# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple

from torch import Tensor

from mmdet3d.registry import MODELS
from mmdet3d.utils import ConfigType, OptConfigType, OptMultiConfig
from .voxelnet import VoxelNet


@MODELS.register_module()
class DynamicVoxelNet(VoxelNet):
    r"""VoxelNet using `dynamic voxelization <https://arxiv.org/abs/1910.06528>`_.
    """

    def __init__(self,
                 voxel_encoder: ConfigType,
                 middle_encoder: ConfigType,
                 backbone: ConfigType,
                 neck: OptConfigType = None,
                 bbox_head: OptConfigType = None,
                 train_cfg: OptConfigType = None,
                 test_cfg: OptConfigType = None,
                 data_preprocessor: OptConfigType = None,
                 init_cfg: OptMultiConfig = None) -> None:
        super().__init__(
            voxel_encoder=voxel_encoder,
            middle_encoder=middle_encoder,
            backbone=backbone,
            neck=neck,
            bbox_head=bbox_head,
            train_cfg=train_cfg,
            test_cfg=test_cfg,
            data_preprocessor=data_preprocessor,
            init_cfg=init_cfg)

    def extract_feat(self, batch_inputs_dict: dict) -> Tuple[Tensor]:
        """Extract features from points."""
        voxel_dict = batch_inputs_dict['voxels']
        voxel_features, feature_coors = self.voxel_encoder(
            voxel_dict['voxels'], voxel_dict['coors'])
        batch_size = voxel_dict['coors'][-1, 0].item() + 1
        x = self.middle_encoder(voxel_features, feature_coors, batch_size)
        x = self.backbone(x)
        if self.with_neck:
            x = self.neck(x)
        return x
