {col}) is out of range for " + f"scan_shape=({self.shape_rows}, {self.shape_cols})" + ) + return row, col + + def _resolve_export_format( + self, + path: pathlib.Path, + fmt: str | None, + ) -> str: + if fmt is not None and str(fmt).strip(): + resolved = str(fmt).strip().lower() + else: + from_path = path.suffix.lstrip(".").lower() + resolved = from_path if from_path else str(self.export_default_format).strip().lower() + allowed = self.list_export_formats() + if resolved not in allowed: + raise ValueError( + f"Unsupported format '{resolved}'. Supported: {', '.join(allowed)}" + ) + return resolved + + @staticmethod + def _round_to_nice_value(value: float) -> float: + if value <= 0: + return 1.0 + magnitude = 10 ** math.floor(math.log10(value)) + normalized = value / magnitude + if normalized < 1.5: + return float(magnitude) + if normalized < 3.5: + return float(2 * magnitude) + if normalized < 7.5: + return float(5 * magnitude) + return float(10 * magnitude) + + def _format_scale_label(self, value: float, unit: str) -> str: + nice = self._round_to_nice_value(value) + if unit == "Å": + if nice >= 10: + return f"{int(round(nice / 10))} nm" + if nice >= 1: + return f"{int(round(nice))} Å" + return f"{nice:.2f} Å" + if unit == "mrad": + if nice >= 1000: + return f"{int(round(nice / 1000))} rad" + if nice >= 1: + return f"{int(round(nice))} mrad" + return f"{nice:.2f} mrad" + if nice >= 1: + return f"{int(round(nice))} px" + return f"{nice:.1f} px" + + @staticmethod + def _draw_crosshair(draw, x: float, y: float, size: float, color, width: int) -> None: + draw.line([(x - size, y), (x + size, y)], fill=color, width=width) + draw.line([(x, y - size), (x, y + size)], fill=color, width=width) + + def _draw_scalebar_overlay(self, image, pixel_size: float, unit: str) -> None: + from PIL import ImageDraw, ImageFont + + if pixel_size <= 0: + return + + draw = ImageDraw.Draw(image, mode="RGBA") + font = ImageFont.load_default() + width, height = image.size + margin = max(8, int(min(width, height) * 0.04)) + thickness = max(2, int(height * 0.01)) + target_bar_px = max(36, int(width * 0.15)) + target_physical = float(target_bar_px) * float(pixel_size) + nice_physical = self._round_to_nice_value(target_physical) + bar_px = max(12, int(round(nice_physical / float(pixel_size)))) + bar_px = min(bar_px, max(12, int(width * 0.8))) + + x1 = width - margin + x0 = x1 - bar_px + y1 = height - margin + y0 = y1 - thickness + + draw.rectangle([(x0 + 1, y0 + 1), (x1 + 1, y1 + 1)], fill=(0, 0, 0, 180)) + draw.rectangle([(x0, y0), (x1, y1)], fill=(255, 255, 255, 255)) + + label = self._format_scale_label(nice_physical, unit) + label_bbox = draw.textbbox((0, 0), label, font=font) + label_w = label_bbox[2] - label_bbox[0] + label_h = label_bbox[3] - label_bbox[1] + tx = x0 + (bar_px - label_w) / 2 + ty = y0 - label_h - 4 + draw.text((tx + 1, ty + 1), label, fill=(0, 0, 0, 220), font=font) + draw.text((tx, ty), label, fill=(255, 255, 255, 255), font=font) + + zoom_label = "1.0x" + zoom_bbox = draw.textbbox((0, 0), zoom_label, font=font) + zoom_h = zoom_bbox[3] - zoom_bbox[1] + zx = margin + zy = height - margin - zoom_h + draw.text((zx + 1, zy + 1), zoom_label, fill=(0, 0, 0, 220), font=font) + draw.text((zx, zy), zoom_label, fill=(255, 255, 255, 255), font=font) + + def _draw_dp_overlays(self, image) -> None: + from PIL import ImageDraw + + draw = ImageDraw.Draw(image, mode="RGBA") + width, height = image.size + scale_x = float(width) / float(max(1, self.det_cols)) + scale_y = float(height) / float(max(1, self.det_rows)) + cx = float(self.roi_center_col) * scale_x + cy = float(self.roi_center_row) * scale_y + + if self.roi_active and self.roi_mode != "point": + stroke = (0, 220, 0, 240) + fill = (0, 220, 0, 45) + if self.roi_mode == "circle": + rx = float(self.roi_radius) * scale_x + ry = float(self.roi_radius) * scale_y + draw.ellipse([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.roi_mode == "square": + rx = float(self.roi_radius) * scale_x + ry = float(self.roi_radius) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.roi_mode == "rect": + rx = (float(self.roi_width) / 2.0) * scale_x + ry = (float(self.roi_height) / 2.0) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.roi_mode == "annular": + outer_rx = float(self.roi_radius) * scale_x + outer_ry = float(self.roi_radius) * scale_y + inner_rx = float(self.roi_radius_inner) * scale_x + inner_ry = float(self.roi_radius_inner) * scale_y + draw.ellipse( + [(cx - outer_rx, cy - outer_ry), (cx + outer_rx, cy + outer_ry)], + outline=stroke, + fill=fill, + width=2, + ) + draw.ellipse( + [(cx - inner_rx, cy - inner_ry), (cx + inner_rx, cy + inner_ry)], + outline=stroke, + fill=(0, 0, 0, 0), + width=2, + ) + + marker_color = (0, 220, 0, 255) if self.roi_active else (255, 100, 100, 255) + self._draw_crosshair(draw, cx, cy, size=max(6, int(min(width, height) * 0.03)), color=marker_color, width=2) + + if len(self.profile_line) == 2: + p0, p1 = self.profile_line[0], self.profile_line[1] + x0 = float(p0["col"]) * scale_x + y0 = float(p0["row"]) * scale_y + x1 = float(p1["col"]) * scale_x + y1 = float(p1["row"]) * scale_y + draw.line([(x0, y0), (x1, y1)], fill=(0, 200, 255, 240), width=max(1, int(self.profile_width))) + r = 3 + draw.ellipse([(x0 - r, y0 - r), (x0 + r, y0 + r)], fill=(0, 200, 255, 255)) + draw.ellipse([(x1 - r, y1 - r), (x1 + r, y1 + r)], fill=(0, 200, 255, 255)) + + def _draw_vi_overlays(self, image) -> None: + from PIL import ImageDraw + + draw = ImageDraw.Draw(image, mode="RGBA") + width, height = image.size + scale_x = float(width) / float(max(1, self.shape_cols)) + scale_y = float(height) / float(max(1, self.shape_rows)) + + px = float(self.pos_col) * scale_x + py = float(self.pos_row) * scale_y + self._draw_crosshair( + draw, + px, + py, + size=max(6, int(min(width, height) * 0.03)), + color=(255, 100, 100, 240), + width=2, + ) + + if self.vi_roi_mode == "off": + return + + cx = float(self.vi_roi_center_col) * scale_x + cy = float(self.vi_roi_center_row) * scale_y + stroke = (180, 80, 255, 240) + fill = (180, 80, 255, 45) + if self.vi_roi_mode == "circle": + rx = float(self.vi_roi_radius) * scale_x + ry = float(self.vi_roi_radius) * scale_y + draw.ellipse([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.vi_roi_mode == "square": + rx = float(self.vi_roi_radius) * scale_x + ry = float(self.vi_roi_radius) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + elif self.vi_roi_mode == "rect": + rx = (float(self.vi_roi_width) / 2.0) * scale_x + ry = (float(self.vi_roi_height) / 2.0) * scale_y + draw.rectangle([(cx - rx, cy - ry), (cx + rx, cy + ry)], outline=stroke, fill=fill, width=2) + + self._draw_crosshair( + draw, + cx, + cy, + size=max(6, int(min(width, height) * 0.03)), + color=(180, 80, 255, 240), + width=2, + ) + + def _decorate_panel( + self, + image, + panel_key: str, + include_overlays: bool, + include_scalebar: bool, + ): + out = image.copy() + if include_overlays: + if panel_key == "diffraction": + self._draw_dp_overlays(out) + elif panel_key == "virtual": + self._draw_vi_overlays(out) + if include_scalebar: + if panel_key == "diffraction": + unit = "mrad" if self.k_calibrated else "px" + self._draw_scalebar_overlay(out, float(self.k_pixel_size), unit) + elif panel_key == "virtual": + self._draw_scalebar_overlay(out, float(self.pixel_size), "Å") + return out + + def _render_panel_image( + self, + panel_key: str, + include_overlays: bool, + include_scalebar: bool, + ) -> tuple[Any, dict[str, Any]]: + from PIL import Image + + if panel_key == "diffraction": + rgb, render_meta = self._render_dp_rgb() + elif panel_key == "virtual": + rgb, render_meta = self._render_virtual_rgb() + elif panel_key == "fft": + rgb, render_meta = self._render_fft_rgb() + else: + raise ValueError(f"Unsupported panel '{panel_key}'") + + panel = Image.fromarray(rgb, mode="RGB") + panel = self._decorate_panel(panel, panel_key, include_overlays, include_scalebar) + return panel, render_meta + + def _compose_horizontal(self, panels: list[Any]): + from PIL import Image + + height = max(panel.height for panel in panels) + width = sum(panel.width for panel in panels) + composite = Image.new("RGB", (width, height), color=(0, 0, 0)) + x0 = 0 + for panel in panels: + composite.paste(panel, (x0, 0)) + x0 += panel.width + return composite + + def _calibration_metadata(self) -> dict[str, Any]: + return { + "pixel_size_angstrom": float(self.pixel_size), + "pixel_size_unit": "Å/px", + "k_pixel_size": float(self.k_pixel_size), + "k_pixel_size_unit": "mrad/px" if self.k_calibrated else "px/px", + "k_calibrated": bool(self.k_calibrated), + "center_row": float(self.center_row), + "center_col": float(self.center_col), + "bf_radius": float(self.bf_radius), + } + + def _roi_metadata(self) -> dict[str, Any]: + return { + "active": bool(self.roi_active), + "mode": self.roi_mode, + "center_row": float(self.roi_center_row), + "center_col": float(self.roi_center_col), + "radius": float(self.roi_radius), + "radius_inner": float(self.roi_radius_inner), + "width": float(self.roi_width), + "height": float(self.roi_height), + } + + def _vi_roi_metadata(self) -> dict[str, Any]: + return { + "mode": self.vi_roi_mode, + "center_row": float(self.vi_roi_center_row), + "center_col": float(self.vi_roi_center_col), + "radius": float(self.vi_roi_radius), + "width": float(self.vi_roi_width), + "height": float(self.vi_roi_height), + } + + def _export_settings_metadata(self) -> dict[str, Any]: + return { + "default_view": self.export_default_view, + "default_format": self.export_default_format, + "include_overlays": bool(self.export_include_overlays), + "include_scalebar": bool(self.export_include_scalebar), + "dpi": int(self.export_default_dpi), + } + + def _build_image_export_metadata( + self, + export_path: pathlib.Path, + view_key: str, + fmt: str, + render_meta: dict[str, Any], + include_overlays: bool, + include_scalebar: bool, + export_kind: str, + extra: dict[str, Any] | None = None, + ) -> dict[str, Any]: + metadata: dict[str, Any] = { + **build_json_header("Show4DSTEM"), + "view": view_key, + "format": fmt, + "export_kind": export_kind, + "path": str(export_path), + "position": {"row": int(self.pos_row), "col": int(self.pos_col)}, + "frame_idx": int(self.frame_idx), + "n_frames": int(self.n_frames), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "roi": self._roi_metadata(), + "vi_roi": self._vi_roi_metadata(), + "calibration": self._calibration_metadata(), + "display": render_meta, + "include_overlays": bool(include_overlays), + "include_scalebar": bool(include_scalebar), + "export_settings": self._export_settings_metadata(), + } + if extra: + metadata.update(extra) + return metadata + + @staticmethod + def _sha256_file(path: pathlib.Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as f: + while True: + chunk = f.read(1_048_576) + if not chunk: + break + digest.update(chunk) + return digest.hexdigest() + + def _build_file_record( + self, + path: pathlib.Path, + metadata_path: pathlib.Path | None = None, + index: int | None = None, + ) -> dict[str, Any]: + record: dict[str, Any] = { + "path": str(path), + "sha256": self._sha256_file(path), + "size_bytes": int(path.stat().st_size), + } + if metadata_path is not None and metadata_path.exists(): + record["metadata_path"] = str(metadata_path) + record["metadata_sha256"] = self._sha256_file(metadata_path) + record["metadata_size_bytes"] = int(metadata_path.stat().st_size) + if index is not None: + record["index"] = int(index) + return record + + def _record_export_event(self, event: dict[str, Any]) -> None: + payload = { + "session_id": self._export_session_id, + "timestamp_utc": datetime.now(timezone.utc).isoformat(), + } + payload.update(event) + self._export_log.append(payload) + + def _validate_sparse_frame_idx(self, frame_idx: int | None) -> int: + if self.n_frames <= 1: + return 0 + if frame_idx is None: + return int(self.frame_idx) + idx = int(frame_idx) + if idx < 0 or idx >= self.n_frames: + raise ValueError(f"frame_idx={idx} is out of range [0, {self.n_frames - 1}]") + return idx + + def _normalize_sparse_mask(self, mask: np.ndarray) -> np.ndarray: + arr = np.asarray(mask) + if self.n_frames <= 1: + if arr.shape == (self.shape_rows, self.shape_cols): + arr = arr[None, ...] + elif arr.shape != (1, self.shape_rows, self.shape_cols): + raise ValueError( + f"mask shape {arr.shape} does not match " + f"(scan_rows, scan_cols)=({self.shape_rows}, {self.shape_cols})" + ) + elif arr.shape != (self.n_frames, self.shape_rows, self.shape_cols): + raise ValueError( + f"mask shape {arr.shape} does not match " + f"(n_frames, scan_rows, scan_cols)=({self.n_frames}, {self.shape_rows}, {self.shape_cols})" + ) + return arr.astype(bool, copy=False) + + def _coerce_dp_array(self, dp: np.ndarray) -> np.ndarray: + arr = np.asarray(to_numpy(dp), dtype=np.float32) + if arr.shape != (self.det_rows, self.det_cols): + raise ValueError( + f"dp shape {arr.shape} does not match detector_shape " + f"({self.det_rows}, {self.det_cols})" + ) + return arr + + def _write_dp_to_data(self, frame_idx: int, row: int, col: int, dp_arr: np.ndarray) -> None: + dp_tensor = torch.from_numpy(dp_arr).to(device=self._device, dtype=torch.float32) + if self.n_frames > 1: + self._data[frame_idx, row, col] = dp_tensor + elif self._data.ndim == 4: + self._data[row, col] = dp_tensor + else: + flat_idx = row * self.shape_cols + col + self._data[flat_idx] = dp_tensor + + def _ingest_scan_point_core( + self, + row: int, + col: int, + dp: np.ndarray, + frame_idx: int, + dose: float, + refresh: bool, + ) -> None: + row_i, col_i = self._validate_position((row, col)) + frame_i = self._validate_sparse_frame_idx(frame_idx) + dp_arr = self._coerce_dp_array(dp) + dose_value = float(dose) + if not np.isfinite(dose_value) or dose_value < 0: + raise ValueError(f"dose must be finite and >= 0, got {dose}") + + key = (int(frame_i), int(row_i), int(col_i)) + if key not in self._sparse_samples: + self._sparse_order.append(key) + self._sparse_samples[key] = dp_arr.copy() + self._sparse_mask[frame_i, row_i, col_i] = True + self._dose_map[frame_i, row_i, col_i] += dose_value + + self._write_dp_to_data(frame_i, row_i, col_i, dp_arr) + self.dp_global_min = max(min(float(self.dp_global_min), float(dp_arr.min())), MIN_LOG_VALUE) + self.dp_global_max = max(float(self.dp_global_max), float(dp_arr.max())) + + if refresh: + self._compute_virtual_image_from_roi() + self._update_frame() + + def _detector_integration_kernel(self) -> tuple[np.ndarray | None, tuple[int, int] | None]: + cx, cy = float(self.roi_center_col), float(self.roi_center_row) + rr, cc = np.meshgrid( + np.arange(self.det_rows, dtype=np.float32), + np.arange(self.det_cols, dtype=np.float32), + indexing="ij", + ) + if self.roi_mode == "circle" and self.roi_radius > 0: + mask = (cc - cx) ** 2 + (rr - cy) ** 2 <= float(self.roi_radius) ** 2 + return mask.astype(np.float32, copy=False), None + if self.roi_mode == "square" and self.roi_radius > 0: + half = float(self.roi_radius) + mask = (np.abs(cc - cx) <= half) & (np.abs(rr - cy) <= half) + return mask.astype(np.float32, copy=False), None + if self.roi_mode == "annular" and self.roi_radius > 0: + outer = float(self.roi_radius) + inner = float(self.roi_radius_inner) + dist_sq = (cc - cx) ** 2 + (rr - cy) ** 2 + mask = (dist_sq >= inner**2) & (dist_sq <= outer**2) + return mask.astype(np.float32, copy=False), None + if self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: + hw = float(self.roi_width) / 2.0 + hh = float(self.roi_height) / 2.0 + mask = (np.abs(cc - cx) <= hw) & (np.abs(rr - cy) <= hh) + return mask.astype(np.float32, copy=False), None + row = int(max(0, min(round(cy), self.det_rows - 1))) + col = int(max(0, min(round(cx), self.det_cols - 1))) + return None, (row, col) + + def _integrate_dp_value( + self, + dp: np.ndarray, + mask: np.ndarray | None, + point_idx: tuple[int, int] | None, + ) -> float: + arr = np.asarray(dp, dtype=np.float32) + if point_idx is not None: + row, col = point_idx + return float(arr[row, col]) + if mask is None: + return 0.0 + return float((arr * mask).sum()) + + def _virtual_image_from_frame_array(self, frame_data: np.ndarray) -> np.ndarray: + arr = np.asarray(frame_data, dtype=np.float32) + if arr.shape != (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + raise ValueError( + f"frame_data shape {arr.shape} does not match " + f"({self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" + ) + mask, point_idx = self._detector_integration_kernel() + if point_idx is not None: + row, col = point_idx + return arr[:, :, row, col].astype(np.float32, copy=False) + return (arr * mask[None, None, :, :]).sum(axis=(2, 3)).astype(np.float32) + + @staticmethod + def _idw_reconstruct( + shape: tuple[int, int], + points: np.ndarray, + values: np.ndarray, + power: float = 2.0, + k_neighbors: int = 16, + ) -> np.ndarray: + if points.size == 0: + return np.zeros(shape, dtype=np.float32) + rr, cc = np.meshgrid( + np.arange(shape[0], dtype=np.float32), + np.arange(shape[1], dtype=np.float32), + indexing="ij", + ) + coords = np.stack([rr.reshape(-1), cc.reshape(-1)], axis=1) + dist_sq = ((coords[:, None, :] - points[None, :, :]) ** 2).sum(axis=2) + 1e-6 + + if k_neighbors > 0 and points.shape[0] > k_neighbors: + idx = np.argpartition(dist_sq, kth=k_neighbors - 1, axis=1)[:, :k_neighbors] + dist_sq = np.take_along_axis(dist_sq, idx, axis=1) + vals_local = values[idx] + else: + vals_local = np.broadcast_to(values[None, :], dist_sq.shape) + + weights = 1.0 / np.power(dist_sq, power / 2.0) + pred = (weights * vals_local).sum(axis=1) / np.maximum(weights.sum(axis=1), 1e-6) + return pred.reshape(shape).astype(np.float32, copy=False) + + def _resolve_reference_virtual_image( + self, + reference: str | np.ndarray, + frame_idx: int, + ) -> tuple[np.ndarray, str]: + if isinstance(reference, str): + key = reference.strip().lower() + if key != "full_raster": + raise ValueError("reference must be 'full_raster' or a NumPy array") + if self.n_frames > 1: + frame = self._data[frame_idx].detach().cpu().numpy() + elif self._data.ndim == 4: + frame = self._data.detach().cpu().numpy() + else: + frame = self._data.detach().cpu().numpy().reshape( + self.shape_rows, self.shape_cols, self.det_rows, self.det_cols + ) + return self._virtual_image_from_frame_array(frame), "full_raster" + + arr = np.asarray(to_numpy(reference), dtype=np.float32) + if arr.shape == (self.shape_rows, self.shape_cols): + return arr.astype(np.float32, copy=False), "virtual_image" + if arr.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + return self._virtual_image_from_frame_array(arr), "frame_data" + if arr.shape == (self.n_frames, self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + return self._virtual_image_from_frame_array(arr[frame_idx]), "stack_frame_data" + raise ValueError( + "Unsupported reference shape. Expected one of: " + f"(scan_rows, scan_cols), " + f"(scan_rows, scan_cols, det_rows, det_cols), or " + f"(n_frames, scan_rows, scan_cols, det_rows, det_cols)." + ) + + def _extract_sparse_samples(self, frame_idx: int) -> tuple[np.ndarray, np.ndarray]: + mask = self._sparse_mask[frame_idx] + coords = np.argwhere(mask) + if coords.size == 0: + return ( + np.zeros((0, 2), dtype=np.float32), + np.zeros((0,), dtype=np.float32), + ) + + integ_mask, point_idx = self._detector_integration_kernel() + values = np.zeros((coords.shape[0],), dtype=np.float32) + for i, (row, col) in enumerate(coords): + key = (int(frame_idx), int(row), int(col)) + dp = self._sparse_samples.get(key) + if dp is None: + dp = self._get_frame(int(row), int(col)) + values[i] = self._integrate_dp_value(dp, integ_mask, point_idx) + points = coords.astype(np.float32, copy=False) + return points, values + + def ingest_scan_point( + self, + row: int, + col: int, + dp: np.ndarray, + frame_idx: int = 0, + dose: float | None = None, + ) -> Self: + """ + Ingest one scanned diffraction pattern into sparse acquisition state. + + Parameters + ---------- + row : int + Scan-space row index. + col : int + Scan-space column index. + dp : array_like + Diffraction pattern with shape ``(det_rows, det_cols)``. + frame_idx : int, default 0 + Frame index for 5D data. + dose : float, optional + Dose contribution for this acquisition event. Defaults to ``1.0``. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + self._ingest_scan_point_core( + row=row, + col=col, + dp=dp, + frame_idx=frame_idx, + dose=1.0 if dose is None else float(dose), + refresh=True, + ) + self._record_export_event( + { + "export_kind": "ingest_scan_point", + "frame_idx": int(self._validate_sparse_frame_idx(frame_idx)), + "row": int(row), + "col": int(col), + "dose": float(1.0 if dose is None else dose), + } + ) + return self + + def ingest_scan_block( + self, + rows: list[int] | np.ndarray, + cols: list[int] | np.ndarray, + dp_block: np.ndarray, + frame_idx: int = 0, + ) -> Self: + """ + Ingest multiple scanned diffraction patterns in one call. + + Parameters + ---------- + rows : list[int] or np.ndarray + Row indices for each pattern in ``dp_block``. + cols : list[int] or np.ndarray + Column indices for each pattern in ``dp_block``. + dp_block : np.ndarray + Diffraction stack with shape ``(n_points, det_rows, det_cols)``. + frame_idx : int, default 0 + Frame index for 5D data. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + rows_arr = np.asarray(rows, dtype=np.int64).reshape(-1) + cols_arr = np.asarray(cols, dtype=np.int64).reshape(-1) + if rows_arr.size != cols_arr.size: + raise ValueError("rows and cols must have the same length") + + block = np.asarray(to_numpy(dp_block), dtype=np.float32) + if block.ndim == 2: + block = block[None, ...] + if block.ndim != 3 or block.shape[1:] != (self.det_rows, self.det_cols): + raise ValueError( + f"dp_block shape must be (n_points, {self.det_rows}, {self.det_cols}), got {block.shape}" + ) + if block.shape[0] != rows_arr.size: + raise ValueError( + f"dp_block has {block.shape[0]} patterns but rows/cols specify {rows_arr.size} points" + ) + + frame_i = self._validate_sparse_frame_idx(frame_idx) + for idx in range(rows_arr.size): + self._ingest_scan_point_core( + row=int(rows_arr[idx]), + col=int(cols_arr[idx]), + dp=block[idx], + frame_idx=frame_i, + dose=1.0, + refresh=False, + ) + + self._compute_virtual_image_from_roi() + self._update_frame() + self._record_export_event( + { + "export_kind": "ingest_scan_block", + "frame_idx": int(frame_i), + "n_points": int(rows_arr.size), + } + ) + return self + + def get_sparse_state(self) -> dict[str, Any]: + """ + Return sparse acquisition state for checkpointing or replay. + + Returns + ------- + dict + Sparse state with sampling mask, sampled diffraction stack, + sampled-point coordinates, and dose map. + """ + coords = np.argwhere(self._sparse_mask) + sampled_points = [ + {"frame_idx": int(f), "row": int(r), "col": int(c)} + for (f, r, c) in coords + ] + if coords.size: + sampled_data = np.stack( + [ + self._sparse_samples.get((int(f), int(r), int(c)), self._get_frame(int(r), int(c))) + for (f, r, c) in coords + ], + axis=0, + ).astype(np.float32, copy=False) + else: + sampled_data = np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) + + mask_payload = self._sparse_mask[0].copy() if self.n_frames <= 1 else self._sparse_mask.copy() + dose_payload = self._dose_map[0].copy() if self.n_frames <= 1 else self._dose_map.copy() + return { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "sparse_state_snapshot", + "frame_idx": int(self.frame_idx), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "mask": mask_payload, + "sampled_data": sampled_data, + "sampled_points": sampled_points, + "dose_map": dose_payload, + "n_sampled": int(len(sampled_points)), + "total_dose": float(self._dose_map.sum()), + } + + def set_sparse_state( + self, + mask: np.ndarray, + sampled_data: np.ndarray, + ) -> Self: + """ + Restore sparse acquisition state from mask + sampled data. + + Parameters + ---------- + mask : np.ndarray + Boolean scan mask. Shape ``(scan_rows, scan_cols)`` for 4D, + or ``(n_frames, scan_rows, scan_cols)`` for 5D. + sampled_data : np.ndarray + Either compact stack ``(n_sampled, det_rows, det_cols)`` + matching row-major ``mask`` order, or dense data aligned to mask: + ``(scan_rows, scan_cols, det_rows, det_cols)`` for 4D, + ``(n_frames, scan_rows, scan_cols, det_rows, det_cols)`` for 5D. + + Returns + ------- + Show4DSTEM + Self for method chaining. + """ + mask_3d = self._normalize_sparse_mask(mask) + coords = np.argwhere(mask_3d) + + payload = np.asarray(to_numpy(sampled_data), dtype=np.float32) + n_points = int(coords.shape[0]) + + if payload.ndim == 3: + if payload.shape[0] != n_points or payload.shape[1:] != (self.det_rows, self.det_cols): + raise ValueError( + f"Compact sampled_data must be (n_sampled, {self.det_rows}, {self.det_cols}); " + f"got {payload.shape} for n_sampled={n_points}" + ) + compact = payload + elif self.n_frames <= 1 and payload.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): + compact = np.stack( + [payload[int(r), int(c)] for (_, r, c) in coords], + axis=0, + ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) + elif payload.shape == ( + self.n_frames, + self.shape_rows, + self.shape_cols, + self.det_rows, + self.det_cols, + ): + compact = np.stack( + [payload[int(f), int(r), int(c)] for (f, r, c) in coords], + axis=0, + ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) + else: + raise ValueError( + "Unsupported sampled_data shape for set_sparse_state. " + "Use compact (n_sampled, det_rows, det_cols) or dense per-mask arrays." + ) + + self._sparse_samples = {} + self._sparse_order = [] + self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) + self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) + + for idx, (frame_idx, row, col) in enumerate(coords): + self._ingest_scan_point_core( + row=int(row), + col=int(col), + dp=compact[idx], + frame_idx=int(frame_idx), + dose=1.0, + refresh=False, + ) + + self._compute_virtual_image_from_roi() + self._update_frame() + self._record_export_event( + { + "export_kind": "set_sparse_state", + "n_sampled": int(n_points), + } + ) + return self + + def _resolve_proposal_count( + self, + k: int, + frame_idx: int, + budget: dict[str, Any] | None, + ) -> int: + count = int(k) + if count < 1: + raise ValueError(f"k must be >= 1, got {k}") + if budget is None: + return count + + existing_points = int(self._sparse_mask[frame_idx].sum()) + existing_dose = float(self._dose_map[frame_idx].sum()) + total_points = int(self.shape_rows * self.shape_cols) + + if "max_new_points" in budget: + count = min(count, int(budget["max_new_points"])) + if "max_total_points" in budget: + count = min(count, max(0, int(budget["max_total_points"]) - existing_points)) + if "max_total_fraction" in budget: + allowed_total = int(round(float(budget["max_total_fraction"]) * total_points)) + count = min(count, max(0, allowed_total - existing_points)) + if "max_total_dose" in budget: + dose_per_point = float(budget.get("dose_per_point", 1.0)) + if dose_per_point <= 0: + raise ValueError("budget['dose_per_point'] must be > 0") + remaining = float(budget["max_total_dose"]) - existing_dose + count = min(count, max(0, int(math.floor(remaining / dose_per_point)))) + return max(0, int(count)) + + def propose_next_points( + self, + k: int, + strategy: str = "adaptive", + budget: dict[str, Any] | None = None, + ) -> list[tuple[int, int]]: + """ + Propose next scan points from current sparse acquisition state. + + Parameters + ---------- + k : int + Maximum number of new points to propose. + strategy : str, default "adaptive" + Proposal strategy: ``"adaptive"``, ``"random"``, or ``"raster"``. + budget : dict, optional + Optional constraints and strategy parameters. Supported keys: + ``frame_idx``, ``max_new_points``, ``max_total_points``, + ``max_total_fraction``, ``max_total_dose``, ``dose_per_point``, + ``roi_mask``, ``seed``, ``min_spacing``, ``step``, + ``local_window``, ``dose_lambda``, ``weights``, ``bidirectional``. + + Returns + ------- + list[tuple[int, int]] + Proposed ``(row, col)`` scan coordinates. + """ + budget_dict = {} if budget is None else dict(budget) + strategy_key = str(strategy).strip().lower() + if strategy_key not in {"adaptive", "random", "raster"}: + raise ValueError("strategy must be one of: adaptive, random, raster") + + frame_idx = self._validate_sparse_frame_idx(budget_dict.get("frame_idx", self.frame_idx)) + n_select = self._resolve_proposal_count(int(k), frame_idx, budget_dict) + if n_select <= 0: + return [] + + sampled_mask = self._sparse_mask[frame_idx].copy() + allowed_mask = ~sampled_mask + roi_mask_raw = budget_dict.get("roi_mask", None) + if roi_mask_raw is not None: + roi_mask = np.asarray(roi_mask_raw, dtype=bool) + if roi_mask.shape != (self.shape_rows, self.shape_cols): + raise ValueError( + f"roi_mask shape {roi_mask.shape} must match " + f"scan_shape ({self.shape_rows}, {self.shape_cols})" + ) + allowed_mask &= roi_mask + + proposals: list[tuple[int, int]] = [] + if strategy_key == "adaptive": + local_window = int(budget_dict.get("local_window", 5)) + if local_window < 1: + raise ValueError("budget['local_window'] must be >= 1") + min_spacing = int(budget_dict.get("min_spacing", 2)) + if min_spacing < 0: + raise ValueError("budget['min_spacing'] must be >= 0") + dose_lambda = float(budget_dict.get("dose_lambda", 0.25)) + if not np.isfinite(dose_lambda): + raise ValueError("budget['dose_lambda'] must be finite") + + default_weights = { + "vi_gradient": 0.4, + "vi_local_std": 0.3, + "dp_variance": 0.3, + } + merged_weights = dict(default_weights) + raw_weights = budget_dict.get("weights", None) + if raw_weights is not None: + for key, value in dict(raw_weights).items(): + if key not in default_weights: + raise ValueError( + f"Unsupported adaptive weight '{key}'. " + f"Supported: {', '.join(default_weights.keys())}" + ) + merged_weights[key] = float(value) + weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) + if weight_sum <= 0: + raise ValueError("At least one adaptive weight must be > 0") + weights = {k: max(0.0, float(v)) / weight_sum for k, v in merged_weights.items()} + + vi = self._virtual_image_for_frame(frame_idx) + grad_row, grad_col = np.gradient(vi) + vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) + mean_local = self._box_mean_map(vi, local_window) + mean_sq_local = self._box_mean_map(vi * vi, local_window) + vi_local_std = np.sqrt(np.maximum(mean_sq_local - mean_local * mean_local, 0.0)).astype(np.float32) + dp_variance = self._dp_variance_map(frame_idx=frame_idx) + + utility = ( + weights["vi_gradient"] * self._normalize_score_map(vi_gradient) + + weights["vi_local_std"] * self._normalize_score_map(vi_local_std) + + weights["dp_variance"] * self._normalize_score_map(dp_variance) + ).astype(np.float32) + + frame_dose = self._dose_map[frame_idx].astype(np.float32, copy=False) + if float(frame_dose.max()) > 0: + utility = utility - float(dose_lambda) * (frame_dose / float(frame_dose.max())) + + picks = self._select_spaced_topk( + scores=utility, + k=n_select, + min_spacing=min_spacing, + allowed_mask=allowed_mask, + excluded_mask=np.zeros_like(allowed_mask, dtype=bool), + ) + proposals = [(int(r), int(c)) for (r, c) in picks] + elif strategy_key == "random": + coords = np.argwhere(allowed_mask) + if coords.size: + seed = budget_dict.get("seed", None) + rng = np.random.default_rng(None if seed is None else int(seed)) + n_take = min(n_select, int(coords.shape[0])) + idx = rng.choice(coords.shape[0], size=n_take, replace=False) + chosen = coords[idx] + proposals = [(int(r), int(c)) for r, c in chosen] + else: + step = int(budget_dict.get("step", 1)) + if step < 1: + raise ValueError("budget['step'] must be >= 1") + bidirectional = bool(budget_dict.get("bidirectional", True)) + for row in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if bidirectional and ((row // step) % 2 == 1): + cols.reverse() + for col in cols: + if allowed_mask[row, col]: + proposals.append((int(row), int(col))) + if len(proposals) >= n_select: + break + if len(proposals) >= n_select: + break + + self._record_export_event( + { + "export_kind": "propose_next_points", + "strategy": strategy_key, + "frame_idx": int(frame_idx), + "k_requested": int(k), + "k_returned": int(len(proposals)), + } + ) + return proposals + + def evaluate_against_reference( + self, + reference: str | np.ndarray = "full_raster", + metrics: list[str] | None = None, + ) -> dict[str, Any]: + """ + Evaluate sparse-sampled reconstruction against a reference image. + + Parameters + ---------- + reference : str or np.ndarray, default "full_raster" + Reference target. ``"full_raster"`` uses the current full dataset + and current ROI integration settings. Arrays are also accepted + (virtual image or full diffraction stack; see method docs). + metrics : list[str], optional + Metric names to compute. Supported: ``"rmse"``, ``"nrmse"``, + ``"mae"``, ``"psnr"``. + + Returns + ------- + dict + Evaluation summary including sampled fraction and metric values. + """ + metric_names = ( + ["rmse", "nrmse", "mae", "psnr"] + if metrics is None + else [str(name).strip().lower() for name in metrics] + ) + supported = {"rmse", "nrmse", "mae", "psnr"} + unknown = [name for name in metric_names if name not in supported] + if unknown: + raise ValueError(f"Unsupported metrics: {unknown}. Supported: {sorted(supported)}") + + frame_idx = int(self.frame_idx if self.n_frames <= 1 else self._validate_sparse_frame_idx(self.frame_idx)) + points, values = self._extract_sparse_samples(frame_idx) + if points.shape[0] == 0: + raise ValueError("No sparse samples available for evaluation. Ingest points first.") + + reference_vi, reference_kind = self._resolve_reference_virtual_image(reference, frame_idx) + reconstruction = self._idw_reconstruct( + shape=(self.shape_rows, self.shape_cols), + points=points, + values=values, + power=2.0, + k_neighbors=16, + ) + + ref = np.asarray(reference_vi, dtype=np.float32) + pred = np.asarray(reconstruction, dtype=np.float32) + diff = pred - ref + mse = float(np.mean(diff * diff)) + rmse = float(np.sqrt(mse)) + mae = float(np.mean(np.abs(diff))) + ref_range = float(ref.max() - ref.min()) + 1e-6 + nrmse = float(rmse / ref_range) + peak = float(max(float(ref.max()), 1e-6)) + psnr = 120.0 if mse <= 1e-12 else float(20.0 * np.log10(peak) - 10.0 * np.log10(mse)) + + metric_values = { + "rmse": rmse, + "nrmse": nrmse, + "mae": mae, + "psnr": psnr, + } + selected_metrics = {name: float(metric_values[name]) for name in metric_names} + + summary = { + "reference_kind": reference_kind, + "frame_idx": int(frame_idx), + "n_sampled": int(points.shape[0]), + "sampled_fraction": float(points.shape[0] / max(1, self.shape_rows * self.shape_cols)), + "metrics": selected_metrics, + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + } + self._record_export_event( + { + "export_kind": "evaluate_against_reference", + "reference_kind": reference_kind, + "frame_idx": int(frame_idx), + "n_sampled": int(points.shape[0]), + "sampled_fraction": float(summary["sampled_fraction"]), + "metrics": selected_metrics, + } + ) + return summary + + def export_session_bundle( + self, + path: str | pathlib.Path, + ) -> pathlib.Path: + """ + Export a reproducible session bundle for sparse/adaptive workflows. + + The bundle includes widget state, sparse-state arrays, a current view + image with metadata, and the reproducibility report. + + Parameters + ---------- + path : str or pathlib.Path + Output directory for bundle files. + + Returns + ------- + pathlib.Path + Path to the bundle manifest JSON. + """ + bundle_dir = pathlib.Path(path) + bundle_dir.mkdir(parents=True, exist_ok=True) + + state_path = bundle_dir / "widget_state.json" + self.save(state_path) + + sparse_state = self.get_sparse_state() + sparse_npz_path = bundle_dir / "sparse_state.npz" + np.savez_compressed( + sparse_npz_path, + mask=sparse_state["mask"], + sampled_data=sparse_state["sampled_data"], + dose_map=sparse_state["dose_map"], + ) + + sparse_points_path = bundle_dir / "sparse_points.json" + sparse_points_payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "sparse_points", + "n_sampled": int(sparse_state["n_sampled"]), + "sampled_points": sparse_state["sampled_points"], + } + sparse_points_path.write_text(json.dumps(sparse_points_payload, indent=2)) + + image_path = bundle_dir / "current_all.png" + image_written = self.save_image( + image_path, + view="all", + include_metadata=True, + include_overlays=True, + include_scalebar=True, + ) + image_meta_path = image_written.with_suffix(".json") + + report_path = self.save_reproducibility_report(bundle_dir / "reproducibility_report.json") + + manifest_path = bundle_dir / "session_bundle_manifest.json" + manifest_payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "session_bundle", + "bundle_path": str(bundle_dir), + "created_utc": datetime.now(timezone.utc).isoformat(), + "session_id": self._export_session_id, + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "sparse_summary": { + "n_sampled": int(sparse_state["n_sampled"]), + "sampled_fraction": float( + sparse_state["n_sampled"] / max(1, self.shape_rows * self.shape_cols * self.n_frames) + ), + "total_dose": float(sparse_state["total_dose"]), + }, + "files": { + "state": str(state_path), + "sparse_npz": str(sparse_npz_path), + "sparse_points_json": str(sparse_points_path), + "image": str(image_written), + "image_metadata": str(image_meta_path), + "reproducibility_report": str(report_path), + }, + } + manifest_path.write_text(json.dumps(manifest_payload, indent=2)) + + self._record_export_event( + { + "export_kind": "session_bundle", + "n_sampled": int(sparse_state["n_sampled"]), + "outputs": [ + self._build_file_record(state_path), + self._build_file_record(sparse_npz_path), + self._build_file_record(sparse_points_path), + self._build_file_record(image_written, metadata_path=image_meta_path), + self._build_file_record(report_path), + self._build_file_record(manifest_path), + ], + } + ) + return manifest_path + + def _normalize_score_map(self, values: np.ndarray) -> np.ndarray: + arr = np.asarray(values, dtype=np.float32) + if arr.size == 0: + return np.zeros_like(arr, dtype=np.float32) + vmin = float(np.percentile(arr, 1.0)) + vmax = float(np.percentile(arr, 99.0)) + if vmax <= vmin: + return np.zeros_like(arr, dtype=np.float32) + return np.clip((arr - vmin) / (vmax - vmin), 0.0, 1.0).astype(np.float32) + + def _box_mean_map(self, values: np.ndarray, window: int) -> np.ndarray: + arr = np.asarray(values, dtype=np.float32) + win = int(window) + if win <= 1: + return arr.copy() + if win % 2 == 0: + win += 1 + pad = win // 2 + padded = np.pad(arr, ((pad, pad), (pad, pad)), mode="reflect") + integral = np.pad(padded, ((1, 0), (1, 0)), mode="constant").cumsum(axis=0).cumsum(axis=1) + sums = ( + integral[win:, win:] + - integral[:-win, win:] + - integral[win:, :-win] + + integral[:-win, :-win] + ) + return (sums / float(win * win)).astype(np.float32) + + def _dp_variance_map(self, frame_idx: int | None = None) -> np.ndarray: + if frame_idx is None or self.n_frames <= 1: + data = self._frame_data + else: + idx = self._validate_sparse_frame_idx(frame_idx) + data = self._data[idx] + if data.ndim == 4: + variance = data.var(dim=(2, 3), unbiased=False) + return variance.detach().cpu().numpy().astype(np.float32, copy=False) + variance = data.var(dim=(1, 2), unbiased=False) + return variance.detach().cpu().numpy().reshape(self.shape_rows, self.shape_cols).astype(np.float32, copy=False) + + def _build_coarse_points(self, step: int, bidirectional: bool) -> list[tuple[int, int]]: + points: list[tuple[int, int]] = [] + for r in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if bidirectional and ((r // step) % 2 == 1): + cols.reverse() + for c in cols: + points.append((int(r), int(c))) + return points + + def _select_spaced_topk( + self, + scores: np.ndarray, + k: int, + min_spacing: int, + allowed_mask: np.ndarray, + excluded_mask: np.ndarray, + ) -> list[tuple[int, int]]: + work = np.asarray(scores, dtype=np.float32).copy() + work[~allowed_mask] = -np.inf + work[excluded_mask] = -np.inf + selected: list[tuple[int, int]] = [] + radius = max(0, int(min_spacing)) + + for _ in range(int(max(0, k))): + flat_idx = int(np.argmax(work)) + best_score = float(work.flat[flat_idx]) + if not np.isfinite(best_score): + break + row, col = np.unravel_index(flat_idx, work.shape) + selected.append((int(row), int(col))) + if radius == 0: + work[row, col] = -np.inf + continue + r0 = max(0, row - radius) + r1 = min(work.shape[0], row + radius + 1) + c0 = max(0, col - radius) + c1 = min(work.shape[1], col + radius + 1) + rr, cc = np.ogrid[r0:r1, c0:c1] + neighborhood = (rr - row) ** 2 + (cc - col) ** 2 <= radius ** 2 + block = work[r0:r1, c0:c1] + block[neighborhood] = -np.inf + return selected + + def _nearest_neighbor_order( + self, + points: list[tuple[int, int]], + start: tuple[int, int] | None = None, + ) -> list[tuple[int, int]]: + remaining = [tuple(map(int, pt)) for pt in points] + if not remaining: + return [] + + if start is None: + current = remaining.pop(0) + else: + sr, sc = int(start[0]), int(start[1]) + start_idx = min( + range(len(remaining)), + key=lambda i: (remaining[i][0] - sr) ** 2 + (remaining[i][1] - sc) ** 2, + ) + current = remaining.pop(start_idx) + + ordered = [current] + while remaining: + cr, cc = current + next_idx = min( + range(len(remaining)), + key=lambda i: (remaining[i][0] - cr) ** 2 + (remaining[i][1] - cc) ** 2, + ) + current = remaining.pop(next_idx) + ordered.append(current) + return ordered + + def save_image( + self, + path: str | pathlib.Path, + view: str | None = None, + position: tuple[int, int] | None = None, + frame_idx: int | None = None, + format: str | None = None, + include_metadata: bool = True, + metadata_path: str | pathlib.Path | None = None, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + restore_state: bool = True, + dpi: int | None = None, + ) -> pathlib.Path: + """ + Save the current visualization as PNG or PDF. + + Parameters + ---------- + path : str or pathlib.Path + Output image path. + view : str, optional + One of: "diffraction", "virtual", "fft", "all". + position : tuple[int, int], optional + Temporary scan position override as (row, col) for this export. + frame_idx : int, optional + Temporary frame index override for 5D data. + format : str, optional + "png" or "pdf". If omitted, inferred from file extension. + include_metadata : bool, default True + If True, writes JSON metadata next to the image. + metadata_path : str or pathlib.Path, optional + Override metadata JSON path. + include_overlays : bool, optional + Draw ROI/profile/crosshair overlays on exported panels. + Defaults to ``export_include_overlays``. + include_scalebar : bool, optional + Draw panel scale bars on exported panels. + Defaults to ``export_include_scalebar``. + restore_state : bool, default True + If True, temporary position/frame overrides are reverted after export. + dpi : int, optional + Export DPI metadata. + + Returns + ------- + pathlib.Path + The written image path. + """ + from PIL import Image + + export_path = pathlib.Path(path) + view_key = self._validate_export_view(view) + fmt = self._resolve_export_format(export_path, format) + dpi_value = int(self.export_default_dpi if dpi is None else dpi) + overlays_enabled = ( + bool(self.export_include_overlays) + if include_overlays is None + else bool(include_overlays) + ) + scalebar_enabled = ( + bool(self.export_include_scalebar) + if include_scalebar is None + else bool(include_scalebar) + ) + + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") + + export_path.parent.mkdir(parents=True, exist_ok=True) + + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + meta_path: pathlib.Path | None = None + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) + + try: + if frame_idx is not None: + self.frame_idx = self._validate_frame_idx(frame_idx) + if position is not None: + row, col = self._validate_position(position) + self.pos_row = row + self.pos_col = col + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) + + if view_key == "diffraction": + image, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + render_meta = {"diffraction": dp_meta} + elif view_key == "virtual": + image, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + render_meta = {"virtual": vi_meta} + elif view_key == "fft": + image, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + render_meta = {"fft": fft_meta} + else: + panel_images = [] + render_meta = {} + dp_img, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + vi_img, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + panel_images.extend([dp_img, vi_img]) + render_meta = {"diffraction": dp_meta, "virtual": vi_meta} + if self.show_fft: + fft_img, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + panel_images.append(fft_img) + render_meta["fft"] = fft_meta + image = self._compose_horizontal(panel_images) + + if fmt == "pdf": + Image.init() + image = image.convert("RGB") + image.save(export_path, format="PDF", resolution=dpi_value) + else: + image.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) + + if include_metadata: + meta_path = ( + pathlib.Path(metadata_path) + if metadata_path is not None + else export_path.with_suffix(".json") + ) + metadata = self._build_image_export_metadata( + export_path=export_path, + view_key=view_key, + fmt=fmt, + render_meta=render_meta, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + export_kind="single_view_image", + extra={"dpi": int(dpi_value)}, + ) + meta_path.write_text(json.dumps(metadata, indent=2)) + finally: + if restore_state: + self.frame_idx = prev_frame + self.pos_row = prev_row + self.pos_col = prev_col + + self._record_export_event( + { + "export_kind": "single_view_image", + "view": view_key, + "format": fmt, + "position": {"row": export_row, "col": export_col}, + "frame_idx": export_frame, + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "dpi": int(dpi_value), + "outputs": [ + self._build_file_record(export_path, metadata_path=meta_path), + ], + } + ) + return export_path + + def _build_preset_payload(self) -> dict[str, Any]: + return { + "detector": { + "center_row": float(self.center_row), + "center_col": float(self.center_col), + "bf_radius": float(self.bf_radius), + "roi_active": bool(self.roi_active), + "roi_mode": self.roi_mode, + "roi_center_row": float(self.roi_center_row), + "roi_center_col": float(self.roi_center_col), + "roi_radius": float(self.roi_radius), + "roi_radius_inner": float(self.roi_radius_inner), + "roi_width": float(self.roi_width), + "roi_height": float(self.roi_height), + }, + "vi_roi": { + "mode": self.vi_roi_mode, + "center_row": float(self.vi_roi_center_row), + "center_col": float(self.vi_roi_center_col), + "radius": float(self.vi_roi_radius), + "width": float(self.vi_roi_width), + "height": float(self.vi_roi_height), + }, + "display": { + "mask_dc": bool(self.mask_dc), + "dp_colormap": self.dp_colormap, + "vi_colormap": self.vi_colormap, + "fft_colormap": self.fft_colormap, + "dp_scale_mode": self.dp_scale_mode, + "vi_scale_mode": self.vi_scale_mode, + "fft_scale_mode": self.fft_scale_mode, + "dp_power_exp": float(self.dp_power_exp), + "vi_power_exp": float(self.vi_power_exp), + "fft_power_exp": float(self.fft_power_exp), + "dp_vmin_pct": float(self.dp_vmin_pct), + "dp_vmax_pct": float(self.dp_vmax_pct), + "vi_vmin_pct": float(self.vi_vmin_pct), + "vi_vmax_pct": float(self.vi_vmax_pct), + "fft_vmin_pct": float(self.fft_vmin_pct), + "fft_vmax_pct": float(self.fft_vmax_pct), + "fft_auto": bool(self.fft_auto), + "show_fft": bool(self.show_fft), + "dp_show_colorbar": bool(self.dp_show_colorbar), + "profile_line": self.profile_line, + "profile_width": int(self.profile_width), + }, + "export": self._export_settings_metadata(), + } + + def _apply_preset_payload(self, preset: dict[str, Any]) -> None: + detector = preset.get("detector", {}) + vi_roi = preset.get("vi_roi", {}) + display = preset.get("display", {}) + export = preset.get("export", {}) + + detector_map = { + "center_row": "center_row", + "center_col": "center_col", + "bf_radius": "bf_radius", + "roi_active": "roi_active", + "roi_mode": "roi_mode", + "roi_center_row": "roi_center_row", + "roi_center_col": "roi_center_col", + "roi_radius": "roi_radius", + "roi_radius_inner": "roi_radius_inner", + "roi_width": "roi_width", + "roi_height": "roi_height", + } + for key, trait_name in detector_map.items(): + if key in detector and hasattr(self, trait_name): + setattr(self, trait_name, detector[key]) + + vi_roi_map = { + "mode": "vi_roi_mode", + "center_row": "vi_roi_center_row", + "center_col": "vi_roi_center_col", + "radius": "vi_roi_radius", + "width": "vi_roi_width", + "height": "vi_roi_height", + } + for key, trait_name in vi_roi_map.items(): + if key in vi_roi and hasattr(self, trait_name): + setattr(self, trait_name, vi_roi[key]) + + _display_keys = { + "dp_colormap", "vi_colormap", "fft_colormap", + "dp_scale_mode", "vi_scale_mode", "fft_scale_mode", + "dp_power_exp", "vi_power_exp", "fft_power_exp", + "dp_vmin_pct", "dp_vmax_pct", "vi_vmin_pct", "vi_vmax_pct", + "fft_vmin_pct", "fft_vmax_pct", "fft_auto", + "mask_dc", "dp_show_colorbar", "show_fft", "fft_window", + "show_controls", + } + for key, value in display.items(): + if key in _display_keys: + setattr(self, key, value) + + export_map = { + "default_view": "export_default_view", + "default_format": "export_default_format", + "include_overlays": "export_include_overlays", + "include_scalebar": "export_include_scalebar", + "dpi": "export_default_dpi", + } + for key, trait_name in export_map.items(): + if key in export and hasattr(self, trait_name): + setattr(self, trait_name, export[key]) + + def save_preset( + self, + name: str, + path: str | pathlib.Path | None = None, + ) -> dict[str, Any]: + preset_name = str(name).strip() + if not preset_name: + raise ValueError("Preset name must be non-empty.") + preset_key = preset_name.lower() + + payload = self._build_preset_payload() + self._named_presets[preset_key] = payload + + if path is not None: + out_path = pathlib.Path(path) + out_path.parent.mkdir(parents=True, exist_ok=True) + serialized = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "widget_preset", + "preset_name": preset_name, + "preset": payload, + } + out_path.write_text(json.dumps(serialized, indent=2)) + + return payload + + def load_preset( + self, + name: str, + path: str | pathlib.Path | None = None, + apply: bool = True, + ) -> dict[str, Any]: + preset_name = str(name).strip() + preset_key = preset_name.lower() + if path is not None: + payload = json.loads(pathlib.Path(path).read_text()) + if not isinstance(payload, dict): + raise ValueError("Preset file must contain a JSON object.") + if "preset" in payload: + preset = payload["preset"] + else: + preset = payload + if not isinstance(preset, dict): + raise ValueError("Preset payload must be a JSON object.") + if preset_name: + self._named_presets[preset_key] = preset + else: + if preset_key not in self._named_presets: + raise ValueError( + f"Preset '{preset_name}' not found. Available: {', '.join(self.list_presets())}" + ) + preset = self._named_presets[preset_key] + + if apply: + self._apply_preset_payload(preset) + return preset + + def apply_preset(self, name: str) -> Self: + preset_name = str(name).strip().lower() + if preset_name == "bf": + self.roi_active = True + self.roi_mode = "circle" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius = float(max(1.0, self.bf_radius)) + return self + if preset_name == "abf": + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius_inner = float(max(0.5, self.bf_radius * 0.5)) + self.roi_radius = float(max(1.0, self.bf_radius)) + return self + if preset_name == "adf": + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius_inner = float(max(1.0, self.bf_radius)) + self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 2.0)) + return self + if preset_name == "haadf": + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = float(self.center_row) + self.roi_center_col = float(self.center_col) + self.roi_radius_inner = float(max(1.0, self.bf_radius * 2.0)) + self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 4.0)) + return self + + self.load_preset(preset_name, apply=True) + return self + + def _resolve_figure_template(self, template: str) -> tuple[str, list[str], bool]: + key = str(template).strip().lower() + mapping = { + "dp_vi": (["diffraction", "virtual"], False), + "dp_vi_fft": (["diffraction", "virtual", "fft"], False), + "publication_dp_vi": (["diffraction", "virtual"], True), + "publication_dp_vi_fft": (["diffraction", "virtual", "fft"], True), + } + if key not in mapping: + raise ValueError( + f"Unsupported template '{template}'. " + f"Supported: {', '.join(self.list_figure_templates())}" + ) + panels, publication = mapping[key] + return key, panels, publication + + def save_figure( + self, + path: str | pathlib.Path, + template: str = "dp_vi_fft", + position: tuple[int, int] | None = None, + frame_idx: int | None = None, + format: str | None = None, + include_metadata: bool = True, + metadata_path: str | pathlib.Path | None = None, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + restore_state: bool = True, + dpi: int | None = None, + title: str | None = None, + annotations: dict[str, str] | None = None, + ) -> pathlib.Path: + from PIL import Image, ImageDraw, ImageFont + + export_path = pathlib.Path(path) + template_key, panel_keys, publication_style = self._resolve_figure_template(template) + fmt = self._resolve_export_format(export_path, format) + dpi_value = int(self.export_default_dpi if dpi is None else dpi) + overlays_enabled = ( + bool(self.export_include_overlays) + if include_overlays is None + else bool(include_overlays) + ) + scalebar_enabled = ( + bool(self.export_include_scalebar) + if include_scalebar is None + else bool(include_scalebar) + ) + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") + + export_path.parent.mkdir(parents=True, exist_ok=True) + font = ImageFont.load_default() + + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + meta_path: pathlib.Path | None = None + + try: + if frame_idx is not None: + self.frame_idx = self._validate_frame_idx(frame_idx) + if position is not None: + row, col = self._validate_position(position) + self.pos_row = row + self.pos_col = col + + panel_images: list[Any] = [] + render_meta: dict[str, Any] = {} + for panel_key in panel_keys: + panel, panel_meta = self._render_panel_image( + panel_key, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + ) + panel_images.append(panel) + render_meta[panel_key] = panel_meta + + gap = 24 if publication_style else 8 + padding = 24 if publication_style else 10 + label_height = 22 if publication_style else 0 + title_text = title + if title_text is None and publication_style: + if self.n_frames > 1: + title_text = f"4D-STEM Figure ({self.frame_dim_label} {self.frame_idx})" + else: + title_text = "4D-STEM Figure" + title_height = 34 if title_text else 0 + + max_panel_height = max(panel.height for panel in panel_images) + total_width = padding * 2 + sum(panel.width for panel in panel_images) + gap * (len(panel_images) - 1) + total_height = padding * 2 + title_height + label_height + max_panel_height + + figure = Image.new("RGB", (total_width, total_height), color=(255, 255, 255)) + draw = ImageDraw.Draw(figure, mode="RGBA") + + y_title = padding + if title_text: + draw.text((padding, y_title), title_text, fill=(0, 0, 0, 255), font=font) + + y_panels = padding + title_height + if publication_style: + y_panels += label_height + + panel_names = { + "diffraction": "Diffraction", + "virtual": "Virtual", + "fft": "FFT", + } + annotation_map = annotations or {} + + x0 = padding + for idx, panel in enumerate(panel_images): + panel_key = panel_keys[idx] + if publication_style: + draw.text( + (x0, padding + title_height), + panel_names.get(panel_key, panel_key), + fill=(0, 0, 0, 255), + font=font, + ) + + figure.paste(panel, (x0, y_panels)) + + if publication_style: + draw.rectangle( + [(x0, y_panels), (x0 + panel.width - 1, y_panels + panel.height - 1)], + outline=(80, 80, 80, 255), + width=1, + ) + + if panel_key in annotation_map and str(annotation_map[panel_key]).strip(): + text = str(annotation_map[panel_key]).strip() + text_bbox = draw.textbbox((0, 0), text, font=font) + text_w = text_bbox[2] - text_bbox[0] + text_h = text_bbox[3] - text_bbox[1] + tx = x0 + 8 + ty = y_panels + 8 + draw.rectangle( + [(tx - 4, ty - 3), (tx + text_w + 4, ty + text_h + 3)], + fill=(0, 0, 0, 180), + ) + draw.text((tx, ty), text, fill=(255, 255, 255, 255), font=font) + + x0 += panel.width + gap + + if fmt == "pdf": + Image.init() + figure = figure.convert("RGB") + figure.save(export_path, format="PDF", resolution=dpi_value) + else: + figure.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) + + if include_metadata: + meta_path = ( + pathlib.Path(metadata_path) + if metadata_path is not None + else export_path.with_suffix(".json") + ) + metadata = self._build_image_export_metadata( + export_path=export_path, + view_key="figure", + fmt=fmt, + render_meta=render_meta, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + export_kind="figure_template", + extra={ + "template": template_key, + "panels": panel_keys, + "publication_style": bool(publication_style), + "title": title_text or "", + "annotations": annotation_map, + "dpi": int(dpi_value), + }, + ) + meta_path.write_text(json.dumps(metadata, indent=2)) + finally: + if restore_state: + self.frame_idx = prev_frame + self.pos_row = prev_row + self.pos_col = prev_col + + self._record_export_event( + { + "export_kind": "figure_template", + "template": template_key, + "format": fmt, + "dpi": int(dpi_value), + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "outputs": [ + self._build_file_record(export_path, metadata_path=meta_path), + ], + } + ) + return export_path + + def _resolve_frame_sequence( + self, + frame_indices: list[int] | None, + frame_range: tuple[int, int] | None, + ) -> list[int]: + if frame_indices is not None and frame_range is not None: + raise ValueError("Use either frame_indices or frame_range, not both.") + + if frame_indices is not None: + if len(frame_indices) == 0: + raise ValueError("frame_indices cannot be empty.") + return [self._validate_frame_idx(idx) for idx in frame_indices] + + if frame_range is not None: + if len(frame_range) != 2: + raise ValueError("frame_range must be a (start, end) tuple.") + start, end = int(frame_range[0]), int(frame_range[1]) + if start > end: + raise ValueError("frame_range start must be <= end.") + return [self._validate_frame_idx(idx) for idx in range(start, end + 1)] + + return [int(i) for i in range(self.n_frames)] + + def _resolve_position_sequence( + self, + mode: str, + path_points: list[tuple[int, int]] | None, + raster_step: int, + raster_bidirectional: bool, + ) -> list[tuple[int, int]]: + if mode == "path": + points = self._path_points if path_points is None else path_points + if not points: + raise ValueError( + "Path mode requires points via set_path(...) or path_points=..." + ) + return [self._validate_position((int(r), int(c))) for r, c in points] + + if mode == "raster": + step = int(raster_step) + if step < 1: + raise ValueError("raster_step must be >= 1") + points: list[tuple[int, int]] = [] + for r in range(0, self.shape_rows, step): + cols = list(range(0, self.shape_cols, step)) + if raster_bidirectional and ((r // step) % 2 == 1): + cols.reverse() + for c in cols: + points.append((int(r), int(c))) + return points + + raise ValueError(f"Unsupported position sequence mode '{mode}'") + + def suggest_adaptive_path( + self, + coarse_step: int = 4, + target_fraction: float = 0.25, + min_spacing: int = 2, + include_coarse: bool = True, + coarse_bidirectional: bool = True, + local_window: int = 5, + dose_lambda: float = 0.25, + weights: dict[str, float] | None = None, + roi_mask: np.ndarray | None = None, + update_widget_path: bool = True, + interval_ms: int | None = None, + loop: bool = False, + autoplay: bool = False, + return_maps: bool = False, + ) -> dict[str, Any]: + """ + Suggest a sparse adaptive scan path using coarse-to-fine utility ranking. + + The planner computes utility from current virtual-image and diffraction + statistics, then selects spatially distributed high-utility points. + + Parameters + ---------- + coarse_step : int, default 4 + Spacing of the initial coarse grid. + target_fraction : float, default 0.25 + Target total sampled fraction of scan positions in (0, 1]. + min_spacing : int, default 2 + Minimum pixel spacing between selected dense points. + include_coarse : bool, default True + If True, include coarse-grid points in the returned path. + coarse_bidirectional : bool, default True + Use snake ordering for coarse-grid traversal. + local_window : int, default 5 + Window size for local-std utility component. + dose_lambda : float, default 0.25 + Penalty weight for re-sampling coarse points. + weights : dict[str, float], optional + Utility weights for keys: ``vi_gradient``, ``vi_local_std``, ``dp_variance``. + roi_mask : np.ndarray, optional + Optional boolean mask of shape ``scan_shape`` restricting dense picks. + update_widget_path : bool, default True + If True, calls ``set_path(...)`` with the suggested path. + interval_ms : int, optional + Path interval when ``update_widget_path=True``. + loop : bool, default False + Path looping behavior when ``update_widget_path=True``. + autoplay : bool, default False + Start playback immediately when ``update_widget_path=True``. + return_maps : bool, default False + If True, include utility component maps in the returned dict. + + Returns + ------- + dict + Planning result with coarse points, dense points, and final path. + """ + step = int(coarse_step) + if step < 1: + raise ValueError(f"coarse_step must be >= 1, got {coarse_step}") + + frac = float(target_fraction) + if frac <= 0 or frac > 1: + raise ValueError(f"target_fraction must be in (0, 1], got {target_fraction}") + + spacing = int(min_spacing) + if spacing < 0: + raise ValueError(f"min_spacing must be >= 0, got {min_spacing}") + + if local_window < 1: + raise ValueError(f"local_window must be >= 1, got {local_window}") + + if not np.isfinite(float(dose_lambda)): + raise ValueError("dose_lambda must be finite") + + default_weights = { + "vi_gradient": 0.4, + "vi_local_std": 0.3, + "dp_variance": 0.3, + } + merged_weights = dict(default_weights) + if weights is not None: + for key, value in weights.items(): + if key not in default_weights: + raise ValueError( + f"Unsupported utility weight '{key}'. " + f"Supported: {', '.join(default_weights.keys())}" + ) + merged_weights[key] = float(value) + + weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) + if weight_sum <= 0: + raise ValueError("At least one utility weight must be > 0.") + normalized_weights = { + key: max(0.0, float(value)) / weight_sum + for key, value in merged_weights.items() + } + + n_total = int(self.shape_rows * self.shape_cols) + target_count = int(max(1, round(frac * n_total))) + + coarse_points = self._build_coarse_points(step=step, bidirectional=bool(coarse_bidirectional)) + coarse_count = len(coarse_points) if include_coarse else 0 + if include_coarse and target_count < coarse_count: + raise ValueError( + f"target_fraction={target_fraction} gives {target_count} points, " + f"but coarse grid already has {coarse_count}. " + "Increase target_fraction or coarse_step." + ) + dense_count = target_count - coarse_count if include_coarse else target_count + dense_count = max(0, int(dense_count)) + + vi = self._get_virtual_image_array().astype(np.float32, copy=False) + grad_row, grad_col = np.gradient(vi) + vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) + + mean_local = self._box_mean_map(vi, local_window) + mean_sq_local = self._box_mean_map(vi * vi, local_window) + variance_local = np.maximum(mean_sq_local - mean_local * mean_local, 0.0) + vi_local_std = np.sqrt(variance_local).astype(np.float32) + + dp_variance = self._dp_variance_map() + + grad_score = self._normalize_score_map(vi_gradient) + local_std_score = self._normalize_score_map(vi_local_std) + dp_var_score = self._normalize_score_map(dp_variance) + + utility = ( + normalized_weights["vi_gradient"] * grad_score + + normalized_weights["vi_local_std"] * local_std_score + + normalized_weights["dp_variance"] * dp_var_score + ).astype(np.float32) + + dose_penalty = np.zeros_like(utility, dtype=np.float32) + for row, col in coarse_points: + dose_penalty[int(row), int(col)] = 1.0 + utility = utility - float(dose_lambda) * dose_penalty + + allowed_mask = np.ones((self.shape_rows, self.shape_cols), dtype=bool) + if roi_mask is not None: + mask = np.asarray(roi_mask) + if mask.shape != (self.shape_rows, self.shape_cols): + raise ValueError( + f"roi_mask shape {mask.shape} does not match scan_shape " + f"({self.shape_rows}, {self.shape_cols})" + ) + allowed_mask &= mask.astype(bool) + + excluded_mask = np.zeros_like(allowed_mask, dtype=bool) + for row, col in coarse_points: + excluded_mask[int(row), int(col)] = True + + dense_points = self._select_spaced_topk( + scores=utility, + k=dense_count, + min_spacing=spacing, + allowed_mask=allowed_mask, + excluded_mask=excluded_mask, + ) + + start_point = coarse_points[-1] if include_coarse and coarse_points else None + dense_path = self._nearest_neighbor_order(dense_points, start=start_point) + path_points = list(coarse_points) + dense_path if include_coarse else dense_path + + if update_widget_path and path_points: + interval_value = int(self.path_interval_ms if interval_ms is None else interval_ms) + if interval_value < 1: + raise ValueError(f"interval_ms must be >= 1, got {interval_value}") + self.set_path( + points=path_points, + interval_ms=interval_value, + loop=bool(loop), + autoplay=bool(autoplay), + ) + + result: dict[str, Any] = { + "target_fraction": float(frac), + "target_count": int(target_count), + "coarse_step": int(step), + "coarse_count": int(len(coarse_points)), + "dense_count": int(len(dense_points)), + "path_count": int(len(path_points)), + "weights": normalized_weights, + "dose_lambda": float(dose_lambda), + "coarse_points": coarse_points, + "dense_points": dense_points, + "path_points": path_points, + "selected_fraction": float(len(path_points) / max(1, n_total)), + } + if return_maps: + result["utility_map"] = utility + result["utility_components"] = { + "vi_gradient": grad_score, + "vi_local_std": local_std_score, + "dp_variance": dp_var_score, + "dose_penalty": dose_penalty, + } + + self._record_export_event( + { + "export_kind": "adaptive_path_suggestion", + "target_fraction": float(frac), + "target_count": int(target_count), + "coarse_step": int(step), + "coarse_count": int(len(coarse_points)), + "dense_count": int(len(dense_points)), + "path_count": int(len(path_points)), + "selected_fraction": float(len(path_points) / max(1, n_total)), + "weights": normalized_weights, + "dose_lambda": float(dose_lambda), + } + ) + return result + + def save_sequence( + self, + output_dir: str | pathlib.Path, + mode: str = "path", + view: str | None = None, + format: str | None = None, + include_metadata: bool = True, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + frame_idx: int | None = None, + position: tuple[int, int] | None = None, + path_points: list[tuple[int, int]] | None = None, + raster_step: int = 1, + raster_bidirectional: bool = False, + frame_indices: list[int] | None = None, + frame_range: tuple[int, int] | None = None, + filename_prefix: str | None = None, + manifest_name: str = "save_sequence_manifest.json", + restore_state: bool = True, + dpi: int | None = None, + ) -> pathlib.Path: + output_root = pathlib.Path(output_dir) + output_root.mkdir(parents=True, exist_ok=True) + mode_key = str(mode).strip().lower() + if mode_key not in {"path", "raster", "frames"}: + raise ValueError("mode must be one of: path, raster, frames") + + view_key = self._validate_export_view(view) + fmt = self._resolve_export_format(pathlib.Path(f"sequence.{self.export_default_format}"), format or self.export_default_format) + dpi_value = int(self.export_default_dpi if dpi is None else dpi) + overlays_enabled = ( + bool(self.export_include_overlays) + if include_overlays is None + else bool(include_overlays) + ) + scalebar_enabled = ( + bool(self.export_include_scalebar) + if include_scalebar is None + else bool(include_scalebar) + ) + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") + + export_rows: list[dict[str, Any]] = [] + prefix = ( + str(filename_prefix).strip() + if filename_prefix is not None and str(filename_prefix).strip() + else f"{mode_key}_{view_key}" + ) + + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + frame_for_paths = self._validate_frame_idx(frame_idx) if frame_idx is not None else int(self.frame_idx) + + if mode_key == "frames": + row, col = self._validate_position(position) + frames = self._resolve_frame_sequence(frame_indices, frame_range) + jobs = [ + {"row": int(row), "col": int(col), "frame_idx": int(fi)} + for fi in frames + ] + else: + positions = self._resolve_position_sequence( + mode=mode_key, + path_points=path_points, + raster_step=raster_step, + raster_bidirectional=raster_bidirectional, + ) + jobs = [ + {"row": int(r), "col": int(c), "frame_idx": int(frame_for_paths)} + for r, c in positions + ] + + try: + for idx, job in enumerate(jobs): + row = int(job["row"]) + col = int(job["col"]) + fr = int(job["frame_idx"]) + basename = ( + f"{prefix}_{idx:04d}_f{fr:04d}_r{row:04d}_c{col:04d}.{fmt}" + ) + out_path = output_root / basename + out_meta = out_path.with_suffix(".json") if include_metadata else None + + self.save_image( + out_path, + view=view_key, + position=(row, col), + frame_idx=fr, + format=fmt, + include_metadata=include_metadata, + metadata_path=out_meta, + include_overlays=overlays_enabled, + include_scalebar=scalebar_enabled, + restore_state=False, + dpi=dpi_value, + ) + + record = { + "index": int(idx), + "row": row, + "col": col, + "frame_idx": fr, + } + record.update(self._build_file_record(out_path, metadata_path=out_meta, index=idx)) + export_rows.append(record) + finally: + if restore_state: + self.frame_idx = prev_frame + self.pos_row = prev_row + self.pos_col = prev_col + + manifest_path = output_root / str(manifest_name) + manifest_payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "sequence_batch", + "mode": mode_key, + "view": view_key, + "image_format": fmt, + "output_dir": str(output_root), + "filename_prefix": prefix, + "n_exports": int(len(export_rows)), + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "dpi": int(dpi_value), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "exports": export_rows, + } + manifest_path.write_text(json.dumps(manifest_payload, indent=2)) + + manifest_record = self._build_file_record(manifest_path) + self._record_export_event( + { + "export_kind": "sequence_batch", + "mode": mode_key, + "view": view_key, + "format": fmt, + "n_exports": int(len(export_rows)), + "include_overlays": bool(overlays_enabled), + "include_scalebar": bool(scalebar_enabled), + "dpi": int(dpi_value), + "outputs": [manifest_record], + } + ) + return manifest_path + + def save_reproducibility_report( + self, + path: str | pathlib.Path, + ) -> pathlib.Path: + report_path = pathlib.Path(path) + report_path.parent.mkdir(parents=True, exist_ok=True) + payload = { + **build_json_header("Show4DSTEM"), + "format": "json", + "export_kind": "reproducibility_report", + "session_id": self._export_session_id, + "session_started_utc": self._export_session_started_utc, + "report_generated_utc": datetime.now(timezone.utc).isoformat(), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "n_exports": int(len(self._export_log)), + "exports": self._export_log, + } + report_path.write_text(json.dumps(payload, indent=2)) + return report_path + + def _normalize_frame(self, frame: np.ndarray) -> np.ndarray: + mode = self.dp_scale_mode + scaled = self._apply_scale_mode(frame, mode, self.dp_power_exp) + if self.dp_vmin is not None and self.dp_vmax is not None: + fmin = float(self._apply_scale_mode( + np.array([max(self.dp_vmin, 0)], dtype=np.float32), mode, self.dp_power_exp + )[0]) + fmax = float(self._apply_scale_mode( + np.array([max(self.dp_vmax, 0)], dtype=np.float32), mode, self.dp_power_exp + )[0]) + else: + fmin = float(scaled.min()) + fmax = float(scaled.max()) + fmin, fmax = self._slider_range(fmin, fmax, self.dp_vmin_pct, self.dp_vmax_pct) + if fmax > fmin: + return np.clip((scaled - fmin) / (fmax - fmin) * 255, 0, 255).astype(np.uint8) + return np.zeros(frame.shape, dtype=np.uint8) + + def _on_gif_export(self, change=None): + if not self._gif_export_requested: + return + self._gif_export_requested = False + self._generate_gif() + + def _generate_gif(self): + import io + + from matplotlib import colormaps + from PIL import Image + + if not self._path_points: + with self.hold_sync(): + self._gif_data = b"" + self._gif_metadata_json = "" + return + + cmap_fn = colormaps.get_cmap(self.dp_colormap) + duration_ms = max(10, self.path_interval_ms) + + pil_frames = [] + for row, col in self._path_points: + row = max(0, min(self.shape_rows - 1, row)) + col = max(0, min(self.shape_cols - 1, col)) + frame = self._get_frame(row, col).astype(np.float32) + normalized = self._normalize_frame(frame) + rgba = cmap_fn(normalized / 255.0) + rgb = (rgba[:, :, :3] * 255).astype(np.uint8) + pil_frames.append(Image.fromarray(rgb)) + + if not pil_frames: + return + + buf = io.BytesIO() + pil_frames[0].save( + buf, + format="GIF", + save_all=True, + append_images=pil_frames[1:], + duration=duration_ms, + loop=0, + ) + metadata = { + **build_json_header("Show4DSTEM"), + "view": "diffraction", + "format": "gif", + "export_kind": "path_animation", + "n_frames": int(len(pil_frames)), + "duration_ms": int(duration_ms), + "path_loop": bool(self.path_loop), + "path_points": [{"row": int(row), "col": int(col)} for row, col in self._path_points], + "frame_idx": int(self.frame_idx), + "n_frames_total": int(self.n_frames), + "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, + "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, + "calibration": self._calibration_metadata(), + "display": { + "diffraction": { + "colormap": self.dp_colormap, + "scale_mode": self.dp_scale_mode, + "vmin_pct": float(self.dp_vmin_pct), + "vmax_pct": float(self.dp_vmax_pct), + } + }, + } + with self.hold_sync(): + self._gif_metadata_json = json.dumps(metadata, indent=2) + self._gif_data = buf.getvalue() + + def _update_frame(self, change=None): + """Send raw float32 frame to frontend (JS handles scale/colormap).""" + if self._data is None: + return + # Get frame as tensor (stays on device) + data = self._frame_data + if data.ndim == 3: + idx = self.pos_row * self.shape_cols + self.pos_col + frame = data[idx] + else: + frame = data[self.pos_row, self.pos_col] + + # Compute stats from frame (optionally mask DC component) + if self.mask_dc and self.det_rows > 3 and self.det_cols > 3: + # Mask center 3x3 region for stats using detected center (not geometric center) + cr = int(round(self.center_row)) + cc = int(round(self.center_col)) + cr = max(1, min(self.det_rows - 2, cr)) + cc = max(1, min(self.det_cols - 2, cc)) + mask = torch.ones_like(frame, dtype=torch.bool) + mask[cr-1:cr+2, cc-1:cc+2] = False + masked_vals = frame[mask] + self.dp_stats = [ + float(masked_vals.mean()), + float(masked_vals.min()), + float(masked_vals.max()), + float(masked_vals.std()), + ] + else: + self.dp_stats = [ + float(frame.mean()), + float(frame.min()), + float(frame.max()), + float(frame.std()), + ] + + # Convert to numpy only for sending bytes to frontend + self.frame_bytes = frame.cpu().numpy().tobytes() + + def _on_roi_change(self, change=None): + """Recompute virtual image when individual ROI params change. + + High-frequency drag updates use the compound roi_center trait instead. + """ + if not self.roi_active: + return + self._compute_virtual_image_from_roi() + + def _on_roi_center_change(self, change=None): + """Handle batched roi_center updates from JS (single observer for row+col). + + This is the fast path for drag operations. JS sends [row, col] as a single + compound trait, so only one observer fires per mouse move. + """ + if not self.roi_active: + return + if change and "new" in change: + row, col = change["new"] + # Sync to individual traits (without triggering _on_roi_change observers) + self.unobserve(self._on_roi_change, names=["roi_center_col", "roi_center_row"]) + self.roi_center_row = row + self.roi_center_col = col + self.observe(self._on_roi_change, names=["roi_center_col", "roi_center_row"]) + self._compute_virtual_image_from_roi() + + def _on_vi_roi_change(self, change=None): + """Compute summed DP when VI ROI changes.""" + if self.vi_roi_mode == "off": + self.summed_dp_bytes = b"" + self.summed_dp_count = 0 + return + self._compute_summed_dp_from_vi_roi() + + def _compute_summed_dp_from_vi_roi(self): + """Sum diffraction patterns from positions inside VI ROI (PyTorch).""" + if self._data is None: + return + # Create mask in scan space using cached coordinates + if self.vi_roi_mode == "circle": + mask = (self._scan_row_coords - self.vi_roi_center_row) ** 2 + (self._scan_col_coords - self.vi_roi_center_col) ** 2 <= self.vi_roi_radius ** 2 + elif self.vi_roi_mode == "square": + half_size = self.vi_roi_radius + mask = (torch.abs(self._scan_row_coords - self.vi_roi_center_row) <= half_size) & (torch.abs(self._scan_col_coords - self.vi_roi_center_col) <= half_size) + elif self.vi_roi_mode == "rect": + half_w = self.vi_roi_width / 2 + half_h = self.vi_roi_height / 2 + mask = (torch.abs(self._scan_row_coords - self.vi_roi_center_row) <= half_h) & (torch.abs(self._scan_col_coords - self.vi_roi_center_col) <= half_w) + else: + return + + # Count positions in mask + n_positions = int(mask.sum()) + if n_positions == 0: + self.summed_dp_bytes = b"" + self.summed_dp_count = 0 + return + + self.summed_dp_count = n_positions + + # Compute average DP using masked sum (vectorized) + data = self._frame_data + if data.ndim == 4: + # (scan_rows, scan_cols, det_rows, det_cols) - sum over masked scan positions + avg_dp = data[mask].mean(dim=0) + else: + # Flattened: (N, det_rows, det_cols) - need to convert mask indices + flat_indices = torch.nonzero(mask.flatten(), as_tuple=True)[0] + avg_dp = data[flat_indices].mean(dim=0) + + # Send raw float32 (consistent with other data paths — JS handles normalization) + self.summed_dp_bytes = avg_dp.cpu().numpy().tobytes() + + def _create_circular_mask(self, cx: float, cy: float, radius: float): + """Create circular mask (boolean tensor on device).""" + mask = (self._det_col_coords - cx) ** 2 + (self._det_row_coords - cy) ** 2 <= radius ** 2 + return mask + + def _create_square_mask(self, cx: float, cy: float, half_size: float): + """Create square mask (boolean tensor on device).""" + mask = (torch.abs(self._det_col_coords - cx) <= half_size) & (torch.abs(self._det_row_coords - cy) <= half_size) + return mask + + def _create_annular_mask( + self, cx: float, cy: float, inner: float, outer: float + ): + """Create annular (donut) mask (boolean tensor on device).""" + dist_sq = (self._det_col_coords - cx) ** 2 + (self._det_row_coords - cy) ** 2 + mask = (dist_sq >= inner ** 2) & (dist_sq <= outer ** 2) + return mask + + def _create_rect_mask(self, cx: float, cy: float, half_width: float, half_height: float): + """Create rectangular mask (boolean tensor on device).""" + mask = (torch.abs(self._det_col_coords - cx) <= half_width) & (torch.abs(self._det_row_coords - cy) <= half_height) + return mask + + def _precompute_common_virtual_images(self): + """Pre-compute BF/ABF/ADF virtual images for instant preset switching.""" + cx, cy, bf = self.center_col, self.center_row, self.bf_radius + # Cache (bytes, stats, min, max) for each preset + bf_arr = self._fast_masked_sum(self._create_circular_mask(cx, cy, bf)) + abf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 0.5, bf)) + adf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf, bf * 4.0)) + + self._cached_bf_virtual = ( + self._to_float32_bytes(bf_arr, update_vi_stats=False), + [float(bf_arr.mean()), float(bf_arr.min()), float(bf_arr.max()), float(bf_arr.std())], + float(bf_arr.min()), float(bf_arr.max()) + ) + self._cached_abf_virtual = ( + self._to_float32_bytes(abf_arr, update_vi_stats=False), + [float(abf_arr.mean()), float(abf_arr.min()), float(abf_arr.max()), float(abf_arr.std())], + float(abf_arr.min()), float(abf_arr.max()) + ) + self._cached_adf_virtual = ( + self._to_float32_bytes(adf_arr, update_vi_stats=False), + [float(adf_arr.mean()), float(adf_arr.min()), float(adf_arr.max()), float(adf_arr.std())], + float(adf_arr.min()), float(adf_arr.max()) + ) + + def _get_cached_preset(self) -> tuple[bytes, list[float], float, float] | None: + """Check if current ROI matches a cached preset and return (bytes, stats, min, max) tuple.""" + # Must be centered on detector center + if abs(self.roi_center_col - self.center_col) >= 1 or abs(self.roi_center_row - self.center_row) >= 1: + return None + + bf = self.bf_radius + + # BF: circle at bf_radius + if (self.roi_mode == "circle" and abs(self.roi_radius - bf) < 1): + return self._cached_bf_virtual + + # ABF: annular at 0.5*bf to bf + if (self.roi_mode == "annular" and + abs(self.roi_radius_inner - bf * 0.5) < 1 and + abs(self.roi_radius - bf) < 1): + return self._cached_abf_virtual + + # ADF: annular at bf to 4*bf (combines LAADF + HAADF) + if (self.roi_mode == "annular" and + abs(self.roi_radius_inner - bf) < 1 and + abs(self.roi_radius - bf * 4.0) < 1): + return self._cached_adf_virtual + + return None + + def _virtual_image_for_frame(self, frame_idx: int) -> np.ndarray: + """Compute virtual image array for a specific frame without mutating traits.""" + data = self._data[frame_idx] if self.n_frames > 1 else self._data + cx, cy = self.roi_center_col, self.roi_center_row + if self.roi_mode == "circle" and self.roi_radius > 0: + mask = self._create_circular_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "square" and self.roi_radius > 0: + mask = self._create_square_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "annular" and self.roi_radius > 0: + mask = self._create_annular_mask(cx, cy, self.roi_radius_inner, self.roi_radius) + elif self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: + mask = self._create_rect_mask(cx, cy, self.roi_width / 2, self.roi_height / 2) + else: + row = int(max(0, min(round(cy), self._det_shape[0] - 1))) + col = int(max(0, min(round(cx), self._det_shape[1] - 1))) + if data.ndim == 4: + vi = data[:, :, row, col] + else: + vi = data[:, row, col].reshape(self._scan_shape) + return vi.cpu().numpy().astype(np.float32, copy=False) + mask_float = mask.float() + n_det = self._det_shape[0] * self._det_shape[1] + n_nonzero = int(mask.sum()) + coverage = n_nonzero / n_det + if coverage < SPARSE_MASK_THRESHOLD: + indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] + n_scan = self._scan_shape[0] * self._scan_shape[1] + data_flat = data.reshape(n_scan, n_det) + result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) + else: + if data.ndim == 3: + data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + else: + data_4d = data + result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) + return result.cpu().numpy().astype(np.float32, copy=False) + + def _fast_masked_sum(self, mask: torch.Tensor) -> torch.Tensor: + """Compute masked sum using PyTorch. + + Uses sparse indexing for small masks (<20% coverage) which is faster + because it only processes non-zero pixels: + - r=10 (1%): ~0.8ms (sparse) vs ~13ms (full) + - r=30 (8%): ~4ms (sparse) vs ~13ms (full) + + For large masks (≥20%), uses full tensordot which has constant ~13ms. + """ + data = self._frame_data + mask_float = mask.float() + n_det = self._det_shape[0] * self._det_shape[1] + n_nonzero = int(mask.sum()) + coverage = n_nonzero / n_det + + if coverage < SPARSE_MASK_THRESHOLD: + # Sparse: faster for small masks + indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] + n_scan = self._scan_shape[0] * self._scan_shape[1] + data_flat = data.reshape(n_scan, n_det) + result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) + else: + # Tensordot: faster for large masks + # Reshape to 4D if needed (3D flattened data) + if data.ndim == 3: + data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + else: + data_4d = data + result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) + + return result + + def _to_float32_bytes(self, arr: torch.Tensor, update_vi_stats: bool = True) -> bytes: + """Convert tensor to float32 bytes.""" + # Compute min/max (fast on GPU) + vmin = float(arr.min()) + vmax = float(arr.max()) + + # Only update traits when requested (avoids side effects during precomputation) + if update_vi_stats: + self.vi_data_min = vmin + self.vi_data_max = vmax + self.vi_stats = [float(arr.mean()), vmin, vmax, float(arr.std())] + + return arr.cpu().numpy().tobytes() + + def _compute_virtual_image_from_roi(self): + """Compute virtual image based on ROI mode.""" + if self._data is None: + return + cached = self._get_cached_preset() + if cached is not None: + # Cached preset returns (bytes, stats, min, max) tuple + vi_bytes, vi_stats, vi_min, vi_max = cached + self.virtual_image_bytes = vi_bytes + self.vi_stats = vi_stats + self.vi_data_min = vi_min + self.vi_data_max = vi_max + return + + cx, cy = self.roi_center_col, self.roi_center_row + + if self.roi_mode == "circle" and self.roi_radius > 0: + mask = self._create_circular_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "square" and self.roi_radius > 0: + mask = self._create_square_mask(cx, cy, self.roi_radius) + elif self.roi_mode == "annular" and self.roi_radius > 0: + mask = self._create_annular_mask(cx, cy, self.roi_radius_inner, self.roi_radius) + elif self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: + mask = self._create_rect_mask(cx, cy, self.roi_width / 2, self.roi_height / 2) + else: + # Point mode: single-pixel indexing + row = int(max(0, min(round(cy), self._det_shape[0] - 1))) + col = int(max(0, min(round(cx), self._det_shape[1] - 1))) + data = self._frame_data + if data.ndim == 4: + virtual_image = data[:, :, row, col] + else: + virtual_image = data[:, row, col].reshape(self._scan_shape) + self.virtual_image_bytes = self._to_float32_bytes(virtual_image) + return + + self.virtual_image_bytes = self._to_float32_bytes(self._fast_masked_sum(mask)) + + +bind_tool_runtime_api(Show4DSTEM, "Show4DSTEM") diff --git a/widget/src/quantem/widget/tool_parity.json b/widget/src/quantem/widget/tool_parity.json new file mode 100644 index 00000000..4271533a --- /dev/null +++ b/widget/src/quantem/widget/tool_parity.json @@ -0,0 +1,93 @@ +{ + "widgets": { + "Show2D": { + "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "roi", "profile", "all"], + "aliases": {} + }, + "Show3D": { + "tool_groups": ["display", "histogram", "stats", "playback", "view", "export", "roi", "profile", "all"], + "aliases": { + "navigation": "playback" + } + }, + "Show3DVolume": { + "tool_groups": ["display", "histogram", "playback", "fft", "navigation", "stats", "export", "view", "volume", "all"], + "aliases": {} + }, + "Show4D": { + "tool_groups": ["display", "roi", "histogram", "profile", "navigation", "playback", "stats", "export", "view", "fft", "all"], + "aliases": {} + }, + "Show4DSTEM": { + "tool_groups": ["display", "histogram", "stats", "navigation", "playback", "view", "export", "roi", "profile", "fft", "virtual", "frame", "all"], + "aliases": {} + }, + "ShowComplex2D": { + "tool_groups": ["display", "histogram", "fft", "roi", "stats", "export", "view", "all"], + "aliases": {} + }, + "Mark2D": { + "tool_groups": ["points", "roi", "profile", "display", "marker_style", "snap", "navigation", "view", "export", "all"], + "aliases": {} + }, + "Edit2D": { + "tool_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "export", "view", "all"], + "aliases": {} + }, + "Align2D": { + "tool_groups": ["alignment", "overlay", "display", "histogram", "stats", "export", "view", "all"], + "aliases": {} + }, + "Align2DBulk": { + "tool_groups": ["display", "histogram", "navigation", "stats", "view", "export", "all"], + "aliases": {} + }, + "Bin4D": { + "tool_groups": ["display", "binning", "mask", "preview", "stats", "export", "all"], + "aliases": {} + }, + "Browse": { + "tool_groups": ["navigation", "filter", "preview", "all"], + "aliases": {} + }, + "Bin2D": { + "tool_groups": ["display", "binning", "histogram", "stats", "navigation", "export", "all"], + "aliases": {} + }, + "Show1D": { + "tool_groups": ["display", "peaks", "stats", "export", "all"], + "aliases": {} + }, + "MetricExplorer": { + "tool_groups": ["display", "export", "all"], + "aliases": {} + }, + "ShowDiffraction": { + "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "spots", "all"], + "aliases": {} + } + }, + "viewer_widgets": ["Show1D", "Show2D", "Show3D", "Show3DVolume", "Show4D", "Show4DSTEM", "ShowComplex2D"], + "control_presets": { + "all": { + "label": "All", + "show_groups": ["*"] + }, + "compact": { + "label": "Compact", + "show_groups": ["mode", "edit", "display", "navigation", "playback", "view", "export", "fft"] + }, + "mask_focus": { + "label": "Mask Focus", + "show_groups": ["edit", "display", "roi", "histogram", "stats", "navigation", "playback", "view", "export", "fft", "virtual", "frame"] + }, + "crop_focus": { + "label": "Crop Focus", + "show_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "view", "export"] + }, + "spectroscopy": { + "label": "Spectroscopy", + "show_groups": ["display", "peaks", "stats"] + } + } +} diff --git a/widget/src/quantem/widget/tool_parity.py b/widget/src/quantem/widget/tool_parity.py new file mode 100644 index 00000000..d5d4f84e --- /dev/null +++ b/widget/src/quantem/widget/tool_parity.py @@ -0,0 +1,184 @@ +"""Shared tool visibility/locking registry and helpers.""" + +from __future__ import annotations + +import json +import pathlib +from functools import lru_cache +from typing import Any + +_REGISTRY_PATH = pathlib.Path(__file__).with_name("tool_parity.json") + + +@lru_cache(maxsize=1) +def _load_registry() -> dict[str, Any]: + return json.loads(_REGISTRY_PATH.read_text()) + + +def get_widget_tool_groups(widget_name: str) -> tuple[str, ...]: + registry = _load_registry() + widgets = registry.get("widgets", {}) + if widget_name not in widgets: + supported = ", ".join(sorted(widgets)) + raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") + return tuple(str(v).strip().lower() for v in widgets[widget_name].get("tool_groups", [])) + + +def get_widget_tool_aliases(widget_name: str) -> dict[str, str]: + registry = _load_registry() + widgets = registry.get("widgets", {}) + if widget_name not in widgets: + supported = ", ".join(sorted(widgets)) + raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") + aliases = widgets[widget_name].get("aliases", {}) + return {str(k).strip().lower(): str(v).strip().lower() for k, v in aliases.items()} + + +def normalize_tool_groups(widget_name: str, tool_groups) -> list[str]: + if tool_groups is None: + return [] + if isinstance(tool_groups, str): + values = [tool_groups] + else: + values = list(tool_groups) + + order = get_widget_tool_groups(widget_name) + aliases = get_widget_tool_aliases(widget_name) + supported = set(order) + normalized: list[str] = [] + seen: set[str] = set() + + for raw in values: + key = str(raw).strip().lower() + if not key: + continue + key = aliases.get(key, key) + if key not in supported: + supported_values = ", ".join(f'"{k}"' for k in order) + raise ValueError( + f"Unknown tool group {raw!r}. Supported values: {supported_values}." + ) + if key == "all": + return ["all"] + if key not in seen: + seen.add(key) + normalized.append(key) + return normalized + + +def build_tool_groups( + widget_name: str, + *, + tool_groups=None, + all_flag: bool = False, + flag_map: dict[str, bool] | None = None, +) -> list[str]: + if all_flag: + return ["all"] + values: list[str] = [] + if tool_groups is not None: + if isinstance(tool_groups, str): + values.append(tool_groups) + else: + values.extend(tool_groups) + for key, enabled in (flag_map or {}).items(): + if enabled: + values.append(key) + return normalize_tool_groups(widget_name, values) + + +def resolve_control_preset_hidden_tools(widget_name: str, preset_id: str) -> list[str]: + preset_key = str(preset_id).strip().lower() + presets = _load_registry().get("control_presets", {}) + if preset_key not in presets: + supported = ", ".join(sorted(presets)) + raise ValueError(f"Unknown control preset {preset_id!r}. Supported presets: {supported}.") + + show_groups = [str(v).strip().lower() for v in presets[preset_key].get("show_groups", [])] + supported_groups = [g for g in get_widget_tool_groups(widget_name) if g != "all"] + if "*" in show_groups: + return [] + show_set = set(show_groups) + hidden = [group for group in supported_groups if group not in show_set] + return normalize_tool_groups(widget_name, hidden) + + +def _flatten_groups(groups: tuple[Any, ...]) -> list[Any]: + if len(groups) == 1 and isinstance(groups[0], (list, tuple, set)): + return list(groups[0]) + return list(groups) + + +def _expanded_without_all(widget_name: str, values) -> list[str]: + normalized = normalize_tool_groups(widget_name, values) + if "all" not in normalized: + return normalized + return [group for group in get_widget_tool_groups(widget_name) if group != "all"] + + +def _ordered_groups(widget_name: str, values: set[str]) -> list[str]: + return [group for group in get_widget_tool_groups(widget_name) if group != "all" and group in values] + + +def bind_tool_runtime_api(cls, widget_name: str) -> None: + """Attach runtime lock/hide helpers to a widget class.""" + + def set_disabled_tools(self, tool_groups) -> Any: + self.disabled_tools = normalize_tool_groups(widget_name, tool_groups) + return self + + def set_hidden_tools(self, tool_groups) -> Any: + self.hidden_tools = normalize_tool_groups(widget_name, tool_groups) + return self + + def lock_tool(self, *tool_groups) -> Any: + new_groups = _flatten_groups(tool_groups) + if not new_groups: + return self + current = _expanded_without_all(widget_name, self.disabled_tools) + requested = _expanded_without_all(widget_name, new_groups) + merged = set(current).union(requested) + self.disabled_tools = _ordered_groups(widget_name, merged) + return self + + def unlock_tool(self, *tool_groups) -> Any: + remove_groups = _flatten_groups(tool_groups) + if not remove_groups: + return self + current = set(_expanded_without_all(widget_name, self.disabled_tools)) + requested = set(_expanded_without_all(widget_name, remove_groups)) + current.difference_update(requested) + self.disabled_tools = _ordered_groups(widget_name, current) + return self + + def hide_tool(self, *tool_groups) -> Any: + new_groups = _flatten_groups(tool_groups) + if not new_groups: + return self + current = _expanded_without_all(widget_name, self.hidden_tools) + requested = _expanded_without_all(widget_name, new_groups) + merged = set(current).union(requested) + self.hidden_tools = _ordered_groups(widget_name, merged) + return self + + def show_tool(self, *tool_groups) -> Any: + remove_groups = _flatten_groups(tool_groups) + if not remove_groups: + return self + current = set(_expanded_without_all(widget_name, self.hidden_tools)) + requested = set(_expanded_without_all(widget_name, remove_groups)) + current.difference_update(requested) + self.hidden_tools = _ordered_groups(widget_name, current) + return self + + def apply_control_preset(self, preset: str) -> Any: + self.hidden_tools = resolve_control_preset_hidden_tools(widget_name, preset) + return self + + cls.set_disabled_tools = set_disabled_tools # type: ignore[attr-defined] + cls.set_hidden_tools = set_hidden_tools # type: ignore[attr-defined] + cls.lock_tool = lock_tool # type: ignore[attr-defined] + cls.unlock_tool = unlock_tool # type: ignore[attr-defined] + cls.hide_tool = hide_tool # type: ignore[attr-defined] + cls.show_tool = show_tool # type: ignore[attr-defined] + cls.apply_control_preset = apply_control_preset # type: ignore[attr-defined] diff --git a/widget/tsconfig.json b/widget/tsconfig.json new file mode 100644 index 00000000..8b4afe79 --- /dev/null +++ b/widget/tsconfig.json @@ -0,0 +1,25 @@ +{ + "include": [ + "js" + ], + "compilerOptions": { + "target": "ES2020", + "module": "ESNext", + "lib": [ + "ES2020", + "DOM", + "DOM.Iterable" + ], + "skipLibCheck": true, + "moduleResolution": "bundler", + "allowImportingTsExtensions": true, + "resolveJsonModule": true, + "isolatedModules": true, + "noEmit": true, + "jsx": "react", + "strict": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "noFallthroughCasesInSwitch": true + } +} \ No newline at end of file diff --git a/widget/vite.config.js b/widget/vite.config.js index 8f303083..291b84aa 100644 --- a/widget/vite.config.js +++ b/widget/vite.config.js @@ -9,14 +9,17 @@ export default defineConfig({ }, build: { outDir: "src/quantem/widget/static", - lib: { - entry: "js/index.jsx", - formats: ["es"], - fileName: "index", - }, + emptyOutDir: true, rollupOptions: { + input: { + show2d: "js/show2d/index.tsx", + show4dstem: "js/show4dstem/index.tsx", + }, output: { - inlineDynamicImports: true, + entryFileNames: "[name].js", + assetFileNames: "[name][extname]", + format: "es", + inlineDynamicImports: false, }, }, }, From 9c3d93013973f9472b336f5def913142046cad6a Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 2 May 2026 14:29:29 -0700 Subject: [PATCH 270/335] refactor: modernize type hints in widget package (PEP 604/585) --- widget/src/quantem/widget/show2d.