Flip the points along given BEV direction. Args: bev_direction (str): Flip direction (horizontal or vertical).

Rotate points with the given rotation matrix or angle. Args: rotation (float | np.ndarray | torch.Tensor): Rotation matrix

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

Initialize parameters for KPConvDeformable. :param kernel_size: Number of kernel points. :param p_dim: dimension of the point

Extract features of images.

Loss function. Args: gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape

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

Scale the points with horizontal and vertical scaling factors. Args: scale_factors (float): Scale factors to scale the points.

Apply dynamic voxelization to points. Args: points (list[torch.Tensor]): Points of each sample. Returns: tuple

Build backbone.

Build backbone.

Extract features from images and points.

implementation of a custom gather operation for faster backwards. :param x: input with shape [N, D_1, ... D_d] :param idx: indexing with

Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical

Evaluation for a single model in nuScenes protocol. Args: result_path (str): Path of the result file. logger (loggi

Convert the results to the standard format. Args: results (list[dict]): Testing results of the dataset. jsonfile_pr

Removes point too close within a certain radius from origin. Args: points (np.ndarray | :obj:`BasePoints`): Sweep points.

Extract features from images and points.

Extract features from images and points.

Extract features from images and points.

Format the results to json (standard format for COCO evaluation). Args: results (list[dict]): Testing results of the dataset.

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

Default initialization for Parameters of Module.

Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical

Pools features with the maximum values. :param x: [n1, d] features matrix :param inds: [n2, max_num] pooling indices :return: [n2, d]

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

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

Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Generate the train/val infos from the raw data. Args: nusc (:obj:`NuScenes`): Dataset class in the nuScenes dataset. train_scenes

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Pad images according to ``self.size``.

points: [N, 18]

Private function to randomly set the scale factor. Args: input_dict (dict): Result dict from loading pipeline. Retu

Private function to randomly set the scale factor. Args: input_dict (dict): Result dict from loading pipeline. Returns:

Private function to rotate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Return

Private function to rotate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. R

Private function to scale bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Returns

Private function to scale bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Re

Private function to translate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Ret

Private function to translate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline.

Computes one-to-one matching based on the weighted costs. This method assign each query prediction to a ground truth or background.

Test function of point cloud branch with augmentaiton. The function implementation process is as follows: - step 1: map features b

Test function of point cloud branch with augmentaiton. The function implementation process is as follows: - step 1: map features

Test function of point cloud branch with augmentaiton. The function implementation process is as follows: - step 1: map features

Test function of point cloud branch with augmentaiton. The function implementation process is as follows: - step 1: map features

Pools features from the closest neighbors. WARNING: this function assumes the neighbors are ordered. :param x: [n1, d] features matrix :p

Convert self to ``dst`` mode. Args: dst (:obj:`CoordMode`): The target Point mode. rt_mat (np.ndarray | torch.Tensor,

generate relative box corners based on length per dim and origin point. Args: dims (float array, shape=[N, ndim]): array of length

Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of

Decode bboxes. Args: all_cls_scores (Tensor): Outputs from the classification head, \ shape [nb_dec, bs, num_qu

Decode bboxes. Args: cls_scores (Tensor): Outputs from the classification head, \ shape [num_query, cls_out_cha

Decode bboxes. Args: cls_scores (Tensor): Outputs from the classification head, \ shape [num_query, cls_out_cha

Evaluation in nuScenes protocol. Args: results (list[dict]): Testing results of the dataset. metric (str | list[str

Extract point and image features of multiple samples.

Extract point and image features of multiple samples.

Extract point and image features of multiple samples.

Extract features of points.

Extract features of points.

Extract features of points.

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch img_feat

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bbox

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bbox

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bbox

Generate one 2D annotation record given various informations on top of the 2D bounding box coordinates. Args: ann_rec (dict): Original

Get the 2D annotation records for a given `sample_data_token`. Args: sample_data_token (str): Sample data token belonging to a camera \

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

Compute regression and classification targets for a batch image. Outputs from a single decoder layer of a single feature level are used.

Block performing a global average over batch pooling :param x: [N, D] input features :param batch_lengths: [B] list of batch lengths

Initialize the kernel point positions in a sphere :return: the tensor of kernel points

Initialize layers of the DeformableDetrTransformer.

Initialize the transformer weights.

Convert the box from ego to global coordinate. Args: info (dict): Info for a specific sample data, including the calibratio

Loss function for outputs from a single decoder layer of a single feature level. Args: cls_scores (Tensor): Box score l

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

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

Compute a radius gaussian (gaussian of distance) :param sq_r: input radiuses [dn, ..., d1, d0] :param sig: extents of gaussians [d1, d0]