def setup_method(self):
self.launcher = MagicMock()
infer_request = MagicMock()
infer_request.wait = Mock(return_value=0)
infer_request.outputs = Mock()
self.launcher.infer_requests = [infer_request]
data = MagicMock(data=MagicMock(), metadata=MagicMock(), identifier=0)
self.preprocessor = Mock()
self.preprocessor.process = Mock(return_value=data)
self.postprocessor = Mock()
self.adapter = MagicMock(return_value=[])
self.input_feeder = Mock()
self.data_reader = Mock(return_value=data)
self.data_reader.data_source = 'source'
annotation_0 = MagicMock()
annotation_0.identifier = 0
annotation_0.metadata = {'data_source': MagicMock()}
annotation_1 = MagicMock()
annotation_1.identifier = 1
annotation_1.metadata = {'data_source': MagicMock()}
annotation_container_0 = MagicMock()
annotation_container_0.values = MagicMock(return_value=[annotation_0])
annotation_container_1 = MagicMock()
annotation_container_1.values = MagicMock(return_value=([annotation_1]))
self.annotations = [[annotation_container_0], [annotation_container_1]]
self.dataset = MagicMock()
self.dataset.__iter__.return_value = self.annotations
self.postprocessor.process_batch = Mock(side_effect=[
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
])
self.postprocessor.process_dataset = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
self.postprocessor.full_process = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
self.metric = Mock()
self.metric.update_metrics_on_batch = Mock()
self.evaluator = ModelEvaluator(
self.launcher,
self.input_feeder,
self.adapter,
self.data_reader,
self.preprocessor,
self.postprocessor,
self.dataset,
self.metric,
True
)
self.evaluator.store_predictions = Mock()
self.evaluator.load = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
def setup_method(self):
self.launcher = MagicMock()
self.launcher.get_async_requests = Mock(return_value=[])
data = MagicMock(data=MagicMock(), metadata=MagicMock(), identifier=0)
self.preprocessor = Mock()
self.preprocessor.process = Mock(return_value=data)
self.postprocessor = Mock()
self.adapter = MagicMock(return_value=[])
self.input_feeder = MagicMock()
self.input_feeder.lstm_inputs = []
annotation_0 = MagicMock()
annotation_0.identifier = 0
annotation_0.metadata = {'data_source': MagicMock()}
annotation_1 = MagicMock()
annotation_1.identifier = 1
annotation_1.metadata = {'data_source': MagicMock()}
annotation_container_0 = MagicMock()
annotation_container_0.values = MagicMock(return_value=[annotation_0])
annotation_container_1 = MagicMock()
annotation_container_1.values = MagicMock(return_value=([annotation_1]))
self.annotations = [[annotation_container_0], [annotation_container_1]]
self.dataset = MagicMock()
self.dataset.__iter__.return_value = [
(range(1), self.annotations[0], data, [0]),
(range(1), self.annotations[1], data, [1])]
self.dataset.multi_infer = False
self.postprocessor.process_batch = Mock(side_effect=[
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
])
self.postprocessor.process_dataset = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
self.postprocessor.full_process = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
self.metric = Mock()
self.metric.update_metrics_on_batch = Mock(return_value=[{}, {}])
self.evaluator = ModelEvaluator(
self.launcher,
self.input_feeder,
self.adapter,
self.preprocessor,
self.postprocessor,
self.dataset,
self.metric,
True,
{}
)
self.evaluator.store_predictions = Mock()
self.evaluator.load = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
def test_preprocessing_config_extra_parameter(self):
preprocessing_config = [{
'type': 'bgr_to_rgb'
}, {
'type': 'resize',
'size': 224,
'something_extra': True
}]
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation': 'annotation',
'preprocessing': preprocessing_config
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
1].message == "datasets.0.preprocessing.1 specifies unknown options: ['something_extra']"
assert config_errors[1].entry == preprocessing_config[1]
assert config_errors[1].field_uri == 'datasets.0.preprocessing.1'
def test_preprocessing_config_unknown_parameter(self):
preprocessing_config = [{
'type': 'bgr_to_rgb'
}, {
'type': 'not_resize',
'size': 224
}]
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation': 'annotation',
'preprocessing': preprocessing_config
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
1].message == "preprocessor not_resize unregistered"
assert config_errors[1].entry == preprocessing_config[1]
