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

Before Change

    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


def group_normalization(x, beta, gamma, num_groups, channel_axis=1, batch_axis=0, eps=1e-05, output_stat=False):

After Change

    adapt_shape[channel_axis] = x.shape[channel_axis]
    adapt_shape = tuple(adapt_shape)

    if beta is not None and beta.shape != adapt_shape:
        assert beta.shape[channel_axis] == adapt_shape[channel_axis],\
            "channel size of beta: {} != channel size of x ({}).".format(beta.shape[channel_axis],
                                                                         adapt_shape[channel_axis])
        beta = broadcast(beta, shape=adapt_shape)

    if gamma is not None and gamma.shape != adapt_shape:
        assert gamma.shape[channel_axis] == adapt_shape[channel_axis], \
            "channel size of gamma: {} != channel size of x ({}).".format(gamma.shape[channel_axis],
                                                                          adapt_shape[channel_axis])

Instances