val_score = run_an_eval_epoch(args, model, val_loader) early_stop = stopper.step(val_score, model) print('epoch {:d}/{:d}, validation {} {:.4f}, best validation {} {:.4f}'.format( epoch + 1, args['num_epochs'], args['metric_name'], val_score, args['metric_name'], stopper.best_score)) if early_stop: break if not args['pre_trained']: stopper.load_checkpoint(model) test_score = run_an_eval_epoch(args, model, test_loader) print('test {} {:.4f}'.format(args['metric_name'], test_score)) if __name__ == "__main__": import argparse from configure import get_exp_configure parser = argparse.ArgumentParser(description='Aromaticity Prediction') parser.add_argument('-m', '--model', type=str, choices=['AttentiveFP'], default='AttentiveFP', help='Model to use') parser.add_argument('-p', '--pre-trained', action='store_true', help='Whether to skip training and use a pre-trained model') args = parser.parse_args().__dict__ args['dataset'] = 'Aromaticity' args['exp'] = '_'.join([args['model'], args['dataset']]) args.update(get_exp_configure(args['exp'])) main(args)
default="MWE-DGCN", help="Model to use") parser.add_argument("-c", "--cuda", type=str, default="none") parser.add_argument( "--postfix", type=str, default="", help="a string appended to the file name of the saved model") parser.add_argument("--rand_seed", type=int, default=-1, help="random seed for torch and numpy") parser.add_argument("--residual", action="store_true") parser.add_argument("--ewnorm", type=str, default="none", choices=["none", "both"]) args = parser.parse_args().__dict__ # Get experiment configuration args["dataset"] = "ogbn-proteins" args["exp_name"] = "_".join([args["model"], args["dataset"]]) args.update(get_exp_configure(args)) if not (args["cuda"] == "none"): args["device"] = torch.device("cuda: " + str(args["cuda"])) else: args["device"] = torch.device("cpu") main(args)