MNIST读取出错RuntimeError: Dataset not found or corrupted. You can use download=True to download it 解决方案

灰太狼 2022-10-16 08:44 977阅读 0赞

### 文章目录 ###

*  1. 前言
 *  2. 下载数据
 *  3. 修改代码
 *   *  3.1 修改逻辑
     *  3.2 代码使用
     *  3.3 附录：mnist.py完整代码

# 1. 前言 #

Pytorch官网教程中，第一个程序是使用简单神经网络对Fashion MNIST数据进行学习和预测，而机器学习/深度学习的处理流程的第一步是：读取数据。代码如下所示：

import torch
    from torch import nn
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torchvision.transforms import ToTensor, Lambda, Compose
    import matplotlib.pyplot as plt
    
    # 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(),
    )

然而不幸的是，由于国情导致的网络问题，往往会导致在下载过程中出错，所以出现如下问题：**RuntimeError: Dataset not found. You can use download=True to download it**，**很多平台和博客提供的解决方案并不完美而且对新手并不友好（不说明逻辑和原因）**。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hlcm9zdW5seQ_size_16_color_FFFFFF_t_70]  
  那该如何解决呢？

# 2. 下载数据 #

可通过迅雷或者其他下载工具对下列4个数据文件进行下载：

*  训练集图像：[http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz][http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_train-images-idx3-ubyte.gz]
 *  训练集标签：[http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz][http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_train-labels-idx1-ubyte.gz]
 *  测试集图像：[http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz][http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_t10k-images-idx3-ubyte.gz]
 *  测试集标签：[http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz][http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_t10k-images-idx3-ubyte.gz]

# 3. 修改代码 #

## 3.1 修改逻辑 ##

首先，我们需要得到修改的代码所处的位置，根据[Python系列课程之模块][Python]的内容，包具有一个特殊的属性： \_\_path\_\_ ，简单来说也就是包所处的具体路径。

import torchvision
    print(torchvision.__path__)
    ['/home/anaconda3/lib/python3.6/site-packages/torchvision']

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hlcm9zdW5seQ_size_16_color_FFFFFF_t_70_pic_center]  
  可以得到mnist.py的具体路径为上述路径下的子路径/datasets/mnist.py。阅读其关键代码可知，download=True不仅会下载.gz文件(图像+标签)，而且会将其保存成torch格式的.pt文件。而我们下载的文件只是.gz文件，所以需要通过代码将.gz转换成.pt。

为了不影响之前的参数和处理逻辑，所以在对象初始化增加了一个参数：load\_gz\_files和对应的处理函数load\_gz\_files()。load\_gz\_files()会借用utils.py中的extract\_archive()函数和check\_integrity()。

def load_gz_files(self):
        """Load the .gz format MNIST data if it exist ."""
        if self._check_exists():
            return
    
        if not os.path.exists(self.processed_folder):
            makedir_exist_ok(self.processed_folder)
    
        for url, md5 in self.resources:
            filename = url.rpartition('/')[2]
            fpath = os.path.join(self.raw_folder, filename)
            if check_integrity(fpath, md5):
                extract_archive(from_path=fpath, to_path=self.processed_folder, remove_finished=False)
    
        training_set = (
            read_image_file(os.path.join(self.processed_folder, 'train-images-idx3-ubyte')),
            read_label_file(os.path.join(self.processed_folder, 'train-labels-idx1-ubyte'))
        )
        test_set = (
            read_image_file(os.path.join(self.processed_folder, 't10k-images-idx3-ubyte')),
            read_label_file(os.path.join(self.processed_folder, 't10k-labels-idx1-ubyte'))
        )
        with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:
            torch.save(training_set, f)
        with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:
            torch.save(test_set, f)
    
        print('Done!')

