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

Update map origin based on current position.

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

Generate relative box corners based on length per dim and origin point. Args: dims (np.ndarray, shape=[N, ndim]): Array of length per dim

Given the raw data, generate the ground truth database. Args: dataset_class_name （str): Name of the input dataset. data_path (str

Encode points with backbone and decode into a semantic segmentation map of the same size as input. Args: points (torch.Te

Extract bounding boxes from 2d detector. Args: img (torch.Tensor): of shape (N, C, H, W) encoding input images. T

Extract features from images and points.

Extract features of points.

Flip the boxes in BEV along given BEV direction. In LIDAR coordinates, it flips the y (horizontal) or x (vertical) axis. Args:

Format the results to pkl file. Args: outputs (list[dict]): Testing results of the dataset. pklfile_prefix (str | Non

Format the results to pkl file. Args: outputs (list[dict]): Testing results of the dataset. pklfile_prefix (str | Non

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

Generate voxels given points.

Get anchors according to feature map sizes. Args: featmap_sizes (list[tuple]): Multi-level feature map sizes. input_m

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:

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 infos. This method gets information from the raw data. Args: num_workers (int): Number of threads to be used. D

Get data infos. This method gets information from the raw data. Args: num_workers (int): Number of threads to be used. D

KITTI annotation format version 2: { [optional]points: [N, 3+] point cloud [optional, for kitti]image: { image_id

KITTI annotation format version 2: { [optional]points: [N, 3+] point cloud [optional, for kitti]image: { image_id

Return current map boundary in map frame.

Get the class of points according to coordinate type. Args: points_type (str): The type of points coordinate. The valid value

Generate targets of vote head. Args: points (list[torch.Tensor]): Points of each batch. gt_bboxes_3d (list[:obj:`Base

Generate targets of proposal module. Args: points (list[torch.Tensor]): Points of each batch. gt_bboxes_3d (list[:obj

Waymo annotation format version like KITTI: { [optional]points: [N, 3+] point cloud [optional, for kitti]image: {

Indoor Evaluation. Evaluate the result of the detection. Args: gt_annos (list[dict]): Ground truth annotations. dt_annos (li

Inference point cloud with the multi-modality detector. Args: model (nn.Module): The loaded detector. pcd (str): Point cloud file

Loss function. Args: gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in

Make divisible function. This function rounds the channel number to the nearest value that can be divisible by the divisor. It is taken from t

Find boxes that are non-empty. A box is considered empty, if either of its side is no larger than threshold. Args:

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

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

Plot the boundary lines of 3D rectangular on 2D images. Args: img (numpy.array): The numpy array of image. num_rects (int): Numbe

Find points that are in boxes (CUDA) Args: points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR coordinate boxes (torch.Tensor):

Find points that are in boxes (CUDA) Args: points (torch.Tensor): [B, M, 3], [x, y, z] in LiDAR coordinate boxes (torch.Tensor):

Initialization before data preparation. Args: results (dict): Dict before data preprocessing. - img_fields (list

Render the local map with a small number of map tiles

Sample features from single level image feature map. Args: img_feats (torch.Tensor): Image feature map in shape (

r"""Randomly sample seeds from all imvotes. Modified from `<https://github.com/facebookresearch/imvotenet/blob/a8856345146bacf29a57266a2f0b874406

Test without augmentation, stage 2. Args: points (list[torch.Tensor], optional): Elements in the list should have

RPN test function.

Convert the model into training mode while keep normalization layer freezed.

Project the semantic point cloud on the BEV map Args: map: np.ndarray with shape (H, W, C). H is the height, W is the wi

`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

`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

`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

Compute assignment by checking whether point is inside bbox. Args: bboxes_3d (BaseInstance3DBoxes): Instance of bounding boxes.

Forward function of roi_extractor and bbox_head used in both training and testing. Args: seg_feats (torch.Tensor): Point-

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

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

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

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

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

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

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