我就废话不多说了，大家还是直接看代码吧~

from torch import nnclass SELayer(nn.Module): def __init__(self, channel, reduction=16): super(SELayer, self).__init__() //返回1X1大小的特征图，通道数不变 self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc = nn.Sequential( nn.Linear(channel, channel // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(channel // reduction, channel, bias=False), nn.Sigmoid() ) def forward(self, x): b, c, _, _ = x.size() //全局平均池化，batch和channel和原来一样保持不变 y = self.avg_pool(x).view(b, c) //全连接层+池化 y = self.fc(y).view(b, c, 1, 1) //和原特征图相乘 return x * y.expand_as(x)

补充知识：pytorch 实现 SE Block

论文模块图

代码

import torch.nn as nnclass SE_Block(nn.Module): def __init__(self, ch_in, reduction=16): super(SE_Block, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) # 全局自适应池化 self.fc = nn.Sequential( nn.Linear(ch_in, ch_in // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(ch_in // reduction, ch_in, bias=False), nn.Sigmoid() ) def forward(self, x): b, c, _, _ = x.size() y = self.avg_pool(x).view(b, c) y = self.fc(y).view(b, c, 1, 1) return x * y.expand_as(x)

现在还有许多关于SE的变形，但大都大同小异

以上这篇pytorch SENet实现案例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持。