-
Notifications
You must be signed in to change notification settings - Fork 2
/
specific_parser.py
140 lines (135 loc) · 7.21 KB
/
specific_parser.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
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
"""
Author: Alessandro Cattoi
Description: This file defines a function to overwrite parsed parameters. If specified is possible to overwrite passed
parameters. As a result is possible to define certain parameters in the initial par of scripts as done in the mains
"""
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #
def specific_parser(parser, log=False, run_folder=None, mode=None, tot_epochs=None, restoring_rep_path=None,
start_from_epoch=None, pretrained_GAN=None, GAN_epoch=None, data_dir_train=None, data_dir_train2=None,
data_dir_test=None, data_dir_test2=None, images_log_freq=None, batch_size=None, batch_size_SN=None,
acc_log_freq=None, loss_log_freq=None, experiment_name=None, run_description=None, prc_train=None,
prc_test=None, prc_val=None, sar_c=None, optical_c=None, N_classes=None, patch_size=None, SN_log_freq=None,
save_model_freq=None, lambda_identity=None, D_training_ratio=None, lambda_A=None, loss_type=None,
lambda_gp=None, res_block_N=None, pool_prc_O=None, pool_prc_S=None, buff_dim=None, th_low=None, th_high=None,
pool=None, conditioned=None, dropping=None, th_b_h_ratio=None, th_b_l_ratio=None, th_b_h_pool=None,
th_b_l_pool=None, drop_prc=None, seed=None):
"""
This is an intermediate layer between the general parser and the config routine to allow who use this code to easily
access parameters and change them when building his experiment
:param parser:
:param log: decide if print or not
:param run_folder: new value for run folder
:param mode: train mode
:param tot_epochs:
:param restoring_rep_path:
:param start_from_epoch:
:param pretrained_GAN:
:param GAN_epoch:
:param data_dir_train:
:param data_dir_train2:
:param data_dir_test:
:param data_dir_test2:
:param images_log_freq:
:param batch_size:
:param batch_size_SN:
:param acc_log_freq:
:param loss_log_freq:
:param experiment_name:
:param run_description:
:param prc_train:
:param prc_test:
:param prc_val:
:param sar_c:
:param optical_c:
:param N_classes:
:param patch_size:
:param SN_log_freq:
:param save_model_freq:
:param lambda_identity:
:param D_training_ratio:
:param lambda_A:
:param loss_type:
:param lambda_gp:
:param res_block_N:
:param pool_prc_O:
:param pool_prc_S:
:param buff_dim:
:param th_low:
:param th_high:
:param pool:
:param conditioned:
:param dropping:
:param th_b_h_ratio:
:param th_b_l_ratio:
:param th_b_h_pool:
:param th_b_l_pool:
:param drop_prc:
:return: args
"""
args = parser.parse_args()
print('SPECIFIC CONFIG')
args.log_dir = update_arg(args.log_dir, run_folder, 'log_dir', log)
args.tot_epochs = update_arg(args.tot_epochs, tot_epochs, 'tot_epochs', log)
args.mode = update_arg(args.mode, mode, 'mode', log)
args.restoring_rep_path = update_arg(args.restoring_rep_path, restoring_rep_path, 'restoring_rep_path', log)
args.start_from_epoch = update_arg(args.start_from_epoch, start_from_epoch, 'start_from_epoch', log)
args.pretrained_GAN = update_arg(args.pretrained_GAN, pretrained_GAN, 'pretrained_GAN', log)
args.GAN_epoch = update_arg(args.GAN_epoch, GAN_epoch, 'GAN_epoch', log)
args.data_dir_train = update_arg(args.data_dir_train, data_dir_train, 'data_dir_train', log)
