def test_save_load(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
sample = next(iter(dataset))
with io.BytesIO() as buffer:
torch.save(sample, buffer)
buffer.seek(0)
assert_samples_equal(torch.load(buffer), sample)
def test_smoke(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
if not isinstance(dataset, IterDataPipe):
raise AssertionError(
f"Loading the dataset should return an IterDataPipe, but got {type(dataset)} instead."
)
def test_num_samples(self, test_home, dataset_mock, config):
mock_info = dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
num_samples = 0
for _ in dataset:
num_samples += 1
assert num_samples == mock_info["num_samples"]
def test_label_matches_path(self, test_home, 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 = datasets.load(dataset_mock.name, **config)
for sample in dataset:
label_from_path = int(Path(sample["path"]).parent.name)
assert sample["label"] == label_from_path
def test_sample_content(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
for sample in dataset:
assert "image" in sample
assert "label" in sample
assert isinstance(sample["image"], Image)
assert isinstance(sample["label"], Label)
assert sample["image"].shape == (1, 16, 16)
def test_decoding(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
undecoded_features = {
key
for key, value in next(iter(dataset)).items()
if isinstance(value, io.IOBase)
}
if undecoded_features:
raise AssertionError(
f"The values of key(s) "
f"{sequence_to_str(sorted(undecoded_features), separate_last='and ')} were not decoded."
)
def test_no_vanilla_tensors(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
vanilla_tensors = {
key
for key, value in next(iter(dataset)).items()
if type(value) is torch.Tensor
}
if vanilla_tensors:
raise AssertionError(
f"The values of key(s) "
f"{sequence_to_str(sorted(vanilla_tensors), separate_last='and ')} contained vanilla tensors."
)
def test_extra_label(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
sample = next(iter(dataset))
for key, type in (
("nist_hsf_series", int),
("nist_writer_id", int),
("digit_index", int),
("nist_label", int),
("global_digit_index", int),
("duplicate", bool),
("unused", bool),
):
assert key in sample and isinstance(sample[key], type)
def test_has_annotations(self, test_home, dataset_mock, config,
annotation_dp_type):
def scan(graph):
for node, sub_graph in graph.items():
yield node
yield from scan(sub_graph)
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
if not any(
type(dp) is annotation_dp_type
for dp in scan(traverse(dataset))):
raise AssertionError(
f"The dataset doesn't contain a {annotation_dp_type.__name__}() datapipe."
)
def main(*names, force=False):
for name in names:
path = BUILTIN_DIR / f"{name}.categories"
if path.exists() and not force:
continue
dataset = datasets.load(name)
try:
categories = dataset._generate_categories()
except NotImplementedError:
continue
with open(path, "w") as file:
writer = csv.writer(file, lineterminator="\n")
for category in categories:
writer.writerow((
category, ) if isinstance(category, str) else category)
def test_sample(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
try:
sample = next(iter(dataset))
except Exception as error:
raise AssertionError(
"Drawing a sample raised the error above.") from error
if not isinstance(sample, dict):
raise AssertionError(
f"Samples should be dictionaries, but got {type(sample)} instead."
)
if not sample:
raise AssertionError("Sample dictionary is empty.")
def new_dataset(self, *, num_workers=0):
return DataLoader2(new_datasets.load(self.name, **self.new_config),
num_workers=num_workers)
def test_traversable(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
traverse(dataset)
def test_transformable(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
next(iter(dataset.map(transforms.Identity())))
def test_serializable(self, test_home, dataset_mock, config):
dataset_mock.prepare(test_home, config)
dataset = datasets.load(dataset_mock.name, **config)
pickle.dumps(dataset)
