Change callers of tf.image.per_image_whitening() to use...

Change callers of tf.image.per_image_whitening() to use tf.image.per_image_standardization(). Once these changes are submitted, per_image_whitening() can be removed.
Change: 137714408

tensorflow/models/image/cifar10/cifar10_input.py

@@ -179,7 +179,7 @@ def distorted_inputs(data_dir, batch_size):
                                              lower=0.2, upper=1.8)
 
   # Subtract off the mean and divide by the variance of the pixels.
-  float_image = tf.image.per_image_whitening(distorted_image)
+  float_image = tf.image.per_image_standardization(distorted_image)
 
   # Ensure that the random shuffling has good mixing properties.
   min_fraction_of_examples_in_queue = 0.4
@@ -234,7 +234,7 @@ def inputs(eval_data, data_dir, batch_size):
                                                          width, height)
 
   # Subtract off the mean and divide by the variance of the pixels.
-  float_image = tf.image.per_image_whitening(resized_image)
+  float_image = tf.image.per_image_standardization(resized_image)
 
   # Ensure that the random shuffling has good mixing properties.
   min_fraction_of_examples_in_queue = 0.4

tensorflow/python/ops/image_ops_test.py

@@ -581,14 +581,14 @@ class PerImageWhiteningTest(test_util.TensorFlowTestCase):
 
     with self.test_session(use_gpu=True):
       x = constant_op.constant(x_np, shape=x_shape)
-      y = image_ops.per_image_whitening(x)
+      y = image_ops.per_image_standardization(x)
       y_tf = y.eval()
       self.assertAllClose(y_tf, y_np, atol=1e-4)
 
   def testUniformImage(self):
     im_np = np.ones([19, 19, 3]).astype(np.float32) * 249
     im = constant_op.constant(im_np)
-    whiten = image_ops.per_image_whitening(im)
+    whiten = image_ops.per_image_standardization(im)
     with self.test_session(use_gpu=True):
       whiten_np = whiten.eval()
       self.assertFalse(np.any(np.isnan(whiten_np)))