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自己实现resnet18网络结构

Hyggelook 2022-01-24 阅读 40

import torch
import torch.nn as nn

# 定义一个Residual模块
class Residual(nn.Module):
    def __init__(self,in_channels,out_channels,stride=1):
        super(Residual, self).__init__()
        self.stride = stride
        self.conv1 = nn.Conv2d(in_channels,out_channels,kernel_size=3,stride=stride,padding=1)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels,out_channels,kernel_size=3,padding=1)
        self.bn2 = nn.BatchNorm2d(out_channels)

        # 核心内容,残差块,x卷积后shape发生变化,
        if in_channels != out_channels:  # 如果输入的通道和输出的通道数不一样,则使用1×1的卷积残差块,也就是shortcut
            self.conv1x1 = nn.Conv2d(in_channels,out_channels,kernel_size=1,stride=stride)
            self.bn = nn.BatchNorm2d(out_channels)
        else:
            self.conv1x1 = None
    def forward(self,x):
        o1 = self.relu(self.bn1(self.conv1(x)))
        o2 = self.bn2(self.conv2(o1))
        if self.conv1x1:
            x = self.bn(self.conv1x1(x))
        out = self.relu(o2 + x)
        return out

# 自己定义一个resnet18网络
class ResNet18(nn.Module):
    def __init__(self,in_channels,num_classes):
        super(ResNet18, self).__init__()
        self.layer0 = nn.Sequential(
            nn.Conv2d(in_channels,64,kernel_size=7,stride=2,padding=3,bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=3,stride=2,padding=1)
        )
        self.layer1 = nn.Sequential(
            Residual(64,64),
            Residual(64,64)
        )
        self.layer2 = nn.Sequential(
            Residual(64,128,stride=2),
            Residual(128,128)
        )
        self.layer3 = nn.Sequential(
            Residual(128,256,stride=2),
            Residual(256,256)
        )
        self.layer4 = nn.Sequential(
            Residual(256,512,stride=2),
            Residual(512,512)
        )
        self.avgpool = nn.AdaptiveAvgPool2d(output_size=(1,1))
        self.fc = nn.Linear(512,1000)
        self.classifier = nn.Sequential(
            nn.Linear(1000,64),
            nn.ReLU(True),
            nn.Dropout(p=0.5,inplace=False),
            nn.Linear(64,num_classes)
        )
    def forward(self,x):
        out0 = self.layer0(x)
        out1 = self.layer1(out0)
        out2 = self.layer2(out1)
        out3 = self.layer3(out2)
        out4 = self.layer4(out3)

        out = self.avgpool(out4)
        out = out.view((x.shape[0],-1))

        out = self.fc(out)

        out = self.classifier(out)

        return out
resnet18 = ResNet18(3,10)
print(resnet18)
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