给出对CLIP预训练模型知识蒸馏的训练代码

以下是一个简单的CLIP预训练模型知识蒸馏的训练代码示例：

import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
from transformers import CLIPModel, CLIPProcessor

# 加载 CLIP 预训练模型和处理器
clip_model = CLIPModel.from_pretrained('openai/clip-vit-base-patch32')
clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')

# 加载原始数据集
train_dataset = MyDataset(...)
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 加载蒸馏数据集
teacher_dataset = MyDataset(...)
teacher_dataloader = DataLoader(teacher_dataset, batch_size=32)

# 定义模型和优化器
student_model = MyModel(...)
optimizer = torch.optim.Adam(student_model.parameters(), lr=1e-5)

# 开始训练
for epoch in range(num_epochs):
    student_model.train()
    for batch_inputs, batch_labels in train_dataloader:
        optimizer.zero_grad()
        # 计算原始模型的输出
        with torch.no_grad():
            batch_inputs_encoded = clip_processor(batch_inputs, return_tensors='pt', padding=True).to(device)
            teacher_outputs = clip_model(**batch_inputs_encoded)['logits']
        # 计算蒸馏模型的输出
        batch_inputs_encoded = clip_processor(batch_inputs, return_tensors='pt', padding=True).to(device)
        student_outputs = student_model(batch_inputs_encoded)
        # 计算蒸馏损失
        kd_loss = F.kl_div(F.log_softmax(student_outputs / temperature, dim=1),
                           F.softmax(teacher_outputs / temperature, dim=1),
                           reduction='batchmean')
        kd_loss.backward()
        optimizer.step()

    # 在验证集上评估模型
    student_model.eval()
    with torch.no_grad():
        total_loss = 0
        for batch_inputs, batch_labels in val_dataloader:
            batch_inputs_encoded = clip_processor(batch_inputs, return_tensors='pt', padding=True).to(device)
            teacher_outputs = clip_model(**batch_inputs_encoded)['logits']
            student_outputs = student_model(batch_inputs_encoded)
            total_loss += F.kl_div(F.log_softmax(student_outputs / temperature, dim=1),
                                   F.softmax(teacher_outputs / temperature, dim=1),
                                   reduction='batchmean')
        avg_loss = total_loss / len(val_dataloader)
        print(f"Epoch {epoch+1}, Validation loss: {avg_loss:.4f}")

这个示例代码中，我们假定 `MyModel` 是一个待训练的模型，它的输入和 CLIP 的输入格式一致。在训练过程中，我们首先计算原始模型在原始数据集上的输出，然后计算蒸馏模型在蒸馏数据集上的输出，并将两者之间的 KL 散度作为损失函数进行优化。最后，在验证集上评估模型的质量。