def METHOD_NAME(exe, compiled_test_program, test_feed_names, test_fetch_list):
    bboxes_list, bbox_nums_list, image_id_list = [], [], []
    with tqdm(
            total=len(val_loader),
            bar_format='Evaluation stage, Run batch:|{bar}| {n_fmt}/{total_fmt}',
            ncols=80) as t:
        for data in val_loader:
            data_all = {k: np.array(v) for k, v in data.items()}
            outs = exe.run(
                compiled_test_program,
                feed={test_feed_names[0]: data_all['image']},
                fetch_list=test_fetch_list,
                return_numpy=False)
            res = {}
            postprocess = YOLOPostProcess(
                score_threshold=0.001, nms_threshold=0.65, multi_label=True)
            res = postprocess(np.array(outs[0]), data_all['scale_factor'])
            bboxes_list.append(res['bbox'])
            bbox_nums_list.append(res['bbox_num'])
            image_id_list.append(np.array(data_all['im_id']))
            t.update()
    map_res = coco_metric(anno_file, bboxes_list, bbox_nums_list, image_id_list)
    return map_res[0]