forked from assassint2017/MICCAI-LITS2017
-
Notifications
You must be signed in to change notification settings - Fork 0
/
parameter.py
86 lines (41 loc) · 2.66 KB
/
parameter.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
# -----------------------路径相关参数---------------------------------------
train_ct_path = '/home/zcy/Desktop/dataset/MICCAI-LITS-2017/train/CT/' # 原始训练集CT数据路径
train_seg_path = '/home/zcy/Desktop/dataset/MICCAI-LITS-2017/train/seg/' # 原始训练集标注数据路径
test_ct_path = '/home/zcy/Desktop/dataset/MICCAI-LITS-2017/test/CT/' # 原始测试集CT数据路径
test_seg_path = '/home/zcy/Desktop/dataset/MICCAI-LITS-2017/test/seg/' # 原始测试集标注数据路径
training_set_path = './train/' # 用来训练网络的数据保存地址
pred_path = '/home/zcy/Desktop/dataset/MICCAI-LITS-2017/test/liver_pred' # 网络预测结果保存路径
crf_path = '/home/zcy/Desktop/dataset/MICCAI-LITS-2017/test/crf' # CRF优化结果保存路径
module_path = './module/net550-0.028-0.022.pth' # 测试模型地址
# -----------------------路径相关参数--------------- ------------------------
# ---------------------训练数据获取相关参数-----------------------------------
size = 48 # 使用48张连续切片作为网络的输入
down_scale = 0.5 # 横断面降采样因子
expand_slice = 20 # 仅使用包含肝脏以及肝脏上下20张切片作为训练样本
slice_thickness = 1 # 将所有数据在z轴的spacing归一化到1mm
upper, lower = 200, -200 # CT数据灰度截断窗口
# ---------------------训练数据获取相关参数-----------------------------------
# -----------------------网络结构相关参数------------------------------------
drop_rate = 0.3 # dropout随机丢弃概率
# -----------------------网络结构相关参数------------------------------------
# ---------------------网络训练相关参数--------------------------------------
gpu = '0' # 使用的显卡序号
Epoch = 1000
learning_rate = 1e-4
learning_rate_decay = [500, 750]
alpha = 0.33 # 深度监督衰减系数
batch_size = 1
num_workers = 3
pin_memory = True
cudnn_benchmark = True
# ---------------------网络训练相关参数--------------------------------------
# ----------------------模型测试相关参数-------------------------------------
threshold = 0.5 # 阈值度阈值
stride = 12 # 滑动取样步长
maximum_hole = 5e4 # 最大的空洞面积
# ----------------------模型测试相关参数-------------------------------------
# ---------------------CRF后处理优化相关参数----------------------------------
z_expand, x_expand, y_expand = 10, 30, 30 # 根据预测结果在三个方向上的扩展数量
max_iter = 20 # CRF迭代次数
s1, s2, s3 = 1, 10, 10 # CRF高斯核参数
# ---------------------CRF后处理优化相关参数----------------------------------