75e3f5e7892de6a0d86c0aa1bb9313b01d3ad485,python/src/nnabla/normalization_functions.py,,instance_normalization,#Any#Any#Any#Any#Any#Any#Any#,425

Before Change

    // check batch axis
    batch_axis = _check_batch_axis_and_force_list(len(x.shape), batch_axis)

    axes = _get_axes_excluding(len(x.shape), [channel_axis, ] + batch_axis)

    if output_stat:
        out, mean, std = tensor_normalization(x, axes, eps, output_stat)

        return out * gamma + beta, mean, std

    return tensor_normalization(x, axes, eps, output_stat) * gamma + beta

After Change

    // Unlike layer_norm and group_norm, only instance_norm can use bn scale bias & scale adaptation
    // by broadcasting channel axis to channel * batch axis. (like [1, C, 1, 1] -> [N, C, 1, 1])

    adapt_shape = [1 for _ in range(len(x.shape))]
    for baxis in batch_axis:
        adapt_shape[baxis] = x.shape[baxis]
    adapt_shape[channel_axis] = x.shape[channel_axis]

Instances