code: def evaluate(self):
      acc = np.zeros(self.num_classes, dtype=float)
      iou = np.zeros(self.num_classes, dtype=float)
      tp = self.conf_matrix.diagonal()[:-1].astype(float)
      pos_gt = np.sum(self.conf_matrix[:-1, :-1], axis=0).astype(float)
      class_weights = pos_gt / np.sum(pos_gt)
      pos_pred = np.sum(self.conf_matrix[:-1, :-1], axis=1).astype(
          float
      )
  
      acc_valid = pos_pred > 0
      acc[acc_valid] = tp[acc_valid] / pos_pred[acc_valid]
      iou_valid = (pos_gt + pos_pred) > 0
      union = pos_gt + pos_pred - tp
      iou[acc_valid] = tp[acc_valid] / union[acc_valid]
      macc = np.sum(acc) / np.sum(acc_valid)
      miou = np.sum(iou) / np.sum(iou_valid)
      fiou = np.sum(iou * class_weights)
      pacc = np.sum(tp) / np.sum(pos_gt)
  
      res = {}
      res["mIoU"] = 100 * miou
      res["fwIoU"] = 100 * fiou
      res["mACC"] = 100 * macc
      res["pACC"] = 100 * pacc
  
      results = build_info_dict(_type_="metric", sem_metrics=res)
      return results
  
file_path: PaddleSeg/contrib/PanopticSeg/paddlepanseg/utils/evaluation/sem_seg_evaluator.py
imports: import numpy
  from paddlepanseg.cvlibs import build_info_dict
  from .evaluator import Evaluator
label_desc: Use image node_id 3 for calling the SemSegEvaluator obj's underlying member method code with example usage: obj.evaluate() and returns: results