在查看pytorch官方文档的时候，在这里链接中https://pytorch.org/tutorials/beginner/basics/data_tutorial.html的Creating a Custom Dataset for your files章节，有提到要自定义数据集，需要用到实际的图片和标签。

在网上找了半天没找到，写了一个脚本将图片和标签文本下载到本地。

import torch
from torch import nn
from torch.utils.data import DataLoader
from torchvision import datasets
from torchvision.transforms import ToTensor

# Download training data from open datasets.
training_data = datasets.FashionMNIST(
    root="data",
    train=True,
    download=True,
    transform=ToTensor(),
)

# Download test data from open datasets.
test_data = datasets.FashionMNIST(
    root="data",
    train=False,
    download=True,
    transform=ToTensor(),
)

# 写入到本地
count=0
for index,x in test_data:
    print(index.size(),x)
    count=count+1
    

    classes = [
        "T-shirttop",
        "Trouser",
        "Pullover",
        "Dress",
        "Coat",
        "Sandal",
        "Shirt",
        "Sneaker",
        "Bag",
        "Ankleboot",
    ]



    import torch
    from torchvision.utils import save_image
    folder_path = './data/imageandlableTest'  # 替换为你的文件夹路径
    filename = '{}{}.jpg'.format(classes[x],count)  # 图片文件名

    # 确保文件夹存在
    import os
    if not os.path.exists(folder_path):
        os.makedirs(folder_path)

    # 保存图片
    save_path = os.path.join(folder_path, filename)
    save_image(index, save_path)



    with open('./data/imageandlableTest/output.txt', 'a') as f:  
        f.write("{},{}\n".format(filename,x))
    print(count)