assert config_errors[1].field_uri == 'datasets.0.preprocessing.1'
def test_empty_config(self):
config_errors = ModelEvaluator.validate_config({'models': [{}]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'models.launchers'
assert config_errors[1].message == 'datasets section is not provided'
assert not config_errors[1].entry
assert config_errors[1].field_uri == 'models.datasets'
def test_unregistered_launcher_config(self):
launcher_config = {'framework': 'foo'}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'launchers': [launcher_config],
'datasets': []
}]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launcher foo is not unregistered'
assert config_errors[0].entry == launcher_config
assert config_errors[0].field_uri == 'models.launchers.0'
assert config_errors[1].message == 'datasets section is not provided'
assert not config_errors[1].entry
assert config_errors[1].field_uri == 'models.datasets'
def test_valid_launcher_config(self):
launcher_config = {
'model': 'foo',
'framework': 'dlsdk',
'device': 'cpu'
}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'launchers': [launcher_config],
'datasets': []
}]})
assert len(config_errors) == 1
assert config_errors[0].message == 'datasets section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'models.datasets'
def test_str_data_reader(self):
dataset_config = {
'name': 'dataset',
'annotation': 'annotation',
'metrics': [{
'type': 'accuracy'
}],
'reader': 'opencv_imread',
'data_source': 'data'
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 1
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
def test_dataset_config_without_metrics(self):
dataset_config = {
'name': 'dataset',
'data_source': 'data',
'annotation': 'annotation'
}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'datasets': [dataset_config]
}]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'models.launchers'
assert config_errors[1].message == 'Metrics are not provided'
assert not config_errors[1].entry
assert config_errors[1].field_uri == 'models.datasets.0'
def test_common_validation_scheme(self):
validation_scheme = ModelEvaluator.validation_scheme()
assert isinstance(validation_scheme, dict)
assert len(validation_scheme) == 1
assert 'models' in validation_scheme
assert len(validation_scheme['models']) == 1
assert contains_all(validation_scheme['models'][0],
['name', 'launchers', 'datasets'])
assert isinstance(validation_scheme['models'][0]['name'], StringField)
model_validation_scheme = validation_scheme['models'][0]
assert model_validation_scheme[
'launchers'].__name__ == Launcher.__name__
assert model_validation_scheme['datasets'].__name__ == Dataset.__name__
assert isinstance(
model_validation_scheme['launchers'].validation_scheme(), list)
assert isinstance(
model_validation_scheme['datasets'].validation_scheme(), list)
def test_data_source_does_not_exists(self):
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data_dir'
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 3
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
-1].message == 'Invalid value "data_dir" for datasets.0.data_source: path does not exist'
assert config_errors[-1].entry == 'data_dir'
assert config_errors[-1].field_uri == 'datasets.0.data_source'
def test_annotation_conversion_without_converter(self):
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation_conversion': {}
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[1].message == 'converter is not found'
assert config_errors[1].entry == {}
assert config_errors[1].field_uri == 'datasets.0.annotation_conversion'
def test_postprocessing_config(self):
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation': 'annotation',
'postprocessing': [{
'type': 'resize_prediction_boxes'
}]
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 1
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
def test_annotation_is_not_provided(self):
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data'
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
1].message == 'annotation_conversion or annotation field should be provided'
assert config_errors[1].entry == dataset_config
assert config_errors[1].field_uri == 'datasets.0'
def test_annotation_conversion_config(self):
conversion_parameters = {
'converter': 'imagenet',
'annotation_file': 'file'
}
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation_conversion': conversion_parameters
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 1
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
