Generate batches of distorted versions of ImageNet images.

  Use this function as the inputs for training a network.

  Distorting images provides a useful technique for augmenting the data
  set during training in order to make the network invariant to aspects
  of the image that do not effect the label.

  Args:
    dataset: instance of Dataset class specifying the dataset.
    batch_size: integer, number of examples in batch
    num_preprocess_threads: integer, total number of preprocessing threads but
      None defaults to FLAGS.num_preprocess_threads.

  Returns:
    images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
                                       FLAGS.image_size, 3].
    labels: 1-D integer Tensor of [batch_size].

def distorted_inputs(dataset, batch_size=None, num_preprocess_threads=None):
  """Generate batches of distorted versions of ImageNet images.

  Use this function as the inputs for training a network.

  Distorting images provides a useful technique for augmenting the data
  set during training in order to make the network invariant to aspects
  of the image that do not effect the label.

  Args:
    dataset: instance of Dataset class specifying the dataset.
    batch_size: integer, number of examples in batch
    num_preprocess_threads: integer, total number of preprocessing threads but
      None defaults to FLAGS.num_preprocess_threads.

  Returns:
    images: Images. 4D tensor of size [batch_size, FLAGS.image_size,
                                       FLAGS.image_size, 3].
    labels: 1-D integer Tensor of [batch_size].
  """
  if not batch_size:
    batch_size = FLAGS.batch_size

  # Force all input processing onto CPU in order to reserve the GPU for
  # the forward inference and back-propagation.
  with tf.device('/cpu:0'):
    images, labels = batch_inputs(
        dataset, batch_size, train=True,
        num_preprocess_threads=num_preprocess_threads,
        num_readers=FLAGS.num_readers)
  return images, labels