code: def evaluate(
      model,
      eval_dataset,
      postprocessor,
      num_workers=0,
      print_detail=True,
      eval_sem=False,
      eval_ins=False,
      precision="fp32",
      amp_level="O1",
      runner=None,
  ):
      """
      Launch evaluation.
  
      Args:
          model（nn.Layer): A panoptic segmentation model.
          eval_dataset (paddle.io.Dataset): Used to read and process validation datasets.
          postprocessor (paddlepanseg.postprocessors.Postprocessor): Used to postprocess model output.
          runner (paddlepanseg.core.runners.PanSegRunner): Used to define how the components interact.
          num_workers (int, optional): The number of workers used by the data loader. Default: 0.
          print_detail (bool, optional): Whether or not to print detailed information about the evaluation process. Default: True.
          eval_sem (bool, optional): Whether or not to calculate semantic segmentation metrics. Default: False.
          eval_ins (bool, optional): Whether or not to calculate instance segmentation metrics. Default: False.
          precision (str, optional): If `precision` is 'fp16', enable automatic mixed precision training. Default: 'fp32'.
          amp_level (str, optional): The auto mixed precision level. Choices are 'O1' and 'O2'. Default: '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()
  
      # By default evaluate the results with panoptic segmentation metrics
      eval_pan = True
  
      # Build batch sampler and data loader
      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,
          collate_fn=eval_dataset.collate
          if hasattr(eval_dataset, "collate")
          else None,
      )
  
      # Bind components to runner
      runner.bind(model=model, postprocessor=postprocessor)
  
      # Create launcher
      launcher = AMPLauncher(
          runner=runner, precision=precision, amp_level=amp_level
      )
  
      total_iters = len(loader)
      if eval_sem:
          sem_evaluator = SemSegEvaluator(
              eval_dataset.num_classes,
              ignore_index=eval_dataset.ignore_index,
          )
      if eval_ins:
          ins_evaluator_ap50 = InsSegEvaluator(
              eval_dataset.num_classes,
              overlaps=0.5,
              thing_ids=eval_dataset.thing_ids,
              ignore_index=eval_dataset.ignore_index,
          )
          ins_evaluator_ap = InsSegEvaluator(
              eval_dataset.num_classes,
              overlaps=list(np.arange(0.5, 1.0, 0.05)),
              thing_ids=eval_dataset.thing_ids,
              ignore_index=eval_dataset.ignore_index,
          )
      if eval_pan:
          pan_evaluator = PanSegEvaluator(
              num_classes=eval_dataset.num_classes,
              thing_ids=eval_dataset.thing_ids,
              label_divisor=eval_dataset.label_divisor,
              convert_id=eval_dataset.convert_id_for_eval,
          )
  
      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)
  
              pp_out = launcher.infer_step(data=data)
  
              if eval_sem:
                  sem_evaluator.update(data, pp_out)
              if eval_ins:
                  ins_evaluator_ap.update(data, pp_out)
                  ins_evaluator_ap50.update(data, pp_out)
              if eval_pan:
                  pan_evaluator.update(data, pp_out)
  
              batch_cost_averager.record(
                  time.time() - batch_start,
                  num_samples=len(data["img"]),
              )
              batch_cost = batch_cost_averager.get_average()
              reader_cost = reader_cost_averager.get_average()
  
              if local_rank == 0:
                  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()
  
      results = build_info_dict(_type_="metric")
  
      if eval_sem:
          sem_results = sem_evaluator.evaluate()
          results["sem_metrics"] = sem_results["sem_metrics"]
      if eval_ins:
          ins_results = build_info_dict(
              _type_="metric", ins_metrics=OrderedDict()
          )
          ins_results_ap = ins_evaluator_ap.evaluate()
          ins_results_ap50 = ins_evaluator_ap50.evaluate()
          ins_results["ins_metrics"]["mAP"] = ins_results_ap[
              "ins_metrics"
          ]["mAP"]
          ins_results["ins_metrics"]["AP"] = ins_results_ap[
              "ins_metrics"
          ]["AP"]
          ins_results["ins_metrics"]["mAP50"] = ins_results_ap50[
              "ins_metrics"
          ]["mAP"]
          ins_results["ins_metrics"]["AP50"] = ins_results_ap50[
              "ins_metrics"
          ]["AP"]
          results["ins_metrics"] = ins_results["ins_metrics"]
      if eval_pan:
          pan_results = pan_evaluator.evaluate()
          results["pan_metrics"] = pan_results["pan_metrics"]
  
      if print_detail:
          if eval_pan:
              logger.info(
                  tabulate_metrics(
                      results["pan_metrics"]["All"],
                      digits=4,
                      title="Pan Metrics of All Classes:",
                  )
              )
              logger.info(
                  tabulate_metrics(
                      results["pan_metrics"]["Things"],
                      digits=4,
                      title="Pan Metrics of Thing Classes:",
                  )
              )
              logger.info(
                  tabulate_metrics(
                      results["pan_metrics"]["Stuff"],
                      digits=4,
                      title="Pan Metrics of Stuff Classes:",
                  )
              )
              logger.info(
                  "PQ: {:.4f}".format(
                      results["pan_metrics"]["All"]["pq"]
                  )
              )
          if eval_sem:
              logger.info(
                  tabulate_metrics(
                      results["sem_metrics"],
                      digits=2,
                      title="Sem metrics:",
                  )
              )
              logger.info(
                  "mIoU: {:.4f}".format(results["sem_metrics"]["mIoU"])
              )
          if eval_ins:
              logger.info(
                  tabulate_metrics(
                      results["ins_metrics"],
                      digits=2,
                      title="Ins metrics:",
                  )
              )
              logger.info(
                  "mAP: {:.4f}, mAP50: {:.4f}".format(
                      results["ins_metrics"]["mAP"],
                      results["ins_metrics"]["mAP50"],
                  )
              )
  
      return results
  
file_path: PaddleSeg/contrib/PanopticSeg/paddlepanseg/core/val.py
imports: from collections import OrderedDict
  import numpy
  import time
  import paddle
  from paddleseg.utils import TimeAverager, logger, progbar
  from paddlepanseg.cvlibs import build_info_dict
  from paddlepanseg.utils.evaluation import SemSegEvaluator, InsSegEvaluator, PanSegEvaluator
  from paddlepanseg.utils import tabulate_metrics
  from paddlepanseg.core.runner import PanSegRunner
  from paddlepanseg.core.launcher import AMPLauncher
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, eval_dataset, postprocessor, num_workers, print_detail, eval_sem, eval_ins, precision, amp_level, runner) and returns: results