(split_name, dataset_dir, file_pattern, reader,
              items_to_descriptions, num_classes)
return slim.dataset.Dataset(
            data_sources=file_pattern,
            reader=reader,
            decoder=decoder,
            num_samples=split_to_sizes[split_name],
            items_to_descriptions=items_to_descriptions,
            num_classes=num_classes,
            labels_to_names=labels_to_names)
Gets a dataset tuple with instructions for reading Pascal VOC dataset.

    Args:
      split_name: A trainval/test split name.
      dataset_dir: The base directory of the dataset sources.
      file_pattern: The file pattern to use when matching the dataset sources.
        It is assumed that the pattern contains a '%s' string so that the split
        name can be inserted.
      reader: The TensorFlow reader type.

    Returns:
      A `Dataset` namedtuple.

    Raises:
        ValueError: if `split_name` is not a valid train/test split.
Gets a dataset tuple with instructions for reading Pascal VOC dataset.
Gets a dataset tuple with instructions for reading Pascal VOC dataset .
def get_split(split_name, dataset_dir, file_pattern, reader,
              items_to_descriptions, num_classes):
    """Gets a dataset tuple with instructions for reading Pascal VOC dataset.

    Args:
      split_name: A trainval/test split name.
      dataset_dir: The base directory of the dataset sources.
      file_pattern: The file pattern to use when matching the dataset sources.
        It is assumed that the pattern contains a '%s' string so that the split
        name can be inserted.
      reader: The TensorFlow reader type.

    Returns:
      A `Dataset` namedtuple.

    Raises:
        ValueError: if `split_name` is not a valid train/test split.
    """
    if split_name not in ['trainval', 'test']:
        raise ValueError('split name %s was not recognized.' % split_name)
    file_pattern = os.path.join(dataset_dir, file_pattern % split_name)

    # Allowing None in the signature so that dataset_factory can use the default.
    if reader is None:
        reader = tf.TFRecordReader
    # Features in Pascal VOC TFRecords.
    keys_to_features = {
        'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
        'image/format': tf.FixedLenFeature((), tf.string, default_value='jpeg'),
        'image/height': tf.FixedLenFeature([1], tf.int64),
        'image/width': tf.FixedLenFeature([1], tf.int64),
        'image/channels': tf.FixedLenFeature([1], tf.int64),
        'image/shape': tf.FixedLenFeature([3], tf.int64),
        'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32),
        'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32),
        'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32),
        'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32),
        'image/object/bbox/label': tf.VarLenFeature(dtype=tf.int64),
        'image/object/bbox/difficult': tf.VarLenFeature(dtype=tf.int64),
        'image/object/bbox/truncated': tf.VarLenFeature(dtype=tf.int64),
    }
    items_to_handlers = {
        'image': slim.tfexample_decoder.Image('image/encoded', 'image/format'),
        'shape': slim.tfexample_decoder.Tensor('image/shape'),
        'object/bbox': slim.tfexample_decoder.BoundingBox(
                ['ymin', 'xmin', 'ymax', 'xmax'], 'image/object/bbox/'),
        'object/label': slim.tfexample_decoder.Tensor('image/object/bbox/label'),
        'object/difficult': slim.tfexample_decoder.Tensor('image/object/bbox/difficult'),
        'object/truncated': slim.tfexample_decoder.Tensor('image/object/bbox/truncated'),
    }
    decoder = slim.tfexample_decoder.TFExampleDecoder(
        keys_to_features, items_to_handlers)

    labels_to_names = None
    if dataset_utils.has_labels(dataset_dir):
        labels_to_names = dataset_utils.read_label_file(dataset_dir)
    # else:
    #     labels_to_names = create_readable_names_for_imagenet_labels()
    #     dataset_utils.write_label_file(labels_to_names, dataset_dir)

    return slim.dataset.Dataset(
            data_sources=file_pattern,
            reader=reader,
            decoder=decoder,
            num_samples=split_to_sizes[split_name],
            items_to_descriptions=items_to_descriptions,
            num_classes=num_classes,
            labels_to_names=labels_to_names)
def get_split ( split_name , dataset_dir , file_pattern , reader , items_to_descriptions , num_classes ) : if split_name not in [ 'trainval' , 'test' ] : raise ValueError ( 'split name %s was not recognized.' % split_name ) file_pattern = os . path . join ( dataset_dir , file_pattern % split_name ) # Allowing None in the signature so that dataset_factory can use the default. if reader is None : reader = tf . TFRecordReader # Features in Pascal VOC TFRecords. keys_to_features = { 'image/encoded' : tf . FixedLenFeature ( ( ) , tf . string , default_value = '' ) , 'image/format' : tf . FixedLenFeature ( ( ) , tf . string , default_value = 'jpeg' ) , 'image/height' : tf . FixedLenFeature ( [ 1 ] , tf . int64 ) , 'image/width' : tf . FixedLenFeature ( [ 1 ] , tf . int64 ) , 'image/channels' : tf . FixedLenFeature ( [ 1 ] , tf . int64 ) , 'image/shape' : tf . FixedLenFeature ( [ 3 ] , tf . int64 ) , 'image/object/bbox/xmin' : tf . VarLenFeature ( dtype = tf . float32 ) , 'image/object/bbox/ymin' : tf . VarLenFeature ( dtype = tf . float32 ) , 'image/object/bbox/xmax' : tf . VarLenFeature ( dtype = tf . float32 ) , 'image/object/bbox/ymax' : tf . VarLenFeature ( dtype = tf . float32 ) , 'image/object/bbox/label' : tf . VarLenFeature ( dtype = tf . int64 ) , 'image/object/bbox/difficult' : tf . VarLenFeature ( dtype = tf . int64 ) , 'image/object/bbox/truncated' : tf . VarLenFeature ( dtype = tf . int64 ) , } items_to_handlers = { 'image' : slim . tfexample_decoder . Image ( 'image/encoded' , 'image/format' ) , 'shape' : slim . tfexample_decoder . Tensor ( 'image/shape' ) , 'object/bbox' : slim . tfexample_decoder . BoundingBox ( [ 'ymin' , 'xmin' , 'ymax' , 'xmax' ] , 'image/object/bbox/' ) , 'object/label' : slim . tfexample_decoder . Tensor ( 'image/object/bbox/label' ) , 'object/difficult' : slim . tfexample_decoder . Tensor ( 'image/object/bbox/difficult' ) , 'object/truncated' : slim . tfexample_decoder . Tensor ( 'image/object/bbox/truncated' ) , } decoder = slim . tfexample_decoder . TFExampleDecoder ( keys_to_features , items_to_handlers ) labels_to_names = None if dataset_utils . has_labels ( dataset_dir ) : labels_to_names = dataset_utils . read_label_file ( dataset_dir ) # else: #     labels_to_names = create_readable_names_for_imagenet_labels() #     dataset_utils.write_label_file(labels_to_names, dataset_dir) return slim . dataset . Dataset ( data_sources = file_pattern , reader = reader , decoder = decoder , num_samples = split_to_sizes [ split_name ] , items_to_descriptions = items_to_descriptions , num_classes = num_classes , labels_to_names = labels_to_names )