remove unused code

research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor.py

@@ -102,7 +102,7 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
     self._depth_multiplier = depth_multiplier
     self._additional_layer_depth = additional_layer_depth
     self._freeze_batchnorm = (not batch_norm_trainable)
-    self._override_base_feature_extractor_hyperparams = 
+    self._override_base_feature_extractor_hyperparams = \
                     override_base_feature_extractor_hyperparams
     self._fpn_min_level = fpn_min_level
     self._fpn_max_level = fpn_max_level
@@ -112,60 +112,6 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
     self.classification_backbone = None
     self._fpn_features_generator = None
 
-  def build(self,):
-    # TODO: Refine doc string
-    """Build Resnet V1 FPN architecture."""
-    # full_resnet_v1_model = self._resnet_v1_base_model(
-    #     batchnorm_training=self._train_batch_norm,
-    #     conv_hyperparams=(self._conv_hyperparams
-    #                       if self._override_base_feature_extractor_hyperparams
-    #                       else None),
-    #     min_depth=self._min_depth,
-    #     depth_multiplier=self._depth_multiplier,
-    #     classes=None,
-    #     weights=None,
-    #     include_top=False)
-    # output_layers = _RESNET_MODEL_OUTPUT_LAYERS[self._resnet_v1_base_model_name]
-    # outputs = [full_resnet_v1_model.get_layer(output_layer_name).output
-    #            for output_layer_name in output_layers]
-    # self.classification_backbone = tf.keras.Model(
-    #     inputs=full_resnet_v1_model.inputs,
-    #     outputs=outputs)
-    # self._depth_fn = lambda d: max(
-    #     int(d * self._depth_multiplier), self._min_depth)
-    # self._base_fpn_max_level = min(self._fpn_max_level, 5)
-    # self._num_levels = self._base_fpn_max_level + 1 - self._fpn_min_level
-    # self._fpn_features_generator = (
-    #     feature_map_generators.KerasFpnTopDownFeatureMaps(
-    #         num_levels=self._num_levels,
-    #         depth=self._depth_fn(self._additional_layer_depth),
-    #         is_training=self._is_training,
-    #         conv_hyperparams=self._conv_hyperparams,
-    #         freeze_batchnorm=self._freeze_batchnorm,
-    #         name='FeatureMaps'))
-    # Construct coarse feature layers
-    depth = self._depth_fn(self._additional_layer_depth)
-    for i in range(self._base_fpn_max_level, self._fpn_max_level):
-      layers = []
-      layer_name = 'bottom_up_block{}'.format(i)
-      layers.append(
-          tf.keras.layers.Conv2D(
-              depth,
-              [3, 3],
-              padding='SAME',
-              strides=2,
-              name=layer_name + '_conv',
-              **self._conv_hyperparams.params()))
-      layers.append(
-          self._conv_hyperparams.build_batch_norm(
-              training=(self._is_training and not self._freeze_batchnorm),
-              name=layer_name + '_batchnorm'))
-      layers.append(
-          self._conv_hyperparams.build_activation_layer(
-              name=layer_name))
-      self._coarse_feature_layers.append(layers)
-    self.built = True
-
   def preprocess(self, resized_inputs):
     """Faster R-CNN Resnet V1 preprocessing.
 
@@ -188,28 +134,6 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
       return resized_inputs - [[channel_means]]
     else:
       return resized_inputs
-  
-  # def _extract_proposal_features(self, preprocessed_inputs, scope=None):
-  #   # TODO: doc string
-  #   """"""
-  #   preprocessed_inputs = shape_utils.check_min_image_dim(
-  #       129, preprocessed_inputs)
-
-  #   with tf.name_scope(scope):
-  #     with tf.name_scope('ResnetV1FPN'):
-  #       image_features = self.classification_backbone(preprocessed_inputs)
-
-  #       feature_block_list = []
-  #       for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
-  #         feature_block_list.append('block{}'.format(level - 1))
-  #       feature_block_map = dict(
-  #           list(zip(self._resnet_block_names, image_features)))
-  #       fpn_input_image_features = [
-  #           (feature_block, feature_block_map[feature_block])
-  #           for feature_block in feature_block_list]
-  #       fpn_features = self._fpn_features_generator(fpn_input_image_features)
-
-  #       return fpn_features
         
