-
Notifications
You must be signed in to change notification settings - Fork 21
/
chair.py
484 lines (397 loc) · 19.6 KB
/
chair.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
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
'''
Copied from: https://github.com/LisaAnne/Hallucination/blob/master/utils/chair.py
Modified by: Maxlinn
1. adapt calculation of CHAIR-i and CHAIR-s for Python3, supports for both json and jsonl file input.
2. integrate synonyms.txt to make the script standalone.
3. remove machine-translation based metrics BLEU-n, CIDEr, ROGUE
4. add new metric Recall, which represents the node words(i.e. lemmas of objects) coverage overall.
5. add pickle cache mechanism to make it fast for repetitive evaluations.
'''
import os
import sys
import nltk
import json
# from pattern.en import singularize
from nltk.corpus import wordnet
from nltk.stem import WordNetLemmatizer
import argparse
import tqdm
import pickle
from collections import defaultdict
# copied from: https://github.com/LisaAnne/Hallucination/blob/master/data/synonyms.txt
synonyms_txt = '''
person, girl, boy, man, woman, kid, child, chef, baker, people, adult, rider, children, baby, worker, passenger, sister, biker, policeman, cop, officer, lady, cowboy, bride, groom, male, female, guy, traveler, mother, father, gentleman, pitcher, player, skier, snowboarder, skater, skateboarder, person, woman, guy, foreigner, child, gentleman, caller, offender, coworker, trespasser, patient, politician, soldier, grandchild, serviceman, walker, drinker, doctor, bicyclist, thief, buyer, teenager, student, camper, driver, solider, hunter, shopper, villager
bicycle, bike, bicycle, bike, unicycle, minibike, trike
car, automobile, van, minivan, sedan, suv, hatchback, cab, jeep, coupe, taxicab, limo, taxi
motorcycle, scooter, motor bike, motor cycle, motorbike, scooter, moped
airplane, jetliner, plane, air plane, monoplane, aircraft, jet, jetliner, airbus, biplane, seaplane
bus, minibus, trolley
train, locomotive, tramway, caboose
truck, pickup, lorry, hauler, firetruck
boat, ship, liner, sailboat, motorboat, dinghy, powerboat, speedboat, canoe, skiff, yacht, kayak, catamaran, pontoon, houseboat, vessel, rowboat, trawler, ferryboat, watercraft, tugboat, schooner, barge, ferry, sailboard, paddleboat, lifeboat, freighter, steamboat, riverboat, battleship, steamship
traffic light, street light, traffic signal, stop light, streetlight, stoplight
fire hydrant, hydrant
stop sign
parking meter
bench, pew
bird, ostrich, owl, seagull, goose, duck, parakeet, falcon, robin, pelican, waterfowl, heron, hummingbird, mallard, finch, pigeon, sparrow, seabird, osprey, blackbird, fowl, shorebird, woodpecker, egret, chickadee, quail, bluebird, kingfisher, buzzard, willet, gull, swan, bluejay, flamingo, cormorant, parrot, loon, gosling, waterbird, pheasant, rooster, sandpiper, crow, raven, turkey, oriole, cowbird, warbler, magpie, peacock, cockatiel, lorikeet, puffin, vulture, condor, macaw, peafowl, cockatoo, songbird
cat, kitten, feline, tabby
dog, puppy, beagle, pup, chihuahua, schnauzer, dachshund, rottweiler, canine, pitbull, collie, pug, terrier, poodle, labrador, doggie, doberman, mutt, doggy, spaniel, bulldog, sheepdog, weimaraner, corgi, cocker, greyhound, retriever, brindle, hound, whippet, husky
horse, colt, pony, racehorse, stallion, equine, mare, foal, palomino, mustang, clydesdale, bronc, bronco
sheep, lamb, ram, lamb, goat, ewe
cow, cattle, oxen, ox, calf, cattle, holstein, heifer, buffalo, bull, zebu, bison
elephant
bear, panda
zebra
giraffe
backpack, knapsack
umbrella
handbag, wallet, purse, briefcase
tie, bow, bow tie
suitcase, suit case, luggage
frisbee
skis, ski
snowboard
sports ball, ball
kite
baseball bat
baseball glove
skateboard
surfboard, longboard, skimboard, shortboard, wakeboard
tennis racket, racket
bottle
wine glass
cup
fork
knife, pocketknife, knive
spoon
bowl, container
banana
apple
sandwich, burger, sub, cheeseburger, hamburger
orange
broccoli
carrot
hot dog
pizza
donut, doughnut, bagel
cake, cheesecake, cupcake, shortcake, coffeecake, pancake
chair, seat, stool
couch, sofa, recliner, futon, loveseat, settee, chesterfield
potted plant, houseplant
bed
dining table, table, desk
toilet, urinal, commode, toilet, lavatory, potty
