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

Get radius of gaussian. Args: det_size (tuple[torch.Tensor]): Size of the detection result. min_overlap (float): Gaussian_overlap

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:

Args: points: (N, 3 + (f)) [bxyz + (f)] voxel_idxs: (N, 4) [bxyz] num_points_in_voxel: (N) Return

Args: points: (N, 3 + (f)) [bxyz + (f)] voxel_idxs: (N, 4) [bxyz] num_points_in_voxel: (N) Return

Args: points: (N, 3 + (f)) [bxyz + (f)] voxel_idxs: (N, 4) [bxyz] num_points_in_voxel: (N) Return

Args: points: (N, 3 + (f)) [bxyz + (f)] voxel_idxs: (N, 4) [bxyz] num_points_in_voxel: (N) Return

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

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

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

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

Get root logger and add a keyword filter to it. The logger will be initialized if it has not been initialized. By default a StreamHandler wil

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

r""" Args: query, key, value: map a query and a set of key-value pairs to an output. See "Attention Is All You Need" for mo

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

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

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

Sampling all categories of bboxes. Args: gt_bboxes (np.ndarray): Ground truth bounding boxes. gt_labels (np.ndarray):

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.

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 Lyft protocol. Args: result_path (str): Path of the result file. logger (logging.Log

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

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

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

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

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

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

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

Return the channel number of classification outputs.

Find the predefined detector config directory.

Load a configuration as a python module.

Load a configuration as a python module.

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

Compute the equation of the plane. Args: vector1 (torch.Tensor): Parallel vector of the plane. vector2 (torch.Tensor)

Return the channel number of regression outputs.

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

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