Propagate zero_centering_image flag in video_input.

PiperOrigin-RevId: 444993865

official/projects/video_ssl/dataloaders/video_ssl_input.py

@@ -128,6 +128,9 @@ def _process_image(image: tf.Tensor,
 
   # Self-supervised pre-training augmentations.
   if is_training and is_ssl:
+    if zero_centering_image:
+      image_1 = 0.5 * (image_1 + 1.0)
+      image_2 = 0.5 * (image_2 + 1.0)
     # Temporally consistent color jittering.
     image_1 = video_ssl_preprocess_ops.random_color_jitter_3d(image_1)
     image_2 = video_ssl_preprocess_ops.random_color_jitter_3d(image_2)
@@ -139,6 +142,8 @@ def _process_image(image: tf.Tensor,
     image_2 = video_ssl_preprocess_ops.random_solarization(image_2)
     image = tf.concat([image_1, image_2], axis=0)
     image = tf.clip_by_value(image, 0., 1.)
+    if zero_centering_image:
+      image = 2 * (image - 0.5)
 
   return image
 
@@ -233,7 +238,8 @@ class Parser(video_input.Parser):
         stride=self._stride,
         num_test_clips=self._num_test_clips,
         min_resize=self._min_resize,
-        crop_size=self._crop_size)
+        crop_size=self._crop_size,
+        zero_centering_image=self._zero_centering_image)
     image = tf.cast(image, dtype=self._dtype)
     features = {'image': image}
 
@@ -255,7 +261,8 @@ class Parser(video_input.Parser):
         num_test_clips=self._num_test_clips,
         min_resize=self._min_resize,
         crop_size=self._crop_size,
-        num_crops=self._num_crops)
+        num_crops=self._num_crops,
+        zero_centering_image=self._zero_centering_image)
     image = tf.cast(image, dtype=self._dtype)
     features = {'image': image}
 

official/vision/configs/video_classification.py

@@ -49,6 +49,7 @@ class DataConfig(cfg.DataConfig):
   cycle_length: int = 10
   drop_remainder: bool = True
   min_image_size: int = 256
+  zero_centering_image: bool = False
   is_multilabel: bool = False
   output_audio: bool = False
   audio_feature: str = ''

official/vision/dataloaders/video_input.py

@@ -271,6 +271,7 @@ class Parser(parser.Parser):
     self._min_resize = input_params.min_image_size
     self._crop_size = input_params.feature_shape[1]
     self._num_crops = input_params.num_test_crops
+    self._zero_centering_image = input_params.zero_centering_image
     self._one_hot_label = input_params.one_hot
     self._num_classes = input_params.num_classes
     self._image_key = image_key
@@ -317,7 +318,8 @@ class Parser(parser.Parser):
         max_aspect_ratio=self._max_aspect_ratio,
         min_area_ratio=self._min_area_ratio,
         max_area_ratio=self._max_area_ratio,
-        augmenter=self._augmenter)
+        augmenter=self._augmenter,
+        zero_centering_image=self._zero_centering_image)
     image = tf.cast(image, dtype=self._dtype)
 
     features = {'image': image}
@@ -349,7 +351,8 @@ class Parser(parser.Parser):
         num_test_clips=self._num_test_clips,
         min_resize=self._min_resize,
         crop_size=self._crop_size,
-        num_crops=self._num_crops)
+        num_crops=self._num_crops,
+        zero_centering_image=self._zero_centering_image)
     image = tf.cast(image, dtype=self._dtype)
     features = {'image': image}