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"]
docstr: Predict full resolution results based on votes 
docstr_tok: ["Predict","full","resolution","results","based","on","votes"]
code: def _predict_full_res(self):
      """Predict full resolution results based on votes"""
      has_prediction = self._vote_counts > 0
      votes = self._votes[has_prediction].div(
          self._vote_counts[has_prediction].unsqueeze(-1)
      )
  
      # Upsample and predict
      full_pred = knn_interpolate(
          votes,
          self._raw_data.pos[has_prediction],
          self._raw_data.pos,
          k=self._k,
      )
      self._full_res_preds = full_pred
  
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 9 for calling the SegmentationVoter obj's underlying member method code with example usage: obj._predict_full_res() without return types