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')),'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,label) in enumerate(zip(test_set[0],test_set[1])): img_path = data_path + str(i) + '.jpg' io.imsave(img_path,'') 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编程小技巧。