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index 08031ac7..7c7ef16a 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -13,7 +13,7 @@ import math import warnings from enum import StrEnum -from typing import Optional, Union, List, Self +from typing import Self import anywidget import matplotlib @@ -253,7 +253,7 @@ class Show2D(anywidget.AnyWidget): image_rotations = traitlets.List(traitlets.Int(), []).tag(sync=True) @classmethod - def _normalize_tool_groups(cls, tool_groups) -> List[str]: + def _normalize_tool_groups(cls, tool_groups) -> list[str]: return normalize_tool_groups("Show2D", tool_groups) @classmethod @@ -269,7 +269,7 @@ def _build_disabled_tools( disable_roi: bool = False, disable_profile: bool = False, disable_all: bool = False, - ) -> List[str]: + ) -> list[str]: return build_tool_groups( "Show2D", tool_groups=disabled_tools, @@ -299,7 +299,7 @@ def _build_hidden_tools( hide_roi: bool = False, hide_profile: bool = False, hide_all: bool = False, - ) -> List[str]: + ) -> list[str]: return build_tool_groups( "Show2D", tool_groups=hidden_tools, @@ -326,10 +326,10 @@ def _validate_hidden_tools(self, proposal): def __init__( self, - data: Union[np.ndarray, List[np.ndarray]], - labels: Optional[List[str]] = None, + data: np.ndarray | list[np.ndarray], + labels: list[str | None] = None, title: str = "", - cmap: Union[str, Colormap] = Colormap.INFERNO, + cmap: str | Colormap = Colormap.INFERNO, pixel_size: float = 0.0, scale_bar_visible: bool = True, show_fft: bool = False, @@ -340,7 +340,7 @@ def __init__( auto_contrast: bool = False, vmin: float | list | None = None, vmax: float | list | None = None, - disabled_tools: Optional[List[str]] = None, + disabled_tools: list[str | None] = None, disable_display: bool = False, disable_histogram: bool = False, disable_stats: bool = False, @@ -350,7 +350,7 @@ def __init__( disable_roi: bool = False, disable_profile: bool = False, disable_all: bool = False, - hidden_tools: Optional[List[str]] = None, + hidden_tools: list[str | None] = None, hide_display: bool = False, hide_histogram: bool = False, hide_stats: bool = False, @@ -371,7 +371,7 @@ def __init__( link_contrast: bool = True, diff_mode: bool = False, view_box: tuple | list | None = None, - display_bin: Union[int, str] = "auto", + display_bin: int | str = "auto", state=None, **kwargs, ): From b245948629fc0ae3fb2d2ad21ee08cdce722c39f Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sat, 2 May 2026 14:33:40 -0700 Subject: [PATCH 271/335] feat: first-class Dataset2d/Dataset3d support, drop cupy, improve errors --- widget/src/quantem/widget/array_utils.py | 291 +++++------------------ widget/src/quantem/widget/show2d.py | 15 +- widget/src/quantem/widget/show4dstem.py | 6 +- 3 files changed, 73 insertions(+), 239 deletions(-) diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py index e86633e6..a38190e6 100644 --- a/widget/src/quantem/widget/array_utils.py +++ b/widget/src/quantem/widget/array_utils.py @@ -1,282 +1,111 @@ """ -Array utilities for handling NumPy, CuPy, and PyTorch arrays uniformly. - -This module provides utilities to convert arrays from different backends -into NumPy arrays for widget processing. +Array utilities for widgets. Supports NumPy + PyTorch input. """ -from typing import Any, Literal +from typing import Literal import numpy as np try: import torch - import torch.nn.functional as F _HAS_TORCH = True except ImportError: _HAS_TORCH = False -ArrayBackend = Literal["numpy", "cupy", "torch", "unknown"] - - -def get_array_backend(data: Any) -> ArrayBackend: - """ - Detect the array backend of the input data. +ArrayBackend = Literal["numpy", "torch", "unknown"] - Parameters - ---------- - data : array-like - Input array (NumPy, CuPy, PyTorch, or other). - Returns - ------- - str - One of: "numpy", "cupy", "torch", "unknown" - """ - # Check PyTorch first (has both .numpy and .detach methods) - if hasattr(data, "detach") and hasattr(data, "numpy"): +def get_array_backend(data) -> ArrayBackend: + """Detect array backend. Returns 'numpy', 'torch', or 'unknown'.""" + if _HAS_TORCH and isinstance(data, torch.Tensor): return "torch" - # Check CuPy (has .get() or __cuda_array_interface__) - if hasattr(data, "__cuda_array_interface__"): - return "cupy" - if hasattr(data, "get") and hasattr(data, "__array__"): - # CuPy arrays have .get() to transfer to CPU - type_name = type(data).__module__ - if "cupy" in type_name: - return "cupy" - # Check NumPy if isinstance(data, np.ndarray): return "numpy" return "unknown" -def to_numpy(data: Any, dtype: np.dtype | None = None) -> np.ndarray: - """ - Convert any array-like (NumPy, CuPy, PyTorch) to a NumPy array. +def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: + """Convert NumPy or PyTorch array to NumPy. Parameters ---------- - data : array-like - Input array from any supported backend. + data : np.ndarray or torch.Tensor + Input array. dtype : np.dtype, optional - Target dtype for the output array. If None, preserves original dtype. + Target dtype. Returns ------- np.ndarray - NumPy array with the same data. Examples -------- >>> import numpy as np - >>> from quantem.widget.array_utils import to_numpy - >>> - >>> # NumPy passthrough - >>> arr = np.random.rand(10, 10) - >>> result = to_numpy(arr) - >>> - >>> # CuPy conversion (if available) - >>> import cupy as cp - >>> gpu_arr = cp.random.rand(10, 10) - >>> cpu_arr = to_numpy(gpu_arr) - >>> - >>> # PyTorch conversion (if available) + >>> to_numpy(np.zeros((4, 4))) >>> import torch - >>> tensor = torch.rand(10, 10) - >>> arr = to_numpy(tensor) + >>> to_numpy(torch.zeros(4, 4)) + + Raises + ------ + TypeError + If `data` is not a NumPy array or PyTorch tensor. """ backend = get_array_backend(data) - if backend == "torch": - # PyTorch tensor: detach from graph, move to CPU, convert to numpy result = data.detach().cpu().numpy() - - elif backend == "cupy": - # CuPy array: use .get() to transfer to CPU - if hasattr(data, "get"): - result = data.get() - else: - # Fallback for __cuda_array_interface__ - import cupy as cp - - result = cp.asnumpy(data) - elif backend == "numpy": - # NumPy array: passthrough (may copy if dtype changes) result = data - else: - # Unknown backend: try np.asarray as fallback - result = np.asarray(data) - - # Apply dtype conversion if specified + # Try np.asarray as last-resort fallback for things like Dataset arrays + try: + result = np.asarray(data) + except Exception as e: + raise TypeError( + f"to_numpy expected a NumPy array or PyTorch tensor, got {type(data).__name__}. " + f"Convert your input via np.asarray(...) or tensor.cpu().numpy() first." + ) from e if dtype is not None: result = np.asarray(result, dtype=dtype) - return result -def bin2d(data, factor: int = 2, mode: str = "mean", edge_mode: str = "crop") -> np.ndarray: - """ - Spatial binning for 2D or 3D arrays. - - Uses torch GPU (MPS/CUDA) when available for large arrays (~5× faster on 4K data). - - Parameters - ---------- - data : array-like - Input array with shape ``(H, W)`` or ``(N, H, W)``. - factor : int, default 2 - Bin factor. - mode : str, default "mean" - Reduction mode: ``"mean"`` or ``"sum"``. - edge_mode : str, default "crop" - How to handle dimensions not divisible by *factor*: - ``"crop"`` trims extra pixels, ``"pad"`` zero-pads to the next - multiple (output shape uses ``ceil(dim / factor)``). - - Returns - ------- - np.ndarray - Binned array, dtype float32. - """ - arr = to_numpy(data) - if arr.dtype != np.float32: - arr = arr.astype(np.float32) - - # Torch GPU fast path: only for arrays between 1M and 500M elements. - # Larger arrays hit MPS memory transfer bottleneck (>2 GB transfer > CPU compute). - import torch - if 1_000_000 < arr.size < 500_000_000 and (torch.backends.mps.is_available() or torch.cuda.is_available()): - dev = torch.device("mps" if torch.backends.mps.is_available() else "cuda") - t = torch.from_numpy(arr).to(dev) - if t.ndim == 2: - h, w = t.shape - oh = h // factor * factor - ow = w // factor * factor - t = t[:oh, :ow].reshape(oh // factor, factor, ow // factor, factor) - t = t.sum(dim=(1, 3)) if mode == "sum" else t.mean(dim=(1, 3)) - elif t.ndim == 3: - n, h, w = t.shape - oh = h // factor * factor - ow = w // factor * factor - t = t[:, :oh, :ow].reshape(n, oh // factor, factor, ow // factor, factor) - t = t.sum(dim=(2, 4)) if mode == "sum" else t.mean(dim=(2, 4)) - return t.cpu().numpy().astype(np.float32) - - # CPU fallback (no GPU available or small array) - reduce = np.ndarray.sum if mode == "sum" else np.ndarray.mean - if arr.ndim == 2: - arr = _pad_or_crop_2d(arr, factor, edge_mode) - h, w = arr.shape - oh, ow = h // factor, w // factor - return reduce(arr.reshape(oh, factor, ow, factor), axis=(1, 3)).astype(np.float32) - # 3D: (N, H, W) - arr = _pad_or_crop_3d(arr, factor, edge_mode) - n, h, w = arr.shape - oh, ow = h // factor, w // factor - return reduce(arr.reshape(n, oh, factor, ow, factor), axis=(2, 4)).astype(np.float32) - - -def _pad_or_crop_2d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: - h, w = arr.shape - if edge_mode == "pad": - pad_h = (factor - h % factor) % factor - pad_w = (factor - w % factor) % factor - if pad_h or pad_w: - arr = np.pad(arr, ((0, pad_h), (0, pad_w)), mode="constant") - else: - oh, ow = h // factor, w // factor - arr = arr[:oh * factor, :ow * factor] - return arr - - -def _pad_or_crop_3d(arr: np.ndarray, factor: int, edge_mode: str) -> np.ndarray: - _, h, w = arr.shape - if edge_mode == "pad": - pad_h = (factor - h % factor) % factor - pad_w = (factor - w % factor) % factor - if pad_h or pad_w: - arr = np.pad(arr, ((0, 0), (0, pad_h), (0, pad_w)), mode="constant") - else: - oh, ow = h // factor, w // factor - arr = arr[:, :oh * factor, :ow * factor] - return arr - - -def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: - """ - Apply sub-pixel shift using bilinear interpolation. - - Uses ``torch.nn.functional.grid_sample`` on GPU when torch is available, - falls back to numpy bilinear interpolation otherwise. - - Parameters - ---------- - img : np.ndarray - 2D image, float32. - dy : float - Shift in y (rows). - dx : float - Shift in x (columns). - - Returns - ------- - np.ndarray - Shifted image, same shape, float32. Out-of-bounds pixels are zero. - """ - if _HAS_TORCH: - h, w = img.shape - device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu") - t = torch.as_tensor(img, dtype=torch.float32, device=device).unsqueeze(0).unsqueeze(0) - base_y = torch.linspace(-1, 1, h, device=device) - base_x = torch.linspace(-1, 1, w, device=device) - gy, gx = torch.meshgrid(base_y, base_x, indexing="ij") - grid = torch.stack([gx - dx * 2.0 / w, gy - dy * 2.0 / h], dim=-1).unsqueeze(0) - result = F.grid_sample(t, grid, mode="bilinear", padding_mode="zeros", align_corners=True) - return result.squeeze().cpu().numpy() - h, w = img.shape - y_src = np.arange(h, dtype=np.float64) - dy - x_src = np.arange(w, dtype=np.float64) - dx - yy, xx = np.meshgrid(y_src, x_src, indexing="ij") - y0 = np.floor(yy).astype(int) - x0 = np.floor(xx).astype(int) - fy = (yy - y0).astype(np.float32) - fx = (xx - x0).astype(np.float32) - valid = (y0 >= 0) & (y0 + 1 < h) & (x0 >= 0) & (x0 + 1 < w) - y0c = np.clip(y0, 0, h - 2) - x0c = np.clip(x0, 0, w - 2) - result = (img[y0c, x0c] * (1 - fy) * (1 - fx) - + img[y0c, x0c + 1] * (1 - fy) * fx - + img[y0c + 1, x0c] * fy * (1 - fx) - + img[y0c + 1, x0c + 1] * fy * fx) - result[~valid] = 0.0 - return result.astype(np.float32) - - def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: - """Resize image using bilinear interpolation (pure numpy, no scipy).""" - h, w = img.shape + """Center-pad an image to (target_h, target_w) with zeros. + Used to align gallery images of different shapes to a common canvas. + """ + h, w = img.shape[-2:] if h == target_h and w == target_w: return img + pad_top = (target_h - h) // 2 + pad_bot = target_h - h - pad_top + pad_left = (target_w - w) // 2 + pad_right = target_w - w - pad_left + return np.pad(img, ((pad_top, pad_bot), (pad_left, pad_right)), mode="constant", constant_values=0) - y_new = np.linspace(0, h - 1, target_h) - x_new = np.linspace(0, w - 1, target_w) - x_grid, y_grid = np.meshgrid(x_new, y_new) - - y0 = np.floor(y_grid).astype(int) - x0 = np.floor(x_grid).astype(int) - y1 = np.minimum(y0 + 1, h - 1) - x1 = np.minimum(x0 + 1, w - 1) - fy = y_grid - y0 - fx = x_grid - x0 - - result = ( - img[y0, x0] * (1 - fy) * (1 - fx) + - img[y0, x1] * (1 - fy) * fx + - img[y1, x0] * fy * (1 - fx) + - img[y1, x1] * fy * fx - ) - return result.astype(img.dtype) +def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: + """Sub-pixel image shift via bilinear interpolation. Used for diff alignment.""" + if not _HAS_TORCH: + # Fallback: integer roll only + return np.roll(img, (int(round(dy)), int(round(dx))), axis=(-2, -1)) + t = torch.from_numpy(img).float() + if t.ndim == 2: + t = t.unsqueeze(0).unsqueeze(0) + h, w = t.shape[-2:] + y = torch.arange(h, dtype=torch.float32) - dy + x = torch.arange(w, dtype=torch.float32) - dx + yy, xx = torch.meshgrid(y, x, indexing="ij") + grid = torch.stack(((xx / (w - 1)) * 2 - 1, (yy / (h - 1)) * 2 - 1), dim=-1).unsqueeze(0) + out = torch.nn.functional.grid_sample(t, grid, mode="bilinear", padding_mode="border", align_corners=True) + return out.squeeze().numpy() + + +def bin2d(img: np.ndarray, factor: int) -> np.ndarray: + """Reduce 2D image by integer binning factor. Mean of f×f blocks.""" + if factor <= 1: + return img + h, w = img.shape[-2:] + h2, w2 = h - h % factor, w - w % factor + img = img[..., :h2, :w2] + return img.reshape(*img.shape[:-2], h2 // factor, factor, w2 // factor, factor).mean(axis=(-3, -1)) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index 7c7ef16a..cd2dff99 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -22,6 +22,7 @@ import numpy as np import traitlets +from quantem.core.datastructures import Dataset2d, Dataset3d from quantem.widget.array_utils import to_numpy, _resize_image from quantem.widget.json_state import resolve_widget_version, save_state_file, unwrap_state_payload from quantem.widget.tool_parity import ( @@ -438,8 +439,12 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, self._display_data = None # initialized after data setup self._display_bin = 1 - # Check if data is a Dataset2d and extract metadata - if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): + # First-class support for quantem Dataset2d / Dataset3d: + # extract array + auto-populate title, pixel_size from sampling+units. + # (Duck-typing fallback below covers any other object exposing the same API.) + if isinstance(data, (Dataset2d, Dataset3d)) or ( + hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling") + ): if not title and data.name: title = data.name if pixel_size == 0.0 and hasattr(data, "units"): @@ -451,7 +456,7 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, pixel_size = sampling_val data = data.array - # Convert input to NumPy (handles NumPy, CuPy, PyTorch) + # Convert NumPy / PyTorch / list inputs to a NumPy array. if isinstance(data, list): images = [to_numpy(d) for d in data] @@ -532,7 +537,7 @@ def _expand(v): if v is None: return [None] * n if isinstance(v, (list, tuple)): if len(v) != n: - raise ValueError(f"vmin/vmax list length {len(v)} != n_images {n}") + raise ValueError(f"vmin/vmax list has length {len(v)} but n_images is {n}. Pass a list of length {n} or a scalar to apply uniformly.") return [None if x is None else float(x) for x in v] return [float(v)] * n self.vmins = _expand(vmin) @@ -1125,7 +1130,7 @@ def rotate(self, idx: int, angle: int) -> Self: Self """ if angle % 90 != 0: - raise ValueError(f"Rotation angle must be a multiple of 90°, got {angle}") + raise ValueError(f"Rotation angle must be a multiple of 90 (got {angle}). Use 0, 90, 180, 270, or -90, -180, -270.") if idx < 0 or idx >= self.n_images: raise IndexError(f"Image index {idx} out of range [0, {self.n_images})") k = (angle // 90) % 4 diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 1fe94273..c514be2f 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -566,7 +566,7 @@ def __init__( self._device = torch.device("cpu") self._data = torch.from_numpy(data_np).to(self._device) else: - raise ValueError(f"Expected 3D, 4D, or 5D array, got {ndim}D") + raise ValueError(f"Show4DSTEM expects a 3D ((N, det_h, det_w) flat-scan), 4D ((scan_h, scan_w, det_h, det_w)), or 5D ((n_frames, scan_h, scan_w, det_h, det_w)) array. Got {ndim}D. Reshape with array.reshape((scan_h, scan_w, det_h, det_w)) or pass a Dataset4dstem.") if _verbose: if str(self._device) == "mps": torch.mps.synchronize() @@ -729,7 +729,7 @@ def set_image(self, data, scan_shape=None): self._det_shape = (data_np.shape[2], data_np.shape[3]) self._data = torch.from_numpy(data_np).to(self._device) else: - raise ValueError(f"Expected 3D, 4D, or 5D array, got {data_np.ndim}D") + raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {data_np.ndim}D. See documentation for accepted shapes.") self.frame_idx = 0 self.shape_rows = self._scan_shape[0] self.shape_cols = self._scan_shape[1] @@ -1865,7 +1865,7 @@ def _render_panel_image( elif panel_key == "fft": rgb, render_meta = self._render_fft_rgb() else: - raise ValueError(f"Unsupported panel '{panel_key}'") + raise ValueError(f"Unsupported panel {panel_key!r}. Valid options: 'diffraction', 'virtual', 'fft', 'all'.") panel = Image.fromarray(rgb, mode="RGB") panel = self._decorate_panel(panel, panel_key, include_overlays, include_scalebar) From 43b54b2ab6b63e9c11baa0ab3f1aefc334d09e7c Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Sun, 3 May 2026 14:24:45 -0700 Subject: [PATCH 272/335] initial demo of Show2D and Show4DSTEM --- .gitignore | 16 +- widget/js/colormaps.ts | 16 +- widget/js/control-customizer.tsx | 174 - widget/js/{webgpu-fft.ts => fft.ts} | 75 +- widget/js/{scalebar.ts => figure.ts} | 33 +- widget/js/histogram.ts | 19 - widget/js/show2d/index.tsx | 362 +- widget/js/show2d/show2d.css | 9 - widget/js/show4dstem/index.tsx | 989 +++--- widget/js/show4dstem/styles.css | 5 - widget/js/stats.ts | 20 + widget/js/tool-parity.ts | 156 - widget/package-lock.json | 1186 +------ widget/package.json | 10 +- widget/src/quantem/widget/__init__.py | 9 +- widget/src/quantem/widget/array_utils.py | 92 +- widget/src/quantem/widget/show2d.py | 316 +- widget/src/quantem/widget/show4dstem.py | 3046 +++-------------- .../widget/{json_state.py => state.py} | 2 - widget/src/quantem/widget/tool_parity.json | 93 - widget/src/quantem/widget/tool_parity.py | 184 - widget/tests/test_fft_parity.py | 200 ++ widget/tests/test_state_dict.py | 168 + widget/vite.config.js | 26 - 24 files changed, 1778 insertions(+), 5428 deletions(-) delete mode 100644 widget/js/control-customizer.tsx rename widget/js/{webgpu-fft.ts => fft.ts} (89%) rename widget/js/{scalebar.ts => figure.ts} (92%) delete mode 100644 widget/js/histogram.ts delete mode 100644 widget/js/show2d/show2d.css delete mode 100644 widget/js/show4dstem/styles.css delete mode 100644 widget/js/tool-parity.ts rename widget/src/quantem/widget/{json_state.py => state.py} (96%) delete mode 100644 widget/src/quantem/widget/tool_parity.json delete mode 100644 widget/src/quantem/widget/tool_parity.py create mode 100644 widget/tests/test_fft_parity.py create mode 100644 widget/tests/test_state_dict.py delete mode 100644 widget/vite.config.js diff --git a/.gitignore b/.gitignore index d87d85c8..92d69e39 100644 --- a/.gitignore +++ b/.gitignore @@ -185,9 +185,23 @@ ipynb-playground/ *.h5 *.npy -# cursor +# cursor/CLI .cursor +.claude +CLAUDE.md +AGENTS.md +AGENT.md # widget (JS build artifacts) node_modules/ widget/src/quantem/widget/static/ + +# widget — local-only (per-developer notebooks, docs scratch, build/test scripts). +# Track only src/, js/, tests/test_*.py for now. +widget/.gitignore +widget/docs/ +widget/notebooks/ +widget/scripts/ +widget/tests/integration/ +widget/tests/snapshots/ + diff --git a/widget/js/colormaps.ts b/widget/js/colormaps.ts index ba160698..40a940b2 100644 --- a/widget/js/colormaps.ts +++ b/widget/js/colormaps.ts @@ -1,3 +1,7 @@ +// ============================================================================ +// Color palettes (LUT control points) +// ============================================================================ + const COLORMAP_POINTS: Record = { inferno: [ [0, 0, 4], [40, 11, 84], [101, 21, 110], [159, 42, 99], @@ -58,6 +62,10 @@ export const COLORMAPS: Record = Object.fromEntries( Object.entries(COLORMAP_POINTS).map(([name, points]) => [name, createColormapLUT(points)]) ); +// ============================================================================ +// CPU colormap (Float32 -> RGBA via 256-entry LUT) +// ============================================================================ + /** Apply colormap LUT to float data, writing into an RGBA Uint8ClampedArray. */ export function applyColormap( data: Float32Array, @@ -119,6 +127,10 @@ export function renderToOffscreenReuse( // 2D dispatch (16×16 workgroups) to stay within WebGPU's 65535 workgroup limit. // 1D dispatch with wg=256 needs ceil(4096*4096/256)=65536 — exceeds the limit by 1. +// ============================================================================ +// WebGPU colormap engine (compute shader, ~300x faster than CPU loop on 4K data) +// ============================================================================ + const COLORMAP_SHADER = /* wgsl */ ` struct Params { width: u32, @@ -1061,9 +1073,9 @@ let gpuColormapEngine: GPUColormapEngine | null = null; /** Get or create the singleton GPU colormap engine. Returns null if WebGPU unavailable. */ export async function getGPUColormapEngine(): Promise { if (gpuColormapEngine) return gpuColormapEngine; - // Reuse the GPU device from webgpu-fft + // Reuse the GPU device from fft try { - const { getGPUDevice } = await import("./webgpu-fft"); + const { getGPUDevice } = await import("./fft"); const device = await getGPUDevice(); if (!device) return null; gpuColormapEngine = new GPUColormapEngine(device); diff --git a/widget/js/control-customizer.tsx b/widget/js/control-customizer.tsx deleted file mode 100644 index 3ca9602d..00000000 --- a/widget/js/control-customizer.tsx +++ /dev/null @@ -1,174 +0,0 @@ -import * as React from "react"; -import Box from "@mui/material/Box"; -import Typography from "@mui/material/Typography"; -import Switch from "@mui/material/Switch"; -import Tooltip from "@mui/material/Tooltip"; -import Divider from "@mui/material/Divider"; -import IconButton from "@mui/material/IconButton"; -import Button from "@mui/material/Button"; -import Menu from "@mui/material/Menu"; -import TuneIcon from "@mui/icons-material/Tune"; - -import { - addToolGroup, - compactToolLabel, - computeToolVisibility, - getControlPresetIds, - getControlPresetLabel, - getWidgetToolGroups, - removeToolGroup, - resolvePresetHiddenTools, -} from "./tool-parity"; - -type ToolSetter = React.Dispatch>; - -type ThemeColors = { - controlBg: string; - text: string; - border: string; - textMuted?: string; - accent?: string; -}; - -type ControlCustomizerProps = { - widgetName: string; - hiddenTools: string[]; - setHiddenTools: ToolSetter; - disabledTools: string[]; - setDisabledTools: ToolSetter; - themeColors: ThemeColors; - labelOverrides?: Record; -}; - -const switchStyles = { - small: { - "& .MuiSwitch-thumb": { width: 12, height: 12 }, - "& .MuiSwitch-switchBase": { padding: "4px" }, - }, -}; - -const presetButton = { - fontSize: 10, - py: 0.25, - px: 1, - minWidth: 0, -}; - -export function ControlCustomizer({ - widgetName, - hiddenTools, - setHiddenTools, - disabledTools, - setDisabledTools, - themeColors, - labelOverrides, -}: ControlCustomizerProps) { - const [anchor, setAnchor] = React.useState(null); - const groups = React.useMemo( - () => getWidgetToolGroups(widgetName).filter((group) => group !== "all"), - [widgetName], - ); - const visibility = React.useMemo( - () => computeToolVisibility(widgetName, disabledTools, hiddenTools), - [widgetName, disabledTools, hiddenTools], - ); - - const setGroupVisible = React.useCallback((group: string, visible: boolean) => { - setHiddenTools((prev) => { - if (visible) return removeToolGroup(widgetName, prev, group); - return addToolGroup(widgetName, prev, group); - }); - }, [setHiddenTools, widgetName]); - - const setGroupLocked = React.useCallback((group: string, locked: boolean) => { - setDisabledTools((prev) => { - if (locked) return addToolGroup(widgetName, prev, group); - return removeToolGroup(widgetName, prev, group); - }); - }, [setDisabledTools, widgetName]); - - const applyPreset = React.useCallback((presetId: string) => { - setHiddenTools(resolvePresetHiddenTools(widgetName, presetId)); - }, [setHiddenTools, widgetName]); - - return ( - <> - - setAnchor(e.currentTarget)} - sx={{ p: 0.25, ml: 0.5, color: themeColors.text }} - > - - - - setAnchor(null)} - anchorOrigin={{ vertical: "bottom", horizontal: "right" }} - transformOrigin={{ vertical: "top", horizontal: "right" }} - PaperProps={{ - sx: { - bgcolor: themeColors.controlBg, - color: themeColors.text, - border: `1px solid ${themeColors.border}`, - p: 0.5, - minWidth: 280, - }, - }} - > - - Presets - - {getControlPresetIds().map((presetId) => ( - - ))} - - - - - Per-group - {groups.map((group) => { - const label = labelOverrides?.[group] ?? compactToolLabel(group); - const hidden = visibility.isHidden(group); - const locked = visibility.isLocked(group); - return ( - - {label} - - Show - setGroupVisible(group, e.target.checked)} - inputProps={{ "aria-label": `show-${group}` }} - sx={switchStyles.small} - /> - Lock - setGroupLocked(group, e.target.checked)} - inputProps={{ "aria-label": `lock-${group}` }} - sx={switchStyles.small} - disabled={hidden} - /> - - - ); - })} - - - - ); -} diff --git a/widget/js/webgpu-fft.ts b/widget/js/fft.ts similarity index 89% rename from widget/js/webgpu-fft.ts rename to widget/js/fft.ts index 2498b755..b2a72ea6 100644 --- a/widget/js/webgpu-fft.ts +++ b/widget/js/fft.ts @@ -85,63 +85,24 @@ export function fftshift(data: Float32Array, width: number, height: number): voi // CPU FFT Web Worker — runs fft2d + fftshift + computeMagnitude off main thread // ============================================================================ +// Build worker source by stringifying the same fft1d/fft2d/fftshift defined +// above. Single source of truth: fix a bug once, both paths get it. Pure +// functions only (no module-state closures), so .toString() captures the full +// behavior. Use Function.name in the onmessage body so minified names still +// match (esbuild may rename `fft2d` -> `a`; the .name property tracks rename). const FFT_WORKER_CODE = ` -function nextPow2(n) { return Math.pow(2, Math.ceil(Math.log2(n))); } -function fft1d(real, imag, inverse) { - var n = real.length; if (n <= 1) return; - var j = 0; - for (var i = 0; i < n - 1; i++) { - if (i < j) { var t = real[i]; real[i] = real[j]; real[j] = t; t = imag[i]; imag[i] = imag[j]; imag[j] = t; } - var k = n >> 1; while (k <= j) { j -= k; k >>= 1; } j += k; - } - var sign = inverse ? 1 : -1; - for (var len = 2; len <= n; len <<= 1) { - var halfLen = len >> 1, angle = (sign * 2 * Math.PI) / len; - var wR = Math.cos(angle), wI = Math.sin(angle); - for (var i = 0; i < n; i += len) { - var cR = 1, cI = 0; - for (var k = 0; k < halfLen; k++) { - var eI = i + k, oI = i + k + halfLen; - var tR = cR * real[oI] - cI * imag[oI], tI = cR * imag[oI] + cI * real[oI]; - real[oI] = real[eI] - tR; imag[oI] = imag[eI] - tI; - real[eI] += tR; imag[eI] += tI; - var nR = cR * wR - cI * wI; cI = cR * wI + cI * wR; cR = nR; - } - } - } - if (inverse) { for (var i = 0; i < n; i++) { real[i] /= n; imag[i] /= n; } } -} -function fft2d(real, imag, width, height, inverse) { - var pW = nextPow2(width), pH = nextPow2(height), pad = pW !== width || pH !== height; - var wR, wI; - if (pad) { - wR = new Float32Array(pW * pH); wI = new Float32Array(pW * pH); - for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { wR[y*pW+x] = real[y*width+x]; wI[y*pW+x] = imag[y*width+x]; } - } else { wR = real; wI = imag; } - var rR = new Float32Array(pW), rI = new Float32Array(pW); - for (var y = 0; y < pH; y++) { - var o = y * pW; for (var x = 0; x < pW; x++) { rR[x] = wR[o+x]; rI[x] = wI[o+x]; } - fft1d(rR, rI, inverse); for (var x = 0; x < pW; x++) { wR[o+x] = rR[x]; wI[o+x] = rI[x]; } - } - var cR = new Float32Array(pH), cI = new Float32Array(pH); - for (var x = 0; x < pW; x++) { - for (var y = 0; y < pH; y++) { cR[y] = wR[y*pW+x]; cI[y] = wI[y*pW+x]; } - fft1d(cR, cI, inverse); for (var y = 0; y < pH; y++) { wR[y*pW+x] = cR[y]; wI[y*pW+x] = cI[y]; } - } - if (pad) { for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) { real[y*width+x] = wR[y*pW+x]; imag[y*width+x] = wI[y*pW+x]; } } -} -function fftshift(data, width, height) { - var hW = width >> 1, hH = height >> 1, temp = new Float32Array(width * height); - for (var y = 0; y < height; y++) for (var x = 0; x < width; x++) temp[((y+hH)%height)*width+((x+hW)%width)] = data[y*width+x]; - data.set(temp); -} +${nextPow2.toString()} +${fft1d.toString()} +${fft2d.toString()} +${fftshift.toString()} self.onmessage = function(e) { - var d = e.data, real = d.real, imag = d.imag, w = d.width, h = d.height; - fft2d(real, imag, w, h, d.inverse); - fftshift(real, w, h); fftshift(imag, w, h); - var n = real.length, mag = new Float32Array(n); - for (var i = 0; i < n; i++) mag[i] = Math.sqrt(real[i]*real[i] + imag[i]*imag[i]); - self.postMessage({ id: d.id, magnitude: mag, real: real, imag: imag }, [mag.buffer, real.buffer, imag.buffer]); + const d = e.data; + ${fft2d.name}(d.real, d.imag, d.width, d.height, d.inverse); + ${fftshift.name}(d.real, d.width, d.height); + ${fftshift.name}(d.imag, d.width, d.height); + const n = d.real.length, mag = new Float32Array(n); + for (let i = 0; i < n; i++) mag[i] = Math.sqrt(d.real[i]*d.real[i] + d.imag[i]*d.imag[i]); + self.postMessage({ id: d.id, magnitude: mag, real: d.real, imag: d.imag }, [mag.buffer, d.real.buffer, d.imag.buffer]); }; `; @@ -189,6 +150,10 @@ export function fft2dAsync( // WebGPU FFT — GPU-accelerated 2D FFT // ============================================================================ +// ============================================================================ +// WebGPU FFT (compute shader, GPU-resident) +// ============================================================================ + const FFT_2D_SHADER = /* wgsl */` fn cmul(a: vec2, b: vec2) -> vec2 { return vec2(a.x*b.x-a.y*b.y, a.x*b.y+a.y*b.x); } fn twiddle(k: u32, N: u32, inverse: f32) -> vec2 { let angle = inverse * 2.0 * 3.14159265359 * f32(k) / f32(N); return vec2(cos(angle), sin(angle)); } diff --git a/widget/js/scalebar.ts b/widget/js/figure.ts similarity index 92% rename from widget/js/scalebar.ts rename to widget/js/figure.ts index 041b4754..b1fd3f2f 100644 --- a/widget/js/scalebar.ts +++ b/widget/js/figure.ts @@ -5,8 +5,6 @@ import { formatNumber } from "./format"; -export type ScaleUnit = "Å" | "mrad" | "px" | "Å⁻¹"; - /** Round a physical value to a "nice" number (1, 2, 5, 10, 20, 50, ...) */ export function roundToNiceValue(value: number): number { if (value <= 0) return 1; @@ -18,23 +16,10 @@ export function roundToNiceValue(value: number): number { return 10 * magnitude; } -/** Format scale bar label with appropriate unit and auto-conversion (Å→nm, mrad→rad, Å⁻¹→nm⁻¹) */ -export function formatScaleLabel(value: number, unit: ScaleUnit): string { +/** Format scale bar label. Unit string is displayed verbatim - no conversion. */ +export function formatScaleLabel(value: number, unit: string): string { const nice = roundToNiceValue(value); - if (unit === "Å") { - if (nice >= 10) return `${Math.round(nice / 10)} nm`; - return nice >= 1 ? `${Math.round(nice)} Å` : `${nice.toFixed(2)} Å`; - } - if (unit === "Å⁻¹") { - // 10 Å⁻¹ = 1 nm⁻¹ - if (nice >= 10) return `${Math.round(nice / 10)} nm⁻¹`; - return nice >= 1 ? `${Math.round(nice)} Å⁻¹` : `${nice.toFixed(2)} Å⁻¹`; - } - if (unit === "px") { - return nice >= 1 ? `${Math.round(nice)} px` : `${nice.toFixed(1)} px`; - } - if (nice >= 1000) return `${Math.round(nice / 1000)} rad`; - return nice >= 1 ? `${Math.round(nice)} mrad` : `${nice.toFixed(2)} mrad`; + return nice >= 1 ? `${Math.round(nice)} ${unit}` : `${nice.toFixed(2)} ${unit}`; } const FONT = "-apple-system, BlinkMacSystemFont, 'Segoe UI', sans-serif"; @@ -48,7 +33,7 @@ export function drawScaleBarHiDPI( dpr: number, zoom: number, pixelSize: number, - unit: "Å" | "mrad" | "px", + unit: string, imageWidth: number, ) { const ctx = canvas.getContext("2d"); @@ -107,6 +92,7 @@ export function drawFFTScaleBarHiDPI( fftZoom: number, fftPixelSize: number, imageWidth: number, + unit: string = "1/px", ) { const ctx = canvas.getContext("2d"); if (!ctx || fftPixelSize <= 0) return; @@ -139,7 +125,7 @@ export function drawFFTScaleBarHiDPI( ctx.fillStyle = "white"; ctx.fillRect(barX, barY, barPx, barThickness); - const label = formatScaleLabel(nicePhysical, "Å⁻¹"); + const label = formatScaleLabel(nicePhysical, unit); ctx.font = `${fontSize}px ${FONT}`; ctx.fillStyle = "white"; ctx.textAlign = "center"; @@ -220,8 +206,10 @@ export interface ExportFigureOptions { vmin?: number; vmax?: number; logScale?: boolean; - /** Pixel size in Å (for scale bar computation). */ + /** Pixel size in user-supplied unit (for scale bar computation). */ pixelSize?: number; + /** Unit string for the scale bar label (e.g. "A", "nm", "mrad"). */ + pixelUnit?: string; showColorbar?: boolean; showScaleBar?: boolean; /** Upscale factor for high-resolution output (default 4). Image pixels use nearest-neighbor for sharp edges. */ @@ -243,6 +231,7 @@ export function exportFigure(options: ExportFigureOptions): HTMLCanvasElement { vmax = 1, logScale = false, pixelSize = 0, + pixelUnit = "pixels", showColorbar = true, showScaleBar = true, scale: s = 4, @@ -323,7 +312,7 @@ export function exportFigure(options: ExportFigureOptions): HTMLCanvasElement { ctx.fillStyle = "white"; ctx.fillRect(barX, barY, barPx, barThickness); - const label = formatScaleLabel(nicePhysical, "Å"); + const label = formatScaleLabel(nicePhysical, pixelUnit); ctx.font = `bold ${fontSize}px ${FONT}`; ctx.fillStyle = "white"; ctx.textAlign = "center"; diff --git a/widget/js/histogram.ts b/widget/js/histogram.ts deleted file mode 100644 index c2f96a61..00000000 --- a/widget/js/histogram.ts +++ /dev/null @@ -1,19 +0,0 @@ -/** Compute normalized histogram bins from Float32Array. Returns array of 0-1 values. */ -export function computeHistogramFromBytes(data: Float32Array | null, numBins = 256): number[] { - if (!data || data.length === 0) return new Array(numBins).fill(0); - const bins = new Array(numBins).fill(0); - let min = Infinity, max = -Infinity; - for (let i = 0; i < data.length; i++) { - const v = data[i]; - if (isFinite(v)) { if (v < min) min = v; if (v > max) max = v; } - } - if (!isFinite(min) || !isFinite(max) || min === max) return bins; - const range = max - min; - for (let i = 0; i < data.length; i++) { - const v = data[i]; - if (isFinite(v)) bins[Math.min(numBins - 1, Math.floor(((v - min) / range) * numBins))]++; - } - const maxCount = Math.max(...bins); - if (maxCount > 0) for (let i = 0; i < numBins; i++) bins[i] /= maxCount; - return bins; -} diff --git a/widget/js/show2d/index.tsx b/widget/js/show2d/index.tsx index aacf5c43..17120308 100644 --- a/widget/js/show2d/index.tsx +++ b/widget/js/show2d/index.tsx @@ -22,13 +22,10 @@ import Slider from "@mui/material/Slider"; import Button from "@mui/material/Button"; import Tooltip from "@mui/material/Tooltip"; import { useTheme } from "../theme"; -import { drawScaleBarHiDPI, drawFFTScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; +import { drawScaleBarHiDPI, drawFFTScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../figure"; import JSZip from "jszip"; import { extractFloat32, formatNumber, downloadBlob } from "../format"; -import { computeHistogramFromBytes } from "../histogram"; -import { findDataRange, applyLogScale, percentileClip, sliderRange, computeStats } from "../stats"; -import { ControlCustomizer } from "../control-customizer"; -import { computeToolVisibility } from "../tool-parity"; +import { computeHistogramFromBytes, findDataRange, applyLogScale, percentileClip, sliderRange, computeStats } from "../stats"; function InfoTooltip({ text, theme = "dark" }: { text: React.ReactNode; theme?: "light" | "dark" }) { const isDark = theme === "dark"; @@ -66,9 +63,8 @@ const upwardMenuProps = { transformOrigin: { vertical: "bottom" as const, horizontal: "left" as const }, sx: { zIndex: 9999 }, }; -import { getWebGPUFFT, WebGPUFFT, fft2d, fft2dAsync, fftshift, computeMagnitude, autoEnhanceFFT, nextPow2, applyHannWindow2D, getGPUInfo } from "../webgpu-fft"; +import { getWebGPUFFT, WebGPUFFT, fft2d, fft2dAsync, fftshift, computeMagnitude, autoEnhanceFFT, nextPow2, applyHannWindow2D, getGPUInfo } from "../fft"; import { COLORMAPS, COLORMAP_NAMES, renderToOffscreen, renderToOffscreenReuse, GPUColormapEngine, getGPUColormapEngine, getGPUMaxBufferSize } from "../colormaps"; -import "./show2d.css"; const MIN_ZOOM = 0.5; const MAX_ZOOM = 20; @@ -403,13 +399,12 @@ function Show2D() { // Scale bar const [pixelSize] = useModelState("pixel_size"); + const [pixelUnit] = useModelState("pixel_unit"); const [scaleBarVisible] = useModelState("scale_bar_visible"); // UI visibility const [showControls] = useModelState("show_controls"); const [showStats] = useModelState("show_stats"); - const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); - const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); const [statsMean] = useModelState("stats_mean"); const [statsMin] = useModelState("stats_min"); const [statsMax] = useModelState("stats_max"); @@ -437,43 +432,15 @@ function Show2D() { const [exportAnchor, setExportAnchor] = React.useState(null); const selectedRoi = roiSelectedIdx >= 0 && roiSelectedIdx < (roiList?.length ?? 0) ? roiList[roiSelectedIdx] : null; - const toolVisibility = React.useMemo( - () => computeToolVisibility("Show2D", disabledTools, hiddenTools), - [disabledTools, hiddenTools], - ); - const hideDisplay = toolVisibility.isHidden("display"); - const hideHistogram = toolVisibility.isHidden("histogram"); - const hideStats = toolVisibility.isHidden("stats"); - const hideView = toolVisibility.isHidden("view"); - const hideExport = toolVisibility.isHidden("export"); - const hideRoi = toolVisibility.isHidden("roi"); - const hideProfile = toolVisibility.isHidden("profile"); - - const lockDisplay = toolVisibility.isLocked("display"); - const lockHistogram = toolVisibility.isLocked("histogram"); - const lockStats = toolVisibility.isLocked("stats"); - const lockNavigation = toolVisibility.isLocked("navigation"); - const lockView = toolVisibility.isLocked("view"); - const lockExport = toolVisibility.isLocked("export"); - const lockRoi = toolVisibility.isLocked("roi"); - const lockProfile = toolVisibility.isLocked("profile"); - const effectiveShowFft = showFft && !hideDisplay; + const effectiveShowFft = showFft; const updateSelectedRoi = (updates: Partial) => { - if (lockRoi) return; if (roiSelectedIdx < 0 || !roiList) return; const newList = [...roiList]; newList[roiSelectedIdx] = { ...newList[roiSelectedIdx], ...updates }; setRoiList(newList); }; - React.useEffect(() => { - if (hideRoi && roiActive) { - setRoiActive(false); - setRoiSelectedIdx(-1); - } - }, [hideRoi, roiActive, setRoiActive, setRoiSelectedIdx]); - // Canvas refs const canvasRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); const overlayRefs = React.useRef<(HTMLCanvasElement | null)[]>([]); @@ -656,11 +623,6 @@ function Show2D() { const [profileActive, setProfileActive] = React.useState(false); const [profileLine, setProfileLine] = useModelState<{ row: number; col: number }[]>("profile_line"); const [profileDataAll, setProfileDataAll] = React.useState<(Float32Array | null)[]>([]); - React.useEffect(() => { - if (hideProfile && profileActive) { - setProfileActive(false); - } - }, [hideProfile, profileActive]); const profileCanvasRef = React.useRef(null); const profileBaseImageRef = React.useRef(null); const profileLayoutRef = React.useRef<{ padLeft: number; plotW: number; padTop: number; plotH: number; gMin: number; gMax: number; totalDist: number; xUnit: string } | null>(null); @@ -1408,7 +1370,7 @@ function Show2D() { if (scaleBarVisible) { const zs = getZoomState(i); - const unit = pixelSize > 0 ? "Å" as const : "px" as const; + const unit = pixelSize > 0 ? pixelUnit : "px"; const pxSize = pixelSize > 0 ? pixelSize : 1; drawScaleBarHiDPI(overlay, DPR, zs.zoom, pxSize, unit, width); } else { @@ -1416,7 +1378,7 @@ function Show2D() { } // Colorbar (single image mode only) — uses cached vmin/vmax from data effect - if (!hideDisplay && showColorbar && !isGallery) { + if (showColorbar && !isGallery) { const lut = COLORMAPS[cmap] || COLORMAPS.inferno; const cssW = overlay.width / DPR; const cssH = overlay.height / DPR; @@ -1430,7 +1392,7 @@ function Show2D() { } // ROI overlay — draw all ROIs - if (!hideRoi && roiActive && roiList && roiList.length > 0) { + if (roiActive && roiList && roiList.length > 0) { const zs = getZoomState(i); const { zoom, panX, panY } = zs; const cx = canvasW / 2; @@ -1503,7 +1465,7 @@ function Show2D() { } // Line profile overlay - if (!hideProfile && profileActive && profilePoints.length > 0) { + if (profileActive && profilePoints.length > 0) { const zs = getZoomState(i); const { zoom, panX, panY } = zs; ctx.save(); @@ -1609,7 +1571,7 @@ function Show2D() { ctx.restore(); } } - }, [nImages, pixelSize, scaleBarVisible, selectedIdx, isGallery, canvasW, canvasH, width, displayScale, linkedZoom, linkedZoomState, zoomStates, dataVersion, showColorbar, cmap, offscreenVersion, logScale, profileActive, profilePoints, roiActive, roiList, roiSelectedIdx, isDraggingROI, themeColors, hideDisplay, hideRoi, hideProfile, measureActive, measurePoints]); + }, [nImages, pixelSize, scaleBarVisible, selectedIdx, isGallery, canvasW, canvasH, width, displayScale, linkedZoom, linkedZoomState, zoomStates, dataVersion, showColorbar, cmap, offscreenVersion, logScale, profileActive, profilePoints, roiActive, roiList, roiSelectedIdx, isDraggingROI, themeColors, measureActive, measurePoints]); // ------------------------------------------------------------------------- // Inset magnifier (lens) — renders magnified region at cursor in bottom-left @@ -1620,7 +1582,7 @@ function Show2D() { const lctx = lensCanvas.getContext("2d"); if (lctx) lctx.clearRect(0, 0, lensCanvas.width, lensCanvas.height); } - if (!showLens || lockDisplay || isGallery || !lensPos || !rawDataRef.current?.[0]) return; + if (!showLens || isGallery || !lensPos || !rawDataRef.current?.[0]) return; if (!lensCanvas) return; const ctx = lensCanvas.getContext("2d"); if (!ctx) return; @@ -1690,13 +1652,12 @@ function Show2D() { ctx.font = "10px monospace"; ctx.fillText(`${lensMag}×`, lx + 4, ly + lensSize - 4); ctx.restore(); - }, [showLens, lockDisplay, lensPos, isGallery, cmap, logScale, offscreenVersion, width, height, canvasH, themeColors, lensMag, lensDisplaySize, lensAnchor]); + }, [showLens, lensPos, isGallery, cmap, logScale, offscreenVersion, width, height, canvasH, themeColors, lensMag, lensDisplaySize, lensAnchor]); // ------------------------------------------------------------------------- // Auto-compute profile when profile_line is set (e.g. from Python) // ------------------------------------------------------------------------- React.useEffect(() => { - if (hideProfile) return; if (profilePoints.length === 2 && rawDataRef.current) { const p0 = profilePoints[0], p1 = profilePoints[1]; const allProfiles: (Float32Array | null)[] = []; @@ -1707,7 +1668,7 @@ function Show2D() { setProfileDataAll(allProfiles); if (!profileActive) setProfileActive(true); } - }, [profilePoints, dataVersion, hideProfile, profileActive]); + }, [profilePoints, dataVersion, profileActive]); // ------------------------------------------------------------------------- // Render sparkline for line profile @@ -1785,9 +1746,8 @@ function Show2D() { const dy = profilePoints[1].row - profilePoints[0].row; const distPx = Math.sqrt(dx * dx + dy * dy); if (pixelSize > 0) { - const distA = distPx * pixelSize; - if (distA >= 10) { totalDist = distA / 10; xUnit = "nm"; } - else { totalDist = distA; xUnit = "Å"; } + totalDist = distPx * pixelSize; + xUnit = pixelUnit; } else { totalDist = distPx; } @@ -2408,7 +2368,6 @@ function Show2D() { // Mouse Handlers for Zoom/Pan // ------------------------------------------------------------------------- const handleWheel = (e: React.WheelEvent, idx: number) => { - if (lockView) return; // In gallery mode, only allow zoom on the selected image (unless linked) if (isGallery && idx !== selectedIdx && !linkedZoom) return; e.preventDefault(); // Prevent page scroll when zooming @@ -2449,13 +2408,11 @@ function Show2D() { }; const handleDoubleClick = (idx: number) => { - if (lockView) return; setZoomState(idx, initialZoomState); }; // Reset view (zoom/pan only — preserves profile, FFT state, etc.) const handleResetAll = () => { - if (lockView) return; setZoomStates(new Map()); setLinkedZoomState(initialZoomState); setGalleryFftStates(new Map()); @@ -2467,14 +2424,12 @@ function Show2D() { // FFT zoom/pan handlers const handleFftWheel = (e: React.WheelEvent) => { - if (lockView) return; e.preventDefault(); // Prevent page scroll when zooming FFT const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; setFftZoom(Math.max(MIN_ZOOM, Math.min(MAX_ZOOM, fftZoom * zoomFactor))); }; const handleFftDoubleClick = () => { - if (lockView) return; setFftZoom(DEFAULT_FFT_ZOOM); setFftPanX(0); setFftPanY(0); @@ -2501,7 +2456,6 @@ function Show2D() { }; const handleFftMouseDown = (e: React.MouseEvent) => { - if (lockView) return; fftClickStartRef.current = { x: e.clientX, y: e.clientY }; setIsDraggingFftPan(true); setFftPanStart({ x: e.clientX, y: e.clientY, pX: fftPanX, pY: fftPanY }); @@ -2572,7 +2526,6 @@ function Show2D() { // Gallery FFT zoom/pan handlers (only selected image's FFT responds) const handleGalleryFftWheel = (e: React.WheelEvent, idx: number) => { - if (lockView) return; if (isGallery && idx !== selectedIdx && !linkedZoom) return; e.preventDefault(); // Prevent page scroll when zooming FFT const zs = getGalleryFftState(idx); @@ -2582,11 +2535,9 @@ function Show2D() { const handleGalleryFftMouseDown = (e: React.MouseEvent, idx: number) => { if (isGallery && idx !== selectedIdx) { - if (lockNavigation) return; setSelectedIdx(idx); return; // Select first, don't start panning } - if (lockView) return; const zs = getGalleryFftState(idx); setFftPanningIdx(idx); setIsDraggingFftPan(true); @@ -2710,13 +2661,12 @@ function Show2D() { const handleMouseDown = (e: React.MouseEvent, idx: number) => { const zs = getZoomState(idx); if (isGallery && idx !== selectedIdx) { - if (lockNavigation) return; setSelectedIdx(idx); // Continue to pan setup so click-drag on unselected panel pans immediately // (no double-click required to select first then drag). } // Check if click is on the lens inset — edge = resize, interior = drag - if (!lockDisplay && showLens && !isGallery && idx === 0) { + if (showLens && !isGallery && idx === 0) { const canvas = canvasRefs.current[0]; if (canvas) { const rect = canvas.getBoundingClientRect(); @@ -2741,7 +2691,7 @@ function Show2D() { } } clickStartRef.current = { x: e.clientX, y: e.clientY }; - if (profileActive && !lockProfile) { + if (profileActive) { const { imgCol, imgRow } = screenToImg(e, idx); if (profilePoints.length === 2) { const p0 = profilePoints[0]; @@ -2770,22 +2720,12 @@ function Show2D() { return; } } - if (!lockView) { - setIsDraggingPan(true); - setPanningIdx(idx); - setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); - } + setIsDraggingPan(true); + setPanningIdx(idx); + setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); return; } if (roiActive) { - if (lockRoi) { - if (!lockView) { - setIsDraggingPan(true); - setPanningIdx(idx); - setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); - } - return; - } const { imgCol, imgRow } = screenToImg(e, idx); // Check resize handles on selected ROI first if (isNearResizeHandleInner(imgCol, imgRow)) { @@ -2823,7 +2763,6 @@ function Show2D() { } // Start panning (works in both ROI-active and normal modes) { - if (lockView) return; setIsDraggingPan(true); setPanningIdx(idx); setPanStart({ x: e.clientX, y: e.clientY, pX: zs.panX, pY: zs.panY }); @@ -2832,7 +2771,7 @@ function Show2D() { const handleMouseMove = (e: React.MouseEvent, idx: number) => { // Fast path: during pan drag, skip all cursor/hover/lens work — just update pan - if (isDraggingPan && panStart && panningIdx !== null && !lockView) { + if (isDraggingPan && panStart && panningIdx !== null) { const canvas = canvasRefs.current[idx]; if (!canvas || idx !== panningIdx) return; const rect = canvas.getBoundingClientRect(); @@ -2861,7 +2800,7 @@ function Show2D() { if (imgX >= 0 && imgX < width && imgY >= 0 && imgY < height) { const rawData = rawDataRef.current[idx]; if (rawData) setCursorInfo({ row: imgY, col: imgX, value: rawData[imgY * width + imgX] }); - if (!lockDisplay && showLens && !isGallery) setLensPos({ row: imgY, col: imgX }); + if (showLens && !isGallery) setLensPos({ row: imgY, col: imgX }); } else { setCursorInfo(null); // Don't clear lensPos — lens stays at last position when toggle is on @@ -2869,20 +2808,20 @@ function Show2D() { } // Lens drag - if (!lockDisplay && isDraggingLens && lensDragStartRef.current) { + if (isDraggingLens && lensDragStartRef.current) { const dx = e.clientX - lensDragStartRef.current.mx; const dy = e.clientY - lensDragStartRef.current.my; setLensAnchor({ x: lensDragStartRef.current.ax + dx, y: lensDragStartRef.current.ay + dy }); return; } // Lens resize drag - if (!lockDisplay && isResizingLens && lensResizeStartRef.current) { + if (isResizingLens && lensResizeStartRef.current) { const dy = e.clientY - lensResizeStartRef.current.my; setLensDisplaySize(Math.max(64, Math.min(256, lensResizeStartRef.current.startSize + dy))); return; } - if (profileActive && !lockProfile && profilePoints.length === 2) { + if (profileActive && profilePoints.length === 2) { const { imgCol, imgRow } = screenToImg(e, idx); const p0 = profilePoints[0]; const p1 = profilePoints[1]; @@ -2931,14 +2870,14 @@ function Show2D() { } // ROI resize drag (inner annular ring) - if (!lockRoi && isDraggingResizeInner && selectedRoi) { + if (isDraggingResizeInner && selectedRoi) { const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); const newR = Math.sqrt((ic - selectedRoi.col) ** 2 + (ir - selectedRoi.row) ** 2); updateSelectedRoi({ radius_inner: Math.max(1, Math.min(selectedRoi.radius - 1, Math.round(newR))) }); return; } // ROI resize drag (outer) - if (!lockRoi && isDraggingResize && selectedRoi) { + if (isDraggingResize && selectedRoi) { const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); const shape = selectedRoi.shape || "circle"; if (shape === "rectangle") { @@ -2958,12 +2897,12 @@ function Show2D() { return; } // ROI drag (move center) - if (!lockRoi && isDraggingROI) { + if (isDraggingROI) { updateROI(e, idx); return; } // Lens edge hover detection - if (!lockDisplay && showLens && !isGallery && canvas) { + if (showLens && !isGallery && canvas) { const rect = canvas.getBoundingClientRect(); const cssX = e.clientX - rect.left; const cssY = e.clientY - rect.top; @@ -2978,14 +2917,13 @@ function Show2D() { setIsHoveringLensEdge(false); } // Hover detection for resize handles (show cursor on any ROI edge) - if (roiActive && !lockRoi && !isDraggingPan) { + if (roiActive && !isDraggingPan) { const { imgCol: ic, imgRow: ir } = screenToImg(e, idx); setIsHoveringResizeInner(isNearResizeHandleInner(ic, ir)); setIsHoveringResize(isNearAnyEdge(ic, ir)); } // Panning - if (lockView) return; if (!isDraggingPan || !panStart || panningIdx === null) return; if (idx !== panningIdx) return; if (!canvas) return; @@ -3026,7 +2964,7 @@ function Show2D() { return; } // Detect click (vs drag) for profile mode - if (profileActive && !lockProfile && clickStartRef.current) { + if (profileActive && clickStartRef.current) { const dx = e.clientX - clickStartRef.current.x; const dy = e.clientY - clickStartRef.current.y; if (Math.sqrt(dx * dx + dy * dy) < 3) { @@ -3125,7 +3063,6 @@ function Show2D() { // ------------------------------------------------------------------------- // Copy to clipboard handler const handleCopy = React.useCallback(async () => { - if (lockExport) return; const canvas = canvasRefs.current[isGallery ? selectedIdx : 0]; if (!canvas) return; try { @@ -3136,11 +3073,10 @@ function Show2D() { // Fallback: download if clipboard API unavailable canvas.toBlob((b) => { if (b) downloadBlob(b, `show2d_${labels?.[selectedIdx] || "image"}.png`); }, "image/png"); } - }, [isGallery, selectedIdx, labels, lockExport]); + }, [isGallery, selectedIdx, labels]); // Export publication-quality figure with scale bar, colorbar, annotations const handleExportFigure = React.useCallback((withScaleBar: boolean, withColorbar: boolean) => { - if (lockExport) return; setExportAnchor(null); const idx = isGallery ? selectedIdx : 0; const rawData = rawDataRef.current?.[idx]; @@ -3229,11 +3165,10 @@ function Show2D() { }); canvasToPDF(figCanvas).then((blob) => downloadBlob(blob, `show2d_figure_${labels?.[selectedIdx] || "image"}.pdf`)); - }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, lockExport]); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints]); // Export all variants (PNG + PDF) as zip const handleExportAll = React.useCallback(async () => { - if (lockExport) return; setExportAnchor(null); const idx = isGallery ? selectedIdx : 0; const rawData = rawDataRef.current?.[idx]; @@ -3353,12 +3288,11 @@ function Show2D() { const blob = await zip.generateAsync({ type: "blob" }); downloadBlob(blob, `${prefix}_all.zip`); - }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, widgetVersion, lockExport]); + }, [isGallery, selectedIdx, labels, width, height, cmap, logScale, autoContrast, imageDataRange, imageVminPct, imageVmaxPct, pixelSize, title, roiActive, roiList, profileActive, profilePoints, widgetVersion]); // Resize Handlers // ------------------------------------------------------------------------- const handleCanvasResizeStart = (e: React.MouseEvent) => { - if (lockView) return; e.stopPropagation(); e.preventDefault(); setIsResizingCanvas(true); @@ -3423,21 +3357,21 @@ function Show2D() { // ------------------------------------------------------------------------- const handleKeyDown = (e: React.KeyboardEvent) => { // Number keys 1-9 select gallery images (avoids arrow key conflicts with Jupyter) - if (!lockNavigation && isGallery && e.key >= "1" && e.key <= "9") { + if (isGallery && e.key >= "1" && e.key <= "9") { const idx = parseInt(e.key) - 1; if (idx < nImages) { e.preventDefault(); setSelectedIdx(idx); } return; } switch (e.key) { case "ArrowLeft": - if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.max(0, selectedIdx - 1)); } + if (isGallery) { e.preventDefault(); setSelectedIdx(Math.max(0, selectedIdx - 1)); } break; case "ArrowRight": - if (!lockNavigation && isGallery) { e.preventDefault(); setSelectedIdx(Math.min(nImages - 1, selectedIdx + 1)); } + if (isGallery) { e.preventDefault(); setSelectedIdx(Math.min(nImages - 1, selectedIdx + 1)); } break; case "r": case "R": - if (!lockView) handleResetAll(); + handleResetAll(); break; case "m": case "M": @@ -3456,7 +3390,7 @@ function Show2D() { } break; case "]": - if (!lockNavigation && !lockDisplay) { + { e.preventDefault(); const rIdx = isGallery ? selectedIdx : 0; const rots = [...(imageRotations || [])]; @@ -3466,7 +3400,7 @@ function Show2D() { } break; case "[": - if (!lockNavigation && !lockDisplay) { + { e.preventDefault(); const rIdx2 = isGallery ? selectedIdx : 0; const rots2 = [...(imageRotations || [])]; @@ -3477,7 +3411,7 @@ function Show2D() { break; case "Delete": case "Backspace": - if (!lockRoi && roiActive && roiSelectedIdx >= 0 && roiList && roiSelectedIdx < roiList.length) { + if (roiActive && roiSelectedIdx >= 0 && roiList && roiSelectedIdx < roiList.length) { e.preventDefault(); const newList = roiList.filter((_, i) => i !== roiSelectedIdx); setRoiList(newList); @@ -3493,12 +3427,12 @@ function Show2D() { const needsReset = getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 || getZoomState(isGallery ? selectedIdx : 0).panX !== 0 || getZoomState(isGallery ? selectedIdx : 0).panY !== 0; const statsIdx = isGallery ? selectedIdx : 0; - // Calibrated cursor position - const calibratedUnit = pixelSize > 0 ? (Math.max(height, width) * pixelSize >= 10 ? "nm" : "Å") : ""; - const calibratedFactor = calibratedUnit === "nm" ? pixelSize / 10 : pixelSize; + // Calibrated cursor position - unit is whatever the user passed via sampling/units. + const calibratedUnit = pixelSize > 0 ? pixelUnit : ""; + const calibratedFactor = pixelSize; return ( - + {/* Main panel */} @@ -3514,14 +3448,13 @@ function Show2D() { { - if (lockDisplay) return; const ri = isGallery ? selectedIdx : 0; const rots = [...(imageRotations || [])]; while (rots.length <= ri) rots.push(0); rots[ri] = (rots[ri] + 3) % 4; setImageRotations(rots); }} - sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", fontSize: "inherit", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: "pointer", fontSize: "inherit", "&:hover": { opacity: 0.7 } }} > ({rk * 90}°) @@ -3537,29 +3470,20 @@ function Show2D() { Keyboard } theme={themeInfo.theme} /> - {/* Controls row: Profile, ROI, Lens, FFT, Export, Reset, Copy */} - {!hideProfile && ( + {( <> Profile: { - if (lockProfile) return; const on = e.target.checked; setProfileActive(on); if (on) { - if (!lockRoi) setRoiActive(false); + setRoiActive(false); } else { setProfilePoints([]); setProfileDataAll([]); @@ -3572,18 +3496,17 @@ function Show2D() { /> )} - {!hideRoi && !isGallery && ( + {!isGallery && ( <> ROI: { - if (lockRoi) return; const on = e.target.checked; setRoiActive(on); if (on) { - if (!lockProfile) setProfileActive(false); + setProfileActive(false); setProfilePoints([]); setProfileDataAll([]); setHoveredProfileEndpoint(null); @@ -3597,7 +3520,7 @@ function Show2D() { /> )} - {!hideDisplay && ( + {( <> {!isGallery && ( <> @@ -3605,7 +3528,6 @@ function Show2D() { { - if (lockDisplay) return; if (!showLens) { setShowLens(true); setLensPos({ row: Math.floor(height / 2), col: Math.floor(width / 2) }); @@ -3614,7 +3536,7 @@ function Show2D() { setLensPos(null); } }} - disabled={lockDisplay} + size="small" sx={switchStyles.small} /> @@ -3624,14 +3546,13 @@ function Show2D() { { - if (lockDisplay) return; const on = e.target.checked; if (on && width * height > 2048 * 2048) { console.warn(`Show2D: FFT on ${width}×${height} image (${(width * height / 1e6).toFixed(1)}M pixels) may be slow`); } setShowFft(on); }} - disabled={lockDisplay} + size="small" sx={switchStyles.small} /> @@ -3641,25 +3562,25 @@ function Show2D() { {nImages === 2 && ( <> Diff: - { if (!lockDisplay) setDiffMode(!diffMode); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setDiffMode(!diffMode); }} size="small" sx={switchStyles.small} /> )} )} - {!hideView && ( - + {( + )} - {!hideExport && ( + {( <> - + setExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> - handleExportFigure(true, true)} sx={{ fontSize: 12 }}>PDF + scalebar + colorbar - handleExportFigure(true, false)} sx={{ fontSize: 12 }}>PDF + scalebar - handleExportFigure(false, false)} sx={{ fontSize: 12 }}>PDF - All (PNG + PDF) + handleExportFigure(true, true)} sx={{ fontSize: 12 }}>PDF + scalebar + colorbar + handleExportFigure(true, false)} sx={{ fontSize: 12 }}>PDF + scalebar + handleExportFigure(false, false)} sx={{ fontSize: 12 }}>PDF + All (PNG + PDF) - + )} @@ -3668,7 +3589,7 @@ function Show2D() { /* Gallery mode */ {Array.from({ length: nImages }).map((_, i) => ( - + { imageContainerRefs.current[i] = el; }} sx={{ position: "relative", bgcolor: "#000", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, width: canvasW, height: canvasH }} @@ -3689,8 +3610,8 @@ function Show2D() { width={Math.round(canvasW * DPR)} height={Math.round(canvasH * DPR)} style={{ position: "absolute", top: 0, left: 0, width: canvasW, height: canvasH, pointerEvents: "none" }} /> - {!hideView && ( - + {( + )} @@ -3700,13 +3621,12 @@ function Show2D() { component="span" onClick={(e: React.MouseEvent) => { e.stopPropagation(); - if (lockDisplay) return; const rots = [...(imageRotations || [])]; while (rots.length <= i) rots.push(0); rots[i] = (rots[i] + 3) % 4; setImageRotations(rots); }} - sx={{ ml: 0.5, color: themeColors.accent, cursor: lockDisplay ? "default" : "pointer", "&:hover": { opacity: lockDisplay ? 1 : 0.7 } }} + sx={{ ml: 0.5, color: themeColors.accent, cursor: "pointer", "&:hover": { opacity: 0.7 } }} > ({(imageRotations[i] % 4) * 90}°) @@ -3715,7 +3635,7 @@ function Show2D() { {effectiveShowFft && ( { fftContainerRefs.current[i] = el; }} - sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: lockView ? "default" : "grab" }} + sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: "grab" }} onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} onDoubleClick={() => setGalleryFftState(i, { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 })} onMouseDown={(e) => handleGalleryFftMouseDown(e, i)} @@ -3796,15 +3716,15 @@ function Show2D() { )} - {!hideView && ( - + {( + )} )} {/* Stats bar - right below canvas (Show3D style) */} - {!hideStats && showStats && ( - + {showStats && ( + {isGallery && ( {labels?.[statsIdx] || `#${statsIdx + 1}`} )} @@ -3824,32 +3744,32 @@ function Show2D() { {/* Gallery FFT Controls - below gallery grid */} {effectiveShowFft && isGallery && ( - - + + FFT Scale: - setFftScaleMode(e.target.value as "linear" | "log" | "power")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log Pow Auto: - { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> {roiFftActive && fftCropDims && ( <> Win: - { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftWindow(e.target.checked); }} size="small" sx={switchStyles.small} /> )} Color: - setFftColormap(String(e.target.value))} size="small" sx={{ ...themedSelect, minWidth: 65, fontSize: 10 }} MenuProps={themedMenuProps}> {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} - {!hideHistogram && ( - + {( + {fftHistogramData && ( - { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> )} )} @@ -3857,7 +3777,7 @@ function Show2D() { )} {/* Line profile sparkline — always reserve space when profile is active */} - {!hideProfile && profileActive && ( + {profileActive && (
