- 论文地址：SA-Net: Shuffle Attention for Deep Convolutional Neural Networks
- 开源代码：https://github.com/wofmanaf/SA-Net
当前的 CNN 中的 attention 机制主要包括：channel attention 和 spatial attention，当前一些方法（GCNet 、CBAM 等）通常将二者集成，容易产生 converging difficulty 和 heavy computation burden 的问题。尽管 ECANet 和 SGE 提出了一些优化方案，但没有充分利用 channel 和 spatial 之间的关系。因此，作者提出一个问题 “ Can one fuse different attention modules in a lighter but more efficient way? ”
为解决这个问题，作者提出了 shuffle attention，整体框架如下图所示。可以看出首先将输入的特征分为
g
g
g组，然后每一组的特征进行split，分成两个分支，分别计算 channel attention 和 spatial attention，两种 attention 都使用全连接 + sigmoid 的方法计算。接着，两个分支的结果拼接到一起，然后合并，得到和输入尺寸一致的 feature map。 最后，用一个 shuffle 层进行处理。
1. 增加ShuffleAttention.yaml文件
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]
# YOLOAir v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[-1, 1, ShuffleAttention, [1024]], # 修改
[[17, 20, 24], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
2. common.py配置
在./models/common.py文件中增加以下模块代码
文章来源：https://www.toymoban.com/news/detail-426105.html
import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter
# https://arxiv.org/pdf/2102.00240.pdf
class ShuffleAttention(nn.Module):
def __init__(self, channel=512,reduction=16,G=8):
super().__init__()
self.G=G
self.channel=channel
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.gn = nn.GroupNorm(channel // (2 * G), channel // (2 * G))
self.cweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
self.cbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
self.sweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
self.sbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
self.sigmoid=nn.Sigmoid()
def init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, mode='fan_out')
if m.bias is not None:
init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
init.constant_(m.weight, 1)
init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
init.normal_(m.weight, std=0.001)
if m.bias is not None:
init.constant_(m.bias, 0)
@staticmethod
def channel_shuffle(x, groups):
b, c, h, w = x.shape
x = x.reshape(b, groups, -1, h, w)
x = x.permute(0, 2, 1, 3, 4)
# flatten
x = x.reshape(b, -1, h, w)
return x
def forward(self, x):
b, c, h, w = x.size()
#group into subfeatures
x=x.view(b*self.G,-1,h,w) #bs*G,c//G,h,w
#channel_split
x_0,x_1=x.chunk(2,dim=1) #bs*G,c//(2*G),h,w
#channel attention
x_channel=self.avg_pool(x_0) #bs*G,c//(2*G),1,1
x_channel=self.cweight*x_channel+self.cbias #bs*G,c//(2*G),1,1
x_channel=x_0*self.sigmoid(x_channel)
#spatial attention
x_spatial=self.gn(x_1) #bs*G,c//(2*G),h,w
x_spatial=self.sweight*x_spatial+self.sbias #bs*G,c//(2*G),h,w
x_spatial=x_1*self.(x_spatial) #bs*G,c//(2*G),h,w
# concatenate along channel axis
out=torch.cat([x_channel,x_spatial],dim=1) #bs*G,c//G,h,w
out=out.contiguous().view(b,-1,h,w)
# channel shuffle
out = self.channel_shuffle(out, 2)
return out
3. yolo.py配置
找到models/yolo.py文件中parse_model()函数的for i, (f, n, m, args) in enumerate(d['backbone'] + d['head'])（258行上下）并其循环内添加如下代码。文章来源地址https://www.toymoban.com/news/detail-426105.html
elif m is ShuffleAttention:
c1, c2 = ch[f], args[0]
if c2 != no:
c2 = make_divisible(c2 * gw, 8)
4. 训练模型
python train.py --cfg ShuffleAttention.yaml
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