code: def validate(val_loader, model, criterion):
      if main_process():
          logger.info(
              ">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>"
          )
      batch_time = AverageMeter()
      model_time = AverageMeter()
      data_time = AverageMeter()
      loss_meter = AverageMeter()
      intersection_meter = AverageMeter()
      union_meter = AverageMeter()
      target_meter = AverageMeter()
      if args.use_coco:
          split_gap = 20
      else:
          split_gap = 5
      class_intersection_meter = [0] * split_gap
      class_union_meter = [0] * split_gap
  
      if args.manual_seed is not None and args.fix_random_seed_val:
          torch.cuda.manual_seed(args.manual_seed)
          np.random.seed(args.manual_seed)
          torch.manual_seed(args.manual_seed)
          torch.cuda.manual_seed_all(args.manual_seed)
          random.seed(args.manual_seed)
  
      model.eval()
      end = time.time()
      if args.split != 999:
          if args.use_coco:
              test_num = 20000
          else:
              test_num = 5000
      else:
          test_num = len(val_loader)
      assert test_num % args.batch_size_val == 0
      iter_num = 0
      total_time = 0
      for e in range(20):
          for i, (
              input,
              target,
              s_input,
              s_mask,
              subcls,
              ori_label,
          ) in enumerate(val_loader):
              if (iter_num - 1) * args.batch_size_val >= test_num:
                  break
              iter_num += 1
              data_time.update(time.time() - end)
              input = input.cuda(non_blocking=True)
              target = target.cuda(non_blocking=True)
              ori_label = ori_label.cuda(non_blocking=True)
              start_time = time.time()
              output = model(s_x=s_input, s_y=s_mask, x=input, y=target)
              total_time = total_time + 1
              model_time.update(time.time() - start_time)
  
              if args.ori_resize:
                  longerside = max(ori_label.size(1), ori_label.size(2))
                  backmask = (
                      torch.ones(
                          ori_label.size(0), longerside, longerside
                      ).cuda()
                      * 255
                  )
                  backmask[
                      0, : ori_label.size(1), : ori_label.size(2)
                  ] = ori_label
                  target = backmask.clone().long()
  
              output = F.interpolate(
                  output,
                  size=target.size()[1:],
                  mode="bilinear",
                  align_corners=True,
              )
              loss = criterion(output, target)
  
              n = input.size(0)
              loss = torch.mean(loss)
  
              output = output.max(1)[1]
  
              intersection, union, new_target = intersectionAndUnionGPU(
                  output, target, args.classes, args.ignore_label
              )
              intersection, union, target, new_target = (
                  intersection.cpu().numpy(),
                  union.cpu().numpy(),
                  target.cpu().numpy(),
                  new_target.cpu().numpy(),
              )
              intersection_meter.update(
                  intersection
              ), union_meter.update(union), target_meter.update(
                  new_target
              )
  
              subcls = subcls[0].cpu().numpy()[0]
              class_intersection_meter[
                  (subcls - 1) % split_gap
              ] += intersection[1]
              class_union_meter[(subcls - 1) % split_gap] += union[1]
  
              accuracy = sum(intersection_meter.val) / (
                  sum(target_meter.val) + 1e-10
              )
              loss_meter.update(loss.item(), input.size(0))
              batch_time.update(time.time() - end)
              end = time.time()
              if ((i + 1) % (test_num / 100) == 0) and main_process():
                  logger.info(
                      "Test: [{}/{}] "
                      "Data {data_time.val:.3f} ({data_time.avg:.3f}) "
                      "Batch {batch_time.val:.3f} ({batch_time.avg:.3f}) "
                      "Loss {loss_meter.val:.4f} ({loss_meter.avg:.4f}) "
                      "Accuracy {accuracy:.4f}.".format(
                          iter_num * args.batch_size_val,
                          test_num,
                          data_time=data_time,
                          batch_time=batch_time,
                          loss_meter=loss_meter,
                          accuracy=accuracy,
                      )
                  )
  
      iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
      accuracy_class = intersection_meter.sum / (
          target_meter.sum + 1e-10
      )
      mIoU = np.mean(iou_class)
      mAcc = np.mean(accuracy_class)
      allAcc = sum(intersection_meter.sum) / (
          sum(target_meter.sum) + 1e-10
      )
  
      class_iou_class = []
      class_miou = 0
      for i in range(len(class_intersection_meter)):
          class_iou = class_intersection_meter[i] / (
              class_union_meter[i] + 1e-10
          )
          class_iou_class.append(class_iou)
          class_miou += class_iou
      class_miou = class_miou * 1.0 / len(class_intersection_meter)
      logger.info(
          "meanIoU---Val result: mIoU {:.4f}.".format(class_miou)
      )
      for i in range(split_gap):
          logger.info(
              "Class_{} Result: iou {:.4f}.".format(
                  i + 1, class_iou_class[i]
              )
          )
  
      if main_process():
          logger.info(
              "FBIoU---Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format(
                  mIoU, mAcc, allAcc
              )
          )
          for i in range(args.classes):
              logger.info(
                  "Class_{} Result: iou/accuracy {:.4f}/{:.4f}.".format(
                      i, iou_class[i], accuracy_class[i]
                  )
              )
          logger.info(
              "<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<"
          )
  
      print(
          "avg inference time: {:.4f}, count: {}".format(
              model_time.avg, test_num
          )
      )
      return loss_meter.avg, mIoU, mAcc, allAcc, class_miou
  
imports: import os
  import random
  import time
  import cv2
  import numpy
  import logging
  import argparse
  import torch
  import torch.backends.cudnn
  import torch.nn
  import torch.nn.functional
  import torch.nn.parallel
  import torch.optim
  import torch.utils.data
  import torch.multiprocessing
  import torch.distributed
  from tensorboardX import SummaryWriter
  from model.PFENet import PFENet
  from util import dataset
  from util import transform, config
  from util.util import AverageMeter, poly_learning_rate, intersectionAndUnionGPU
label_desc: Use image node_id 7 for calling a global function with example usage: validate(val_loader, model, criterion) and returns: loss_meter,  mIoU,  mAcc,  allAcc,  class_miou