def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training')
parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
parser.add_argument('--workers', type=int, default=4, help='number of workers for dataloader')
parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment')
parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
parser.add_argument('--local_rank', type=int, default=50, help='local rank for distributed training')
parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
help='set extra config keys if needed')
parser.add_argument('--max_waiting_mins', type=int, default=30, help='max waiting minutes')
parser.add_argument('--start_epoch', type=int, default=0, help='')
parser.add_argument('--eval_tag', type=str, default='default', help='eval tag for this experiment')
parser.add_argument('--eval_all', action='store_true', default=False, help='whether to evaluate all checkpoints')
parser.add_argument('--ckpt_dir', type=str, default=None, help='specify a ckpt directory to be evaluated if needed')
parser.add_argument('--save_to_file', action='store_true', default=False, help='')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
cfg.TAG = Path(args.cfg_file).stem
cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml'
np.random.seed(1024)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default='/home/syang/Data/data_object_velodyne/output/kitti_models/centernet_multihead/0124_single_head/centernet_multihead.yaml', help='specify the config for training')
parser.add_argument('--batch_size', type=int, default=1, required=False, help='batch size for training')
parser.add_argument('--workers', type=int, default=16, help='number of workers for dataloader')
parser.add_argument('--extra_tag', type=str, default='0129_twostage_first_4', help='extra tag for this experiment')
# os.path.abspath('..') + '/output/robosense_models/robosense_pointpillar/BResampl_LR001/ckpt/checkpoint_epoch_30.pth'
parser.add_argument('--ckpt', type=str, default='/home/syang/Data/data_object_velodyne/output/kitti_models/centernet_multihead/0124_single_head/ckpt/checkpoint_epoch_80.pth', help='checkpoint to start from')
parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
help='set extra config keys if needed')
parser.add_argument('--max_waiting_mins', type=int, default=30, help='max waiting minutes')
parser.add_argument('--start_epoch', type=int, default=0, help='')
parser.add_argument('--eval_tag', type=str, default='default', help='eval tag for this experiment')
parser.add_argument('--eval_all', action='store_true', default=False, help='whether to evaluate all checkpoints')
parser.add_argument('--ckpt_dir', type=str, default=None, help='specify a ckpt directory to be evaluated if needed')
parser.add_argument('--save_to_file', action='store_true', default=False, help='')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
cfg.TAG = Path(args.cfg_file).stem
cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml'
np.random.seed(1024)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file', type=str, default=None, help='specify the config for training')
parser.add_argument('--batch_size', type=int, default=None, required=False, help='batch size for training')
parser.add_argument('--epochs', type=int, default=None, required=False, help='number of epochs to train for')
parser.add_argument('--workers', type=int, default=8, help='number of workers for dataloader')
parser.add_argument('--extra_tag', type=str, default='default', help='extra tag for this experiment')
parser.add_argument('--ckpt', type=str, default=None, help='checkpoint to start from')
parser.add_argument('--pretrained_model', type=str, default=None, help='pretrained_model')
parser.add_argument('--launcher', choices=['none', 'pytorch', 'slurm'], default='none')
parser.add_argument('--tcp_port', type=int, default=18888, help='tcp port for distrbuted training')
parser.add_argument('--sync_bn', action='store_true', default=False, help='whether to use sync bn')
parser.add_argument('--fix_random_seed', action='store_true', default=False, help='')
parser.add_argument('--ckpt_save_interval', type=int, default=1, help='number of training epochs')
parser.add_argument('--local_rank', type=int, default=0, help='local rank for distributed training')
parser.add_argument('--max_ckpt_save_num', type=int, default=30, help='max number of saved checkpoint')
parser.add_argument('--merge_all_iters_to_one_epoch', action='store_true', default=False, help='')
parser.add_argument('--set', dest='set_cfgs', default=None, nargs=argparse.REMAINDER,
help='set extra config keys if needed')
parser.add_argument('--max_waiting_mins', type=int, default=0, help='max waiting minutes')
parser.add_argument('--start_epoch', type=int, default=0, help='')
parser.add_argument('--save_to_file', action='store_true', default=False, help='')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
cfg.TAG = Path(args.cfg_file).stem
cfg.EXP_GROUP_PATH = '/'.join(args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml'
