(dataset,
             batch_size,
             augment=False,
             central_crop_size=None,
             shuffle_config=None,
             shuffle=True)
return InputEndpoints(
      images=images,
      images_orig=images_orig,
      labels=labels,
      labels_one_hot=labels_one_hot)
Wraps calls to DatasetDataProviders and shuffle_batch.

  For more details about supported Dataset objects refer to datasets/fsns.py.

  Args:
    dataset: a slim.data.dataset.Dataset object.
    batch_size: number of samples per batch.
    augment: optional, if True does random image distortion.
    central_crop_size: A CharLogittuple (crop_width, crop_height).
    shuffle_config: A namedtuple ShuffleBatchConfig.
    shuffle: if True use data shuffling.

  Returns:
Wraps calls to DatasetDataProviders and shuffle_batch.
Wraps calls to DatasetDataProviders and shuffle_batch .
def get_data(dataset,
             batch_size,
             augment=False,
             central_crop_size=None,
             shuffle_config=None,
             shuffle=True):
  """Wraps calls to DatasetDataProviders and shuffle_batch.

  For more details about supported Dataset objects refer to datasets/fsns.py.

  Args:
    dataset: a slim.data.dataset.Dataset object.
    batch_size: number of samples per batch.
    augment: optional, if True does random image distortion.
    central_crop_size: A CharLogittuple (crop_width, crop_height).
    shuffle_config: A namedtuple ShuffleBatchConfig.
    shuffle: if True use data shuffling.

  Returns:

  """
  if not shuffle_config:
    shuffle_config = DEFAULT_SHUFFLE_CONFIG

  provider = slim.dataset_data_provider.DatasetDataProvider(
      dataset,
      shuffle=shuffle,
      common_queue_capacity=2 * batch_size,
      common_queue_min=batch_size)
  image_orig, label = provider.get(['image', 'label'])
  
  image = preprocess_image(
      image_orig, augment, central_crop_size, num_towers=dataset.num_of_views)
  label_one_hot = slim.one_hot_encoding(label, dataset.num_char_classes)

  images, images_orig, labels, labels_one_hot = (tf.train.shuffle_batch(
      [image, image_orig, label, label_one_hot],
      batch_size=batch_size,
      num_threads=shuffle_config.num_batching_threads,
      capacity=shuffle_config.queue_capacity,
      min_after_dequeue=shuffle_config.min_after_dequeue))

  return InputEndpoints(
      images=images,
      images_orig=images_orig,
      labels=labels,
      labels_one_hot=labels_one_hot)
def get_data ( dataset , batch_size , augment = False , central_crop_size = None , shuffle_config = None , shuffle = True ) : if not shuffle_config : shuffle_config = DEFAULT_SHUFFLE_CONFIG provider = slim . dataset_data_provider . DatasetDataProvider ( dataset , shuffle = shuffle , common_queue_capacity = 2 * batch_size , common_queue_min = batch_size ) image_orig , label = provider . get ( [ 'image' , 'label' ] ) image = preprocess_image ( image_orig , augment , central_crop_size , num_towers = dataset . num_of_views ) label_one_hot = slim . one_hot_encoding ( label , dataset . num_char_classes ) images , images_orig , labels , labels_one_hot = ( tf . train . shuffle_batch ( [ image , image_orig , label , label_one_hot ] , batch_size = batch_size , num_threads = shuffle_config . num_batching_threads , capacity = shuffle_config . queue_capacity , min_after_dequeue = shuffle_config . min_after_dequeue ) ) return InputEndpoints ( images = images , images_orig = images_orig , labels = labels , labels_one_hot = labels_one_hot )