Python region_similarity_calculator_builder.build方法代码示例

# 需要导入模块: from object_detection.builders import region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_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 region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_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 region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_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 region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_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 region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_builder import build [as 别名]
def testBuildIoaSimilarityCalculator(self):
    similarity_calc_text_proto = """
      ioa_similarity {
      }
    """
    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
    similarity_calc = region_similarity_calculator_builder.build(
        similarity_calc_proto)
    self.assertTrue(isinstance(similarity_calc,
                               region_similarity_calculator.IoaSimilarity)) 

# 需要导入模块: from object_detection.builders import region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_builder import build [as 别名]
def testBuildIouSimilarityCalculator(self):
    similarity_calc_text_proto = """
      iou_similarity {
      }
    """
    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
    similarity_calc = region_similarity_calculator_builder.build(
        similarity_calc_proto)
    self.assertTrue(isinstance(similarity_calc,
                               region_similarity_calculator.IouSimilarity)) 

# 需要导入模块: from object_detection.builders import region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_builder import build [as 别名]
def testBuildNegSqDistSimilarityCalculator(self):
    similarity_calc_text_proto = """
      neg_sq_dist_similarity {
      }
    """
    similarity_calc_proto = sim_calc_pb2.RegionSimilarityCalculator()
    text_format.Merge(similarity_calc_text_proto, similarity_calc_proto)
    similarity_calc = region_similarity_calculator_builder.build(
        similarity_calc_proto)
    self.assertTrue(isinstance(similarity_calc,
                               region_similarity_calculator.
                               NegSqDistSimilarity)) 

# 需要导入模块: from object_detection.builders import region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_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
  pad_to_multiple = feature_extractor_config.pad_to_multiple
  batch_norm_trainable = feature_extractor_config.batch_norm_trainable
  use_explicit_padding = feature_extractor_config.use_explicit_padding
  use_depthwise = feature_extractor_config.use_depthwise
  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(is_training, depth_multiplier, min_depth,
                                 pad_to_multiple, conv_hyperparams,
                                 batch_norm_trainable, reuse_weights,
                                 use_explicit_padding, use_depthwise) 

# 需要导入模块: from object_detection.builders import region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_builder import build [as 别名]
def build(model_config, is_training, add_summaries=True,
          add_background_class=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.
    add_background_class: Whether to add an implicit background class to one-hot
      encodings of groundtruth labels. Set to false if using groundtruth labels
      with an explicit background class or using multiclass scores instead of
      truth in the case of distillation. Ignored in the case of faster_rcnn.
  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,
                            add_background_class)
  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 region_similarity_calculator_builder [as 别名]
# 或者: from object_detection.builders.region_similarity_calculator_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
  pad_to_multiple = feature_extractor_config.pad_to_multiple
  use_explicit_padding = feature_extractor_config.use_explicit_padding
  use_depthwise = feature_extractor_config.use_depthwise
  conv_hyperparams = hyperparams_builder.build(
      feature_extractor_config.conv_hyperparams, is_training)
  override_base_feature_extractor_hyperparams = (
      feature_extractor_config.override_base_feature_extractor_hyperparams)

  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(
      is_training, depth_multiplier, min_depth, pad_to_multiple,
      conv_hyperparams, reuse_weights, use_explicit_padding, use_depthwise,
      override_base_feature_extractor_hyperparams)