def build_evaluator()

in ubteacher/engine/trainer.py [0:0]

• 19 lines of code
• 6 McCabe index (conditional complexity)

    def build_evaluator(cls, cfg, dataset_name, output_folder=None):
        if output_folder is None:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
        evaluator_list = []
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type

        if evaluator_type == "coco":
            evaluator_list.append(COCOEvaluator(
                dataset_name, output_dir=output_folder))
        elif evaluator_type == "pascal_voc":
            return PascalVOCDetectionEvaluator(dataset_name)
        if len(evaluator_list) == 0:
            raise NotImplementedError(
                "no Evaluator for the dataset {} with the type {}".format(
                    dataset_name, evaluator_type
                )
            )
        elif len(evaluator_list) == 1:
            return evaluator_list[0]

        return DatasetEvaluators(evaluator_list)