code: def eval():
      place = (
          paddle.CUDAPlace(0)
          if FLAGS.devices == "gpu"
          else paddle.CPUPlace()
      )
      exe = paddle.static.Executor(place)
  
      (
          val_program,
          feed_target_names,
          fetch_targets,
      ) = paddle.static.load_inference_model(
          global_config["model_dir"].rstrip("/"),
          exe,
          model_filename=global_config["model_filename"],
          params_filename=global_config["params_filename"],
      )
      print("Loaded model from: {}".format(global_config["model_dir"]))
  
      metric = global_config["metric"]
      for batch_id, data in enumerate(val_loader):
          data_all = convert_numpy_data(data, metric)
          data_input = {}
          for k, v in data.items():
              if isinstance(global_config["input_list"], list):
                  if k in global_config["input_list"]:
                      data_input[k] = np.array(v)
              elif isinstance(global_config["input_list"], dict):
                  if k in global_config["input_list"].keys():
                      data_input[
                          global_config["input_list"][k]
                      ] = np.array(v)
  
          outs = exe.run(
              val_program,
              feed=data_input,
              fetch_list=fetch_targets,
              return_numpy=False,
          )
          res = {}
          if (
              "arch" in global_config
              and global_config["arch"] == "PPYOLOE"
          ):
              postprocess = PPYOLOEPostProcess(
                  score_threshold=0.01, nms_threshold=0.6
              )
              res = postprocess(
                  np.array(outs[0]), data_all["scale_factor"]
              )
          else:
              for out in outs:
                  v = np.array(out)
                  if len(v.shape) > 1:
                      res["bbox"] = v
                  else:
                      res["bbox_num"] = v
          metric.update(data_all, res)
          if batch_id % 100 == 0:
              print("Eval iter:", batch_id)
      metric.accumulate()
      metric.log()
      metric.reset()
  
file_path: PaddleDetection/deploy/auto_compression/eval.py
imports: import os
  import sys
  import numpy
  import argparse
  import paddle
  from ppdet.core.workspace import load_config, merge_config
  from ppdet.core.workspace import create
  from ppdet.metrics import COCOMetric, VOCMetric, KeyPointTopDownCOCOEval
  from paddleslim.auto_compression.config_helpers import load_config
  from post_process import PPYOLOEPostProcess
label_desc: Use image node_id 4 for calling a global function with example usage: eval() without return types