-
Notifications
You must be signed in to change notification settings - Fork 1
/
test.py
104 lines (86 loc) · 3.77 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
# python imports
import os
import glob
# external imports
import torch
import numpy as np
import torchsnooper
import SimpleITK as sitk
# internal imports
from Model import losses
from Model.config import args
from Model.model import U_Network, SpatialTransformer
def make_dirs():
if not os.path.exists(args.result_dir):
os.makedirs(args.result_dir)
def save_image(img, ref_img, name):
img = sitk.GetImageFromArray(img[0, 0, ...].cpu().detach().numpy())
img.SetOrigin(ref_img.GetOrigin())
img.SetDirection(ref_img.GetDirection())
img.SetSpacing(ref_img.GetSpacing())
sitk.WriteImage(img, os.path.join(args.result_dir, name))
def compute_label_dice(gt, pred):
# 需要计算的标签类别，不包括背景和图像中不存在的区域
cls_lst = [21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 61, 62,
63, 64, 65, 66, 67, 68, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 101, 102, 121, 122, 161, 162,
163, 164, 165, 166]
dice_lst = []
for cls in cls_lst:
dice = losses.DSC(gt == cls, pred == cls)
dice_lst.append(dice)
return np.mean(dice_lst)
# @torchsnooper.snoop()
def test():
make_dirs()
device = torch.device('cuda:{}'.format(args.gpu) if torch.cuda.is_available() else 'cpu')
print(args.checkpoint_path)
f_img = sitk.ReadImage(args.atlas_file)
input_fixed = sitk.GetArrayFromImage(f_img)[np.newaxis, np.newaxis, ...]
vol_size = input_fixed.shape[2:]
# set up atlas tensor
input_fixed = torch.from_numpy(input_fixed).to(device).float()
# Test file and anatomical labels we want to evaluate
test_file_lst = glob.glob(os.path.join(args.test_dir, "*.nii.gz"))
print("The number of test data: ", len(test_file_lst))
# Prepare the vm1 or vm2 model and send to device
nf_enc = [16, 32, 32, 32]
if args.model == "vm1":
nf_dec = [32, 32, 32, 32, 8, 8]
else:
nf_dec = [32, 32, 32, 32, 32, 16, 16]
# Set up model
UNet = U_Network(len(vol_size), nf_enc, nf_dec).to(device)
UNet.load_state_dict(torch.load(args.checkpoint_path))
STN_img = SpatialTransformer(vol_size).to(device)
STN_label = SpatialTransformer(vol_size, mode="nearest").to(device)
UNet.eval()
STN_img.eval()
STN_label.eval()
DSC = []
# fixed图像对应的label
fixed_label = sitk.GetArrayFromImage(sitk.ReadImage(os.path.join(args.label_dir, "S01.delineation.structure.label.nii.gz")))
for file in test_file_lst:
name = os.path.split(file)[1]
# 读入moving图像
input_moving = sitk.GetArrayFromImage(sitk.ReadImage(file))[np.newaxis, np.newaxis, ...]
input_moving = torch.from_numpy(input_moving).to(device).float()
# 读入moving图像对应的label
label_file = glob.glob(os.path.join(args.label_dir, name[:3] + "*"))[0]
input_label = sitk.GetArrayFromImage(sitk.ReadImage(label_file))[np.newaxis, np.newaxis, ...]
input_label = torch.from_numpy(input_label).to(device).float()
# 获得配准后的图像和label
pred_flow = UNet(input_moving, input_fixed)
pred_img = STN_img(input_moving, pred_flow)
pred_label = STN_label(input_label, pred_flow)
# 计算DSC
dice = compute_label_dice(fixed_label, pred_label[0, 0, ...].cpu().detach().numpy())
print("dice: ", dice)
DSC.append(dice)
if '7' in file:
save_image(pred_img, f_img, "7_warped.nii.gz")
save_image(pred_flow.permute(0, 2, 3, 4, 1)[np.newaxis, ...], f_img, "7_flow.nii.gz")
save_image(pred_label, f_img, "7_label.nii.gz")
del pred_flow, pred_img, pred_label
print("mean(DSC): ", np.mean(DSC), " std(DSC): ", np.std(DSC))
if __name__ == "__main__":
test()