-
Notifications
You must be signed in to change notification settings - Fork 14
/
engine.py
202 lines (154 loc) · 7.67 KB
/
engine.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
#encoding:utf-8
# -----------------------------------------------------------
# "Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information"
# Yuan, Zhiqiang and Zhang, Wenkai and Changyuan Tian and Xuee, Rong and Zhengyuan Zhang and Wang, Hongqi and Fu, Kun and Sun, Xian
# Writen by YuanZhiqiang, 2021. Our code is depended on AMFMN
# ------------------------------------------------------------
import time
import torch
import numpy as np
import sys
from torch.autograd import Variable
import utils
import tensorboard_logger as tb_logger
import logging
from torch.nn.utils.clip_grad import clip_grad_norm
def train(train_loader, model, optimizer, epoch, opt={}):
# extract value
grad_clip = opt['optim']['grad_clip']
max_violation = opt['optim']['max_violation']
margin = opt['optim']['margin']
loss_name = opt['model']['name'] + "_" + opt['dataset']['datatype']
print_freq = opt['logs']['print_freq']
# switch to train mode
model.train()
batch_time = utils.AverageMeter()
data_time = utils.AverageMeter()
train_logger = utils.LogCollector()
end = time.time()
params = list(model.parameters())
for i, train_data in enumerate(train_loader):
images, local_rep, local_adj, captions, lengths, ids= train_data
batch_size = images.size(0)
margin = float(margin)
# measure data loading time
data_time.update(time.time() - end)
model.logger = train_logger
input_visual = Variable(images)
input_local_rep = Variable(local_rep)
input_local_adj = Variable(local_adj)
input_text = Variable(captions)
if torch.cuda.is_available():
input_visual = input_visual.cuda()
input_local_rep = input_local_rep.cuda()
input_local_adj = input_local_adj.cuda()
input_text = input_text.cuda()
scores = model(input_visual, input_local_rep, input_local_adj, input_text, lengths)
torch.cuda.synchronize()
loss = utils.calcul_loss(scores, input_visual.size(0), margin, max_violation=max_violation, )
if grad_clip > 0:
clip_grad_norm(params, grad_clip)
train_logger.update('L', loss.cpu().data.numpy())
optimizer.zero_grad()
loss.backward()
torch.cuda.synchronize()
optimizer.step()
torch.cuda.synchronize()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % print_freq == 0:
logging.info(
'Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f}\t'
'{elog}\t'
.format(epoch, i, len(train_loader),
batch_time=batch_time,
elog=str(train_logger)))
utils.log_to_txt(
'Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f}\t'
'{elog}\t'
.format(epoch, i, len(train_loader),
batch_time=batch_time,
elog=str(train_logger)),
opt['logs']['ckpt_save_path']+ opt['model']['name'] + "_" + opt['dataset']['datatype'] +".txt"
)
tb_logger.log_value('epoch', epoch, step=model.Eiters)
tb_logger.log_value('step', i, step=model.Eiters)
tb_logger.log_value('batch_time', batch_time.val, step=model.Eiters)
train_logger.tb_log(tb_logger, step=model.Eiters)
def validate(val_loader, model):
model.eval()
val_logger = utils.LogCollector()
model.logger = val_logger
start = time.time()
input_visual = np.zeros((len(val_loader.dataset), 3, 256, 256))
input_local_rep = np.zeros((len(val_loader.dataset), 20, 20))
input_local_adj = np.zeros((len(val_loader.dataset), 20, 20))
input_text = np.zeros((len(val_loader.dataset), 47), dtype=np.int64)
input_text_lengeth = [0]*len(val_loader.dataset)
for i, val_data in enumerate(val_loader):
images, local_rep, local_adj, captions, lengths, ids = val_data
for (id, img,rep,adj, cap, l) in zip(ids, (images.numpy().copy()),(local_rep.numpy().copy()),(local_adj.numpy().copy()), (captions.numpy().copy()), lengths):
input_visual[id] = img
input_local_rep[id] = rep
input_local_adj[id] = adj
input_text[id, :captions.size(1)] = cap
input_text_lengeth[id] = l
input_visual = np.array([input_visual[i] for i in range(0, len(input_visual), 5)])
input_local_rep = np.array([input_local_rep[i] for i in range(0, len(input_local_rep), 5)])
input_local_adj = np.array([input_local_adj[i] for i in range(0, len(input_local_adj), 5)])
d = utils.shard_dis_GaLR(input_visual, input_local_rep, input_local_adj, input_text, model, lengths=input_text_lengeth)
end = time.time()
print("calculate similarity time:", end - start)
(r1i, r5i, r10i, medri, meanri), _ = utils.acc_i2t2(d)
logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1i, r5i, r10i, medri, meanri))
(r1t, r5t, r10t, medrt, meanrt), _ = utils.acc_t2i2(d)
logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" %
(r1t, r5t, r10t, medrt, meanrt))
currscore = (r1t + r5t + r10t + r1i + r5i + r10i)/6.0
all_score = "r1i:{} r5i:{} r10i:{} medri:{} meanri:{}\n r1t:{} r5t:{} r10t:{} medrt:{} meanrt:{}\n sum:{}\n ------\n".format(
r1i, r5i, r10i, medri, meanri, r1t, r5t, r10t, medrt, meanrt, currscore
)
tb_logger.log_value('r1i', r1i, step=model.Eiters)
tb_logger.log_value('r5i', r5i, step=model.Eiters)
tb_logger.log_value('r10i', r10i, step=model.Eiters)
tb_logger.log_value('medri', medri, step=model.Eiters)
tb_logger.log_value('meanri', meanri, step=model.Eiters)
tb_logger.log_value('r1t', r1t, step=model.Eiters)
tb_logger.log_value('r5t', r5t, step=model.Eiters)
tb_logger.log_value('r10t', r10t, step=model.Eiters)
tb_logger.log_value('medrt', medrt, step=model.Eiters)
tb_logger.log_value('meanrt', meanrt, step=model.Eiters)
tb_logger.log_value('rsum', currscore, step=model.Eiters)
return currscore, all_score
def validate_test(val_loader, model):
model.eval()
val_logger = utils.LogCollector()
model.logger = val_logger
start = time.time()
input_visual = np.zeros((len(val_loader.dataset), 3, 256, 256))
input_local_rep = np.zeros((len(val_loader.dataset), 20, 20))
input_local_adj = np.zeros((len(val_loader.dataset), 20, 20))
input_text = np.zeros((len(val_loader.dataset), 47), dtype=np.int64)
input_text_lengeth = [0] * len(val_loader.dataset)
embed_start = time.time()
for i, val_data in enumerate(val_loader):
images,local_rep, local_adj, captions, lengths, ids = val_data
for (id, img,rep,adj, cap, l) in zip(ids, (images.numpy().copy()),(local_rep.numpy().copy()),(local_adj.numpy().copy()), (captions.numpy().copy()), lengths):
input_visual[id] = img
input_local_rep[id] = rep
input_local_adj[id] = adj
input_text[id, :captions.size(1)] = cap
input_text_lengeth[id] = l
input_visual = np.array([input_visual[i] for i in range(0, len(input_visual), 5)])
input_local_rep = np.array([input_local_rep[i] for i in range(0, len(input_local_rep), 5)])
input_local_adj = np.array([input_local_adj[i] for i in range(0, len(input_local_adj), 5)])
embed_end = time.time()
print("embedding time: {}".format(embed_end-embed_start))
d = utils.shard_dis_GaLR(input_visual, input_local_rep, input_local_adj, input_text, model, lengths=input_text_lengeth)
end = time.time()
print("calculate similarity time:", end - start)
return d