为了避免load\_gz\_files()函数和原有的download()函数互相影响，所以简单修改了下代码(**完整代码在文章最后，使用的时候将其复制并覆盖mnist.py文件内容即可**)：

if load_gz_files:
        self.load_gz_files()
    else:
        if download:
            self.download()

## 3.2 代码使用 ##

修改后代码如何使用呢？

1.  新建文件夹，如/home/data/FashionMNIST/raw，并把下载的四个.gz文件放入其中。
2.  读取文件代码如下(**需要注意的是root路径是新建文件夹的根路径**)：

import torch
    from torch import nn
    from torch.utils.data import DataLoader
    from torchvision import datasets
    from torchvision.transforms import ToTensor, Lambda, Compose
    import matplotlib.pyplot as plt
    
    training_data = datasets.FashionMNIST(
        root="/home/data/",
        train=True,
        download=False,
        transform=ToTensor(),
        load_gz_files=True
    )
    
    test_data = datasets.FashionMNIST(
        root="/home/data/",
        train=False,
        download=False,
        transform=ToTensor(),
        load_gz_files=True
    )

如果在from .utils import语句中报错：ImportError: cannot import name ‘makedir\_exist\_ok’，如下所示：  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hlcm9zdW5seQ_size_16_color_FFFFFF_t_70 1]  
  只须在utils.py中添加小段函数代码：

def makedir_exist_ok(dirpath):
        """ Python2 support for os.makedirs(.., exist_ok=True) """
        try:
            os.makedirs(dirpath)
        except OSError as e:
            if e.errno == errno.EEXIST:
                pass
            else:
                raise

## 3.3 附录：mnist.py完整代码 ##

为了方便大家修改，所以提供源码如下：

from __future__ import print_function
    from .vision import VisionDataset
    import warnings
    from PIL import Image
    import os
    import os.path
    import numpy as np
    import torch
    import codecs
    from .utils import download_url, download_and_extract_archive, extract_archive, \
        makedir_exist_ok, verify_str_arg, check_integrity
    
    
    class MNIST(VisionDataset):
        """`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset. Args: root (string): Root directory of dataset where ``MNIST/processed/training.pt`` and ``MNIST/processed/test.pt`` exist. train (bool, optional): If True, creates dataset from ``training.pt``, otherwise from ``test.pt``. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. """
    
        resources = [
            ("http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz", "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
            ("http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz", "d53e105ee54ea40749a09fcbcd1e9432"),
            ("http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz", "9fb629c4189551a2d022fa330f9573f3"),
            ("http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz", "ec29112dd5afa0611ce80d1b7f02629c")
        ]
    
        training_file = 'training.pt'
        test_file = 'test.pt'
        classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
                   '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']
    
        @property
        def train_labels(self):
            warnings.warn("train_labels has been renamed targets")
            return self.targets
    
        @property
        def test_labels(self):
            warnings.warn("test_labels has been renamed targets")
            return self.targets
    
        @property
        def train_data(self):
            warnings.warn("train_data has been renamed data")
            return self.data
    
        @property
        def test_data(self):
            warnings.warn("test_data has been renamed data")
            return self.data
    
        def __init__(self, root, train=True, transform=None, target_transform=None,
                     download=False, load_gz_files=False):
            super(MNIST, self).__init__(root, transform=transform,
                                        target_transform=target_transform)
            self.train = train  # training set or test set
    
            if load_gz_files:
                self.load_gz_files()
            else:
                if download:
                    self.download()
            
            if not self._check_exists():
                raise RuntimeError('Dataset not found.' +
                                   ' You can use download=True to download it')
    
            if self.train:
                data_file = self.training_file
            else:
                data_file = self.test_file
            self.data, self.targets = torch.load(os.path.join(self.processed_folder, data_file))
    
        def __getitem__(self, index):
            """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class. """
            img, target = self.data[index], int(self.targets[index])
    
            # doing this so that it is consistent with all other datasets
            # to return a PIL Image
            img = Image.fromarray(img.numpy(), mode='L')
    
            if self.transform is not None:
                img = self.transform(img)
    
            if self.target_transform is not None:
                target = self.target_transform(target)
    
            return img, target
    
        def __len__(self):
            return len(self.data)
    
