def test_num_dataloader_batches(tmpdir):
"""Tests that the correct number of batches are allocated."""
# when we have fewer batches in the dataloader we should use those instead of the limit
model = BoringModel()
trainer = Trainer(limit_val_batches=100,
limit_train_batches=100,
max_epochs=1,
default_root_dir=tmpdir)
trainer.fit(model)
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert isinstance(trainer.num_val_batches, list)
assert trainer.num_val_batches[0] == 64
assert trainer.num_training_batches == 64
# when we have more batches in the dataloader we should limit them
model = BoringModel()
trainer = Trainer(limit_val_batches=7,
limit_train_batches=7,
max_epochs=1,
default_root_dir=tmpdir)
trainer.fit(model)
assert len(model.train_dataloader()) == 64
assert len(model.val_dataloader()) == 64
assert isinstance(trainer.num_val_batches, list)
assert trainer.num_val_batches[0] == 7
assert trainer.num_training_batches == 7
def test_warning_with_few_workers(_, tmpdir, ckpt_path, stage):
""" Test that error is raised if dataloader with only a few workers is used """
model = BoringModel()
train_dl = model.train_dataloader()
train_dl.num_workers = 0
val_dl = model.val_dataloader()
val_dl.num_workers = 0
trainer = Trainer(
default_root_dir=tmpdir,
max_epochs=1,
limit_val_batches=0.1,
limit_train_batches=0.2,
)
with pytest.warns(
UserWarning,
match=f'The dataloader, {stage} dataloader{" 0" if stage != "train" else ""}, does not have many workers'
):
if stage == 'test':
ckpt_path = trainer.checkpoint_callback.best_model_path if ckpt_path == 'specific' else ckpt_path
trainer.test(model, test_dataloaders=train_dl, ckpt_path=ckpt_path)
else:
trainer.fit(model, train_dataloader=train_dl, val_dataloaders=val_dl)
def test_error_raised_with_float_limited_eval_batches():
"""Test that an error is raised if there are not enough batches when passed with float value of
limit_eval_batches."""
model = BoringModel()
dl_size = len(model.val_dataloader())
limit_val_batches = 1 / (dl_size + 2)
trainer = Trainer(limit_val_batches=limit_val_batches)
trainer._data_connector.attach_data(model)
with pytest.raises(
MisconfigurationException,
match=
rf"{limit_val_batches} \* {dl_size} < 1. Please increase the `limit_val_batches`",
):
trainer._data_connector._reset_eval_dataloader(RunningStage.VALIDATING,
model)
