def test_get_len():
assert get_len(DataLoader(RandomDataset(1, 1))) == 1
value = get_len(DataLoader(RandomIterableDataset(1, 1)))
assert isinstance(value, float)
assert value == float("inf")
def test_has_len():
assert has_len(DataLoader(RandomDataset(1, 1)))
with pytest.raises(ValueError, match="`Dataloader` returned 0 length."):
assert has_len(DataLoader(RandomDataset(0, 0)))
assert not has_len(DataLoader(RandomIterableDataset(1, 1)))
def test_has_len():
assert has_len(DataLoader(RandomDataset(1, 1)))
with pytest.warns(UserWarning, match="`DataLoader` returned 0 length."):
assert has_len(DataLoader(RandomDataset(0, 0)))
assert not has_len(DataLoader(RandomIterableDataset(1, 1)))
def test_num_stepping_batches_iterable_dataset():
"""Test the stepping batches with iterable dataset configured with max steps."""
max_steps = 1000
trainer = Trainer(max_steps=max_steps)
model = BoringModel()
train_dl = DataLoader(RandomIterableDataset(size=7, count=1e10))
trainer._data_connector.attach_data(model, train_dataloaders=train_dl)
trainer.strategy.connect(model)
assert trainer.estimated_stepping_batches == max_steps
def test_has_iterable_dataset():
assert has_iterable_dataset(DataLoader(RandomIterableDataset(1, 1)))
assert not has_iterable_dataset(DataLoader(RandomDataset(1, 1)))
class MockDatasetWithoutIterableDataset(RandomDataset):
def __iter__(self):
yield 1
return self
assert not has_iterable_dataset(
DataLoader(MockDatasetWithoutIterableDataset(1, 1)))
def test_dataloader_kwargs_replacement_with_iterable_dataset(mode):
"""Test that DataLoader kwargs are not replaced when using Iterable Dataset."""
dataset = RandomIterableDataset(7, 100)
dataloader = DataLoader(dataset, batch_size=32)
dl_kwargs = _get_dataloader_init_kwargs(dataloader,
dataloader.sampler,
mode=mode)
assert dl_kwargs["sampler"] is None
assert dl_kwargs["batch_sampler"] is None
assert dl_kwargs["batch_size"] is dataloader.batch_size
assert dl_kwargs["dataset"] is dataloader.dataset
assert dl_kwargs["collate_fn"] is dataloader.collate_fn
@RunIf(rich=True)
def test_rich_progress_bar_refresh_rate():
progress_bar = RichProgressBar(refresh_rate_per_second=1)
assert progress_bar.is_enabled
assert not progress_bar.is_disabled
progress_bar = RichProgressBar(refresh_rate_per_second=0)
assert not progress_bar.is_enabled
assert progress_bar.is_disabled
@RunIf(rich=True)
@mock.patch(
"pytorch_lightning.callbacks.progress.rich_progress.Progress.update")
@pytest.mark.parametrize(
"dataset", [RandomDataset(32, 64),
RandomIterableDataset(32, 64)])
def test_rich_progress_bar(progress_update, tmpdir, dataset):
class TestModel(BoringModel):
def train_dataloader(self):
return DataLoader(dataset=dataset)
def val_dataloader(self):
return DataLoader(dataset=dataset)
def test_dataloader(self):
return DataLoader(dataset=dataset)
def predict_dataloader(self):
return DataLoader(dataset=dataset)
model = TestModel()
assert isinstance(trainer.progress_bar_callback, RichProgressBar)
@RunIf(rich=True)
def test_rich_progress_bar_refresh_rate_enabled():
progress_bar = RichProgressBar(refresh_rate=1)
assert progress_bar.is_enabled
assert not progress_bar.is_disabled
progress_bar = RichProgressBar(refresh_rate=0)
assert not progress_bar.is_enabled
assert progress_bar.is_disabled
@RunIf(rich=True)
@mock.patch("pytorch_lightning.callbacks.progress.rich_progress.Progress.update")
@pytest.mark.parametrize("dataset", [RandomDataset(32, 64), RandomIterableDataset(32, 64)])
def test_rich_progress_bar(progress_update, tmpdir, dataset):
class TestModel(BoringModel):
def train_dataloader(self):
return DataLoader(dataset=dataset)
def val_dataloader(self):
return DataLoader(dataset=dataset)
def test_dataloader(self):
return DataLoader(dataset=dataset)
def predict_dataloader(self):
return DataLoader(dataset=dataset)
model = TestModel()
def train_dataloader(self):
train_ds = (RandomIterableDataset(32, count=max_steps +
100) if use_infinite_dataset else
RandomDataset(32, length=data_samples_train))
return DataLoader(train_ds)
