code: def evaluate(
      model,
      eval_dataset,
      aug_eval=False,
      scales=1.0,
      flip_horizontal=True,
      flip_vertical=False,
      is_slide=False,
      stride=None,
      crop_size=None,
      num_workers=0,
      print_detail=True,
  ):
      """
      Launch evalution.
  
      Args:
          model（nn.Layer): A sementic segmentation model.
          eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
          aug_eval (bool, optional): Whether to use mulit-scales and flip augment for evaluation. Default: False.
          scales (list|float, optional): Scales for augment. It is valid when `aug_eval` is True. Default: 1.0.
          flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_eval` is True. Default: True.
          flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_eval` is True. Default: False.
          is_slide (bool, optional): Whether to evaluate by sliding window. Default: False.
          stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
              It should be provided when `is_slide` is True.
          crop_size (tuple|list, optional):  The crop size of sliding window, the first is width and the second is height.
              It should be provided when `is_slide` is True.
          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.
      """
      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,
      )
  
      total_iters = len(loader)
      intersect_area_all = 0
      pred_area_all = 0
      label_area_all = 0
  
      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()
      with paddle.no_grad():
          for iter, data in enumerate(loader):
              reader_cost_averager.record(time.time() - batch_start)
              preds = []
              label = data[3].astype("int64")
              for ii in range(3):
                  im = data[ii]
                  ori_shape = im.shape[-2:]
                  if aug_eval:
                      pred = infer_crop.aug_inference(
                          model,
                          im,
                          ori_shape=ori_shape,
                          transforms=eval_dataset.transforms.transforms,
                          scales=scales,
                          flip_horizontal=flip_horizontal,
                          flip_vertical=flip_vertical,
                          is_slide=is_slide,
                          stride=stride,
                          crop_size=crop_size,
                      )
                  else:
                      pred = infer_crop.inference(
                          model,
                          im,
                          ori_shape=ori_shape,
                          transforms=eval_dataset.transforms.transforms,
                          is_slide=is_slide,
                          stride=stride,
                          crop_size=crop_size,
                      )
                  preds.append(pred)
  
              left_ensem = (
                  preds[0][:, :, :, 640:1280] + preds[1][:, :, :, 0:640]
              ) / 2
              right_ensem = (
                  preds[1][:, :, :, 640:1280] + preds[2][:, :, :, 0:640]
              ) / 2
              pred_ensem = paddle.concat(
                  [
                      preds[0][:, :, :, 0:640],
                      left_ensem,
                      right_ensem,
                      preds[2][:, :, :, 640:1280],
                  ],
                  axis=3,
              )
              pred = paddle.argmax(
                  pred_ensem, axis=1, keepdim=True, dtype="int32"
              )
  
              (
                  intersect_area,
                  pred_area,
                  label_area,
              ) = metrics.calculate_area(
                  pred,
                  label,
                  eval_dataset.num_classes,
                  ignore_index=eval_dataset.ignore_index,
              )
  
              # Gather from all ranks
              if nranks > 1:
                  intersect_area_list = []
                  pred_area_list = []
                  label_area_list = []
                  paddle.distributed.all_gather(
                      intersect_area_list, intersect_area
                  )
                  paddle.distributed.all_gather(
                      pred_area_list, pred_area
                  )
                  paddle.distributed.all_gather(
                      label_area_list, label_area
                  )
  
                  # Some image has been evaluated and should be eliminated in last iter
                  if (iter + 1) * nranks > len(eval_dataset):
                      valid = len(eval_dataset) - iter * nranks
                      intersect_area_list = intersect_area_list[:valid]
                      pred_area_list = pred_area_list[:valid]
                      label_area_list = label_area_list[:valid]
  
                  for i in range(len(intersect_area_list)):
                      intersect_area_all = (
                          intersect_area_all + intersect_area_list[i]
                      )
                      pred_area_all = pred_area_all + pred_area_list[i]
                      label_area_all = (
                          label_area_all + label_area_list[i]
                      )
              else:
                  intersect_area_all = (
                      intersect_area_all + intersect_area
                  )
                  pred_area_all = pred_area_all + pred_area
                  label_area_all = label_area_all + label_area
  
              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()
  
      class_iou, miou = metrics.mean_iou(
          intersect_area_all, pred_area_all, label_area_all
      )
      class_acc, acc = metrics.accuracy(
          intersect_area_all, pred_area_all
      )
      kappa = metrics.kappa(
          intersect_area_all, pred_area_all, label_area_all
      )
  
      if print_detail:
          logger.info(
              "[EVAL] #Images={} mIoU={:.4f} Acc={:.4f} Kappa={:.4f} ".format(
                  len(eval_dataset), miou, acc, kappa
              )
          )
          logger.info(
              "[EVAL] Class IoU: \n" + str(np.round(class_iou, 4))
          )
          logger.info(
              "[EVAL] Class Acc: \n" + str(np.round(class_acc, 4))
          )
      return miou, acc
  
file_path: PaddleSeg/contrib/AutoNUE/core/val_crop.py
imports: import os
  import numpy
  import time
  import paddle
  import paddle.nn.functional
  from paddleseg.utils import metrics, TimeAverager, calculate_eta, logger, progbar
  from core import infer_crop
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, eval_dataset, aug_eval, scales, flip_horizontal, flip_vertical, is_slide, stride, crop_size, num_workers, print_detail) and returns: miou,  acc