text: import torch
  
  
  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.' '''
      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):
      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
  