def test_getattr(self):
feature = feature_lib.Sequence(
feature_lib.ClassLabel(names=['left', 'right']),)
self.assertEqual(feature.names, ['left', 'right'])
feature = feature_lib.Sequence({
'label': feature_lib.ClassLabel(names=['left', 'right']),
})
self.assertEqual(feature['label'].names, ['left', 'right'])
def test_metadata(self):
feature = feature_lib.Sequence(feature_lib.ClassLabel(num_classes=2))
feature.feature.names = ['left', 'right']
with testing.tmp_dir() as tmp_dir:
feature.save_metadata(data_dir=tmp_dir, feature_name='test')
feature2 = feature_lib.Sequence(feature_lib.ClassLabel(num_classes=2))
feature2.load_metadata(data_dir=tmp_dir, feature_name='test')
self.assertEqual(feature2.feature.names, ['left', 'right'])
def test_empty(self):
# Encoding should works if num_classes=0
labels = features.ClassLabel(num_classes=0)
self.assertEqual(0, labels.num_classes)
self.assertEqual(0, len(labels.names))
self.assertEqual(-1, labels.encode_example(-1))
labels = features.ClassLabel(names=[])
self.assertEqual(0, labels.num_classes)
self.assertEqual(0, len(labels.names))
self.assertEqual(-1, labels.encode_example(-1))
def expectations(self):
return [
test_utils.FeatureExpectation(
name='label',
feature=features.ClassLabel(num_classes=10),
dtype=tf.int64,
shape=(),
tests=[
test_utils.FeatureExpectationItem(
value=3,
expected=3,
),
test_utils.FeatureExpectationItem(
value='3',
expected=3,
),
test_utils.FeatureExpectationItem(
value=10,
raise_cls=ValueError,
raise_msg='greater than configured num_classes',
),
test_utils.FeatureExpectationItem(
value='10',
raise_cls=ValueError,
raise_msg='Invalid',
),
]
),
test_utils.FeatureExpectation(
name='directions',
feature=features.ClassLabel(names=['left', 'right']),
dtype=tf.int64,
shape=(),
tests=[
test_utils.FeatureExpectationItem(
value=1,
expected=1,
),
test_utils.FeatureExpectationItem(
value='left',
expected=0,
),
test_utils.FeatureExpectationItem(
value='right',
expected=1,
),
]
),
]
def test_feature(self):
self.assertFeature(
feature=features.ClassLabel(num_classes=10),
dtype=tf.int64,
shape=(),
tests=[
testing.FeatureExpectationItem(
value=3,
expected=3,
),
testing.FeatureExpectationItem(
value='3',
expected=3,
),
testing.FeatureExpectationItem(
value=10,
raise_cls=ValueError,
raise_msg='greater than configured num_classes',
),
testing.FeatureExpectationItem(
value='10',
raise_cls=ValueError,
raise_msg='Invalid',
),
],
test_attributes=dict(
num_classes=10,
names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'],
))
def test_label(self):
self.assertFeature(
feature=feature_lib.Sequence(
feature_lib.ClassLabel(names=['left', 'right']),
),
shape=(None,),
dtype=tf.int64,
tests=[
testing.FeatureExpectationItem(
value=['right', 'left', 'left'],
expected=[1, 0, 0],
),
# Variable sequence length
testing.FeatureExpectationItem(
value=['right', 'left', 'right', 'left'],
expected=[1, 0, 1, 0],
),
# Empty sequence length
testing.FeatureExpectationItem(
value=[],
expected=[],
),
],
)
def test_file_path(tmp_path):
label_file = tmp_path / 'label_names.txt'
# Empty lines are ignored
content = textwrap.dedent("""
label1
label0
""")
label_file.write_text(content)
# Both Path and str are supported
labels = features.ClassLabel(names_file=label_file)
labels_2 = features.ClassLabel(names_file=str(label_file))
assert labels.names == labels_2.names
assert labels.names == ['label1', 'label0'] # Order is kept
def test_label(self):
self.assertFeature(
feature=feature_lib.Sequence(
{
'label': feature_lib.ClassLabel(names=['left', 'right']),
},
length=None),
shape={'label': (None, )},
dtype={'label': tf.int64},
serialized_info={
'label': feature_lib.TensorInfo(shape=(None, ),
dtype=tf.int64),
},
tests=[
testing.FeatureExpectationItem(
value={'label': ['right', 'left', 'left']},
