-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathdata.py
177 lines (136 loc) · 6.04 KB
/
data.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
"""Data provider"""
import torch
import torch.utils.data as data
import torch.distributed as dist
from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer
import os
import nltk
import h5py
import numpy as np
import string
class PrecompDataset(data.Dataset):
"""
Load precomputed captions and image features
Possible options: f30k_precomp, coco_precomp
"""
def __init__(self, data_path, data_split, vocab):
self.vocab = vocab
self.loc = data_path + '/'
self.img_path = self.loc+'%s.h5' % data_split
self.data_split = data_split
self.images = None
# load the raw captions
self.captions = []
for line in open(self.loc+'%s_caps.txt' % data_split, 'rb'):
self.captions.append(line.strip())
# load the image features
#self.images = h5py.File(self.loc+'%s.h5' % data_split, 'r')
self.length = len(self.captions)
if data_split == 'train':
self.im_div = 5
else:
self.im_div = 1
# the development set for coco is large and so validation would be slow
if data_split == 'dev':
self.length = 5000
self.captions = self.captions[0:self.length]
# CIDEr
self.trans_captions = self.captions
# TFIDF matrix compute
self.tfidf_map = self.tfidf_compute(self.captions)
self.tfidf_shape = (self.tfidf_map.shape[0], self.tfidf_map.shape[1])
self.idx2GT = np.zeros((self.tfidf_map.shape[0]), dtype=int)
for i in range(0, self.tfidf_map.shape[0], 5):
self.idx2GT[i: i + 5] = i
def tfidf_compute(self, corpus, eps=1e-6):
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
transformer = TfidfTransformer()
tfidf_mat = transformer.fit_transform(X).toarray()
return tfidf_mat
def __getitem__(self, index):
# handle the image redundancy
if self.images is None:
self.images = h5py.File(self.loc+'%s.h5' % self.data_split, 'r')
img_id = index//self.im_div
image = torch.Tensor(self.images['feat'][img_id])
caption = self.captions[index]
# get tfidf vector of indexed caption
sem_single = self.tfidf_map[index]
# get tfidf vectors of positive captions of indexed images
sem_GT = self.tfidf_map[self.idx2GT[index]: self.idx2GT[index] + 5, :]
#print(type(sem_GT)
vocab = self.vocab
# -------- The main difference between python2.7 and python3.6 --------#
# The suggestion from Hongguang Zhu(https://github.com/KevinLight831)
# ---------------------------------------------------------------------#
# tokens = nltk.tokenize.word_tokenize(str(caption).lower())
# convert caption (string) to word ids.
tokens = nltk.tokenize.word_tokenize(caption.lower().decode('utf-8'))
caption = []
caption.append(vocab('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
target = torch.Tensor(caption)
return image, target, index, img_id, sem_GT, sem_single
def __len__(self):
return self.length
def collate_fn(data):
"""
Build mini-batch tensors from a list of (image, caption, index, img_id) tuples.
Args:
data: list of (image, target, index, img_id) tuple.
- image: torch tensor of shape (36, 2048).
- target: torch tensor of shape (?) variable length.
Returns:
- images: torch tensor of shape (batch_size, 36, 2048).
- targets: torch tensor of shape (batch_size, padded_length).
- lengths: list; valid length for each padded caption.
"""
# Sort a data list by caption length
data.sort(key=lambda x: len(x[1]), reverse=True)
images, captions, ids, img_ids, sem_GT, sem_single = zip(*data)
# Merge images (convert tuple of 2D tensor to 3D tensor)
images = torch.stack(images, 0)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
lengths = [len(cap) for cap in captions]
targets = torch.zeros(len(captions), max(lengths)).long()
for i, cap in enumerate(captions):
end = lengths[i]
targets[i, :end] = cap[:end]
# CIDEr
sem_GT = torch.Tensor(np.stack(sem_GT, axis=0))
sem_single = torch.Tensor(np.stack(sem_single, axis=0))
return images, targets, lengths, ids, sem_GT, sem_single
def get_precomp_loader(data_path, data_split, vocab, opt, batch_size=100,
shuffle=True, num_workers=2):
dset = PrecompDataset(data_path, data_split, vocab)
if data_split == 'train':
sampler = torch.utils.data.distributed.DistributedSampler(dset)
else:
sampler = None
data_loader = torch.utils.data.DataLoader(dataset=dset,
batch_size=batch_size,
sampler=sampler,
num_workers=num_workers,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn)
return data_loader, sampler
def get_loaders(data_name, vocab, batch_size, workers, opt):
# get the data path
dpath = os.path.join(opt.data_path, data_name)
# get the train_loader
train_loader, train_sampler = get_precomp_loader(dpath, 'train', vocab, opt,
batch_size, True, workers)
# get the val_loader
val_loader, _ = get_precomp_loader(dpath, 'dev', vocab, opt,
100, False, workers)
return train_loader, val_loader, train_sampler
def get_test_loader(split_name, data_name, vocab, batch_size, workers, opt):
# get the data path
dpath = os.path.join(opt.data_path, data_name)
# get the test_loader
test_loader, test_sampler = get_precomp_loader(dpath, split_name, vocab, opt,
100, False, workers)
return test_loader