如何在PyTorch中组合数据集以同时返回图像和numpy文件

import torch
from torch.utils.data import Dataset

class CustomDataset(Dataset):
    def __init__(self, image_paths, numpy_files):
        self.image_paths = image_paths
        self.numpy_files = numpy_files

    def __len__(self):
        return len(self.image_paths)

    def __getitem__(self, index):
        image_path = self.image_paths[index]
        numpy_file = self.numpy_files[index]

        # 加载图像和numpy文件
        image = load_image(image_path)
        numpy_data = load_numpy(numpy_file)

        return image, numpy_data

from torch.utils.data import DataLoader

# 假设已经准备好了图像路径和numpy文件路径的列表
image_paths = ['path/to/image1.jpg', 'path/to/image2.jpg', ...]
numpy_files = ['path/to/file1.npy', 'path/to/file2.npy', ...]

# 创建自定义数据集实例
dataset = CustomDataset(image_paths, numpy_files)

# 创建数据加载器
batch_size = 32
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

for images, numpy_data in data_loader:
    # 在这里进行模型训练或其他操作
    ...