Furen Xiao
a516408c4e
- Implemented 09_run_iterative.py for MONAI to orchestrate iterative pseudo-labeling rounds, including training, predicting, and evaluation. - Created 01_prepare_dataset.py, 02_plan_preprocess.py, 06_pseudo_label.py, 07_train.py, 08_eval.py, and 09_run_iterative.py for nnU-Net to manage dataset preparation, planning, training, evaluation, and iterative pseudo-labeling. - Enhanced error handling and logging throughout the scripts for better traceability. - Added functionality to handle negative pseudo-labels and TTA options during inference. - Included visualization of Dice scores across rounds for both MONAI and nnU-Net.
33 lines
No EOL
1.6 KiB
Python
33 lines
No EOL
1.6 KiB
Python
"""Plan + preprocess the nnU-Net dataset (always --clean), then write the
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subject-level splits_final.json: val = split_val cases, train = the rest
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(all base + pseudo cases), same 5-fold content so -f 0 is the one we use.
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Usage: python scripts_nnu/02_nnu_plan_preprocess.py --val data/manifests/split_val.jsonl [--npp 8]
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"""
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import argparse
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from src.common import load_jsonl
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from nnu_common import run, nnu_env, plan_preprocess_cmd, dataset_case_keys, write_splits, preproc_ds
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def main():
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ap = argparse.ArgumentParser()
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ap.add_argument("--val", required=True, help="jsonl of internal-validation cases (subject-disjoint)")
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ap.add_argument("--npp", type=int, default=8, help="preprocess processes")
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ap.add_argument("--log", default=None)
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args = ap.parse_args()
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log = args.log or os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "logs", "nnu_plan.log")
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run(plan_preprocess_cmd(args.npp), log, env=nnu_env())
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val_keys = [r["key"] for r in load_jsonl(args.val)]
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all_keys = dataset_case_keys()
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assert all_keys, "raw dataset is empty; run 01_nnu_prepare_dataset.py first"
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assert set(val_keys) <= set(all_keys), f"val keys not in dataset: {set(val_keys) - set(all_keys)}"
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n_tr, n_va = write_splits(all_keys, val_keys)
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print(f"[nnu:plan] splits_final.json written to {preproc_ds()}: train={n_tr} val={n_va}", flush=True)
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if __name__ == "__main__":
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main() |