-
Notifications
You must be signed in to change notification settings - Fork 0
/
test_stage1.py
126 lines (85 loc) · 3.97 KB
/
test_stage1.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
import os
from options.test_options import TestOptions
from data import CreateDataLoader
from models import create_model
from util.visualizer import Visualizer
from util import html
from data_custom.data_load import load_nifty_volume_as_array
from data_custom.data_load import save_array_as_nifty_volume
import numpy as np
import torch
import math
if __name__ == '__main__':
opt = TestOptions().parse()
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
# data_loader = CreateDataLoader(opt)
# dataset = data_loader.load_data()
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
# web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
# webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
# test
in_type1=opt.input1
in_type2=opt.input2
out_type=opt.out
for phase in ['train','val','test']:
target_path=opt.dataroot+'/'+phase
patients=os.listdir(target_path)
order_c=opt.order
for i in range(len(patients)):
# if i >= opt.how_many:
# break
target_subject=os.path.join(target_path,patients[i])+'/'
in_im1=np.float32(load_nifty_volume_as_array(filename=target_subject+in_type1+'.nii'))
in_im2=np.float32(load_nifty_volume_as_array(filename=target_subject+in_type2+'.nii'))
out_im1=np.float32(load_nifty_volume_as_array(filename=target_subject+out_type+'.nii'))
out_im1[out_im1<0] = 0
in_im1[in_im1<0] = 0
in_im2[in_im2<0] = 0
#subject based normalization
out_im1=1.0*out_im1/out_im1.max()
in_im1=1.0*in_im1/in_im1.max()
in_im2=1.0*in_im2/in_im2.max()
shapes=out_im1.shape
s1=shapes[0]
s2=shapes[1]
s3=shapes[2]
if order_c[0]=='A':
slice_size=s1
elif order_c[0]=='C':
slice_size=s2
else:
slice_size=s3
fake_syn=np.zeros([s1,s2,s3])
for ind in range(slice_size):
if order_c[0]=='A':
data_x=np.array([in_im1[ind,:,:],in_im2[ind,:,:]])
data_y=np.array([out_im1[ind,:,:]])
elif order_c[0]=='C':
data_x=np.array([in_im1[:,ind,:],in_im2[:,ind,:]])
data_y=np.array([out_im1[:,ind,:]])
else:
data_x=np.array([in_im1[:,:,ind],in_im2[:,:,ind]])
data_y=np.array([out_im1[:,:,ind]])
data_x=(data_x*1.0-0.5)*2
data_y=(data_y*1.0-0.5)*2
# data_x=np.float32(data_x)
# data_y=np.float32(data_y)
data_x=np.expand_dims(data_x,axis=0)
data_y=np.expand_dims(data_y,axis=0)
data={'A': torch.from_numpy(data_x), 'B':torch.from_numpy(data_y), 'A_paths':target_subject, 'B_paths':target_subject}
model.set_input(data)
model.test()
fake_im=model.fake_B.cpu().data.numpy()
fake_im=fake_im*0.5+0.5
if order_c[0]=='A':
fake_syn[ind,:,:]=fake_im
elif order_c[0]=='C':
fake_syn[:,ind,:]=fake_im
else:
fake_syn[:,:,ind]=fake_im
save_array_as_nifty_volume(fake_syn, filename=target_subject+out_type+'_syn_3dpro_'+order_c[0]+'_1.nii')