code: def main(args):
      if args.seed is not None:
          paddle.seed(args.seed)
          np.random.seed(args.seed)
          random.seed(args.seed)
  
      env_info = get_sys_env()
      info = ["{}: {}".format(k, v) for k, v in env_info.items()]
      info = "\n".join(
          ["", format("Environment Information", "-^48s")]
          + info
          + ["-" * 48]
      )
      logger.info(info)
  
      if (
          args.device == "gpu"
          and env_info["Paddle compiled with cuda"]
          and env_info["GPUs used"]
      ):
          place = "gpu"
      elif args.device == "xpu" and paddle.is_compiled_with_xpu():
          place = "xpu"
      elif (
          args.device == "npu"
          and paddle.is_compiled_with_custom_device("npu")
      ):
          place = "npu"
      elif args.device == "mlu" and paddle.is_compiled_with_mlu():
          place = "mlu"
      else:
          place = "cpu"
  
      paddle.set_device(place)
      if not args.cfg:
          raise RuntimeError("No configuration file specified.")
  
      nranks = paddle.distributed.ParallelEnv().nranks
      # Limit cv2 threads if too many subprocesses are spawned.
      # This should reduce resource allocation and thus boost performance.
      if nranks >= 8 and args.num_workers >= 8:
          logger.warning(
              "The number of threads used by OpenCV is set to 1 to improve performance."
          )
          cv2.setNumThreads(1)
  
      cfg = Config(
          args.cfg,
          learning_rate=args.learning_rate,
          iters=args.iters,
          batch_size=args.batch_size,
          opts=args.opts,
      )
  
      # Only support for the DeepLabv3+ model
      if args.data_format == "NHWC":
          if cfg.dic["model"]["type"] != "DeepLabV3P":
              raise ValueError(
                  'The "NHWC" data format only support the DeepLabV3P model!'
              )
          cfg.dic["model"]["data_format"] = args.data_format
          cfg.dic["model"]["backbone"]["data_format"] = args.data_format
          loss_len = len(cfg.dic["loss"]["types"])
          for i in range(loss_len):
              cfg.dic["loss"]["types"][i][
                  "data_format"
              ] = args.data_format
  
      train_dataset = cfg.train_dataset
      if train_dataset is None:
          raise RuntimeError(
              "The training dataset is not specified in the configuration file."
          )
      elif len(train_dataset) == 0:
          raise ValueError(
              "The length of train_dataset is 0. Please check if your dataset is valid"
          )
  
      if args.repeats > 1:
          train_dataset.file_list *= args.repeats
  
      val_dataset = cfg.val_dataset if args.do_eval else None
      losses = cfg.loss
  
      msg = "\n---------------Config Information---------------\n"
      msg += str(cfg)
      msg += "------------------------------------------------"
      logger.info(msg)
  
      # convert bn to sync_bn if necessary
      if place == "gpu" and paddle.distributed.ParallelEnv().nranks > 1:
          model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(
              cfg.model
          )
      else:
          model = cfg.model
  
      train(
          model,
          train_dataset,
          val_dataset=val_dataset,
          optimizer=cfg.optimizer,
          save_dir=args.save_dir,
          iters=cfg.iters,
          batch_size=cfg.batch_size,
          resume_model=args.resume_model,
          save_interval=args.save_interval,
          log_iters=args.log_iters,
          num_workers=args.num_workers,
          use_vdl=args.use_vdl,
          losses=losses,
          keep_checkpoint_max=args.keep_checkpoint_max,
          test_config=cfg.test_config,
          precision=args.precision,
          amp_level=args.amp_level,
          profiler_options=args.profiler_options,
          to_static_training=cfg.to_static_training,
      )
  
file_path: PaddleSeg/contrib/RSSegBenchmark/train.py
imports: import argparse
  import random
  import paddle
  import numpy
  import cv2
  import backbones
  from paddleseg.cvlibs import manager, Config
  from paddleseg.utils import get_sys_env, logger
  from paddleseg.core import train
label_desc: Use image node_id 2 for calling a global function with example usage: main(args) without return types