1 files changed, 19 insertions, 132 deletions
diff --git a/tensorflow/models/image/cifar10/cifar10.py b/tensorflow/models/image/cifar10/cifar10.py
index b9b89473e8..32234db496 100644
--- a/tensorflow/models/image/cifar10/cifar10.py
+++ b/tensorflow/models/image/cifar10/cifar10.py
@@ -43,11 +43,9 @@ import tarfile
 
 import tensorflow.python.platform
 from six.moves import urllib
-from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
 
 from tensorflow.models.image.cifar10 import cifar10_input
-from tensorflow.python.platform import gfile
 
 FLAGS = tf.app.flags.FLAGS
 
@@ -57,15 +55,12 @@ tf.app.flags.DEFINE_integer('batch_size', 128,
 tf.app.flags.DEFINE_string('data_dir', '/tmp/cifar10_data',
                            """Path to the CIFAR-10 data directory.""")
 
-# Process images of this size. Note that this differs from the original CIFAR
-# image size of 32 x 32. If one alters this number, then the entire model
-# architecture will change and any model would need to be retrained.
-IMAGE_SIZE = 24
-
 # Global constants describing the CIFAR-10 data set.
-NUM_CLASSES = 10
-NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = 50000
-NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = 10000
+IMAGE_SIZE = cifar10_input.IMAGE_SIZE
+NUM_CLASSES = cifar10_input.NUM_CLASSES
+NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
+NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = cifar10_input.NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
+
 
 # Constants describing the training process.
 MOVING_AVERAGE_DECAY = 0.9999     # The decay to use for the moving average.
@@ -139,91 +134,21 @@ def _variable_with_weight_decay(name, shape, stddev, wd):
   return var
 
 
-def _generate_image_and_label_batch(image, label, min_queue_examples):
-  """Construct a queued batch of images and labels.
-
-  Args:
-    image: 3-D Tensor of [IMAGE_SIZE, IMAGE_SIZE, 3] of type.float32.
-    label: 1-D Tensor of type.int32
-    min_queue_examples: int32, minimum number of samples to retain
-      in the queue that provides of batches of examples.
-
-  Returns:
-    images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
-    labels: Labels. 1D tensor of [batch_size] size.
-  """
-  # Create a queue that shuffles the examples, and then
-  # read 'FLAGS.batch_size' images + labels from the example queue.
-  num_preprocess_threads = 16
-  images, label_batch = tf.train.shuffle_batch(
-      [image, label],
-      batch_size=FLAGS.batch_size,
-      num_threads=num_preprocess_threads,
-      capacity=min_queue_examples + 3 * FLAGS.batch_size,
-      min_after_dequeue=min_queue_examples)
-
-  # Display the training images in the visualizer.
-  tf.image_summary('images', images)
-
-  return images, tf.reshape(label_batch, [FLAGS.batch_size])
-
-
 def distorted_inputs():
   """Construct distorted input for CIFAR training using the Reader ops.
 
-  Raises:
-    ValueError: if no data_dir
-
   Returns:
     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(FLAGS.data_dir, 'cifar-10-batches-bin',
-                            'data_batch_%d.bin' % i)
-               for i in xrange(1, 6)]
-  for f in filenames:
-    if not gfile.Exists(f):
-      raise ValueError('Failed to find file: ' + f)
-
-  # Create a queue that produces the filenames to read.
-  filename_queue = tf.train.string_input_producer(filenames)
 
-  # Read examples from files in the filename queue.
-  read_input = cifar10_input.read_cifar10(filename_queue)
-  reshaped_image = tf.cast(read_input.uint8image, tf.float32)
-
-  height = IMAGE_SIZE
-  width = IMAGE_SIZE
-
-  # Image processing for training the network. Note the many random
-  # distortions applied to the image.
-
-  # Randomly crop a [height, width] section of the image.
-  distorted_image = tf.image.random_crop(reshaped_image, [height, width])
-
-  # Randomly flip the image horizontally.
-  distorted_image = tf.image.random_flip_left_right(distorted_image)
-
-  # Because these operations are not commutative, consider randomizing
-  # randomize 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)
-
-  # Subtract off the mean and divide by the variance of the pixels.
-  float_image = tf.image.per_image_whitening(distorted_image)
-
-  # 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)
-
-  # 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)
+  Raises:
+    ValueError: If no data_dir
+  """
+  if not FLAGS.data_dir:
+    raise ValueError('Please supply a data_dir')
+  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
+  return cifar10_input.distorted_inputs(data_dir=data_dir,
+                                        batch_size=FLAGS.batch_size)
 
 
 def inputs(eval_data):
@@ -232,56 +157,18 @@ def inputs(eval_data):
   Args:
     eval_data: bool, indicating if one should use the train or eval data set.
 
-  Raises:
-    ValueError: if no data_dir
-
   Returns:
     images: Images. 4D tensor of [batch_size, IMAGE_SIZE, IMAGE_SIZE, 3] size.
     labels: Labels. 1D tensor of [batch_size] size.
+
+  Raises:
+    ValueError: If no data_dir
   """
   if not FLAGS.data_dir:
     raise ValueError('Please supply a data_dir')
-
-  if not eval_data:
-    filenames = [os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin',
-                              '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(FLAGS.data_dir, 'cifar-10-batches-bin',
-                              'test_batch.bin')]
-    num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL
-
-  for f in filenames:
-    if not gfile.Exists(f):
-      raise ValueError('Failed to find file: ' + f)
-
-  # Create a queue that produces the filenames to read.
-  filename_queue = tf.train.string_input_producer(filenames)
-
-  # Read examples from files in the filename queue.
-  read_input = cifar10_input.read_cifar10(filename_queue)
-  reshaped_image = tf.cast(read_input.uint8image, tf.float32)
-
-  height = IMAGE_SIZE
-  width = IMAGE_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)
-
-  # Subtract off the mean and divide by the variance of the pixels.
-  float_image = tf.image.per_image_whitening(resized_image)
-
-  # 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)
-
-  # 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)
+  data_dir = os.path.join(FLAGS.data_dir, 'cifar-10-batches-bin')
+  return cifar10_input.inputs(eval_data=eval_data, data_dir=data_dir,
+                              batch_size=FLAGS.batch_size)
 
 
 def inference(images):