Placeholder of forward function.

Forward function' Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxes_3d (list[:obj:`BaseIn

Forward function' Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxes_3d (list[:obj:`Bas

Calls either simple_test or aug_test depending on the length of outer list of points. If len(points) == 1, call simple_test. Otherwise

During inference, the functionary of batch norm layers is turned off but only the mean and var alone channels are used, which exposes the chance t

During inference, the functionary of batch norm layers is turned off but only the mean and var alone channels are used, which exposes the chance t

use lidar2global to get gt map layers

Get the 2D annotation records for a given info. Args: info: Information of the given sample data. occluded: Integer (0, 1, 2, 3)

Get the 2D annotation records for a given info. Args: info: Information of the given sample data. occluded: Integer (0, 1, 2, 3)

Get annotation info according to the given index. Args: index (int): Index of the annotation data to get. Returns:

Get annotation info according to the given index. Args: index (int): Index of the annotation data to get. Returns:

Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation.

Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation.

Generate bboxes from vote head predictions. Args: points (torch.Tensor): Input points. bbox_preds (dict): Predictions

Generate bboxes from bbox head predictions. Args: preds_dicts (tuple[list[dict]]): Prediction results. img_metas (list

obtain bev features.

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

Encode direction to 0 ~ num_bins-1. Args: anchors (torch.Tensor): Concatenated multi-level anchor. reg_targets (torch.Tensor): Bb

Get the label of the data Args: data_dir (str): fetch the label from the data_dir

Calculate loss of voting module. Args: seed_points (torch.Tensor): Coordinate of the seed points. vote_points (torch.

Create boolean mask by actually number of a padded tensor. Args: actual_num (torch.Tensor): Actual number of points in each voxel.

Get points according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. dtype (t

Get the reference points used in SCA and TSA. Args: H, W: spatial shape of bev. Z: hight of pillar. D:

Gets the confusion matrix that only considers the sub-indices Args: indices: List[int] to_probability: bool

Given the tile index, return the tile boundary index

Check whether the points are in the given range. Args: point_range (list | torch.Tensor): The range of point in o

Inference with slide/whole style. Args: points (torch.Tensor): Input points of shape [B, N, 3+C]. img_metas (list[dic

Inference point cloud with the detector. Args: model (nn.Module): The loaded detector. pcd (str): Point cloud files. Returns

Inference image with the monocular 3D detector. Args: model (nn.Module): The loaded detector. image (str): Image files. a

Inference point cloud with the segmentor. Args: model (nn.Module): The loaded segmentor. pcd (str): Point cloud files. Retur

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Rasterize a single line to mask. Args: line_ego (LineString): line mask (array): semantic mask to paint on

Rasterize a single line to mask. Args: line_ego (LineString): line mask (array): semantic mask to paint on

Prepare data related to Lyft dataset. Related data consists of '.pkl' files recording basic infos. Although the ground truth database and 2D

Prepare data related to Lyft dataset. Related data consists of '.pkl' files recording basic infos. Although the ground truth database and 2D

Map voxel features to its corresponding points. Args: pts_coors (torch.Tensor): Voxel coordinate of each point. voxel

Map the centers of voxels to its corresponding points. Args: pts_coors (torch.Tensor): The coordinates of each points, shape

Multi-class NMS for box head. Note: This function has large overlap with the `box3d_multiclass_nms` implemented in `m

Random rotate or remove each groundtruth independently. use kitti viewer to test this function points_transform_ Args: gt_boxes (np.n

Prepare data related to nuScenes dataset. Related data consists of '.pkl' files recording basic infos, 2D annotations and groundtruth databas

Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. senso

Convert the output to the box class in the nuScenes. Args: detection (dict): Detection results. - boxes_3d (:obj:`BaseInstan

Convert the output to the box class in the nuScenes. Args: detection (dict): Detection results. - boxes_3d (:obj:`BaseInsta

Project points from camera coordicates to image coordinates. Args: points_3d (torch.Tensor): Points in shape (N, 3). proj_mat (to

Check points is in 3d convex polygons. Args: points (np.ndarray): Input points with shape of (num_points, 3). polygon_surfaces (n

Apply transforms to points and box centers. Args: points (np.ndarray): Input points. centers (np.ndarray): Input box centers.

Get the intersection of the convex hull of the reprojected bbox corners and the image canvas, return None if no intersection. Args: c

Get the intersection of the convex hull of the reprojected bbox corners and the image canvas, return None if no intersection. Args: c

Prepare data for testing. Args: index (int): Index for accessing the target data. Returns: dict: Testing dat

Training image preparation. Args: index (int): Index for accessing the target image data. Returns: dict: Tra

Extract labels of each point in the pcd from image Args: pcd: np.ndarray with shape (M, N), point cloud of size N with di

Flip 3D data randomly. Args: input_dict (dict): Result dict from loading pipeline. direction (str): Flip direction. D

Remove the points in the sampled bounding boxes. Args: points (:obj:`BasePoints`): Input point cloud array. boxes (np