def test_data_source_is_file(self):
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'annotation': 'annotation',
'data_source': 'data'
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
1].message == 'Invalid value "data" for datasets.0.data_source: path is not a directory'
assert config_errors[1].entry == 'data'
assert config_errors[1].field_uri == 'datasets.0.data_source'
def test_postprocessing_config_unknown_type(self):
postprocessing_config = [{'type': 'unknown', 'size': 224}]
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation': 'annotation',
'postprocessing': postprocessing_config
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[1].message == 'postprocessor unknown unregistered'
assert config_errors[1].entry == postprocessing_config[0]
assert config_errors[1].field_uri == 'datasets.0.postprocessing.0'
def test_input_without_name(self):
launcher_config = {
'model': 'foo',
'framework': 'dlsdk',
'device': 'cpu',
'inputs': [{
"type": 'INPUT'
}]
}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'launchers': [launcher_config],
'datasets': []
}]})
assert len(config_errors) == 2
assert config_errors[0].message.endswith('input name is not provided')
assert config_errors[0].field_uri == 'models.launchers.0.inputs.0'
assert config_errors[1].message == 'datasets section is not provided'
assert not config_errors[1].entry
assert config_errors[1].field_uri == 'models.datasets'
def test_annotation_conversion_missed_parameter(self):
conversion_parameters = {'converter': 'imagenet'}
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation_conversion': conversion_parameters
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
1].message == 'Invalid config for datasets.0.annotation_conversion: missing required fields: annotation_file'
assert config_errors[1].entry == conversion_parameters
assert config_errors[1].field_uri == 'datasets.0.annotation_conversion'
def test_data_reader_without_data_source(self):
dataset_config = {
'name': 'dataset',
'annotation': 'annotation',
'metrics': [{
'type': 'accuracy'
}]
}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'datasets': [dataset_config]
}]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'models.launchers'
assert config_errors[
1].message == 'Invalid value "None" for models.datasets.0.data_source: models.datasets.0.data_source is not allowed to be None'
assert not config_errors[1].entry
assert config_errors[1].field_uri == 'models.datasets.0.data_source'
def test_unregistered_data_reader(self):
dataset_config = {
'name': 'dataset',
'annotation': 'annotation',
'metrics': [{
'type': 'accuracy'
}],
'reader': 'unknown'
}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'datasets': [dataset_config]
}]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'models.launchers'
assert config_errors[1].message.startswith(
'Invalid value "unknown" for models.datasets.0.reader')
assert config_errors[-1].entry == 'unknown'
assert config_errors[-1].field_uri == 'models.datasets.0.reader'
def test_unregistered_adapter_config(self):
launcher_config = {
'model': 'foo',
'framework': 'dlsdk',
'device': 'cpu',
'adapter': 'not_classification'
}
config_errors = ModelEvaluator.validate_config(
{'models': [{
'launchers': [launcher_config],
'datasets': []
}]})
assert len(config_errors) == 2
assert config_errors[0].message.startswith(
'Invalid value "not_classification"')
assert config_errors[0].entry == 'not_classification'
assert config_errors[0].field_uri.startswith(
'models.launchers.0') and config_errors[0].field_uri.endswith(
'adapter')
assert config_errors[1].message == 'datasets section is not provided'
assert not config_errors[1].entry
assert config_errors[1].field_uri == 'models.datasets'
def test_annotation_conversion_extra_parameter(self):
conversion_parameters = {
'converter': 'imagenet',
'annotation_file': 'file',
'something_extra': 'extra'
}
dataset_config = {
'name': 'dataset',
'metrics': [{
'type': 'accuracy'
}],
'data_source': 'data',
'annotation_conversion': conversion_parameters
}
config_errors = ModelEvaluator.validate_config(
{'datasets': [dataset_config]})
assert len(config_errors) == 2
assert config_errors[0].message == 'launchers section is not provided'
assert not config_errors[0].entry
assert config_errors[0].field_uri == 'launchers'
assert config_errors[
1].message == "datasets.0.annotation_conversion specifies unknown options: ['something_extra']"
assert config_errors[1].entry == conversion_parameters
assert config_errors[1].field_uri == 'datasets.0.annotation_conversion'
