Python pipeline_pb2.TrainEvalPipelineConfig方法代碼示例

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def get_configs_from_pipeline_file():
  """Reads evaluation configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads evaluation config from file specified by pipeline_config_path flag.

  Returns:
    model_config: a model_pb2.DetectionModel
    eval_config: a eval_pb2.EvalConfig
    input_config: a input_reader_pb2.InputReader
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)

  model_config = pipeline_config.model
  if FLAGS.eval_training_data:
    eval_config = pipeline_config.train_config
  else:
    eval_config = pipeline_config.eval_config
  input_config = pipeline_config.eval_input_reader

  return model_config, eval_config, input_config 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_export_frozen_graph_with_moving_averages(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=True)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = True
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_export_model_with_all_output_nodes(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(add_detection_masks=True)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path)
    inference_graph = self._load_inference_graph(inference_graph_path)
    with self.test_session(graph=inference_graph):
      inference_graph.get_tensor_by_name('image_tensor:0')
      inference_graph.get_tensor_by_name('detection_boxes:0')
      inference_graph.get_tensor_by_name('detection_scores:0')
      inference_graph.get_tensor_by_name('detection_classes:0')
      inference_graph.get_tensor_by_name('detection_masks:0')
      inference_graph.get_tensor_by_name('num_detections:0') 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_export_model_with_detection_only_nodes(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(add_detection_masks=False)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path)
    inference_graph = self._load_inference_graph(inference_graph_path)
    with self.test_session(graph=inference_graph):
      inference_graph.get_tensor_by_name('image_tensor:0')
      inference_graph.get_tensor_by_name('detection_boxes:0')
      inference_graph.get_tensor_by_name('detection_scores:0')
      inference_graph.get_tensor_by_name('detection_classes:0')
      inference_graph.get_tensor_by_name('num_detections:0')
      with self.assertRaises(KeyError):
        inference_graph.get_tensor_by_name('detection_masks:0') 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: input_reader_pb2.InputReader
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)

  model_config = pipeline_config.model
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_export_frozen_graph(self):
    checkpoint_path = os.path.join(self.get_temp_dir(), 'model-ckpt')
    self._save_checkpoint_from_mock_model(checkpoint_path,
                                          use_moving_averages=False)
    inference_graph_path = os.path.join(self.get_temp_dir(),
                                        'exported_graph.pb')
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel(num_classes=1)
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      pipeline_config.eval_config.use_moving_averages = False
      exporter.export_inference_graph(
          input_type='image_tensor',
          pipeline_config=pipeline_config,
          checkpoint_path=checkpoint_path,
          inference_graph_path=inference_graph_path) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_get_configs_from_pipeline_file(self):
    """Test that proto configs can be read from pipeline config file."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    self.assertProtoEquals(pipeline_config.model, configs["model"])
    self.assertProtoEquals(pipeline_config.train_config,
                           configs["train_config"])
    self.assertProtoEquals(pipeline_config.train_input_reader,
                           configs["train_input_config"])
    self.assertProtoEquals(pipeline_config.eval_config,
                           configs["eval_config"])
    self.assertProtoEquals(pipeline_config.eval_input_reader,
                           configs["eval_input_configs"]) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_create_configs_from_pipeline_proto(self):
    """Tests creating configs dictionary from pipeline proto."""

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    configs = config_util.create_configs_from_pipeline_proto(pipeline_config)
    self.assertProtoEquals(pipeline_config.model, configs["model"])
    self.assertProtoEquals(pipeline_config.train_config,
                           configs["train_config"])
    self.assertProtoEquals(pipeline_config.train_input_reader,
                           configs["train_input_config"])
    self.assertProtoEquals(pipeline_config.eval_config, configs["eval_config"])
    self.assertProtoEquals(pipeline_config.eval_input_reader,
                           configs["eval_input_configs"]) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_create_pipeline_proto_from_configs(self):
    """Tests that proto can be reconstructed from configs dictionary."""
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))
    self.assertEqual(pipeline_config, pipeline_config_reconstructed) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def test_save_pipeline_config(self):
    """Tests that the pipeline config is properly saved to disk."""
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.model.faster_rcnn.num_classes = 10
    pipeline_config.train_config.batch_size = 32
    pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
    pipeline_config.eval_config.num_examples = 20
    pipeline_config.eval_input_reader.add().queue_capacity = 100

