def get_loss()

in models/loss_helper.py [0:0]

• 31 lines of code
• 2 McCabe index (conditional complexity)

def get_loss(end_points, config):
    """ Loss functions

    Args:
        end_points: dict
            {   
                seed_xyz, seed_inds, vote_xyz,
                center,
                heading_scores, heading_residuals_normalized,
                size_scores, size_residuals_normalized,
                sem_cls_scores, #seed_logits,#
                center_label,
                heading_class_label, heading_residual_label,
                size_class_label, size_residual_label,
                sem_cls_label,
                box_label_mask,
                vote_label, vote_label_mask
            }
        config: dataset config instance
    Returns:
        loss: pytorch scalar tensor
        end_points: dict
    """

    # Vote loss
    vote_loss = compute_vote_loss(end_points)
    end_points['vote_loss'] = vote_loss

    # Obj loss
    objectness_loss, objectness_label, objectness_mask, object_assignment = \
        compute_objectness_loss(end_points)
    end_points['objectness_loss'] = objectness_loss
    end_points['objectness_label'] = objectness_label
    end_points['objectness_mask'] = objectness_mask
    end_points['object_assignment'] = object_assignment
    total_num_proposal = objectness_label.shape[0]*objectness_label.shape[1]
    end_points['pos_ratio'] = \
        torch.sum(objectness_label.float().cuda())/float(total_num_proposal)
    end_points['neg_ratio'] = \
        torch.sum(objectness_mask.float())/float(total_num_proposal) - end_points['pos_ratio']

    # Box loss and sem cls loss
    center_loss, heading_cls_loss, heading_reg_loss, size_cls_loss, size_reg_loss, sem_cls_loss = \
        compute_box_and_sem_cls_loss(end_points, config)
    end_points['center_loss'] = center_loss
    end_points['heading_cls_loss'] = heading_cls_loss
    end_points['heading_reg_loss'] = heading_reg_loss
    end_points['size_cls_loss'] = size_cls_loss
    end_points['size_reg_loss'] = size_reg_loss
    end_points['sem_cls_loss'] = sem_cls_loss
    box_loss = center_loss + 0.1*heading_cls_loss + heading_reg_loss + 0.1*size_cls_loss + size_reg_loss
    end_points['box_loss'] = box_loss

    # Final loss function
    loss = vote_loss + 0.5*objectness_loss + box_loss + 0.1*sem_cls_loss
    loss *= 10
    end_points['loss'] = loss

    # --------------------------------------------
    # Some other statistics
    obj_pred_val = torch.argmax(end_points['objectness_scores'], 2) # B,K
    obj_acc = torch.sum((obj_pred_val==objectness_label.long()).float()*objectness_mask)/(torch.sum(objectness_mask)+1e-6)
    end_points['obj_acc'] = obj_acc

    return loss, end_points