code: def train(
      model,
      train_dataset,
      val_dataset=None,
      optimizer=None,
      save_dir="output",
      iters=10000,
      batch_size=2,
      resume_model=None,
      save_interval=1000,
      log_iters=10,
      num_workers=0,
      use_vdl=False,
      losses=None,
      keep_checkpoint_max=5,
      threshold=0.1,
      nms_kernel=7,
      top_k=200,
  ):
      """
      Launch training.
  
      Args:
          model（nn.Layer): A sementic segmentation model.
          train_dataset (paddle.io.Dataset): Used to read and process training datasets.
          val_dataset (paddle.io.Dataset, optional): Used to read and process validation datasets.
          optimizer (paddle.optimizer.Optimizer): The optimizer.
          save_dir (str, optional): The directory for saving the model snapshot. Default: 'output'.
          iters (int, optional): How may iters to train the model. Defualt: 10000.
          batch_size (int, optional): Mini batch size of one gpu or cpu. Default: 2.
          resume_model (str, optional): The path of resume model.
          save_interval (int, optional): How many iters to save a model snapshot once during training. Default: 1000.
          log_iters (int, optional): Display logging information at every log_iters. Default: 10.
          num_workers (int, optional): Num workers for data loader. Default: 0.
          use_vdl (bool, optional): Whether to record the data to VisualDL during training. Default: False.
          losses (dict): A dict including 'types' and 'coef'. The length of coef should equal to 1 or len(losses['types']).
              The 'types' item is a list of object of paddleseg.models.losses while the 'coef' item is a list of the relevant coefficient.
          keep_checkpoint_max (int, optional): Maximum number of checkpoints to save. Default: 5.
          threshold (float, optional): A Float, threshold applied to center heatmap score. Default: 0.1.
          nms_kernel (int, optional): An Integer, NMS max pooling kernel size. Default: 7.
          top_k (int, optional): An Integer, top k centers to keep. Default: 200.
      """
      model.train()
      nranks = paddle.distributed.ParallelEnv().nranks
      local_rank = paddle.distributed.ParallelEnv().local_rank
  
      start_iter = 0
      if resume_model is not None:
          start_iter = resume(model, optimizer, resume_model)
  
      if not os.path.isdir(save_dir):
          if os.path.exists(save_dir):
              os.remove(save_dir)
          os.makedirs(save_dir)
  
      if nranks > 1:
          # Initialize parallel environment if not done.
          if (
              not paddle.distributed.parallel.parallel_helper._is_parallel_ctx_initialized()
          ):
              paddle.distributed.init_parallel_env()
              ddp_model = paddle.DataParallel(model)
          else:
              ddp_model = paddle.DataParallel(model)
  
      batch_sampler = paddle.io.DistributedBatchSampler(
          train_dataset,
          batch_size=batch_size,
          shuffle=True,
          drop_last=True,
      )
  
      loader = paddle.io.DataLoader(
          train_dataset,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          return_list=True,
      )
  
      if use_vdl:
          from visualdl import LogWriter
  
          log_writer = LogWriter(save_dir)
  
      avg_loss = 0.0
      avg_loss_list = []
      iters_per_epoch = len(batch_sampler)
      best_pq = -1.0
      best_model_iter = -1
      reader_cost_averager = TimeAverager()
      batch_cost_averager = TimeAverager()
      save_models = deque()
      batch_start = time.time()
  
      iter = start_iter
      while iter < iters:
          for data in loader:
              iter += 1
              if iter > iters:
                  break
              reader_cost_averager.record(time.time() - batch_start)
              images = data[0]
              semantic = data[1]
              semantic_weights = data[2]
              center = data[3]
              center_weights = data[4]
              offset = data[5]
              offset_weights = data[6]
              foreground = data[7]
  
              if nranks > 1:
                  logits_list = ddp_model(images)
              else:
                  logits_list = model(images)
  
              loss_list = loss_computation(
                  logits_list=logits_list,
                  losses=losses,
                  semantic=semantic,
                  semantic_weights=semantic_weights,
                  center=center,
                  center_weights=center_weights,
                  offset=offset,
                  offset_weights=offset_weights,
              )
              loss = sum(loss_list)
              loss.backward()
  
              optimizer.step()
              lr = optimizer.get_lr()
              if isinstance(
                  optimizer._learning_rate,
                  paddle.optimizer.lr.LRScheduler,
              ):
                  optimizer._learning_rate.step()
              model.clear_gradients()
              avg_loss += float(loss)
              if not avg_loss_list:
                  avg_loss_list = [l.numpy() for l in loss_list]
              else:
                  for i in range(len(loss_list)):
                      avg_loss_list[i] += loss_list[i].numpy()
              batch_cost_averager.record(
                  time.time() - batch_start, num_samples=batch_size
              )
  
              if (iter) % log_iters == 0 and local_rank == 0:
                  avg_loss /= log_iters
                  avg_loss_list = [
                      l.item() / log_iters for l in avg_loss_list
                  ]
                  remain_iters = iters - iter
                  avg_train_batch_cost = (
                      batch_cost_averager.get_average()
                  )
                  avg_train_reader_cost = (
                      reader_cost_averager.get_average()
                  )
                  eta = calculate_eta(
                      remain_iters, avg_train_batch_cost
                  )
                  logger.info(
                      "[TRAIN] epoch={}, iter={}/{}, loss={:.4f}, lr={:.6f}, batch_cost={:.4f}, reader_cost={:.5f}, ips={:.4f} samples/sec | ETA {}".format(
                          (iter - 1) // iters_per_epoch + 1,
                          iter,
                          iters,
                          avg_loss,
                          lr,
                          avg_train_batch_cost,
                          avg_train_reader_cost,
                          batch_cost_averager.get_ips_average(),
                          eta,
                      )
                  )
                  logger.info(
                      "[LOSS] loss={:.4f}, semantic_loss={:.4f}, center_loss={:.4f}, offset_loss={:.4f}".format(
                          avg_loss,
                          avg_loss_list[0],
                          avg_loss_list[1],
                          avg_loss_list[2],
                      )
                  )
                  if use_vdl:
                      log_writer.add_scalar(
                          "Train/loss", avg_loss, iter
                      )
                      # Record all losses if there are more than 2 losses.
                      if len(avg_loss_list) > 1:
                          avg_loss_dict = {}
                          for i, value in enumerate(avg_loss_list):
                              avg_loss_dict["loss_" + str(i)] = value
                          for key, value in avg_loss_dict.items():
                              log_tag = "Train/" + key
                              log_writer.add_scalar(
                                  log_tag, value, iter
                              )
  
                      log_writer.add_scalar("Train/lr", lr, iter)
                      log_writer.add_scalar(
                          "Train/batch_cost", avg_train_batch_cost, iter
                      )
                      log_writer.add_scalar(
                          "Train/reader_cost",
                          avg_train_reader_cost,
                          iter,
                      )
  
                  avg_loss = 0.0
                  avg_loss_list = []
                  reader_cost_averager.reset()
                  batch_cost_averager.reset()
  
              # save model
              if (
                  iter % save_interval == 0 or iter == iters
              ) and local_rank == 0:
                  current_save_dir = os.path.join(
                      save_dir, "iter_{}".format(iter)
                  )
                  if not os.path.isdir(current_save_dir):
                      os.makedirs(current_save_dir)
                  paddle.save(
                      model.state_dict(),
                      os.path.join(current_save_dir, "model.pdparams"),
                  )
                  paddle.save(
                      optimizer.state_dict(),
                      os.path.join(current_save_dir, "model.pdopt"),
                  )
                  save_models.append(current_save_dir)
                  if len(save_models) > keep_checkpoint_max > 0:
                      model_to_remove = save_models.popleft()
                      shutil.rmtree(model_to_remove)
  
              # eval model
              if (
                  (iter % save_interval == 0 or iter == iters)
                  and (val_dataset is not None)
                  and local_rank == 0
                  and iter > iters // 2
              ):
                  num_workers = 1 if num_workers > 0 else 0
                  (
                      panoptic_results,
                      semantic_results,
                      instance_results,
                  ) = evaluate(
                      model,
                      val_dataset,
                      threshold=threshold,
                      nms_kernel=nms_kernel,
                      top_k=top_k,
                      num_workers=num_workers,
                      print_detail=False,
                  )
                  pq = panoptic_results["pan_seg"]["All"]["pq"]
                  miou = semantic_results["sem_seg"]["mIoU"]
                  map = instance_results["ins_seg"]["mAP"]
                  map50 = instance_results["ins_seg"]["mAP50"]
                  logger.info(
                      "[EVAL] PQ: {:.4f}, mIoU: {:.4f}, mAP: {:.4f}, mAP50: {:.4f}".format(
                          pq, miou, map, map50
                      )
                  )
                  model.train()
  
              # save best model and add evaluate results to vdl
              if (
                  iter % save_interval == 0 or iter == iters
              ) and local_rank == 0:
                  if val_dataset is not None and iter > iters // 2:
                      if pq > best_pq:
                          best_pq = pq
                          best_model_iter = iter
                          best_model_dir = os.path.join(
                              save_dir, "best_model"
                          )
                          paddle.save(
                              model.state_dict(),
                              os.path.join(
                                  best_model_dir, "model.pdparams"
                              ),
                          )
                      logger.info(
                          "[EVAL] The model with the best validation pq ({:.4f}) was saved at iter {}.".format(
                              best_pq, best_model_iter
                          )
                      )
  
                      if use_vdl:
                          log_writer.add_scalar("Evaluate/PQ", pq, iter)
                          log_writer.add_scalar(
                              "Evaluate/mIoU", miou, iter
                          )
                          log_writer.add_scalar(
                              "Evaluate/mAP", map, iter
                          )
                          log_writer.add_scalar(
                              "Evaluate/mAP50", map50, iter
                          )
              batch_start = time.time()
  
      # Calculate flops.
      if local_rank == 0:
  
          def count_syncbn(m, x, y):
              x = x[0]
              nelements = x.numel()
              m.total_ops += int(2 * nelements)
  
          _, c, h, w = images.shape
          flops = paddle.flops(
              model,
              [1, c, h, w],
              custom_ops={paddle.nn.SyncBatchNorm: count_syncbn},
          )
  
      # Sleep for half a second to let dataloader release resources.
      time.sleep(0.5)
      if use_vdl:
          log_writer.close()
  
file_path: PaddleSeg/contrib/PanopticDeepLab/core/train.py
imports: import os
  import time
  from collections import deque
  import shutil
  import paddle
  import paddle.nn.functional
  from paddleseg.utils import TimeAverager, calculate_eta, resume, logger
  from core.val import evaluate
label_desc: Use image node_id 3 for calling a global function with example usage: train(model, train_dataset, val_dataset, optimizer, save_dir, iters, batch_size, resume_model, save_interval, log_iters, num_workers, use_vdl, losses, keep_checkpoint_max, threshold, nms_kernel, top_k) without return types