| | |
| | | "training phase. If None is given, it is decided according the number " |
| | | "of training samples automatically .", |
| | | ) |
| | | parser.add_argument( |
| | | "--use_tensorboard", |
| | | type=str2bool, |
| | | default=True, |
| | | help="Enable tensorboard logging", |
| | | ) |
| | | |
| | | # pretrained model related |
| | | parser.add_argument( |
| | |
| | | prepare_data(args, distributed_option) |
| | | |
| | | model = build_model(args) |
| | | model = model.to( |
| | | dtype=getattr(torch, args.train_dtype), |
| | | device="cuda" if args.ngpu > 0 else "cpu", |
| | | ) |
| | | optimizers = build_optimizer(args, model=model) |
| | | schedulers = build_scheduler(args, optimizers) |
| | | |
| | |
| | | distributed_option.dist_rank, |
| | | distributed_option.local_rank)) |
| | | logging.info(pytorch_cudnn_version()) |
| | | logging.info("Args: {}".format(args)) |
| | | logging.info(model_summary(model)) |
| | | logging.info("Optimizer: {}".format(optimizers)) |
| | | logging.info("Scheduler: {}".format(schedulers)) |