Method _set_aux_loss

tutorials/motr/motr_det.py:480–485

(self, outputs_class, outputs_coord)

    @torch.jit.unused
    def _set_aux_loss(self, outputs_class, outputs_coord):
        # this is a workaround to make torchscript happy, as torchscript
        # doesn't support dictionary with non-homogeneous values, such
        # as a dict having both a Tensor and a list.
        return [{'pred_logits': a, 'pred_boxes': b, }
                for a, b in zip(outputs_class[:-1], outputs_coord[:-1])]

    def _forward_single_image(self, samples, track_instances: Instances):
        features, pos = self.backbone(samples)