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import torch
import torch.nn.functional as F
# The code was inspired from https://github.com/naoto0804/pytorch-AdaIN.
def calc_mean_std(feat, eps=1e-5, mask=None):
# eps is a small value added to the variance to avoid divide-by-zero.
size = feat.size()
assert (len(size) == 4)
if mask is not None:
msize = mask.size()
assert (msize[0] == size[0] and msize[2] == size[2] and msize[3] == size[3])
N, C = size[:2]
if mask is not None:
cnt = mask.view(N, 1, -1).sum(2)
mf = mask * feat
mf = mf.view(N, C, -1).sum(2)
mf /= cnt
feat_mean = mf.view(N, C, 1, 1)
vf = ((feat - feat_mean) ** 2) * mask
vf = vf.view(N, C, -1).sum(2)
vf = vf / (cnt - 1)
vf += eps
feat_std = vf.sqrt().view(N, C, 1, 1)
else:
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, c_mask=None, s_mask=None):
assert (content_feat.size()[:2] == style_feat.size()[:2])
size = content_feat.size()
H, W = size[2], size[3]
msk = None
if c_mask is not None:
msk = F.interpolate(c_mask, (H, W))
s_size = style_feat.size()
s_H, s_W = s_size[2], s_size[3]
s_msk = None
if s_mask is not None:
s_msk = F.interpolate(s_mask, (s_H, s_W))
style_mean, style_std = calc_mean_std(style_feat, mask=s_msk)
content_mean, content_std = calc_mean_std(content_feat, mask=msk)
normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(size)
if msk is not None:
return (normalized_feat * style_std.expand(size) + style_mean.expand(size)) * msk + content_feat * (1 - msk)
else:
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())
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