YOLOv9独家改进|使用HWD(小波下采样)模块改进ADown-编程知识

YOLOv9独家改进|使用HWD(小波下采样)模块改进ADown

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、改进点介绍

HWD是一种下采样模型，应用了小波变换的方法。

ADown是YOLOv9中的下采样模块，对不同的数据场景具有一定的可学习能力。

二、HWD-ADown模块详解

2.1 模块简介

HWD-ADown的主要思想：使用HWD替换ADown中的Conv模块。

三、 HWD-ADown模块使用教程

3.1 HWD-ADown模块的代码

try:from mmcv.cnn import build_activation_layer, build_norm_layerfrom mmcv.ops.modulated_deform_conv import ModulatedDeformConv2dfrom mmengine.model import constant_init, normal_init
except ImportError as e:pass"""
论文地址：https://arxiv.org/pdf/2208.03641v1.pdf
"""class HWD_ADown(nn.Module):def __init__(self, c1, c2):  # ch_in, ch_out, shortcut, kernels, groups, expandsuper().__init__()self.c = c2 // 2# self.cv1 = Conv(c1 // 2, self.c, 3, 2, 1)self.cv1 = HWD(c1 // 2, self.c, 3, 1, 1)self.cv2 = Conv(c1 // 2, self.c, 1, 1, 0)def forward(self, x):x = 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)class HWD(nn.Module):def __init__(self, in_ch, out_ch, k, s, p):super(HWD, self).__init__()from pytorch_wavelets import DWTForwardself.wt = DWTForward(J=1, mode='zero', wave='haar')self.conv = Conv(in_ch * 4, out_ch, k, s, p)def forward(self, x):yL, yH = self.wt(x)y_HL = yH[0][:, :, 0, ::]y_LH = yH[0][:, :, 1, ::]y_HH = yH[0][:, :, 2, ::]x = torch.cat([yL, y_HL, y_LH, y_HH], dim=1)x = self.conv(x)return x

3.2 在YOlO v9中的添加教程

阅读YOLOv9添加模块教程或使用下文操作

1. 将YOLOv9工程中models下common.py文件中的最下行增加模块的代码。

2. 将YOLOv9工程中models下yolo.py文件中的第681行（可能因版本变化而变化）增加以下代码。

            RepNCSPELAN4, SPPELAN, HWD_ADown}:

3.3 运行配置文件

# YOLOv9
# Powered bu https://blog.csdn.net/StopAndGoyyy# parameters
nc: 80  # number of classes
#depth_multiple: 0.33  # model depth multiple
depth_multiple: 1  # model depth multiple
#width_multiple: 0.25  # layer channel multiple
width_multiple: 1  # 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, HWD_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# detection head# detect[[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)]