def parge_config():
parser = argparse.ArgumentParser(description="GAN model inference")
parser.add_argument("resume", help="the checkpoint file to test")
parser.add_argument("--config", help="test config file path")
parser.add_argument(
"--launcher",
type=str,
choices=["none", "pytorch", "slurm"],
default="none",
help="job launcher",
)
parser.add_argument("--tcp-port", type=str, default="5017")
parser.add_argument(
"--set",
dest="set_cfgs",
default=None,
nargs=argparse.REMAINDER,
help="set extra config keys if needed",
)
args = parser.parse_args()
args.resume = Path(args.resume)
cfg.work_dir = args.resume.parent
if not args.config:
args.config = cfg.work_dir / "config.yaml"
cfg_from_yaml_file(args.config, cfg)
cfg.launcher = args.launcher
cfg.tcp_port = args.tcp_port
cfg.MODEL.sync_bn = False # not required for inference
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
return args, cfg
def parge_config():
parser = argparse.ArgumentParser(description='UDA_TP training')
parser.add_argument('config', help='train config file path')
parser.add_argument('--work-dir',
help='the dir to save logs and models',
default='')
parser.add_argument('--resume-from',
help='the checkpoint file to resume from')
parser.add_argument('--launcher',
type=str,
choices=['none', 'pytorch', 'slurm'],
default='none',
help='job launcher')
parser.add_argument('--tcp-port', type=str, default='5017')
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.config, cfg)
cfg.launcher = args.launcher
cfg.tcp_port = args.tcp_port
if not args.work_dir:
args.work_dir = Path(args.config).stem
cfg.work_dir = cfg.LOGS_ROOT / args.work_dir
mkdir_if_missing(cfg.work_dir)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
shutil.copy(args.config, cfg.work_dir / 'config.yaml')
return args, cfg
def parge_config():
parser = argparse.ArgumentParser(description="CycleGAN training")
parser.add_argument("config", help="train config file path")
parser.add_argument("--work-dir",
help="the dir to save logs and models",
default="")
parser.add_argument("--resume-from",
help="the checkpoint file to resume from")
parser.add_argument(
"--launcher",
type=str,
choices=["none", "pytorch", "slurm"],
default="none",
help="job launcher",
)
parser.add_argument("--tcp-port", type=str, default="5017")
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.config, cfg)
assert len(list(cfg.TRAIN.datasets.keys()))==2, \
"the number of datasets for domain-translation training should be two"
cfg.launcher = args.launcher
cfg.tcp_port = args.tcp_port
if not args.work_dir:
args.work_dir = Path(args.config).stem
cfg.work_dir = cfg.LOGS_ROOT / args.work_dir
mkdir_if_missing(cfg.work_dir)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs, cfg)
shutil.copy(args.config, cfg.work_dir / "config.yaml")
return args, cfg
