-
Notifications
You must be signed in to change notification settings - Fork 15
/
Copy pathtest_21pairs_axial.py
145 lines (125 loc) · 4.78 KB
/
test_21pairs_axial.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
# test phase
import os
import torch
from torch.autograd import Variable
from net import NestFuse_light2_nodense, Fusion_network, Fusion_strategy, RFN_decoder
import utils
from args_fusion import args
import numpy as np
from vit_model import VisionTransformer
def load_model(path_auto, path_fusion, fs_type, flag_img):
if flag_img is True:
nc = 3
else:
nc =1
input_nc = nc
output_nc = nc
nb_filter = [64, 112, 160, 208]
nest_model = NestFuse_light2_nodense(nb_filter, input_nc, output_nc, deepsupervision=False)
nest_model.load_state_dict(torch.load(path_auto))
fusion_model = Fusion_network(nb_filter, fs_type)
fusion_model.load_state_dict(torch.load("models/train/fusionnet/6.0/fusion_axial/fusion_axial.model"))
fusion_strategy = Fusion_strategy(fs_type)
para = sum([np.prod(list(p.size())) for p in nest_model.parameters()])
type_size = 4
print('Model {} : params: {:4f}M'.format(nest_model._get_name(), para * type_size / 1000 / 1000))
para = sum([np.prod(list(p.size())) for p in fusion_model.parameters()])
type_size = 4
print('Model {} : params: {:4f}M'.format(fusion_model._get_name(), para * type_size / 1000 / 1000))
nest_model.eval()
fusion_model.eval()
#trans_model.eval()
nest_model.cuda()
fusion_model.cuda()
#trans_model.cuda()
return nest_model, fusion_model, fusion_strategy
def run_demo(nest_model, fusion_model, fusion_strategy, infrared_path, visible_path, output_path_root, name_ir, fs_type, use_strategy, flag_img, alpha):
img_ir, h, w, c = utils.get_test_image(infrared_path, flag=flag_img) # True for rgb
img_vi, h, w, c = utils.get_test_image(visible_path, flag=flag_img)
# dim = img_ir.shape
if c is 1:
if args.cuda:
img_ir = img_ir.cuda()
img_vi = img_vi.cuda()
img_ir = Variable(img_ir, requires_grad=False)
img_vi = Variable(img_vi, requires_grad=False)
print(img_ir.shape)
# encoder
en_r = nest_model.encoder(img_ir)
en_v = nest_model.encoder(img_vi)
# fusion
f = fusion_model(en_r, en_v)
# decoder
img_fusion_list = nest_model.decoder_eval(f)
else:
# fusion each block
img_fusion_blocks = []
for i in range(c):
# encoder
img_vi_temp = img_vi[i]
img_ir_temp = img_ir[i]
if args.cuda:
img_vi_temp = img_vi_temp.cuda()
img_ir_temp = img_ir_temp.cuda()
img_vi_temp = Variable(img_vi_temp, requires_grad=False)
img_ir_temp = Variable(img_ir_temp, requires_grad=False)
# encoder
en_r = nest_model.encoder(img_ir)
en_v = nest_model.encoder(img_vi)
# fusion
f = fusion_model(en_r, en_v)
# decoder
img_fusion_temp = nest_model.decoder_eval(f)
img_fusion_blocks.append(img_fusion_temp)
img_fusion_list = utils.recons_fusion_images(img_fusion_blocks, h, w)
# ########################### multi-outputs ##############################################
output_count = 0
for img_fusion in img_fusion_list:
file_name = 'fused_' + alpha + '_' + name_ir
output_path = output_path_root + file_name
output_count += 1
# save images
utils.save_image_test(img_fusion, output_path)
print(output_path)
def main():
# False - gray
flag_img = False
# ################# gray scale ########################################
test_path = "images/21_pairs_tno/ir/"
path_auto = args.resume_nestfuse
output_path_root = "./outputs/alpha_1e4_21/"
if os.path.exists(output_path_root) is False:
os.mkdir(output_path_root)
fs_type = 'res' # res (RFN), add, avg, max, spa, nuclear
use_strategy = False # True - static strategy; False - RFN
path_fusion_root = args.fusion_model
with torch.no_grad():
# alpha_list = [2500, 5000, 15000, 20000, 25000]
alpha_list = [700]
w_all_list = [[6.0, 3.0]]
for alpha in alpha_list:
for w_all in w_all_list:
w, w2 = w_all
temp = 'rfnnest_' + str(alpha) + '_wir_' + str(w) + '_wvi_' + str(w2)+ 'axial'
output_path_list = 'fused_' + temp + '_21' + '_' + fs_type
output_path1 = output_path_root + output_path_list + '/'
if os.path.exists(output_path1) is False:
os.mkdir(output_path1)
output_path = output_path1
# load network
path_fusion = path_fusion_root + str(w) + '/' + 'Final_epoch_2_alpha_' + str(alpha) + '_wir_' + str(w) + '_wvi_' + str(w2) + '_ssim_vi.model'
model, fusion_model, fusion_strategy = load_model(path_auto, path_fusion, fs_type, flag_img)
imgs_paths_ir, names = utils.list_images(test_path)
num = len(imgs_paths_ir)
for i in range(num):
name_ir = names[i]
infrared_path = imgs_paths_ir[i]
visible_path = infrared_path.replace('ir/', 'vis/')
if visible_path.__contains__('IR'):
visible_path = visible_path.replace('IR', 'VIS')
else:
visible_path = visible_path.replace('i.', 'v.')
run_demo(model, fusion_model, fusion_strategy, infrared_path, visible_path, output_path, name_ir, fs_type, use_strategy, flag_img, temp)
print('Done......')
if __name__ == '__main__':
main()