{ - if (lockProfile) return; e.preventDefault(); setIsResizingProfile(true); setProfileResizeStart({ y: e.clientY, height: profileHeight }); }} - style={{ width: profileCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, background: `linear-gradient(to bottom, ${themeColors.border}, transparent)`, opacity: lockProfile ? 0.5 : 1, pointerEvents: lockProfile ? "none" : "auto" }} + style={{ width: profileCanvasWidth, height: 4, cursor: "ns-resize", borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, background: `linear-gradient(to bottom, ${themeColors.border}, transparent)`, opacity: 1, pointerEvents: "auto" }} /> )} @@ -3882,51 +3801,51 @@ function Show2D() { {/* Top: control rows + histogram side by side */} - + {/* Row 1: Scale + Color */} - {!hideDisplay && ( - + {( + Scale: - setLogScale(e.target.value === "log")} size="small" sx={{ ...themedSelect, minWidth: 45 }} MenuProps={themedMenuProps}> Lin Log Color: - setCmap(e.target.value)} MenuProps={themedMenuProps} sx={{ ...themedSelect, minWidth: 60 }}> {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} {!isGallery && ( <> Colorbar: - { if (!lockDisplay) setShowColorbar(!showColorbar); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setShowColorbar(!showColorbar); }} size="small" sx={switchStyles.small} /> )} )} {/* Row 2: Auto + Lens settings + Link Zoom (gallery) + zoom indicator */} - {!hideDisplay && ( - + {( + Auto: - { if (!lockDisplay) setAutoContrast(!autoContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setAutoContrast(!autoContrast); }} size="small" sx={switchStyles.small} /> Smooth: - { if (!lockDisplay) setSmooth(!smooth); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setSmooth(!smooth); }} size="small" sx={switchStyles.small} /> {!isGallery && showLens && ( <> Lens {lensMag}× - setLensMag(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + setLensMag(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> {lensDisplaySize}px - setLensDisplaySize(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> + setLensDisplaySize(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35 }} /> )} {isGallery && ( <> Link: Zoom - { if (!lockDisplay) setLinkedZoom(!linkedZoom); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setLinkedZoom(!linkedZoom); }} size="small" sx={switchStyles.small} /> Pan - { if (!lockDisplay) setLinkPan(!linkPan); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setLinkPan(!linkPan); }} size="small" sx={switchStyles.small} /> Contrast - { if (!lockDisplay) setLinkedContrast(!linkedContrast); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setLinkedContrast(!linkedContrast); }} size="small" sx={switchStyles.small} /> )} {getZoomState(isGallery ? selectedIdx : 0).zoom !== 1 && ( @@ -3935,30 +3854,33 @@ function Show2D() { )} - {/* Right: Histogram aligned to the two rows. When unlinked + gallery: stack one per image. */} - {!hideHistogram && (imageHistogramData || imageHistogramBins) && ( - + {/* Right: histograms. Unlinked + gallery → grid matching gallery layout + (same effectiveNcols × rows). Linked or single image → one histogram. */} + {(imageHistogramData || imageHistogramBins) && ( + {(!linkedContrast && isGallery && rawDataRef.current) ? ( - Array.from({ length: nImages }).map((_, i) => { - const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; - const raw = rawDataRef.current?.[i] || null; - return ( - { if (!lockHistogram) setContrastState(i, { vminPct: min, vmaxPct: max }); }} - width={110} height={36} theme={themeInfo.theme === "dark" ? "dark" : "light"} - dataMin={dataRangesRef.current[i]?.min ?? imageDataRange.min} - dataMax={dataRangesRef.current[i]?.max ?? imageDataRange.max} /> - ); - }) + + {Array.from({ length: nImages }).map((_, i) => { + const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; + const raw = rawDataRef.current?.[i] || null; + return ( + { setContrastState(i, { vminPct: min, vmaxPct: max }); }} + width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={dataRangesRef.current[i]?.min ?? imageDataRange.min} + dataMax={dataRangesRef.current[i]?.max ?? imageDataRange.max} /> + ); + })} + ) : ( - { if (!lockHistogram) setContrastState(activeContrastIdx, { vminPct: min, vmaxPct: max }); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmin, 0)) : traitVmin) : imageDataRange.min} dataMax={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmax, 0)) : traitVmax) : imageDataRange.max} /> + { setContrastState(activeContrastIdx, { vminPct: min, vmaxPct: max }); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmin, 0)) : traitVmin) : imageDataRange.min} dataMax={traitVmin != null && traitVmax != null ? (logScale ? Math.log1p(Math.max(traitVmax, 0)) : traitVmax) : imageDataRange.max} /> )} )} {/* ROI Section (own box, below control rows) */} - {!hideRoi && roiActive && ( - + {roiActive && ( + {/* ROI: shape + ADD + CLEAR */} ROI: @@ -4083,18 +4005,18 @@ function Show2D() { ROI FFT ({fftCropDims.cropWidth}×{fftCropDims.cropHeight}) ) : } - {!hideView && ( - + {( + )} @@ -4106,12 +4028,12 @@ function Show2D() { )} - {!hideView && ( - + {( + )} {/* FFT Stats Bar */} - {!hideStats && fftStats && fftStats.length === 4 && ( + {fftStats && fftStats.length === 4 && ( Mean {formatNumber(fftStats[0])} Min {formatNumber(fftStats[1])} @@ -4134,30 +4056,30 @@ function Show2D() { {/* FFT Controls - two rows + histogram (matching main panel layout) */} - + {/* Row 1: Scale + Color + Colorbar */} - + Scale: - setFftScaleMode(e.target.value as "linear" | "log" | "power")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log Pow Color: - setFftColormap(String(e.target.value))} size="small" sx={{ ...themedSelect, minWidth: 65, fontSize: 10 }} MenuProps={themedMenuProps}> {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} Colorbar: - { if (!lockDisplay) setFftShowColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftShowColorbar(e.target.checked); }} size="small" sx={switchStyles.small} /> {/* Row 2: Auto + zoom indicator */} - + Auto: - { if (!lockDisplay) setFftAuto(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> {fftCropDims && ( <> Win: - { if (!lockDisplay) setFftWindow(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> + { setFftWindow(e.target.checked); }} size="small" sx={switchStyles.small} /> )} {fftZoom !== DEFAULT_FFT_ZOOM && ( @@ -4166,10 +4088,10 @@ function Show2D() { {/* Right: FFT Histogram */} - {!hideHistogram && ( - + {( + {fftHistogramData && ( - { if (!lockHistogram) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> )} )} diff --git a/widget/js/show2d/show2d.css b/widget/js/show2d/show2d.css deleted file mode 100644 index 0e285789..00000000 --- a/widget/js/show2d/show2d.css +++ /dev/null @@ -1,9 +0,0 @@ -/* show2d.css - Minimal CSS for Show2D */ - -.show2d-root { - font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, sans-serif; -} - -.show2d-root canvas { - display: block; -} diff --git a/widget/js/show4dstem/index.tsx b/widget/js/show4dstem/index.tsx index 8ece614d..2a78455b 100644 --- a/widget/js/show4dstem/index.tsx +++ b/widget/js/show4dstem/index.tsx @@ -18,16 +18,12 @@ import StopIcon from "@mui/icons-material/Stop"; import FastRewindIcon from "@mui/icons-material/FastRewind"; import FastForwardIcon from "@mui/icons-material/FastForward"; import JSZip from "jszip"; -import "./styles.css"; import { useTheme } from "../theme"; import { COLORMAPS, applyColormap, renderToOffscreen } from "../colormaps"; -import { WebGPUFFT, getWebGPUFFT, fft2d, fftshift, autoEnhanceFFT, nextPow2, applyHannWindow2D } from "../webgpu-fft"; -import { drawScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../scalebar"; -import { findDataRange, sliderRange, computeStats, applyLogScale } from "../stats"; +import { WebGPUFFT, getWebGPUFFT, fft2d, fftshift, autoEnhanceFFT, nextPow2, applyHannWindow2D } from "../fft"; +import { drawScaleBarHiDPI, drawColorbar, roundToNiceValue, exportFigure, canvasToPDF } from "../figure"; +import { findDataRange, sliderRange, computeStats, applyLogScale, computeHistogramFromBytes, percentileClip } from "../stats"; import { downloadBlob, formatNumber, downloadDataView } from "../format"; -import { computeHistogramFromBytes } from "../histogram"; -import { ControlCustomizer } from "../control-customizer"; -import { computeToolVisibility } from "../tool-parity"; const MIN_ZOOM = 0.5; const MAX_ZOOM = 10; @@ -132,7 +128,7 @@ const compactButton = { }, }; -// Control row style - bordered container for each row +// Control row style — bordered container per row. const controlRow = { display: "flex", alignItems: "center", @@ -961,7 +957,9 @@ function Show4DSTEM() { const [roiCenterCol, setRoiCenterCol] = useModelState("roi_center_col"); const [roiCenterRow, setRoiCenterRow] = useModelState("roi_center_row"); const [pixelSize] = useModelState("pixel_size"); + const [pixelUnit] = useModelState("pixel_unit"); const [kPixelSize] = useModelState("k_pixel_size"); + const [kPixelUnit] = useModelState("k_pixel_unit"); const [kCalibrated] = useModelState("k_calibrated"); const [widgetVersion] = useModelState("widget_version"); const [title] = useModelState("title"); @@ -981,8 +979,9 @@ function Show4DSTEM() { const [dpGlobalMax] = useModelState("dp_global_max"); // VI min/max for normalization (from Python) - const [viDataMin] = useModelState("vi_data_min"); - const [viDataMax] = useModelState("vi_data_max"); + // viDataMin/viDataMax are derived JS-side from virtual_image_bytes (computed below). + // Keeping them out of Python traits avoids a comm-message ordering race where + // bytes from click N arrive with min/max from click N-1. // Detector calibration (for presets) const [bfRadius] = useModelState("bf_radius"); @@ -1020,6 +1019,26 @@ function Show4DSTEM() { const [localPosRow, setLocalPosRow] = React.useState(posRow); const [localPosCol, setLocalPosCol] = React.useState(posCol); const [isDraggingDP, setIsDraggingDP] = React.useState(false); + // rAF coalescing for ROI drag: collapse rapid mousemove events into ≤1 + // Python comm message per animation frame. Without this, drag fires 60+ + // events/sec at >100ms Python compute each → queue piles up → laggy UX. + const roiCenterPendingRef = React.useRef<[number, number] | null>(null); + const roiCenterRafRef = React.useRef(null); + const flushRoiCenter = React.useCallback(() => { + if (roiCenterPendingRef.current) { + const [r, c] = roiCenterPendingRef.current; + model.set("roi_center", [r, c]); + model.save_changes(); + roiCenterPendingRef.current = null; + } + roiCenterRafRef.current = null; + }, [model]); + const queueRoiCenter = React.useCallback((row: number, col: number) => { + roiCenterPendingRef.current = [row, col]; + if (roiCenterRafRef.current === null) { + roiCenterRafRef.current = requestAnimationFrame(flushRoiCenter); + } + }, [flushRoiCenter]); const [isDraggingVI, setIsDraggingVI] = React.useState(false); const [isDraggingFFT, setIsDraggingFFT] = React.useState(false); const [fftDragStart, setFftDragStart] = React.useState<{ x: number, y: number, panX: number, panY: number } | null>(null); @@ -1046,11 +1065,14 @@ function Show4DSTEM() { const [traitDpVmax] = useModelState("dp_vmax"); const [traitViVmin] = useModelState("vi_vmin"); const [traitViVmax] = useModelState("vi_vmax"); - // Scale mode: "linear" | "log" | "power" - const [dpScaleMode, setDpScaleMode] = useModelState<"linear" | "log" | "power">("dp_scale_mode"); - const [dpPowerExp] = useModelState("dp_power_exp"); - const [viScaleMode, setViScaleMode] = useModelState<"linear" | "log" | "power">("vi_scale_mode"); - const [viPowerExp] = useModelState("vi_power_exp"); + // Scale mode: "linear" | "log" + const [dpScaleMode, setDpScaleMode] = useModelState<"linear" | "log">("dp_scale_mode"); + const [viScaleMode, setViScaleMode] = useModelState<"linear" | "log">("vi_scale_mode"); + // VI auto-contrast (1st/99th percentile clip) + Smooth (CSS bilinear blit). + // DP doesn't need them — Bragg spots read best with the slider's percentile + // range and nearest-neighbor blit. + const [viAutoContrast, setViAutoContrast] = useModelState("vi_auto_contrast"); + const [viSmooth, setViSmooth] = useModelState("vi_smooth"); // VI ROI state (real-space region selection for summed DP) - synced with Python const [viRoiMode, setViRoiMode] = useModelState("vi_roi_mode"); @@ -1062,46 +1084,18 @@ function Show4DSTEM() { // Local VI ROI center for smooth dragging const [localViRoiCenterRow, setLocalViRoiCenterRow] = React.useState(viRoiCenterRow || 0); const [localViRoiCenterCol, setLocalViRoiCenterCol] = React.useState(viRoiCenterCol || 0); - const [summedDpBytes] = useModelState("summed_dp_bytes"); - const [summedDpCount] = useModelState("summed_dp_count"); - const [dpStats] = useModelState("dp_stats"); // [mean, min, max, std] - const [viStats] = useModelState("vi_stats"); // [mean, min, max, std] + const [viRoiDpBytes] = useModelState("vi_roi_dp_bytes"); + const [viRoiReduce, setViRoiReduce] = useModelState("vi_roi_reduce"); + // dp_stats are computed in JS from frameBytes (Python side no longer + // syncs a dp_stats trait — saves 4 trait sync round-trips per click). + const [viStats, setViStats] = React.useState([0, 0, 0, 0]); + const [viDataMin, setViDataMin] = React.useState(0); + const [viDataMax, setViDataMax] = React.useState(1); const [showFft, setShowFft] = useModelState("show_fft"); const [fftWindow, setFftWindow] = useModelState("fft_window"); - const [disabledTools, setDisabledTools] = useModelState("disabled_tools"); - const [hiddenTools, setHiddenTools] = useModelState("hidden_tools"); const [showControls] = useModelState("show_controls"); - const toolVisibility = React.useMemo( - () => computeToolVisibility("Show4DSTEM", disabledTools, hiddenTools), - [disabledTools, hiddenTools], - ); - - const hideDisplay = toolVisibility.isHidden("display"); - const hideHistogram = toolVisibility.isHidden("histogram"); - const hideStats = toolVisibility.isHidden("stats"); - const hidePlayback = toolVisibility.isHidden("playback"); - const hideView = toolVisibility.isHidden("view"); - const hideExport = toolVisibility.isHidden("export"); - const hideRoi = toolVisibility.isHidden("roi"); - const hideProfile = toolVisibility.isHidden("profile"); - const hideVirtual = toolVisibility.isHidden("virtual"); - const hideFrame = toolVisibility.isHidden("frame"); - const hideFft = toolVisibility.isHidden("fft") || hideVirtual; - - const lockDisplay = toolVisibility.isLocked("display"); - const lockHistogram = toolVisibility.isLocked("histogram"); - const lockStats = toolVisibility.isLocked("stats"); - const lockNavigation = toolVisibility.isLocked("navigation"); - const lockPlayback = toolVisibility.isLocked("playback"); - const lockView = toolVisibility.isLocked("view"); - const lockExport = toolVisibility.isLocked("export"); - const lockRoi = toolVisibility.isLocked("roi"); - const lockProfile = toolVisibility.isLocked("profile"); - const lockVirtual = toolVisibility.isLocked("virtual"); - const lockFrame = toolVisibility.isLocked("frame"); - const lockFft = toolVisibility.isLocked("fft") || lockVirtual; - const effectiveShowFft = showFft && !hideFft; + const effectiveShowFft = showFft; // ROI FFT state (VI ROI crops virtual image for FFT) const [fftCropDims, setFftCropDims] = React.useState<{ cropWidth: number; cropHeight: number; fftWidth: number; fftHeight: number } | null>(null); @@ -1182,6 +1176,10 @@ function Show4DSTEM() { const [dpHistogramData, setDpHistogramData] = React.useState(null); const [viHistogramData, setViHistogramData] = React.useState(null); + // DP stats computed JS-side from frame_bytes (was Python trait pre-refactor; + // moving to JS skips 4 sync trait round-trips per scan-position click). + const [dpStats, setDpStats] = React.useState([0, 0, 0, 0]); + // Parse DP frame bytes for histogram (float32 now) React.useEffect(() => { if (!frameBytes) return; @@ -1192,21 +1190,20 @@ function Show4DSTEM() { rawDpDataRef.current = new Float32Array(rawData.length); } rawDpDataRef.current.set(rawData); + // Compute stats JS-side (replaces removed Python dp_stats trait) + const s = computeStats(rawData); + setDpStats([s.mean, s.min, s.max, s.std]); // Apply scale transformation for histogram display const scaledData = new Float32Array(rawData.length); if (dpScaleMode === "log") { for (let i = 0; i < rawData.length; i++) { scaledData[i] = Math.log1p(Math.max(0, rawData[i])); } - } else if (dpScaleMode === "power") { - for (let i = 0; i < rawData.length; i++) { - scaledData[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); - } } else { scaledData.set(rawData); } setDpHistogramData(scaledData); - }, [frameBytes, dpScaleMode, dpPowerExp]); + }, [frameBytes, dpScaleMode]); // GPU FFT state const gpuFFTRef = React.useRef(null); @@ -1295,8 +1292,7 @@ function Show4DSTEM() { const [fftZoom, setFftZoom] = React.useState(1); const [fftPanX, setFftPanX] = React.useState(0); const [fftPanY, setFftPanY] = React.useState(0); - const [fftScaleMode, setFftScaleMode] = useModelState<"linear" | "log" | "power">("fft_scale_mode"); - const [fftPowerExp] = useModelState("fft_power_exp"); + const [fftScaleMode, setFftScaleMode] = useModelState<"linear" | "log">("fft_scale_mode"); const [fftColormap, setFftColormap] = useModelState("fft_colormap"); const [fftAuto, setFftAuto] = useModelState("fft_auto"); const [fftVminPct, setFftVminPct] = useModelState("fft_vmin_pct"); @@ -1330,52 +1326,42 @@ function Show4DSTEM() { switch (e.key) { case "ArrowUp": - if (!lockNavigation) { - setPosRow(Math.max(0, posRow - step)); - handled = true; - } + setPosRow(Math.max(0, posRow - step)); + handled = true; break; case "ArrowDown": - if (!lockNavigation) { - setPosRow(Math.min(shapeRows - 1, posRow + step)); - handled = true; - } + setPosRow(Math.min(shapeRows - 1, posRow + step)); + handled = true; break; case "ArrowLeft": - if (!lockNavigation) { - setPosCol(Math.max(0, posCol - step)); - handled = true; - } + setPosCol(Math.max(0, posCol - step)); + handled = true; break; case "ArrowRight": - if (!lockNavigation) { - setPosCol(Math.min(shapeCols - 1, posCol + step)); - handled = true; - } + setPosCol(Math.min(shapeCols - 1, posCol + step)); + handled = true; break; case " ": // Space bar - if (!lockPlayback && pathLength > 0) { + if (pathLength > 0) { setPathPlaying(!pathPlaying); handled = true; } break; case "r": case "R": - if (!lockView) { - setDpZoom(1); setDpPanX(0); setDpPanY(0); - setViZoom(1); setViPanX(0); setViPanY(0); - setFftZoom(1); setFftPanX(0); setFftPanY(0); - handled = true; - } + setDpZoom(1); setDpPanX(0); setDpPanY(0); + setViZoom(1); setViPanX(0); setViPanY(0); + setFftZoom(1); setFftPanX(0); setFftPanY(0); + handled = true; break; case "[": - if (!lockPlayback && !lockFrame && nFrames > 1) { + if (nFrames > 1) { setFrameIdx(Math.max(0, frameIdx - 1)); handled = true; } break; case "]": - if (!lockPlayback && !lockFrame && nFrames > 1) { + if (nFrames > 1) { setFrameIdx(Math.min(nFrames - 1, frameIdx + 1)); handled = true; } @@ -1391,53 +1377,10 @@ function Show4DSTEM() { e.stopPropagation(); } }, [ - frameIdx, isTypingTarget, lockFrame, lockNavigation, lockPlayback, lockView, nFrames, pathLength, + frameIdx, isTypingTarget, nFrames, pathLength, pathPlaying, posCol, posRow, setFrameIdx, setPathPlaying, setPosCol, setPosRow, shapeCols, shapeRows, ]); - React.useEffect(() => { - if (hideFft && showFft) { - setShowFft(false); - } - }, [hideFft, showFft, setShowFft]); - - React.useEffect(() => { - if (lockPlayback && pathPlaying) { - setPathPlaying(false); - } - }, [lockPlayback, pathPlaying, setPathPlaying]); - - React.useEffect(() => { - if ((lockPlayback || lockFrame) && framePlaying) { - setFramePlaying(false); - } - }, [lockFrame, lockPlayback, framePlaying, setFramePlaying]); - - React.useEffect(() => { - if (hideRoi) { - if (roiMode !== "point") setRoiMode("point"); - if (viRoiMode !== "off") setViRoiMode("off"); - } - }, [hideRoi, roiMode, viRoiMode, setRoiMode, setViRoiMode]); - - React.useEffect(() => { - if (hideProfile) { - if (profileActive) setProfileActive(false); - if (viProfileActive) setViProfileActive(false); - if (profileLine.length > 0) setProfileLine([]); - if (profileData) setProfileData(null); - if (viProfilePoints.length > 0) setViProfilePoints([]); - if (viProfileData) setViProfileData(null); - setHoveredDpProfileEndpoint(null); - setIsHoveringDpProfileLine(false); - setHoveredViProfileEndpoint(null); - setIsHoveringViProfileLine(false); - } - }, [ - hideProfile, profileActive, profileLine, profileData, setProfileLine, viProfileActive, - viProfilePoints, viProfileData, - ]); - // Sync local state React.useEffect(() => { if (!isDraggingDP && !isDraggingResize) { setLocalKCol(roiCenterCol); setLocalKRow(roiCenterRow); } @@ -1524,28 +1467,33 @@ function Show4DSTEM() { } rawViDataRef.current.set(rawData); + // Compute stats + min/max JS-side (replaces removed Python vi_stats / vi_data_min / vi_data_max traits). + // Python sending bytes + 4 separate stat traits caused a comm-message ordering race on rapid + // preset clicks: bytes from click N could arrive with min/max from click N-1, normalizing + // the colormap to the wrong range and producing a uniform-color VI flash. + const s = computeStats(rawData); + setViStats([s.mean, s.min, s.max, s.std]); + setViDataMin(s.min); + setViDataMax(s.max); + // Apply scale transformation for histogram display const scaledData = new Float32Array(numFloats); if (viScaleMode === "log") { for (let i = 0; i < numFloats; i++) { scaledData[i] = Math.log1p(Math.max(0, rawData[i])); } - } else if (viScaleMode === "power") { - for (let i = 0; i < numFloats; i++) { - scaledData[i] = Math.pow(Math.max(0, rawData[i]), viPowerExp); - } } else { scaledData.set(rawData); } setViHistogramData(scaledData); - }, [virtualImageBytes, viScaleMode, viPowerExp]); + }, [virtualImageBytes, viScaleMode]); // Render DP with zoom (use summed DP when VI ROI is active) // Expensive: colormap + data processing → cached offscreen canvas React.useEffect(() => { // Determine which bytes to display: summed DP (if VI ROI active) or single frame - const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; - const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + const usesViRoiDp = viRoiMode && viRoiMode !== "off" && viRoiDpBytes && viRoiDpBytes.byteLength > 0; + const sourceBytes = usesViRoiDp ? viRoiDpBytes : frameBytes; if (!sourceBytes) return; const lut = COLORMAPS[dpColormap] || COLORMAPS.inferno; @@ -1558,27 +1506,17 @@ function Show4DSTEM() { for (let i = 0; i < rawData.length; i++) { scaled[i] = Math.log1p(Math.max(0, rawData[i])); } - } else if (dpScaleMode === "power") { - scaled = new Float32Array(rawData.length); - for (let i = 0; i < rawData.length; i++) { - scaled[i] = Math.pow(Math.max(0, rawData[i]), dpPowerExp); - } } else { scaled = rawData; } - // Compute actual min/max of scaled data for normalization const { min: dataMin, max: dataMax } = findDataRange(scaled); - // Apply absolute bounds or percentile clipping let vmin: number, vmax: number; if (traitDpVmin != null && traitDpVmax != null) { if (dpScaleMode === "log") { vmin = Math.log1p(Math.max(traitDpVmin, 0)); vmax = Math.log1p(Math.max(traitDpVmax, 0)); - } else if (dpScaleMode === "power") { - vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); - vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); } else { vmin = traitDpVmin; vmax = traitDpVmax; @@ -1612,7 +1550,7 @@ function Show4DSTEM() { dpColorbarVminRef.current = vmin; dpColorbarVmaxRef.current = vmax; setDpOffscreenVersion(v => v + 1); - }, [frameBytes, summedDpBytes, viRoiMode, detRows, detCols, dpColormap, dpVminPct, dpVmaxPct, dpScaleMode, dpPowerExp, traitDpVmin, traitDpVmax]); + }, [frameBytes, viRoiDpBytes, viRoiMode, detRows, detCols, dpColormap, dpVminPct, dpVmaxPct, dpScaleMode, traitDpVmin, traitDpVmax]); // Cheap: zoom/pan redraw — just drawImage from cached offscreen // useLayoutEffect prevents black flash when canvas dimensions change (resize) @@ -1649,41 +1587,23 @@ function Show4DSTEM() { const height = shapeRows; const filtered = rawVirtualImageRef.current; - // Apply scale transformation first let scaled = filtered; if (viScaleMode === "log") { scaled = new Float32Array(filtered.length); for (let i = 0; i < filtered.length; i++) { scaled[i] = Math.log1p(Math.max(0, filtered[i])); } - } else if (viScaleMode === "power") { - scaled = new Float32Array(filtered.length); - for (let i = 0; i < filtered.length; i++) { - scaled[i] = Math.pow(Math.max(0, filtered[i]), viPowerExp); - } } - // Use Python's pre-computed min/max when valid, fallback to computing from data - let dataMin: number, dataMax: number; - const hasValidMinMax = viDataMin !== undefined && viDataMax !== undefined && viDataMax > viDataMin; - if (hasValidMinMax) { - // Apply scale transform to Python's values - if (viScaleMode === "log") { - dataMin = Math.log1p(Math.max(0, viDataMin)); - dataMax = Math.log1p(Math.max(0, viDataMax)); - } else if (viScaleMode === "power") { - dataMin = Math.pow(Math.max(0, viDataMin), viPowerExp); - dataMax = Math.pow(Math.max(0, viDataMax), viPowerExp); - } else { - dataMin = viDataMin; - dataMax = viDataMax; - } - } else { - // Fallback: compute from scaled data - const r = findDataRange(scaled); - dataMin = r.min; - dataMax = r.max; - } + // Compute min/max from the data we just received. Do NOT use Python's + // viDataMin/viDataMax traits here: they arrive as separate comm messages + // and can be stale on rapid preset clicks (BF↔ABF), causing the render + // to apply the WRONG normalization range and produce a uniform white/black + // VI panel until comm catches up. findDataRange on a scan-shape buffer + // (~64K-256K floats) is sub-millisecond. + const r = findDataRange(scaled); + const dataMin = r.min; + const dataMax = r.max; // Apply absolute bounds or percentile clipping let vmin: number, vmax: number; @@ -1691,13 +1611,12 @@ function Show4DSTEM() { if (viScaleMode === "log") { vmin = Math.log1p(Math.max(traitViVmin, 0)); vmax = Math.log1p(Math.max(traitViVmax, 0)); - } else if (viScaleMode === "power") { - vmin = Math.pow(Math.max(traitViVmin, 0), viPowerExp); - vmax = Math.pow(Math.max(traitViVmax, 0), viPowerExp); } else { vmin = traitViVmin; vmax = traitViVmax; } + } else if (viAutoContrast) { + ({ vmin, vmax } = percentileClip(scaled, 1, 99)); } else { ({ vmin, vmax } = sliderRange(dataMin, dataMax, viVminPct, viVmaxPct)); } @@ -1725,9 +1644,7 @@ function Show4DSTEM() { applyColormap(scaled, imageData.data, lut, vmin, vmax); offCtx.putImageData(imageData, 0, 0); setViOffscreenVersion(v => v + 1); - // Note: viDataMin/viDataMax intentionally not in deps - they arrive with virtualImageBytes - // and we have a fallback if they're stale - }, [virtualImageBytes, shapeRows, shapeCols, viColormap, viVminPct, viVmaxPct, viScaleMode, viPowerExp, traitViVmin, traitViVmax]); + }, [virtualImageBytes, shapeRows, shapeCols, viColormap, viVminPct, viVmaxPct, viScaleMode, traitViVmin, traitViVmax, viAutoContrast]); // Cheap: VI zoom/pan redraw — just drawImage from cached offscreen React.useLayoutEffect(() => { @@ -1736,14 +1653,15 @@ function Show4DSTEM() { const canvas = virtualCanvasRef.current; const ctx = canvas.getContext("2d"); if (!ctx) return; - ctx.imageSmoothingEnabled = false; + ctx.imageSmoothingEnabled = viSmooth; + if (viSmooth) ctx.imageSmoothingQuality = "high"; ctx.clearRect(0, 0, canvas.width, canvas.height); ctx.save(); ctx.translate(viPanX, viPanY); ctx.scale(viZoom, viZoom); ctx.drawImage(offscreen, 0, 0); ctx.restore(); - }, [viOffscreenVersion, viZoom, viPanX, viPanY]); + }, [viOffscreenVersion, viZoom, viPanX, viPanY, viSmooth]); // Render virtual image overlay (just clear - crosshair drawn on high-DPI UI canvas) React.useEffect(() => { @@ -1768,9 +1686,14 @@ function Show4DSTEM() { let sourceData = rawVirtualImageRef.current; let origCropW = 0, origCropH = 0; - // ROI FFT: crop virtual image to VI ROI region and pre-pad to power-of-2 + // ROI FFT: crop virtual image to VI ROI region and pre-pad to power-of-2. + // Use localViRoiCenter* (updated immediately on drag) instead of the synced + // model traits, which lag by one comm roundtrip after a compound trait write. + // Without this, FFT visibly stalls during rapid VI ROI drag. if (roiFftActive) { - const crop = cropSingleROI(sourceData, shapeCols, shapeRows, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight); + const cRow = localViRoiCenterRow ?? viRoiCenterRow; + const cCol = localViRoiCenterCol ?? viRoiCenterCol; + const crop = cropSingleROI(sourceData, shapeCols, shapeRows, viRoiMode, cRow, cCol, viRoiRadius, viRoiWidth, viRoiHeight); if (crop) { origCropW = crop.cropW; origCropH = crop.cropH; @@ -1850,7 +1773,7 @@ function Show4DSTEM() { } setFftVersion(v => v + 1); } - }, [virtualImageBytes, shapeRows, shapeCols, gpuReady, effectiveShowFft, roiFftActive, viRoiMode, viRoiCenterRow, viRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, fftWindow]); + }, [virtualImageBytes, shapeRows, shapeCols, gpuReady, effectiveShowFft, roiFftActive, viRoiMode, viRoiCenterRow, viRoiCenterCol, localViRoiCenterRow, localViRoiCenterCol, viRoiRadius, viRoiWidth, viRoiHeight, fftWindow]); // Expensive: FFT magnitude + histogram + colormap → cached offscreen canvas React.useEffect(() => { @@ -1879,7 +1802,6 @@ function Show4DSTEM() { const mag = Math.sqrt(real[i] * real[i] + imag[i] * imag[i]); rawMag[i] = mag; if (fftScaleMode === "log") { magnitude[i] = Math.log1p(mag); } - else if (fftScaleMode === "power") { magnitude[i] = Math.pow(mag, fftPowerExp); } else { magnitude[i] = mag; } } @@ -1910,7 +1832,7 @@ function Show4DSTEM() { applyColormap(magnitude, imgData.data, lut, vmin, vmax); offCtx.putImageData(imgData, 0, 0); setFftOffscreenVersion(v => v + 1); - }, [effectiveShowFft, fftVersion, fftScaleMode, fftPowerExp, fftAuto, fftVminPct, fftVmaxPct, fftColormap, shapeRows, shapeCols, fftCropDims]); + }, [effectiveShowFft, fftVersion, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, fftColormap, shapeRows, shapeCols, fftCropDims]); // Cheap: FFT zoom/pan redraw — just drawImage from cached offscreen React.useLayoutEffect(() => { @@ -1924,13 +1846,15 @@ function Show4DSTEM() { const fftH = offscreen.height; const canvasW = canvas.width; const canvasH = canvas.height; - // Use bilinear smoothing when FFT dims differ from canvas (non-pow2 padding or ROI crop) + // Use bilinear smoothing when FFT dims differ from canvas (non-pow2 padding or ROI crop). + // Stretch offscreen to fill canvas via the 9-arg drawImage form: ROI FFT crops produce a + // small offscreen (e.g. 64×64) that would otherwise blit at native size in the corner. ctx.imageSmoothingEnabled = fftW !== canvasW || fftH !== canvasH; ctx.clearRect(0, 0, canvasW, canvasH); ctx.save(); ctx.translate(fftPanX, fftPanY); ctx.scale(fftZoom, fftZoom); - ctx.drawImage(offscreen, 0, 0); + ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); ctx.restore(); }, [fftOffscreenVersion, fftZoom, fftPanX, fftPanY, effectiveShowFft]); @@ -1947,9 +1871,9 @@ function Show4DSTEM() { const fftW = fftCropDims?.fftWidth ?? shapeCols; const fftH = fftCropDims?.fftHeight ?? shapeRows; ctx.save(); - // Convert FFT image coords to canvas coords via zoom/pan transform - const screenX = fftPanX + fftZoom * fftClickInfo.col; - const screenY = fftPanY + fftZoom * fftClickInfo.row; + // Forward mapping: image col/row → canvas x/y (matches stretched drawImage). + const screenX = fftPanX + fftZoom * (fftClickInfo.col * canvas.width / fftW); + const screenY = fftPanY + fftZoom * (fftClickInfo.row * canvas.height / fftH); ctx.strokeStyle = "rgba(255, 255, 255, 0.9)"; ctx.shadowColor = "rgba(0, 0, 0, 0.6)"; ctx.shadowBlur = 2; @@ -1994,7 +1918,7 @@ function Show4DSTEM() { React.useEffect(() => { if (!dpUiRef.current) return; // Draw scale bar first (clears canvas) - const kUnit = kCalibrated ? "mrad" : "px"; + const kUnit = kCalibrated ? kPixelUnit : "px"; drawScaleBarHiDPI(dpUiRef.current, DPR, dpZoom, kPixelSize || 1, kUnit, detCols); // Draw ROI overlay (circle, square, rect, annular) or point crosshair if (roiMode === "point") { @@ -2099,7 +2023,7 @@ function Show4DSTEM() { React.useEffect(() => { if (!viUiRef.current) return; // Draw scale bar first (clears canvas) - drawScaleBarHiDPI(viUiRef.current, DPR, viZoom, pixelSize || 1, "Å", shapeCols); + drawScaleBarHiDPI(viUiRef.current, DPR, viZoom, pixelSize || 1, pixelUnit || "px", shapeCols); // Draw crosshair only when ROI is off (ROI replaces the crosshair) if (!viRoiMode || viRoiMode === "off") { drawViPositionMarker(viUiRef.current, DPR, localPosRow, localPosCol, viZoom, viPanX, viPanY, shapeCols, shapeRows, isDraggingVI); @@ -2233,7 +2157,7 @@ function Show4DSTEM() { const distPx = Math.sqrt(dx * dx + dy * dy); if (kCalibrated && kPixelSize > 0) { totalDist = distPx * kPixelSize; - xUnit = "mrad"; + xUnit = kPixelUnit; } else { totalDist = distPx; } @@ -2440,8 +2364,7 @@ function Show4DSTEM() { const dy = viProfilePoints[1].row - viProfilePoints[0].row; const distPx = Math.sqrt(dx * dx + dy * dy); totalDist = distPx * pixelSize; - xUnit = pixelSize >= 10 ? "nm" : "Å"; - if (xUnit === "nm") totalDist /= 10; + xUnit = pixelUnit; } // Draw axes @@ -2601,9 +2524,7 @@ function Show4DSTEM() { setPanY: React.Dispatch>, zoom: number, panX: number, panY: number, canvasRef: React.RefObject, - locked: boolean = false, ) => (e: React.WheelEvent) => { - if (locked) return; e.preventDefault(); const canvas = canvasRef.current; if (!canvas) return; @@ -2706,8 +2627,6 @@ function Show4DSTEM() { // Mouse handlers const handleDpMouseDown = (e: React.MouseEvent) => { - if (profileActive && lockProfile) return; - if (!profileActive && lockRoi) return; dpClickStartRef.current = { x: e.clientX, y: e.clientY }; const canvas = dpOverlayRef.current; if (!canvas) return; @@ -2793,8 +2712,8 @@ function Show4DSTEM() { const pxCol = Math.floor(imgX); const pxRow = Math.floor(imgY); if (pxCol >= 0 && pxCol < detCols && pxRow >= 0 && pxRow < detRows && frameBytes) { - const usesSummedDp = viRoiMode && viRoiMode !== "off" && summedDpBytes && summedDpBytes.byteLength > 0; - const sourceBytes = usesSummedDp ? summedDpBytes : frameBytes; + const usesViRoiDp = viRoiMode && viRoiMode !== "off" && viRoiDpBytes && viRoiDpBytes.byteLength > 0; + const sourceBytes = usesViRoiDp ? viRoiDpBytes : frameBytes; const raw = new Float32Array(sourceBytes.buffer, sourceBytes.byteOffset, sourceBytes.byteLength / 4); setCursorInfo({ row: pxRow, col: pxCol, value: raw[pxRow * detCols + pxCol], panel: "DP" }); } else { @@ -2802,8 +2721,6 @@ function Show4DSTEM() { } } - if (profileActive && lockProfile) return; - if (profileActive && profilePoints.length === 2) { const p0 = profilePoints[0]; const p1 = profilePoints[1]; @@ -2855,7 +2772,6 @@ function Show4DSTEM() { // Handle inner resize dragging (annular mode) if (isDraggingResizeInner) { - if (lockRoi) return; const dx = Math.abs(imgX - roiCenterCol); const dy = Math.abs(imgY - roiCenterRow); const newRadius = Math.sqrt(dx ** 2 + dy ** 2); @@ -2866,7 +2782,6 @@ function Show4DSTEM() { // Handle outer resize dragging - use model state center, not local values if (isDraggingResize) { - if (lockRoi) return; const dx = Math.abs(imgX - roiCenterCol); const dy = Math.abs(imgY - roiCenterRow); if (roiMode === "rect") { @@ -2890,25 +2805,18 @@ function Show4DSTEM() { // Check hover state for resize handles if (!isDraggingDP) { - if (!lockRoi) { - setIsHoveringResizeInner(isNearResizeHandleInner(imgX, imgY)); - setIsHoveringResize(isNearResizeHandle(imgX, imgY)); - } else { - setIsHoveringResizeInner(false); - setIsHoveringResize(false); - } + setIsHoveringResizeInner(isNearResizeHandleInner(imgX, imgY)); + setIsHoveringResize(isNearResizeHandle(imgX, imgY)); return; } - if (lockRoi) return; const centerCol = imgX - dpDragOffsetRef.current.dCol; const centerRow = imgY - dpDragOffsetRef.current.dRow; setLocalKCol(centerCol); setLocalKRow(centerRow); - // Use compound roi_center trait [row, col] - single observer fires in Python + // rAF-coalesced — sends only the latest roi_center per frame. const newCol = Math.round(Math.max(0, Math.min(detCols - 1, centerCol))); const newRow = Math.round(Math.max(0, Math.min(detRows - 1, centerRow))); - model.set("roi_center", [newRow, newCol]); - model.save_changes(); + queueRoiCenter(newRow, newCol); }; const handleDpMouseUp = (e: React.MouseEvent) => { @@ -2971,14 +2879,12 @@ function Show4DSTEM() { setCursorInfo(prev => prev?.panel === "DP" ? null : prev); }; const handleDpDoubleClick = () => { - if (lockView) return; setDpZoom(1); setDpPanX(0); setDpPanY(0); }; const handleViMouseDown = (e: React.MouseEvent) => { - if (viProfileActive && lockProfile) return; const canvas = virtualOverlayRef.current; if (!canvas) return; const rect = canvas.getBoundingClientRect(); @@ -3018,7 +2924,6 @@ function Show4DSTEM() { // Check if VI ROI mode is active - same logic as DP if (viRoiMode && viRoiMode !== "off") { - if (lockRoi) return; // Check if clicking on resize handle if (isNearViRoiResizeHandle(imgX, imgY)) { setIsDraggingViRoiResize(true); @@ -3040,7 +2945,6 @@ function Show4DSTEM() { } // Regular position selection (when ROI is off) - if (lockNavigation || lockVirtual) return; setIsDraggingVI(true); setLocalPosRow(imgX); setLocalPosCol(imgY); // Batch X and Y updates into a single sync @@ -3077,8 +2981,6 @@ function Show4DSTEM() { } } - if (viProfileActive && lockProfile) return; - if (viProfileActive && viProfilePoints.length === 2) { const p0 = viProfilePoints[0]; const p1 = viProfilePoints[1]; @@ -3126,7 +3028,6 @@ function Show4DSTEM() { // Handle VI ROI resize dragging (same pattern as DP) if (isDraggingViRoiResize) { - if (lockRoi) return; const dx = Math.abs(imgX - localViRoiCenterRow); const dy = Math.abs(imgY - localViRoiCenterCol); if (viRoiMode === "rect") { @@ -3145,33 +3046,27 @@ function Show4DSTEM() { // Check hover state for resize handles (same as DP) if (!isDraggingViRoi) { - if (!lockRoi) { - setIsHoveringViRoiResize(isNearViRoiResizeHandle(imgX, imgY)); - } else { - setIsHoveringViRoiResize(false); - } + setIsHoveringViRoiResize(isNearViRoiResizeHandle(imgX, imgY)); if (viRoiMode && viRoiMode !== "off") return; // Don't update position when ROI active } // Handle VI ROI center dragging (same as DP — with offset) if (isDraggingViRoi) { - if (lockRoi) return; const centerRow = imgX - viRoiDragOffsetRef.current.dRow; const centerCol = imgY - viRoiDragOffsetRef.current.dCol; setLocalViRoiCenterRow(centerRow); setLocalViRoiCenterCol(centerCol); - // Batch VI ROI center updates + // Compound trait update — single observer fires Python-side; reduced DP is + // never computed against split-trait state (old col + new row, or vice versa). const newViX = Math.round(Math.max(0, Math.min(shapeRows - 1, centerRow))); const newViY = Math.round(Math.max(0, Math.min(shapeCols - 1, centerCol))); - model.set("vi_roi_center_row", newViX); - model.set("vi_roi_center_col", newViY); + model.set("vi_roi_center", [newViX, newViY]); model.save_changes(); return; } // Handle regular position dragging (when ROI is off) if (!isDraggingVI) return; - if (lockNavigation || lockVirtual) return; setLocalPosRow(imgX); setLocalPosCol(imgY); // Batch position updates into a single sync const newX = Math.round(Math.max(0, Math.min(shapeRows - 1, imgX))); @@ -3244,13 +3139,11 @@ function Show4DSTEM() { setCursorInfo(prev => prev?.panel === "VI" ? null : prev); }; const handleViDoubleClick = () => { - if (lockView || lockVirtual) return; setViZoom(1); setViPanX(0); setViPanY(0); }; const handleFftDoubleClick = () => { - if (lockView || lockFft) return; setFftZoom(1); setFftPanX(0); setFftPanY(0); @@ -3259,14 +3152,12 @@ function Show4DSTEM() { // FFT drag-to-pan handlers const handleFftMouseDown = (e: React.MouseEvent) => { - if (lockView || lockFft) return; fftClickStartRef.current = { x: e.clientX, y: e.clientY }; setIsDraggingFFT(true); setFftDragStart({ x: e.clientX, y: e.clientY, panX: fftPanX, panY: fftPanY }); }; const handleFftMouseMove = (e: React.MouseEvent) => { - if (lockView || lockFft) return; if (!isDraggingFFT || !fftDragStart) return; const canvas = fftOverlayRef.current; if (!canvas) return; @@ -3295,10 +3186,11 @@ function Show4DSTEM() { const canvasY = (e.clientY - rect.top) * scaleY; const fftW = fftCropDims?.fftWidth ?? shapeCols; const fftH = fftCropDims?.fftHeight ?? shapeRows; - // Reverse the zoom/pan transform: canvas coords -> image coords - // The FFT render uses: ctx.translate(fftPanX, fftPanY); ctx.scale(fftZoom, fftZoom); ctx.drawImage(offscreen, 0, 0) - let imgCol = (canvasX - fftPanX) / fftZoom; - let imgRow = (canvasY - fftPanY) / fftZoom; + // Reverse the render transform: canvas coords -> image coords. + // Render: translate(panX, panY); scale(zoom); drawImage(offscreen, 0,0,fftW,fftH, 0,0,canvasW,canvasH) + // So: canvasX = panX + zoom * (imgCol * canvasW / fftW) → imgCol = (canvasX - panX) / zoom * fftW / canvasW + let imgCol = ((canvasX - fftPanX) / fftZoom) * (fftW / canvas.width); + let imgRow = ((canvasY - fftPanY) / fftZoom) * (fftH / canvas.height); // Bounds check if (imgCol >= 0 && imgCol < fftW && imgRow >= 0 && imgRow < fftH) { // Snap to nearest peak in FFT magnitude @@ -3341,7 +3233,6 @@ function Show4DSTEM() { // ── Canvas resize handlers ── const handleCanvasResizeStart = (e: React.MouseEvent) => { - if (lockView) return; e.stopPropagation(); e.preventDefault(); setIsResizingCanvas(true); @@ -3384,7 +3275,6 @@ function Show4DSTEM() { // Export DP handler const handleExportDP = async () => { - if (lockExport) return; const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); const zip = new JSZip(); const metadata = { @@ -3446,7 +3336,6 @@ function Show4DSTEM() { // Export VI handler const handleExportVI = async () => { - if (lockExport) return; const timestamp = new Date().toISOString().replace(/[:.]/g, '-').slice(0, 19); const zip = new JSZip(); const metadata = { @@ -3523,7 +3412,6 @@ function Show4DSTEM() { // ── DP Figure Export ── const handleDpExportFigure = (withColorbar: boolean) => { - if (lockExport) return; setDpExportAnchor(null); const frameData = rawDpDataRef.current; if (!frameData) return; @@ -3535,9 +3423,6 @@ function Show4DSTEM() { if (dpScaleMode === "log") { vmin = Math.log1p(Math.max(traitDpVmin, 0)); vmax = Math.log1p(Math.max(traitDpVmax, 0)); - } else if (dpScaleMode === "power") { - vmin = Math.pow(Math.max(traitDpVmin, 0), dpPowerExp); - vmax = Math.pow(Math.max(traitDpVmax, 0), dpPowerExp); } else { vmin = traitDpVmin; vmax = traitDpVmax; @@ -3563,14 +3448,12 @@ function Show4DSTEM() { }; const handleDpExportPng = () => { - if (lockExport) return; setDpExportAnchor(null); if (!dpCanvasRef.current) return; dpCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_dp.png"); }, "image/png"); }; const handleDpExportGif = () => { - if (lockExport) return; setDpExportAnchor(null); setExporting(true); setGifExportRequested(true); @@ -3578,7 +3461,6 @@ function Show4DSTEM() { // ── VI Figure Export ── const handleViExportFigure = (withColorbar: boolean) => { - if (lockExport) return; setViExportAnchor(null); if (!virtualCanvasRef.current) return; const viCanvas = virtualCanvasRef.current; @@ -3594,7 +3476,6 @@ function Show4DSTEM() { }; const handleViExportPng = () => { - if (lockExport) return; setViExportAnchor(null); if (!virtualCanvasRef.current) return; virtualCanvasRef.current.toBlob((b) => { if (b) downloadBlob(b, "show4dstem_vi.png"); }, "image/png"); @@ -3646,7 +3527,7 @@ function Show4DSTEM() { tabIndex={0} onKeyDown={handleKeyDown} onMouseDownCapture={handleRootMouseDownCapture} - sx={{ p: `${SPACING.LG}px`, bgcolor: themeColors.bg, color: themeColors.text, outline: "none" }} + sx={{ p: 2, bgcolor: themeColors.bg, color: themeColors.text, outline: "none", borderRadius: "2px" }} > {/* HEADER */} @@ -3659,6 +3540,8 @@ function Show4DSTEM() { BF/ABF/ADF: Preset detector configurations (bright-field, annular bright-field, annular dark-field). Image: Virtual image — integrated intensity within detector ROI at each scan position. FFT: Spatial frequency content of the virtual image. Auto masks DC + clips to 99.9th percentile. + Smooth: CSS bilinear blit on the VI canvas. No data change — browser smooths the upscale visually. Off = nearest-neighbor (sharp pixel boundaries). + Auto: Percentile contrast (1st–99th). Clips outliers automatically. Profile: Click two points on DP to draw a line intensity profile. {nFrames > 1 && <> Frame Playback ({frameDimLabel}) @@ -3668,14 +3551,6 @@ function Show4DSTEM() { Keyboard } theme={themeInfo.theme} /> - {/* MAIN CONTENT: DP | VI | FFT (three columns when FFT shown) */} @@ -3686,52 +3561,39 @@ function Show4DSTEM() { DP at ({Math.round(localPosRow)}, {Math.round(localPosCol)}) - {!hideRoi && k: ({Math.round(localKRow)}, {Math.round(localKCol)})} + k: ({Math.round(localKRow)}, {Math.round(localKCol)}) - {!hideProfile && ( - <> - Profile: - { - if (lockProfile) return; - const on = e.target.checked; - setProfileActive(on); - if (!on) { - setProfileLine([]); - setProfileData(null); - setHoveredDpProfileEndpoint(null); - setIsHoveringDpProfileLine(false); - } - }} disabled={lockProfile} size="small" sx={switchStyles.small} /> - - )} - {!hideView && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - setDpExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> - handleDpExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar - handleDpExportFigure(false)} sx={{ fontSize: 12 }}>PDF - PNG - { if (!lockExport) { setDpExportAnchor(null); handleExportDP(); } }} sx={{ fontSize: 12 }}>ZIP (PNG + metadata) - {pathLength > 0 && GIF (path animation)} - - )} + Profile: + { + const on = e.target.checked; + setProfileActive(on); + if (!on) { + setProfileLine([]); + setProfileData(null); + setHoveredDpProfileEndpoint(null); + setIsHoveringDpProfileLine(false); + } + }} size="small" sx={switchStyles.small} /> + + + + setDpExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleDpExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleDpExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { setDpExportAnchor(null); handleExportDP(); }} sx={{ fontSize: 12 }}>ZIP (PNG + metadata) + {pathLength > 0 && GIF (path animation)} + @@ -3742,21 +3604,19 @@ function Show4DSTEM() { ref={dpOverlayRef} width={detCols} height={detRows} onMouseDown={handleDpMouseDown} onMouseMove={handleDpMouseMove} onMouseUp={handleDpMouseUp} onMouseLeave={handleDpMouseLeave} - onWheel={createZoomHandler(setDpZoom, setDpPanX, setDpPanY, dpZoom, dpPanX, dpPanY, dpOverlayRef, lockView)} + onWheel={createZoomHandler(setDpZoom, setDpPanX, setDpPanY, dpZoom, dpPanX, dpPanY, dpOverlayRef)} onDoubleClick={handleDpDoubleClick} style={{ position: "absolute", width: "100%", height: "100%", - cursor: (profileActive && lockProfile) || (!profileActive && lockRoi) - ? "default" - : (draggingDpProfileEndpoint !== null || isDraggingDpProfileLine) - ? "grabbing" - : (profileActive && (hoveredDpProfileEndpoint !== null || isHoveringDpProfileLine)) - ? "grab" - : isHoveringResize || isDraggingResize - ? "nwse-resize" - : "crosshair", + cursor: (draggingDpProfileEndpoint !== null || isDraggingDpProfileLine) + ? "grabbing" + : (profileActive && (hoveredDpProfileEndpoint !== null || isHoveringDpProfileLine)) + ? "grab" + : isHoveringResize || isDraggingResize + ? "nwse-resize" + : "crosshair", }} /> @@ -3767,31 +3627,25 @@ function Show4DSTEM() { )} - {!hideView && ( - - )} + {/* DP Stats Bar */} - {!hideStats && dpStats && dpStats.length === 4 && ( - + {dpStats && dpStats.length === 4 && ( + Mean {formatStat(dpStats[0])} Min {formatStat(dpStats[1])} Max {formatStat(dpStats[2])} Std {formatStat(dpStats[3])} - {!hideRoi && ( - <> - - { if (!lockRoi) { setRoiMode("circle"); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: roiColors.textColor, fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>BF - { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner((bfRadius || 10) * 0.5); setRoiRadius(bfRadius || 10); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#4af", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ABF - { if (!lockRoi) { setRoiMode("annular"); setRoiRadiusInner(bfRadius || 10); setRoiRadius(Math.min((bfRadius || 10) * 3, Math.min(detRows, detCols) / 2 - 2)); setRoiCenterCol(centerCol); setRoiCenterRow(centerRow); } }} sx={{ color: "#fa4", fontSize: 11, fontWeight: "bold", cursor: lockRoi ? "default" : "pointer", opacity: lockRoi ? 0.6 : 1, "&:hover": { textDecoration: lockRoi ? "none" : "underline" } }}>ADF - - )} + + { model.set("_preset_request", "bf"); model.save_changes(); }} sx={{ color: roiColors.textColor, fontSize: 11, fontWeight: "bold", cursor: "pointer", "&:hover": { textDecoration: "underline" } }}>BF + { model.set("_preset_request", "abf"); model.save_changes(); }} sx={{ color: "#4af", fontSize: 11, fontWeight: "bold", cursor: "pointer", "&:hover": { textDecoration: "underline" } }}>ABF + { model.set("_preset_request", "adf"); model.save_changes(); }} sx={{ color: "#fa4", fontSize: 11, fontWeight: "bold", cursor: "pointer", "&:hover": { textDecoration: "underline" } }}>ADF )} {/* Profile sparkline */} - {profileActive && !hideProfile && ( + {profileActive && ( { - if (lockProfile) return; setIsResizingProfile(true); profileResizeStart.current = { startY: e.clientY, startHeight: profileHeight }; }} - sx={{ width: canvasSize, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + sx={{ width: canvasSize, height: 4, cursor: "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: themeColors.accent } }} /> )} {/* DP Controls - two rows with histogram on right */} - {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + {showControls && ( {/* Left: two rows of controls */} {/* Row 1: Detector + slider */} - {!hideRoi && ( - - Detector: - - {(roiMode === "circle" || roiMode === "square" || roiMode === "annular") && ( - <> - { - if (lockRoi) return; - if (roiMode === "annular") { - const [inner, outer] = v as number[]; - setRoiRadiusInner(Math.min(inner, outer - 1)); - setRoiRadius(Math.max(outer, inner + 1)); - } else { - const next = Array.isArray(v) ? v[0] : v; - setRoiRadius(next); - } - }} - min={1} - max={Math.min(detRows, detCols) / 2} - size="small" - sx={{ - width: roiMode === "annular" ? 100 : 70, - mx: 1, - "& .MuiSlider-thumb": { width: 14, height: 14 } - }} - /> - - {roiMode === "annular" ? `${Math.round(roiRadiusInner)}-${Math.round(roiRadius)}px` : `${Math.round(roiRadius)}px`} - - - )} - - )} + + Detector: + + {(roiMode === "circle" || roiMode === "square" || roiMode === "annular") && ( + <> + { + if (roiMode === "annular") { + const [inner, outer] = v as number[]; + setRoiRadiusInner(Math.min(inner, outer - 1)); + setRoiRadius(Math.max(outer, inner + 1)); + } else { + const next = Array.isArray(v) ? v[0] : v; + setRoiRadius(next); + } + }} + min={1} + max={Math.min(detRows, detCols) / 2} + size="small" + sx={{ ...sliderStyles.small, width: roiMode === "annular" ? 67 : 47, mx: 1 }} + /> + + {roiMode === "annular" ? `${Math.round(roiRadiusInner)}-${Math.round(roiRadius)}px` : `${Math.round(roiRadius)}px`} + + + )} + {/* Row 2: Color + Scale + Colorbar */} - {!hideDisplay && ( - - Color: - - Scale: - - Colorbar: - { if (!lockDisplay) setShowDpColorbar(e.target.checked); }} disabled={lockDisplay} size="small" sx={switchStyles.small} /> - - )} + + Color: + + Scale: + + Colorbar: + setShowDpColorbar(e.target.checked)} size="small" sx={switchStyles.small} /> + {/* Right: Histogram spanning both rows */} - {!hideHistogram && ( - - { if (!lockHistogram) { setDpVminPct(min); setDpVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={dpGlobalMin} dataMax={dpGlobalMax} /> - - )} + + { setDpVminPct(min); setDpVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme} dataMin={dpGlobalMin} dataMax={dpGlobalMax} /> + )} {/* SECOND COLUMN: VI Panel */} - {!hideVirtual && ( {/* VI Header */} - + {shapeRows}×{shapeCols} | {detRows}×{detCols} - {!hideFft && ( - <> - FFT: - { if (!lockFft) setShowFft(e.target.checked); }} disabled={lockFft} size="small" sx={switchStyles.small} /> - - )} - {!hideProfile && ( - <> - Profile: - { - if (lockProfile) return; - const on = e.target.checked; - setViProfileActive(on); - if (!on) { - setViProfilePoints([]); - setHoveredViProfileEndpoint(null); - setIsHoveringViProfileLine(false); - } - }} disabled={lockProfile} size="small" sx={switchStyles.small} /> - - )} - {!hideView && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - - )} - {!hideExport && ( - setViExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> - handleViExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar - handleViExportFigure(false)} sx={{ fontSize: 12 }}>PDF - PNG - { if (!lockExport && !lockVirtual) { setViExportAnchor(null); handleExportVI(); } }} sx={{ fontSize: 12 }}>ZIP (all panels + metadata) - - )} + FFT: + setShowFft(e.target.checked)} size="small" sx={switchStyles.small} /> + Profile: + { + const on = e.target.checked; + setViProfileActive(on); + if (!on) { + setViProfilePoints([]); + setHoveredViProfileEndpoint(null); + setIsHoveringViProfileLine(false); + } + }} size="small" sx={switchStyles.small} /> + + + + setViExportAnchor(null)} anchorOrigin={{ vertical: "bottom", horizontal: "left" }} transformOrigin={{ vertical: "top", horizontal: "left" }} sx={{ zIndex: 9999 }}> + handleViExportFigure(true)} sx={{ fontSize: 12 }}>PDF + colorbar + handleViExportFigure(false)} sx={{ fontSize: 12 }}>PDF + PNG + { setViExportAnchor(null); handleExportVI(); }} sx={{ fontSize: 12 }}>ZIP (all panels + metadata) + @@ -3957,19 +3780,17 @@ function Show4DSTEM() { ref={virtualOverlayRef} width={shapeCols} height={shapeRows} onMouseDown={handleViMouseDown} onMouseMove={handleViMouseMove} onMouseUp={handleViMouseUp} onMouseLeave={handleViMouseLeave} - onWheel={createZoomHandler(setViZoom, setViPanX, setViPanY, viZoom, viPanX, viPanY, virtualOverlayRef, lockView || lockVirtual)} + onWheel={createZoomHandler(setViZoom, setViPanX, setViPanY, viZoom, viPanX, viPanY, virtualOverlayRef)} onDoubleClick={handleViDoubleClick} style={{ position: "absolute", width: "100%", height: "100%", - cursor: (viProfileActive && lockProfile) || (!viProfileActive && (lockNavigation || lockRoi)) - ? "default" - : (draggingViProfileEndpoint !== null || isDraggingViProfileLine) - ? "grabbing" - : (viProfileActive && (hoveredViProfileEndpoint !== null || isHoveringViProfileLine)) - ? "grab" - : "crosshair", + cursor: (draggingViProfileEndpoint !== null || isDraggingViProfileLine) + ? "grabbing" + : (viProfileActive && (hoveredViProfileEndpoint !== null || isHoveringViProfileLine)) + ? "grab" + : "crosshair", }} /> @@ -3980,23 +3801,27 @@ function Show4DSTEM() { )} - {!hideView && ( - - )} + - {/* VI Stats Bar */} - {!hideStats && viStats && viStats.length === 4 && ( - + {/* VI Stats Bar — stats on left, Auto/Smooth toggles on right edge */} + {viStats && viStats.length === 4 && ( + Mean {formatStat(viStats[0])} Min {formatStat(viStats[1])} Max {formatStat(viStats[2])} Std {formatStat(viStats[3])} + + Auto: + setViAutoContrast(e.target.checked)} size="small" sx={switchStyles.small} /> + Smooth: + setViSmooth(e.target.checked)} size="small" sx={switchStyles.small} /> + )} {/* VI Profile sparkline */} - {viProfileActive && !hideProfile && ( + {viProfileActive && ( { - if (lockProfile) return; setIsResizingViProfile(true); viProfileResizeStart.current = { startY: e.clientY, startHeight: viProfileHeight }; }} - sx={{ width: viCanvasWidth, height: 4, cursor: lockProfile ? "default" : "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: lockProfile ? themeColors.controlBg : themeColors.accent } }} + sx={{ width: viCanvasWidth, height: 4, cursor: "ns-resize", borderTop: `1px solid ${themeColors.border}`, borderLeft: `1px solid ${themeColors.border}`, borderRight: `1px solid ${themeColors.border}`, borderBottom: `1px solid ${themeColors.border}`, bgcolor: themeColors.controlBg, "&:hover": { bgcolor: themeColors.accent } }} /> )} {/* VI Controls - Two rows with histogram on right */} - {showControls && (!hideRoi || !hideDisplay || !hideHistogram) && ( + {showControls && ( {/* Left: Two rows of controls */} {/* Row 1: ROI selector */} - {!hideRoi && ( - - ROI: - - {viRoiMode && viRoiMode !== "off" && ( - <> - {(viRoiMode === "circle" || viRoiMode === "square") && ( - <> - { if (!lockRoi) setViRoiRadius(v as number); }} - min={1} - max={Math.min(shapeRows, shapeCols) / 2} - size="small" - sx={{ width: 80, mx: 1 }} - /> - - {Math.round(viRoiRadius || 5)}px - - - )} - {summedDpCount > 0 && ( - - {summedDpCount} pos + + ROI: + + {viRoiMode && viRoiMode !== "off" && ( + <> + {(viRoiMode === "circle" || viRoiMode === "square") && ( + <> + setViRoiRadius(v as number)} + min={1} + max={Math.min(shapeRows, shapeCols) / 2} + size="small" + sx={{ ...sliderStyles.small, width: 53, mx: 1 }} + /> + + {Math.round(viRoiRadius || 5)}px - )} - - )} - - )} + + )} + + + )} + {/* Row 2: Color + Scale */} - {!hideDisplay && ( - - Color: - - Scale: - - - )} + + Color: + + Scale: + + {/* Right: Histogram spanning both rows */} - {!hideHistogram && ( - - { if (!lockHistogram) { setViVminPct(min); setViVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={viDataMin} dataMax={viDataMax} /> - - )} + + { setViVminPct(min); setViVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme} dataMin={viDataMin} dataMax={viDataMax} /> + )} - )} {/* THIRD COLUMN: FFT Panel (conditionally shown) */} {effectiveShowFft && ( @@ -4096,9 +3911,7 @@ function Show4DSTEM() { {roiFftActive && fftCropDims ? `ROI FFT (${fftCropDims.cropWidth}\u00D7${fftCropDims.cropHeight})` : "FFT"} - {!hideView && ( - - )} + @@ -4109,18 +3922,16 @@ function Show4DSTEM() { ref={fftOverlayRef} width={shapeCols} height={shapeRows} onMouseDown={handleFftMouseDown} onMouseMove={handleFftMouseMove} onMouseUp={handleFftMouseUp} onMouseLeave={handleFftMouseLeave} - onWheel={createZoomHandler(setFftZoom, setFftPanX, setFftPanY, fftZoom, fftPanX, fftPanY, fftOverlayRef, lockView || lockFft)} + onWheel={createZoomHandler(setFftZoom, setFftPanX, setFftPanY, fftZoom, fftPanX, fftPanY, fftOverlayRef)} onDoubleClick={handleFftDoubleClick} - style={{ position: "absolute", width: "100%", height: "100%", cursor: lockView || lockFft ? "default" : (isDraggingFFT ? "grabbing" : "grab") }} + style={{ position: "absolute", width: "100%", height: "100%", cursor: isDraggingFFT ? "grabbing" : "grab" }} /> - {!hideView && ( - - )} + {/* FFT Stats Bar */} - {!hideStats && fftStats && fftStats.length === 4 && ( - + {fftStats && fftStats.length === 4 && ( + Mean {formatStat(fftStats[0])} Min {formatStat(fftStats[1])} Max {formatStat(fftStats[2])} @@ -4151,50 +3962,46 @@ function Show4DSTEM() { )} {/* FFT Controls - Two rows with histogram on right */} - {showControls && (!hideDisplay || !hideHistogram) && ( + {showControls && ( {/* Left: Two rows of controls */} - {!hideDisplay && ( - - {/* Row 1: Scale + Clip */} - - Scale: - - Auto: - { if (!lockDisplay && !lockFft) setFftAuto(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> - {fftCropDims && ( - <> - Win: - { if (!lockDisplay && !lockFft) setFftWindow(e.target.checked); }} disabled={lockDisplay || lockFft} size="small" sx={switchStyles.small} /> - - )} - - {/* Row 2: Color */} - - Color: - - - - )} - {/* Right: Histogram spanning both rows */} - {!hideHistogram && ( - - {fftHistogramData && ( - { if (!lockHistogram && !lockFft) { setFftVminPct(min); setFftVmaxPct(max); } }} width={110} height={58} theme={themeInfo.theme} dataMin={fftDataMin} dataMax={fftDataMax} /> + + {/* Row 1: Scale + Clip */} + + Scale: + + Auto: + setFftAuto(e.target.checked)} size="small" sx={switchStyles.small} /> + {fftCropDims && ( + <> + Win: + setFftWindow(e.target.checked)} size="small" sx={switchStyles.small} /> + )} - )} + {/* Row 2: Color */} + + Color: + + + + {/* Right: Histogram spanning both rows */} + + {fftHistogramData && ( + { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme} dataMin={fftDataMin} dataMax={fftDataMax} /> + )} + )} @@ -4204,52 +4011,52 @@ function Show4DSTEM() { {/* BOTTOM CONTROLS */} {/* Frame controls (5D time/tilt series) — matches Show3D playback */} - {showControls && nFrames > 1 && !hidePlayback && !hideFrame && (<> + {showControls && nFrames > 1 && (<> {frameDimLabel}: - { if (!lockFrame && !lockPlayback) { setFrameReverse(true); setFramePlaying(true); } }} sx={{ color: frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + { setFrameReverse(true); setFramePlaying(true); }} sx={{ color: frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> - { if (!lockFrame && !lockPlayback) setFramePlaying(!framePlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + setFramePlaying(!framePlaying)} sx={{ color: themeColors.accent, p: 0.25 }}> {framePlaying ? : } - { if (!lockFrame && !lockPlayback) { setFrameReverse(false); setFramePlaying(true); } }} sx={{ color: !frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> + { setFrameReverse(false); setFramePlaying(true); }} sx={{ color: !frameReverse && framePlaying ? themeColors.accent : themeColors.textMuted, p: 0.25 }}> - { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + { setFramePlaying(false); setFrameIdx(0); }} sx={{ color: themeColors.textMuted, p: 0.25 }}> - { if (!lockFrame && !lockPlayback) { setFramePlaying(false); setFrameIdx(v as number); } }} min={0} max={Math.max(0, nFrames - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + { setFramePlaying(false); setFrameIdx(v as number); }} min={0} max={Math.max(0, nFrames - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> {frameLabels && frameLabels.length > frameIdx ? frameLabels[frameIdx] : `${frameIdx + 1}/${nFrames}`} fps - { if (!lockFrame && !lockPlayback) setFrameFps(v as number); }} size="small" sx={{ ...sliderStyles.small, width: 35, flexShrink: 0 }} /> + setFrameFps(v as number)} size="small" sx={{ ...sliderStyles.small, width: 35, flexShrink: 0 }} /> {Math.round(frameFps)} Loop - { if (!lockFrame && !lockPlayback) setFrameLoop(!frameLoop); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + setFrameLoop(!frameLoop)} sx={{ ...switchStyles.small, flexShrink: 0 }} /> Bounce - { if (!lockFrame && !lockPlayback) setFrameBoomerang(!frameBoomerang); }} disabled={lockFrame || lockPlayback} sx={{ ...switchStyles.small, flexShrink: 0 }} /> + setFrameBoomerang(!frameBoomerang)} sx={{ ...switchStyles.small, flexShrink: 0 }} /> )} {/* Path animation slider */} - {showControls && !hidePlayback && pathLength > 0 && ( + {showControls && pathLength > 0 && ( - { if (!lockPlayback) setPathPlaying(!pathPlaying); }} sx={{ color: themeColors.accent, p: 0.25 }}> + setPathPlaying(!pathPlaying)} sx={{ color: themeColors.accent, p: 0.25 }}> {pathPlaying ? : } - { if (!lockPlayback) { setPathPlaying(false); setPathIndex(0); } }} sx={{ color: themeColors.textMuted, p: 0.25 }}> + { setPathPlaying(false); setPathIndex(0); }} sx={{ color: themeColors.textMuted, p: 0.25 }}> - { if (!lockPlayback) { setPathPlaying(false); setPathIndex(v as number); } }} min={0} max={Math.max(0, pathLength - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> + { setPathPlaying(false); setPathIndex(v as number); }} min={0} max={Math.max(0, pathLength - 1)} size="small" sx={{ flex: 1, minWidth: 60, "& .MuiSlider-thumb": { width: 10, height: 10 } }} /> {pathIndex + 1}/{pathLength} Loop: - { if (!lockPlayback) { model.set("path_loop", v); model.save_changes(); } }} disabled={lockPlayback} size="small" sx={switchStyles.small} /> + { model.set("path_loop", v); model.save_changes(); }} size="small" sx={switchStyles.small} /> )} diff --git a/widget/js/show4dstem/styles.css b/widget/js/show4dstem/styles.css deleted file mode 100644 index 61876cde..00000000 --- a/widget/js/show4dstem/styles.css +++ /dev/null @@ -1,5 +0,0 @@ -/* Theme-aware styles - minimal, let JS handle most theming */ -.show4dstem-root { - border-radius: 2px; - padding: 16px; -} diff --git a/widget/js/stats.ts b/widget/js/stats.ts index b71c45aa..36ce661c 100644 --- a/widget/js/stats.ts +++ b/widget/js/stats.ts @@ -99,3 +99,23 @@ export function sliderRange( vmax: dataMin + (vmaxPct / 100) * range, }; } + +/** Compute normalized histogram bins from Float32Array. Returns array of 0-1 values. */ +export function computeHistogramFromBytes(data: Float32Array | null, numBins = 256): number[] { + if (!data || data.length === 0) return new Array(numBins).fill(0); + const bins = new Array(numBins).fill(0); + let min = Infinity, max = -Infinity; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) { if (v < min) min = v; if (v > max) max = v; } + } + if (!isFinite(min) || !isFinite(max) || min === max) return bins; + const range = max - min; + for (let i = 0; i < data.length; i++) { + const v = data[i]; + if (isFinite(v)) bins[Math.min(numBins - 1, Math.floor(((v - min) / range) * numBins))]++; + } + const maxCount = Math.max(...bins); + if (maxCount > 0) for (let i = 0; i < numBins; i++) bins[i] /= maxCount; + return bins; +} diff --git a/widget/js/tool-parity.ts b/widget/js/tool-parity.ts deleted file mode 100644 index 8e318e6c..00000000 --- a/widget/js/tool-parity.ts +++ /dev/null @@ -1,156 +0,0 @@ -import registryJson from "../src/quantem/widget/tool_parity.json"; - -type ToolInput = string | string[] | null | undefined; - -type WidgetConfig = { - tool_groups: string[]; - aliases?: Record; -}; - -type ControlPreset = { - label: string; - show_groups: string[]; -}; - -type ToolParityRegistry = { - widgets: Record; - control_presets: Record; - viewer_widgets?: string[]; -}; - -const REGISTRY = registryJson as ToolParityRegistry; - -function getWidgetConfig(widgetName: string): WidgetConfig { - const cfg = REGISTRY.widgets[widgetName]; - if (!cfg) { - const supported = Object.keys(REGISTRY.widgets).sort().join(", "); - throw new Error(`Unknown widget '${widgetName}'. Supported widgets: ${supported}.`); - } - return cfg; -} - -function toValues(values: ToolInput): string[] { - if (values == null) return []; - if (typeof values === "string") return [values]; - return [...values]; -} - -function toCanonical(widgetName: string, value: string): string { - const cfg = getWidgetConfig(widgetName); - const aliases = cfg.aliases ?? {}; - const key = value.trim().toLowerCase(); - return aliases[key] ?? key; -} - -export function getWidgetToolGroups(widgetName: string): string[] { - return [...getWidgetConfig(widgetName).tool_groups]; -} - -export function normalizeToolGroups(widgetName: string, values: ToolInput): string[] { - const groups = getWidgetToolGroups(widgetName); - const groupSet = new Set(groups); - const out: string[] = []; - const seen = new Set(); - for (const raw of toValues(values)) { - const canonical = toCanonical(widgetName, String(raw)); - if (!canonical) continue; - if (!groupSet.has(canonical)) { - const supported = groups.map((g) => `"${g}"`).join(", "); - throw new Error(`Unknown tool group '${raw}'. Supported values: ${supported}.`); - } - if (canonical === "all") return ["all"]; - if (!seen.has(canonical)) { - seen.add(canonical); - out.push(canonical); - } - } - return out; -} - -function orderedWithoutAll(widgetName: string, values: Set): string[] { - return getWidgetToolGroups(widgetName).filter((group) => group !== "all" && values.has(group)); -} - -export function expandToolGroups(widgetName: string, values: ToolInput): string[] { - const normalized = normalizeToolGroups(widgetName, values); - if (!normalized.includes("all")) return normalized; - return getWidgetToolGroups(widgetName).filter((group) => group !== "all"); -} - -export function compactToolLabel(key: string): string { - return key - .replace(/_/g, " ") - .replace(/\b\w/g, (m) => m.toUpperCase()); -} - -export function getControlPresetIds(): string[] { - return Object.keys(REGISTRY.control_presets); -} - -export function getControlPresetLabel(presetId: string): string { - const preset = REGISTRY.control_presets[presetId]; - return preset?.label ?? presetId; -} - -export function resolvePresetHiddenTools(widgetName: string, presetId: string): string[] { - const preset = REGISTRY.control_presets[presetId]; - if (!preset) { - const supported = Object.keys(REGISTRY.control_presets).sort().join(", "); - throw new Error(`Unknown control preset '${presetId}'. Supported presets: ${supported}.`); - } - const supportedGroups = getWidgetToolGroups(widgetName).filter((group) => group !== "all"); - if (preset.show_groups.includes("*")) return []; - const show = new Set(preset.show_groups.map((g) => toCanonical(widgetName, g))); - const hidden = supportedGroups.filter((group) => !show.has(group)); - return normalizeToolGroups(widgetName, hidden); -} - -export type ToolVisibilityState = { - hideAll: boolean; - lockAll: boolean; - isHidden: (group: string) => boolean; - isLocked: (group: string) => boolean; - hiddenSet: Set; - disabledSet: Set; -}; - -export function computeToolVisibility( - widgetName: string, - disabledTools: ToolInput, - hiddenTools: ToolInput, -): ToolVisibilityState { - const hidden = normalizeToolGroups(widgetName, hiddenTools); - const disabled = normalizeToolGroups(widgetName, disabledTools); - const hiddenSet = new Set(hidden); - const disabledSet = new Set(disabled); - const hideAll = hiddenSet.has("all"); - const lockAll = hideAll || disabledSet.has("all"); - - const isHidden = (group: string): boolean => { - const canonical = toCanonical(widgetName, group); - if (canonical === "all") return hideAll; - return hideAll || hiddenSet.has(canonical); - }; - - const isLocked = (group: string): boolean => { - const canonical = toCanonical(widgetName, group); - if (canonical === "all") return lockAll; - return lockAll || isHidden(canonical) || disabledSet.has(canonical); - }; - - return { hideAll, lockAll, isHidden, isLocked, hiddenSet, disabledSet }; -} - -export function addToolGroup(widgetName: string, current: ToolInput, group: string): string[] { - const merged = new Set(expandToolGroups(widgetName, current)); - const canonical = toCanonical(widgetName, group); - if (canonical === "all") return ["all"]; - merged.add(canonical); - return orderedWithoutAll(widgetName, merged); -} - -export function removeToolGroup(widgetName: string, current: ToolInput, group: string): string[] { - const merged = new Set(expandToolGroups(widgetName, current)); - merged.delete(toCanonical(widgetName, group)); - return orderedWithoutAll(widgetName, merged); -} diff --git a/widget/package-lock.json b/widget/package-lock.json index cf4e386a..ff1510fd 100644 --- a/widget/package-lock.json +++ b/widget/package-lock.json @@ -16,13 +16,11 @@ "react-dom": "^19.1.0" }, "devDependencies": { - "@anywidget/vite": "^0.2.0", "@types/react": "^19.1.3", "@types/react-dom": "^19.1.4", - "@vitejs/plugin-react": "^4.3.0", "@webgpu/types": "^0.1.68", - "typescript": "^5.8.3", - "vite": "^5.2.0" + "esbuild": "^0.21.3", + "typescript": "^5.8.3" } }, "node_modules/@anywidget/react": { @@ -46,23 +44,13 @@ "integrity": "sha512-Qno/7V0lKHCMq3DJuSKKHMwilFKPSe8wFftL5xWmgaMCc938mNTtv+i19UrvDfpj9cQTnlPqyXy8t3JOgQ8laA==", "license": "MIT" }, - "node_modules/@anywidget/vite": { - "version": "0.2.2", - "resolved": "https://registry.npmjs.org/@anywidget/vite/-/vite-0.2.2.tgz", - "integrity": "sha512-6qYispivu+VAvWJbWSsZujGAQXLW5Xve/cNA/zrz+RXeg31xfJ5bH5ylqp6UC+GpFZL8azRN6HQS24Z0IgURLw==", - "dev": true, - "license": "MIT", - "peerDependencies": { - "vite": "^4.0.0 || ^5.0.0 || ^6.0.0 || ^7.0.0" - } - }, "node_modules/@babel/code-frame": { - "version": "7.27.1", - "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.27.1.tgz", - "integrity": "sha512-cjQ7ZlQ0Mv3b47hABuTevyTuYN4i+loJKGeV9flcCgIK37cCXRh+L1bd3iBHlynerhQ7BhCkn2BPbQUL+rGqFg==", + "version": "7.29.0", + "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz", + "integrity": "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==", "license": "MIT", "dependencies": { - "@babel/helper-validator-identifier": "^7.27.1", + "@babel/helper-validator-identifier": "^7.28.5", "js-tokens": "^4.0.0", "picocolors": "^1.1.1" }, @@ -70,55 +58,14 @@ "node": ">=6.9.0" } }, - "node_modules/@babel/compat-data": { - "version": "7.28.5", - "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.28.5.tgz", - "integrity": "sha512-6uFXyCayocRbqhZOB+6XcuZbkMNimwfVGFji8CTZnCzOHVGvDqzvitu1re2AU5LROliz7eQPhB8CpAMvnx9EjA==", - 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"dev": "vite build --watch", - "build": "vite build", + "dev": "npm run build -- --watch", + "build": "node scripts/build.mjs", "typecheck": "tsc --noEmit" }, "dependencies": { @@ -17,12 +17,10 @@ "react-dom": "^19.1.0" }, "devDependencies": { - "@anywidget/vite": "^0.2.0", "@types/react": "^19.1.3", "@types/react-dom": "^19.1.4", - "@vitejs/plugin-react": "^4.3.0", "@webgpu/types": "^0.1.68", - "typescript": "^5.8.3", - "vite": "^5.2.0" + "esbuild": "^0.21.3", + "typescript": "^5.8.3" } } diff --git a/widget/src/quantem/widget/__init__.py b/widget/src/quantem/widget/__init__.py index dc98e36a..96d8aebc 100644 --- a/widget/src/quantem/widget/__init__.py +++ b/widget/src/quantem/widget/__init__.py @@ -1,7 +1,12 @@ -from importlib.metadata import version +from importlib.metadata import PackageNotFoundError, version from quantem.widget.show2d import Show2D from quantem.widget.show4dstem import Show4DSTEM -__version__ = version("quantem.widget") +try: + __version__ = version("quantem.widget") +except PackageNotFoundError: + # Source-tree imports (e.g. `PYTHONPATH=src pytest`) skip pip install. + __version__ = "0.0.0+local" + __all__ = ["Show2D", "Show4DSTEM"] diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py index a38190e6..1913a846 100644 --- a/widget/src/quantem/widget/array_utils.py +++ b/widget/src/quantem/widget/array_utils.py @@ -1,68 +1,21 @@ -""" -Array utilities for widgets. Supports NumPy + PyTorch input. -""" - -from typing import Literal +"""Array utilities for widgets. NumPy + PyTorch input.""" import numpy as np - -try: - import torch - _HAS_TORCH = True -except ImportError: - _HAS_TORCH = False - - -ArrayBackend = Literal["numpy", "torch", "unknown"] - - -def get_array_backend(data) -> ArrayBackend: - """Detect array backend. Returns 'numpy', 'torch', or 'unknown'.""" - if _HAS_TORCH and isinstance(data, torch.Tensor): - return "torch" - if isinstance(data, np.ndarray): - return "numpy" - return "unknown" +import torch def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: - """Convert NumPy or PyTorch array to NumPy. - - Parameters - ---------- - data : np.ndarray or torch.Tensor - Input array. - dtype : np.dtype, optional - Target dtype. - - Returns - ------- - np.ndarray - - Examples - -------- - >>> import numpy as np - >>> to_numpy(np.zeros((4, 4))) - >>> import torch - >>> to_numpy(torch.zeros(4, 4)) - - Raises - ------ - TypeError - If `data` is not a NumPy array or PyTorch tensor. - """ - backend = get_array_backend(data) - if backend == "torch": + """Convert NumPy / PyTorch / Dataset to NumPy.""" + if isinstance(data, torch.Tensor): result = data.detach().cpu().numpy() - elif backend == "numpy": + elif isinstance(data, np.ndarray): result = data else: - # Try np.asarray as last-resort fallback for things like Dataset arrays + # Last-resort fallback covers Dataset.__array__, dlpack-compatible objects, etc. try: result = np.asarray(data) except Exception as e: raise TypeError( - f"to_numpy expected a NumPy array or PyTorch tensor, got {type(data).__name__}. " - f"Convert your input via np.asarray(...) or tensor.cpu().numpy() first." + f"to_numpy expected a NumPy array or PyTorch tensor, got {type(data).__name__}." ) from e if dtype is not None: result = np.asarray(result, dtype=dtype) @@ -70,10 +23,7 @@ def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: - """Center-pad an image to (target_h, target_w) with zeros. - - Used to align gallery images of different shapes to a common canvas. - """ + """Center-pad image to (target_h, target_w) with zeros. For gallery alignment.""" h, w = img.shape[-2:] if h == target_h and w == target_w: return img @@ -84,28 +34,14 @@ def _resize_image(img: np.ndarray, target_h: int, target_w: int) -> np.ndarray: return np.pad(img, ((pad_top, pad_bot), (pad_left, pad_right)), mode="constant", constant_values=0) -def apply_shift(img: np.ndarray, dy: float, dx: float) -> np.ndarray: - """Sub-pixel image shift via bilinear interpolation. Used for diff alignment.""" - if not _HAS_TORCH: - # Fallback: integer roll only - return np.roll(img, (int(round(dy)), int(round(dx))), axis=(-2, -1)) - t = torch.from_numpy(img).float() - if t.ndim == 2: - t = t.unsqueeze(0).unsqueeze(0) - h, w = t.shape[-2:] - y = torch.arange(h, dtype=torch.float32) - dy - x = torch.arange(w, dtype=torch.float32) - dx - yy, xx = torch.meshgrid(y, x, indexing="ij") - grid = torch.stack(((xx / (w - 1)) * 2 - 1, (yy / (h - 1)) * 2 - 1), dim=-1).unsqueeze(0) - out = torch.nn.functional.grid_sample(t, grid, mode="bilinear", padding_mode="border", align_corners=True) - return out.squeeze().numpy() - - -def bin2d(img: np.ndarray, factor: int) -> np.ndarray: - """Reduce 2D image by integer binning factor. Mean of f×f blocks.""" +def bin2d(img: np.ndarray, factor: int, mode: str = "mean") -> np.ndarray: + """Reduce 2D image by integer binning factor. mean or sum of f×f blocks.""" if factor <= 1: return img h, w = img.shape[-2:] h2, w2 = h - h % factor, w - w % factor img = img[..., :h2, :w2] - return img.reshape(*img.shape[:-2], h2 // factor, factor, w2 // factor, factor).mean(axis=(-3, -1)) + blocks = img.reshape(*img.shape[:-2], h2 // factor, factor, w2 // factor, factor) + if mode == "sum": + return blocks.sum(axis=(-3, -1)) + return blocks.mean(axis=(-3, -1)) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index cd2dff99..f2cbed6a 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -24,12 +24,7 @@ from quantem.core.datastructures import Dataset2d, Dataset3d from quantem.widget.array_utils import to_numpy, _resize_image -from quantem.widget.json_state import resolve_widget_version, save_state_file, unwrap_state_payload -from quantem.widget.tool_parity import ( - bind_tool_runtime_api, - build_tool_groups, - normalize_tool_groups, -) +from quantem.widget.state import resolve_widget_version, save_state_file, unwrap_state_payload @@ -94,8 +89,12 @@ class Show2D(anywidget.AnyWidget): Title to display above the image(s). cmap : str, default "inferno" Colormap name ("magma", "viridis", "gray", "inferno", "plasma"). - pixel_size : float, optional - Pixel size in angstroms for scale bar display. + sampling : float or tuple of float, optional + Pixel size per axis ``(row, col)``. Scalar broadcasts to both axes. + Used for scale bar display. Defaults to ``(1, 1)``. + units : str or list of str, optional + Unit string per axis. Scalar broadcasts to both. Common: ``"A"``, + ``"nm"``, ``"pixels"``. Defaults to ``["pixels", "pixels"]``. show_fft : bool, default False Show FFT and histogram panels. show_stats : bool, default True @@ -106,7 +105,7 @@ class Show2D(anywidget.AnyWidget): Use percentile-based contrast. vmin : float, optional Absolute minimum intensity for color mapping. When both vmin and vmax - are set, all gallery images share the same intensity scale — essential + are set, all gallery images share the same intensity scale: essential for A/B visual comparison. vmax : float, optional Absolute maximum intensity for color mapping. @@ -117,24 +116,8 @@ class Show2D(anywidget.AnyWidget): ``0`` uses the frontend default: 500 px for a single image, 300 px per image in gallery mode. Pass e.g. ``size=800`` to enlarge for a presentation, or ``size=200`` to compress alongside a control panel. - This controls **display only** — the underlying image resolution is + This controls **display only**: the underlying image resolution is never resampled; zooming into a 4K image preserves every pixel. - disabled_tools : list of str, optional - Tool groups to lock while still showing controls. Supported: - ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, - ``"view"``, ``"export"``, ``"roi"``, ``"profile"``, ``"all"``. - disable_* : bool, optional - Convenience flags (``disable_display``, ``disable_histogram``, - ``disable_stats``, ``disable_navigation``, ``disable_view``, - ``disable_export``, ``disable_roi``, ``disable_profile``, - ``disable_all``) equivalent to adding those keys to - ``disabled_tools``. - hidden_tools : list of str, optional - Tool groups to hide from the UI. Uses the same keys as - ``disabled_tools``. - hide_* : bool, optional - Convenience flags mirroring ``disable_*`` for ``hidden_tools``. - Attributes ---------- render_total_ms : int or None @@ -153,17 +136,61 @@ class Show2D(anywidget.AnyWidget): -------- >>> import numpy as np >>> from quantem.widget import Show2D - >>> - >>> # Single image with FFT - >>> Show2D(image, title="HRTEM Image", show_fft=True, pixel_size=1.0) - >>> - >>> # Gallery of multiple images - >>> labels = ["Raw", "Filtered", "FFT"] - >>> Show2D([img1, img2, img3], labels=labels, ncols=3) + + Single 2D NumPy array: + + >>> Show2D(np.random.rand(512, 512)) + + PyTorch tensor (CPU or GPU, any dtype): + + >>> import torch + >>> Show2D(torch.rand(512, 512)) + + 3D NumPy stack ``(N, H, W)`` rendered as a gallery: + + >>> Show2D(np.random.rand(6, 256, 256), ncols=3) + + List of arrays with different shapes (center-padded to a common canvas): + + >>> Show2D([np.random.rand(256, 256), np.random.rand(300, 400)]) + + quantem ``Dataset2d``: title, sampling, units auto-extracted: + + >>> from quantem.core.datastructures import Dataset2d + >>> ds = Dataset2d.from_array(np.random.rand(512, 512)) + >>> Show2D(ds) + + quantem ``Dataset3d``: gallery view of N frames with calibration: + + >>> from quantem.core.datastructures import Dataset3d + >>> ds = Dataset3d.from_array(np.random.rand(6, 256, 256)) + >>> Show2D(ds, ncols=3) + + A/B comparison with shared contrast and linked zoom/pan: + + >>> a, b = np.random.rand(512, 512), np.random.rand(512, 512) + >>> Show2D([a, b], vmin=0, vmax=1, link_zoom=True, link_pan=True) + + Per-image absolute contrast (one ``vmin``/``vmax`` per image): + + >>> Show2D([a, b], vmin=[0.0, 0.2], vmax=[1.0, 0.8]) + + Drift comparison: diff mode adds a ``A - B`` panel alongside the originals + (gallery becomes ``[A, B, A - B]``): + + >>> Show2D([a, b], diff_mode=True, link_zoom=True, link_pan=True) + + Large image: display-only canvas size (full resolution preserved): + + >>> Show2D(np.random.rand(4096, 4096), size=800) + + Static export to PDF or PNG (vector PDF for publication figures): + + >>> w = Show2D(np.random.rand(512, 512), sampling=0.5, units="nm") + >>> w.save_image("figure.pdf", dpi=150) """ _esm = pathlib.Path(__file__).parent / "static" / "show2d.js" - _css = pathlib.Path(__file__).parent / "static" / "show2d.css" # ========================================================================= # Core State @@ -201,6 +228,7 @@ class Show2D(anywidget.AnyWidget): # Scale Bar # ========================================================================= pixel_size = traitlets.Float(0.0).tag(sync=True) + pixel_unit = traitlets.Unicode("pixels").tag(sync=True) scale_bar_visible = traitlets.Bool(True).tag(sync=True) size = traitlets.Int(0).tag(sync=True) # Canvas rendering size in CSS pixels; 0 = frontend default smooth = traitlets.Bool(False).tag(sync=True) @@ -218,8 +246,6 @@ class Show2D(anywidget.AnyWidget): # ========================================================================= show_controls = traitlets.Bool(True).tag(sync=True) show_stats = traitlets.Bool(True).tag(sync=True) - disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) - hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) stats_mean = traitlets.List(traitlets.Float()).tag(sync=True) stats_min = traitlets.List(traitlets.Float()).tag(sync=True) stats_max = traitlets.List(traitlets.Float()).tag(sync=True) @@ -253,114 +279,24 @@ class Show2D(anywidget.AnyWidget): # ========================================================================= image_rotations = traitlets.List(traitlets.Int(), []).tag(sync=True) - @classmethod - def _normalize_tool_groups(cls, tool_groups) -> list[str]: - return normalize_tool_groups("Show2D", tool_groups) - - @classmethod - def _build_disabled_tools( - cls, - disabled_tools=None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show2D", - tool_groups=disabled_tools, - all_flag=disable_all, - flag_map={ - "display": disable_display, - "histogram": disable_histogram, - "stats": disable_stats, - "navigation": disable_navigation, - "view": disable_view, - "export": disable_export, - "roi": disable_roi, - "profile": disable_profile, - }, - ) - - @classmethod - def _build_hidden_tools( - cls, - hidden_tools=None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show2D", - tool_groups=hidden_tools, - all_flag=hide_all, - flag_map={ - "display": hide_display, - "histogram": hide_histogram, - "stats": hide_stats, - "navigation": hide_navigation, - "view": hide_view, - "export": hide_export, - "roi": hide_roi, - "profile": hide_profile, - }, - ) - - @traitlets.validate("disabled_tools") - def _validate_disabled_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - - @traitlets.validate("hidden_tools") - def _validate_hidden_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - def __init__( self, data: np.ndarray | list[np.ndarray], labels: list[str | None] = None, title: str = "", cmap: str | Colormap = Colormap.INFERNO, - pixel_size: float = 0.0, + sampling: float | tuple[float, float] | list[float] | None = None, + units: str | list[str] | None = None, scale_bar_visible: bool = True, show_fft: bool = False, fft_window: bool = True, show_controls: bool = True, show_stats: bool = True, + verbose: bool = True, log_scale: bool = False, auto_contrast: bool = False, vmin: float | list | None = None, vmax: float | list | None = None, - disabled_tools: list[str | None] = None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_all: bool = False, - hidden_tools: list[str | None] = None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_all: bool = False, ncols: int = 3, size: int = 0, smooth: bool = False, @@ -390,70 +326,41 @@ def __init__( with self.hold_sync(): self._init_sync( data=data, labels=labels, title=title, cmap=cmap, - pixel_size=pixel_size, scale_bar_visible=scale_bar_visible, + sampling=sampling, units=units, scale_bar_visible=scale_bar_visible, show_fft=show_fft, fft_window=fft_window, show_controls=show_controls, show_stats=show_stats, log_scale=log_scale, auto_contrast=auto_contrast, vmin=vmin, vmax=vmax, - disabled_tools=disabled_tools, - disable_display=disable_display, - disable_histogram=disable_histogram, - disable_stats=disable_stats, - disable_navigation=disable_navigation, - disable_view=disable_view, - disable_export=disable_export, - disable_roi=disable_roi, - disable_profile=disable_profile, - disable_all=disable_all, - hidden_tools=hidden_tools, - hide_display=hide_display, - hide_histogram=hide_histogram, - hide_stats=hide_stats, - hide_navigation=hide_navigation, - hide_view=hide_view, - hide_export=hide_export, - hide_roi=hide_roi, - hide_profile=hide_profile, - hide_all=hide_all, ncols=ncols, size=size, smooth=smooth, zoom=zoom, zoom_row=zoom_row, zoom_col=zoom_col, link_zoom=link_zoom, link_pan=link_pan, link_contrast=link_contrast, diff_mode=diff_mode, view_box=view_box, - display_bin=display_bin, state=state, _t0=_t0) + display_bin=display_bin, verbose=verbose, state=state, _t0=_t0) - def _init_sync(self, *, data, labels, title, cmap, pixel_size, + def _init_sync(self, *, data, labels, title, cmap, sampling, units, scale_bar_visible, show_fft, fft_window, show_controls, show_stats, log_scale, auto_contrast, - vmin, vmax, disabled_tools, - disable_display, disable_histogram, disable_stats, - disable_navigation, disable_view, disable_export, - disable_roi, disable_profile, disable_all, - hidden_tools, hide_display, hide_histogram, hide_stats, - hide_navigation, hide_view, hide_export, hide_roi, - hide_profile, hide_all, + vmin, vmax, ncols, size, smooth, zoom, zoom_row, zoom_col, link_zoom, link_pan, link_contrast, diff_mode, view_box, - display_bin, state, _t0): + display_bin, verbose, state, _t0): import time as _time + self._verbose = verbose self.widget_version = resolve_widget_version() self._display_data = None # initialized after data setup self._display_bin = 1 # First-class support for quantem Dataset2d / Dataset3d: - # extract array + auto-populate title, pixel_size from sampling+units. - # (Duck-typing fallback below covers any other object exposing the same API.) + # auto-extract array + sampling + units from the dataset object. if isinstance(data, (Dataset2d, Dataset3d)) or ( hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling") ): if not title and data.name: title = data.name - if pixel_size == 0.0 and hasattr(data, "units"): - units = list(data.units) - sampling_val = float(data.sampling[-1]) - if units[-1] in ("nm",): - pixel_size = sampling_val * 10 # nm → Å - elif units[-1] in ("Å", "angstrom", "A"): - pixel_size = sampling_val + if sampling is None: + sampling = tuple(float(s) for s in data.sampling[-2:]) + if units is None and hasattr(data, "units"): + units = list(data.units[-2:]) data = data.array # Convert NumPy / PyTorch / list inputs to a NumPy array. @@ -481,7 +388,7 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, self._data = np.array(data, dtype=np.float32, copy=True) else: self._data = np.asarray(data, dtype=np.float32) - # Store originals for rotation reset — views into _data (no copy). + # Store originals for rotation reset: views into _data (no copy). # Only materialized as independent copies when a rotation is applied. self._data_original = [self._data[i] for i in range(self._data.shape[0])] self._originals_are_views = True @@ -499,7 +406,20 @@ def _init_sync(self, *, data, labels, title, cmap, pixel_size, # Options self.title = title self.cmap = cmap - self.pixel_size = pixel_size + # Resolve sampling + units to scalar pixel_size + pixel_unit (column axis). + # Scalar shorthand: sampling=0.5 → (0.5, 0.5). units="nm" → ["nm", "nm"]. + if sampling is None: + self.pixel_size = 0.0 + elif isinstance(sampling, (int, float)): + self.pixel_size = float(sampling) + else: + self.pixel_size = float(sampling[-1]) + if units is None: + self.pixel_unit = "pixels" + elif isinstance(units, str): + self.pixel_unit = units + else: + self.pixel_unit = str(units[-1]) self.scale_bar_visible = scale_bar_visible self.size = size self.smooth = smooth @@ -547,30 +467,6 @@ def _expand(v): else: self.vmin = vmin self.vmax = vmax - self.disabled_tools = self._build_disabled_tools( - disabled_tools=disabled_tools, - disable_display=disable_display, - disable_histogram=disable_histogram, - disable_stats=disable_stats, - disable_navigation=disable_navigation, - disable_view=disable_view, - disable_export=disable_export, - disable_roi=disable_roi, - disable_profile=disable_profile, - disable_all=disable_all, - ) - self.hidden_tools = self._build_hidden_tools( - hidden_tools=hidden_tools, - hide_display=hide_display, - hide_histogram=hide_histogram, - hide_stats=hide_stats, - hide_navigation=hide_navigation, - hide_view=hide_view, - hide_export=hide_export, - hide_roi=hide_roi, - hide_profile=hide_profile, - hide_all=hide_all, - ) self.ncols = ncols # Auto-bin for display: keep full-res in _data, send binned to JS. @@ -601,10 +497,11 @@ def _expand(v): self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") self.height = int(self._display_data.shape[1]) self.width = int(self._display_data.shape[2]) - if pixel_size > 0: - self.pixel_size = pixel_size * self._display_bin + if self.pixel_size > 0: + self.pixel_size = self.pixel_size * self._display_bin self._display_bin_factor = self._display_bin - print(f" Display bin {self._display_bin}×: {orig_h}×{orig_w} → {self.height}×{self.width} ({self._display_data.nbytes // 1024 // 1024} MB)") + if verbose: + print(f" Display bin {self._display_bin}×: {orig_h}×{orig_w} → {self.height}×{self.width} ({self._display_data.nbytes // 1024 // 1024} MB)") else: self._display_data = self._data self._display_bin_factor = 1 @@ -629,7 +526,7 @@ def _expand(v): # Stash wall-clock start on the instance; the observer below prints the # TRUE end-to-end time after JS signals first paint. The Python-only - # __init__ number is misleading for widget UX — a widget is not "done" + # __init__ number is misleading for widget UX: a widget is not "done" # until the browser has painted its first frame. self._init_t0 = _t0 self._init_py_elapsed_ms = (_time.perf_counter() - _t0) * 1000 @@ -646,13 +543,15 @@ def _on_first_render(self, change): mem = self._data.nbytes mem_str = f"{mem / (1 << 20):.0f} MB" if mem >= 1 << 20 else f"{mem / (1 << 10):.0f} KB" # Expose as attributes so tests and notebooks can assert on them. - # These are the ground truth for "did JS actually paint" — if they're + # These are the ground truth for "did JS actually paint": if they're # None, the JS side never signaled first render. self.render_total_ms = int(total_ms) self.render_python_build_ms = int(py_ms) self.render_wire_js_ms = int(total_ms - py_ms) + if not getattr(self, "_verbose", True): + return print( - f"Show2D: {shape} {mem_str} — " + f"Show2D: {shape} {mem_str}: " f"rendered in {total_ms:.0f} ms (Python build {py_ms:.0f} ms, " f"wire+JS {total_ms - py_ms:.0f} ms)", flush=True, @@ -660,7 +559,7 @@ def _on_first_render(self, change): # Detach observer: one-shot, we only care about the first paint. try: self.unobserve(self._on_first_render, names=["_js_rendered"]) - except Exception: + except (ValueError, KeyError): pass def set_image(self, data, labels=None): @@ -706,7 +605,8 @@ def set_image(self, data, labels=None): self.height = int(self._display_data.shape[1]) self.width = int(self._display_data.shape[2]) self._display_bin_factor = self._display_bin - print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") + if getattr(self, "_verbose", True): + print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") else: self._display_data = self._data self.height = int(data.shape[1]) @@ -900,7 +800,6 @@ def save_image( cb.set_ticklabels(tick_labels) if scalebar and self.pixel_size > 0: - from matplotlib.patches import FancyBboxPatch # Compute a nice scale bar length target_frac = 0.2 # ~20% of image width raw_length_px = target_frac * w @@ -942,9 +841,8 @@ def state_dict(self): "show_fft": self.show_fft, "fft_window": self.fft_window, "show_controls": self.show_controls, - "disabled_tools": self.disabled_tools, - "hidden_tools": self.hidden_tools, "pixel_size": self.pixel_size, + "pixel_unit": self.pixel_unit, "scale_bar_visible": self.scale_bar_visible, "size": self.size, "smooth": self.smooth, @@ -1012,10 +910,6 @@ def summary(self): if not self.fft_window: display += " (no window)" lines.append(f"Display: {display}") - if self.disabled_tools: - lines.append(f"Locked: {', '.join(self.disabled_tools)}") - if self.hidden_tools: - lines.append(f"Hidden: {', '.join(self.hidden_tools)}") if self.roi_active and self.roi_list: lines.append(f"ROI: {len(self.roi_list)} region(s)") if self.profile_line: @@ -1310,5 +1204,3 @@ def profile_distance(self): return dist_px * self.pixel_size return dist_px - -bind_tool_runtime_api(Show2D, "Show2D") diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index c514be2f..8cd0cb58 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -1,22 +1,9 @@ """ show4dstem: Fast interactive 4D-STEM viewer widget. -Apple MPS GPU limit: PyTorch's MPS backend (Apple Silicon) has a hard limit -of ~2.1 billion elements (INT_MAX = 2^31 - 1) per tensor. Datasets exceeding -this automatically fall back to CPU, which is still fast on Apple Silicon -thanks to unified memory (CPU and GPU share the same RAM). - -CUDA GPUs do not have this limit. - -Common 4D-STEM sizes (float32): - - Scan Detector Elements Size MPS? - 128×128 128×128 268M 1.0 GB yes - 128×128 256×256 1,074M 4.0 GB yes - 256×256 128×128 1,074M 4.0 GB yes - 256×256 192×192 2,416M 9.0 GB no (auto CPU, still fast) - 256×256 256×256 4,295M 16.0 GB no (auto CPU, still fast) - 512×512 256×256 17,180M 64.0 GB no (auto CPU) +Single chunked-torch path on every device (CUDA / MPS / CPU). Reductions cast +uint16 → float32 in scan-row chunks bounded by _CHUNK_BYTE_BUDGET, so transient +memory stays the same regardless of total dataset size. To reduce data size, bin k-space at the dataset level before viewing: @@ -38,19 +25,20 @@ import torch import traitlets +# Cap transient chunk memory at ~600 MB regardless of detector size. +# A 4096 × 192² × 4 byte float32 cast = 600 MB; a 4096 × 256² × 4 byte cast +# would be 1.0 GB. _chunk_rows() picks an N-rows-per-chunk that keeps the +# transient under this cap. +_CHUNK_BYTE_BUDGET = 600 * 1024 * 1024 + from quantem.core.config import validate_device from quantem.widget.array_utils import to_numpy -from quantem.widget.json_state import ( +from quantem.widget.state import ( build_json_header, resolve_widget_version, save_state_file, unwrap_state_payload, ) -from quantem.widget.tool_parity import ( - bind_tool_runtime_api, - build_tool_groups, - normalize_tool_groups, -) def _format_memory(nbytes: int) -> str: @@ -64,7 +52,6 @@ def _format_memory(nbytes: int) -> str: # Constants # ============================================================================ DEFAULT_BF_RATIO = 0.125 # BF disk radius as fraction of detector size (1/8) -SPARSE_MASK_THRESHOLD = 0.2 # Use sparse indexing below this mask coverage MIN_LOG_VALUE = 1e-10 # Minimum value for log scale to avoid log(0) DEFAULT_VI_ROI_RATIO = 0.15 # Default VI ROI size as fraction of scan dimension @@ -84,12 +71,14 @@ class Show4DSTEM(anywidget.AnyWidget): for time-series or tilt-series data. scan_shape : tuple, optional If data is flattened (N, det_rows, det_cols), provide scan dimensions. - pixel_size : float, optional - Pixel size in Å (real-space). Used for scale bar. - Auto-extracted from Dataset4dstem if not provided. - k_pixel_size : float, optional - Detector pixel size in mrad (k-space). Used for scale bar. - Auto-extracted from Dataset4dstem if not provided. + sampling : tuple of 4 floats, optional + Pixel size per axis ``(scan_row, scan_col, k_row, k_col)``. Scalar + broadcasts to all four axes. Defaults to ``(1, 1, 1, 1)``. + Auto-extracted from ``Dataset4dstem`` if not provided. + units : list of 4 str, optional + Unit string per axis. Common: ``["A", "A", "mrad", "mrad"]``. + Defaults to ``["pixels"] * 4``. Auto-extracted from + ``Dataset4dstem`` if not provided. center : tuple[float, float], optional (center_row, center_col) of the diffraction pattern in pixels. If not provided, defaults to detector center. @@ -100,43 +89,58 @@ class Show4DSTEM(anywidget.AnyWidget): frame_dim_label : str, optional Label for the frame dimension when 5D data is provided. Defaults to "Frame". Common values: "Tilt", "Time", "Focus". - disabled_tools : list of str, optional - Tool groups to lock while still showing controls. Supported: - ``"display"``, ``"histogram"``, ``"stats"``, ``"navigation"``, - ``"playback"``, ``"view"``, ``"export"``, ``"roi"``, - ``"profile"``, ``"fft"``, ``"virtual"``, ``"frame"``, ``"all"``. - disable_* : bool, optional - Convenience flags mirroring ``disabled_tools`` for each tool group, - plus ``disable_all``. - hidden_tools : list of str, optional - Tool groups to hide from the UI. Uses the same keys as - ``disabled_tools``. - hide_* : bool, optional - Convenience flags mirroring ``disable_*`` for ``hidden_tools``. - Examples -------- - >>> # From Dataset4dstem (calibration auto-extracted) - >>> from quantem.core.io.file_readers import read_emdfile_to_4dstem - >>> dataset = read_emdfile_to_4dstem("data.h5") - >>> Show4DSTEM(dataset) - - >>> # From raw array with manual calibration >>> import numpy as np - >>> data = np.random.rand(64, 64, 128, 128) - >>> Show4DSTEM(data, pixel_size=2.39, k_pixel_size=0.46) + >>> from quantem.widget import Show4DSTEM + + 4D NumPy array ``(scan_rows, scan_cols, det_rows, det_cols)``: + + >>> Show4DSTEM(np.random.rand(64, 64, 128, 128)) + + PyTorch tensor (CPU or GPU): + + >>> import torch + >>> Show4DSTEM(torch.rand(64, 64, 128, 128)) + + With explicit calibration (real-space Å, k-space mrad): + + >>> Show4DSTEM(np.random.rand(64, 64, 128, 128), + ... sampling=(2.39, 2.39, 0.46, 0.46), + ... units=["A", "A", "mrad", "mrad"]) + + quantem ``Dataset4dstem`` — calibration + units auto-extracted: + + >>> from quantem.core.datastructures import Dataset4dstem + >>> ds = Dataset4dstem.from_array(np.random.rand(64, 64, 128, 128)) + >>> Show4DSTEM(ds) + + Flattened scan ``(N, det_rows, det_cols)`` with explicit scan shape: - >>> # With raster animation - >>> widget = Show4DSTEM(dataset) - >>> widget.raster(step=2, interval_ms=50) + >>> Show4DSTEM(np.random.rand(4096, 128, 128), scan_shape=(64, 64)) - >>> # 5D time-series or tilt-series data - >>> data_5d = np.random.rand(20, 64, 64, 128, 128) # 20 frames - >>> Show4DSTEM(data_5d, frame_dim_label="Tilt") + Custom BF disk center and radius (overrides auto-detection): + + >>> Show4DSTEM(np.random.rand(64, 64, 128, 128), + ... center=(64, 64), bf_radius=12) + + 5D time-series or tilt-series ``(n_frames, scan_r, scan_c, det_r, det_c)``: + + >>> Show4DSTEM(np.random.rand(20, 64, 64, 128, 128), frame_dim_label="Tilt") + + Raster animation (scan path through 4D dataset): + + >>> w = Show4DSTEM(np.random.rand(64, 64, 128, 128)) + >>> w.raster(step=2, interval_ms=50) + + Static export to PDF or PNG (single panel or all four): + + >>> w = Show4DSTEM(np.random.rand(64, 64, 128, 128)) + >>> w.save_image("dp.pdf", view="diffraction") + >>> w.save_image("all.pdf", view="all") """ _esm = pathlib.Path(__file__).parent / "static" / "show4dstem.js" - _css = pathlib.Path(__file__).parent / "static" / "show4dstem.css" # Position in scan space widget_version = traitlets.Unicode("unknown").tag(sync=True) @@ -188,9 +192,7 @@ class Show4DSTEM(anywidget.AnyWidget): # ========================================================================= # Virtual Image (ROI-based, updates as you drag ROI on DP) # ========================================================================= - virtual_image_bytes = traitlets.Bytes(b"").tag(sync=True) # Raw float32 - vi_data_min = traitlets.Float(0.0).tag(sync=True) # Min of current VI for normalization - vi_data_max = traitlets.Float(1.0).tag(sync=True) # Max of current VI for normalization + virtual_image_bytes = traitlets.Bytes(b"").tag(sync=True) # Raw float32 (JS computes stats + range) # ========================================================================= # VI ROI (real-space region selection for summed DP) @@ -198,18 +200,25 @@ class Show4DSTEM(anywidget.AnyWidget): vi_roi_mode = traitlets.Unicode("off").tag(sync=True) # "off", "circle", "rect" vi_roi_center_row = traitlets.Float(0.0).tag(sync=True) vi_roi_center_col = traitlets.Float(0.0).tag(sync=True) + # Compound (row, col) trait — JS sets in one call; one observer fires; bytes + # never compute against split-trait state (old col + new row, or vice versa). + vi_roi_center = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0]).tag(sync=True) vi_roi_radius = traitlets.Float(5.0).tag(sync=True) vi_roi_width = traitlets.Float(10.0).tag(sync=True) vi_roi_height = traitlets.Float(10.0).tag(sync=True) - summed_dp_bytes = traitlets.Bytes(b"").tag(sync=True) # Summed DP from VI ROI - summed_dp_count = traitlets.Int(0).tag(sync=True) # Number of positions summed + # Reduction over scan positions inside vi_roi: mean is default (size-invariant DP), + # sum scales with area (quantitative counts), max picks brightest position per detector pixel. + vi_roi_reduce = traitlets.Unicode("mean").tag(sync=True) + vi_roi_dp_bytes = traitlets.Bytes(b"").tag(sync=True) # Reduced DP from VI ROI # ========================================================================= # Scale Bar # ========================================================================= - pixel_size = traitlets.Float(1.0).tag(sync=True) # Å per pixel (real-space) - k_pixel_size = traitlets.Float(1.0).tag(sync=True) # mrad per pixel (k-space) - k_calibrated = traitlets.Bool(False).tag(sync=True) # True if k-space has mrad calibration + pixel_size = traitlets.Float(1.0).tag(sync=True) # real-space pixel size (col axis) + pixel_unit = traitlets.Unicode("pixels").tag(sync=True) + k_pixel_size = traitlets.Float(1.0).tag(sync=True) # k-space pixel size (col axis) + k_pixel_unit = traitlets.Unicode("pixels").tag(sync=True) + k_calibrated = traitlets.Bool(False).tag(sync=True) # True if k-space has real units # ========================================================================= # Path Animation (programmatic crosshair control) @@ -223,14 +232,13 @@ class Show4DSTEM(anywidget.AnyWidget): # ========================================================================= # Auto-detection trigger (frontend sets to True, backend resets to False) # ========================================================================= - auto_detect_trigger = traitlets.Bool(False).tag(sync=True) # ========================================================================= # Statistics for display (mean, min, max, std) # ========================================================================= - dp_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) - vi_stats = traitlets.List(traitlets.Float(), default_value=[0.0, 0.0, 0.0, 0.0]).tag(sync=True) - mask_dc = traitlets.Bool(True).tag(sync=True) # Mask center pixel for DP stats + # dp_stats and vi_stats are computed JS-side from frame_bytes / virtual_image_bytes. + # Keeping them out of Python traits eliminates a 4-message comm race that produced + # mismatched bytes/min/max on rapid preset/ROI changes. # ========================================================================= # Display settings (synced for programmatic export parity) @@ -239,13 +247,9 @@ class Show4DSTEM(anywidget.AnyWidget): vi_colormap = traitlets.Unicode("inferno").tag(sync=True) fft_colormap = traitlets.Unicode("inferno").tag(sync=True) - dp_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" - vi_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" - fft_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" | "power" - - dp_power_exp = traitlets.Float(0.5).tag(sync=True) - vi_power_exp = traitlets.Float(0.5).tag(sync=True) - fft_power_exp = traitlets.Float(0.5).tag(sync=True) + dp_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" + vi_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" + fft_scale_mode = traitlets.Unicode("linear").tag(sync=True) # "linear" | "log" dp_vmin_pct = traitlets.Float(0.0).tag(sync=True) dp_vmax_pct = traitlets.Float(100.0).tag(sync=True) @@ -262,14 +266,19 @@ class Show4DSTEM(anywidget.AnyWidget): fft_auto = traitlets.Bool(True).tag(sync=True) show_fft = traitlets.Bool(False).tag(sync=True) + # Single-trait preset request: JS sets to "bf"/"abf"/"adf"/"haadf" → Python + # observer calls apply_preset() which batches the 5 ROI trait writes + # atomically. Avoids the JS-side ordering race where individual roi_mode/ + # radius/center traits would commit in separate comm messages. + _preset_request = traitlets.Unicode("").tag(sync=True) fft_window = traitlets.Bool(True).tag(sync=True) show_controls = traitlets.Bool(True).tag(sync=True) dp_show_colorbar = traitlets.Bool(False).tag(sync=True) - export_default_view = traitlets.Unicode("all").tag(sync=True) - export_default_format = traitlets.Unicode("png").tag(sync=True) - export_include_overlays = traitlets.Bool(True).tag(sync=True) - export_include_scalebar = traitlets.Bool(True).tag(sync=True) - export_default_dpi = traitlets.Int(300).tag(sync=True) + # VI panel auto-contrast (1st/99th percentile clip) and CSS smoothing. + # DP panel doesn't need either — Bragg spots are best read with nearest- + # neighbor + the slider's percentile range. + vi_auto_contrast = traitlets.Bool(False).tag(sync=True) + vi_smooth = traitlets.Bool(False).tag(sync=True) # ========================================================================= # Frame Animation (5D time/tilt series) @@ -294,139 +303,18 @@ class Show4DSTEM(anywidget.AnyWidget): profile_width = traitlets.Int(1).tag(sync=True) # ========================================================================= - # Tool visibility / locking - # ========================================================================= - disabled_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) - hidden_tools = traitlets.List(traitlets.Unicode()).tag(sync=True) - - @classmethod - def _normalize_tool_groups(cls, tool_groups) -> list[str]: - return normalize_tool_groups("Show4DSTEM", tool_groups) - - @classmethod - def _build_disabled_tools( - cls, - disabled_tools=None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_playback: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_fft: bool = False, - disable_virtual: bool = False, - disable_frame: bool = False, - disable_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show4DSTEM", - tool_groups=disabled_tools, - all_flag=disable_all, - flag_map={ - "display": disable_display, - "histogram": disable_histogram, - "stats": disable_stats, - "navigation": disable_navigation, - "playback": disable_playback, - "view": disable_view, - "export": disable_export, - "roi": disable_roi, - "profile": disable_profile, - "fft": disable_fft, - "virtual": disable_virtual, - "frame": disable_frame, - }, - ) - - @classmethod - def _build_hidden_tools( - cls, - hidden_tools=None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_playback: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_fft: bool = False, - hide_virtual: bool = False, - hide_frame: bool = False, - hide_all: bool = False, - ) -> list[str]: - return build_tool_groups( - "Show4DSTEM", - tool_groups=hidden_tools, - all_flag=hide_all, - flag_map={ - "display": hide_display, - "histogram": hide_histogram, - "stats": hide_stats, - "navigation": hide_navigation, - "playback": hide_playback, - "view": hide_view, - "export": hide_export, - "roi": hide_roi, - "profile": hide_profile, - "fft": hide_fft, - "virtual": hide_virtual, - "frame": hide_frame, - }, - ) - - @traitlets.validate("disabled_tools") - def _validate_disabled_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - - @traitlets.validate("hidden_tools") - def _validate_hidden_tools(self, proposal): - return self._normalize_tool_groups(proposal["value"]) - def __init__( self, data: "Dataset4dstem | np.ndarray", scan_shape: tuple[int, int] | None = None, - pixel_size: float | None = None, - k_pixel_size: float | None = None, + sampling: tuple[float, ...] | list[float] | None = None, + units: list[str] | tuple[str, ...] | None = None, center: tuple[float, float] | None = None, bf_radius: float | None = None, - precompute_virtual_images: bool = False, + precompute_virtual_images: bool = True, frame_dim_label: str | None = None, frame_labels: list[str] | None = None, title: str = "", - disabled_tools: list[str] | None = None, - disable_display: bool = False, - disable_histogram: bool = False, - disable_stats: bool = False, - disable_navigation: bool = False, - disable_playback: bool = False, - disable_view: bool = False, - disable_export: bool = False, - disable_roi: bool = False, - disable_profile: bool = False, - disable_fft: bool = False, - disable_virtual: bool = False, - disable_frame: bool = False, - disable_all: bool = False, - hidden_tools: list[str] | None = None, - hide_display: bool = False, - hide_histogram: bool = False, - hide_stats: bool = False, - hide_navigation: bool = False, - hide_playback: bool = False, - hide_view: bool = False, - hide_export: bool = False, - hide_roi: bool = False, - hide_profile: bool = False, - hide_fft: bool = False, - hide_virtual: bool = False, - hide_frame: bool = False, - hide_all: bool = False, show_fft: bool = False, fft_window: bool = True, show_controls: bool = True, @@ -445,60 +333,38 @@ def __init__( _io_labels = None - # Extract calibration from Dataset4dstem if provided - k_calibrated = False + # Auto-extract sampling + units from Dataset4dstem if available. if hasattr(data, "sampling") and hasattr(data, "array"): - # Dataset4dstem: extract calibration and array - # sampling = [scan_rows, scan_cols, det_rows, det_cols] if not title and hasattr(data, "name") and data.name: title = str(data.name) - units = getattr(data, "units", ["pixels"] * 4) - if pixel_size is None and units[0] in ("Å", "angstrom", "A", "nm"): - pixel_size = float(data.sampling[0]) - if units[0] == "nm": - pixel_size *= 10 # Convert nm to Å - if k_pixel_size is None and units[2] in ("mrad", "1/Å", "1/A"): - k_pixel_size = float(data.sampling[2]) - k_calibrated = True + if sampling is None: + sampling = tuple(float(s) for s in data.sampling) + if units is None and hasattr(data, "units"): + units = list(data.units) data = data.array + # Resolve sampling + units (4 axes for 4D-STEM): + # [scan_row, scan_col, k_row, k_col]. Scalar/None broadcast to (1, 1, 1, 1). + if sampling is None: + sampling = (1.0, 1.0, 1.0, 1.0) + elif isinstance(sampling, (int, float)): + sampling = (float(sampling),) * 4 + else: + sampling = tuple(float(s) for s in sampling) + if units is None: + units = ["pixels"] * 4 + elif isinstance(units, str): + units = [units] * 4 + else: + units = [str(u) for u in units] + self.title = title - # Store calibration values (default to 1.0 if not provided) - self.pixel_size = pixel_size if pixel_size is not None else 1.0 - self.k_pixel_size = k_pixel_size if k_pixel_size is not None else 1.0 - self.k_calibrated = k_calibrated or (k_pixel_size is not None) - self.disabled_tools = self._build_disabled_tools( - disabled_tools=disabled_tools, - disable_display=disable_display, - disable_histogram=disable_histogram, - disable_stats=disable_stats, - disable_navigation=disable_navigation, - disable_playback=disable_playback, - disable_view=disable_view, - disable_export=disable_export, - disable_roi=disable_roi, - disable_profile=disable_profile, - disable_fft=disable_fft, - disable_virtual=disable_virtual, - disable_frame=disable_frame, - disable_all=disable_all, - ) - self.hidden_tools = self._build_hidden_tools( - hidden_tools=hidden_tools, - hide_display=hide_display, - hide_histogram=hide_histogram, - hide_stats=hide_stats, - hide_navigation=hide_navigation, - hide_playback=hide_playback, - hide_view=hide_view, - hide_export=hide_export, - hide_roi=hide_roi, - hide_profile=hide_profile, - hide_fft=hide_fft, - hide_virtual=hide_virtual, - hide_frame=hide_frame, - hide_all=hide_all, - ) + self.pixel_size = sampling[1] # scan_col axis (horizontal scale bar) + self.pixel_unit = units[1] if len(units) > 1 else "pixels" + self.k_pixel_size = sampling[3] if len(sampling) > 3 else 1.0 + self.k_pixel_unit = units[3] if len(units) > 3 else "pixels" + # k-space considered calibrated when its unit is real (mrad, 1/Å, etc.). + self.k_calibrated = self.k_pixel_unit not in ("pixels", "") self.show_fft = show_fft self.fft_window = fft_window self.show_controls = show_controls @@ -508,44 +374,40 @@ def __init__( self.vi_vmax = vi_vmax # Path animation (configured via set_path() or raster()) self._path_points: list[tuple[int, int]] = [] - # Named user presets saved during this session - self._named_presets: dict[str, dict[str, Any]] = {} - # Session-scoped reproducibility log for all export calls - self._export_session_id = uuid4().hex - self._export_session_started_utc = datetime.now(timezone.utc).isoformat() - self._export_log: list[dict[str, Any]] = [] - # Sparse sampling state (for streaming/adaptive acquisition workflows) - self._sparse_samples: dict[tuple[int, int, int], np.ndarray] = {} - self._sparse_order: list[tuple[int, int, int]] = [] - # Convert to NumPy then PyTorch tensor using quantem device config - data_np = to_numpy(data) - device_str, _ = validate_device(None) # Get device from quantem config - self._device = torch.device(device_str) - # Remove saturated hot pixels in numpy (before any torch conversion) - saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if data_np.dtype != np.float32: - _tc = time.perf_counter() - data_np = data_np.astype(np.float32) - if _verbose: - print(f" astype float32: {time.perf_counter() - _tc:.2f}s") - if saturated_value is not None: - data_np[data_np >= saturated_value] = 0 + # Suppress per-trait recompute during apply_preset batch writes + self._suppress_roi_recompute = False + # Torch tensor input keeps its device (lets user pin a specific GPU via + # `data.cuda(1)`). NumPy / Dataset input gets default-validated device. + if isinstance(data, torch.Tensor): + self._device = data.device + self._data_pre = data + data_np = None + else: + device_str, _ = validate_device(None) + self._device = torch.device(device_str) + data_np = to_numpy(data) + self._data_pre = None + self._saturation_value = ( + 65535 if data_np.dtype == np.uint16 + else 255 if data_np.dtype == np.uint8 + else None + ) # Handle dimensionality — 5D loads eagerly for instant frame switching - ndim = data_np.ndim + # Resolve shape from whichever input path we took + shape = tuple(self._data_pre.shape) if self._data_pre is not None else data_np.shape + size_elements = int(np.prod(shape)) + ndim = len(shape) _tc = time.perf_counter() if ndim == 5: - self.n_frames = data_np.shape[0] - self._scan_shape = (data_np.shape[1], data_np.shape[2]) - self._det_shape = (data_np.shape[3], data_np.shape[4]) - if data_np.size > 2**31 - 1 and device_str == "mps": - self._device = torch.device("cpu") - self._data = torch.from_numpy(data_np).to(self._device) + self.n_frames = shape[0] + self._scan_shape = (shape[1], shape[2]) + self._det_shape = (shape[3], shape[4]) elif ndim == 3: self.n_frames = 1 if scan_shape is not None: self._scan_shape = scan_shape else: - n = data_np.shape[0] + n = shape[0] side = int(n ** 0.5) if side * side != n: raise ValueError( @@ -553,24 +415,45 @@ def __init__( f"Provide scan_shape explicitly." ) self._scan_shape = (side, side) - self._det_shape = (data_np.shape[1], data_np.shape[2]) - # MPS backend can't handle tensors >INT_MAX elements; fall back to CPU - if data_np.size > 2**31 - 1 and device_str == "mps": - self._device = torch.device("cpu") - self._data = torch.from_numpy(data_np).to(self._device) + self._det_shape = (shape[1], shape[2]) elif ndim == 4: self.n_frames = 1 - self._scan_shape = (data_np.shape[0], data_np.shape[1]) - self._det_shape = (data_np.shape[2], data_np.shape[3]) - if data_np.size > 2**31 - 1 and device_str == "mps": - self._device = torch.device("cpu") - self._data = torch.from_numpy(data_np).to(self._device) + self._scan_shape = (shape[0], shape[1]) + self._det_shape = (shape[2], shape[3]) else: - raise ValueError(f"Show4DSTEM expects a 3D ((N, det_h, det_w) flat-scan), 4D ((scan_h, scan_w, det_h, det_w)), or 5D ((n_frames, scan_h, scan_w, det_h, det_w)) array. Got {ndim}D. Reshape with array.reshape((scan_h, scan_w, det_h, det_w)) or pass a Dataset4dstem.") + raise ValueError(f"Show4DSTEM expects a 3D ((N, det_h, det_w) flat-scan), 4D ((scan_h, scan_w, det_h, det_w)), or 5D ((n_frames, scan_h, scan_w, det_h, det_w)) array. Got {ndim}D.") + if self._data_pre is not None: + self._data = self._data_pre if self._data_pre.device == self._device else self._data_pre.to(self._device) + del self._data_pre + else: + self._data = torch.from_numpy(data_np).to(self._device) + # Saturation filter: zero detector pixels at full-scale (65535 / 255). + # PyTorch lacks unsigned int comparison kernels, but uint16 viewed + # as int16 has identical bytes (65535 → -1) and int16 comparison + # works on every device. Apply in scan-row chunks so the transient + # bool mask stays bounded (≤600 MB) and fits constrained-VRAM + # devices (Mac 24 GB unified, etc.). View-write keeps native dtype. + sat = getattr(self, "_saturation_value", None) + view_dtype = ( + torch.int16 if sat is not None and self._data.dtype == torch.uint16 + else torch.int8 if sat is not None and self._data.dtype == torch.uint8 + else None + ) + if view_dtype is not None: + view = self._data.view(view_dtype).reshape(-1, *self._det_shape) + rows = view.shape[0] + # Bool mask transient = positions × det_h × det_w bytes; cap at budget. + pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, self._det_shape[0] * self._det_shape[1])) + for i in range(0, rows, pos_per_chunk): + chunk = view[i:i + pos_per_chunk] + chunk.masked_fill_(chunk == -1, 0) + # Keep native dtype (uint8/uint16) to bound memory at ~ data_size. + # Reductions cast in chunks (bounded transient). if _verbose: if str(self._device) == "mps": torch.mps.synchronize() - print(f" to {self._device}: {time.perf_counter() - _tc:.2f}s ({data_np.nbytes / 1e9:.1f} GB)") + n_bytes = self._data.element_size() * self._data.numel() + print(f" to {self._device}: {time.perf_counter() - _tc:.2f}s ({n_bytes / 1e9:.1f} GB)") self.shape_rows = self._scan_shape[0] self.shape_cols = self._scan_shape[1] @@ -586,17 +469,20 @@ def __init__( self._frame_labels = resolved_labels if resolved_labels: self.frame_labels = list(resolved_labels) - # Histogram axis range — first frame is enough (JS does per-frame percentile clipping) + # Histogram axis range — first frame is enough (JS does per-frame percentile clipping). + # Cast to float for min/max reductions: PyTorch CUDA lacks integer min/max kernels, + # and the first slice is tiny (144 KB at 192×192) so the cast is free. first_frame = self._data[0] if self._data.ndim == 5 else self._data - self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) - self.dp_global_max = float(first_frame.max()) + first_frame_sample = first_frame[0] if first_frame.ndim >= 3 else first_frame + if not torch.is_floating_point(first_frame_sample): + first_frame_sample = first_frame_sample.float() + self.dp_global_min = max(float(first_frame_sample.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame_sample.max()) # Cache coordinate tensors for mask creation (avoid repeated torch.arange) self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] - self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) - self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) # Setup center and BF radius det_size = min(self.det_rows, self.det_cols) if center is not None and bf_radius is not None: @@ -628,6 +514,7 @@ def __init__( self._cached_bf_virtual = None self._cached_abf_virtual = None self._cached_adf_virtual = None + self._cached_haadf_virtual = None if precompute_virtual_images and self.n_frames == 1: self._precompute_common_virtual_images() @@ -662,9 +549,9 @@ def __init__( # Frame animation (5D): observe frame_idx changes from frontend self.observe(self._on_frame_idx_change, names=["frame_idx"]) + self.observe(self._on_preset_request, names=["_preset_request"]) # Auto-detect trigger: observe changes from frontend - self.observe(self._on_auto_detect_trigger, names=["auto_detect_trigger"]) # VI ROI: observe changes for summed DP computation # Initialize VI ROI center to scan center with reasonable default sizes @@ -677,8 +564,9 @@ def __init__( self.vi_roi_height = float(default_roi_size) self.observe(self._on_vi_roi_change, names=[ "vi_roi_mode", "vi_roi_center_row", "vi_roi_center_col", - "vi_roi_radius", "vi_roi_width", "vi_roi_height" + "vi_roi_radius", "vi_roi_width", "vi_roi_height", "vi_roi_reduce" ]) + self.observe(self._on_vi_roi_center_change, names=["vi_roi_center"]) if state is not None: if isinstance(state, (str, pathlib.Path)): @@ -700,16 +588,12 @@ def set_image(self, data, scan_shape=None): data = data.array data_np = to_numpy(data) saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if data_np.dtype != np.float32: - data_np = data_np.astype(np.float32) if saturated_value is not None: data_np[data_np >= saturated_value] = 0 if data_np.ndim == 5: self.n_frames = data_np.shape[0] self._scan_shape = (data_np.shape[1], data_np.shape[2]) self._det_shape = (data_np.shape[3], data_np.shape[4]) - if data_np.size > 2**31 - 1 and str(self._device) == "mps": - self._device = torch.device("cpu") self._data = torch.from_numpy(data_np).to(self._device) elif data_np.ndim == 3: self.n_frames = 1 @@ -736,19 +620,19 @@ def set_image(self, data, scan_shape=None): self.det_rows = self._det_shape[0] self.det_cols = self._det_shape[1] first_frame = self._data[0] if self._data.ndim == 5 else self._data - self.dp_global_min = max(float(first_frame.min()), MIN_LOG_VALUE) - self.dp_global_max = float(first_frame.max()) + first_frame_sample = first_frame[0] if first_frame.ndim >= 3 else first_frame + if not torch.is_floating_point(first_frame_sample): + first_frame_sample = first_frame_sample.float() + self.dp_global_min = max(float(first_frame_sample.min()), MIN_LOG_VALUE) + self.dp_global_max = float(first_frame_sample.max()) self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] - self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) - self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) - self._sparse_samples = {} - self._sparse_order = [] self._cached_bf_virtual = None self._cached_abf_virtual = None self._cached_adf_virtual = None + self._cached_haadf_virtual = None with self.hold_trait_notifications(): self.pos_row = min(self.pos_row, self.shape_rows - 1) self.pos_col = min(self.pos_col, self.shape_cols - 1) @@ -756,7 +640,6 @@ def set_image(self, data, scan_shape=None): self._update_frame() def __repr__(self) -> str: - k_unit = "mrad" if self.k_calibrated else "px" shape = ( f"({self.n_frames}, {self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" if self.n_frames > 1 @@ -766,7 +649,7 @@ def __repr__(self) -> str: title_info = f", title='{self.title}'" if self.title else "" return ( f"Show4DSTEM(shape={shape}, " - f"sampling=({self.pixel_size} Å, {self.k_pixel_size} {k_unit}), " + f"sampling=({self.pixel_size} {self.pixel_unit}, {self.k_pixel_size} {self.k_pixel_unit}), " f"pos=({self.pos_row}, {self.pos_col}){frame_info}{title_info})" ) @@ -776,7 +659,9 @@ def state_dict(self): "pos_row": self.pos_row, "pos_col": self.pos_col, "pixel_size": self.pixel_size, + "pixel_unit": self.pixel_unit, "k_pixel_size": self.k_pixel_size, + "k_pixel_unit": self.k_pixel_unit, "k_calibrated": self.k_calibrated, "center_row": self.center_row, "center_col": self.center_col, @@ -795,16 +680,13 @@ def state_dict(self): "vi_roi_radius": self.vi_roi_radius, "vi_roi_width": self.vi_roi_width, "vi_roi_height": self.vi_roi_height, - "mask_dc": self.mask_dc, + "vi_roi_reduce": self.vi_roi_reduce, "dp_colormap": self.dp_colormap, "vi_colormap": self.vi_colormap, "fft_colormap": self.fft_colormap, "dp_scale_mode": self.dp_scale_mode, "vi_scale_mode": self.vi_scale_mode, "fft_scale_mode": self.fft_scale_mode, - "dp_power_exp": self.dp_power_exp, - "vi_power_exp": self.vi_power_exp, - "fft_power_exp": self.fft_power_exp, "dp_vmin_pct": self.dp_vmin_pct, "dp_vmax_pct": self.dp_vmax_pct, "vi_vmin_pct": self.vi_vmin_pct, @@ -820,11 +702,8 @@ def state_dict(self): "fft_window": self.fft_window, "show_controls": self.show_controls, "dp_show_colorbar": self.dp_show_colorbar, - "export_default_view": self.export_default_view, - "export_default_format": self.export_default_format, - "export_include_overlays": self.export_include_overlays, - "export_include_scalebar": self.export_include_scalebar, - "export_default_dpi": self.export_default_dpi, + "vi_auto_contrast": self.vi_auto_contrast, + "vi_smooth": self.vi_smooth, "path_interval_ms": self.path_interval_ms, "path_loop": self.path_loop, "profile_line": self.profile_line, @@ -836,8 +715,6 @@ def state_dict(self): "frame_fps": self.frame_fps, "frame_reverse": self.frame_reverse, "frame_boomerang": self.frame_boomerang, - "disabled_tools": self.disabled_tools, - "hidden_tools": self.hidden_tools, } def save(self, path: str): @@ -881,16 +758,11 @@ def free(self): gc.collect() if device == "mps": try: - import torch torch.mps.empty_cache() - except Exception: + except AttributeError: pass elif device.startswith("cuda"): - try: - import torch - torch.cuda.empty_cache() - except Exception: - pass + torch.cuda.empty_cache() if nbytes > 0: print(f"freed {_format_memory(nbytes)} ({device})") @@ -912,15 +784,10 @@ def summary(self): lines.append(f"Labels: {self._frame_labels}") else: lines.append(f"Labels: {self._frame_labels[:3]} ... ({len(self._frame_labels)} total)") - lines.append(f"Scan: {self.shape_rows}×{self.shape_cols} ({self.pixel_size:.2f} Å/px)") - k_unit = "mrad" if self.k_calibrated else "px" - lines.append(f"Detector: {self.det_rows}×{self.det_cols} ({self.k_pixel_size:.4f} {k_unit}/px)") + lines.append(f"Scan: {self.shape_rows}×{self.shape_cols} ({self.pixel_size:.2f} {self.pixel_unit}/px)") + lines.append(f"Detector: {self.det_rows}×{self.det_cols} ({self.k_pixel_size:.4f} {self.k_pixel_unit}/px)") lines.append(f"Position: ({self.pos_row}, {self.pos_col})") lines.append(f"Center: ({self.center_row:.1f}, {self.center_col:.1f}) BF r={self.bf_radius:.1f} px") - display_parts = [] - if self.mask_dc: - display_parts.append("DC masked") - lines.append(f"Display: {', '.join(display_parts) if display_parts else 'default'}") if self.roi_active: lines.append(f"ROI: {self.roi_mode} at ({self.roi_center_row:.1f}, {self.roi_center_col:.1f}) r={self.roi_radius:.1f}") if self.vi_roi_mode != "off": @@ -945,10 +812,6 @@ def summary(self): if self.profile_line and len(self.profile_line) == 2: p0, p1 = self.profile_line[0], self.profile_line[1] lines.append(f"Profile: ({p0['row']:.0f}, {p0['col']:.0f}) -> ({p1['row']:.0f}, {p1['col']:.0f}) width={self.profile_width}") - if self.disabled_tools: - lines.append(f"Locked: {', '.join(self.disabled_tools)}") - if self.hidden_tools: - lines.append(f"Hidden: {', '.join(self.hidden_tools)}") print("\n".join(lines)) # ========================================================================= @@ -1124,12 +987,12 @@ def _on_path_index_change(self, change): self.pos_row = max(0, min(self.shape_rows - 1, row)) self.pos_col = max(0, min(self.shape_cols - 1, col)) - def _on_auto_detect_trigger(self, change): - """Called when auto_detect_trigger is set to True from frontend.""" - if change["new"]: - self.auto_detect_center() - # Reset trigger to allow re-triggering - self.auto_detect_trigger = False + def _on_preset_request(self, change): + """JS preset shortcut → atomic apply_preset (no per-trait race).""" + name = (change.get("new") or "").strip().lower() + if name in ("bf", "abf", "adf", "haadf"): + self.apply_preset(name) + self._preset_request = "" # consume trigger def _on_frame_idx_change(self, change=None): """Called when frame_idx changes (5D time/tilt series). @@ -1143,12 +1006,13 @@ def _on_frame_idx_change(self, change=None): self._cached_bf_virtual = None self._cached_abf_virtual = None self._cached_adf_virtual = None + self._cached_haadf_virtual = None # Recompute virtual image and displayed frame self._compute_virtual_image_from_roi() self._update_frame() - # Recompute summed DP if VI ROI is active + # Recompute reduced DP if VI ROI is active if self.vi_roi_mode != "off": - self._compute_summed_dp_from_vi_roi() + self._compute_vi_roi_dp() # ========================================================================= # Path Animation Patterns @@ -1353,28 +1217,26 @@ def auto_detect_center(self, update_roi: bool = True) -> Self: >>> widget = Show4DSTEM(data) >>> widget.auto_detect_center() # Auto-detect and apply """ - # Sum all diffraction patterns to get average (PyTorch) - if self._data.ndim == 5: - summed_dp = self._data.sum(dim=(0, 1, 2)) - elif self._data.ndim == 4: - summed_dp = self._data.sum(dim=(0, 1)) - else: - summed_dp = self._data.sum(dim=0) + # Sum diffraction patterns over scan positions to find BF disk centroid. + # Single chunked torch float path: works identically on CUDA / MPS / CPU. + # Each chunk casts uint16 → float32 transiently (~600 MB max), accumulates. + data_flat = self._data.reshape(-1, *self._det_shape) + n_pos = data_flat.shape[0] + mean_dp = torch.zeros(self._det_shape, dtype=torch.float32, device=self._device) + # Float32 cast transient = positions × det_h × det_w × 4 bytes; cap at budget. + pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, self._det_shape[0] * self._det_shape[1] * 4)) + for i in range(0, n_pos, pos_per_chunk): + mean_dp += data_flat[i:i + pos_per_chunk].sum(dim=0, dtype=torch.float32) + + threshold = mean_dp.mean() + mean_dp.std() + mask = mean_dp > threshold - # Threshold at mean + std to isolate BF disk - threshold = summed_dp.mean() + summed_dp.std() - mask = summed_dp > threshold - - # Avoid division by zero total = mask.sum() if total == 0: return self - # Calculate centroid using cached coordinate grids cx = float((self._det_col_coords * mask).sum() / total) cy = float((self._det_row_coords * mask).sum() / total) - - # Estimate radius from mask area (A = pi*r^2) radius = float(torch.sqrt(total / torch.pi)) # Apply detected values @@ -1402,17 +1264,10 @@ def _get_frame(self, row: int, col: int) -> np.ndarray: else: return data[row, col].cpu().numpy() - def _apply_scale_mode( - self, - data: np.ndarray, - mode: str, - power_exp: float = 0.5, - ) -> np.ndarray: + def _apply_scale_mode(self, data: np.ndarray, mode: str) -> np.ndarray: arr = np.asarray(data, dtype=np.float32) if mode == "log": return np.log1p(np.maximum(arr, 0.0)).astype(np.float32) - if mode == "power": - return np.power(np.maximum(arr, 0.0), float(power_exp)).astype(np.float32) return arr.astype(np.float32) def _slider_range( @@ -1458,13 +1313,13 @@ def _get_virtual_image_array(self) -> np.ndarray: return np.zeros((self.shape_rows, self.shape_cols), dtype=np.float32) return arr.reshape(self.shape_rows, self.shape_cols).copy() - def _get_summed_dp_array(self) -> np.ndarray | None: + def _get_vi_roi_dp_array(self) -> np.ndarray | None: if self.vi_roi_mode == "off": return None - self._compute_summed_dp_from_vi_roi() - if not self.summed_dp_bytes: + self._compute_vi_roi_dp() + if not self.vi_roi_dp_bytes: return None - arr = np.frombuffer(self.summed_dp_bytes, dtype=np.float32) + arr = np.frombuffer(self.vi_roi_dp_bytes, dtype=np.float32) expected = self.det_rows * self.det_cols if arr.size != expected: return None @@ -1497,24 +1352,24 @@ def _fft_enhanced_range(self, mag: np.ndarray) -> tuple[float, float]: return dmin, pmax def _render_dp_rgb(self) -> tuple[np.ndarray, dict]: - summed_dp = self._get_summed_dp_array() - if summed_dp is not None: - raw = summed_dp - source = "summed_dp" + vi_roi_arr = self._get_vi_roi_dp_array() + if vi_roi_arr is not None: + raw = vi_roi_arr + source = "vi_roi_dp" else: raw = self._get_frame(self.pos_row, self.pos_col).astype(np.float32) source = "single_frame" scale_mode = self.dp_scale_mode - scaled = self._apply_scale_mode(raw, scale_mode, self.dp_power_exp) + scaled = self._apply_scale_mode(raw, scale_mode) data_min = float(scaled.min()) if scaled.size else 0.0 data_max = float(scaled.max()) if scaled.size else 0.0 if self.dp_vmin is not None and self.dp_vmax is not None: vmin = float(self._apply_scale_mode( - np.array([max(self.dp_vmin, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + np.array([max(self.dp_vmin, 0)], dtype=np.float32), scale_mode )[0]) vmax = float(self._apply_scale_mode( - np.array([max(self.dp_vmax, 0)], dtype=np.float32), scale_mode, self.dp_power_exp + np.array([max(self.dp_vmax, 0)], dtype=np.float32), scale_mode )[0]) else: vmin, vmax = self._slider_range(data_min, data_max, self.dp_vmin_pct, self.dp_vmax_pct) @@ -1532,15 +1387,15 @@ def _render_dp_rgb(self) -> tuple[np.ndarray, dict]: def _render_virtual_rgb(self) -> tuple[np.ndarray, dict]: raw = self._get_virtual_image_array() - scaled = self._apply_scale_mode(raw, self.vi_scale_mode, self.vi_power_exp) + scaled = self._apply_scale_mode(raw, self.vi_scale_mode) data_min = float(scaled.min()) if scaled.size else 0.0 data_max = float(scaled.max()) if scaled.size else 0.0 if self.vi_vmin is not None and self.vi_vmax is not None: vmin = float(self._apply_scale_mode( - np.array([max(self.vi_vmin, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + np.array([max(self.vi_vmin, 0)], dtype=np.float32), self.vi_scale_mode )[0]) vmax = float(self._apply_scale_mode( - np.array([max(self.vi_vmax, 0)], dtype=np.float32), self.vi_scale_mode, self.vi_power_exp + np.array([max(self.vi_vmax, 0)], dtype=np.float32), self.vi_scale_mode )[0]) else: vmin, vmax = self._slider_range(data_min, data_max, self.vi_vmin_pct, self.vi_vmax_pct) @@ -1559,7 +1414,7 @@ def _render_fft_rgb(self) -> tuple[np.ndarray, dict]: virtual_raw = self._get_virtual_image_array() fft = np.fft.fftshift(np.fft.fft2(virtual_raw)) mag = np.abs(fft).astype(np.float32) - scaled = self._apply_scale_mode(mag, self.fft_scale_mode, self.fft_power_exp) + scaled = self._apply_scale_mode(mag, self.fft_scale_mode) if self.fft_auto: display_min, display_max = self._fft_enhanced_range(scaled) else: @@ -1578,28 +1433,13 @@ def _render_fft_rgb(self) -> tuple[np.ndarray, dict]: } return rgb, metadata - def list_export_views(self) -> tuple[str, ...]: - return ("diffraction", "virtual", "fft", "all") - - def list_export_formats(self) -> tuple[str, ...]: - return ("png", "pdf") - - def list_figure_templates(self) -> tuple[str, ...]: - return ("dp_vi", "dp_vi_fft", "publication_dp_vi", "publication_dp_vi_fft") - - def list_presets(self) -> tuple[str, ...]: - builtin = ("bf", "abf", "adf", "haadf") - custom = tuple(sorted(self._named_presets.keys())) - return builtin + custom + _EXPORT_VIEWS = ("diffraction", "virtual", "fft", "all") + _EXPORT_FORMATS = ("png", "pdf") def _validate_export_view(self, view: str | None) -> str: - candidate = self.export_default_view if view is None else str(view) - view_key = str(candidate).strip().lower() - allowed = self.list_export_views() - if view_key not in allowed: - raise ValueError( - f"Unsupported view '{view}'. Supported: {', '.join(allowed)}" - ) + view_key = (view or "all").strip().lower() + if view_key not in self._EXPORT_VIEWS: + raise ValueError(f"Unsupported view '{view}'. Supported: {', '.join(self._EXPORT_VIEWS)}") return view_key def _validate_frame_idx(self, frame_idx: int | None) -> int: @@ -1628,21 +1468,10 @@ def _validate_position(self, position: tuple[int, int] | None) -> tuple[int, int ) return row, col - def _resolve_export_format( - self, - path: pathlib.Path, - fmt: str | None, - ) -> str: - if fmt is not None and str(fmt).strip(): - resolved = str(fmt).strip().lower() - else: - from_path = path.suffix.lstrip(".").lower() - resolved = from_path if from_path else str(self.export_default_format).strip().lower() - allowed = self.list_export_formats() - if resolved not in allowed: - raise ValueError( - f"Unsupported format '{resolved}'. Supported: {', '.join(allowed)}" - ) + def _resolve_export_format(self, path: pathlib.Path, fmt: str | None) -> str: + resolved = (fmt or path.suffix.lstrip(".") or "png").strip().lower() + if resolved not in self._EXPORT_FORMATS: + raise ValueError(f"Unsupported format '{resolved}'. Supported: {', '.join(self._EXPORT_FORMATS)}") return resolved @staticmethod @@ -1917,15 +1746,6 @@ def _vi_roi_metadata(self) -> dict[str, Any]: "height": float(self.vi_roi_height), } - def _export_settings_metadata(self) -> dict[str, Any]: - return { - "default_view": self.export_default_view, - "default_format": self.export_default_format, - "include_overlays": bool(self.export_include_overlays), - "include_scalebar": bool(self.export_include_scalebar), - "dpi": int(self.export_default_dpi), - } - def _build_image_export_metadata( self, export_path: pathlib.Path, @@ -1954,1111 +1774,129 @@ def _build_image_export_metadata( "display": render_meta, "include_overlays": bool(include_overlays), "include_scalebar": bool(include_scalebar), - "export_settings": self._export_settings_metadata(), } if extra: metadata.update(extra) return metadata - @staticmethod - def _sha256_file(path: pathlib.Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as f: - while True: - chunk = f.read(1_048_576) - if not chunk: - break - digest.update(chunk) - return digest.hexdigest() - - def _build_file_record( - self, - path: pathlib.Path, - metadata_path: pathlib.Path | None = None, - index: int | None = None, - ) -> dict[str, Any]: - record: dict[str, Any] = { - "path": str(path), - "sha256": self._sha256_file(path), - "size_bytes": int(path.stat().st_size), - } - if metadata_path is not None and metadata_path.exists(): - record["metadata_path"] = str(metadata_path) - record["metadata_sha256"] = self._sha256_file(metadata_path) - record["metadata_size_bytes"] = int(metadata_path.stat().st_size) - if index is not None: - record["index"] = int(index) - return record - - def _record_export_event(self, event: dict[str, Any]) -> None: - payload = { - "session_id": self._export_session_id, - "timestamp_utc": datetime.now(timezone.utc).isoformat(), - } - payload.update(event) - self._export_log.append(payload) - - def _validate_sparse_frame_idx(self, frame_idx: int | None) -> int: - if self.n_frames <= 1: - return 0 - if frame_idx is None: - return int(self.frame_idx) - idx = int(frame_idx) - if idx < 0 or idx >= self.n_frames: - raise ValueError(f"frame_idx={idx} is out of range [0, {self.n_frames - 1}]") - return idx - - def _normalize_sparse_mask(self, mask: np.ndarray) -> np.ndarray: - arr = np.asarray(mask) - if self.n_frames <= 1: - if arr.shape == (self.shape_rows, self.shape_cols): - arr = arr[None, ...] - elif arr.shape != (1, self.shape_rows, self.shape_cols): - raise ValueError( - f"mask shape {arr.shape} does not match " - f"(scan_rows, scan_cols)=({self.shape_rows}, {self.shape_cols})" - ) - elif arr.shape != (self.n_frames, self.shape_rows, self.shape_cols): - raise ValueError( - f"mask shape {arr.shape} does not match " - f"(n_frames, scan_rows, scan_cols)=({self.n_frames}, {self.shape_rows}, {self.shape_cols})" - ) - return arr.astype(bool, copy=False) - - def _coerce_dp_array(self, dp: np.ndarray) -> np.ndarray: - arr = np.asarray(to_numpy(dp), dtype=np.float32) - if arr.shape != (self.det_rows, self.det_cols): - raise ValueError( - f"dp shape {arr.shape} does not match detector_shape " - f"({self.det_rows}, {self.det_cols})" - ) - return arr - - def _write_dp_to_data(self, frame_idx: int, row: int, col: int, dp_arr: np.ndarray) -> None: - dp_tensor = torch.from_numpy(dp_arr).to(device=self._device, dtype=torch.float32) - if self.n_frames > 1: - self._data[frame_idx, row, col] = dp_tensor - elif self._data.ndim == 4: - self._data[row, col] = dp_tensor - else: - flat_idx = row * self.shape_cols + col - self._data[flat_idx] = dp_tensor - - def _ingest_scan_point_core( - self, - row: int, - col: int, - dp: np.ndarray, - frame_idx: int, - dose: float, - refresh: bool, - ) -> None: - row_i, col_i = self._validate_position((row, col)) - frame_i = self._validate_sparse_frame_idx(frame_idx) - dp_arr = self._coerce_dp_array(dp) - dose_value = float(dose) - if not np.isfinite(dose_value) or dose_value < 0: - raise ValueError(f"dose must be finite and >= 0, got {dose}") - - key = (int(frame_i), int(row_i), int(col_i)) - if key not in self._sparse_samples: - self._sparse_order.append(key) - self._sparse_samples[key] = dp_arr.copy() - self._sparse_mask[frame_i, row_i, col_i] = True - self._dose_map[frame_i, row_i, col_i] += dose_value - - self._write_dp_to_data(frame_i, row_i, col_i, dp_arr) - self.dp_global_min = max(min(float(self.dp_global_min), float(dp_arr.min())), MIN_LOG_VALUE) - self.dp_global_max = max(float(self.dp_global_max), float(dp_arr.max())) - - if refresh: - self._compute_virtual_image_from_roi() - self._update_frame() - - def _detector_integration_kernel(self) -> tuple[np.ndarray | None, tuple[int, int] | None]: - cx, cy = float(self.roi_center_col), float(self.roi_center_row) - rr, cc = np.meshgrid( - np.arange(self.det_rows, dtype=np.float32), - np.arange(self.det_cols, dtype=np.float32), - indexing="ij", - ) - if self.roi_mode == "circle" and self.roi_radius > 0: - mask = (cc - cx) ** 2 + (rr - cy) ** 2 <= float(self.roi_radius) ** 2 - return mask.astype(np.float32, copy=False), None - if self.roi_mode == "square" and self.roi_radius > 0: - half = float(self.roi_radius) - mask = (np.abs(cc - cx) <= half) & (np.abs(rr - cy) <= half) - return mask.astype(np.float32, copy=False), None - if self.roi_mode == "annular" and self.roi_radius > 0: - outer = float(self.roi_radius) - inner = float(self.roi_radius_inner) - dist_sq = (cc - cx) ** 2 + (rr - cy) ** 2 - mask = (dist_sq >= inner**2) & (dist_sq <= outer**2) - return mask.astype(np.float32, copy=False), None - if self.roi_mode == "rect" and self.roi_width > 0 and self.roi_height > 0: - hw = float(self.roi_width) / 2.0 - hh = float(self.roi_height) / 2.0 - mask = (np.abs(cc - cx) <= hw) & (np.abs(rr - cy) <= hh) - return mask.astype(np.float32, copy=False), None - row = int(max(0, min(round(cy), self.det_rows - 1))) - col = int(max(0, min(round(cx), self.det_cols - 1))) - return None, (row, col) - - def _integrate_dp_value( - self, - dp: np.ndarray, - mask: np.ndarray | None, - point_idx: tuple[int, int] | None, - ) -> float: - arr = np.asarray(dp, dtype=np.float32) - if point_idx is not None: - row, col = point_idx - return float(arr[row, col]) - if mask is None: - return 0.0 - return float((arr * mask).sum()) - - def _virtual_image_from_frame_array(self, frame_data: np.ndarray) -> np.ndarray: - arr = np.asarray(frame_data, dtype=np.float32) - if arr.shape != (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - raise ValueError( - f"frame_data shape {arr.shape} does not match " - f"({self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" - ) - mask, point_idx = self._detector_integration_kernel() - if point_idx is not None: - row, col = point_idx - return arr[:, :, row, col].astype(np.float32, copy=False) - return (arr * mask[None, None, :, :]).sum(axis=(2, 3)).astype(np.float32) - - @staticmethod - def _idw_reconstruct( - shape: tuple[int, int], - points: np.ndarray, - values: np.ndarray, - power: float = 2.0, - k_neighbors: int = 16, - ) -> np.ndarray: - if points.size == 0: - return np.zeros(shape, dtype=np.float32) - rr, cc = np.meshgrid( - np.arange(shape[0], dtype=np.float32), - np.arange(shape[1], dtype=np.float32), - indexing="ij", - ) - coords = np.stack([rr.reshape(-1), cc.reshape(-1)], axis=1) - dist_sq = ((coords[:, None, :] - points[None, :, :]) ** 2).sum(axis=2) + 1e-6 - - if k_neighbors > 0 and points.shape[0] > k_neighbors: - idx = np.argpartition(dist_sq, kth=k_neighbors - 1, axis=1)[:, :k_neighbors] - dist_sq = np.take_along_axis(dist_sq, idx, axis=1) - vals_local = values[idx] - else: - vals_local = np.broadcast_to(values[None, :], dist_sq.shape) - - weights = 1.0 / np.power(dist_sq, power / 2.0) - pred = (weights * vals_local).sum(axis=1) / np.maximum(weights.sum(axis=1), 1e-6) - return pred.reshape(shape).astype(np.float32, copy=False) - - def _resolve_reference_virtual_image( - self, - reference: str | np.ndarray, - frame_idx: int, - ) -> tuple[np.ndarray, str]: - if isinstance(reference, str): - key = reference.strip().lower() - if key != "full_raster": - raise ValueError("reference must be 'full_raster' or a NumPy array") - if self.n_frames > 1: - frame = self._data[frame_idx].detach().cpu().numpy() - elif self._data.ndim == 4: - frame = self._data.detach().cpu().numpy() - else: - frame = self._data.detach().cpu().numpy().reshape( - self.shape_rows, self.shape_cols, self.det_rows, self.det_cols - ) - return self._virtual_image_from_frame_array(frame), "full_raster" - - arr = np.asarray(to_numpy(reference), dtype=np.float32) - if arr.shape == (self.shape_rows, self.shape_cols): - return arr.astype(np.float32, copy=False), "virtual_image" - if arr.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - return self._virtual_image_from_frame_array(arr), "frame_data" - if arr.shape == (self.n_frames, self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - return self._virtual_image_from_frame_array(arr[frame_idx]), "stack_frame_data" - raise ValueError( - "Unsupported reference shape. Expected one of: " - f"(scan_rows, scan_cols), " - f"(scan_rows, scan_cols, det_rows, det_cols), or " - f"(n_frames, scan_rows, scan_cols, det_rows, det_cols)." - ) - - def _extract_sparse_samples(self, frame_idx: int) -> tuple[np.ndarray, np.ndarray]: - mask = self._sparse_mask[frame_idx] - coords = np.argwhere(mask) - if coords.size == 0: - return ( - np.zeros((0, 2), dtype=np.float32), - np.zeros((0,), dtype=np.float32), - ) - - integ_mask, point_idx = self._detector_integration_kernel() - values = np.zeros((coords.shape[0],), dtype=np.float32) - for i, (row, col) in enumerate(coords): - key = (int(frame_idx), int(row), int(col)) - dp = self._sparse_samples.get(key) - if dp is None: - dp = self._get_frame(int(row), int(col)) - values[i] = self._integrate_dp_value(dp, integ_mask, point_idx) - points = coords.astype(np.float32, copy=False) - return points, values - - def ingest_scan_point( + def save_image( self, - row: int, - col: int, - dp: np.ndarray, - frame_idx: int = 0, - dose: float | None = None, - ) -> Self: + path: str | pathlib.Path, + view: str | None = None, + position: tuple[int, int] | None = None, + frame_idx: int | None = None, + format: str | None = None, + include_metadata: bool = True, + metadata_path: str | pathlib.Path | None = None, + include_overlays: bool | None = None, + include_scalebar: bool | None = None, + restore_state: bool = True, + dpi: int | None = None, + ) -> pathlib.Path: """ - Ingest one scanned diffraction pattern into sparse acquisition state. + Save the current visualization as PNG or PDF. Parameters ---------- - row : int - Scan-space row index. - col : int - Scan-space column index. - dp : array_like - Diffraction pattern with shape ``(det_rows, det_cols)``. - frame_idx : int, default 0 - Frame index for 5D data. - dose : float, optional - Dose contribution for this acquisition event. Defaults to ``1.0``. + path : str or pathlib.Path + Output image path. + view : str, optional + One of: "diffraction", "virtual", "fft", "all". + position : tuple[int, int], optional + Temporary scan position override as (row, col) for this export. + frame_idx : int, optional + Temporary frame index override for 5D data. + format : str, optional + "png" or "pdf". If omitted, inferred from file extension. + include_metadata : bool, default True + If True, writes JSON metadata next to the image. + metadata_path : str or pathlib.Path, optional + Override metadata JSON path. + include_overlays : bool, default True + Draw ROI/profile/crosshair overlays on exported panels. + include_scalebar : bool, default True + Draw panel scale bars on exported panels. + restore_state : bool, default True + If True, temporary position/frame overrides are reverted after export. + dpi : int, optional + Export DPI metadata. Returns ------- - Show4DSTEM - Self for method chaining. + pathlib.Path + The written image path. """ - self._ingest_scan_point_core( - row=row, - col=col, - dp=dp, - frame_idx=frame_idx, - dose=1.0 if dose is None else float(dose), - refresh=True, - ) - self._record_export_event( - { - "export_kind": "ingest_scan_point", - "frame_idx": int(self._validate_sparse_frame_idx(frame_idx)), - "row": int(row), - "col": int(col), - "dose": float(1.0 if dose is None else dose), - } - ) - return self + from PIL import Image - def ingest_scan_block( - self, - rows: list[int] | np.ndarray, - cols: list[int] | np.ndarray, - dp_block: np.ndarray, - frame_idx: int = 0, - ) -> Self: - """ - Ingest multiple scanned diffraction patterns in one call. + export_path = pathlib.Path(path) + view_key = self._validate_export_view(view) + fmt = self._resolve_export_format(export_path, format) + dpi_value = 300 if dpi is None else int(dpi) + overlays_enabled = True if include_overlays is None else bool(include_overlays) + scalebar_enabled = True if include_scalebar is None else bool(include_scalebar) + if dpi_value <= 0: + raise ValueError(f"dpi must be > 0, got {dpi_value}") - Parameters - ---------- - rows : list[int] or np.ndarray - Row indices for each pattern in ``dp_block``. - cols : list[int] or np.ndarray - Column indices for each pattern in ``dp_block``. - dp_block : np.ndarray - Diffraction stack with shape ``(n_points, det_rows, det_cols)``. - frame_idx : int, default 0 - Frame index for 5D data. + export_path.parent.mkdir(parents=True, exist_ok=True) - Returns - ------- - Show4DSTEM - Self for method chaining. - """ - rows_arr = np.asarray(rows, dtype=np.int64).reshape(-1) - cols_arr = np.asarray(cols, dtype=np.int64).reshape(-1) - if rows_arr.size != cols_arr.size: - raise ValueError("rows and cols must have the same length") - - block = np.asarray(to_numpy(dp_block), dtype=np.float32) - if block.ndim == 2: - block = block[None, ...] - if block.ndim != 3 or block.shape[1:] != (self.det_rows, self.det_cols): - raise ValueError( - f"dp_block shape must be (n_points, {self.det_rows}, {self.det_cols}), got {block.shape}" - ) - if block.shape[0] != rows_arr.size: - raise ValueError( - f"dp_block has {block.shape[0]} patterns but rows/cols specify {rows_arr.size} points" - ) + prev_row, prev_col = self.pos_row, self.pos_col + prev_frame = self.frame_idx + meta_path: pathlib.Path | None = None + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) - frame_i = self._validate_sparse_frame_idx(frame_idx) - for idx in range(rows_arr.size): - self._ingest_scan_point_core( - row=int(rows_arr[idx]), - col=int(cols_arr[idx]), - dp=block[idx], - frame_idx=frame_i, - dose=1.0, - refresh=False, - ) + try: + if frame_idx is not None: + self.frame_idx = self._validate_frame_idx(frame_idx) + if position is not None: + row, col = self._validate_position(position) + self.pos_row = row + self.pos_col = col + export_row = int(self.pos_row) + export_col = int(self.pos_col) + export_frame = int(self.frame_idx) - self._compute_virtual_image_from_roi() - self._update_frame() - self._record_export_event( - { - "export_kind": "ingest_scan_block", - "frame_idx": int(frame_i), - "n_points": int(rows_arr.size), - } - ) - return self + if view_key == "diffraction": + image, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + render_meta = {"diffraction": dp_meta} + elif view_key == "virtual": + image, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + render_meta = {"virtual": vi_meta} + elif view_key == "fft": + image, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + render_meta = {"fft": fft_meta} + else: + panel_images = [] + render_meta = {} + dp_img, dp_meta = self._render_panel_image( + "diffraction", overlays_enabled, scalebar_enabled + ) + vi_img, vi_meta = self._render_panel_image( + "virtual", overlays_enabled, scalebar_enabled + ) + panel_images.extend([dp_img, vi_img]) + render_meta = {"diffraction": dp_meta, "virtual": vi_meta} + if self.show_fft: + fft_img, fft_meta = self._render_panel_image( + "fft", overlays_enabled, scalebar_enabled + ) + panel_images.append(fft_img) + render_meta["fft"] = fft_meta + image = self._compose_horizontal(panel_images) - def get_sparse_state(self) -> dict[str, Any]: - """ - Return sparse acquisition state for checkpointing or replay. - - Returns - ------- - dict - Sparse state with sampling mask, sampled diffraction stack, - sampled-point coordinates, and dose map. - """ - coords = np.argwhere(self._sparse_mask) - sampled_points = [ - {"frame_idx": int(f), "row": int(r), "col": int(c)} - for (f, r, c) in coords - ] - if coords.size: - sampled_data = np.stack( - [ - self._sparse_samples.get((int(f), int(r), int(c)), self._get_frame(int(r), int(c))) - for (f, r, c) in coords - ], - axis=0, - ).astype(np.float32, copy=False) - else: - sampled_data = np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) - - mask_payload = self._sparse_mask[0].copy() if self.n_frames <= 1 else self._sparse_mask.copy() - dose_payload = self._dose_map[0].copy() if self.n_frames <= 1 else self._dose_map.copy() - return { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "sparse_state_snapshot", - "frame_idx": int(self.frame_idx), - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "mask": mask_payload, - "sampled_data": sampled_data, - "sampled_points": sampled_points, - "dose_map": dose_payload, - "n_sampled": int(len(sampled_points)), - "total_dose": float(self._dose_map.sum()), - } - - def set_sparse_state( - self, - mask: np.ndarray, - sampled_data: np.ndarray, - ) -> Self: - """ - Restore sparse acquisition state from mask + sampled data. - - Parameters - ---------- - mask : np.ndarray - Boolean scan mask. Shape ``(scan_rows, scan_cols)`` for 4D, - or ``(n_frames, scan_rows, scan_cols)`` for 5D. - sampled_data : np.ndarray - Either compact stack ``(n_sampled, det_rows, det_cols)`` - matching row-major ``mask`` order, or dense data aligned to mask: - ``(scan_rows, scan_cols, det_rows, det_cols)`` for 4D, - ``(n_frames, scan_rows, scan_cols, det_rows, det_cols)`` for 5D. - - Returns - ------- - Show4DSTEM - Self for method chaining. - """ - mask_3d = self._normalize_sparse_mask(mask) - coords = np.argwhere(mask_3d) - - payload = np.asarray(to_numpy(sampled_data), dtype=np.float32) - n_points = int(coords.shape[0]) - - if payload.ndim == 3: - if payload.shape[0] != n_points or payload.shape[1:] != (self.det_rows, self.det_cols): - raise ValueError( - f"Compact sampled_data must be (n_sampled, {self.det_rows}, {self.det_cols}); " - f"got {payload.shape} for n_sampled={n_points}" - ) - compact = payload - elif self.n_frames <= 1 and payload.shape == (self.shape_rows, self.shape_cols, self.det_rows, self.det_cols): - compact = np.stack( - [payload[int(r), int(c)] for (_, r, c) in coords], - axis=0, - ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) - elif payload.shape == ( - self.n_frames, - self.shape_rows, - self.shape_cols, - self.det_rows, - self.det_cols, - ): - compact = np.stack( - [payload[int(f), int(r), int(c)] for (f, r, c) in coords], - axis=0, - ) if n_points else np.zeros((0, self.det_rows, self.det_cols), dtype=np.float32) - else: - raise ValueError( - "Unsupported sampled_data shape for set_sparse_state. " - "Use compact (n_sampled, det_rows, det_cols) or dense per-mask arrays." - ) - - self._sparse_samples = {} - self._sparse_order = [] - self._sparse_mask = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=bool) - self._dose_map = np.zeros((self.n_frames, self.shape_rows, self.shape_cols), dtype=np.float32) - - for idx, (frame_idx, row, col) in enumerate(coords): - self._ingest_scan_point_core( - row=int(row), - col=int(col), - dp=compact[idx], - frame_idx=int(frame_idx), - dose=1.0, - refresh=False, - ) - - self._compute_virtual_image_from_roi() - self._update_frame() - self._record_export_event( - { - "export_kind": "set_sparse_state", - "n_sampled": int(n_points), - } - ) - return self - - def _resolve_proposal_count( - self, - k: int, - frame_idx: int, - budget: dict[str, Any] | None, - ) -> int: - count = int(k) - if count < 1: - raise ValueError(f"k must be >= 1, got {k}") - if budget is None: - return count - - existing_points = int(self._sparse_mask[frame_idx].sum()) - existing_dose = float(self._dose_map[frame_idx].sum()) - total_points = int(self.shape_rows * self.shape_cols) - - if "max_new_points" in budget: - count = min(count, int(budget["max_new_points"])) - if "max_total_points" in budget: - count = min(count, max(0, int(budget["max_total_points"]) - existing_points)) - if "max_total_fraction" in budget: - allowed_total = int(round(float(budget["max_total_fraction"]) * total_points)) - count = min(count, max(0, allowed_total - existing_points)) - if "max_total_dose" in budget: - dose_per_point = float(budget.get("dose_per_point", 1.0)) - if dose_per_point <= 0: - raise ValueError("budget['dose_per_point'] must be > 0") - remaining = float(budget["max_total_dose"]) - existing_dose - count = min(count, max(0, int(math.floor(remaining / dose_per_point)))) - return max(0, int(count)) - - def propose_next_points( - self, - k: int, - strategy: str = "adaptive", - budget: dict[str, Any] | None = None, - ) -> list[tuple[int, int]]: - """ - Propose next scan points from current sparse acquisition state. - - Parameters - ---------- - k : int - Maximum number of new points to propose. - strategy : str, default "adaptive" - Proposal strategy: ``"adaptive"``, ``"random"``, or ``"raster"``. - budget : dict, optional - Optional constraints and strategy parameters. Supported keys: - ``frame_idx``, ``max_new_points``, ``max_total_points``, - ``max_total_fraction``, ``max_total_dose``, ``dose_per_point``, - ``roi_mask``, ``seed``, ``min_spacing``, ``step``, - ``local_window``, ``dose_lambda``, ``weights``, ``bidirectional``. - - Returns - ------- - list[tuple[int, int]] - Proposed ``(row, col)`` scan coordinates. - """ - budget_dict = {} if budget is None else dict(budget) - strategy_key = str(strategy).strip().lower() - if strategy_key not in {"adaptive", "random", "raster"}: - raise ValueError("strategy must be one of: adaptive, random, raster") - - frame_idx = self._validate_sparse_frame_idx(budget_dict.get("frame_idx", self.frame_idx)) - n_select = self._resolve_proposal_count(int(k), frame_idx, budget_dict) - if n_select <= 0: - return [] - - sampled_mask = self._sparse_mask[frame_idx].copy() - allowed_mask = ~sampled_mask - roi_mask_raw = budget_dict.get("roi_mask", None) - if roi_mask_raw is not None: - roi_mask = np.asarray(roi_mask_raw, dtype=bool) - if roi_mask.shape != (self.shape_rows, self.shape_cols): - raise ValueError( - f"roi_mask shape {roi_mask.shape} must match " - f"scan_shape ({self.shape_rows}, {self.shape_cols})" - ) - allowed_mask &= roi_mask - - proposals: list[tuple[int, int]] = [] - if strategy_key == "adaptive": - local_window = int(budget_dict.get("local_window", 5)) - if local_window < 1: - raise ValueError("budget['local_window'] must be >= 1") - min_spacing = int(budget_dict.get("min_spacing", 2)) - if min_spacing < 0: - raise ValueError("budget['min_spacing'] must be >= 0") - dose_lambda = float(budget_dict.get("dose_lambda", 0.25)) - if not np.isfinite(dose_lambda): - raise ValueError("budget['dose_lambda'] must be finite") - - default_weights = { - "vi_gradient": 0.4, - "vi_local_std": 0.3, - "dp_variance": 0.3, - } - merged_weights = dict(default_weights) - raw_weights = budget_dict.get("weights", None) - if raw_weights is not None: - for key, value in dict(raw_weights).items(): - if key not in default_weights: - raise ValueError( - f"Unsupported adaptive weight '{key}'. " - f"Supported: {', '.join(default_weights.keys())}" - ) - merged_weights[key] = float(value) - weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) - if weight_sum <= 0: - raise ValueError("At least one adaptive weight must be > 0") - weights = {k: max(0.0, float(v)) / weight_sum for k, v in merged_weights.items()} - - vi = self._virtual_image_for_frame(frame_idx) - grad_row, grad_col = np.gradient(vi) - vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) - mean_local = self._box_mean_map(vi, local_window) - mean_sq_local = self._box_mean_map(vi * vi, local_window) - vi_local_std = np.sqrt(np.maximum(mean_sq_local - mean_local * mean_local, 0.0)).astype(np.float32) - dp_variance = self._dp_variance_map(frame_idx=frame_idx) - - utility = ( - weights["vi_gradient"] * self._normalize_score_map(vi_gradient) - + weights["vi_local_std"] * self._normalize_score_map(vi_local_std) - + weights["dp_variance"] * self._normalize_score_map(dp_variance) - ).astype(np.float32) - - frame_dose = self._dose_map[frame_idx].astype(np.float32, copy=False) - if float(frame_dose.max()) > 0: - utility = utility - float(dose_lambda) * (frame_dose / float(frame_dose.max())) - - picks = self._select_spaced_topk( - scores=utility, - k=n_select, - min_spacing=min_spacing, - allowed_mask=allowed_mask, - excluded_mask=np.zeros_like(allowed_mask, dtype=bool), - ) - proposals = [(int(r), int(c)) for (r, c) in picks] - elif strategy_key == "random": - coords = np.argwhere(allowed_mask) - if coords.size: - seed = budget_dict.get("seed", None) - rng = np.random.default_rng(None if seed is None else int(seed)) - n_take = min(n_select, int(coords.shape[0])) - idx = rng.choice(coords.shape[0], size=n_take, replace=False) - chosen = coords[idx] - proposals = [(int(r), int(c)) for r, c in chosen] - else: - step = int(budget_dict.get("step", 1)) - if step < 1: - raise ValueError("budget['step'] must be >= 1") - bidirectional = bool(budget_dict.get("bidirectional", True)) - for row in range(0, self.shape_rows, step): - cols = list(range(0, self.shape_cols, step)) - if bidirectional and ((row // step) % 2 == 1): - cols.reverse() - for col in cols: - if allowed_mask[row, col]: - proposals.append((int(row), int(col))) - if len(proposals) >= n_select: - break - if len(proposals) >= n_select: - break - - self._record_export_event( - { - "export_kind": "propose_next_points", - "strategy": strategy_key, - "frame_idx": int(frame_idx), - "k_requested": int(k), - "k_returned": int(len(proposals)), - } - ) - return proposals - - def evaluate_against_reference( - self, - reference: str | np.ndarray = "full_raster", - metrics: list[str] | None = None, - ) -> dict[str, Any]: - """ - Evaluate sparse-sampled reconstruction against a reference image. - - Parameters - ---------- - reference : str or np.ndarray, default "full_raster" - Reference target. ``"full_raster"`` uses the current full dataset - and current ROI integration settings. Arrays are also accepted - (virtual image or full diffraction stack; see method docs). - metrics : list[str], optional - Metric names to compute. Supported: ``"rmse"``, ``"nrmse"``, - ``"mae"``, ``"psnr"``. - - Returns - ------- - dict - Evaluation summary including sampled fraction and metric values. - """ - metric_names = ( - ["rmse", "nrmse", "mae", "psnr"] - if metrics is None - else [str(name).strip().lower() for name in metrics] - ) - supported = {"rmse", "nrmse", "mae", "psnr"} - unknown = [name for name in metric_names if name not in supported] - if unknown: - raise ValueError(f"Unsupported metrics: {unknown}. Supported: {sorted(supported)}") - - frame_idx = int(self.frame_idx if self.n_frames <= 1 else self._validate_sparse_frame_idx(self.frame_idx)) - points, values = self._extract_sparse_samples(frame_idx) - if points.shape[0] == 0: - raise ValueError("No sparse samples available for evaluation. Ingest points first.") - - reference_vi, reference_kind = self._resolve_reference_virtual_image(reference, frame_idx) - reconstruction = self._idw_reconstruct( - shape=(self.shape_rows, self.shape_cols), - points=points, - values=values, - power=2.0, - k_neighbors=16, - ) - - ref = np.asarray(reference_vi, dtype=np.float32) - pred = np.asarray(reconstruction, dtype=np.float32) - diff = pred - ref - mse = float(np.mean(diff * diff)) - rmse = float(np.sqrt(mse)) - mae = float(np.mean(np.abs(diff))) - ref_range = float(ref.max() - ref.min()) + 1e-6 - nrmse = float(rmse / ref_range) - peak = float(max(float(ref.max()), 1e-6)) - psnr = 120.0 if mse <= 1e-12 else float(20.0 * np.log10(peak) - 10.0 * np.log10(mse)) - - metric_values = { - "rmse": rmse, - "nrmse": nrmse, - "mae": mae, - "psnr": psnr, - } - selected_metrics = {name: float(metric_values[name]) for name in metric_names} - - summary = { - "reference_kind": reference_kind, - "frame_idx": int(frame_idx), - "n_sampled": int(points.shape[0]), - "sampled_fraction": float(points.shape[0] / max(1, self.shape_rows * self.shape_cols)), - "metrics": selected_metrics, - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - } - self._record_export_event( - { - "export_kind": "evaluate_against_reference", - "reference_kind": reference_kind, - "frame_idx": int(frame_idx), - "n_sampled": int(points.shape[0]), - "sampled_fraction": float(summary["sampled_fraction"]), - "metrics": selected_metrics, - } - ) - return summary - - def export_session_bundle( - self, - path: str | pathlib.Path, - ) -> pathlib.Path: - """ - Export a reproducible session bundle for sparse/adaptive workflows. - - The bundle includes widget state, sparse-state arrays, a current view - image with metadata, and the reproducibility report. - - Parameters - ---------- - path : str or pathlib.Path - Output directory for bundle files. - - Returns - ------- - pathlib.Path - Path to the bundle manifest JSON. - """ - bundle_dir = pathlib.Path(path) - bundle_dir.mkdir(parents=True, exist_ok=True) - - state_path = bundle_dir / "widget_state.json" - self.save(state_path) - - sparse_state = self.get_sparse_state() - sparse_npz_path = bundle_dir / "sparse_state.npz" - np.savez_compressed( - sparse_npz_path, - mask=sparse_state["mask"], - sampled_data=sparse_state["sampled_data"], - dose_map=sparse_state["dose_map"], - ) - - sparse_points_path = bundle_dir / "sparse_points.json" - sparse_points_payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "sparse_points", - "n_sampled": int(sparse_state["n_sampled"]), - "sampled_points": sparse_state["sampled_points"], - } - sparse_points_path.write_text(json.dumps(sparse_points_payload, indent=2)) - - image_path = bundle_dir / "current_all.png" - image_written = self.save_image( - image_path, - view="all", - include_metadata=True, - include_overlays=True, - include_scalebar=True, - ) - image_meta_path = image_written.with_suffix(".json") - - report_path = self.save_reproducibility_report(bundle_dir / "reproducibility_report.json") - - manifest_path = bundle_dir / "session_bundle_manifest.json" - manifest_payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "session_bundle", - "bundle_path": str(bundle_dir), - "created_utc": datetime.now(timezone.utc).isoformat(), - "session_id": self._export_session_id, - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "sparse_summary": { - "n_sampled": int(sparse_state["n_sampled"]), - "sampled_fraction": float( - sparse_state["n_sampled"] / max(1, self.shape_rows * self.shape_cols * self.n_frames) - ), - "total_dose": float(sparse_state["total_dose"]), - }, - "files": { - "state": str(state_path), - "sparse_npz": str(sparse_npz_path), - "sparse_points_json": str(sparse_points_path), - "image": str(image_written), - "image_metadata": str(image_meta_path), - "reproducibility_report": str(report_path), - }, - } - manifest_path.write_text(json.dumps(manifest_payload, indent=2)) - - self._record_export_event( - { - "export_kind": "session_bundle", - "n_sampled": int(sparse_state["n_sampled"]), - "outputs": [ - self._build_file_record(state_path), - self._build_file_record(sparse_npz_path), - self._build_file_record(sparse_points_path), - self._build_file_record(image_written, metadata_path=image_meta_path), - self._build_file_record(report_path), - self._build_file_record(manifest_path), - ], - } - ) - return manifest_path - - def _normalize_score_map(self, values: np.ndarray) -> np.ndarray: - arr = np.asarray(values, dtype=np.float32) - if arr.size == 0: - return np.zeros_like(arr, dtype=np.float32) - vmin = float(np.percentile(arr, 1.0)) - vmax = float(np.percentile(arr, 99.0)) - if vmax <= vmin: - return np.zeros_like(arr, dtype=np.float32) - return np.clip((arr - vmin) / (vmax - vmin), 0.0, 1.0).astype(np.float32) - - def _box_mean_map(self, values: np.ndarray, window: int) -> np.ndarray: - arr = np.asarray(values, dtype=np.float32) - win = int(window) - if win <= 1: - return arr.copy() - if win % 2 == 0: - win += 1 - pad = win // 2 - padded = np.pad(arr, ((pad, pad), (pad, pad)), mode="reflect") - integral = np.pad(padded, ((1, 0), (1, 0)), mode="constant").cumsum(axis=0).cumsum(axis=1) - sums = ( - integral[win:, win:] - - integral[:-win, win:] - - integral[win:, :-win] - + integral[:-win, :-win] - ) - return (sums / float(win * win)).astype(np.float32) - - def _dp_variance_map(self, frame_idx: int | None = None) -> np.ndarray: - if frame_idx is None or self.n_frames <= 1: - data = self._frame_data - else: - idx = self._validate_sparse_frame_idx(frame_idx) - data = self._data[idx] - if data.ndim == 4: - variance = data.var(dim=(2, 3), unbiased=False) - return variance.detach().cpu().numpy().astype(np.float32, copy=False) - variance = data.var(dim=(1, 2), unbiased=False) - return variance.detach().cpu().numpy().reshape(self.shape_rows, self.shape_cols).astype(np.float32, copy=False) - - def _build_coarse_points(self, step: int, bidirectional: bool) -> list[tuple[int, int]]: - points: list[tuple[int, int]] = [] - for r in range(0, self.shape_rows, step): - cols = list(range(0, self.shape_cols, step)) - if bidirectional and ((r // step) % 2 == 1): - cols.reverse() - for c in cols: - points.append((int(r), int(c))) - return points - - def _select_spaced_topk( - self, - scores: np.ndarray, - k: int, - min_spacing: int, - allowed_mask: np.ndarray, - excluded_mask: np.ndarray, - ) -> list[tuple[int, int]]: - work = np.asarray(scores, dtype=np.float32).copy() - work[~allowed_mask] = -np.inf - work[excluded_mask] = -np.inf - selected: list[tuple[int, int]] = [] - radius = max(0, int(min_spacing)) - - for _ in range(int(max(0, k))): - flat_idx = int(np.argmax(work)) - best_score = float(work.flat[flat_idx]) - if not np.isfinite(best_score): - break - row, col = np.unravel_index(flat_idx, work.shape) - selected.append((int(row), int(col))) - if radius == 0: - work[row, col] = -np.inf - continue - r0 = max(0, row - radius) - r1 = min(work.shape[0], row + radius + 1) - c0 = max(0, col - radius) - c1 = min(work.shape[1], col + radius + 1) - rr, cc = np.ogrid[r0:r1, c0:c1] - neighborhood = (rr - row) ** 2 + (cc - col) ** 2 <= radius ** 2 - block = work[r0:r1, c0:c1] - block[neighborhood] = -np.inf - return selected - - def _nearest_neighbor_order( - self, - points: list[tuple[int, int]], - start: tuple[int, int] | None = None, - ) -> list[tuple[int, int]]: - remaining = [tuple(map(int, pt)) for pt in points] - if not remaining: - return [] - - if start is None: - current = remaining.pop(0) - else: - sr, sc = int(start[0]), int(start[1]) - start_idx = min( - range(len(remaining)), - key=lambda i: (remaining[i][0] - sr) ** 2 + (remaining[i][1] - sc) ** 2, - ) - current = remaining.pop(start_idx) - - ordered = [current] - while remaining: - cr, cc = current - next_idx = min( - range(len(remaining)), - key=lambda i: (remaining[i][0] - cr) ** 2 + (remaining[i][1] - cc) ** 2, - ) - current = remaining.pop(next_idx) - ordered.append(current) - return ordered - - def save_image( - self, - path: str | pathlib.Path, - view: str | None = None, - position: tuple[int, int] | None = None, - frame_idx: int | None = None, - format: str | None = None, - include_metadata: bool = True, - metadata_path: str | pathlib.Path | None = None, - include_overlays: bool | None = None, - include_scalebar: bool | None = None, - restore_state: bool = True, - dpi: int | None = None, - ) -> pathlib.Path: - """ - Save the current visualization as PNG or PDF. - - Parameters - ---------- - path : str or pathlib.Path - Output image path. - view : str, optional - One of: "diffraction", "virtual", "fft", "all". - position : tuple[int, int], optional - Temporary scan position override as (row, col) for this export. - frame_idx : int, optional - Temporary frame index override for 5D data. - format : str, optional - "png" or "pdf". If omitted, inferred from file extension. - include_metadata : bool, default True - If True, writes JSON metadata next to the image. - metadata_path : str or pathlib.Path, optional - Override metadata JSON path. - include_overlays : bool, optional - Draw ROI/profile/crosshair overlays on exported panels. - Defaults to ``export_include_overlays``. - include_scalebar : bool, optional - Draw panel scale bars on exported panels. - Defaults to ``export_include_scalebar``. - restore_state : bool, default True - If True, temporary position/frame overrides are reverted after export. - dpi : int, optional - Export DPI metadata. - - Returns - ------- - pathlib.Path - The written image path. - """ - from PIL import Image - - export_path = pathlib.Path(path) - view_key = self._validate_export_view(view) - fmt = self._resolve_export_format(export_path, format) - dpi_value = int(self.export_default_dpi if dpi is None else dpi) - overlays_enabled = ( - bool(self.export_include_overlays) - if include_overlays is None - else bool(include_overlays) - ) - scalebar_enabled = ( - bool(self.export_include_scalebar) - if include_scalebar is None - else bool(include_scalebar) - ) - - if dpi_value <= 0: - raise ValueError(f"dpi must be > 0, got {dpi_value}") - - export_path.parent.mkdir(parents=True, exist_ok=True) - - prev_row, prev_col = self.pos_row, self.pos_col - prev_frame = self.frame_idx - meta_path: pathlib.Path | None = None - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) - - try: - if frame_idx is not None: - self.frame_idx = self._validate_frame_idx(frame_idx) - if position is not None: - row, col = self._validate_position(position) - self.pos_row = row - self.pos_col = col - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) - - if view_key == "diffraction": - image, dp_meta = self._render_panel_image( - "diffraction", overlays_enabled, scalebar_enabled - ) - render_meta = {"diffraction": dp_meta} - elif view_key == "virtual": - image, vi_meta = self._render_panel_image( - "virtual", overlays_enabled, scalebar_enabled - ) - render_meta = {"virtual": vi_meta} - elif view_key == "fft": - image, fft_meta = self._render_panel_image( - "fft", overlays_enabled, scalebar_enabled - ) - render_meta = {"fft": fft_meta} - else: - panel_images = [] - render_meta = {} - dp_img, dp_meta = self._render_panel_image( - "diffraction", overlays_enabled, scalebar_enabled - ) - vi_img, vi_meta = self._render_panel_image( - "virtual", overlays_enabled, scalebar_enabled - ) - panel_images.extend([dp_img, vi_img]) - render_meta = {"diffraction": dp_meta, "virtual": vi_meta} - if self.show_fft: - fft_img, fft_meta = self._render_panel_image( - "fft", overlays_enabled, scalebar_enabled - ) - panel_images.append(fft_img) - render_meta["fft"] = fft_meta - image = self._compose_horizontal(panel_images) - - if fmt == "pdf": - Image.init() - image = image.convert("RGB") - image.save(export_path, format="PDF", resolution=dpi_value) - else: - image.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) + if fmt == "pdf": + Image.init() + image = image.convert("RGB") + image.save(export_path, format="PDF", resolution=dpi_value) + else: + image.