code: def train(self, validate=False):
      assert self.mode == "train", "Model not in 'train' mode"
      Init_mark = False
      if validate:
          self.cfg["EvalDataset"] = self.cfg.EvalDataset = create(
              "EvalDataset"
          )()
  
      model = self.model
      if self.cfg.get("to_static", False):
          model = apply_to_static(self.cfg, model)
      sync_bn = (
          getattr(self.cfg, "norm_type", None) == "sync_bn"
          and (self.cfg.use_gpu or self.cfg.use_mlu)
          and self._nranks > 1
      )
      if sync_bn:
          model = paddle.nn.SyncBatchNorm.convert_sync_batchnorm(model)
  
      # enabel auto mixed precision mode
      if self.use_amp:
          scaler = paddle.amp.GradScaler(
              enable=self.cfg.use_gpu
              or self.cfg.use_npu
              or self.cfg.use_mlu,
              init_loss_scaling=self.cfg.get("init_loss_scaling", 1024),
          )
      # get distributed model
      if self.cfg.get("fleet", False):
          model = fleet.distributed_model(model)
          self.optimizer = fleet.distributed_optimizer(self.optimizer)
      elif self._nranks > 1:
          find_unused_parameters = (
              self.cfg["find_unused_parameters"]
              if "find_unused_parameters" in self.cfg
              else False
          )
          model = paddle.DataParallel(
              model, find_unused_parameters=find_unused_parameters
          )
  
      self.status.update(
          {
              "epoch_id": self.start_epoch,
              "step_id": 0,
              "steps_per_epoch": len(self.loader),
          }
      )
  
      self.status["batch_time"] = stats.SmoothedValue(
          self.cfg.log_iter, fmt="{avg:.4f}"
      )
      self.status["data_time"] = stats.SmoothedValue(
          self.cfg.log_iter, fmt="{avg:.4f}"
      )
      self.status["training_staus"] = stats.TrainingStats(
          self.cfg.log_iter
      )
  
      if self.cfg.get("print_flops", False):
          flops_loader = create(
              "{}Reader".format(self.mode.capitalize())
          )(self.dataset, self.cfg.worker_num)
          self._flops(flops_loader)
      profiler_options = self.cfg.get("profiler_options", None)
  
      self._compose_callback.on_train_begin(self.status)
  
      use_fused_allreduce_gradients = (
          self.cfg["use_fused_allreduce_gradients"]
          if "use_fused_allreduce_gradients" in self.cfg
          else False
      )
  
      for epoch_id in range(self.start_epoch, self.cfg.epoch):
          self.status["mode"] = "train"
          self.status["epoch_id"] = epoch_id
          self._compose_callback.on_epoch_begin(self.status)
          self.loader.dataset.set_epoch(epoch_id)
          model.train()
          iter_tic = time.time()
          for step_id, data in enumerate(self.loader):
              self.status["data_time"].update(time.time() - iter_tic)
              self.status["step_id"] = step_id
              profiler.add_profiler_step(profiler_options)
              self._compose_callback.on_step_begin(self.status)
              data["epoch_id"] = epoch_id
              if (
                  self.cfg.get("to_static", False)
                  and "image_file" in data.keys()
              ):
                  data.pop("image_file")
  
              if self.use_amp:
                  if (
                      isinstance(model, paddle.DataParallel)
                      and use_fused_allreduce_gradients
                  ):
                      with model.no_sync():
                          with paddle.amp.auto_cast(
                              enable=self.cfg.use_gpu
                              or self.cfg.use_npu
                              or self.cfg.use_mlu,
                              custom_white_list=self.custom_white_list,
                              custom_black_list=self.custom_black_list,
                              level=self.amp_level,
                          ):
                              # model forward
                              outputs = model(data)
                              loss = outputs["loss"]
                          # model backward
                          scaled_loss = scaler.scale(loss)
                          scaled_loss.backward()
                      fused_allreduce_gradients(
                          list(model.parameters()), None
                      )
                  else:
                      with paddle.amp.auto_cast(
                          enable=self.cfg.use_gpu
                          or self.cfg.use_npu
                          or self.cfg.use_mlu,
                          custom_white_list=self.custom_white_list,
                          custom_black_list=self.custom_black_list,
                          level=self.amp_level,
                      ):
                          # model forward
                          outputs = model(data)
                          loss = outputs["loss"]
                      # model backward
                      scaled_loss = scaler.scale(loss)
                      scaled_loss.backward()
                  # in dygraph mode, optimizer.minimize is equal to optimizer.step
                  scaler.minimize(self.optimizer, scaled_loss)
              else:
                  if (
                      isinstance(model, paddle.DataParallel)
                      and use_fused_allreduce_gradients
                  ):
                      with model.no_sync():
                          # model forward
                          outputs = model(data)
                          loss = outputs["loss"]
                          # model backward
                          loss.backward()
                      fused_allreduce_gradients(
                          list(model.parameters()), None
                      )
                  else:
                      # model forward
                      outputs = model(data)
                      loss = outputs["loss"]
                      # model backward
                      loss.backward()
                  self.optimizer.step()
              curr_lr = self.optimizer.get_lr()
              self.lr.step()
              if self.cfg.get("unstructured_prune"):
                  self.pruner.step()
              self.optimizer.clear_grad()
              self.status["learning_rate"] = curr_lr
  
