要使用 PyTorch 实现卷积形式的 ResNet（残差网络），你需要遵循几个主要步骤。首先，让我们概述 ResNet 的基本结构。ResNet 通过添加所谓的“残差连接”（或跳跃连接）来解决深度神经网络中的梯度消失/爆炸问题。这些连接允许梯度直接流过网络，从而改善了训练过程。

1. 导入必需的库

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

2. 定义残差块

残差块包括两个卷积层和一个跳跃连接。

class ResidualBlock(nn.Module):def __init__(self, in_channels, out_channels, stride=1, downsample=None):super(ResidualBlock, self).__init__()self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)self.bn1 = nn.BatchNorm2d(out_channels)self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)self.bn2 = nn.BatchNorm2d(out_channels)self.downsample = downsampledef forward(self, x):residual = xout = self.conv1(x)out = F.relu(self.bn1(out))out = self.conv2(out)out = self.bn2(out)if self.downsample:residual = self.downsample(x)out += residualout = F.relu(out)return out

3. 构建 ResNet 网络

这里以 ResNet-18 为例，但可以根据需要调整层数。

class ResNet(nn.Module):def __init__(self, block, layers, num_classes=1000):super(ResNet, self).__init__()self.in_channels = 64self.conv = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)self.bn = nn.BatchNorm2d(64)self.relu = nn.ReLU(inplace=True)self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)self.layer1 = self.make_layer(block, 64, layers[0])self.layer2 = self.make_layer(block, 128, layers[1], 2)self.layer3 = self.make_layer(block, 256, layers[2], 2)self.layer4 = self.make_layer(block, 512, layers[3], 2)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(512, num_classes)def make_layer(self, block, out_channels, blocks, stride=1):downsample = Noneif (stride != 1) or (self.in_channels != out_channels):downsample = nn.Sequential(nn.Conv2d(self.in_channels, out_channels, kernel_size=1, stride=stride, bias=False),nn.BatchNorm2d(out_channels))layers = []layers.append(block(self.in_channels, out_channels, stride, downsample))self.in_channels = out_channelsfor i in range(1, blocks):layers.append(block(out_channels, out_channels))return nn.Sequential(*layers)def forward(self, x):x = self.conv(x)x = self.bn(x)x = self.relu(x)x = self.maxpool(x)x = self.layer1(x)x = self.layer2(x)x = self.layer3(x)x = self.layer4(x)x = self.avgpool(x)x = x.view(x.size(0), -1)x = self.fc(x)return x

4. 实例化网络和训练

创建 ResNet 实例并进行训练。

model = ResNet(ResidualBlock, [2, 2, 2, 2])  # ResNet-18
# 接下来是训练代码，包括数据加载、损失函数、优化器等