def main():
args = parse_train_args()
cfg = load_config(args)
logger = setup_logger("TSN", save_dir=cfg.OUTPUT.DIR)
logger.info(args)
logger.info("Environment info:\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
if args.config_file:
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
launch_job(args, cfg, train)
def main():
args = parse_test_args()
cfg = load_test_config(args)
logger = logging.setup_logging(__name__, output_dir=cfg.OUTPUT_DIR)
logger.info(args)
logger.info("Environment info:\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
if args.config_file:
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
launch_job(cfg=cfg, init_method=args.init_method, func=test)
def main():
parser = argparse.ArgumentParser(description='TSN Test With PyTorch')
parser.add_argument("rgb_config_file",
default="",
metavar="RGB_CONFIG_FILE",
help="path to config file",
type=str)
parser.add_argument('rgb_pretrained',
default="",
metavar='RGB_PRETRAINED_FILE',
help="path to pretrained model",
type=str)
parser.add_argument("rgbdiff_config_file",
default="",
metavar="RGBDIFF_CONFIG_FILE",
help="path to config file",
type=str)
parser.add_argument('rgbdiff_pretrained',
default="",
metavar='RGBDIFF_PRETRAINED_FILE',
help="path to pretrained model",
type=str)
parser.add_argument('--output', default="./outputs/test", type=str)
args = parser.parse_args()
if not os.path.isfile(args.rgb_config_file) and not os.path.isfile(
args.rgb_pretrained):
raise ValueError('需要输入RGB模态配置文件和预训练模型路径')
if not os.path.isfile(args.rgbdiff_config_file) or not os.path.isfile(
args.rgbdiff_pretrained):
raise ValueError('需要输入RGBDIFF模态配置文件和预训练模型路径')
if not os.path.exists(args.output):
os.makedirs(args.output)
logger = logging.setup_logging(output_dir=args.output)
logger.info(args)
logger.info("Environment info:\n" + collect_env_info())
test(args)
def main():
parser = argparse.ArgumentParser(description='TSN Test With PyTorch')
parser.add_argument("config_file", default="", metavar="CONFIG_FILE",
help="path to config file", type=str)
parser.add_argument('pretrained', default="", metavar='PRETRAINED_FILE',
help="path to pretrained model", type=str)
parser.add_argument('--output', default="./outputs/test", type=str)
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
if not os.path.isfile(args.config_file) or not os.path.isfile(args.pretrained):
raise ValueError('需要输入配置文件和预训练模型路径')
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.MODEL.PRETRAINED = args.pretrained
cfg.OUTPUT.DIR = args.output
cfg.freeze()
if not os.path.exists(cfg.OUTPUT.DIR):
os.makedirs(cfg.OUTPUT.DIR)
logger = setup_logger("TSN", save_dir=cfg.OUTPUT.DIR)
logger.info(args)
logger.info("Environment info:\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
if args.config_file:
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
test(cfg)
def main():
parser = argparse.ArgumentParser(description='TSN Training With PyTorch')
parser.add_argument("--config_file",
default="",
metavar="FILE",
help="path to config file",
type=str)
parser.add_argument('--log_step',
default=10,
type=int,
help='Print logs every log_step')
parser.add_argument('--save_step',
default=2500,
type=int,
help='Save checkpoint every save_step')
parser.add_argument('--stop_save', default=False, action='store_true')
parser.add_argument(
'--eval_step',
default=2500,
type=int,
help='Evaluate dataset every eval_step, disabled when eval_step < 0')
parser.add_argument('--stop_eval', default=False, action='store_true')
parser.add_argument('--resume',
default=False,
action='store_true',
help='Resume training')
parser.add_argument('--use_tensorboard', default=1, type=int)
parser.add_argument('-n',
'--nodes',
default=1,
type=int,
metavar='N',
help='number of machines (default: 1)')
parser.add_argument('-g',
'--gpus',
default=1,
type=int,
help='number of gpus per node')
parser.add_argument('-nr',
'--nr',
default=0,
type=int,
help='ranking within the nodes')
parser.add_argument(
"opts",
help="Modify config options using the command-line",
default=None,
nargs=argparse.REMAINDER,
)
args = parser.parse_args()
if args.config_file:
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
if not os.path.exists(cfg.OUTPUT.DIR):
os.makedirs(cfg.OUTPUT.DIR)
logger = setup_logger("TSN", save_dir=cfg.OUTPUT.DIR)
logger.info(args)
logger.info("Environment info:\n" + collect_env_info())
logger.info("Loaded configuration file {}".format(args.config_file))
if args.config_file:
with open(args.config_file, "r") as cf:
config_str = "\n" + cf.read()
logger.info(config_str)
logger.info("Running with config:\n{}".format(cfg))
args.world_size = args.gpus * args.nodes
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '14028'
mp.spawn(train, nprocs=args.gpus, args=(args, cfg))