import torch def calc_mean_std(feat, eps=1e-5): # eps is a small value added to the variance to avoid divide-by-zero. size = feat.size() assert (len(size) == 4) N, C = size[:2] feat_var = feat.view(N, C, -1).var(dim=2) + eps feat_std = feat_var.sqrt().view(N, C, 1, 1) feat_mean = feat.view(N, C, -1).mean(dim=2).view(N, C, 1, 1) return feat_mean, feat_std def adaptive_instance_normalization(content_feat, style_feat): assert (content_feat.size()[:2] == style_feat.size()[:2]) size = content_feat.size() style_mean, style_std = calc_mean_std(style_feat) content_mean, content_std = calc_mean_std(content_feat) 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 assert (feat.size()[0] == 3) assert (isinstance(feat, torch.FloatTensor)) feat_flatten = feat.view(3, -1) mean = feat_flatten.mean(dim=-1, keepdim=True) std = feat_flatten.std(dim=-1, keepdim=True) return feat_flatten, mean, std def _mat_sqrt(x): U, D, V = torch.svd(x) return torch.mm(torch.mm(U, D.pow(0.5).diag()), V.t()) def coral(source, target): # assume both source and target are 3D array (C, H, W) # Note: flatten -> f source_f, source_f_mean, source_f_std = _calc_feat_flatten_mean_std(source) source_f_norm = (source_f - source_f_mean.expand_as( source_f)) / source_f_std.expand_as(source_f) source_f_cov_eye = \ torch.mm(source_f_norm, source_f_norm.t()) + torch.eye(3) target_f, target_f_mean, target_f_std = _calc_feat_flatten_mean_std(target) target_f_norm = (target_f - target_f_mean.expand_as( target_f)) / target_f_std.expand_as(target_f) target_f_cov_eye = \ torch.mm(target_f_norm, target_f_norm.t()) + torch.eye(3) source_f_norm_transfer = torch.mm( _mat_sqrt(target_f_cov_eye), torch.mm(torch.inverse(_mat_sqrt(source_f_cov_eye)), source_f_norm) ) source_f_transfer = source_f_norm_transfer * \ target_f_std.expand_as(source_f_norm) + \ target_f_mean.expand_as(source_f_norm) return source_f_transfer.view(source.size())