| import argparse |
| import datetime |
| import importlib |
| import os |
| from shutil import copyfile |
|
|
| import torch |
| import torch.distributed as dist |
| from omegaconf import OmegaConf |
|
|
| from utils.dist_utils import ( |
| get_world_size, |
| ) |
| from utils.utils import seed_all |
|
|
|
|
| parser = argparse.ArgumentParser(description="VFI") |
| parser.add_argument("-c", "--config", type=str) |
| parser.add_argument("-p", "--port", default="23455", type=str) |
| parser.add_argument("--local_rank", default="0") |
|
|
| args = parser.parse_args() |
|
|
|
|
| def main_worker(rank, config): |
| if "local_rank" not in config: |
| config["local_rank"] = config["global_rank"] = rank |
| if torch.cuda.is_available(): |
| print(f"Rank {rank} is available") |
| config["device"] = f"cuda:{rank}" |
| if config["distributed"]: |
| dist.init_process_group(backend="nccl", timeout=datetime.timedelta(seconds=5400)) |
| else: |
| config["device"] = "cpu" |
|
|
| cfg_name = os.path.basename(args.config).split(".")[0] |
| config["exp_name"] = cfg_name + "_" + config["exp_name"] |
| config["save_dir"] = os.path.join(config["save_dir"], config["exp_name"]) |
|
|
| if (not config["distributed"]) or rank == 0: |
| os.makedirs(config["save_dir"], exist_ok=True) |
| os.makedirs(f"{config['save_dir']}/ckpts", exist_ok=True) |
| config_path = os.path.join(config["save_dir"], args.config.split("/")[-1]) |
| if not os.path.isfile(config_path): |
| copyfile(args.config, config_path) |
| print("[**] create folder {}".format(config["save_dir"])) |
|
|
| trainer_name = config.get("trainer_type", "base_trainer") |
| print(f"using GPU {rank} for training") |
| if rank == 0: |
| print(trainer_name) |
| trainer_pack = importlib.import_module("trainers." + trainer_name) |
| trainer = trainer_pack.Trainer(config) |
|
|
| trainer.train() |
|
|
|
|
| if __name__ == "__main__": |
| torch.backends.cudnn.benchmark = True |
| cfg = OmegaConf.load(args.config) |
| seed_all(cfg.seed) |
| rank = int(args.local_rank) |
| torch.cuda.set_device(torch.device(f"cuda:{rank}")) |
| |
| cfg["world_size"] = get_world_size() |
| cfg["local_rank"] = rank |
| if rank == 0: |
| print("world_size: ", cfg["world_size"]) |
| main_worker(rank, cfg) |
|
|