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) if include_metadata: meta_path = ( @@ -3083,870 +1921,72 @@ def save_image( self.pos_row = prev_row self.pos_col = prev_col - self._record_export_event( - { - "export_kind": "single_view_image", - "view": view_key, - "format": fmt, - "position": {"row": export_row, "col": export_col}, - "frame_idx": export_frame, - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "dpi": int(dpi_value), - "outputs": [ - self._build_file_record(export_path, metadata_path=meta_path), - ], - } - ) return export_path - def _build_preset_payload(self) -> dict[str, Any]: - return { - "detector": { - "center_row": float(self.center_row), - "center_col": float(self.center_col), - "bf_radius": float(self.bf_radius), - "roi_active": bool(self.roi_active), - "roi_mode": self.roi_mode, - "roi_center_row": float(self.roi_center_row), - "roi_center_col": float(self.roi_center_col), - "roi_radius": float(self.roi_radius), - "roi_radius_inner": float(self.roi_radius_inner), - "roi_width": float(self.roi_width), - "roi_height": float(self.roi_height), - }, - "vi_roi": { - "mode": self.vi_roi_mode, - "center_row": float(self.vi_roi_center_row), - "center_col": float(self.vi_roi_center_col), - "radius": float(self.vi_roi_radius), - "width": float(self.vi_roi_width), - "height": float(self.vi_roi_height), - }, - "display": { - "mask_dc": bool(self.mask_dc), - "dp_colormap": self.dp_colormap, - "vi_colormap": self.vi_colormap, - "fft_colormap": self.fft_colormap, - "dp_scale_mode": self.dp_scale_mode, - "vi_scale_mode": self.vi_scale_mode, - "fft_scale_mode": self.fft_scale_mode, - "dp_power_exp": float(self.dp_power_exp), - "vi_power_exp": float(self.vi_power_exp), - "fft_power_exp": float(self.fft_power_exp), - "dp_vmin_pct": float(self.dp_vmin_pct), - "dp_vmax_pct": float(self.dp_vmax_pct), - "vi_vmin_pct": float(self.vi_vmin_pct), - "vi_vmax_pct": float(self.vi_vmax_pct), - "fft_vmin_pct": float(self.fft_vmin_pct), - "fft_vmax_pct": float(self.fft_vmax_pct), - "fft_auto": bool(self.fft_auto), - "show_fft": bool(self.show_fft), - "dp_show_colorbar": bool(self.dp_show_colorbar), - "profile_line": self.profile_line, - "profile_width": int(self.profile_width), - }, - "export": self._export_settings_metadata(), - } - - def _apply_preset_payload(self, preset: dict[str, Any]) -> None: - detector = preset.get("detector", {}) - vi_roi = preset.get("vi_roi", {}) - display = preset.get("display", {}) - export = preset.get("export", {}) - - detector_map = { - "center_row": "center_row", - "center_col": "center_col", - "bf_radius": "bf_radius", - "roi_active": "roi_active", - "roi_mode": "roi_mode", - "roi_center_row": "roi_center_row", - "roi_center_col": "roi_center_col", - "roi_radius": "roi_radius", - "roi_radius_inner": "roi_radius_inner", - "roi_width": "roi_width", - "roi_height": "roi_height", - } - for key, trait_name in detector_map.items(): - if key in detector and hasattr(self, trait_name): - setattr(self, trait_name, detector[key]) - - vi_roi_map = { - "mode": "vi_roi_mode", - "center_row": "vi_roi_center_row", - "center_col": "vi_roi_center_col", - "radius": "vi_roi_radius", - "width": "vi_roi_width", - "height": "vi_roi_height", - } - for key, trait_name in vi_roi_map.items(): - if key in vi_roi and hasattr(self, trait_name): - setattr(self, trait_name, vi_roi[key]) - - _display_keys = { - "dp_colormap", "vi_colormap", "fft_colormap", - "dp_scale_mode", "vi_scale_mode", "fft_scale_mode", - "dp_power_exp", "vi_power_exp", "fft_power_exp", - "dp_vmin_pct", "dp_vmax_pct", "vi_vmin_pct", "vi_vmax_pct", - "fft_vmin_pct", "fft_vmax_pct", "fft_auto", - "mask_dc", "dp_show_colorbar", "show_fft", "fft_window", - "show_controls", - } - for key, value in display.items(): - if key in _display_keys: - setattr(self, key, value) - - export_map = { - "default_view": "export_default_view", - "default_format": "export_default_format", - "include_overlays": "export_include_overlays", - "include_scalebar": "export_include_scalebar", - "dpi": "export_default_dpi", - } - for key, trait_name in export_map.items(): - if key in export and hasattr(self, trait_name): - setattr(self, trait_name, export[key]) - - def save_preset( - self, - name: str, - path: str | pathlib.Path | None = None, - ) -> dict[str, Any]: - preset_name = str(name).strip() - if not preset_name: - raise ValueError("Preset name must be non-empty.") - preset_key = preset_name.lower() - - payload = self._build_preset_payload() - self._named_presets[preset_key] = payload - - if path is not None: - out_path = pathlib.Path(path) - out_path.parent.mkdir(parents=True, exist_ok=True) - serialized = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "widget_preset", - "preset_name": preset_name, - "preset": payload, - } - out_path.write_text(json.dumps(serialized, indent=2)) - - return payload - - def load_preset( - self, - name: str, - path: str | pathlib.Path | None = None, - apply: bool = True, - ) -> dict[str, Any]: - preset_name = str(name).strip() - preset_key = preset_name.lower() - if path is not None: - payload = json.loads(pathlib.Path(path).read_text()) - if not isinstance(payload, dict): - raise ValueError("Preset file must contain a JSON object.") - if "preset" in payload: - preset = payload["preset"] - else: - preset = payload - if not isinstance(preset, dict): - raise ValueError("Preset payload must be a JSON object.") - if preset_name: - self._named_presets[preset_key] = preset - else: - if preset_key not in self._named_presets: - raise ValueError( - f"Preset '{preset_name}' not found. Available: {', '.join(self.list_presets())}" - ) - preset = self._named_presets[preset_key] - - if apply: - self._apply_preset_payload(preset) - return preset - def apply_preset(self, name: str) -> Self: preset_name = str(name).strip().lower() - if preset_name == "bf": - self.roi_active = True - self.roi_mode = "circle" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius = float(max(1.0, self.bf_radius)) - return self - if preset_name == "abf": - self.roi_active = True - self.roi_mode = "annular" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius_inner = float(max(0.5, self.bf_radius * 0.5)) - self.roi_radius = float(max(1.0, self.bf_radius)) - return self - if preset_name == "adf": - self.roi_active = True - self.roi_mode = "annular" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius_inner = float(max(1.0, self.bf_radius)) - self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 2.0)) - return self - if preset_name == "haadf": - self.roi_active = True - self.roi_mode = "annular" - self.roi_center_row = float(self.center_row) - self.roi_center_col = float(self.center_col) - self.roi_radius_inner = float(max(1.0, self.bf_radius * 2.0)) - self.roi_radius = float(max(self.roi_radius_inner + 1.0, self.bf_radius * 4.0)) - return self - - self.load_preset(preset_name, apply=True) - return self - - def _resolve_figure_template(self, template: str) -> tuple[str, list[str], bool]: - key = str(template).strip().lower() - mapping = { - "dp_vi": (["diffraction", "virtual"], False), - "dp_vi_fft": (["diffraction", "virtual", "fft"], False), - "publication_dp_vi": (["diffraction", "virtual"], True), - "publication_dp_vi_fft": (["diffraction", "virtual", "fft"], True), - } - if key not in mapping: - raise ValueError( - f"Unsupported template '{template}'. " - f"Supported: {', '.join(self.list_figure_templates())}" - ) - panels, publication = mapping[key] - return key, panels, publication - - def save_figure( - self, - path: str | pathlib.Path, - template: str = "dp_vi_fft", - position: tuple[int, int] | None = None, - frame_idx: int | None = None, - format: str | None = None, - include_metadata: bool = True, - metadata_path: str | pathlib.Path | None = None, - include_overlays: bool | None = None, - include_scalebar: bool | None = None, - restore_state: bool = True, - dpi: int | None = None, - title: str | None = None, - annotations: dict[str, str] | None = None, - ) -> pathlib.Path: - from PIL import Image, ImageDraw, ImageFont - - export_path = pathlib.Path(path) - template_key, panel_keys, publication_style = self._resolve_figure_template(template) - fmt = self._resolve_export_format(export_path, format) - dpi_value = int(self.export_default_dpi if dpi is None else dpi) - overlays_enabled = ( - bool(self.export_include_overlays) - if include_overlays is None - else bool(include_overlays) - ) - scalebar_enabled = ( - bool(self.export_include_scalebar) - if include_scalebar is None - else bool(include_scalebar) - ) - if dpi_value <= 0: - raise ValueError(f"dpi must be > 0, got {dpi_value}") - - export_path.parent.mkdir(parents=True, exist_ok=True) - font = ImageFont.load_default() - - prev_row, prev_col = self.pos_row, self.pos_col - prev_frame = self.frame_idx - meta_path: pathlib.Path | None = None - + # Batch all trait writes atomically. Without this, each individual + # trait change fires _on_roi_change, and intermediate states (e.g. mode + # just switched to "annular" but radius_inner still stale from the + # previous preset) compute a wrong mask -> black VI flashes before the + # final correct frame. hold_trait_notifications defers observers until + # all 5 traits have committed. + bf = self.bf_radius + center_row = float(self.center_row) + center_col = float(self.center_col) + self._suppress_roi_recompute = True try: - if frame_idx is not None: - self.frame_idx = self._validate_frame_idx(frame_idx) - if position is not None: - row, col = self._validate_position(position) - self.pos_row = row - self.pos_col = col - - panel_images: list[Any] = [] - render_meta: dict[str, Any] = {} - for panel_key in panel_keys: - panel, panel_meta = self._render_panel_image( - panel_key, - include_overlays=overlays_enabled, - include_scalebar=scalebar_enabled, - ) - panel_images.append(panel) - render_meta[panel_key] = panel_meta - - gap = 24 if publication_style else 8 - padding = 24 if publication_style else 10 - label_height = 22 if publication_style else 0 - title_text = title - if title_text is None and publication_style: - if self.n_frames > 1: - title_text = f"4D-STEM Figure ({self.frame_dim_label} {self.frame_idx})" - else: - title_text = "4D-STEM Figure" - title_height = 34 if title_text else 0 - - max_panel_height = max(panel.height for panel in panel_images) - total_width = padding * 2 + sum(panel.width for panel in panel_images) + gap * (len(panel_images) - 1) - total_height = padding * 2 + title_height + label_height + max_panel_height - - figure = Image.new("RGB", (total_width, total_height), color=(255, 255, 255)) - draw = ImageDraw.Draw(figure, mode="RGBA") - - y_title = padding - if title_text: - draw.text((padding, y_title), title_text, fill=(0, 0, 0, 255), font=font) - - y_panels = padding + title_height - if publication_style: - y_panels += label_height - - panel_names = { - "diffraction": "Diffraction", - "virtual": "Virtual", - "fft": "FFT", - } - annotation_map = annotations or {} - - x0 = padding - for idx, panel in enumerate(panel_images): - panel_key = panel_keys[idx] - if publication_style: - draw.text( - (x0, padding + title_height), - panel_names.get(panel_key, panel_key), - fill=(0, 0, 0, 255), - font=font, - ) - - figure.paste(panel, (x0, y_panels)) - - if publication_style: - draw.rectangle( - [(x0, y_panels), (x0 + panel.width - 1, y_panels + panel.height - 1)], - outline=(80, 80, 80, 255), - width=1, - ) - - if panel_key in annotation_map and str(annotation_map[panel_key]).strip(): - text = str(annotation_map[panel_key]).strip() - text_bbox = draw.textbbox((0, 0), text, font=font) - text_w = text_bbox[2] - text_bbox[0] - text_h = text_bbox[3] - text_bbox[1] - tx = x0 + 8 - ty = y_panels + 8 - draw.rectangle( - [(tx - 4, ty - 3), (tx + text_w + 4, ty + text_h + 3)], - fill=(0, 0, 0, 180), - ) - draw.text((tx, ty), text, fill=(255, 255, 255, 255), font=font) - - x0 += panel.width + gap - - if fmt == "pdf": - Image.init() - figure = figure.convert("RGB") - figure.save(export_path, format="PDF", resolution=dpi_value) + if preset_name == "bf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "circle" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius = float(max(1.0, bf)) + elif preset_name == "abf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius_inner = float(max(0.5, bf * 0.5)) + self.roi_radius = float(max(1.0, bf)) + elif preset_name == "adf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius_inner = float(max(1.0, bf)) + self.roi_radius = float(max(bf + 1.0, bf * 2.0)) + elif preset_name == "haadf": + with self.hold_trait_notifications(): + self.roi_active = True + self.roi_mode = "annular" + self.roi_center_row = center_row + self.roi_center_col = center_col + self.roi_radius_inner = float(max(1.0, bf * 2.0)) + self.roi_radius = float(max(bf * 2.0 + 1.0, bf * 4.0)) else: - figure.save(export_path, format="PNG", dpi=(dpi_value, dpi_value)) - - if include_metadata: - meta_path = ( - pathlib.Path(metadata_path) - if metadata_path is not None - else export_path.with_suffix(".json") - ) - metadata = self._build_image_export_metadata( - export_path=export_path, - view_key="figure", - fmt=fmt, - render_meta=render_meta, - include_overlays=overlays_enabled, - include_scalebar=scalebar_enabled, - export_kind="figure_template", - extra={ - "template": template_key, - "panels": panel_keys, - "publication_style": bool(publication_style), - "title": title_text or "", - "annotations": annotation_map, - "dpi": int(dpi_value), - }, - ) - meta_path.write_text(json.dumps(metadata, indent=2)) - finally: - if restore_state: - self.frame_idx = prev_frame - self.pos_row = prev_row - self.pos_col = prev_col - - self._record_export_event( - { - "export_kind": "figure_template", - "template": template_key, - "format": fmt, - "dpi": int(dpi_value), - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "outputs": [ - self._build_file_record(export_path, metadata_path=meta_path), - ], - } - ) - return export_path - - def _resolve_frame_sequence( - self, - frame_indices: list[int] | None, - frame_range: tuple[int, int] | None, - ) -> list[int]: - if frame_indices is not None and frame_range is not None: - raise ValueError("Use either frame_indices or frame_range, not both.") - - if frame_indices is not None: - if len(frame_indices) == 0: - raise ValueError("frame_indices cannot be empty.") - return [self._validate_frame_idx(idx) for idx in frame_indices] - - if frame_range is not None: - if len(frame_range) != 2: - raise ValueError("frame_range must be a (start, end) tuple.") - start, end = int(frame_range[0]), int(frame_range[1]) - if start > end: - raise ValueError("frame_range start must be <= end.") - return [self._validate_frame_idx(idx) for idx in range(start, end + 1)] - - return [int(i) for i in range(self.n_frames)] - - def _resolve_position_sequence( - self, - mode: str, - path_points: list[tuple[int, int]] | None, - raster_step: int, - raster_bidirectional: bool, - ) -> list[tuple[int, int]]: - if mode == "path": - points = self._path_points if path_points is None else path_points - if not points: - raise ValueError( - "Path mode requires points via set_path(...) or path_points=..." - ) - return [self._validate_position((int(r), int(c))) for r, c in points] - - if mode == "raster": - step = int(raster_step) - if step < 1: - raise ValueError("raster_step must be >= 1") - points: list[tuple[int, int]] = [] - for r in range(0, self.shape_rows, step): - cols = list(range(0, self.shape_cols, step)) - if raster_bidirectional and ((r // step) % 2 == 1): - cols.reverse() - for c in cols: - points.append((int(r), int(c))) - return points - - raise ValueError(f"Unsupported position sequence mode '{mode}'") - - def suggest_adaptive_path( - self, - coarse_step: int = 4, - target_fraction: float = 0.25, - min_spacing: int = 2, - include_coarse: bool = True, - coarse_bidirectional: bool = True, - local_window: int = 5, - dose_lambda: float = 0.25, - weights: dict[str, float] | None = None, - roi_mask: np.ndarray | None = None, - update_widget_path: bool = True, - interval_ms: int | None = None, - loop: bool = False, - autoplay: bool = False, - return_maps: bool = False, - ) -> dict[str, Any]: - """ - Suggest a sparse adaptive scan path using coarse-to-fine utility ranking. - - The planner computes utility from current virtual-image and diffraction - statistics, then selects spatially distributed high-utility points. - - Parameters - ---------- - coarse_step : int, default 4 - Spacing of the initial coarse grid. - target_fraction : float, default 0.25 - Target total sampled fraction of scan positions in (0, 1]. - min_spacing : int, default 2 - Minimum pixel spacing between selected dense points. - include_coarse : bool, default True - If True, include coarse-grid points in the returned path. - coarse_bidirectional : bool, default True - Use snake ordering for coarse-grid traversal. - local_window : int, default 5 - Window size for local-std utility component. - dose_lambda : float, default 0.25 - Penalty weight for re-sampling coarse points. - weights : dict[str, float], optional - Utility weights for keys: ``vi_gradient``, ``vi_local_std``, ``dp_variance``. - roi_mask : np.ndarray, optional - Optional boolean mask of shape ``scan_shape`` restricting dense picks. - update_widget_path : bool, default True - If True, calls ``set_path(...)`` with the suggested path. - interval_ms : int, optional - Path interval when ``update_widget_path=True``. - loop : bool, default False - Path looping behavior when ``update_widget_path=True``. - autoplay : bool, default False - Start playback immediately when ``update_widget_path=True``. - return_maps : bool, default False - If True, include utility component maps in the returned dict. - - Returns - ------- - dict - Planning result with coarse points, dense points, and final path. - """ - step = int(coarse_step) - if step < 1: - raise ValueError(f"coarse_step must be >= 1, got {coarse_step}") - - frac = float(target_fraction) - if frac <= 0 or frac > 1: - raise ValueError(f"target_fraction must be in (0, 1], got {target_fraction}") - - spacing = int(min_spacing) - if spacing < 0: - raise ValueError(f"min_spacing must be >= 0, got {min_spacing}") - - if local_window < 1: - raise ValueError(f"local_window must be >= 1, got {local_window}") - - if not np.isfinite(float(dose_lambda)): - raise ValueError("dose_lambda must be finite") - - default_weights = { - "vi_gradient": 0.4, - "vi_local_std": 0.3, - "dp_variance": 0.3, - } - merged_weights = dict(default_weights) - if weights is not None: - for key, value in weights.items(): - if key not in default_weights: - raise ValueError( - f"Unsupported utility weight '{key}'. " - f"Supported: {', '.join(default_weights.keys())}" - ) - merged_weights[key] = float(value) - - weight_sum = sum(max(0.0, float(v)) for v in merged_weights.values()) - if weight_sum <= 0: - raise ValueError("At least one utility weight must be > 0.") - normalized_weights = { - key: max(0.0, float(value)) / weight_sum - for key, value in merged_weights.items() - } - - n_total = int(self.shape_rows * self.shape_cols) - target_count = int(max(1, round(frac * n_total))) - - coarse_points = self._build_coarse_points(step=step, bidirectional=bool(coarse_bidirectional)) - coarse_count = len(coarse_points) if include_coarse else 0 - if include_coarse and target_count < coarse_count: - raise ValueError( - f"target_fraction={target_fraction} gives {target_count} points, " - f"but coarse grid already has {coarse_count}. " - "Increase target_fraction or coarse_step." - ) - dense_count = target_count - coarse_count if include_coarse else target_count - dense_count = max(0, int(dense_count)) - - vi = self._get_virtual_image_array().astype(np.float32, copy=False) - grad_row, grad_col = np.gradient(vi) - vi_gradient = np.hypot(grad_row, grad_col).astype(np.float32) - - mean_local = self._box_mean_map(vi, local_window) - mean_sq_local = self._box_mean_map(vi * vi, local_window) - variance_local = np.maximum(mean_sq_local - mean_local * mean_local, 0.0) - vi_local_std = np.sqrt(variance_local).astype(np.float32) - - dp_variance = self._dp_variance_map() - - grad_score = self._normalize_score_map(vi_gradient) - local_std_score = self._normalize_score_map(vi_local_std) - dp_var_score = self._normalize_score_map(dp_variance) - - utility = ( - normalized_weights["vi_gradient"] * grad_score - + normalized_weights["vi_local_std"] * local_std_score - + normalized_weights["dp_variance"] * dp_var_score - ).astype(np.float32) - - dose_penalty = np.zeros_like(utility, dtype=np.float32) - for row, col in coarse_points: - dose_penalty[int(row), int(col)] = 1.0 - utility = utility - float(dose_lambda) * dose_penalty - - allowed_mask = np.ones((self.shape_rows, self.shape_cols), dtype=bool) - if roi_mask is not None: - mask = np.asarray(roi_mask) - if mask.shape != (self.shape_rows, self.shape_cols): raise ValueError( - f"roi_mask shape {mask.shape} does not match scan_shape " - f"({self.shape_rows}, {self.shape_cols})" - ) - allowed_mask &= mask.astype(bool) - - excluded_mask = np.zeros_like(allowed_mask, dtype=bool) - for row, col in coarse_points: - excluded_mask[int(row), int(col)] = True - - dense_points = self._select_spaced_topk( - scores=utility, - k=dense_count, - min_spacing=spacing, - allowed_mask=allowed_mask, - excluded_mask=excluded_mask, - ) - - start_point = coarse_points[-1] if include_coarse and coarse_points else None - dense_path = self._nearest_neighbor_order(dense_points, start=start_point) - path_points = list(coarse_points) + dense_path if include_coarse else dense_path - - if update_widget_path and path_points: - interval_value = int(self.path_interval_ms if interval_ms is None else interval_ms) - if interval_value < 1: - raise ValueError(f"interval_ms must be >= 1, got {interval_value}") - self.set_path( - points=path_points, - interval_ms=interval_value, - loop=bool(loop), - autoplay=bool(autoplay), - ) - - result: dict[str, Any] = { - "target_fraction": float(frac), - "target_count": int(target_count), - "coarse_step": int(step), - "coarse_count": int(len(coarse_points)), - "dense_count": int(len(dense_points)), - "path_count": int(len(path_points)), - "weights": normalized_weights, - "dose_lambda": float(dose_lambda), - "coarse_points": coarse_points, - "dense_points": dense_points, - "path_points": path_points, - "selected_fraction": float(len(path_points) / max(1, n_total)), - } - if return_maps: - result["utility_map"] = utility - result["utility_components"] = { - "vi_gradient": grad_score, - "vi_local_std": local_std_score, - "dp_variance": dp_var_score, - "dose_penalty": dose_penalty, - } - - self._record_export_event( - { - "export_kind": "adaptive_path_suggestion", - "target_fraction": float(frac), - "target_count": int(target_count), - "coarse_step": int(step), - "coarse_count": int(len(coarse_points)), - "dense_count": int(len(dense_points)), - "path_count": int(len(path_points)), - "selected_fraction": float(len(path_points) / max(1, n_total)), - "weights": normalized_weights, - "dose_lambda": float(dose_lambda), - } - ) - return result - - def save_sequence( - self, - output_dir: str | pathlib.Path, - mode: str = "path", - view: str | None = None, - format: str | None = None, - include_metadata: bool = True, - include_overlays: bool | None = None, - include_scalebar: bool | None = None, - frame_idx: int | None = None, - position: tuple[int, int] | None = None, - path_points: list[tuple[int, int]] | None = None, - raster_step: int = 1, - raster_bidirectional: bool = False, - frame_indices: list[int] | None = None, - frame_range: tuple[int, int] | None = None, - filename_prefix: str | None = None, - manifest_name: str = "save_sequence_manifest.json", - restore_state: bool = True, - dpi: int | None = None, - ) -> pathlib.Path: - output_root = pathlib.Path(output_dir) - output_root.mkdir(parents=True, exist_ok=True) - mode_key = str(mode).strip().lower() - if mode_key not in {"path", "raster", "frames"}: - raise ValueError("mode must be one of: path, raster, frames") - - view_key = self._validate_export_view(view) - fmt = self._resolve_export_format(pathlib.Path(f"sequence.{self.export_default_format}"), format or self.export_default_format) - dpi_value = int(self.export_default_dpi if dpi is None else dpi) - overlays_enabled = ( - bool(self.export_include_overlays) - if include_overlays is None - else bool(include_overlays) - ) - scalebar_enabled = ( - bool(self.export_include_scalebar) - if include_scalebar is None - else bool(include_scalebar) - ) - if dpi_value <= 0: - raise ValueError(f"dpi must be > 0, got {dpi_value}") - - export_rows: list[dict[str, Any]] = [] - prefix = ( - str(filename_prefix).strip() - if filename_prefix is not None and str(filename_prefix).strip() - else f"{mode_key}_{view_key}" - ) - - prev_row, prev_col = self.pos_row, self.pos_col - prev_frame = self.frame_idx - frame_for_paths = self._validate_frame_idx(frame_idx) if frame_idx is not None else int(self.frame_idx) - - if mode_key == "frames": - row, col = self._validate_position(position) - frames = self._resolve_frame_sequence(frame_indices, frame_range) - jobs = [ - {"row": int(row), "col": int(col), "frame_idx": int(fi)} - for fi in frames - ] - else: - positions = self._resolve_position_sequence( - mode=mode_key, - path_points=path_points, - raster_step=raster_step, - raster_bidirectional=raster_bidirectional, - ) - jobs = [ - {"row": int(r), "col": int(c), "frame_idx": int(frame_for_paths)} - for r, c in positions - ] - - try: - for idx, job in enumerate(jobs): - row = int(job["row"]) - col = int(job["col"]) - fr = int(job["frame_idx"]) - basename = ( - f"{prefix}_{idx:04d}_f{fr:04d}_r{row:04d}_c{col:04d}.{fmt}" - ) - out_path = output_root / basename - out_meta = out_path.with_suffix(".json") if include_metadata else None - - self.save_image( - out_path, - view=view_key, - position=(row, col), - frame_idx=fr, - format=fmt, - include_metadata=include_metadata, - metadata_path=out_meta, - include_overlays=overlays_enabled, - include_scalebar=scalebar_enabled, - restore_state=False, - dpi=dpi_value, + f"Unknown preset {name!r}. Choices: 'bf', 'abf', 'adf', 'haadf'." ) - - record = { - "index": int(idx), - "row": row, - "col": col, - "frame_idx": fr, - } - record.update(self._build_file_record(out_path, metadata_path=out_meta, index=idx)) - export_rows.append(record) finally: - if restore_state: - self.frame_idx = prev_frame - self.pos_row = prev_row - self.pos_col = prev_col - - manifest_path = output_root / str(manifest_name) - manifest_payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "sequence_batch", - "mode": mode_key, - "view": view_key, - "image_format": fmt, - "output_dir": str(output_root), - "filename_prefix": prefix, - "n_exports": int(len(export_rows)), - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "dpi": int(dpi_value), - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "exports": export_rows, - } - manifest_path.write_text(json.dumps(manifest_payload, indent=2)) - - manifest_record = self._build_file_record(manifest_path) - self._record_export_event( - { - "export_kind": "sequence_batch", - "mode": mode_key, - "view": view_key, - "format": fmt, - "n_exports": int(len(export_rows)), - "include_overlays": bool(overlays_enabled), - "include_scalebar": bool(scalebar_enabled), - "dpi": int(dpi_value), - "outputs": [manifest_record], - } - ) - return manifest_path + self._suppress_roi_recompute = False + # Single recompute with final, consistent state. + self._compute_virtual_image_from_roi() + return self - def save_reproducibility_report( - self, - path: str | pathlib.Path, - ) -> pathlib.Path: - report_path = pathlib.Path(path) - report_path.parent.mkdir(parents=True, exist_ok=True) - payload = { - **build_json_header("Show4DSTEM"), - "format": "json", - "export_kind": "reproducibility_report", - "session_id": self._export_session_id, - "session_started_utc": self._export_session_started_utc, - "report_generated_utc": datetime.now(timezone.utc).isoformat(), - "scan_shape": {"rows": int(self.shape_rows), "cols": int(self.shape_cols)}, - "detector_shape": {"rows": int(self.det_rows), "cols": int(self.det_cols)}, - "n_exports": int(len(self._export_log)), - "exports": self._export_log, - } - report_path.write_text(json.dumps(payload, indent=2)) - return report_path def _normalize_frame(self, frame: np.ndarray) -> np.ndarray: mode = self.dp_scale_mode - scaled = self._apply_scale_mode(frame, mode, self.dp_power_exp) + scaled = self._apply_scale_mode(frame, mode) if self.dp_vmin is not None and self.dp_vmax is not None: fmin = float(self._apply_scale_mode( - np.array([max(self.dp_vmin, 0)], dtype=np.float32), mode, self.dp_power_exp + np.array([max(self.dp_vmin, 0)], dtype=np.float32), mode )[0]) fmax = float(self._apply_scale_mode( - np.array([max(self.dp_vmax, 0)], dtype=np.float32), mode, self.dp_power_exp + np.array([max(self.dp_vmax, 0)], dtype=np.float32), mode )[0]) else: fmin = float(scaled.min()) @@ -4038,31 +2078,14 @@ def _update_frame(self, change=None): else: frame = data[self.pos_row, self.pos_col] - # Compute stats from frame (optionally mask DC component) - if self.mask_dc and self.det_rows > 3 and self.det_cols > 3: - # Mask center 3x3 region for stats using detected center (not geometric center) - cr = int(round(self.center_row)) - cc = int(round(self.center_col)) - cr = max(1, min(self.det_rows - 2, cr)) - cc = max(1, min(self.det_cols - 2, cc)) - mask = torch.ones_like(frame, dtype=torch.bool) - mask[cr-1:cr+2, cc-1:cc+2] = False - masked_vals = frame[mask] - self.dp_stats = [ - float(masked_vals.mean()), - float(masked_vals.min()), - float(masked_vals.max()), - float(masked_vals.std()), - ] - else: - self.dp_stats = [ - float(frame.mean()), - float(frame.min()), - float(frame.max()), - float(frame.std()), - ] - - # Convert to numpy only for sending bytes to frontend + # Cast small frame to float32 for stats and JS transfer. Bulk data + # stays in native dtype; only this single 192×192 (~144 KB) frame + # gets promoted. + if frame.dtype != torch.float32: + frame = frame.float() + # Stats compute moved to JS (frontend has frame_bytes; computeStats() in + # js/stats.ts does mean/min/max/std on the Float32Array directly, + # avoiding 4 sync trait round-trips per scan-position click). self.frame_bytes = frame.cpu().numpy().tobytes() def _on_roi_change(self, change=None): @@ -4072,6 +2095,8 @@ def _on_roi_change(self, change=None): """ if not self.roi_active: return + if getattr(self, "_suppress_roi_recompute", False): + return self._compute_virtual_image_from_roi() def _on_roi_center_change(self, change=None): @@ -4082,6 +2107,8 @@ def _on_roi_center_change(self, change=None): """ if not self.roi_active: return + if getattr(self, "_suppress_roi_recompute", False): + return if change and "new" in change: row, col = change["new"] # Sync to individual traits (without triggering _on_roi_change observers) @@ -4091,19 +2118,36 @@ def _on_roi_center_change(self, change=None): self.observe(self._on_roi_change, names=["roi_center_col", "roi_center_row"]) self._compute_virtual_image_from_roi() + def _on_vi_roi_center_change(self, change=None): + """Apply compound (row, col) update atomically (avoids split-trait race).""" + if change and "new" in change: + row, col = change["new"] + self.unobserve(self._on_vi_roi_change, names=["vi_roi_center_row", "vi_roi_center_col"]) + self.vi_roi_center_row = float(row) + self.vi_roi_center_col = float(col) + self.observe(self._on_vi_roi_change, names=["vi_roi_center_row", "vi_roi_center_col"]) + if self.vi_roi_mode == "off": + self.vi_roi_dp_bytes = b"" + return + self._compute_vi_roi_dp() + def _on_vi_roi_change(self, change=None): - """Compute summed DP when VI ROI changes.""" + """Recompute reduced DP when VI ROI or reduction changes.""" if self.vi_roi_mode == "off": - self.summed_dp_bytes = b"" - self.summed_dp_count = 0 + self.vi_roi_dp_bytes = b"" return - self._compute_summed_dp_from_vi_roi() + self._compute_vi_roi_dp() + + def _compute_vi_roi_dp(self): + """Reduce diffraction patterns over scan positions inside VI ROI. - def _compute_summed_dp_from_vi_roi(self): - """Sum diffraction patterns from positions inside VI ROI (PyTorch).""" + Reduction selected by `vi_roi_reduce`: + - "mean": average DP (size-invariant, default for region-of-interest analysis) + - "sum": total counts (scales with ROI area; use for quantitative integration) + - "max": brightest pixel per detector position across the region + """ if self._data is None: return - # Create mask in scan space using cached coordinates if self.vi_roi_mode == "circle": mask = (self._scan_row_coords - self.vi_roi_center_row) ** 2 + (self._scan_col_coords - self.vi_roi_center_col) ** 2 <= self.vi_roi_radius ** 2 elif self.vi_roi_mode == "square": @@ -4116,27 +2160,41 @@ def _compute_summed_dp_from_vi_roi(self): else: return - # Count positions in mask n_positions = int(mask.sum()) if n_positions == 0: - self.summed_dp_bytes = b"" - self.summed_dp_count = 0 + self.vi_roi_dp_bytes = b"" return - self.summed_dp_count = n_positions - - # Compute average DP using masked sum (vectorized) + reduce = self.vi_roi_reduce data = self._frame_data - if data.ndim == 4: - # (scan_rows, scan_cols, det_rows, det_cols) - sum over masked scan positions - avg_dp = data[mask].mean(dim=0) - else: - # Flattened: (N, det_rows, det_cols) - need to convert mask indices - flat_indices = torch.nonzero(mask.flatten(), as_tuple=True)[0] - avg_dp = data[flat_indices].mean(dim=0) + # Single chunked torch path. For each scan-row chunk: cast to float32 and + # broadcast-multiply by the mask (no `chunk[row_mask]` slab, which would + # roughly duplicate the chunk in memory when the mask is dense). Sum/mean + # use einsum over scan dims; max masks zero rows then takes amax. + data_4d = data if data.ndim == 4 else data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + rows_per_chunk = self._chunk_rows() + if reduce == "sum" or reduce == "mean": + dp = torch.zeros(self._det_shape, dtype=torch.float32, device=self._device) + else: # max + dp = torch.full(self._det_shape, -float("inf"), dtype=torch.float32, device=self._device) + for i in range(0, self._scan_shape[0], rows_per_chunk): + row_mask = mask[i:i + rows_per_chunk] + if not bool(row_mask.any()): + continue + chunk = data_4d[i:i + rows_per_chunk] + if not torch.is_floating_point(chunk): + chunk = chunk.float() + row_mask_f = row_mask.float() + if reduce == "max": + # Outside-mask positions become 0; doesn't affect amax provided + # the data has any non-negative pixels (true for detector counts). + dp = torch.maximum(dp, (chunk * row_mask_f[..., None, None]).amax(dim=(0, 1))) + else: + dp += torch.einsum("rcij,rc->ij", chunk, row_mask_f) + if reduce == "mean": + dp /= float(n_positions) - # Send raw float32 (consistent with other data paths — JS handles normalization) - self.summed_dp_bytes = avg_dp.cpu().numpy().tobytes() + self.vi_roi_dp_bytes = dp.cpu().numpy().tobytes() def _create_circular_mask(self, cx: float, cy: float, radius: float): """Create circular mask (boolean tensor on device).""" @@ -4162,31 +2220,24 @@ def _create_rect_mask(self, cx: float, cy: float, half_width: float, half_height return mask def _precompute_common_virtual_images(self): - """Pre-compute BF/ABF/ADF virtual images for instant preset switching.""" + """Pre-compute BF/ABF/ADF/HAADF virtual image bytes. Annular ranges match + apply_preset() so the cache always hits on preset clicks.""" cx, cy, bf = self.center_col, self.center_row, self.bf_radius - # Cache (bytes, stats, min, max) for each preset - bf_arr = self._fast_masked_sum(self._create_circular_mask(cx, cy, bf)) - abf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 0.5, bf)) - adf_arr = self._fast_masked_sum(self._create_annular_mask(cx, cy, bf, bf * 4.0)) - - self._cached_bf_virtual = ( - self._to_float32_bytes(bf_arr, update_vi_stats=False), - [float(bf_arr.mean()), float(bf_arr.min()), float(bf_arr.max()), float(bf_arr.std())], - float(bf_arr.min()), float(bf_arr.max()) + self._cached_bf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_circular_mask(cx, cy, bf)) + ) + self._cached_abf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 0.5, bf)) ) - self._cached_abf_virtual = ( - self._to_float32_bytes(abf_arr, update_vi_stats=False), - [float(abf_arr.mean()), float(abf_arr.min()), float(abf_arr.max()), float(abf_arr.std())], - float(abf_arr.min()), float(abf_arr.max()) + self._cached_adf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_annular_mask(cx, cy, bf, bf * 2.0)) ) - self._cached_adf_virtual = ( - self._to_float32_bytes(adf_arr, update_vi_stats=False), - [float(adf_arr.mean()), float(adf_arr.min()), float(adf_arr.max()), float(adf_arr.std())], - float(adf_arr.min()), float(adf_arr.max()) + self._cached_haadf_virtual = self._to_float32_bytes( + self._fast_masked_sum(self._create_annular_mask(cx, cy, bf * 2.0, bf * 4.0)) ) - def _get_cached_preset(self) -> tuple[bytes, list[float], float, float] | None: - """Check if current ROI matches a cached preset and return (bytes, stats, min, max) tuple.""" + def _get_cached_preset(self) -> bytes | None: + """Return cached preset bytes if current ROI matches BF/ABF/ADF preset shape.""" # Must be centered on detector center if abs(self.roi_center_col - self.center_col) >= 1 or abs(self.roi_center_row - self.center_row) >= 1: return None @@ -4203,16 +2254,25 @@ def _get_cached_preset(self) -> tuple[bytes, list[float], float, float] | None: abs(self.roi_radius - bf) < 1): return self._cached_abf_virtual - # ADF: annular at bf to 4*bf (combines LAADF + HAADF) + # ADF: annular at bf to 2*bf if (self.roi_mode == "annular" and abs(self.roi_radius_inner - bf) < 1 and - abs(self.roi_radius - bf * 4.0) < 1): + abs(self.roi_radius - bf * 2.0) < 1): return self._cached_adf_virtual + # HAADF: annular at 2*bf to 4*bf + if (self.roi_mode == "annular" and + abs(self.roi_radius_inner - bf * 2.0) < 1 and + abs(self.roi_radius - bf * 4.0) < 1): + return self._cached_haadf_virtual + return None def _virtual_image_for_frame(self, frame_idx: int) -> np.ndarray: - """Compute virtual image array for a specific frame without mutating traits.""" + """Compute virtual image for a specific 5D frame without mutating traits. + + Single chunked-torch path matching _fast_masked_sum. + """ data = self._data[frame_idx] if self.n_frames > 1 else self._data cx, cy = self.roi_center_col, self.roi_center_row if self.roi_mode == "circle" and self.roi_radius > 0: @@ -4231,68 +2291,65 @@ def _virtual_image_for_frame(self, frame_idx: int) -> np.ndarray: else: vi = data[:, row, col].reshape(self._scan_shape) return vi.cpu().numpy().astype(np.float32, copy=False) - mask_float = mask.float() - n_det = self._det_shape[0] * self._det_shape[1] - n_nonzero = int(mask.sum()) - coverage = n_nonzero / n_det - if coverage < SPARSE_MASK_THRESHOLD: - indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] - n_scan = self._scan_shape[0] * self._scan_shape[1] - data_flat = data.reshape(n_scan, n_det) - result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) - else: - if data.ndim == 3: - data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) - else: - data_4d = data - result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) - return result.cpu().numpy().astype(np.float32, copy=False) + data_4d = data if data.ndim == 4 else data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) + mask_f = mask.float() + rows_per_chunk = self._chunk_rows() + out = torch.zeros(self._scan_shape, dtype=torch.float32, device=self._device) + for i in range(0, data_4d.shape[0], rows_per_chunk): + chunk = data_4d[i:i + rows_per_chunk] + if not torch.is_floating_point(chunk): + chunk = chunk.float() + out[i:i + rows_per_chunk] = torch.tensordot(chunk, mask_f, dims=([2, 3], [0, 1])) + return out.cpu().numpy().astype(np.float32, copy=False) + + def _chunk_rows(self) -> int: + """Pick rows-per-chunk so float32 transient stays under _CHUNK_BYTE_BUDGET. + + Float32 cast of one chunk = rows × scan_cols × det_h × det_w × 4 bytes. + Selected slabs (e.g. vi_roi reduce) inherit the same per-row budget. + """ + per_row = self._scan_shape[1] * self._det_shape[0] * self._det_shape[1] * 4 + return max(1, _CHUNK_BYTE_BUDGET // max(1, per_row)) def _fast_masked_sum(self, mask: torch.Tensor) -> torch.Tensor: - """Compute masked sum using PyTorch. - - Uses sparse indexing for small masks (<20% coverage) which is faster - because it only processes non-zero pixels: - - r=10 (1%): ~0.8ms (sparse) vs ~13ms (full) - - r=30 (8%): ~4ms (sparse) vs ~13ms (full) + """Sum data over scan positions weighted by detector mask. - For large masks (≥20%), uses full tensordot which has constant ~13ms. + Chunked tensordot. Per-chunk float32 cast bounded by _CHUNK_BYTE_BUDGET. + Identical math on CUDA / MPS / CPU. """ data = self._frame_data - mask_float = mask.float() - n_det = self._det_shape[0] * self._det_shape[1] - n_nonzero = int(mask.sum()) - coverage = n_nonzero / n_det - - if coverage < SPARSE_MASK_THRESHOLD: - # Sparse: faster for small masks - indices = torch.nonzero(mask_float.flatten(), as_tuple=True)[0] - n_scan = self._scan_shape[0] * self._scan_shape[1] - data_flat = data.reshape(n_scan, n_det) - result = data_flat[:, indices].sum(dim=1).reshape(self._scan_shape) + if data.ndim == 3: + data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) else: - # Tensordot: faster for large masks - # Reshape to 4D if needed (3D flattened data) - if data.ndim == 3: - data_4d = data.reshape(self._scan_shape[0], self._scan_shape[1], *self._det_shape) - else: - data_4d = data - result = torch.tensordot(data_4d, mask_float, dims=([2, 3], [0, 1])) - - return result - - def _to_float32_bytes(self, arr: torch.Tensor, update_vi_stats: bool = True) -> bytes: - """Convert tensor to float32 bytes.""" - # Compute min/max (fast on GPU) - vmin = float(arr.min()) - vmax = float(arr.max()) + data_4d = data + # Single chunked torch path. Per scan-row chunk: cast to float32, contract + # with mask via tensordot. Transient memory bounded by chunk size. Same + # code on CUDA / MPS / CPU. Identical results regardless of device. + mask_f = mask.float() + n_rows = data_4d.shape[0] + out = torch.zeros(self._scan_shape, dtype=torch.float32, device=self._device) + # Convert positions chunk size to row chunks based on scan width. + rows_per_chunk = self._chunk_rows() + for i in range(0, n_rows, rows_per_chunk): + chunk = data_4d[i:i + rows_per_chunk] + if not torch.is_floating_point(chunk): + chunk = chunk.float() + out[i:i + rows_per_chunk] = torch.tensordot(chunk, mask_f, dims=([2, 3], [0, 1])) + return out - # Only update traits when requested (avoids side effects during precomputation) - if update_vi_stats: - self.vi_data_min = vmin - self.vi_data_max = vmax - self.vi_stats = [float(arr.mean()), vmin, vmax, float(arr.std())] + def _to_float32_bytes(self, arr: torch.Tensor) -> bytes: + """Convert tensor (any numeric dtype) to float32 bytes for JS transfer. + Cast to float32 only at the small output. Integer reductions (uint16 sums, + int64 accumulators) get promoted here so the multi-GB raw data never gets + copied to float. Stats (min/max/mean/std) are computed JS-side from the + same Float32Array — keeping them out of separate traits avoids a + comm-message ordering race where bytes from click N arrive with stats + from click N-1, producing a wrong colormap normalization (uniform white + flash on rapid preset switching). + """ + if arr.dtype != torch.float32: + arr = arr.float() return arr.cpu().numpy().tobytes() def _compute_virtual_image_from_roi(self): @@ -4301,12 +2358,7 @@ def _compute_virtual_image_from_roi(self): return cached = self._get_cached_preset() if cached is not None: - # Cached preset returns (bytes, stats, min, max) tuple - vi_bytes, vi_stats, vi_min, vi_max = cached - self.virtual_image_bytes = vi_bytes - self.vi_stats = vi_stats - self.vi_data_min = vi_min - self.vi_data_max = vi_max + self.virtual_image_bytes = cached return cx, cy = self.roi_center_col, self.roi_center_row @@ -4333,5 +2385,3 @@ def _compute_virtual_image_from_roi(self): self.virtual_image_bytes = self._to_float32_bytes(self._fast_masked_sum(mask)) - -bind_tool_runtime_api(Show4DSTEM, "Show4DSTEM") diff --git a/widget/src/quantem/widget/json_state.py b/widget/src/quantem/widget/state.py similarity index 96% rename from widget/src/quantem/widget/json_state.py rename to widget/src/quantem/widget/state.py index 4874981f..d7710287 100644 --- a/widget/src/quantem/widget/json_state.py +++ b/widget/src/quantem/widget/state.py @@ -12,8 +12,6 @@ def resolve_widget_version() -> str: return importlib.metadata.version("quantem-widget") except importlib.metadata.PackageNotFoundError: return "unknown" - except Exception: - return "unknown" def build_json_header(widget_name: str) -> dict[str, Any]: diff --git a/widget/src/quantem/widget/tool_parity.json b/widget/src/quantem/widget/tool_parity.json deleted file mode 100644 index 4271533a..00000000 --- a/widget/src/quantem/widget/tool_parity.json +++ /dev/null @@ -1,93 +0,0 @@ -{ - "widgets": { - "Show2D": { - "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "roi", "profile", "all"], - "aliases": {} - }, - "Show3D": { - "tool_groups": ["display", "histogram", "stats", "playback", "view", "export", "roi", "profile", "all"], - "aliases": { - "navigation": "playback" - } - }, - "Show3DVolume": { - "tool_groups": ["display", "histogram", "playback", "fft", "navigation", "stats", "export", "view", "volume", "all"], - "aliases": {} - }, - "Show4D": { - "tool_groups": ["display", "roi", "histogram", "profile", "navigation", "playback", "stats", "export", "view", "fft", "all"], - "aliases": {} - }, - "Show4DSTEM": { - "tool_groups": ["display", "histogram", "stats", "navigation", "playback", "view", "export", "roi", "profile", "fft", "virtual", "frame", "all"], - "aliases": {} - }, - "ShowComplex2D": { - "tool_groups": ["display", "histogram", "fft", "roi", "stats", "export", "view", "all"], - "aliases": {} - }, - "Mark2D": { - "tool_groups": ["points", "roi", "profile", "display", "marker_style", "snap", "navigation", "view", "export", "all"], - "aliases": {} - }, - "Edit2D": { - "tool_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "export", "view", "all"], - "aliases": {} - }, - "Align2D": { - "tool_groups": ["alignment", "overlay", "display", "histogram", "stats", "export", "view", "all"], - "aliases": {} - }, - "Align2DBulk": { - "tool_groups": ["display", "histogram", "navigation", "stats", "view", "export", "all"], - "aliases": {} - }, - "Bin4D": { - "tool_groups": ["display", "binning", "mask", "preview", "stats", "export", "all"], - "aliases": {} - }, - "Browse": { - "tool_groups": ["navigation", "filter", "preview", "all"], - "aliases": {} - }, - "Bin2D": { - "tool_groups": ["display", "binning", "histogram", "stats", "navigation", "export", "all"], - "aliases": {} - }, - "Show1D": { - "tool_groups": ["display", "peaks", "stats", "export", "all"], - "aliases": {} - }, - "MetricExplorer": { - "tool_groups": ["display", "export", "all"], - "aliases": {} - }, - "ShowDiffraction": { - "tool_groups": ["display", "histogram", "stats", "navigation", "view", "export", "spots", "all"], - "aliases": {} - } - }, - "viewer_widgets": ["Show1D", "Show2D", "Show3D", "Show3DVolume", "Show4D", "Show4DSTEM", "ShowComplex2D"], - "control_presets": { - "all": { - "label": "All", - "show_groups": ["*"] - }, - "compact": { - "label": "Compact", - "show_groups": ["mode", "edit", "display", "navigation", "playback", "view", "export", "fft"] - }, - "mask_focus": { - "label": "Mask Focus", - "show_groups": ["edit", "display", "roi", "histogram", "stats", "navigation", "playback", "view", "export", "fft", "virtual", "frame"] - }, - "crop_focus": { - "label": "Crop Focus", - "show_groups": ["mode", "edit", "display", "histogram", "stats", "navigation", "view", "export"] - }, - "spectroscopy": { - "label": "Spectroscopy", - "show_groups": ["display", "peaks", "stats"] - } - } -} diff --git a/widget/src/quantem/widget/tool_parity.py b/widget/src/quantem/widget/tool_parity.py deleted file mode 100644 index d5d4f84e..00000000 --- a/widget/src/quantem/widget/tool_parity.py +++ /dev/null @@ -1,184 +0,0 @@ -"""Shared tool visibility/locking registry and helpers.""" - -from __future__ import annotations - -import json -import pathlib -from functools import lru_cache -from typing import Any - -_REGISTRY_PATH = pathlib.Path(__file__).with_name("tool_parity.json") - - -@lru_cache(maxsize=1) -def _load_registry() -> dict[str, Any]: - return json.loads(_REGISTRY_PATH.read_text()) - - -def get_widget_tool_groups(widget_name: str) -> tuple[str, ...]: - registry = _load_registry() - widgets = registry.get("widgets", {}) - if widget_name not in widgets: - supported = ", ".join(sorted(widgets)) - raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") - return tuple(str(v).strip().lower() for v in widgets[widget_name].get("tool_groups", [])) - - -def get_widget_tool_aliases(widget_name: str) -> dict[str, str]: - registry = _load_registry() - widgets = registry.get("widgets", {}) - if widget_name not in widgets: - supported = ", ".join(sorted(widgets)) - raise ValueError(f"Unknown widget {widget_name!r}. Supported widgets: {supported}.") - aliases = widgets[widget_name].get("aliases", {}) - return {str(k).strip().lower(): str(v).strip().lower() for k, v in aliases.items()} - - -def normalize_tool_groups(widget_name: str, tool_groups) -> list[str]: - if tool_groups is None: - return [] - if isinstance(tool_groups, str): - values = [tool_groups] - else: - values = list(tool_groups) - - order = get_widget_tool_groups(widget_name) - aliases = get_widget_tool_aliases(widget_name) - supported = set(order) - normalized: list[str] = [] - seen: set[str] = set() - - for raw in values: - key = str(raw).strip().lower() - if not key: - continue - key = aliases.get(key, key) - if key not in supported: - supported_values = ", ".join(f'"{k}"' for k in order) - raise ValueError( - f"Unknown tool group {raw!r}. Supported values: {supported_values}." - ) - if key == "all": - return ["all"] - if key not in seen: - seen.add(key) - normalized.append(key) - return normalized - - -def build_tool_groups( - widget_name: str, - *, - tool_groups=None, - all_flag: bool = False, - flag_map: dict[str, bool] | None = None, -) -> list[str]: - if all_flag: - return ["all"] - values: list[str] = [] - if tool_groups is not None: - if isinstance(tool_groups, str): - values.append(tool_groups) - else: - values.extend(tool_groups) - for key, enabled in (flag_map or {}).items(): - if enabled: - values.append(key) - return normalize_tool_groups(widget_name, values) - - -def resolve_control_preset_hidden_tools(widget_name: str, preset_id: str) -> list[str]: - preset_key = str(preset_id).strip().lower() - presets = _load_registry().get("control_presets", {}) - if preset_key not in presets: - supported = ", ".join(sorted(presets)) - raise ValueError(f"Unknown control preset {preset_id!r}. Supported presets: {supported}.") - - show_groups = [str(v).strip().lower() for v in presets[preset_key].get("show_groups", [])] - supported_groups = [g for g in get_widget_tool_groups(widget_name) if g != "all"] - if "*" in show_groups: - return [] - show_set = set(show_groups) - hidden = [group for group in supported_groups if group not in show_set] - return normalize_tool_groups(widget_name, hidden) - - -def _flatten_groups(groups: tuple[Any, ...]) -> list[Any]: - if len(groups) == 1 and isinstance(groups[0], (list, tuple, set)): - return list(groups[0]) - return list(groups) - - -def _expanded_without_all(widget_name: str, values) -> list[str]: - normalized = normalize_tool_groups(widget_name, values) - if "all" not in normalized: - return normalized - return [group for group in get_widget_tool_groups(widget_name) if group != "all"] - - -def _ordered_groups(widget_name: str, values: set[str]) -> list[str]: - return [group for group in get_widget_tool_groups(widget_name) if group != "all" and group in values] - - -def bind_tool_runtime_api(cls, widget_name: str) -> None: - """Attach runtime lock/hide helpers to a widget class.""" - - def set_disabled_tools(self, tool_groups) -> Any: - self.disabled_tools = normalize_tool_groups(widget_name, tool_groups) - return self - - def set_hidden_tools(self, tool_groups) -> Any: - self.hidden_tools = normalize_tool_groups(widget_name, tool_groups) - return self - - def lock_tool(self, *tool_groups) -> Any: - new_groups = _flatten_groups(tool_groups) - if not new_groups: - return self - current = _expanded_without_all(widget_name, self.disabled_tools) - requested = _expanded_without_all(widget_name, new_groups) - merged = set(current).union(requested) - self.disabled_tools = _ordered_groups(widget_name, merged) - return self - - def unlock_tool(self, *tool_groups) -> Any: - remove_groups = _flatten_groups(tool_groups) - if not remove_groups: - return self - current = set(_expanded_without_all(widget_name, self.disabled_tools)) - requested = set(_expanded_without_all(widget_name, remove_groups)) - current.difference_update(requested) - self.disabled_tools = _ordered_groups(widget_name, current) - return self - - def hide_tool(self, *tool_groups) -> Any: - new_groups = _flatten_groups(tool_groups) - if not new_groups: - return self - current = _expanded_without_all(widget_name, self.hidden_tools) - requested = _expanded_without_all(widget_name, new_groups) - merged = set(current).union(requested) - self.hidden_tools = _ordered_groups(widget_name, merged) - return self - - def show_tool(self, *tool_groups) -> Any: - remove_groups = _flatten_groups(tool_groups) - if not remove_groups: - return self - current = set(_expanded_without_all(widget_name, self.hidden_tools)) - requested = set(_expanded_without_all(widget_name, remove_groups)) - current.difference_update(requested) - self.hidden_tools = _ordered_groups(widget_name, current) - return self - - def apply_control_preset(self, preset: str) -> Any: - self.hidden_tools = resolve_control_preset_hidden_tools(widget_name, preset) - return self - - cls.set_disabled_tools = set_disabled_tools # type: ignore[attr-defined] - cls.set_hidden_tools = set_hidden_tools # type: ignore[attr-defined] - cls.lock_tool = lock_tool # type: ignore[attr-defined] - cls.unlock_tool = unlock_tool # type: ignore[attr-defined] - cls.hide_tool = hide_tool # type: ignore[attr-defined] - cls.show_tool = show_tool # type: ignore[attr-defined] - cls.apply_control_preset = apply_control_preset # type: ignore[attr-defined] diff --git a/widget/tests/test_fft_parity.py b/widget/tests/test_fft_parity.py new file mode 100644 index 00000000..4e38b85f --- /dev/null +++ b/widget/tests/test_fft_parity.py @@ -0,0 +1,200 @@ +"""FFT parity: JS fft1d/fft2d/fftshift line-ported to Python, validated against numpy. + +Why ports instead of running the JS directly: pytest can't drive a TypeScript +module without a Node bridge or browser harness, both of which add fragility +and slow CI. Instead we mirror js/fft.ts:14-82 line-for-line in Python below +and assert against numpy.fft. If the JS algorithm has a bug, the line-port +inherits it and this test fails — surfacing the bug at unit-test speed. + +When js/fft.ts changes, update the ports here in the same commit. The +side-by-side structure makes drift visually obvious during review. +""" +import numpy as np + + +def _next_pow2(n: int) -> int: + p = 1 + while p < n: + p <<= 1 + return p + + +def _js_fft1d(real: np.ndarray, imag: np.ndarray, inverse: bool = False) -> None: + """Line-port of js/fft.ts fft1d. In-place. Iterative radix-2 Cooley-Tukey.""" + n = real.size + if n <= 1: + return + # Bit-reversal permutation. + j = 0 + for i in range(n - 1): + if i < j: + real[i], real[j] = real[j], real[i] + imag[i], imag[j] = imag[j], imag[i] + k = n >> 1 + while k <= j: + j -= k + k >>= 1 + j += k + sign = 1 if inverse else -1 + length = 2 + while length <= n: + half = length >> 1 + angle = (sign * 2 * np.pi) / length + w_real = np.cos(angle) + w_imag = np.sin(angle) + for i in range(0, n, length): + cur_real = 1.0 + cur_imag = 0.0 + for k in range(half): + even = i + k + odd = i + k + half + t_real = cur_real * real[odd] - cur_imag * imag[odd] + t_imag = cur_real * imag[odd] + cur_imag * real[odd] + real[odd] = real[even] - t_real + imag[odd] = imag[even] - t_imag + real[even] += t_real + imag[even] += t_imag + new_real = cur_real * w_real - cur_imag * w_imag + cur_imag = cur_real * w_imag + cur_imag * w_real + cur_real = new_real + length <<= 1 + if inverse: + real /= n + imag /= n + + +def _js_fft2d(real: np.ndarray, imag: np.ndarray, width: int, height: int, inverse: bool = False) -> None: + """Line-port of js/fft.ts fft2d. In-place on (height*width) flattened arrays.""" + padded_w = _next_pow2(width) + padded_h = _next_pow2(height) + needs_padding = padded_w != width or padded_h != height + if needs_padding: + work_real = np.zeros(padded_w * padded_h, dtype=np.float64) + work_imag = np.zeros(padded_w * padded_h, dtype=np.float64) + for y in range(height): + for x in range(width): + work_real[y * padded_w + x] = real[y * width + x] + work_imag[y * padded_w + x] = imag[y * width + x] + else: + work_real = real + work_imag = imag + row_real = np.empty(padded_w, dtype=np.float64) + row_imag = np.empty(padded_w, dtype=np.float64) + for y in range(padded_h): + offset = y * padded_w + row_real[:] = work_real[offset:offset + padded_w] + row_imag[:] = work_imag[offset:offset + padded_w] + _js_fft1d(row_real, row_imag, inverse) + work_real[offset:offset + padded_w] = row_real + work_imag[offset:offset + padded_w] = row_imag + col_real = np.empty(padded_h, dtype=np.float64) + col_imag = np.empty(padded_h, dtype=np.float64) + for x in range(padded_w): + for y in range(padded_h): + col_real[y] = work_real[y * padded_w + x] + col_imag[y] = work_imag[y * padded_w + x] + _js_fft1d(col_real, col_imag, inverse) + for y in range(padded_h): + work_real[y * padded_w + x] = col_real[y] + work_imag[y * padded_w + x] = col_imag[y] + if needs_padding: + for y in range(height): + for x in range(width): + real[y * width + x] = work_real[y * padded_w + x] + imag[y * width + x] = work_imag[y * padded_w + x] + + +def _js_fftshift(data: np.ndarray, width: int, height: int) -> None: + """Line-port of js/fft.ts fftshift. In-place.""" + half_w = width >> 1 + half_h = height >> 1 + temp = np.empty(width * height, dtype=data.dtype) + for y in range(height): + for x in range(width): + temp[((y + half_h) % height) * width + ((x + half_w) % width)] = data[y * width + x] + data[:] = temp + + +# --------------------------------------------------------------------------- + +def test_fft1d_matches_numpy_pow2(): + """1D FFT on power-of-2 input matches numpy.fft.fft.""" + rng = np.random.default_rng(0) + n = 64 + x = rng.standard_normal(n) + real = x.astype(np.float64).copy() + imag = np.zeros(n, dtype=np.float64) + _js_fft1d(real, imag, inverse=False) + js = real + 1j * imag + expected = np.fft.fft(x) + np.testing.assert_allclose(js, expected, atol=1e-9) + + +def test_fft1d_inverse_roundtrip(): + """fft1d(fft1d(x), inverse=True) ≈ x.""" + rng = np.random.default_rng(1) + n = 128 + x = rng.standard_normal(n) + real = x.astype(np.float64).copy() + imag = np.zeros(n, dtype=np.float64) + _js_fft1d(real, imag, inverse=False) + _js_fft1d(real, imag, inverse=True) + np.testing.assert_allclose(real, x, atol=1e-9) + np.testing.assert_allclose(imag, np.zeros(n), atol=1e-9) + + +def test_fft2d_matches_numpy_pow2(): + """2D FFT on power-of-2 dims matches numpy.fft.fft2.""" + rng = np.random.default_rng(2) + h, w = 32, 64 + img = rng.standard_normal((h, w)) + real = img.astype(np.float64).flatten() + imag = np.zeros(h * w, dtype=np.float64) + _js_fft2d(real, imag, w, h, inverse=False) + js = (real + 1j * imag).reshape(h, w) + expected = np.fft.fft2(img) + np.testing.assert_allclose(js, expected, atol=1e-9) + + +def test_fft2d_non_pow2_zero_pads(): + """Non-power-of-2 input gets zero-padded; FFT of padded matches numpy of padded.""" + rng = np.random.default_rng(3) + h, w = 30, 50 + img = rng.standard_normal((h, w)) + real = img.astype(np.float64).flatten() + imag = np.zeros(h * w, dtype=np.float64) + _js_fft2d(real, imag, w, h, inverse=False) + # JS contract: only the (h, w) region of the result is written back to the input arrays. + js = (real + 1j * imag).reshape(h, w) + pw, ph = _next_pow2(w), _next_pow2(h) + padded = np.zeros((ph, pw)) + padded[:h, :w] = img + expected = np.fft.fft2(padded)[:h, :w] + np.testing.assert_allclose(js, expected, atol=1e-9) + + +def test_fftshift_matches_numpy(): + """fftshift matches numpy.fft.fftshift on 2D data.""" + rng = np.random.default_rng(4) + h, w = 16, 16 + img = rng.standard_normal((h, w)) + flat = img.flatten().copy() + _js_fftshift(flat, w, h) + js_shifted = flat.reshape(h, w) + expected = np.fft.fftshift(img) + np.testing.assert_array_equal(js_shifted, expected) + + +def test_fft2d_then_fftshift_matches_numpy(): + """Combined FFT + fftshift matches numpy reference.""" + rng = np.random.default_rng(5) + h, w = 32, 32 + img = rng.standard_normal((h, w)) + real = img.astype(np.float64).flatten() + imag = np.zeros(h * w, dtype=np.float64) + _js_fft2d(real, imag, w, h, inverse=False) + _js_fftshift(real, w, h) + _js_fftshift(imag, w, h) + js = (real + 1j * imag).reshape(h, w) + expected = np.fft.fftshift(np.fft.fft2(img)) + np.testing.assert_allclose(js, expected, atol=1e-9) diff --git a/widget/tests/test_state_dict.py b/widget/tests/test_state_dict.py new file mode 100644 index 00000000..9247c979 --- /dev/null +++ b/widget/tests/test_state_dict.py @@ -0,0 +1,168 @@ +"""state_dict roundtrip tests for Show2D and Show4DSTEM. + +For each widget: +1. Construct with default data. +2. Mutate every trait in state_dict() to a non-default value. +3. Get state_dict. +4. Construct a fresh widget and load_state_dict. +5. Assert every trait on the restored widget equals what we set. + +Catches silent regressions when traits are added, renamed, or dropped without +updating the state_dict roundtrip path. +""" +import json +import pathlib + +import numpy as np +import pytest + +from quantem.widget import Show2D, Show4DSTEM + + +def _flip_value(default): + """Return a value distinct from `default` for the same type.""" + if isinstance(default, bool): + return not default + if isinstance(default, int): + return int(default) + 7 + if isinstance(default, float): + return float(default) + 0.123 + if isinstance(default, str): + return default + "_x" if default else "x" + if isinstance(default, list): + return [_flip_value(default[0])] if default else [0] + return default + + +def _mutate_state(state: dict) -> dict: + """Build a new state dict with every key changed to a non-default value.""" + out = {} + for k, v in state.items(): + # Skip values our flipper can't safely tweak (None defaults, nested dicts/lists-of-dicts, bytes). + if v is None or isinstance(v, (dict, bytes)): + out[k] = v + continue + # Lists hold structured items (dicts, tuples) for ROI / profile / labels; + # mutating them generically is fragile. The roundtrip-defaults test already + # covers list trait persistence — here we only mutate scalars. + if isinstance(v, list): + out[k] = v + continue + out[k] = _flip_value(v) + return out + + +# --------------------------------------------------------------------------- +# Show4DSTEM +# --------------------------------------------------------------------------- + +@pytest.fixture +def show4dstem_widget(): + data = np.random.default_rng(0).poisson(5, (8, 8, 16, 16)).astype(np.uint16) + data[:, :, 6:10, 6:10] += 500 # synthetic BF disk + return Show4DSTEM(data, verbose=False) + + +def test_show4dstem_state_dict_keys(show4dstem_widget): + """state_dict returns a non-empty dict of public traits.""" + s = show4dstem_widget.state_dict() + assert isinstance(s, dict) + assert len(s) > 10 + # Required keys for the widget's user-facing display state + for required in ("title", "dp_colormap", "vi_colormap", "roi_mode", "vi_roi_reduce"): + assert required in s, f"state_dict missing key {required!r}" + + +def test_show4dstem_state_dict_roundtrip_defaults(show4dstem_widget): + """save → load on default widget preserves state.""" + original = show4dstem_widget.state_dict() + data = np.random.default_rng(0).poisson(5, (8, 8, 16, 16)).astype(np.uint16) + data[:, :, 6:10, 6:10] += 500 + fresh = Show4DSTEM(data, state=original, verbose=False) + restored = fresh.state_dict() + for k in original: + assert restored[k] == original[k], f"{k}: {original[k]!r} -> {restored[k]!r}" + + +def test_show4dstem_state_dict_roundtrip_mutated(show4dstem_widget): + """Mutating every trait then roundtripping preserves the mutations.""" + # Position / frame indices are clamped to valid range by trait validators + # against the data dimensions; mutating them generically is meaningless here. + skip = {"pos_row", "pos_col", "frame_idx", "path_index", "path_length", + "vi_roi_center_row", "vi_roi_center_col"} + mutated = _mutate_state(show4dstem_widget.state_dict()) + show4dstem_widget.load_state_dict(mutated) + out = show4dstem_widget.state_dict() + for k, v in mutated.items(): + if k in skip: + continue + if isinstance(v, float): + assert abs(out[k] - v) < 1e-3, f"{k}: expected {v}, got {out[k]}" + else: + assert out[k] == v, f"{k}: expected {v!r}, got {out[k]!r}" + + +def test_show4dstem_save_and_load(tmp_path, show4dstem_widget): + """save() writes a versioned envelope JSON, state= kwarg loads it.""" + show4dstem_widget.dp_colormap = "viridis" + show4dstem_widget.vi_colormap = "magma" + show4dstem_widget.show_fft = True + path = tmp_path / "show4dstem_state.json" + show4dstem_widget.save(str(path)) + + payload = json.loads(path.read_text()) + assert payload["widget_name"] == "Show4DSTEM" + assert "metadata_version" in payload + assert "state" in payload + + data = np.random.default_rng(0).poisson(5, (8, 8, 16, 16)).astype(np.uint16) + data[:, :, 6:10, 6:10] += 500 + fresh = Show4DSTEM(data, state=str(path), verbose=False) + assert fresh.dp_colormap == "viridis" + assert fresh.vi_colormap == "magma" + assert fresh.show_fft is True + + +# --------------------------------------------------------------------------- +# Show2D +# --------------------------------------------------------------------------- + +@pytest.fixture +def show2d_widget(): + return Show2D(np.random.default_rng(0).standard_normal((32, 32)).astype(np.float32), verbose=False) + + +def test_show2d_state_dict_keys(show2d_widget): + s = show2d_widget.state_dict() + assert isinstance(s, dict) + assert len(s) > 5 + for required in ("cmap", "log_scale"): + assert required in s, f"state_dict missing key {required!r}" + + +def test_show2d_state_dict_roundtrip_defaults(show2d_widget): + original = show2d_widget.state_dict() + fresh = Show2D(np.random.default_rng(0).standard_normal((32, 32)).astype(np.float32), + state=original, verbose=False) + restored = fresh.state_dict() + for k in original: + if isinstance(original[k], float): + assert abs(restored[k] - original[k]) < 1e-3, f"{k}: {original[k]} -> {restored[k]}" + else: + assert restored[k] == original[k], f"{k}: {original[k]!r} -> {restored[k]!r}" + + +def test_show2d_save_and_load(tmp_path, show2d_widget): + show2d_widget.cmap = "viridis" + show2d_widget.log_scale = True + path = tmp_path / "show2d_state.json" + show2d_widget.save(str(path)) + + payload = json.loads(path.read_text()) + assert payload["widget_name"] == "Show2D" + assert "state" in payload + + fresh = Show2D(np.random.default_rng(0).standard_normal((32, 32)).astype(np.float32), + state=str(path), verbose=False) + assert fresh.cmap == "viridis" + assert fresh.log_scale is True diff --git a/widget/vite.config.js b/widget/vite.config.js deleted file mode 100644 index 291b84aa..00000000 --- a/widget/vite.config.js +++ /dev/null @@ -1,26 +0,0 @@ -import { defineConfig } from "vite"; -import anywidget from "@anywidget/vite"; -import react from "@vitejs/plugin-react"; - -export default defineConfig({ - plugins: [anywidget(), 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widget/docs/ widget/notebooks/ widget/scripts/ +!widget/scripts/build.mjs widget/tests/integration/ widget/tests/snapshots/ diff --git a/widget/js/show2d/index.tsx b/widget/js/show2d/index.tsx index 17120308..1693f177 100644 --- a/widget/js/show2d/index.tsx +++ b/widget/js/show2d/index.tsx @@ -585,6 +585,24 @@ function Show2D() { const [fftColormap, setFftColormap] = React.useState("inferno"); const [fftScaleMode, setFftScaleMode] = React.useState<"linear" | "log" | "power">("linear"); const [fftAuto, setFftAuto] = React.useState(true); + const [fftSmooth, setFftSmooth] = React.useState(true); + const [fftLinkedZoom, setFftLinkedZoom] = React.useState(false); + const [fftLinkPan, setFftLinkPan] = React.useState(false); + const [fftLinkedContrast, setFftLinkedContrast] = React.useState(true); + // Per-image FFT contrast (used when fftLinkedContrast=false) + const [fftContrastStates, setFftContrastStates] = React.useState>(new Map()); + const fftContrastFor = React.useCallback((idx: number) => { + if (fftLinkedContrast) return { vminPct: fftVminPct, vmaxPct: fftVmaxPct }; + return fftContrastStates.get(idx) || { vminPct: 0, vmaxPct: 100 }; + }, [fftLinkedContrast, fftVminPct, fftVmaxPct, fftContrastStates]); + const setFftContrastFor = React.useCallback((idx: number, val: { vminPct: number; vmaxPct: number }) => { + if (fftLinkedContrast) { + setFftVminPct(val.vminPct); + setFftVmaxPct(val.vmaxPct); + } else { + setFftContrastStates(prev => new Map(prev).set(idx, val)); + } + }, [fftLinkedContrast]); const [fftStats, setFftStats] = React.useState(null); const [fftShowColorbar, setFftShowColorbar] = React.useState(false); @@ -640,16 +658,34 @@ function Show2D() { const [linkedFftZoomState, setLinkedFftZoomState] = React.useState({ zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }); const [fftPanningIdx, setFftPanningIdx] = React.useState(null); const getGalleryFftState = React.useCallback((idx: number) => { - if (linkedZoom) return linkedFftZoomState; - return galleryFftStates.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; - }, [linkedZoom, linkedFftZoomState, galleryFftStates]); + const per = galleryFftStates.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; + return { + zoom: fftLinkedZoom ? linkedFftZoomState.zoom : per.zoom, + panX: fftLinkPan ? linkedFftZoomState.panX : per.panX, + panY: fftLinkPan ? linkedFftZoomState.panY : per.panY, + }; + }, [fftLinkedZoom, fftLinkPan, linkedFftZoomState, galleryFftStates]); const setGalleryFftState = React.useCallback((idx: number, state: ZoomState) => { - if (linkedZoom) { - setLinkedFftZoomState(state); - } else { - setGalleryFftStates(prev => new Map(prev).set(idx, state)); + if (fftLinkedZoom || fftLinkPan) { + setLinkedFftZoomState(prev => ({ + zoom: fftLinkedZoom ? state.zoom : prev.zoom, + panX: fftLinkPan ? state.panX : prev.panX, + panY: fftLinkPan ? state.panY : prev.panY, + })); } - }, [linkedZoom]); + if (!fftLinkedZoom || !fftLinkPan) { + setGalleryFftStates(prev => { + const cur = prev.get(idx) || { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 }; + const next = new Map(prev); + next.set(idx, { + zoom: fftLinkedZoom ? cur.zoom : state.zoom, + panX: fftLinkPan ? cur.panX : state.panX, + panY: fftLinkPan ? cur.panY : state.panY, + }); + return next; + }); + } + }, [fftLinkedZoom, fftLinkPan]); // Resizable state (gallery starts smaller) const [canvasSize, setCanvasSize] = React.useState(nImages > 1 ? GALLERY_IMAGE_TARGET : SINGLE_IMAGE_TARGET); @@ -875,12 +911,12 @@ function Show2D() { if (!off) return; const ctx = canvas.getContext("2d"); if (!ctx) return; - ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.imageSmoothingEnabled = fftSmooth; ctx.clearRect(0, 0, canvas.width, canvas.height); ctx.drawImage(off, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); })(); return () => { cancelled = true; }; - }, [effectiveShowFft, showDiffPanel, nImages, dataVersion, width, height, fftWindow, fftColormap, canvasW, canvasH]); + }, [effectiveShowFft, showDiffPanel, nImages, dataVersion, width, height, fftWindow, fftColormap, canvasW, canvasH, fftSmooth]); // Diff panels render — DYNAMIC. One per non-reference image: image[ref] − image[i]. // Computed at canvas resolution from raw float data, re-running on zoom/pan/align change. @@ -2075,7 +2111,7 @@ function Show2D() { const fftH = offscreen.height; // Use bilinear smoothing when FFT is smaller than canvas (avoids blocky upscaling) - ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.imageSmoothingEnabled = fftSmooth || (fftW < canvasW || fftH < canvasH); ctx.clearRect(0, 0, canvasW, canvasH); ctx.save(); @@ -2087,7 +2123,7 @@ function Show2D() { // Stretch cropped FFT to fill the full canvas (no layout change during drag) ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); ctx.restore(); - }, [effectiveShowFft, isGallery, fftOffscreenVersion, canvasW, canvasH, fftZoom, fftPanX, fftPanY]); + }, [effectiveShowFft, isGallery, fftOffscreenVersion, canvasW, canvasH, fftZoom, fftPanX, fftPanY, fftSmooth]); // ------------------------------------------------------------------------- // Render FFT overlay (scale bar + colorbar + d-spacing marker) @@ -2317,7 +2353,8 @@ function Show2D() { } else { ({ min: displayMin, max: displayMax } = findDataRange(displayData)); } - const { vmin, vmax } = sliderRange(displayMin, displayMax, fftVminPct, fftVmaxPct); + const fc = fftContrastFor(idx); + const { vmin, vmax } = sliderRange(displayMin, displayMax, fc.vminPct, fc.vmaxPct); const offscreen = renderToOffscreen(displayData, fftW, fftH, lut, vmin, vmax); if (!offscreen) continue; @@ -2335,7 +2372,7 @@ function Show2D() { setFftDataRange(findDataRange(histData)); } setGalleryFftOffscreenVersion(v => v + 1); - }, [effectiveShowFft, isGallery, nImages, width, height, galleryFftMagVersion, fftColormap, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, selectedIdx]); + }, [effectiveShowFft, isGallery, nImages, width, height, galleryFftMagVersion, fftColormap, fftScaleMode, fftAuto, fftVminPct, fftVmaxPct, selectedIdx, fftLinkedContrast, fftContrastStates]); // Gallery FFT draw effect: cheap drawImage from cached offscreens (zoom/pan changes) React.useLayoutEffect(() => { @@ -2351,7 +2388,7 @@ function Show2D() { if (!ctx) continue; const { zoom, panX, panY } = getGalleryFftState(idx); - ctx.imageSmoothingEnabled = fftW < canvasW || fftH < canvasH; + ctx.imageSmoothingEnabled = fftSmooth; ctx.clearRect(0, 0, canvasW, canvasH); ctx.save(); const cx = canvasW / 2; @@ -2362,7 +2399,7 @@ function Show2D() { ctx.drawImage(offscreen, 0, 0, fftW, fftH, 0, 0, canvasW, canvasH); ctx.restore(); } - }, [effectiveShowFft, isGallery, nImages, canvasW, canvasH, width, height, galleryFftOffscreenVersion, galleryFftStates, linkedZoom, linkedFftZoomState]); + }, [effectiveShowFft, isGallery, nImages, canvasW, canvasH, width, height, galleryFftOffscreenVersion, galleryFftStates, fftLinkedZoom, linkedFftZoomState, fftSmooth]); // ------------------------------------------------------------------------- // Mouse Handlers for Zoom/Pan @@ -2526,7 +2563,7 @@ function Show2D() { // Gallery FFT zoom/pan handlers (only selected image's FFT responds) const handleGalleryFftWheel = (e: React.WheelEvent, idx: number) => { - if (isGallery && idx !== selectedIdx && !linkedZoom) return; + if (isGallery && idx !== selectedIdx && !fftLinkedZoom) return; e.preventDefault(); // Prevent page scroll when zooming FFT const zs = getGalleryFftState(idx); const zoomFactor = e.deltaY > 0 ? 0.9 : 1.1; @@ -3636,7 +3673,7 @@ function Show2D() { { fftContainerRefs.current[i] = el; }} sx={{ mt: 0.5, position: "relative", border: `2px solid ${i === selectedIdx ? themeColors.accent : themeColors.border}`, borderRadius: 0, bgcolor: "#000", cursor: "grab" }} - onWheel={(i === selectedIdx || linkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} + onWheel={(i === selectedIdx || fftLinkedZoom) ? (e) => handleGalleryFftWheel(e, i) : undefined} onDoubleClick={() => setGalleryFftState(i, { zoom: DEFAULT_FFT_ZOOM, panX: 0, panY: 0 })} onMouseDown={(e) => handleGalleryFftMouseDown(e, i)} onMouseMove={(e) => handleGalleryFftMouseMove(e, i)} @@ -3747,13 +3784,10 @@ function Show2D() { FFT Scale: - setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log - Pow - Auto: - { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> {roiFftActive && fftCropDims && ( <> Win: @@ -3765,11 +3799,69 @@ function Show2D() { {COLORMAP_NAMES.map((name) => ({name.charAt(0).toUpperCase() + name.slice(1)}))} + {/* FFT Row 2: Auto + Smooth + Link Zoom/Pan/Contrast (mirrors main image Row 2) */} + + Auto: + { setFftAuto(e.target.checked); }} size="small" sx={switchStyles.small} /> + Smooth: + { setFftSmooth(e.target.checked); }} size="small" sx={switchStyles.small} /> + {isGallery && ( + <> + Link: + Zoom + { setFftLinkedZoom(!fftLinkedZoom); }} size="small" sx={switchStyles.small} /> + Pan + { setFftLinkPan(!fftLinkPan); }} size="small" sx={switchStyles.small} /> + Contrast + { setFftLinkedContrast(!fftLinkedContrast); }} size="small" sx={switchStyles.small} /> + + )} + {( {fftHistogramData && ( - { setFftVminPct(min); setFftVmaxPct(max); }} width={110} height={58} theme={themeInfo.theme === "dark" ? "dark" : "light"} dataMin={fftDataRange.min} dataMax={fftDataRange.max} /> + !fftLinkedContrast && isGallery ? ( + + {Array.from({ length: nImages }).map((_, i) => { + const fc = fftContrastFor(i); + const mag = fftMagCacheGalleryRef.current[i]; + let perData: Float32Array | null = null; + if (mag) { + if (fftScaleMode === "log") perData = applyLogScale(mag); + else if (fftScaleMode === "power") { + perData = new Float32Array(mag.length); + for (let j = 0; j < mag.length; j++) perData[j] = Math.sqrt(mag[j]); + } else perData = mag; + } + const dr = perData ? findDataRange(perData) : fftDataRange; + return ( + { setFftContrastFor(i, { vminPct: min, vmaxPct: max }); }} + width={110} height={58} + theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={dr.min} dataMax={dr.max} + /> + ); + })} + + ) : (() => { + const fc = fftContrastFor(selectedIdx); + return ( + { setFftContrastFor(selectedIdx, { vminPct: min, vmaxPct: max }); }} + width={110} height={58} + theme={themeInfo.theme === "dark" ? "dark" : "light"} + dataMin={fftDataRange.min} dataMax={fftDataRange.max} + /> + ); + })() )} )} @@ -3798,7 +3890,7 @@ function Show2D() { {/* Controls: two rows left + histogram right, ROI below */} {showControls && ( - + {/* Top: control rows + histogram side by side */} @@ -3856,10 +3948,10 @@ function Show2D() { {/* Right: histograms. Unlinked + gallery → grid matching gallery layout (same effectiveNcols × rows). Linked or single image → one histogram. */} - {(imageHistogramData || imageHistogramBins) && ( + {(imageHistogramData || imageHistogramBins || (isGallery && !linkedContrast && rawDataRef.current)) && ( {(!linkedContrast && isGallery && rawDataRef.current) ? ( - + {Array.from({ length: nImages }).map((_, i) => { const cs = contrastStates.get(i) || { vminPct: 0, vmaxPct: 100 }; const raw = rawDataRef.current?.[i] || null; @@ -4060,10 +4152,9 @@ function Show2D() { {/* Row 1: Scale + Color + Colorbar */} Scale: - setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log - Pow Color: setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> + @@ -4152,7 +4222,7 @@ function Show2D() { {/* Row 1: Scale + Color + Colorbar */} Scale: - setFftScaleMode(e.target.value as "linear" | "log")} size="small" sx={{ ...themedSelect, minWidth: 50, fontSize: 10 }} MenuProps={themedMenuProps}> Lin Log diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index f2cbed6a..edcd01db 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -303,8 +303,8 @@ def __init__( zoom: float = 1.0, zoom_row: float | None = None, zoom_col: float | None = None, - link_zoom: bool = False, - link_pan: bool = False, + link_zoom: bool | None = None, + link_pan: bool | None = None, link_contrast: bool = True, diff_mode: bool = False, view_box: tuple | list | None = None, @@ -362,6 +362,17 @@ def _init_sync(self, *, data, labels, title, cmap, sampling, units, if units is None and hasattr(data, "units"): units = list(data.units[-2:]) data = data.array + # Same auto-extract for list/tuple of Dataset2d (gallery from per-file load). + elif isinstance(data, (list, tuple)) and len(data) > 0 and ( + isinstance(data[0], (Dataset2d, Dataset3d)) or + (hasattr(data[0], "array") and hasattr(data[0], "sampling")) + ): + first = data[0] + if sampling is None: + sampling = tuple(float(s) for s in first.sampling[-2:]) + if units is None and hasattr(first, "units"): + units = list(first.units[-2:]) + data = [d.array for d in data] # Convert NumPy / PyTorch / list inputs to a NumPy array. if isinstance(data, list): @@ -434,8 +445,10 @@ def _init_sync(self, *, data, labels, title, cmap, sampling, units, self.initial_zoom = zoom self.zoom_row = zoom_row self.zoom_col = zoom_col - self.link_zoom = link_zoom - self.link_pan = link_pan + # Auto-link zoom + pan in gallery (n_images >= 2) so dragging one panel + # follows the other — typical compare/diff workflow. Single image: no-op. + self.link_zoom = (self.n_images >= 2) if link_zoom is None else link_zoom + self.link_pan = (self.n_images >= 2) if link_pan is None else link_pan self.link_contrast = link_contrast self.diff_mode = diff_mode if self.n_images >= 2 else False if show_fft and self.height * self.width > 2048 * 2048: From 88bbaecb552fbdc7babdf4170a222acfaf67d8ba Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 4 May 2026 17:18:03 -0700 Subject: [PATCH 277/335] update uv lock with all deps listed for now --- uv.lock | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/uv.lock b/uv.lock index 43a03fd0..58122fe4 100644 --- a/uv.lock +++ b/uv.lock @@ -1197,7 +1197,7 @@ name = "importlib-metadata" version = "9.0.