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])
))