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Tests: refactor trainer dataloaders (#1690)

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* refactor default model

* drop redundant seeds

* refactor dataloaders tests

* fix multiple

* fix conf

* flake8

* Apply suggestions from code review

Co-authored-by: William Falcon <waf2107@columbia.edu>

Co-authored-by: William Falcon <waf2107@columbia.edu>
This commit is contained in:
Jirka Borovec
2020-05-05 12:31:15 -04:00
committed by GitHub
co-authored by William Falcon
parent a6de1b8d75
commit 2a2f303ae9
8 changed files with 195 additions and 220 deletions
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pytorch_lightning/trainer/data_loading.py
+10 -10
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@@ -46,8 +46,8 @@ def _has_len(dataloader: DataLoader) -> bool:
try:
# try getting the length
if len(dataloader) == 0:
raise ValueError('Dataloader returned 0 length. Please make sure'
' that your Dataloader atleast returns 1 batch')
raise ValueError('`Dataloader` returned 0 length.'
' Please make sure that your Dataloader at least returns 1 batch')
return True
except TypeError:
return False
@@ -186,10 +186,10 @@ class TrainerDataLoadingMixin(ABC):
self.val_check_batch = float('inf')
else:
raise MisconfigurationException(
'When using an infinite DataLoader (e.g. with an IterableDataset or when '
'DataLoader does not implement `__len__`) for `train_dataloader`, '
'`Trainer(val_check_interval)` must be `1.0` or an int. An int k specifies '
'checking validation every k training batches.')
'When using an infinite DataLoader (e.g. with an IterableDataset'
' or when DataLoader does not implement `__len__`) for `train_dataloader`,'
' `Trainer(val_check_interval)` must be `1.0` or an int. An int k specifies'
' checking validation every k training batches.')
else:
self._percent_range_check('val_check_interval')
@@ -240,9 +240,9 @@ class TrainerDataLoadingMixin(ABC):
num_batches = int(num_batches * percent_check)
elif percent_check not in (0.0, 1.0):
raise MisconfigurationException(
'When using an infinite DataLoader (e.g. with an IterableDataset or when '
f'DataLoader does not implement `__len__`) for `{mode}_dataloader`, '
f'`Trainer({mode}_percent_check)` must be `0.0` or `1.0`.')
'When using an infinite DataLoader (e.g. with an IterableDataset'
f' or when DataLoader does not implement `__len__`) for `{mode}_dataloader`,'
f' `Trainer({mode}_percent_check)` must be `0.0` or `1.0`.')
return num_batches, dataloaders
def reset_val_dataloader(self, model: LightningModule) -> None:
@@ -252,7 +252,7 @@ class TrainerDataLoadingMixin(ABC):
model: The current `LightningModule`
"""
if self.is_overriden('validation_step'):
self.num_val_batches, self.val_dataloaders =\
self.num_val_batches, self.val_dataloaders = \
self._reset_eval_dataloader(model, 'val')
def reset_test_dataloader(self, model) -> None: