-
Notifications
You must be signed in to change notification settings - Fork 43
/
config_args.py
147 lines (111 loc) · 5.91 KB
/
config_args.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
import os.path as path
import os
import numpy as np
from pdb import set_trace as stop
def get_args(parser,eval=False):
parser.add_argument('--dataroot', type=str, default='./data/')
parser.add_argument('--dataset', type=str, choices=['coco', 'voc','coco1000','nus','vg','news','cub'], default='coco')
parser.add_argument('--workers', type=int, default=10)
parser.add_argument('--results_dir', type=str, default='results/')
parser.add_argument('--test_known', type=int, default=0)
# Optimization
parser.add_argument('--optim', type=str, choices=['adam', 'sgd'], default='adam')
parser.add_argument('--lr', type=float, default=0.0002)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--test_batch_size', type=int, default=-1)
parser.add_argument('--grad_ac_steps', type=int, default=1)
parser.add_argument('--scheduler_step', type=int, default=1000)
parser.add_argument('--scheduler_gamma', type=float, default=0.1)
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--int_loss', type=float, default=0.0)
parser.add_argument('--aux_loss', type=float, default=0.0)
parser.add_argument('--loss_type', type=str, choices=['bce', 'mixed','class_ce','soft_margin'], default='bce')
parser.add_argument('--scheduler_type', type=str, choices=['plateau', 'step'], default='plateau')
parser.add_argument('--loss_labels', type=str, choices=['all', 'unk'], default='all')
parser.add_argument('--lr_decay', type=float, default=0)
parser.add_argument('--weight_decay', type=float, default=1e-4)
parser.add_argument('--max_samples', type=int, default=-1)
parser.add_argument('--max_batches', type=int, default=-1)
parser.add_argument('--warmup_scheduler', action='store_true',help='')
# Model
parser.add_argument('--layers', type=int, default=3)
parser.add_argument('--heads', type=int, default=4)
parser.add_argument('--dropout', type=float, default=0.1)
parser.add_argument('--pos_emb', action='store_true',help='positional encoding')
parser.add_argument('--use_lmt', dest='use_lmt', action='store_true',help='label mask training')
parser.add_argument('--freeze_backbone', action='store_true')
parser.add_argument('--no_x_features', action='store_true')
# CUB
parser.add_argument('--attr_group_dict', type=str, default='')
parser.add_argument('--n_groups', type=int, default=10,help='groups for CUB test time intervention')
# Image Sizes
parser.add_argument('--scale_size', type=int, default=640)
parser.add_argument('--crop_size', type=int, default=576)
# Testing Models
parser.add_argument('--inference', action='store_true')
parser.add_argument('--resume', action='store_true')
parser.add_argument('--saved_model_name', type=str, default='')
parser.add_argument('--overwrite', action='store_true')
parser.add_argument('--name', type=str, default='')
args = parser.parse_args()
model_name = args.dataset
if args.dataset == 'voc':
args.num_labels = 20
elif args.dataset == 'nus':
args.num_labels = 1000
elif args.dataset == 'coco1000':
args.num_labels = 1000
elif args.dataset == 'coco':
args.num_labels = 80
elif args.dataset == 'vg':
args.num_labels = 500
elif args.dataset == 'news':
args.num_labels = 500
elif args.dataset == 'cub':
args.num_labels = 112
else:
print('dataset not included')
exit()
model_name += '.'+str(args.layers)+'layer'
model_name += '.bsz_{}'.format(int(args.batch_size * args.grad_ac_steps))
model_name += '.'+args.optim+str(args.lr)#.split('.')[1]
if args.use_lmt:
model_name += '.lmt'
args.loss_labels = 'unk'
model_name += '.unk_loss'
args.train_known_labels = 100
else:
args.train_known_labels = 0
if args.pos_emb:
model_name += '.pos_emb'
if args.int_loss != 0.0:
model_name += '.int_loss'+str(args.int_loss).split('.')[1]
if args.aux_loss != 0.0:
model_name += '.aux_loss'+str(args.aux_loss).replace('.','')
if args.no_x_features:
model_name += '.no_x_features'
args.test_known_labels = int(args.test_known*0.01*args.num_labels)
if args.dataset == 'cub':
# reset the TOTAL number of labels to be concepts+classes
model_name += '.step_{}'.format(args.scheduler_step)
model_name += '.'+args.loss_type+'_loss'
args.num_labels = 112+200
args.attr_group_dict = {0: [0, 1, 2, 3], 1: [4, 5, 6, 7, 8, 9], 2: [10, 11, 12, 13, 14, 15], 3: [16, 17, 18, 19, 20, 21], 4: [22, 23, 24], 5: [25, 26, 27, 28, 29, 30], 6: [31], 7: [32, 33, 34, 35, 36], 8: [37, 38], 9: [39, 40, 41, 42, 43, 44], 10: [45, 46, 47, 48, 49], 11: [50], 12: [51, 52], 13: [53, 54, 55, 56, 57, 58], 14: [59, 60, 61, 62, 63], 15: [64, 65, 66, 67, 68, 69], 16: [70, 71, 72, 73, 74, 75], 17: [76, 77], 18: [78, 79, 80], 19: [81, 82], 20: [83, 84, 85], 21: [86, 87, 88], 22: [89], 23: [90, 91, 92, 93, 94, 95], 24: [96, 97, 98], 25: [99, 100, 101], 26: [102, 103, 104, 105, 106, 107], 27: [108, 109, 110, 111]}
if args.name != '':
model_name += '.'+args.name
if not os.path.exists(args.results_dir):
os.makedirs(args.results_dir)
model_name = os.path.join(args.results_dir,model_name)
args.model_name = model_name
if args.inference:
args.epochs = 1
if os.path.exists(args.model_name) and (not args.overwrite) and (not 'test' in args.name) and (not eval) and (not args.inference) and (not args.resume):
print(args.model_name)
overwrite_status = input('Already Exists. Overwrite?: ')
if overwrite_status == 'rm':
os.system('rm -rf '+args.model_name)
elif not 'y' in overwrite_status:
exit(0)
elif not os.path.exists(args.model_name):
os.makedirs(args.model_name)
return args