Revert addition of explicit `training` kwarg to feature map and box predictor

PiperOrigin-RevId: 362098420

research/object_detection/models/feature_map_generators.py

@@ -305,7 +305,7 @@ class KerasMultiResolutionFeatureMaps(tf.keras.Model):
       # this net must be appended only once it's been filled with layers
       self.convolutions.append(net)
 
-  def call(self, image_features, training=None):
+  def call(self, image_features):
     """Generate the multi-resolution feature maps.
 
     Executed when calling the `.__call__` method on input.
@@ -313,11 +313,6 @@ class KerasMultiResolutionFeatureMaps(tf.keras.Model):
     Args:
       image_features: A dictionary of handles to activation tensors from the
         base feature extractor.
-      training: A boolean, True when in training mode. If not specified,
-        defaults to (in order of priority): the training mode of the outer
-        `Layer.call`; the default mode set by
-        `tf.keras.backend.set_learning_phase`; or the default value for
-        `training` in the call signature.
 
     Returns:
       feature_maps: an OrderedDict mapping keys (feature map names) to
@@ -333,7 +328,7 @@ class KerasMultiResolutionFeatureMaps(tf.keras.Model):
       else:
         feature_map = feature_maps[-1]
         for layer in self.convolutions[index]:
-          feature_map = layer(feature_map, training=training)
+          feature_map = layer(feature_map)
         layer_name = self.convolutions[index][-1].name
         feature_map_keys.append(layer_name)
       feature_maps.append(feature_map)
@@ -621,7 +616,7 @@ class KerasFpnTopDownFeatureMaps(tf.keras.Model):
       self.reshape_blocks.append(reshaped_residual)
       self.conv_layers.append(conv_net)
 
-  def call(self, image_features, training=None):
+  def call(self, image_features):
     """Generate the multi-resolution feature maps.
 
     Executed when calling the `.__call__` method on input.
@@ -630,11 +625,6 @@ class KerasFpnTopDownFeatureMaps(tf.keras.Model):
       image_features: list of tuples of (tensor_name, image_feature_tensor).
         Spatial resolutions of succesive tensors must reduce exactly by a factor
         of 2.
-      training: A boolean, True when in training mode. If not specified,
-        defaults to (in order of priority): the training mode of the outer
-        `Layer.call`; the default mode set by
-        `tf.keras.backend.set_learning_phase`; or the default value for
-        `training` in the call signature.
 
     Returns:
       feature_maps: an OrderedDict mapping keys (feature map names) to
@@ -646,7 +636,7 @@ class KerasFpnTopDownFeatureMaps(tf.keras.Model):
     with tf.name_scope(self.scope):
       top_down = image_features[-1][1]
       for layer in self.top_layers:
-        top_down = layer(top_down, training=training)
+        top_down = layer(top_down)
       output_feature_maps_list.append(top_down)
       output_feature_map_keys.append('top_down_%s' % image_features[-1][0])
 
@@ -655,14 +645,14 @@ class KerasFpnTopDownFeatureMaps(tf.keras.Model):
         residual = image_features[level][1]
         top_down = output_feature_maps_list[-1]
         for layer in self.residual_blocks[index]:
-          residual = layer(residual, training=training)
+          residual = layer(residual)
         for layer in self.top_down_blocks[index]:
-          top_down = layer(top_down, training=training)
+          top_down = layer(top_down)
         for layer in self.reshape_blocks[index]:
-          top_down = layer([residual, top_down], training=training)
+          top_down = layer([residual, top_down])
         top_down += residual
         for layer in self.conv_layers[index]:
-          top_down = layer(top_down, training=training)
+          top_down = layer(top_down)
         output_feature_maps_list.append(top_down)
         output_feature_map_keys.append('top_down_%s' % image_features[level][0])
     return collections.OrderedDict(reversed(

research/object_detection/predictors/convolutional_keras_box_predictor.py

@@ -197,7 +197,7 @@ class ConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
 
       # Apply shared conv layers before the head predictors.
       for layer in self._shared_nets[index]:
-        net = layer(net, training=self._is_training)
+        net = layer(net)
 
       for head_name in self._sorted_head_names:
         head_obj = self._prediction_heads[head_name][index]
@@ -458,13 +458,13 @@ class WeightSharedConvolutionalBoxPredictor(box_predictor.KerasBoxPredictor):
 
     def _apply_layers(base_tower_layers, image_feature):
       for layer in base_tower_layers:
-        image_feature = layer(image_feature, training=self._is_training)
+        image_feature = layer(image_feature)
       return image_feature
 
     for (index, image_feature) in enumerate(image_features):
       # Apply additional projection layers to image features
       for layer in self._additional_projection_layers[index]:
-        image_feature = layer(image_feature, training=self._is_training)
+        image_feature = layer(image_feature)
 
       # Apply box tower layers.
       box_tower_feature = _apply_layers(