This repository has been archived by the owner on Jul 15, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 0
/
test.py
140 lines (113 loc) · 4.11 KB
/
test.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
import argparse
import json
import os
import os.path as osp
import sys
import torch
from mmcv import Config
from dataset import build_data_loader
from models import build_model
from models.utils import fuse_module
from utils import AverageMeter, Corrector, ResultFormat, Visualizer
def model_structure(model):
blank = ' '
print('-' * 90)
print('|' + ' ' * 11 + 'weight name' + ' ' * 10 + '|' \
+ ' ' * 15 + 'weight shape' + ' ' * 15 + '|' \
+ ' ' * 3 + 'number' + ' ' * 3 + '|')
print('-' * 90)
num_para = 0
for index, (key, w_variable) in enumerate(model.named_parameters()):
if len(key) <= 30:
key = key + (30 - len(key)) * blank
shape = str(w_variable.shape)
if len(shape) <= 40:
shape = shape + (40 - len(shape)) * blank
each_para = 1
for k in w_variable.shape:
each_para *= k
num_para += each_para
str_num = str(each_para)
if len(str_num) <= 10:
str_num = str_num + (10 - len(str_num)) * blank
print('| {} | {} | {} |'.format(key, shape, str_num))
print('-' * 90)
print('The total number of parameters: ' + str(num_para))
print('The parameters of Model {}: {:4f}M'.format(
model._get_name(), num_para / 1e6))
print('-' * 90)
def report_speed(outputs, speed_meters):
total_time = 0
for key in outputs:
if 'time' in key:
total_time += outputs[key]
speed_meters[key].update(outputs[key])
print('%s: %.4f' % (key, speed_meters[key].avg))
speed_meters['total_time'].update(total_time)
print('FPS: %.1f' % (1.0 / speed_meters['total_time'].avg))
def test(test_loader, model, cfg):
model.eval()
rf = ResultFormat(cfg.data.test.type, cfg.test_cfg.result_path)
print('Start testing %d images' % len(test_loader))
cfg.debug = False
cfg.report_speed = False
for idx, data in enumerate(test_loader):
print('Testing %d/%d\r' % (idx, len(test_loader)), end='', flush=True)
# prepare input
data['imgs'] = data['imgs'].cuda()
data.update(dict(cfg=cfg))
# forward
with torch.no_grad():
outputs = model(**data)
# save result
image_name, _ = osp.splitext(osp.basename(test_loader.dataset.img_paths[idx]))
image_path=test_loader.dataset.img_paths[idx]
rf.write_result(image_name, image_path, outputs)
print('Done!')
def main(args):
cfg = Config.fromfile(args.config)
for d in [cfg, cfg.data.test]:
d.update(dict(report_speed=args.report_speed))
print(json.dumps(cfg._cfg_dict, indent=4))
# data loader
data_loader = build_data_loader(cfg.data.test)
test_loader = torch.utils.data.DataLoader(
data_loader,
batch_size=1,
shuffle=False,
num_workers=2,
)
# model
if hasattr(cfg.model, 'recognition_head'):
cfg.model.recognition_head.update(
dict(
voc=data_loader.voc,
char2id=data_loader.char2id,
id2char=data_loader.id2char,
))
model = build_model(cfg.model)
model = model.cuda()
if cfg.test_cfg.pretrain is not None:
if os.path.isfile(cfg.test_cfg.pretrain):
print("Loading model and optimizer from checkpoint '{}'".format(
cfg.test_cfg.pretrain))
checkpoint = torch.load(cfg.test_cfg.pretrain)
d = dict()
for key, value in checkpoint['state_dict'].items():
tmp = key[7:]
d[tmp] = value
model.load_state_dict(d)
else:
print("No checkpoint found at '{}'".format(args.resume))
raise
# fuse conv and bn
model = fuse_module(model)
model_structure(model)
# test
test(test_loader, model, cfg)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Hyperparams')
parser.add_argument('config', default='config/pan_pp/pan_pp_test.py', help='config file path')
parser.add_argument('--report_speed', action='store_true')
args = parser.parse_args()
main(args)