pytorch 把MNIST数据集转换成图片和txt的方法

  • Z时代
  • 2024-01-10
  • 分类：综合

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)

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