   def get_proposal_feature_extractor_model(self, name=None):
     """Returns a model that extracts first stage RPN features.
@@ -262,32 +186,16 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
         for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
           feature_block_list.append('block{}'.format(level - 1))
         feature_block_map = dict(
-            list(zip(self._resnet_block_names, image_features)))
+            list(zip(self._resnet_block_names, backbone_outputs)))
         fpn_input_image_features = [
             (feature_block, feature_block_map[feature_block])
             for feature_block in feature_block_list]
         fpn_features = self._fpn_features_generator(fpn_input_image_features)
 
         feature_extractor_model = tf.keras.models.Model(
-            inputs=self.full_resnet_v1_model.inputs, outputs=fpn_features)
+            inputs=full_resnet_v1_model.inputs, outputs=fpn_features)
         return feature_extractor_model
 
-  # def _extract_box_classifier_features(self, proposal_feature_maps, scope=None):
-  #   with tf.name_scope(scope):
-  #     with tf.name_scope('ResnetV1FPN'):
-  #       feature_maps = []
-  #       for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
-  #         feature_maps.append(proposal_feature_maps['top_down_block{}'.format(level-1)])
-  #       self.last_feature_map = proposal_feature_maps['top_down_block{}'.format(
-  #           self._base_fpn_max_level - 1)]
-
-  #       for coarse_feature_layers in self._coarse_feature_layers:
-  #         for layer in coarse_feature_layers:
-  #           last_feature_map = layer(last_feature_map)
-  #         feature_maps.append(self.last_feature_map)
-
-  #       return feature_maps
-
   def get_box_classifier_feature_extractor_model(self, name=None):
     """Returns a model that extracts second stage box classifier features.
 
@@ -309,8 +217,8 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
     with tf.name_scope(name):
       with tf.name_scope('ResnetV1FPN'):
         feature_extractor_model = tf.keras.models.Sequential([
-          Dense(unit=1024, activation='ReLU'),
-          Dense(unit=1024, activation='ReLU')
+          tf.keras.layers.Dense(unit=1024, activation='ReLU'),
+          tf.keras.layers.Dense(unit=1024, activation='ReLU')
         ])
         return feature_extractor_model
 
@@ -346,10 +254,10 @@ class FasterRCNNResnet50FPNKerasFeatureExtractor(
       additional_layer_depth: See base class.
       override_base_feature_extractor_hyperparams: See base class.
     """
-    super(FasterRCNNResnet50KerasFeatureExtractor, self).__init__(
+    super(FasterRCNNResnet50FPNKerasFeatureExtractor, self).__init__(
         is_training=is_training,
         first_stage_features_stride=first_stage_features_stride,
-        conv_hyperparams=conv_hyperparameters,
+        conv_hyperparams=conv_hyperparams,
         min_depth=min_depth,
         depth_multiplier=depth_multiplier,
         resnet_v1_base_model=resnet_v1.resnet_v1_50,
@@ -391,10 +299,10 @@ class FasterRCNNResnet101FPNKerasFeatureExtractor(
       additional_layer_depth: See base class.
       override_base_feature_extractor_hyperparams: See base class.
     """
-    super(FasterRCNNResnet50KerasFeatureExtractor, self).__init__(
+    super(FasterRCNNResnet101FPNKerasFeatureExtractor, self).__init__(
         is_training=is_training,
         first_stage_features_stride=first_stage_features_stride,
-        conv_hyperparams=conv_hyperparameters,
+        conv_hyperparams=conv_hyperparams,
         min_depth=min_depth,
         depth_multiplier=depth_multiplier,
         resnet_v1_base_model=resnet_v1.resnet_v1_101,
@@ -438,10 +346,10 @@ class FasterRCNNResnet152FPNKerasFeatureExtractor(
       additional_layer_depth: See base class.
       override_base_feature_extractor_hyperparams: See base class.
     """
-    super(FasterRCNNResnet50KerasFeatureExtractor, self).__init__(
+    super(FasterRCNNResnet152FPNKerasFeatureExtractor, self).__init__(
         is_training=is_training,
         first_stage_features_stride=first_stage_features_stride,
-        conv_hyperparams=conv_hyperparameters,
+        conv_hyperparams=conv_hyperparams,
         min_depth=min_depth,
         depth_multiplier=depth_multiplier,
         resnet_v1_base_model=resnet_v1.resnet_v1_152,