def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() config = load_config(args.config_path) run_train_model(config)
def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() config = load_config(args.config_path) run_grid_search(config)
def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() config = load_config(args.config_path) build_inference_data(config)
def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() config: ConfigBase = load_config(args.config_path) config.model.predict()
def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() config: ConfigBase = load_config(args.config_path) config.data_builder.build_training_data()
def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() logger.info("load config") config: ConfigBase = load_config(args.config_path) logger.info("start beautify") beautifier = config.data_beautifier beautifier.beautify()
def main(): parser = argparse.ArgumentParser() parser.add_argument("config_path") args = parser.parse_args() config = load_config(args.config_path) train_data_loader = DataLoader(config.train_dataset, batch_size=config.batch_size, shuffle=True, pin_memory=True, num_workers=config.num_workers) val_data_loader = DataLoader(config.val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=config.num_workers) model = config.model model_save_path = config.model_save_path os.makedirs(model_save_path, exist_ok=True) logger_path = os.path.join(model_save_path, "log.txt") setup_logger(out_file=logger_path) trainer = config.trainer_cls(model=model, train_data_loader=train_data_loader, val_data_loader=val_data_loader, epoch_count=config.epoch_count, optimizer=config.optimizer, scheduler=config.scheduler, loss_calculator=config.loss_calculator, metric_calculator=config.metric_calculator, print_frequency=config.print_frequency) trainer.run()