Проблемная загрузка / обеспечение CIFAR-100 | нейронные сети

Question

Я пытался реализовать бинаризованные нейронные сети по этой ссылке , но у меня есть ошибка, связанная с загрузкой / предоставлением набора данных CIFAR-100.

def get_data_provider(name, training=True, mnist_input=None):
    if name == 'imagenet':
        return DataProvider(__read_imagenet(IMAGENET_PATH), [1280000, 224, 224, 3], True)
    elif name == 'cifar10':
        path = os.path.join(DATA_DIR,'cifar10')
        url = URLs['cifar10']
        def post_f(f): return tarfile.open(f, 'r:gz').extractall(path)
        __maybe_download(url, path,post_f)
        data_dir = os.path.join(path, 'cifar-10-batches-bin/')
        if training:
            return DataProvider(__read_cifar([os.path.join(data_dir, 'data_batch_%d.bin' % i)
                                    for i in range(1, 6)]), [50000, 32,32,3], True)
        else:
            return DataProvider(__read_cifar([os.path.join(data_dir, 'test_batch.bin')]),
                                [10000, 32,32, 3], False)
    elif name == 'cifar100':
        path = os.path.join(DATA_DIR,'cifar100')
        url = URLs['cifar100']
        def post_f(f): return tarfile.open(f, 'r:gz').extractall(path)
        __maybe_download(url, path,post_f)
        data_dir = os.path.join(path, 'cifar-100-binary/')
        if training:
            print(os.path.join(data_dir, 'train.bin'))
            return DataProvider([os.path.join(data_dir, 'train.bin')], 50000, True, __read_cifar)
        else:
            return DataProvider([os.path.join(data_dir, 'test.bin')],
                               10000, False, __read_cifar)

Я получаю следующая ошибка: TypeError: __init __ () принимает от 2 до 4 позиционных аргументов, но было дано 5 для строки return DataProvider ([os.path.join (data_dir, 'train.bin')]] , 50000, True, __read_cifar). Я упоминаю, что загрузка / предоставление CIFAR-10 работает отлично. Я поставлю здесь код для DataProvider и _read_cifar . Версия Tensorflow 1.2.1.

class DataProvider:
    def __init__(self, data, size=None, training=True):
        self.size = size or [None]*4
        self.data = data
        self.training = training

    def generate_batches(self, batch_size, min_queue_examples=1000, num_threads=8):
        """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.

        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.

        image, label = self.data
        if self.training:
            images, label_batch = tf.train.shuffle_batch(
            [preprocess_training(image, height=self.size[1], width=self.size[2]), label],
            batch_size=batch_size,
            num_threads=num_threads,
            capacity=min_queue_examples + 3 * batch_size,
            min_after_dequeue=min_queue_examples)
        else:
            images, label_batch = tf.train.batch(
            [preprocess_evaluation(image, height=self.size[1], width=self.size[2]), label],
            batch_size=batch_size,
            num_threads=num_threads,
            capacity=min_queue_examples + 3 * batch_size)



        return images, tf.reshape(label_batch, [batch_size])

def __read_cifar(filenames, shuffle=True, cifar100=False):
  """Reads and parses examples from CIFAR data files.
  """
  # 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.
  filename_queue = tf.train.string_input_producer(filenames, shuffle=shuffle,num_epochs=None)

  label_bytes = 1  # 2 for CIFAR-100
  if cifar100:
      label_bytes = 2
  height = 32
  width = 32
  depth = 3
  image_bytes = height * width * 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)
  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.
  label = tf.cast(
      tf.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.slice(record_bytes, [label_bytes], [image_bytes]),
                           [depth, height, width])
  # Convert from [depth, height, width] to [height, width, depth].
  image = tf.transpose(depth_major, [1, 2, 0])

  return tf.cast(image, tf.float32), label