Furen Xiao a491ae0523 feat(train): implement checkpointing and robust data loading ... Enhance the training pipeline with stateful checkpointing and improve the resilience of the data loading process against filesystem latency and transient I/O errors. - Implement auto-resuming in `train_ddp` by loading model, optimizer, and scheduler states from `state.pt`. - Add atomic state saving using temporary files to prevent corruption. - Introduce `_read_nii` with exponential backoff retries to handle transient NFS/filesystem failures during NIfTI reading. - Add explicit error handling for missing or unreadable label files in `PatchDataset`. - Update `sliding_window_probs` to conditionally apply Test-Time Augmentation (TTA) based on the `tta` parameter. - Add `scripts/test_dataloader.py` for verifying dataset integrity.		2026-09-26 03:44:12 +08:00
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common.py	feat: initial project structure	2026-09-25 16:00:37 +08:00
dataset.py	feat(train): implement checkpointing and robust data loading	2026-09-26 03:44:12 +08:00
losses.py	feat: initial project structure	2026-09-25 16:00:37 +08:00
training.py	feat(train): implement checkpointing and robust data loading	2026-09-26 03:44:12 +08:00
unet3d.py	feat: initial project structure	2026-09-25 16:00:37 +08:00