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)] = self._acc
      metrics["{}_macc".format(self._stage)] = self._macc
      metrics["{}_miou".format(self._stage)] = self._miou
  
      if verbose:
          metrics[
              "{}_iou_per_class".format(self._stage)
          ] = self._iou_per_class
      return metrics
  
file_path: torch-points3d/torch_points3d/metrics/segmentation_tracker.py
imports: from typing import Dict, Any
  import torch
  import wandb
  import numpy
  from torch_points3d.metrics.confusion_matrix import ConfusionMatrix
  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.models import model_interface
label_desc: Use image node_id 7 for calling the SegmentationTracker obj's underlying member method code with example usage: obj.get_metrics(verbose) and returns: metrics