-
Notifications
You must be signed in to change notification settings - Fork 12
/
option.py
56 lines (45 loc) · 2.86 KB
/
option.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
import argparse
parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox")
# Hardware specifications
parser.add_argument("--gpu_id", type=str, default='0')
# Data specifications
# parser.add_argument('--data_root', type=str, default='../../datasets/', help='dataset directory')
parser.add_argument('--data_root', type=str, default='/data/zyx_data/datasets/', help='dataset directory')
parser.add_argument('--data_path_CAVE', default='/data/zyx_data/datasets/CAVE_512_28/', type=str,
help='path of data')
parser.add_argument('--data_path_KAIST', default='/data/zyx_data/datasets/KAIST_CVPR2021/', type=str,
help='path of data')
parser.add_argument('--mask_path', default='/data/zyx_data/datasets/TSA_real_data/mask.mat', type=str,
help='path of mask')
# parser.add_argument('--mask_path', default='/home/zyx22/code/DGSMP/Real/Data/mask.mat', type=str,
# help='path of mask')
# Saving specifications
parser.add_argument('--outf', type=str, default='./exp/bisrnet/', help='saving_path')
# Model specifications
parser.add_argument('--method', type=str, default='bisrnet', help='method name')
parser.add_argument('--pretrained_model_path', type=str, default=None, help='pretrained model directory')
parser.add_argument("--input_setting", type=str, default='H',
help='the input measurement of the network: H, HM or Y')
parser.add_argument("--input_mask", type=str, default='Phi',
help='the input mask of the network: Phi, Phi_PhiPhiT, Mask or None') # Phi: shift_mask Mask: mask
# Training specifications
parser.add_argument("--size", default=96, type=int, help='cropped patch size')
parser.add_argument("--epoch_sam_num", default=5000, type=int, help='total number of trainset')
parser.add_argument("--seed", default=1, type=int, help='Random_seed')
parser.add_argument('--batch_size', type=int, default=8, help='the number of HSIs per batch')
parser.add_argument("--isTrain", default=True, type=bool, help='train or test')
parser.add_argument("--max_epoch", type=int, default=500, help='total epoch')
parser.add_argument("--scheduler", type=str, default='MultiStepLR', help='MultiStepLR or CosineAnnealingLR')
parser.add_argument("--milestones", type=int, default=[50,100,150,200,250], help='milestones for MultiStepLR')
parser.add_argument("--gamma", type=float, default=0.1, help='learning rate decay for MultiStepLR')
parser.add_argument("--learning_rate", type=float, default=0.0001)
opt = parser.parse_args()
opt.input_setting = 'H'
opt.input_mask = 'Mask'
opt.scheduler = 'CosineAnnealingLR'
opt.trainset_num = 20000 // ((opt.size // 96) ** 2)
for arg in vars(opt):
if vars(opt)[arg] == 'True':
vars(opt)[arg] = True
elif vars(opt)[arg] == 'False':
vars(opt)[arg] = False