tv, monitor, televison, television
laptop, computer, notebook, netbook, lenovo, macbook, laptop computer
mouse
remote
keyboard
cell phone, mobile phone, phone, cellphone, telephone, phon, smartphone, iPhone
microwave
oven, stovetop, stove, stove top oven
toaster
sink
refrigerator, fridge, fridge, freezer
book
clock
vase
scissors
teddy bear, teddybear
hair drier, hairdryer
toothbrush
'''
def combine_coco_captions(annotation_path):
if not os.path.exists('%s/captions_%s2014.json' %(annotation_path, 'val')):
raise Exception("Please download MSCOCO caption annotations for val set")
if not os.path.exists('%s/captions_%s2014.json' %(annotation_path, 'train')):
raise Exception("Please download MSCOCO caption annotations for train set")
val_caps = json.load(open('%s/captions_%s2014.json' %(annotation_path, 'val')))
train_caps = json.load(open('%s/captions_%s2014.json' %(annotation_path, 'train')))
all_caps = {'info': train_caps['info'],
'licenses': train_caps['licenses'],
'images': val_caps['images'] + train_caps['images'],
'annotations': val_caps['annotations'] + train_caps['annotations']}
return all_caps
def combine_coco_instances(annotation_path):
if not os.path.exists('%s/instances_%s2014.json' %(annotation_path, 'val')):
raise Exception("Please download MSCOCO instance annotations for val set")
if not os.path.exists('%s/instances_%s2014.json' %(annotation_path, 'train')):
raise Exception("Please download MSCOCO instance annotations for train set")
val_instances = json.load(open('%s/instances_%s2014.json' %(annotation_path, 'val')))
train_instances = json.load(open('%s/instances_%s2014.json' %(annotation_path, 'train')))
all_instances = {'info': train_instances['info'],
'licenses': train_instances['licenses'],
'type': train_instances['licenses'],
'categories': train_instances['categories'],
'images': train_instances['images'] + val_instances['images'],
'annotations': val_instances['annotations'] + train_instances['annotations']}
return all_instances
class CHAIR(object):
def __init__(self, coco_path):
self.imid_to_objects = defaultdict(list) # later become a dict of sets
self.coco_path = coco_path
#read in synonyms
synonyms = synonyms_txt.splitlines()
synonyms = [s.strip().split(', ') for s in synonyms]
self.mscoco_objects = [] #mscoco objects and *all* synonyms
self.inverse_synonym_dict = {}
for synonym in synonyms:
self.mscoco_objects.extend(synonym)
for s in synonym:
self.inverse_synonym_dict[s] = synonym[0]
#Some hard coded rules for implementing CHAIR metrics on MSCOCO
#common 'double words' in MSCOCO that should be treated as a single word
coco_double_words = ['motor bike', 'motor cycle', 'air plane', 'traffic light', 'street light', 'traffic signal', 'stop light', 'fire hydrant', 'stop sign', 'parking meter', 'suit case', 'sports ball', 'baseball bat', 'baseball glove', 'tennis racket', 'wine glass', 'hot dog', 'cell phone', 'mobile phone', 'teddy bear', 'hair drier', 'potted plant', 'bow tie', 'laptop computer', 'stove top oven', 'hot dog', 'teddy bear', 'home plate', 'train track']
#Hard code some rules for special cases in MSCOCO
#qualifiers like 'baby' or 'adult' animal will lead to a false fire for the MSCOCO object 'person'. 'baby bird' --> 'bird'.
animal_words = ['bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'animal', 'cub']
#qualifiers like 'passenger' vehicle will lead to a false fire for the MSCOCO object 'person'. 'passenger jet' --> 'jet'.
vehicle_words = ['jet', 'train']
#double_word_dict will map double words to the word they should be treated as in our analysis
self.double_word_dict = {}
for double_word in coco_double_words:
self.double_word_dict[double_word] = double_word
for animal_word in animal_words:
self.double_word_dict['baby %s' %animal_word] = animal_word
self.double_word_dict['adult %s' %animal_word] = animal_word
for vehicle_word in vehicle_words:
self.double_word_dict['passenger %s' %vehicle_word] = vehicle_word
self.double_word_dict['bow tie'] = 'tie'
self.double_word_dict['toilet seat'] = 'toilet'
self.double_word_dict['wine glas'] = 'wine glass'
self.get_annotations()
def _load_generated_captions_into_evaluator(self, cap_file, image_id_key, caption_key):
'''
Meant to save time so imid_to_objects does not always need to be recomputed.