              if self._nranks < 2 or self._local_rank == 0:
                  self.status["training_staus"].update(outputs)
  
              self.status["batch_time"].update(time.time() - iter_tic)
              self._compose_callback.on_step_end(self.status)
              if self.use_ema:
                  self.ema.update()
              iter_tic = time.time()
  
          if self.cfg.get("unstructured_prune"):
              self.pruner.update_params()
  
          is_snapshot = (
              self._nranks < 2
              or (
                  self._local_rank == 0
                  or self.cfg.metric == "Pose3DEval"
              )
          ) and (
              (epoch_id + 1) % self.cfg.snapshot_epoch == 0
              or epoch_id == self.end_epoch - 1
          )
          if is_snapshot and self.use_ema:
              # apply ema weight on model
              weight = copy.deepcopy(self.model.state_dict())
              self.model.set_dict(self.ema.apply())
              self.status["weight"] = weight
  
          self._compose_callback.on_epoch_end(self.status)
  
          if validate and is_snapshot:
              if not hasattr(self, "_eval_loader"):
                  # build evaluation dataset and loader
                  self._eval_dataset = self.cfg.EvalDataset
                  self._eval_batch_sampler = paddle.io.BatchSampler(
                      self._eval_dataset,
                      batch_size=self.cfg.EvalReader["batch_size"],
                  )
                  # If metric is VOC, need to be set collate_batch=False.
                  if self.cfg.metric == "VOC":
                      self.cfg["EvalReader"]["collate_batch"] = False
                  if self.cfg.metric == "Pose3DEval":
                      self._eval_loader = create("EvalReader")(
                          self._eval_dataset, self.cfg.worker_num
                      )
                  else:
                      self._eval_loader = create("EvalReader")(
                          self._eval_dataset,
                          self.cfg.worker_num,
                          batch_sampler=self._eval_batch_sampler,
                      )
              # if validation in training is enabled, metrics should be re-init
              # Init_mark makes sure this code will only execute once
              if validate and Init_mark == False:
                  Init_mark = True
                  self._init_metrics(validate=validate)
                  self._reset_metrics()
  
              with paddle.no_grad():
                  self.status["save_best_model"] = True
                  self._eval_with_loader(self._eval_loader)
  
          if is_snapshot and self.use_ema:
              # reset original weight
              self.model.set_dict(weight)
              self.status.pop("weight")
  
      self._compose_callback.on_train_end(self.status)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import sys
  import copy
  import time
  from tqdm import tqdm
  import numpy
  import typing
  from PIL import Image, ImageOps, ImageFile
  import paddle
  import paddle.nn
  import paddle.distributed
  from paddle.distributed import fleet
  from paddle.static import InputSpec
  from ppdet.optimizer import ModelEMA
  from ppdet.core.workspace import create
  from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
  from ppdet.utils.visualizer import visualize_results, save_result
  from ppdet.metrics import get_infer_results, KeyPointTopDownCOCOEval, KeyPointTopDownCOCOWholeBadyHandEval, KeyPointTopDownMPIIEval, Pose3DEval
  from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, RBoxMetric, JDEDetMetric, SNIPERCOCOMetric, CULaneMetric
  from ppdet.data.source.sniper_coco import SniperCOCODataSet
  from ppdet.data.source.category import get_categories
  import ppdet.utils.stats
  from ppdet.utils.fuse_utils import fuse_conv_bn
  from ppdet.utils import profiler
  from ppdet.modeling.post_process import multiclass_nms
  from ppdet.modeling.lane_utils import imshow_lanes
  from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter, SniperProposalsGenerator, WandbCallback, SemiCheckpointer, SemiLogPrinter
  from .export_utils import _dump_infer_config, _prune_input_spec, apply_to_static
  from paddle.distributed.fleet.utils.hybrid_parallel_util import fused_allreduce_gradients
  from ppdet.utils.logger import setup_logger
label_desc: Use image node_id 10 for calling the Trainer obj's underlying member method code with example usage: obj.train(validate) without return types