1、前言

移动端设备也需要既准确又快的小模型。为了满足这些需求，一些轻量级的CNN网络如MobileNet和ShuffleNet被提出，它们在速度和准确度之间做了很好地平衡。ShuffleNetv2是旷视2018年提出的ShuffleNet升级版本，并被ECCV2018收录。

当然也可以修改YOLOv5模型，具体参考= = = = =>YOLOv5改进（四）–轻量化模型ShuffleNetv2

2、ShuffleNetV2代码实现

2.1、创建ShuffleNet类

在ultralytics/nn文件夹中新建ShuffleNet.py文件

import torch
import torch.nn as nn


class Conv_maxpool(nn.Module):
    def __init__(self, c1, c2):  # ch_in, ch_out
        super().__init__()
        self.conv= nn.Sequential(
            nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False),
            nn.BatchNorm2d(c2),
            nn.ReLU(inplace=True),
        )
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)

    def forward(self, x):
        return self.maxpool(self.conv(x))

class ShuffleNetV2(nn.Module):
    def __init__(self, inp, oup, stride):  # ch_in, ch_out, stride
        super().__init__()

        self.stride = stride

        branch_features = oup // 2
        assert (self.stride != 1) or (inp == branch_features << 1)

        if self.stride == 2:
            # copy input
            self.branch1 = nn.Sequential(
                nn.Conv2d(inp, inp, kernel_size=3, stride=self.stride, padding=1, groups=inp),
                nn.BatchNorm2d(inp),
                nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
                nn.BatchNorm2d(branch_features),
                nn.ReLU(inplace=True))
        else:
            self.branch1 = nn.Sequential()

        self.branch2 = nn.Sequential(
            nn.Conv2d(inp if (self.stride == 2) else branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),

            nn.Conv2d(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1, groups=branch_features),
            nn.BatchNorm2d(branch_features),

            nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
            nn.BatchNorm2d(branch_features),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        if self.stride == 1:
            x1, x2 = x.chunk(2, dim=1)
            out = torch.cat((x1, self.branch2(x2)), dim=1)
        else:
            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)

        out = self.channel_shuffle(out, 2)

        return out

    def channel_shuffle(self, x, groups):
        N, C, H, W = x.size()
        out = x.view(N, groups, C // groups, H, W).permute(0, 2, 1, 3, 4).contiguous().view(N, C, H, W)

        return out

2.2、修改tasks.py

修改ultralytics/nn/tasks.py的 parse_model()函数：添加以下代码

elif m in [ShuffleNetV2, Conv_maxpool]:
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(c2 * width, 8)
            args = [c1, c2, *args[1:]]    

2.3、创建shufflenetv2.yaml文件

在ultralytics/yolo/cfg目录下创建shufflenetv2.yaml

#  Ultralytics YOLO 🚀, GPL-3.0 license

# Parameters
nc: 6  # number of classes
depth_multiple: 0.33  # scales module repeats
width_multiple: 0.50  # scales convolution channels

# YOLOv8.0n backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv_maxpool, [24]]    # 0-P2/4
  - [-1, 1, ShuffleNetV2, [116, 2]] # 1-P3/8
  - [-1, 3, ShuffleNetV2, [116, 1]] # 2
  - [-1, 1, ShuffleNetV2, [232, 2]] # 3-P4/16
  - [-1, 7, ShuffleNetV2, [232, 1]] # 4
  - [-1, 1, ShuffleNetV2, [464, 2]] # 5-P5/32
  - [-1, 3, ShuffleNetV2, [464, 1]] # 6
  - [-1, 1, SPPF, [1024, 5]]  # 7

# YOLOv8.0n head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 10

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 2], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 13 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 10], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 7], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)

  - [[13, 16, 19], 1, Detect, [nc]]  # Detect(P3, P4, P5)

2.4、跑通示例

核查是否修改成功，见下图，至此全部修改成功。

3、碰到的问题

File “/public/home/miniconda/envs/yolov8/lib/python3.8/site-packages/ultralytics/nn/tasks.py”, line 855, in parse_model

m = getattr(torch.nn, m[3:]) if “nn.” in m else globals()[m] # get module KeyError: ‘Conv_maxpool’

说明你没有真正修改tasks.py文件，需要你重新将2、ShuffleNetV2代码实现重新弄一遍，注意本次要来到 /public/home/miniconda/envs/yolov8/lib/python3.8/site-packages/ultralytics/nn 创建 ShuffleNet.py文件和修改task.py文件

4、目标检测系列文章

1. YOLOv5s网络模型讲解(一看就会)
2. 生活垃圾数据集（YOLO版）
3. YOLOv5如何训练自己的数据集
4. 双向控制舵机（树莓派版）
5. 树莓派部署YOLOv5目标检测（详细篇）
6. YOLO_Tracking 实践 （环境搭建 & 案例测试）
7. 目标检测：数据集划分 & XML数据集转YOLO标签
8. DeepSort行人车辆识别系统（实现目标检测+跟踪+统计）
9. YOLOv5参数大全(parse_opt篇)
10. YOLOv5改进（一）-- 轻量化YOLOv5s模型
11. YOLOv5改进（二）-- 目标检测优化点（添加小目标头检测）
12. YOLOv5改进（三）-- 引进Focaler-IoU损失函数
13. YOLOv5改进（四）–轻量化模型ShuffleNetv2
14. YOLOv5改进（五）-- 轻量化模型MobileNetv3
15. YOLOv5改进（六）–引入YOLOv8中C2F模块