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(ICCV 2021)Crossnorm-Selfnorm(领域泛化).py
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(ICCV 2021)Crossnorm-Selfnorm(领域泛化).py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import functools
import numpy as np
# 论文：CrossNorm and SelfNorm for Generalization under Distribution Shifts
# 论文地址；https://arxiv.org/pdf/2102.02811
def calc_ins_mean_std(x, eps=1e-5):
"""extract feature map statistics"""
# eps is a small value added to the variance to avoid divide-by-zero.
size = x.size()
assert (len(size) == 4)
N, C = size[:2]
var = x.contiguous().view(N, C, -1).var(dim=2) + eps
std = var.sqrt().view(N, C, 1, 1)
mean = x.contiguous().view(N, C, -1).mean(dim=2).view(N, C, 1, 1)
return mean, std
def instance_norm_mix(content_feat, style_feat):
"""replace content statistics with style statistics"""
assert (content_feat.size()[:2] == style_feat.size()[:2])
size = content_feat.size()
style_mean, style_std = calc_ins_mean_std(style_feat)
content_mean, content_std = calc_ins_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 cn_rand_bbox(size, beta, bbx_thres):
"""sample a bounding box for cropping."""
W = size[2]
H = size[3]
while True:
ratio = np.random.beta(beta, beta)
cut_rat = np.sqrt(ratio)
cut_w = np.int(W * cut_rat)
cut_h = np.int(H * cut_rat)
# uniform
cx = np.random.randint(W)
cy = np.random.randint(H)
bbx1 = np.clip(cx - cut_w // 2, 0, W)
bby1 = np.clip(cy - cut_h // 2, 0, H)
bbx2 = np.clip(cx + cut_w // 2, 0, W)
bby2 = np.clip(cy + cut_h // 2, 0, H)
ratio = float(bbx2 - bbx1) * (bby2 - bby1) / (W * H)
if ratio > bbx_thres:
break
return bbx1, bby1, bbx2, bby2
def cn_op_2ins_space_chan(x, crop='neither', beta=1, bbx_thres=0.1, lam=None, chan=False):
"""2-instance crossnorm with cropping."""
assert crop in ['neither', 'style', 'content', 'both']
ins_idxs = torch.randperm(x.size()[0]).to(x.device)
if crop in ['style', 'both']:
bbx3, bby3, bbx4, bby4 = cn_rand_bbox(x.size(), beta=beta, bbx_thres=bbx_thres)
x2 = x[ins_idxs, :, bbx3:bbx4, bby3:bby4]
else:
x2 = x[ins_idxs]
if chan:
chan_idxs = torch.randperm(x.size()[1]).to(x.device)
x2 = x2[:, chan_idxs, :, :]
if crop in ['content', 'both']:
x_aug = torch.zeros_like(x)
bbx1, bby1, bbx2, bby2 = cn_rand_bbox(x.size(), beta=beta, bbx_thres=bbx_thres)
x_aug[:, :, bbx1:bbx2, bby1:bby2] = instance_norm_mix(content_feat=x[:, :, bbx1:bbx2, bby1:bby2],
style_feat=x2)
mask = torch.ones_like(x, requires_grad=False)
mask[:, :, bbx1:bbx2, bby1:bby2] = 0.
x_aug = x * mask + x_aug
else:
x_aug = instance_norm_mix(content_feat=x, style_feat=x2)
if lam is not None:
x = x * lam + x_aug * (1-lam)
else:
x = x_aug
return x
class CrossNorm(nn.Module):
"""CrossNorm module"""
def __init__(self, crop=None, beta=None):
super(CrossNorm, self).__init__()
self.active = False
self.cn_op = functools.partial(cn_op_2ins_space_chan,
crop=crop, beta=beta)
def forward(self, x):
if self.training and self.active:
x = self.cn_op(x)
self.active = False
return x
class SelfNorm(nn.Module):
"""SelfNorm module"""
def __init__(self, chan_num, is_two=False):
super(SelfNorm, self).__init__()
# channel-wise fully connected layer
self.g_fc = nn.Conv1d(chan_num, chan_num, kernel_size=2,
bias=False, groups=chan_num)
self.g_bn = nn.BatchNorm1d(chan_num)
if is_two is True:
self.f_fc = nn.Conv1d(chan_num, chan_num, kernel_size=2,
bias=False, groups=chan_num)
self.f_bn = nn.BatchNorm1d(chan_num)
else:
self.f_fc = None
def forward(self, x):
b, c, _, _ = x.size()
mean, std = calc_ins_mean_std(x, eps=1e-12)
statistics = torch.cat((mean.squeeze(3), std.squeeze(3)), -1)
g_y = self.g_fc(statistics)
g_y = self.g_bn(g_y)
g_y = torch.sigmoid(g_y)
g_y = g_y.view(b, c, 1, 1)
if self.f_fc is not None:
f_y = self.f_fc(statistics)
f_y = self.f_bn(f_y)
f_y = torch.sigmoid(f_y)
f_y = f_y.view(b, c, 1, 1)
return x * g_y.expand_as(x) + mean.expand_as(x) * (f_y.expand_as(x)-g_y.expand_as(x))
else:
return x * g_y.expand_as(x)
class CNSN(nn.Module):
"""A module to combine CrossNorm and SelfNorm"""
def __init__(self, crossnorm, selfnorm):
super(CNSN, self).__init__()
self.crossnorm = crossnorm
self.selfnorm = selfnorm
def forward(self, x):
if self.crossnorm and self.crossnorm.active:
x = self.crossnorm(x)
if self.selfnorm:
x = self.selfnorm(x)
return x
if __name__ == '__main__':
# block = CrossNorm()
# block = SelfNorm(chan_num=3)
# 创建 CrossNorm 和 SelfNorm 的实例
crossnorm = CrossNorm()
selfnorm = SelfNorm(chan_num=3)
block = CNSN(crossnorm, selfnorm)
input = torch.rand(32, 3, 224, 224)
output = block(input)
print(input.size())
print(output.size())