网站首页 > 厂商资讯 > 声网 >

PyTorch搭建ResNet的代码示例

在深度学习领域，卷积神经网络（CNN）因其强大的特征提取能力而备受关注。其中，ResNet（残差网络）作为一种经典的CNN架构，因其能够有效解决深层网络训练中的梯度消失问题而广受欢迎。本文将为您介绍如何使用PyTorch搭建ResNet模型，并通过实际案例展示其应用。

ResNet的基本原理

ResNet的核心思想是引入残差块（Residual Block），通过将输入直接传递到下一层，从而缓解梯度消失问题。残差块包含两个卷积层，并使用恒等映射（Identity Mapping）连接输入和输出，使得网络能够学习到更深的特征。

PyTorch搭建ResNet

以下是一个使用PyTorch搭建ResNet的代码示例：

import torch

import torch.nn as nn



class ResidualBlock(nn.Module):

    def __init__(self, in_channels, out_channels, stride=1):

        super(ResidualBlock, self).__init__()

        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, stride=1, padding=1)

        self.bn2 = nn.BatchNorm2d(out_channels)

        self.shortcut = nn.Sequential()

        if stride != 1 or in_channels != out_channels:

            self.shortcut = nn.Sequential(

                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride),

                nn.BatchNorm2d(out_channels)

            )



    def forward(self, x):

        identity = x

        out = self.conv1(x)

        out = self.bn1(out)

        out = self.relu(out)

        out = self.conv2(out)

        out = self.bn2(out)

        out += self.shortcut(identity)

        out = self.relu(out)

        return out



class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000):

        super(ResNet, self).__init__()

        self.in_channels = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)

        self.bn1 = 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], stride=2)

        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)

        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        self.fc = nn.Linear(512 * block.expansion, num_classes)



    def _make_layer(self, block, out_channels, blocks, stride=1):

        strides = [stride] + [1] * (blocks - 1)

        layers = []

        for stride in strides:

            layers.append(block(self.in_channels, out_channels, stride))

            self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)



    def forward(self, x):

        x = self.conv1(x)

        x = self.bn1(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 = torch.flatten(x, 1)

        x = self.fc(x)

        return x



# 创建ResNet模型

model = ResNet(block=ResidualBlock, layers=[2, 2, 2, 2], num_classes=1000)

案例分析

以下是一个使用ResNet进行图像分类的案例：

import torch.optim as optim

from torchvision import datasets, transforms

from torch.utils.data import DataLoader



# 数据预处理

transform = transforms.Compose([

    transforms.Resize((224, 224)),

    transforms.ToTensor(),

    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])

])



# 加载数据集

train_dataset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)



# 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()

optimizer = optim.Adam(model.parameters(), lr=0.001)



# 训练模型

for epoch in range(10):

    for i, (images, labels) in enumerate(train_loader):

        optimizer.zero_grad()

        outputs = model(images)

        loss = criterion(outputs, labels)

        loss.backward()

        optimizer.step()

        if (i + 1) % 100 == 0:

            print(f'Epoch [{epoch + 1}/{10}], Step [{i + 1}/{len(train_loader)}], Loss: {loss.item():.4f}')

通过以上代码，我们可以使用ResNet模型对CIFAR-10数据集进行图像分类。在实际应用中，您可以根据需要调整模型结构、超参数等，以获得更好的性能。