code: def evaluate(
      model,
      eval_dataset,
      num_workers=0,
      print_detail=True,
      save_dir="output/results",
      save_results=True,
      metrics="sad",
      precision="fp32",
      amp_level="O1",
  ):
      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()
  
      loader = paddle.io.DataLoader(
          eval_dataset,
          batch_size=1,
          drop_last=False,
          num_workers=num_workers,
          return_list=True,
      )
  
      total_iters = len(loader)
      # Get metric instances and data saving
      metrics_ins = {}
      metrics_data = {}
      if isinstance(metrics, str):
          metrics = [metrics]
      elif not isinstance(metrics, list):
          metrics = ["sad"]
      for key in metrics:
          key = key.lower()
          metrics_ins[key] = metrics_class_dict[key]()
          metrics_data[key] = 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()
  
      img_name = ""
      i = 0
      with paddle.no_grad():
          for iter, data in enumerate(loader):
              reader_cost_averager.record(time.time() - batch_start)
              if precision == "fp16":
                  with paddle.amp.auto_cast(
                      level=amp_level,
                      enable=True,
                      custom_white_list={
                          "elementwise_add",
                          "batch_norm",
                          "sync_batch_norm",
                      },
                      custom_black_list={"bilinear_interp_v2", "pad3d"},
                  ):
                      alpha_pred = model(data)
                      alpha_pred = reverse_transform(
                          alpha_pred, data["trans_info"]
                      )
              else:
                  alpha_pred = model(data)
                  alpha_pred = reverse_transform(
                      alpha_pred, data["trans_info"]
                  )
  
              alpha_pred = alpha_pred.numpy()
  
              alpha_gt = data["alpha"].numpy() * 255
              trimap = data.get("ori_trimap")
              if trimap is not None:
                  trimap = trimap.numpy().astype("uint8")
              alpha_pred = np.round(alpha_pred * 255)
              for key in metrics_ins.keys():
                  metrics_data[key] = metrics_ins[key].update(
                      alpha_pred, alpha_gt, trimap
                  )
  
              if save_results:
                  alpha_pred_one = alpha_pred[0].squeeze()
                  if trimap is not None:
                      trimap = trimap.squeeze().astype("uint8")
                      alpha_pred_one[trimap == 255] = 255
                      alpha_pred_one[trimap == 0] = 0
  
                  save_name = data["img_name"][0]
                  name, ext = os.path.splitext(save_name)
                  if save_name == img_name:
                      save_name = name + "_" + str(i) + ext
                      i += 1
                  else:
                      img_name = save_name
                      save_name = name + "_" + str(i) + ext
                      i = 1
  
                  save_alpha_pred(
                      alpha_pred_one, os.path.join(save_dir, save_name)
                  )
  
              batch_cost_averager.record(
                  time.time() - batch_start, num_samples=len(alpha_gt)
              )
              batch_cost = batch_cost_averager.get_average()
              reader_cost = reader_cost_averager.get_average()
  
              if local_rank == 0 and print_detail:
                  show_list = [(k, v) for k, v in metrics_data.items()]
                  show_list = show_list + [
                      ("batch_cost", batch_cost),
                      ("reader cost", reader_cost),
                  ]
                  progbar_val.update(iter + 1, show_list)
  
              reader_cost_averager.reset()
              batch_cost_averager.reset()
              batch_start = time.time()
  
      for key in metrics_ins.keys():
          metrics_data[key] = metrics_ins[key].evaluate()
      log_str = "[EVAL] "
      for key, value in metrics_data.items():
          log_str = log_str + key + ": {:.4f}, ".format(value)
      log_str = log_str[:-2]
  
      logger.info(log_str)
      return metrics_data
  
file_path: PaddleSeg/Matting/ppmatting/core/val.py
imports: import os
  import cv2
  import numpy
  import time
  import paddle
  import paddle.nn.functional
  from paddleseg.utils import TimeAverager, calculate_eta, logger, progbar
  from ppmatting.metrics import metrics_class_dict
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, eval_dataset, num_workers, print_detail, save_dir, save_results, metrics, precision, amp_level) and returns: metrics_data