docstr: Returns a dictionnary of all metrics and losses being tracked
          
docstr_tok: ["Returns","a","dictionnary","of","all","metrics","and","losses","being","tracked"]
code: def get_metrics(self, verbose=False) -> Dict[str, Any]:
      """Returns a dictionnary of all metrics and losses being tracked"""
      metrics = super().get_metrics(verbose)
  
      metrics["{}_acc".format(self._stage)] = meter_value(self._obj_acc)
      metrics["{}_pos".format(self._stage)] = meter_value(
          self._pos_ratio
      )
      metrics["{}_neg".format(self._stage)] = meter_value(
          self._neg_ratio
      )
  
      if self._has_box_data:
          for thresh, ap in self._ap.items():
              mAP = sum(ap.values()) / len(ap)
              metrics["{}_map{}".format(self._stage, thresh)] = mAP
  
      if verbose and self._has_box_data:
          for thresh in self._ap:
              metrics[
                  "{}_class_rec{}".format(self._stage, thresh)
              ] = self._dict_to_str(self._rec[thresh])
              metrics[
                  "{}_class_ap{}".format(self._stage, thresh)
              ] = self._dict_to_str(self._ap[thresh])
  
      return metrics
  
imports: from typing import Dict, List, Any
  import torchnet
  import torch
  from collections import OrderedDict
  from torch_points3d.models.model_interface import TrackerInterface
  from torch_points3d.metrics.base_tracker import BaseTracker, meter_value
  from torch_points3d.metrics.meters import APMeter
  from torch_points3d.datasets.segmentation import IGNORE_LABEL
  from torch_points3d.modules.VoteNet import VoteNetResults
  from torch_points3d.datasets.object_detection.box_data import BoxData
  from .box_detection.ap import eval_detection
label_desc: Use image node_id 6 for calling the ObjectDetectionTracker obj's underlying member method code with example usage: obj.get_metrics(verbose) and returns: metrics