expected={'label': [1, 0, 0]},
),
# Variable sequence length
testing.FeatureExpectationItem(
value={'label': ['right', 'left', 'right', 'left']},
expected={'label': [1, 0, 1, 0]},
),
# Empty sequence length
testing.FeatureExpectationItem(
value={'label': []},
expected={'label': []},
),
],
test_attributes=dict(_length=None))
def test_label(self):
self.assertFeatureEagerOnly(
feature=feature_lib.Dataset(
{
'label': feature_lib.ClassLabel(names=['left', 'right']),
},
length=None),
shape={'label': ()},
dtype={'label': tf.int64},
serialized_info={
'label': feature_lib.TensorInfo(shape=(None,), dtype=tf.int64),
},
tests=[
testing.FeatureExpectationItem(
value=[{
'label': 'right'
}, {
'label': 'left'
}, {
'label': 'left'
}],
expected=tf.data.Dataset.from_tensor_slices(
{'label': [1, 0, 0]}),
),
# Variable sequence length
testing.FeatureExpectationItem(
value=dataset_utils.as_numpy(
tf.data.Dataset.from_tensor_slices(
{'label': ['right', 'left', 'right', 'left']})),
expected=tf.data.Dataset.from_tensor_slices(
{'label': [1, 0, 1, 0]}),
),
],
test_attributes=dict(_length=None))
def test_feature(self):
self.assertFeature(
feature=features.ClassLabel(num_classes=10),
dtype=tf.int64,
shape=(),
tests=[
testing.FeatureExpectationItem(
value=3,
expected=3,
),
testing.FeatureExpectationItem(
value='3',
expected=3,
),
testing.FeatureExpectationItem(
value=10,
raise_cls=ValueError,
raise_msg='greater than configured num_classes',
),
testing.FeatureExpectationItem(
value='10',
raise_cls=ValueError,
raise_msg='Invalid',
),
])
def test_feature_save_load_metadata_slashes(self):
with testing.tmp_dir() as data_dir:
fd = features_lib.FeaturesDict({
'image/frame': features_lib.Image(shape=(32, 32, 3)),
'image/label': features_lib.ClassLabel(num_classes=2),
})
fd.save_metadata(data_dir)
fd.load_metadata(data_dir)
def _info(self):
return dataset_info.DatasetInfo(
builder=self,
features=features.FeaturesDict({
"image": features.Image(shape=(28, 28, 1)),
"label": features.ClassLabel(num_classes=10),
}),
)
def _info(self):
return dataset_info.DatasetInfo(
builder=self,
features=features.FeaturesDict({
'image': features.Image(shape=(28, 28, 1)),
'label': features.ClassLabel(num_classes=10),
}),
description='Mnist description.',
)
def test_save_load(self):
labels1 = features.ClassLabel(names=['label3', 'label1', 'label2'])
labels2 = features.ClassLabel(num_classes=None)
labels3 = features.ClassLabel(num_classes=1)
with test_utils.tmp_dir(self.get_temp_dir()) as tmp_dir:
labels1.save_metadata(tmp_dir, 'test-labels')
labels2.load_metadata(tmp_dir, 'test-labels')
with self.assertRaisesWithPredicateMatch(
ValueError, 'number of names do not match the defined num_classes'):
labels3.load_metadata(tmp_dir, 'test-labels')
# labels2 should have been copied from label1
self.assertEqual(3, labels2.num_classes)
self.assertEqual(labels2.names, [
'label3',
'label1',
'label2',
])
def _info(self):
return dataset_info.DatasetInfo(
builder=self,
features=features.FeaturesDict({
"image": features.Image(shape=(16, 16, 1)),
"label": features.ClassLabel(names=["dog", "cat"]),
"id": tf.int32,
}),
supervised_keys=("x", "x"),
metadata=dataset_info.BeamMetadataDict(),
)
def test_names(self):
labels = features.ClassLabel(names=['label3', 'label1', 'label2'])
with self.assertRaisesWithPredicateMatch(
ValueError, 'overwrite already defined ClassLabel'):
labels.names = ['other', 'labels']
labels = features.ClassLabel()
labels.names = ['label3', 'label1', 'label2']
with self.assertRaisesWithPredicateMatch(
ValueError, 'overwrite already defined ClassLabel'):
labels.names = ['other', 'labels']
labels = features.ClassLabel(num_classes=3)
labels.names = ['label3', 'label1', 'label2']
labels = features.ClassLabel(num_classes=3)
