fix coding style

research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor.py

@@ -35,15 +35,15 @@ _RESNET_MODEL_OUTPUT_LAYERS = {
 class ResnetFPN(tf.keras.layers.Layer):
   """Construct Resnet FPN layer."""
 
-    def __init__(self,
-                 backbone_classifier,
-                 fpn_features_generator,
-                 coarse_feature_layers,
-                 pad_to_multiple,
-                 fpn_min_level,
-                 resnet_block_names,
-                 base_fpn_max_level):
-      """Constructor.
+  def __init__(self,
+               backbone_classifier,
+               fpn_features_generator,
+               coarse_feature_layers,
+               pad_to_multiple,
+               fpn_min_level,
+               resnet_block_names,
+               base_fpn_max_level):
+    """Constructor.
 
     Args:
       backbone_classifier: Classifier backbone. Should be one of 'resnet_v1_50',
@@ -56,41 +56,51 @@ class ResnetFPN(tf.keras.layers.Layer):
       resnet_block_names: a list of block names of resnet.
       base_fpn_max_level: maximum level of fpn without coarse feature layers.
     """
-      super(ResnetFPN, self).__init__()
-      self.classification_backbone = backbone_classifier
-      self.fpn_features_generator = fpn_features_generator
-      self.coarse_feature_layers = coarse_feature_layers
-      self.pad_to_multiple = pad_to_multiple
-      self._fpn_min_level = fpn_min_level
-      self._resnet_block_names = resnet_block_names
-      self._base_fpn_max_level = base_fpn_max_level
-
-    def call(self, inputs):
-      inputs = ops.pad_to_multiple(inputs, self.pad_to_multiple)
-      backbone_outputs = self.classification_backbone(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, 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_maps = []
-      for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
-        feature_maps.append(fpn_features['top_down_block{}'.format(level-1)])
-      last_feature_map = fpn_features['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(last_feature_map)
-
-      return feature_maps
+    super(ResnetFPN, self).__init__()
+    self.classification_backbone = backbone_classifier
+    self.fpn_features_generator = fpn_features_generator
+    self.coarse_feature_layers = coarse_feature_layers
+    self.pad_to_multiple = pad_to_multiple
+    self._fpn_min_level = fpn_min_level
+    self._resnet_block_names = resnet_block_names
+    self._base_fpn_max_level = base_fpn_max_level
+
+  def call(self, inputs):
+    """Create ResnetFPN layer.
+
+    Args:
+      inputs: A [batch, height_out, width_out, channels] float32 tensor
+        representing a batch of images.
+
+    Return:
+      feature_maps: A list of tensors with shape [batch, height, width, depth]
+        represent extracted features.
+    """
+    inputs = ops.pad_to_multiple(inputs, self.pad_to_multiple)
+    backbone_outputs = self.classification_backbone(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, 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_maps = []
+    for level in range(self._fpn_min_level, self._base_fpn_max_level + 1):
+      feature_maps.append(fpn_features['top_down_block{}'.format(level-1)])
+    last_feature_map = fpn_features['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(last_feature_map)
+
+    return feature_maps
 
 
 class FasterRCNNResnetV1FpnKerasFeatureExtractor(
@@ -229,7 +239,7 @@ class FasterRCNNResnetV1FpnKerasFeatureExtractor(
                 conv_hyperparams=self._conv_hyperparams,
                 freeze_batchnorm=self._freeze_batchnorm,
                 name='FeatureMaps'))
-        
+
         # Construct coarse feature layers
         for i in range(self._base_fpn_max_level, self._fpn_max_level):
           layers = []
@@ -250,14 +260,14 @@ class FasterRCNNResnetV1FpnKerasFeatureExtractor(
               self._conv_hyperparams.build_activation_layer(
                   name=layer_name))
           self._coarse_feature_layers.append(layers)
-        
+
         feature_extractor_model = ResnetFPN(self.classification_backbone,
-                           self._fpn_features_generator,
-                           self._coarse_feature_layers,
-                           self._pad_to_multiple,
-                           self._fpn_min_level,
-                           self._resnet_block_names,
-                           self._base_fpn_max_level)
+                                            self._fpn_features_generator,
+                                            self._coarse_feature_layers,
+                                            self._pad_to_multiple,
+                                            self._fpn_min_level,
+                                            self._resnet_block_names,
+                                            self._base_fpn_max_level)
         return feature_extractor_model
 
   def get_box_classifier_feature_extractor_model(self, name=None):