给出对CLIP预训练模型知识蒸馏的训练代码，要求有kl_div loss和soft loss和hard loss

以下是一个基于PyTorch的CLIP模型知识蒸馏训练代码，包括KL散度损失、软标签损失和硬标签损失：

import torch
import torch.nn.functional as F
from torch import nn, optim
from torchvision import transforms, models
from torch.utils.data import DataLoader

# 加载预训练模型
teacher_model = models.clip_vit_large(pretrained=True)
student_model = models.clip_vit_base(pretrained=False)

# 定义数据预处理和增强
transformations = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = ImageFolder('train_folder', transformations)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# 定义损失函数
kl_div_loss = nn.KLDivLoss(reduction='batchmean')
soft_loss = nn.CrossEntropyLoss(reduction='mean')
hard_loss = nn.CrossEntropyLoss(reduction='mean')

# 定义优化器
optimizer = optim.Adam(student_model.parameters(), lr=0.001)

# 开始训练
for epoch in range(10):
    for inputs, targets in train_loader:
        optimizer.zero_grad()

        # 计算教师模型输出和标签
        with torch.no_grad():
            teacher_outputs = teacher_model.encode_image(inputs)
            teacher_targets = F.softmax(teacher_outputs / 0.5, dim=-1)

        # 计算学生模型输出和标签
        student_outputs = student_model.encode_image(inputs)
        student_targets = F.softmax(student_outputs / 0.5, dim=-1)

        # 计算KL散度损失
        kl_loss = kl_div_loss(student_targets.log(), teacher_targets)

        # 计算软标签损失
        soft_loss_value = soft_loss(student_outputs, targets)

        # 计算硬标签损失
        hard_loss_value = hard_loss(student_outputs, targets)

        # 计算总损失
        loss = kl_loss + 0.5 * soft_loss_value + 0.5 * hard_loss_value

        # 反向传播和更新参数
        loss.backward()
        optimizer.step()

    print(f'Epoch {epoch+1} Loss: {loss.item()}')

需要注意的是，这只是一个简单的示例代码，实际应用中还需要根据数据集和模型做出一些调整和优化。另外，由于CLIP模型较为复杂，可能需要较长的训练时间和更多的计算资源。