docstr: generic tracker for registration task.
  to track results, it measures the loss, and the accuracy.
  only useful for the training.
docstr_tok: ["generic","tracker","for","registration","task",".","to","track","results",",","it","measures","the","loss",",","and","the","accuracy",".","only","useful","for","the","training","."]
code: def __init__(
      self,
      dataset,
      stage="train",
      wandb_log=False,
      use_tensorboard: bool = False,
  ):
      """
      generic tracker for registration task.
      to track results, it measures the loss, and the accuracy.
      only useful for the training.
      """
  
      super(PatchRegistrationTracker, self).__init__(
          stage, wandb_log, use_tensorboard
      )
  
      self.reset(stage)
  
imports: from typing import Dict, Any
  import torchnet
  import torch
  from .base_tracker import BaseTracker
  from .registration_metrics import compute_accuracy
  from .registration_metrics import compute_hit_ratio
  from .registration_metrics import compute_transfo_error
  from .registration_metrics import compute_scaled_registration_error
  from torch_points3d.models import model_interface
  from torch_points3d.utils.registration import estimate_transfo, fast_global_registration, get_matches
label_desc: Use image node_id 1 to create a new PatchRegistrationTracker object from inherited base classes: BaseTracker with example: obj = PatchRegistrationTracker(dataset, stage, wandb_log, use_tensorboard)