text: # coding=utf-8
  # Copyright 2019 Deepmind Technologies Limited.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # 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
  # limitations under the License.
  
  r"""Loads images from the 80M@200K training set and saves them in PNG format.
  
  Usage:
      cd /path/to/deepmind_research
      python -m unsupervised_adversarial_training.save_example_images \
          --data_bin_path=/path/to/tiny_images.bin
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  from absl import app
  from absl import flags
  import numpy as np
  from PIL import Image
  
  DIR_NAME = os.path.dirname(__file__)
  FLAGS = flags.FLAGS
  flags.DEFINE_string('data_bin_path', None,
                      'path to 80M Tiny Images data binary')
  flags.DEFINE_string('idxs_path', os.path.join(DIR_NAME, 'tiny_200K_idxs.txt'),
                      'path to file of indices indicating subset of 80M dataset')
  flags.DEFINE_string('output_dir', os.path.join(DIR_NAME, 'images'),
                      'path to output directory for images')
  flags.mark_flag_as_required('data_bin_path')
  
  CIFAR_LABEL_IDX_TO_NAME = ['airplane', 'automobile', 'bird', 'cat', 'deer',
                             'dog', 'frog', 'horse', 'ship', 'truck']
  DATASET_SIZE = 79302017
  
  
  def _load_dataset_as_array(ds_path):
    dataset = np.memmap(filename=ds_path, dtype=np.uint8, mode='r',
                        shape=(DATASET_SIZE, 3, 32, 32))
    return dataset.transpose([0, 3, 2, 1])
  
  
  def main(unused_argv):
    dataset = _load_dataset_as_array(FLAGS.data_bin_path)
  
    # Load the indices and labels of the 80M@200K training set
    data_idxs, data_labels = np.loadtxt(
        FLAGS.idxs_path,
        delimiter=',',
        dtype=[('index', np.uint64), ('label', np.uint8)],
        unpack=True)
  
    # Save images as PNG files
    if not os.path.exists(FLAGS.output_dir):
      os.makedirs(FLAGS.output_dir)
    for i in range(100):
      class_name = CIFAR_LABEL_IDX_TO_NAME[data_labels[i]]
      file_name = 'im{}_{}.png'.format(i, class_name)
      file_path = os.path.join(FLAGS.output_dir, file_name)
      img = dataset[data_idxs[i]]
      Image.fromarray(img).save(file_path)
  
  
  if __name__ == '__main__':
    app.run(main)
  