'''
#Read in captions
self.caps, self.eval_imids = load_generated_captions(cap_file, image_id_key, caption_key)
assert len(self.caps) == len(self.eval_imids)
def get_wordnet_pos(self, tag):
if tag.startswith('J'):
return wordnet.ADJ
elif tag.startswith('V'):
return wordnet.VERB
elif tag.startswith('N'):
return wordnet.NOUN
elif tag.startswith('R'):
return wordnet.ADV
else:
return None
def caption_to_words(self, caption):
'''
Input: caption
Output: MSCOCO words in the caption
'''
#standard preprocessing
words = nltk.word_tokenize(caption.lower())
tagged_sent = nltk.pos_tag(words)
lemmas_sent = []
wnl = WordNetLemmatizer()
for tag in tagged_sent:
wordnet_pos = self.get_wordnet_pos(tag[1]) or wordnet.NOUN
lemmas_sent.append(wnl.lemmatize(tag[0], pos=wordnet_pos))
# words = [singularize(w) for w in words]
words = lemmas_sent
#replace double words
i = 0
double_words = []
idxs = []
while i < len(words):
idxs.append(i)
double_word = ' '.join(words[i:i+2])
if double_word in self.double_word_dict:
double_words.append(self.double_word_dict[double_word])
i += 2
else:
double_words.append(words[i])
i += 1
words = double_words
#toilet seat is not chair (sentences like "the seat of the toilet" will fire for "chair" if we do not include this line)
if ('toilet' in words) & ('seat' in words): words = [word for word in words if word != 'seat']
#get synonyms for all words in the caption
idxs = [idxs[idx] for idx, word in enumerate(words) \
if word in set(self.mscoco_objects)]
words = [word for word in words if word in set(self.mscoco_objects)]
node_words = []
for word in words:
node_words.append(self.inverse_synonym_dict[word])
#return all the MSCOCO objects in the caption
return words, node_words, idxs, double_words
def get_annotations_from_segments(self):
'''
Add objects taken from MSCOCO segmentation masks
'''
coco_segments = combine_coco_instances(self.coco_path )
segment_annotations = coco_segments['annotations']
#make dict linking object name to ids
id_to_name = {} #dict with id to synsets
for cat in coco_segments['categories']:
id_to_name[cat['id']] = cat['name']
for i, annotation in enumerate(segment_annotations):
sys.stdout.write("\rGetting annotations for %d/%d segmentation masks"
%(i, len(segment_annotations)))
imid = annotation['image_id']
node_word = self.inverse_synonym_dict[id_to_name[annotation['category_id']]]
self.imid_to_objects[imid].append(node_word)
print("\n")
def get_annotations_from_captions(self):
'''
Add objects taken from MSCOCO ground truth captions
'''
coco_caps = combine_coco_captions(self.coco_path)
caption_annotations = coco_caps['annotations']
for i, annotation in enumerate(caption_annotations):
sys.stdout.write('\rGetting annotations for %d/%d ground truth captions'
%(i, len(coco_caps['annotations'])))
imid = annotation['image_id']
_, node_words, _, _ = self.caption_to_words(annotation['caption'])
# note here is update, so call get_annotations_from_segments first
self.imid_to_objects[imid].extend(node_words)
print("\n")
def get_annotations(self):
'''
Get annotations from both segmentation and captions. Need both annotation types for CHAIR metric.
'''
self.get_annotations_from_segments()
self.get_annotations_from_captions()
# deduplicate
for imid in self.imid_to_objects:
self.imid_to_objects[imid] = set(self.imid_to_objects[imid])
def compute_chair(self, cap_file, image_id_key, caption_key):
'''
Given ground truth objects and generated captions, determine which sentences have hallucinated words.
'''
self._load_generated_captions_into_evaluator(cap_file, image_id_key, caption_key)
imid_to_objects = self.imid_to_objects
caps = self.caps
eval_imids = self.eval_imids
num_caps = 0.
num_hallucinated_caps = 0.
hallucinated_word_count = 0.
coco_word_count = 0.
len_caps = 0.
# :add:
num_recall_gt_objects = 0.
num_gt_objects = 0.
