Python datasets.dataset_utils.write_label_file() Examples

The following are 30 code examples of datasets.dataset_utils.write_label_file(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module datasets.dataset_utils , or try the search function .
Example #1
Source File: download_and_convert_mnist.py    From tumblr-emotions with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #2
Source File: download_and_convert_cifar10.py    From Action_Recognition_Zoo with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #3
Source File: download_and_convert_flowers.py    From hands-detection with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #4
Source File: download_and_convert_mnist.py    From hands-detection with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #5
Source File: convert_flowers.py    From SSD_tensorflow_VOC with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
    """Runs the download and conversion operation.

    Args:
        dataset_dir: The dataset directory where the dataset is stored.
    """
    if not tf.gfile.Exists(dataset_dir):
        tf.gfile.MakeDirs(dataset_dir)

    if _dataset_exists(dataset_dir):
        print('Dataset files already exist. Exiting without re-creating them.')
        return

    dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
    photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
    class_names_to_ids = dict(zip(class_names, range(len(class_names))))

    # Divide into train and test:
    random.seed(_RANDOM_SEED)
    random.shuffle(photo_filenames)
    training_filenames = photo_filenames[_NUM_VALIDATION:]
    validation_filenames = photo_filenames[:_NUM_VALIDATION]

    # First, convert the training and validation sets.
    _convert_dataset('train', training_filenames, class_names_to_ids,
                                     dataset_dir)
    _convert_dataset('validation', validation_filenames, class_names_to_ids,
                                     dataset_dir)

    # Finally, write the labels file:
    labels_to_class_names = dict(zip(range(len(class_names)), class_names))
    dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

    _clean_up_temporary_files(dataset_dir)
    print('\nFinished converting the Flowers dataset!')
Example #6
Source File: download_and_convert_cifar10.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #7
Source File: download_and_convert_flowers.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #8
Source File: download_and_convert_mnist.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #9
Source File: download_and_convert_cifar10.py    From MBMD with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #10
Source File: download_and_convert_flowers.py    From MBMD with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #11
Source File: download_and_convert_mnist.py    From MBMD with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #12
Source File: download_and_convert_cifar10.py    From hands-detection with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #13
Source File: download_and_convert_cifar10.py    From tensorflow-densenet with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #14
Source File: download_and_convert_svhn.py    From TwinGAN with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    # labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    # labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the svhn dataset!')
Example #15
Source File: convert.py    From tensorflow-kubernetes-art-classification with Apache License 2.0 5 votes vote down vote up 
def main(_):
  """Runs the conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """

  if not FLAGS.dataset_dir:
    raise ValueError('Please specify the dataset directory with --dataset_dir')

  if _dataset_exists(FLAGS.dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  photo_filenames, class_names = _get_filenames_and_classes(FLAGS.dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  if FLAGS.check_image:
    _check_image(photo_filenames)
    return

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  _NUM_VALIDATION = int(len(photo_filenames) * _PERCENT_VALIDATION)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   FLAGS.dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   FLAGS.dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, FLAGS.dataset_dir)

  print('\nFinished converting the Arts dataset!')
Example #16
Source File: download_and_convert_flowers.py    From tensorflow-densenet with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #17
Source File: download_and_convert_flowers.py    From tumblr-emotions with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #18
Source File: download_and_convert_cifar10.py    From tumblr-emotions with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #19
Source File: download_and_convert_mnist.py    From tensorflow_yolo2 with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #20
Source File: download_and_convert_flowers.py    From tensorflow_yolo2 with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #21
Source File: download_and_convert_cifar10.py    From tensorflow_yolo2 with MIT License 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #22
Source File: download_and_convert_mnist.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #23
Source File: download_and_convert_flowers.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #24
Source File: download_and_convert_cifar10.py    From MAX-Image-Segmenter with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #25
Source File: download_and_convert_mnist.py    From MobileNet with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #26
Source File: download_and_convert_flowers.py    From MobileNet with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #27
Source File: download_and_convert_cifar10.py    From MobileNet with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')
Example #28
Source File: download_and_convert_mnist.py    From hops-tensorflow with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  _download_dataset(dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TRAIN_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TRAIN_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 60000, tfrecord_writer)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    data_filename = os.path.join(dataset_dir, _TEST_DATA_FILENAME)
    labels_filename = os.path.join(dataset_dir, _TEST_LABELS_FILENAME)
    _add_to_tfrecord(data_filename, labels_filename, 10000, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the MNIST dataset!')
Example #29
Source File: download_and_convert_flowers.py    From hops-tensorflow with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  if _dataset_exists(dataset_dir):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)
  photo_filenames, class_names = _get_filenames_and_classes(dataset_dir)
  class_names_to_ids = dict(zip(class_names, range(len(class_names))))

  # Divide into train and test:
  random.seed(_RANDOM_SEED)
  random.shuffle(photo_filenames)
  training_filenames = photo_filenames[_NUM_VALIDATION:]
  validation_filenames = photo_filenames[:_NUM_VALIDATION]

  # First, convert the training and validation sets.
  _convert_dataset('train', training_filenames, class_names_to_ids,
                   dataset_dir)
  _convert_dataset('validation', validation_filenames, class_names_to_ids,
                   dataset_dir)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(class_names)), class_names))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Flowers dataset!')
Example #30
Source File: download_and_convert_cifar10.py    From hops-tensorflow with Apache License 2.0 5 votes vote down vote up 
def run(dataset_dir):
  """Runs the download and conversion operation.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
  """
  if not tf.gfile.Exists(dataset_dir):
    tf.gfile.MakeDirs(dataset_dir)

  training_filename = _get_output_filename(dataset_dir, 'train')
  testing_filename = _get_output_filename(dataset_dir, 'test')

  if tf.gfile.Exists(training_filename) and tf.gfile.Exists(testing_filename):
    print('Dataset files already exist. Exiting without re-creating them.')
    return

  dataset_utils.download_and_uncompress_tarball(_DATA_URL, dataset_dir)

  # First, process the training data:
  with tf.python_io.TFRecordWriter(training_filename) as tfrecord_writer:
    offset = 0
    for i in range(_NUM_TRAIN_FILES):
      filename = os.path.join(dataset_dir,
                              'cifar-10-batches-py',
                              'data_batch_%d' % (i + 1))  # 1-indexed.
      offset = _add_to_tfrecord(filename, tfrecord_writer, offset)

  # Next, process the testing data:
  with tf.python_io.TFRecordWriter(testing_filename) as tfrecord_writer:
    filename = os.path.join(dataset_dir,
                            'cifar-10-batches-py',
                            'test_batch')
    _add_to_tfrecord(filename, tfrecord_writer)

  # Finally, write the labels file:
  labels_to_class_names = dict(zip(range(len(_CLASS_NAMES)), _CLASS_NAMES))
  dataset_utils.write_label_file(labels_to_class_names, dataset_dir)

  _clean_up_temporary_files(dataset_dir)
  print('\nFinished converting the Cifar10 dataset!')