def parse_args(): parser = argparse.ArgumentParser( description="Train classification models on ImageNet", formatter_class=argparse.ArgumentDefaultsHelpFormatter) models.add_model_args(parser) fit.add_fit_args(parser) data.add_data_args(parser) dali.add_dali_args(parser) data.add_data_aug_args(parser) return parser.parse_args()
def get_args(): parser = ArgumentParser() base_group = parser.add_argument_group('base') base_group.add_argument('--nmr', action='store_true', help='determine whether use nmr data or not') base_group.add_argument( '--drop_smile', action='store_true', help='If true, do not use SMILES data for prediction') add_data_util_args(parser) add_model_args(parser) add_progress_args(parser) add_training_args(parser) add_learning_rate_option(parser) return parser.parse_args()
def get_training_args(): parser = argparse.ArgumentParser() # Dynet dynn.command_line.add_dynet_args(parser) # Data data_group = parser.add_argument_group("Data arguments") data_group.add_argument("--dataset", default="sst", choices=["sst", "amazon"]) data_group.add_argument("--data-dir", default="data") data_group.add_argument("--reprocess-data", action="store_true") data_group.add_argument("--lowercase", action="store_true") # Optimization optim_group = parser.add_argument_group("Optimization arguments") optim_group.add_argument("--batch-size", default=150, type=int) optim_group.add_argument("--max-tokens-per-batch", default=4000, type=int) optim_group.add_argument("--n-epochs", default=10, type=int) optim_group.add_argument("--patience", default=2, type=int) optim_group.add_argument("--lr", default=0.001, type=float) optim_group.add_argument("--lr-decay", default=0.1, type=float) # Model model_group = parser.add_argument_group("Model arguments") model_group.add_argument("--model-type", default="sopa", choices=models.supported_model_types) model_group.add_argument("--model-file", default="sopa_sst.npz") model_group.add_argument("--pretrained-embeds", default=None, type=str) model_group.add_argument("--freeze-embeds", action="store_true") model_group.add_argument("--normalize-embeds", action="store_true") # Misc misc_group = parser.add_argument_group("Miscellaneous arguments") model_group.add_argument("--verbose", action="store_true") misc_group.add_argument("--log-file", default=None, type=str) misc_group.add_argument("--n-explain", default=10, type=int) misc_group.add_argument("--n-top-contrib", default=10, type=int) # Parse args to get model type args, _ = parser.parse_known_args() # Add model specific arguments models.add_model_args(args.model_type, parser) # Parse again to get all arguments args = parser.parse_args() return args
def run_training_pipeline(parser): parser.add_argument("-f", "--config", default=False, type=str, help="Path to a YAML config file.") parser.add_argument( "--optimizer", default=False, type=str, help="Optimizer to be used during training.", ) parser.add_argument( "--scheduler", default=False, type=str, help="LR scheduler to be used during training.", ) parser.add_argument( "--model", default=False, type=str, help="The estimator architecture we we wish to use.", ) args, _ = parser.parse_known_args() if not args.optimizer and not args.scheduler and not args.model: optimizer, scheduler, model = get_main_args_from_yaml(args) else: optimizer = args.optimizer scheduler = args.scheduler model = args.model parser = add_optimizer_args(parser, optimizer) parser = add_scheduler_args(parser, scheduler) parser = add_model_args(parser, model) parser = add_trainer_specific_args(parser) hparams = load_yaml_args(parser=parser, log=log) set_seed(hparams.seed) model = build_model(hparams) trainer = setup_training(hparams) if hparams.load_weights: model.load_weights(hparams.load_weights) log.info(f"{model.__class__.__name__} train starting:") trainer.fit(model)
parser.add_argument('--patience_factor', default=2, type=int) parser.add_argument('--max_patience', default=64, type=int) parser.add_argument('--min_lr', default=1e-6, type=float) parser.add_argument('--threshold', default=1e-4, type=float) # optim.lr_scheduler.common parser.add_argument('--gamma', default=0.25, type=float) parser.add_argument('--early_stopping', default=128, type=int) parser.add_argument( '--model_summary', default=False, action='store_true', ) # development parser.add_argument('--devrun', default=False, action='store_true') parser.add_argument('--nosave', default=False, action='store_true') tmp_args, _ = parser.parse_known_args() parser = add_model_args(parser, tmp_args) args = parser.parse_args() main(args)