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added docs (#944)

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William Falcon
2020-02-25 15:05:56 -05:00
committed by GitHub
parent be83e7515b
commit 96b058c5fa
1 changed files with 36 additions and 11 deletions
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pytorch_lightning/core/lightning.py
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@@ -930,16 +930,23 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
:return: PyTorch DataLoader
Called by lightning during training loop. Make sure to use the @pl.data_loader decorator,
this ensures not calling this function until the data are needed.
If you want to change the data during every epoch DON'T use the data_loader decorator.
Return a dataloader. It will not be called every epoch unless you set
```Trainer(reload_dataloaders_every_epoch=True)```.
It's recommended that all data downloads and preparation happen in prepare_data().
.. note:: Lightning adds the correct sampler for distributed and arbitrary hardware. No need to set yourself.
- .fit()
- ...
- prepare_data()
- train_dataloader
Example
-------
.. code-block:: python
@pl.data_loader
def train_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
@@ -967,8 +974,19 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
def test_dataloader(self):
r"""
Called by lightning during test loop. Make sure to use the @pl.data_loader decorator,
this ensures not calling this function until the data are needed.
Return a dataloader. It will not be called every epoch unless you set
```Trainer(reload_dataloaders_every_epoch=True)```.
It's recommended that all data downloads and preparation happen in prepare_data().
- .fit()
- ...
- prepare_data()
- train_dataloader
- val_dataloader
- test_dataloader
.. note:: Lightning adds the correct sampler for distributed and arbitrary hardware. No need to set yourself.
Return:
PyTorch DataLoader
@@ -978,7 +996,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
.. code-block:: python
@pl.data_loader
def test_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
@@ -1000,8 +1017,18 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
def val_dataloader(self):
r"""
Called by lightning during validation loop. Make sure to use the @pl.data_loader decorator,
this ensures not calling this function until the data are needed.
Return a dataloader. It will not be called every epoch unless you set
```Trainer(reload_dataloaders_every_epoch=True)```.
It's recommended that all data downloads and preparation happen in prepare_data().
- .fit()
- ...
- prepare_data()
- train_dataloader
- val_dataloader
.. note:: Lightning adds the correct sampler for distributed and arbitrary hardware No need to set yourself.
Return:
PyTorch DataLoader
@@ -1011,7 +1038,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
.. code-block:: python
@pl.data_loader
def val_dataloader(self):
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
@@ -1024,7 +1050,6 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
return loader
# can also return multiple dataloaders
@pl.data_loader
def val_dataloader(self):
return [loader_a, loader_b, ..., loader_n]