Add `offset` argument to `Rescaling`.

PiperOrigin-RevId: 313101675
Change-Id: Id59e6dcbe4f038d627c7d71fdf4dfeb58e8e05cd

tensorflow/python/keras/layers/preprocessing/image_preprocessing.py

@@ -292,11 +292,16 @@ class RandomCrop(Layer):
 
 @keras_export('keras.layers.experimental.preprocessing.Rescaling')
 class Rescaling(Layer):
-  """Multiply inputs by `scale`.
+  """Multiply inputs by `scale` and adds `offset`.
 
-  For instance, to rescale an input in the `[0, 255]` range
+  For instance:
+
+  1. To rescale an input in the `[0, 255]` range
   to be in the `[0, 1]` range, you would pass `scale=1./255`.
 
+  2. To rescale an input in the `[0, 255]` range to be in the `[-1, 1]` range,
+  you would pass `scale=1./127.5, offset=-1`.
+
   The rescaling is applied both during training and inference.
 
   Input shape:
@@ -307,16 +312,20 @@ class Rescaling(Layer):
 
   Arguments:
     scale: Float, the scale to apply to the inputs.
+    offset: Float, the offset to apply to the inputs.
     name: A string, the name of the layer.
   """
 
-  def __init__(self, scale, name=None, **kwargs):
+  def __init__(self, scale, offset=0., name=None, **kwargs):
     self.scale = scale
+    self.offset = offset
     super(Rescaling, self).__init__(name=name, **kwargs)
 
   def call(self, inputs):
     dtype = self._compute_dtype
-    return math_ops.cast(inputs, dtype) * math_ops.cast(self.scale, dtype)
+    scale = math_ops.cast(self.scale, dtype)
+    offset = math_ops.cast(self.offset, dtype)
+    return math_ops.cast(inputs, dtype) * scale + offset
 
   def compute_output_shape(self, input_shape):
     return input_shape
@@ -324,6 +333,7 @@ class Rescaling(Layer):
   def get_config(self):
     config = {
         'scale': self.scale,
+        'offset': self.offset,
     }
     base_config = super(Rescaling, self).get_config()
     return dict(list(base_config.items()) + list(config.items()))

tensorflow/python/keras/layers/preprocessing/image_preprocessing_test.py

@@ -306,7 +306,7 @@ class RescalingTest(keras_parameterized.TestCase):
 
   @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
   def test_rescaling_base(self):
-    kwargs = {'scale': 0.004}
+    kwargs = {'scale': 1./127.5, 'offset': -1.}
     testing_utils.layer_test(
         image_preprocessing.Rescaling,
         kwargs=kwargs,
@@ -315,18 +315,18 @@ class RescalingTest(keras_parameterized.TestCase):
 
   @tf_test_util.run_v2_only
   def test_rescaling_correctness_float(self):
-    layer = image_preprocessing.Rescaling(0.004)
+    layer = image_preprocessing.Rescaling(scale=1./127.5, offset=-1.)
     inputs = random_ops.random_uniform((2, 4, 5, 3))
     outputs = layer(inputs)
-    self.assertAllClose(outputs.numpy(), inputs.numpy() * 0.004)
+    self.assertAllClose(outputs.numpy(), inputs.numpy() * (1./127.5) - 1)
 
   @tf_test_util.run_v2_only
   def test_rescaling_correctness_int(self):
-    layer = image_preprocessing.Rescaling(0.004)
+    layer = image_preprocessing.Rescaling(scale=1./127.5, offset=-1)
     inputs = random_ops.random_uniform((2, 4, 5, 3), 0, 100, dtype='int32')
     outputs = layer(inputs)
     self.assertEqual(outputs.dtype.name, 'float32')
-    self.assertAllClose(outputs.numpy(), inputs.numpy() * 0.004)
+    self.assertAllClose(outputs.numpy(), inputs.numpy() * (1./127.5) - 1)
 
   def test_config_with_custom_name(self):
     layer = image_preprocessing.Rescaling(0.5, name='rescaling')

tensorflow/tools/api/golden/v1/tensorflow.keras.layers.experimental.preprocessing.-rescaling.pbtxt

@@ -113,7 +113,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'scale\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'scale\', \'offset\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.0\', \'None\'], "
   }
   member_method {
     name: "add_loss"

tensorflow/tools/api/golden/v2/tensorflow.keras.layers.experimental.preprocessing.-rescaling.pbtxt

@@ -113,7 +113,7 @@ tf_class {
   }
   member_method {
     name: "__init__"
-    argspec: "args=[\'self\', \'scale\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'None\'], "
+    argspec: "args=[\'self\', \'scale\', \'offset\', \'name\'], varargs=None, keywords=kwargs, defaults=[\'0.0\', \'None\'], "
   }
   member_method {
     name: "add_loss"