Arbitrary Style Transfer in Real
2019-07-22 本文已影响0人
Cat丹
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目标：实时任意风格转移
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方法：adaptive instance normalization
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原理：图像的风格就是特征图各个feature channel跨空间的统计信息，比如mean和variance。迁移各个channel的mean和variance就可以实现风格迁移。
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效果：可实时实现任意风格图片转移，并且可以控制content-style trade-off,style interpolation,color,spatial
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code片段：
def adaptive_instance_normalization(content_feat, style_feat):
assert (content_feat.data.size()[:2] == style_feat.data.size()[:2])
size = content_feat.data.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 style_transfer(vgg, decoder, content, style, alpha=1.0):
assert (0.0 <= alpha <= 1.0)
content_f = vgg(content)
style_f = vgg(style)
feat = adaptive_instance_normalization(content_f, style_f)
feat = feat * alpha + content_f * (1 - alpha)
return decoder(feat)
network.png
demo.png
