code: def evaluate(model, data_loader, device):
      cpu_device = torch.device("cpu")
      model.eval()
      metric_logger = utils.MetricLogger(delimiter="  ")
      header = "Test: "
  
      det_metric = EvalCOCOMetric(
          data_loader.dataset.coco,
          iou_type="bbox",
          results_file_name="det_results.json",
      )
      seg_metric = EvalCOCOMetric(
          data_loader.dataset.coco,
          iou_type="segm",
          results_file_name="seg_results.json",
      )
      for image, targets in metric_logger.log_every(
          data_loader, 100, header
      ):
          image = list(img.to(device) for img in image)
  
          # 当使用CPU时，跳过GPU相关指令
          if device != torch.device("cpu"):
              torch.cuda.synchronize(device)
  
          model_time = time.time()
          outputs = model(image)
  
          outputs = [
              {k: v.to(cpu_device) for k, v in t.items()}
              for t in outputs
          ]
          model_time = time.time() - model_time
  
          det_metric.update(targets, outputs)
          seg_metric.update(targets, outputs)
          metric_logger.update(model_time=model_time)
  
      # gather the stats from all processes
      metric_logger.synchronize_between_processes()
      print("Averaged stats:", metric_logger)
  
      # 同步所有进程中的数据
      det_metric.synchronize_results()
      seg_metric.synchronize_results()
  
      if utils.is_main_process():
          coco_info = det_metric.evaluate()
          seg_info = seg_metric.evaluate()
      else:
          coco_info = None
          seg_info = None
  
      return coco_info, seg_info
  
file_path: deep-learning-for-image-processing/pytorch_object_detection/mask_rcnn/train_utils/train_eval_utils.py
imports: import math
  import sys
  import time
  import torch
  import train_utils.distributed_utils
  from .coco_eval import EvalCOCOMetric
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, data_loader, device) and returns: coco_info,  seg_info