【YOLO改进】主干插入ECAAttention模块(基于MMYOLO)

ECAAttention模块

论文链接：https://arxiv.org/abs/1910.03151

将ECAAttention模块添加到MMYOLO中

1. 将开源代码ECAAttention.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 ECAAttention 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 ECAAttention(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“ECAAttention”，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ECAAttention.py

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

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

    def __init__(self, in_channels, kernel_size=3):
        super().__init__()
        self.gap = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2)
        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)

    def forward(self, x):
        y = self.gap(x)  # bs,c,1,1
        y = y.squeeze(-1).permute(0, 2, 1)  # bs,1,c
        y = self.conv(y)  # bs,1,c
        y = self.sigmoid(y)  # bs,1,c
        y = y.permute(0, 2, 1).unsqueeze(-1)  # bs,c,1,1
        return x * y.expand_as(x)


if __name__ == '__main__':
    input = torch.randn(50, 512, 7, 7)
    eca = ECAAttention(kernel_size=3)
    output = eca(input)
    print(output.shape)

修改后的__init__.py

# Copyright (c) OpenMMLab. All rights reserved.
from .cbam import CBAM
from .Biformer import BiLevelRoutingAttention
from .A2Attention import DoubleAttention
from .CoordAttention import CoordAtt
from .CoTAttention import CoTAttention
from .ECA import ECAAttention
__all__ = ['CBAM', 'BiLevelRoutingAttention', 'DoubleAttention', 'CoordAtt','CoTAttention','ECAAt