🐞 Fix issues when validation and test split modes set to none #1703
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Signed-off-by: Willy Fitra Hendria <willyfitrahendria@gmail.com>
Signed-off-by: Willy Fitra Hendria <willyfitrahendria@gmail.com>
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I have a few questions regarding this fix. I am not clear on the assumed intention behind passing split mode as None. From what I understand it is only to split the datamodule. But it should not restrict users if they pass test or validation dataset separately. But I don't have a lot of familiarity with data modules. Maybe @djdameln can chime in.
| @@ -671,7 +675,12 @@ def train( | |||
| self.trainer.validate(model, val_dataloaders, None, verbose=False, datamodule=datamodule) | |||
| else: | |||
| self.trainer.fit(model, train_dataloaders, val_dataloaders, datamodule, ckpt_path) | |||
| self.trainer.test(model, test_dataloaders, ckpt_path=ckpt_path, datamodule=datamodule) | |||
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| if datamodule is not None and datamodule.test_split_mode == TestSplitMode.NONE: | |||
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What happens if datamodule is not None and test_dataloaders is not None
| Returns: | ||
| DictConfig | ListConfig: Updated configurable parameters in DictConfig object. | ||
| """ | ||
| if config.data.init_args.val_split_mode == ValSplitMode.NONE and config.trainer.limit_val_batches != 0.0: |
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Are we sure that if val_split_mode is None, that validation dataset is not provided separately?
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📝 Description
This PR is based on the close PR #1628:
when val_split_mode is 'NONE' , skip the validation stage by setting
config.trainer.limit_val_batchesto 0.0when test_split_mode is 'NONE', skip the test stage by including a conditional check
datamodule.test_split_mode == TestSplitMode.NONE. To address a comment in the closed PR, a warning is provided to check the length of the test set that is being skipped.🛠️ Fixes 🐞 v1 - Issues when validation and test split modes are set to 'none' #1629
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