/
demo_server.py
175 lines (152 loc) · 6 KB
/
demo_server.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
import os
import time
import yaml
import json
import argparse
import re
import base64
import torch
from torch.autograd import Variable
from PIL import Image
from io import BytesIO
from pprint import pprint
from werkzeug.wrappers import Request, Response
from werkzeug.serving import run_simple
import torchvision.transforms as transforms
import vqa.lib.utils as utils
import vqa.datasets as datasets
import vqa.models as models
import vqa.models.convnets as convnets
from vqa.datasets.vqa_processed import tokenize_mcb
from train import load_checkpoint
parser = argparse.ArgumentParser(
description='Demo server',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--dir_logs', type=str,
#default='logs/vqa2/blocmutan_noatt_fbresnet152torchported_save_all',
default='logs/vqa2/mutan_att_train',
help='dir logs')
parser.add_argument('--path_opt', type=str,
#default='logs/vqa2/blocmutan_noatt_fbresnet152torchported_save_all/blocmutan_noatt.yaml',
default='logs/vqa2/mutan_att_train/mutan_att_train.yaml',
help='path to a yaml options file')
parser.add_argument('--resume', type=str,
default='best',
help='path to latest checkpoint')
parser.add_argument('--cuda', type=bool,
const=True,
nargs='?',
help='path to latest checkpoint')
@Request.application
def application(request):
print('')
if 'visual' in request.form and 'question' in request.form:
visual = process_visual(request.form['visual'])
question = process_question(request.form['question'])
answer = process_answer(model(visual, question))
response = Response(answer)
elif 'question' not in request.form:
response = Response('Question missing')
elif 'visual' not in request.form:
response = Response('Image missing')
else:
response = Response('what?')
response.headers.add('Access-Control-Allow-Origin', '*')
response.headers.add('Access-Control-Allow-Methods', 'GET,PUT,POST,DELETE,PATCH')
response.headers.add('Access-Control-Allow-Headers', 'Content-Type, Authorization')
response.headers.add('X-XSS-Protection', '0')
return response
def process_visual(visual_strb64):
visual_strb64 = re.sub('^data:image/.+;base64,', '', visual_strb64)
visual_PIL = Image.open(BytesIO(base64.b64decode(visual_strb64)))
visual_tensor = transform(visual_PIL)
visual_data = torch.FloatTensor(1, 3,
visual_tensor.size(1),
visual_tensor.size(2))
visual_data[0][0] = visual_tensor[0]
visual_data[0][1] = visual_tensor[1]
visual_data[0][2] = visual_tensor[2]
print('visual', visual_data.size(), visual_data.mean())
if args.cuda:
visual_data = visual_data.cuda(async=True)
visual_input = Variable(visual_data, volatile=True)
visual_features = cnn(visual_input)
if 'NoAtt' in options['model']['arch']:
nb_regions = visual_features.size(2) * visual_features.size(3)
visual_features = visual_features.sum(3).sum(2).div(nb_regions).view(-1, 2048)
return visual_features
def process_question(question_str):
question_tokens = tokenize_mcb(question_str)
question_data = torch.LongTensor(1, len(question_tokens))
for i, word in enumerate(question_tokens):
if word in trainset.word_to_wid:
question_data[0][i] = trainset.word_to_wid[word]
else:
question_data[0][i] = trainset.word_to_wid['UNK']
if args.cuda:
question_data = question_data.cuda(async=True)
question_input = Variable(question_data, volatile=True)
print('question', question_str, question_tokens, question_data)
return question_input
def process_answer(answer_var):
answer_sm = torch.nn.functional.softmax(answer_var.data[0].cpu())
max_, aid = answer_sm.topk(5, 0, True, True)
ans = []
val = []
for i in range(5):
ans.append(trainset.aid_to_ans[aid.data[i]])
val.append(max_.data[i])
att = []
for x_att in model.list_att:
img = x_att.view(1,14,14).cpu()
img = transforms.ToPILImage()(img)
buffer_ = BytesIO()
img.save(buffer_, format="PNG")
img_str = base64.b64encode(buffer_.getvalue()).decode()
img_str = 'data:image/png;base64,'+img_str
att.append(img_str)
answer = {'ans':ans,'val':val,'att':att}
answer_str = json.dumps(answer)
return answer_str
def main():
global args, options, model, cnn, transform, trainset
args = parser.parse_args()
options = {
'logs': {
'dir_logs': args.dir_logs
}
}
if args.path_opt is not None:
with open(args.path_opt, 'r') as handle:
options_yaml = yaml.load(handle)
options = utils.update_values(options, options_yaml)
print('## args'); pprint(vars(args))
print('## options'); pprint(options)
trainset = datasets.factory_VQA(options['vqa']['trainsplit'],
options['vqa'])
#options['coco'])
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.Scale(options['coco']['size']),
transforms.CenterCrop(options['coco']['size']),
transforms.ToTensor(),
normalize,
])
opt_factory_cnn = {
'arch': options['coco']['arch']
}
cnn = convnets.factory(opt_factory_cnn, cuda=args.cuda, data_parallel=False)
model = models.factory(options['model'],
trainset.vocab_words(),
trainset.vocab_answers(),
cuda=args.cuda,
data_parallel=False)
model.eval()
start_epoch, best_acc1, _ = load_checkpoint(model, None,
os.path.join(options['logs']['dir_logs'], args.resume))
my_local_ip = '192.168.0.32'
my_local_port = 3456
run_simple(my_local_ip, my_local_port, application)
if __name__ == '__main__':
main()