Rotated box iou running in gpu. 500x faster than cpu version (take 5ms in one example with numba.cuda code). convert from [this project]( http

Rotation 2d points based on origin point clockwise when angle positive. Args: points (np.ndarray): Points to be rotated with shape \

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

Parse and save the images in png format. Args: frame (:obj:`Frame`): Open dataset frame proto. file_idx (int): Curren

save semantic image or map

save semantic image or map

Semantic Segmentation Evaluation. Evaluate the result of the Semantic Segmentation. Args: gt_labels (list[torch.Tensor]): Ground tr

Visualize the points cloud. Args: save_path (str): path to save image. Default: None.

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 of

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 of

Test model with single gpu. This method tests model with single gpu and gives the 'show' option. By setting ``show=True``, it saves the visua

Generate grid anchors of a single level feature map. This function is usually called by method ``self.grid_anchors``. Args:

Return if a tile is in current map_tile.

Return if an existing map tile is active in the dynamic map.

Apply hard voxelization to points.

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

Call function to apply noise to each ground truth in the scene. Args: input_dict (dict): Result dict from loading pipeline.

Call function to load multiple types annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. R

Call function to collect keys in results. The keys in ``meta_keys`` will be converted to :obj:`mmcv.DataContainer`. Args:

Call function to transform and format common fields in results. Args: results (dict): Result dict contains the data to convert.

Initialize a DetectionEval object. :param nusc: A NuScenes object. :param config: A DetectionConfig object. :param re

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.

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.

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.

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.

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: 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

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

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