args.data_dir_train2 = update_arg(args.data_dir_train2, data_dir_train2, 'data_dir_train2', log)
args.data_dir_test = update_arg(args.data_dir_test, data_dir_test, 'data_dir_test', log)
args.data_dir_test2 = update_arg(args.data_dir_test2, data_dir_test2, 'data_dir_test2', log)
args.images_log_freq = update_arg(args.images_log_freq, images_log_freq, 'images_log_freq', log)
args.batch_size = update_arg(args.batch_size, batch_size, 'batch_size', log)
args.batch_size_SN = update_arg(args.batch_size_SN, batch_size_SN, 'batch_size_SN', log)
args.acc_log_freq = update_arg(args.acc_log_freq, acc_log_freq, 'acc_log_freq', log)
args.loss_log_freq = update_arg(args.loss_log_freq, loss_log_freq, 'loss_log_freq', log)
args.experiment_name = update_arg(args.experiment_name, experiment_name, 'experiment_name', log)
args.run_description = update_arg(args.run_description, run_description, 'run_description', log)
args.prc_train = update_arg(args.prc_train, prc_train, 'prc_train', log)
args.prc_test = update_arg(args.prc_test, prc_test, 'prc_test', log)
args.prc_val = update_arg(args.prc_val, prc_val, 'prc_val', log)
args.sar_c = update_arg(args.sar_c, sar_c, 'sar_c', log)
args.optical_c = update_arg(args.optical_c, optical_c, 'optical_c', log)
args.N_classes = update_arg(args.N_classes, N_classes, 'N_classes', log)
args.patch_size = update_arg(args.patch_size, patch_size, 'patch_size', log)
args.SN_log_freq = update_arg(args.SN_log_freq, SN_log_freq, 'SN_log_freq', log)
args.save_model_freq = update_arg(args.save_model_freq, save_model_freq, 'save_model_freq', log)
args.lambda_identity = update_arg(args.lambda_identity, lambda_identity, 'lambda_identity', log)
args.D_training_ratio = update_arg(args.D_training_ratio, D_training_ratio, 'D_training_ratio', log)
args.lambda_A = update_arg(args.lambda_A, lambda_A, 'lambda_A', log)
args.loss_type = update_arg(args.loss_type, loss_type, 'loss_type', log)
args.lambda_gp = update_arg(args.lambda_gp, lambda_gp, 'lambda_gp', log)
args.res_block_N = update_arg(args.res_block_N, res_block_N, 'res_block_N', log)
args.pool_prc_O = update_arg(args.pool_prc_O, pool_prc_O, 'pool_prc_O', log)
args.pool_prc_S = update_arg(args.pool_prc_S, pool_prc_S, 'pool_prc_S', log)
args.buff_dim = update_arg(args.buff_dim, buff_dim, 'buff_dim', log)
args.th_low = update_arg(args.th_low, th_low, 'th_low', log)
args.th_high = update_arg(args.th_high, th_high, 'th_high', log)
args.pool = update_arg(args.pool, pool, 'pool', log)
args.conditioned = update_arg(args.conditioned, conditioned, 'conditioned', log)
args.dropping = update_arg(args.dropping, dropping, 'dropping', log)
args.th_b_h_ratio = update_arg(args.th_b_h_ratio, th_b_h_ratio, 'th_b_h_ratio', log)
args.th_b_l_ratio = update_arg(args.th_b_l_ratio, th_b_l_ratio, 'th_b_l_ratio', log)
args.th_b_h_pool = update_arg(args.th_b_h_pool, th_b_h_pool, 'th_b_h_pool', log)
args.th_b_l_pool = update_arg(args.th_b_l_pool, th_b_l_pool, 'th_b_l_pool', log)
args.drop_prc = update_arg(args.drop_prc, drop_prc, 'drop_prc', log)
args.seed = update_arg(args.seed, seed, 'seed', log)
return args
def update_arg(original, new_val, name, log=False):
"""
Decide if update value or keep the original
:param original:
:param new_val:
:param name: name of the variable
:param log: decide if print or not
:return:
"""
if new_val is None:
out_val = original
else:
out_val = new_val
if log:
print(' - ' + name + ' = {}'.format(out_val))
return out_val