Generate grid anchors in multiple feature levels. Args: featmap_sizes (list[tuple]): List of feature map sizes in

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

Compute loss. Args: bbox_preds (dict): Predictions from forward of h3d bbox head. points (list[torch.Tensor]): Input

Match points to plane. Args: plane (torch.Tensor): Equation of the plane. points (torch.Tensor): Points of input.

Rotate boxes with points (optional) with the given angle or \ rotation matrix. Args: angles (float | torch.Tensor | np.nd

Rotate boxes with points (optional) with the given angle or \ rotation matrix. Args: angle (float | torch.Tensor | np.nda

mPtr[((i1 * mShape[1] + i2) * mShape[2] + i3) * mShape[3] + i4];

Sample specific number of ground truths. Args: num (int): Sampled number. Returns: list[dict]: Sampled groun

Convert results into format that is directly readable for meshlab. Args: points (np.ndarray): Points. gt_seg (np.ndarray): Ground

Split predicted features to specific parts. Args: cls_preds (torch.Tensor): Class predicted features to split. reg_pr

Initialize conv layers as a prediction branch.

Assign anchors and encode positive anchors. Args: bbox_assigner (BaseAssigner): assign positive and negative boxes. a

Multi-class nms for 3D boxes. Args: mlvl_bboxes (torch.Tensor): Multi-level boxes with shape (N, M). M is the dimensions of b

Build segmentor.

Extract features from images and points.

Extract features from images and points.

Extract features of multiple samples.

Format the results to json (standard format for COCO evaluation). Args: results (list[tuple | numpy.ndarray]): Testing results o

Limit the value into a period for periodic function. Args: val (torch.Tensor): The value to be converted. offset (float, optional

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory of

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory

Args: input_ch(int) : the number of input channel out_features (list[str]): name of the layers whose outputs should

Generate targets of line primitive. Args: point_mask (torch.Tensor): Tensor to store the ground truth of mask.

Draw points on visualizer. Args: points (numpy.array | torch.tensor, shape=[N, 3+C]): points to visualize. vis (:obj:

Repeat x `num` times to form a list.

Given feats and indexes, returns the gathered feats. Args: feats (torch.Tensor): Features to be transposed and gathered

Grab configs necessary to create a segmentor. These are deep copied to allow for safe modification of parameters without influencing other te

Write points into ``obj`` format for meshlab visualization. Args: points (np.ndarray): Points in shape (N, dim). out_filename (st

3d nms for aligned boxes. Args: boxes (torch.Tensor): Aligned box with shape [n, 6]. scores (torch.Tensor): Scores of each box.

Convert 3D detection results to kitti format for evaluation and test submission. Args: net_outputs (list[np.ndarray]): Li

Convert points in camera coordinate to lidar coordinate. Args: points (np.ndarray, shape=[N, 3]): Points in camera coordinate. r_

Collect the information of the running environments.

torch.Tensor: a tensor with 8 corners of each box.

Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP. Args: gt_annos (dict): Must from get_label_annos() in kitti_common

Directly extract features from the img backbone+neck.

Forward function for training. Args: points (list[torch.Tensor]): List of points of shape [N, C]. img_metas (list): I

Provides the default colors based on the category names. This method works for the general nuScenes categories, as well as the nuScenes det

Only keep loading image, points and annotations related configuration. Args: pipeline (list[dict] | list[:obj:`Pipeline`]): D

Compute surface and line center of bounding boxes. Returns: torch.Tensor: Surface and line center of bounding boxes.

Initialize weights of shared MLP layers and BN layers.

Match points to corresponding line. Args: points (torch.Tensor): Points of input. corners (torch.Tensor): Eight corne

Multi-class nms in single batch. Args: obj_scores (torch.Tensor): Objectness score of bounding boxes. sem_scores (tor

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

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

Obtain image features using points. Args: img_meta (dict): Meta info. img_features (torch.Tensor): 1 x C x H x W image features.

Renders various PR and TP curves. :param metrics: DetectionMetrics instance. :param md_list: DetectionMetricDataList instance

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory of

`example` type: <class 'dict'> keys: 'img_metas', 'gt_bboxes_3d', 'gt_labels_3d', 'img'; all keys type is 'Data

`example` type: <class 'dict'> keys: 'img_metas', 'gt_bboxes_3d', 'gt_labels_3d', 'img'; all keys type is 'Data

Add shared or separable branch.

Create a superset of inputs needed to run test or train batches. Args: input_shape (tuple): input batch dimensions n

Draw bbox on visualizer and change the color of points inside bbox3d. Args: bbox3d (numpy.array | torch.tensor, shape=[M, 7]):

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

Get axis_align_matrix from info. If not exist, return identity mat. Args: info (dict): one data info term. Returns:

Grab configs necessary to create a bbox_head. These are deep copied to allow for safe modification of parameters without influencing other te

Grab configs necessary to create a pts_bbox_head. These are deep copied to allow for safe modification of parameters without influencing othe

Grab configs necessary to create a vote_head. These are deep copied to allow for safe modification of parameters without influencing other te

Convert the rotation difference to difference in sine function. Args: boxes1 (torch.Tensor): Original Boxes in shape (NxC), where

Generate anchors in a single range. Args: feature_size (list[float] | tuple[float]): Feature map size. It is eith

Convert continuous angle to a discrete class and a residual. Convert continuous angle to a discrete class and a small regression numb

Convert 2D detection results to kitti format for evaluation and test submission. Args: net_outputs (list[np.ndarray]): Li

Convert a list of bounding boxes to roi format. Args: bbox_list (list[torch.Tensor]): A list of bounding boxes corresponding

Calculate nearest 3D IoU. Note: This function first finds the nearest 2D boxes in bird eye view (BEV), and then calculates the 2D

Convert kitti locations, dimensions and angles to corners. format: center(xy), dims(xy), angles(clockwise when positive) Args: center

Inverse function to angle2class. Args: angle_cls (torch.Tensor): Angle class to decode. angle_res (torch.Tensor): Ang