models/loss_helper.py [215:237]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    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
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



models/loss_helper_boxnet.py [87:109]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    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
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -