text: import numpy as np
  import torch
  import torch.nn.functional
  import torch.optim as optim
  import torch.utils.data
  from torch.optim.lr_scheduler import StepLR
  from torchvision import transforms
  from evaluator import JerseyEvaluator, JerseyDetEvaluator
  from model import JerseyModel, ResNetJersey
  import glob
  import os
  
  import argparse
  parser = argparse.ArgumentParser()
  parser.add_argument('-a', '--best_acc', default=89.7, help='..')
  parser.add_argument('-m', '--models_path', default='./work_dirs/r18_randaug_fulldata/',  help='..')
  
  
  def main():
      args = parser.parse_args()
      models_path = args.models_path
  
      model = JerseyModel(inter_size=7)
      model.restore('./work_dirs/basic_randaug_fulldata/model-best.pth')
      model.cuda()
  
  
      ### This checkpoint gives 92.6 in 1.17 minutes time
      # evaluator = JerseyDetEvaluator(
      #     config_file='/home/ubuntu/oljike/BallTracking/mmdetection/configs/yolo_jersey/yolov3_d53_320_273e_jersey.py',
      #     checkpoint_file='/home/ubuntu/oljike/BallTracking/mmdetection/work_dirs/jersey_region_yolov3-320_fullData/epoch_150.pth'
      # )
  
      # This checkpoint gives 92.3 in 1.04 minutes time.
      # evaluator = JerseyDetEvaluator(
      #     config_file='/home/ubuntu/oljike/BallTracking/mmdetection/configs/yolo_jersey/yolov3_d53_320_273e_jersey_smallres.py',
      #     checkpoint_file='/home/ubuntu/oljike/BallTracking/mmdetection/work_dirs/jersey_region_yolov3-320_fullData_smallRes/epoch_90.pth'
      # )
  
      evaluator = JerseyEvaluator('./data/jersey_crops_val.txt')
  
      best_accuracy = args.best_acc
      accuracy = evaluator.evaluate(model)
      print('==> accuracy = %f, best accuracy %f' % (accuracy, best_accuracy))
  
  
  if __name__ == '__main__':
      main()