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

Args: input_ch(int) : the number of input channel out_features (list[str]): name of the layers whose outputs should

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

Merge augmented detection 3D bboxes and scores. Args: aug_results (list[dict]): The dict of detection results. The dict conta

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

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

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

Computes one-to-one matching based on the weighted costs. This method assign each query prediction to a ground truth or background. Th

1x1 convolution with padding

3x3 convolution with padding

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

Decode bboxes. Args: all_cls_scores (Tensor): Outputs from the classification head, \ shape [nb_dec, bs, num_query

Extract features from images and points.

Extract features from images and points.

Extract features from images and points.

Extract features from images and points.

Extract features of images.

Extract features of images.

Extract features of images.

Extract features of points.

Extract features of points.

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

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

Default initialization for Parameters of Module.

Flip 3D data randomly. Args: input_dict (dict): Result dict from loading pipeline. direction (str): Flip direction. D

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

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

Pad images according to ``self.size``.

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

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

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

Test function with augmentaiton.

Test function with augmentaiton.

Test function with augmentaiton.

Test function with augmentaiton.

Test function of point cloud branch with augmentaiton.

Test function of point cloud branch with augmentaiton.

Test function of point cloud branch with augmentaiton.

Test function of point cloud branch with augmentaiton.

Map bboxes from testing scale to original image scale. Args: bboxes (:obj:`BaseInstance3DBoxes`): Boxes to be mapped back. scale_

Decode bboxes. Args: cls_scores (Tensor): Outputs from the classification head, \ shape [num_query, cls_out_channe

3x3 convolution with padding

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

Extract point and image features of multiple samples.

Extract point and image features of multiple samples.

Extract point and image features of multiple samples.

Extract point and image features of multiple samples.

Extract features of images.

Extract features of points.

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxe

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxe

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxe

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxe

Forward training function. Args: points (list[torch.Tensor], optional): Points of each sample. Defaults to None.

Forward training function. Args: points (list[torch.Tensor], optional): Points of each sample. Defaults to None.

Forward training function. Args: points (list[torch.Tensor], optional): Points of each sample. Defaults to None.

Forward training function. Args: points (list[torch.Tensor], optional): Points of each sample. Defaults to None.

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

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.

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

Compute regression and classification targets for a batch image. Outputs from a single decoder layer of a single feature level are used.

Compute regression and classification targets for a batch image. Outputs from a single decoder layer of a single feature level are used.

Default initialization for Parameters of Module.

Initialize weights of the depth head.

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

Loss function for outputs from a single decoder layer of a single feature level. Args: cls_scores (Tensor): Box score logi

Loss function for outputs from a single decoder layer of a single feature level. Args: cls_scores (Tensor): Box score logi

make encoder layers using sparse convs. Args: make_block (method): A bounded function to build blocks. norm_cfg (dict

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