Bump version to 0.1.9#263
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Optimizer mixin hotfix
…-20260302 chore: Update dependencies
…tter for printing?
change kwargs in read4stem
move tagging and release to deploy action
Overhaul of vector
…-20260309 chore: Update dependencies
…ion, constructors, define_lattice() and AutoSerialize implementation.
…-20260601 chore: Update dependencies
…raphyDatasetBase.
Per-parameter Learning Rate (PPLR) Implementation, Tensor Decomposition Models, and Tomography Updates
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.
Fix latent bugs in tomography optimizer wiring, constraints, and radon
Tomography Slow/Fast Tests
…-20260608 chore: Update dependencies
…-20260615 chore: Update dependencies
Normalizing by the quantile now an option in Tomo Dataset
…er than comparing the shapes within the tilt series stack.
Tomography Dataset Hotfix
…-20260629 chore: Update dependencies
…-20260706 chore: Update dependencies
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.
Remove torch.compile from INR ray helpers for Windows compatibility
gvarnavi
approved these changes
Jul 9, 2026
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This PR was automatically created because the submitted version
0.1.8matched the current release onmain.It bumps the patch version to
0.1.9and starts a new release process.