class_docstr: Streaming interface to allow for any source of predictions. 
  Initialize it, count predictions one by one, then print confusion matrix and intersection-union score
class_docstr_tok: ["Streaming","interface","to","allow","for","any","source","of","predictions",".","Initialize","it",",","count","predictions","one","by","one",",","then","print","confusion","matrix","and","intersection-union","score"]
docstr: Get the mIoU metric by ignoring missing labels. 
  If missing_as_one is True then treats missing classes in the IoU as 1
docstr_tok: ["Get","the","mIoU","metric","by","ignoring","missing","labels",".","If","missing_as_one","is","True","then","treats","missing","classes","in","the","IoU","as","1"]
code: def get_average_intersection_union(self, missing_as_one=False):
      """Get the mIoU metric by ignoring missing labels.
      If missing_as_one is True then treats missing classes in the IoU as 1
      """
      (
          values,
          existing_classes_mask,
      ) = self.get_intersection_union_per_class()
      if np.sum(existing_classes_mask) == 0:
          return 0
      if missing_as_one:
          values[~existing_classes_mask] = 1
          existing_classes_mask[:] = True
      return np.sum(values[existing_classes_mask]) / np.sum(
          existing_classes_mask
      )
  
file_path: torch-points3d/torch_points3d/metrics/confusion_matrix.py
imports: import numpy
  import torch
  import os
label_desc: Use image node_id 8 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.get_average_intersection_union(missing_as_one) and returns: unknown, int