        @property
        def raw_folder(self):
            return os.path.join(self.root, self.__class__.__name__, 'raw')
    
        @property
        def processed_folder(self):
            return os.path.join(self.root, self.__class__.__name__, 'processed')
    
        @property
        def class_to_idx(self):
            return { _class: i for i, _class in enumerate(self.classes)}
    
        def _check_exists(self):
            return (os.path.exists(os.path.join(self.processed_folder,
                                                self.training_file)) and
                    os.path.exists(os.path.join(self.processed_folder,
                                                self.test_file)))
    
        def load_gz_files(self):
            """Load the .gz format MNIST data if it exist ."""
            if self._check_exists():
                return
    
            if not os.path.exists(self.processed_folder):
                makedir_exist_ok(self.processed_folder)
        
            for url, md5 in self.resources:
                filename = url.rpartition('/')[2]
                fpath = os.path.join(self.raw_folder, filename)
                if check_integrity(fpath, md5):
                    extract_archive(from_path=fpath, to_path=self.processed_folder, remove_finished=False)
    
            training_set = (
                read_image_file(os.path.join(self.processed_folder, 'train-images-idx3-ubyte')),
                read_label_file(os.path.join(self.processed_folder, 'train-labels-idx1-ubyte'))
            )
            test_set = (
                read_image_file(os.path.join(self.processed_folder, 't10k-images-idx3-ubyte')),
                read_label_file(os.path.join(self.processed_folder, 't10k-labels-idx1-ubyte'))
            )
            with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:
                torch.save(training_set, f)
            with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:
                torch.save(test_set, f)
    
            print('Done!')
    
        def download(self):
            """Download the MNIST data if it doesn't exist in processed_folder already."""
    
            if self._check_exists():
                return
    
            makedir_exist_ok(self.raw_folder)
            makedir_exist_ok(self.processed_folder)
    
            # download files
            for url, md5 in self.resources:
                filename = url.rpartition('/')[2]
                download_and_extract_archive(url, download_root=self.raw_folder, filename=filename, md5=md5)
    
            # process and save as torch files
            print('Processing...')
    
            training_set = (
                read_image_file(os.path.join(self.raw_folder, 'train-images-idx3-ubyte')),
                read_label_file(os.path.join(self.raw_folder, 'train-labels-idx1-ubyte'))
            )
            test_set = (
                read_image_file(os.path.join(self.raw_folder, 't10k-images-idx3-ubyte')),
                read_label_file(os.path.join(self.raw_folder, 't10k-labels-idx1-ubyte'))
            )
            with open(os.path.join(self.processed_folder, self.training_file), 'wb') as f:
                torch.save(training_set, f)
            with open(os.path.join(self.processed_folder, self.test_file), 'wb') as f:
                torch.save(test_set, f)
    
            print('Done!')
    
        def extra_repr(self):
            return "Split: {}".format("Train" if self.train is True else "Test")
    
    
    class FashionMNIST(MNIST):
        """`Fashion-MNIST <https://github.com/zalandoresearch/fashion-mnist>`_ Dataset. Args: root (string): Root directory of dataset where ``Fashion-MNIST/processed/training.pt`` and ``Fashion-MNIST/processed/test.pt`` exist. train (bool, optional): If True, creates dataset from ``training.pt``, otherwise from ``test.pt``. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. """
        resources = [
            ("http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz",
             "8d4fb7e6c68d591d4c3dfef9ec88bf0d"),
            ("http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz",
             "25c81989df183df01b3e8a0aad5dffbe"),
            ("http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz",
             "bef4ecab320f06d8554ea6380940ec79"),
            ("http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz",
             "bb300cfdad3c16e7a12a480ee83cd310")
        ]
        classes = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal',
                   'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
    
