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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
scripts feat(train): implement checkpointing and robust data loading 2026-09-26 03:44:12 +08:00
src feat(train): implement checkpointing and robust data loading 2026-09-26 03:44:12 +08:00
.gitignore feat: initial project structure 2026-09-25 16:00:37 +08:00
AGENTS.md feat: initial project structure 2026-09-25 16:00:37 +08:00
README.md first commit 2026-09-25 10:15:20 +08:00

README.md