Internal change

PiperOrigin-RevId: 397467113

Author: tensorflower-gardener

official/vision/beta/modeling/retinanet_model.py

@@ -77,6 +77,7 @@ def call(self,
            images: tf.Tensor,
            image_shape: Optional[tf.Tensor] = None,
            anchor_boxes: Optional[Mapping[str, tf.Tensor]] = None,
+           output_intermediate_features: bool = False,
            training: bool = None) -> Mapping[str, tf.Tensor]:
     """Forward pass of the RetinaNet model.
 
@@ -92,6 +93,8 @@ def call(self,
         - key: `str`, the level of the multilevel predictions.
         - values: `Tensor`, the anchor coordinates of a particular feature
             level, whose shape is [height_l, width_l, num_anchors_per_location].
+      output_intermediate_features: `bool` indicating whether to return the
+        intermediate feature maps generated by backbone and decoder.
       training: `bool`, indicating whether it is in training mode.
 
     Returns:
@@ -112,19 +115,26 @@ def call(self,
             feature level, whose shape is
             [batch, height_l, width_l, att_size * num_anchors_per_location].
     """
+    outputs = {}
     # Feature extraction.
     features = self.backbone(images)
+    if output_intermediate_features:
+      outputs.update(
+          {'backbone_{}'.format(k): v for k, v in features.items()})
     if self.decoder:
       features = self.decoder(features)
+      if output_intermediate_features:
+        outputs.update(
+            {'decoder_{}'.format(k): v for k, v in features.items()})
 
     # Dense prediction. `raw_attributes` can be empty.
     raw_scores, raw_boxes, raw_attributes = self.head(features)
 
     if training:
-      outputs = {
+      outputs.update({
           'cls_outputs': raw_scores,
           'box_outputs': raw_boxes,
-      }
+      })
       if raw_attributes:
         outputs.update({'attribute_outputs': raw_attributes})
       return outputs
@@ -145,12 +155,13 @@ def call(self,
               [tf.shape(images)[0], 1, 1, 1])
 
       # Post-processing.
-      final_results = self.detection_generator(
-          raw_boxes, raw_scores, anchor_boxes, image_shape, raw_attributes)
-      outputs = {
+      final_results = self.detection_generator(raw_boxes, raw_scores,
+                                               anchor_boxes, image_shape,
+                                               raw_attributes)
+      outputs.update({
           'cls_outputs': raw_scores,
           'box_outputs': raw_boxes,
-      }
+      })
       if self.detection_generator.get_config()['apply_nms']:
         outputs.update({
             'detection_boxes': final_results['detection_boxes'],

official/vision/beta/modeling/retinanet_model_test.py

@@ -147,8 +147,10 @@ def test_build_model(self, use_separable_conv, build_anchor_boxes,
           ],
           training=[True, False],
           has_att_heads=[True, False],
+          output_intermediate_features=[True, False],
       ))
-  def test_forward(self, strategy, image_size, training, has_att_heads):
+  def test_forward(self, strategy, image_size, training, has_att_heads,
+                   output_intermediate_features):
     """Test for creation of a R50-FPN RetinaNet."""
     tf.keras.backend.set_image_data_format('channels_last')
     num_classes = 3
@@ -202,6 +204,7 @@ def test_forward(self, strategy, image_size, training, has_att_heads):
           images,
           image_shape,
           anchor_boxes,
+          output_intermediate_features=output_intermediate_features,
           training=training)
 
     if training:
@@ -247,6 +250,19 @@ def test_forward(self, strategy, image_size, training, has_att_heads):
         self.assertAllEqual(
             [2, 10, 1],
             model_outputs['detection_attributes']['depth'].numpy().shape)
+    if output_intermediate_features:
+      for l in range(2, 6):
+        self.assertIn('backbone_{}'.format(l), model_outputs)
+        self.assertAllEqual([
+            2, image_size[0] // 2**l, image_size[1] // 2**l,
+            backbone.output_specs[str(l)].as_list()[-1]
+        ], model_outputs['backbone_{}'.format(l)].numpy().shape)
+      for l in range(min_level, max_level + 1):
+        self.assertIn('decoder_{}'.format(l), model_outputs)
+        self.assertAllEqual([
+            2, image_size[0] // 2**l, image_size[1] // 2**l,
+            decoder.output_specs[str(l)].as_list()[-1]
+        ], model_outputs['decoder_{}'.format(l)].numpy().shape)
 
   def test_serialize_deserialize(self):
     """Validate the network can be serialized and deserialized."""