def test_train_val_dataloaders_passed_to_fit(tmpdir):
""" Verify that train & val dataloader 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, val 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))
results = trainer.fit(model, **fit_options)
assert len(trainer.get_val_dataloaders()) == 1, \
f'`val_dataloaders` not initiated properly, got {trainer.get_val_dataloaders()}'
def test_inf_val_dataloader(tmpdir):
"""Test inf val data loader (e.g. IterableDataset)"""
tutils.reset_seed()
class CurrentTestModel(LightInfValDataloader, LightningTestModel):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# fit model
with pytest.raises(MisconfigurationException):
trainer = Trainer(default_save_path=tmpdir,
max_epochs=1,
val_percent_check=0.5)
trainer.fit(model)
# logger file to get meta
trainer = Trainer(default_save_path=tmpdir, max_epochs=1)
result = trainer.fit(model)
# verify training completed
assert result == 1
def test_running_test_without_val(tmpdir):
"""Verify `test()` works on a model with no `val_loader`."""
tutils.reset_seed()
class CurrentTestModel(LightningTestMixin, LightningTestModelBase):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# logger file to get meta
logger = tutils.get_test_tube_logger(tmpdir, False)
# logger file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
show_progress_bar=False,
max_epochs=1,
train_percent_check=0.4,
val_percent_check=0.2,
test_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
early_stop_callback=False
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
assert result == 1, 'training failed to complete'
trainer.test()
# test we have good test accuracy
tutils.assert_ok_model_acc(trainer)
def test_trains_logger(tmpdir):
"""Verify that basic functionality of TRAINS logger works."""
tutils.reset_seed()
hparams = tutils.get_hparams()
model = LightningTestModel(hparams)
TrainsLogger.set_bypass_mode(True)
TrainsLogger.set_credentials(api_host='http://integration.trains.allegro.ai:8008',
files_host='http://integration.trains.allegro.ai:8081',
web_host='http://integration.trains.allegro.ai:8080', )
logger = TrainsLogger(project_name="lightning_log", task_name="pytorch lightning test")
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
train_percent_check=0.05,
logger=logger
)
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
print('result finished')
logger.finalize()
assert result == 1, "Training failed"
def test_dataloader_config_errors(tmpdir):
tutils.reset_seed()
class CurrentTestModel(
LightTrainDataloader,
TestModelBase,
):
pass
hparams = tutils.get_hparams()
model = CurrentTestModel(hparams)
# percent check < 0
# logger file to get meta
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
train_percent_check=-0.1,
)
# fit model
trainer = Trainer(**trainer_options)
with pytest.raises(ValueError):
trainer.fit(model)
# percent check > 1
# logger file to get meta
trainer_options = dict(
default_save_path=tmpdir,
max_epochs=1,
train_percent_check=1.1,
)
# fit model
trainer = Trainer(**trainer_options)
with pytest.raises(ValueError):
trainer.fit(model)
# int val_check_interval > num batches
# logger file to get meta
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
val_check_interval=10000)
# fit model
trainer = Trainer(**trainer_options)
with pytest.raises(ValueError):
trainer.fit(model)
# float val_check_interval > 1
# logger file to get meta
trainer_options = dict(default_save_path=tmpdir,
max_epochs=1,
val_check_interval=1.1)
# fit model
trainer = Trainer(**trainer_options)
with pytest.raises(ValueError):
trainer.fit(model)