    
    class KMNIST(MNIST):
        """`Kuzushiji-MNIST <https://github.com/rois-codh/kmnist>`_ Dataset. Args: root (string): Root directory of dataset where ``KMNIST/processed/training.pt`` and ``KMNIST/processed/test.pt`` exist. train (bool, optional): If True, creates dataset from ``training.pt``, otherwise from ``test.pt``. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. """
        resources = [
            ("http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-images-idx3-ubyte.gz", "bdb82020997e1d708af4cf47b453dcf7"),
            ("http://codh.rois.ac.jp/kmnist/dataset/kmnist/train-labels-idx1-ubyte.gz", "e144d726b3acfaa3e44228e80efcd344"),
            ("http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-images-idx3-ubyte.gz", "5c965bf0a639b31b8f53240b1b52f4d7"),
            ("http://codh.rois.ac.jp/kmnist/dataset/kmnist/t10k-labels-idx1-ubyte.gz", "7320c461ea6c1c855c0b718fb2a4b134")
        ]
        classes = ['o', 'ki', 'su', 'tsu', 'na', 'ha', 'ma', 'ya', 're', 'wo']
    
    
    class EMNIST(MNIST):
        """`EMNIST <https://www.westernsydney.edu.au/bens/home/reproducible_research/emnist>`_ Dataset. Args: root (string): Root directory of dataset where ``EMNIST/processed/training.pt`` and ``EMNIST/processed/test.pt`` exist. split (string): The dataset has 6 different splits: ``byclass``, ``bymerge``, ``balanced``, ``letters``, ``digits`` and ``mnist``. This argument specifies which one to use. train (bool, optional): If True, creates dataset from ``training.pt``, otherwise from ``test.pt``. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. """
        # Updated URL from https://www.nist.gov/node/1298471/emnist-dataset since the
        # _official_ download link
        # https://cloudstor.aarnet.edu.au/plus/s/ZNmuFiuQTqZlu9W/download
        # is (currently) unavailable
        url = 'http://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/gzip.zip'
        md5 = "58c8d27c78d21e728a6bc7b3cc06412e"
        splits = ('byclass', 'bymerge', 'balanced', 'letters', 'digits', 'mnist')
    
        def __init__(self, root, split, **kwargs):
            self.split = verify_str_arg(split, "split", self.splits)
            self.training_file = self._training_file(split)
            self.test_file = self._test_file(split)
            super(EMNIST, self).__init__(root, **kwargs)
    
        @staticmethod
        def _training_file(split):
            return 'training_{}.pt'.format(split)
    
        @staticmethod
        def _test_file(split):
            return 'test_{}.pt'.format(split)
    
        def download(self):
            """Download the EMNIST data if it doesn't exist in processed_folder already."""
            import shutil
    
            if self._check_exists():
                return
    
            makedir_exist_ok(self.raw_folder)
            makedir_exist_ok(self.processed_folder)
    
            # download files
            print('Downloading and extracting zip archive')
            download_and_extract_archive(self.url, download_root=self.raw_folder, filename="emnist.zip",
                                         remove_finished=True, md5=self.md5)
            gzip_folder = os.path.join(self.raw_folder, 'gzip')
            for gzip_file in os.listdir(gzip_folder):
                if gzip_file.endswith('.gz'):
                    extract_archive(os.path.join(gzip_folder, gzip_file), gzip_folder)
    
            # process and save as torch files
            for split in self.splits:
                print('Processing ' + split)
                training_set = (
                    read_image_file(os.path.join(gzip_folder, 'emnist-{}-train-images-idx3-ubyte'.format(split))),
                    read_label_file(os.path.join(gzip_folder, 'emnist-{}-train-labels-idx1-ubyte'.format(split)))
                )
                test_set = (
                    read_image_file(os.path.join(gzip_folder, 'emnist-{}-test-images-idx3-ubyte'.format(split))),
                    read_label_file(os.path.join(gzip_folder, 'emnist-{}-test-labels-idx1-ubyte'.format(split)))
                )
                with open(os.path.join(self.processed_folder, self._training_file(split)), 'wb') as f:
                    torch.save(training_set, f)
                with open(os.path.join(self.processed_folder, self._test_file(split)), 'wb') as f:
                    torch.save(test_set, f)
            shutil.rmtree(gzip_folder)
    
            print('Done!')
    
