MMDetection中对Resnet增加注意力机制Attention的简单方法

# -*- encoding: utf-8 -*-
'''
@File    :   resnet_with_attention.py
@Time    :   2023/03/25 08:55:30
@Author  :   RainfyLee 
@Version :   1.0
@Contact :   [email protected]
'''
 
# here put the import lib
 
import torch
from mmdet.models.backbones import ResNet
from fightingcv_attention.attention.CoordAttention import CoordAtt
from fightingcv_attention.attention.SEAttention import SEAttention
from mmdet.models.builder import BACKBONES
 
 
# 定义带attention的resnet18基类
class ResNetWithAttention(ResNet):
    def __init__(self , **kwargs):
        super(ResNetWithAttention, self).__init__(**kwargs)
        # 目前将注意力模块加在最后的三个输出特征层
        # resnet输出四个特征层
        if self.depth in (18, 34):
            self.dims = (64, 128, 256, 512)
        elif self.depth in (50, 101, 152):
            self.dims = (256, 512, 1024, 2048)
        else:
            raise Exception()
        self.attention1 = self.get_attention_module(self.dims[1])     
        self.attention2 = self.get_attention_module(self.dims[2])     
        self.attention3 = self.get_attention_module(self.dims[3])     
    
    # 子类只需要实现该attention即可
    def get_attention_module(self, dim):
        raise NotImplementedError()
    
    def forward(self, x):
        outs = super().forward(x)
        outs = list(outs)
        outs[1] = self.attention1(outs[1])
        outs[2] = self.attention2(outs[2])
        outs[3] = self.attention3(outs[3])    
        outs = tuple(outs)
        return outs
    
@BACKBONES.register_module()
class ResNetWithCoordAttention(ResNetWithAttention):
    def __init__(self , **kwargs):
        super(ResNetWithCoordAttention, self).__init__(**kwargs)
 
    # 子类只需要实现该attention即可
    def get_attention_module(self, dim):
        return CoordAtt(inp=dim, oup=dim, reduction=32)
    
@BACKBONES.register_module()
class ResNetWithSEAttention(ResNetWithAttention):
    def __init__(self , **kwargs):
        super(ResNetWithSEAttention, self).__init__(**kwargs)
 
    # 子类只需要实现该attention即可
    def get_attention_module(self, dim):
        return SEAttention(channel=dim, reduction=16)
 
 
if __name__ == "__main__":
    # model = ResNet(depth=18)
    # model = ResNet(depth=34)
    # model = ResNet(depth=50)
    # model = ResNet(depth=101)    
    # model = ResNet(depth=152)
    # model = ResNetWithCoordAttention(depth=18)
    model = ResNetWithSEAttention(depth=18)
    x = torch.rand(1, 3, 224, 224)
    outs = model(x)
    # print(outs.shape)
    for i, out in enumerate(outs):
        print(i, out.shape)

以resnet为例子，我在多个尺度的特征层输出增加注意力机制，以此编写一个基类，子类只需要实现这个attention即可。

参考开源仓库实现attention：

GitHub - xmu-xiaoma666/External-Attention-pytorch: Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

当然也可以直接pip调用：

pip install fightingcv-attention

测试完模型输出后可以利用注册到mmdetection：

简单的方法是，添加backbone注册修饰器，并在train.py和test.py中，import 该文件。

在配置上将model的type从Resnet更改为ResNetWithSEAttention或者ResNetWithSEAttention即可。