Internal change

PiperOrigin-RevId: 422863802

Author: fyangf

official/vision/beta/serving/semantic_segmentation.py

@@ -46,13 +46,13 @@ def _build_inputs(self, image):
                                            offset=MEAN_RGB,
                                            scale=STDDEV_RGB)
 
-    image, _ = preprocess_ops.resize_and_crop_image(
+    image, image_info = preprocess_ops.resize_and_crop_image(
         image,
         self._input_image_size,
         padded_size=self._input_image_size,
         aug_scale_min=1.0,
         aug_scale_max=1.0)
-    return image
+    return image, image_info
 
   def serve(self, images):
     """Cast image to float and run inference.
@@ -64,21 +64,27 @@ def serve(self, images):
     """
     # Skip image preprocessing when input_type is tflite so it is compatible
     # with TFLite quantization.
+    image_info = None
     if self._input_type != 'tflite':
       with tf.device('cpu:0'):
         images = tf.cast(images, dtype=tf.float32)
+        images_spec = tf.TensorSpec(
+            shape=self._input_image_size + [3], dtype=tf.float32)
+        image_info_spec = tf.TensorSpec(shape=[4, 2], dtype=tf.float32)
 
-        images = tf.nest.map_structure(
+        images, image_info = tf.nest.map_structure(
             tf.identity,
             tf.map_fn(
                 self._build_inputs,
                 elems=images,
-                fn_output_signature=tf.TensorSpec(
-                    shape=self._input_image_size + [3], dtype=tf.float32),
+                fn_output_signature=(images_spec, image_info_spec),
                 parallel_iterations=32))
 
     outputs = self.inference_step(images)
     outputs['logits'] = tf.image.resize(
         outputs['logits'], self._input_image_size, method='bilinear')
 
+    if image_info is not None:
+      outputs.update({'image_info': image_info})
+
     return outputs

official/vision/beta/serving/semantic_segmentation_test.py

@@ -93,13 +93,15 @@ def test_export(self, input_type='image_tensor'):
 
     images = self._get_dummy_input(input_type)
     if input_type != 'tflite':
-      processed_images = tf.nest.map_structure(
+      processed_images, _ = tf.nest.map_structure(
           tf.stop_gradient,
           tf.map_fn(
               module._build_inputs,
               elems=tf.zeros((1, 112, 112, 3), dtype=tf.uint8),
-              fn_output_signature=tf.TensorSpec(
-                  shape=[112, 112, 3], dtype=tf.float32)))
+              fn_output_signature=(tf.TensorSpec(
+                  shape=[112, 112, 3], dtype=tf.float32),
+                                   tf.TensorSpec(
+                                       shape=[4, 2], dtype=tf.float32))))
     else:
       processed_images = images
     expected_output = tf.image.resize(