text: # Copyright (c) Facebook, Inc. and its affiliates.
  
  # This source code is licensed under the MIT license found in the
  # LICENSE file in the root directory of this source tree.
  
  """
  Full credits: https://github.com/facebookresearch/moco/blob/main/detection/train_net.py  # NOQA
  """
  
  
  import os
  
  from detectron2.checkpoint import DetectionCheckpointer
  from detectron2.config import get_cfg
  from detectron2.engine import (
      DefaultTrainer,
      default_argument_parser,
      default_setup,
      launch as d2_launch,
  )
  from detectron2.evaluation import COCOEvaluator, PascalVOCDetectionEvaluator
  from detectron2.layers import get_norm
  from detectron2.modeling.roi_heads import ROI_HEADS_REGISTRY, Res5ROIHeads
  
  
  @ROI_HEADS_REGISTRY.register()
  class Res5ROIHeadsExtraNorm(Res5ROIHeads):
      """
      As described in the MOCO paper, there is an extra BN layer
      following the res5 stage.
      """
  
      def __init__(self, cfg, input_shape):
          super().__init__(cfg, input_shape)
          norm = cfg.MODEL.RESNETS.NORM
          norm = get_norm(norm, self.res5[-1].out_channels)
          self.res5.add_module("norm", norm)
  
  
  class Trainer(DefaultTrainer):
      @classmethod
      def build_evaluator(cls, cfg, dataset_name, output_folder=None):
          if output_folder is None:
              output_folder = os.path.join(cfg.OUTPUT_DIR, "inference")
          if "coco" in dataset_name:
              return COCOEvaluator(dataset_name, cfg, True, output_folder)
          else:
              assert "voc" in dataset_name, "Dataset must be coco or voc"
              return PascalVOCDetectionEvaluator(dataset_name)
  
  
  def main(args):
      # setup the config file
      cfg = get_cfg()
      cfg.merge_from_file(args.config_file)
      cfg.merge_from_list(args.opts)
      cfg.freeze()
      default_setup(cfg, 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)
          return res
  
      trainer = Trainer(cfg)
      trainer.resume_or_load(resume=args.resume)
      return trainer.train()
  
  
  if __name__ == "__main__":
      args = default_argument_parser().parse_args()
      print("Arguments:", args)
      d2_launch(
          main,
          args.num_gpus,
          num_machines=args.num_machines,
          machine_rank=args.machine_rank,
          dist_url=args.dist_url,
          args=(args,),
      )
  