用pytorch构建resnet18的详细步骤

好的，下面是构建resnet18的详细步骤：

Step 1: 加载必要的库和模块

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

Step 2: 定义基本的块

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,
                               stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion*planes,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

Step 3: 定义完整的ResNet18网络结构

class ResNet18(nn.Module):
    def __init__(self, num_classes=10):
        super(ResNet18, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                               stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = nn.Sequential(
            BasicBlock(64, 64, stride=1),
            BasicBlock(64, 64, stride=1)
        )
        self.layer2 = nn.Sequential(
            BasicBlock(64, 128, stride=2),
            BasicBlock(128, 128, stride=1)
        )
        self.layer3 = nn.Sequential(
            BasicBlock(128, 256, stride=2),
            BasicBlock(256, 256, stride=1)
        )
        self.layer4 = nn.Sequential(
            BasicBlock(256, 512, stride=2),
            BasicBlock(512, 512, stride=1)
        )
        self.linear = nn.Linear(512, num_classes)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

Step 4: 实例化 ResNet18 模型

resnet18 = ResNet18()