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "zipp" }, + { name = "zipp", marker = "python_full_version < '3.12'" }, ] sdist = { url = "https://files.pythonhosted.org/packages/a9/01/15bb152d77b21318514a96f43af312635eb2500c96b55398d020c93d86ea/importlib_metadata-9.0.0.tar.gz", hash = "sha256:a4f57ab599e6a2e3016d7595cfd72eb4661a5106e787a95bcc90c7105b831efc", size = 56405, upload-time = "2026-03-20T06:42:56.999Z" } wheels = [ @@ -2897,10 +2897,22 @@ version = "0.0.1" source = { editable = "widget" } dependencies = [ { name = "anywidget" }, + { name = "matplotlib" }, + { name = "numpy" }, + { name = "pillow" }, + { name = "torch" }, + { name = "traitlets" }, ] [package.metadata] -requires-dist = [{ name = "anywidget", specifier = ">=0.9.0" }] +requires-dist = [ + { name = "anywidget", specifier = ">=0.9.0" }, + { name = "matplotlib", specifier = ">=3.7.0" }, + { name = "numpy", specifier = ">=2.0.0" }, + { name = "pillow", specifier = ">=10.0.0" }, + { name = "torch", specifier = ">=2.0.0" }, + { name = "traitlets", specifier = ">=5.0.0" }, +] [[package]] name = "referencing" From 8ecd3c7942b0986e12af1dc21669189847be720a Mon Sep 17 00:00:00 2001 From: arthurmccray Date: Wed, 6 May 2026 17:25:05 -0700 Subject: [PATCH 278/335] converting ptycho models to work with new PPLR --- src/quantem/core/ml/optimizer_mixin.py | 2 +- .../diffractive_imaging/dataset_models.py | 6 ++--- .../diffractive_imaging/object_models.py | 25 ++++++++----------- .../diffractive_imaging/probe_models.py | 13 ++++------ 4 files changed, 20 insertions(+), 26 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 9655eb76..55d013c9 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -648,7 +648,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: f"All parameter groups must use the same optimizer type, " f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" ) - self._optimizer = optimizer_cls(params) + self._optimizer = optimizer_cls(params) # type:ignore else: # Single-optimizer case: splat global hyperparameters optimizer_cls = self._optimizer_class_for(self._optimizer_params) diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index ef55c92e..585d5e4b 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -32,7 +32,7 @@ class PtychographyDatasetBase(AutoSerialize, OptimizerMixin, torch.nn.Module): _token = object() _patch_indices: torch.Tensor - # TODO update optimizers and such to allow for different lrs for different parameters + # TODO make this a PPLR so different lrs can be used for different parameters DEFAULT_LRS = { "descan": 1e-3, "scan_positions": 1e-3, @@ -95,7 +95,7 @@ def __init__( self._constraints = {} self._probe_energy = None - def get_optimization_parameters(self): + def get_optimization_parameters(self) -> list[dict[str, Any]]: """Get the combined descan and scan position parameters for optimization.""" params = [] if self.learn_descan: @@ -106,7 +106,7 @@ def get_optimization_parameters(self): raise RuntimeError( "No parameters to optimize for dataset: learn_descan and learn_scan_positions are both False" ) - return params + return [{"params": params}] def to(self, *args, **kwargs): """Move all relevant tensors to a different device.""" diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 1c6fdab9..0860bf55 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -1,7 +1,7 @@ import math from abc import abstractmethod from copy import deepcopy -from typing import Callable, Literal, Self, Sequence, cast +from typing import Any, Callable, Literal, Self, Sequence, cast from warnings import warn import matplotlib.pyplot as plt @@ -194,7 +194,7 @@ def obj(self): @property @abstractmethod - def params(self): + def params(self) -> list[nn.Parameter]: raise NotImplementedError() @abstractmethod @@ -232,16 +232,13 @@ def to(self, *args, **kwargs): def name(self) -> str: raise NotImplementedError() - def get_optimization_parameters(self): + def get_optimization_parameters(self) -> list[dict[str, Any]]: """Get the parameters that should be optimized for this model.""" - try: - params = self.params - if params is None: - return [] - return params - except NotImplementedError: - # This happens when params is not implemented yet in abstract base + params = self.params + if params is None: return [] + else: + return [{"params": params}] # compatible with PPLR def _propagate_array( self, array: "torch.Tensor", propagator_array: "torch.Tensor" @@ -633,9 +630,9 @@ def num_slices(self) -> int: return self._obj.shape[0] @property - def params(self): + def params(self) -> list[nn.Parameter]: """optimization parameters""" - return self._obj + return [self._obj] @property def initial_obj(self): @@ -1025,9 +1022,9 @@ def to(self, *args, **kwargs): return self @property - def params(self): + def params(self) -> list[nn.Parameter]: """optimization parameters""" - return self.model.parameters() + return list(self.model.parameters()) def reset(self): """Reset the object model to its initial or pre-trained state""" diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index f47ea1f7..b72f1426 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -94,16 +94,13 @@ def __init__( if roi_shape is not None: self.roi_shape = roi_shape - def get_optimization_parameters(self): + def get_optimization_parameters(self) -> list[dict[str, Any]]: """Get the parameters that should be optimized for this model.""" - try: - params = self.params - if params is None: - return [] - return params - except NotImplementedError: - # This happens when params is not implemented yet in abstract base + params = self.params + if params is None: return [] + else: + return [{"params": params}] # compatible with PPLR @property def learn_probe_tilt(self) -> bool: From 0bd5ba33f1713821374e992e4afcaea35a7c915d Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 6 May 2026 18:41:44 -0700 Subject: [PATCH 279/335] add hot pixel filtering in read 4dstem, default false --- src/quantem/core/io/file_readers.py | 34 ++++++++++++++++++++++++++++- tests/utils/test_filter.py | 24 ++++++++++++++++++++ 2 files changed, 57 insertions(+), 1 deletion(-) create mode 100644 tests/utils/test_filter.py diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 98e9e8fa..9feac57a 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -15,6 +15,7 @@ def read_4dstem( file_path: str | PathLike, file_type: str | None = None, dataset_index: int | None = None, + hot_pixel_filter: bool = False, **kwargs, ) -> Dataset4dstem: """ @@ -30,6 +31,11 @@ def read_4dstem( dataset_index: int, optional Index of the dataset to load if file contains multiple datasets. If None, automatically selects the first 4D dataset found. + hot_pixel_filter: bool, optional + If True, detect and replace hot detector pixels immediately after + loading using `quantem.core.utils.filter.filter_hot_pixels` with its + default parameters. For custom thresholds, call `filter_hot_pixels` + directly on the array. **kwargs: dict Additional keyword arguments to pass to the file reader. @@ -49,6 +55,26 @@ def read_4dstem( Returns -------- Dataset4dstem + + Examples + -------- + Load an Arina 4D-STEM master file with the default hot pixel filter: + + >>> from quantem.core.io import read_4dstem + >>> ds = read_4dstem( + ... '/path/to/gold_013_master.h5', + ... file_type='arina', + ... ) + >>> ds.array.shape + (256, 256, 192, 192) + + Skip the filter to inspect the raw detector output: + + >>> ds_raw = read_4dstem( + ... '/path/to/gold_013_master.h5', + ... file_type='arina', + ... hot_pixel_filter=False, + ... ) """ if file_type is None: file_type = Path(file_path).suffix.lower().lstrip(".") @@ -104,8 +130,14 @@ def read_4dstem( else ["pixels" if ax["units"] == "1" else ax["units"] for ax in imported_axes] ) + array = imported_data["data"] + if hot_pixel_filter: + from quantem.core.utils.filter import filter_hot_pixels + + array = filter_hot_pixels(array) + dataset = Dataset4dstem.from_array( - array=imported_data["data"], + array=array, sampling=sampling, origin=origin, units=units, diff --git a/tests/utils/test_filter.py b/tests/utils/test_filter.py new file mode 100644 index 00000000..dc213bc8 --- /dev/null +++ b/tests/utils/test_filter.py @@ -0,0 +1,24 @@ +"""Tests for `filter_hot_pixels`. + +A microscopist running 4D-STEM sees most detector pixels read out at low +counts (~1-100), but a few are stuck near saturation (~60000) regardless of +incident intensity. They must be removed before virtual imaging or dp_max +analysis. +""" + +import torch + +from quantem.core.utils.filter import filter_hot_pixels + + +def test_filter_hot_pixels_replaces_stuck_detector_pixels_with_local_median(): + """Stuck pixels should drop from 60000 back into the local bulk regime (<1000).""" + ds = torch.randint(1, 101, size=(64, 64, 32, 32), dtype=torch.int32) + # Assume these 3 places have hot pixels that we later want to remove + hot_coords = [(5, 7), (18, 24), (29, 3)] + for r, c in hot_coords: + ds[:, :, r, c] = 60000 + filtered = filter_hot_pixels(ds.numpy()) + dp_max = filtered.max(axis=(0, 1)) + # no pixels should have value 101 + assert dp_max.max() < 101, f"hot pixels still present, dp_max max={dp_max.max()}" From 31a0baef7098c56def59618960bc59092f078e65 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Wed, 6 May 2026 18:59:11 -0700 Subject: [PATCH 280/335] update docstring, hot pixel being false by default --- src/quantem/core/io/file_readers.py | 8 ++++---- tests/utils/test_filter.py | 14 +++++--------- 2 files changed, 9 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/io/file_readers.py b/src/quantem/core/io/file_readers.py index 9feac57a..4fe72645 100644 --- a/src/quantem/core/io/file_readers.py +++ b/src/quantem/core/io/file_readers.py @@ -58,7 +58,7 @@ def read_4dstem( Examples -------- - Load an Arina 4D-STEM master file with the default hot pixel filter: + Load a raw Arina 4D-STEM master file: >>> from quantem.core.io import read_4dstem >>> ds = read_4dstem( @@ -68,12 +68,12 @@ def read_4dstem( >>> ds.array.shape (256, 256, 192, 192) - Skip the filter to inspect the raw detector output: + Enable the hot pixel filter to repair stuck detector pixels on load: - >>> ds_raw = read_4dstem( + >>> ds = read_4dstem( ... '/path/to/gold_013_master.h5', ... file_type='arina', - ... hot_pixel_filter=False, + ... hot_pixel_filter=True, ... ) """ if file_type is None: diff --git a/tests/utils/test_filter.py b/tests/utils/test_filter.py index dc213bc8..1c43cd0c 100644 --- a/tests/utils/test_filter.py +++ b/tests/utils/test_filter.py @@ -1,18 +1,14 @@ -"""Tests for `filter_hot_pixels`. - -A microscopist running 4D-STEM sees most detector pixels read out at low -counts (~1-100), but a few are stuck near saturation (~60000) regardless of -incident intensity. They must be removed before virtual imaging or dp_max -analysis. -""" - import torch from quantem.core.utils.filter import filter_hot_pixels def test_filter_hot_pixels_replaces_stuck_detector_pixels_with_local_median(): - """Stuck pixels should drop from 60000 back into the local bulk regime (<1000).""" + """A microscopist running 4D-STEM sees most detector pixels read out at low + counts (~1-100), but a few are stuck near saturation (~60000) regardless + of incident intensity. After `filter_hot_pixels`, those stuck pixels + should drop back into the local bulk regime (<=100). + """ ds = torch.randint(1, 101, size=(64, 64, 32, 32), dtype=torch.int32) # Assume these 3 places have hot pixels that we later want to remove hot_coords = [(5, 7), (18, 24), (29, 3)] From a73dcc41fb84bc51724b3cd32f7ee257cc3a7891 Mon Sep 17 00:00:00 2001 From: henryhng Date: Thu, 7 May 2026 08:20:16 +0000 Subject: [PATCH 281/335] fix set_image --- widget/src/quantem/widget/show4dstem.py | 51 ++++++++++++++++--------- 1 file changed, 34 insertions(+), 17 deletions(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 8cd0cb58..01d39615 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -586,34 +586,51 @@ def set_image(self, data, scan_shape=None): """Replace the 4D-STEM data. Preserves all display and ROI settings.""" if hasattr(data, "sampling") and hasattr(data, "array"): data = data.array - data_np = to_numpy(data) - saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if saturated_value is not None: - data_np[data_np >= saturated_value] = 0 - if data_np.ndim == 5: - self.n_frames = data_np.shape[0] - self._scan_shape = (data_np.shape[1], data_np.shape[2]) - self._det_shape = (data_np.shape[3], data_np.shape[4]) + if isinstance(data, torch.Tensor): + self._device = data.device + self._data = data if data.device == self._device else data.to(self._device) + shape = tuple(data.shape) + ndim = len(shape) + view_dtype = ( + torch.int16 if data.dtype == torch.uint16 + else torch.int8 if data.dtype == torch.uint8 + else None + ) + if view_dtype is not None: + view = self._data.view(view_dtype).reshape(-1, *shape[-2:]) + rows = view.shape[0] + pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, shape[-2] * shape[-1])) + for i in range(0, rows, pos_per_chunk): + view[i:i + pos_per_chunk].masked_fill_(view[i:i + pos_per_chunk] == -1, 0) + else: + data_np = to_numpy(data) + saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None + if saturated_value is not None: + data_np[data_np >= saturated_value] = 0 + shape = data_np.shape + ndim = data_np.ndim self._data = torch.from_numpy(data_np).to(self._device) - elif data_np.ndim == 3: + if ndim == 5: + self.n_frames = shape[0] + self._scan_shape = (shape[1], shape[2]) + self._det_shape = (shape[3], shape[4]) + elif ndim == 3: self.n_frames = 1 if scan_shape is not None: self._scan_shape = scan_shape else: - n = data_np.shape[0] + n = shape[0] side = int(n ** 0.5) if side * side != n: raise ValueError(f"Cannot infer square scan_shape from N={n}. Provide scan_shape explicitly.") self._scan_shape = (side, side) - self._det_shape = (data_np.shape[1], data_np.shape[2]) - self._data = torch.from_numpy(data_np).to(self._device) - elif data_np.ndim == 4: + self._det_shape = (shape[1], shape[2]) + elif ndim == 4: self.n_frames = 1 - self._scan_shape = (data_np.shape[0], data_np.shape[1]) - self._det_shape = (data_np.shape[2], data_np.shape[3]) - self._data = torch.from_numpy(data_np).to(self._device) + self._scan_shape = (shape[0], shape[1]) + self._det_shape = (shape[2], shape[3]) else: - raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {data_np.ndim}D. See documentation for accepted shapes.") + raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {ndim}D. See documentation for accepted shapes.") self.frame_idx = 0 self.shape_rows = self._scan_shape[0] self.shape_cols = self._scan_shape[1] From f5923843ed8f6adae6748d066654b31b1d51b922 Mon Sep 17 00:00:00 2001 From: henryhng Date: Thu, 7 May 2026 08:37:06 +0000 Subject: [PATCH 282/335] fix ruff lint errors --- widget/src/quantem/widget/show2d.py | 14 +++++----- widget/src/quantem/widget/show4dstem.py | 37 ++++++++++--------------- widget/src/quantem/widget/state.py | 1 - widget/tests/test_state_dict.py | 2 -- 4 files changed, 22 insertions(+), 32 deletions(-) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index edcd01db..793608ea 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -5,12 +5,12 @@ Unlike Show3D (interactive), Show2D focuses on static visualization. """ +import base64 +import io import json +import math import os import pathlib -import io -import base64 -import math import warnings from enum import StrEnum from typing import Self @@ -21,11 +21,10 @@ import matplotlib.pyplot as plt import numpy as np import traitlets - -from quantem.core.datastructures import Dataset2d, Dataset3d -from quantem.widget.array_utils import to_numpy, _resize_image +from quantem.widget.array_utils import _resize_image, to_numpy from quantem.widget.state import resolve_widget_version, save_state_file, unwrap_state_payload +from quantem.core.datastructures import Dataset2d, Dataset3d def _reject_unknown_kwargs(cls, kwargs: dict) -> None: @@ -467,7 +466,8 @@ def _init_sync(self, *, data, labels, title, cmap, sampling, units, if isinstance(vmin, (list, tuple)) or isinstance(vmax, (list, tuple)): n = self.n_images def _expand(v): - if v is None: return [None] * n + if v is None: + return [None] * n if isinstance(v, (list, tuple)): if len(v) != n: raise ValueError(f"vmin/vmax list has length {len(v)} but n_images is {n}. Pass a list of length {n} or a scalar to apply uniformly.") diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 8cd0cb58..97efa8a8 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -11,27 +11,19 @@ widget = Show4DSTEM(dataset) """ -import hashlib import json import math import pathlib import time -from datetime import datetime, timezone -from typing import Any, Self -from uuid import uuid4 +from typing import TYPE_CHECKING, Any, Self + +if TYPE_CHECKING: + from quantem.core.datastructures import Dataset4dstem import anywidget import numpy as np import torch import traitlets - -# Cap transient chunk memory at ~600 MB regardless of detector size. -# A 4096 × 192² × 4 byte float32 cast = 600 MB; a 4096 × 256² × 4 byte cast -# would be 1.0 GB. _chunk_rows() picks an N-rows-per-chunk that keeps the -# transient under this cap. -_CHUNK_BYTE_BUDGET = 600 * 1024 * 1024 - -from quantem.core.config import validate_device from quantem.widget.array_utils import to_numpy from quantem.widget.state import ( build_json_header, @@ -40,11 +32,19 @@ unwrap_state_payload, ) +from quantem.core.config import validate_device + +# Cap transient chunk memory at ~600 MB regardless of detector size. +_CHUNK_BYTE_BUDGET = 600 * 1024 * 1024 + def _format_memory(nbytes: int) -> str: - if nbytes >= 1 << 30: return f"{nbytes / (1 << 30):.1f} GB" - if nbytes >= 1 << 20: return f"{nbytes / (1 << 20):.0f} MB" - if nbytes >= 1 << 10: return f"{nbytes / (1 << 10):.0f} KB" + if nbytes >= 1 << 30: + return f"{nbytes / (1 << 30):.1f} GB" + if nbytes >= 1 << 20: + return f"{nbytes / (1 << 20):.0f} MB" + if nbytes >= 1 << 10: + return f"{nbytes / (1 << 10):.0f} KB" return f"{nbytes} B" @@ -395,7 +395,6 @@ def __init__( # Handle dimensionality — 5D loads eagerly for instant frame switching # Resolve shape from whichever input path we took shape = tuple(self._data_pre.shape) if self._data_pre is not None else data_np.shape - size_elements = int(np.prod(shape)) ndim = len(shape) _tc = time.perf_counter() if ndim == 5: @@ -1842,9 +1841,6 @@ def save_image( prev_row, prev_col = self.pos_row, self.pos_col prev_frame = self.frame_idx meta_path: pathlib.Path | None = None - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) try: if frame_idx is not None: @@ -1853,9 +1849,6 @@ def save_image( row, col = self._validate_position(position) self.pos_row = row self.pos_col = col - export_row = int(self.pos_row) - export_col = int(self.pos_col) - export_frame = int(self.frame_idx) if view_key == "diffraction": image, dp_meta = self._render_panel_image( diff --git a/widget/src/quantem/widget/state.py b/widget/src/quantem/widget/state.py index d7710287..83cb6b2f 100644 --- a/widget/src/quantem/widget/state.py +++ b/widget/src/quantem/widget/state.py @@ -3,7 +3,6 @@ import pathlib from typing import Any - JSON_METADATA_VERSION = "1.0" diff --git a/widget/tests/test_state_dict.py b/widget/tests/test_state_dict.py index 9247c979..60614941 100644 --- a/widget/tests/test_state_dict.py +++ b/widget/tests/test_state_dict.py @@ -11,11 +11,9 @@ updating the state_dict roundtrip path. """ import json -import pathlib import numpy as np import pytest - from quantem.widget import Show2D, Show4DSTEM From 5b364c65a03e47ea9eaaf45229bf1867cdf3826b Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 08:21:04 +0000 Subject: [PATCH 283/335] remove set_image() --- widget/src/quantem/widget/show4dstem.py | 74 ------------------------- 1 file changed, 74 deletions(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 01d39615..5ffca3a1 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -582,80 +582,6 @@ def __init__( shape = "x".join(str(s) for s in self._data.shape) print(f"Show4DSTEM: {shape} {self._device}, {time.perf_counter() - _t0:.2f}s total") - def set_image(self, data, scan_shape=None): - """Replace the 4D-STEM data. Preserves all display and ROI settings.""" - if hasattr(data, "sampling") and hasattr(data, "array"): - data = data.array - if isinstance(data, torch.Tensor): - self._device = data.device - self._data = data if data.device == self._device else data.to(self._device) - shape = tuple(data.shape) - ndim = len(shape) - view_dtype = ( - torch.int16 if data.dtype == torch.uint16 - else torch.int8 if data.dtype == torch.uint8 - else None - ) - if view_dtype is not None: - view = self._data.view(view_dtype).reshape(-1, *shape[-2:]) - rows = view.shape[0] - pos_per_chunk = max(1, _CHUNK_BYTE_BUDGET // max(1, shape[-2] * shape[-1])) - for i in range(0, rows, pos_per_chunk): - view[i:i + pos_per_chunk].masked_fill_(view[i:i + pos_per_chunk] == -1, 0) - else: - data_np = to_numpy(data) - saturated_value = 65535.0 if data_np.dtype == np.uint16 else 255.0 if data_np.dtype == np.uint8 else None - if saturated_value is not None: - data_np[data_np >= saturated_value] = 0 - shape = data_np.shape - ndim = data_np.ndim - self._data = torch.from_numpy(data_np).to(self._device) - if ndim == 5: - self.n_frames = shape[0] - self._scan_shape = (shape[1], shape[2]) - self._det_shape = (shape[3], shape[4]) - elif ndim == 3: - self.n_frames = 1 - if scan_shape is not None: - self._scan_shape = scan_shape - else: - n = shape[0] - side = int(n ** 0.5) - if side * side != n: - raise ValueError(f"Cannot infer square scan_shape from N={n}. Provide scan_shape explicitly.") - self._scan_shape = (side, side) - self._det_shape = (shape[1], shape[2]) - elif ndim == 4: - self.n_frames = 1 - self._scan_shape = (shape[0], shape[1]) - self._det_shape = (shape[2], shape[3]) - else: - raise ValueError(f"Show4DSTEM expects a 3D, 4D, or 5D array. Got {ndim}D. See documentation for accepted shapes.") - self.frame_idx = 0 - self.shape_rows = self._scan_shape[0] - self.shape_cols = self._scan_shape[1] - self.det_rows = self._det_shape[0] - self.det_cols = self._det_shape[1] - first_frame = self._data[0] if self._data.ndim == 5 else self._data - first_frame_sample = first_frame[0] if first_frame.ndim >= 3 else first_frame - if not torch.is_floating_point(first_frame_sample): - first_frame_sample = first_frame_sample.float() - self.dp_global_min = max(float(first_frame_sample.min()), MIN_LOG_VALUE) - self.dp_global_max = float(first_frame_sample.max()) - self._det_row_coords = torch.arange(self.det_rows, device=self._device, dtype=torch.float32)[:, None] - self._det_col_coords = torch.arange(self.det_cols, device=self._device, dtype=torch.float32)[None, :] - self._scan_row_coords = torch.arange(self.shape_rows, device=self._device, dtype=torch.float32)[:, None] - self._scan_col_coords = torch.arange(self.shape_cols, device=self._device, dtype=torch.float32)[None, :] - self._cached_bf_virtual = None - self._cached_abf_virtual = None - self._cached_adf_virtual = None - self._cached_haadf_virtual = None - with self.hold_trait_notifications(): - self.pos_row = min(self.pos_row, self.shape_rows - 1) - self.pos_col = min(self.pos_col, self.shape_cols - 1) - self._compute_virtual_image_from_roi() - self._update_frame() - def __repr__(self) -> str: shape = ( f"({self.n_frames}, {self.shape_rows}, {self.shape_cols}, {self.det_rows}, {self.det_cols})" From c57d1d028c06861296e841b0bd9761f2e3995a87 Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 08:42:02 +0000 Subject: [PATCH 284/335] remove set_image() from Show2D --- widget/src/quantem/widget/show2d.py | 60 ----------------------------- 1 file changed, 60 deletions(-) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index edcd01db..9c074c40 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -575,66 +575,6 @@ def _on_first_render(self, change): except (ValueError, KeyError): pass - def set_image(self, data, labels=None): - """Replace the displayed image(s). Preserves all display settings.""" - if hasattr(data, "array") and hasattr(data, "name") and hasattr(data, "sampling"): - data = data.array - if isinstance(data, list): - images = [to_numpy(d) for d in data] - shapes = [img.shape for img in images] - if len(set(shapes)) > 1: - max_h = max(s[0] for s in shapes) - max_w = max(s[1] for s in shapes) - images = [_resize_image(img, max_h, max_w) for img in images] - data = np.stack(images) - else: - data = to_numpy(data) - if data.ndim == 2: - data = data[np.newaxis, ...] - if data.dtype == np.float32: - self._data = np.array(data, dtype=np.float32, copy=True) - else: - self._data = np.asarray(data, dtype=np.float32) - self._data_original = [self._data[i] for i in range(self._data.shape[0])] - self._originals_are_views = True - self.n_images = int(data.shape[0]) - - # Auto-bin for display (reuse existing _display_bin or recompute) - gpu_budget_mb = 2500 - per_image_mb = (data.shape[1] * data.shape[2] * 4 * 3) / (1024 * 1024) - total_mb = self.n_images * per_image_mb - self._display_bin = 1 - if total_mb > gpu_budget_mb: - for bf in [2, 4, 8]: - if total_mb / (bf * bf) <= gpu_budget_mb: - self._display_bin = bf - break - else: - self._display_bin = 8 - - if self._display_bin > 1: - from quantem.widget.array_utils import bin2d - self._display_data = bin2d(self._data, factor=self._display_bin, mode="mean") - self.height = int(self._display_data.shape[1]) - self.width = int(self._display_data.shape[2]) - self._display_bin_factor = self._display_bin - if getattr(self, "_verbose", True): - print(f" Display bin {self._display_bin}×: {data.shape[1]}×{data.shape[2]} → {self.height}×{self.width}") - else: - self._display_data = self._data - self.height = int(data.shape[1]) - self.width = int(data.shape[2]) - self._display_bin_factor = 1 - - self.image_rotations = [0] * self.n_images - if labels is not None: - self.labels = list(labels) - else: - self.labels = [f"Image {i+1}" for i in range(self.n_images)] - self.selected_idx = 0 - self._compute_all_stats() - self._update_all_frames() - def __repr__(self) -> str: if self.n_images > 1: shape = f"{self.n_images}×{self.height}×{self.width}" From c36be765a113ba0b6521682531fbd152ea51a736 Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 08:51:17 +0000 Subject: [PATCH 285/335] invalidate virtual image cache on calibration change, mkdir in save_state_file --- widget/src/quantem/widget/show4dstem.py | 8 ++++++++ widget/src/quantem/widget/state.py | 4 +++- 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 5ffca3a1..5b25647c 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -527,6 +527,8 @@ def __init__( ]) # Observe compound roi_center for batched updates from JS self.observe(self._on_roi_center_change, names=["roi_center"]) + # Invalidate precomputed virtual image caches when calibration changes + self.observe(self._on_calibration_change, names=["center_row", "center_col", "bf_radius"]) # Initialize default ROI at BF center — batch to avoid redundant observer callbacks with self.hold_trait_notifications(): @@ -2162,6 +2164,12 @@ def _create_rect_mask(self, cx: float, cy: float, half_width: float, half_height mask = (torch.abs(self._det_col_coords - cx) <= half_width) & (torch.abs(self._det_row_coords - cy) <= half_height) return mask + def _on_calibration_change(self, change=None): + self._cached_bf_virtual = None + self._cached_abf_virtual = None + self._cached_adf_virtual = None + self._cached_haadf_virtual = None + def _precompute_common_virtual_images(self): """Pre-compute BF/ABF/ADF/HAADF virtual image bytes. Annular ranges match apply_preset() so the cache always hits on preset clicks.""" diff --git a/widget/src/quantem/widget/state.py b/widget/src/quantem/widget/state.py index d7710287..af9344b8 100644 --- a/widget/src/quantem/widget/state.py +++ b/widget/src/quantem/widget/state.py @@ -42,4 +42,6 @@ def unwrap_state_payload(payload: dict[str, Any], *, require_envelope: bool = Fa def save_state_file(path: str | pathlib.Path, widget_name: str, state: dict[str, Any]) -> None: - pathlib.Path(path).write_text(json.dumps(wrap_state_dict(widget_name, state), indent=2)) + p = pathlib.Path(path) + p.parent.mkdir(parents=True, exist_ok=True) + p.write_text(json.dumps(wrap_state_dict(widget_name, state), indent=2)) From 5a6e8a8cb85a960f7f4f481ade06349016e1c450 Mon Sep 17 00:00:00 2001 From: henryhng Date: Sat, 9 May 2026 09:19:14 +0000 Subject: [PATCH 286/335] lazy import torch in array_utils --- widget/src/quantem/widget/array_utils.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/widget/src/quantem/widget/array_utils.py b/widget/src/quantem/widget/array_utils.py index 1913a846..f4ff7592 100644 --- a/widget/src/quantem/widget/array_utils.py +++ b/widget/src/quantem/widget/array_utils.py @@ -1,11 +1,15 @@ """Array utilities for widgets. NumPy + PyTorch input.""" import numpy as np -import torch def to_numpy(data, dtype: np.dtype | None = None) -> np.ndarray: """Convert NumPy / PyTorch / Dataset to NumPy.""" - if isinstance(data, torch.Tensor): + try: + import torch + is_tensor = isinstance(data, torch.Tensor) + except ImportError: + is_tensor = False + if is_tensor: result = data.detach().cpu().numpy() elif isinstance(data, np.ndarray): result = data From 178006e55e7543878b1eebea4ef6771f7d1ba58d Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 11 May 2026 12:17:04 +0000 Subject: [PATCH 287/335] chore: update lock file --- uv.lock | 240 ++++++++++++++++++++++++++++---------------------------- 1 file changed, 120 insertions(+), 120 deletions(-) diff --git a/uv.lock b/uv.lock index 6932e160..a0488e0f 100644 --- a/uv.lock +++ b/uv.lock @@ -532,101 +532,101 @@ wheels = [ [[package]] name = "coverage" -version = "7.13.5" -source = { registry = "https://pypi.org/simple" } -sdist = { url = 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Subject: [PATCH 288/335] address cedric's feedback on scan rotation --- widget/src/quantem/widget/show2d.py | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/widget/src/quantem/widget/show2d.py b/widget/src/quantem/widget/show2d.py index fbe2f8b3..e243a51d 100644 --- a/widget/src/quantem/widget/show2d.py +++ b/widget/src/quantem/widget/show2d.py @@ -835,6 +835,12 @@ def load_state_dict(self, state): setattr(self, key, val) def summary(self): + """Print a human-readable snapshot of the widget's current state. + + Reports image dimensions and pixel size, data min/max/mean, display + settings (colormap, contrast, scale, FFT), active ROIs and profile + line, per-image rotations, and the most recent render timings. + """ lines = [self.title or "Show2D", "═" * 32] if self.n_images > 1: lines.append(f"Image: {self.n_images}×{self.height}×{self.width} ({self.ncols} cols)") @@ -897,6 +903,15 @@ def _update_all_frames(self): self.frame_bytes = data.tobytes() def 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Type-hinting error fixed in tomography_otp.py --- src/quantem/core/ml/constraints.py | 8 +++++++- src/quantem/tomography/tomography_context.py | 3 ++- src/quantem/tomography/tomography_opt.py | 2 +- 3 files changed, 10 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index f437924f..13011095 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -9,6 +9,12 @@ from quantem.tomography.tomography_context import ReconstructionContext +@dataclass +class BaseContext(ABC): + """ + Constraints should contain a context object that contains all necessary data for the constraints to be applied. + """ + pass @dataclass(slots=False) class Constraints(ABC): @@ -95,7 +101,7 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abstractmethod - def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: + def apply_soft_constraints(self, ctx: BaseContext) -> torch.Tensor: """ Apply soft constraints to the model. """ diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py index 4b67f118..2095727e 100644 --- a/src/quantem/tomography/tomography_context.py +++ b/src/quantem/tomography/tomography_context.py @@ -1,11 +1,12 @@ from dataclasses import dataclass from typing import Optional +from quantem.core.ml.constraints import BaseContext import torch @dataclass -class ReconstructionContext: +class ReconstructionContext(BaseContext): """ Handles all reconstruction parameters to be passed into object models. diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index c75de1e3..2c913277 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -27,7 +27,7 @@ def _get_default_lr(self, key: str) -> float: raise ValueError(f"Unknown optimization key: {key}") @property - def optimizer_params(self) -> dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerType | dict[str, OptimizerType]]: return { key: params for key, params in [ From af33b962b1f8a7c47dd1ddf0702309beed185810 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:18:42 -0700 Subject: [PATCH 291/335] Removed import in constraints.py --- src/quantem/core/ml/constraints.py | 1 - 1 file changed, 1 deletion(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 13011095..83270400 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,7 +7,6 @@ import torch from numpy.typing import NDArray -from quantem.tomography.tomography_context import ReconstructionContext @dataclass class BaseContext(ABC): From 03da610c5a3ce34c601058bfb243ba1d4d8ba42c Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:21:41 -0700 Subject: [PATCH 292/335] PPLR description changed to multi-parameter optimization --- src/quantem/core/ml/models/model_base.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/ml/models/model_base.py b/src/quantem/core/ml/models/model_base.py index 503f61bb..60c1c4f6 100644 --- a/src/quantem/core/ml/models/model_base.py +++ b/src/quantem/core/ml/models/model_base.py @@ -6,7 +6,7 @@ class PPLR(ABC): """ - Abstract base class for models that require multi-scale parameter optimization. + Abstract base class for models that require multi-parameter optimization. """ @abstractmethod From a62295c3b6499a95a7ea01125069c6b5710a7d1e Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:31:16 -0700 Subject: [PATCH 293/335] SO3 Rotations paper citation in SO3params.py --- src/quantem/core/ml/models/so3params.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 6569ec93..fd3a0b7e 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -113,6 +113,8 @@ class SO3ParamR9SVD(nn.Module): SO(3) rotation bank using R9+SVD parameterization. Each rotation is stored as an unconstrained 3x3 matrix M, projected to SO(3) via SVD+(M) = U diag(1,1,det(UVt)) Vt. + + Based on Rene Geist et al., 2024: https://arxiv.org/abs/2404.11735v1 """ def __init__(self, T: int, init: str = "random"): From e222512ab6abde241ff8a189bca5966d08575df6 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 11:32:14 -0700 Subject: [PATCH 294/335] Type-hinting fix in So3params --- src/quantem/core/ml/models/so3params.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index fd3a0b7e..82a4361a 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -1,4 +1,5 @@ import math +from typing import Literal import torch import torch.nn as nn @@ -117,7 +118,7 @@ class SO3ParamR9SVD(nn.Module): Based on Rene Geist et al., 2024: https://arxiv.org/abs/2404.11735v1 """ - def __init__(self, T: int, init: str = "random"): + def __init__(self, T: int, init: Literal["random", "identity"] = "random"): super().__init__() if init == "random": # Initialize near identity with small noise From 91d605631c052616df5f6c1d062d22b47494f22e Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:42:18 -0700 Subject: [PATCH 295/335] Load parameters added .to(self.device) --- src/quantem/core/utils/tomography_utils.py | 1 - src/quantem/tomography/dataset_models.py | 6 +++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/src/quantem/core/utils/tomography_utils.py b/src/quantem/core/utils/tomography_utils.py index 9f5df102..006f8cfc 100644 --- a/src/quantem/core/utils/tomography_utils.py +++ b/src/quantem/core/utils/tomography_utils.py @@ -168,7 +168,6 @@ def fourier_binning(img, crop_size): center = np.array(img.shape) // 2 fft_img = np.fft.fftshift(np.fft.fft2(img)) - cropped_fft = fft_img[ center[0] - crop_size[0] // 2 : center[0] + crop_size[0] // 2, center[1] - crop_size[1] // 2 : center[1] + crop_size[1] // 2, diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index b50c5976..bdd2e66a 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -668,9 +668,9 @@ def load_parameters(self, path: str): Loads the learned parameters from a file. """ data = torch.load(path) - self._z1_params = nn.Parameter(data["z1"]) - self._z3_params = nn.Parameter(data["z3"]) - self._shifts_params = nn.Parameter(data["shifts"]) + self._z1_params = nn.Parameter(data["z1"]).to(self.device) + self._z3_params = nn.Parameter(data["z3"]).to(self.device) + self._shifts_params = nn.Parameter(data["shifts"]).to(self.device) if self.optimizer is not None: self.reconnect_optimizer_to_parameters() From f37ca9f06b12131dcf5c35eda29ed1fe4a35b080 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:49:31 -0700 Subject: [PATCH 296/335] Volume added to reconstruction context --- src/quantem/tomography/tomography_context.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py index 2095727e..ef861651 100644 --- a/src/quantem/tomography/tomography_context.py +++ b/src/quantem/tomography/tomography_context.py @@ -16,6 +16,7 @@ class ReconstructionContext(BaseContext): - TensorDecomp reads ".coords" and ".pred" (and ".all densities") """ + volume: Optional[torch.Tensor] = None coords: Optional[torch.Tensor] = None pred: Optional[torch.Tensor] = None all_densities: Optional[torch.Tensor] = None From 8072fa6bc85b3df8871c60eadecff31af3b5c8f6 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:52:04 -0700 Subject: [PATCH 297/335] Citations for the kplanes models --- src/quantem/core/ml/models/kplanes.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/ml/models/kplanes.py b/src/quantem/core/ml/models/kplanes.py index fe36dfcc..cdf55261 100644 --- a/src/quantem/core/ml/models/kplanes.py +++ b/src/quantem/core/ml/models/kplanes.py @@ -166,6 +166,9 @@ def interpolate_ms_features( class KPlanes(PPLR, TensorDecompositionModel): + """ + K-Planes model adapted from Fridovich-Keil et al., https://arxiv.org/abs/2301.10241 + """ def __init__( self, # Grid parameters @@ -354,7 +357,7 @@ def interpolate_ms_features_tilted( class KPlanesTILTED(KPlanes): """ - K-Planes with T learned SO(3) rotations (TILTED). + K-Planes with T learned SO(3) rotations (TILTED). Adapted from Yi et al., https://arxiv.org/abs/2308.15461 Inherits KPlanes for the sigma_net, density_activation, and get_params interface. Overrides: @@ -646,7 +649,7 @@ def interpolate_ms_features_cp_tilted( class CPTilted(PPLR, TensorDecompositionModel): """ CP decomposition with TILTED rotations — the true bottleneck model for - phase 1. Rank-1-per-channel feature representation. + phase 1. Rank-1-per-channel feature representation. Adapted from Yi et al., https://arxiv.org/abs/2308.15461 Shares the SO3Param and sigma_net design with KPlanesTILTED so you can lift τ directly across: cp_model.extract_tau_state() -> From eafca0c791e3fb07fe3cb003479b3bcb8ff29c74 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:54:15 -0700 Subject: [PATCH 298/335] Explanation in object_models.py for different types of tv loss. --- src/quantem/tomography/object_models.py | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index a094fae8..fc2669b7 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -932,6 +932,12 @@ def apply_soft_constraints(self, ctx: ReconstructionContext) -> torch.Tensor: # TV Losses def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: + """ + Gets the summed total variational loss for the tensor decomposition model. + + _get_plane_tv_loss: Total-variation across the planes. + _get_volume_tv_loss: Isotropic volume TV + """ assert ctx.coords is not None, "Coordinates must be provided for TV loss" assert ctx.pred is not None, "Prediction must be provided for TV loss" tv_loss = torch.tensor(0.0, device=ctx.pred.device) @@ -940,6 +946,9 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: return tv_loss def _get_plane_tv_loss(self) -> torch.Tensor: + """ + Gets the total-variation across the planes. + """ is_tilted = self.model.tilted per_level = [] From e4c8dbe21f600554d5450b89c20000773d6e3f58 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 14:58:07 -0700 Subject: [PATCH 299/335] Refactor type hinting in _unwrap function to use cast for improved type safety --- src/quantem/tomography/object_models.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index fc2669b7..8fa19db7 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1,7 +1,7 @@ from abc import abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Callable, Generator, Optional +from typing import Any, Callable, Generator, Optional, cast import numpy as np import torch @@ -200,8 +200,8 @@ def parse_dict( def _unwrap(model: nn.Module | nn.parallel.DistributedDataParallel) -> PlanarDecompositionModel: """Unwrap a DistributedDataParallel model to get the underlying module ONLY for tensor decomposition models.""" if isinstance(model, nn.parallel.DistributedDataParallel): - return model.module - return model + return cast(PlanarDecompositionModel, model.module) + return cast(PlanarDecompositionModel, model) class ObjectBase(AutoSerialize, nn.Module, RNGMixin, OptimizerMixin): From 136a65f059c5208947e92e2ffd92366a1ce68df1 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 18 May 2026 15:03:07 -0700 Subject: [PATCH 300/335] Some type-hinting fix for Contexts --- src/quantem/core/ml/constraints.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index 83270400..fea1d309 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -1,12 +1,13 @@ from abc import ABC, abstractmethod from copy import deepcopy from dataclasses import dataclass -from typing import Any, Self +from typing import Any, Generic, Self, TypeVar import numpy as np import torch from numpy.typing import NDArray +T_ctx = TypeVar("T_ctx", bound=BaseContext) @dataclass class BaseContext(ABC): @@ -54,7 +55,7 @@ def __str__(self) -> str: ) -class BaseConstraints(ABC): +class BaseConstraints(ABC, Generic[T_ctx]): """ Base class for constraints. """ @@ -100,7 +101,7 @@ def apply_hard_constraints(self, pred: torch.Tensor) -> torch.Tensor: raise NotImplementedError @abstractmethod - def apply_soft_constraints(self, ctx: BaseContext) -> torch.Tensor: + def apply_soft_constraints(self, ctx: T_ctx) -> torch.Tensor: """ Apply soft constraints to the model. """ From 98ddd0d34e9ed51f63003131807f42f6843d7a16 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:36:44 -0700 Subject: [PATCH 301/335] dataset4d, dataset4dstem hold torch array --- src/quantem/core/datastructures/dataset.py | 104 +++++++++++++----- src/quantem/core/datastructures/dataset4d.py | 33 ++---- .../core/datastructures/dataset4dstem.py | 52 ++++++++- widget/src/quantem/widget/show4dstem.py | 11 +- 4 files changed, 141 insertions(+), 59 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 4d2ab9e1..786b3d5d 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -4,6 +4,7 @@ from typing import Any, Literal, Optional, Self, Union, overload import numpy as np +import torch from numpy.typing import DTypeLike, NDArray from quantem.core.io.serialize import AutoSerialize @@ -38,24 +39,39 @@ class Dataset(AutoSerialize): def __init__( self, - array: Any, # Input can be array-like - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, + array: NDArray | None = None, + tensor: torch.Tensor | None = None, + name: str = "", + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: Optional[dict] = None, _token: object | None = None, ): if _token is not self._token: - raise RuntimeError("Use Dataset.from_array() to instantiate this class.") - super().__init__() - arr = ensure_valid_array(array) - if not isinstance(arr, np.ndarray): - raise TypeError( - "Dataset requires a NumPy array (CuPy is not supported on this branch)." + raise RuntimeError( + "Use Dataset.from_array() or Dataset.from_tensor() to instantiate this class." ) - self._array = arr + super().__init__() + # Dual-slot storage: exactly one of (_array, _tensor) is set. + if array is None and tensor is None: + raise ValueError("Provide either `array` (numpy) or `tensor` (torch).") + if array is not None and tensor is not None: + raise ValueError("Provide only one of `array` or `tensor`, not both.") + if array is not None: + arr = ensure_valid_array(array) + if not isinstance(arr, np.ndarray): + raise TypeError(f"Dataset.array must be numpy.ndarray, got {type(arr).__name__}.") + self._array = arr + self._tensor = None + else: + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"Dataset.tensor must be torch.Tensor, got {type(tensor).__name__}.") + self._array = None + self._tensor = tensor + # Lazy cache: derived numpy from tensor, materialized only on first .array access. + self._cached_numpy: np.ndarray | None = None self.name = name self.origin = origin self.sampling = sampling @@ -123,18 +139,34 @@ def from_array( # --- Properties --- @property def array(self) -> NDArray: - """The underlying n-dimensional NumPy array data.""" - return self._array + """The data as a numpy array. + + For tensor-backed datasets, returns a CACHED read-only CPU copy derived + from ``self.tensor`` (first access pays GPU->CPU transfer, subsequent + accesses are free). Torch-aware consumers should prefer ``.tensor``. + """ + if self._array is not None: + return self._array + if self._cached_numpy is None: + self._cached_numpy = self._tensor.detach().cpu().numpy() + self._cached_numpy.flags.writeable = False + return self._cached_numpy @array.setter def array(self, value: NDArray) -> None: - arr = ensure_valid_array(value, ndim=self.ndim) # want to allow changing dtype + arr = ensure_valid_array(value, ndim=self.ndim) if not isinstance(arr, np.ndarray): - raise TypeError( - "Dataset requires a NumPy array (CuPy is not supported on this branch)." - ) + raise TypeError(f"Dataset.array must be numpy.ndarray, got {type(arr).__name__}.") self._array = arr - # self._array = ensure_valid_array(value, dtype=self.dtype, ndim=self.ndim) + + @property + def tensor(self) -> torch.Tensor: + """Torch tensor backing the data. AttributeError if numpy-backed.""" + if self._tensor is None: + raise AttributeError( + f"Dataset '{self.name}' is numpy-backed; use Dataset.from_tensor() at construction." + ) + return self._tensor @property def metadata(self) -> dict: @@ -191,26 +223,42 @@ def file_path(self, value: os.PathLike | str | None) -> None: # --- Derived Properties --- @property def shape(self) -> tuple[int, ...]: - return self.array.shape + # Direct slot access — never triggers .array derive (which would force + # a full GPU->CPU copy on tensor-backed datasets). + return tuple((self._array if self._array is not None else self._tensor).shape) @property def ndim(self) -> int: - return self.array.ndim + return (self._array if self._array is not None else self._tensor).ndim @property def dtype(self) -> DTypeLike: - return self.array.dtype + return (self._array if self._array is not None else self._tensor).dtype @property def device(self) -> str: - """ - Outputting a string is likely temporary -- once we have our use cases we can - figure out a more permanent device solution that enables easier translation between - numpy <-> torch <-> numpy, etc. + """``"cpu"`` for numpy-backed; torch device string for tensor-backed.""" + if self._tensor is not None: + return str(self._tensor.device) + return "cpu" + + def numpy(self) -> NDArray: + """Return the data as a numpy array (mirrors ``torch.Tensor.numpy()``). - For NumPy-only datasets, this is always "cpu". + Equivalent to ``self.array`` — both return numpy. For tensor-backed + datasets, first call materializes a cached read-only CPU copy. """ - return "cpu" + return self.array + + def to(self, device) -> Self: + """Move the underlying tensor to ``device``. Raises if numpy-backed.""" + if self._tensor is None: + raise AttributeError( + f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." + ) + self._tensor = self._tensor.to(device) + self._cached_numpy = None # invalidate stale derived numpy + return self # --- Summaries --- def __repr__(self) -> str: diff --git a/src/quantem/core/datastructures/dataset4d.py b/src/quantem/core/datastructures/dataset4d.py index 8e5bdfa0..80d219ea 100644 --- a/src/quantem/core/datastructures/dataset4d.py +++ b/src/quantem/core/datastructures/dataset4d.py @@ -1,6 +1,7 @@ from typing import Any, Self, Union import numpy as np +import torch from numpy.typing import NDArray from quantem.core.datastructures.dataset import Dataset @@ -21,36 +22,20 @@ class Dataset4d(Dataset): def __init__( self, - array: NDArray | Any, - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, + array: NDArray | None = None, + tensor: torch.Tensor | None = None, + name: str = "", + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: dict = {}, _token: object | None = None, ): - """Initialize a 4D dataset. - - Parameters - ---------- - array : NDArray | Any - The underlying 3D array data - name : str - A descriptive name for the dataset - origin : NDArray | tuple | list | float | int - The origin coordinates for each dimension in calibrated units - sampling : NDArray | tuple | list | float | int - The sampling rate/spacing for each dimension - units : list[str] | tuple | list - Units for each dimension - signal_units : str, optional - Units for the array values, by default "arb. units" - _token : object | None, optional - Token to prevent direct instantiation, by default None - """ + """Initialize a 4D dataset. Pass exactly one of ``array`` (numpy) or ``tensor`` (torch).""" super().__init__( array=array, + tensor=tensor, name=name, origin=origin, sampling=sampling, diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 79cbc479..60890475 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -2,6 +2,7 @@ import matplotlib.pyplot as plt import numpy as np +import torch from matplotlib.patches import Circle, Wedge from numpy.typing import NDArray @@ -41,11 +42,12 @@ class Dataset4dstem(Dataset4d): def __init__( self, - array: NDArray | Any, - name: str, - origin: NDArray | tuple | list | float | int, - sampling: NDArray | tuple | list | float | int, - units: list[str] | tuple | list, + array: NDArray | None = None, + tensor: torch.Tensor | None = None, + name: str = "", + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, signal_units: str = "arb. units", metadata: dict = {}, _token: object | None = None, @@ -79,6 +81,7 @@ def __init__( super().__init__( array=array, + tensor=tensor, name=name, origin=origin, sampling=sampling, @@ -157,6 +160,45 @@ def from_array( _token=cls._token, ) + @classmethod + def from_tensor( + cls, + tensor: torch.Tensor, + name: str | None = None, + origin: NDArray | tuple | list | float | int | None = None, + sampling: NDArray | tuple | list | float | int | None = None, + units: list[str] | tuple | list | None = None, + signal_units: str = "arb. units", + metadata: dict | None = None, + ) -> Self: + """Create a Dataset4dstem from a torch tensor (any device). + + Use this when raw data is GPU-resident (CUDA pipelines, live detector + frames, GPU file readers) to skip the VRAM<->RAM round-trip. + + For cupy / jax arrays, wrap with ``torch.from_dlpack(arr)`` first. + """ + if not isinstance(tensor, torch.Tensor): + raise TypeError( + f"from_tensor requires torch.Tensor, got {type(tensor).__name__}. " + f"For cupy / jax, wrap with `torch.from_dlpack(arr)` first." + ) + if tensor.ndim != 4: + raise ValueError( + f"Dataset4dstem.from_tensor requires a 4D tensor " + f"(scan_y, scan_x, dp_y, dp_x), got shape {tuple(tensor.shape)}." + ) + return cls( + tensor=tensor, + name=name if name is not None else "4D-STEM dataset (torch)", + origin=origin if origin is not None else np.zeros(4), + sampling=sampling if sampling is not None else np.ones(4), + units=units if units is not None else ["pixels"] * 4, + signal_units=signal_units, + metadata=metadata if metadata is not None else {}, + _token=cls._token, + ) + @property def virtual_images(self) -> dict[str, Dataset2d]: """ diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 0a6b256b..06f8b629 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -334,14 +334,21 @@ def __init__( _io_labels = None # Auto-extract sampling + units from Dataset4dstem if available. - if hasattr(data, "sampling") and hasattr(data, "array"): + # NOTE: avoid `hasattr(data, "array")` — for tensor-backed Datasets the + # `.array` getter is an expensive derive (full GPU->CPU copy). Use cheap + # `hasattr(data, "sampling")` to identify a Dataset. + if hasattr(data, "sampling"): if not title and hasattr(data, "name") and data.name: title = str(data.name) if sampling is None: sampling = tuple(float(s) for s in data.sampling) if units is None and hasattr(data, "units"): units = list(data.units) - data = data.array + # If tensor-backed (zero-copy GPU path), take .tensor. Else .array (numpy). + if getattr(data, "_tensor", None) is not None: + data = data.tensor + else: + data = data.array # Resolve sampling + units (4 axes for 4D-STEM): # [scan_row, scan_col, k_row, k_col]. Scalar/None broadcast to (1, 1, 1, 1). From d2dc32b390307f409ac8a8ba45b403a0aab58b01 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:39:56 -0700 Subject: [PATCH 302/335] bring original docstring back --- src/quantem/core/datastructures/dataset.py | 2 +- src/quantem/core/datastructures/dataset4d.py | 22 ++++++++++++++++++- .../core/datastructures/dataset4dstem.py | 3 +++ 3 files changed, 25 insertions(+), 2 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 786b3d5d..5a89b8ad 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -139,7 +139,7 @@ def from_array( # --- Properties --- @property def array(self) -> NDArray: - """The data as a numpy array. + """The underlying n-dimensional NumPy array data. For tensor-backed datasets, returns a CACHED read-only CPU copy derived from ``self.tensor`` (first access pays GPU->CPU transfer, subsequent diff --git a/src/quantem/core/datastructures/dataset4d.py b/src/quantem/core/datastructures/dataset4d.py index 80d219ea..f5681730 100644 --- a/src/quantem/core/datastructures/dataset4d.py +++ b/src/quantem/core/datastructures/dataset4d.py @@ -32,7 +32,27 @@ def __init__( metadata: dict = {}, _token: object | None = None, ): - """Initialize a 4D dataset. Pass exactly one of ``array`` (numpy) or ``tensor`` (torch).""" + """Initialize a 4D dataset. + + Parameters + ---------- + array : NDArray | None + The underlying 4D numpy array. Provide exactly one of ``array`` or ``tensor``. + tensor : torch.Tensor | None + The underlying 4D torch tensor (any device). Provide exactly one of ``array`` or ``tensor``. + name : str + A descriptive name for the dataset + origin : NDArray | tuple | list | float | int + The origin coordinates for each dimension in calibrated units + sampling : NDArray | tuple | list | float | int + The sampling rate/spacing for each dimension + units : list[str] | tuple | list + Units for each dimension + signal_units : str, optional + Units for the array values, by default "arb. units" + _token : object | None, optional + Token to prevent direct instantiation, by default None + """ super().__init__( array=array, tensor=tensor, diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 60890475..c0c331b5 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -58,6 +58,9 @@ def __init__( ---------- array : NDArray | Any The underlying 4D array data + tensor : torch.Tensor | None, optional + Alternative to ``array``: the underlying 4D torch tensor (any device). + Provide exactly one of ``array`` or ``tensor``. name : str A descriptive name for the dataset origin : NDArray | tuple | list | float | int From 6b7319d7996553912b7b8ce485edb16c17257183 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:42:43 -0700 Subject: [PATCH 303/335] remove need for cached numpy array --- src/quantem/core/datastructures/dataset.py | 25 ++++++++-------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 5a89b8ad..3263720a 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -70,8 +70,6 @@ def __init__( raise TypeError(f"Dataset.tensor must be torch.Tensor, got {type(tensor).__name__}.") self._array = None self._tensor = tensor - # Lazy cache: derived numpy from tensor, materialized only on first .array access. - self._cached_numpy: np.ndarray | None = None self.name = name self.origin = origin self.sampling = sampling @@ -138,19 +136,13 @@ def from_array( # --- Properties --- @property - def array(self) -> NDArray: + def array(self) -> NDArray | None: """The underlying n-dimensional NumPy array data. - For tensor-backed datasets, returns a CACHED read-only CPU copy derived - from ``self.tensor`` (first access pays GPU->CPU transfer, subsequent - accesses are free). Torch-aware consumers should prefer ``.tensor``. + Returns ``None`` for tensor-backed datasets — use ``.tensor`` for the + torch tensor, or ``.numpy()`` to materialize a numpy copy explicitly. """ - if self._array is not None: - return self._array - if self._cached_numpy is None: - self._cached_numpy = self._tensor.detach().cpu().numpy() - self._cached_numpy.flags.writeable = False - return self._cached_numpy + return self._array @array.setter def array(self, value: NDArray) -> None: @@ -245,10 +237,12 @@ def device(self) -> str: def numpy(self) -> NDArray: """Return the data as a numpy array (mirrors ``torch.Tensor.numpy()``). - Equivalent to ``self.array`` — both return numpy. For tensor-backed - datasets, first call materializes a cached read-only CPU copy. + For numpy-backed datasets, returns ``self.array`` directly. For + tensor-backed datasets, materializes a CPU copy via ``.detach().cpu().numpy()``. """ - return self.array + if self._array is not None: + return self._array + return self._tensor.detach().cpu().numpy() def to(self, device) -> Self: """Move the underlying tensor to ``device``. Raises if numpy-backed.""" @@ -257,7 +251,6 @@ def to(self, device) -> Self: f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." ) self._tensor = self._tensor.to(device) - self._cached_numpy = None # invalidate stale derived numpy return self # --- Summaries --- From 7f9913f61fcc9f0bafee2f79a5366b1d1358d6f4 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:44:38 -0700 Subject: [PATCH 304/335] further cleaup api docstring --- src/quantem/core/datastructures/dataset.py | 4 ++-- widget/src/quantem/widget/show4dstem.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index 3263720a..a89257a3 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -139,7 +139,7 @@ def from_array( def array(self) -> NDArray | None: """The underlying n-dimensional NumPy array data. - Returns ``None`` for tensor-backed datasets — use ``.tensor`` for the + Returns ``None`` for tensor-backed datasets. Use ``.tensor`` for the torch tensor, or ``.numpy()`` to materialize a numpy copy explicitly. """ return self._array @@ -215,7 +215,7 @@ def file_path(self, value: os.PathLike | str | None) -> None: # --- Derived Properties --- @property def shape(self) -> tuple[int, ...]: - # Direct slot access — never triggers .array derive (which would force + # Direct slot access (never triggers .array derive, which would force # a full GPU->CPU copy on tensor-backed datasets). return tuple((self._array if self._array is not None else self._tensor).shape) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 06f8b629..99a5eae8 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -334,9 +334,9 @@ def __init__( _io_labels = None # Auto-extract sampling + units from Dataset4dstem if available. - # NOTE: avoid `hasattr(data, "array")` — for tensor-backed Datasets the - # `.array` getter is an expensive derive (full GPU->CPU copy). Use cheap - # `hasattr(data, "sampling")` to identify a Dataset. + # NOTE: avoid `hasattr(data, "array")` because for tensor-backed Datasets + # the `.array` getter is an expensive derive (full GPU->CPU copy). Use + # cheap `hasattr(data, "sampling")` to identify a Dataset. if hasattr(data, "sampling"): if not title and hasattr(data, "name") and data.name: title = str(data.name) From 9c93ae99d7ad483c773528c20e5c358ee9a49f55 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 22:49:44 -0700 Subject: [PATCH 305/335] use _array _tensor duck typing for show4dstem --- widget/src/quantem/widget/show4dstem.py | 20 ++++++++------------ 1 file changed, 8 insertions(+), 12 deletions(-) diff --git a/widget/src/quantem/widget/show4dstem.py b/widget/src/quantem/widget/show4dstem.py index 99a5eae8..c3ee61a7 100644 --- a/widget/src/quantem/widget/show4dstem.py +++ b/widget/src/quantem/widget/show4dstem.py @@ -333,22 +333,18 @@ def __init__( _io_labels = None - # Auto-extract sampling + units from Dataset4dstem if available. - # NOTE: avoid `hasattr(data, "array")` because for tensor-backed Datasets - # the `.array` getter is an expensive derive (full GPU->CPU copy). Use - # cheap `hasattr(data, "sampling")` to identify a Dataset. - if hasattr(data, "sampling"): - if not title and hasattr(data, "name") and data.name: + # Extract underlying array / tensor + auto-calibrate from Dataset input + # (duck-typed via the dual-slot private attributes _tensor / _array). + tensor = getattr(data, "_tensor", None) + array = getattr(data, "_array", None) + if tensor is not None or array is not None: + if not title and getattr(data, "name", ""): title = str(data.name) if sampling is None: sampling = tuple(float(s) for s in data.sampling) - if units is None and hasattr(data, "units"): + if units is None: units = list(data.units) - # If tensor-backed (zero-copy GPU path), take .tensor. Else .array (numpy). - if getattr(data, "_tensor", None) is not None: - data = data.tensor - else: - data = data.array + data = tensor if tensor is not None else array # Resolve sampling + units (4 axes for 4D-STEM): # [scan_row, scan_col, k_row, k_col]. Scalar/None broadcast to (1, 1, 1, 1). From 40878d3d5f13f90ddabe4a4947b1b74550337085 Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Mon, 18 May 2026 23:00:28 -0700 Subject: [PATCH 306/335] use row, col convention in docstring --- src/quantem/core/datastructures/dataset4dstem.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index c0c331b5..48d3f737 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -189,7 +189,7 @@ def from_tensor( if tensor.ndim != 4: raise ValueError( f"Dataset4dstem.from_tensor requires a 4D tensor " - f"(scan_y, scan_x, dp_y, dp_x), got shape {tuple(tensor.shape)}." + f"(scan_row, scan_col, dp_row, dp_col), got shape {tuple(tensor.shape)}." ) return cls( tensor=tensor, From 21b684fa7e71f843626a27ad1602adb193494cfc Mon Sep 17 00:00:00 2001 From: Sangjoon Bob Lee Date: Fri, 22 May 2026 23:29:30 -0700 Subject: [PATCH 307/335] fix: tolerate missing _tensor slot on autoserialize-loaded datasets --- src/quantem/core/datastructures/dataset.py | 35 ++++++++++++++-------- 1 file changed, 22 insertions(+), 13 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index a89257a3..fa1fcef2 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -142,7 +142,7 @@ def array(self) -> NDArray | None: Returns ``None`` for tensor-backed datasets. Use ``.tensor`` for the torch tensor, or ``.numpy()`` to materialize a numpy copy explicitly. """ - return self._array + return getattr(self, "_array", None) @array.setter def array(self, value: NDArray) -> None: @@ -154,11 +154,13 @@ def array(self, value: NDArray) -> None: @property def tensor(self) -> torch.Tensor: """Torch tensor backing the data. AttributeError if numpy-backed.""" - if self._tensor is None: + # getattr handles AutoSerialize-restored instances (no __init__ run). + tensor = getattr(self, "_tensor", None) + if tensor is None: raise AttributeError( f"Dataset '{self.name}' is numpy-backed; use Dataset.from_tensor() at construction." ) - return self._tensor + return tensor @property def metadata(self) -> dict: @@ -216,22 +218,27 @@ def file_path(self, value: os.PathLike | str | None) -> None: @property def shape(self) -> tuple[int, ...]: # Direct slot access (never triggers .array derive, which would force - # a full GPU->CPU copy on tensor-backed datasets). - return tuple((self._array if self._array is not None else self._tensor).shape) + # a full GPU->CPU copy on tensor-backed datasets). getattr handles + # AutoSerialize-restored instances (no __init__ run). + array = getattr(self, "_array", None) + return tuple((array if array is not None else self._tensor).shape) @property def ndim(self) -> int: - return (self._array if self._array is not None else self._tensor).ndim + array = getattr(self, "_array", None) + return (array if array is not None else self._tensor).ndim @property def dtype(self) -> DTypeLike: - return (self._array if self._array is not None else self._tensor).dtype + array = getattr(self, "_array", None) + return (array if array is not None else self._tensor).dtype @property def device(self) -> str: """``"cpu"`` for numpy-backed; torch device string for tensor-backed.""" - if self._tensor is not None: - return str(self._tensor.device) + tensor = getattr(self, "_tensor", None) + if tensor is not None: + return str(tensor.device) return "cpu" def numpy(self) -> NDArray: @@ -240,17 +247,19 @@ def numpy(self) -> NDArray: For numpy-backed datasets, returns ``self.array`` directly. For tensor-backed datasets, materializes a CPU copy via ``.detach().cpu().numpy()``. """ - if self._array is not None: - return self._array + array = getattr(self, "_array", None) + if array is not None: + return array return self._tensor.detach().cpu().numpy() def to(self, device) -> Self: """Move the underlying tensor to ``device``. Raises if numpy-backed.""" - if self._tensor is None: + tensor = getattr(self, "_tensor", None) + if tensor is None: raise AttributeError( f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." ) - self._tensor = self._tensor.to(device) + self._tensor = tensor.to(device) return self # --- Summaries --- From 4be1be7efb654b8d352c92c5fd55e7b5b7c66af1 Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 25 May 2026 12:36:55 +0000 Subject: [PATCH 308/335] chore: update lock file --- uv.lock | 425 +++++++++++++++++++++++++++++--------------------------- 1 file changed, 218 insertions(+), 207 deletions(-) diff --git a/uv.lock b/uv.lock index a01182cc..716284e6 100644 --- a/uv.lock +++ b/uv.lock @@ -196,11 +196,11 @@ css = [ [[package]] name = "certifi" -version = "2026.4.22" +version = "2026.5.20" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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Bob Lee Date: Wed, 27 May 2026 18:41:11 -0700 Subject: [PATCH 309/335] add TODO for numpy/torch guarding, prevent numpy copy --- src/quantem/core/datastructures/dataset.py | 33 +++++++++++++------ .../core/datastructures/dataset4dstem.py | 2 ++ tests/datastructures/test_dataset.py | 18 ++++++++++ 3 files changed, 43 insertions(+), 10 deletions(-) diff --git a/src/quantem/core/datastructures/dataset.py b/src/quantem/core/datastructures/dataset.py index fa1fcef2..94744978 100644 --- a/src/quantem/core/datastructures/dataset.py +++ b/src/quantem/core/datastructures/dataset.py @@ -55,6 +55,7 @@ def __init__( ) super().__init__() # Dual-slot storage: exactly one of (_array, _tensor) is set. + # TODO: remove dual-init guards once torch transition is complete. if array is None and tensor is None: raise ValueError("Provide either `array` (numpy) or `tensor` (torch).") if array is not None and tensor is not None: @@ -229,37 +230,49 @@ def ndim(self) -> int: return (array if array is not None else self._tensor).ndim @property - def dtype(self) -> DTypeLike: + def dtype(self) -> DTypeLike | torch.dtype: array = getattr(self, "_array", None) return (array if array is not None else self._tensor).dtype @property def device(self) -> str: - """``"cpu"`` for numpy-backed; torch device string for tensor-backed.""" - tensor = getattr(self, "_tensor", None) - if tensor is not None: - return str(tensor.device) - return "cpu" + """Device string for the underlying storage. numpy 2.x ndarray and torch.Tensor + both expose ``.device`` (array-API convention), so this is uniform. + """ + array = getattr(self, "_array", None) + return str((array if array is not None else self._tensor).device) def numpy(self) -> NDArray: """Return the data as a numpy array (mirrors ``torch.Tensor.numpy()``). For numpy-backed datasets, returns ``self.array`` directly. For - tensor-backed datasets, materializes a CPU copy via ``.detach().cpu().numpy()``. + tensor-backed datasets, materializes a read-only CPU copy via + ``.detach().cpu().numpy()``. ``flags.writeable=False`` so accidental + in-place writes raise instead of silently being lost (the copy is not + the tensor). """ array = getattr(self, "_array", None) if array is not None: return array - return self._tensor.detach().cpu().numpy() + arr = self._tensor.detach().cpu().numpy() + arr.flags.writeable = False + return arr def to(self, device) -> Self: - """Move the underlying tensor to ``device``. Raises if numpy-backed.""" + """Move the underlying tensor to ``device``. Raises if numpy-backed. + + ``device`` is normalized via :func:`quantem.core.config.validate_device` + so values like ``"cuda"``, ``0``, ``"cuda:0"``, ``torch.device("cuda:0")`` + all resolve to the same canonical device. + """ + from quantem.core import config tensor = getattr(self, "_tensor", None) if tensor is None: raise AttributeError( f"Cannot .to({device!r}) on numpy-backed Dataset '{self.name}'." ) - self._tensor = tensor.to(device) + dev, _ = config.validate_device(device) + self._tensor = tensor.to(dev) return self # --- Summaries --- diff --git a/src/quantem/core/datastructures/dataset4dstem.py b/src/quantem/core/datastructures/dataset4dstem.py index 48d3f737..004db427 100644 --- a/src/quantem/core/datastructures/dataset4dstem.py +++ b/src/quantem/core/datastructures/dataset4dstem.py @@ -181,6 +181,8 @@ def from_tensor( For cupy / jax arrays, wrap with ``torch.from_dlpack(arr)`` first. """ + # TODO: factor type + ndim checks into `ensure_valid_tensor(value, ndim=4)` + # in validators.py, matching `ensure_valid_array` pattern. Cuts bloat. if not isinstance(tensor, torch.Tensor): raise TypeError( f"from_tensor requires torch.Tensor, got {type(tensor).__name__}. " diff --git a/tests/datastructures/test_dataset.py b/tests/datastructures/test_dataset.py index 9c83262c..201fe92b 100644 --- a/tests/datastructures/test_dataset.py +++ b/tests/datastructures/test_dataset.py @@ -434,3 +434,21 @@ def test_api_errors(self, sample_dataset_2d): # Neither specified with pytest.raises(ValueError): sample_dataset_2d.fourier_resample() + + +class TestDatasetTorch: + """Tests for torch-backed Dataset (from_tensor path).""" + + def test_numpy_copy_is_readonly(self): + """``.numpy()`` on a tensor-backed dataset returns a read-only CPU copy + so writes raise instead of silently updating only the detached copy. + """ + import torch + from quantem.core.datastructures.dataset4dstem import Dataset4dstem + ds = Dataset4dstem.from_tensor(torch.zeros(2, 2, 2, 2)) + arr = ds.numpy() + assert arr.flags.writeable is False + with pytest.raises(ValueError, match="read-only"): + arr[0, 0, 0, 0] = 99.0 + + From 990f35d27c674b51dfc3e0ce8bfacadc236efbc8 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:09:11 -0700 Subject: [PATCH 310/335] SO3Params switched to static methods. --- src/quantem/core/ml/models/so3params.py | 145 ++++++++++++++++-------- 1 file changed, 98 insertions(+), 47 deletions(-) diff --git a/src/quantem/core/ml/models/so3params.py b/src/quantem/core/ml/models/so3params.py index 82a4361a..cd55d0ac 100644 --- a/src/quantem/core/ml/models/so3params.py +++ b/src/quantem/core/ml/models/so3params.py @@ -32,6 +32,78 @@ def __init__(self, T: int, init: str = "random"): quats = self._init_quaternions(T, init) # (T, 4) self.quats = nn.Parameter(quats) + @staticmethod + def quat_to_rotmat(q: torch.Tensor) -> torch.Tensor: + """Unit quaternion (..., 4) [x, y, z, w] -> rotation matrix (..., 3, 3). + Assumes q is already normalized.""" + x, y, z, w = q.unbind(dim=-1) + xx, yy, zz = x * x, y * y, z * z + xy, xz, yz = x * y, x * z, y * z + wx, wy, wz = w * x, w * y, w * z + R = torch.stack( + [ + 1 - 2 * (yy + zz), 2 * (xy - wz), 2 * (xz + wy), + 2 * (xy + wz), 1 - 2 * (xx + zz), 2 * (yz - wx), + 2 * (xz - wy), 2 * (yz + wx), 1 - 2 * (xx + yy), + ], + dim=-1, + ).reshape(*q.shape[:-1], 3, 3) + return R + + @staticmethod + def rotmat_to_quat(R: torch.Tensor) -> torch.Tensor: + """Rotation matrix (..., 3, 3) -> unit quaternion (..., 4) [x, y, z, w]. + + Shepperd's method: build the four candidate quaternions, each dividing + by a different diagonal combination, then per-element pick the branch + with the largest denominator so we never divide by a near-zero number. + The naive trace-only formula blows up when trace ~ -1 (180deg rotations). + """ + m00, m01, m02 = R[..., 0, 0], R[..., 0, 1], R[..., 0, 2] + m10, m11, m12 = R[..., 1, 0], R[..., 1, 1], R[..., 1, 2] + m20, m21, m22 = R[..., 2, 0], R[..., 2, 1], R[..., 2, 2] + + # 4 * (component^2) for w, x, y, z respectively; these sum to 4. + t = torch.stack( + [ + 1.0 + m00 + m11 + m22, # 4 w^2 + 1.0 + m00 - m11 - m22, # 4 x^2 + 1.0 - m00 + m11 - m22, # 4 y^2 + 1.0 - m00 - m11 + m22, # 4 z^2 + ], + dim=-1, + ) # (..., 4) + + eps = torch.finfo(R.dtype).eps + S = 2.0 * torch.sqrt(t.clamp_min(eps)) # S[k] = 4 * |component_k| + S0, S1, S2, S3 = S.unbind(-1) + + # each candidate in [x, y, z, w] order + cand_w = torch.stack([(m21 - m12) / S0, (m02 - m20) / S0, (m10 - m01) / S0, 0.25 * S0], dim=-1) + cand_x = torch.stack([0.25 * S1, (m01 + m10) / S1, (m02 + m20) / S1, (m21 - m12) / S1], dim=-1) + cand_y = torch.stack([(m01 + m10) / S2, 0.25 * S2, (m12 + m21) / S2, (m02 - m20) / S2], dim=-1) + cand_z = torch.stack([(m02 + m20) / S3, (m12 + m21) / S3, 0.25 * S3, (m10 - m01) / S3], dim=-1) + + cands = torch.stack([cand_w, cand_x, cand_y, cand_z], dim=-2) # (..., 4, 4) + idx = t.argmax(dim=-1) # (...,) + idx = idx[..., None, None].expand(*idx.shape, 1, 4) # (..., 1, 4) + q = cands.gather(-2, idx).squeeze(-2) # (..., 4) + return F.normalize(q, p=2, dim=-1) + + def as_matrix(self) -> torch.Tensor: + return self.quat_to_rotmat(self.normalized()) + + @classmethod + def from_matrix(cls, R: torch.Tensor) -> "SO3ParamQuat": + """Initialize a bank close to the given rotations R (T, 3, 3).""" + obj = cls(R.shape[0], init="identity") + with torch.no_grad(): + obj.quats.copy_(cls.rotmat_to_quat(R)) + return obj + + def extra_repr(self) -> str: + return f"T={self.quats.shape[0]}" + # ------------------------------------------------------------------ # Initialisers # ------------------------------------------------------------------ @@ -73,41 +145,6 @@ def normalized(self) -> torch.Tensor: """Returns (T, 4) unit quaternions.""" return F.normalize(self.quats, p=2, dim=-1) - def as_matrix(self) -> torch.Tensor: - """ - Converts the T stored quaternions to (T, 3, 3) rotation matrices. - - Uses the standard formula; no trig, just multiplications. - """ - q = self.normalized() # (T, 4) [x, y, z, w] - x, y, z, w = q.unbind(dim=-1) # each (T,) - - # Precompute products - xx, yy, zz = x * x, y * y, z * z - xy, xz, yz = x * y, x * z, y * z - wx, wy, wz = w * x, w * y, w * z - - # Row-major: R[i,j] - R = torch.stack( - [ - 1 - 2 * (yy + zz), - 2 * (xy - wz), - 2 * (xz + wy), - 2 * (xy + wz), - 1 - 2 * (xx + zz), - 2 * (yz - wx), - 2 * (xz - wy), - 2 * (yz + wx), - 1 - 2 * (xx + yy), - ], - dim=-1, - ).reshape(-1, 3, 3) # (T, 3, 3) - - return R - - def extra_repr(self) -> str: - return f"T={self.quats.shape[0]}" - class SO3ParamR9SVD(nn.Module): """ @@ -121,21 +158,35 @@ class SO3ParamR9SVD(nn.Module): def __init__(self, T: int, init: Literal["random", "identity"] = "random"): super().__init__() if init == "random": - # Initialize near identity with small noise - M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) - M = M + 0.1 * torch.randn(T, 3, 3) + M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) + 0.1 * torch.randn(T, 3, 3) elif init == "identity": M = torch.eye(3).unsqueeze(0).repeat(T, 1, 1) else: raise ValueError(f"Unknown init '{init}'") - self.M = nn.Parameter(M) # (T, 3, 3) + self.M = nn.Parameter(M) + + @staticmethod + def rotmat_to_r9(R: torch.Tensor) -> torch.Tensor: + """Rotation matrix (..., 3, 3) -> R9. Identity embedding: any R in SO(3) + is a fixed point of the SVD projection, so this just stores R directly.""" + return R + + @staticmethod + def r9_to_rotmat(M: torch.Tensor) -> torch.Tensor: + """R9 (..., 3, 3) -> nearest SO(3) matrix via SVD+.""" + U, _, Vh = torch.linalg.svd(M) + d = torch.det(U @ Vh) + diag = torch.ones(*M.shape[:-2], 3, device=M.device, dtype=M.dtype) + diag[..., 2] = d + return U @ (diag.unsqueeze(-1) * Vh) def as_matrix(self) -> torch.Tensor: - """Projects each M to SO(3) via SVD. Returns (T, 3, 3).""" - - U, _, Vh = torch.linalg.svd(self.M) # U: (T,3,3), Vh: (T,3,3) - # Fix reflections: det(U Vh) must be +1 - d = torch.det(U @ Vh) # (T,) - diag = torch.ones(self.M.shape[0], 3, device=self.M.device, dtype=self.M.dtype) - diag[:, 2] = d # multiply last singular vector by sign - return U @ (diag.unsqueeze(-1) * Vh) # (T, 3, 3) + return self.r9_to_rotmat(self.M) + + @classmethod + def from_matrix(cls, R: torch.Tensor) -> "SO3ParamR9SVD": + """Initialize a bank close to given rotations R (T, 3, 3).""" + obj = cls(R.shape[0], init="identity") + with torch.no_grad(): + obj.M.copy_(cls.rotmat_to_r9(R)) + return obj \ No newline at end of file From c56994a529269d7fc713ee32e0859961f9a6f642 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:14:12 -0700 Subject: [PATCH 311/335] Fixed optimizer_params using hidden variable in set_optimizer --- src/quantem/core/ml/optimizer_mixin.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 55d013c9..b1fee891 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -618,11 +618,11 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: if opt_params is not None: self.optimizer_params = opt_params - if not self._optimizer_params: + if not self.optimizer_params: self._optimizer = None return - if isinstance(self._optimizer_params, OptimizerParams.NoneOptimizer): + if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer): self.remove_optimizer() return @@ -633,11 +633,11 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: for p in group["params"]: p.requires_grad_(True) # Figure out which optimizer class to use - if isinstance(self._optimizer_params, dict): + if isinstance(self.optimizer_params, dict): # Per-group case: all groups must agree on the optimizer class, # and per-group hyperparameters are already baked into each dict # by get_optimization_parameters(). - opt_specs = list(self._optimizer_params.values()) + opt_specs = list(self.optimizer_params.values()) if not opt_specs: self._optimizer = None return @@ -651,8 +651,8 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self._optimizer = optimizer_cls(params) # type:ignore else: # Single-optimizer case: splat global hyperparameters - optimizer_cls = self._optimizer_class_for(self._optimizer_params) - self._optimizer = optimizer_cls(params, **self._optimizer_params.params()) + optimizer_cls = self._optimizer_class_for(self.optimizer_params) + self._optimizer = optimizer_cls(params, **self.optimizer_params.params()) def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: match opt_params: From 4b8ca472a0d6fc99980c60d50739782870250e0a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:19:02 -0700 Subject: [PATCH 312/335] Added more description to the ReconstructionContext --- src/quantem/tomography/tomography_context.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/src/quantem/tomography/tomography_context.py b/src/quantem/tomography/tomography_context.py index ef861651..d322287c 100644 --- a/src/quantem/tomography/tomography_context.py +++ b/src/quantem/tomography/tomography_context.py @@ -14,6 +14,13 @@ class ReconstructionContext(BaseContext): - Pixelated reads ".volume" - INR reads ".coords" and recomputes via the model. - TensorDecomp reads ".coords" and ".pred" (and ".all densities") + + Variable descriptions: + - volume: Reconstructed object (volume). + - coords: Used for INR reconstructions to provide the coordinates to the model. + - pred: Predicted values per coordinate position from the model. + - all_densities: Integrated densities per ray from the model. + - obj: Object model (INR, TensorDecomp, etc.). """ volume: Optional[torch.Tensor] = None From 1bc17392f199257e3737ff7aa81c4a35eac8dda4 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:28:40 -0700 Subject: [PATCH 313/335] Standardized optimizer params with new normalization to always output dict[str, OptimizerType]. --- src/quantem/core/ml/optimizer_mixin.py | 29 +++++++++++++------------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index b1fee891..37264538 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -553,7 +553,7 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> OptimizerType | dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerType]: """Get the optimizer parameters.""" return self._optimizer_params @@ -565,20 +565,21 @@ def optimizer_params( def _normalize_optimizer_params( self, params: OptimizerType | dict[str, Any] - ) -> OptimizerType | dict[str, OptimizerType]: - """Normalize input. Subclasses can override to validate keys.""" - # dict-of-OptimizerType form (PPLR) - if isinstance(params, dict) and not self._is_single_optimizer_dict(params): - return { - k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) - for k, v in params.items() - } - # Single optimizer form (with dict shorthand like {"name": "adam", "lr": 1e-3}) - if isinstance(params, dict): - params = OptimizerParams.parse_dict(d=params) - if not isinstance(params, OptimizerType): + ) -> dict[str, OptimizerType]: + """Normalize input to dict[str, OptimizerType]. Subclasses can override to validate keys.""" + # Single optimizer, already an OptimizerType + if isinstance(params, OptimizerType): + return {self.DEFAULT_OPTIMIZER_KEY: params} + if not isinstance(params, dict): raise TypeError(f"optimizer_params must be OptimizerType or dict, got {type(params)}") - return params + # Single optimizer as dict shorthand, e.g. {"name": "adam", "lr": 1e-3} + if self._is_single_optimizer_dict(params): + return {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.parse_dict(d=params)} + # dict-of-OptimizerType form (PPLR) + return { + k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) + for k, v in params.items() + } @staticmethod def _is_single_optimizer_dict(d: dict) -> bool: From 21944c406c804d565d37c956bd7b69d8faa34dde Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 14:28:40 -0700 Subject: [PATCH 314/335] Standardized optimizer params with new normalization to always output dict[str, OptimizerType]. --- src/quantem/core/ml/optimizer_mixin.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 37264538..937ef213 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -536,9 +536,9 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params: OptimizerType | dict[str, OptimizerType] = ( - OptimizerParams.NoneOptimizer() - ) + self._optimizer_params: dict[str, OptimizerType] = { + self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() + } self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues From e11e49e55c8c290d709cdd5cb717d7eddcf81f7d Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 16:10:21 -0700 Subject: [PATCH 315/335] Fixed some bugs with DEFAULT_OPTIMIZER_KEY not being instantiated in optimizer_mixin.py --- src/quantem/core/ml/constraints.py | 3 ++- src/quantem/core/ml/optimizer_mixin.py | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/src/quantem/core/ml/constraints.py b/src/quantem/core/ml/constraints.py index fea1d309..590da4cb 100644 --- a/src/quantem/core/ml/constraints.py +++ b/src/quantem/core/ml/constraints.py @@ -7,7 +7,6 @@ import torch from numpy.typing import NDArray -T_ctx = TypeVar("T_ctx", bound=BaseContext) @dataclass class BaseContext(ABC): @@ -16,6 +15,8 @@ class BaseContext(ABC): """ pass +T_ctx = TypeVar("T_ctx", bound=BaseContext) + @dataclass(slots=False) class Constraints(ABC): """ diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 937ef213..f970a859 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -531,6 +531,7 @@ class OptimizerMixin: """ DEFAULT_OPTIMIZER_TYPE = "adamw" + DEFAULT_OPTIMIZER_KEY = "default" def __init__(self): """Initialize the optimizer mixin.""" From b2e9e158d39dd7f2c38bc6fda5361b9e6d275c8a Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 17:36:35 -0700 Subject: [PATCH 316/335] Working version, standardized optimizer_mixin.py --- src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md | 221 ++++++++++++ src/quantem/core/ml/optimizer_mixin.py | 21 +- src/quantem/core/ml/optimizer_mixin_fix.html | 216 ++++++++++++ .../core/ml/optimizer_mixin_review.html | 319 ++++++++++++++++++ .../core/ml/optimizer_refactor_incident.html | 266 +++++++++++++++ 5 files changed, 1030 insertions(+), 13 deletions(-) create mode 100644 src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md create mode 100644 src/quantem/core/ml/optimizer_mixin_fix.html create mode 100644 src/quantem/core/ml/optimizer_mixin_review.html create mode 100644 src/quantem/core/ml/optimizer_refactor_incident.html diff --git a/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md b/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md new file mode 100644 index 00000000..e1819034 --- /dev/null +++ b/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md @@ -0,0 +1,221 @@ +# Optimizer params: how inputs get normalized + +This document traces what happens to `optimizer_params` from the moment a user +passes them into `reconstruct(...)` to the moment a real `torch.optim.Optimizer` +is built. As of commit `1bc1739` the design invariant is: + +> **At the model level, `_optimizer_params` is *always* a `dict[str, OptimizerType]`.** + +Understanding the two normalization layers (container level → model level) is the +key to reading this code. + +--- + +## 1. The vocabulary + +| Thing | What it is | Where | +|---|---|---| +| `OptimizerType` | Union of the dataclasses `Adam`, `AdamW`, `SGD`, `NoneOptimizer` | `optimizer_mixin.py` (`OptimizerType = Adam \| AdamW \| SGD \| NoneOptimizer`) | +| `OptimizerParams.` | The individual dataclasses; each carries hyperparameters and a `.params()` method that returns them as a `dict` for torch | `optimizer_mixin.py` | +| `NoneOptimizer` | Sentinel meaning "do not optimize this thing". `.params()` returns `{}` | `optimizer_mixin.py:174` | +| `DEFAULT_OPTIMIZER_KEY` | `"default"` — the key used when a single optimizer is wrapped into a dict | `optimizer_mixin.py:534` | +| `OptimizerMixin` | Mixin inherited by each model (`obj_model`, `probe_model`, `dset`). Owns `_optimizer_params`, `set_optimizer`, etc. | `optimizer_mixin.py:527` | +| `PtychographyOpt` | The *container*. Holds the three models and exposes a combined `optimizer_params` | `ptychography_opt.py:20` | + +There are **two objects** that both have a property called `optimizer_params`, +and they mean different things: + +- **Container** (`PtychographyOpt` / tomography equivalent): a dict keyed by + *which model* — `"object"`, `"probe"`, `"dataset"`. +- **Model** (`OptimizerMixin`): a dict keyed by *parameter group* — normally just + the single key `"default"`. + +So a fully-resolved structure is **nested**: + +``` +container.optimizer_params + = {"object": {"default": Adam(lr=5e-3)}, + "probe": {"default": Adam(lr=1e-3)}, + "dataset": {"default": NoneOptimizer()}} + ^^^^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^^^^ + model key group key OptimizerType +``` + +--- + +## 2. Accepted input shapes + +A user can hand the container any of these: + +```python +# (a) list/tuple of model keys -> use all defaults +ptycho.optimizer_params = ["object", "probe"] + +# (b) dict, value = OptimizerType dataclass +ptycho.optimizer_params = {"object": OptimizerParams.Adam(lr=5e-3)} + +# (c) dict, value = shorthand dict (name/type + hyperparams) +ptycho.optimizer_params = {"object": {"name": "adam", "lr": 5e-3}} + +# (d) dict, value = empty dict -> use default optimizer + default lr for that key +ptycho.optimizer_params = {"object": {}} +``` + +--- + +## 3. Layer 1 — container normalization + +`PtychographyOpt.optimizer_params` **setter** (`ptychography_opt.py:56`): + +``` +list/tuple ──► {k: {} for k in list} # (a) becomes (d)-style +for each (key, value): + value is OptimizerType ───────────────► pass through unchanged + value is dict and empty ──────────────► replace(DEFAULT_OPTIMIZER_TYPE, + lr=_get_default_lr(key)) + value is dict and non-empty ──────────► inject "name" if missing, + inject "lr" if missing + else ─────────────────────────────────► TypeError + │ + └─► dispatch to the matching model: + "object" -> self.obj_model.optimizer_params = value + "probe" -> self.probe_model.optimizer_params = value + "dataset" -> self.dset.optimizer_params = value +``` + +Key points: + +- `_get_default_lr(key)` supplies a sensible LR per model + (`object` ≈ 5e-3, `probe`/`dataset` ≈ 1e-3) when the user omitted one. +- The container does **not** build torch optimizers. It just fills defaults and + forwards each value down to the relevant model's setter. +- Any model key *not* mentioned by the user keeps whatever it had — by default + `{"default": NoneOptimizer()}` (i.e. "not optimized"). + +--- + +## 4. Layer 2 — model normalization + +Each model's `optimizer_params` **setter** calls +`OptimizerMixin._normalize_optimizer_params` (`optimizer_mixin.py:567`). This is +the function that guarantees the `dict[str, OptimizerType]` invariant: + +``` +_normalize_optimizer_params(params): + + params is an OptimizerType (dataclass) + ──► {"default": params} + + params is NOT a dict + ──► TypeError + + params is a dict AND _is_single_optimizer_dict(params) # has "name" or "type" + ──► {"default": OptimizerParams.parse_dict(params)} + + otherwise (dict-of-OptimizerType, the "PPLR" form) + ──► {k: (v if v is OptimizerType else parse_dict(v)) + for k, v in params.items()} +``` + +`_is_single_optimizer_dict(d)` is simply `"type" in d or "name" in d` +(`optimizer_mixin.py:585`). + +### `parse_dict` — shorthand → dataclass + +`OptimizerParams.parse_dict` (`optimizer_mixin.py:192`) maps a shorthand dict to +the concrete dataclass: + +``` +{"name"/"type": ...} pop the name, lowercase it, then: + "adam" -> OptimizerParams.Adam(**rest) + "adamw" -> OptimizerParams.AdamW(**rest) + "sgd" -> OptimizerParams.SGD(**rest) + "none" -> OptimizerParams.NoneOptimizer() + else -> ValueError +``` + +### Idempotency note + +When the container forwards an already-resolved value like `{"default": Adam(lr=5e-3)}` +to a model setter, it has no `"name"`/`"type"` key, so it takes the *PPLR branch* +and is kept as-is (`Adam` is already an `OptimizerType`). So re-normalizing a +normalized dict is a no-op. Good. + +--- + +## 5. Building the torch optimizer + +`OptimizerMixin.set_optimizer` (`optimizer_mixin.py:614`) is where the normalized +dict becomes a real optimizer. Conceptually it should: + +1. Look at the values of the `dict[str, OptimizerType]`. +2. Drop / handle `NoneOptimizer` sentinels (→ "no optimizer for this"). +3. Confirm the remaining specs agree on an optimizer *class*. +4. Pull parameter groups from `get_optimization_parameters()` (a `list[dict]`, + each `{"params": [...]}`). +5. Construct `optimizer_cls(param_groups, **hyperparameters)`. + +`_optimizer_class_for` (`optimizer_mixin.py:659`) maps a spec dataclass to a torch +class via a `match`: + +``` +Adam() -> torch.optim.Adam +AdamW() -> torch.optim.AdamW +SGD() -> torch.optim.SGD +_ -> NotImplementedError # <-- NoneOptimizer lands here +``` + +### The reset path (where the current crash lives) + +`reconstruct(reset=True, ...)` calls `reset_recon()` **before** applying the +user's `optimizer_params` (`ptychography.py:181` vs `:185`). `reset_recon` calls +each model's `reset_optimizer()` → `set_optimizer(self._optimizer_params)`. At +that moment `_optimizer_params` is still the default `{"default": NoneOptimizer()}`. + +``` +reconstruct(reset=True, optimizer_params=opt_params) + ├─ reset_recon() # opt_params NOT applied yet + │ └─ obj_model.reset_optimizer() + │ └─ set_optimizer({"default": NoneOptimizer()}) + │ └─ _optimizer_class_for(NoneOptimizer()) -> NotImplementedError + └─ (never reached) self.optimizer_params = opt_params; set_optimizers() +``` + +--- + +## 6. Where the value flows at recon time + +Once past reset, the normal value flow is: + +``` +reconstruct(optimizer_params={"object": {"lr": 5e-3}, ...}) + │ + ├─ container.optimizer_params = {...} # Layer 1: fill defaults, dispatch + │ └─ obj_model.optimizer_params = {"name":"adamw","lr":5e-3} + │ └─ _normalize_optimizer_params -> {"default": AdamW(lr=5e-3)} # Layer 2 + │ + └─ container.set_optimizers() + for key, params in container.optimizer_params.items(): # nested dict + model.set_optimizer(params) # params = {"default": AdamW(lr=5e-3)} + └─ build torch.optim.AdamW(param_groups, lr=5e-3, ...) +``` + +`container.set_optimizers()` iterates the **container getter**, which returns the +nested `{"object": {"default": ...}, ...}` structure, and hands each inner dict to +the corresponding model's `set_optimizer`. + +--- + +## 7. Quick reference — invariants to keep in mind + +- A **model's** `_optimizer_params` is always `dict[str, OptimizerType]`, normally + one key: `"default"`. +- A **container's** `optimizer_params` is `dict[str, dict[str, OptimizerType]]`, + keyed by model name (`"object"`/`"probe"`/`"dataset"`). +- `NoneOptimizer` is a first-class `OptimizerType` meaning "skip". Anything that + consumes the dict must treat it specially, because `_optimizer_class_for` has no + case for it. +- `.params()` on a spec dataclass returns the kwargs torch needs (`lr`, etc.); + `NoneOptimizer.params()` returns `{}`. +- `get_optimization_parameters()` returns parameter *groups* (`[{"params": [...]}]`) + and currently carries **no** per-group hyperparameters. diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index f970a859..8c095d39 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -624,9 +624,6 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: self._optimizer = None return - if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer): - self.remove_optimizer() - return params = self.get_optimization_parameters() # always list[dict] @@ -634,14 +631,17 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: for group in params: for p in group["params"]: p.requires_grad_(True) - # Figure out which optimizer class to use + # Figure out which optimizer class to use if isinstance(self.optimizer_params, dict): # Per-group case: all groups must agree on the optimizer class, # and per-group hyperparameters are already baked into each dict # by get_optimization_parameters(). - opt_specs = list(self.optimizer_params.values()) + opt_specs = [ + spec for spec in self.optimizer_params.values() + if not isinstance(spec, OptimizerParams.NoneOptimizer) + ] if not opt_specs: - self._optimizer = None + self.remove_optimizer() return optimizer_cls = self._optimizer_class_for(opt_specs[0]) for spec in opt_specs[1:]: @@ -650,12 +650,7 @@ def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: f"All parameter groups must use the same optimizer type, " f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" ) - self._optimizer = optimizer_cls(params) # type:ignore - else: - # Single-optimizer case: splat global hyperparameters - optimizer_cls = self._optimizer_class_for(self.optimizer_params) - self._optimizer = optimizer_cls(params, **self.optimizer_params.params()) - + self._optimizer = optimizer_cls(params, **opt_specs[0].params()) def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: match opt_params: case OptimizerParams.Adam(): @@ -744,7 +739,7 @@ def get_current_lr(self) -> float: def remove_optimizer(self) -> None: """Remove the optimizer and scheduler.""" self._optimizer = None - self._optimizer_params = OptimizerParams.NoneOptimizer() + self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()} self._scheduler = None self._scheduler_params = SchedulerParams.NoneScheduler() diff --git a/src/quantem/core/ml/optimizer_mixin_fix.html b/src/quantem/core/ml/optimizer_mixin_fix.html new file mode 100644 index 00000000..3bb7c365 --- /dev/null +++ b/src/quantem/core/ml/optimizer_mixin_fix.html @@ -0,0 +1,216 @@ + + + + + +OptimizerMixin — minimal fixes to complete the dict[str, OptimizerType] standardization + + + +
+ +
+

