code: def compute_metrics(self, results: list) -> Dict[str, float]:
      logger: MMLogger = MMLogger.get_current_instance()
  
      correct_image = 0
      num_image = 0
      nt = {"TP": 0, "TN": 0, "FP": 0, "FN": 0}
  
      for result in results:
          img_id = result["img_id"]
          TP = 0
  
          ann_ids = self.coco.getAnnIds(imgIds=img_id)
          target = self.coco.loadAnns(ann_ids[0])
  
          converted_bbox_all = []
          no_target_flag = False
          for one_target in target:
              if one_target["category_id"] == -1:
                  no_target_flag = True
              target_bbox = one_target["bbox"]
              converted_bbox = [
                  target_bbox[0],
                  target_bbox[1],
                  target_bbox[2] + target_bbox[0],
                  target_bbox[3] + target_bbox[1],
              ]
              converted_bbox_all.append(
                  np.array(converted_bbox).reshape(-1, 4)
              )
          gt_bbox_all = np.concatenate(converted_bbox_all, axis=0)
  
          idx = result["scores"] >= self.thresh_score
          filtered_boxes = result["bboxes"][idx]
  
          iou = bbox_overlaps(filtered_boxes.numpy(), gt_bbox_all)
          iou = torch.from_numpy(iou)
  
          num_prediction = filtered_boxes.shape[0]
          num_gt = gt_bbox_all.shape[0]
          if no_target_flag:
              if num_prediction >= 1:
                  nt["FN"] += 1
              else:
                  nt["TP"] += 1
              if num_prediction >= 1:
                  f_1 = 0.0
              else:
                  f_1 = 1.0
          else:
              if num_prediction >= 1:
                  nt["TN"] += 1
              else:
                  nt["FP"] += 1
              for i in range(min(num_prediction, num_gt)):
                  top_value, top_index = torch.topk(
                      iou.flatten(0, 1), 1
                  )
                  if top_value < self.iou_thrs:
                      break
                  else:
                      top_index_x = top_index // num_gt
                      top_index_y = top_index % num_gt
                      TP += 1
                      iou[top_index_x[0], :] = 0.0
                      iou[:, top_index_y[0]] = 0.0
              FP = num_prediction - TP
              FN = num_gt - TP
              f_1 = 2 * TP / (2 * TP + FP + FN)
  
          if f_1 >= self.thresh_f1:
              correct_image += 1
          num_image += 1
  
      score = correct_image / max(num_image, 1)
      results = {
          "F1_score": score,
          "T_acc": nt["TN"] / (nt["TN"] + nt["FP"]),
          "N_acc": nt["TP"] / (nt["TP"] + nt["FN"]),
      }
      logger.info(results)
      return results
  
file_path: mmdetection/mmdet/evaluation/metrics/grefcoco_metric.py
imports: from typing import Dict, Optional, Sequence
  import numpy
  import torch
  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 gRefCOCOMetric obj's underlying member method code with example usage: obj.compute_metrics(results) and returns: results