【目标检测实验系列】YOLOv5模型改进：引入轻量化多维动态卷积ODConv，减少计算量的同时保持精度稳定或略微上涨！(内含源代码，超详细改进代码流程)

1. 文章主要内容

本篇博客主要涉及轻量化多维动态卷积ODConv，融合到YOLOv5模型中，减少计算量的同时保持精度稳定或略微上涨。（通读本篇博客需要7分钟左右的时间）。

2. 介绍

ODconv沿着空间、输入通道、输出通道以及卷积核空间的核维度学习更丰富的注意力，且采用更少的卷积核，使其在取得更优性能的同时也能降低计算量。
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3. 详细代码改进流程

3.1 ODconv源代码

源代码如下：

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autogradclass ODConv(nn.Sequential):def __init__(self, in_planes, out_planes, kernel_size=3, stride=1, groups=1, norm_layer=nn.BatchNorm2d,reduction=0.0625, kernel_num=1):padding = (kernel_size - 1) // 2super(ODConv, self).__init__(ODConv2d(in_planes, out_planes, kernel_size, stride, padding, groups=groups,reduction=reduction, kernel_num=kernel_num),norm_layer(out_planes),nn.SiLU())class Attention(nn.Module):def __init__(self, in_planes, out_planes, kernel_size,groups=1,reduction=0.0625,kernel_num=4,min_channel=16):super(Attention, self).__init__()attention_channel = max(int(in_planes * reduction), min_channel)self.kernel_size = kernel_sizeself.kernel_num = kernel_numself.temperature = 1.0self.avgpool = nn.AdaptiveAvgPool2d(1)self.fc = nn.Conv2d(in_planes, attention_channel, 1, bias=False)self.bn = nn.BatchNorm2d(attention_channel)self.relu = nn.ReLU(inplace=True)self.channel_fc = nn.Conv2d(attention_channel, in_planes, 1, bias=True)self.func_channel = self.get_channel_attentionif in_planes == groups and in_planes == out_planes:  # depth-wise convolutionself.func_filter = self.skipelse:self.filter_fc = nn.Conv2d(attention_channel, out_planes, 1, bias=True)self.func_filter = self.get_filter_attentionif kernel_size == 1:  # point-wise convolutionself.func_spatial = self.skipelse:self.spatial_fc = nn.Conv2d(attention_channel, kernel_size * kernel_size, 1, bias=True)self.func_spatial = self.get_spatial_attentionif kernel_num == 1:self.func_kernel = self.skipelse:self.kernel_fc = nn.Conv2d(attention_channel, kernel_num, 1, bias=True)self.func_kernel = self.get_kernel_attentionself.bn_1 = nn.LayerNorm([attention_channel,1,1])self._initialize_weights()def _initialize_weights(self):for m in self.modules():if isinstance(m, nn.Conv2d):nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')if m.bias is not None:nn.init.constant_(m.bias, 0)if isinstance(m, nn.BatchNorm2d):nn.init.constant_(m.weight, 1)nn.init.constant_(m.bias, 0)def update_temperature(self, temperature):self.temperature = temperature@staticmethoddef skip(_):return 1.0def get_channel_attention(self, x):channel_attention = torch.sigmoid(self.channel_fc(x).view(x.size(0), -1, 1, 1) / self.temperature)return channel_attentiondef get_filter_attention(self, x):filter_attention = torch.sigmoid(self.filter_fc(x).view(x.size(0), -1, 1, 1) / self.temperature)return filter_attentiondef get_spatial_attention(self, x):spatial_attention = self.spatial_fc(x).view(x.size(0), 1, 1, 1, self.kernel_size, self.kernel_size)spatial_attention = torch.sigmoid(spatial_attention / self.temperature)return spatial_attentiondef get_kernel_attention(self, x):kernel_attention = self.kernel_fc(x).view(x.size(0), -1, 1, 1, 1, 1)kernel_attention = F.softmax(kernel_attention / self.temperature, dim=1)return kernel_attentiondef forward(self, x):x = self.avgpool(x)x = self.fc(x)x = self.bn_1(x)x = self.relu(x)return self.func_channel(x), self.func_filter(x), self.func_spatial(x), self.func_kernel(x)class ODConv2d(nn.Module):def __init__(self,in_planes,out_planes,kernel_size=3,stride=1,padding=0,dilation=1,groups=1,reduction=0.0625,kernel_num=1):super(ODConv2d, self).__init__()self.in_planes = in_planesself.out_planes = out_planesself.kernel_size = kernel_sizeself.stride = strideself.padding = paddingself.dilation = dilationself.groups = groupsself.kernel_num = kernel_numself.attention = Attention(in_planes, out_planes, kernel_size, groups=groups,reduction=reduction, kernel_num=kernel_num)self.weight = nn.Parameter(torch.randn(kernel_num, out_planes, in_planes//groups, kernel_size, kernel_size),requires_grad=True)self._initialize_weights()if self.kernel_size == 1 and self.kernel_num == 1:self._forward_impl = self._forward_impl_pw1xelse:self._forward_impl = self._forward_impl_commondef _initialize_weights(self):for i in range(self.kernel_num):nn.init.kaiming_normal_(self.weight[i], mode='fan_out', nonlinearity='relu')def update_temperature(self, temperature):self.attention.update_temperature(temperature)def _forward_impl_common(self, x):channel_attention, filter_attention, spatial_attention, kernel_attention = self.attention(x)batch_size, in_planes, height, width = x.size()x = x * channel_attentionx = x.reshape(1, -1, height, width)aggregate_weight = spatial_attention * kernel_attention * self.weight.unsqueeze(dim=0)aggregate_weight = torch.sum(aggregate_weight, dim=1).view([-1, self.in_planes // self.groups, self.kernel_size, self.kernel_size])output = F.conv2d(x, weight=aggregate_weight, bias=None, stride=self.stride, padding=self.padding,dilation=self.dilation, groups=self.groups * batch_size)output = output.view(batch_size, self.out_planes, output.size(-2), output.size(-1))output = output * filter_attentionreturn outputdef _forward_impl_pw1x(self, x):channel_attention, filter_attention, spatial_attention, kernel_attention = self.attention(x)x = x * channel_attentionoutput = F.conv2d(x, weight=self.weight.squeeze(dim=0), bias=None, stride=self.stride, padding=self.padding,dilation=self.dilation, groups=self.groups)output = output * filter_attentionreturn outputdef forward(self, x):return self._forward_impl(x)

3.2 建立一个yolov5-odconv.yaml文件

注意到，这里博主直接使用ODConv代替Head P5前一层的标准卷积(事实上可以替换结构中的任意标准卷积), 另外注意nc改为自己数据集的类别数。

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license# Parameters
nc: 10  # 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  小目标- [30,61, 62,45, 59,119]  # P4/16 中目标- [116,90, 156,198, 373,326]  # P5/32  大目标# YOLOv5 v6.0 backbone
backbone:# [from, number, module, args][[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2  output_channel, kernel_size, stride, padding[-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]# 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, 14], 1, Concat, [1]],  # cat head P4[-1, 3, C3, [512, False]],  # 20 (P4/16-medium)[-1, 1, ODConv, [512, 3, 2]],[[-1, 10], 1, Concat, [1]],  # cat head P5[-1, 3, C3, [1024, False]],  # 23 (P5/32-large)[[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)]