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mymnist.py
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mymnist.py
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from __future__ import print_function
import torch.utils.data as data
from PIL import Image
import os
import os.path
import errno
import torch
import json
import codecs
import numpy as np
from torchvision import transforms
'''
This is my mnist dataset class for super-resolution
Here is the difference :
__getitem__ : target becomes original image
'''
class MNIST(data.Dataset):
urls = [
'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
]
raw_folder = 'raw'
processed_folder = 'processed'
training_file = 'training.pt'
test_file = 'test.pt'
def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
self.root = root
self.transform = transform
self.target_transform = target_transform
self.train = train # training set or test set
if download:
self.download()
if not self._check_exists():
raise RuntimeError('Dataset not found.' +
' You can use download=True to download it')
if self.train:
self.train_data, self.train_labels = torch.load(
os.path.join(root, self.processed_folder, self.training_file))
else:
self.test_data, self.test_labels = torch.load(os.path.join(root, self.processed_folder, self.test_file))
def __getitem__(self, index):
if self.train:
origin_img= self.train_data[index]
else:
origin_img = self.test_data[index]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
origin_img = Image.fromarray(origin_img.numpy(), mode='L')
scale = transforms.Scale(14)
small_img = scale(origin_img)
if self.transform is not None:
origin_img = self.transform(origin_img)
small_img = self.transform(small_img)
return small_img, origin_img
def __len__(self):
if self.train:
return 60000
else:
return 10000
def _check_exists(self):
return os.path.exists(os.path.join(self.root, self.processed_folder, self.training_file)) and \
os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
def download(self):
from six.moves import urllib
import gzip
if self._check_exists():
return
# download files
try:
os.makedirs(os.path.join(self.root, self.raw_folder))
os.makedirs(os.path.join(self.root, self.processed_folder))
except OSError as e:
if e.errno == errno.EEXIST:
pass
else:
raise
for url in self.urls:
print('Downloading ' + url)
data = urllib.request.urlopen(url)
filename = url.rpartition('/')[2]
file_path = os.path.join(self.root, self.raw_folder, filename)
with open(file_path, 'wb') as f:
f.write(data.read())
with open(file_path.replace('.gz', ''), 'wb') as out_f, \
gzip.GzipFile(file_path) as zip_f:
out_f.write(zip_f.read())
os.unlink(file_path)
# process and save as torch files
print('Processing...')
training_set = (
read_image_file(os.path.join(self.root, self.raw_folder, 'train-images-idx3-ubyte')),
read_label_file(os.path.join(self.root, self.raw_folder, 'train-labels-idx1-ubyte'))
)
test_set = (
read_image_file(os.path.join(self.root, self.raw_folder, 't10k-images-idx3-ubyte')),
read_label_file(os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'))
)
with open(os.path.join(self.root, self.processed_folder, self.training_file), 'wb') as f:
torch.save(training_set, f)
with open(os.path.join(self.root, self.processed_folder, self.test_file), 'wb') as f:
torch.save(test_set, f)
print('Done!')
def get_int(b):
return int(codecs.encode(b, 'hex'), 16)
def parse_byte(b):
if isinstance(b, str):
return ord(b)
return b
def read_label_file(path):
with open(path, 'rb') as f:
data = f.read()
assert get_int(data[:4]) == 2049
length = get_int(data[4:8])
labels = [parse_byte(b) for b in data[8:]]
assert len(labels) == length
return torch.LongTensor(labels)
def read_image_file(path):
with open(path, 'rb') as f:
data = f.read()
assert get_int(data[:4]) == 2051
length = get_int(data[4:8])
num_rows = get_int(data[8:12])
num_cols = get_int(data[12:16])
images = []
idx = 16
for l in range(length):
img = []
images.append(img)
for r in range(num_rows):
row = []
img.append(row)
for c in range(num_cols):
row.append(parse_byte(data[idx]))
idx += 1
assert len(images) == length
return torch.ByteTensor(images).view(-1, 28, 28)