    
    class QMNIST(MNIST):
        """`QMNIST <https://github.com/facebookresearch/qmnist>`_ Dataset. Args: root (string): Root directory of dataset whose ``processed'' subdir contains torch binary files with the datasets. what (string,optional): Can be 'train', 'test', 'test10k', 'test50k', or 'nist' for respectively the mnist compatible training set, the 60k qmnist testing set, the 10k qmnist examples that match the mnist testing set, the 50k remaining qmnist testing examples, or all the nist digits. The default is to select 'train' or 'test' according to the compatibility argument 'train'. compat (bool,optional): A boolean that says whether the target for each example is class number (for compatibility with the MNIST dataloader) or a torch vector containing the full qmnist information. Default=True. download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, ``transforms.RandomCrop`` target_transform (callable, optional): A function/transform that takes in the target and transforms it. train (bool,optional,compatibility): When argument 'what' is not specified, this boolean decides whether to load the training set ot the testing set. Default: True. """
    
        subsets = { 
            'train': 'train',
            'test': 'test',
            'test10k': 'test',
            'test50k': 'test',
            'nist': 'nist'
        }
        resources = { 
            'train': [('https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-train-images-idx3-ubyte.gz',
                       'ed72d4157d28c017586c42bc6afe6370'),
                      ('https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-train-labels-idx2-int.gz',
                       '0058f8dd561b90ffdd0f734c6a30e5e4')],
            'test': [('https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-test-images-idx3-ubyte.gz',
                      '1394631089c404de565df7b7aeaf9412'),
                     ('https://raw.githubusercontent.com/facebookresearch/qmnist/master/qmnist-test-labels-idx2-int.gz',
                      '5b5b05890a5e13444e108efe57b788aa')],
            'nist': [('https://raw.githubusercontent.com/facebookresearch/qmnist/master/xnist-images-idx3-ubyte.xz',
                      '7f124b3b8ab81486c9d8c2749c17f834'),
                     ('https://raw.githubusercontent.com/facebookresearch/qmnist/master/xnist-labels-idx2-int.xz',
                      '5ed0e788978e45d4a8bd4b7caec3d79d')]
        }
        classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
                   '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']
    
        def __init__(self, root, what=None, compat=True, train=True, **kwargs):
            if what is None:
                what = 'train' if train else 'test'
            self.what = verify_str_arg(what, "what", tuple(self.subsets.keys()))
            self.compat = compat
            self.data_file = what + '.pt'
            self.training_file = self.data_file
            self.test_file = self.data_file
            super(QMNIST, self).__init__(root, train, **kwargs)
    
        def download(self):
            """Download the QMNIST data if it doesn't exist in processed_folder already. Note that we only download what has been asked for (argument 'what'). """
            if self._check_exists():
                return
            makedir_exist_ok(self.raw_folder)
            makedir_exist_ok(self.processed_folder)
            split = self.resources[self.subsets[self.what]]
            files = []
    
            # download data files if not already there
            for url, md5 in split:
                filename = url.rpartition('/')[2]
                file_path = os.path.join(self.raw_folder, filename)
                if not os.path.isfile(file_path):
                    download_url(url, root=self.raw_folder, filename=filename, md5=md5)
                files.append(file_path)
    
