1. SE注意力机制论文

论文题目：Squeeze-and-Excitation Networks
论文链接：Squeeze-and-Excitation Networks

２. SE注意力机制原理

３. SE注意力机制的配置

３.１common.py配置

./models/common.py文件增加以下模块

在最后添加如下代码块：

import numpy as np
import torch
from torch import nn
from torch.nn import initclass SEAttention(nn.Module):def __init__(self, channel=512,reduction=16):super().__init__()self.avg_pool = nn.AdaptiveAvgPool2d(1)self.fc = nn.Sequential(nn.Linear(channel, channel // reduction, bias=False),nn.ReLU(inplace=True),nn.Linear(channel // reduction, channel, bias=False),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)def forward(self, x):b, c, _, _ = x.size()# # AIEAGNYy = self.avg_pool(x).view(b, c)y = self.fc(y).view(b, c, 1, 1)return x * y.expand_as(x)

３.２yolo.py配置

在 models/yolo.py文件夹下加入SEAttention

首先定位到parse_model函数中
for i, (f, n, m, args) in enumerate(d[‘backbone’] + d[‘head’])模块：

插入如下代码：

elif m is SEAttention:c1, c2 = ch[f], args[0]if c2 != no:c2 = make_divisible(c2 * gw, 8)

如图为：

３.３yaml文件配置

这里以YOLOv5的6.0版本为例：
可以在骨架网络当中添加：

# YOLOv5 v6.0 backbone
backbone:[[-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, SEAttention,[1024]], #add  SEAttention[-1, 1, SPPF, [1024, 5]],  # 9]

可以在head当中添加：

# YOLOv5 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, SEAttention, [1024]],[[17, 20, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

添加的位置可以任意，就像搭积木一样，但是要注意通道数是否正确。

持续更新🔥🔥🔥