def do_test()

in train_net.py [0:0]

• 35 lines of code
• 13 McCabe index (conditional complexity)

def do_test(cfg, model):
    results = OrderedDict()
    for d, dataset_name in enumerate(cfg.DATASETS.TEST):
        if cfg.MODEL.RESET_CLS_TESTS:
            reset_cls_test(
                model,
                cfg.MODEL.TEST_CLASSIFIERS[d],
                cfg.MODEL.TEST_NUM_CLASSES[d])
        mapper = None if cfg.INPUT.TEST_INPUT_TYPE == 'default' \
            else DatasetMapper(
                cfg, False, augmentations=build_custom_augmentation(cfg, False))
        data_loader = build_detection_test_loader(cfg, dataset_name, mapper=mapper)
        output_folder = os.path.join(
            cfg.OUTPUT_DIR, "inference_{}".format(dataset_name))
        evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type

        if evaluator_type == "lvis" or cfg.GEN_PSEDO_LABELS:
            evaluator = LVISEvaluator(dataset_name, cfg, True, output_folder)
        elif evaluator_type == 'coco':
            if dataset_name == 'coco_generalized_zeroshot_val':
                # Additionally plot mAP for 'seen classes' and 'unseen classes'
                evaluator = CustomCOCOEvaluator(dataset_name, cfg, True, output_folder)
            else:
                evaluator = COCOEvaluator(dataset_name, cfg, True, output_folder)
        elif evaluator_type == 'oid':
            evaluator = OIDEvaluator(dataset_name, cfg, True, output_folder)
        else:
            assert 0, evaluator_type
            
        results[dataset_name] = inference_on_dataset(
            model, data_loader, evaluator)
        if comm.is_main_process():
            logger.info("Evaluation results for {} in csv format:".format(
                dataset_name))
            print_csv_format(results[dataset_name])
    if len(results) == 1:
        results = list(results.values())[0]
    return results