-
Notifications
You must be signed in to change notification settings - Fork 0
/
triplet_mnist_loader.py
211 lines (176 loc) · 7.7 KB
/
triplet_mnist_loader.py
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
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
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
import csv
class MNIST_t(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'
train_triplet_file = 'train_triplets.txt'
test_triplet_file = 'test_triplets.txt'
def __init__(self, root, n_train_triplets=50000, n_test_triplets=10000, train=True, transform=None, target_transform=None, download=False):
self.root = root
self.transform = 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))
self.make_triplet_list(n_train_triplets)
triplets = []
for line in open(os.path.join(root, self.processed_folder, self.train_triplet_file)):
if len(line.split()) <3: continue
triplets.append((int(line.split()[0]), int(line.split()[1]), int(line.split()[2]))) # anchor, close, far
self.triplets_train = triplets
else:
self.test_data, self.test_labels = torch.load(os.path.join(root, self.processed_folder, self.test_file))
self.make_triplet_list(n_test_triplets)
triplets = []
for line in open(os.path.join(root, self.processed_folder, self.test_triplet_file)):
if len(line.split()) <3: continue
triplets.append((int(line.split()[0]), int(line.split()[1]), int(line.split()[2]))) # anchor, close, far
self.triplets_test = triplets
def __getitem__(self, index):
if self.train:
idx1, idx2, idx3 = self.triplets_train[index]
img1, img2, img3 = self.train_data[idx1], self.train_data[idx2], self.train_data[idx3]
else:
idx1, idx2, idx3 = self.triplets_test[index]
img1, img2, img3 = self.test_data[idx1], self.test_data[idx2], self.test_data[idx3]
# doing this so that it is consistent with all other datasets
# to return a PIL Image
img1 = Image.fromarray(img1.numpy(), mode='L')
img2 = Image.fromarray(img2.numpy(), mode='L')
img3 = Image.fromarray(img3.numpy(), mode='L')
if self.transform is not None:
img1 = self.transform(img1)
img2 = self.transform(img2)
img3 = self.transform(img3)
return img1, img2, img3
def __len__(self):
if self.train:
return len(self.triplets_train)
else:
return len(self.triplets_test)
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 _check_triplets_exists(self):
return os.path.exists(os.path.join(self.root, self.processed_folder, self.train_triplet_file)) and \
os.path.exists(os.path.join(self.root, self.processed_folder, self.test_triplet_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 make_triplet_list(self, ntriplets):
if self._check_triplets_exists():
return
print('Processing Triplet Generation ...')
if self.train:
np_labels = self.train_labels.numpy()
filename = self.train_triplet_file
else:
np_labels = self.test_labels.numpy()
filename = self.test_triplet_file
triplets = []
for class_idx in range(10):
a = np.random.choice(np.where(np_labels==class_idx)[0], int(ntriplets/10), replace=True)
b = np.random.choice(np.where(np_labels==class_idx)[0], int(ntriplets/10), replace=True)
while np.any((a-b)==0):
np.random.shuffle(b)
c = np.random.choice(np.where(np_labels!=class_idx)[0], int(ntriplets/10), replace=True)
for i in range(a.shape[0]):
#triplets.append([int(a[i]), int(c[i]), int(b[i])])
triplets.append([int(a[i]), int(b[i]), int(c[i])])
with open(os.path.join(self.root, self.processed_folder, filename), "w") as f:
writer = csv.writer(f, delimiter=' ')
writer.writerows(triplets)
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)