-
Notifications
You must be signed in to change notification settings - Fork 353
/
dataset.py
193 lines (170 loc) · 7.1 KB
/
dataset.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
import os
import math
import numpy as np
import pandas as pd
from tqdm import tqdm
from utils.coco.coco import COCO
from utils.vocabulary import Vocabulary
class DataSet(object):
def __init__(self,
image_ids,
image_files,
batch_size,
word_idxs=None,
masks=None,
is_train=False,
shuffle=False):
self.image_ids = np.array(image_ids)
self.image_files = np.array(image_files)
self.word_idxs = np.array(word_idxs)
self.masks = np.array(masks)
self.batch_size = batch_size
self.is_train = is_train
self.shuffle = shuffle
self.setup()
def setup(self):
""" Setup the dataset. """
self.count = len(self.image_ids)
self.num_batches = int(np.ceil(self.count * 1.0 / self.batch_size))
self.fake_count = self.num_batches * self.batch_size - self.count
self.idxs = list(range(self.count))
self.reset()
def reset(self):
""" Reset the dataset. """
self.current_idx = 0
if self.shuffle:
np.random.shuffle(self.idxs)
def next_batch(self):
""" Fetch the next batch. """
assert self.has_next_batch()
if self.has_full_next_batch():
start, end = self.current_idx, \
self.current_idx + self.batch_size
current_idxs = self.idxs[start:end]
else:
start, end = self.current_idx, self.count
current_idxs = self.idxs[start:end] + \
list(np.random.choice(self.count, self.fake_count))
image_files = self.image_files[current_idxs]
if self.is_train:
word_idxs = self.word_idxs[current_idxs]
masks = self.masks[current_idxs]
self.current_idx += self.batch_size
return image_files, word_idxs, masks
else:
self.current_idx += self.batch_size
return image_files
def has_next_batch(self):
""" Determine whether there is a batch left. """
return self.current_idx < self.count
def has_full_next_batch(self):
""" Determine whether there is a full batch left. """
return self.current_idx + self.batch_size <= self.count
def prepare_train_data(config):
""" Prepare the data for training the model. """
coco = COCO(config.train_caption_file)
coco.filter_by_cap_len(config.max_caption_length)
print("Building the vocabulary...")
vocabulary = Vocabulary(config.vocabulary_size)
if not os.path.exists(config.vocabulary_file):
vocabulary.build(coco.all_captions())
vocabulary.save(config.vocabulary_file)
else:
vocabulary.load(config.vocabulary_file)
print("Vocabulary built.")
print("Number of words = %d" %(vocabulary.size))
coco.filter_by_words(set(vocabulary.words))
print("Processing the captions...")
if not os.path.exists(config.temp_annotation_file):
captions = [coco.anns[ann_id]['caption'] for ann_id in coco.anns]
image_ids = [coco.anns[ann_id]['image_id'] for ann_id in coco.anns]
image_files = [os.path.join(config.train_image_dir,
coco.imgs[image_id]['file_name'])
for image_id in image_ids]
annotations = pd.DataFrame({'image_id': image_ids,
'image_file': image_files,
'caption': captions})
annotations.to_csv(config.temp_annotation_file)
else:
annotations = pd.read_csv(config.temp_annotation_file)
captions = annotations['caption'].values
image_ids = annotations['image_id'].values
image_files = annotations['image_file'].values
if not os.path.exists(config.temp_data_file):
word_idxs = []
masks = []
for caption in tqdm(captions):
current_word_idxs_ = vocabulary.process_sentence(caption)
current_num_words = len(current_word_idxs_)
current_word_idxs = np.zeros(config.max_caption_length,
dtype = np.int32)
current_masks = np.zeros(config.max_caption_length)
current_word_idxs[:current_num_words] = np.array(current_word_idxs_)
current_masks[:current_num_words] = 1.0
word_idxs.append(current_word_idxs)
masks.append(current_masks)
word_idxs = np.array(word_idxs)
masks = np.array(masks)
data = {'word_idxs': word_idxs, 'masks': masks}
np.save(config.temp_data_file, data)
else:
data = np.load(config.temp_data_file).item()
word_idxs = data['word_idxs']
masks = data['masks']
print("Captions processed.")
print("Number of captions = %d" %(len(captions)))
print("Building the dataset...")
dataset = DataSet(image_ids,
image_files,
config.batch_size,
word_idxs,
masks,
True,
True)
print("Dataset built.")
return dataset
def prepare_eval_data(config):
""" Prepare the data for evaluating the model. """
coco = COCO(config.eval_caption_file)
image_ids = list(coco.imgs.keys())
image_files = [os.path.join(config.eval_image_dir,
coco.imgs[image_id]['file_name'])
for image_id in image_ids]
print("Building the vocabulary...")
if os.path.exists(config.vocabulary_file):
vocabulary = Vocabulary(config.vocabulary_size,
config.vocabulary_file)
else:
vocabulary = build_vocabulary(config)
print("Vocabulary built.")
print("Number of words = %d" %(vocabulary.size))
print("Building the dataset...")
dataset = DataSet(image_ids, image_files, config.batch_size)
print("Dataset built.")
return coco, dataset, vocabulary
def prepare_test_data(config):
""" Prepare the data for testing the model. """
files = os.listdir(config.test_image_dir)
image_files = [os.path.join(config.test_image_dir, f) for f in files
if f.lower().endswith('.jpg') or f.lower().endswith('.jpeg')]
image_ids = list(range(len(image_files)))
print("Building the vocabulary...")
if os.path.exists(config.vocabulary_file):
vocabulary = Vocabulary(config.vocabulary_size,
config.vocabulary_file)
else:
vocabulary = build_vocabulary(config)
print("Vocabulary built.")
print("Number of words = %d" %(vocabulary.size))
print("Building the dataset...")
dataset = DataSet(image_ids, image_files, config.batch_size)
print("Dataset built.")
return dataset, vocabulary
def build_vocabulary(config):
""" Build the vocabulary from the training data and save it to a file. """
coco = COCO(config.train_caption_file)
coco.filter_by_cap_len(config.max_caption_length)
vocabulary = Vocabulary(config.vocabulary_size)
vocabulary.build(coco.all_captions())
vocabulary.save(config.vocabulary_file)
return vocabulary