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