Traceback (most recent call last):
  File "train.py", line 98, in <module>
    main(opt)
  File "train.py", line 65, in main
    model, opt.load_model, trainer.optimizer, opt.resume, opt.lr, opt.lr_step)
  File "/home/sonic/sw/FairMOT/src/lib/models/model.py", line 37, in load_model
    print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch']))
KeyError: 'epoch'

load_model def in model.py is this
def load_model(model, model_path, optimizer=None, resume=False, 
               lr=None, lr_step=None):
  start_epoch = 0
  checkpoint = torch.load(model_path, map_location=lambda storage, loc: storage)
  print('loaded {}, epoch {}'.format(model_path, checkpoint['epoch']))
  state_dict_ = checkpoint['state_dict']
  state_dict = {}

  # convert data_parallal to model
  for k in state_dict_:
    if k.startswith('module') and not k.startswith('module_list'):
      state_dict[k[7:]] = state_dict_[k]
    else:
      state_dict[k] = state_dict_[k]
  model_state_dict = model.state_dict()

  # check loaded parameters and created model parameters
  msg = 'If you see this, your model does not fully load the ' + \
        'pre-trained weight. Please make sure ' + \
        'you have correctly specified --arch xxx ' + \
        'or set the correct --num_classes for your own dataset.'
  for k in state_dict:
    if k in model_state_dict:
      if state_dict[k].shape != model_state_dict[k].shape:
        print('Skip loading parameter {}, required shape{}, '\
              'loaded shape{}. {}'.format(
          k, model_state_dict[k].shape, state_dict[k].shape, msg))
        state_dict[k] = model_state_dict[k]
    else:
      print('Drop parameter {}.'.format(k) + msg)
  for k in model_state_dict:
    if not (k in state_dict):
      print('No param {}.'.format(k) + msg)
      state_dict[k] = model_state_dict[k]
  model.load_state_dict(state_dict, strict=False)

  # resume optimizer parameters
  if optimizer is not None and resume:
    if 'optimizer' in checkpoint:
      optimizer.load_state_dict(checkpoint['optimizer'])
      start_epoch = checkpoint['epoch']
      start_lr = lr
      for step in lr_step:
        if start_epoch >= step:
          start_lr *= 0.1
      for param_group in optimizer.param_groups:
        param_group['lr'] = start_lr
      print('Resumed optimizer with start lr', start_lr)
    else:
      print('No optimizer parameters in checkpoint.')
  if optimizer is not None:
    return model, optimizer, start_epoch
  else:
    return model

Thank you.