Removed some tests

This commit is contained in:
kinaryml 2022-09-29 02:18:10 -07:00
parent 72319ecbf5
commit 8ad5918229

View File

@ -92,36 +92,6 @@ class ImageClassifierTest(parameterized.TestCase):
test_util.get_test_data_path(_IMAGE_FILE))
self.model_path = test_util.get_test_data_path(_MODEL_FILE)
def test_create_from_file_succeeds_with_valid_model_path(self):
# Creates with default option and valid model file successfully.
with _ImageClassifier.create_from_model_path(self.model_path) as classifier:
self.assertIsInstance(classifier, _ImageClassifier)
def test_create_from_options_succeeds_with_valid_model_path(self):
# Creates with options containing model file successfully.
base_options = _BaseOptions(file_name=self.model_path)
options = _ImageClassifierOptions(base_options=base_options)
with _ImageClassifier.create_from_options(options) as classifier:
self.assertIsInstance(classifier, _ImageClassifier)
def test_create_from_options_fails_with_invalid_model_path(self):
# Invalid empty model path.
with self.assertRaisesRegex(
ValueError,
r"ExternalFile must specify at least one of 'file_content', "
r"'file_name' or 'file_descriptor_meta'."):
base_options = _BaseOptions(file_name='')
options = _ImageClassifierOptions(base_options=base_options)
_ImageClassifier.create_from_options(options)
def test_create_from_options_succeeds_with_valid_model_content(self):
# Creates with options containing model content successfully.
with open(self.model_path, 'rb') as f:
base_options = _BaseOptions(file_content=f.read())
options = _ImageClassifierOptions(base_options=base_options)
classifier = _ImageClassifier.create_from_options(options)
self.assertIsInstance(classifier, _ImageClassifier)
@parameterized.parameters(
(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
@ -151,141 +121,6 @@ class ImageClassifierTest(parameterized.TestCase):
# a context.
classifier.close()
@parameterized.parameters(
(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
def test_classify_in_context(self, model_file_type, max_results,
expected_classification_result):
if model_file_type is ModelFileType.FILE_NAME:
base_options = _BaseOptions(file_name=self.model_path)
elif model_file_type is ModelFileType.FILE_CONTENT:
with open(self.model_path, 'rb') as f:
model_content = f.read()
base_options = _BaseOptions(file_content=model_content)
else:
# Should never happen
raise ValueError('model_file_type is invalid.')
classifier_options = _ClassifierOptions(max_results=max_results)
options = _ImageClassifierOptions(
base_options=base_options, classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs object detection on the input.
image_result = classifier.classify(self.test_image)
# Comparing results.
self.assertEqual(image_result, expected_classification_result)
def test_score_threshold_option(self):
classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
classifications = image_result.classifications
for classification in classifications:
for entry in classification.entries:
score = entry.categories[0].score
self.assertGreaterEqual(
score, _SCORE_THRESHOLD,
f'Classification with score lower than threshold found. '
f'{classification}')
def test_max_results_option(self):
classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
categories = image_result.classifications[0].entries[0].categories
self.assertLessEqual(
len(categories), _MAX_RESULTS, 'Too many results returned.')
def test_allow_list_option(self):
classifier_options = _ClassifierOptions(category_allowlist=_ALLOW_LIST)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
classifications = image_result.classifications
for classification in classifications:
for entry in classification.entries:
label = entry.categories[0].category_name
self.assertIn(label, _ALLOW_LIST,
f'Label {label} found but not in label allow list')
def test_deny_list_option(self):
classifier_options = _ClassifierOptions(category_denylist=_DENY_LIST)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
classifications = image_result.classifications
for classification in classifications:
for entry in classification.entries:
label = entry.categories[0].category_name
self.assertNotIn(label, _DENY_LIST,
f'Label {label} found but in deny list.')
def test_combined_allowlist_and_denylist(self):
# Fails with combined allowlist and denylist
with self.assertRaisesRegex(
ValueError,
r'`category_allowlist` and `category_denylist` are mutually '
r'exclusive options.'):
classifier_options = _ClassifierOptions(category_allowlist=['foo'],
category_denylist=['bar'])
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as unused_classifier:
pass
def test_empty_classification_outputs(self):
classifier_options = _ClassifierOptions(score_threshold=1)
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
classifier_options=classifier_options)
with _ImageClassifier.create_from_options(options) as classifier:
# Performs image classification on the input.
image_result = classifier.classify(self.test_image)
self.assertEmpty(image_result.classifications[0].entries[0].categories)
def test_missing_result_callback(self):
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
running_mode=_RUNNING_MODE.LIVE_STREAM)
with self.assertRaisesRegex(ValueError,
r'result callback must be provided'):
with _ImageClassifier.create_from_options(options) as unused_classifier:
pass
@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
def test_illegal_result_callback(self, running_mode):
def pass_through(unused_result: _ClassificationResult):
pass
options = _ImageClassifierOptions(
base_options=_BaseOptions(file_name=self.model_path),
running_mode=running_mode,
result_callback=pass_through)
with self.assertRaisesRegex(ValueError,
r'result callback should not be provided'):
with _ImageClassifier.create_from_options(options) as unused_classifier:
pass
if __name__ == '__main__':
absltest.main()