Removed some tests
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kinaryml
parent
72319ecbf5
commit
8ad5918229
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@ -92,36 +92,6 @@ class ImageClassifierTest(parameterized.TestCase):
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test_util.get_test_data_path(_IMAGE_FILE))
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test_util.get_test_data_path(_IMAGE_FILE))
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _ImageClassifier.create_from_model_path(self.model_path) as classifier:
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self.assertIsInstance(classifier, _ImageClassifier)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(file_name=self.model_path)
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options = _ImageClassifierOptions(base_options=base_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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self.assertIsInstance(classifier, _ImageClassifier)
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def test_create_from_options_fails_with_invalid_model_path(self):
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# Invalid empty model path.
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(file_name='')
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options = _ImageClassifierOptions(base_options=base_options)
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_ImageClassifier.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(file_content=f.read())
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options = _ImageClassifierOptions(base_options=base_options)
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classifier = _ImageClassifier.create_from_options(options)
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self.assertIsInstance(classifier, _ImageClassifier)
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@parameterized.parameters(
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
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(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
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@ -151,141 +121,6 @@ class ImageClassifierTest(parameterized.TestCase):
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# a context.
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# a context.
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classifier.close()
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classifier.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_CLASSIFICATION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_CLASSIFICATION_RESULT))
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def test_classify_in_context(self, model_file_type, max_results,
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expected_classification_result):
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(file_name=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(file_content=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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classifier_options = _ClassifierOptions(max_results=max_results)
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options = _ImageClassifierOptions(
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base_options=base_options, classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs object detection on the input.
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image_result = classifier.classify(self.test_image)
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# Comparing results.
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self.assertEqual(image_result, expected_classification_result)
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def test_score_threshold_option(self):
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classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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classifications = image_result.classifications
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for classification in classifications:
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for entry in classification.entries:
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score = entry.categories[0].score
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self.assertGreaterEqual(
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score, _SCORE_THRESHOLD,
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f'Classification with score lower than threshold found. '
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f'{classification}')
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def test_max_results_option(self):
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classifier_options = _ClassifierOptions(score_threshold=_SCORE_THRESHOLD)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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categories = image_result.classifications[0].entries[0].categories
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self.assertLessEqual(
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len(categories), _MAX_RESULTS, 'Too many results returned.')
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def test_allow_list_option(self):
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classifier_options = _ClassifierOptions(category_allowlist=_ALLOW_LIST)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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classifications = image_result.classifications
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for classification in classifications:
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for entry in classification.entries:
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label = entry.categories[0].category_name
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self.assertIn(label, _ALLOW_LIST,
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f'Label {label} found but not in label allow list')
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def test_deny_list_option(self):
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classifier_options = _ClassifierOptions(category_denylist=_DENY_LIST)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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classifications = image_result.classifications
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for classification in classifications:
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for entry in classification.entries:
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label = entry.categories[0].category_name
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self.assertNotIn(label, _DENY_LIST,
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f'Label {label} found but in deny list.')
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def test_combined_allowlist_and_denylist(self):
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# Fails with combined allowlist and denylist
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with self.assertRaisesRegex(
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ValueError,
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r'`category_allowlist` and `category_denylist` are mutually '
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r'exclusive options.'):
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classifier_options = _ClassifierOptions(category_allowlist=['foo'],
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category_denylist=['bar'])
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as unused_classifier:
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pass
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def test_empty_classification_outputs(self):
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classifier_options = _ClassifierOptions(score_threshold=1)
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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classifier_options=classifier_options)
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with _ImageClassifier.create_from_options(options) as classifier:
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# Performs image classification on the input.
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image_result = classifier.classify(self.test_image)
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self.assertEmpty(image_result.classifications[0].entries[0].categories)
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def test_missing_result_callback(self):
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM)
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with self.assertRaisesRegex(ValueError,
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r'result callback must be provided'):
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with _ImageClassifier.create_from_options(options) as unused_classifier:
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pass
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@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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def test_illegal_result_callback(self, running_mode):
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def pass_through(unused_result: _ClassificationResult):
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pass
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options = _ImageClassifierOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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running_mode=running_mode,
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result_callback=pass_through)
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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with _ImageClassifier.create_from_options(options) as unused_classifier:
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pass
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if __name__ == '__main__':
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if __name__ == '__main__':
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absltest.main()
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absltest.main()
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