def METHOD_NAME(in_file, out_file, num_classes):
    """Convert keys in checkpoints.
    There can be some breaking changes during the development of mmdetection,
    and this tool is used for upgrading checkpoints trained with old versions
    to the latest one.
    """
    checkpoint = torch.load(in_file)
    in_state_dict = checkpoint.pop('state_dict')
    out_state_dict = OrderedDict()
    meta_info = checkpoint['meta']
    is_two_stage, is_ssd, is_retina, reg_cls_agnostic = parse_config(
        '#' + meta_info['config'])
    if meta_info['mmdet_version'] <= '0.5.3' and is_retina:
        upgrade_retina = True
    else:
        upgrade_retina = False
    # MMDetection v2.5.0 unifies the class order in RPN
    # if the model is trained in version<v2.5.0
    # The RPN model should be upgraded to be used in version>=2.5.0
    if meta_info['mmdet_version'] < '2.5.0':
        upgrade_rpn = True
    else:
        upgrade_rpn = False
    for key, val in in_state_dict.items():
        new_key = key
        new_val = val
        if is_two_stage and is_head(key):
            new_key = 'roi_head.{}'.format(key)
        # classification
        if upgrade_rpn:
            m = re.search(
                r'(conv_cls|retina_cls|rpn_cls|fc_cls|fcos_cls|'
                r'fovea_cls).(weight|bias)', new_key)
        else:
            m = re.search(
                r'(conv_cls|retina_cls|fc_cls|fcos_cls|'
                r'fovea_cls).(weight|bias)', new_key)
        if m is not None:
            print(f'reorder cls channels of {new_key}')
            new_val = reorder_cls_channel(val, num_classes)
        # regression
        if upgrade_rpn:
            m = re.search(r'(fc_reg).(weight|bias)', new_key)
        else:
            m = re.search(r'(fc_reg|rpn_reg).(weight|bias)', new_key)
        if m is not None and not reg_cls_agnostic:
            print(f'truncate regression channels of {new_key}')
            new_val = truncate_reg_channel(val, num_classes)
        # mask head
        m = re.search(r'(conv_logits).(weight|bias)', new_key)
        if m is not None:
            print(f'truncate mask prediction channels of {new_key}')
            new_val = truncate_cls_channel(val, num_classes)
        m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key)
        # Legacy issues in RetinaNet since V1.x
        # Use ConvModule instead of nn.Conv2d in RetinaNet
        # cls_convs.0.weight -> cls_convs.0.conv.weight
        if m is not None and upgrade_retina:
            param = m.groups()[1]
            new_key = key.replace(param, f'conv.{param}')
            out_state_dict[new_key] = val
            print(f'rename the name of {key} to {new_key}')
            continue
        m = re.search(r'(cls_convs).\d.(weight|bias)', key)
        if m is not None and is_ssd:
            print(f'reorder cls channels of {new_key}')
            new_val = reorder_cls_channel(val, num_classes)
        out_state_dict[new_key] = new_val
    checkpoint['state_dict'] = out_state_dict
    torch.save(checkpoint, out_file)