Python box_coder_builder.build方法代码示例

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def build(model_config, is_training):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture)) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.
  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture)) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_keypoint_box_coder_with_non_default_parameters(self):
    box_coder_text_proto = """
      keypoint_box_coder {
        num_keypoints: 6
        y_scale: 6.0
        x_scale: 3.0
        height_scale: 7.0
        width_scale: 8.0
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
    self.assertEqual(box_coder_object._num_keypoints, 6)
    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def build(model_config, is_training, add_summaries=True):
  """Builds a DetectionModel based on the model config.

  Args:
    model_config: A model.proto object containing the config for the desired
      DetectionModel.
    is_training: True if this model is being built for training purposes.
    add_summaries: Whether to add tensorflow summaries in the model graph.

  Returns:
    DetectionModel based on the config.

  Raises:
    ValueError: On invalid meta architecture or model.
  """
  if not isinstance(model_config, model_pb2.DetectionModel):
    raise ValueError('model_config not of type model_pb2.DetectionModel.')
  meta_architecture = model_config.WhichOneof('model')
  if meta_architecture == 'ssd':
    return _build_ssd_model(model_config.ssd, is_training, add_summaries)
  if meta_architecture == 'faster_rcnn':
    return _build_faster_rcnn_model(model_config.faster_rcnn, is_training,
                                    add_summaries)
  raise ValueError('Unknown meta architecture: {}'.format(meta_architecture)) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def _build_ssd_feature_extractor(feature_extractor_config, is_training,
                                 reuse_weights=None):
  """Builds a ssd_meta_arch.SSDFeatureExtractor based on config.

  Args:
    feature_extractor_config: A SSDFeatureExtractor proto config from ssd.proto.
    is_training: True if this feature extractor is being built for training.
    reuse_weights: if the feature extractor should reuse weights.

  Returns:
    ssd_meta_arch.SSDFeatureExtractor based on config.

  Raises:
    ValueError: On invalid feature extractor type.
  """
  feature_type = feature_extractor_config.type
  depth_multiplier = feature_extractor_config.depth_multiplier
  min_depth = feature_extractor_config.min_depth
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)

  if feature_type not in SSD_FEATURE_EXTRACTOR_CLASS_MAP:
    raise ValueError('Unknown ssd feature_extractor: {}'.format(feature_type))

  feature_extractor_class = SSD_FEATURE_EXTRACTOR_CLASS_MAP[feature_type]
  return feature_extractor_class(depth_multiplier, min_depth, conv_hyperparams,
                                 reuse_weights) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_faster_rcnn_box_coder_with_defaults(self):
    box_coder_text_proto = """
      faster_rcnn_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(isinstance(box_coder_object,
                               faster_rcnn_box_coder.FasterRcnnBoxCoder))
    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_faster_rcnn_box_coder_with_non_default_parameters(self):
    box_coder_text_proto = """
      faster_rcnn_box_coder {
        y_scale: 6.0
        x_scale: 3.0
        height_scale: 7.0
        width_scale: 8.0
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(isinstance(box_coder_object,
                               faster_rcnn_box_coder.FasterRcnnBoxCoder))
    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_square_box_coder_with_defaults(self):
    box_coder_text_proto = """
      square_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(
        isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_square_box_coder_with_non_default_parameters(self):
    box_coder_text_proto = """
      square_box_coder {
        y_scale: 6.0
        x_scale: 3.0
        length_scale: 7.0
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertTrue(
        isinstance(box_coder_object, square_box_coder.SquareBoxCoder))
    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_raise_error_on_empty_box_coder(self):
    box_coder_text_proto = """
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    with self.assertRaises(ValueError):
      box_coder_builder.build(box_coder_proto) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_faster_rcnn_box_coder_with_defaults(self):
    box_coder_text_proto = """
      faster_rcnn_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertIsInstance(box_coder_object,
                          faster_rcnn_box_coder.FasterRcnnBoxCoder)
    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_faster_rcnn_box_coder_with_non_default_parameters(self):
    box_coder_text_proto = """
      faster_rcnn_box_coder {
        y_scale: 6.0
        x_scale: 3.0
        height_scale: 7.0
        width_scale: 8.0
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertIsInstance(box_coder_object,
                          faster_rcnn_box_coder.FasterRcnnBoxCoder)
    self.assertEqual(box_coder_object._scale_factors, [6.0, 3.0, 7.0, 8.0]) 

# 需要导入模块: from object_detection.builders import box_coder_builder [as 别名]
# 或者: from object_detection.builders.box_coder_builder import build [as 别名]
def test_build_keypoint_box_coder_with_defaults(self):
    box_coder_text_proto = """
      keypoint_box_coder {
      }
    """
    box_coder_proto = box_coder_pb2.BoxCoder()
    text_format.Merge(box_coder_text_proto, box_coder_proto)
    box_coder_object = box_coder_builder.build(box_coder_proto)
    self.assertIsInstance(box_coder_object, keypoint_box_coder.KeypointBoxCoder)
    self.assertEqual(box_coder_object._scale_factors, [10.0, 10.0, 5.0, 5.0])