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fix typos in validation_step and test_step docs (#5438) 

* fixed docs in lightning.py

* few more

Co-authored-by: Rohit Gupta <rohitgr1998@gmail.com>
Co-authored-by: Nicki Skafte <skaftenicki@gmail.com>
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Poons 2021-01-11 16:32:30 +05:30 committed by GitHub
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pytorch_lightning/core/lightning.py
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@ -621,14 +621,14 @@ class LightningModule(
for val_batch in val_data: for val_batch in val_data:
out = validation_step(val_batch) out = validation_step(val_batch)
val_outs.append(out) val_outs.append(out)
validation_epoch_end(val_outs) validation_epoch_end(val_outs)
Args: Args:
batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]): batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list. The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): The index of this batch batch_idx (int): The index of this batch
dataloader_idx (int): The index of the dataloader that produced this batch dataloader_idx (int): The index of the dataloader that produced this batch
(only if multiple val datasets used) (only if multiple val dataloaders used)
Return: Return:
Any of. Any of.
@ -677,11 +677,11 @@ class LightningModule(
# log the outputs! # log the outputs!
self.log_dict({'val_loss': loss, 'val_acc': val_acc}) self.log_dict({'val_loss': loss, 'val_acc': val_acc})
If you pass in multiple val datasets, validation_step will have an additional argument. If you pass in multiple val dataloaders, :meth:`validation_step` will have an additional argument.
.. code-block:: python .. code-block:: python
# CASE 2: multiple validation datasets # CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx): def validation_step(self, batch, batch_idx, dataloader_idx):
# dataloader_idx tells you which dataset this is. # dataloader_idx tells you which dataset this is.
@ -813,7 +813,7 @@ class LightningModule(
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list. The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): The index of this batch. batch_idx (int): The index of this batch.
dataloader_idx (int): The index of the dataloader that produced this batch dataloader_idx (int): The index of the dataloader that produced this batch
(only if multiple test datasets used). (only if multiple test dataloaders used).
Return: Return:
Any of. Any of.
@ -853,17 +853,17 @@ class LightningModule(
# log the outputs! # log the outputs!
self.log_dict({'test_loss': loss, 'test_acc': test_acc}) self.log_dict({'test_loss': loss, 'test_acc': test_acc})
If you pass in multiple validation datasets, :meth:`test_step` will have an additional If you pass in multiple test dataloaders, :meth:`test_step` will have an additional
argument. argument.
.. code-block:: python .. code-block:: python
# CASE 2: multiple test datasets # CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx): def test_step(self, batch, batch_idx, dataloader_idx):
# dataloader_idx tells you which dataset this is. # dataloader_idx tells you which dataset this is.
Note: Note:
If you don't need to validate you don't need to implement this method. If you don't need to test you don't need to implement this method.
Note: Note:
When the :meth:`test_step` is called, the model has been put in eval mode and When the :meth:`test_step` is called, the model has been put in eval mode and