Fix coding style for feature extractor.

research/object_detection/models/faster_rcnn_resnet_v1_fpn_keras_feature_extractor.py

@@ -20,9 +20,7 @@ import tensorflow.compat.v1 as tf
 from object_detection.meta_architectures import faster_rcnn_meta_arch
 from object_detection.models import feature_map_generators
 from object_detection.models.keras_models import resnet_v1
-from object_detection.models.keras_models import model_utils
-from object_detection.utils import ops
-from object_detection.utils import shape_utils
+
 
 _RESNET_MODEL_OUTPUT_LAYERS = {
     'resnet_v1_50': ['conv2_block3_out', 'conv3_block4_out',
@@ -35,7 +33,7 @@ _RESNET_MODEL_OUTPUT_LAYERS = {
 
 
 class FasterRCNNResnetV1FPNKerasFeatureExtractor(
-    faster_rcnn_meta_arch.FasterRCNNFeatureExtractor):
+    faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor):
   """Faster RCNN Feature Extractor using Keras-based Resnet V1 FPN features."""
 
   def __init__(self,
@@ -52,30 +50,24 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
                fpn_max_level=7,
                additional_layer_depth=256,
                override_base_feature_extractor_hyperparams=False):
-    # FIXME: fix doc string for fpn min level and fpn max level
     """Constructor.
 
     Args:
       is_training: See base class.
+      resnet_v1_base_model: base resnet v1 network to use. One of
+        the resnet_v1.resnet_v1_{50,101,152} models.
+      resnet_v1_base_model_name: model name under which to construct resnet v1.
       first_stage_features_stride: See base class.
-
       conv_hyperparameters: a `hyperparams_builder.KerasLayerHyperparams` object
         containing convolution hyperparameters for the layers added on top of
         the base feature extractor.
       min_depth: Minimum number of filters in the convolutional layers.
       depth_multiplier: The depth multiplier to modify the number of filters
         in the convolutional layers.
-      resnet_v1_base_model: base resnet v1 network to use. One of
-        the resnet_v1.resnet_v1_{50,101,152} models.
-      resnet_v1_base_model_name: model name under which to construct resnet v1.
-
       batch_norm_trainable: See base class.
       weight_decay: See base class.
-
       fpn_min_level: the highest resolution feature map to use in FPN. The valid
-        values are {2, 3, 4, 5} which map to MobileNet v1 layers
-        {Conv2d_3_pointwise, Conv2d_5_pointwise, Conv2d_11_pointwise,
-        Conv2d_13_pointwise}, respectively.
+        values are {2, 3, 4, 5} which map to Resnet v1 layers.
       fpn_max_level: the smallest resolution feature map to construct or use in
         FPN. FPN constructions uses features maps starting from fpn_min_level
         upto the fpn_max_level. In the case that there are not enough feature
@@ -92,22 +84,24 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
     """
     if first_stage_features_stride != 8 and first_stage_features_stride != 16:
       raise ValueError('`first_stage_features_stride` must be 8 or 16.')
+
     super(FasterRCNNResnetV1FPNKerasFeatureExtractor, self).__init__(
-          is_training=is_training,
-          first_stage_features_stride=first_stage_features_stride,
-          batch_norm_trainable=batch_norm_trainable,
-          weight_decay=weight_decay)
+        is_training=is_training,
+        first_stage_features_stride=first_stage_features_stride,
+        batch_norm_trainable=batch_norm_trainable,
+        weight_decay=weight_decay)
+
+    self._resnet_v1_base_model = resnet_v1_base_model
+    self._resnet_v1_base_model_name = resnet_v1_base_model_name
     self._conv_hyperparams = conv_hyperparams
     self._min_depth = min_depth
     self._depth_multiplier = depth_multiplier
+    self._fpn_min_level = fpn_min_level
+    self._fpn_max_level = fpn_max_level
     self._additional_layer_depth = additional_layer_depth
     self._freeze_batchnorm = (not batch_norm_trainable)
     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
-    self._resnet_v1_base_model = resnet_v1_base_model
-    self._resnet_v1_base_model_name = resnet_v1_base_model_name
     self._resnet_block_names = ['block1', 'block2', 'block3', 'block4']
     self.classification_backbone = None
     self._fpn_features_generator = None
@@ -134,11 +128,11 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
       return resized_inputs - [[channel_means]]
     else:
       return resized_inputs
-        
+
   def get_proposal_feature_extractor_model(self, name=None):
     """Returns a model that extracts first stage RPN features.
 
-    Extracts features using the first half of the Resnet v1 network.
+    Extracts features using the Resnet v1 FPN network.
 
     Args:
       name: A scope name to construct all variables within.
@@ -147,6 +141,9 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
       A Keras model that takes preprocessed_inputs:
         A [batch, height, width, channels] float32 tensor
         representing a batch of images.
+
+      And returns rpn_feature_map:
+        A list of tensors with shape [batch, height, width, depth]
     """
     with tf.name_scope(name):
       with tf.name_scope('ResnetV1FPN'):
@@ -162,7 +159,7 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
             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]
+                   for output_layer_name in output_layers]
         self.classification_backbone = tf.keras.Model(
             inputs=full_resnet_v1_model.inputs,
             outputs=outputs)
@@ -181,7 +178,7 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
                 conv_hyperparams=self._conv_hyperparams,
                 freeze_batchnorm=self._freeze_batchnorm,
                 name='FeatureMaps'))
-        
+
         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))
@@ -200,7 +197,7 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
   def get_box_classifier_feature_extractor_model(self, name=None):
     """Returns a model that extracts second stage box classifier features.
 
-    TODO: doc
+    Construct two fully connected layer to extract the box classifier features.
 
     Args:
       name: A scope name to construct all variables within.
@@ -210,17 +207,18 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
         A 4-D float tensor with shape
         [batch_size * self.max_num_proposals, crop_height, crop_width, depth]
         representing the feature map cropped to each proposal.
+
       And returns proposal_classifier_features:
         A 4-D float tensor with shape
-        [batch_size * self.max_num_proposals, height, width, depth]
+        [batch_size * self.max_num_proposals, 1024]
         representing box classifier features for each proposal.
     """
     with tf.name_scope(name):
       with tf.name_scope('ResnetV1FPN'):
         feature_extractor_model = tf.keras.models.Sequential([
-          tf.keras.layers.Flatten(),
-          tf.keras.layers.Dense(units=1024, activation='relu'),
-          tf.keras.layers.Dense(units=1024, activation='relu')
+            tf.keras.layers.Flatten(),
+            tf.keras.layers.Dense(units=1024, activation='relu'),
+            tf.keras.layers.Dense(units=1024, activation='relu')
         ])
         return feature_extractor_model
 
@@ -228,7 +226,7 @@ class FasterRCNNResnetV1FPNKerasFeatureExtractor(
 class FasterRCNNResnet50FPNKerasFeatureExtractor(
     FasterRCNNResnetV1FPNKerasFeatureExtractor):
   """Faster RCNN with Resnet50 FPN feature extractor implementation."""
-  
+
   def __init__(self,
                is_training,
                first_stage_features_stride=16,
@@ -271,6 +269,7 @@ class FasterRCNNResnet50FPNKerasFeatureExtractor(
         additional_layer_depth=additional_layer_depth,
         override_base_feature_extractor_hyperparams=override_base_feature_extractor_hyperparams)
 
+
 class FasterRCNNResnet101FPNKerasFeatureExtractor(
     FasterRCNNResnetV1FPNKerasFeatureExtractor):
   """Faster RCNN with Resnet101 FPN feature extractor implementation."""