def test_all_paired_dataset_providers_should_provide_correct_labels(
dataset_provider_cls_name):
provider_cls = from_class_name(dataset_provider_cls_name)
raw_data_provider = FakeRawDataProvider(curated=True)
dataset_spec = DatasetSpec(raw_data_provider,
DatasetType.TEST,
with_excludes=False,
encoding=False)
provider = provider_cls(raw_data_provider)
dataset = provider.supply_dataset(dataset_spec, batch_size=1).take(100)
for batch in dataset:
left_img = np.rint(
(batch[0][consts.LEFT_FEATURE_IMAGE].numpy().flatten()[0] + 0.5) *
10)
right_img = np.rint(
(batch[0][consts.RIGHT_FEATURE_IMAGE].numpy().flatten()[0] + 0.5) *
10)
pair_label = batch[1][consts.TFRECORD_PAIR_LABEL].numpy().flatten()[0]
left_label = batch[1][consts.TFRECORD_LEFT_LABEL].numpy().flatten()[0]
right_label = batch[1][
consts.TFRECORD_RIGHT_LABEL].numpy().flatten()[0]
assert pair_label == (1 if left_img == right_img else 0)
assert pair_label == (1 if left_label == right_label else 0)
def test_all_unpaired_dataset_providers_should_honor_excludes(
dataset_provider_cls_name, patched_excluded):
provider_cls = from_class_name(dataset_provider_cls_name)
raw_data_provider = FakeRawDataProvider(curated=True)
dataset_spec = DatasetSpec(raw_data_provider,
DatasetType.TRAIN,
with_excludes=False,
encoding=False,
paired=False)
provider = provider_cls(raw_data_provider)
dataset = provider.supply_dataset(dataset_spec, batch_size=1).take(100)
encountered_labels = set()
for batch in dataset:
image = np.rint(
(batch[0][consts.FEATURES].numpy().flatten()[0] + 0.5) * 10)
label = batch[1][consts.LABELS].numpy().flatten()[0]
encountered_labels.update((image, ))
encountered_labels.update((label, ))
assert_that((np.rint(list(encountered_labels))),
only_contains(not_(is_in(list(patched_excluded.numpy())))))
assert_that((np.rint(list(encountered_labels))),
only_contains((is_in([0, 1, 4]))))
def test_all_unpaired_dataset_providers_should_provide_correct_labels(
dataset_provider_cls_name):
provider_cls = from_class_name(dataset_provider_cls_name)
raw_data_provider = FakeRawDataProvider(curated=True)
dataset_spec = DatasetSpec(raw_data_provider,
DatasetType.TRAIN,
with_excludes=False,
encoding=False,
paired=False)
provider = provider_cls(raw_data_provider)
dataset = provider.supply_dataset(dataset_spec, batch_size=1).take(100)
for batch in dataset:
images = np.rint(
(batch[0][consts.FEATURES].numpy().flatten()[0] + 0.5) * 10)
labels = batch[1][consts.LABELS].numpy().flatten()[0]
assert images == labels
def dataset_spec(
description: DataDescription = None,
raw_dataset_fragment: RawDatasetFragment = None,
type=DatasetType.TRAIN,
with_excludes=False,
encoding=True,
paired=True,
repeating_pairs=True,
identical_pairs=False,
):
from testing_utils.testing_classes import FakeRawDataProvider
return DatasetSpec(raw_data_provider=FakeRawDataProvider(
description, raw_dataset_fragment, curated=True),
type=type,
with_excludes=with_excludes,
encoding=encoding,
paired=paired,
repeating_pairs=repeating_pairs,
identical_pairs=identical_pairs)
def test_all_unpaired_dataset_providers_should_get_features_from_raw_data_provider(
description, dataset_provider_cls_name):
provider = dataset_provider_cls_name(
FakeRawDataProvider(curated=True, description=description))
image_dims = provider.raw_data_provider.description.image_dimensions
batch_size = 12
dataset_spec = gen.dataset_spec(description=description,
type=DatasetType.TEST,
with_excludes=False,
encoding=False,
paired=False)
dataset = provider.supply_dataset(dataset_spec,
batch_size=batch_size).take(100)
images, labels = tf_helpers.unpack_first_batch(dataset)
assert images.shape == (batch_size, *image_dims)
assert labels.shape == (batch_size, )
def test_all_paired_dataset_providers_should_honor_excludes(
dataset_provider_cls_name, patched_excluded):
provider_cls = from_class_name(dataset_provider_cls_name)
raw_data_provider = FakeRawDataProvider(curated=True)
dataset_spec = DatasetSpec(raw_data_provider,
DatasetType.TEST,
with_excludes=False,
encoding=False)
provider = provider_cls(raw_data_provider)
dataset = provider.supply_dataset(dataset_spec, batch_size=1).take(100)
encountered_labels = set()
for batch in dataset:
left_label = batch[0][
consts.LEFT_FEATURE_IMAGE].numpy().flatten()[0] + 0.5
right_label = batch[0][
consts.RIGHT_FEATURE_IMAGE].numpy().flatten()[0] + 0.5
encountered_labels.update((left_label, right_label))
assert_that((np.rint(list(encountered_labels)) * 10),
only_contains(not_(is_in(list(patched_excluded.numpy())))))
assert_that((np.rint(list(encountered_labels)) * 10),
only_contains((is_in([0, 1, 4]))))
from testing_utils import tf_helpers, gen
from testing_utils.testing_classes import FakeRawDataProvider, FAKE_DATA_DESCRIPTION, FAKE_MNIST_DESCRIPTION
from testing_utils.tf_helpers import run_eagerly
class FakeDatasetProvider(AbstractDatasetProvider):
def build_dataset(self, dataset_spec: DatasetSpec) -> tf.data.Dataset:
pass
class FakeTrainUnpairedDatasetProvider(TFRecordTrainUnpairedDatasetProvider):
def build_dataset(self, dataset_spec: DatasetSpec) -> tf.data.Dataset:
pass
raw_data_provider = FakeRawDataProvider()
default_provider = FakeDatasetProvider(raw_data_provider)
train_unpaired_dataset_provider = FakeTrainUnpairedDatasetProvider(
raw_data_provider)
@pytest.fixture()
def patched_dataset_building(mocker):
return mocker.patch.object(default_provider,
'build_dataset',
autospec=True)
def test_train_input_fn_should_search_for_dataset_with_correct_spec(
patched_dataset_building):
default_provider.train_input_fn()
def raw_data_provider(self) -> AbstractRawDataProvider:
return FakeRawDataProvider(description=self.description)