code: def evaluate(
      model,
      eval_dataset,
      losses,
      num_workers=0,
      print_detail=True,
      auc_roc=False,
      writer=None,
      save_dir=None,
      sw_num=None,
      is_save_data=True,
      has_dataset_json=True,
  ):
      """
      Launch evalution.
      Args:
          model（nn.Layer): A sementic segmentation model.
          eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
          losses(dict): Used to calculate the loss. e.g: {"types":[loss_1...], "coef": [0.5,...]}
          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.
          auc_roc(bool, optional): whether add auc_roc metric.
          writer: visualdl log writer.
          save_dir(str, optional): the path to save predicted result.
          sw_num:sw batch size.
          is_save_data:use savedata function
          has_dataset_json:has dataset_json
      Returns:
          float: The mIoU of validation datasets.
          float: The accuracy of validation datasets.
      """
      new_loss = dict()
      new_loss["types"] = [losses["types"][0]]
      new_loss["coef"] = [losses["coef"][0]]
      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=False
      )
      loader = paddle.io.DataLoader(
          eval_dataset,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          return_list=True,
      )
  
      if has_dataset_json:
          with open(
              eval_dataset.dataset_json_path, "r", encoding="utf-8"
          ) as f:
              dataset_json_dict = json.load(f)
  
      total_iters = len(loader)
      logits_all = None
      label_all = None
  
      if print_detail:
          logger.info(
              "Start evaluating (total_samples: {}, total_iters: {})...".format(
                  len(eval_dataset), total_iters
              )
          )
      progbar_val = progbar.Progbar(
          target=total_iters, verbose=1 if nranks < 2 else 2
      )
      reader_cost_averager = TimeAverager()
      batch_cost_averager = TimeAverager()
      batch_start = time.time()
  
      mdice = 0.0
      channel_dice_array = np.array([])
      loss_all = 0.0
  
      with paddle.no_grad():
          for iter, (im, label, idx) in enumerate(loader):
              reader_cost_averager.record(time.time() - batch_start)
  
              if has_dataset_json:
                  image_json = dataset_json_dict["training"][
                      idx[0].split("/")[-1].split(".")[0]
                  ]
              else:
                  image_json = None
  
              label = label.astype("int32")
  
              if sw_num:
                  (
                      pred,
                      logits,
                  ) = infer.inference(  # reverse transform here
                      model,
                      im,
                      ori_shape=label.shape[-3:],
                      transforms=eval_dataset.transforms.transforms,
                      sw_num=sw_num,
                  )
  
              else:
                  (
                      pred,
                      logits,
                  ) = infer.inference(  # reverse transform here
                      model,
                      im,
                      ori_shape=label.shape[-3:],
                      transforms=eval_dataset.transforms.transforms,
                  )
  
              if (
                  writer is not None
              ):  # TODO visualdl single channel pseudo label map transfer to
                  pass
  
              if hasattr(model, "postprocess"):
                  logits, label = model.postprocess(logits, label)
                  # Update pred from postprocessed logits
                  pred = paddle.argmax(
                      logits[0], axis=1, keepdim=True, dtype="int32"
                  )
  
              # logits [N, num_classes, D, H, W] Compute loss to get dice
              loss, per_channel_dice = loss_computation(
                  logits, label, new_loss
              )
              loss = sum(loss)
  
              if auc_roc:
                  logits = F.softmax(logits, axis=1)
                  if logits_all is None:
                      logits_all = logits.numpy()
                      label_all = label.numpy()
                  else:
                      logits_all = np.concatenate(
                          [logits_all, logits.numpy()]
                      )  # (KN, C, H, W)
                      label_all = np.concatenate(
                          [label_all, label.numpy()]
                      )
  
              loss_all += loss.numpy()
              mdice += np.mean(per_channel_dice)
              if channel_dice_array.size == 0:
                  channel_dice_array = per_channel_dice
              else:
                  channel_dice_array += per_channel_dice
              if is_save_data:
                  if iter < 5:
                      if image_json is None:
                          raise ValueError(
                              "No json file is loaded. Please check if the dataset is preprocessed and `has_dataset_json` is True."
                          )
                      save_array(
                          save_path=os.path.join(save_dir, str(iter)),
                          save_content={
                              "pred": pred.numpy(),
                              "label": label.numpy(),
                              "img": im.numpy(),
                          },
                          form=("npy", "nii.gz"),
                          image_infor={
                              "spacing": image_json.get(
                                  "spacing_resample",
                                  image_json["spacing"],
                              ),
                              "direction": image_json["direction"],
                              "origin": image_json["origin"],
                              "format": "xyz",
                          },
                      )
  
              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:
                  progbar_val.update(
                      iter + 1,
                      [
                          ("batch_cost", batch_cost),
                          ("reader cost", reader_cost),
                      ],
                  )
              reader_cost_averager.reset()
              batch_cost_averager.reset()
              batch_start = time.time()
  
      mdice /= total_iters
      channel_dice_array /= total_iters
      loss_all /= total_iters
  
      result_dict = {"mdice": mdice}
      if auc_roc:
          auc_roc = metric.auc_roc(
              logits_all,
              label_all,
              num_classes=eval_dataset.num_classes,
          )
          auc_infor = "Auc_roc: {:.4f}".format(auc_roc)
          result_dict["auc_roc"] = auc_roc
  
      if print_detail:
          infor = (
              "[EVAL] #Images: {}, Dice: {:.4f}, Loss: {:6f}".format(
                  len(eval_dataset), mdice, loss_all[0]
              )
          )
          infor = infor + auc_infor if auc_roc else infor
          logger.info(infor)
          logger.info(
              "[EVAL] Class dice: \n"
              + str(np.round(channel_dice_array, 4))
          )
  
      return result_dict
  
file_path: PaddleSeg/contrib/MedicalSeg/medicalseg/core/val.py
imports: import os
  import time
  import json
  import numpy
  import paddle
  import paddle.nn.functional
  from medicalseg.core import infer
  from medicalseg.utils import metric, TimeAverager, calculate_eta, logger, progbar, loss_computation, add_image_vdl, save_array
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, eval_dataset, losses, num_workers, print_detail, auc_roc, writer, save_dir, sw_num, is_save_data, has_dataset_json) and returns: result_dict