YOLOv9中加入SCConv模块！
专栏介绍：YOLOv9改进系列 | 包含深度学习最新创新，主力高效涨点！！！
一、本文介绍
本文将一步步演示如何在YOLOv9中添加 / 替换新模块，寻找模型上的创新！
适用检测目标： YOLOv9模块通用改进
二、改进步骤
《YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information》
论文地址： https://arxiv.org/abs/2402.13616
代码地址： https://github.com/WongKinYiu/yolov9
2.1 创建一个脚本存放新模块
为方便调用，这里我将脚本放在models包下，命名为extra.py。
2.2 将模块复制到脚本中，并导入需要的包（以SCConv为例）
我们将SCConv的代码复制到刚刚创建的extra.py脚本中。
import torch
import torch.nn as nn
import torch.nn.functional as F
from models.common import Conv
class SCConv(nn.Module):
"""https://github.com/MCG-NKU/SCNet/blob/master/scnet.py"""
def __init__(self, inplanes, planes, stride=1, padding=1, dilation=1, groups=1, pooling_r=4):
super(SCConv, self).__init__()
self.k2 = nn.Sequential(
nn.AvgPool2d(kernel_size=pooling_r, stride=pooling_r),
Conv(inplanes, planes, k=3, s=1, p=padding, d=dilation, g=groups, act=False))
self.k3 = Conv(inplanes, planes, k=3, s=1, p=padding, d=dilation, g=groups, act=False)
self.k4 = Conv(inplanes, planes, k=3, s=1, p=padding, d=dilation, g=groups, act=False)
def forward(self, x):
identity = x
out = torch.sigmoid(torch.add(identity, F.interpolate(self.k2(x), identity.size()[2:]))) # sigmoid(identity + k2)
out = torch.mul(self.k3(x), out) # k3 * sigmoid(identity + k2)
out = self.k4(out) # k4
return out
2.3 对yolo.py操作
打开models包下的yolo.py文件夹，将刚才创建的脚本导入。并在下方第700行的位置(位置可能因v9版本更新变动)加入下方代码。
2.4 运行配置文件
创建模型配置文件（yaml文件），将我们所作改进加入到配置文件中（这一步的配置文件可以复制models - > detect 下的yaml修改。）。对YOLO系列yaml文件不熟悉的同学可以看我往期的yaml详解教学！
# YOLOv9
# parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.25 # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()
# anchors
anchors: 3
# YOLOv9 backbone
backbone:
[
[-1, 1, Silence, []],
# conv down
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 3
# avg-conv down
[-1, 1, ADown, [256]], # 4-P3/8
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 5
# avg-conv down
[-1, 1, ADown, [512]], # 6-P4/16
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 7
# avg-conv down
[-1, 1, ADown, [512]], # 8-P5/32
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 9
]
# YOLOv9 head
head:
[
# elan-spp block
[-1, 1, SPPELAN, [512, 256]], # 10
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 7], 1, Concat, [1]], # cat backbone P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 13
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 5], 1, Concat, [1]], # cat backbone P3
# elan-2 block
[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 16 (P3/8-small)
# avg-conv-down merge
[-1, 1, ADown, [256]],
[[-1, 13], 1, Concat, [1]], # cat head P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 19 (P4/16-medium)
# avg-conv-down merge
[-1, 1, ADown, [512]],
[[-1, 10], 1, Concat, [1]], # cat head P5
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 22 (P5/32-large)
# multi-level reversible auxiliary branch
# routing
[5, 1, CBLinear, [[256]]], # 23
[7, 1, CBLinear, [[256, 512]]], # 24
[9, 1, CBLinear, [[256, 512, 512]]], # 25
# conv down
[0, 1, Conv, [64, 3, 2]], # 26-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 27-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 28
# avg-conv down fuse
[-1, 1, ADown, [256]], # 29-P3/8
[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 31
# avg-conv down fuse
[-1, 1, ADown, [512]], # 32-P4/16
[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 34
# avg-conv down fuse
[-1, 1, ADown, [512]], # 35-P5/32
[[25, -1], 1, CBFuse, [[2]]], # 36
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 37
[-1, 1, SCConv, []], # 38
# detection head
# detect
[[31, 34, 38, 16, 19, 22], 1, DualDDetect, [nc]], # DualDDetect(A3, A4, A5, P3, P4, P5)
]
3.4 训练过程
最后，复制我们创建的模型配置，填入训练脚本（train_dual）中（不会训练的同学可以参考我之前的文章。），运行即可。
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原文地址：https://blog.csdn.net/StopAndGoyyy/article/details/136390378
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