-
Notifications
You must be signed in to change notification settings - Fork 56
/
data.py
304 lines (225 loc) · 9.75 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
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
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
import os
import json
import numpy as np
import pandas as pd
import torch
import torch.nn.functional as F
from fairseq.data import FairseqDataset, data_utils
from torch.utils.data.dataloader import default_collate
from PIL import Image
def split_file(split):
return os.path.join('splits', f'karpathy_{split}_images.txt')
def read_split_image_ids_and_paths(split):
split_df = pd.read_csv(split_file(split), sep=' ', header=None)
return split_df.iloc[:,1].to_numpy(), split_df.iloc[:,0].to_numpy()
def read_split_image_ids(split):
return read_split_image_ids_and_paths(split)[0]
def read_image_ids(file, non_redundant=False):
with open(file, 'r') as f:
image_ids = [int(line) for line in f]
if non_redundant:
return list(set(image_ids))
else:
return image_ids
def read_image_metadata(file):
df = pd.read_csv(file)
md = {}
for img_id, img_h, img_w, num_boxes in zip(df['image_id'], df['image_h'], df['image_w'], df['num_boxes']):
md[img_id] = {
'image_h': np.float32(img_h),
'image_w': np.float32(img_w),
'num_boxes': num_boxes
}
return md
class ImageDataset(torch.utils.data.Dataset):
def __init__(self, image_ids, image_paths, transform=lambda x: x):
self.image_ids = image_ids
self.image_paths = image_paths
self.transform = transform
def __len__(self):
return len(self.image_ids)
def __getitem__(self, idx):
image_path = self.image_paths[idx]
with Image.open(image_path).convert('RGB') as img:
return self.transform(img), self.image_ids[idx]
class FeaturesDataset(FairseqDataset):
def __init__(self, features_dir, image_ids, num_objects):
self.features_dir = features_dir
self.image_ids = image_ids
self.num_objects = num_objects
def __getitem__(self, index):
return self.read_data(self.image_ids[index])
def __len__(self):
return len(self.image_ids)
def num_tokens(self, index):
return self.num_objects[index]
def size(self, index):
return self.num_objects[index]
@property
def sizes(self):
return self.num_objects
def read_data(self, image_id):
raise NotImplementedError
def collater(self, samples):
num_objects = [features.shape[0] for features, _ in samples]
max_objects = max(num_objects)
feature_samples_padded = []
location_samples_padded = []
for (features, locations), n in zip(samples, num_objects):
features_padded = F.pad(features, pad=[0, 0, 0, max_objects-n], mode='constant', value=0.0)
locations_padded = F.pad(locations, pad=[0, 0, 0, max_objects-n], mode='constant', value=0.0)
feature_samples_padded.append(features_padded)
location_samples_padded.append(locations_padded)
return default_collate(feature_samples_padded), default_collate(location_samples_padded)
class GridFeaturesDataset(FeaturesDataset):
def __init__(self, features_dir, image_ids, grid_shape=(8, 8)):
super().__init__(features_dir=features_dir,
image_ids=image_ids,
num_objects=np.ones(len(image_ids), dtype=np.int) * np.prod(grid_shape))
self.grid_shape = grid_shape
self.locations = self.tile_locations(grid_shape)
def read_data(self, image_id):
features_file = os.path.join(self.features_dir, f'{image_id}.npy')
features = np.load(features_file)
return torch.as_tensor(features), self.locations
@staticmethod
def tile_locations(grid_shape):
num_tiles = np.prod(grid_shape)
rel_tile_w = 1. / grid_shape[1]
rel_tile_h = 1. / grid_shape[0]
rel_tile_area = 1. / num_tiles
rel_tile_locations = np.zeros(shape=(grid_shape[0], grid_shape[1], 5), dtype=np.float32)
for i in range(grid_shape[0]):
for j in range(grid_shape[1]):
rel_tile_locations[i, j] = np.array([
j * rel_tile_w,
i * rel_tile_h,
(j+1) * rel_tile_w,
(i+1) * rel_tile_h,
rel_tile_area
], dtype=np.float32)
return torch.as_tensor(rel_tile_locations).view(num_tiles, 5)
class ObjectFeaturesDataset(FeaturesDataset):
def __init__(self, features_dir, image_ids, image_metadata):
super().__init__(features_dir=features_dir,
image_ids=image_ids,
num_objects=np.array([image_metadata[image_id]['num_boxes'] for image_id in image_ids]))
self.image_metadata = image_metadata
def read_data(self, image_id):
features_file = os.path.join(self.features_dir, f'{image_id}.npy')
features = np.load(features_file)
boxes_file = os.path.join(self.features_dir, f'{image_id}-boxes.npy')
boxes = np.load(boxes_file)
# Normalize box coordinates
boxes[:, [0, 2]] /= self.image_metadata[image_id]['image_w']
boxes[:, [1, 3]] /= self.image_metadata[image_id]['image_h']
# Normalized box areas
areas = (boxes[:, 2] - boxes[:, 0]) * \
(boxes[:, 3] - boxes[:, 1])
return torch.as_tensor(features), \
torch.as_tensor(np.c_[boxes, areas])
class CaptionsDataset(FairseqDataset):
"""Captions dataset used for self-critical sequence training (SCST) only.
