Internal change

PiperOrigin-RevId: 481804149

Author: fyangf

official/projects/basnet/serving/basnet.py

@@ -20,10 +20,6 @@
 from official.vision.serving import semantic_segmentation
 
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
-
 class BASNetModule(semantic_segmentation.SegmentationModule):
   """BASNet Module."""
 

official/projects/basnet/tasks/basnet.py

@@ -33,7 +33,7 @@
 def build_basnet_model(
     input_specs: tf.keras.layers.InputSpec,
     model_config: exp_cfg.BASNetModel,
-    l2_regularizer: tf.keras.regularizers.Regularizer = None):
+    l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None):
   """Builds BASNet model."""
   norm_activation_config = model_config.norm_activation
   backbone = basnet_model.BASNetEncoder(

official/vision/dataloaders/classification_input.py

@@ -23,9 +23,6 @@
 from official.vision.ops import augment
 from official.vision.ops import preprocess_ops
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
 DEFAULT_IMAGE_FIELD_KEY = 'image/encoded'
 DEFAULT_LABEL_FIELD_KEY = 'image/class/label'
 
@@ -223,7 +220,7 @@ def _parse_train_image(self, decoded_tensors):
 
     # Normalizes image with mean and std pixel values.
     image = preprocess_ops.normalize_image(
-        image, offset=MEAN_RGB, scale=STDDEV_RGB)
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
 
     # Random erasing after the image has been normalized
     if self._random_erasing is not None:
@@ -258,7 +255,7 @@ def _parse_eval_image(self, decoded_tensors):
 
     # Normalizes image with mean and std pixel values.
     image = preprocess_ops.normalize_image(
-        image, offset=MEAN_RGB, scale=STDDEV_RGB)
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
 
     # Convert image to self._dtype.
     image = tf.image.convert_image_dtype(image, self._dtype)
@@ -284,6 +281,6 @@ def inference_fn(cls,
 
     # Normalizes image with mean and std pixel values.
     image = preprocess_ops.normalize_image(
-        image, offset=MEAN_RGB, scale=STDDEV_RGB)
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
     image.set_shape(input_image_size + [num_channels])
     return image

official/vision/examples/starter/example_input.py

@@ -26,9 +26,6 @@
 from official.vision.dataloaders import parser
 from official.vision.ops import preprocess_ops
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
 
 class Decoder(decoder.Decoder):
   """A tf.Example decoder for classification task."""
@@ -102,7 +99,7 @@ def _parse_data(
 
     # Normalizes image with mean and std pixel values.
     image = preprocess_ops.normalize_image(
-        image, offset=MEAN_RGB, scale=STDDEV_RGB)
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
 
     image = tf.image.convert_image_dtype(image, self._dtype)
     return image, label

official/vision/serving/detection.py

@@ -27,10 +27,6 @@
 from official.vision.serving import export_base
 
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
-
 class DetectionModule(export_base.ExportModule):
   """Detection Module."""
 
@@ -74,9 +70,8 @@ def _build_inputs(self, image):
     """Builds detection model inputs for serving."""
     model_params = self.params.task.model
     # Normalizes image with mean and std pixel values.
-    image = preprocess_ops.normalize_image(image,
-                                           offset=MEAN_RGB,
-                                           scale=STDDEV_RGB)
+    image = preprocess_ops.normalize_image(
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
 
     image, image_info = preprocess_ops.resize_and_crop_image(
         image,

official/vision/serving/image_classification.py

@@ -21,10 +21,6 @@
 from official.vision.serving import export_base
 
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
-
 class ClassificationModule(export_base.ExportModule):
   """classification Module."""
 
@@ -50,9 +46,8 @@ def _build_inputs(self, image):
         image, [self._input_image_size[0], self._input_image_size[1], 3])
 
     # Normalizes image with mean and std pixel values.
-    image = preprocess_ops.normalize_image(image,
-                                           offset=MEAN_RGB,
-                                           scale=STDDEV_RGB)
+    image = preprocess_ops.normalize_image(
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
     return image
 
   def serve(self, images):

official/vision/serving/semantic_segmentation.py

@@ -21,10 +21,6 @@
 from official.vision.serving import export_base
 
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
-
 class SegmentationModule(export_base.ExportModule):
   """Segmentation Module."""
 
@@ -41,9 +37,8 @@ def _build_inputs(self, image):
     """Builds classification model inputs for serving."""
 
     # Normalizes image with mean and std pixel values.
-    image = preprocess_ops.normalize_image(image,
-                                           offset=MEAN_RGB,
-                                           scale=STDDEV_RGB)
+    image = preprocess_ops.normalize_image(
+        image, offset=preprocess_ops.MEAN_RGB, scale=preprocess_ops.STDDEV_RGB)
 
     if self.params.task.train_data.preserve_aspect_ratio:
       image, image_info = preprocess_ops.resize_and_crop_image(

official/vision/serving/video_classification.py

@@ -21,9 +21,6 @@
 from official.vision.serving import export_base
 from official.vision.tasks import video_classification
 
-MEAN_RGB = (0.485 * 255, 0.456 * 255, 0.406 * 255)
-STDDEV_RGB = (0.229 * 255, 0.224 * 255, 0.225 * 255)
-
 
 class VideoClassificationModule(export_base.ExportModule):
   """Video classification Module."""