ResNet18复现「建议收藏」

全栈程序员站长

发布于 2022-09-01 14:37:10

3380

发布于 2022-09-01 14:37:10

文章被收录于专栏：全栈程序员必看

大家好，又见面了，我是你们的朋友全栈君。

ResNet18的网络架构图

首先将网络分为四层（layers），每层有两个模块组成，除了第一层是两个普通的残差块组成，其它三层有一个普通的残差块和下采样的卷积块组成。输入图像为3x224x224格式，经过卷积池化后为64x112x112格式进入主网络架构。

代码如下：

import torch
from torch import nn
from torch.nn import functional as F

class BasicBlock(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size,stride):
        super(BasicBlock,self).__init__()
        self.conv1=nn.Conv2d(in_channels,out_channels,kernel_size,stride,padding=1)
        self.bn1=nn.BatchNorm2d(out_channels)
        self.conv2=nn.Conv2d(out_channels,out_channels,kernel_size,stride,padding=1)
        self.bn2=nn.BatchNorm2d(out_channels)
        
    def forward(self,x):
        output=self.bn1(self.conv1(x))
        output=self.bn2(self.conv2(output))
        return F.relu(x+output)
    

class BasicDownBlock(nn.Module):
    def __init__(self,in_channels,out_channels,kernel_size,stride):
        super(BasicDownBlock,self).__init__()     
        self.conv1=nn.Conv2d(in_channels,out_channels,kernel_size[0],stride[0],padding=1)
        self.bn1=nn.BatchNorm2d(out_channels)
        self.conv2=nn.Conv2d(out_channels,out_channels,kernel_size[0],stride[1],padding=1)
        self.bn2=nn.BatchNorm2d(out_channels)
        self.conv3=nn.Conv2d(in_channels,out_channels,kernel_size[1],stride[0])
        self.bn3=nn.BatchNorm2d(out_channels)
        
    def forward(self,x):
        output=self.bn1(self.conv1(x))
        output=self.bn2(self.conv2(output))
        output1=self.bn3(self.conv3(x))
        return F.relu(output1+output)

class ResNet18(nn.Module):
    def __init__(self):
        super().__init__()
        # 3x224x224-->64x112x112
        self.conv1=nn.Conv2d(in_channels=3,out_channels=64,kernel_size=7,stride=2,padding=3)
        self.bn1=nn.BatchNorm2d(64)
        # 64x112x112-->64x56x56
        self.pool1=nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
        
        # 64x56x56-->64x56x56
        self.layer1=nn.Sequential(
            BasicBlock(64,64,3,1),
            BasicBlock(64,64,3,1)
        )
        # 64x56x56-->128*28*28
        self.layer2=nn.Sequential(
            BasicDownBlock(64,128,[3,1],[2,1]),
            BasicBlock(128,128,3,1)
        )
        # 128*28*28-->256*14*14
        self.layer3=nn.Sequential(
            BasicDownBlock(128,256,[3,1],[2,1]),
            BasicBlock(256,256,3,1)
        )
        # 256*14*14-->512x7x7
        self.layer4=nn.Sequential(
            BasicDownBlock(256,512,[7,1],[2,1]),
            BasicBlock(512,512,3,1)
        )
        # 512x7x7-->512x1x1
        self.avgpool=nn.AdaptiveMaxPool2d(output_size=(1,1))
        self.flat=nn.Flatten()
        self.linear=nn.Linear(512,10)
        
    def forward(self,x):
        output=self.pool1(F.relu(self.bn1(self.conv1(x))))
        output=self.layer1(output)
        output=self.layer2(output)
        output=self.layer3(output)
        output=self.layer4(output)
        output=self.avgpool(output)
        output=self.flat(output)
        output=self.linear(output)
        return output
    

net=ResNet18()
x=torch.randn(32,3,224,224)
print(x.shape)
y=net(x)
print(y.shape)

代码中BasicBlock为普通的残差块，注意步长和卷积核的大小，BasicDownBlock为下采样的残差块，然后将四层的网络表示出来，最后进行验证x.shape为torch.Size([32, 3, 224, 224])，y.shape为torch.Size([32, 10])。

发布者：全栈程序员栈长，转载请注明出处：https://javaforall.cn/141545.html原文链接：https://javaforall.cn

本文参与腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。

原始发表：2022年5月2，如有侵权请联系 cloudcommunity@tencent.com 删除

https