我就废话不多说了，大家还是直接看代码吧~
代码语言：javascript复制from torch import nn
class 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
代码语言：javascript复制import torch.nn as nn
class 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。
