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model_checkpoint to save all models (#1359) 

* model_checkpoint to save all models

* changelog

* rise if

Co-authored-by: jamesjjcondon <jamesjjcondon@gmail.com>
Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
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William Falcon 2020-04-05 09:56:26 -04:00 committed by GitHub
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commit f1e11d8b38
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CHANGELOG.md
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@ -56,7 +56,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
### Fixed
- `Trainer.add_argparse_args` classmethod fixed. Now it adds a type for the arguments ([#1147](https://github.com/PyTorchLightning/pytorch-lightning/pull/1147)).
- Fixed `model_checkpoint` when saving all models ([#1359](https://github.com/PyTorchLightning/pytorch-lightning/pull/1359))
- `Trainer.add_argparse_args` classmethod fixed. Now it adds a type for the arguments ([#1147](https://github.com/PyTorchLightning/pytorch-lightning/pull/1147))
- Fixed bug related to type cheking of `ReduceLROnPlateau` lr schedulers([#1114](https://github.com/PyTorchLightning/pytorch-lightning/issues/1114))
- Fixed a bug to ensure lightning checkpoints to be backward compatible ([#1132](https://github.com/PyTorchLightning/pytorch-lightning/pull/1132))
- Fixed a bug that created an extra dataloader with active `reload_dataloaders_every_epoch` ([#1181](https://github.com/PyTorchLightning/pytorch-lightning/issues/1181)

4
pytorch_lightning/callbacks/model_checkpoint.py
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@ -82,7 +82,7 @@ class ModelCheckpoint(Callback):
save_top_k: int = 1, save_weights_only: bool = False,
mode: str = 'auto', period: int = 1, prefix: str = ''):
super().__init__()
if save_top_k and os.path.isdir(filepath) and len(os.listdir(filepath)) > 0:
if save_top_k > 0 and os.path.isdir(filepath) and len(os.listdir(filepath)) > 0:
warnings.warn(
f"Checkpoint directory {filepath} exists and is not empty with save_top_k != 0."
"All files in this directory will be deleted when a checkpoint is saved!"
@ -219,7 +219,7 @@ class ModelCheckpoint(Callback):
def _do_check_save(self, filepath, current, epoch):
# remove kth
if len(self.best_k_models) == self.save_top_k:
if len(self.best_k_models) == self.save_top_k and self.save_top_k > 0:
delpath = self.kth_best_model
self.best_k_models.pop(self.kth_best_model)
self._del_model(delpath)