            # process and save as torch files
            print('Processing...')
            data = read_sn3_pascalvincent_tensor(files[0])
            assert(data.dtype == torch.uint8)
            assert(data.ndimension() == 3)
            targets = read_sn3_pascalvincent_tensor(files[1]).long()
            assert(targets.ndimension() == 2)
            if self.what == 'test10k':
                data = data[0:10000, :, :].clone()
                targets = targets[0:10000, :].clone()
            if self.what == 'test50k':
                data = data[10000:, :, :].clone()
                targets = targets[10000:, :].clone()
            with open(os.path.join(self.processed_folder, self.data_file), 'wb') as f:
                torch.save((data, targets), f)
    
        def __getitem__(self, index):
            # redefined to handle the compat flag
            img, target = self.data[index], self.targets[index]
            img = Image.fromarray(img.numpy(), mode='L')
            if self.transform is not None:
                img = self.transform(img)
            if self.compat:
                target = int(target[0])
            if self.target_transform is not None:
                target = self.target_transform(target)
            return img, target
    
        def extra_repr(self):
            return "Split: {}".format(self.what)
    
    
    def get_int(b):
        return int(codecs.encode(b, 'hex'), 16)
    
    
    def open_maybe_compressed_file(path):
        """Return a file object that possibly decompresses 'path' on the fly. Decompression occurs when argument `path` is a string and ends with '.gz' or '.xz'. """
        if not isinstance(path, torch._six.string_classes):
            return path
        if path.endswith('.gz'):
            import gzip
            return gzip.open(path, 'rb')
        if path.endswith('.xz'):
            import lzma
            return lzma.open(path, 'rb')
        return open(path, 'rb')
    
    
    def read_sn3_pascalvincent_tensor(path, strict=True):
        """Read a SN3 file in "Pascal Vincent" format (Lush file 'libidx/idx-io.lsh'). Argument may be a filename, compressed filename, or file object. """
        # typemap
        if not hasattr(read_sn3_pascalvincent_tensor, 'typemap'):
            read_sn3_pascalvincent_tensor.typemap = { 
                8: (torch.uint8, np.uint8, np.uint8),
                9: (torch.int8, np.int8, np.int8),
                11: (torch.int16, np.dtype('>i2'), 'i2'),
                12: (torch.int32, np.dtype('>i4'), 'i4'),
                13: (torch.float32, np.dtype('>f4'), 'f4'),
                14: (torch.float64, np.dtype('>f8'), 'f8')}
        # read
        with open_maybe_compressed_file(path) as f:
            data = f.read()
        # parse
        magic = get_int(data[0:4])
        nd = magic % 256
        ty = magic // 256
        assert nd >= 1 and nd <= 3
        assert ty >= 8 and ty <= 14
        m = read_sn3_pascalvincent_tensor.typemap[ty]
        s = [get_int(data[4 * (i + 1): 4 * (i + 2)]) for i in range(nd)]
        parsed = np.frombuffer(data, dtype=m[1], offset=(4 * (nd + 1)))
        assert parsed.shape[0] == np.prod(s) or not strict
        return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
    
    
    def read_label_file(path):
        with open(path, 'rb') as f:
            x = read_sn3_pascalvincent_tensor(f, strict=False)
        assert(x.dtype == torch.uint8)
        assert(x.ndimension() == 1)
        return x.long()
    
    
    def read_image_file(path):
        with open(path, 'rb') as f:
            x = read_sn3_pascalvincent_tensor(f, strict=False)
        assert(x.dtype == torch.uint8)
        assert(x.ndimension() == 3)
        return x

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[http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_train-images-idx3-ubyte.gz]: http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz
[http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_train-labels-idx1-ubyte.gz]: http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz
[http_fashion-mnist.s3-website.eu-central-1.amazonaws.com_t10k-images-idx3-ubyte.gz]: http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz
[Python]: https://blog.csdn.net/herosunly/article/details/116895553
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hlcm9zdW5seQ_size_16_color_FFFFFF_t_70_pic_center]: /images/20221014/212e9dd6f9574b94b83bbdd565fa3568.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L2hlcm9zdW5seQ_size_16_color_FFFFFF_t_70 1]: /images/20221014/b40b5799a4d94dbcae59683e4d839ee7.png