allow continuation of model training #386
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hughes036
reviewed
Jun 4, 2024
cyto_dl/train.py
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| if cfg.get("weights_only"): | ||
| # load model from state dict to get around trainer.max_epochs limit, useful for resuming model training from existing weights | ||
| ckpt_path = cfg.get("ckpt_path") | ||
| state_dict = torch.load(ckpt_path)["state_dict"] |
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Is there any logic validating that when weights_only, the other keys need to exist?
hughes036
reviewed
Jun 4, 2024
| @@ -97,9 +97,11 @@ def train(cfg: DictConfig) -> Tuple[dict, dict]: | |||
| log.info("Starting training!") | |||
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| if cfg.get("weights_only"): | |||
| assert cfg.get( | |||
| "ckpt_path" | |||
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Nice!
Any unit tests that exercise this?
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manual testing only at the moment... at the moment I'm reluctant to add more unit tests when we're already timing out on windows.
hughes036
approved these changes
Jun 4, 2024
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What does this PR do?
weights_onlytop-level config arg to resume training from model weights (ignoring lightning optimizer metadata etc.)Before submitting
pytestcommand?pre-commit run -acommand?Did you have fun?
Make sure you had fun coding 🙃