def _parse_losses()

in models/base.py [0:0]

• 19 lines of code
• 10 McCabe index (conditional complexity)

    def _parse_losses(losses):
        """Parse the raw outputs (losses) of the network.
        Args:
            losses (dict): Raw output of the network, which usually contain
                losses and other necessary information.
        Returns:
            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor
                which may be a weighted sum of all losses, log_vars contains
                all the variables to be sent to the logger.
        """
        log_vars = OrderedDict()
        for loss_name, loss_value in losses.items():
            if isinstance(loss_value, torch.Tensor):
                log_vars[loss_name] = loss_value.mean()
            elif isinstance(loss_value, list):
                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
            else:
                raise TypeError(
                    f'{loss_name} is not a tensor or list of tensors')

        loss = sum(_value for _key, _value in log_vars.items()
                   if 'loss' in _key)

        log_vars['loss'] = loss
        
        for loss_name, loss_value in log_vars.items():
            # reduce loss when distributed training
            if dist.is_available() and dist.is_initialized():
                loss_value = loss_value.data.clone()
                dist.all_reduce(loss_value.div_(dist.get_world_size()))
            log_vars[loss_name] = loss_value.item()

        return loss, log_vars