text: from __future__ import print_function
  import os
  import argparse
  import torch
  from data import cfg_mnet, cfg_re50
  from layers.functions.prior_box import PriorBox
  from models.retinaface import RetinaFace
  
  parser = argparse.ArgumentParser(description='Test')
  parser.add_argument('-m', '--trained_model', default='./weights/mobilenet0.25_Final.pth',
                      type=str, help='Trained state_dict file path to open')
  parser.add_argument('--network', default='mobile0.25', help='Backbone network mobile0.25 or resnet50')
  parser.add_argument('--long_side', default=640, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
  parser.add_argument('--cpu', action="store_true", default=True, help='Use cpu inference')
  
  args = parser.parse_args()
  
  
  def check_keys(model, pretrained_state_dict):
      ckpt_keys = set(pretrained_state_dict.keys())
      model_keys = set(model.state_dict().keys())
      used_pretrained_keys = model_keys & ckpt_keys
      unused_pretrained_keys = ckpt_keys - model_keys
      missing_keys = model_keys - ckpt_keys
      print('Missing keys:{}'.format(len(missing_keys)))
      print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
      print('Used keys:{}'.format(len(used_pretrained_keys)))
      assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
      return True
  
  
  def remove_prefix(state_dict, prefix):
      ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
      print('remove prefix \'{}\''.format(prefix))
      f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
      return {f(key): value for key, value in state_dict.items()}
  
  
  def load_model(model, pretrained_path, load_to_cpu):
      print('Loading pretrained model from {}'.format(pretrained_path))
      if load_to_cpu:
          pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
      else:
          device = torch.cuda.current_device()
          pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
      if "state_dict" in pretrained_dict.keys():
          pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
      else:
          pretrained_dict = remove_prefix(pretrained_dict, 'module.')
      check_keys(model, pretrained_dict)
      model.load_state_dict(pretrained_dict, strict=False)
      return model
  
  
  if __name__ == '__main__':
      torch.set_grad_enabled(False)
      cfg = None
      if args.network == "mobile0.25":
          cfg = cfg_mnet
      elif args.network == "resnet50":
          cfg = cfg_re50
      # net and model
      net = RetinaFace(cfg=cfg, phase = 'test')
      net = load_model(net, args.trained_model, args.cpu)
      net.eval()
      print('Finished loading model!')
      print(net)
      device = torch.device("cpu" if args.cpu else "cuda")
      net = net.to(device)
  
      # ------------------------ export -----------------------------
      output_onnx = 'FaceDetector.onnx'
      print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
      input_names = ["input0"]
      output_names = ["output0"]
      inputs = torch.randn(1, 3, args.long_side, args.long_side).to(device)
  
      torch_out = torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False,
                                     input_names=input_names, output_names=output_names)
  
  
  