Python box_predictor_builder.build方法代码示例

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_build_default_mask_rcnn_box_predictor(self):
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
        hyperparams_pb2.Hyperparams.FC)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock.Mock(return_value='arg_scope'),
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertFalse(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.5)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 4)
    self.assertFalse(box_predictor._predict_instance_masks)
    self.assertFalse(box_predictor._predict_keypoints) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_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_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def _get_second_stage_box_predictor(self, num_classes, is_training,
                                      predict_masks, masks_are_class_agnostic):
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(self._get_second_stage_box_predictor_text_proto(),
                      box_predictor_proto)
    if predict_masks:
      text_format.Merge(
          self._add_mask_to_second_stage_box_predictor_text_proto(
              masks_are_class_agnostic),
          box_predictor_proto)

    return box_predictor_builder.build(
        hyperparams_builder.build,
        box_predictor_proto,
        num_classes=num_classes,
        is_training=is_training) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_build_default_mask_rcnn_box_predictor(self):
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
        hyperparams_pb2.Hyperparams.FC)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock.Mock(return_value='arg_scope'),
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    box_head = box_predictor._box_prediction_head
    class_head = box_predictor._class_prediction_head
    self.assertFalse(box_head._use_dropout)
    self.assertFalse(class_head._use_dropout)
    self.assertAlmostEqual(box_head._dropout_keep_prob, 0.5)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_head._box_code_size, 4)
    self.assertEqual(len(box_predictor._third_stage_heads.keys()), 0) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_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_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_construct_default_conv_box_predictor(self):
    box_predictor_text_proto = """
      weight_shared_convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l1_regularizer {
            }
          }
          initializer {
            truncated_normal_initializer {
            }
          }
        }
      }"""
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=hyperparams_builder.build,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor._depth, 0)
    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_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_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def _build_arg_scope_with_hyperparams(self,
                                        hyperparams_text_proto,
                                        is_training):
    hyperparams = hyperparams_pb2.Hyperparams()
    text_format.Merge(hyperparams_text_proto, hyperparams)
    return hyperparams_builder.build(hyperparams, is_training=is_training) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def _get_second_stage_box_predictor(self, num_classes, is_training):
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(self._get_second_stage_box_predictor_text_proto(),
                      box_predictor_proto)
    return box_predictor_builder.build(
        hyperparams_builder.build,
        box_predictor_proto,
        num_classes=num_classes,
        is_training=is_training) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_construct_default_conv_box_predictor(self):
    box_predictor_text_proto = """
      convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l1_regularizer {
            }
          }
          initializer {
            truncated_normal_initializer {
            }
          }
        }
      }"""
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=hyperparams_builder.build,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor._min_depth, 0)
    self.assertEqual(box_predictor._max_depth, 0)
    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
    self.assertTrue(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.8)
    self.assertFalse(box_predictor._apply_sigmoid_to_scores)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_non_default_mask_rcnn_box_predictor(self):
    fc_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
      op: FC
    """
    box_predictor_text_proto = """
      mask_rcnn_box_predictor {
        use_dropout: true
        dropout_keep_probability: 0.8
        box_code_size: 3
      }
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
    def mock_fc_argscope_builder(fc_hyperparams_arg, is_training):
      return (fc_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_fc_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertTrue(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.8)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 3) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_build_box_predictor_with_mask_branch(self):
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.op = (
        hyperparams_pb2.Hyperparams.FC)
    box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams.op = (
        hyperparams_pb2.Hyperparams.CONV)
    box_predictor_proto.mask_rcnn_box_predictor.predict_instance_masks = True
    box_predictor_proto.mask_rcnn_box_predictor.mask_prediction_conv_depth = 512
    mock_argscope_fn = mock.Mock(return_value='arg_scope')
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_argscope_fn,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    mock_argscope_fn.assert_has_calls(
        [mock.call(box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams,
                   True),
         mock.call(box_predictor_proto.mask_rcnn_box_predictor.conv_hyperparams,
                   True)], any_order=True)
    self.assertFalse(box_predictor._use_dropout)
    self.assertAlmostEqual(box_predictor._dropout_keep_prob, 0.5)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 4)
    self.assertTrue(box_predictor._predict_instance_masks)
    self.assertEqual(box_predictor._mask_prediction_conv_depth, 512)
    self.assertFalse(box_predictor._predict_keypoints) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_non_default_rfcn_box_predictor(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
    """
    box_predictor_text_proto = """
      rfcn_box_predictor {
        num_spatial_bins_height: 4
        num_spatial_bins_width: 4
        depth: 4
        box_code_size: 3
        crop_height: 16
        crop_width: 16
      }
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(conv_hyperparams_text_proto, hyperparams_proto)
    def mock_conv_argscope_builder(conv_hyperparams_arg, is_training):
      return (conv_hyperparams_arg, is_training)

    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor_proto.rfcn_box_predictor.conv_hyperparams.CopyFrom(
        hyperparams_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=mock_conv_argscope_builder,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertEqual(box_predictor._box_code_size, 3)
    self.assertEqual(box_predictor._num_spatial_bins, [4, 4])
    self.assertEqual(box_predictor._crop_size, [16, 16]) 

# 需要导入模块: from object_detection.builders import box_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_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_predictor_builder [as 别名]
# 或者: from object_detection.builders.box_predictor_builder import build [as 别名]
def test_construct_default_conv_box_predictor(self):
    box_predictor_text_proto = """
      convolutional_box_predictor {
        conv_hyperparams {
          regularizer {
            l1_regularizer {
            }
          }
          initializer {
            truncated_normal_initializer {
            }
          }
        }
      }"""
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    text_format.Merge(box_predictor_text_proto, box_predictor_proto)
    box_predictor = box_predictor_builder.build(
        argscope_fn=hyperparams_builder.build,
        box_predictor_config=box_predictor_proto,
        is_training=True,
        num_classes=90)
    class_head = box_predictor._class_prediction_head
    self.assertEqual(box_predictor._min_depth, 0)
    self.assertEqual(box_predictor._max_depth, 0)
    self.assertEqual(box_predictor._num_layers_before_predictor, 0)
    self.assertTrue(class_head._use_dropout)
    self.assertAlmostEqual(class_head._dropout_keep_prob, 0.8)
    self.assertFalse(class_head._apply_sigmoid_to_scores)
    self.assertEqual(class_head._num_class_slots, 91)
    self.assertEqual(box_predictor.num_classes, 90)
    self.assertTrue(box_predictor._is_training)
    self.assertFalse(class_head._use_depthwise)