class_docstr: This class is a helper to perform full point cloud prediction by having votes interpolated using knn
class_docstr_tok: ["This","class","is","a","helper","to","perform","full","point","cloud","prediction","by","having","votes","interpolated","using","knn"]
code: def __init__(
      self,
      raw_data,
      num_classes,
      conv_type,
      class_seg_map=None,
      k: int = 1,
  ):
      assert k > 0
      self._raw_data = raw_data
      self._num_pos = raw_data.pos.shape[0]
      self._votes = torch.zeros(
          (self._num_pos, num_classes), dtype=torch.float
      )
      self._vote_counts = torch.zeros(self._num_pos, dtype=torch.float)
      self._full_res_preds = None
      self._conv_type = conv_type
      self._class_seg_map = class_seg_map
      self._k = k
      self._num_votes = 0
  
file_path: torch-points3d/torch_points3d/metrics/segmentation_helpers.py
imports: import numpy
  import torch
  from torch_points3d.core.data_transform import SaveOriginalPosId
  from torch_geometric.nn.unpool import knn_interpolate
label_desc: Use image node_id 1 to create a new SegmentationVoter object  with example: obj = SegmentationVoter(raw_data, num_classes, conv_type, class_seg_map, k)