output = {'sentences': []}
for i in tqdm.trange(len(caps)):
cap :str = caps[i]
imid :int = eval_imids[i]
#get all words in the caption, as well as corresponding node word
# pos = cap.rfind('.')
# cap = cap[:pos+1]
words, node_words, idxs, raw_words = self.caption_to_words(cap)
gt_objects = imid_to_objects[imid]
cap_dict = {'image_id': imid,
'caption': cap,
'mscoco_hallucinated_words': [],
'mscoco_gt_words': list(gt_objects),
'mscoco_generated_words': list(node_words),
'hallucination_idxs': [],
'words': raw_words
}
# :add:
cap_dict['metrics'] = {'CHAIRs': 0,
'CHAIRi': 0,
'Recall': 0,
'Len': 0,
}
#count hallucinated words
coco_word_count += len(node_words)
hallucinated = False
# add
recall_gt_objects = set()
for word, node_word, idx in zip(words, node_words, idxs):
if node_word not in gt_objects:
hallucinated_word_count += 1
cap_dict['mscoco_hallucinated_words'].append((word, node_word))
cap_dict['hallucination_idxs'].append(idx)
hallucinated = True
else:
recall_gt_objects.add(node_word)
#count hallucinated caps
num_caps += 1
len_caps += len(raw_words)
if hallucinated:
num_hallucinated_caps += 1
# add
num_gt_objects += len(gt_objects)
num_recall_gt_objects += len(recall_gt_objects)
cap_dict['metrics']['CHAIRs'] = int(hallucinated)
cap_dict['metrics']['CHAIRi'] = 0.
cap_dict['metrics']['Recall'] = 0.
cap_dict['metrics']['Len'] = 0.
if len(words) > 0:
cap_dict['metrics']['CHAIRi'] = len(cap_dict['mscoco_hallucinated_words'])/float(len(words))
# add
if len(gt_objects) > 0:
cap_dict['metrics']['Recall'] = len(recall_gt_objects) / len(gt_objects)
output['sentences'].append(cap_dict)
chair_s = (num_hallucinated_caps/num_caps)
chair_i = (hallucinated_word_count/coco_word_count)
# add
recall = num_recall_gt_objects / num_gt_objects
avg_len = (0.01*len_caps/num_caps)
output['overall_metrics'] = {'CHAIRs': chair_s,
'CHAIRi': chair_i,
'Recall': recall,
'Len': avg_len,}
return output
def load_generated_captions(cap_file, image_id_key:str, caption_key:str):
#Read in captions
# it should be list of dict
ext = os.path.splitext(cap_file)[-1]
if ext == '.json':
caps = json.load(open(cap_file))
elif ext == '.jsonl':
caps = [json.loads(s) for s in open(cap_file)]
else:
raise ValueError(f'Unspported extension {ext} for cap_file: {cap_file}')
# list of int
imids = [obj[image_id_key] for obj in caps]
# list of str
caps = [obj[caption_key] for obj in caps]
return caps, imids
def save_hallucinated_words(cap_file, cap_dict):
with open(cap_file, 'w') as f:
json.dump(cap_dict, f, indent=2, ensure_ascii=False)
def print_metrics(hallucination_cap_dict, quiet=False):
sentence_metrics = hallucination_cap_dict['overall_metrics']
for k, v in sentence_metrics.items():
k_str = str(k).ljust(10)
v_str = f'{v * 100:.01f}'
print(k_str, v_str, sep=': ')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--cap_file", type=str, default='',
help="path towards json or jsonl saving image ids and their captions in list of dict.")
parser.add_argument("--image_id_key", type=str, default="image_id",
help="in each dict of cap_file, which key stores image id of coco.")
parser.add_argument("--caption_key", type=str, default="caption",
help="in each dict of cap_file, which key stores caption of the image.")
parser.add_argument("--cache", type=str, default="chair.pkl",
help="pre inited CHAIR evaluator object, for fast loading.")
parser.add_argument("--coco_path", type=str, default='coco_annotations',
help="only use for regenerating CHAIR evaluator object, will be ignored if uses cached evaluator.")
parser.add_argument("--save_path", type=str, default="",
help="saving CHAIR evaluate and results to json, useful for debugging the caption model.")
args = parser.parse_args()
if args.cache and os.path.exists(args.cache):
evaluator = pickle.load(open(args.cache, 'rb'))
print(f"loaded evaluator from cache: {args.cache}")
else:
print(f"cache not setted or not exist yet, building from scratch...")
evaluator = CHAIR(args.coco_path)
pickle.dump(evaluator, open(args.cache, 'wb'))
print(f"cached evaluator to: {args.cache}")
cap_dict = evaluator.compute_chair(args.cap_file, args.image_id_key, args.caption_key)
print_metrics(cap_dict)
if args.save_path:
save_hallucinated_words(args.save_path, cap_dict)
# CUDA_VISIBLE_DEVICES=5 python chair.py \
# --cap_file ../POPE-Adv/text_feat/chair-eval/instructblip/ours.jsonl \
# --image_id_key image_id --caption_key caption \
# --coco_path /mnt/petrelfs/share_data/wangjiaqi/mllm-data-alg/COCO_2014/ori/annotations_trainval2014/annotations/ \
# --save_path ../POPE-Adv/text_feat/chair-eval/instructblip/ours_outputs.json