def test_extractors_context_manager():
"""
Unit test for context manager Extractors
"""
for characteristic in Characteristics:
with Extractors(characteristic) as extractors:
assert isinstance(extractors, Extractors)
def test_acceleration_discovery(monkeypatch):
"""
Unit test for acceleration_discovery staticmethod
"""
@staticmethod
def mocked___init__(characteristics):
"""
Closure to mock __init__ behaviour
"""
del characteristics
@staticmethod
def mocked_acceleration_discovery():
"""
Closure to mock acceleration_discovery behaviour
"""
return True
monkeypatch.setattr(
Extractors,
'__init__',
mocked___init__
)
monkeypatch.setattr(
Extractors,
'acceleration_discovery',
mocked_acceleration_discovery
)
assert Extractors(
Characteristics.LIGHTWEIGHT_REGULAR_PRECISION
).acceleration_discovery()
def test_network_instance():
"""
Unit test for network instantiation
"""
for characteristic in Characteristics:
with Extractors(characteristic) as extractor:
assert extractor._infer_network()[1].name == extractor.network.name
def test_extract():
"""
Unit test for extract method
"""
for characteristic in Characteristics:
with Extractors(characteristic) as extractor:
assert extractor.extract(abspath(TEST_LOCAL)).shape[0] == \
extractor.network.output_shape[1]
def test_preprocessor():
"""
Unit test for preprocessor method
"""
for characteristic in Characteristics:
with Extractors(characteristic) as extractor:
assert extractor.preprocessor(abspath(TEST_LOCAL)).shape == \
(1, *extractor.image_input_shape, 3)
def index(self, uri, **kwargs):
"""
Performs image indexing.
Parameters
----------
uri: str
Directory or file, or http(s) location.
**check_unique (optional): bool
If, during the index process, imported images
should have their unicity verified (to avoid duplicates).
"""
with Extractors(
characteristics=self._characteristic) as extractor, Index(
extractor.output_shape, data_dir=self._data_dir) as index:
self._index_configuration('w', feature_size=extractor.output_shape)
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = {
executor.submit(self.image_database.insert, rank,
uri_from_file): rank
for rank, uri_from_file in enumerate(
extractor.scan_images(uri))
}
ranks = []
for future in extractor.progress(
concurrent.futures.as_completed(futures),
message='Importing images:'):
ranks.append(futures[future])
for rank in extractor.progress(sorted(ranks),
precise=True,
message='Indexing images:'):
features_tensor_name = self.image_database.\
mount_file_name(
rank,
'npy'
)
extractor.save_tensor(
extractor.extract,
self.image_database.mount_file_name(rank, 'jpg'),
features_tensor_name)
check_unique = kwargs.get('check_unique')
if check_unique is None:
check_unique = False
index.append(extractor.load_tensor(features_tensor_name),
check_unique=check_unique)
os.remove(features_tensor_name)
def search(self, query, top=4):
"""
Executes the search for a created database
Parameters
----------
query: str
URI of a image to query
top (optional)(default: 4): int
How many results should be returned.
"""
with Extractors(characteristics=self._characteristic) as extractor:
with Index(extractor.output_shape,
data_dir=self._data_dir) as index:
for result in index.search(extractor.extract(query),
results=top):
yield result
def test_append_check_unique():
"""Unit test for method append, check unique case."""
with warnings.catch_warnings(record=True) as caught_warnings:
warnings.simplefilter('always')
with Extractors(
Characteristics.LIGHTWEIGHT_REGULAR_PRECISION
) as extractor, \
Index(extractor.output_shape, volatile=True) as index:
for image in extractor.scan(TEST_CHECK_UNIQUE):
index.append(
extractor.extract(image),
check_unique=True
)
assert len(caught_warnings) == 1
assert issubclass(caught_warnings[-1].category, UserWarning)
assert str(
caught_warnings[-1].message
) == 'Tensor being indexed already exists in the database ' + \
'and the check for duplicates are on. Refusing to store ' + \
'again this tensor.'
def test_unknown_network_instance(self):
"""Unit test for an unknown network characteristic."""
with self.assertRaises(UnknownCharacteristics):
Extractors(UNKNOWN_CHARACTERISTICS)
def test_output_shape_property():
"""Unit test for property output_shape."""
for characteristic in Characteristics:
with Extractors(characteristic) as extractor:
assert extractor.output_shape == extractor._features_output_shape