Class FCAF3DHeadRotMat

embodiedscan/models/dense_heads/fcaf3d_head.py:828–1725

r"""FCAF3D head with a 6D representation for rotation of boxes. Actually here we store both the sparse 3D FPN and a head. The neck and the head can not be simply separated as pruning score on the i-th level of FPN requires classification scores from i+1-th level of the head. Args:

@MODELS.register_module()
class FCAF3DHeadRotMat(BaseModel):
    r"""FCAF3D head with a 6D representation for rotation of boxes.

    Actually here we store both the sparse 3D FPN and a head. The neck and
    the head can not be simply separated as pruning score on the i-th level
    of FPN requires classification scores from i+1-th level of the head.

    Args:
        num_classes (int): Number of classes.
        in_channels (tuple(int)): Number of channels in input tensors.
        out_channels (int): Number of channels in the neck output tensors.
        num_reg_outs (int): Number of regression layer channels.
        voxel_size (float): Voxel size in meters.
        pts_prune_threshold (int): Pruning threshold on each feature level.
        pts_assign_threshold (int): Box to location assigner parameter.
            Assigner selects the maximum feature level with more locations
            inside the box than pts_assign_threshold.
        pts_center_threshold (int): Box to location assigner parameter.
            After feature level for the box is determined, assigner selects
            pts_center_threshold locations closest to the box center.
        center_loss (dict): Config of centerness loss. Defaults to
            dict(type='mmdet.CrossEntropyLoss', use_sigmoid=True).
        bbox_loss (dict): Config of bbox loss. Defaults to
            dict(type='BBoxCDLoss', mode='l1', loss_weight=1.0, group='g8').
        cls_loss (dict): Config of classification loss. Defaults to
            dict = dict(type='mmdet.FocalLoss').
        train_cfg (dict, optional): Config for train stage. Defaults to None.
        test_cfg (dict, optional): Config for test stage. Defaults to None.
        init_cfg (dict, optional): Config for weight initialization.
            Defaults to None.
    """

    def __init__(self,
                 num_classes: int,
                 in_channels: Tuple[int],
                 out_channels: int,
                 num_reg_outs: int,
                 voxel_size: float,
                 pts_prune_threshold: int,
                 pts_assign_threshold: int,
                 pts_center_threshold: int,
                 center_loss: dict = dict(type='mmdet.CrossEntropyLoss',
                                          use_sigmoid=True),
                 bbox_loss: dict = dict(type='BBoxCDLoss',
                                        mode='l1',
                                        loss_weight=1.0,
                                        group='g8'),
                 cls_loss: dict = dict(type='mmdet.FocalLoss'),
                 decouple_bbox_loss: bool = False,
                 decouple_groups: int = 3,
                 decouple_weights: Optional[list] = None,
                 norm_decouple_loss: bool = False,
                 train_cfg: Optional[dict] = None,
                 test_cfg: Optional[dict] = None,
                 init_cfg: Optional[dict] = None):
        super(FCAF3DHeadRotMat, self).__init__(init_cfg)
        if ME is None:
            raise ImportError(