text: # Copyright (c) OpenMMLab. All rights reserved.
  from os import path as osp
  
  import mmcv
  import numpy as np
  import torch
  from mmcv.parallel import DataContainer as DC
  from mmcv.runner import auto_fp16
  
  from mmdet3d.core import show_seg_result
  from mmseg.models.segmentors import BaseSegmentor
  
  
  class Base3DSegmentor(BaseSegmentor):
      """Base class for 3D segmentors.
  
      The main difference with `BaseSegmentor` is that we modify the keys in
      data_dict and use a 3D seg specific visualization function.
      """
  
      @property
      def with_regularization_loss(self):
          """bool: whether the segmentor has regularization loss for weight"""
          return hasattr(self, 'loss_regularization') and \
              self.loss_regularization is not None
  
      def forward_test(self, points, img_metas, **kwargs):
          """Calls either simple_test or aug_test depending on the length of
          outer list of points. If len(points) == 1, call simple_test. Otherwise
          call aug_test to aggregate the test results by e.g. voting.
  
          Args:
              points (list[list[torch.Tensor]]): the outer list indicates
                  test-time augmentations and inner torch.Tensor should have a
                  shape BXNxC, which contains all points in the batch.
              img_metas (list[list[dict]]): the outer list indicates test-time
                  augs (multiscale, flip, etc.) and the inner list indicates
                  images in a batch.
          """
          for var, name in [(points, 'points'), (img_metas, 'img_metas')]:
              if not isinstance(var, list):
                  raise TypeError(f'{name} must be a list, but got {type(var)}')
  
          num_augs = len(points)
          if num_augs != len(img_metas):
              raise ValueError(f'num of augmentations ({len(points)}) != '
                               f'num of image meta ({len(img_metas)})')
  
          if num_augs == 1:
              return self.simple_test(points[0], img_metas[0], **kwargs)
          else:
              return self.aug_test(points, img_metas, **kwargs)
  
      @auto_fp16(apply_to=('points'))
      def forward(self, return_loss=True, **kwargs):
          """Calls either forward_train or forward_test depending on whether
          return_loss=True.
  
          Note this setting will change the expected inputs. When
          `return_loss=True`, point and img_metas are single-nested (i.e.
          torch.Tensor and list[dict]), and when `resturn_loss=False`, point and
          img_metas should be double nested (i.e.  list[torch.Tensor],
          list[list[dict]]), with the outer list indicating test time
          augmentations.
          """
          if return_loss:
              return self.forward_train(**kwargs)
          else:
              return self.forward_test(**kwargs)
  
      def show_results(self,
                       data,
                       result,
                       palette=None,
                       out_dir=None,
                       ignore_index=None,
                       show=False,
                       score_thr=None):
          """Results visualization.
  
          Args:
              data (list[dict]): Input points and the information of the sample.
              result (list[dict]): Prediction results.
              palette (list[list[int]]] | np.ndarray): The palette of
                  segmentation map. If None is given, random palette will be
                  generated. Default: None
              out_dir (str): Output directory of visualization result.
              ignore_index (int, optional): The label index to be ignored, e.g.
                  unannotated points. If None is given, set to len(self.CLASSES).
                  Defaults to None.
              show (bool, optional): Determines whether you are
                  going to show result by open3d.
                  Defaults to False.
              TODO: implement score_thr of Base3DSegmentor.
              score_thr (float, optional): Score threshold of bounding boxes.
                  Default to None.
                  Not implemented yet, but it is here for unification.
          """
          assert out_dir is not None, 'Expect out_dir, got none.'
          if palette is None:
              if self.PALETTE is None:
                  palette = np.random.randint(
                      0, 255, size=(len(self.CLASSES), 3))
              else:
                  palette = self.PALETTE
          palette = np.array(palette)
          for batch_id in range(len(result)):
              if isinstance(data['points'][0], DC):
                  points = data['points'][0]._data[0][batch_id].numpy()
              elif mmcv.is_list_of(data['points'][0], torch.Tensor):
                  points = data['points'][0][batch_id]
              else:
                  ValueError(f"Unsupported data type {type(data['points'][0])} "
                             f'for visualization!')
              if isinstance(data['img_metas'][0], DC):
                  pts_filename = data['img_metas'][0]._data[0][batch_id][
                      'pts_filename']
              elif mmcv.is_list_of(data['img_metas'][0], dict):
                  pts_filename = data['img_metas'][0][batch_id]['pts_filename']
              else:
                  ValueError(
                      f"Unsupported data type {type(data['img_metas'][0])} "
                      f'for visualization!')
              file_name = osp.split(pts_filename)[-1].split('.')[0]
  
              pred_sem_mask = result[batch_id]['semantic_mask'].cpu().numpy()
  
              show_seg_result(
                  points,
                  None,
                  pred_sem_mask,
                  out_dir,
                  file_name,
                  palette,
                  ignore_index,
                  show=show)
  