pytorch 把MNIST数据集转换成图片和txt的方法

import os# third-party libraryimport torchimport torch.nn as nnfrom torch.autograd import Variableimport torch.utils.data as Dataimport torchvisionimport matplotlib.pyplot as plt # torch.manual_seed(1)  # reproducibleDOWNLOAD_MNIST = False # Mnist digits datasetif not(os.path.exists('./mnist/')) or not os.listdir('./mnist/'):  # not mnist dir or mnist is empyt dir  DOWNLOAD_MNIST = True train_data = torchvision.datasets.MNIST(  root='./mnist/',  train=True,                   # this is training data  transform=torchvision.transforms.ToTensor(),  # Converts a PIL.Image or numpy.ndarray to                          # torch.FloatTensor of shape (C x H x W) and normalize in the range [0.0, 1.0]  download=DOWNLOAD_MNIST,)

import osfrom skimage import ioimport torchvision.datasets.mnist as mnistimport numpy root = "./mnist/raw/"train_set = (  mnist.read_image_file(os.path.join(root, 'train-images-idx3-ubyte')),  mnist.read_label_file(os.path.join(root, 'train-labels-idx1-ubyte'))) test_set = (  mnist.read_image_file(os.path.join(root,'t10k-images-idx3-ubyte')),  mnist.read_label_file(os.path.join(root,'t10k-labels-idx1-ubyte'))) print("train set:", train_set[0].size())print("test set:", test_set[0].size()) def convert_to_img(train=True):  if(train):    f = open(root + 'train.txt', 'w')    data_path = root + '/train/'    if(not os.path.exists(data_path)):      os.makedirs(data_path)    for i, (img, label) in enumerate(zip(train_set[0], train_set[1])):      img_path = data_path + str(i) + '.jpg'      io.imsave(img_path, img.numpy())      int_label = str(label).replace('tensor(', '')      int_label = int_label.replace(')', '')      f.write(img_path + ' ' + str(int_label) + '/n')    f.close()  else:    f = open(root + 'test.txt', 'w')    data_path = root + '/test/'    if (not os.path.exists(data_path)):      os.makedirs(data_path)    for i, (img, label) in enumerate(zip(test_set[0], test_set[1])):      img_path = data_path + str(i) + '.jpg'      io.imsave(img_path, img.numpy())      int_label = str(label).replace('tensor(', '')      int_label = int_label.replace(')', '')      f.write(img_path + ' ' + str(int_label) + '/n')    f.close() convert_to_img(True)convert_to_img(False)