pytorch 把MNIST数据集转换成图片和txt的方法

　　本文介绍了pytorch 把MNIST数据集转换成图片和txt的方法，分享给大家，具体如下：

　　1.下载Mnist 数据集

　　 import os # third-party library import torch import torch.nn as nn from torch.autograd import Variable import torch.utils.data as Data import torchvision import matplotlib.pyplot as plt # torch.manual_seed(1) # reproducible DOWNLOAD_MNIST = False # Mnist digits dataset if 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, )

　　下载下来的其实可以直接用了，但是我们这边想把它们转换成图片和txt，这样好看些，为后面用自己的图片和txt作为准备

　　2. 保存为图片和txt

　　 import os from skimage import io import torchvision.datasets.mnist as mnist import 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)

　　以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。