with self.assertRaisesWithPredicateMatch(
ValueError, 'number of names do not match the defined num_classes'):
labels.names = ['label3', 'label1']
def test_feature__repr__(self):
label = features_lib.ClassLabel(names=['m', 'f'])
feature_dict = features_lib.FeaturesDict({
'metadata': features_lib.Sequence({
'frame': features_lib.Image(shape=(32, 32, 3)),
}),
'label': features_lib.Sequence(label),
})
self.assertEqual(repr(feature_dict), FEATURE_STR)
def test_num_classes(self):
labels = features.ClassLabel(num_classes=10)
self.assertEqual(10, labels.num_classes)
self.assertEqual(10, len(labels.names))
self.assertEqual(1, labels.str2int('1'))
self.assertEqual(u'1', labels.int2str(1))
with self.assertRaisesWithPredicateMatch(ValueError, 'Invalid'):
labels.str2int('10')
with self.assertRaisesWithPredicateMatch(ValueError, 'Invalid'):
labels.int2str(10)
def _info(self) -> dataset_info.DatasetInfo:
return dataset_info.DatasetInfo(
builder=self,
description='Generic image classification dataset.',
features=features_lib.FeaturesDict({
'image':
features_lib.Image(),
'label':
features_lib.ClassLabel(),
'image/filename':
features_lib.Text(),
}),
supervised_keys=('image', 'label'),
)
def _info(self):
return dataset_info.DatasetInfo(
builder=self,
features=features.FeaturesDict({
'image':
features.Image(shape=(16, 16, 1)),
'label':
features.ClassLabel(names=['dog', 'cat']),
'id':
tf.int32,
}),
supervised_keys=('x', 'x'),
metadata=dataset_info.BeamMetadataDict(),
)
def expectations(self):
return [
test_utils.FeatureExpectation(
name='label',
feature=features.ClassLabel(10),
dtype=tf.int64,
shape=(),
tests=[
test_utils.FeatureExpectationItem(
value=3,
expected=3,
),
test_utils.FeatureExpectationItem(
value=10,
raise_cls=ValueError,
raise_msg='greater than configured num_classes',
),
]),
]
def test_str_classes(self):
labels = features.ClassLabel(names=[
'label3',
'label1',
'label2',
])
self.assertEqual(3, labels.num_classes)
self.assertEqual(labels.names, [
'label3',
'label1',
'label2',
])
self.assertEqual(labels.str2int('label3'), 0)
self.assertEqual(labels.str2int('label1'), 1)
self.assertEqual(labels.str2int('label2'), 2)
self.assertEqual(labels.int2str(0), 'label3')
self.assertEqual(labels.int2str(1), 'label1')
self.assertEqual(labels.int2str(2), 'label2')
def test_labels(self):
self.assertFeature(
feature=features.ClassLabel(names=['left', 'right']),
dtype=tf.int64,
shape=(),
tests=[
testing.FeatureExpectationItem(
value=1,
expected=1,
),
testing.FeatureExpectationItem(
value='left',
expected=0,
),
testing.FeatureExpectationItem(
value='right',
expected=1,
),
])
def test_feature__repr__(self):
label = features_lib.ClassLabel(names=['m', 'f'])
feature_dict = features_lib.FeaturesDict({
'metadata':
features_lib.Sequence({
'frame': features_lib.Image(shape=(32, 32, 3)),
}),
'label':
features_lib.Sequence(label),
})
self.assertEqual(
repr(feature_dict),
textwrap.dedent("""\
FeaturesDict({
'label': Sequence(ClassLabel(shape=(), dtype=tf.int64, num_classes=2)),
'metadata': Sequence({
'frame': Image(shape=(32, 32, 3), dtype=tf.uint8),
}),
})"""),
)
def test_label(self):
self.assertFeatureEagerOnly(
feature=feature_lib.Dataset(
feature_lib.ClassLabel(names=['left', 'right']), ),
shape=(),
dtype=tf.int64,
tests=[
testing.FeatureExpectationItem(
value=['right', 'left', 'left'],
expected=tf.data.Dataset.from_tensor_slices([1, 0, 0]),
),
# Variable sequence length
testing.FeatureExpectationItem(
value=['right', 'left', 'right', 'left'],
expected=tf.data.Dataset.from_tensor_slices([1, 0, 1, 0]),
),
# Empty sequence length
testing.FeatureExpectationItem(
value=[],
expected=[],
),
],
)
def test_extract_features_values():
features = features_lib.FeaturesDict({
'img': features_lib.Image(shape=(256, 256, 3)),
'img2': features_lib.Image(shape=(256, 256, 3)),
