将cnn模型进行知识蒸馏的实例pytorch代码

以下是一个简单的示例代码，用于将一个已经训练好的CNN模型进行知识蒸馏：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
from distillation import KnowledgeDistiller

# 定义教师和学生模型
class TeacherModel(nn.Module):
    def __init__(self):
        super(TeacherModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(64 * 8 * 8, 512)
        self.bn3 = nn.BatchNorm1d(512)
        self.relu3 = nn.ReLU(inplace=True)

        self.fc2 = nn.Linear(512, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)

        x = x.view(-1, 64 * 8 * 8)
        x = self.fc1(x)
        x = self.bn3(x)
        x = self.relu3(x)

        x = self.fc2(x)
        return x

class StudentModel(nn.Module):
    def __init__(self):
        super(StudentModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(32 * 8 * 8, 256)
        self.bn3 = nn.BatchNorm1d(256)
        self.relu3 = nn.ReLU(inplace=True)

        self.fc2 = nn.Linear(256, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)

        x = x.view(-1, 32 * 8 * 8)
        x = self.fc1(x)
        x = self.bn3(x)
        x = self.relu3(x)

        x = self.fc2(x)
        return x

# 定义数据加载器和训练函数
train_transforms = 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))])

test_transforms = transforms.Compose([transforms.ToTensor(),
                                      transforms.Normalize((0.4914, 0.4822, 0.4465),
                                                           (0.2023, 0.1994, 0.2010))])

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

train_loader = DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)
test_loader = DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)

def train(model, dataloader, criterion, optimizer, device):
    model.train()
    running_loss = 0.0
    for i, (inputs, labels) in enumerate(dataloader):
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * inputs.size(0)
    epoch_loss = running_loss / len(dataloader.dataset)
    return epoch_loss

# 定义知识蒸馏对象
teacher_model = TeacherModel()
teacher_model.load_state_dict(torch.load('teacher_model.pth'))
student_model = StudentModel()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(student_model.parameters(), lr=0.1, momentum=0.9)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
kd = KnowledgeDistiller(teacher_model, student_model, criterion, optimizer, device)

# 开始知识蒸馏训练
num_epochs = 10
for epoch in range(num_epochs):
    train_loss = train(student_model, train_loader, criterion, optimizer, device)
    print('Epoch {}/{}: train_loss = {:.4f}'.format(epoch+1, num_epochs, train_loss))
    kd.train(train_loader, epoch)

在上面的代码中，我们首先定义了一个带有两个卷积层和两个全连接层的教师模型（`TeacherModel`）和一个带有两个卷积层和两个全连接层的学生模型（`StudentModel`）。这些模型都是用PyTorch的`nn.Module`基类定义的。我们还定义了一个数据加载器和一个训练函数。

然后，我们定义了一个知识蒸馏对象（`KnowledgeDistiller`），该对象使用教师模型来生成软目标，并将其传递给学生模型进行训练。在知识蒸馏训练期间，我们通过调用`kd.train()`函数来更新学生模型的参数。我们还使用`train()`函数来计算学生模型在每个epoch上的训练损失。

请注意，`KnowledgeDistiller`类的实现不在这里提供，但您可以在GitHub上找到大量的开源实现。