int: Number of points in the current object.

Assign and sample proposals for training. Args: proposal_list (list[dict]): Proposals produced by RPN. gt_bboxes_3d (

Run forward function and calculate loss for auxiliary head in training.

Forward training function of roi_extractor and bbox_head. Args: seg_feats (torch.Tensor): Point-wise semantic features.

Build the default pipeline for this dataset.

Build the default pipeline for this dataset.

Build the default pipeline for this dataset.

Make ann_files as list/tuple.

Check if rotation matrix is valid for self.rotation_axis. Args: rot_mat (np.ndarray): Rotation matrix to be applied.

Make scene_idxs as list/tuple.

Convert class_id in loaded segmentation mask to label.

Convert class_id in loaded segmentation mask to label.

Run forward function and calculate loss for decode head in inference.

Run forward function and calculate loss for decode head in training.

Draw bbox on visualizer and change the color or points inside bbox3d with indices. Args: bbox3d (numpy.array | torch.tensor, shape=[M

Load data using input pipeline and extract data according to key. Args: index (int): Index for accessing the target data.

Load data using input pipeline and extract data according to key. Args: index (int): Index for accessing the target data.

Extract inputs from features dictionary. Args: feat_dict (dict): Feature dict from backbone. Returns: torch.

Extract inputs from features dictionary. Args: feat_dict (dict): Feature dict from backbone. Returns: torch.

Extract inputs from features dictionary. Args: feat_dict (dict): Feature dict from backbone. Returns: list[t

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

Generate the train/val infos from the raw data. Args: lyft (:obj:`LyftDataset`): Dataset class in the Lyft dataset. train_scenes

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

Gather feature map. Given feature map and index, return indexed feature map. Args: feat (torch.tensor): Feature map with

Gather feature map. Given feature map and index, return indexed feature map. Args: feat (torch.tensor): Feature map with

Gather feature map. Given feature map and index, return indexed feature map. Args: feat (torch.tensor): Feature map with

Return an activation function given a string

Return an activation function given a string

Return an activation function given a string

Return an activation function given a string

Return an activation function given a string

Transform outputs for a single batch item into bbox predictions. Args: cls_scores (list[Tensor]): Box scores for a single scale l

Return the channel number of classification outputs.

Find the predefined detector config directory.

Find the predefined detector config directory.

Find the predefined detector config directory.

Find the predefined detector config directory.

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 model. These are deep copied to allow for safe modification of parameters without influencing other tests.

Grab configs necessary to create a parta2_bbox_head. These are deep copied to allow for safe modification of parameters without influencing o

Get data loading pipeline in self.show/evaluate function. Args: pipeline (list[dict] | None): Input pipeline. If None is given, \

Get data loading pipeline in self.show/evaluate function. Args: pipeline (list[dict] | None): Input pipeline. If None is given, \

Get points of a single scale level.

Get point cloud filename according to the given index. Args: index (int): Index of the point cloud file to get. Returns:

Return the channel number of regression outputs.

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

Load annotations from ann_file. Args: ann_file (str): Path of the annotation file. Returns: list[dict]: List

r""" Performs the in-projection step of the attention operation. This is simply a triple of linear projections, with shape constraints on th

r""" Performs the in-projection step of the attention operation, using packed weights. Output is a triple containing projection tensors for

Initialize the target assigner and sampler of the head.

Initialize ``auxiliary_head``

Initialize classification conv layers of the head.

Initialize ``decode_head``

Initialize layers of the head.

Initialize neural network layers of the head.

Initialize predictor layers of the head.

Initialize bbox regression conv layers of the head.

Generating model input. Generate input by subtracting patch center and adding additional \ features. Currently support colors and

Generating model input. Generate input by subtracting patch center and adding additional \ features. Currently support colors and

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Returns

Private function to load 3D bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

Private function to load 2.5D bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Returns

Private function to load 3D mask annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Retur

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

Private function to load 3D semantic segmentation annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset

Patch points sampling. First sample a valid patch. Then sample points within that patch to a certain number. Args:

Args: points (np.ndarray): Input points with shape of (num_points, 3). polygon_surfaces (np.ndarray): Polygon surfaces with shape

convert kitti points(N, >=3) to voxels. Args: points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \ points[:

convert kitti points(N, >=3) to voxels. Args: points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \ points[:

Randomly get another item with the same flag. Returns: int: Another index of item with the same flag.

Randomly get another item with the same flag. Returns: int: Another index of item with the same flag.

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

Reset the index of batchsampler to zero.