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()
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.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.metric.profiler = None
self.evaluator = ModelEvaluator(self.launcher, self.input_feeder,
self.adapter, 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_store_only(self):
self.postprocessor.has_dataset_processors = False
self.evaluator.process_dataset('path', None, store_only=True)
assert self.evaluator.store_predictions.called
assert not self.evaluator.load.called
assert self.launcher.predict.called
assert not self.postprocessor.process_batch.called
assert not self.metric.update_metrics_on_batch.called
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(
'openvino.tools.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(
'openvino.tools.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.metric.profiler = None
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.launcher.dyn_input_layers = 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_infer_queue.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.launcher.dyn_input_layers = False
self.evaluator.process_dataset('path', None)
assert not self.evaluator.load.called
assert not self.launcher.predict.called
assert self.launcher.get_infer_queue.called
def test_process_dataset_with_loading_predictions_and_without_dataset_processors(
self, mocker):
mocker.patch(
'openvino.tools.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.launcher.dynamic_shapes_policy = 'static'
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)