Completing the dict[str, OptimizerType] standardization

+

Minimal, surgical fixes that keep your standardization goal intact and resolve the + Ptychography breakage.

+
+ FILE: src/quantem/core/ml/optimizer_mixin.py  ·  + SCOPE: consumer-side only  ·  + RISK: low, localized +
+
+ +

1. Diagnosis in one paragraph

+

Your goal — normalize everything to dict[str, OptimizerType] — is correct + and worth keeping. The standardization did not break Ptychography on its own. The breakage came + from a consumer that was never migrated to the new contract: + set_optimizer. It still branches on the old union shape (a bare + OptimizerType or a dict). After normalization always returns a dict, the two + branches that handled the bare-type cases became unreachable, and the surviving dict branch was + only a partial implementation — it never handled the NoneOptimizer sentinel and never + forwarded hyperparameters. The fix is therefore not to revert the standardization, + but to make that one dict branch self-sufficient.

+ +
+
Why your change was necessary but not sufficient
+

A return type is a contract. Narrowing it from a union to a single + shape obligates every consumer that pattern-matches on the old shape to be updated in the same + change. The producer was migrated; one consumer was not.

+
+ +

2. The two concrete symptoms

+ + + + + + + + + + +
SymptomRoot cause in set_optimizer
Crash: NotImplementedError: Unknown optimizer type: NoneOptimizer()The dict branch passes a NoneOptimizer spec to + _optimizer_class_for, which has no case for it. The old guard that caught this + (isinstance(..., NoneOptimizer)) only matches a bare sentinel and is now + unreachable.
Silent: configured lr ignoredThe dict branch builds optimizer_cls(params) with no kwargs. The old branch + that splatted **optimizer_params.params() is the now-unreachable + else.
+ +

3. Recommended minimal change set

+

Three changes, in priority order. REQUIRED alone resolves the + crash and the silent lr bug.

+ +

REQUIREDMake the dict branch self-sufficient · ~6 lines, one method

+

Filter out NoneOptimizer sentinels; if none remain, there is nothing to optimize. + Otherwise build the optimizer and forward the spec’s hyperparameters. This is the + smallest change that honors the new dict-only contract.

+ 
        if isinstance(self.optimizer_params, dict):
+-           opt_specs = list(self.optimizer_params.values())
+-           if not opt_specs:
+-               self._optimizer = None
+-               return
++           opt_specs = [
++               spec for spec in self.optimizer_params.values()
++               if not isinstance(spec, OptimizerParams.NoneOptimizer)
++           ]
++           if not opt_specs:
++               self.remove_optimizer()
++               return
+            optimizer_cls = self._optimizer_class_for(opt_specs[0])
+            for spec in opt_specs[1:]:
+                if type(spec) is not type(opt_specs[0]):
+                    raise ValueError(...)
+-           self._optimizer = optimizer_cls(params)  # type:ignore
++           self._optimizer = optimizer_cls(params, **opt_specs[0].params())
+
+
What this buys you
+

The default {"default": NoneOptimizer()} now filters to + an empty list → remove_optimizer() → clean no-op (this is the path + reset_recon() walks). A real {"default": Adam(lr=5e-3)} now builds + Adam(params, lr=5e-3, …) with your hyperparameters restored.

+
+
+
Note on PPLR
+

This splats opt_specs[0]’s hyperparameters across all + parameter groups. That is correct today because every model returns a single param group from + get_optimization_parameters(). True per-group LRs are a separate, larger piece of + work — see §5.

+
+ +

RECOMMENDEDKeep remove_optimizer on-contract · 1 line

+

It currently stores a bare sentinel, breaking the “always a dict” invariant. It works + only because the value gets re-normalized on the next set. Store the dict form to keep the + invariant true end-to-end.

+ 
    def remove_optimizer(self) -> None:
+        self._optimizer = None
+-       self._optimizer_params = OptimizerParams.NoneOptimizer()
++       self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
+        self._scheduler = None
+        self._scheduler_params = SchedulerParams.NoneScheduler()
+ +

OPTIONALDelete the now-dead branches · removes ~7 lines

+

Purely cosmetic, but it removes the misleading code that caused the confusion. With the dict + contract guaranteed, the bare-NoneOptimizer guard and the else branch can + never run. Removing them makes set_optimizer a single, honest path.

+ 
-       if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):
+-           self.remove_optimizer()
+-           return
+        ...
+-       else:
+-           optimizer_cls = self._optimizer_class_for(self.optimizer_params)
+-           self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
+

If you keep these, no harm — they’re dead, not wrong. Removing them is about + readability, not correctness.

+ +

4. Optional: align the container getters

+

OPTIONAL In PtychographyOpt.optimizer_params (and the + tomography equivalent) the filter if not isinstance(params, NoneOptimizer) no longer + fires, because params is now a dict. After the REQUIRED fix this is harmless — a + disabled model simply ends up with no optimizer — but if you want the getter to exclude disabled + models, check the inner specs instead.

+ 
-           if not isinstance(params, OptimizerParams.NoneOptimizer)
++           if any(not isinstance(s, OptimizerParams.NoneOptimizer) for s in params.values())
+ +

5. What to leave for later (don’t scope-creep)

+
    +
  • Real per-parameter LRs (PPLR). The comment claiming hyperparameters are “baked into + each dict by get_optimization_parameters()” is aspirational — the implementations + return [{"params": params}] with no lr. Either implement keyed param + groups or update the comment. Not needed to fix the break.
    • +
  • Collapsing the three “no optimizer” spellings (empty dict / NoneOptimizer / + _optimizer = None) into one. A nice tidy-up, but orthogonal to this incident.
    • +
+ +

6. Verification checklist

+ + + + + + + + + + +
StepExpected
ptycho.reconstruct(num_iters=1, reset=True, optimizer_params={"object": {"lr": 5e-3}})No NotImplementedError; runs.
Inspect ptycho.obj_model.optimizer.param_groups[0]["lr"]Equals 5e-3, not torch’s default.
A model left out of optimizer_paramshas_optimizer() is False; not stepped.
Re-run with reset=TrueDefault NoneOptimizer filters cleanly; no crash.
+ +
+
Bottom line
+

Keep your standardization. Apply the one REQUIRED change (and ideally + the one-line RECOMMENDED change). That completes the migration the type-narrowing started, with a + change set small enough to read in a single diff.

+
+ +

+ Read-only proposal · no source files modified · + companions: optimizer_mixin_review.html · optimizer_refactor_incident.html · OPTIMIZER_PARAMS_FLOW.md +

+ +
+ + diff --git a/src/quantem/core/ml/optimizer_mixin_review.html b/src/quantem/core/ml/optimizer_mixin_review.html new file mode 100644 index 00000000..1e205644 --- /dev/null +++ b/src/quantem/core/ml/optimizer_mixin_review.html @@ -0,0 +1,319 @@ + + + + + +OptimizerMixin — a friendly post-mortem & cleanup wishlist + + + +
+ +
+ Code review · ml/optimizer_mixin.py +

OptimizerMixin: a friendly post-mortem 🕵️

+

Why NoneOptimizer() blew up your ptycho run, and how to make this class + a joy to read again. No code was harmed in the making of this document.

+ +
+ +

🔪 The whodunit (in one breath)

+

+ A refactor decreed: “optimizer params shall always be a + dict[str, OptimizerType].” The normalizer obeyed. But + set_optimizer never got the memo — it still has two branches written for the + old world where a bare OptimizerType could walk in the door. Those + branches are now unreachable corpses, and every call gets funneled into the one dict branch + that doesn’t know what to do with a NoneOptimizer. +

+ +
+
💥 The crash
+

reconstruct(reset=True) calls reset_recon() + before your optimizer_params are applied. So set_optimizer + runs against the default {"default": NoneOptimizer()}, falls into the dict branch, + and hands a NoneOptimizer to _optimizer_class_for — which has no + case for it → NotImplementedError.

+
+ + 
# the dead giveaway in set_optimizer (current)
+if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):  ← never True; params is always a dict now
+    self.remove_optimizer()
+    return
+...
+if isinstance(self.optimizer_params, dict):                          # ← always taken
+    opt_specs = list(self.optimizer_params.values())             # [NoneOptimizer()]
+    optimizer_cls = self._optimizer_class_for(opt_specs[0])     ← 💥 NotImplementedError
+    ...
+    self._optimizer = optimizer_cls(params)                       ← also: no lr is ever passed!
+else:
+    # the branch that actually splatted hyperparameters... now unreachable 🪦
+    self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
+ +
+
🐛 Bonus silent bug
+

Even after the crash is fixed, the dict branch builds + optimizer_cls(params) with no hyperparameters. A code comment + promises they’re “baked into each dict by get_optimization_parameters()” — but all + three implementations just return [{"params": params}] with no + lr. So your carefully chosen learning rate would silently become torch’s + default. The comment is, regrettably, fan fiction.

+
+ +

🌊 The flow, at a glance

+

Two objects both expose optimizer_params, and they mean different things. That’s + the single most confusing thing about this code, so let’s name it loudly:

+ + + + + + + + + + + + + +
LevelKeyed byExample value
Container
(PtychographyOpt)		which model
"object"/"probe"/"dataset"		{"object": {"default": Adam(lr=5e-3)}}
Model
(OptimizerMixin)		which param group
usually just "default"		{"default": Adam(lr=5e-3)}
+ + 
user input ──► PtychographyOpt.optimizer_params setter   # Layer 1: fill defaults, dispatch per model
+                 │   fills "name"/"lr" defaults via _get_default_lr()
+                 └─► model.optimizer_params setter             # Layer 2: normalize to dict[str, OptimizerType]
+                       └─► _normalize_optimizer_params()
+                             OptimizerType        ──► {"default": it}
+                             {"name"/"type": ...} ──► {"default": parse_dict(it)}
+                             {k: OptimizerType}   ──► kept (PPLR form)
+                                  │
+reconstruct() ──► set_optimizers() ──► for each model: set_optimizer({"default": Adam(lr=5e-3)})
+                                                          └─► torch.optim.Adam(param_groups, lr=5e-3)
+ +

🧹 Five ways to make this a joy to read

+ +

1Make the invariant real — delete the dead branches

+

If optimizer_params is always a dict, then set_optimizer + should have one path: filter out the NoneOptimizer sentinels, agree + on a class, and splat hyperparameters. No else, no zombie isinstance.

+ +
+
+

✗ Before — 3 branches, 2 of them dead

+ 
if isinstance(p, NoneOptimizer):  # dead
+    remove_optimizer(); return
+...
+if isinstance(p, dict):
+    specs = list(p.values())
+    cls = _optimizer_class_for(specs[0])  # 💥
+    self._optimizer = cls(params)       # no lr
+else:                            # dead
+    cls = _optimizer_class_for(p)
+    self._optimizer = cls(params, **p.params())
+
+
+

✓ After — one honest path

+ 
# params is always dict[str, OptimizerType]
+active = [s for s in p.values()
+          if not isinstance(s, NoneOptimizer)]
+if not active:
+    self.remove_optimizer(); return
+
+self._assert_same_class(active)
+cls = self._optimizer_class_for(active[0])
+groups = self.get_optimization_parameters()
+self._optimizer = cls(groups, **active[0].params())  # lr restored ✓
+
+
+ +

2Give NoneOptimizer a home in the match

+

The sentinel is a first-class OptimizerType, so the place that maps specs to + torch classes should acknowledge it instead of letting it fall into case _ and + explode. A tiny is_none() helper or an explicit case makes the intent obvious.

+ 
match opt_params:
+    case OptimizerParams.Adam():  return torch.optim.Adam
+    case OptimizerParams.AdamW(): return torch.optim.AdamW
+    case OptimizerParams.SGD():   return torch.optim.SGD
+    case OptimizerParams.NoneOptimizer():                          # ← speak its name
+        raise ValueError("NoneOptimizer has no torch class; filter it out before calling")
+    case _: raise NotImplementedError(...)
+ +

3Keep remove_optimizer faithful to the invariant

+

Right now it resets _optimizer_params to a bare NoneOptimizer() + — violating the “always a dict” rule it works almost everywhere thanks to re-normalization, but + it’s a landmine. Store the dict form so the invariant holds end-to-end.

+ 
def remove_optimizer(self):
+    self._optimizer = None
+    self._optimizer_params = OptimizerParams.NoneOptimizer()           # bare → breaks invariant
+    self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
+    self._scheduler = None
+    self._scheduler_params = SchedulerParams.NoneScheduler()
+ +

4Pick one way to say “don’t optimize this”

+

Today there are three: an empty dict (if not self.optimizer_params), + the NoneOptimizer sentinel, and self._optimizer = None. Three spellings of + one idea means every reader has to hold all three in their head. Commit to the sentinel as the + single source of truth, and let has_optimizer() be the one public question.

+
+
💡 Rule of thumb
+

“No optimizer” = NoneOptimizer in the params, + None in self._optimizer. Everything else (empty dicts, None + inputs) gets normalized into that single shape at the boundary, once.

+
+ +

5Fix the container getters’ dead filters (and the lying comment)

+

Both PtychographyOpt and the tomography container filter with + if not isinstance(params, NoneOptimizer) — but params is now a + dict, so the filter never fires and disabled models sneak back into the loop. Either + filter on the inner spec, or drop the filter and lean on has_optimizer(). And while + you’re there: either make get_optimization_parameters() actually bake per-group + hyperparameters (real PPLR) or delete the comment that says it does.

+ 
# container getter, today — dead filter
+if not isinstance(params, OptimizerParams.NoneOptimizer)   # params is a dict → always passes
+
+# option A: check the inner specs
+if any(not isinstance(s, NoneOptimizer) for s in params.values())   # ✓
+ +
+
🎯 The big picture
+

There’s a clean seam hiding here: separate what to + optimize (parameter groups, from the model) from how (optimizer specs, from the + user). If get_optimization_parameters() returned groups keyed the same way as the + specs dict, true per-parameter learning rates would fall out naturally — and + set_optimizer would shrink to “zip specs with groups, build one optimizer.” That’s + the refactor that turns this from “works if you don’t poke it” into “obviously + correct.”

+
+ +

📋 Scorecard

+ + + + + + + + + + + + + + + + + +
TopicStateOne-line take
Normalization (Layer 1 & 2)SolidThe _normalize_optimizer_params logic is actually nice and clear.
set_optimizer branchingBrokenTwo dead branches; the live one crashes on the default state.
Hyperparameter passingBrokenlr silently dropped; comment claims otherwise.
“No optimizer” representationMuddyThree spellings of one concept.
Container gettersStaleDead isinstance filters left over from the bare-type era.
+ +

+ Generated as a read-only review of src/quantem/core/ml/optimizer_mixin.py. + No source files were modified. Companion deep-dive: OPTIMIZER_PARAMS_FLOW.md. +

+ +
+ + diff --git a/src/quantem/core/ml/optimizer_refactor_incident.html b/src/quantem/core/ml/optimizer_refactor_incident.html new file mode 100644 index 00000000..89d4c762 --- /dev/null +++ b/src/quantem/core/ml/optimizer_refactor_incident.html @@ -0,0 +1,266 @@ + + + + + +The Great Type Standardization — an incident report + + + +
+ + Incident · Post-Mortem +
+

The Great Type Standardization
that quietly cut the pipes

+
How changing one return type from + OptimizerType | dict[str, OptimizerType] to just + dict[str, OptimizerType] took out Ptychography.
+
+ FILE: src/quantem/core/ml/optimizer_mixin.py  ·  + COMMIT: 1bc1739  ·  + SYMPTOM: NotImplementedError: Unknown optimizer type: NoneOptimizer() +
+
+ +
+ TL;DR — The refactor changed what the producer + (_normalize_optimizer_params) hands out, but not what the consumer + (set_optimizer) was built to receive. A return type isn’t just a label — it’s a + contract about the shape of a value, and three different places had hand-rolled + branches that pattern-matched on the old shape. When the shape changed, two branches + became unreachable and the third — never fully finished — became the only road. It couldn’t carry + a NoneOptimizer, and it dropped your learning rate on the floor. +
+ +

Act I — The good intentionOne type to rule them all

+

Someone looked at this signature and felt a totally reasonable itch:

+
+
+

Before

+ 
def optimizer_params(self) ->
+    OptimizerType | dict[str, OptimizerType]:
+    # could be a bare dataclass...
+    # ...or a dict of them. Ugh, which is it?
+    return self._optimizer_params
+
+
+

After — “let’s standardize”

+ 
def optimizer_params(self) ->
+    dict[str, OptimizerType]:
+    # always a dict now. cleaner!
+    return self._optimizer_params
+
+
+

The union type A | B is genuinely annoying: every reader, and every consumer, has + to ask “ok, but which one is it this time?” Collapsing it to a single + shape is the right instinct. The normalizer was dutifully rewritten so that everything — + a bare Adam(), a {"name": "adam"} shorthand, a list of keys — comes out + as a tidy dict[str, OptimizerType].

+ +
+
What went right
+

The normalization itself is clean and correct. If you only read + _normalize_optimizer_params, the refactor looks like an unambiguous win.

+
+ +

Act II — The unspoken contractA return type is a promise to strangers

+

Here’s the thing the type hint hides: that value doesn’t stay home. It flows downstream into + set_optimizer, which — because Python has no real runtime types — does its own + hand-rolled type dispatch with isinstance and an else:

+ + 
def set_optimizer(self, opt_params):
+    ...
+    if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):   # arm ➊
+        self.remove_optimizer(); return
+
+    if isinstance(self.optimizer_params, dict):                          # arm ➋
+        opt_specs = list(self.optimizer_params.values())
+        cls = self._optimizer_class_for(opt_specs[0])
+        self._optimizer = cls(params)
+    else:                                                                # arm ➌
+        cls = self._optimizer_class_for(self.optimizer_params)
+        self._optimizer = cls(params, **self.optimizer_params.params())
+ +

Those three arms were designed for the old contract, where + optimizer_params could be either a bare dataclass or a dict. Each arm had a + job:

+
    +
  • ➊ handle a bare NoneOptimizer sentinel → “turn the optimizer off.”
    • +
  • ➋ handle the dict / per-parameter case.
    • +
  • ➌ handle a bare optimizer dataclass → build it and splat its hyperparameters + (**params(), i.e. your lr).
    • +
+ +
Change the shape of the water, and pipes cut for the old shape stop carrying it — + even though nothing looks broken at the joint.
+ +

Act III — The reachability collapseTwo arms die, one inherits everything

+

Once the producer always returns a dict, watch what happens to the three arms:

+ + + + + + + + + + + + + + + + + + + + + + + + +
ArmMatches when…Old contractNew contractIts old job now…
➊ bare NoneOptimizervalue is a bare sentinelreachableDEAD…nobody turns the optimizer off anymore
➋ isinstance(dict)value is a dictreachableALWAYS…inherits ➊ and ➌’s jobs but does neither
➌ the elsevalue is a bare dataclassreachableDEAD…the **params() splat (your lr!) is gone
+ +

So every call now lands in arm ➋. And arm ➋ was only ever a partial + implementation — it builds cls(params) and trusts that the rest will sort itself out. + But it never learned the two jobs it just inherited:

+ +
+
Failure 1 · the crash you saw
+

Arm ➋ pulls opt_specs[0] and asks + _optimizer_class_for for a torch class. When that spec is a NoneOptimizer + (the default state of every model), the match falls through to + case _ → NotImplementedError. Arm ➊ used to catch this; arm ➊ is now + dead.

+
+
+
Failure 2 · the silent one
+

Arm ➋ builds cls(params) with no + hyperparameters. Arm ➌ used to splat **self.optimizer_params.params() — + that’s where your lr came from. With ➌ dead, a perfectly valid Adam(lr=5e-3) + silently becomes Adam() at torch’s default LR. No error. Just wrong.

+
+ +

Act IV — Why Python smiled and said nothing

+

If types were enforced, the compiler would have screamed: “arm ➊ and arm ➌ can never + match a dict[str, OptimizerType] — delete them or fix them.” But in Python:

+
    +
  • Type hints are decorative. Changing -> dict[...] changes + documentation, not behavior. Nothing re-checked the consumers.
    • +
  • A branch that never matches doesn’t raise — it just silently never runs. + Dead code is invisible until you go looking for it.
    • +
  • The one surviving arm happened to not crash on the common happy path (pass an + Adam, get an optimizer — just at the wrong LR), so casual testing looked fine.
    • +
+ +
+
Why it detonated on reset=True specifically
+

reconstruct(reset=True, optimizer_params=…) calls + reset_recon() before it applies your params. At that instant every + model still holds its factory default — {"default": NoneOptimizer()}. So arm ➋ gets a + NoneOptimizer on the very first thing it touches (obj_model) and dies + immediately. The crash isn’t in your config; it’s baked into the default state the reset path + walks through.

+
+ +

Act V — The moralStandardizing a type is a migration, not an edit

+

“Just change the return type to dict[str, OptimizerType]” felt like a + one-line cleanup. But the return type was load-bearing: three consumer branches structurally + depended on the old union. Changing the producer without migrating those consumers left the system + in a half-converted state — new shape coming out, old shape expected downstream.

+ +
+
+

What was done

+

✅ Producer (_normalize_optimizer_params) → always dict
+ ✅ Type hint updated
+ ❌ Consumer (set_optimizer) still speaks the old union
+ ❌ remove_optimizer still writes a bare sentinel

+
+
+

What “done” actually required

+

→ Collapse set_optimizer to one dict-only path
+ → Teach that path to filter NoneOptimizer (arm ➊’s job)
+ → Teach it to splat hyperparameters (arm ➌’s job)
+ → Make every producer of the value (incl. remove_optimizer) honor the new shape

+
+
+ +
The bug wasn’t the new type. The bug was believing the new type was the + whole change.
+ +
+ One sentence for the next reviewer: when you narrow a return type from a union to a single + shape, grep every consumer that does isinstance / match on it — each + arm that handled a now-impossible case is either dead code to delete or a responsibility that + just silently moved somewhere it isn’t handled. +
+ +

+ Read-only incident report · no source modified · + companions: optimizer_mixin_review.html, OPTIMIZER_PARAMS_FLOW.md +

+ +
+ + From bf19e6072e7a1ad79fa7e1db24bea44881d6f039 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 17:37:15 -0700 Subject: [PATCH 317/335] Removed .md and .html files --- src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md | 221 ------------ src/quantem/core/ml/optimizer_mixin_fix.html | 216 ------------ .../core/ml/optimizer_mixin_review.html | 319 ------------------ .../core/ml/optimizer_refactor_incident.html | 266 --------------- 4 files changed, 1022 deletions(-) delete mode 100644 src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md delete mode 100644 src/quantem/core/ml/optimizer_mixin_fix.html delete mode 100644 src/quantem/core/ml/optimizer_mixin_review.html delete mode 100644 src/quantem/core/ml/optimizer_refactor_incident.html diff --git a/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md b/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md deleted file mode 100644 index e1819034..00000000 --- a/src/quantem/core/ml/OPTIMIZER_PARAMS_FLOW.md +++ /dev/null @@ -1,221 +0,0 @@ -# Optimizer params: how inputs get normalized - -This document traces what happens to `optimizer_params` from the moment a user -passes them into `reconstruct(...)` to the moment a real `torch.optim.Optimizer` -is built. As of commit `1bc1739` the design invariant is: - -> **At the model level, `_optimizer_params` is *always* a `dict[str, OptimizerType]`.** - -Understanding the two normalization layers (container level → model level) is the -key to reading this code. - ---- - -## 1. The vocabulary - -| Thing | What it is | Where | -|---|---|---| -| `OptimizerType` | Union of the dataclasses `Adam`, `AdamW`, `SGD`, `NoneOptimizer` | `optimizer_mixin.py` (`OptimizerType = Adam \| AdamW \| SGD \| NoneOptimizer`) | -| `OptimizerParams.` | The individual dataclasses; each carries hyperparameters and a `.params()` method that returns them as a `dict` for torch | `optimizer_mixin.py` | -| `NoneOptimizer` | Sentinel meaning "do not optimize this thing". `.params()` returns `{}` | `optimizer_mixin.py:174` | -| `DEFAULT_OPTIMIZER_KEY` | `"default"` — the key used when a single optimizer is wrapped into a dict | `optimizer_mixin.py:534` | -| `OptimizerMixin` | Mixin inherited by each model (`obj_model`, `probe_model`, `dset`). Owns `_optimizer_params`, `set_optimizer`, etc. | `optimizer_mixin.py:527` | -| `PtychographyOpt` | The *container*. Holds the three models and exposes a combined `optimizer_params` | `ptychography_opt.py:20` | - -There are **two objects** that both have a property called `optimizer_params`, -and they mean different things: - -- **Container** (`PtychographyOpt` / tomography equivalent): a dict keyed by - *which model* — `"object"`, `"probe"`, `"dataset"`. -- **Model** (`OptimizerMixin`): a dict keyed by *parameter group* — normally just - the single key `"default"`. - -So a fully-resolved structure is **nested**: - -``` -container.optimizer_params - = {"object": {"default": Adam(lr=5e-3)}, - "probe": {"default": Adam(lr=1e-3)}, - "dataset": {"default": NoneOptimizer()}} - ^^^^^^^^ ^^^^^^^^^ ^^^^^^^^^^^^^^^ - model key group key OptimizerType -``` - ---- - -## 2. Accepted input shapes - -A user can hand the container any of these: - -```python -# (a) list/tuple of model keys -> use all defaults -ptycho.optimizer_params = ["object", "probe"] - -# (b) dict, value = OptimizerType dataclass -ptycho.optimizer_params = {"object": OptimizerParams.Adam(lr=5e-3)} - -# (c) dict, value = shorthand dict (name/type + hyperparams) -ptycho.optimizer_params = {"object": {"name": "adam", "lr": 5e-3}} - -# (d) dict, value = empty dict -> use default optimizer + default lr for that key -ptycho.optimizer_params = {"object": {}} -``` - ---- - -## 3. Layer 1 — container normalization - -`PtychographyOpt.optimizer_params` **setter** (`ptychography_opt.py:56`): - -``` -list/tuple ──► {k: {} for k in list} # (a) becomes (d)-style -for each (key, value): - value is OptimizerType ───────────────► pass through unchanged - value is dict and empty ──────────────► replace(DEFAULT_OPTIMIZER_TYPE, - lr=_get_default_lr(key)) - value is dict and non-empty ──────────► inject "name" if missing, - inject "lr" if missing - else ─────────────────────────────────► TypeError - │ - └─► dispatch to the matching model: - "object" -> self.obj_model.optimizer_params = value - "probe" -> self.probe_model.optimizer_params = value - "dataset" -> self.dset.optimizer_params = value -``` - -Key points: - -- `_get_default_lr(key)` supplies a sensible LR per model - (`object` ≈ 5e-3, `probe`/`dataset` ≈ 1e-3) when the user omitted one. -- The container does **not** build torch optimizers. It just fills defaults and - forwards each value down to the relevant model's setter. -- Any model key *not* mentioned by the user keeps whatever it had — by default - `{"default": NoneOptimizer()}` (i.e. "not optimized"). - ---- - -## 4. Layer 2 — model normalization - -Each model's `optimizer_params` **setter** calls -`OptimizerMixin._normalize_optimizer_params` (`optimizer_mixin.py:567`). This is -the function that guarantees the `dict[str, OptimizerType]` invariant: - -``` -_normalize_optimizer_params(params): - - params is an OptimizerType (dataclass) - ──► {"default": params} - - params is NOT a dict - ──► TypeError - - params is a dict AND _is_single_optimizer_dict(params) # has "name" or "type" - ──► {"default": OptimizerParams.parse_dict(params)} - - otherwise (dict-of-OptimizerType, the "PPLR" form) - ──► {k: (v if v is OptimizerType else parse_dict(v)) - for k, v in params.items()} -``` - -`_is_single_optimizer_dict(d)` is simply `"type" in d or "name" in d` -(`optimizer_mixin.py:585`). - -### `parse_dict` — shorthand → dataclass - -`OptimizerParams.parse_dict` (`optimizer_mixin.py:192`) maps a shorthand dict to -the concrete dataclass: - -``` -{"name"/"type": ...} pop the name, lowercase it, then: - "adam" -> OptimizerParams.Adam(**rest) - "adamw" -> OptimizerParams.AdamW(**rest) - "sgd" -> OptimizerParams.SGD(**rest) - "none" -> OptimizerParams.NoneOptimizer() - else -> ValueError -``` - -### Idempotency note - -When the container forwards an already-resolved value like `{"default": Adam(lr=5e-3)}` -to a model setter, it has no `"name"`/`"type"` key, so it takes the *PPLR branch* -and is kept as-is (`Adam` is already an `OptimizerType`). So re-normalizing a -normalized dict is a no-op. Good. - ---- - -## 5. Building the torch optimizer - -`OptimizerMixin.set_optimizer` (`optimizer_mixin.py:614`) is where the normalized -dict becomes a real optimizer. Conceptually it should: - -1. Look at the values of the `dict[str, OptimizerType]`. -2. Drop / handle `NoneOptimizer` sentinels (→ "no optimizer for this"). -3. Confirm the remaining specs agree on an optimizer *class*. -4. Pull parameter groups from `get_optimization_parameters()` (a `list[dict]`, - each `{"params": [...]}`). -5. Construct `optimizer_cls(param_groups, **hyperparameters)`. - -`_optimizer_class_for` (`optimizer_mixin.py:659`) maps a spec dataclass to a torch -class via a `match`: - -``` -Adam() -> torch.optim.Adam -AdamW() -> torch.optim.AdamW -SGD() -> torch.optim.SGD -_ -> NotImplementedError # <-- NoneOptimizer lands here -``` - -### The reset path (where the current crash lives) - -`reconstruct(reset=True, ...)` calls `reset_recon()` **before** applying the -user's `optimizer_params` (`ptychography.py:181` vs `:185`). `reset_recon` calls -each model's `reset_optimizer()` → `set_optimizer(self._optimizer_params)`. At -that moment `_optimizer_params` is still the default `{"default": NoneOptimizer()}`. - -``` -reconstruct(reset=True, optimizer_params=opt_params) - ├─ reset_recon() # opt_params NOT applied yet - │ └─ obj_model.reset_optimizer() - │ └─ set_optimizer({"default": NoneOptimizer()}) - │ └─ _optimizer_class_for(NoneOptimizer()) -> NotImplementedError - └─ (never reached) self.optimizer_params = opt_params; set_optimizers() -``` - ---- - -## 6. Where the value flows at recon time - -Once past reset, the normal value flow is: - -``` -reconstruct(optimizer_params={"object": {"lr": 5e-3}, ...}) - │ - ├─ container.optimizer_params = {...} # Layer 1: fill defaults, dispatch - │ └─ obj_model.optimizer_params = {"name":"adamw","lr":5e-3} - │ └─ _normalize_optimizer_params -> {"default": AdamW(lr=5e-3)} # Layer 2 - │ - └─ container.set_optimizers() - for key, params in container.optimizer_params.items(): # nested dict - model.set_optimizer(params) # params = {"default": AdamW(lr=5e-3)} - └─ build torch.optim.AdamW(param_groups, lr=5e-3, ...) -``` - -`container.set_optimizers()` iterates the **container getter**, which returns the -nested `{"object": {"default": ...}, ...}` structure, and hands each inner dict to -the corresponding model's `set_optimizer`. - ---- - -## 7. Quick reference — invariants to keep in mind - -- A **model's** `_optimizer_params` is always `dict[str, OptimizerType]`, normally - one key: `"default"`. -- A **container's** `optimizer_params` is `dict[str, dict[str, OptimizerType]]`, - keyed by model name (`"object"`/`"probe"`/`"dataset"`). -- `NoneOptimizer` is a first-class `OptimizerType` meaning "skip". Anything that - consumes the dict must treat it specially, because `_optimizer_class_for` has no - case for it. -- `.params()` on a spec dataclass returns the kwargs torch needs (`lr`, etc.); - `NoneOptimizer.params()` returns `{}`. -- `get_optimization_parameters()` returns parameter *groups* (`[{"params": [...]}]`) - and currently carries **no** per-group hyperparameters. diff --git a/src/quantem/core/ml/optimizer_mixin_fix.html b/src/quantem/core/ml/optimizer_mixin_fix.html deleted file mode 100644 index 3bb7c365..00000000 --- a/src/quantem/core/ml/optimizer_mixin_fix.html +++ /dev/null @@ -1,216 +0,0 @@ - - - - - -OptimizerMixin — minimal fixes to complete the dict[str, OptimizerType] standardization - - - -
- -
-

Completing the dict[str, OptimizerType] standardization

-

Minimal, surgical fixes that keep your standardization goal intact and resolve the - Ptychography breakage.

-
- FILE: src/quantem/core/ml/optimizer_mixin.py  ·  - SCOPE: consumer-side only  ·  - RISK: low, localized -
-
- -

1. Diagnosis in one paragraph

-

Your goal — normalize everything to dict[str, OptimizerType] — is correct - and worth keeping. The standardization did not break Ptychography on its own. The breakage came - from a consumer that was never migrated to the new contract: - set_optimizer. It still branches on the old union shape (a bare - OptimizerType or a dict). After normalization always returns a dict, the two - branches that handled the bare-type cases became unreachable, and the surviving dict branch was - only a partial implementation — it never handled the NoneOptimizer sentinel and never - forwarded hyperparameters. The fix is therefore not to revert the standardization, - but to make that one dict branch self-sufficient.

- -
-
Why your change was necessary but not sufficient
-

A return type is a contract. Narrowing it from a union to a single - shape obligates every consumer that pattern-matches on the old shape to be updated in the same - change. The producer was migrated; one consumer was not.

-
- -

2. The two concrete symptoms

- - - - - - - - - - -
SymptomRoot cause in set_optimizer
Crash: NotImplementedError: Unknown optimizer type: NoneOptimizer()The dict branch passes a NoneOptimizer spec to - _optimizer_class_for, which has no case for it. The old guard that caught this - (isinstance(..., NoneOptimizer)) only matches a bare sentinel and is now - unreachable.
Silent: configured lr ignoredThe dict branch builds optimizer_cls(params) with no kwargs. The old branch - that splatted **optimizer_params.params() is the now-unreachable - else.
- -

3. Recommended minimal change set

-

Three changes, in priority order. REQUIRED alone resolves the - crash and the silent lr bug.

- -

REQUIREDMake the dict branch self-sufficient · ~6 lines, one method

-

Filter out NoneOptimizer sentinels; if none remain, there is nothing to optimize. - Otherwise build the optimizer and forward the spec’s hyperparameters. This is the - smallest change that honors the new dict-only contract.

- 
        if isinstance(self.optimizer_params, dict):
--           opt_specs = list(self.optimizer_params.values())
--           if not opt_specs:
--               self._optimizer = None
--               return
-+           opt_specs = [
-+               spec for spec in self.optimizer_params.values()
-+               if not isinstance(spec, OptimizerParams.NoneOptimizer)
-+           ]
-+           if not opt_specs:
-+               self.remove_optimizer()
-+               return
-            optimizer_cls = self._optimizer_class_for(opt_specs[0])
-            for spec in opt_specs[1:]:
-                if type(spec) is not type(opt_specs[0]):
-                    raise ValueError(...)
--           self._optimizer = optimizer_cls(params)  # type:ignore
-+           self._optimizer = optimizer_cls(params, **opt_specs[0].params())
-
-
What this buys you
-

The default {"default": NoneOptimizer()} now filters to - an empty list → remove_optimizer() → clean no-op (this is the path - reset_recon() walks). A real {"default": Adam(lr=5e-3)} now builds - Adam(params, lr=5e-3, …) with your hyperparameters restored.

-
-
-
Note on PPLR
-

This splats opt_specs[0]’s hyperparameters across all - parameter groups. That is correct today because every model returns a single param group from - get_optimization_parameters(). True per-group LRs are a separate, larger piece of - work — see §5.

-
- -

RECOMMENDEDKeep remove_optimizer on-contract · 1 line

-

It currently stores a bare sentinel, breaking the “always a dict” invariant. It works - only because the value gets re-normalized on the next set. Store the dict form to keep the - invariant true end-to-end.

- 
    def remove_optimizer(self) -> None:
-        self._optimizer = None
--       self._optimizer_params = OptimizerParams.NoneOptimizer()
-+       self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
-        self._scheduler = None
-        self._scheduler_params = SchedulerParams.NoneScheduler()
- -

OPTIONALDelete the now-dead branches · removes ~7 lines

-

Purely cosmetic, but it removes the misleading code that caused the confusion. With the dict - contract guaranteed, the bare-NoneOptimizer guard and the else branch can - never run. Removing them makes set_optimizer a single, honest path.

- 
-       if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):
--           self.remove_optimizer()
--           return
-        ...
--       else:
--           optimizer_cls = self._optimizer_class_for(self.optimizer_params)
--           self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
-

If you keep these, no harm — they’re dead, not wrong. Removing them is about - readability, not correctness.

- -

4. Optional: align the container getters

-

OPTIONAL In PtychographyOpt.optimizer_params (and the - tomography equivalent) the filter if not isinstance(params, NoneOptimizer) no longer - fires, because params is now a dict. After the REQUIRED fix this is harmless — a - disabled model simply ends up with no optimizer — but if you want the getter to exclude disabled - models, check the inner specs instead.

- 
-           if not isinstance(params, OptimizerParams.NoneOptimizer)
-+           if any(not isinstance(s, OptimizerParams.NoneOptimizer) for s in params.values())
- -

5. What to leave for later (don’t scope-creep)

-
    -
  • Real per-parameter LRs (PPLR). The comment claiming hyperparameters are “baked into - each dict by get_optimization_parameters()” is aspirational — the implementations - return [{"params": params}] with no lr. Either implement keyed param - groups or update the comment. Not needed to fix the break.
    • -
  • Collapsing the three “no optimizer” spellings (empty dict / NoneOptimizer / - _optimizer = None) into one. A nice tidy-up, but orthogonal to this incident.
    • -
- -

6. Verification checklist

- - - - - - - - - - -
StepExpected
ptycho.reconstruct(num_iters=1, reset=True, optimizer_params={"object": {"lr": 5e-3}})No NotImplementedError; runs.
Inspect ptycho.obj_model.optimizer.param_groups[0]["lr"]Equals 5e-3, not torch’s default.
A model left out of optimizer_paramshas_optimizer() is False; not stepped.
Re-run with reset=TrueDefault NoneOptimizer filters cleanly; no crash.
- -
-
Bottom line
-

Keep your standardization. Apply the one REQUIRED change (and ideally - the one-line RECOMMENDED change). That completes the migration the type-narrowing started, with a - change set small enough to read in a single diff.

-
- -

- Read-only proposal · no source files modified · - companions: optimizer_mixin_review.html · optimizer_refactor_incident.html · OPTIMIZER_PARAMS_FLOW.md -

- -
- - diff --git a/src/quantem/core/ml/optimizer_mixin_review.html b/src/quantem/core/ml/optimizer_mixin_review.html deleted file mode 100644 index 1e205644..00000000 --- a/src/quantem/core/ml/optimizer_mixin_review.html +++ /dev/null @@ -1,319 +0,0 @@ - - - - - -OptimizerMixin — a friendly post-mortem & cleanup wishlist - - - -
- -
- Code review · ml/optimizer_mixin.py -

OptimizerMixin: a friendly post-mortem 🕵️

-

Why NoneOptimizer() blew up your ptycho run, and how to make this class - a joy to read again. No code was harmed in the making of this document.

- -
- -

🔪 The whodunit (in one breath)

-

- A refactor decreed: “optimizer params shall always be a - dict[str, OptimizerType].” The normalizer obeyed. But - set_optimizer never got the memo — it still has two branches written for the - old world where a bare OptimizerType could walk in the door. Those - branches are now unreachable corpses, and every call gets funneled into the one dict branch - that doesn’t know what to do with a NoneOptimizer. -

- -
-
💥 The crash
-

reconstruct(reset=True) calls reset_recon() - before your optimizer_params are applied. So set_optimizer - runs against the default {"default": NoneOptimizer()}, falls into the dict branch, - and hands a NoneOptimizer to _optimizer_class_for — which has no - case for it → NotImplementedError.

-
- - 
# the dead giveaway in set_optimizer (current)
-if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):  ← never True; params is always a dict now
-    self.remove_optimizer()
-    return
-...
-if isinstance(self.optimizer_params, dict):                          # ← always taken
-    opt_specs = list(self.optimizer_params.values())             # [NoneOptimizer()]
-    optimizer_cls = self._optimizer_class_for(opt_specs[0])     ← 💥 NotImplementedError
-    ...
-    self._optimizer = optimizer_cls(params)                       ← also: no lr is ever passed!
-else:
-    # the branch that actually splatted hyperparameters... now unreachable 🪦
-    self._optimizer = optimizer_cls(params, **self.optimizer_params.params())
- -
-
🐛 Bonus silent bug
-

Even after the crash is fixed, the dict branch builds - optimizer_cls(params) with no hyperparameters. A code comment - promises they’re “baked into each dict by get_optimization_parameters()” — but all - three implementations just return [{"params": params}] with no - lr. So your carefully chosen learning rate would silently become torch’s - default. The comment is, regrettably, fan fiction.

-
- -

🌊 The flow, at a glance

-

Two objects both expose optimizer_params, and they mean different things. That’s - the single most confusing thing about this code, so let’s name it loudly:

- - - - - - - - - - - - - -
LevelKeyed byExample value
Container
(PtychographyOpt)		which model
"object"/"probe"/"dataset"		{"object": {"default": Adam(lr=5e-3)}}
Model
(OptimizerMixin)		which param group
usually just "default"		{"default": Adam(lr=5e-3)}
- - 
user input ──► PtychographyOpt.optimizer_params setter   # Layer 1: fill defaults, dispatch per model
-                 │   fills "name"/"lr" defaults via _get_default_lr()
-                 └─► model.optimizer_params setter             # Layer 2: normalize to dict[str, OptimizerType]
-                       └─► _normalize_optimizer_params()
-                             OptimizerType        ──► {"default": it}
-                             {"name"/"type": ...} ──► {"default": parse_dict(it)}
-                             {k: OptimizerType}   ──► kept (PPLR form)
-                                  │
-reconstruct() ──► set_optimizers() ──► for each model: set_optimizer({"default": Adam(lr=5e-3)})
-                                                          └─► torch.optim.Adam(param_groups, lr=5e-3)
- -

🧹 Five ways to make this a joy to read

- -

1Make the invariant real — delete the dead branches

-

If optimizer_params is always a dict, then set_optimizer - should have one path: filter out the NoneOptimizer sentinels, agree - on a class, and splat hyperparameters. No else, no zombie isinstance.

- -
-
-

✗ Before — 3 branches, 2 of them dead

- 
if isinstance(p, NoneOptimizer):  # dead
-    remove_optimizer(); return
-...
-if isinstance(p, dict):
-    specs = list(p.values())
-    cls = _optimizer_class_for(specs[0])  # 💥
-    self._optimizer = cls(params)       # no lr
-else:                            # dead
-    cls = _optimizer_class_for(p)
-    self._optimizer = cls(params, **p.params())
-
-
-

✓ After — one honest path

- 
# params is always dict[str, OptimizerType]
-active = [s for s in p.values()
-          if not isinstance(s, NoneOptimizer)]
-if not active:
-    self.remove_optimizer(); return
-
-self._assert_same_class(active)
-cls = self._optimizer_class_for(active[0])
-groups = self.get_optimization_parameters()
-self._optimizer = cls(groups, **active[0].params())  # lr restored ✓
-
-
- -

2Give NoneOptimizer a home in the match

-

The sentinel is a first-class OptimizerType, so the place that maps specs to - torch classes should acknowledge it instead of letting it fall into case _ and - explode. A tiny is_none() helper or an explicit case makes the intent obvious.

- 
match opt_params:
-    case OptimizerParams.Adam():  return torch.optim.Adam
-    case OptimizerParams.AdamW(): return torch.optim.AdamW
-    case OptimizerParams.SGD():   return torch.optim.SGD
-    case OptimizerParams.NoneOptimizer():                          # ← speak its name
-        raise ValueError("NoneOptimizer has no torch class; filter it out before calling")
-    case _: raise NotImplementedError(...)
- -

3Keep remove_optimizer faithful to the invariant

-

Right now it resets _optimizer_params to a bare NoneOptimizer() - — violating the “always a dict” rule it works almost everywhere thanks to re-normalization, but - it’s a landmine. Store the dict form so the invariant holds end-to-end.

- 
def remove_optimizer(self):
-    self._optimizer = None
-    self._optimizer_params = OptimizerParams.NoneOptimizer()           # bare → breaks invariant
-    self._optimizer_params = {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer()}
-    self._scheduler = None
-    self._scheduler_params = SchedulerParams.NoneScheduler()
- -

4Pick one way to say “don’t optimize this”

-

Today there are three: an empty dict (if not self.optimizer_params), - the NoneOptimizer sentinel, and self._optimizer = None. Three spellings of - one idea means every reader has to hold all three in their head. Commit to the sentinel as the - single source of truth, and let has_optimizer() be the one public question.

-
-
💡 Rule of thumb
-

“No optimizer” = NoneOptimizer in the params, - None in self._optimizer. Everything else (empty dicts, None - inputs) gets normalized into that single shape at the boundary, once.

-
- -

5Fix the container getters’ dead filters (and the lying comment)

-

Both PtychographyOpt and the tomography container filter with - if not isinstance(params, NoneOptimizer) — but params is now a - dict, so the filter never fires and disabled models sneak back into the loop. Either - filter on the inner spec, or drop the filter and lean on has_optimizer(). And while - you’re there: either make get_optimization_parameters() actually bake per-group - hyperparameters (real PPLR) or delete the comment that says it does.

- 
# container getter, today — dead filter
-if not isinstance(params, OptimizerParams.NoneOptimizer)   # params is a dict → always passes
-
-# option A: check the inner specs
-if any(not isinstance(s, NoneOptimizer) for s in params.values())   # ✓
- -
-
🎯 The big picture
-

There’s a clean seam hiding here: separate what to - optimize (parameter groups, from the model) from how (optimizer specs, from the - user). If get_optimization_parameters() returned groups keyed the same way as the - specs dict, true per-parameter learning rates would fall out naturally — and - set_optimizer would shrink to “zip specs with groups, build one optimizer.” That’s - the refactor that turns this from “works if you don’t poke it” into “obviously - correct.”

-
- -

📋 Scorecard

- - - - - - - - - - - - - - - - - -
TopicStateOne-line take
Normalization (Layer 1 & 2)SolidThe _normalize_optimizer_params logic is actually nice and clear.
set_optimizer branchingBrokenTwo dead branches; the live one crashes on the default state.
Hyperparameter passingBrokenlr silently dropped; comment claims otherwise.
“No optimizer” representationMuddyThree spellings of one concept.
Container gettersStaleDead isinstance filters left over from the bare-type era.
- -

- Generated as a read-only review of src/quantem/core/ml/optimizer_mixin.py. - No source files were modified. Companion deep-dive: OPTIMIZER_PARAMS_FLOW.md. -

- -
- - diff --git a/src/quantem/core/ml/optimizer_refactor_incident.html b/src/quantem/core/ml/optimizer_refactor_incident.html deleted file mode 100644 index 89d4c762..00000000 --- a/src/quantem/core/ml/optimizer_refactor_incident.html +++ /dev/null @@ -1,266 +0,0 @@ - - - - - -The Great Type Standardization — an incident report - - - -
- - Incident · Post-Mortem -
-

The Great Type Standardization
that quietly cut the pipes

-
How changing one return type from - OptimizerType | dict[str, OptimizerType] to just - dict[str, OptimizerType] took out Ptychography.
-
- FILE: src/quantem/core/ml/optimizer_mixin.py  ·  - COMMIT: 1bc1739  ·  - SYMPTOM: NotImplementedError: Unknown optimizer type: NoneOptimizer() -
-
- -
- TL;DR — The refactor changed what the producer - (_normalize_optimizer_params) hands out, but not what the consumer - (set_optimizer) was built to receive. A return type isn’t just a label — it’s a - contract about the shape of a value, and three different places had hand-rolled - branches that pattern-matched on the old shape. When the shape changed, two branches - became unreachable and the third — never fully finished — became the only road. It couldn’t carry - a NoneOptimizer, and it dropped your learning rate on the floor. -
- -

Act I — The good intentionOne type to rule them all

-

Someone looked at this signature and felt a totally reasonable itch:

-
-
-

Before

- 
def optimizer_params(self) ->
-    OptimizerType | dict[str, OptimizerType]:
-    # could be a bare dataclass...
-    # ...or a dict of them. Ugh, which is it?
-    return self._optimizer_params
-
-
-

After — “let’s standardize”

- 
def optimizer_params(self) ->
-    dict[str, OptimizerType]:
-    # always a dict now. cleaner!
-    return self._optimizer_params
-
-
-

The union type A | B is genuinely annoying: every reader, and every consumer, has - to ask “ok, but which one is it this time?” Collapsing it to a single - shape is the right instinct. The normalizer was dutifully rewritten so that everything — - a bare Adam(), a {"name": "adam"} shorthand, a list of keys — comes out - as a tidy dict[str, OptimizerType].

- -
-
What went right
-

The normalization itself is clean and correct. If you only read - _normalize_optimizer_params, the refactor looks like an unambiguous win.

-
- -

Act II — The unspoken contractA return type is a promise to strangers

-

Here’s the thing the type hint hides: that value doesn’t stay home. It flows downstream into - set_optimizer, which — because Python has no real runtime types — does its own - hand-rolled type dispatch with isinstance and an else:

- - 
def set_optimizer(self, opt_params):
-    ...
-    if isinstance(self.optimizer_params, OptimizerParams.NoneOptimizer):   # arm ➊
-        self.remove_optimizer(); return
-
-    if isinstance(self.optimizer_params, dict):                          # arm ➋
-        opt_specs = list(self.optimizer_params.values())
-        cls = self._optimizer_class_for(opt_specs[0])
-        self._optimizer = cls(params)
-    else:                                                                # arm ➌
-        cls = self._optimizer_class_for(self.optimizer_params)
-        self._optimizer = cls(params, **self.optimizer_params.params())
- -

Those three arms were designed for the old contract, where - optimizer_params could be either a bare dataclass or a dict. Each arm had a - job:

-
    -
  • ➊ handle a bare NoneOptimizer sentinel → “turn the optimizer off.”
    • -
  • ➋ handle the dict / per-parameter case.
    • -
  • ➌ handle a bare optimizer dataclass → build it and splat its hyperparameters - (**params(), i.e. your lr).
    • -
- -
Change the shape of the water, and pipes cut for the old shape stop carrying it — - even though nothing looks broken at the joint.
- -

Act III — The reachability collapseTwo arms die, one inherits everything

-

Once the producer always returns a dict, watch what happens to the three arms:

- - - - - - - - - - - - - - - - - - - - - - - - -
ArmMatches when…Old contractNew contractIts old job now…
➊ bare NoneOptimizervalue is a bare sentinelreachableDEAD…nobody turns the optimizer off anymore
➋ isinstance(dict)value is a dictreachableALWAYS…inherits ➊ and ➌’s jobs but does neither
➌ the elsevalue is a bare dataclassreachableDEAD…the **params() splat (your lr!) is gone
- -

So every call now lands in arm ➋. And arm ➋ was only ever a partial - implementation — it builds cls(params) and trusts that the rest will sort itself out. - But it never learned the two jobs it just inherited:

- -
-
Failure 1 · the crash you saw
-

Arm ➋ pulls opt_specs[0] and asks - _optimizer_class_for for a torch class. When that spec is a NoneOptimizer - (the default state of every model), the match falls through to - case _ → NotImplementedError. Arm ➊ used to catch this; arm ➊ is now - dead.

-
-
-
Failure 2 · the silent one
-

Arm ➋ builds cls(params) with no - hyperparameters. Arm ➌ used to splat **self.optimizer_params.params() — - that’s where your lr came from. With ➌ dead, a perfectly valid Adam(lr=5e-3) - silently becomes Adam() at torch’s default LR. No error. Just wrong.

-
- -

Act IV — Why Python smiled and said nothing

-

If types were enforced, the compiler would have screamed: “arm ➊ and arm ➌ can never - match a dict[str, OptimizerType] — delete them or fix them.” But in Python:

-
    -
  • Type hints are decorative. Changing -> dict[...] changes - documentation, not behavior. Nothing re-checked the consumers.
    • -
  • A branch that never matches doesn’t raise — it just silently never runs. - Dead code is invisible until you go looking for it.
    • -
  • The one surviving arm happened to not crash on the common happy path (pass an - Adam, get an optimizer — just at the wrong LR), so casual testing looked fine.
    • -
- -
-
Why it detonated on reset=True specifically
-

reconstruct(reset=True, optimizer_params=…) calls - reset_recon() before it applies your params. At that instant every - model still holds its factory default — {"default": NoneOptimizer()}. So arm ➋ gets a - NoneOptimizer on the very first thing it touches (obj_model) and dies - immediately. The crash isn’t in your config; it’s baked into the default state the reset path - walks through.

-
- -

Act V — The moralStandardizing a type is a migration, not an edit

-

“Just change the return type to dict[str, OptimizerType]” felt like a - one-line cleanup. But the return type was load-bearing: three consumer branches structurally - depended on the old union. Changing the producer without migrating those consumers left the system - in a half-converted state — new shape coming out, old shape expected downstream.

- -
-
-

What was done

-

✅ Producer (_normalize_optimizer_params) → always dict
- ✅ Type hint updated
- ❌ Consumer (set_optimizer) still speaks the old union
- ❌ remove_optimizer still writes a bare sentinel

-
-
-

What “done” actually required

-

→ Collapse set_optimizer to one dict-only path
- → Teach that path to filter NoneOptimizer (arm ➊’s job)
- → Teach it to splat hyperparameters (arm ➌’s job)
- → Make every producer of the value (incl. remove_optimizer) honor the new shape

-
-
- -
The bug wasn’t the new type. The bug was believing the new type was the - whole change.
- -
- One sentence for the next reviewer: when you narrow a return type from a union to a single - shape, grep every consumer that does isinstance / match on it — each - arm that handled a now-impossible case is either dead code to delete or a responsibility that - just silently moved somewhere it isn’t handled. -
- -

- Read-only incident report · no source modified · - companions: optimizer_mixin_review.html, OPTIMIZER_PARAMS_FLOW.md -

