def rand_augment_transform()

in slowfast/datasets/rand_augment.py [0:0]

• 33 lines of code
• 13 McCabe index (conditional complexity)

def rand_augment_transform(config_str, hparams):
    """
    RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719

    Create a RandAugment transform
    :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
    dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
    sections, not order sepecific determine
        'm' - integer magnitude of rand augment
        'n' - integer num layers (number of transform ops selected per image)
        'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
        'mstd' -  float std deviation of magnitude noise applied
        'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
    Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
    'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2
    :param hparams: Other hparams (kwargs) for the RandAugmentation scheme
    :return: A PyTorch compatible Transform
    """
    magnitude = _MAX_LEVEL  # default to _MAX_LEVEL for magnitude (currently 10)
    num_layers = 2  # default to 2 ops per image
    weight_idx = None  # default to no probability weights for op choice
    transforms = _RAND_TRANSFORMS
    config = config_str.split("-")
    assert config[0] == "rand"
    config = config[1:]
    for c in config:
        cs = re.split(r"(\d.*)", c)
        if len(cs) < 2:
            continue
        key, val = cs[:2]
        if key == "mstd":
            # noise param injected via hparams for now
            hparams.setdefault("magnitude_std", float(val))
        elif key == "inc":
            if bool(val):
                transforms = _RAND_INCREASING_TRANSFORMS
        elif key == "m":
            magnitude = int(val)
        elif key == "n":
            num_layers = int(val)
        elif key == "w":
            weight_idx = int(val)
        else:
            assert NotImplementedError
    ra_ops = rand_augment_ops(
        magnitude=magnitude, hparams=hparams, transforms=transforms
    )
    choice_weights = (
        None if weight_idx is None else _select_rand_weights(weight_idx)
    )
    return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)