零基础教程：Yolov5模型改进-添加13种注意力机制

1.准备工作

先给出13种注意力机制的下载地址：

https://github.com/z1069614715/objectdetection_script

2.加入注意力机制

1.以添加SimAM注意力机制为例（不需要接收通道数的注意力机制）

1.在models文件下新建py文件，取名叫SimAM.py

将以下代码复制到SimAM.py文件种

import torch
import torch.nn as nnclass SimAM(torch.nn.Module):# 不需要接收通道数输入def __init__(self, e_lambda=1e-4):super(SimAM, self).__init__()self.activaton = nn.Sigmoid()self.e_lambda = e_lambdadef __repr__(self):s = self.__class__.__name__ + '('s += ('lambda=%f)' % self.e_lambda)return s@staticmethoddef get_module_name():return "simam"def forward(self, x):b, c, h, w = x.size()n = w * h - 1x_minus_mu_square = (x - x.mean(dim=[2, 3], keepdim=True)).pow(2)y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2, 3], keepdim=True) / n + self.e_lambda)) + 0.5return x * self.activaton(y)

2.在yolo.py头部导入SimAM这个类

3.然后复制yolov5s.yaml到同级目录，取名为yolov5s-SimAM.yaml

在某一层添加注意力机制

[from,number,module,args]

注意：！！！！！！！！！！！！！！！！！！！

添加完一层注意力机制之后，会对后面层数造成影响，记得在检测头那里要改层数

2.添加SE注意力机制（需要接收通道数的注意力机制）

1.新建SE.py

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()y = self.avg_pool(x).view(b, c)y = self.fc(y).view(b, c, 1, 1)return x * y.expand_as(x)

2.修改yolo.py

添加这两行代码

        elif m is SEAttention:args = [ch[f]]

3.models下新建yolov5s-SE.yaml

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license# Parameters
nc: 80  # number of classes  coco数据集的种类
depth_multiple: 0.33  # model depth multiple  用来控制模型的大小  与每一层的number相乘再取整
width_multiple: 0.50  # layer channel multiple  与每一层的channel相乘 例如64*0.5、128*0.5
# anchors指的是我们使用的anchor的大小，anchor分为3组，每组3个
anchors:- [10,13, 16,30, 33,23]  # P3/8 第一组anchor作用在feature,feature大小是原图的1/8的stride大小。anchor比较小。因为是浅层的特征，感受野比较小。- [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]  args:参数 arg是argument（参数）的缩写，是每一层输出的一个参数[[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2  arguments 输出通道数为64（也是卷积核的个数），Conv卷积核的大小为6*6 stride=2 padding=2 此时特征图大小为原图的1/2[-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  对于SPP(不同尺度特征层的融合)的改进-SPPF]# YOLOv5 v6.0 head  bottleneck（除了检测以外的部分）+detect 瓶颈+检测
head:[[-1, 1, Conv, [512, 1, 1]],[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 6], 1, Concat, [1]],  # cat backbone P4[-1, 1,SEAttention, [16]],      # ----------这一层添加了SEAttention注意力机制,此注意力的通道数512也不用写在这里，[]里面写除了通道数以外的其他参数：reduction=16[-1, 3, C3, [512, False]],  # 14 -------从原来的13层改成14层[-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, 15], 1, Concat, [1]],  # cat head P4   ------这里从原来的14改成15[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5  ------注意力机制加在10层之后，所以不会对第10层有影响[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5) ----从原来的17,20,23改成18,21,24]