文章目录
- 1. 构建ResNet模型
- 1.1 前置条件
- 1.2 构建Residual Block
- 1.3 构建ResNet-18
- 1.4 模型测试
- 2. 训练与评估
- 2.1 数据预处理与加载
- 2.2 模型训练
- 2.3 模型评估
- Reference
1. 构建ResNet模型
我们将使用PyTorch框架来实现一个简化版的ResNet-18模型。我们的目标是构建一个可以在CIFAR-10数据集上进行分类任务的模型。
1.1 前置条件
pip install torch torchvision
1.2 构建Residual Block
首先,让我们实现一个残差块。这是前面章节已经介绍过的内容。
import torch
import torch.nn as nnclass 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))
1.3 构建ResNet-18
接下来,我们使用残差块来构建完整的ResNet-18模型。
class ResNet18(nn.Module):def __init__(self, num_classes=10):super(ResNet18, self).__init__()self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)self.bn1 = nn.BatchNorm2d(64)self.relu = nn.ReLU(inplace=True)self.layer1 = self._make_layer(64, 64, 2)self.layer2 = self._make_layer(64, 128, 2, stride=2)self.layer3 = self._make_layer(128, 256, 2, stride=2)self.layer4 = self._make_layer(256, 512, 2, stride=2)self.avgpool = nn.AdaptiveAvgPool2d((1, 1))self.fc = nn.Linear(512, num_classes)def _make_layer(self, in_channels, out_channels, blocks, stride=1):layers = []layers.append(ResidualBlock(in_channels, out_channels, stride))for _ in range(1, blocks):layers.append(ResidualBlock(out_channels, out_channels))return nn.Sequential(*layers)def forward(self, x):x = self.conv1(x)x = self.bn1(x)x = self.relu(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
以上代码定义了一个用于CIFAR-10分类任务的ResNet-18模型。在这个模型中,我们使用了前面定义的ResidualBlock
类,并通过_make_layer
函数来堆叠多个残差块。
1.4 模型测试
接下来,我们可以测试这个模型以确保其结构是正确的。
# 创建一个模拟输入
x = torch.randn(64, 3, 32, 32)# 实例化模型
model = ResNet18(num_classes=10)# 前向传播
output = model(x)# 输出形状应为(64, 10),因为我们有64个样本和10个类别
print(output.shape) # 输出:torch.Size([64, 10])
2. 训练与评估
在成功构建了ResNet-18模型之后,下一步就是进行模型的训练和评估。在这一部分,我们将介绍如何在CIFAR-10数据集上完成这两个步骤。
2.1 数据预处理与加载
首先,我们需要准备数据。使用PyTorch的torchvision
库,我们可以非常方便地下载和预处理CIFAR-10数据集。
import torch
import torchvision
import torchvision.transforms as transforms# 数据预处理
transform = transforms.Compose([transforms.RandomCrop(32, padding=4),transforms.RandomHorizontalFlip(),transforms.ToTensor(),transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])# 加载数据集
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True)testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False)
2.2 模型训练
训练模型通常需要指定损失函数和优化器,并反复进行前向传播、计算损失、反向传播和参数更新。
import torch.optim as optim# 实例化模型并移至GPU
model = ResNet18(num_classes=10).cuda()# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)# 训练模型
for epoch in range(10): # 运行10个周期for i, data in enumerate(trainloader, 0):inputs, labels = datainputs, labels = inputs.cuda(), labels.cuda()# 清零梯度缓存optimizer.zero_grad()# 前向传播,计算损失,反向传播outputs = model(inputs)loss = criterion(outputs, labels)loss.backward()# 更新参数optimizer.step()
2.3 模型评估
训练完成后,我们需要评估模型的性能。这通常通过在测试集上计算模型的准确率来完成。
# 切换模型为评估模式
model.eval()correct = 0
total = 0
with torch.no_grad():for data in testloader:images, labels = dataimages, labels = images.cuda(), labels.cuda()outputs = model(images)_, predicted = torch.max(outputs.data, 1)total += labels.size(0)correct += (predicted == labels).sum().item()print(f'Accuracy of the network on the 10000 test images: {100 * correct / total}%')
Reference
理论内容:https://blog.csdn.net/magicyangjay111/article/details/132553872