1，本文介绍
本文将利用YOLOv9的ADown模块改进卷积。
关于ADown的详细介绍可以看论文：https://arxiv.org/abs/2402.13616
本文将讲解如何将ADown融合进yolov8
话不多说，上代码！
2， 将ADown融合进yolov8
2.1 步骤一
找到如下的目录'ultralytics/nn/modules'，然后在这个目录下创建一个ADown.py文件，文件名字可以根据你自己的习惯起，然后将ADown的核心代码复制进去。
import torch
import torch.nn as nn__all__ = ['ADown']def autopad(k, p=None, d=1): # kernel, padding, dilation# Pad to 'same' shape outputsif d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-padreturn pclass Conv(nn.Module):# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)default_act = nn.SiLU() # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):return self.act(self.conv(x))class ADown(nn.Module):def __init__(self, c1, c2): # ch_in, ch_out, shortcut, kernels, groups, expandsuper().__init__()self.c = c2 // 2self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)def forward(self, x):x = torch.nn.functional.avg_pool2d(x, 2, 1, 0, False, True)x1,x2 = x.chunk(2, 1)x1 = self.cv1(x1)x2 = torch.nn.functional.max_pool2d(x2, 3, 2, 1)x2 = self.cv2(x2)return torch.cat((x1, x2), 1)def autopad(k, p=None, d=1): # kernel, padding, dilation"""Pad to 'same' shape outputs."""if d > 1:k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-sizeif p is None:p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-padreturn pclass Conv(nn.Module):"""Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)."""default_act = nn.SiLU() # default activationdef __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):"""Initialize Conv layer with given arguments including activation."""super().__init__()self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)self.bn = nn.BatchNorm2d(c2)self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()def forward(self, x):"""Apply convolution, batch normalization and activation to input tensor."""return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):"""Perform transposed convolution of 2D data."""return self.act(self.conv(x))2.2 步骤二
在task.py导入我们的模块
from .modules.ADown import Adown2.3 步骤三
在task.py的parse_model方法里面注册我们的模块
到此注册成功,复制后面的yaml文件直接运行即可
yaml文件
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPss: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPsm: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPsl: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2- [-1, 1, ADown, [128]] # 1-P2/4- [-1, 3, C2f, [128, True]]- [-1, 1, ADown, [256]] # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, ADown, [512]] # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, ADown, [1024]] # 7-P5/32- [-1, 3, C2f, [1024, True]]- [-1, 1, SPPF, [1024, 5]] # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]] # cat backbone P4- [-1, 3, C2f, [512]] # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]] # cat backbone P3- [-1, 3, C2f, [256]] # 15 (P3/8-small)- [-1, 1, ADown, [256]]- [[-1, 12], 1, Concat, [1]] # cat head P4- [-1, 3, C2f, [512]] # 18 (P4/16-medium)- [-1, 1, ADown, [512]]- [[-1, 9], 1, Concat, [1]] # cat head P5- [-1, 3, C2f, [1024]] # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)# 关于ADown的位置大家可以自行调换，位置不同结果不同
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