def test_multiple_val_dataloader(tmpdir):
"""Verify multiple val_dataloader."""
model = EvalModelTemplate()
model.val_dataloader = model.val_dataloader__multiple
model.validation_step = model.validation_step__multiple_dataloaders
model.validation_epoch_end = model.validation_epoch_end__multiple_dataloaders
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_val_batches=0.1,
limit_train_batches=1.0,
)
result = trainer.fit(model)
# verify training completed
assert result == 1
# verify there are 2 val loaders
assert len(trainer.val_dataloaders) == 2, \
'Multiple val_dataloaders not initiated properly'
# make sure predictions are good for each val set
for dataloader in trainer.val_dataloaders:
tutils.run_prediction(dataloader, trainer.model)
def test_multiple_test_dataloader(tmpdir, ckpt_path):
"""Verify multiple test_dataloader."""
model_template = EvalModelTemplate()
class MultipleTestDataloaderModel(EvalModelTemplate):
def test_dataloader(self):
return model_template.test_dataloader__multiple()
def test_step(self, batch, batch_idx, *args, **kwargs):
return model_template.test_step__multiple_dataloaders(
batch, batch_idx, *args, **kwargs)
model = MultipleTestDataloaderModel()
# fit model
trainer = Trainer(default_root_dir=tmpdir,
max_epochs=1,
limit_val_batches=0.1,
limit_train_batches=0.2)
trainer.fit(model)
if ckpt_path == 'specific':
ckpt_path = trainer.checkpoint_callback.best_model_path
trainer.test(ckpt_path=ckpt_path)
# verify there are 2 test loaders
assert len(trainer.test_dataloaders) == 2, \
'Multiple test_dataloaders not initiated properly'
# make sure predictions are good for each test set
for dataloader in trainer.test_dataloaders:
tutils.run_prediction(dataloader, trainer.model)
# run the test method
trainer.test(ckpt_path=ckpt_path)
def test_multiple_test_dataloader(tmpdir):
"""Verify multiple test_dataloader."""
model = EvalModelTemplate()
model.test_dataloader = model.test_dataloader__multiple
model.test_step = model.test_step__multiple_dataloaders
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=0.2
)
trainer.fit(model)
trainer.test()
# verify there are 2 test loaders
assert len(trainer.test_dataloaders) == 2, \
'Multiple test_dataloaders not initiated properly'
# make sure predictions are good for each test set
for dataloader in trainer.test_dataloaders:
tutils.run_prediction(dataloader, trainer.model)
# run the test method
trainer.test()
def test_multiple_test_dataloader(tmpdir):
"""Verify multiple test_dataloader."""
class CurrentTestModel(
LightTrainDataloader,
LightTestMultipleDataloadersMixin,
LightEmptyTestStep,
TestModelBase,
):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
# fit model
trainer = Trainer(default_root_dir=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=0.2)
trainer.fit(model)
trainer.test()
# verify there are 2 test loaders
assert len(trainer.test_dataloaders) == 2, \
'Multiple test_dataloaders not initiated properly'
# make sure predictions are good for each test set
for dataloader in trainer.test_dataloaders:
tutils.run_prediction(dataloader, trainer.model)
# run the test method
trainer.test()
def test_multiple_val_dataloader(tmpdir):
"""Verify multiple val_dataloader."""
class CurrentTestModel(
LightTrainDataloader,
LightValidationMultipleDataloadersMixin,
TestModelBase,
):
pass
hparams = tutils.get_default_hparams()
model = CurrentTestModel(hparams)
# fit model
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
val_percent_check=0.1,
train_percent_check=1.0,
)
result = trainer.fit(model)
# verify training completed
assert result == 1
# verify there are 2 val loaders
assert len(trainer.val_dataloaders) == 2, \
'Multiple val_dataloaders not initiated properly'
# make sure predictions are good for each val set
for dataloader in trainer.val_dataloaders:
tutils.run_prediction(dataloader, trainer.model)
def assert_good_acc():
assert new_trainer.current_epoch == real_global_epoch and new_trainer.current_epoch > 0
# if model and state loaded correctly, predictions will be good even though we
# haven't trained with the new loaded model
dp_model = new_trainer.model
dp_model.eval()
dataloader = trainer.train_dataloader
tutils.run_prediction(dataloader, dp_model, dp=True)
def test_running_test_pretrained_model_distrib(tmpdir, backend):
"""Verify `test()` on pretrained model."""
tutils.reset_seed()
tutils.set_random_master_port()
hparams = tutils.get_default_hparams()
model = LightningTestModel(hparams)
# exp file to get meta
logger = tutils.get_default_logger(tmpdir)
# exp file to get weights
checkpoint = tutils.init_checkpoint_callback(logger)
trainer_options = dict(
progress_bar_refresh_rate=0,
max_epochs=2,
train_percent_check=0.4,
val_percent_check=0.2,
checkpoint_callback=checkpoint,
logger=logger,
gpus=[0, 1],
distributed_backend=backend,
)
# fit model
trainer = Trainer(**trainer_options)
result = trainer.fit(model)
log.info(os.listdir(tutils.get_data_path(logger, path_dir=tmpdir)))
# correct result and ok accuracy
assert result == 1, 'training failed to complete'
pretrained_model = tutils.load_model(logger,
trainer.checkpoint_callback.dirpath,
module_class=LightningTestModel)
# run test set
new_trainer = Trainer(**trainer_options)
new_trainer.test(pretrained_model)
# test we have good test accuracy
tutils.assert_ok_model_acc(new_trainer)
dataloaders = model.test_dataloader()
if not isinstance(dataloaders, list):
dataloaders = [dataloaders]
for dataloader in dataloaders:
tutils.run_prediction(dataloader, pretrained_model)
