code: def compute_metrics(self, results: list) -> Dict[str, float]:
      logger: MMLogger = MMLogger.get_current_instance()
  
      dataset2score = {
          "refcoco": {k: 0.0 for k in self.topk},
          "refcoco+": {k: 0.0 for k in self.topk},
          "refcocog": {k: 0.0 for k in self.topk},
      }
      dataset2count = {"refcoco": 0.0, "refcoco+": 0.0, "refcocog": 0.0}
  
      for result in results:
          img_id = result["img_id"]
  
          ann_ids = self.coco.getAnnIds(imgIds=img_id)
          assert len(ann_ids) == 1
          img_info = self.coco.loadImgs(img_id)[0]
          target = self.coco.loadAnns(ann_ids[0])
  
          target_bbox = target[0]["bbox"]
          converted_bbox = [
              target_bbox[0],
              target_bbox[1],
              target_bbox[2] + target_bbox[0],
              target_bbox[3] + target_bbox[1],
          ]
          iou = bbox_overlaps(
              result["bboxes"], np.array(converted_bbox).reshape(-1, 4)
          )
          for k in self.topk:
              if max(iou[:k]) >= self.iou_thrs:
                  dataset2score[img_info["dataset_name"]][k] += 1.0
          dataset2count[img_info["dataset_name"]] += 1.0
  
      for key, value in dataset2score.items():
          for k in self.topk:
              try:
                  value[k] /= dataset2count[key]
              except Exception as e:
                  print(e)
  
      results = {}
      mean_precision = 0.0
      for key, value in dataset2score.items():
          results[key] = sorted([v for k, v in value.items()])
          mean_precision += sum(results[key])
          logger.info(
              f" Dataset: {key} - Precision @ 1, 5, 10: {results[key]}"
          )
  
      # `mean_precision` key is used for saving the best checkpoint
      out_results = {"mean_precision": mean_precision / 9.0}
  
      for i, k in enumerate(self.topk):
          out_results[f"refcoco_precision@{k}"] = results["refcoco"][i]
      for i, k in enumerate(self.topk):
          out_results[f"refcoco+_precision@{k}"] = results["refcoco+"][
              i
          ]
      for i, k in enumerate(self.topk):
          out_results[f"refcocog_precision@{k}"] = results["refcocog"][
              i
          ]
      return out_results
  
file_path: mmdetection/mmdet/evaluation/metrics/refexp_metric.py
imports: from typing import Dict, Optional, Sequence
  import numpy
  from mmengine.evaluator import BaseMetric
  from mmengine.fileio import get_local_path
  from mmengine.logging import MMLogger
  from mmdet.datasets.api_wrappers import COCO
  from mmdet.registry import METRICS
  from ..functional import bbox_overlaps
label_desc: Use image node_id 3 for calling the RefExpMetric obj's underlying member method code with example usage: obj.compute_metrics(results) and returns: out_results