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file',
type=str,
default=None,
help='specify the config for training')
parser.add_argument('--data_dir', type=str, default=None)
parser.add_argument('--batch_size',
type=int,
default=16,
required=False,
help='batch size for training')
parser.add_argument('--epochs',
type=int,
default=80,
required=False,
help='number of epochs to train for')
parser.add_argument('--workers',
type=int,
default=4,
help='number of workers for dataloader')
parser.add_argument('--extra_tag',
type=str,
default='default',
help='extra tag for this experiment')
parser.add_argument('--ckpt',
type=str,
default=None,
help='checkpoint to start from')
parser.add_argument('--pretrained_model',
type=str,
default=None,
help='pretrained_model')
parser.add_argument('--launcher',
choices=['none', 'pytorch', 'slurm'],
default='none')
parser.add_argument('--federated',
choices=['none', 'sync', 'async'],
default='none') #NOTE: for federated learning
parser.add_argument('--tcp_port',
type=int,
default=18888,
help='tcp port for distributed training')
parser.add_argument('--sync_bn',
action='store_true',
default=False,
help='whether to use sync bn')
parser.add_argument('--fix_random_seed',
action='store_true',
default=False,
help='whether to use sync bn')
parser.add_argument('--ckpt_save_interval',
type=int,
default=2,
help='number of training epochs')
parser.add_argument('--local_rank',
type=int,
default=0,
help='local rank for distributed training')
parser.add_argument('--max_ckpt_save_num',
type=int,
default=30,
help='max number of saved checkpoint')
parser.add_argument('--set',
dest='set_cfgs',
default=None,
nargs=argparse.REMAINDER,
help='set extra config keys if needed')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
cfg.TAG = Path(args.cfg_file).stem
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg
def parse_config():
parser = argparse.ArgumentParser(description='arg parser')
parser.add_argument('--cfg_file',
type=str,
default=None,
help='specify the config for training')
parser.add_argument('--batch_size',
type=int,
default=None,
required=False,
help='batch size for training')
parser.add_argument('--epochs',
type=int,
default=None,
required=False,
help='number of epochs to train for')
parser.add_argument('--workers',
type=int,
default=8,
help='number of workers for dataloader')
parser.add_argument('--extra_tag',
type=str,
default='default',
help='extra tag for this experiment')
parser.add_argument('--ckpt',
type=str,
default=None,
help='checkpoint to start from')
parser.add_argument('--pretrained_model',
type=str,
default=None,
help='pretrained_model')
parser.add_argument('--launcher',
choices=['none', 'pytorch', 'slurm'],
default='none')
parser.add_argument('--tcp_port',
type=int,
default=18888,
help='tcp port for distrbuted training')
parser.add_argument('--sync_bn',
action='store_true',
default=False,
help='whether to use sync bn')
parser.add_argument('--fix_random_seed',
action='store_true',
default=False,
help='')
parser.add_argument('--ckpt_save_interval',
type=int,
default=1,
help='number of training epochs')
parser.add_argument('--local_rank',
type=int,
default=0,
help='local rank for distributed training')
parser.add_argument('--max_ckpt_save_num',
type=int,
default=30,
help='max number of saved checkpoint')
parser.add_argument('--merge_all_iters_to_one_epoch',
action='store_true',
default=False,
help='')
parser.add_argument('--set',
dest='set_cfgs',
default=None,
nargs=argparse.REMAINDER,
help='set extra config keys if needed')
parser.add_argument('--max_waiting_mins',
type=int,
default=0,
help='max waiting minutes')
parser.add_argument('--start_epoch', type=int, default=0, help='')
parser.add_argument('--save_to_file',
action='store_true',
default=False,
help='')
parser.add_argument('--adv',
action='store_true',
default=False,
help='adv defense or not')
parser.add_argument('--norm', type=str, default='inf', help='norm type')
parser.add_argument('--epsilon',
type=float,
default=0.01,
help='epsilon value')
parser.add_argument('--rec_type',
type=str,
default='both',
help='both: attack to points and reflectance'
'points: attack to points only'
'reflectance: attack to reflectance only')
parser.add_argument('--iterations',
type=int,
default=1,
help='iterations of different method')
parser.add_argument(
'--pgd',
type=bool,
default=False,
help=
'pgd adversarial type, when pgd is True, momentum should be False and iterations should be 10'
)
parser.add_argument(
'--momentum',
type=bool,
default=False,
help=
'adversarial type momentum, when momentum is True, pgd should be False and iterations should be 10'
)
parser.add_argument('--cfg_root_dir',
type=str,
default='',
help='model and relative informations save dir')
args = parser.parse_args()
cfg_from_yaml_file(args.cfg_file, cfg)
cfg.TAG = Path(args.cfg_file).stem
cfg.EXP_GROUP_PATH = '/'.join(
args.cfg_file.split('/')[1:-1]) # remove 'cfgs' and 'xxxx.yaml'
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg