TensorFlow/squeezenet/src/generate_cifar_tfrecords.py [74:162]:
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def _add_to_tfrecord(filename, tfrecord_writer, offset=0):
  """Loads data from the cifar10 pickle files and writes files to a TFRecord.

  Args:
    filename: The filename of the cifar10 pickle file.
    tfrecord_writer: The TFRecord writer to use for writing.
    offset: An offset into the absolute number of images previously written.

  Returns:
    The new offset.
  """
  with tf.gfile.Open(filename, 'rb') as f:
    if sys.version_info < (3,):
      data = cPickle.load(f)
    else:
      data = cPickle.load(f, encoding='bytes')

  images = data[b'data']
  num_images = images.shape[0]

  images = images.reshape((num_images, 3, 32, 32))
  labels = data[b'labels']

  with tf.Graph().as_default():
    image_placeholder = tf.placeholder(dtype=tf.uint8)
    encoded_image = tf.image.encode_png(image_placeholder)

    with tf.Session('') as sess:

      for j in range(num_images):
        sys.stdout.write('\r>> Reading file [%s] image %d/%d' % (
            filename, offset + j + 1, offset + num_images))
        sys.stdout.flush()

        image = np.squeeze(images[j]).transpose((1, 2, 0))
        label = labels[j]

        png_string = sess.run(encoded_image,
                              feed_dict={image_placeholder: image})

        example = dataset_utils.image_to_tfexample(
            png_string, b'png', _IMAGE_SIZE, _IMAGE_SIZE, label)
        tfrecord_writer.write(example.SerializeToString())

  return offset + num_images


def _get_output_filename(dataset_dir, split_name):
  """Creates the output filename.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
    split_name: The name of the train/test split.

  Returns:
    An absolute file path.
  """
  return '%s/cifar10_%s.tfrecord' % (dataset_dir, split_name)


def _download_and_uncompress_dataset(dataset_dir):
  """Downloads cifar10 and uncompresses it locally.

  Args:
    dataset_dir: The directory where the temporary files are stored.
  """
  filename = _DATA_URL.split('/')[-1]
  filepath = os.path.join(dataset_dir, filename)

  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(_DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
    tarfile.open(filepath, 'r:gz').extractall(dataset_dir)


def _clean_up_temporary_files(dataset_dir):
  """Removes temporary files used to create the dataset.

  Args:
    dataset_dir: The directory where the temporary files are stored.
  """
  filename = _DATA_URL.split('/')[-1]
  filepath = os.path.join(dataset_dir, filename)
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TensorFlow/squeezenet/src/models/research/slim/datasets/download_and_convert_cifar10.py [64:152]:
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def _add_to_tfrecord(filename, tfrecord_writer, offset=0):
  """Loads data from the cifar10 pickle files and writes files to a TFRecord.

  Args:
    filename: The filename of the cifar10 pickle file.
    tfrecord_writer: The TFRecord writer to use for writing.
    offset: An offset into the absolute number of images previously written.

  Returns:
    The new offset.
  """
  with tf.gfile.Open(filename, 'rb') as f:
    if sys.version_info < (3,):
      data = cPickle.load(f)
    else:
      data = cPickle.load(f, encoding='bytes')

  images = data[b'data']
  num_images = images.shape[0]

  images = images.reshape((num_images, 3, 32, 32))
  labels = data[b'labels']

  with tf.Graph().as_default():
    image_placeholder = tf.placeholder(dtype=tf.uint8)
    encoded_image = tf.image.encode_png(image_placeholder)

    with tf.Session('') as sess:

      for j in range(num_images):
        sys.stdout.write('\r>> Reading file [%s] image %d/%d' % (
            filename, offset + j + 1, offset + num_images))
        sys.stdout.flush()

        image = np.squeeze(images[j]).transpose((1, 2, 0))
        label = labels[j]

        png_string = sess.run(encoded_image,
                              feed_dict={image_placeholder: image})

        example = dataset_utils.image_to_tfexample(
            png_string, b'png', _IMAGE_SIZE, _IMAGE_SIZE, label)
        tfrecord_writer.write(example.SerializeToString())

  return offset + num_images


def _get_output_filename(dataset_dir, split_name):
  """Creates the output filename.

  Args:
    dataset_dir: The dataset directory where the dataset is stored.
    split_name: The name of the train/test split.

  Returns:
    An absolute file path.
  """
  return '%s/cifar10_%s.tfrecord' % (dataset_dir, split_name)


def _download_and_uncompress_dataset(dataset_dir):
  """Downloads cifar10 and uncompresses it locally.

  Args:
    dataset_dir: The directory where the temporary files are stored.
  """
  filename = _DATA_URL.split('/')[-1]
  filepath = os.path.join(dataset_dir, filename)

  if not os.path.exists(filepath):
    def _progress(count, block_size, total_size):
      sys.stdout.write('\r>> Downloading %s %.1f%%' % (
          filename, float(count * block_size) / float(total_size) * 100.0))
      sys.stdout.flush()
    filepath, _ = urllib.request.urlretrieve(_DATA_URL, filepath, _progress)
    print()
    statinfo = os.stat(filepath)
    print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
    tarfile.open(filepath, 'r:gz').extractall(dataset_dir)


def _clean_up_temporary_files(dataset_dir):
  """Removes temporary files used to create the dataset.

  Args:
    dataset_dir: The directory where the temporary files are stored.
  """
  filename = _DATA_URL.split('/')[-1]
  filepath = os.path.join(dataset_dir, filename)
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