diff --git a/tools/eval_utils/eval_utils.py b/tools/eval_utils/eval_utils.py
new file mode 100644
index 0000000..92abd25
--- /dev/null
+++ b/tools/eval_utils/eval_utils.py
@@ -0,0 +1,140 @@
+import pickle
+import time
+
+import numpy as np
+import torch
+import tqdm
+
+from pcdet.models import load_data_to_gpu
+from pcdet.utils import common_utils
+
+
+def statistics_info(cfg, ret_dict, metric, disp_dict):
+    for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
+        metric['recall_roi_%s' % str(cur_thresh)] += ret_dict.get('roi_%s' % str(cur_thresh), 0)
+        metric['recall_rcnn_%s' % str(cur_thresh)] += ret_dict.get('rcnn_%s' % str(cur_thresh), 0)
+    metric['gt_num'] += ret_dict.get('gt', 0)
+    min_thresh = cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST[0]
+    disp_dict['recall_%s' % str(min_thresh)] = \
+        '(%d, %d) / %d' % (metric['recall_roi_%s' % str(min_thresh)], metric['recall_rcnn_%s' % str(min_thresh)], metric['gt_num'])
+
+
+def eval_one_epoch(cfg, args, model, dataloader, epoch_id, logger, dist_test=False, result_dir=None):
+    result_dir.mkdir(parents=True, exist_ok=True)
+
+    final_output_dir = result_dir / 'final_result' / 'data'
+    if args.save_to_file:
+        final_output_dir.mkdir(parents=True, exist_ok=True)
+
+    metric = {
+        'gt_num': 0,
+    }
+    for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
+        metric['recall_roi_%s' % str(cur_thresh)] = 0
+        metric['recall_rcnn_%s' % str(cur_thresh)] = 0
+
+    dataset = dataloader.dataset
+    class_names = dataset.class_names
+    det_annos = []
+
+    if getattr(args, 'infer_time', False):
+        start_iter = int(len(dataloader) * 0.1)
+        infer_time_meter = common_utils.AverageMeter()
+
+    logger.info('*************** EPOCH %s EVALUATION *****************' % epoch_id)
+    if dist_test:
+        num_gpus = torch.cuda.device_count()
+        local_rank = cfg.LOCAL_RANK % num_gpus
+        model = torch.nn.parallel.DistributedDataParallel(
+                model,
+                device_ids=[local_rank],
+                broadcast_buffers=False
+        )
+    model.eval()
+
+    if cfg.LOCAL_RANK == 0:
+        progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval', dynamic_ncols=True)
+    start_time = time.time()
+    for i, batch_dict in enumerate(dataloader):
+        load_data_to_gpu(batch_dict)
+
+        if getattr(args, 'infer_time', False):
+            start_time = time.time()
+
+        with torch.no_grad():
+            pred_dicts, ret_dict = model(batch_dict)
+
+        disp_dict = {}
+
+        if getattr(args, 'infer_time', False):
+            inference_time = time.time() - start_time
+            infer_time_meter.update(inference_time * 1000)
+            # use ms to measure inference time
+            disp_dict['infer_time'] = f'{infer_time_meter.val:.2f}({infer_time_meter.avg:.2f})'
+
+        statistics_info(cfg, ret_dict, metric, disp_dict)
+        annos = dataset.generate_prediction_dicts(
+            batch_dict, pred_dicts, class_names,
+            output_path=final_output_dir if args.save_to_file else None
+        )
+        det_annos += annos
+        if cfg.LOCAL_RANK == 0:
+            progress_bar.set_postfix(disp_dict)
+            progress_bar.update()
+
+    if cfg.LOCAL_RANK == 0:
+        progress_bar.close()
+
+    if dist_test:
+        rank, world_size = common_utils.get_dist_info()
+        det_annos = common_utils.merge_results_dist(det_annos, len(dataset), tmpdir=result_dir / 'tmpdir')
+        metric = common_utils.merge_results_dist([metric], world_size, tmpdir=result_dir / 'tmpdir')
+
+    logger.info('*************** Performance of EPOCH %s *****************' % epoch_id)
+    sec_per_example = (time.time() - start_time) / len(dataloader.dataset)
+    logger.info('Generate label finished(sec_per_example: %.4f second).' % sec_per_example)
+
+    if cfg.LOCAL_RANK != 0:
+        return {}
+
+    ret_dict = {}
+    if dist_test:
+        for key, val in metric[0].items():
+            for k in range(1, world_size):
+                metric[0][key] += metric[k][key]
+        metric = metric[0]
+
+    gt_num_cnt = metric['gt_num']
+    for cur_thresh in cfg.MODEL.POST_PROCESSING.RECALL_THRESH_LIST:
+        cur_roi_recall = metric['recall_roi_%s' % str(cur_thresh)] / max(gt_num_cnt, 1)
+        cur_rcnn_recall = metric['recall_rcnn_%s' % str(cur_thresh)] / max(gt_num_cnt, 1)
+        logger.info('recall_roi_%s: %f' % (cur_thresh, cur_roi_recall))
+        logger.info('recall_rcnn_%s: %f' % (cur_thresh, cur_rcnn_recall))
+        ret_dict['recall/roi_%s' % str(cur_thresh)] = cur_roi_recall
+        ret_dict['recall/rcnn_%s' % str(cur_thresh)] = cur_rcnn_recall
+
+    total_pred_objects = 0
+    for anno in det_annos:
+        total_pred_objects += anno['name'].__len__()
+    logger.info('Average predicted number of objects(%d samples): %.3f'
+                % (len(det_annos), total_pred_objects / max(1, len(det_annos))))
+
+    with open(result_dir / 'result.pkl', 'wb') as f:
+        pickle.dump(det_annos, f)
+
+    result_str, result_dict = dataset.evaluation(
+        det_annos, class_names,
+        eval_metric=cfg.MODEL.POST_PROCESSING.EVAL_METRIC,
+        output_path=final_output_dir
+    )
+
+    logger.info(result_str)
+    ret_dict.update(result_dict)
+
+    logger.info('Result is saved to %s' % result_dir)
+    logger.info('****************Evaluation done.*****************')
+    return ret_dict
+
+
+if __name__ == '__main__':
+    pass