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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())
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