code: def evaluate(
      model,
      eval_dataset,
      num_workers=0,
      print_detail=True,
      save_img=True,
  ):
      """
      Launch evalution.
  
      Args:
          model（nn.Layer): A sementic segmentation model.
          eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
          num_workers (int, optional): Num workers for data loader. Default: 0.
          print_detail (bool, optional): Whether to print detailed information about the evaluation process. Default: True.
  
      Returns:
          float: The mIoU of validation datasets.
          float: The accuracy of validation datasets.
      """
      logger.info("Validating")
      evaluator = Eval(eval_dataset.NUM_CLASSES)
      evaluator.reset()
  
      model.eval()
  
      nranks = paddle.distributed.ParallelEnv().nranks
      local_rank = paddle.distributed.ParallelEnv().local_rank
      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()
  
      batch_sampler = paddle.io.DistributedBatchSampler(
          eval_dataset, batch_size=1, shuffle=False, drop_last=True
      )
      loader = paddle.io.DataLoader(
          eval_dataset,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          return_list=True,
      )
  
      progbar_val = progbar.Progbar(
          target=len(loader), verbose=0 if nranks < 2 else 2
      )
  
      reader_cost_averager = TimeAverager()
      batch_cost_averager = TimeAverager()
      batch_start = time.time()
  
      with paddle.no_grad():
          for idx, (x, y, _, item) in enumerate(loader):
              reader_cost_averager.record(time.time() - batch_start)
  
              # Forward
              y = y.astype("int64")
              pred = model(x)  # 1, c, h, w
              if len(pred) > 1:
                  pred = pred[0]
  
              # Convert to numpy
              label = y.squeeze(axis=1).numpy()  #
              argpred = np.argmax(pred.numpy(), axis=1)  # 1, 1, H, W
              if save_img:
                  save_imgs(argpred, item, "./output/")
  
              # Add to evaluator
              evaluator.add_batch(label, argpred)
  
              batch_cost_averager.record(
                  time.time() - batch_start, num_samples=len(label)
              )
              batch_cost = batch_cost_averager.get_average()
              reader_cost = reader_cost_averager.get_average()
  
              if local_rank == 0 and print_detail and idx % 10 == 0:
                  progbar_val.update(
                      idx + 1,
                      [
                          ("batch_cost", batch_cost),
                          ("reader cost", reader_cost),
                      ],
                  )
              reader_cost_averager.reset()
              batch_cost_averager.reset()
              batch_start = time.time()
  
          PA = evaluator.pixel_accuracy()
          MPA = evaluator.mean_pixel_accuracy()
          MIoU = evaluator.mean_iou()
          FWIoU = evaluator.fwiou()
          PC = evaluator.mean_precision()
          logger.info(
              "PA1:{:.3f}, MPA1:{:.3f}, MIoU1:{:.3f}, FWIoU1:{:.3f}, PC:{:.3f}".format(
                  PA, MPA, MIoU, FWIoU, PC
              )
          )
  
      return PA, MPA, MIoU, FWIoU
  
imports: import os
  import time
  import tqdm
  import numpy
  from PIL import Image
  import paddle
  import paddle.nn.functional
  from paddleseg.utils import metrics, TimeAverager, calculate_eta, logger, progbar
  import datasets
  from utils import save_imgs
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, eval_dataset, num_workers, print_detail, save_img) and returns: PA,  MPA,  MIoU,  FWIoU