-
Notifications
You must be signed in to change notification settings - Fork 6
/
eval_utils_h.py
197 lines (165 loc) · 7.53 KB
/
eval_utils_h.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
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import json
from json import encoder
import random
import string
import time
import os
import sys
import misc.utils as utils
import ipdb
from tqdm import tqdm
bad_endings = ['a', 'an', 'the', 'in', 'for', 'at', 'of', 'with', 'before', 'after', 'on', 'upon', 'near', 'to', 'is', 'are', 'am']
bad_endings += ['the']
def count_bad(sen):
sen = sen.split(' ')
if sen[-1] in bad_endings:
return 1
else:
return 0
def language_eval(dataset, preds, model_id, split):
import sys
sys.path.append("coco-caption")
if 'coco' in dataset:
annFile = 'coco-caption/annotations/captions_val2014.json'
elif 'flickr30k' in dataset or 'f30k' in dataset:
annFile = 'coco-caption/f30k_captions4eval.json'
from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
# encoder.FLOAT_REPR = lambda o: format(o, '.3f')
if '+' in model_id: # ensemble
save_to = 'eval_results/ensemble'
else:
save_to = 'eval_results/single'
if not os.path.isdir(save_to):
os.mkdir(save_to)
cache_path = os.path.join(save_to, model_id + '_' + split + '.json')
# cache_path = os.path.join(save_to, 'tmp_' + split + '.json')
coco = COCO(annFile)
valids = coco.getImgIds()
# filter results to only those in MSCOCO validation set (will be about a third)
preds_filt = [p for p in preds if p['image_id'] in valids]
print('using %d/%d predictions' % (len(preds_filt), len(preds)))
# json.dump(preds_filt, open(cache_path, 'w')) # serialize to temporary json file. Sigh, COCO API...
with open(cache_path, 'w') as f:
json.dump(preds_filt, f)
print("Write prediction results to {}".format(cache_path))
cocoRes = coco.loadRes(cache_path)
cocoEval = COCOEvalCap(coco, cocoRes)
cocoEval.params['image_id'] = cocoRes.getImgIds()
cocoEval.evaluate()
# create output dictionary
out = {}
for metric, score in cocoEval.eval.items():
out[metric] = score
imgToEval = cocoEval.imgToEval
for p in preds_filt:
image_id, caption = p['image_id'], p['caption']
imgToEval[image_id]['caption'] = caption
out['bad_count_rate'] = sum([count_bad(_['caption']) for _ in preds_filt]) / float(len(preds_filt))
outfile_path = os.path.join(save_to, model_id + '_' + split + '_imgToEval.json')
# outfile_path = os.path.join(save_to, 'tmp_' + split + '_imgToEval.json')
with open(outfile_path, 'w') as outfile:
json.dump({'overall': out, 'imgToEval': imgToEval, 'predCaption': preds_filt}, outfile)
print("Write prediction results to {}".format(outfile_path))
return out
def eval_split(model, crit, loader, eval_kwargs={}):
verbose = eval_kwargs.get('verbose', True)
verbose = False
# Print beam search
verbose_beam = eval_kwargs.get('verbose_beam', 1)
verbose_beam = 0
verbose_loss = eval_kwargs.get('verbose_loss', 1)
num_images = eval_kwargs.get('num_images', eval_kwargs.get('val_images_use', -1))
split = eval_kwargs.get('split', 'val')
lang_eval = eval_kwargs.get('language_eval', 1)
dataset = eval_kwargs.get('dataset', 'coco')
beam_size = eval_kwargs.get('beam_size', 1)
remove_bad_endings = eval_kwargs.get('remove_bad_endings', 0)
os.environ["REMOVE_BAD_ENDINGS"] = str(remove_bad_endings) # Use this nasty way to make other code clean since it's a global configuration
# Make sure in the evaluation mode
model.eval()
loader.reset_iterator(split)
n = 0
loss = 0
loss_sum = 0
loss_evals = 1e-8
predictions = []
st1 = time.time()
while True:
data = loader.get_batch(split)
n = n + loader.batch_size
if data.get('labels', None) is not None and verbose_loss:
# forward the model to get loss
tmp = [data['fc_feats'], data['att_feats'], data['flag_feats'], data['labels'], data['masks'],
data['att_masks']]
tmp = [_.cuda() if _ is not None else _ for _ in tmp]
ffc_feats, att_feats, flag_feats, labels, masks, att_masks = tmp
with torch.no_grad():
loss = crit(model(ffc_feats, att_feats, flag_feats, labels, att_masks), labels[:, 1:], masks[:, 1:])
loss_sum = loss_sum + loss
loss_evals = loss_evals + 1
# forward the model to also get generated samples for each image
# Only leave one feature for each image, in case duplicate sample
tmp = [data['fc_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['flag_feats'][np.arange(loader.batch_size) * loader.seq_per_img],
data['att_masks'][np.arange(loader.batch_size) * loader.seq_per_img] if data['att_masks'] is not None else None]
tmp = [_.cuda() if _ is not None else _ for _ in tmp]
fc_feats, att_feats, flag_feats, att_masks = tmp
eval_kwargs['batch_info'] = data['infos']
# forward the model to also get generated samples for each image
with torch.no_grad():
# import ipdb; ipdb.set_trace()
seq = model(fc_feats, att_feats, flag_feats, att_masks, opt=eval_kwargs, mode='sample')[0].data
# Print beam search
if beam_size > 1 and verbose_beam:
for i in range(loader.batch_size):
print('\n'.join([utils.decode_sequence(loader.get_vocab(), _['seq'].unsqueeze(0))[0] for _ in model.done_beams[i]]))
print('--' * 10)
sents = utils.decode_sequence(loader.get_vocab(), seq)
for k, sent in enumerate(sents):
entry = {'image_id': data['infos'][k]['id'], 'caption': sent}
if eval_kwargs.get('dump_path', 0) == 1:
entry['file_name'] = data['infos'][k]['file_path']
predictions.append(entry)
if eval_kwargs.get('dump_images', 0) == 1:
# dump the raw image to vis/ folder
cmd = 'cp "' + os.path.join(eval_kwargs['image_root'], data['infos'][k]['file_path']) + '" vis/imgs/img' + str(len(predictions)) + '.jpg' # bit gross
print(cmd)
os.system(cmd)
if verbose:
print('image %s: %s' % (entry['image_id'], entry['caption']))
# if we wrapped around the split or used up val imgs budget then bail
ix0 = data['bounds']['it_pos_now']
ix1 = data['bounds']['it_max']
if num_images != -1:
ix1 = min(ix1, num_images)
for i in range(n - ix1):
predictions.pop()
if verbose:
print('evaluating validation preformance... %d/%d (%f)' % (ix0 - 1, ix1, loss))
if data['bounds']['wrapped']:
break
print(n)
if num_images >= 0 and n >= num_images:
break
lang_stats = None
if lang_eval == 1:
model_id = eval_kwargs['id']
if '+' in model_id:
result_file = model_id
else:
model_name = os.path.basename(eval_kwargs['model']).split('.')[0]
result_file = '_'.join(eval_kwargs['model'].split("/")[-3:-1] + [model_name, model_id])
lang_stats = language_eval(dataset, predictions, result_file, split)
end1 = time.time()
print("Evaluation Done! {}mins {:.1f} seconds".format((end1 - st1) // 60, (end1 - st1) % 60))
# Switch back to training mode
model.train()
return loss_sum / loss_evals, predictions, lang_stats