def single_output_forward()

in criterion.py [0:0]

• 29 lines of code
• 8 McCabe index (conditional complexity)

    def single_output_forward(self, outputs, targets):
        gious = generalized_box3d_iou(
            outputs["box_corners"],
            targets["gt_box_corners"],
            targets["nactual_gt"],
            rotated_boxes=torch.any(targets["gt_box_angles"] > 0).item(),
            needs_grad=(self.loss_weight_dict["loss_giou_weight"] > 0),
        )

        outputs["gious"] = gious
        center_dist = torch.cdist(
            outputs["center_normalized"], targets["gt_box_centers_normalized"], p=1
        )
        outputs["center_dist"] = center_dist
        assignments = self.matcher(outputs, targets)

        losses = {}

        for k in self.loss_functions:
            loss_wt_key = k + "_weight"
            if (
                loss_wt_key in self.loss_weight_dict
                and self.loss_weight_dict[loss_wt_key] > 0
            ) or loss_wt_key not in self.loss_weight_dict:
                # only compute losses with loss_wt > 0
                # certain losses like cardinality are only logged and have no loss weight
                curr_loss = self.loss_functions[k](outputs, targets, assignments)
                losses.update(curr_loss)

        final_loss = 0
        for k in self.loss_weight_dict:
            if self.loss_weight_dict[k] > 0:
                losses[k.replace("_weight", "")] *= self.loss_weight_dict[k]
                final_loss += losses[k.replace("_weight", "")]
        return final_loss, losses