"""
def __init__(self, captions_file, image_ids):
self.image_ids = image_ids
self.num_objects = np.zeros(len(image_ids), dtype=np.int)
with open(captions_file) as f:
self.captions = json.load(f)
for i, image_id in enumerate(image_ids):
captions = self.captions[str(image_id)]
caption_sizes = [len(caption.split(' ')) for caption in captions]
self.num_objects[i] = np.max(caption_sizes)
def __getitem__(self, index):
return self.captions[str(self.image_ids[index])]
def __len__(self):
return len(self.image_ids)
def num_tokens(self, index):
return self.num_objects[index]
def size(self, index):
return self.num_objects[index]
@property
def sizes(self):
return self.num_objects
def collater(self, samples):
return samples
class ImageCaptionDataset(FairseqDataset):
def __init__(self, img_ds, cap_ds, cap_dict, scst=False, shuffle=False):
self.img_ds = img_ds
self.cap_ds = cap_ds
self.cap_dict = cap_dict
self.scst = scst
self.shuffle = shuffle
def __getitem__(self, index):
source_features, source_locations = self.img_ds[index]
target = self.cap_ds[index]
return {
'id': index,
'source_features': source_features,
'source_locations': source_locations,
'target': target
}
def __len__(self):
return len(self.img_ds)
def num_tokens(self, index):
return self.size(index)[1]
def size(self, index):
# number of image feature vectors, number of tokens in caption
return self.img_ds.sizes[index], self.cap_ds.sizes[index]
def ordered_indices(self):
if self.shuffle:
indices = np.random.permutation(len(self))
else:
indices = np.arange(len(self))
# Inspired by LanguagePairDataset.ordered_indices
indices = indices[np.argsort(self.cap_ds.sizes[indices], kind='mergesort')]
return indices[np.argsort(self.img_ds.sizes[indices], kind='mergesort')]
def collater(self, samples):
indices = []
source_feature_samples = []
source_location_samples = []
source_lengths = []
target_samples = []
target_ntokens = 0
for sample in samples:
index = sample['id']
indices.append(index)
source_feature_samples.append(sample['source_features'])
source_location_samples.append(sample['source_locations'])
source_lengths.append(self.img_ds.sizes[index])
target_samples.append(sample['target'])
target_ntokens += self.cap_ds.sizes[index]
num_sentences = len(samples)
# FIXME: workaround for edge case in parallel processing
# (framework passes empty samples list
# to collater under certain conditions)
if num_sentences == 0:
return None
indices = torch.tensor(indices, dtype=torch.long)
source_feature_batch, source_location_batch = \
self.img_ds.collater(list(zip(source_feature_samples, source_location_samples)))
# TODO: switch depending on SCST or CE training
if self.scst:
target_batch = target_samples
rotate_batch = None
else:
target_batch = data_utils.collate_tokens(target_samples,
pad_idx=self.cap_dict.pad(),
eos_idx=self.cap_dict.eos(),
move_eos_to_beginning=False)
rotate_batch = data_utils.collate_tokens(target_samples,
pad_idx=self.cap_dict.pad(),
eos_idx=self.cap_dict.eos(),
move_eos_to_beginning=True)
return {
'id': indices,
'net_input': {
'src_tokens': source_feature_batch,
'src_locations': source_location_batch,
'src_lengths': source_lengths,
'prev_output_tokens': rotate_batch,
},
'target': target_batch,
'ntokens': target_ntokens,
'nsentences': num_sentences,
}