-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathargparser.py
173 lines (151 loc) · 9.54 KB
/
argparser.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
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import argparse
import torch
class Argparser:
"""
The actual argparser
"""
def __init__(self):
self.args = self.prepare_arg_parser().parse_args()
def prepare_arg_parser(self):
"""
Add all args to the argparser
"""
arg_parser = argparse.ArgumentParser()
# Hardware specifications
arg_parser.add_argument('--GPU_ID', type=str, default='1',
help='Which gpu to run on your program')
arg_parser.add_argument('--cpu', action='store_true',
help='use cpu only')
arg_parser.add_argument('--chop', action='store_true',
help='enable memory-efficient forward')
arg_parser.add_argument('--n_GPUs', type=int, default=1,
help='number of GPUs')
arg_parser.add_argument('--device', type=str,
default= torch.device("cuda"),
help='use the given device (cuda/cpu) for training')
arg_parser.add_argument('--precision', type=str, default='single',
choices=('single', 'half'),
help='FP precision for test (single | half)')
# Train, Val, Test DataSet specifications
arg_parser.add_argument('--input_dir', type=str,
default='/data/dkjangid/superresolution/Material_Dataset/Ti7_Deformed_Afterdef_HomoCubo',
help=' directory path of input datasets')
arg_parser.add_argument('--lr_data_dir', type=str,
default='LR_Images/X4/train',
help=' path of low resolution training dataset')
arg_parser.add_argument('--hr_data_dir', type=str,
default='HR_Images/train',
help=' path of high resolution training datasets')
arg_parser.add_argument('--val_lr_data_dir', type=str,
default='LR_Images/X4/val',
help='path of low resolution validation dataset')
arg_parser.add_argument('--val_hr_data_dir', type=str,
default='HR_Images/val',
help='path of high resolution validation datasets')
arg_parser.add_argument('--batch_size', type=int, default=16,
help='Batch size of training datasets')
arg_parser.add_argument('--val_batch_size', type=int, default=1,
help='batch size of validation datasets')
arg_parser.add_argument('--patch_size', type=int, default=64,
help='output patch size from the network')
arg_parser.add_argument('--rgb_range', type=int, default=2,
help='maximum value of RGB')
arg_parser.add_argument('--n_colors', type=int, default=4,
help='number of channels to use')
arg_parser.add_argument('--scalar_first', action='store_true',
help = 'Format for Quaternion data')
arg_parser.add_argument('--scale', type=int, default=4,
help='super resolution scale')
arg_parser.add_argument('--noise', type=str, default='.',
help='Gaussian noise std.')
arg_parser.add_argument('--test_dataset_type', type=str, default='val',
help='which dataset you want to test')
# Models specificaitons
arg_parser.add_argument('--model', default='san',
help='name of super-resolution model')
arg_parser.add_argument('--act', type=str, default='relu',
help='activation function')
arg_parser.add_argument('--n_resblocks', type=int, default=20,
help='number of residual blocks')
arg_parser.add_argument('--n_resgroups', type=int, default=10,
help='number of residual groups')
arg_parser.add_argument('--reduction', type=int, default=16,
help='number of feature maps reduction')
arg_parser.add_argument('--n_feats', type=int, default=128,
help='number of feature maps')
arg_parser.add_argument('--res_scale', type=float, default=1,
help='residual scaling')
arg_parser.add_argument('--shift_mean', default=True,
help='subtract pixel mean from the input')
arg_parser.add_argument('--save_results', action='store_false',
help='Save output results')
arg_parser.add_argument('--test_only', action='store_true',
help='set this option to test the model')
arg_parser.add_argument('--print_model', action='store_true',
help='print model')
# Parameters saving specificaitons
arg_parser.add_argument('--print_every', type=int, default=1,
help='how many batches to wait before logging training status')
arg_parser.add_argument('--save_model_freq', type=int, default=500,
help='how many epoch after we save the model')
arg_parser.add_argument('--pre_train', type=str, default='.',
help='pre-trained model directory')
arg_parser.add_argument('--save', type=str, default='SAN_Misorientation_Cubo_Symm_debug',
help='file name to save trained model')
arg_parser.add_argument('--load', type=str, default='.',
help='file name to load')
arg_parser.add_argument('--save_models', action='store_false',
help = 'save all intermediate models')
arg_parser.add_argument('--self_ensemble', action='store_true',
help='use self-ensemble method for test')
arg_parser.add_argument('--model_to_load', type=str, default='model_best',
help='file name to load')
#Training Parameters
arg_parser.add_argument('--leak_value', type=float, default=0.2,
help='leak value in leaky relu')
arg_parser.add_argument('--lr', type=float, default=0.0002,
help='learning rate')
arg_parser.add_argument('--lr_decay', type=int, default=100,
help='learning rate decay per N epochs')
arg_parser.add_argument('--optim_lr_min', type=float, default=1e-6,
help=' minimum learning rate for optimizer')
arg_parser.add_argument('--decay_type', type=str, default='step',
help='learning rate decay type')
arg_parser.add_argument('--gamma', type=float, default=0.6,
help='learning rate decay factor for step decay')
arg_parser.add_argument('--optimizer', default='ADAM',
choices=('SGD', 'ADAM', 'RMSprop'),
help='optimizer to use (SGD | ADAM | RMSprop)')
arg_parser.add_argument('--momentum', type=float, default=0.9,
help='SGD momentum')
arg_parser.add_argument('--beta1', type=float, default=0.9,
help='ADAM beta1')
arg_parser.add_argument('--beta2', type=float, default=0.99,
help='ADAM beta2')
arg_parser.add_argument('--epsilon', type=float, default=1e-8,
help='ADAM epsilon for numerical stability')
arg_parser.add_argument('--weight_decay', type=float, default=0,
help='weight decay')
arg_parser.add_argument('--epochs', type=int, default=1501,
help='number of epochs to train')
arg_parser.add_argument('--current_epoch', type=int, default=0,
help='current epoch training/epoch to start with')
arg_parser.add_argument('--reset', action='store_true',
help='reset the training')
arg_parser.add_argument('--loss', type=str, default='1*MisOrientation',
help='loss function configuration')
arg_parser.add_argument('--dist_type', type=str, default='rot_dist_approx',
help='select from [l1, l2, rot_dist,rot_dist_approx ]')
arg_parser.add_argument('--act_loss', type=str, default=None,
help='select from [None, tanhc ]')
arg_parser.add_argument('--syms_req', action='store_true',
help='want hcp syms or not')
arg_parser.add_argument('--skip_threshold', type=float, default='1e6',
help='skipping batch that has large error')
arg_parser.add_argument('--extend', type=str, default='.',
help='pre-trained model directory')
arg_parser.add_argument('--resume', type=int, default=0,
help='resume from specific checkpoint')
arg_parser.add_argument('--val_freq', type=int, default=100,
help='number of epochs to train')
return arg_parser