A function warpper for building 3D detector or segmentor according to cfg. Should be deprecated in the future.

Generate bboxes from bbox head predictions. Args: preds_dicts (tuple[list[dict]]): Prediction results. img_metas (lis

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

input_data (dict): sensors' data - "imgs" - "map_mask"

Get data info according to the given index. Args: index (int): Index of the sample data to get. Returns: dic

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

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

Parse the raw outputs (losses) of the network. Args: losses (dict): Raw output of the network, which usually contain

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

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

Get the 2D annotation records for a given `sample_data_token`. Args: sample_data_token (str): Sample data token belonging to a camera \

Get available scenes from the input nuscenes class. Given the raw data, get the information of available scenes for further info generation.

The loss() interfaces of the heads in the mmdetection3d are not unified. This is an adaptor.

Prepare data related to Lyft dataset. Related data consists of '.pkl' files recording basic infos. Although the ground truth database and 2D

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

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_siz

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

Get map mask in nuscenes dataset Args: loc (list[float]): global location of the lidar. (x,y) angle (float):

Get map mask in nuscenes dataset Args: loc (list[float]): global location of the lidar. (x,y) angle (float):

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.

Private function to rotate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Return

Sample specific number of ground truths and return indices. Args: num (int): Sampled number. Returns: list[i

Private function to scale bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Returns

Private function to translate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Ret

Evaluation in nuScenes protocol. Args: results (list[dict]): Testing results of the dataset. metric (str | list[str],

TODO: surpport test time augmentation.

Generate one 2D annotation record given various informations on top of the 2D bounding box coordinates. Args: ann_rec (dict): Origina

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:

Rotate nms for each task. Args: num_class_with_bg (int): Number of classes for the current task. batch_cls_preds (lis

L2 loss. Args: pred (torch.Tensor): The prediction. target (torch.Tensor): The learning target of the prediction. Returns:

Convert the box from ego to global coordinate. Args: info (dict): Info for a specific sample data, including the calibration

Evaluation API for Lyft dataset. Args: lyft (:obj:`LyftDataset`): Lyft class in the sdk. data_root (str): Root of data for readin

Make divisible function. This function rounds the channel number down to the nearest value that can be divisible by the divisor. Args:

Convert the output to the box class in the nuScenes. Args: detection (dict): Detection results. - boxes_3d (:obj:`BaseInstan

Get the intersection of the convex hull of the reprojected bbox corners and the image canvas, return None if no intersection. Args: c

Sampling all categories of bboxes. Args: gt_info (dict): - gt_bboxes (np.ndarray): Ground truth bou

Sampling specific categories of bounding boxes. Args: name (str): Class of objects to be sampled. num (int): Number o