Vgg19bn、Inc-v3、Res50和Dense169这些模型是怎样调用的，请复现一下代码

在论文中提到的Vgg19bn、Inc-v3、Res50和Dense169等模型是作为教师模型（teacher models）来指导知识蒸馏过程中的学生模型（student model）训练的。具体来说，这些模型被用来生成软标签（soft labels），然后通过知识蒸馏的方法将这些软标签传递给学生模型。

以下是基于PyTorch的一个示例代码，展示如何使用这些预训练的教师模型进行知识蒸馏：

### 导入必要的库

import torch
import torch.nn as nn
import torchvision.models as models
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

### 定义教师模型

def load_teacher_models():
    vgg19bn = models.vgg19_bn(pretrained=True)
    inc_v3 = models.inception_v3(pretrained=True, aux_logits=False)
    res50 = models.resnet50(pretrained=True)
    dense169 = models.densenet169(pretrained=True)

    # 将模型设置为评估模式
    vgg19bn.eval()
    inc_v3.eval()
    res50.eval()
    dense169.eval()

    return vgg19bn, inc_v3, res50, dense169

### 定义数据加载器

def get_data_loader(batch_size):
    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])
    ])

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

    return train_loader

### 定义学生模型

class StudentModel(nn.Module):
    def __init__(self):
        super(StudentModel, self).__init__()
        self.model = models.resnet18(pretrained=False)
        num_ftrs = self.model.fc.in_features
        self.model.fc = nn.Linear(num_ftrs, 10)  # CIFAR-10 has 10 classes

    def forward(self, x):
        return self.model(x)

### 计算软标签

def compute_soft_labels(images, teacher_models, temperature):
    with torch.no_grad():
        soft_labels = []
        for model in teacher_models:
            logits = model(images)
            soft_labels.append(torch.softmax(logits / temperature, dim=1))
        
        # 可以选择平均或随机选择一个教师模型的软标签
        if len(soft_labels) > 1:
            soft_labels = sum(soft_labels) / len(soft_labels)
        else:
            soft_labels = soft_labels[0]
    
    return soft_labels

### 训练学生模型

def train_student_model(student_model, teacher_models, train_loader, epochs, temperature, device):
    student_model.to(device)
    optimizer = torch.optim.Adam(student_model.parameters(), lr=0.001)
    criterion = nn.KLDivLoss(reduction='batchmean')

    for epoch in range(epochs):
        running_loss = 0.0
        for images, _ in train_loader:
            images = images.to(device)
            
            # 计算软标签
            soft_labels = compute_soft_labels(images, teacher_models, temperature).to(device)
            
            # 前向传播
            student_outputs = student_model(images)
            student_soft_outputs = torch.log_softmax(student_outputs / temperature, dim=1)
            
            # 计算损失
            loss = criterion(student_soft_outputs, soft_labels)
            
            # 反向传播和优化
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            
            running_loss += loss.item() * images.size(0)
        
        epoch_loss = running_loss / len(train_loader.dataset)
        print(f"Epoch [{epoch+1}/{epochs}], Loss: {epoch_loss:.4f}")

### 主函数

if __name__ == "__main__":
    batch_size = 64
    epochs = 10
    temperature = 10
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # 加载教师模型
    teacher_models = load_teacher_models()

    # 获取数据加载器
    train_loader = get_data_loader(batch_size)

    # 初始化学生模型
    student_model = StudentModel()

    # 训练学生模型
    train_student_model(student_model, teacher_models, train_loader, epochs, temperature, device)

这个示例代码展示了如何使用多个预训练的教师模型进行知识蒸馏，并训练一个学生模型。你可以根据需要调整参数和模型结构。希望这对你有所帮助！