added `_parse_data` with `is_training` flag

official/vision/beta/projects/panoptic_maskrcnn/dataloaders/panoptic_deeplab_input.py

@@ -149,6 +149,7 @@ class Parser(parser.Parser):
           self._groundtruth_padded_size[0],
           self._groundtruth_padded_size[1])
     mask -= 1
+    
     # Assign ignore label to the padded region.
     mask = tf.where(
         tf.equal(mask, -1),
@@ -157,8 +158,7 @@ class Parser(parser.Parser):
     mask = tf.squeeze(mask, axis=0)
     return mask
 
-  def _parse_train_data(self, data):
-    """Parses data for training."""
+  def _parse_data(self, data, is_training):
     image = data['image']
     image = preprocess_ops.normalize_image(image)
 
@@ -170,7 +170,7 @@ class Parser(parser.Parser):
         dtype=tf.float32)
 
     # Flips image randomly during training.
-    if self._aug_rand_hflip:
+    if self._aug_rand_hflip and is_training:
       masks = tf.stack([category_mask, instance_mask], axis=0)
       image, _, masks = preprocess_ops.random_horizontal_flip(
           image=image, masks=masks)
@@ -182,22 +182,22 @@ class Parser(parser.Parser):
         image,
         self._output_size,
         self._output_size,
-        aug_scale_min=self._aug_scale_min,
-        aug_scale_max=self._aug_scale_max)
+        aug_scale_min=self._aug_scale_min if is_training else 1.0,
+        aug_scale_max=self._aug_scale_max if is_training else 1.0)
 
     category_mask = self._resize_and_crop_mask(
         category_mask,
         image_info,
-        is_training=True)
+        is_training=is_training)
     instance_mask = self._resize_and_crop_mask(
         instance_mask,
         image_info,
-        is_training=True)
+        is_training=is_training)
 
     centers_heatmap, centers_offset = self._encode_centers_and_offets(
         instance_mask=instance_mask[:, :, 0])
 
-    # Cast image as self._dtype
+    # Cast image and labels as self._dtype
     image = tf.cast(image, dtype=self._dtype)
     category_mask = tf.cast(category_mask, dtype=self._dtype)
     instance_mask = tf.cast(instance_mask, dtype=self._dtype)
@@ -216,57 +216,13 @@ class Parser(parser.Parser):
     }
     return image, labels
 
+  def _parse_train_data(self, data):
+    """Parses data for training."""
+    return self._parse_data(data=data, is_training=True)
+
   def _parse_eval_data(self, data):
     """Parses data for evaluation."""
-    image = data['image']
-    image = preprocess_ops.normalize_image(image)
-
-    # shape of masks: [H, W]
-    category_mask = tf.cast(
-        data['groundtruth_panoptic_category_mask'][:, :, 0],
-        dtype=tf.float32)
-    instance_mask = tf.cast(
-        data['groundtruth_panoptic_instance_mask'][:, :, 0],
-        dtype=tf.float32)
-
-    # Resizes and crops image.
-    image, image_info = preprocess_ops.resize_and_crop_image(
-        image,
-        self._output_size,
-        self._output_size,
-        aug_scale_min=1.0,
-        aug_scale_max=1.0)
-
-    category_mask = self._resize_and_crop_mask(
-        category_mask,
-        image_info,
-        is_training=False)
-    instance_mask = self._resize_and_crop_mask(
-        instance_mask,
-        image_info,
-        is_training=False)
-
-    centers_heatmap, centers_offset = self._encode_centers_and_offets(
-        instance_mask=instance_mask[:, :, 0])
-
-    # Cast image as self._dtype
-    image = tf.cast(image, dtype=self._dtype)
-    category_mask = tf.cast(category_mask, dtype=self._dtype)
-    instance_mask = tf.cast(instance_mask, dtype=self._dtype)
-    centers_heatmap = tf.cast(centers_heatmap, dtype=self._dtype)
-    centers_offset = tf.cast(centers_offset, dtype=self._dtype)
-    things_mask = tf.cast(
-        tf.not_equal(instance_mask, self._ignore_label),
-        dtype=self._dtype)
-
-    labels = {
-        'category_mask': category_mask,
-        'instance_mask': instance_mask,
-        'centers_heatmap': centers_heatmap,
-        'centers_offset': centers_offset,
-        'things_mask': things_mask
-    }
-    return image, labels
+    return self._parse_data(data=data, is_training=False)
 
   def _encode_centers_and_offets(self, instance_mask):
     """Generates center heatmaps and offets from instance id mask