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: returns list of lists of integers; use it as result[ground_truth][predicted]
  to know how many samples of class ground_truth were reported as class predicted
docstr_tok: ["returns","list","of","lists","of","integers",";","use","it","as","result","[","ground_truth","]","[","predicted","]","to","know","how","many","samples","of","class","ground_truth","were","reported","as","class","predicted"]
code: def get_confusion_matrix(self):
      """returns list of lists of integers; use it as result[ground_truth][predicted]
      to know how many samples of class ground_truth were reported as class predicted
      """
      return self.confusion_matrix
  
file_path: torch-points3d/torch_points3d/metrics/confusion_matrix.py
imports: import numpy
  import torch
  import os
label_desc: Use image node_id 5 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.get_confusion_matrix() and returns: self