-
Notifications
You must be signed in to change notification settings - Fork 4
/
evaluation.py
292 lines (247 loc) · 10.4 KB
/
evaluation.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
from __future__ import print_function
import os, sys
import pickle
import torch
import numpy
from data import get_test_loader
import time
import numpy as np
from model import DIME
from collections import OrderedDict
from tqdm import tqdm
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=0):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / (.0001 + self.count)
def __str__(self):
"""String representation for logging
"""
# for values that should be recorded exactly e.g. iteration number
if self.count == 0:
return str(self.val)
# for stats
return '%.4f (%.4f)' % (self.val, self.avg)
class LogCollector(object):
"""A collection of logging objects that can change from train to val"""
def __init__(self):
# to keep the order of logged variables deterministic
self.meters = OrderedDict()
def update(self, k, v, n=0):
# create a new meter if previously not recorded
if k not in self.meters:
self.meters[k] = AverageMeter()
self.meters[k].update(v, n)
def __str__(self):
"""Concatenate the meters in one log line
"""
s = ''
for i, (k, v) in enumerate(self.meters.iteritems()):
if i > 0:
s += ' '
if(k == 'lr'):
v = '{:.3e}'.format(v.val)
s += k + ' ' + str(v)
return s
def tb_log(self, tb_logger, prefix='', step=None):
"""Log using tensorboard
"""
for k, v in self.meters.iteritems():
tb_logger.log_value(prefix + k, v.val, step=step)
def encode_data(model, data_loader, opt, log_step=10, logging=print):
"""Encode all images and captions loadable by `data_loader`
"""
batch_time = AverageMeter()
val_logger = LogCollector()
# switch to evaluate mode
model.val_start()
end = time.time()
max_n_word = 0
l_idx = 0
for i, batch_data in enumerate(data_loader):
images, input_ids, lengths, ids, attention_mask, token_type_ids = batch_data
max_n_word = max(max_n_word, max(lengths))
# numpy array to keep all the embeddings
is_init = True
for i, batch_data in enumerate(data_loader):
# make sure val logger is used
model.logger = val_logger
images, input_ids, lengths, ids, attention_mask, token_type_ids = batch_data
# compute the embeddings
img_emb, self_att_emb, cap_emb, word_emb = model.forward_emb(batch_data, volatile=True)
# initialize the numpy arrays given the size of the embeddings
if is_init:
is_init = False
rgn = np.zeros((len(data_loader.dataset), self_att_emb.size(1), self_att_emb.size(2)), dtype=np.float32)
img = np.zeros((len(data_loader.dataset), img_emb.size(1)), dtype=np.float32)
wrd = np.zeros((len(data_loader.dataset), max_n_word, word_emb.size(2)), dtype=np.float32)
stc = np.zeros((len(data_loader.dataset), cap_emb.size(1)), dtype=np.float32)
stc_lens = np.zeros(len(data_loader.dataset), dtype=np.int)
rgn[ids] = self_att_emb.detach().cpu().numpy().copy()
img[ids] = img_emb.data.cpu().numpy().copy()
cur_max_len = word_emb.size(1)
wrd[ids, :cur_max_len, :] = word_emb.data.cpu().numpy().copy()
stc[ids] = cap_emb.data.cpu().numpy().copy()
# preserve the lengths of sentences
stc_lens[ids] = np.asarray(lengths, dtype=np.int)
del batch_data
return rgn, img, wrd, stc, stc_lens
def evalrank(model_path, data_path=None, split='dev', fold5=False):
"""
Evaluate a trained model on either dev or test. If `fold5=True`, 5 fold
cross-validation is done (only for MSCOCO). Otherwise, the full data is
used for evaluation.
"""
# load model and options
checkpoint = torch.load(model_path)
opt = checkpoint['opt']
if data_path is not None:
opt.data_path = data_path
# construct model
model = DIME(opt)
# load model state
model.load_state_dict(checkpoint['model'])
print('Loading dataset')
data_loader = get_test_loader(split, opt.data_name,
opt.batch_size, opt.workers, opt)
print('Computing results...')