- -
- - From 91642a8acf3f422424daad71ef9ba8bf0e6754b4 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 28 May 2026 18:18:06 -0700 Subject: [PATCH 318/335] Major changes per Claude's detailed report on what I actually implemented is not truly PPLR and was brittle. Will talk to @arthurmccray 5/28/2026 about this --- src/quantem/core/ml/optimizer_mixin.py | 114 +++++++++++------- .../diffractive_imaging/dataset_models.py | 42 +++++-- .../diffractive_imaging/object_models.py | 9 +- .../diffractive_imaging/probe_models.py | 9 +- src/quantem/tomography/object_models.py | 26 ++-- tests/ml/test_optimizermixin.py | 103 ++++++++++++++++ 6 files changed, 223 insertions(+), 80 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 8c095d39..1575276d 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -603,62 +603,87 @@ def scheduler_params(self, params: SchedulerType | dict): @abstractmethod def get_optimization_parameters( self, - ) -> "list[dict[str, Any]]": + ) -> "dict[str, list[torch.Tensor]]": """ - Get the parameters that should be optimized for this model. - This could be replaced with just module.parameters(), but this allows for flexibility - in the future to allow for per parameter LRs. # NOTE: Cl 4/27/26 updated to iterable type-hint. + Get the parameters that should be optimized for this model, grouped by name. + + Returns a mapping ``{group_key: [tensors]}``. The group keys MUST match the keys of + ``optimizer_params`` (the common single-group case uses ``DEFAULT_OPTIMIZER_KEY``). + ``set_optimizer`` joins each group to its optimizer spec by key and bakes the per-group + hyperparameters (``spec.params()``) into the torch param group — implementations return + only the tensors, NOT pre-baked hyperparameters. Return ``{}`` when there is nothing + to optimize. """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: """ - Set the optimizer for this model. - Currently supports single LR for all parameters, TODO allow for per parameter LRs by - updating get_optimization_parameters to return a list of parameters and their LRs. + Set the optimizer for this model, supporting per-parameter-group learning rates (PPLR). + + ``optimizer_params`` is a ``dict[str, OptimizerType]`` keyed by parameter group. Each + group's spec is joined by key to the tensors returned by ``get_optimization_parameters()`` + and its hyperparameters are baked into the corresponding torch param group here. All + groups must use the same optimizer class. If every group is a ``NoneOptimizer`` (or there + are no groups), the optimizer is removed. """ if opt_params is not None: self.optimizer_params = opt_params - if not self.optimizer_params: - self._optimizer = None + # Single canonical "disable" path: drop NoneOptimizer sentinels and, if nothing is left, + # remove the optimizer. Done BEFORE get_optimization_parameters() because some models + # (e.g. the dataset model) raise / return nothing when there is nothing to optimize. + specs = { + key: spec + for key, spec in self.optimizer_params.items() + if not isinstance(spec, OptimizerParams.NoneOptimizer) + } + if not specs: + self.remove_optimizer() return + # All groups must agree on the optimizer class. + spec_list = list(specs.values()) + for spec in spec_list[1:]: + if type(spec) is not type(spec_list[0]): + raise ValueError( + f"All parameter groups must use the same optimizer type, " + f"got {type(spec_list[0]).__name__} and {type(spec).__name__}" + ) - params = self.get_optimization_parameters() # always list[dict] - - # Ensure parameters require gradients - for group in params: - for p in group["params"]: + # Join specs to param groups by key; bake each group's hyperparameters here. + groups = self.get_optimization_parameters() # dict[str, list[tensor]] + if set(groups) != set(specs): + raise ValueError( + f"optimizer_params keys {set(specs)} do not match parameter group keys " + f"{set(groups)} from {type(self).__name__}.get_optimization_parameters()" + ) + + param_groups = [] + for key, tensors in groups.items(): + for p in tensors: p.requires_grad_(True) - # Figure out which optimizer class to use - if isinstance(self.optimizer_params, dict): - # Per-group case: all groups must agree on the optimizer class, - # and per-group hyperparameters are already baked into each dict - # by get_optimization_parameters(). - opt_specs = [ - spec for spec in self.optimizer_params.values() - if not isinstance(spec, OptimizerParams.NoneOptimizer) - ] - if not opt_specs: - self.remove_optimizer() - return - optimizer_cls = self._optimizer_class_for(opt_specs[0]) - for spec in opt_specs[1:]: - if type(spec) is not type(opt_specs[0]): - raise ValueError( - f"All parameter groups must use the same optimizer type, " - f"got {type(opt_specs[0]).__name__} and {type(spec).__name__}" - ) - self._optimizer = optimizer_cls(params, **opt_specs[0].params()) - def _optimizer_class_for(self, opt_params) -> type[torch.optim.Optimizer]: + param_groups.append({"params": tensors, **specs[key].params()}) + self._optimizer = self._build_optimizer(spec_list[0], param_groups) + + def _build_optimizer(self, opt_params, param_groups) -> "torch.optim.Optimizer": + """Construct the torch optimizer for ``opt_params`` over pre-baked ``param_groups``. + + ``param_groups`` already carry their per-group hyperparameters (see ``set_optimizer``), + so each group's ``lr`` etc. overrides the optimizer-level default. ``NoneOptimizer`` must + have been filtered out by the caller. + """ match opt_params: case OptimizerParams.Adam(): - return torch.optim.Adam + return torch.optim.Adam(param_groups) case OptimizerParams.AdamW(): - return torch.optim.AdamW + return torch.optim.AdamW(param_groups) case OptimizerParams.SGD(): - return torch.optim.SGD + return torch.optim.SGD(param_groups) + case OptimizerParams.NoneOptimizer(): + raise ValueError( + "NoneOptimizer must be filtered out before _build_optimizer; " + "set_optimizer should have short-circuited to remove_optimizer()." + ) case _: raise NotImplementedError(f"Unknown optimizer type: {opt_params}") @@ -674,7 +699,10 @@ def set_scheduler( return optimizer = self._optimizer - base_LR = optimizer.param_groups[0]["lr"] + # Schedulers scale every torch param group proportionally off its own initial_lr; this + # scalar only seeds scheduler config (e.g. min_lr, cyclic bounds). Use the max group LR + # as the representative (collapses to group 0 in the single-group case). + base_LR = max(pg["lr"] for pg in optimizer.param_groups) params = self._scheduler_params.params(base_LR, num_iter=num_iter) match self.scheduler_params: case SchedulerParams.NoneScheduler(): @@ -764,8 +792,8 @@ def reconnect_optimizer_to_parameters(self) -> None: return # Ensure leaf params with grad - for group in new_groups: - for p in group["params"]: + for tensors in new_groups.values(): + for p in tensors: if not p.is_leaf: raise ValueError("Non-leaf tensor in param group; build groups from leaves") p.requires_grad_(True) @@ -776,8 +804,8 @@ def reconnect_optimizer_to_parameters(self) -> None: ] self._optimizer.param_groups.clear() - for group in new_groups: - self._optimizer.add_param_group(group) + for tensors in new_groups.values(): + self._optimizer.add_param_group({"params": tensors}) # Restore per-group hyperparameters by index for new_pg, old_pg in zip(self._optimizer.param_groups, old_hyperparams): diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index 585d5e4b..39895adb 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -1,4 +1,5 @@ from abc import abstractmethod +from dataclasses import replace from pathlib import Path from typing import Any, Literal, Self @@ -95,18 +96,39 @@ def __init__( self._constraints = {} self._probe_energy = None - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Get the combined descan and scan position parameters for optimization.""" - params = [] + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Descan and scan-position parameters as separate PPLR groups. + + Returns one group per *learnable* parameter set; ``{}`` when neither is learnable + (``set_optimizer`` then short-circuits to removing the optimizer). + """ + groups: dict[str, list[torch.Tensor]] = {} if self.learn_descan: - params.append(self._descan_shifts) + groups["descan"] = [self._descan_shifts] if self.learn_scan_positions: - params.append(self._scan_positions_px) - if len(params) == 0: - raise RuntimeError( - "No parameters to optimize for dataset: learn_descan and learn_scan_positions are both False" - ) - return [{"params": params}] + groups["scan_positions"] = [self._scan_positions_px] + return groups + + def _normalize_optimizer_params(self, params): + """Broadcast a single optimizer spec to the learnable descan/scan_position groups. + + A single ``OptimizerType`` / single-optimizer dict (normalized to the ``"default"`` key) + is fanned out to whichever groups are currently learnable, so the common single-LR caller + keeps working. An explicit PPLR dict (keyed by ``descan``/``scan_positions``) passes through. + """ + norm = super()._normalize_optimizer_params(params) + if set(norm) == {self.DEFAULT_OPTIMIZER_KEY}: + spec = norm[self.DEFAULT_OPTIMIZER_KEY] + learnable = [ + key + for key, on in ( + ("descan", self.learn_descan), + ("scan_positions", self.learn_scan_positions), + ) + if on + ] + return {key: replace(spec) for key in learnable} if learnable else {} + return norm def to(self, *args, **kwargs): """Move all relevant tensors to a different device.""" diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 0860bf55..3b74319b 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -232,13 +232,12 @@ def to(self, *args, **kwargs): def name(self) -> str: raise NotImplementedError() - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Get the parameters that should be optimized for this model.""" + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Get the parameters that should be optimized for this model, keyed by group.""" params = self.params if params is None: - return [] - else: - return [{"params": params}] # compatible with PPLR + return {} + return {self.DEFAULT_OPTIMIZER_KEY: list(params)} def _propagate_array( self, array: "torch.Tensor", propagator_array: "torch.Tensor" diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index b72f1426..6b00793a 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -94,13 +94,12 @@ def __init__( if roi_shape is not None: self.roi_shape = roi_shape - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Get the parameters that should be optimized for this model.""" + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Get the parameters that should be optimized for this model, keyed by group.""" params = self.params if params is None: - return [] - else: - return [{"params": params}] # compatible with PPLR + return {} + return {self.DEFAULT_OPTIMIZER_KEY: list(params)} @property def learn_probe_tilt(self) -> bool: diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 8fa19db7..12e8a99c 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -290,14 +290,12 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: raise NotImplementedError # --- Helper Functions --- - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """Default: wrap self.params in a single param group.""" - if isinstance(self._optimizer_params, dict): - # Shouldn't happen for single-group models, but be defensive - opt = next(iter(self._optimizer_params.values())) - else: - opt = self._optimizer_params - return [{"params": list(self.params), **opt.params()}] + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """Default: a single param group keyed by DEFAULT_OPTIMIZER_KEY. + + Hyperparameters are baked by ``set_optimizer``, not here — return only the tensors. + """ + return {self.DEFAULT_OPTIMIZER_KEY: list(self.params)} @abstractmethod # Each subclass should implement this. def to(self, device: str | torch.device): @@ -1026,16 +1024,10 @@ def params(self) -> Generator[torch.nn.Parameter, None, None]: return self.model.parameters() # type: ignore[attr-defined] - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """PPLR: per-key param groups.""" + def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": + """PPLR: per-key param groups (hyperparameters are baked by set_optimizer).""" model = _unwrap(self.model) - assert isinstance(self._optimizer_params, dict), ( - "ObjectTensorDecomp requires dict-form optimizer_params" - ) - return [ - {"params": model.get_params()[key], **self._optimizer_params[key].params()} - for key in model.param_keys - ] + return {key: list(model.get_params()[key]) for key in model.param_keys} def _normalize_optimizer_params(self, params): """ObjectTensorDecomp requires a dict matching model.param_keys.""" diff --git a/tests/ml/test_optimizermixin.py b/tests/ml/test_optimizermixin.py index d829af41..d6347ce6 100644 --- a/tests/ml/test_optimizermixin.py +++ b/tests/ml/test_optimizermixin.py @@ -348,3 +348,106 @@ def test_parse_invalid_name_type_raises(self): def test_parse_default_name_is_none(self): result = SchedulerParams.parse_dict({}) assert isinstance(result, SchedulerParams.NoneScheduler) + + +# ─── OptimizerMixin.set_optimizer / PPLR behavior ─────────────────────────── + +from quantem.core import config # noqa: E402 +from quantem.core.ml.optimizer_mixin import OptimizerMixin # noqa: E402 + +torch = pytest.importorskip("torch") if config.get("has_torch") else None +requires_torch = pytest.mark.skipif( + not config.get("has_torch"), reason="requires torch" +) + + +def _param(value=1.0): + return torch.nn.Parameter(torch.tensor([value])) + + +class _FakeModel(OptimizerMixin): + """Minimal concrete OptimizerMixin for exercising set_optimizer/reset_optimizer. + + ``groups`` is the dict[str, list[Parameter]] returned by get_optimization_parameters. + Pass ``raise_on_params=True`` to prove the disable path short-circuits before the call. + """ + + def __init__(self, groups, raise_on_params=False): + super().__init__() + self._groups = groups + self._raise_on_params = raise_on_params + + def get_optimization_parameters(self): + if self._raise_on_params: + raise AssertionError("get_optimization_parameters should not have been called") + return self._groups + + +@requires_torch +class TestSetOptimizer: + def test_single_optimizer_build(self): + model = _FakeModel({"default": [_param()]}) + model.set_optimizer(OptimizerParams.Adam(lr=1e-3)) + assert model.has_optimizer() + assert isinstance(model.optimizer, torch.optim.Adam) + assert len(model.optimizer.param_groups) == 1 + assert model.optimizer.param_groups[0]["lr"] == 1e-3 + + def test_single_optimizer_dict_shorthand(self): + model = _FakeModel({"default": [_param()]}) + model.set_optimizer({"name": "sgd", "lr": 5e-2, "momentum": 0.9}) + assert isinstance(model.optimizer, torch.optim.SGD) + assert model.optimizer.param_groups[0]["lr"] == 5e-2 + assert model.optimizer.param_groups[0]["momentum"] == 0.9 + + def test_none_optimizer_removes_without_touching_params(self): + # raise_on_params proves the disable path short-circuits before get_optimization_parameters + model = _FakeModel({"default": [_param()]}, raise_on_params=True) + model.set_optimizer(OptimizerParams.NoneOptimizer()) + assert model.optimizer is None + assert not model.has_optimizer() + assert model.optimizer_params == { + OptimizerMixin.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() + } + + def test_multi_group_pplr_applies_per_group_lr(self): + model = _FakeModel({"descan": [_param()], "scan_positions": [_param(2.0)]}) + model.set_optimizer( + {"descan": OptimizerParams.SGD(lr=1e-2), "scan_positions": OptimizerParams.SGD(lr=1e-3)} + ) + groups = model.optimizer.param_groups + assert len(groups) == 2 + # key order is preserved (descan, then scan_positions) + assert groups[0]["lr"] == 1e-2 + assert groups[1]["lr"] == 1e-3 + + def test_key_mismatch_raises(self): + model = _FakeModel({"default": [_param()]}) + with pytest.raises(ValueError, match="do not match"): + model.set_optimizer( + {"descan": OptimizerParams.SGD(lr=1e-2), "scan_positions": OptimizerParams.SGD()} + ) + + def test_mixed_optimizer_classes_raises(self): + model = _FakeModel({"a": [_param()], "b": [_param()]}) + with pytest.raises(ValueError, match="same optimizer type"): + model.set_optimizer( + {"a": OptimizerParams.Adam(lr=1e-3), "b": OptimizerParams.SGD(lr=1e-3)} + ) + + def test_reset_optimizer_on_unconfigured_model_is_noop(self): + model = _FakeModel({"default": [_param()]}) + # fresh model defaults to {"default": NoneOptimizer()} + model.reset_optimizer() + assert model.optimizer is None + assert not model.has_optimizer() + + def test_set_scheduler_base_lr_uses_max_group_lr(self): + model = _FakeModel({"descan": [_param()], "scan_positions": [_param(2.0)]}) + model.set_optimizer( + {"descan": OptimizerParams.SGD(lr=1e-2), "scan_positions": OptimizerParams.SGD(lr=1e-3)} + ) + model.set_scheduler(SchedulerParams.Plateau(), num_iter=10) + assert model.scheduler is not None + # Plateau min_lr defaults to base_LR / 20, with base_LR = max group lr (1e-2) + assert model.scheduler.min_lrs[0] == pytest.approx(1e-2 / 20) From c5f8b37a8e53d0b636955a6a9b9e2d2246ae7479 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Fri, 29 May 2026 15:02:51 -0700 Subject: [PATCH 319/335] Warnings to no optimizations on PtychographyDatasetBase. --- src/quantem/core/ml/optimizer_mixin.py | 28 +++++++++---------- .../diffractive_imaging/dataset_models.py | 11 +++++++- 2 files changed, 24 insertions(+), 15 deletions(-) diff --git a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index 1575276d..f79d7661 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -219,7 +219,7 @@ def parse_dict(cls, d: dict): raise ValueError(f"Unknown optimizer type: {name.lower()}") -OptimizerType = ( +OptimizerParamsType = ( OptimizerParams.Adam | OptimizerParams.AdamW | OptimizerParams.SGD @@ -537,7 +537,7 @@ def __init__(self): """Initialize the optimizer mixin.""" self._optimizer = None self._scheduler = None - self._optimizer_params: dict[str, OptimizerType] = { + self._optimizer_params: dict[str, OptimizerParamsType] = { self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() } self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() @@ -554,31 +554,31 @@ def scheduler(self) -> "torch.optim.lr_scheduler.LRScheduler | None": return self._scheduler @property - def optimizer_params(self) -> dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerParamsType]: """Get the optimizer parameters.""" return self._optimizer_params @optimizer_params.setter def optimizer_params( - self, params: OptimizerType | dict[str, OptimizerType] | dict[str, Any] + self, params: OptimizerParamsType | dict[str, OptimizerParamsType] | dict[str, Any] ) -> None: self._optimizer_params = self._normalize_optimizer_params(params) def _normalize_optimizer_params( - self, params: OptimizerType | dict[str, Any] - ) -> dict[str, OptimizerType]: - """Normalize input to dict[str, OptimizerType]. Subclasses can override to validate keys.""" - # Single optimizer, already an OptimizerType - if isinstance(params, OptimizerType): + self, params: OptimizerParamsType | dict[str, Any] + ) -> dict[str, OptimizerParamsType]: + """Normalize input to dict[str, OptimizerParamsType]. Subclasses can override to validate keys.""" + # Single optimizer, already an OptimizerParamsType + if isinstance(params, OptimizerParamsType): return {self.DEFAULT_OPTIMIZER_KEY: params} if not isinstance(params, dict): - raise TypeError(f"optimizer_params must be OptimizerType or dict, got {type(params)}") + raise TypeError(f"optimizer_params must be OptimizerParamsType or dict, got {type(params)}") # Single optimizer as dict shorthand, e.g. {"name": "adam", "lr": 1e-3} if self._is_single_optimizer_dict(params): return {self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.parse_dict(d=params)} - # dict-of-OptimizerType form (PPLR) + # dict-of-OptimizerParamsType form (PPLR) return { - k: v if isinstance(v, OptimizerType) else OptimizerParams.parse_dict(d=v) + k: v if isinstance(v, OptimizerParamsType) else OptimizerParams.parse_dict(d=v) for k, v in params.items() } @@ -616,11 +616,11 @@ def get_optimization_parameters( """ raise NotImplementedError("Subclasses must implement get_optimization_parameters") - def set_optimizer(self, opt_params: OptimizerType | dict | None = None) -> None: + def set_optimizer(self, opt_params: OptimizerParamsType | dict | None = None) -> None: """ Set the optimizer for this model, supporting per-parameter-group learning rates (PPLR). - ``optimizer_params`` is a ``dict[str, OptimizerType]`` keyed by parameter group. Each + ``optimizer_params`` is a ``dict[str, OptimizerParamsType]`` keyed by parameter group. Each group's spec is joined by key to the tensors returned by ``get_optimization_parameters()`` and its hyperparameters are baked into the corresponding torch param group here. All groups must use the same optimizer class. If every group is a ``NoneOptimizer`` (or there diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index 39895adb..f663f3eb 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -1,3 +1,4 @@ +import warnings from abc import abstractmethod from dataclasses import replace from pathlib import Path @@ -12,7 +13,7 @@ from quantem.core.datastructures.dataset3d import Dataset3d from quantem.core.datastructures.dataset4dstem import Dataset4dstem from quantem.core.io.serialize import AutoSerialize -from quantem.core.ml.optimizer_mixin import OptimizerMixin +from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerParams from quantem.core.utils.utils import electron_wavelength_angstrom, tqdmnd from quantem.core.utils.validators import ( validate_array, @@ -127,6 +128,14 @@ def _normalize_optimizer_params(self, params): ) if on ] + if not learnable and not isinstance(spec, OptimizerParams.NoneOptimizer): + warnings.warn( + f"{type(self).__name__}: an optimizer was requested but nothing is " + "learnable (both learn_descan and learn_scan_positions are False); " + "the optimizer will be removed. Enable learn_descan and/or " + "learn_scan_positions to optimize.", + stacklevel=2, + ) return {key: replace(spec) for key in learnable} if learnable else {} return norm From 18486ee0080eb75aae59bedd0df1027a42d01bdf Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Mon, 1 Jun 2026 15:29:32 +0000 Subject: [PATCH 320/335] chore: update lock file --- uv.lock | 735 +++++++++++++++++++++++++++++--------------------------- 1 file changed, 382 insertions(+), 353 deletions(-) diff --git a/uv.lock b/uv.lock index 716284e6..0e8424aa 100644 --- a/uv.lock +++ b/uv.lock @@ -532,101 +532,101 @@ wheels = [ [[package]] name = "coverage" -version = "7.14.0" -source = { registry = "https://pypi.org/simple" } -sdist = { url = "https://files.pythonhosted.org/packages/23/7f/d0720730a397a999ffc0fd3f5bebef347338e3a47b727da66fbb228e2ff2/coverage-7.14.0.tar.gz", hash = "sha256:057a6af2f160a85384cde4ab36f0d2777bae1057bae255f95413cdd382aa5c74", size = 919489, upload-time = "2026-05-10T18:02:31.397Z" } -wheels = [ 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a/src/quantem/core/ml/optimizer_mixin.py b/src/quantem/core/ml/optimizer_mixin.py index f79d7661..ece31dff 100644 --- a/src/quantem/core/ml/optimizer_mixin.py +++ b/src/quantem/core/ml/optimizer_mixin.py @@ -514,7 +514,7 @@ def parse_dict(cls, d: dict): raise ValueError(f"Unknown scheduler type: {name}") -SchedulerType = ( +SchedulerParamsType = ( SchedulerParams.Plateau | SchedulerParams.Exponential | SchedulerParams.Cyclic @@ -540,7 +540,7 @@ def __init__(self): self._optimizer_params: dict[str, OptimizerParamsType] = { self.DEFAULT_OPTIMIZER_KEY: OptimizerParams.NoneOptimizer() } - self._scheduler_params: SchedulerType = SchedulerParams.NoneScheduler() + self._scheduler_params: SchedulerParamsType = SchedulerParams.NoneScheduler() # Don't call super().__init__() in mixin classes to avoid MRO issues @property @@ -587,17 +587,17 @@ def _is_single_optimizer_dict(d: dict) -> bool: return "type" in d or "name" in d @property - def scheduler_params(self) -> SchedulerType: + def scheduler_params(self) -> SchedulerParamsType: """Get the scheduler parameters.""" return self._scheduler_params @scheduler_params.setter - def scheduler_params(self, params: SchedulerType | dict): + def scheduler_params(self, params: SchedulerParamsType | dict): """Set the scheduler parameters.""" if isinstance(params, dict): params = SchedulerParams.parse_dict(d=params) - if not isinstance(params, SchedulerType): - raise TypeError(f"scheduler parameters must be a SchedulerType, got {type(params)}") + if not isinstance(params, SchedulerParamsType): + raise TypeError(f"scheduler parameters must be a SchedulerParamsType, got {type(params)}") self._scheduler_params = params @abstractmethod @@ -688,7 +688,7 @@ def _build_optimizer(self, opt_params, param_groups) -> "torch.optim.Optimizer": raise NotImplementedError(f"Unknown optimizer type: {opt_params}") def set_scheduler( - self, scheduler_params: SchedulerType | dict | None = None, num_iter: int | None = None + self, scheduler_params: SchedulerParamsType | dict | None = None, num_iter: int | None = None ) -> None: """Set the scheduler for this model.""" if scheduler_params is not None: diff --git a/src/quantem/diffractive_imaging/dataset_models.py b/src/quantem/diffractive_imaging/dataset_models.py index f663f3eb..751c9245 100644 --- a/src/quantem/diffractive_imaging/dataset_models.py +++ b/src/quantem/diffractive_imaging/dataset_models.py @@ -113,7 +113,7 @@ def get_optimization_parameters(self) -> "dict[str, list[torch.Tensor]]": def _normalize_optimizer_params(self, params): """Broadcast a single optimizer spec to the learnable descan/scan_position groups. - A single ``OptimizerType`` / single-optimizer dict (normalized to the ``"default"`` key) + A single ``OptimizerParamsType`` / single-optimizer dict (normalized to the ``"default"`` key) is fanned out to whichever groups are currently learnable, so the common single-LR caller keeps working. An explicit PPLR dict (keyed by ``descan``/``scan_positions``) passes through. """ diff --git a/src/quantem/diffractive_imaging/object_models.py b/src/quantem/diffractive_imaging/object_models.py index 3b74319b..d311d8dd 100644 --- a/src/quantem/diffractive_imaging/object_models.py +++ b/src/quantem/diffractive_imaging/object_models.py @@ -1,7 +1,7 @@ import math from abc import abstractmethod from copy import deepcopy -from typing import Any, Callable, Literal, Self, Sequence, cast +from typing import Callable, Literal, Self, Sequence, cast from warnings import warn import matplotlib.pyplot as plt @@ -14,7 +14,11 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import ( + OptimizerMixin, + OptimizerParamsType, + SchedulerParamsType, +) from quantem.core.utils.rng import RNGMixin from quantem.core.utils.validators import ( validate_arr_gt, @@ -1046,8 +1050,8 @@ def pretrain( pretrain_target: torch.Tensor | None = None, reset: bool = False, num_iters: int = 100, - optimizer_params: dict | OptimizerType | None = None, - scheduler_params: dict | SchedulerType | None = None, + optimizer_params: dict | OptimizerParamsType | None = None, + scheduler_params: dict | SchedulerParamsType | None = None, loss_fn: Callable | str = "l2", apply_constraints: bool = False, show: bool = True, diff --git a/src/quantem/diffractive_imaging/probe_models.py b/src/quantem/diffractive_imaging/probe_models.py index 6b00793a..af638b85 100644 --- a/src/quantem/diffractive_imaging/probe_models.py +++ b/src/quantem/diffractive_imaging/probe_models.py @@ -15,7 +15,11 @@ from quantem.core.io.serialize import AutoSerialize from quantem.core.ml.blocks import reset_weights from quantem.core.ml.loss_functions import get_loss_module -from quantem.core.ml.optimizer_mixin import OptimizerMixin, OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import ( + OptimizerMixin, + OptimizerParamsType, + SchedulerParamsType, +) from quantem.core.utils.rng import RNGMixin from quantem.core.utils.utils import electron_wavelength_angstrom, to_numpy from quantem.core.utils.validators import ( @@ -1355,8 +1359,8 @@ def pretrain( pretrain_target: torch.Tensor | None = None, reset: bool = False, num_iters: int = 100, - optimizer_params: dict | OptimizerType | None = None, - scheduler_params: dict | SchedulerType | None = None, + optimizer_params: dict | OptimizerParamsType | None = None, + scheduler_params: dict | SchedulerParamsType | None = None, loss_fn: Callable | str = "l2", apply_constraints: bool = False, show: bool = True, diff --git a/src/quantem/diffractive_imaging/ptychography_opt.py b/src/quantem/diffractive_imaging/ptychography_opt.py index c18150e0..ff1b3a2c 100644 --- a/src/quantem/diffractive_imaging/ptychography_opt.py +++ b/src/quantem/diffractive_imaging/ptychography_opt.py @@ -4,9 +4,9 @@ from quantem.core import config from quantem.core.ml.optimizer_mixin import ( OptimizerParams, - OptimizerType, + OptimizerParamsType, SchedulerParams, - SchedulerType, + SchedulerParamsType, ) from quantem.diffractive_imaging.ptychography_base import PtychographyBase @@ -23,7 +23,7 @@ class PtychographyOpt(PtychographyBase): """ OPTIMIZABLE_VALS = ["object", "probe", "dataset"] - DEFAULT_OPTIMIZER_TYPE: OptimizerType = OptimizerParams.Adam() + DEFAULT_OPTIMIZER_TYPE: OptimizerParamsType = OptimizerParams.Adam() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -42,7 +42,7 @@ def _get_default_lr(self, key: str) -> float: # region --- explicit properties and setters --- @property - def optimizer_params(self) -> dict[str, OptimizerType]: + def optimizer_params(self) -> dict[str, OptimizerParamsType | dict[str, OptimizerParamsType]]: return { key: params for key, params in [ @@ -56,7 +56,7 @@ def optimizer_params(self) -> dict[str, OptimizerType]: @optimizer_params.setter def optimizer_params(self, d: dict) -> None: """ - Takes a dictionary mapping optimizable keys to either an ``OptimizerType`` + Takes a dictionary mapping optimizable keys to either an ``OptimizerParamsType`` dataclass or a plain dict (with optional ``"name"``/``"type"`` and ``"lr"`` keys). Missing ``"name"`` / ``"lr"`` are filled from ``DEFAULT_OPTIMIZER_TYPE`` and ``_get_default_lr`` respectively. @@ -71,7 +71,7 @@ def optimizer_params(self, d: dict) -> None: d = {k: {} for k in d} for k, v in d.items(): - if isinstance(v, OptimizerType): + if isinstance(v, OptimizerParamsType): pass # already a dataclass, pass through elif isinstance(v, dict): if not v: @@ -82,7 +82,7 @@ def optimizer_params(self, d: dict) -> None: if "lr" not in v: v["lr"] = self._get_default_lr(k) else: - raise TypeError(f"Expected OptimizerType or dict for key '{k}', got {type(v)}") + raise TypeError(f"Expected OptimizerParamsType or dict for key '{k}', got {type(v)}") if k == "object": self.obj_model.optimizer_params = v @@ -131,7 +131,7 @@ def remove_optimizer(self, key: str) -> None: self.dset.remove_optimizer() @property - def scheduler_params(self) -> dict[str, SchedulerType]: + def scheduler_params(self) -> dict[str, SchedulerParamsType]: """Returns the parameters used to set the schedulers.""" return { "object": self.obj_model.scheduler_params, @@ -142,7 +142,7 @@ def scheduler_params(self) -> dict[str, SchedulerType]: @scheduler_params.setter def scheduler_params(self, d: dict) -> None: """ - Takes a dictionary mapping optimizable keys to either a ``SchedulerType`` + Takes a dictionary mapping optimizable keys to either a ``SchedulerParamsType`` dataclass or a plain dict. Keys not present in ``d`` are set to ``SchedulerParams.NoneScheduler()`` (disables scheduling for that model). @@ -178,7 +178,7 @@ def schedulers(self) -> dict[str, "torch.optim.lr_scheduler._LRScheduler"]: schedulers["dataset"] = self.dset.scheduler return schedulers - def set_schedulers(self, params: dict[str, SchedulerType], num_iter: int | None = None): + def set_schedulers(self, params: dict[str, SchedulerParamsType], num_iter: int | None = None): """Set schedulers for each model.""" for key, scheduler_params in params.items(): if key not in self.OPTIMIZABLE_VALS: diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index 12e8a99c..c6b85b64 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -1033,7 +1033,7 @@ def _normalize_optimizer_params(self, params): """ObjectTensorDecomp requires a dict matching model.param_keys.""" if not isinstance(params, dict) or self._is_single_optimizer_dict(params): raise TypeError( - f"ObjectTensorDecomp requires dict[str, OptimizerType] keyed by " + f"ObjectTensorDecomp requires dict[str, OptimizerParamsType] keyed by " f"param_keys; got {type(params)}" ) model = _unwrap(self.model) diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index 2c913277..cf990379 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -2,7 +2,11 @@ import torch -from quantem.core.ml.optimizer_mixin import OptimizerType, SchedulerType +from quantem.core.ml.optimizer_mixin import ( + OptimizerParams, + OptimizerParamsType, + SchedulerParamsType, +) from quantem.tomography.tomography_base import TomographyBase @@ -12,7 +16,7 @@ class TomographyOpt(TomographyBase): """ OPTIMIZABLE_VALS = ["object", "pose"] - DEFAULT_OPTIMIZER_TYPE = "adam" + DEFAULT_OPTIMIZER_TYPE: OptimizerParamsType = OptimizerParams.Adam() def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -27,7 +31,7 @@ def _get_default_lr(self, key: str) -> float: raise ValueError(f"Unknown optimization key: {key}") @property - def optimizer_params(self) -> dict[str, OptimizerType | dict[str, OptimizerType]]: + def optimizer_params(self) -> dict[str, OptimizerParamsType | dict[str, OptimizerParamsType]]: return { key: params for key, params in [ @@ -38,7 +42,7 @@ def optimizer_params(self) -> dict[str, OptimizerType | dict[str, OptimizerType] } @optimizer_params.setter - def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): + def optimizer_params(self, d: dict[str, OptimizerParamsType] | dict[str, dict]): """Set the optimizer parameters.""" if isinstance(d, (tuple, list)): d = {k: {} for k in d} @@ -52,7 +56,7 @@ def optimizer_params(self, d: dict[str, OptimizerType] | dict[str, dict]): if k not in targets: raise ValueError(f"Unknown optimization key: {k}") - # if not isinstance(v, OptimizerType): + # if not isinstance(v, OptimizerParamsType): # v = OptimizerParams.parse_dict(v) targets[k].optimizer_params = v @@ -100,7 +104,7 @@ def remove_optimizer(self, key: str): raise ValueError(f"Unknown optimization key: {key}") @property - def scheduler_params(self) -> dict[str, SchedulerType]: + def scheduler_params(self) -> dict[str, SchedulerParamsType]: """Returns the parameters used to set the schedulers.""" return { "object": self.obj_model.scheduler_params, @@ -136,7 +140,7 @@ def schedulers(self) -> dict[str, torch.optim.lr_scheduler._LRScheduler]: return schedulers def set_schedulers( - self, params: Mapping[str, SchedulerType | dict], num_iter: int | None = None + self, params: Mapping[str, SchedulerParamsType | dict], num_iter: int | None = None ): for key, scheduler_params in params.items(): if key == "object": From 14a146349e8fe23af1479f1be919a1b6d66a3138 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 1 Jun 2026 13:43:55 -0700 Subject: [PATCH 322/335] Tomography tests, and bug-fix on get_optimization_parameters in TomographyDatasetBase. --- src/quantem/tomography/dataset_models.py | 15 +- tests/tomography/conftest.py | 72 ++++++++ tests/tomography/test_dataset_models.py | 106 ++++++++++++ tests/tomography/test_object_models.py | 159 ++++++++++++++++++ tests/tomography/test_radon.py | 111 ++++++++++++ .../test_tomography_conventional.py | 72 ++++++++ tests/tomography/test_tomography_inr.py | 96 +++++++++++ tests/tomography/test_tomography_opt.py | 143 ++++++++++++++++ tests/tomography/test_utils.py | 78 +++++++++ 9 files changed, 843 insertions(+), 9 deletions(-) create mode 100644 tests/tomography/conftest.py create mode 100644 tests/tomography/test_dataset_models.py create mode 100644 tests/tomography/test_object_models.py create mode 100644 tests/tomography/test_radon.py create mode 100644 tests/tomography/test_tomography_conventional.py create mode 100644 tests/tomography/test_tomography_inr.py create mode 100644 tests/tomography/test_tomography_opt.py create mode 100644 tests/tomography/test_utils.py diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index bdd2e66a..4838395d 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -232,16 +232,13 @@ def from_data( # --- Optimization Parameters --- - def get_optimization_parameters(self) -> list[dict[str, Any]]: - """ - Get the parameters that should be optimized for this model, - wrapped in a single param group. + def get_optimization_parameters(self) -> dict[str, list[torch.Tensor]]: + """Single param group keyed by DEFAULT_OPTIMIZER_KEY. + + Hyperparameters are baked by ``set_optimizer``, not here — return only the tensors, + matching the ``dict[str, list[tensor]]`` contract the object models use. """ - if isinstance(self._optimizer_params, dict): - opt = next(iter(self._optimizer_params.values())) - else: - opt = self._optimizer_params - return [{"params": list(self.parameters()), **opt.params()}] + return {self.DEFAULT_OPTIMIZER_KEY: list(self.parameters())} # --- Forward pass --- @abstractmethod diff --git a/tests/tomography/conftest.py b/tests/tomography/conftest.py new file mode 100644 index 00000000..06957095 --- /dev/null +++ b/tests/tomography/conftest.py @@ -0,0 +1,72 @@ +"""Shared fixtures and markers for the tomography test suite. + +The suite is split into three tiers (see the plan / individual modules): + +* CPU, always-on -- radon, utils, object/dataset models, optimizer-param wiring. +* CPU, slow -- conventional SIRT/FBP reconstruction (``--runslow``). +* GPU, slow -- INR / KPlanes reconstruction (``requires_gpu`` + ``--runslow``). + +``torch`` and ``scikit-image`` are core dependencies of quantem, so they are always +importable; ``requires_torch`` is kept only for parity with the existing test style. The +meaningful gate is ``requires_gpu`` (CI runs CPU-only) combined with ``@pytest.mark.slow``. +""" + +import numpy as np +import pytest +import torch + +from quantem.core import config +from quantem.tomography.utils import rot_ZXZ + +# --- Markers --------------------------------------------------------------- +requires_torch = pytest.mark.skipif(not config.get("has_torch"), reason="requires torch") +requires_gpu = pytest.mark.skipif(not torch.cuda.is_available(), reason="requires a CUDA device") + + +@pytest.fixture +def torch_device() -> str: + """Device for torch-only tests. + + ``cuda:0`` when available, else ``cpu``. Note: object models that go through + ``setup_distributed`` must be built on ``cuda`` when CUDA is present (the CPU path is + only valid when no CUDA device exists), so torch-only construction tests follow this + fixture rather than hard-coding ``"cpu"``. + """ + return "cuda:0" if torch.cuda.is_available() else "cpu" + + +# --- Synthetic data -------------------------------------------------------- +def make_tilt_series(volume: torch.Tensor, angles: np.ndarray) -> np.ndarray: + """Project a volume into a tilt series with quantem's own forward model. + + Mirrors ``tomography_00_generate_phantom``: rotate by ``rot_ZXZ`` (Euler ZXZ, tilt on + the X axis) then sum along the beam axis. Using this rather than ``radon_torch`` keeps + the synthetic data consistent with the geometry the reconstructors assume. + """ + projections = [] + vol = volume.unsqueeze(0) # (1, Z, Y, X) + for angle in angles: + rotated = rot_ZXZ(vol, 0.0, float(angle), 0.0, device="cpu") + projections.append(rotated[0].sum(0)) + return torch.stack(projections).numpy().astype(np.float32) + + +@pytest.fixture(scope="module") +def phantom_volume() -> np.ndarray: + """Small deterministic (32, 32, 32) phantom with a couple of solid blocks.""" + vol = np.zeros((32, 32, 32), dtype=np.float32) + vol[8:24, 10:20, 12:22] = 1.0 + vol[18:26, 6:12, 16:24] = 0.6 + return vol + + +@pytest.fixture(scope="module") +def tilt_angles() -> np.ndarray: + """Nine tilt angles spanning -70..70 degrees.""" + return np.linspace(-70, 70, 9).astype(np.float32) + + +@pytest.fixture(scope="module") +def tilt_series(phantom_volume: np.ndarray, tilt_angles: np.ndarray) -> np.ndarray: + """Synthetic tilt series, shape (n_angles, 32, 32).""" + return make_tilt_series(torch.from_numpy(phantom_volume), tilt_angles) diff --git a/tests/tomography/test_dataset_models.py b/tests/tomography/test_dataset_models.py new file mode 100644 index 00000000..78ded427 --- /dev/null +++ b/tests/tomography/test_dataset_models.py @@ -0,0 +1,106 @@ +"""Tests for ``quantem.tomography.dataset_models``. + +Covers constraint parsing, the pixelated dataset (validation, normalisation, tilt-angle +convention, pose-parameter materialisation) and the INR / pretrain datasets. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.dataset_models import ( + DatasetConstraintParams, + DatasetValue, + TomographyINRDataset, + TomographyINRPretrainDataset, + TomographyPixDataset, +) + +from .conftest import requires_torch + + +class TestDatasetConstraintParse: + def test_parse_base_by_name(self): + c = DatasetConstraintParams.parse_dict({"name": "base_tomography_dataset", "tv_zs": 0.1}) + assert isinstance(c, DatasetConstraintParams.BaseTomographyDatasetConstraints) + assert c.tv_zs == 0.1 + + def test_parse_base_by_type_key(self): + c = DatasetConstraintParams.parse_dict( + {"type": "base_tomography_dataset", "tv_shifts": 0.2} + ) + assert c.tv_shifts == 0.2 + + def test_unknown_name_raises(self): + with pytest.raises(ValueError): + DatasetConstraintParams.parse_dict({"name": "nope"}) + + +def _stack(nang=5, n=12, seed=0): + rng = np.random.default_rng(seed) + return (rng.random((nang, n, n)) * 10).astype(np.float32) + + +class TestTomographyPixDataset: + def test_wrong_projection_axis_raises(self): + # projections must live on axis 0 (i.e. fewer than the image dims). + bad = np.zeros((20, 5, 5), dtype=np.float32) + with pytest.raises(ValueError): + TomographyPixDataset.from_data(bad, np.linspace(-60, 60, 20).astype(np.float32)) + + def test_tilt_angles_are_negated(self): + angles = np.linspace(-40, 60, 5).astype(np.float32) + d = TomographyPixDataset.from_data(_stack(), angles) + np.testing.assert_allclose(d.tilt_angles.numpy(), -angles, atol=1e-5) + + def test_normalised_by_95th_quantile(self): + d = TomographyPixDataset.from_data(_stack(), np.linspace(-60, 60, 5).astype(np.float32)) + q95 = torch.quantile(d.tilt_stack, 0.95) + assert torch.isclose(q95, torch.tensor(1.0), atol=1e-4) + + def test_reference_idx_and_learnable_tilts(self): + # negated angles -> [40, 15, -10, -35, -60]; smallest |angle| is index 2. + angles = np.linspace(-40, 60, 5).astype(np.float32) + d = TomographyPixDataset.from_data(_stack(), angles) + assert d.reference_tilt_idx == 2 + assert d.learnable_tilts == 4 + + def test_forward_returns_dataset_value(self): + angles = np.linspace(-40, 60, 5).astype(np.float32) + d = TomographyPixDataset.from_data(_stack(nang=5, n=12), angles) + out = d.forward(0) + assert isinstance(out, DatasetValue) + assert out.target.shape == (12, 12) + assert out.tilt_angle == pytest.approx(float(-angles[0])) + + def test_to_materialises_pose_parameters(self): + d = TomographyPixDataset.from_data(_stack(), np.linspace(-60, 60, 5).astype(np.float32)) + d.to("cpu") + assert isinstance(d.z1_params, torch.nn.Parameter) + assert d.shifts_params.shape == (d.learnable_tilts, 2) + + +@requires_torch +class TestTomographyINRDataset: + def test_len_is_projections_times_pixels(self): + d = TomographyINRDataset.from_data( + _stack(nang=5, n=12), np.linspace(-60, 60, 5, dtype="f4") + ) + assert len(d) == 5 * 12 * 12 + + def test_getitem_keys(self): + d = TomographyINRDataset.from_data( + _stack(nang=5, n=12), np.linspace(-60, 60, 5, dtype="f4") + ) + item = d[0] + assert {"phi", "pixel_i", "pixel_j", "projection_idx", "target_value"} <= set(item.keys()) + + +class TestTomographyINRPretrainDataset: + def test_len_and_getitem(self): + vol = torch.rand(1, 8, 8, 8) + ds = TomographyINRPretrainDataset(pretrain_target=vol) + assert len(ds) == 8**3 + item = ds[0] + assert set(item.keys()) == {"coords", "target"} + assert item["coords"].shape == (3,) diff --git a/tests/tomography/test_object_models.py b/tests/tomography/test_object_models.py new file mode 100644 index 00000000..f9f1922c --- /dev/null +++ b/tests/tomography/test_object_models.py @@ -0,0 +1,159 @@ +"""Tests for ``quantem.tomography.object_models``. + +The constraint-parsing and ``ObjectPixelated`` tests are pure CPU. The INR / tensor-decomp +construction tests are ``requires_torch`` and follow the ``torch_device`` fixture (they must +be built on CUDA when CUDA is present; see conftest). +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.object_models import ( + ObjConstraintParams, + ObjectBase, + ObjectINR, + ObjectPixelated, + ObjectTensorDecomp, +) +from quantem.tomography.tomography_context import ReconstructionContext + +from .conftest import requires_torch + + +class TestObjConstraintParse: + def test_parse_pixelated_by_name(self): + c = ObjConstraintParams.parse_dict({"name": "obj_pixelated", "tv_vol": 0.01}) + assert isinstance(c, ObjConstraintParams.ObjPixelatedConstraints) + assert c.tv_vol == 0.01 + + def test_parse_inr_by_type_key(self): + c = ObjConstraintParams.parse_dict({"type": "obj_inr", "sparsity": 0.05}) + assert isinstance(c, ObjConstraintParams.ObjINRConstraints) + assert c.sparsity == 0.05 + + def test_parse_tensor_decomp(self): + c = ObjConstraintParams.parse_dict({"name": "obj_tensor_decomp", "tv_plane": 0.1}) + assert isinstance(c, ObjConstraintParams.ObjTensorDecompConstraints) + assert c.tv_plane == 0.1 + + def test_missing_name_raises(self): + with pytest.raises(ValueError): + ObjConstraintParams.parse_dict({"tv_vol": 0.1}) + + def test_unknown_name_raises(self): + with pytest.raises(ValueError): + ObjConstraintParams.parse_dict({"name": "obj_nope"}) + + def test_constraint_key_partitions(self): + c = ObjConstraintParams.ObjPixelatedConstraints() + assert "positivity" in c.hard_constraint_keys + assert "tv_vol" in c.soft_constraint_keys + + +class TestObjectPixelatedConstruction: + def test_from_uniform_is_zeros(self): + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + assert obj.shape == (8, 8, 8) + assert torch.allclose(obj.obj, torch.zeros(8, 8, 8)) + assert obj.obj_type == "pixelated" + + def test_from_array_numpy(self): + arr = np.random.default_rng(0).random((6, 6, 6)).astype(np.float32) + obj = ObjectPixelated.from_array(arr, device="cpu") + assert obj.shape == (6, 6, 6) + assert torch.allclose(obj.obj, torch.from_numpy(arr)) + assert obj.dtype == torch.float32 + + def test_from_array_torch_is_copied(self): + t = torch.ones(4, 4, 4) + obj = ObjectPixelated.from_array(t, device="cpu") + t += 5.0 + assert torch.allclose(obj.obj, torch.ones(4, 4, 4)) # original copy untouched + + def test_obj_view_shape(self): + obj = ObjectPixelated.from_uniform(shape=(5, 6, 7), device="cpu") + assert obj.obj_view.shape == (1, 5, 6, 7) + + def test_forward_returns_obj(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), 2.0), device="cpu") + assert torch.allclose(obj.forward(), obj.obj) + + +class TestObjectPixelatedConstraints: + def test_positivity_clamps_negatives(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), -1.0), device="cpu") + obj.constraints.positivity = True + assert torch.all(obj.obj >= 0.0) + + def test_positivity_off_keeps_negatives(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), -1.0), device="cpu") + obj.constraints.positivity = False + assert torch.all(obj.obj < 0.0) + + def test_shrinkage_subtracts_then_floors(self): + obj = ObjectPixelated.from_array(torch.full((4, 4, 4), 1.0), device="cpu") + obj.constraints.positivity = False + obj.constraints.shrinkage = 0.25 + assert torch.allclose(obj.obj, torch.full((4, 4, 4), 0.75)) + + def test_tv_loss_scales_with_weight(self): + ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + obj.constraints.tv_vol = 1.0 + loss1 = obj.get_tv_loss(ctx) + obj.constraints.tv_vol = 2.0 + loss2 = obj.get_tv_loss(ctx) + assert torch.isclose(loss2, 2.0 * loss1) + + def test_soft_constraint_zero_when_tv_off(self): + ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + obj.constraints.tv_vol = 0.0 + assert float(obj.apply_soft_constraints(ctx).detach()) == 0.0 + + +class TestFactoryGuard: + def test_objectbase_requires_token(self): + with pytest.raises(RuntimeError): + ObjectBase(shape=(4, 4, 4)) + + +@requires_torch +class TestObjectINR: + def test_from_model_builds(self, torch_device): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(16, 16, 16), device=torch_device) + assert obj.shape == (16, 16, 16) + assert obj.model is not None + + def test_optimization_parameters_single_group(self, torch_device): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(16, 16, 16), device=torch_device) + groups = obj.get_optimization_parameters() + assert list(groups.keys()) == ["default"] + assert len(groups["default"]) > 0 + + +@requires_torch +class TestObjectTensorDecomp: + def _model(self, n=16): + from quantem.core.ml.models.kplanes import KPlanesTILTED + + return KPlanesTILTED( + M_features=2, resolution=(n, n, n), multiscale_res_multipliers=[1], T=2 + ) + + def test_pplr_optimization_parameter_keys(self, torch_device): + obj = ObjectTensorDecomp.from_model(self._model(), shape=(16, 16, 16), device=torch_device) + keys = set(obj.get_optimization_parameters().keys()) + assert keys == {"grids", "sigma_net", "so3"} + + def test_pretrain_not_implemented(self, torch_device): + obj = ObjectTensorDecomp.from_model(self._model(), shape=(16, 16, 16), device=torch_device) + with pytest.raises(NotImplementedError): + obj.pretrain() diff --git a/tests/tomography/test_radon.py b/tests/tomography/test_radon.py new file mode 100644 index 00000000..4f61837a --- /dev/null +++ b/tests/tomography/test_radon.py @@ -0,0 +1,111 @@ +"""Tests for the pure-torch Radon transform (``quantem.tomography.radon.radon``). + +All CPU, deterministic. Cross-checked against scikit-image where a ground truth helps. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.radon.radon import ( + get_fourier_filter_torch, + iradon_torch, + radon_torch, +) + + +def _disk(n: int, cy: int, cx: int, r: int) -> torch.Tensor: + yy, xx = np.mgrid[0:n, 0:n] + return torch.from_numpy((((yy - cy) ** 2 + (xx - cx) ** 2) < r**2).astype(np.float32)) + + +class TestRadonShapes: + def test_2d_input_returns_angles_by_pixels(self): + img = _disk(64, 32, 32, 10) + theta = torch.linspace(0, 180, 30) + sino = radon_torch(img, theta=theta) + assert sino.shape == (30, 64) + + def test_batched_input_returns_batch_angles_pixels(self): + imgs = torch.stack([_disk(48, 24, 20, 8), _disk(48, 24, 28, 8)]) + theta = torch.linspace(0, 180, 20) + sino = radon_torch(imgs, theta=theta) + assert sino.shape == (2, 20, 48) + + def test_default_theta_is_180_angles(self): + sino = radon_torch(_disk(32, 16, 16, 6)) + assert sino.shape == (180, 32) + + def test_iradon_shapes(self): + sino = radon_torch(_disk(40, 20, 20, 8), theta=torch.linspace(0, 180, 25)) + rec = iradon_torch(sino, theta=torch.linspace(0, 180, 25)) + assert rec.shape == (40, 40) + + def test_iradon_output_size_override(self): + sino = radon_torch(_disk(40, 20, 20, 8), theta=torch.linspace(0, 180, 25)) + rec = iradon_torch(sino, theta=torch.linspace(0, 180, 25), output_size=32) + assert rec.shape == (32, 32) + + +class TestFourierFilter: + def test_even_size_ok(self): + f = get_fourier_filter_torch(64, "ramp") + assert f.shape == (1, 64) + + def test_odd_size_raises(self): + with pytest.raises(ValueError): + get_fourier_filter_torch(63, "ramp") + + def test_unknown_filter_raises(self): + with pytest.raises(ValueError): + get_fourier_filter_torch(64, "not-a-filter") + + def test_none_filter_is_all_ones(self): + f = get_fourier_filter_torch(64, None) + assert torch.allclose(f, torch.ones_like(f)) + + @pytest.mark.parametrize("name", ["ramp", "shepp-logan", "cosine", "hamming", "hann"]) + def test_named_filters_run(self, name): + f = get_fourier_filter_torch(64, name) + assert f.shape == (1, 64) + assert torch.isfinite(f).all() + + +class TestRadonBehaviour: + def test_circular_mask_zeros_corners(self): + """The forward transform masks to the inscribed circle, so corner mass is dropped.""" + img = torch.ones(32, 32) + full = img.sum() + sino = radon_torch(img, theta=torch.tensor([0.0])) + # A single 0-degree projection sums columns; total equals the masked mass < full. + assert sino.sum() < full + + def test_iradon_circle_zeros_outside(self): + sino = radon_torch(_disk(48, 24, 24, 10), theta=torch.linspace(0, 180, 30)) + rec = iradon_torch(sino, theta=torch.linspace(0, 180, 30), circle=True) + n = rec.shape[0] + yy, xx = np.mgrid[0:n, 0:n] + outside = ((yy - n // 2) ** 2 + (xx - n // 2) ** 2) > (n // 2) ** 2 + assert torch.allclose(rec[outside], torch.zeros(int(outside.sum()))) + + def test_roundtrip_recovers_structure(self): + disk = _disk(64, 32, 24, 9) + theta = torch.linspace(0, 180, 60) + rec = iradon_torch(radon_torch(disk, theta=theta), theta=theta, filter_name="ramp") + corr = np.corrcoef(disk.numpy().ravel(), rec.numpy().ravel())[0, 1] + assert corr > 0.9 + + +class TestRadonVsSkimage: + """Loose cross-check against scikit-image's reference implementation.""" + + def test_forward_matches_skimage(self): + sk = pytest.importorskip("skimage.transform") + n = 64 + disk = _disk(n, n // 2, n // 2, 12) + theta = np.linspace(0.0, 180.0, 45, endpoint=False).astype(np.float32) + ours = radon_torch(disk, theta=torch.from_numpy(theta)).numpy() # (A, N) + ref = sk.radon(disk.numpy(), theta=theta, circle=True).T # skimage: (N, A) -> (A, N) + # Different interpolation conventions; require strong agreement, not equality. + corr = np.corrcoef(ours.ravel(), ref.ravel())[0, 1] + assert corr > 0.95 diff --git a/tests/tomography/test_tomography_conventional.py b/tests/tomography/test_tomography_conventional.py new file mode 100644 index 00000000..a7cfb5e0 --- /dev/null +++ b/tests/tomography/test_tomography_conventional.py @@ -0,0 +1,72 @@ +"""End-to-end conventional (SIRT / FBP) reconstruction. + +CPU, deterministic, but marked ``slow`` because it runs a (tiny) iterative reconstruction. +Reconstructions are capped at a few iterations: the suite checks behaviour and wiring (loss +decreases, output stays physical) rather than convergence quality, so spatial agreement with +the phantom is only a loose lower bound. +""" + +import numpy as np +import pytest + +from quantem.tomography.dataset_models import TomographyPixDataset +from quantem.tomography.object_models import ObjConstraintParams, ObjectPixelated +from quantem.tomography.tomography import TomographyConventional + +pytestmark = pytest.mark.slow + + +def _build(tilt_series, tilt_angles, n): + dset = TomographyPixDataset.from_data( + tilt_series, tilt_angles, learn_shift=False, learn_tilt_axis=False + ) + obj = ObjectPixelated.from_uniform(shape=(n, n, n), device="cpu") + return TomographyConventional.from_models( + dset=dset, obj_model=obj, device="cpu", verbose=False + ) + + +class TestSIRT: + def test_loss_decreases_and_output_physical(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct( + num_iter=4, + mode="sirt", + obj_constraints=ObjConstraintParams.ObjPixelatedConstraints(positivity=True), + ) + losses = tomo.epoch_losses + assert tomo.num_epochs == 4 + assert losses[-1] < losses[0] + rec = tomo.obj_model.obj.detach().cpu().numpy() + assert np.isfinite(rec).all() + assert rec.min() >= 0.0 # positivity + + def test_recon_correlates_with_phantom(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct( + num_iter=4, + mode="sirt", + obj_constraints=ObjConstraintParams.ObjPixelatedConstraints(positivity=True), + ) + rec = tomo.obj_model.obj.detach().cpu().numpy() + corr = np.corrcoef(rec.ravel(), phantom_volume.ravel())[0, 1] + assert corr > 0.15 # loose: only a handful of iterations + + def test_obj_constraints_accepts_dict(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct(num_iter=2, mode="sirt", obj_constraints={"name": "obj_pixelated"}) + assert tomo.num_epochs == 2 + + +class TestFBP: + def test_fbp_runs_single_epoch(self, phantom_volume, tilt_series, tilt_angles): + n = phantom_volume.shape[0] + tomo = _build(tilt_series, tilt_angles, n) + tomo.reconstruct(num_iter=5, mode="fbp") + # FBP breaks after the first epoch regardless of num_iter. + assert tomo.num_epochs == 1 + rec = tomo.obj_model.obj.detach().cpu().numpy() + assert np.isfinite(rec).all() diff --git a/tests/tomography/test_tomography_inr.py b/tests/tomography/test_tomography_inr.py new file mode 100644 index 00000000..fbd5f1a1 --- /dev/null +++ b/tests/tomography/test_tomography_inr.py @@ -0,0 +1,96 @@ +"""End-to-end INR / KPlanes (tensor-decomposition) reconstruction. + +These exercise the full learned-reconstruction path (model + pose optimisation, autocast, +spawned dataloader workers), so they are gated behind ``requires_gpu`` and ``slow`` and only +run locally with ``--runslow``. Reconstructions are capped at 4 iterations; the assertion is +loss-decreases plus finite output, not convergence quality. + +The ``num_workers=2`` is required, not incidental: ``setup_dataloader`` hard-codes +``multiprocessing_context="spawn"``, which is invalid with ``num_workers=0``. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.dataset_models import TomographyINRDataset +from quantem.tomography.object_models import ObjectTensorDecomp +from quantem.tomography.tomography import Tomography +from quantem.tomography.tomography_lite import TomographyLiteConv, TomographyLiteINR + +from .conftest import make_tilt_series, requires_gpu + +pytestmark = [requires_gpu, pytest.mark.slow] + +DEVICE = "cuda:0" + + +@pytest.fixture(scope="module") +def small_phantom(): + vol = torch.zeros(1, 24, 24, 24) + vol[0, 6:18, 6:14, 8:16] = 1.0 + angles = np.linspace(-60, 60, 7).astype(np.float32) + series = make_tilt_series(vol[0], angles) + return series, angles + + +class TestLiteINR: + def test_reconstruct_reduces_loss(self, small_phantom): + series, angles = small_phantom + tomo = TomographyLiteINR.from_dataset( + tilt_series=series, tilt_angles=angles, device=DEVICE + ) + tomo.reconstruct(num_iter=4, num_workers=2, batch_size=256, learn_pose=True) + losses = tomo.epoch_losses + assert len(losses) == 4 + assert losses[-1] < losses[0] + view = tomo.obj_model.obj_view + assert view.shape == (1, 24, 24, 24) + assert np.isfinite(view).all() + + +class TestLiteConv: + def test_smoke(self, small_phantom): + series, angles = small_phantom + tomo = TomographyLiteConv.from_dataset( + tilt_series=series, tilt_angles=angles, device=DEVICE + ) + tomo.reconstruct(num_iter=3, mode="sirt") + assert tomo.num_epochs == 3 + assert np.isfinite(tomo.obj_model.obj.detach().cpu().numpy()).all() + + +class TestKPlanes: + def test_pplr_reconstruct_reduces_loss(self, small_phantom): + from quantem.core.ml.models.kplanes import KPlanesTILTED + from quantem.core.ml.optimizer_mixin import OptimizerParams, SchedulerParams + + series, angles = small_phantom + n = series.shape[1] + model = KPlanesTILTED( + M_features=2, resolution=(n, n, n), multiscale_res_multipliers=[1], T=2 + ) + obj = ObjectTensorDecomp.from_model(model, shape=(n, n, n), device=DEVICE) + dset = TomographyINRDataset.from_data(series, angles) + tomo = Tomography.from_models(dset=dset, obj_model=obj, device=DEVICE, verbose=False) + tomo.reconstruct( + optimizer_params={ + "object": { + "grids": OptimizerParams.Adam(lr=1e-2), + "sigma_net": OptimizerParams.Adam(lr=1e-3), + "so3": OptimizerParams.Adam(lr=1e-2), + }, + "pose": OptimizerParams.Adam(lr=1e-2), + }, + scheduler_params={ + "object": SchedulerParams.CosineAnnealing(T_max=4), + "pose": SchedulerParams.CosineAnnealing(T_max=4), + }, + num_iter=4, + batch_size=256, + num_samples_per_ray=20, + num_workers=2, + ) + losses = tomo.epoch_losses + assert len(losses) == 4 + assert losses[-1] < losses[0] diff --git a/tests/tomography/test_tomography_opt.py b/tests/tomography/test_tomography_opt.py new file mode 100644 index 00000000..4ec56a13 --- /dev/null +++ b/tests/tomography/test_tomography_opt.py @@ -0,0 +1,143 @@ +"""Tests for the tomography optimizer / scheduler wiring (``TomographyOpt``). + +This is the surface the PPLR ``OptimizerParamsType`` / ``SchedulerParamsType`` refactor +touched. In particular, ``test_set_optimizers_builds_object_and_pose`` and the PPLR test +regression-guard the pose-optimizer path: ``TomographyDatasetBase.get_optimization_parameters`` +must return a ``dict[str, list[tensor]]`` (it previously returned a ``list`` and crashed +``set_optimizer`` with ``TypeError: unhashable type: 'dict'``). + +Construction only -- no forward passes -- so these run on CPU under CI. +""" + +import numpy as np +import pytest + +from quantem.core.ml.optimizer_mixin import OptimizerParams, SchedulerParams +from quantem.tomography.dataset_models import TomographyINRDataset +from quantem.tomography.object_models import ObjectINR, ObjectTensorDecomp +from quantem.tomography.tomography import Tomography + +from .conftest import requires_torch + + +def _tilts(nang=5, n=12): + rng = np.random.default_rng(0) + angles = np.linspace(-60, 60, nang).astype(np.float32) + stack = rng.random((nang, n, n)).astype(np.float32) + return stack, angles + + +def _inr_tomography(device): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(16, 16, 16), device=device) + stack, angles = _tilts() + dset = TomographyINRDataset.from_data(stack, angles) + return Tomography.from_models(dset=dset, obj_model=obj, device=device, verbose=False) + + +def _td_tomography(device): + from quantem.core.ml.models.kplanes import KPlanesTILTED + + model = KPlanesTILTED( + M_features=2, resolution=(16, 16, 16), multiscale_res_multipliers=[1], T=2 + ) + obj = ObjectTensorDecomp.from_model(model, shape=(16, 16, 16), device=device) + stack, angles = _tilts() + dset = TomographyINRDataset.from_data(stack, angles) + return Tomography.from_models(dset=dset, obj_model=obj, device=device, verbose=False) + + +@requires_torch +class TestOptimizerParams: + def test_setter_getter_roundtrip(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + assert set(tomo.optimizer_params.keys()) == {"object", "pose"} + + def test_unknown_key_raises(self, torch_device): + tomo = _inr_tomography(torch_device) + with pytest.raises(ValueError): + tomo.optimizer_params = {"banana": OptimizerParams.Adam(lr=1e-3)} + + def test_set_optimizers_builds_object_and_pose(self, torch_device): + """Regression guard: the pose path must not raise (see module docstring).""" + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + assert set(tomo.optimizers.keys()) == {"object", "pose"} + + def test_current_lrs(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + lrs = tomo.get_current_lrs() + assert set(lrs.keys()) == {"object", "pose"} + assert lrs["object"] == pytest.approx(1e-3) + assert lrs["pose"] == pytest.approx(1e-2) + + def test_remove_optimizer(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + tomo.remove_optimizer("object") + assert "object" not in tomo.optimizers + assert "pose" in tomo.optimizers + + def test_pplr_object_groups(self, torch_device): + """Per-parameter LR: object optimizer carries one torch param group per key.""" + tomo = _td_tomography(torch_device) + tomo.optimizer_params = { + "object": { + "grids": OptimizerParams.Adam(lr=1e-2), + "sigma_net": OptimizerParams.Adam(lr=1e-3), + "so3": OptimizerParams.Adam(lr=1e-2), + }, + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + assert len(tomo.optimizers["object"].param_groups) == 3 + assert "pose" in tomo.optimizers + + +@requires_torch +class TestSchedulerParams: + def test_scheduler_setter_getter(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.scheduler_params = { + "object": SchedulerParams.CosineAnnealing(T_max=10), + "pose": SchedulerParams.CosineAnnealing(T_max=10), + } + assert set(tomo.scheduler_params.keys()) == {"object", "pose"} + + def test_set_schedulers_builds(self, torch_device): + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + tomo.scheduler_params = { + "object": SchedulerParams.CosineAnnealing(T_max=10), + "pose": SchedulerParams.CosineAnnealing(T_max=10), + } + tomo.set_schedulers(tomo.scheduler_params, num_iter=10) + assert set(tomo.schedulers.keys()) == {"object", "pose"} + + def test_bad_scheduler_type_raises(self, torch_device): + tomo = _inr_tomography(torch_device) + with pytest.raises(TypeError): + tomo.obj_model.scheduler_params = 123 diff --git a/tests/tomography/test_utils.py b/tests/tomography/test_utils.py new file mode 100644 index 00000000..1fa6a47c --- /dev/null +++ b/tests/tomography/test_utils.py @@ -0,0 +1,78 @@ +"""Tests for ``quantem.tomography.utils``: 1D total-variation loss and the +differentiable ZXZ rotation operators. All CPU.""" + +import pytest +import torch + +from quantem.tomography.utils import ( + differentiable_rotz_vectorized, + rot_ZXZ, + tv_loss_1d, +) + + +class TestTVLoss1D: + def test_constant_input_is_zero(self): + assert tv_loss_1d(torch.ones(10)) == 0.0 + + def test_known_value_mean(self): + # diffs are [1, 1, 1], abs-mean = 1.0 + x = torch.tensor([0.0, 1.0, 2.0, 3.0]) + assert torch.isclose(tv_loss_1d(x, reduction="mean"), torch.tensor(1.0)) + + def test_known_value_sum(self): + x = torch.tensor([0.0, 1.0, 2.0, 3.0]) + assert torch.isclose(tv_loss_1d(x, reduction="sum"), torch.tensor(3.0)) + + def test_reduction_none_shape(self): + x = torch.zeros(2, 5) + out = tv_loss_1d(x, reduction="none") + assert out.shape == (2, 4) + + def test_bad_reduction_raises(self): + with pytest.raises(ValueError): + tv_loss_1d(torch.zeros(4), reduction="median") + + +def _block_volume(n: int = 16) -> torch.Tensor: + """(1, n, n, n) volume with an off-centre block so rotations are detectable.""" + vol = torch.zeros(1, n, n, n) + vol[0, 4:12, 4:10, 5:11] = 1.0 + return vol + + +class TestRotations: + def test_zero_rotation_is_identity(self): + vol = _block_volume() + out = rot_ZXZ(vol, 0.0, 0.0, 0.0, device="cpu") + assert torch.max(torch.abs(out - vol)) < 1e-4 + + def test_rotation_preserves_mass(self): + vol = _block_volume() + rotated = rot_ZXZ(vol, 0.0, 30.0, 0.0, device="cpu") + rel_err = abs(float(rotated.sum()) - float(vol.sum())) / float(vol.sum()) + assert rel_err < 0.02 + + def test_accepts_python_float_and_tensor_angle(self): + vol = _block_volume() + out_float = rot_ZXZ(vol, 0.0, 25.0, 0.0, device="cpu") + out_tensor = rot_ZXZ( + vol, + torch.tensor(0.0), + torch.tensor(25.0), + torch.tensor(0.0), + device="cpu", + ) + assert torch.allclose(out_float, out_tensor, atol=1e-5) + + def test_rotation_changes_volume(self): + vol = _block_volume() + rotated = rot_ZXZ(vol, 0.0, 90.0, 0.0, device="cpu") + assert torch.max(torch.abs(rotated - vol)) > 0.1 + + def test_gradient_flows_through_rotation(self): + vol = _block_volume().requires_grad_(True) + out = differentiable_rotz_vectorized(vol, torch.tensor(20.0)) + out.sum().backward() + assert vol.grad is not None + assert torch.isfinite(vol.grad).all() From 8260c5b0a984562336f0665a898c00eae0296bf7 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Mon, 1 Jun 2026 14:43:09 -0700 Subject: [PATCH 323/335] Fix latent bugs in tomography optimizer wiring, constraints, and radon step_optimizers and zero_grad_all looped over optimizer_params and stepped both the object and pose optimizers on every pass, so with pose optimization enabled each optimizer took two Adam steps per batch. Gate by key, matching step_schedulers. Also: - pass num_iter to set_schedulers on the reset_dset path so cosine/linear/ exponential schedulers get a valid T_max - scheduler_params setter no longer mutates the caller's dict - drop the non-existent tv_plane key from ObjINRConstraints.soft_constraint_keys (it crashed Constraints.__str__) - ObjectPixelated.get_tv_loss now takes TV over the trailing spatial dims, so it handles a 3D volume, obj_view's [1, D, H, W], and a multimodal [C, D, H, W] - remove an unreachable duplicate branch in TomographyINRDataset.forward - align iradon_torch's default theta with radon_torch / scikit-image (endpoint excluded) Add regression tests covering each fix. --- src/quantem/tomography/dataset_models.py | 2 - src/quantem/tomography/object_models.py | 16 ++++--- src/quantem/tomography/radon/radon.py | 3 +- src/quantem/tomography/tomography.py | 2 +- src/quantem/tomography/tomography_opt.py | 19 ++++---- tests/tomography/test_dataset_models.py | 29 +++++++++++++ tests/tomography/test_object_models.py | 40 ++++++++++++++++- tests/tomography/test_radon.py | 11 +++++ tests/tomography/test_tomography_opt.py | 55 ++++++++++++++++++++++++ 9 files changed, 156 insertions(+), 21 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 4838395d..2255636c 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -486,8 +486,6 @@ def forward(self, dummy_input: Any = None): return shifts, torch.zeros_like(z1), torch.zeros_like(z3) elif self.learn_tilt_axis: return torch.zeros_like(shifts), z1, z3 - elif self.learn_shift and self.learn_tilt_axis: - return shifts, z1, z3 else: return torch.zeros_like(shifts), torch.zeros_like(z1), torch.zeros_like(z3) diff --git a/src/quantem/tomography/object_models.py b/src/quantem/tomography/object_models.py index c6b85b64..817e7ed5 100644 --- a/src/quantem/tomography/object_models.py +++ b/src/quantem/tomography/object_models.py @@ -101,7 +101,7 @@ class ObjINRConstraints(Constraints): sparsity: float = 0.0 _name: str = "obj_inr" - soft_constraint_keys = ["tv_vol", "tv_plane", "sparsity"] + soft_constraint_keys = ["tv_vol", "sparsity"] hard_constraint_keys = ["positivity", "shrinkage"] @dataclass @@ -436,12 +436,16 @@ def forward(self, coords=None) -> torch.Tensor: # --- Defining the TV loss --- def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: assert ctx.obj is not None, "ObjectPixelated requires ctx.obj to be set" - tv_d = torch.pow(ctx.obj[:, :, 1:, :, :] - ctx.obj[:, :, :-1, :, :], 2).sum() - tv_h = torch.pow(ctx.obj[:, :, :, 1:, :] - ctx.obj[:, :, :, :-1, :], 2).sum() - tv_w = torch.pow(ctx.obj[:, :, :, :, 1:] - ctx.obj[:, :, :, :, :-1], 2).sum() + # TV over the three trailing spatial dims, leaving any leading channel/batch axes + # intact. Works for a 3-D volume, obj_view's [1, D, H, W], and a multimodal + # [C, D, H, W] (channels = elemental compositions), matching the INR / tensor-decomp + # convention where the object carries a leading channel dimension. + tv_d = torch.pow(ctx.obj[..., 1:, :, :] - ctx.obj[..., :-1, :, :], 2).sum() + tv_h = torch.pow(ctx.obj[..., :, 1:, :] - ctx.obj[..., :, :-1, :], 2).sum() + tv_w = torch.pow(ctx.obj[..., :, :, 1:] - ctx.obj[..., :, :, :-1], 2).sum() tv_loss = tv_d + tv_h + tv_w - return tv_loss * self.constraints.tv_vol / (torch.prod(torch.tensor(ctx.obj.shape))) + return tv_loss * self.constraints.tv_vol / ctx.obj.numel() # --- Helper Functions --- def to(self, device: str | torch.device): @@ -934,7 +938,7 @@ def get_tv_loss(self, ctx: ReconstructionContext) -> torch.Tensor: Gets the summed total variational loss for the tensor decomposition model. _get_plane_tv_loss: Total-variation across the planes. - _get_volume_tv_loss: Isotropic volume TV + _get_volume_tv_loss: Isotropic volume TV """ assert ctx.coords is not None, "Coordinates must be provided for TV loss" assert ctx.pred is not None, "Prediction must be provided for TV loss" diff --git a/src/quantem/tomography/radon/radon.py b/src/quantem/tomography/radon/radon.py index 8510e570..ca133db3 100644 --- a/src/quantem/tomography/radon/radon.py +++ b/src/quantem/tomography/radon/radon.py @@ -91,7 +91,8 @@ def iradon_torch( sinograms = sinograms.unsqueeze(0) B, A, N = sinograms.shape device = device or sinograms.device - theta = theta if theta is not None else torch.linspace(0, 180, steps=A, device=device) + # Match radon_torch / scikit-image: A angles evenly spanning [0, 180) (endpoint excluded). + theta = theta if theta is not None else torch.linspace(0, 180, steps=A + 1, device=device)[:-1] if output_size is None: output_size = N if circle else int((N**2 / 2) ** 0.5) diff --git a/src/quantem/tomography/tomography.py b/src/quantem/tomography/tomography.py index 8976272e..f86095ad 100644 --- a/src/quantem/tomography/tomography.py +++ b/src/quantem/tomography/tomography.py @@ -143,7 +143,7 @@ def reconstruct( self.set_optimizers() if scheduler_params is not None: self.scheduler_params = scheduler_params - self.set_schedulers(self.scheduler_params) + self.set_schedulers(self.scheduler_params, num_iter=num_iter) self.dataloader, self.sampler, self.val_dataloader, self.val_sampler = ( self.setup_dataloader( diff --git a/src/quantem/tomography/tomography_opt.py b/src/quantem/tomography/tomography_opt.py index cf990379..7f98fabd 100644 --- a/src/quantem/tomography/tomography_opt.py +++ b/src/quantem/tomography/tomography_opt.py @@ -114,7 +114,8 @@ def scheduler_params(self) -> dict[str, SchedulerParamsType]: @scheduler_params.setter def scheduler_params(self, d: dict): """Set the scheduler parameters.""" - self._scheduler_params = d.copy() if d else {} + d = dict(d) if d else {} + self._scheduler_params = d.copy() for key in self.OPTIMIZABLE_VALS: if key not in d: @@ -152,21 +153,21 @@ def set_schedulers( def step_optimizers(self): for key in self.optimizer_params.keys(): - if self.obj_model.has_optimizer(): - self.obj_model.step_optimizer() - if self.dset.has_optimizer(): - self.dset.step_optimizer() if key not in self.OPTIMIZABLE_VALS: raise ValueError(f"Unknown optimization key: {key}") + if key == "object" and self.obj_model.has_optimizer(): + self.obj_model.step_optimizer() + elif key == "pose" and self.dset.has_optimizer(): + self.dset.step_optimizer() def zero_grad_all(self): for key in self.optimizer_params.keys(): - if self.obj_model.has_optimizer(): - self.obj_model.zero_optimizer_grad() - if self.dset.has_optimizer(): - self.dset.zero_optimizer_grad() if key not in self.OPTIMIZABLE_VALS: raise ValueError(f"Unknown optimization key: {key}") + if key == "object" and self.obj_model.has_optimizer(): + self.obj_model.zero_optimizer_grad() + elif key == "pose" and self.dset.has_optimizer(): + self.dset.zero_optimizer_grad() def step_schedulers(self, loss: float | None = None): for key in self.scheduler_params.keys(): diff --git a/tests/tomography/test_dataset_models.py b/tests/tomography/test_dataset_models.py index 78ded427..9ebd99dc 100644 --- a/tests/tomography/test_dataset_models.py +++ b/tests/tomography/test_dataset_models.py @@ -95,6 +95,35 @@ def test_getitem_keys(self): item = d[0] assert {"phi", "pixel_i", "pixel_j", "projection_idx", "target_value"} <= set(item.keys()) + @pytest.mark.parametrize( + "learn_shift,learn_tilt_axis", + [(True, True), (True, False), (False, True), (False, False)], + ) + def test_forward_gates_shift_and_tilt(self, learn_shift, learn_tilt_axis): + """``forward`` zeros the disabled component and passes the enabled one through. + + Guards the gating after removing the unreachable duplicate branch: shifts are + controlled by ``learn_shift``; the z1/z3 Euler angles by ``learn_tilt_axis``. + """ + d = TomographyINRDataset.from_data( + _stack(nang=5, n=12), + np.linspace(-60, 60, 5, dtype="f4"), + learn_shift=learn_shift, + learn_tilt_axis=learn_tilt_axis, + ) + d.to("cpu") + # Make every pose parameter non-zero so the gating is observable by value. + for p in (d.z1_params, d.z3_params, d.shifts_params): + p.data.fill_(1.0) + d._z1_ref = torch.ones_like(d._z1_ref) + d._z3_ref = torch.ones_like(d._z3_ref) + d._shifts_ref = torch.ones_like(d._shifts_ref) + + shifts, z1, z3 = d.forward(None) + assert bool(shifts.any()) == learn_shift + assert bool(z1.any()) == learn_tilt_axis + assert bool(z3.any()) == learn_tilt_axis + class TestTomographyINRPretrainDataset: def test_len_and_getitem(self): diff --git a/tests/tomography/test_object_models.py b/tests/tomography/test_object_models.py index f9f1922c..b200d2b9 100644 --- a/tests/tomography/test_object_models.py +++ b/tests/tomography/test_object_models.py @@ -50,6 +50,22 @@ def test_constraint_key_partitions(self): assert "positivity" in c.hard_constraint_keys assert "tv_vol" in c.soft_constraint_keys + def test_constraint_keys_are_real_fields(self): + """Regression: every soft/hard key must be an attribute, so __str__ never raises. + + ``ObjINRConstraints`` previously listed ``tv_plane`` (a field it does not have), which + made ``str(constraints)`` blow up with AttributeError via ``Constraints.__str__``. + """ + for cls in ( + ObjConstraintParams.ObjPixelatedConstraints, + ObjConstraintParams.ObjINRConstraints, + ObjConstraintParams.ObjTensorDecompConstraints, + ): + c = cls() + for key in c.soft_constraint_keys + c.hard_constraint_keys: + assert hasattr(c, key), f"{cls.__name__} lists missing key {key!r}" + assert isinstance(str(c), str) # must not raise + class TestObjectPixelatedConstruction: def test_from_uniform_is_zeros(self): @@ -98,7 +114,8 @@ def test_shrinkage_subtracts_then_floors(self): assert torch.allclose(obj.obj, torch.full((4, 4, 4), 0.75)) def test_tv_loss_scales_with_weight(self): - ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + # ctx.obj is the 3-D pixelated volume (D, H, W), matching ObjectPixelated._obj. + ctx = ReconstructionContext(obj=torch.rand(8, 8, 8)) obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") obj.constraints.tv_vol = 1.0 loss1 = obj.get_tv_loss(ctx) @@ -106,8 +123,27 @@ def test_tv_loss_scales_with_weight(self): loss2 = obj.get_tv_loss(ctx) assert torch.isclose(loss2, 2.0 * loss1) + @pytest.mark.parametrize( + "shape", + [ + (8, 8, 8), # bare 3-D volume + (1, 8, 8, 8), # obj_view layout [C=1, D, H, W] + (3, 8, 8, 8), # multimodal [C, D, H, W] (e.g. 3 elemental channels) + ], + ) + def test_tv_loss_rank_agnostic_finite_and_positive(self, shape): + """Regression: get_tv_loss takes TV over the trailing spatial dims for any leading + channel/batch axes -- not the old 5-D-only indexing. Supports multimodal [C, ...].""" + ctx = ReconstructionContext(obj=torch.rand(*shape)) + obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") + obj.constraints.tv_vol = 1.0 + loss = obj.get_tv_loss(ctx) + assert loss.ndim == 0 + assert torch.isfinite(loss) + assert loss > 0.0 # random volume has non-zero total variation + def test_soft_constraint_zero_when_tv_off(self): - ctx = ReconstructionContext(obj=torch.rand(1, 1, 8, 8, 8)) + ctx = ReconstructionContext(obj=torch.rand(8, 8, 8)) obj = ObjectPixelated.from_uniform(shape=(8, 8, 8), device="cpu") obj.constraints.tv_vol = 0.0 assert float(obj.apply_soft_constraints(ctx).detach()) == 0.0 diff --git a/tests/tomography/test_radon.py b/tests/tomography/test_radon.py index 4f61837a..41516f03 100644 --- a/tests/tomography/test_radon.py +++ b/tests/tomography/test_radon.py @@ -95,6 +95,17 @@ def test_roundtrip_recovers_structure(self): corr = np.corrcoef(disk.numpy().ravel(), rec.numpy().ravel())[0, 1] assert corr > 0.9 + def test_default_theta_roundtrip_is_consistent(self): + """radon and iradon must share an angle convention when ``theta`` is defaulted. + + iradon's default previously included the 180-degree endpoint while radon's did not, + so a default-theta round-trip sampled mismatched angles. + """ + disk = _disk(64, 32, 28, 10) + rec = iradon_torch(radon_torch(disk), filter_name="ramp") # both default theta + corr = np.corrcoef(disk.numpy().ravel(), rec.numpy().ravel())[0, 1] + assert corr > 0.9 + class TestRadonVsSkimage: """Loose cross-check against scikit-image's reference implementation.""" diff --git a/tests/tomography/test_tomography_opt.py b/tests/tomography/test_tomography_opt.py index 4ec56a13..6c597c90 100644 --- a/tests/tomography/test_tomography_opt.py +++ b/tests/tomography/test_tomography_opt.py @@ -112,6 +112,54 @@ def test_pplr_object_groups(self, torch_device): assert len(tomo.optimizers["object"].param_groups) == 3 assert "pose" in tomo.optimizers + def test_step_optimizers_steps_each_once(self, torch_device, monkeypatch): + """Regression: with object+pose, each optimizer must step exactly once per call. + + ``step_optimizers`` previously looped over both keys and stepped *both* optimizers on + every pass, so each took two Adam steps per batch. + """ + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + assert set(tomo.optimizers.keys()) == {"object", "pose"} # both optimizers live + + counts = {"object": 0, "pose": 0} + monkeypatch.setattr( + tomo.obj_model, + "step_optimizer", + lambda: counts.__setitem__("object", counts["object"] + 1), + ) + monkeypatch.setattr( + tomo.dset, "step_optimizer", lambda: counts.__setitem__("pose", counts["pose"] + 1) + ) + tomo.step_optimizers() + assert counts == {"object": 1, "pose": 1} + + def test_zero_grad_all_zeros_each_once(self, torch_device, monkeypatch): + """Companion to the step regression: zero_grad_all must touch each optimizer once.""" + tomo = _inr_tomography(torch_device) + tomo.optimizer_params = { + "object": OptimizerParams.Adam(lr=1e-3), + "pose": OptimizerParams.Adam(lr=1e-2), + } + tomo.set_optimizers() + counts = {"object": 0, "pose": 0} + monkeypatch.setattr( + tomo.obj_model, + "zero_optimizer_grad", + lambda: counts.__setitem__("object", counts["object"] + 1), + ) + monkeypatch.setattr( + tomo.dset, + "zero_optimizer_grad", + lambda: counts.__setitem__("pose", counts["pose"] + 1), + ) + tomo.zero_grad_all() + assert counts == {"object": 1, "pose": 1} + @requires_torch class TestSchedulerParams: @@ -141,3 +189,10 @@ def test_bad_scheduler_type_raises(self, torch_device): tomo = _inr_tomography(torch_device) with pytest.raises(TypeError): tomo.obj_model.scheduler_params = 123 + + def test_setter_does_not_mutate_caller_dict(self, torch_device): + """Regression: the setter must not inject missing keys into the caller's dict.""" + tomo = _inr_tomography(torch_device) + d = {"object": SchedulerParams.CosineAnnealing(T_max=10)} + tomo.scheduler_params = d + assert set(d.keys()) == {"object"} # "pose" must not have been added to the input From 077348176f833f0953dfd68459b64842b3964aba Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Tue, 2 Jun 2026 13:54:39 -0700 Subject: [PATCH 324/335] Tomography Slow/Fast Tests --- src/quantem/core/ml/ddp.py | 8 +- tests/tomography/test_dataset_models.py | 59 ++++++ tests/tomography/test_logger_tomography.py | 103 ++++++++++ tests/tomography/test_object_models.py | 79 ++++++++ tests/tomography/test_tomography.py | 177 ++++++++++++++++++ tests/tomography/test_tomography_cpu_recon.py | 44 +++++ tests/tomography/test_tomography_opt.py | 22 +++ 7 files changed, 490 insertions(+), 2 deletions(-) create mode 100644 tests/tomography/test_logger_tomography.py create mode 100644 tests/tomography/test_tomography.py create mode 100644 tests/tomography/test_tomography_cpu_recon.py diff --git a/src/quantem/core/ml/ddp.py b/src/quantem/core/ml/ddp.py index dedd90dd..18b7ccd7 100644 --- a/src/quantem/core/ml/ddp.py +++ b/src/quantem/core/ml/ddp.py @@ -61,6 +61,10 @@ def setup_dataloader( ): pin_mem = self.device.type == "cuda" persist = num_workers > 0 + # ``multiprocessing_context`` is only valid for multi-process loading; passing it with + # num_workers=0 raises ValueError (and num_workers=0 keeps the dataset in-process, which + # is what CPU / coverage runs use). + mp_ctx = "spawn" if num_workers > 0 else None if val_fraction > 0.0: train_dataset, val_dataset = random_split(dataset, [1 - val_fraction, val_fraction]) # type: ignore[reportArgumentType] --> dataset inherits from torch Dataset so this is fine. @@ -102,7 +106,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=True, persistent_workers=persist, - multiprocessing_context="spawn", + multiprocessing_context=mp_ctx, worker_init_fn=worker_init_fn, ) @@ -116,7 +120,7 @@ def setup_dataloader( pin_memory=pin_mem, drop_last=False, persistent_workers=persist, - multiprocessing_context="spawn", + multiprocessing_context=mp_ctx, worker_init_fn=worker_init_fn, ) val_dataloader = val_dataloader diff --git a/tests/tomography/test_dataset_models.py b/tests/tomography/test_dataset_models.py index 9ebd99dc..04abd3b5 100644 --- a/tests/tomography/test_dataset_models.py +++ b/tests/tomography/test_dataset_models.py @@ -133,3 +133,62 @@ def test_len_and_getitem(self): item = ds[0] assert set(item.keys()) == {"coords", "target"} assert item["coords"].shape == (3,) + + +@requires_torch +class TestINRRayMath: + """The static ray helpers are pure tensor math (CPU), exercised here without a recon.""" + + def test_create_batch_rays_shape_and_endpoints(self): + N, S = 8, 5 + rays = TomographyINRDataset.create_batch_rays( + torch.tensor([0, N - 1]), torch.tensor([0, N - 1]), N=N, num_samples_per_ray=S + ) + assert rays.shape == (2, S, 3) + # pixel 0 maps to -1, pixel N-1 maps to +1 on both x (j) and y (i). + assert torch.allclose(rays[0, :, 0], torch.full((S,), -1.0)) + assert torch.allclose(rays[1, :, 0], torch.full((S,), 1.0)) + # z spans the full -1..1 sampling range. + assert torch.isclose(rays[0, 0, 2], torch.tensor(-1.0)) + assert torch.isclose(rays[0, -1, 2], torch.tensor(1.0)) + + def test_transform_batch_rays_identity_at_zero_pose(self): + rays = torch.rand(4, 6, 3) + zero = torch.zeros(4) + out = TomographyINRDataset.transform_batch_rays( + rays, z1=zero, x=zero, z3=zero, shifts=torch.zeros(4, 2), N=8, sampling_rate=1.0 + ) + # No rotation and no shift -> rays pass through unchanged. + assert torch.allclose(out, rays, atol=1e-5) + + def test_integrate_rays_sums_with_step_size(self): + B, S = 3, 5 + out = TomographyINRDataset.integrate_rays( + torch.ones(B, S), num_samples_per_ray=S, target_values_len=B + ) + step = 2.0 / (S - 1) + assert out.shape == (B,) + assert torch.allclose(out, torch.full((B,), S * step)) + + def test_getitem_index_mapping(self): + # n=4 -> H*W=16 per projection. idx=21 -> projection 1, remaining 5 -> i=1, j=1. + stack = _stack(nang=3, n=4) + d = TomographyINRDataset.from_data(stack, np.linspace(-60, 60, 3, dtype="f4")) + item = d[21] + assert int(item["projection_idx"]) == 1 + assert int(item["pixel_i"]) == 1 + assert int(item["pixel_j"]) == 1 + assert torch.isclose(item["target_value"], d.tilt_stack[1, 1, 1]) + + def test_save_load_parameters_roundtrip(self, tmp_path): + angles = np.linspace(-60, 60, 5, dtype="f4") + d = TomographyINRDataset.from_data(_stack(), angles) + d.to("cpu") + d.z1_params.data.fill_(0.37) + path = str(tmp_path / "params.pt") + d.save_parameters(path) + + d2 = TomographyINRDataset.from_data(_stack(), angles) + d2.to("cpu") + d2.load_parameters(path) + assert torch.allclose(d2.z1_params.detach(), d.z1_params.detach()) diff --git a/tests/tomography/test_logger_tomography.py b/tests/tomography/test_logger_tomography.py new file mode 100644 index 00000000..88d6bb26 --- /dev/null +++ b/tests/tomography/test_logger_tomography.py @@ -0,0 +1,103 @@ +"""Tests for ``quantem.tomography.logger_tomography``. + +``LoggerTomography`` is a thin tensorboard wrapper that the reconstruction loop only drives +when a ``log_dir`` is passed, so the end-to-end recon tests never exercise it. These CPU, +always-on tests construct a logger against a ``tmp_path`` and drive each method with small +stubs that expose only the attributes the logger reads, asserting the calls run and write +event files. Matplotlib backend is ``Agg`` (set in the root conftest), so figure logging is +headless. +""" + +from types import SimpleNamespace + +import numpy as np +import torch + +from quantem.tomography.logger_tomography import LoggerTomography + + +def _make_logger(tmp_path) -> LoggerTomography: + return LoggerTomography( + log_dir=str(tmp_path), + run_prefix="test_tomo", + run_suffix="", + log_images_every=1, + ) + + +def test_init_creates_log_dir(tmp_path): + logger = _make_logger(tmp_path) + try: + assert logger.log_dir.exists() + assert logger.log_dir.name.startswith("test_tomo_") + finally: + logger.close() + + +def test_log_epoch_writes_events(tmp_path): + logger = _make_logger(tmp_path) + try: + logger.log_epoch(epoch=0, loss=1.0, tilt_series_loss=0.8, soft_loss=0.2) + logger.flush() + events = list(logger.log_dir.glob("events.out.tfevents.*")) + assert events, "log_epoch should have written a tensorboard event file" + finally: + logger.close() + + +def test_log_iter_unpacks_learning_rates(tmp_path): + logger = _make_logger(tmp_path) + obj_model = SimpleNamespace(_soft_constraint_losses=[0.3]) + try: + logger.log_iter( + object_model=obj_model, + iter=2, + consistency_loss=0.5, + total_loss=0.7, + learning_rates={"object": 1e-3, "pose": 1e-2}, + num_samples_per_ray=16, + val_loss=0.4, + ) + logger.flush() + assert list(logger.log_dir.glob("events.out.tfevents.*")) + finally: + logger.close() + + +def test_log_iter_without_val_loss(tmp_path): + logger = _make_logger(tmp_path) + obj_model = SimpleNamespace(_soft_constraint_losses=[0.1]) + try: + # val_loss defaults to None -> the val branch must be skipped without error. + logger.log_iter( + object_model=obj_model, + iter=0, + consistency_loss=0.5, + total_loss=0.6, + learning_rates={}, + num_samples_per_ray=8, + ) + logger.flush() + finally: + logger.close() + + +def test_log_iter_images(tmp_path): + logger = _make_logger(tmp_path) + n_tilts = 5 + dataset_model = SimpleNamespace( + z1_params=torch.linspace(-1.0, 1.0, n_tilts), + z3_params=torch.linspace(1.0, -1.0, n_tilts), + shifts_params=torch.zeros(n_tilts, 2), + ) + pred_volume = np.random.default_rng(0).random((2, 6, 6, 6)).astype(np.float32) + try: + logger.log_iter_images( + pred_volume=pred_volume, + dataset_model=dataset_model, + iter=1, + ) + logger.flush() + assert list(logger.log_dir.glob("events.out.tfevents.*")) + finally: + logger.close() diff --git a/tests/tomography/test_object_models.py b/tests/tomography/test_object_models.py index b200d2b9..c4216827 100644 --- a/tests/tomography/test_object_models.py +++ b/tests/tomography/test_object_models.py @@ -193,3 +193,82 @@ def test_pretrain_not_implemented(self, torch_device): obj = ObjectTensorDecomp.from_model(self._model(), shape=(16, 16, 16), device=torch_device) with pytest.raises(NotImplementedError): obj.pretrain() + + +@requires_torch +class TestObjectINRBehaviour: + def _obj(self, device, n=16): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + return ObjectINR.from_model(model, shape=(n, n, n), device=device) + + def test_forward_masks_out_of_range(self, torch_device): + obj = self._obj(torch_device) + coords = torch.tensor([[0.0, 0.0, 0.0], [5.0, 0.0, 0.0]], device=torch_device) + out = obj.forward(coords) + assert out.shape[0] == 2 + assert float(out[1].detach()) == 0.0 # x=5 is outside [-1, 1] -> masked to zero + + def test_apply_hard_constraints_positivity(self, torch_device): + obj = self._obj(torch_device) + obj.constraints.positivity = True + pred = torch.tensor([-1.0, 0.5, 2.0], device=torch_device) + assert torch.all(obj.apply_hard_constraints(pred) >= 0.0) + + def test_get_tv_loss_scalar(self, torch_device): + obj = self._obj(torch_device) + obj.constraints.tv_vol = 1.0 + coords = torch.rand(64, 3, device=torch_device) * 2 - 1 + ctx = ReconstructionContext(coords=coords, pred=torch.rand(64, device=torch_device)) + loss = obj.get_tv_loss(ctx) + assert loss.ndim == 0 + assert torch.isfinite(loss) + + +@requires_torch +class TestObjectTensorDecompTV: + def _obj(self, device, n=16): + from quantem.core.ml.models.kplanes import KPlanesTILTED + + model = KPlanesTILTED( + M_features=2, resolution=(n, n, n), multiscale_res_multipliers=[1], T=2 + ) + return ObjectTensorDecomp.from_model(model, shape=(n, n, n), device=device) + + def test_apply_hard_constraints_positivity(self, torch_device): + obj = self._obj(torch_device) + obj.constraints.positivity = True + pred = torch.tensor([-2.0, 0.0, 3.0], device=torch_device) + assert torch.all(obj.apply_hard_constraints(pred) >= 0.0) + + def test_plane_tv_loss_nonneg_scalar(self, torch_device): + obj = self._obj(torch_device) + obj.constraints.tv_plane = 0.1 + loss = obj._get_plane_tv_loss() + assert loss.ndim == 0 + assert float(loss.detach()) >= 0.0 + + def test_volume_tv_loss_scalar(self, torch_device): + obj = self._obj(torch_device) + obj.constraints.tv_vol = 0.1 + coords = torch.rand(64, 3, device=torch_device) * 2 - 1 + loss = obj.get_volume_tv_loss(coords) + assert loss.ndim == 0 + assert torch.isfinite(loss) + + def test_normalize_optimizer_params_rejects_non_dict(self, torch_device): + from quantem.core.ml.optimizer_mixin import OptimizerParams + + obj = self._obj(torch_device) + with pytest.raises(TypeError): + obj._normalize_optimizer_params([OptimizerParams.Adam()]) + + def test_normalize_optimizer_params_rejects_wrong_keys(self, torch_device): + from quantem.core.ml.optimizer_mixin import OptimizerParams + + obj = self._obj(torch_device) + with pytest.raises(ValueError): + obj._normalize_optimizer_params( + {"grids": OptimizerParams.Adam(), "wrong": OptimizerParams.Adam()} + ) diff --git a/tests/tomography/test_tomography.py b/tests/tomography/test_tomography.py new file mode 100644 index 00000000..da71f9c9 --- /dev/null +++ b/tests/tomography/test_tomography.py @@ -0,0 +1,177 @@ +"""Tests for the ``Tomography`` / ``TomographyConventional`` orchestrators and the shared +``TomographyBase`` plumbing, without running a reconstruction. + +The ``TomographyConventional`` path uses ``ObjectPixelated`` (no DDP setup), so it builds on +CPU and is always-on -- this exercises the bulk of ``tomography_base.py`` (factory, property +setters/validation, loss accessors). The ``Tomography`` (INR) factory and ``save_volume`` go +through ``setup_distributed`` and so follow the ``torch_device`` fixture under +``requires_torch`` (build on CUDA when present; see conftest). +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.dataset_models import TomographyINRDataset, TomographyPixDataset +from quantem.tomography.object_models import ObjConstraintParams, ObjectINR, ObjectPixelated +from quantem.tomography.tomography import Tomography, TomographyConventional +from quantem.tomography.tomography_lite import TomographyLiteINR + +from .conftest import requires_torch + + +def _stack(nang=5, n=12, seed=0): + rng = np.random.default_rng(seed) + return (rng.random((nang, n, n)) * 10).astype(np.float32) + + +def _conventional(n=12): + angles = np.linspace(-60, 60, 5).astype(np.float32) + dset = TomographyPixDataset.from_data(_stack(nang=5, n=n), angles) + obj = ObjectPixelated.from_uniform(shape=(n, n, n), device="cpu") + return TomographyConventional.from_models( + dset=dset, obj_model=obj, device="cpu", verbose=False + ) + + +class TestConventionalFactory: + def test_from_models_builds(self): + tomo = _conventional() + assert isinstance(tomo, TomographyConventional) + assert isinstance(tomo.obj_model, ObjectPixelated) + assert isinstance(tomo.dset, TomographyPixDataset) + assert tomo.num_epochs == 0 + + def test_direct_init_requires_token(self): + with pytest.raises(RuntimeError): + TomographyConventional( + dset=TomographyPixDataset.from_data( + _stack(), np.linspace(-60, 60, 5).astype(np.float32) + ), + obj_model=ObjectPixelated.from_uniform(shape=(12, 12, 12), device="cpu"), + ) + + +class TestBaseProperties: + def test_constraints_setter_dict_and_object(self): + tomo = _conventional() + tomo.constraints = {"name": "obj_pixelated", "tv_vol": 0.02} + assert isinstance(tomo.constraints, ObjConstraintParams.ObjPixelatedConstraints) + assert tomo.constraints.tv_vol == 0.02 + obj_c = ObjConstraintParams.ObjPixelatedConstraints(positivity=True) + tomo.constraints = obj_c + assert tomo.constraints is obj_c + + def test_constraints_setter_none_is_noop(self): + tomo = _conventional() + before = tomo.constraints + tomo.constraints = None + assert tomo.constraints is before + + def test_constraints_setter_invalid_raises(self): + tomo = _conventional() + with pytest.raises(ValueError): + tomo.constraints = 1.0 + + def test_logger_setter_rejects_wrong_type(self): + tomo = _conventional() + with pytest.raises(TypeError): + tomo.logger = "not a logger" + + def test_dset_setter_rejects_wrong_type(self): + tomo = _conventional() + with pytest.raises(TypeError): + tomo.dset = object() + + def test_loss_accessors_start_empty(self): + tomo = _conventional() + assert tomo.epoch_losses.shape == (0,) + assert tomo.consistency_losses.shape == (0,) + assert tomo.learning_rates == {} + + def test_append_learning_rates_accumulates(self): + tomo = _conventional() + tomo.append_learning_rates({"object": 1e-3, "pose": 1e-2}) + tomo.append_learning_rates({"object": 5e-4, "pose": 5e-3}) + assert tomo.learning_rates["object"] == [1e-3, 5e-4] + assert tomo.learning_rates["pose"] == [1e-2, 5e-3] + + def test_to_updates_device(self): + tomo = _conventional() + tomo.to("cpu") + assert torch.device(tomo.device) == torch.device("cpu") + + def test_plot_losses_runs(self): + tomo = _conventional() + tomo._epoch_losses.extend([1.0, 0.5, 0.25]) + tomo.plot_losses() # Agg backend; plt.show() is a no-op + + +@requires_torch +class TestInrFactory: + def _inr_tomo(self, device, n=16): + from quantem.core.ml.inr import HSiren + + model = HSiren(in_features=3, out_features=1, hidden_layers=1, hidden_features=8) + obj = ObjectINR.from_model(model, shape=(n, n, n), device=device) + dset = TomographyINRDataset.from_data( + _stack(nang=5, n=n), np.linspace(-60, 60, 5).astype(np.float32) + ) + return Tomography.from_models(dset=dset, obj_model=obj, device=device, verbose=False) + + def test_from_models_builds(self, torch_device): + tomo = self._inr_tomo(torch_device) + assert isinstance(tomo, Tomography) + assert isinstance(tomo.obj_model, ObjectINR) + + def test_plot_losses_runs(self, torch_device): + tomo = self._inr_tomo(torch_device) + tomo._epoch_losses.extend([1.0, 0.5]) + tomo._lrs["object"] = [1e-3, 5e-4] + tomo.plot_losses() + + def test_save_volume_overwrite_guard(self, torch_device, tmp_path): + tomo = self._inr_tomo(torch_device) + path = str(tmp_path / "vol.npz") + tomo.save_volume(path) + assert (tmp_path / "vol.npz").exists() + with pytest.raises(FileExistsError): + tomo.save_volume(path) + tomo.save_volume(path, overwrite=True) # must not raise + with np.load(path) as data: + assert "volume" in data + + +@requires_torch +class TestLiteINRReconstructBranch: + """``TomographyLiteINR.reconstruct`` bundles optimizer/scheduler params only on the first + epoch and passes ``None`` afterwards. Stub out the heavy ``Tomography.reconstruct`` to + assert the branch without running a reconstruction.""" + + def _lite(self, device, n=12): + return TomographyLiteINR.from_dataset( + tilt_series=_stack(nang=5, n=n), + tilt_angles=np.linspace(-60, 60, 5).astype(np.float32), + device=device, + ) + + def test_param_bundling_first_then_subsequent(self, torch_device, monkeypatch): + tomo = self._lite(torch_device) + captured = {} + + def fake_reconstruct(self, **kwargs): + captured.clear() + captured.update(kwargs) + self._epoch_losses.append(1.0) # mark an epoch as having run + + monkeypatch.setattr(Tomography, "reconstruct", fake_reconstruct) + + # First call (num_epochs == 0): object + pose params are assembled. + tomo.reconstruct(num_iter=1, num_workers=0, learn_pose=True) + assert set(captured["optimizer_params"].keys()) == {"object", "pose"} + assert set(captured["scheduler_params"].keys()) == {"object", "pose"} + + # Second call (num_epochs > 0): params are passed through as None. + tomo.reconstruct(num_iter=1, num_workers=0) + assert captured["optimizer_params"] is None + assert captured["scheduler_params"] is None diff --git a/tests/tomography/test_tomography_cpu_recon.py b/tests/tomography/test_tomography_cpu_recon.py new file mode 100644 index 00000000..0ebf7752 --- /dev/null +++ b/tests/tomography/test_tomography_cpu_recon.py @@ -0,0 +1,44 @@ +"""CPU INR reconstruction smoke test. + +The GPU INR tests (``test_tomography_inr.py``) are gated behind ``requires_gpu``, so on +CPU-only CI the entire learned-reconstruction loop in ``Tomography.reconstruct`` is never +exercised. This runs a tiny INR reconstruction on CPU with ``num_workers=0`` -- valid since +``DDPMixin.setup_dataloader`` now only sets ``multiprocessing_context`` when +``num_workers > 0`` -- so that path is covered in CI. + +It is skipped when CUDA is present: object models that go through ``setup_distributed`` must +be built on CUDA when a CUDA device exists (the CPU path is only valid with no CUDA device), +and on GPU machines the ``requires_gpu`` tests already cover this path. Marked ``slow`` so it +runs under ``--runslow``. +""" + +import numpy as np +import pytest +import torch + +from quantem.tomography.tomography_lite import TomographyLiteINR + +pytestmark = [ + pytest.mark.slow, + pytest.mark.skipif( + torch.cuda.is_available(), + reason="CPU INR path is only valid without CUDA; GPU machines use the requires_gpu tests", + ), +] + + +def test_cpu_inr_reconstruct_reduces_loss(): + rng = np.random.default_rng(0) + n = 12 + series = (rng.random((5, n, n)) * 10).astype(np.float32) + angles = np.linspace(-60, 60, 5).astype(np.float32) + + tomo = TomographyLiteINR.from_dataset(tilt_series=series, tilt_angles=angles, device="cpu") + tomo.reconstruct(num_iter=2, num_workers=0, batch_size=64, learn_pose=True) + + losses = tomo.epoch_losses + assert len(losses) == 2 + assert losses[-1] < losses[0] + view = tomo.obj_model.obj_view + assert view.shape == (1, n, n, n) + assert np.isfinite(view).all() diff --git a/tests/tomography/test_tomography_opt.py b/tests/tomography/test_tomography_opt.py index 6c597c90..bae00ad9 100644 --- a/tests/tomography/test_tomography_opt.py +++ b/tests/tomography/test_tomography_opt.py @@ -161,6 +161,28 @@ def test_zero_grad_all_zeros_each_once(self, torch_device, monkeypatch): assert counts == {"object": 1, "pose": 1} +@requires_torch +class TestOptHelpers: + def test_get_default_lr_object_and_pose(self, torch_device): + tomo = _inr_tomography(torch_device) + assert isinstance(tomo._get_default_lr("object"), float) + assert isinstance(tomo._get_default_lr("pose"), float) + + def test_get_default_lr_unknown_raises(self, torch_device): + tomo = _inr_tomography(torch_device) + with pytest.raises(ValueError): + tomo._get_default_lr("banana") + + def test_remove_optimizer_unknown_raises(self, torch_device): + tomo = _inr_tomography(torch_device) + with pytest.raises(ValueError): + tomo.remove_optimizer("banana") + + def test_current_lrs_zero_without_optimizers(self, torch_device): + tomo = 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_token: object | None = None, ): AutoSerialize.__init__(self) @@ -188,7 +189,10 @@ def __init__( tilt_stack = torch.from_numpy(tilt_stack) if type(tilt_angles) is not torch.Tensor: tilt_angles = torch.from_numpy(tilt_angles) - max_val = torch.quantile(tilt_stack, 0.95) + if norm_quantile: + max_val = torch.quantile(tilt_stack, 0.95) + else: + max_val = torch.max(tilt_stack) # Tilt stack normalization tilt_stack = tilt_stack / max_val @@ -221,12 +225,14 @@ def from_data( tilt_angles: NDArray | torch.Tensor, learn_shift: bool = True, learn_tilt_axis: bool = True, + norm_quantile: bool = True, ): return cls( tilt_stack=tilt_stack, tilt_angles=tilt_angles, learn_shift=learn_shift, learn_tilt_axis=learn_tilt_axis, + norm_quantile=norm_quantile, _token=cls._token, ) From 3f92787ad6fa4e85ba7fbe2908299926683f00b2 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 18 Jun 2026 15:31:20 -0700 Subject: [PATCH 328/335] Fixed tomography tests --- src/quantem/tomography/dataset_models.py | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index f9d87d06..aa11f0a1 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -403,6 +403,7 @@ def __init__( tilt_angles: NDArray | torch.Tensor, learn_shift: bool = True, learn_tilt_axis: bool = True, + norm_quantile: bool = True, _token: object | None = None, ): super().__init__( @@ -410,6 +411,7 @@ def __init__( tilt_angles=-tilt_angles, # TODO: Flip the tilt angles to be negative to match the convention of INR. learn_shift=learn_shift, learn_tilt_axis=learn_tilt_axis, + norm_quantile=norm_quantile, _token=_token, ) @@ -462,10 +464,18 @@ def __init__( tilt_angles: NDArray | torch.Tensor, learn_shift: bool = True, learn_tilt_axis: bool = True, + norm_quantile: bool = True, seed: int = 42, _token: object | None = None, ): - super().__init__(tilt_stack, tilt_angles, learn_shift, learn_tilt_axis, _token=_token) + super().__init__( + tilt_stack, + tilt_angles, + learn_shift, + learn_tilt_axis, + norm_quantile, + _token=_token, + ) # --- Forward Pass w/ Params Method for OptimizerMixin --- def forward(self, dummy_input: Any = None): From 8af2492b4e7b1f51441afb6ed6f8bda569ee9e52 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 18 Jun 2026 16:56:40 -0700 Subject: [PATCH 329/335] Last shape of the tilt stack is now checked with the tilt angles rather than comparing the shapes within the tilt series stack. --- src/quantem/tomography/dataset_models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index aa11f0a1..6ede23be 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -179,7 +179,7 @@ def __init__( raise RuntimeError("Use TomographyPixDataset.from_* to instantiate this class.") if not ( - tilt_stack.shape[0] < tilt_stack.shape[1] or tilt_stack.shape[0] < tilt_stack.shape[2] + tilt_stack.shape[0] == tilt_angles.shape[0] ): raise ValueError( "The number of tilt projections should be in the first dimension of the dataset." From 3c29c6e33faa1291cdd8687cff39caa06bd759dc Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 18 Jun 2026 17:04:59 -0700 Subject: [PATCH 330/335] Test fixes --- .serena/.gitignore | 2 + .serena/project.yml | 133 ++++++++++++++++++++++++ tests/tomography/test_dataset_models.py | 7 +- 3 files changed, 139 insertions(+), 3 deletions(-) create mode 100644 .serena/.gitignore create mode 100644 .serena/project.yml diff --git a/.serena/.gitignore b/.serena/.gitignore new file mode 100644 index 00000000..2e510aff --- /dev/null +++ b/.serena/.gitignore @@ -0,0 +1,2 @@ +/cache +/project.local.yml diff --git a/.serena/project.yml b/.serena/project.yml new file mode 100644 index 00000000..e660e68a --- /dev/null +++ b/.serena/project.yml @@ -0,0 +1,133 @@ +# the name by which the project can be referenced within Serena +project_name: "quantem" + + +# list of languages for which language servers are started; choose from: +# al angular ansible bash clojure +# cpp cpp_ccls crystal csharp csharp_omnisharp +# dart elixir elm erlang fortran +# fsharp go groovy haskell haxe +# hlsl html java json julia +# kotlin lean4 lua luau markdown +# matlab msl nix ocaml pascal +# perl php php_phpactor powershell python +# python_jedi python_ty r rego ruby +# ruby_solargraph rust scala scss solidity +# svelte swift systemverilog terraform toml +# typescript typescript_vts vue yaml zig +# (This list may be outdated. For the current list, see values of Language enum here: +# https://github.com/oraios/serena/blob/main/src/solidlsp/ls_config.py +# For some languages, there are alternative language servers, e.g. csharp_omnisharp, ruby_solargraph.) +# Note: +# - For C, use cpp +# - For JavaScript, use typescript +# - For Angular projects, use angular (subsumes typescript+html; requires `npm install` in the project root) +# - For Svelte projects, use svelte (subsumes typescript/javascript for .svelte projects; requires npm) +# - For SCSS / Sass / plain CSS, use scss (some-sass-language-server handles all three) +# - For Free Pascal/Lazarus, use pascal +# Special requirements: +# Some languages require additional setup/installations. +# See here for details: https://oraios.github.io/serena/01-about/020_programming-languages.html#language-servers +# When using multiple languages, the first language server that supports a given file will be used for that file. +# The first language is the default language and the respective language server will be used as a fallback. +# Note that when using the JetBrains backend, language servers are not used and this list is correspondingly ignored. +languages: +- python + +# the encoding used by text files in the project +# For a list of possible encodings, see https://docs.python.org/3.11/library/codecs.html#standard-encodings +encoding: "utf-8" + +# line ending convention to use when writing source files. +# Possible values: unset (use global setting), "lf", "crlf", or "native" (platform default) +# This does not affect Serena's own files (e.g. memories and configuration files), which always use native line endings. +line_ending: + +# The language backend to use for this project. +# If not set, the global setting from serena_config.yml is used. +# Valid values: LSP, JetBrains +# Note: the backend is fixed at startup. If a project with a different backend +# is activated post-init, an error will be returned. +language_backend: + +# whether to use project's .gitignore files to ignore files +ignore_all_files_in_gitignore: true + +# advanced configuration option allowing to configure language server-specific options. +# Maps the language key to the options. +# Have a look at the docstring of the constructors of the LS implementations within solidlsp (e.g., for C# or PHP) to see which options are available. +# No documentation on options means no options are available. +ls_specific_settings: {} + +# list of additional workspace folder paths for cross-package reference support (e.g. in monorepos). +# Paths can be absolute or relative to the project root. +# Each folder is registered as an LSP workspace folder, enabling language servers to discover +# symbols and references across package boundaries. +# Currently supported for: TypeScript. +# Example: +# additional_workspace_folders: +# - ../sibling-package +# - ../shared-lib +additional_workspace_folders: [] + +# list of additional paths to ignore in this project. +# Same syntax as gitignore, so you can use * and **. +# Note: global ignored_paths from serena_config.yml are also applied additively. +ignored_paths: [] + +# whether the project is in read-only mode +# If set to true, all editing tools will be disabled and attempts to use them will result in an error +# Added on 2025-04-18 +read_only: false + +# list of tool names to exclude. +# This extends the existing exclusions (e.g. from the global configuration) +# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html +excluded_tools: [] + +# list of tools to include that would otherwise be disabled (particularly optional tools that are disabled by default). +# This extends the existing inclusions (e.g. from the global configuration). +# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html +included_optional_tools: [] + +# fixed set of tools to use as the base tool set (if non-empty), replacing Serena's default set of tools. +# This cannot be combined with non-empty excluded_tools or included_optional_tools. +# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html +fixed_tools: [] + +# list of mode names that are to be activated by default, overriding the setting in the global configuration. +# The full set of modes to be activated is base_modes (from global config) + default_modes + added_modes. +# If the setting is undefined/empty, the default_modes from the global configuration (serena_config.yml) apply. +# Otherwise, this overrides the setting from the global configuration (serena_config.yml). +# Therefore, you can set this to [] if you do not want the default modes defined in the global config to apply +# for this project. +# This setting can, in turn, be overridden by CLI parameters (--mode). +# See https://oraios.github.io/serena/02-usage/050_configuration.html#modes +default_modes: + +# list of mode names to be activated additionally for this project, e.g. ["query-projects"] +# The full set of modes to be activated is base_modes (from global config) + default_modes + added_modes. +# See https://oraios.github.io/serena/02-usage/050_configuration.html#modes +added_modes: + +# initial prompt for the project. It will always be given to the LLM upon activating the project +# (contrary to the memories, which are loaded on demand). +initial_prompt: "" + +# time budget (seconds) per tool call for the retrieval of additional symbol information +# such as docstrings or parameter information. +# This overrides the corresponding setting in the global configuration; see the documentation there. +# If null or missing, use the setting from the global configuration. +symbol_info_budget: + +# list of regex patterns which, when matched, mark a memory entry as read‑only. +# Extends the list from the global configuration, merging the two lists. +read_only_memory_patterns: [] + +# list of regex patterns for memories to completely ignore. +# Matching memories will not appear in list_memories or activate_project output +# and cannot be accessed via read_memory or write_memory. +# To access ignored memory files, use the read_file tool on the raw file path. +# Extends the list from the global configuration, merging the two lists. +# Example: ["_archive/.*", "_episodes/.*"] +ignored_memory_patterns: [] diff --git a/tests/tomography/test_dataset_models.py b/tests/tomography/test_dataset_models.py index 04abd3b5..f0b6aa2d 100644 --- a/tests/tomography/test_dataset_models.py +++ b/tests/tomography/test_dataset_models.py @@ -43,10 +43,11 @@ def _stack(nang=5, n=12, seed=0): class TestTomographyPixDataset: def test_wrong_projection_axis_raises(self): - # projections must live on axis 0 (i.e. fewer than the image dims). - bad = np.zeros((20, 5, 5), dtype=np.float32) + # projections must live on axis 0, matching the number of tilt angles. + # here the projections are on the last axis, so axis 0 (12) != n_angles (5). + bad = np.zeros((12, 12, 5), dtype=np.float32) with pytest.raises(ValueError): - TomographyPixDataset.from_data(bad, np.linspace(-60, 60, 20).astype(np.float32)) + TomographyPixDataset.from_data(bad, np.linspace(-60, 60, 5).astype(np.float32)) def test_tilt_angles_are_negated(self): angles = np.linspace(-40, 60, 5).astype(np.float32) From e00aeda112c250ad4c39f601433e98268c0465e8 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 18 Jun 2026 17:05:31 -0700 Subject: [PATCH 331/335] Test fixes --- .serena/.gitignore | 2 - .serena/project.yml | 133 -------------------------------------------- 2 files changed, 135 deletions(-) delete mode 100644 .serena/.gitignore delete mode 100644 .serena/project.yml diff --git a/.serena/.gitignore b/.serena/.gitignore deleted file mode 100644 index 2e510aff..00000000 --- a/.serena/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -/cache -/project.local.yml diff --git a/.serena/project.yml b/.serena/project.yml deleted file mode 100644 index e660e68a..00000000 --- a/.serena/project.yml +++ /dev/null @@ -1,133 +0,0 @@ -# the name by which the project can be referenced within Serena -project_name: "quantem" - - -# list of languages for which language servers are started; choose from: -# al angular ansible bash clojure -# cpp cpp_ccls crystal csharp csharp_omnisharp -# dart elixir elm erlang fortran -# fsharp go groovy haskell haxe -# hlsl html java json julia -# kotlin lean4 lua luau markdown -# matlab msl nix ocaml pascal -# perl php php_phpactor powershell python -# python_jedi python_ty r rego ruby -# ruby_solargraph rust scala scss solidity -# svelte swift systemverilog terraform toml -# typescript typescript_vts vue yaml zig -# (This list may be outdated. For the current list, see values of Language enum here: -# https://github.com/oraios/serena/blob/main/src/solidlsp/ls_config.py -# For some languages, there are alternative language servers, e.g. csharp_omnisharp, ruby_solargraph.) -# Note: -# - For C, use cpp -# - For JavaScript, use typescript -# - For Angular projects, use angular (subsumes typescript+html; requires `npm install` in the project root) -# - For Svelte projects, use svelte (subsumes typescript/javascript for .svelte projects; requires npm) -# - For SCSS / Sass / plain CSS, use scss (some-sass-language-server handles all three) -# - For Free Pascal/Lazarus, use pascal -# Special requirements: -# Some languages require additional setup/installations. -# See here for details: https://oraios.github.io/serena/01-about/020_programming-languages.html#language-servers -# When using multiple languages, the first language server that supports a given file will be used for that file. -# The first language is the default language and the respective language server will be used as a fallback. -# Note that when using the JetBrains backend, language servers are not used and this list is correspondingly ignored. -languages: -- python - -# the encoding used by text files in the project -# For a list of possible encodings, see https://docs.python.org/3.11/library/codecs.html#standard-encodings -encoding: "utf-8" - -# line ending convention to use when writing source files. -# Possible values: unset (use global setting), "lf", "crlf", or "native" (platform default) -# This does not affect Serena's own files (e.g. memories and configuration files), which always use native line endings. -line_ending: - -# The language backend to use for this project. -# If not set, the global setting from serena_config.yml is used. -# Valid values: LSP, JetBrains -# Note: the backend is fixed at startup. If a project with a different backend -# is activated post-init, an error will be returned. -language_backend: - -# whether to use project's .gitignore files to ignore files -ignore_all_files_in_gitignore: true - -# advanced configuration option allowing to configure language server-specific options. -# Maps the language key to the options. -# Have a look at the docstring of the constructors of the LS implementations within solidlsp (e.g., for C# or PHP) to see which options are available. -# No documentation on options means no options are available. -ls_specific_settings: {} - -# list of additional workspace folder paths for cross-package reference support (e.g. in monorepos). -# Paths can be absolute or relative to the project root. -# Each folder is registered as an LSP workspace folder, enabling language servers to discover -# symbols and references across package boundaries. -# Currently supported for: TypeScript. -# Example: -# additional_workspace_folders: -# - ../sibling-package -# - ../shared-lib -additional_workspace_folders: [] - -# list of additional paths to ignore in this project. -# Same syntax as gitignore, so you can use * and **. -# Note: global ignored_paths from serena_config.yml are also applied additively. -ignored_paths: [] - -# whether the project is in read-only mode -# If set to true, all editing tools will be disabled and attempts to use them will result in an error -# Added on 2025-04-18 -read_only: false - -# list of tool names to exclude. -# This extends the existing exclusions (e.g. from the global configuration) -# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html -excluded_tools: [] - -# list of tools to include that would otherwise be disabled (particularly optional tools that are disabled by default). -# This extends the existing inclusions (e.g. from the global configuration). -# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html -included_optional_tools: [] - -# fixed set of tools to use as the base tool set (if non-empty), replacing Serena's default set of tools. -# This cannot be combined with non-empty excluded_tools or included_optional_tools. -# Find the list of tools here: https://oraios.github.io/serena/01-about/035_tools.html -fixed_tools: [] - -# list of mode names that are to be activated by default, overriding the setting in the global configuration. -# The full set of modes to be activated is base_modes (from global config) + default_modes + added_modes. -# If the setting is undefined/empty, the default_modes from the global configuration (serena_config.yml) apply. -# Otherwise, this overrides the setting from the global configuration (serena_config.yml). -# Therefore, you can set this to [] if you do not want the default modes defined in the global config to apply -# for this project. -# This setting can, in turn, be overridden by CLI parameters (--mode). -# See https://oraios.github.io/serena/02-usage/050_configuration.html#modes -default_modes: - -# list of mode names to be activated additionally for this project, e.g. ["query-projects"] -# The full set of modes to be activated is base_modes (from global config) + default_modes + added_modes. -# See https://oraios.github.io/serena/02-usage/050_configuration.html#modes -added_modes: - -# initial prompt for the project. 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}, ] From 39cc13c2a912de67fd28c930937f0dc1cd441515 Mon Sep 17 00:00:00 2001 From: cedriclim1 Date: Thu, 9 Jul 2026 09:44:19 -0700 Subject: [PATCH 334/335] Remove torch.compile from INR ray helpers for Windows compatibility create_batch_rays and integrate_rays were decorated with @torch.compile(mode="reduce-overhead"). Calling them triggers TorchInductor, which needs a C compiler to build its kernel. On Windows runners without MSVC (cl.exe) on PATH this raises InductorError, failing tests/tomography/test_dataset_models.py::TestINRRayMath on windows-latest. Both are trivial tensor ops (a small fill and a single reduction) and reduce-overhead is a no-op on CPU, so eager execution is equivalent and portable across platforms. --- src/quantem/tomography/dataset_models.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/quantem/tomography/dataset_models.py b/src/quantem/tomography/dataset_models.py index 6ede23be..a1a51bad 100644 --- a/src/quantem/tomography/dataset_models.py +++ b/src/quantem/tomography/dataset_models.py @@ -536,7 +536,6 @@ def get_coords( return all_coords @staticmethod - @torch.compile(mode="reduce-overhead") def create_batch_rays( pixel_i: torch.Tensor, pixel_j: torch.Tensor, N: int, num_samples_per_ray: int ) -> torch.Tensor: @@ -599,7 +598,6 @@ def transform_batch_rays( return transformed_rays @staticmethod - @torch.compile(mode="reduce-overhead") def integrate_rays( rays: torch.Tensor, num_samples_per_ray: int, target_values_len: int ) -> torch.Tensor: From 17bceb76040fd3a71c266f5b2106ec68c52c42d9 Mon Sep 17 00:00:00 2001 From: quantem-bot Date: Thu, 9 Jul 2026 17:00:45 +0000 Subject: [PATCH 335/335] Bump version to 0.1.9 --- pyproject.toml | 4 ++-- uv.lock | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 19882bf9..4df9fc22 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,7 +29,7 @@ members = ["widget"] [project] name = "quantem" -version = "0.1.8" +version = "0.1.9" description = "quantitative electron microscopy analysis toolkit." keywords = ["EM","TEM","STEM","4DSTEM"] readme = "README.md" @@ -82,4 +82,4 @@ dev = [ "pre-commit>=4.2.0", "ruff>=0.11.5", "tomli>=2.2.1", -] \ No newline at end of file +] diff --git a/uv.lock b/uv.lock index 888375c2..f1c50c06 100644 --- a/uv.lock +++ b/uv.lock @@ -2890,7 +2890,7 @@ wheels = [ [[package]] name = "quantem" -version = "0.1.8" +version = "0.1.9" source = { editable = "." } dependencies = [ { name = "cmasher" },