text: #!/usr/bin/env python3
  # Copyright (c) Facebook, Inc. and its affiliates.
  
  """
  PointRend Training Script.
  
  This script is a simplified version of the training script in detectron2/tools.
  """
  
  import os
  import torch
  
  import detectron2.data.transforms as T
  import detectron2.utils.comm as comm
  from detectron2.checkpoint import DetectionCheckpointer
  from detectron2.config import get_cfg
  from detectron2.data import DatasetMapper, MetadataCatalog, build_detection_train_loader
  from detectron2.engine import DefaultTrainer, default_argument_parser, default_setup, launch
  from detectron2.evaluation import (
      # CityscapesInstanceEvaluator,
      # CityscapesSemSegEvaluator,
      # COCOEvaluator,
      DatasetEvaluators,
      # LVISEvaluator,
      SemSegEvaluator,
      verify_results,
  )
  from point_rend_ipalm import ColorAugSSDTransform, add_pointrend_config
  
  
  def build_sem_seg_train_aug(cfg):
      augs = [
          T.ResizeShortestEdge(
              cfg.INPUT.MIN_SIZE_TRAIN, cfg.INPUT.MAX_SIZE_TRAIN, cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
          )
      ]
      if cfg.INPUT.CROP.ENABLED:
          augs.append(
              T.RandomCrop_CategoryAreaConstraint(
                  cfg.INPUT.CROP.TYPE,
                  cfg.INPUT.CROP.SIZE,
                  cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
                  cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
              )
          )
      if cfg.INPUT.COLOR_AUG_SSD:
          augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
      augs.append(T.RandomFlip())
      return augs
  
  
  class Trainer(DefaultTrainer):
      """
      We use the "DefaultTrainer" which contains a number pre-defined 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 use the cleaner
      "SimpleTrainer", or write your own training loop.
      """
  
      @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 == "lvis":
          #     return LVISEvaluator(dataset_name, output_dir=output_folder)
          # if evaluator_type == "coco":
          #     return COCOEvaluator(dataset_name, output_dir=output_folder)
          # if evaluator_type == "sem_seg":
          return SemSegEvaluator(
              dataset_name,
              distributed=True,
              output_dir=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)
          # if len(evaluator_list) == 0:
          #     raise NotImplementedError(
          #         "no Evaluator for the dataset {} with the type {}".format(
          #             dataset_name, evaluator_type
          #         )
          #     )
          # if len(evaluator_list) == 1:
          #     return evaluator_list[0]
          # return DatasetEvaluators(evaluator_list)
  
      @classmethod
      def build_train_loader(cls, cfg):
          # if "SemanticSegmentor" in cfg.MODEL.META_ARCHITECTURE:
          mapper = DatasetMapper(cfg, is_train=True, augmentations=build_sem_seg_train_aug(cfg))
          # else:
          #     mapper = None
          return build_detection_train_loader(cfg, mapper=mapper)
  
  
  def setup(args):
      """
      Create config and perform basic setups.
      """
      cfg = get_cfg()
      add_pointrend_config(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
  
      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,),
      )
  