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| 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 calc_mean_std1(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) | |
| WH,N, C = size | |
| feat_var = feat.var(dim=0) + eps | |
| feat_std = feat_var.sqrt() | |
| feat_mean = feat.mean(dim=0) | |
| return feat_mean, feat_std | |
| def normal(feat, eps=1e-5): | |
| feat_mean, feat_std= calc_mean_std(feat, eps) | |
| normalized=(feat-feat_mean)/feat_std | |
| return normalized | |
| def normal_style(feat, eps=1e-5): | |
| feat_mean, feat_std= calc_mean_std1(feat, eps) | |
| normalized=(feat-feat_mean)/feat_std | |
| return normalized | |
| 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()) | |