def test_update_dataloader_with_multiprocessing_context():
"""This test verifies that replace_sampler conserves multiprocessing context."""
train = RandomDataset(32, 64)
context = "spawn"
train = DataLoader(train,
batch_size=32,
num_workers=2,
multiprocessing_context=context,
shuffle=True)
trainer = Trainer()
new_data_loader = trainer._update_dataloader(
train, SequentialSampler(train.dataset))
assert new_data_loader.multiprocessing_context == train.multiprocessing_context
def test_dataloaders_with_missing_keyword_arguments():
trainer = Trainer()
ds = RandomDataset(10, 20)
class TestDataLoader(DataLoader):
def __init__(self, dataset):
super().__init__(dataset)
loader = TestDataLoader(ds)
sampler = SequentialSampler(ds)
match = escape(
"missing arguments are ['batch_sampler', 'sampler', 'shuffle']")
with pytest.raises(MisconfigurationException, match=match):
trainer._update_dataloader(loader, sampler, mode="fit")
match = escape(
"missing arguments are ['batch_sampler', 'batch_size', 'drop_last', 'sampler', 'shuffle']"
)
with pytest.raises(MisconfigurationException, match=match):
trainer._update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, dataset, *args, **kwargs):
super().__init__(dataset)
loader = TestDataLoader(ds)
sampler = SequentialSampler(ds)
trainer._update_dataloader(loader, sampler, mode="fit")
trainer._update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, *foo, **bar):
super().__init__(*foo, **bar)
loader = TestDataLoader(ds)
sampler = SequentialSampler(ds)
trainer._update_dataloader(loader, sampler, mode="fit")
trainer._update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, num_feat, dataset, *args, shuffle=False):
self.num_feat = num_feat
super().__init__(dataset)
loader = TestDataLoader(1, ds)
sampler = SequentialSampler(ds)
match = escape("missing arguments are ['batch_sampler', 'sampler']")
with pytest.raises(MisconfigurationException, match=match):
trainer._update_dataloader(loader, sampler, mode="fit")
match = escape(
"missing arguments are ['batch_sampler', 'batch_size', 'drop_last', 'sampler']"
)
with pytest.raises(MisconfigurationException, match=match):
trainer._update_dataloader(loader, sampler, mode="predict")
class TestDataLoader(DataLoader):
def __init__(self, num_feat, dataset, **kwargs):
self.feat_num = num_feat
super().__init__(dataset)
loader = TestDataLoader(1, ds)
sampler = SequentialSampler(ds)
match = escape("missing attributes are ['num_feat']")
with pytest.raises(MisconfigurationException, match=match):
trainer._update_dataloader(loader, sampler, mode="fit")
match = escape("missing attributes are ['num_feat']")
with pytest.raises(MisconfigurationException, match=match):
trainer._update_dataloader(loader, sampler, mode="predict")
def test_update_dataloader_raises():
trainer = Trainer()
with pytest.raises(ValueError,
match="needs to subclass `torch.utils.data.DataLoader"):
trainer._update_dataloader(object(), object(), mode="fit")
