44 lines
1.7 KiB
ReStructuredText
44 lines
1.7 KiB
ReStructuredText
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Accessing DataLoaders
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=====================
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In the case that you require access to the :class:`torch.utils.data.DataLoader` or :class:`torch.utils.data.Dataset` objects, DataLoaders for each step can be accessed
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via the trainer properties :meth:`~lightning.pytorch.trainer.trainer.Trainer.train_dataloader`,
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:meth:`~lightning.pytorch.trainer.trainer.Trainer.val_dataloaders`,
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:meth:`~lightning.pytorch.trainer.trainer.Trainer.test_dataloaders`, and
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:meth:`~lightning.pytorch.trainer.trainer.Trainer.predict_dataloaders`.
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.. code-block:: python
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dataloaders = trainer.train_dataloader
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dataloaders = trainer.val_dataloaders
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dataloaders = trainer.test_dataloaders
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dataloaders = trainer.predict_dataloaders
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These properties will match exactly what was returned in your ``*_dataloader`` hooks or passed to the ``Trainer``,
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meaning that if you returned a dictionary of dataloaders, these will return a dictionary of dataloaders.
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Replacing DataLoaders
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---------------------
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If you are using a :class:`~lightning.pytorch.utilities.CombinedLoader`. A flattened list of DataLoaders can be accessed by doing:
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.. code-block:: python
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from lightning.pytorch.utilities import CombinedLoader
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iterables = {"dl1": dl1, "dl2": dl2}
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combined_loader = CombinedLoader(iterables)
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# access the original iterables
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assert combined_loader.iterables is iterables
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# the `.flattened` property can be convenient
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assert combined_loader.flattened == [dl1, dl2]
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# for example, to do a simple loop
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updated = []
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for dl in combined_loader.flattened:
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new_dl = apply_some_transformation_to(dl)
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updated.append(new_dl)
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# it also allows you to easily replace the dataloaders
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combined_loader.flattened = updated
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