def get_rec_iter()

in image_classification/common/data.py [0:0]

• 58 lines of code
• 7 McCabe index (conditional complexity)

def get_rec_iter(args, kv=None):
    image_shape = tuple([int(l) for l in args.image_shape.split(',')])
    if 'benchmark' in args and args.benchmark:
        data_shape = (args.batch_size,) + image_shape
        train = SyntheticDataIter(args.num_classes, data_shape,
                args.num_examples / args.batch_size, np.float32)
        return (train, None)
    if kv:
        (rank, nworker) = (kv.rank, kv.num_workers)
    else:
        (rank, nworker) = (0, 1)
    rgb_mean = [float(i) for i in args.rgb_mean.split(',')]
    train = mx.io.ImageRecordIter(
        path_imgrec         = args.data_train,
        path_imgidx         = args.data_train_idx,
        label_width         = 1,
        mean_r              = rgb_mean[0],
        mean_g              = rgb_mean[1],
        mean_b              = rgb_mean[2],
        data_name           = 'data',
        label_name          = 'softmax_label',
        data_shape          = image_shape,
        batch_size          = args.batch_size,
        rand_crop           = args.random_crop,
        max_random_scale    = args.max_random_scale,
        pad                 = args.pad_size,
        fill_value          = 127,
        min_random_scale    = args.min_random_scale,
        max_aspect_ratio    = args.max_random_aspect_ratio,
        random_h            = args.max_random_h,
        random_s            = args.max_random_s,
        random_l            = args.max_random_l,
        max_rotate_angle    = args.max_random_rotate_angle,
        max_shear_ratio     = args.max_random_shear_ratio,
        rand_mirror         = args.random_mirror,
        preprocess_threads  = args.data_nthreads,
        shuffle             = True,
        num_parts           = nworker,
        part_index          = rank)
    if args.data_val is None:
        return (train, None)
    val = mx.io.ImageRecordIter(
        path_imgrec         = args.data_val,
        path_imgidx         = args.data_val_idx,
        label_width         = 1,
        mean_r              = rgb_mean[0],
        mean_g              = rgb_mean[1],
        mean_b              = rgb_mean[2],
        data_name           = 'data',
        label_name          = 'softmax_label',
        batch_size          = args.batch_size,
        data_shape          = image_shape,
        preprocess_threads  = args.data_nthreads,
        rand_crop           = False,
        rand_mirror         = False,
        num_parts           = nworker,
        part_index          = rank)
    return (train, val)