Method test

detectron2/engine/defaults.py:503–553

Args: cfg (CfgNode): model (nn.Module): evaluators (list[DatasetEvaluator] or None): if None, will call :meth:`build_evaluator`. Otherwise, must have the same length as ``cfg.DATASETS.TEST``. Returns: d

(cls, cfg, model, evaluators=None)

    @classmethod
    def test(cls, cfg, model, evaluators=None):
        """
        Args:
            cfg (CfgNode):
            model (nn.Module):
            evaluators (list[DatasetEvaluator] or None): if None, will call
                :meth:`build_evaluator`. Otherwise, must have the same length as
                ``cfg.DATASETS.TEST``.

        Returns:
            dict: a dict of result metrics
        """
        logger = logging.getLogger(__name__)
        if isinstance(evaluators, DatasetEvaluator):
            evaluators = [evaluators]
        if evaluators is not None:
            assert len(cfg.DATASETS.TEST) == len(evaluators), "{} != {}".format(
                len(cfg.DATASETS.TEST), len(evaluators)
            )

        results = OrderedDict()
        for idx, dataset_name in enumerate(cfg.DATASETS.TEST):
            data_loader = cls.build_test_loader(cfg, dataset_name)
            # When evaluators are passed in as arguments,
            # implicitly assume that evaluators can be created before data_loader.
            if evaluators is not None:
                evaluator = evaluators[idx]
            else:
                try:
                    evaluator = cls.build_evaluator(cfg, dataset_name)
                except NotImplementedError:
                    logger.warn(
                        "No evaluator found. Use `DefaultTrainer.test(evaluators=)`, "
                        "or implement its `build_evaluator` method."
                    )
                    results[dataset_name] = {}
                    continue
            results_i = inference_on_dataset(model, data_loader, evaluator)
            results[dataset_name] = results_i
            if comm.is_main_process():
                assert isinstance(
                    results_i, dict
                ), "Evaluator must return a dict on the main process. Got {} instead.".format(
                    results_i
                )
                logger.info("Evaluation results for {} in csv format:".format(dataset_name))
                print_csv_format(results_i)

        if len(results) == 1:
            results = list(results.values())[0]
        return results

    @staticmethod
    def auto_scale_workers(cfg, num_workers: int):