def train(cfg):
# Set up environment.
init_distributed_training(cfg)
local_rank_id = get_local_rank()
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED + 10 * local_rank_id)
torch.manual_seed(cfg.RNG_SEED + 10 * local_rank_id)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
# Setup logging format.
logging.setup_logging(cfg.OUTPUT_DIR)
logger.info('init start')
# 迭代轮数从１开始计数
arguments = {"cur_epoch": 1}
device = get_device(local_rank_id)
model = build_recognizer(cfg, device)
criterion = build_criterion(cfg, device)
optimizer = build_optimizer(cfg, model)
lr_scheduler = build_lr_scheduler(cfg, optimizer)
checkpointer = CheckPointer(model,
optimizer=optimizer,
scheduler=lr_scheduler,
save_dir=cfg.OUTPUT_DIR,
save_to_disk=True)
if cfg.TRAIN.RESUME:
logger.info('resume start')
extra_checkpoint_data = checkpointer.load(map_location=device)
if isinstance(extra_checkpoint_data, dict):
arguments['cur_epoch'] = extra_checkpoint_data['cur_epoch']
if cfg.LR_SCHEDULER.IS_WARMUP:
logger.info('warmup start')
if lr_scheduler.finished:
optimizer.load_state_dict(
lr_scheduler.after_scheduler.optimizer.state_dict())
else:
optimizer.load_state_dict(
lr_scheduler.optimizer.state_dict())
lr_scheduler.optimizer = optimizer
lr_scheduler.after_scheduler.optimizer = optimizer
logger.info('warmup end')
logger.info('resume end')
train_data_loader = build_data(cfg, is_train=True)
test_data_loader = build_data(cfg, is_train=False)
logger.info('init end')
synchronize()
do_train(cfg, arguments, train_data_loader, test_data_loader, model,
criterion, optimizer, lr_scheduler, checkpointer, device)
def main():
args = parse_test_args()
cfg = load_test_config(args)
logging.setup_logging(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 test(cfg):
# Set up environment.
init_distributed_training(cfg)
local_rank_id = get_local_rank()
# Set random seed from configs.
np.random.seed(cfg.RNG_SEED + 10 * local_rank_id)
torch.manual_seed(cfg.RNG_SEED + 10 * local_rank_id)
torch.backends.cudnn.deterministic = False
torch.backends.cudnn.benchmark = True
logging.setup_logging(cfg.OUTPUT_DIR)
device = get_device(local_rank=local_rank_id)
model = build_recognizer(cfg, device=device)
synchronize()
do_evaluation(cfg, model, device)
def main():
args = parse_args()
cfg = load_config(args)
if not os.path.exists(cfg.OUTPUT_DIR):
os.makedirs(cfg.OUTPUT_DIR)
# Setup logging format.
logging.setup_logging(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=cfg.INIT_METHOD, func=train)