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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>
(cherry picked from commit 499d5031e8)
This commit is contained in:
Poons 2021-01-11 16:32:30 +05:30 committed by Jirka Borovec
parent 64b701f72e
commit defbeeccd3
1 changed files with 8 additions and 9 deletions
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17
pytorch_lightning/core/lightning.py
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@ -620,14 +620,14 @@ class LightningModule(
for val_batch in val_data:
out = validation_step(val_batch)
val_outs.append(out)
validation_epoch_end(val_outs)
validation_epoch_end(val_outs)
Args:
batch (:class:`~torch.Tensor` | (:class:`~torch.Tensor`, ...) | [:class:`~torch.Tensor`, ...]):
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): The index of 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:
Any of.
@ -675,11 +675,11 @@ class LightningModule(
# log the outputs!
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
# CASE 2: multiple validation datasets
# CASE 2: multiple validation dataloaders
def validation_step(self, batch, batch_idx, dataloader_idx):
# dataloader_idx tells you which dataset this is.
@ -811,7 +811,7 @@ class LightningModule(
The output of your :class:`~torch.utils.data.DataLoader`. A tensor, tuple or list.
batch_idx (int): The index of 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:
Any of.
@ -850,17 +850,16 @@ class LightningModule(
# log the outputs!
self.log_dict({'test_loss': loss, 'test_acc': test_acc})
If you pass in multiple validation datasets, :meth:`test_step` will have an additional
argument.
If you pass in multiple test dataloaders, :meth:`test_step` will have an additional argument.
.. code-block:: python
# CASE 2: multiple test datasets
# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx):
# dataloader_idx tells you which dataset this is.
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:
When the :meth:`test_step` is called, the model has been put in eval mode and