Add C0326 bad-whitespace error to pylint sanity check.

PiperOrigin-RevId: 183689499

1 files changed, 30 insertions, 22 deletions

diff --git a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py
index d07fece4bc..6a3b535eb4 100644
--- a/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py
+++ b/tensorflow/contrib/model_pruning/examples/cifar10/cifar10_input.py
@@ -58,6 +58,7 @@ def read_cifar10(filename_queue):
 
   class CIFAR10Record(object):
     pass
+
   result = CIFAR10Record()
 
   # Dimensions of the images in the CIFAR-10 dataset.
@@ -147,8 +148,9 @@ def distorted_inputs(data_dir, batch_size):
     images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
     labels: Labels. 1D tensor of [batch_size] size.
   """
-  filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
-               for i in xrange(1, 6)]
+  filenames = [
+      os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)
+  ]
   for f in filenames:
     if not tf.gfile.Exists(f):
       raise ValueError('Failed to find file: ' + f)
@@ -174,10 +176,9 @@ def distorted_inputs(data_dir, batch_size):
 
   # Because these operations are not commutative, consider randomizing
   # the order their operation.
-  distorted_image = tf.image.random_brightness(distorted_image,
-                                               max_delta=63)
-  distorted_image = tf.image.random_contrast(distorted_image,
-                                             lower=0.2, upper=1.8)
+  distorted_image = tf.image.random_brightness(distorted_image, max_delta=63)
+  distorted_image = tf.image.random_contrast(
+      distorted_image, lower=0.2, upper=1.8)
 
   # Subtract off the mean and divide by the variance of the pixels.
   float_image = tf.image.per_image_standardization(distorted_image)
@@ -188,15 +189,18 @@ def distorted_inputs(data_dir, batch_size):
 
   # Ensure that the random shuffling has good mixing properties.
   min_fraction_of_examples_in_queue = 0.4
-  min_queue_examples = int(NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN *
-                           min_fraction_of_examples_in_queue)
-  print ('Filling queue with %d CIFAR images before starting to train. '
-         'This will take a few minutes.' % min_queue_examples)
+  min_queue_examples = int(
+      NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN * min_fraction_of_examples_in_queue)
+  print('Filling queue with %d CIFAR images before starting to train. '
+        'This will take a few minutes.' % min_queue_examples)
 
   # Generate a batch of images and labels by building up a queue of examples.
-  return _generate_image_and_label_batch(float_image, read_input.label,
-                                         min_queue_examples, batch_size,
-                                         shuffle=True)
+  return _generate_image_and_label_batch(
+      float_image,
+      read_input.label,
+      min_queue_examples,
+      batch_size,
+      shuffle=True)
 
 
 def inputs(eval_data, data_dir, batch_size):
@@ -212,8 +216,9 @@ def inputs(eval_data, data_dir, batch_size):
     labels: Labels. 1D tensor of [batch_size] size.
   """
   if not eval_data:
-    filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i)
-                 for i in xrange(1, 6)]
+    filenames = [
+        os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in xrange(1, 6)
+    ]
     num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
   else:
     filenames = [os.path.join(data_dir, 'test_batch.bin')]
@@ -235,8 +240,8 @@ def inputs(eval_data, data_dir, batch_size):
 
   # Image processing for evaluation.
   # Crop the central [height, width] of the image.
-  resized_image = tf.image.resize_image_with_crop_or_pad(reshaped_image,
-                                                         width, height)
+  resized_image = tf.image.resize_image_with_crop_or_pad(
+      reshaped_image, width, height)
 
   # Subtract off the mean and divide by the variance of the pixels.
   float_image = tf.image.per_image_standardization(resized_image)
@@ -247,10 +252,13 @@ def inputs(eval_data, data_dir, batch_size):
 
   # Ensure that the random shuffling has good mixing properties.
   min_fraction_of_examples_in_queue = 0.4
-  min_queue_examples = int(num_examples_per_epoch *
-                           min_fraction_of_examples_in_queue)
+  min_queue_examples = int(
+      num_examples_per_epoch * min_fraction_of_examples_in_queue)
 
   # Generate a batch of images and labels by building up a queue of examples.
-  return _generate_image_and_label_batch(float_image, read_input.label,
-                                         min_queue_examples, batch_size,
-                                         shuffle=False)
+  return _generate_image_and_label_batch(
+      float_image,
+      read_input.label,
+      min_queue_examples,
+      batch_size,
+      shuffle=False)