docstr:         tracker for registration tasks (we learn feature for each fragments like segmentation network)
  it measures loss, feature match recall, hit ratio, rotation error, translation error.
          
docstr_tok: ["tracker","for","registration","tasks","(","we","learn","feature","for","each","fragments","like","segmentation","network",")","it","measures","loss",",","feature","match","recall",",","hit","ratio",",","rotation","error",",","translation","error","."]
code: def __init__(
      self,
      num_points=5000,
      tau_1=0.1,
      tau_2=0.05,
      rot_thresh=5,
      trans_thresh=2,
      stage="train",
      wandb_log=False,
      use_tensorboard: bool = False,
  ):
      """
              tracker for registration tasks (we learn feature for each fragments like segmentation network)
      it measures loss, feature match recall, hit ratio, rotation error, translation error.
      """
      super(FragmentRegistrationTracker, self).__init__(
          stage, wandb_log, use_tensorboard
      )
  
      self.reset(stage)
      self.num_points = num_points
      self.tau_1 = tau_1
      self.tau_2 = tau_2
      self.rot_thresh = rot_thresh
      self.trans_thresh = trans_thresh
  
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 FragmentRegistrationTracker object from inherited base classes: BaseTracker with example: obj = FragmentRegistrationTracker(num_points, tau_1, tau_2, rot_thresh, trans_thresh, stage, wandb_log, use_tensorboard)