7f99fd7e1ea3f3a8b956232234b336ef410e6f91,function.py,,adaptive_instance_normalization,#Any#Any#,4

Before Change

    style_std = style_feat.std(dim=2).view(N, C, 1, 1).expand(size)
    style_mean = style_feat.mean(dim=2).view(N, C, 1, 1).expand(size)
    content_feat_3d = content_feat.view(N, C, -1)
    content_std = content_feat_3d.std(dim=2).view(N, C, , 1).expand(size)
    content_mean = content_feat_3d.mean(dim=2).view(N, C, 1, 1).expand(size)
    normalized_feat = (content_feat - content_mean) / content_std
    return normalized_feat * style_std + style_mean

After Change

    content_mean = content_feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
    normalized_feat = (content_feat - content_mean.expand(
        size)) / content_std.expand(size)
    return normalized_feat * style_std.expand(size) + style_mean.expand(size)


def calc_feat_flatten_mean_std(feat):
    // takes 3D feat (C, H, W), return mean and std of array within channels

Instances