pytorch 把MNIST数据集转换成图片和txt

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)

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