80 lines
2.2 KiB
ReStructuredText
80 lines
2.2 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.core.lightning import LightningModule
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.. _multiple_loaders:
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Multiple Datasets
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=================
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Lightning supports multiple dataloaders in a few ways.
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1. Create a dataloader that iterates both datasets under the hood.
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2. In the validation and test loop you also have the option to return multiple dataloaders
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which lightning will call sequentially.
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----------
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Multiple training dataloaders
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-----------------------------
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For training, the best way to use multiple-dataloaders is to create a Dataloader class
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which wraps both your dataloaders. (This of course also works for testing and validation
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dataloaders).
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(`reference <https://discuss.pytorch.org/t/train-simultaneously-on-two-datasets/649/2>`_)
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.. testcode::
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class ConcatDataset(torch.utils.data.Dataset):
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def __init__(self, *datasets):
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self.datasets = datasets
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def __getitem__(self, i):
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return tuple(d[i] for d in self.datasets)
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def __len__(self):
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return min(len(d) for d in self.datasets)
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class LitModel(LightningModule):
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def train_dataloader(self):
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concat_dataset = ConcatDataset(
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datasets.ImageFolder(traindir_A),
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datasets.ImageFolder(traindir_B)
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)
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loader = torch.utils.data.DataLoader(
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concat_dataset,
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batch_size=args.batch_size,
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shuffle=True,
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num_workers=args.workers,
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pin_memory=True
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)
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return loader
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def val_dataloader(self):
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# SAME
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...
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def test_dataloader(self):
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# SAME
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...
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----------
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Test/Val dataloaders
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--------------------
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For validation, test dataloaders lightning also gives you the additional
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option of passing in multiple dataloaders back from each call.
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See the following for more details:
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- :meth:`~pytorch_lightning.core.LightningModule.val_dataloader`
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- :meth:`~pytorch_lightning.core.LightningModule.test_dataloader`
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.. testcode::
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def val_dataloader(self):
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loader_1 = Dataloader()
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loader_2 = Dataloader()
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return [loader_1, loader_2]
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