code: def evaluate(model, data_loader, epoch):
      loss_function = torch.nn.CrossEntropyLoss()
  
      model.eval()
  
      accu_num = torch.zeros(1).cuda()  # 累计预测正确的样本数
      accu_loss = torch.zeros(1).cuda()  # 累计损失
  
      sample_num = 0
      data_loader = tqdm(data_loader, file=sys.stdout)
      for step, data in enumerate(data_loader):
          images, labels = data
          sample_num += images.shape[0]
  
          pred = model(images.cuda())
          pred_classes = torch.max(pred, dim=1)[1]
          accu_num += torch.eq(pred_classes, labels.cuda()).sum()
  
          loss = loss_function(pred, labels.cuda())
          accu_loss += loss
  
          data_loader.desc = (
              "[valid epoch {}] loss: {:.3f}, acc: {:.3f}".format(
                  epoch,
                  accu_loss.item() / (step + 1),
                  accu_num.item() / sample_num,
              )
          )
  
      return accu_loss.item() / (step + 1), accu_num.item() / sample_num
  
file_path: deep-learning-for-image-processing/deploying_service/deploying_pytorch/convert_tensorrt/convert_resnet34/utils.py
imports: import os
  import sys
  import json
  import pickle
  import random
  import torch
  from tqdm import tqdm
label_desc: Use image node_id 5 for calling a global function with example usage: evaluate(model, data_loader, epoch) and returns: unknown,  unknown