改进YOLOv5:添加EMA注意力机制

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前言

本文主要介绍一种在YOLOv5-7.0中添加EMA注意力机制的方法。EMA注意力机制原论文地址，有关EMA注意力机制的解读可参考文章。

新建EMA.py文件

在yolov5的models文件中新建一个名为EMA.py文件，将下述代码复制到EMA.py文件中并保存。

import torch
from torch import nnclass EMA(nn.Module):def __init__(self, channels, factor=8):super(EMA, self).__init__()self.groups = factorassert channels // self.groups > 0self.softmax = nn.Softmax(-1)self.agp = nn.AdaptiveAvgPool2d((1, 1))self.pool_h = nn.AdaptiveAvgPool2d((None, 1))self.pool_w = nn.AdaptiveAvgPool2d((1, None))self.gn = nn.GroupNorm(channels // self.groups, channels // self.groups)self.conv1x1 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=1, stride=1, padding=0)self.conv3x3 = nn.Conv2d(channels // self.groups, channels // self.groups, kernel_size=3, stride=1, padding=1)def forward(self, x):b, c, h, w = x.size()group_x = x.reshape(b * self.groups, -1, h, w)  # b*g,c//g,h,wx_h = self.pool_h(group_x)x_w = self.pool_w(group_x).permute(0, 1, 3, 2)hw = self.conv1x1(torch.cat([x_h, x_w], dim=2))x_h, x_w = torch.split(hw, [h, w], dim=2)x1 = self.gn(group_x * x_h.sigmoid() * x_w.permute(0, 1, 3, 2).sigmoid())x2 = self.conv3x3(group_x)x11 = self.softmax(self.agp(x1).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x12 = x2.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hwx21 = self.softmax(self.agp(x2).reshape(b * self.groups, -1, 1).permute(0, 2, 1))x22 = x1.reshape(b * self.groups, c // self.groups, -1)  # b*g, c//g, hwweights = (torch.matmul(x11, x12) + torch.matmul(x21, x22)).reshape(b * self.groups, 1, h, w)return (group_x * weights.sigmoid()).reshape(b, c, h, w)

修改yolo.py文件

1.导入EMA.py

在yolo.py文件开头导入EMA.py，代码如下：

from models.EMA import EMA

代码放在yolo.py位置如下图所示：

2.修改parse_model

这里主要是添加通道参数，再添加一个elif，把EMA添加进去，代码如下：

 elif m is EMA:   args = [ch[f], *args]

添加上述代码的位置可参考下图：

修改yaml文件（yolov5s为例）

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# 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, EMA, [8]],[-1, 1, SPPF, [1024, 5]],  # 9]# 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, 15], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, Conv, [512, 3, 2]],[[-1, 11], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[18, 21, 24], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]

上述代码将EMA注意力机制模块加在backbone层中最后C3模块后面，SPPF模块前面，仅供参考，具体添加位置要根据个人数据集的不同合理的添加。

[-1, 1, EMA, [8]], #-1代表连接上一层通道数，1是个数，8是EMA所需的参数（factor=8）

说明：因为在yolo.py文件parse_model函数中修改了通道参数，因此在yaml文件中无需添加通道参数，只需添加EMA函数所需的其他参数。在backbone中添加一层注意力机制模块，因此后续的层数都要加一，在head层中做如下改动。

[[-1, 15], 1, Concat, [1]],  #未改动前的第14层，在经过上述改动后改为15
[[-1, 11], 1, Concat, [1]],  #未改动前的第10层，在记过上述改动后改为11
[[18, 21, 24], 1, Detect, [nc, anchors]],  #17，20，23层改为18，21，24

运行train.py文件可以在输出终端窗口看到上图网络结构，可以看到在第9层已经成功添加EMA注意力机制模块。

                from  n    params  module                                  arguments                     0                -1  1      3520  models.common.Conv                      [3, 32, 6, 2, 2]              1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                2                -1  1     18816  models.common.C3                        [64, 64, 1]                   3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               4                -1  2    115712  models.common.C3                        [128, 128, 2]                 5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              6                -1  3    625152  models.common.C3                        [256, 256, 3]                 7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              8                -1  1   1182720  models.common.C3                        [512, 512, 1]                 9                -1  1     41216  models.EMA.EMA                          [512, 8]                      10                -1  1    656896  models.common.SPPF                      [512, 512, 5]                 11                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              12                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          13           [-1, 6]  1         0  models.common.Concat                    [1]                           14                -1  1    361984  models.common.C3                        [512, 256, 1, False]          15                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              16                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          17           [-1, 4]  1         0  models.common.Concat                    [1]                           18                -1  1     90880  models.common.C3                        [256, 128, 1, False]          19                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              20          [-1, 15]  1         0  models.common.Concat                    [1]                           21                -1  1    296448  models.common.C3                        [256, 256, 1, False]          22                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              23          [-1, 11]  1         0  models.common.Concat                    [1]                           24                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          25      [18, 21, 24]  1     16182  models.yolo.Detect                      [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
YOLOv5sEMA summary: 222 layers, 7063542 parameters, 7063542 gradients, 16.2 GFLOPs

参考

https://www.bilibili.com/video/BV1s84y1775U/?spm_id_from=333.788&vd_source=f83457e2adc10b543ae4c742fba1e3b2
https://blog.csdn.net/hhhhhhhhhhwwwwwwwwww/article/details/131347981