def main(args, unknown_args): args, config = parse_args_uargs(args, unknown_args) set_global_seeds(args.seed) modules = prepare_modules(expdir=args.expdir) model = Registry.get_model(**config["model_params"]) datasource = modules["data"].DataSource() data_params = config.get("data_params", {}) or {} loaders = datasource.prepare_loaders(mode="infer", n_workers=args.workers, batch_size=args.batch_size, **data_params) runner = modules["model"].ModelRunner(model=model) callbacks_params = config.get("callbacks_params", {}) or {} callbacks = runner.prepare_callbacks(mode="infer", resume=args.resume, out_prefix=args.out_prefix, **callbacks_params) runner.infer(loaders=loaders, callbacks=callbacks, verbose=args.verbose)
def main(args, unknown_args): args, config = parse_args_uargs(args, unknown_args, dump_config=True) set_global_seeds(args.seed) assert args.baselogdir is not None or args.logdir is not None if args.logdir is None: modules_ = prepare_modules(expdir=args.expdir) logdir = modules_["model"].prepare_logdir(config=config) args.logdir = str(pathlib.Path(args.baselogdir).joinpath(logdir)) os.makedirs(args.logdir, exist_ok=True) save_config(config=config, logdir=args.logdir) modules = prepare_modules(expdir=args.expdir, dump_dir=args.logdir) model = Registry.get_model(**config["model_params"]) datasource = modules["data"].DataSource() runner = modules["model"].ModelRunner(model=model) runner.train_stages(datasource=datasource, args=args, stages_config=config["stages"], verbose=args.verbose)
def get_model(self, stage: str) -> _Model: model = Registry.get_model(**self._config["model_params"]) model = self._preprocess_model_for_stage(stage, model) model = self._postprocess_model_for_stage(stage, model) return model