code: def eval(self):
      self.model.eval()
      raw_metrics = []
      total_frame = 0.0
      total_time = 0.0
      for i, batch in tqdm(
          enumerate(self.validate_loader),
          total=len(self.validate_loader),
          desc="test model",
      ):
          with paddle.no_grad():
              start = time.time()
              preds = self.model(batch["img"])
              boxes, scores = self.post_process(
                  batch,
                  preds,
                  is_output_polygon=self.metric_cls.is_output_polygon,
              )
              total_frame += batch["img"].shape[0]
              total_time += time.time() - start
              raw_metric = self.metric_cls.validate_measure(
                  batch, (boxes, scores)
              )
              raw_metrics.append(raw_metric)
      metrics = self.metric_cls.gather_measure(raw_metrics)
      print("FPS:{}".format(total_frame / total_time))
      return {
          "recall": metrics["recall"].avg,
          "precision": metrics["precision"].avg,
          "fmeasure": metrics["fmeasure"].avg,
      }
  
file_path: PaddleOCR/benchmark/PaddleOCR_DBNet/tools/eval.py
imports: import os
  import sys
  import pathlib
  import argparse
  import time
  import paddle
  from tqdm.auto import tqdm
label_desc: Use image node_id 2 for calling the EVAL obj's underlying member method code with example usage: obj.eval() and returns: dict