    config_util.save_pipeline_config(pipeline_config, self.get_temp_dir())
    configs = config_util.get_configs_from_pipeline_file(
        os.path.join(self.get_temp_dir(), "pipeline.config"))
    pipeline_config_reconstructed = (
        config_util.create_pipeline_proto_from_configs(configs))

    self.assertEqual(pipeline_config, pipeline_config_reconstructed) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def testNewMomentumOptimizerValue(self):
    """Tests that new momentum value is updated appropriately."""
    original_momentum_value = 0.4
    hparams = tf.contrib.training.HParams(momentum_optimizer_value=1.1)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    optimizer_config = pipeline_config.train_config.optimizer.rms_prop_optimizer
    optimizer_config.momentum_optimizer_value = original_momentum_value
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    optimizer_config = configs["train_config"].optimizer.rms_prop_optimizer
    new_momentum_value = optimizer_config.momentum_optimizer_value
    self.assertAlmostEqual(1.0, new_momentum_value)  # Clipped to 1.0. 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def testNewFocalLossParameters(self):
    """Tests that the loss weight ratio is updated appropriately."""
    original_alpha = 1.0
    original_gamma = 1.0
    new_alpha = 0.3
    new_gamma = 2.0
    hparams = tf.contrib.training.HParams(
        focal_loss_alpha=new_alpha, focal_loss_gamma=new_gamma)
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    classification_loss = pipeline_config.model.ssd.loss.classification_loss
    classification_loss.weighted_sigmoid_focal.alpha = original_alpha
    classification_loss.weighted_sigmoid_focal.gamma = original_gamma
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    configs = config_util.merge_external_params_with_configs(configs, hparams)
    classification_loss = configs["model"].ssd.loss.classification_loss
    self.assertAlmostEqual(new_alpha,
                           classification_loss.weighted_sigmoid_focal.alpha)
    self.assertAlmostEqual(new_gamma,
                           classification_loss.weighted_sigmoid_focal.gamma) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def testMergingKeywordArguments(self):
    """Tests that keyword arguments get merged as do hyperparameters."""
    original_num_train_steps = 100
    desired_num_train_steps = 10
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    pipeline_config.train_config.num_steps = original_num_train_steps
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"train_steps": desired_num_train_steps}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    train_steps = configs["train_config"].num_steps
    self.assertEqual(desired_num_train_steps, train_steps) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def testNewTrainInputPath(self):
    """Tests that train input path can be overwritten with single file."""
    original_train_path = ["path/to/data"]
    new_train_path = "another/path/to/data"
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"train_input_path": new_train_path}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual([new_train_path], final_path) 

# 需要導入模塊: from object_detection.protos import pipeline_pb2 [as 別名]
# 或者: from object_detection.protos.pipeline_pb2 import TrainEvalPipelineConfig [as 別名]
def testNewTrainInputPathList(self):
    """Tests that train input path can be overwritten with multiple files."""
    original_train_path = ["path/to/data"]
    new_train_path = ["another/path/to/data", "yet/another/path/to/data"]
    pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")

    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    reader_config = pipeline_config.train_input_reader.tf_record_input_reader
    reader_config.input_path.extend(original_train_path)
    _write_config(pipeline_config, pipeline_config_path)

    configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
    override_dict = {"train_input_path": new_train_path}
    configs = config_util.merge_external_params_with_configs(
        configs, kwargs_dict=override_dict)
    reader_config = configs["train_input_config"].tf_record_input_reader
    final_path = reader_config.input_path
    self.assertEqual(new_train_path, final_path)