MR-zero submission

submissions/MR-zero/simulation_mr0.py

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+"""MR-zero simulation and timing reference for all five cases.
+
+Writes ``case0.h5`` … ``case4.h5`` next to this file (raw ADC, acquisition
+order). Prints the wall-clock of the MR-zero run itself, after the phantom
+and the sequence are loaded, and names the CPU or GPU that ran it. The same
+lines are written to ``timings.txt`` as each case finishes. Copy your own
+wall-clock into ``runtime_s`` and the time recorded here into
+``mrzero_runtime_s``. Evaluation divides the two.
+
+Install::
+
+    pip install mrzerocore==1.0.8 numpy h5py
+
+The GPU is used when one is present. Pass ``cpu`` or ``gpu`` to choose::
+
+    python submissions/003_MR-zero/simulation_mr0.py
+    python submissions/003_MR-zero/simulation_mr0.py cpu
+    python submissions/003_MR-zero/simulation_mr0.py gpu
+"""
+import sys
+import time
+from pathlib import Path
+
+import h5py
+import MRzeroCore as mr0
+import numpy as np
+import torch
+
+here = Path(__file__).resolve().parent
+root = here.parents[0] #modify this so it points to git base directory to run
+LARMOR = 127.7292e6  # Hz. Published with the sequences.
+
+# Example solver setup, not a challenge rule.
+MAX_STATES = 200
+MIN_STATE = 1e-3
+
+# Case 4 repeats the EPI file on the phantom that carries T2'.
+CASES = (
+    (0, 'case0_fid.seq', 'challenge-brain-4mm.json'),
+    (1, 'case1_gre.seq', 'challenge-brain-4mm.json'),
+    (2, 'case2_tse.seq', 'challenge-brain-4mm.json'),
+    (3, 'case3_epi.seq', 'challenge-brain-4mm.json'),
+    (4, 'case3_epi.seq', 'challenge-brain-4mm-t2star.json'),
+)
+
+
+def use_gpu(argv: list[str]) -> bool:
+    choice = argv[1] if len(argv) > 1 else 'auto'
+    if choice == 'cpu':
+        return False
+    if choice == 'gpu' and not torch.cuda.is_available():
+        raise SystemExit('gpu was requested, but CUDA is not available')
+    if choice not in {'auto', 'gpu'}:
+        raise SystemExit('usage: simulation_mr0.py [cpu|gpu]')
+    return torch.cuda.is_available()
+
+
+def hardware(gpu: bool) -> str:
+    if gpu:
+        return torch.cuda.get_device_name(0)
+    return 'cpu'
+
+
+def load_phantom(name: str, gpu: bool):
+    obj = mr0.TissueDict.load(str(root / 'phantoms' / 'brainweb' / name)).build()
+    return obj.cuda() if gpu else obj
+
+
+def load_sequence(name: str, gpu: bool):
+    seq = mr0.Sequence.import_file(str(root / 'sequences' / name), larmor_hz=LARMOR)
+    return seq.cuda() if gpu else seq
+
+
+def write_signal(index: int, signal: np.ndarray) -> Path:
+    path = here / f'case{index}.h5'
+    with h5py.File(path, 'w') as handle:
+        handle.create_dataset('signal_real', data=signal.real)
+        handle.create_dataset('signal_imag', data=signal.imag)
+    return path
+
+
+def write_timings(where: str, lines: list[str]) -> Path:
+    path = here / 'timings.txt'
+    path.write_text('hardware: ' + where + '\n' + '\n'.join(lines) + '\n')
+    return path
+
+
+def main() -> None:
+    gpu = use_gpu(sys.argv)
+    where = hardware(gpu)
+    phantoms: dict[str, object] = {}
+    lines: list[str] = []
+    print(f'hardware: {where}')
+    for index, seq_name, phantom_name in CASES:
+        if phantom_name not in phantoms:
+            phantoms[phantom_name] = load_phantom(phantom_name, gpu)
+        seq = load_sequence(seq_name, gpu)
+        obj = phantoms[phantom_name]
+        if gpu:
+            torch.cuda.synchronize()
+        started = time.perf_counter()
+        graph = mr0.compute_graph(seq, obj, max_state_count=MAX_STATES, min_state_mag=MIN_STATE)
+        signal = mr0.execute_graph(graph, seq, obj, print_progress=False)
+        if gpu:
+            torch.cuda.synchronize()
+        seconds = time.perf_counter() - started
+        adc = np.asarray(signal.detach().cpu()).reshape(-1)
+        path = write_signal(index, adc)
+        line = f'case {index}  {seconds:8.1f} s  {adc.size:5d} samples  {path.name}'
+        print(line)
+        lines.append(line)
+        write_timings(where, lines)
+    print(f'wrote {here / "timings.txt"}')
+
+
+if __name__ == '__main__':
+    main()

submissions/MR-zero/submission.json

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+{
+  "group": "zMRI Lab (Prof. Zaiss)",
+  "tool_name": "MR-zero",
+  "tool_version": "1.0.8",
+  "tool_commit": "",
+  "tool_date": "",
+  "solver": "",
+  "project_page": "https://github.com/MRsources/MRzero-Core",
+  "paper": "",
+  "hardware": "Intel Core i7-12700K",
+  "cases": [
+    {"case": 0, "runtime_s": 3.5, "mrzero_runtime_s": 3.5},
+    {"case": 1, "runtime_s": 25, "mrzero_runtime_s": 25},
+    {"case": 2, "runtime_s": 10.8, "mrzero_runtime_s": 10.8},
+    {"case": 3, "runtime_s": 13.1, "mrzero_runtime_s": 13.1},
+    {"case": 4, "runtime_s": 13.5, "mrzero_runtime_s": 13.5}
+  ],
+  "setup_notes": "Maximum RAM usage minus baseline usage in GB: case0: 9, case1: 2, case2: 2, case3: 37, case4: 37"
+}