code: def evaluate(
      model,
      eval_dataset,
      losses,
      num_workers=0,
      print_detail=True,
      auc_roc=False,
      writer=None,
      save_dir=None,
  ):
      """
      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.
  
      Returns:
          float: The mIoU of validation datasets.
          float: The accuracy of validation datasets.
      """
      model.eval()
      loader = eval_dataset
      total_iters = len(loader)
  
      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)
      reader_cost_averager = TimeAverager()
      batch_cost_averager = TimeAverager()
      batch_start = time.time()
  
      online_eval_foreground_dc = []
      online_eval_tp_list = []
      online_eval_fp_list = []
      online_eval_fn_list = []
  
      with paddle.no_grad():
          for iter, (im, label) in enumerate(loader):
              reader_cost_averager.record(time.time() - batch_start)
  
              label = label[0].astype("int32")
  
              if iter >= eval_dataset.num_batches_per_epoch:
                  break
              output = model(im)[0]
              num_classes = output.shape[1]
  
              output_softmax = F.softmax(output, axis=1)
              output_seg = paddle.argmax(output_softmax, axis=1)
  
              target = label[:, 0, ...]
              axes = tuple(range(1, len(target.shape)))
              tp_hard = paddle.zeros((target.shape[0], num_classes - 1))
              fp_hard = paddle.zeros((target.shape[0], num_classes - 1))
              fn_hard = paddle.zeros((target.shape[0], num_classes - 1))
              for c in range(1, num_classes):
                  tp_hard[:, c - 1] = sum_tensor(
                      (output_seg == c).astype("float32")
                      * (target == c).astype("float32"),
                      axes=axes,
                  )
                  fp_hard[:, c - 1] = sum_tensor(
                      (output_seg == c).astype("float32")
                      * (target != c).astype("float32"),
                      axes=axes,
                  )
                  fn_hard[:, c - 1] = sum_tensor(
                      (output_seg != c).astype("float32")
                      * (target == c).astype("float32"),
                      axes=axes,
                  )
  
              tp_hard = tp_hard.sum(0, keepdim=False).numpy()
              fp_hard = fp_hard.sum(0, keepdim=False).numpy()
              fn_hard = fn_hard.sum(0, keepdim=False).numpy()
  
              online_eval_foreground_dc.append(
                  list(
                      (2 * tp_hard)
                      / (2 * tp_hard + fp_hard + fn_hard + 1e-8)
                  )
              )
              online_eval_tp_list.append(list(tp_hard))
              online_eval_fp_list.append(list(fp_hard))
              online_eval_fn_list.append(list(fn_hard))
  
              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 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()
  
      online_eval_tp = np.sum(online_eval_tp_list, 0)
      online_eval_fp = np.sum(online_eval_fp_list, 0)
      online_eval_fn = np.sum(online_eval_fn_list, 0)
  
      global_dc_per_class = [
          i
          for i in [
              2 * i / (2 * i + j + k)
              for i, j, k in zip(
                  online_eval_tp, online_eval_fp, online_eval_fn
              )
          ]
          if not np.isnan(i)
      ]
      mdice = np.mean(global_dc_per_class)
  
      logger.info(
          "Average global foreground Dice: {}".format(
              [np.round(i, 4) for i in global_dc_per_class]
          )
      )
      logger.info(
          "(interpret this as an estimate for the Dice of the different classes. This is not "
          "exact.)"
      )
      result_dict = {"mdice": mdice}
      return result_dict
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/core/val.py
imports: import os
  import time
  import numpy
  import paddle
  import paddle.nn.functional
  from medicalseg.utils import metric, TimeAverager, calculate_eta, logger, progbar, loss_computation, add_image_vdl
  from nnunet.utils import sum_tensor
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) and returns: result_dict