text: #Modified code of cifar10_input.py from the cifar10 tutorial of Tensorflow (see https://github.com/tensorflow/models/tree/master/tutorials/image/cifar10)
  
  """Routine for decoding the CIFAR-10 binary file format."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  
  from six.moves import xrange  # pylint: disable=redefined-builtin
  import tensorflow as tf
  
  # Process images of this size.
  IMAGE_SIZE = 32
  
  FLAGS = tf.app.flags.FLAGS
  
  
  # 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
  
  
  def read_cifar10(filename_queue):
    """Reads and parses examples from CIFAR10 data files.
  
    Recommendation: if you want N-way read parallelism, call this function
    N times.  This will give you N independent Readers reading different
    files & positions within those files, which will give better mixing of
    examples.
  
    Args:
      filename_queue: A queue of strings with the filenames to read from.
  
    Returns:
      An object representing a single example, with the following fields:
        height: number of rows in the result (32)
        width: number of columns in the result (32)
        depth: number of color channels in the result (3)
        key: a scalar string Tensor describing the filename & record number
          for this example.
        label: an int32 Tensor with the label in the range 0..9.
        uint8image: a [height, width, depth] uint8 Tensor with the image data
    """
  
    class CIFAR10Record(object):
      pass
    result = CIFAR10Record()
  
    # Dimensions of the images in the CIFAR-10 dataset.
    # See http://www.cs.toronto.edu/~kriz/cifar.html for a description of the
    # input format.
    label_bytes = 1  # 2 for CIFAR-100
    result.height = 32
    result.width = 32
    result.depth = 3
    image_bytes = result.height * result.width * result.depth
    # Every record consists of a label followed by the image, with a
    # fixed number of bytes for each.
    record_bytes = label_bytes + image_bytes
  
    # Read a record, getting filenames from the filename_queue.  No
    # header or footer in the CIFAR-10 format, so we leave header_bytes
    # and footer_bytes at their default of 0.
    reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
    result.key, value = reader.read(filename_queue)
  
    # Convert from a string to a vector of uint8 that is record_bytes long.
    record_bytes = tf.decode_raw(value, tf.uint8)
  
    # The first bytes represent the label, which we convert from uint8->int32.
    result.label = tf.cast(
        tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
  
    # The remaining bytes after the label represent the image, which we reshape
    # from [depth * height * width] to [depth, height, width].
    depth_major = tf.reshape(
        tf.strided_slice(record_bytes, [label_bytes],
                         [label_bytes + image_bytes]),
        [result.depth, result.height, result.width])
    # Convert from [depth, height, width] to [height, width, depth].
    result.uint8image = tf.transpose(depth_major, [1, 2, 0])
  
    return result
  
  
  def _generate_image_and_label_batch(image, label, min_queue_examples,
                                      batch_size, shuffle):
    """Construct a queued batch of images and labels.
  
    Args:
      image: 3-D Tensor of [height, width, 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.
      batch_size: Number of images per batch.
      shuffle: boolean indicating whether to use a shuffling queue.
  
    Returns:
      images: Images. 4D tensor of [batch_size, height, width, 3] size.
      labels: Labels. 1D tensor of [batch_size] size.
    """
    # Create a queue that shuffles the examples, and then
    # read 'batch_size' images + labels from the example queue.
    num_preprocess_threads = 1
    if shuffle:
      images, label_batch = tf.train.shuffle_batch(
          [image, label],
          batch_size=batch_size,
          num_threads=num_preprocess_threads,
          capacity=min_queue_examples + 3 * batch_size,
          min_after_dequeue=min_queue_examples)
    else:
      images, label_batch = tf.train.batch(
          [image, label],
          batch_size=batch_size,
          num_threads=num_preprocess_threads,
          capacity=min_queue_examples + 3 * batch_size)
  
    # Display the training images in the visualizer.
    tf.summary.image('images', images)
  
    return images, tf.reshape(label_batch, [batch_size])
  
  
  def distorted_inputs(data_dir, batch_size,start,stop):
    """Construct distorted input for CIFAR training using the Reader ops.
  
    Args:
      data_dir: Path to the CIFAR-10 data directory.
      batch_size: Number of images per batch.
  
    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(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)
  
    # 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 = read_cifar10(filename_queue)
    reshaped_image = tf.cast(read_input.uint8image, tf.float32)
  
    height = IMAGE_SIZE
    width = IMAGE_SIZE
  
    # Subtract off the mean and divide by the variance of the pixels.
    float_image = tf.image.per_image_standardization(reshaped_image)
  
    # Set the shapes of tensors.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])
  
    # 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.
    images, labels = _generate_image_and_label_batch(float_image, read_input.label,
                                           min_queue_examples, batch_size,
                                           shuffle=True)
    return images,labels
  
  def inputs(data_dir, batch_size):
    """Construct input for CIFAR evaluation using the Reader ops.
  
    Args:
      eval_data: bool, indicating if one should use the train or eval data set.
      data_dir: Path to the CIFAR-10 data directory.
      batch_size: Number of images per batch.
  
    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(data_dir, "test_batch.bin")
    num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN
    
  
    # 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 = 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,
                                                             height, width)
  
    # Subtract off the mean and divide by the variance of the pixels.
    float_image = tf.image.per_image_standardization(resized_image)
  
    # Set the shapes of tensors.
    float_image.set_shape([height, width, 3])
    read_input.label.set_shape([1])
  
    # 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, batch_size,
                                           shuffle=False)
  
  def get_local_data(current_worker_id=0,total_number_of_worker=1):
    examples_per_worker = 60000/total_number_of_worker
    if not FLAGS.data_dir:
      raise ValueError('Please supply a data_dir')
    return distorted_inputs(data_dir=FLAGS.data_dir,batch_size=FLAGS.batch_size,start=int((current_worker_id-1)*examples_per_worker),stop=int((current_worker_id)*examples_per_worker))
  
  def get_test_set():
    if not FLAGS.data_dir:
      raise ValueError('Please supply a data_dir')
    return inputs(data_dir=FLAGS.data_dir, batch_size=FLAGS.batch_size)
  