使用pytorch加载多模式数据

import torch
from torch.utils.data import Dataset

class MultiModalDataset(Dataset):
    def __init__(self, image_paths, text_data, audio_data, labels):
        self.image_paths = image_paths
        self.text_data = text_data
        self.audio_data = audio_data
        self.labels = labels

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

    def __getitem__(self, index):
        image = self.load_image(self.image_paths[index])
        text = self.text_data[index]
        audio = self.load_audio(self.audio_data[index])
        label = self.labels[index]

        return image, text, audio, label

    def load_image(self, image_path):
        # 从路径加载图像数据的代码

    def load_audio(self, audio_path):
        # 从路径加载音频数据的代码

# 创建数据集实例
image_paths = ['path/to/image1.jpg', 'path/to/image2.jpg', ...]
text_data = ['text1', 'text2', ...]
audio_data = ['path/to/audio1.wav', 'path/to/audio2.wav', ...]
labels = [0, 1, ...]

dataset = MultiModalDataset(image_paths, text_data, audio_data, labels)

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

# 在训练循环中使用多模态数据
for images, texts, audios, labels in data_loader:
    # 在这里进行模型的训练或推理操作