Calculate 3D IoU using cuda implementation. Note: This function calculates the IoU of 3D boxes based on their volumes. IoU calcul

Transform 3D boxes. Args: boxes (np.ndarray): 3D boxes to be transformed. loc_transform (np.ndarray): Location transform to be ap

Covert boxes in camera coordinate to lidar coordinate. Args: data (np.ndarray, shape=[N, 7]): Boxes in camera coordinate. r_rect

Build data config for loading visualization data.

Build data config for loading visualization data.

Build middle level encoder.

Build roi layers using `layer_cfg`

Calculate the Euclidian distance between two sets of points. Args: xyz1 (torch.Tensor): (N, 3), the first set of points. xyz2 (to

Fast iou algorithm. this function can be used independently to do result analysis. Must be used in CAMERA coordinate system. Args: gt

Convert transformation matrix in Cartesian coordinates to homogeneous format. Args: mat (np.ndarray): Transformation matr

Convert transformation matrix in Cartesian coordinates to homogeneous format. Args: mat (np.ndarray): Transformation matr

Check if a box is visible inside an image without accounting for occlusions. :param box: The box to be checked. :param intrinsic: <float:

Center to minmax. Args: centers (np.ndarray): Center points. dims (np.ndarray): Dimensions. origin (list or array or floa

Calculate Chamfer Distance of two sets. Args: src (torch.Tensor): Source set with shape [B, N, C] to calculate Chamfer Distan

Whether the mean of points to plane distance is lower than thresh. Args: plane_equ (torch.Tensor): Plane to be checked.

Check whether is a horizontal plane. Args: points (torch.Tensor): Points of input. Returns: Bool: Flag of re

Class agnostic nms for single batch. Args: mlvl_bboxes (torch.Tensor): Bboxes from Multi-level. mlvl_bboxes_for_nms (

Plot the true positive curve for the specified class. :param md_list: DetectionMetricDataList instance. :param metrics: DetectionMetrics

Sigmoid function for input feature. Args: x (torch.Tensor): Input feature map with the shape of [B, N, H, W]. eps (float): Lower

Combine predictions in waymo format for each sample together. Args: pathnames (str): Paths to save predictions. Returns:

Combine predictions in waymo format for each sample together. Args: pathnames (str): Paths to save predictions. Returns:

Compute loss of primitive module. Args: primitive_center (torch.Tensor): Pridictions of primitive center. primitive_s

Concat data_infos from several datasets to form self.data_infos. Args: data_infos (list[list[dict]])

Concat scene_idxs from several datasets to form self.scene_idxs. Needs to manually add offset to scene_idxs[1, 2, ...]. Args:

Convert keys in pycls pretrained RegNet models to mmdet style.

Convert keys in pycls pretrained RegNet models to mmdet style.

Convert action.

Convert boxes from `src` mode to `dst` mode. Args: box (tuple | list | np.ndarray | torch.Tensor | BaseInstance3D

Convert self to ``dst`` mode. Args: dst (:obj:`Box3DMode`): the target Box mode rt_mat (np.ndarray | torch.Tensor): T

Convert the predicted boxes into valid ones. Args: box_dict (dict): Box dictionaries to be converted. - boxes_3d

Convert the predicted boxes into valid ones. Args: box_dict (dict): Box dictionaries to be converted. - boxes_3d

Convert the boxes into valid format. Args: box_dict (dict): Bounding boxes to be converted. - boxes_3d (:obj:``L

Transform 2d pixel coordinates according to img_meta. Args: img_meta(dict): Meta info regarding data transformation. coord_2d (to

convert 3d box corners from corner function above to surfaces that normal vectors all direct to internal. Args: corners (np.ndarray):

Convert 3d box corners from corner function above to surfaces that normal vectors all direct to internal. Args: corners (np.ndarray):

Create info file of av2 dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of th

Create info file of av2 dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path of th

Create folder for data preprocessing.

Create folder for data preprocessing.

Create folder for data conversion.

Create folder for data conversion.

Generate the grid information including the lower bound, interval, and size. Args: x (tuple(float)): Config of grid alone

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

Encode bitmap mask to RLE code. Semantic Masks only Args: mask_results (list | tuple[list]): bitmap mask results. In mask s

Apply element-wise weight and reduce loss. Args: loss (Tensor): num_sample, num_dir weight (Tensor): Element-wise weights.

Apply element-wise weight and reduce loss. Args: loss (Tensor): num_sample, num_order, num_pts, num_coords weight (Tensor): Eleme

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

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

Forward of upsample and residual block. Args: x_lateral (:obj:`SparseConvTensor`): Lateral tensor. x_bottom (:obj:`Sp

Convert depth map to points. Args: depth (np.array, shape=[H, W]): Depth map which the row of [0~`trunc_pixel`] are truncated

Initialize from serialized content.

Compute rotated iou on device. Args: rbox1 (np.ndarray, shape=[5]): Rotated 2d box. rbox2 (np.ndarray, shape=[5]): Rotated 2d box

Get gaussian masked heatmap. Args: heatmap (torch.Tensor): Heatmap to be masked. center (torch.Tensor): Center coord of the heatm

Drop irrelevant ground truths by name. Args: gt_names (list[str]): Names of ground truths. used_classes (list[str]):

3x3 convolution with padding

Generic functions to compute precision/recall for object detection for a single class. Args: pred (dict): Predictions mapping from im