class TestModelEvaluator:
def setup_method(self):
self.launcher = Mock()
self.launcher.predict.return_value = []
data = MagicMock(data=MagicMock(), metadata=MagicMock(), identifier=0)
self.preprocessor = Mock()
self.preprocessor.process = Mock(return_value=data)
self.postprocessor = Mock()
self.adapter = MagicMock(return_value=[])
self.input_feeder = Mock()
self.data_reader = Mock(return_value=data)
self.data_reader.data_source = 'source'
annotation_0 = MagicMock()
annotation_0.identifier = 0
annotation_0.metadata = {'data_source': MagicMock()}
annotation_1 = MagicMock()
annotation_1.identifier = 1
annotation_1.metadata = {'data_source': MagicMock()}
annotation_container_0 = MagicMock()
annotation_container_0.values = MagicMock(return_value=[annotation_0])
annotation_container_1 = MagicMock()
annotation_container_1.values = MagicMock(return_value=([annotation_1]))
self.annotations = [[annotation_container_0], [annotation_container_1]]
self.dataset = MagicMock()
self.dataset.__iter__.return_value = [(range(1), self.annotations[0]), (range(1), self.annotations[1])]
self.postprocessor.process_batch = Mock(side_effect=[
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
])
self.postprocessor.process_dataset = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
self.postprocessor.full_process = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
self.metric = Mock()
self.metric.update_metrics_on_batch = Mock()
self.evaluator = ModelEvaluator(
self.launcher,
self.input_feeder,
self.adapter,
self.data_reader,
self.preprocessor,
self.postprocessor,
self.dataset,
self.metric,
False
)
self.evaluator.store_predictions = Mock()
self.evaluator.load = Mock(return_value=(
([annotation_container_0], [annotation_container_0]), ([annotation_container_1], [annotation_container_1])
))
def test_process_dataset_without_storing_predictions_and_dataset_processors(self):
self.postprocessor.has_dataset_processors = False
self.evaluator.process_dataset(None, None)
assert not self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert self.launcher.predict.called
assert self.postprocessor.process_batch.called
assert self.metric.update_metrics_on_batch.call_count == len(self.annotations)
assert not self.postprocessor.process_dataset.called
assert not self.postprocessor.full_process.called
def test_process_dataset_with_storing_predictions_and_without_dataset_processors(self):
self.postprocessor.has_dataset_processors = False
self.evaluator.process_dataset('path', None)
assert self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert self.launcher.predict.called
assert self.postprocessor.process_batch.called
assert self.metric.update_metrics_on_batch.call_count == len(self.annotations)
assert not self.postprocessor.process_dataset.called
assert not self.postprocessor.full_process.called
def test_process_dataset_with_loading_predictions_and_without_dataset_processors(self, mocker):
mocker.patch('accuracy_checker.evaluators.model_evaluator.get_path')
self.postprocessor.has_dataset_processors = False
self.evaluator.process_dataset('path', None)
assert self.evaluator.load.called
assert not self.launcher.predict.called
assert not self.postprocessor.process_batch.called
assert self.metric.update_metrics_on_batch.call_count == 1
assert not self.postprocessor.process_dataset.called
assert self.postprocessor.full_process.called
def test_process_dataset_with_loading_predictions_and_with_dataset_processors(self, mocker):
mocker.patch('accuracy_checker.evaluators.model_evaluator.get_path')
self.postprocessor.has_dataset_processors = True
self.evaluator.process_dataset('path', None)
assert not self.evaluator.store_predictions.called
assert self.evaluator.load.called
assert not self.launcher.predict.called
assert not self.postprocessor.process_batch.called
assert self.metric.update_metrics_on_batch.call_count == 1
assert not self.postprocessor.process_dataset.called
assert self.postprocessor.full_process.called
class TestModelEvaluatorAsync:
def setup_method(self):
self.launcher = MagicMock()
self.launcher.get_async_requests = Mock(return_value=[])
data = MagicMock(data=MagicMock(), metadata=MagicMock(), identifier=0)
self.preprocessor = Mock()
self.preprocessor.process = Mock(return_value=data)
self.postprocessor = Mock()
self.adapter = MagicMock(return_value=[])
self.input_feeder = MagicMock()
self.input_feeder.lstm_inputs = []
annotation_0 = MagicMock()
annotation_0.identifier = 0
