Python object_detection.core.box_predictor.MASK_PREDICTIONS Examples

The following are 30 code examples of object_detection.core.box_predictor.MASK_PREDICTIONS(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.core.box_predictor , or try the search function .
Example #1
Source File: convolutional_box_predictor.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 6 votes vote down vote up 
def _predict_head(self, head_name, head_obj, image_feature, box_tower_feature,
                    feature_index, has_different_feature_channels,
                    target_channel, inserted_layer_counter,
                    num_predictions_per_location):
    if head_name == CLASS_PREDICTIONS_WITH_BACKGROUND:
      tower_name_scope = 'ClassPredictionTower'
    elif head_name == MASK_PREDICTIONS:
      tower_name_scope = 'MaskPredictionTower'
    else:
      raise ValueError('Unknown head')
    if self._share_prediction_tower:
      head_tower_feature = box_tower_feature
    else:
      head_tower_feature = self._compute_base_tower(
          tower_name_scope=tower_name_scope,
          image_feature=image_feature,
          feature_index=feature_index,
          has_different_feature_channels=has_different_feature_channels,
          target_channel=target_channel,
          inserted_layer_counter=inserted_layer_counter)
    return head_obj.predict(
        features=head_tower_feature,
        num_predictions_per_location=num_predictions_per_location)
Example #2
Source File: convolutional_box_predictor.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def _predict_head(self, head_name, head_obj, image_feature, box_tower_feature,
                    feature_index, has_different_feature_channels,
                    target_channel, inserted_layer_counter,
                    num_predictions_per_location):
    if head_name == CLASS_PREDICTIONS_WITH_BACKGROUND:
      tower_name_scope = 'ClassPredictionTower'
    elif head_name == MASK_PREDICTIONS:
      tower_name_scope = 'MaskPredictionTower'
    else:
      raise ValueError('Unknown head')
    if self._share_prediction_tower:
      head_tower_feature = box_tower_feature
    else:
      head_tower_feature = self._compute_base_tower(
          tower_name_scope=tower_name_scope,
          image_feature=image_feature,
          feature_index=feature_index,
          has_different_feature_channels=has_different_feature_channels,
          target_channel=target_channel,
          inserted_layer_counter=inserted_layer_counter)
    return head_obj.predict(
        features=head_tower_feature,
        num_predictions_per_location=num_predictions_per_location)
Example #3
Source File: box_predictor_test.py    From mtl-ssl with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #4
Source File: box_predictor_test.py    From motion-rcnn with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True,
        num_layers_before_mask_prediction=2)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #5
Source File: box_predictor_test.py    From AniSeg with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #6
Source File: box_predictor_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #7
Source File: box_predictor_test.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #8
Source File: box_predictor_test.py    From Elphas with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #9
Source File: box_predictor_test.py    From MBMD with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #10
Source File: box_predictor_test.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #11
Source File: box_predictor_test.py    From hands-detection with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #12
Source File: box_predictor_test.py    From moveo_ros with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #13
Source File: box_predictor_test.py    From ros_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #14
Source File: box_predictor_test.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #15
Source File: box_predictor_test.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #16
Source File: box_predictor_test.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #17
Source File: box_predictor_test.py    From Person-Detection-and-Tracking with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams_fn=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams_fn=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #18
Source File: box_predictor_test.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #19
Source File: box_predictor_test.py    From object_detector_app with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #20
Source File: convolutional_box_predictor.py    From vehicle_counting_tensorflow with MIT License 6 votes vote down vote up 
def _predict_head(self, head_name, head_obj, image_feature, box_tower_feature,
                    feature_index, has_different_feature_channels,
                    target_channel, inserted_layer_counter,
                    num_predictions_per_location):
    if head_name == CLASS_PREDICTIONS_WITH_BACKGROUND:
      tower_name_scope = 'ClassPredictionTower'
    elif head_name == MASK_PREDICTIONS:
      tower_name_scope = 'MaskPredictionTower'
    else:
      raise ValueError('Unknown head')
    if self._share_prediction_tower:
      head_tower_feature = box_tower_feature
    else:
      head_tower_feature = self._compute_base_tower(
          tower_name_scope=tower_name_scope,
          image_feature=image_feature,
          feature_index=feature_index,
          has_different_feature_channels=has_different_feature_channels,
          target_channel=target_channel,
          inserted_layer_counter=inserted_layer_counter)
    return head_obj.predict(
        features=head_tower_feature,
        num_predictions_per_location=num_predictions_per_location)
Example #21
Source File: box_predictor_test.py    From ros_people_object_detection_tensorflow with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #22
Source File: box_predictor_test.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #23
Source File: box_predictor_test.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #24
Source File: box_predictor_test.py    From Traffic-Rule-Violation-Detection-System with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        [image_features],
        num_predictions_per_location=[1],
        scope='BoxPredictor',
        predict_boxes_and_classes=True,
        predict_auxiliary_outputs=True)
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #25
Source File: box_predictor_test.py    From HereIsWally with MIT License 6 votes vote down vote up 
def test_get_instance_masks(self):
    image_features = tf.random_uniform([2, 7, 7, 3], dtype=tf.float32)
    mask_box_predictor = box_predictor.MaskRCNNBoxPredictor(
        is_training=False,
        num_classes=5,
        fc_hyperparams=self._build_arg_scope_with_hyperparams(),
        use_dropout=False,
        dropout_keep_prob=0.5,
        box_code_size=4,
        conv_hyperparams=self._build_arg_scope_with_hyperparams(
            op_type=hyperparams_pb2.Hyperparams.CONV),
        predict_instance_masks=True)
    box_predictions = mask_box_predictor.predict(
        image_features, num_predictions_per_location=1, scope='BoxPredictor')
    mask_predictions = box_predictions[box_predictor.MASK_PREDICTIONS]
    self.assertListEqual([2, 1, 5, 14, 14],
                         mask_predictions.get_shape().as_list())
Example #26
Source File: test_utils.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def _predict(self, image_features, **kwargs):
    image_feature = image_features[0]
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_feature)
    batch_size = combined_feature_shape[0]
    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
    code_size = 4
    zero = tf.reduce_sum(0 * image_feature)
    num_class_slots = self.num_classes
    if self._add_background_class:
      num_class_slots = num_class_slots + 1
    box_encodings = zero + tf.zeros(
        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
    class_predictions_with_background = zero + tf.zeros(
        (batch_size, num_anchors, num_class_slots), dtype=tf.float32)
    masks = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
         DEFAULT_MASK_SIZE),
        dtype=tf.float32)
    predictions_dict = {
        box_predictor.BOX_ENCODINGS:
            box_encodings,
        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background
    }
    if self._predict_mask:
      predictions_dict[box_predictor.MASK_PREDICTIONS] = masks
    return predictions_dict
Example #27
Source File: test_utils.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def _predict(self, image_features, num_predictions_per_location):
    image_feature = image_features[0]
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_feature)
    batch_size = combined_feature_shape[0]
    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
    code_size = 4
    zero = tf.reduce_sum(0 * image_feature)
    num_class_slots = self.num_classes
    if self._add_background_class:
      num_class_slots = num_class_slots + 1
    box_encodings = zero + tf.zeros(
        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
    class_predictions_with_background = zero + tf.zeros(
        (batch_size, num_anchors, num_class_slots), dtype=tf.float32)
    masks = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
         DEFAULT_MASK_SIZE),
        dtype=tf.float32)
    predictions_dict = {
        box_predictor.BOX_ENCODINGS:
            box_encodings,
        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background
    }
    if self._predict_mask:
      predictions_dict[box_predictor.MASK_PREDICTIONS] = masks

