Python box_predictor_pb2.BoxPredictor方法代码示例

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [as 别名]
def build_score_converter(score_converter_config, is_training):
  """Builds score converter based on the config.

  Builds one of [tf.identity, tf.sigmoid] score converters based on the config
  and whether the BoxPredictor is for training or inference.

  Args:
    score_converter_config:
      box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.score_converter.
    is_training: Indicates whether the BoxPredictor is in training mode.

  Returns:
    Callable score converter op.

  Raises:
    ValueError: On unknown score converter.
  """
  if score_converter_config == (
      box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.IDENTITY):
    return tf.identity
  if score_converter_config == (
      box_predictor_pb2.WeightSharedConvolutionalBoxPredictor.SIGMOID):
    return tf.identity if is_training else tf.sigmoid
  raise ValueError('Unknown score converter.') 

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [as 别名]
def test_box_predictor_builder_calls_fc_argscope_fn(self):
    fc_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
          weight: 0.0003
        }
      }
      initializer {
        truncated_normal_initializer {
          mean: 0.0
          stddev: 0.3
        }
      }
      activation: RELU_6
      op: FC
    """
    hyperparams_proto = hyperparams_pb2.Hyperparams()
    text_format.Merge(fc_hyperparams_text_proto, hyperparams_proto)
    box_predictor_proto = box_predictor_pb2.BoxPredictor()
    box_predictor_proto.mask_rcnn_box_predictor.fc_hyperparams.CopyFrom(
        hyperparams_proto)
    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=False,
        num_classes=10)
    mock_argscope_fn.assert_called_with(hyperparams_proto, False)
    self.assertEqual(box_predictor._fc_hyperparams, 'arg_scope') 

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [as 别名]
def test_default_rfcn_box_predictor(self):
    conv_hyperparams_text_proto = """
      regularizer {
        l1_regularizer {
        }
      }
      initializer {
        truncated_normal_initializer {
        }
      }
      activation: RELU_6
    """
    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()
    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, 4)
    self.assertEqual(box_predictor._num_spatial_bins, [3, 3])
    self.assertEqual(box_predictor._crop_size, [12, 12]) 

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [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) 

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [as 别名]
def test_construct_default_conv_box_predictor_with_custom_mask_head(self):
    box_predictor_text_proto = """
      convolutional_box_predictor {
        mask_head {
          mask_height: 7
          mask_width: 7
          masks_are_class_agnostic: false
        }
        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.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
                    box_predictor._other_heads)
    mask_prediction_head = (
        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
    )
    self.assertEqual(mask_prediction_head._mask_height, 7)
    self.assertEqual(mask_prediction_head._mask_width, 7)
    self.assertFalse(mask_prediction_head._masks_are_class_agnostic) 

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [as 别名]
def test_construct_weight_shared_predictor_with_default_mask_head(self):
    box_predictor_text_proto = """
      weight_shared_convolutional_box_predictor {
        mask_head {
        }
        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.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
                    box_predictor._other_heads)
    weight_shared_convolutional_mask_head = (
        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
    )
    self.assertIsInstance(weight_shared_convolutional_mask_head,
                          mask_head.WeightSharedConvolutionalMaskHead)
    self.assertEqual(weight_shared_convolutional_mask_head._mask_height, 15)
    self.assertEqual(weight_shared_convolutional_mask_head._mask_width, 15)
    self.assertTrue(
        weight_shared_convolutional_mask_head._masks_are_class_agnostic) 

# 需要导入模块: from object_detection.protos import box_predictor_pb2 [as 别名]
# 或者: from object_detection.protos.box_predictor_pb2 import BoxPredictor [as 别名]
def test_construct_weight_shared_predictor_with_custom_mask_head(self):
    box_predictor_text_proto = """
      weight_shared_convolutional_box_predictor {
        mask_head {
          mask_height: 7
          mask_width: 7
          masks_are_class_agnostic: false
        }
        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.assertTrue(convolutional_box_predictor.MASK_PREDICTIONS in
                    box_predictor._other_heads)
    weight_shared_convolutional_mask_head = (
        box_predictor._other_heads[convolutional_box_predictor.MASK_PREDICTIONS]
    )
    self.assertIsInstance(weight_shared_convolutional_mask_head,
                          mask_head.WeightSharedConvolutionalMaskHead)
    self.assertEqual(weight_shared_convolutional_mask_head._mask_height, 7)
    self.assertEqual(weight_shared_convolutional_mask_head._mask_width, 7)
    self.assertFalse(
        weight_shared_convolutional_mask_head._masks_are_class_agnostic)