【YOLO改进】主干插入ShuffleAttention模块(基于MMYOLO)

ShuffleAttention模块

论文链接:https://arxiv.org/abs/2102.00240

将ShuffleAttention模块添加到MMYOLO中

1. 将开源代码ShuffleAttention.py文件复制到mmyolo/models/plugins目录下

2. 导入MMYOLO用于注册模块的包: from mmyolo.registry import MODELS

3. 确保 class ShuffleAttention中的输入维度为in_channels(因为MMYOLO会提前传入输入维度参数，所以要保持参数名的一致)

4. 利用@MODELS.register_module()将“class ShuffleAttention(nn.Module)”注册:

5. 修改mmyolo/models/plugins/__init__.py文件

6. 在终端运行:

   python setup.py install

7. 修改对应的配置文件，并且将plugins的参数“type”设置为“ShuffleAttention”，可参考【YOLO改进】主干插入注意力机制模块CBAM（基于MMYOLO）-优快云博客

修改后的ShuffleAttention.py

import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter
from mmyolo.registry import MODELS

@MODELS.register_module()
class ShuffleAttention(nn.Module):

    def __init__(self, in_channels=512, reduction=16, G=8):
        super().__init__()
        self.G = G
        self.channel = in_channels
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.gn = nn.GroupNorm(in_channels // (2 * G), in_channels // (2 * G))
        self.cweight = Parameter(torch.zeros(1, in_channels // (2 * G), 1, 1))
        self.cbias = Parameter(torch.ones(1, in_channels // (2 * G), 1, 1))
        self.sweight = Parameter(torch.zeros(1, in_channels // (2 * G), 1, 1))
        self.sbias = Parameter(torch.ones(1, in_channels // (2 * G), 1, 1))
        self.sigmoid = nn.Sigmoid()

    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    @staticmethod
    def channel_shuffle(x, groups):
        b, c, h, w = x.shape
        x = x.reshape(b, groups, -1, h, w)
        x = x.permute(0, 2, 1, 3, 4)

        # flatten
        x = x.reshape(b, -1, h, w)

        return x

    def forward(self, x):
        b, c, h, w = x.size()
        # group into subfeatures
        x = x.view(b * self.G, -1, h, w)  # bs*G,c//G,h,w

        # channel_split
        x_0, x_1 = x.chunk(2, dim=1)  # bs*G,c//(2*G),h,w

        # channel attention
        x_channel = self.avg_pool(x_0)  # bs*G,c//(2*G),1,1