def test_multiple_val_dataloader(tmpdir):
"""Verify multiple val_dataloader."""
tutils.reset_seed()
class CurrentTestModel(LightningValidationMultipleDataloadersMixin,
LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=1.0,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
# verify training completed
assert result == 1
# verify there are 2 val loaders
assert len(trainer.get_val_dataloaders()) == 2, \
'Multiple val_dataloaders not initiated properly'
# make sure predictions are good for each val set
for dataloader in trainer.get_val_dataloaders():
tutils.run_prediction(dataloader, trainer.model)
def test_mixing_of_dataloader_options(tmpdir):
"""Verify that dataloaders can be passed to fit"""
tutils.reset_seed()
class CurrentTestModel(LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=0.2)
# fit model
trainer = Trainer(**trainer_options)
fit_options = dict(val_dataloader=model._dataloader(train=False))
results = trainer.fit(model, **fit_options)
# fit model
trainer = Trainer(**trainer_options)
fit_options = dict(val_dataloader=model._dataloader(train=False),
test_dataloader=model._dataloader(train=False))
results = trainer.fit(model, **fit_options)
assert len(trainer.get_val_dataloaders()) == 1, \
f'`val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
assert len(trainer.get_test_dataloaders()) == 1, \
f'`test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
def test_multiple_dataloaders_passed_to_fit(tmpdir):
""" Verify that multiple val & test dataloaders can be passed to fit """
tutils.reset_seed()
class CurrentTestModel(LightningTestModelBaseWithoutDataloader):
pass
hparams = tutils.get_hparams()
# logger file to get meta
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=0.2)
# train, multiple val and multiple test passed to fit
model = CurrentTestModel(hparams)
trainer = Trainer(**trainer_options)
fit_options = dict(train_dataloader=model._dataloader(train=True),
val_dataloader=[
model._dataloader(train=False),
model._dataloader(train=False)
],
test_dataloader=[
model._dataloader(train=False),
model._dataloader(train=False)
])
results = trainer.fit(model, **fit_options)
assert len(trainer.get_val_dataloaders()) == 2, \
f'Multiple `val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
assert len(trainer.get_test_dataloaders()) == 2, \
f'Multiple `test_dataloaders` not initiated properly, got {trainer.get_test_dataloaders()}'
