倒置残差的理解

倒残差与线性瓶颈浅析 - MobileNetV2_晓野豬的博客-CSDN博客_倒残差结构 

首先理解倒置残差要先了解残差形式

残差结构：

1.采用1*1卷积降维，比如输入是256，降维到64

2.采用卷积核为3*3形式

3.采用1*1卷积升维。，比如64变成256

了解完残差结构后，现在开始学习倒置残差结构

1.采用1*1卷积升维，比如输入是64，降维到256

2.采用卷积核为深度可分离的3*3形式

3.采用1*1卷积 降维。比如256，降维到64

这里的激活函数采用的是relu6

# _*_coding:utf-8_*_
import torch
import torch.nn as nn


class InvertedResidualsBlock(nn.Module):
    def __init__(self, in_channels, out_channels, expansion, stride):
        super(InvertedResidualsBlock, self).__init__()
        channels = expansion * in_channels
        self.stride = stride

        self.basic_block = nn.Sequential(
            nn.Conv2d(in_channels, channels, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(channels),
            nn.ReLU6(inplace=True),
            nn.Conv2d(channels, channels, kernel_size=3, stride=stride, padding=1, groups=channels, bias=False),
            nn.BatchNorm2d(channels),
            nn.ReLU6(inplace=True),
            nn.Conv2d(channels, out_channels, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(out_channels)
        )
        # The shortcut operation does not affect the number of channels
        self.shortcut = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, bias=False),
            nn.BatchNorm2d(out_channels)
        )

    def forward(self, x):
        out = self.basic_block(x)
        if self.stride == 1:
            print("With shortcut!")
            out = out + self.shortcut(x)
        else:
            print("No shortcut!")
        print(out.size())

        return out


if __name__ == "__main__":
    x = torch.randn(16, 3, 32, 32)
    # no shortcut
    net1 = InvertedResidualsBlock(3, 6, 6, 2)
    # with shortcut
    net2 = InvertedResidualsBlock(3, 6, 6, 1)
    y1, y2 = net1(x), net2(x)