rgn, img, wrd, stc, stc_lens = encode_data(model, data_loader, opt)
if not fold5:
# no cross-validation, full evaluation
embs = (rgn, img, wrd, stc)
r, rt, sims = i2t(model, embs, stc_lens, opt, return_ranks=True)
ri, rti = t2i(model, sims, opt, return_ranks=True)
ar = (r[0] + r[1] + r[2]) / 3
ari = (ri[0] + ri[1] + ri[2]) / 3
rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2]
print("rsum: %.1f" % rsum)
print("Average i2t Recall: %.1f" % ar)
print("Image to text: %.1f %.1f %.1f %.1f %.1f" % r)
print("Average t2i Recall: %.1f" % ari)
print("Text to image: %.1f %.1f %.1f %.1f %.1f" % ri)
else:
# 5fold cross-validation, only for MSCOCO
results = []
for i in range(5):
if opt.itr == 'stc_rgn' or opt.itr == 'stc_rgn_max':
embs = (rgn[i * 5000:(i + 1) * 5000], None, None, stc[i * 5000:(i + 1) * 5000])
elif opt.itr == 'img_wrd':
embs = (None, img[i * 5000:(i + 1) * 5000], wrd[i * 5000:(i + 1) * 5000], None)
elif opt.itr == 'rgn_wrd':
embs = (rgn[i * 5000:(i + 1) * 5000], img[i * 5000:(i + 1) * 5000], wrd[i * 5000:(i + 1) * 5000], \
stc[i * 5000:(i + 1) * 5000])
else:
embs = (None, img[i * 5000:(i + 1) * 5000], None, stc[i * 5000:(i + 1) * 5000])
r, rt0, sims = i2t(model, embs, opt, return_ranks=True)
print("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % r)
ri, rti0 = t2i(model, sims, opt, return_ranks=True)
if i == 0:
rt, rti = rt0, rti0
print("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % ri)
ar = (r[0] + r[1] + r[2]) / 3
ari = (ri[0] + ri[1] + ri[2]) / 3
rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2]
print("rsum: %.1f ar: %.1f ari: %.1f" % (rsum, ar, ari))
results += [list(r) + list(ri) + [ar, ari, rsum]]
print("-----------------------------------")
print("Mean metrics: ")
mean_metrics = tuple(np.array(results).mean(axis=0).flatten())
print("rsum: %.1f" % (mean_metrics[10] * 6))
print("Average i2t Recall: %.1f" % mean_metrics[11])
print("Image to text: %.1f %.1f %.1f %.1f %.1f" %
mean_metrics[:5])
print("Average t2i Recall: %.1f" % mean_metrics[12])
print("Text to image: %.1f %.1f %.1f %.1f %.1f" %
mean_metrics[5:10])
torch.save({'rt': rt, 'rti': rti}, 'ranks.pth.tar')
def calItr(model, embs, stc_lens, opt, shard_size=128):
"""
Computer pairwise i2t image-caption distance with locality sharding
"""
rgn, img, wrd, stc = embs
n_img = len(rgn)
n_stc = len(wrd)
t0 = time.time()
n_im_shard = (n_img-1) // shard_size + 1
n_cap_shard = (n_stc-1) // shard_size + 1
d = np.zeros((n_img, n_stc))
if sys.stdout.isatty():
pbar = tqdm(total=(n_im_shard * n_cap_shard))
for i in range(n_im_shard):
im_start, im_end = shard_size*i, min(shard_size*(i+1), n_img)
for j in range(n_cap_shard):
cap_start, cap_end = shard_size*j, min(shard_size*(j+1), n_stc)
cur_stc_lens = stc_lens[cap_start: cap_end]
with torch.no_grad():
rgn_block = torch.from_numpy(rgn[im_start:im_end]).cuda()
stc_block = torch.from_numpy(stc[cap_start:cap_end]).cuda()
img_block = torch.from_numpy(img[im_start:im_end]).cuda()
wrd_block = torch.from_numpy(wrd[cap_start:cap_end]).cuda()
sim = model.itr_module(rgn_block, img_block, wrd_block, stc_block, cur_stc_lens)
d[im_start:im_end, cap_start:cap_end] = sim.data.cpu().numpy()
if sys.stdout.isatty():
pbar.update(1)
if sys.stdout.isatty():
pbar.close()
print('Calculate similarity matrix elapses: {:.3f}s'.format(time.time() - t0))
return d
def i2t(model, embs, stc_lens, opt, npts=None, return_ranks=False):
t0 = time.time()
rgn, img, wrd, stc = embs
rgn = numpy.array([rgn[i] for i in range(0, len(rgn), 5)])
img = numpy.array([img[i] for i in range(0, len(img), 5)])
npts = len(rgn)
embs = (rgn, img, wrd, stc)
sims = calItr(model, embs, stc_lens, opt, shard_size=int(opt.batch_size * 2))
ranks = np.zeros(npts)
top1 = np.zeros(npts)
for index in range(npts):
inds = np.argsort(sims[index])[::-1]
# Score
rank = 1e20
for i in range(5 * index, 5 * index + 5, 1):
tmp = np.where(inds == i)[0][0]
if tmp < rank:
rank = tmp
ranks[index] = rank
top1[index] = inds[0]
# Compute metrics
r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
medr = np.floor(np.median(ranks)) + 1
meanr = ranks.mean() + 1
if return_ranks:
return (r1, r5, r10, medr, meanr), (ranks, top1), sims
else:
return (r1, r5, r10, medr, meanr), sims
def t2i(model, sims, opt, npts=None, return_ranks=False):
t0 = time.time()
npts = sims.shape[0]
ranks = np.zeros(5 * npts)
top1 = np.zeros(5 * npts)
sims = sims.T
for index in range(npts):
for i in range(5):
inds = np.argsort(sims[5 * index + i])[::-1]
ranks[5 * index + i] = np.where(inds == index)[0][0]
top1[5 * index + i] = inds[0]
# Compute metrics
r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
medr = np.floor(np.median(ranks)) + 1
meanr = ranks.mean() + 1
if return_ranks:
return (r1, r5, r10, medr, meanr), (ranks, top1)
else:
return (r1, r5, r10, medr, meanr)