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# Copyright 2015 The TensorFlow Authors. All Rights Reserved.

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

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#

#     http://www.apache.org/licenses/LICENSE-2.0

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# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

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# ==============================================================================

"""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. 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

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 = 16

  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):

  """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

  # 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.random_crop(reshaped_image, [height, width, 3])

  # Randomly flip the image horizontally.

  distorted_image = tf.image.random_flip_left_right(distorted_image)

  # Because these operations are not commutative, consider randomizing

  # the order their operation.

  # NOTE: since per_image_standardization zeros the mean and makes

  # the stddev unit, this likely has no effect see tensorflow#1458.

  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)

  # 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.

  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):

  """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.

  """

  if not eval_data:

    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')]

    num_examples_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_EVAL

  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

  # 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)