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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
|
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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
|
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服务器之家。
原文链接:http://www.waitingfy.com/archives/3539