forked from donnyyou/torchcv
/
main.py
executable file
·204 lines (177 loc) · 10.6 KB
/
main.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
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
#!/usr/bin/env python
# -*- coding:utf-8 -*-
# Author: Donny You(youansheng@gmail.com)
# Main Scripts for computer vision.
import os
import json
import time
import random
import argparse
import torch
import torch.backends.cudnn as cudnn
from methods.method_selector import MethodSelector
from methods.tools.controller import Controller
from utils.tools.configer import Configer
from utils.tools.logger import Logger as Log
def str2bool(v):
""" Usage:
parser.add_argument('--pretrained', type=str2bool, nargs='?', const=True,
dest='pretrained', help='Whether to use pretrained models.')
"""
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Unsupported value encountered.')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--hypes', default=None, type=str,
dest='hypes', help='The file of the hyper parameters.')
parser.add_argument('--phase', default='train', type=str,
dest='phase', help='The phase of module.')
parser.add_argument('--gpu', default=[0, 1, 2, 3], nargs='+', type=int,
dest='gpu', help='The gpu list used.')
# *********** Params for data. **********
parser.add_argument('--data_dir', default=None, type=str,
dest='data:data_dir', help='The Directory of the data.')
parser.add_argument('--tag', default=None, type=str,
dest='data:tag', help='The Tag of the data.')
parser.add_argument('--include_val', type=str2bool, nargs='?', default=False,
dest='data:include_val', help='Include validation set for final training.')
parser.add_argument('--drop_last', type=str2bool, nargs='?', default=False,
dest='data:drop_last', help='Fix bug for syncbn.')
parser.add_argument('--workers', default=None, type=int,
dest='data:workers', help='The number of workers to load data.')
parser.add_argument('--train_batch_size', default=None, type=int,
dest='train:batch_size', help='The batch size of training.')
parser.add_argument('--val_batch_size', default=None, type=int,
dest='val:batch_size', help='The batch size of validation.')
# *********** Params for model. **********
parser.add_argument('--model_name', default=None, type=str,
dest='network:model_name', help='The name of model.')
parser.add_argument('--checkpoints_root', default=None, type=str,
dest='network:checkpoints_root', help='The root dir of model save path.')
parser.add_argument('--checkpoints_name', default=None, type=str,
dest='network:checkpoints_name', help='The name of checkpoint model.')
parser.add_argument('--backbone', default=None, type=str,
dest='network:backbone', help='The base network of model.')
parser.add_argument('--norm_type', default=None, type=str,
dest='network:norm_type', help='The BN type of the network.')
parser.add_argument('--multi_grid', default=None, nargs='+', type=int,
dest='network:multi_grid', help='The multi_grid for resnet backbone.')
parser.add_argument('--pretrained', type=str, default=None,
dest='network:pretrained', help='The path to pretrained model.')
parser.add_argument('--resume', default=None, type=str,
dest='network:resume', help='The path of checkpoints.')
parser.add_argument('--resume_strict', type=str2bool, nargs='?', default=True,
dest='network:resume_strict', help='Fully match keys or not.')
parser.add_argument('--resume_continue', type=str2bool, nargs='?', default=False,
dest='network:resume_continue', help='Whether to continue training.')
parser.add_argument('--resume_val', type=str2bool, nargs='?', default=True,
dest='network:resume_val', help='Whether to validate during resume.')
parser.add_argument('--gathered', type=str2bool, nargs='?', default=True,
dest='network:gathered', help='Whether to gather the output of model.')
parser.add_argument('--loss_balance', type=str2bool, nargs='?', default=False,
dest='network:loss_balance', help='Whether to balance GPU usage.')
# *********** Params for solver. **********
parser.add_argument('--optim_method', default=None, type=str,
dest='solver:optim:optim_method', help='The optim method that used.')
parser.add_argument('--base_lr', default=None, type=float,
dest='solver:lr:base_lr', help='The learning rate.')
parser.add_argument('--nbb_mult', default=1.0, type=float,
dest='solver:lr:nbb_mult', help='The not backbone mult ratio of learning rate.')
parser.add_argument('--lr_policy', default=None, type=str,
dest='solver:lr:lr_policy', help='The policy of lr during training.')
parser.add_argument('--max_epoch', default=None, type=int,
dest='solver:max_epoch', help='The max epoch of training.')
parser.add_argument('--max_iters', default=None, type=int,
dest='solver:max_iters', help='The max iters of training.')
parser.add_argument('--display_iter', default=None, type=int,
dest='solver:display_iter', help='The display iteration of train logs.')
parser.add_argument('--test_interval', default=None, type=int,
dest='solver:test_interval', help='The test interval of validation.')
parser.add_argument('--save_iters', default=None, type=int,
dest='solver:save_iters', help='The saving iters of checkpoint model.')
parser.add_argument('--save_epoch', default=None, type=int,
dest='solver:save_epoch', help='The saving epoch of checkpoint model.')
