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rely on patched datasets home rather than passing it around #5998

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Merged
merged 5 commits into from
May 12, 2022
Merged

rely on patched datasets home rather than passing it around #5998

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ee16272
rely on patched datasets home rather than passing it around
pmeier May 12, 2022
07ad331
Merge branch 'main' into datasets-test-home
pmeier May 12, 2022
60316bb
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pmeier May 12, 2022
a78f99b
Merge branch 'datasets-test-home' of https://github.com/pmeier/vision…
pmeier May 12, 2022
8437e79
Merge branch 'main' into datasets-test-home
pmeier May 12, 2022
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6 changes: 4 additions & 2 deletions test/builtin_dataset_mocks.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,8 +62,10 @@ def _parse_mock_info(self, mock_info):

return mock_info

def prepare(self, home, config):
root = home / self.name
def prepare(self, config):
# `datasets.home()` is patched to a temporary directory through the autouse fixture `test_home` in
# test/test_prototype_builtin_datasets.py
root = pathlib.Path(datasets.home()) / self.name
root.mkdir(exist_ok=True)

mock_info = self._parse_mock_info(self.mock_data_fn(root, config))
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63 changes: 32 additions & 31 deletions test/test_prototype_builtin_datasets.py
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Expand Up @@ -25,9 +25,10 @@ def extract_datapipes(dp):
return get_all_graph_pipes(traverse(dp, only_datapipe=True))


@pytest.fixture
@pytest.fixture(autouse=True)
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Although this patch is now applied by default, if required, a test can still request this fixture the normal way.

def test_home(mocker, tmp_path):
mocker.patch("torchvision.prototype.datasets._api.home", return_value=str(tmp_path))
mocker.patch("torchvision.prototype.datasets.home", return_value=str(tmp_path))
yield tmp_path


Expand All @@ -54,17 +55,17 @@ def test_info(self, name):
raise AssertionError("Info should be a dictionary with string keys.")

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_smoke(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_smoke(self, dataset_mock, config):
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

if not isinstance(dataset, datasets.utils.Dataset):
raise AssertionError(f"Loading the dataset should return an Dataset, but got {type(dataset)} instead.")

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_sample(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_sample(self, dataset_mock, config):
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

Expand All @@ -82,16 +83,16 @@ def test_sample(self, test_home, dataset_mock, config):
raise AssertionError("Sample dictionary is empty.")

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_num_samples(self, test_home, dataset_mock, config):
mock_info = dataset_mock.prepare(test_home, config)
def test_num_samples(self, dataset_mock, config):
mock_info = dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

assert len(list(dataset)) == mock_info["num_samples"]

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_no_vanilla_tensors(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_no_vanilla_tensors(self, dataset_mock, config):
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

Expand All @@ -103,24 +104,24 @@ def test_no_vanilla_tensors(self, test_home, dataset_mock, config):
)

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_transformable(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_transformable(self, dataset_mock, config):
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

next(iter(dataset.map(transforms.Identity())))

@pytest.mark.parametrize("only_datapipe", [False, True])
@parametrize_dataset_mocks(DATASET_MOCKS)
def test_traversable(self, test_home, dataset_mock, config, only_datapipe):
dataset_mock.prepare(test_home, config)
def test_traversable(self, dataset_mock, config, only_datapipe):
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)

traverse(dataset, only_datapipe=only_datapipe)

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_serializable(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_serializable(self, dataset_mock, config):
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)

pickle.dumps(dataset)
Expand All @@ -133,8 +134,8 @@ def _collate_fn(self, batch):

@pytest.mark.parametrize("num_workers", [0, 1])
@parametrize_dataset_mocks(DATASET_MOCKS)
def test_data_loader(self, test_home, dataset_mock, config, num_workers):
dataset_mock.prepare(test_home, config)
def test_data_loader(self, dataset_mock, config, num_workers):
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)

dl = DataLoader(
Expand All @@ -151,17 +152,17 @@ def test_data_loader(self, test_home, dataset_mock, config, num_workers):
# contain a custom test for that, but we opted to wait for a potential solution / test from torchdata for now.
@parametrize_dataset_mocks(DATASET_MOCKS)
@pytest.mark.parametrize("annotation_dp_type", (Shuffler, ShardingFilter))
def test_has_annotations(self, test_home, dataset_mock, config, annotation_dp_type):
def test_has_annotations(self, dataset_mock, config, annotation_dp_type):

dataset_mock.prepare(test_home, config)
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)

if not any(isinstance(dp, annotation_dp_type) for dp in extract_datapipes(dataset)):
raise AssertionError(f"The dataset doesn't contain a {annotation_dp_type.__name__}() datapipe.")

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_save_load(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_save_load(self, dataset_mock, config):
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)
sample = next(iter(dataset))

Expand All @@ -171,8 +172,8 @@ def test_save_load(self, test_home, dataset_mock, config):
assert_samples_equal(torch.load(buffer), sample)

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_infinite_buffer_size(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_infinite_buffer_size(self, dataset_mock, config):
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)

for dp in extract_datapipes(dataset):
Expand All @@ -182,17 +183,17 @@ def test_infinite_buffer_size(self, test_home, dataset_mock, config):
assert dp.buffer_size == INFINITE_BUFFER_SIZE

@parametrize_dataset_mocks(DATASET_MOCKS)
def test_has_length(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_has_length(self, dataset_mock, config):
dataset_mock.prepare(config)
dataset = datasets.load(dataset_mock.name, **config)

assert len(dataset) > 0


@parametrize_dataset_mocks(DATASET_MOCKS["qmnist"])
class TestQMNIST:
def test_extra_label(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_extra_label(self, dataset_mock, config):
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

Expand All @@ -211,13 +212,13 @@ def test_extra_label(self, test_home, dataset_mock, config):

@parametrize_dataset_mocks(DATASET_MOCKS["gtsrb"])
class TestGTSRB:
def test_label_matches_path(self, test_home, dataset_mock, config):
def test_label_matches_path(self, dataset_mock, config):
# We read the labels from the csv files instead. But for the trainset, the labels are also part of the path.
# This test makes sure that they're both the same
if config["split"] != "train":
return

dataset_mock.prepare(test_home, config)
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

Expand All @@ -228,8 +229,8 @@ def test_label_matches_path(self, test_home, dataset_mock, config):

@parametrize_dataset_mocks(DATASET_MOCKS["usps"])
class TestUSPS:
def test_sample_content(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
def test_sample_content(self, dataset_mock, config):
dataset_mock.prepare(config)

dataset = datasets.load(dataset_mock.name, **config)

Expand Down