text: #!/usr/bin/env python
  # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
  """
  Detection Training Script.
  
  This scripts reads a given config file and runs the training or evaluation.
  It is an entry point that is made to train standard models in detectron2.
  
  In order to let one script support training of many models,
  this script contains logic that are specific to these built-in models and therefore
  may not be suitable for your own project.
  For example, your research project perhaps only needs a single "evaluator".
  
  Therefore, we recommend you to use detectron2 as an library and take
  this file as an example of how to use the library.
  You may want to write your own script with your datasets and other customizations.
  """
  
  import logging
  import os
  from collections import OrderedDict
  import torch
  
  import detectron2.utils.comm as comm
  from detectron2.checkpoint import DetectionCheckpointer
  from detectron2.config import get_cfg
  from detectron2.data import MetadataCatalog, build_detection_train_loader
  from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, hooks, launch
  from detectron2.evaluation import (
      CityscapesInstanceEvaluator,
      CityscapesSemSegEvaluator,
      COCOEvaluator,
      COCOPanopticEvaluator,
      DatasetEvaluators,
      LVISEvaluator,
      PascalVOCDetectionEvaluator,
      SemSegEvaluator,
      verify_results,
  )
  from center_net import TrafficLightDatasetMapper
  
  class Trainer(DefaultTrainer):
      """
      We use the "DefaultTrainer" which contains pre-defined default logic for
      standard training workflow. They may not work for you, especially if you
      are working on a new research project. In that case you can write your
      own training loop. You can use "tools/plain_train_net.py" as an example.
      """
  
  
      @classmethod
      def build_evaluator(cls, cfg, dataset_name, output_folder=None):
          """
          Create evaluator(s) for a given dataset.
          This uses the special metadata "evaluator_type" associated with each builtin dataset.
          For your own dataset, you can simply create an evaluator manually in your
          script and do not have to worry about the hacky if-else logic here.
          """
          if output_folder is None:
              output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
          evaluator_list = []
          evaluator_type = MetadataCatalog.get(dataset_name).evaluator_type
          if evaluator_type in ["sem_seg", "coco_panoptic_seg"]:
              evaluator_list.append(
                  SemSegEvaluator(
                      dataset_name,
                      distributed=True,
                      num_classes=cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
                      ignore_label=cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
                      output_dir=output_folder,
                  )
              )
          if evaluator_type in ["coco", "coco_panoptic_seg"]:
              evaluator_list.append(COCOEvaluator(dataset_name, cfg, True, output_folder))
          if evaluator_type == "coco_panoptic_seg":
              evaluator_list.append(COCOPanopticEvaluator(dataset_name, output_folder))
          if evaluator_type == "cityscapes_instance":
              assert (
                  torch.cuda.device_count() >= comm.get_rank()
              ), "CityscapesEvaluator currently do not work with multiple machines."
              return CityscapesInstanceEvaluator(dataset_name)
          if evaluator_type == "cityscapes_sem_seg":
              assert (
                  torch.cuda.device_count() >= comm.get_rank()
              ), "CityscapesEvaluator currently do not work with multiple machines."
              return CityscapesSemSegEvaluator(dataset_name)
          elif evaluator_type == "pascal_voc":
              return PascalVOCDetectionEvaluator(dataset_name)
          elif evaluator_type == "lvis":
              return LVISEvaluator(dataset_name, cfg, True, output_folder)
          if len(evaluator_list) == 0:
              raise NotImplementedError(
                  "no Evaluator for the dataset {} with the type {}".format(
                      dataset_name, evaluator_type
                  )
              )
          elif len(evaluator_list) == 1:
              return evaluator_list[0]
          return DatasetEvaluators(evaluator_list)
  
  
      @classmethod
      def build_train_loader(cls, cfg):
          mapper = TrafficLightDatasetMapper(cfg, is_train=True)
          return build_detection_train_loader(cfg, mapper=mapper)
  
  
  def setup(args):
      """
      Create configs and perform basic setups.
      """
      cfg = get_cfg()
      cfg.merge_from_file(args.config_file)
      cfg.merge_from_list(args.opts)
      cfg.freeze()
      default_setup(cfg, args)
      return cfg
  
  
  def main(args):
      cfg = setup(args)
  
      if args.eval_only:
          model = Trainer.build_model(cfg)
          DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(
              cfg.MODEL.WEIGHTS, resume=args.resume
          )
          res = Trainer.test(cfg, model)
          if comm.is_main_process():
              verify_results(cfg, res)
          return res
  
      """
      If you'd like to do anything fancier than the standard training logic,
      consider writing your own training loop (see plain_train_net.py) or
      subclassing the trainer.
      """
      trainer = Trainer(cfg)
      trainer.resume_or_load(resume=args.resume)
      return trainer.train()
  
  
  if __name__ == "__main__":
      args = default_argument_parser().parse_args()
      print("Command Line Args:", args)
      launch(
          main,
          args.num_gpus,
          num_machines=args.num_machines,
          machine_rank=args.machine_rank,
          dist_url=args.dist_url,
          args=(args,),
      )
  