    return predictions_dict
Example #28
Source File: faster_rcnn_meta_arch.py    From mtl-ssl with Apache License 2.0 5 votes vote down vote up 
def predict_edgemask(self, prediction_dict):
    input_feature = prediction_dict['rpn_features_to_crop']
    edgemask_predictions = self._edgemask_predictor.predict(
      input_feature, scope=self.edgemask_predictor_scope)
    prediction_dict['edgemask_predictions'] = edgemask_predictions[mask_predictor.MASK_PREDICTIONS]
    return prediction_dict
Example #29
Source File: test_utils.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def _predict(self, image_features, num_predictions_per_location):
    image_feature = image_features[0]
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_feature)
    batch_size = combined_feature_shape[0]
    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
    code_size = 4
    zero = tf.reduce_sum(0 * image_feature)
    num_class_slots = self.num_classes
    if self._add_background_class:
      num_class_slots = num_class_slots + 1
    box_encodings = zero + tf.zeros(
        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
    class_predictions_with_background = zero + tf.zeros(
        (batch_size, num_anchors, num_class_slots), dtype=tf.float32)
    masks = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
         DEFAULT_MASK_SIZE),
        dtype=tf.float32)
    predictions_dict = {
        box_predictor.BOX_ENCODINGS:
            box_encodings,
        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background
    }
    if self._predict_mask:
      predictions_dict[box_predictor.MASK_PREDICTIONS] = masks

    return predictions_dict
Example #30
Source File: test_utils.py    From vehicle_counting_tensorflow with MIT License 5 votes vote down vote up 
def _predict(self, image_features, **kwargs):
    image_feature = image_features[0]
    combined_feature_shape = shape_utils.combined_static_and_dynamic_shape(
        image_feature)
    batch_size = combined_feature_shape[0]
    num_anchors = (combined_feature_shape[1] * combined_feature_shape[2])
    code_size = 4
    zero = tf.reduce_sum(0 * image_feature)
    num_class_slots = self.num_classes
    if self._add_background_class:
      num_class_slots = num_class_slots + 1
    box_encodings = zero + tf.zeros(
        (batch_size, num_anchors, 1, code_size), dtype=tf.float32)
    class_predictions_with_background = zero + tf.zeros(
        (batch_size, num_anchors, num_class_slots), dtype=tf.float32)
    masks = zero + tf.zeros(
        (batch_size, num_anchors, self.num_classes, DEFAULT_MASK_SIZE,
         DEFAULT_MASK_SIZE),
        dtype=tf.float32)
    predictions_dict = {
        box_predictor.BOX_ENCODINGS:
            box_encodings,
        box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND:
            class_predictions_with_background
    }
    if self._predict_mask:
      predictions_dict[box_predictor.MASK_PREDICTIONS] = masks
    return predictions_dict