def _write_losses()

in ubteacher/engine/hooks.py [0:0]

• 12 lines of code
• 7 McCabe index (conditional complexity)

    def _write_losses(self, metrics_dict):
        # gather metrics among all workers for logging
        # This assumes we do DDP-style training, which is currently the only
        # supported method in detectron2.
        comm.synchronize()
        all_metrics_dict = comm.gather(metrics_dict, dst=0)

        if comm.is_main_process():
            # average the rest metrics
            metrics_dict = {
                "val_" + k: np.mean([x[k] for x in all_metrics_dict])
                for k in all_metrics_dict[0].keys()
            }
            total_losses_reduced = sum(loss for loss in metrics_dict.values())

            self.trainer.storage.put_scalar("val_total_loss_val", total_losses_reduced)
            if len(metrics_dict) > 1:
                self.trainer.storage.put_scalars(**metrics_dict)