# *********** Params for loss. **********
parser.add_argument('--loss_type', default=None, type=str,
dest='loss:loss_type', help='The loss type of the network.')
# *********** Params for logging. **********
parser.add_argument('--logfile_level', default=None, type=str,
dest='logging:logfile_level', help='To set the log level to files.')
parser.add_argument('--stdout_level', default=None, type=str,
dest='logging:stdout_level', help='To set the level to print to screen.')
parser.add_argument('--log_file', default=None, type=str,
dest='logging:log_file', help='The path of log files.')
parser.add_argument('--rewrite', type=str2bool, nargs='?', default=True,
dest='logging:rewrite', help='Whether to rewrite files.')
parser.add_argument('--log_to_file', type=str2bool, nargs='?', default=True,
dest='logging:log_to_file', help='Whether to write logging into files.')
# *********** Params for test or submission. **********
parser.add_argument('--test_img', default=None, type=str,
dest='test:test_img', help='The test path of image.')
parser.add_argument('--test_dir', default=None, type=str,
dest='test:test_dir', help='The test directory of images.')
parser.add_argument('--root_dir', default=None, type=str,
dest='test:root_dir', help='The root directory of images.')
parser.add_argument('--out_dir', default='none', type=str,
dest='test:out_dir', help='The test out directory of images.')
# *********** Params for env. **********
parser.add_argument('--seed', default=None, type=int, help='manual seed')
parser.add_argument('--cudnn', type=str2bool, nargs='?', default=True, help='Use CUDNN.')
args_parser = parser.parse_args()
if args_parser.seed is not None:
random.seed(args_parser.seed)
torch.manual_seed(args_parser.seed)
cudnn.enabled = True
cudnn.benchmark = args_parser.cudnn
configer = Configer(args_parser=args_parser)
abs_data_dir = os.path.expanduser(configer.get('data', 'data_dir'))
configer.update(['data', 'data_dir'], abs_data_dir)
if configer.get('gpu') is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = ','.join(str(gpu_id) for gpu_id in configer.get('gpu'))
if configer.get('network', 'norm_type') is None:
configer.update(['network', 'norm_type'], 'batchnorm')
if configer.get('phase') == 'train':
assert len(configer.get('gpu')) > 1 or 'sync' not in configer.get('network', 'norm_type')
project_dir = os.path.dirname(os.path.realpath(__file__))
configer.add(['project_dir'], project_dir)
if configer.get('logging', 'log_to_file'):
log_file = configer.get('logging', 'log_file')
new_log_file = '{}_{}'.format(log_file, time.strftime("%Y-%m-%d_%X", time.localtime()))
configer.update(['logging', 'log_file'], new_log_file)
else:
configer.update(['logging', 'logfile_level'], None)
Log.init(logfile_level=configer.get('logging', 'logfile_level'),
stdout_level=configer.get('logging', 'stdout_level'),
log_file=configer.get('logging', 'log_file'),
log_format=configer.get('logging', 'log_format'),
rewrite=configer.get('logging', 'rewrite'))
Log.info('BN Type is {}.'.format(configer.get('network', 'norm_type')))
Log.info('Config Dict: {}'.format(json.dumps(configer.to_dict(), indent=2)))
method_selector = MethodSelector(configer)
runner = None
if configer.get('task') == 'pose':
runner = method_selector.select_pose_method()
elif configer.get('task') == 'seg':
runner = method_selector.select_seg_method()
elif configer.get('task') == 'det':
runner = method_selector.select_det_method()
elif configer.get('task') == 'cls':
runner = method_selector.select_cls_method()
elif configer.get('task') == 'gan':
runner = method_selector.select_gan_method()
else:
Log.error('Task: {} is not valid.'.format(configer.get('task')))
exit(1)
if configer.get('phase') == 'train':
if configer.get('network', 'resume') is None:
Controller.init(runner)
Controller.train(runner)
elif configer.get('phase') == 'test' and configer.get('network', 'resume') is not None:
Controller.test(runner)
else:
Log.error('Phase: {} is not valid.'.format(configer.get('phase')))
exit(1)