Python anchor_generator_builder.build方法代码示例

# 需要导入模块: from object_detection.builders import anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_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 anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_builder import build [as 别名]
def test_build_grid_anchor_generator_with_defaults(self):
    anchor_generator_text_proto = """
      grid_anchor_generator {
      }
     """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               grid_anchor_generator.GridAnchorGenerator))
    self.assertListEqual(anchor_generator_object._scales, [])
    self.assertListEqual(anchor_generator_object._aspect_ratios, [])
    with self.test_session() as sess:
      base_anchor_size, anchor_offset, anchor_stride = sess.run(
          [anchor_generator_object._base_anchor_size,
           anchor_generator_object._anchor_offset,
           anchor_generator_object._anchor_stride])
    self.assertAllEqual(anchor_offset, [0, 0])
    self.assertAllEqual(anchor_stride, [16, 16])
    self.assertAllEqual(base_anchor_size, [256, 256]) 

# 需要导入模块: from object_detection.builders import anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_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 anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_builder import build [as 别名]
def test_build_ssd_anchor_generator_with_custom_scales(self):
    anchor_generator_text_proto = """
      ssd_anchor_generator {
        aspect_ratios: [1.0]
        scales: [0.1, 0.15, 0.2, 0.4, 0.6, 0.8]
        reduce_boxes_in_lowest_layer: false
      }
    """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               multiple_grid_anchor_generator.
                               MultipleGridAnchorGenerator))
    for actual_scales, expected_scales in zip(
        list(anchor_generator_object._scales),
        [(0.1, math.sqrt(0.1 * 0.15)),
         (0.15, math.sqrt(0.15 * 0.2)),
         (0.2, math.sqrt(0.2 * 0.4)),
         (0.4, math.sqrt(0.4 * 0.6)),
         (0.6, math.sqrt(0.6 * 0.8)),
         (0.8, math.sqrt(0.8 * 1.0))]):
      self.assert_almost_list_equal(expected_scales, actual_scales, delta=1e-2) 

# 需要导入模块: from object_detection.builders import anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_builder import build [as 别名]
def test_build_multiscale_anchor_generator_custom_aspect_ratios(self):
    anchor_generator_text_proto = """
      multiscale_anchor_generator {
        aspect_ratios: [1.0]
      }
    """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               multiscale_grid_anchor_generator.
                               MultiscaleGridAnchorGenerator))
    for level, anchor_grid_info in zip(
        range(3, 8), anchor_generator_object._anchor_grid_info):
      self.assertEqual(set(anchor_grid_info.keys()), set(['level', 'info']))
      self.assertTrue(level, anchor_grid_info['level'])
      self.assertEqual(len(anchor_grid_info['info']), 4)
      self.assertAllClose(anchor_grid_info['info'][0], [2**0, 2**0.5])
      self.assertTrue(anchor_grid_info['info'][1], 1.0)
      self.assertAllClose(anchor_grid_info['info'][2],
                          [4.0 * 2**level, 4.0 * 2**level])
      self.assertAllClose(anchor_grid_info['info'][3], [2**level, 2**level])
      self.assertTrue(anchor_generator_object._normalize_coordinates) 

# 需要导入模块: from object_detection.builders import anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_builder import build [as 别名]
def test_build_multiscale_anchor_generator_with_anchors_in_pixel_coordinates(
      self):
    anchor_generator_text_proto = """
      multiscale_anchor_generator {
        aspect_ratios: [1.0]
        normalize_coordinates: false
      }
    """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               multiscale_grid_anchor_generator.
                               MultiscaleGridAnchorGenerator))
    self.assertFalse(anchor_generator_object._normalize_coordinates) 

# 需要导入模块: from object_detection.builders import anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_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 anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_builder import build [as 别名]
def test_build_ssd_anchor_generator_with_custom_interpolated_scale(self):
    anchor_generator_text_proto = """
      ssd_anchor_generator {
        aspect_ratios: [0.5]
        interpolated_scale_aspect_ratio: 0.5
        reduce_boxes_in_lowest_layer: false
      }
    """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               multiple_grid_anchor_generator.
                               MultipleGridAnchorGenerator))
    for actual_aspect_ratio, expected_aspect_ratio in zip(
        list(anchor_generator_object._aspect_ratios),
        6 * [(0.5, 0.5)]):
      self.assert_almost_list_equal(expected_aspect_ratio, actual_aspect_ratio) 

# 需要导入模块: from object_detection.builders import anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_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 anchor_generator_builder [as 别名]
# 或者: from object_detection.builders.anchor_generator_builder import build [as 别名]
def test_build_grid_anchor_generator_with_non_default_parameters(self):
    anchor_generator_text_proto = """
      grid_anchor_generator {
        height: 128
        width: 512
        height_stride: 10
        width_stride: 20
        height_offset: 30
        width_offset: 40
        scales: [0.4, 2.2]
        aspect_ratios: [0.3, 4.5]
      }
     """
    anchor_generator_proto = anchor_generator_pb2.AnchorGenerator()
    text_format.Merge(anchor_generator_text_proto, anchor_generator_proto)
    anchor_generator_object = anchor_generator_builder.build(
        anchor_generator_proto)
    self.assertTrue(isinstance(anchor_generator_object,
                               grid_anchor_generator.GridAnchorGenerator))
    self.assert_almost_list_equal(anchor_generator_object._scales,
                                  [0.4, 2.2])
    self.assert_almost_list_equal(anchor_generator_object._aspect_ratios,
                                  [0.3, 4.5])
    with self.test_session() as sess:
      base_anchor_size, anchor_offset, anchor_stride = sess.run(
          [anchor_generator_object._base_anchor_size,
           anchor_generator_object._anchor_offset,
           anchor_generator_object._anchor_stride])
    self.assertAllEqual(anchor_offset, [30, 40])
    self.assertAllEqual(anchor_stride, [10, 20])
    self.assertAllEqual(base_anchor_size, [128, 512])