annotation_0.metadata = {'data_source': MagicMock()}
annotation_1 = MagicMock()
annotation_1.identifier = 1
annotation_1.metadata = {'data_source': MagicMock()}
annotation_container_0 = MagicMock()
annotation_container_0.values = MagicMock(return_value=[annotation_0])
annotation_container_1 = MagicMock()
annotation_container_1.values = MagicMock(
return_value=([annotation_1]))
self.annotations = [[annotation_container_0], [annotation_container_1]]
self.dataset = MagicMock()
self.dataset.__iter__.return_value = [
(range(1), self.annotations[0], data, [0]),
(range(1), self.annotations[1], data, [1])
]
self.dataset.multi_infer = False
self.postprocessor.process_batch = Mock(
side_effect=[([annotation_container_0], [annotation_container_0]),
([annotation_container_1], [annotation_container_1])])
self.postprocessor.process_dataset = Mock(
return_value=(([annotation_container_0], [annotation_container_0]),
([annotation_container_1],
[annotation_container_1])))
self.postprocessor.full_process = Mock(
return_value=(([annotation_container_0], [annotation_container_0]),
([annotation_container_1],
[annotation_container_1])))
self.metric = Mock()
self.metric.update_metrics_on_batch = Mock(return_value=[{}, {}])
self.evaluator = ModelEvaluator(self.launcher, self.input_feeder,
self.adapter, self.preprocessor,
self.postprocessor, self.dataset,
self.metric, True)
self.evaluator.store_predictions = Mock()
self.evaluator.load = Mock(return_value=(([annotation_container_0],
[annotation_container_0]),
([annotation_container_1],
[annotation_container_1])))
def test_process_dataset_without_storing_predictions_and_dataset_processors(
self):
self.postprocessor.has_dataset_processors = False
self.launcher.allow_reshape_input = False
self.preprocessor.has_multi_infer_transformations = False
self.evaluator.process_dataset(None, None)
assert not self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert not self.launcher.predict.called
assert self.launcher.get_async_requests.called
def test_process_dataset_with_storing_predictions_and_without_dataset_processors(
self):
self.postprocessor.has_dataset_processors = False
self.launcher.allow_reshape_input = False
self.preprocessor.has_multi_infer_transformations = False
self.dataset.multi_infer = False
self.evaluator.process_dataset('path', None)
assert not self.evaluator.load.called
assert not self.launcher.predict.called
assert self.launcher.get_async_requests.called
def test_process_dataset_with_loading_predictions_and_without_dataset_processors(
self, mocker):
mocker.patch('accuracy_checker.evaluators.model_evaluator.get_path')
self.postprocessor.has_dataset_processors = False
self.evaluator.process_dataset('path', None)
assert self.evaluator.load.called
assert not self.launcher.predict.called
assert not self.launcher.predict_async.called
assert not self.postprocessor.process_batch.called
assert self.metric.update_metrics_on_batch.call_count == 1
assert not self.postprocessor.process_dataset.called
assert self.postprocessor.full_process.called
def test_switch_to_sync_predict_if_need_reshaping(self):
self.postprocessor.has_dataset_processors = False
self.launcher.allow_reshape_input = True
self.preprocessor.has_multi_infer_transformations = False
self.evaluator.process_dataset(None, None)
assert not self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert self.launcher.predict.called
assert not self.launcher.predict_async.called
assert self.metric.update_metrics_on_batch.call_count == len(
self.annotations)
def test_switch_to_sync_predict_if_need_multi_infer_after_preprocessing(
self):
self.postprocessor.has_dataset_processors = False
self.launcher.allow_reshape_input = False
self.preprocessor.has_multi_infer_transformations = True
self.evaluator.process_dataset(None, None)
assert not self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert self.launcher.predict.called
assert not self.launcher.predict_async.called
assert self.metric.update_metrics_on_batch.call_count == len(
self.annotations)
def test_switch_to_sync_predict_if_need_multi_infer(self):
self.postprocessor.has_dataset_processors = False
self.launcher.allow_reshape_input = False
self.preprocessor.has_multi_infer_transformations = False
self.dataset.multi_infer = True
self.evaluator.process_dataset(None, None)
assert not self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert self.launcher.predict.called
assert not self.launcher.predict_async.called
assert self.metric.update_metrics_on_batch.call_count == len(
self.annotations)
