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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,

)

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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)