'metadata': {
'label': features_lib.ClassLabel(num_classes=4),
'other': tf.string,
},
'sequence': features_lib.Sequence({
'x': tf.int64,
'y': tf.int64,
}),
'sequence_flat': features_lib.Sequence(tf.int64),
})
result = _extract_features(
feature=features,
expected_feature={
'img': True,
'img2': False,
'unknown_key': False, # Extra keys are filtered
'metadata': ['label'],
'sequence': {'y'},
'sequence_flat': True,
},
)
testing.assert_features_equal(
result,
features_lib.FeaturesDict({
'img': features_lib.Image(shape=(256, 256, 3)),
'metadata': {
'label': features_lib.ClassLabel(num_classes=4),
},
'sequence': features_lib.Sequence({
'y': tf.int64,
}),
'sequence_flat': features_lib.Sequence(tf.int64),
}),
)
result = _extract_features(
feature=features,
expected_feature={'metadata', 'sequence'},
)
testing.assert_features_equal(
result,
features_lib.FeaturesDict({
'metadata': {
'label': features_lib.ClassLabel(num_classes=4),
'other': tf.string,
},
'sequence': features_lib.Sequence({
'x': tf.int64,
'y': tf.int64,
}),
}),
)
# Test, mixing Features with non-features.
result = _extract_features(
feature=features,
expected_feature={
'img': features_lib.Image(),
'sequence': {
'x': tf.int64,
'y': False,
},
},
)
testing.assert_features_equal(
result,
features_lib.FeaturesDict({
'img': features_lib.Image(shape=(256, 256, 3)),
'sequence': features_lib.Sequence({
'x': tf.int64,
}),
}),
)
def test_num_classes(self):
self.assertEqual(10, features.ClassLabel(10).num_classes)
def test_duplicate_names(self):
with self.assertRaisesWithPredicateMatch(ValueError,
'label names are duplicated'):
features.ClassLabel(names=['label1', 'label1', 'label2'])
def test_extract_features():
features = features_lib.FeaturesDict({
'img': features_lib.Image(shape=(256, 256, 3)),
'img2': features_lib.Image(shape=(256, 256, 3)),
'metadata': {
'label': features_lib.ClassLabel(num_classes=4),
'other': tf.string,
},
'sequence': features_lib.Sequence({
'x': tf.int64,
'y': tf.int64,
}),
'sequence_flat': features_lib.Sequence(tf.int64),
})
result = _extract_features(
feature=features,
expected_feature={},
)
testing.assert_features_equal(result, features_lib.FeaturesDict({}))
# Feature spec accepted
result = _extract_features(
feature=features,
expected_feature={
'img': features_lib.Image(shape=(None, None, 3)),
'metadata': {
'other': tf.string,
},
'sequence': features_lib.Sequence({
'x': tf.int64,
}),
},
)
testing.assert_features_equal(
result,
features_lib.FeaturesDict({
'img': features_lib.Image(shape=(256, 256, 3)),
'metadata': {
'other': tf.string,
},
'sequence': features_lib.Sequence({
'x': tf.int64,
}),
}),
)
# Failure mode:
# * Structure not matching
# * Type not matching
# * Shape/dtype not matching
# * Sequence values not matching (e.g. try bad dtype)
with pytest.raises(ValueError, match="Missing expected feature 'unknown'"):
_extract_features(
feature=features,
expected_feature={
'sequence': features_lib.Sequence({
'unknown': tf.bool,
})
},
)
with pytest.raises(ValueError, match="Missing expected feature 'non_exista"):
_extract_features(
feature=features,
expected_feature={
'non_existant': features_lib.Image(shape=(None, None, 3)),
},
)
with pytest.raises(TypeError, match='Expected: Tensor.*. Got: Image'):
_extract_features(
feature=features,
expected_feature={
'img': features_lib.Tensor(shape=(256, 256, 3), dtype=tf.uint8),
},
)
with pytest.raises(ValueError, match='Expected: Image.*. Got: Image'):
_extract_features(
feature=features,
expected_feature={
'img': features_lib.Image(shape=(None, None, 1)),
},
)
with pytest.raises(ValueError, match='Expected: Tensor.*. Got: Tensor'):
_extract_features(
feature=features,
expected_feature={
'sequence_flat': features_lib.Sequence(tf.float32), # Wrong dtype
},
)
