text: # Copyright (c) SenseTime. All Rights Reserved.
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  
  import logging
  
  import torch
  
  
  logger = logging.getLogger('global')
  
  
  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
      # filter 'num_batches_tracked'
      missing_keys = [x for x in missing_keys
                      if not x.endswith('num_batches_tracked')]
      if len(missing_keys) > 0:
          logger.info('[Warning] missing keys: {}'.format(missing_keys))
          logger.info('missing keys:{}'.format(len(missing_keys)))
      if len(unused_pretrained_keys) > 0:
          logger.info('[Warning] unused_pretrained_keys: {}'.format(
              unused_pretrained_keys))
          logger.info('unused checkpoint keys:{}'.format(
              len(unused_pretrained_keys)))
      logger.info('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
      share common prefix 'module.' '''
      logger.info('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_pretrain(model, pretrained_path):
      logger.info('load pretrained model from {}'.format(pretrained_path))
      device = torch.cuda.current_device()
      pretrained_dict = torch.load(pretrained_path,
          map_location=lambda storage, loc: storage)
      if "state_dict" in pretrained_dict.keys():
          pretrained_dict = remove_prefix(pretrained_dict['state_dict'],
                                          'module.')
      else:
          pretrained_dict = remove_prefix(pretrained_dict, 'module.')
  
      try:
          check_keys(model, pretrained_dict)
      except:
          logger.info('[Warning]: using pretrain as features.\
                  Adding "features." as prefix')
          new_dict = {}
          for k, v in pretrained_dict.items():
              k = 'features.' + k
              new_dict[k] = v
          pretrained_dict = new_dict
          check_keys(model, pretrained_dict)
      model.load_state_dict(pretrained_dict, strict=False)
      return model
  
  
  def restore_from(model, optimizer, ckpt_path):
      device = torch.cuda.current_device()
      ckpt = torch.load(ckpt_path,
          map_location=lambda storage, loc: storage.cuda(device))
      epoch = ckpt['epoch']
  
      ckpt_model_dict = remove_prefix(ckpt['state_dict'], 'module.')
      check_keys(model, ckpt_model_dict)
      model.load_state_dict(ckpt_model_dict, strict=False)
  
      check_keys(optimizer, ckpt['optimizer'])
      optimizer.load_state_dict(ckpt['optimizer'])
      return model, optimizer, epoch
  