code: def __init__(
      self,
      config,
      model,
      criterion,
      train_loader,
      validate_loader,
      metric_cls,
      post_process=None,
  ):
      config["trainer"]["output_dir"] = os.path.join(
          str(pathlib.Path(os.path.abspath(__name__)).parent),
          config["trainer"]["output_dir"],
      )
      config["name"] = config["name"] + "_" + model.name
      self.save_dir = config["trainer"]["output_dir"]
      self.checkpoint_dir = os.path.join(self.save_dir, "checkpoint")
  
      os.makedirs(self.checkpoint_dir, exist_ok=True)
  
      self.global_step = 0
      self.start_epoch = 0
      self.config = config
      self.criterion = criterion
      # logger and tensorboard
      self.visualdl_enable = self.config["trainer"].get(
          "visual_dl", False
      )
      self.epochs = self.config["trainer"]["epochs"]
      self.log_iter = self.config["trainer"]["log_iter"]
      if paddle.distributed.get_rank() == 0:
          anyconfig.dump(
              config, os.path.join(self.save_dir, "config.yaml")
          )
          self.logger = setup_logger(
              os.path.join(self.save_dir, "train.log")
          )
          self.logger_info(pformat(self.config))
  
      self.model = self.apply_to_static(model)
  
      # device
      if (
          paddle.device.cuda.device_count() > 0
          and paddle.device.is_compiled_with_cuda()
      ):
          self.with_cuda = True
          random.seed(self.config["trainer"]["seed"])
          np.random.seed(self.config["trainer"]["seed"])
          paddle.seed(self.config["trainer"]["seed"])
      else:
          self.with_cuda = False
      self.logger_info(
          "train with and paddle {}".format(paddle.__version__)
      )
      # metrics
      self.metrics = {
          "recall": 0,
          "precision": 0,
          "hmean": 0,
          "train_loss": float("inf"),
          "best_model_epoch": 0,
      }
  
      self.train_loader = train_loader
      if validate_loader is not None:
          assert post_process is not None and metric_cls is not None
      self.validate_loader = validate_loader
      self.post_process = post_process
      self.metric_cls = metric_cls
      self.train_loader_len = len(train_loader)
  
      if self.validate_loader is not None:
          self.logger_info(
              "train dataset has {} samples,{} in dataloader, validate dataset has {} samples,{} in dataloader".format(
                  len(self.train_loader.dataset),
                  self.train_loader_len,
                  len(self.validate_loader.dataset),
                  len(self.validate_loader),
              )
          )
      else:
          self.logger_info(
              "train dataset has {} samples,{} in dataloader".format(
                  len(self.train_loader.dataset), self.train_loader_len
              )
          )
  
      self._initialize_scheduler()
  
      self._initialize_optimizer()
  
      # resume or finetune
      if self.config["trainer"]["resume_checkpoint"] != "":
          self._load_checkpoint(
              self.config["trainer"]["resume_checkpoint"], resume=True
          )
      elif self.config["trainer"]["finetune_checkpoint"] != "":
          self._load_checkpoint(
              self.config["trainer"]["finetune_checkpoint"],
              resume=False,
          )
  
      if self.visualdl_enable and paddle.distributed.get_rank() == 0:
          from visualdl import LogWriter
  
          self.writer = LogWriter(self.save_dir)
  
      # 混合精度训练
      self.amp = self.config.get("amp", None)
      if self.amp == "None":
          self.amp = None
      if self.amp:
          self.amp["scaler"] = paddle.amp.GradScaler(
              init_loss_scaling=self.amp.get("scale_loss", 1024),
              use_dynamic_loss_scaling=self.amp.get(
                  "use_dynamic_loss_scaling", True
              ),
          )
          self.model, self.optimizer = paddle.amp.decorate(
              models=self.model,
              optimizers=self.optimizer,
              level=self.amp.get("amp_level", "O2"),
          )
  
      # 分布式训练
      if paddle.device.cuda.device_count() > 1:
          self.model = paddle.DataParallel(self.model)
      # make inverse Normalize
      self.UN_Normalize = False
      for t in self.config["dataset"]["train"]["dataset"]["args"][
          "transforms"
      ]:
          if t["type"] == "Normalize":
              self.normalize_mean = t["args"]["mean"]
              self.normalize_std = t["args"]["std"]
              self.UN_Normalize = True
  
imports: import os
  import pathlib
  import shutil
  from pprint import pformat
  import anyconfig
  import paddle
  import numpy
  import random
  from paddle.jit import to_static
  from paddle.static import InputSpec
  from utils import setup_logger
label_desc: Use image node_id 1 to create a new BaseTrainer object  with example: obj = BaseTrainer(config, model, criterion, train_loader, validate_loader, metric_cls, post_process)