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 nuScenes protocol. Args: result_path (str): Path of the result file. logger (loggi

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

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

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_pr

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

Perform feature aggregation using pooling operation. Args: features (torch.Tensor): (B, C, N, K) Features of loca

Prepare input point pairs features for self.ScoreNet. Args: points_xyz (torch.Tensor): (B, 3, npoint, K) Coordina

Removes point too close within a certain radius from origin. Args: points (np.ndarray | :obj:`BasePoints`): Sweep points.

Removes point too close within a certain radius from origin. Args: points (np.ndarray | :obj:`BasePoints`): Sweep points.

Rotate 2D boxes. Args: corners (np.ndarray): Corners of boxes. angle (float): Rotation angle. rot_mat_T (np.ndarray): Tra

Perform point sampling based on inputs. If `indices` is specified, directly sample corresponding points. Else if `target_xyz` is spec

Sample points for each points subset. Args: points (np.ndarray): Points subset to be sampled. sampler (VoxelGenerator

Select transform. Args: transform (np.ndarray): Transforms to select from. indices (np.ndarray): Mask to indicate which transform

Set flag according to image aspect ratio. Images with aspect ratio greater than 1 will be set as group 1, otherwise group 0. In 3D da

Split coordinates and features of input points. Args: points (torch.Tensor): Point coordinates with features, wit

Export oriented (around Z axis) scene bbox to meshes. Args: scene_bbox(list[ndarray] or ndarray): xyz pos of center and 3 len

Add bounding box to visualizer. Args: bbox3d (numpy.array, shape=[M, 7]): 3D bbox (x, y, z, dx, dy, dz, yaw) to b

Add segmentation mask to visualizer via per-point colorization. Args: seg_mask_colors (numpy.array, shape=[N, 6]):

Compute regression and classification targets for anchors. Args: anchor_list (list[list]): Multi level anchors of each image.

Test function with augmentation, stage 2. Args: points (list[list[torch.Tensor]], optional): the outer list indic

Calculate average precision (for single or multiple scales). Args: recalls (np.ndarray): Recalls with shape of (num_scales, num_dets) \

Convert 3D detection results to kitti format for evaluation and test submission. Args: net_outputs (list[np.ndarray]): Li

Convert results to kitti format for evaluation and test submission. Args: net_outputs (List[np.ndarray]): list of array storing t

Convert 2D detection results to kitti format for evaluation and test submission. Args: net_outputs (list[np.ndarray]): Li

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

Box collision test. Args: boxes (np.ndarray): Corners of current boxes. qboxes (np.ndarray): Boxes to be avoid colliding.

Build RoI feature extractor.

Build voxel encoder.

Calculating square distance between a and b. Args: point_feat_a (Tensor): (B, N, C) Feature vector of each point. point_feat_b (T

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

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

Convert kitti locations, dimensions and angles to corners. Args: centers (np.ndarray): Locations in kitti label file with shape (N, 3).

Circular NMS. An object is only counted as positive if no other center with a higher confidence exists within a radius r using a bird-eye

Classify each points.

1x1 convolution with padding

3x3 convolution with padding

Convert action.

Convert config's naiveSyncBN to BN. Args: config (str or :obj:`mmcv.Config`): Config file path or the config object.

Convert range images to point cloud. Args: frame (:obj:`Frame`): Open dataset frame. range_images (dict): Mapping fro

Convert range images to point cloud. Args: frame (:obj:`Frame`): Open dataset frame. range_images (dict): Mapping fro

Convert self to ``dst`` mode. Args: dst (:obj:`Box3DMode`): The target Box mode. rt_mat (np.ndarray | torch.Tensor):

Convert self to ``dst`` mode. Args: dst (:obj:`CoordMode`): The target Point mode. rt_mat (np.ndarray | torch.Tensor)

Convert self to ``dst`` mode. Args: dst (:obj:`CoordMode`): The target Point mode. rt_mat (np.ndarray | torch.Tensor)

Convert self to ``dst`` mode. Args: dst (:obj:`CoordMode`): The target Point mode. rt_mat (np.ndarray | torch.Tensor)

Generate the frustum template for each image. Args: depth_cfg (tuple(float)): Config of grid alone depth axis in format

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

Test model with multiple gpus. This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu mo

each line with 1 meter width pred_lines: num_preds, List [npts, 2] gt_lines: num_gts, npts, 2 gt_mask: num_gts, npts,

A function wrapper for launching model training according to cfg. Because we need different eval_hook in runner. Should be deprecated in the

Decode center, size residuals and class to corners. Only useful for axis-aligned bounding boxes, so angle isn't considered. Args:

str: The device of the boxes are on.

Draw a polyline onto an image using given line segments. Args: line: Array of shape (K, 2) representing the coordinates of line. v

Enlarge the length, width and height boxes. Args: extra_width (float | torch.Tensor): Extra width to enlarge the box. Re

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

Extract image augmentation information from img_meta. Args: img_meta(dict): Meta info regarding data transformation. tensor(torch

Extract features from points.

Extract features from points.

Extract features from images and points.

Extract 3d features from the backbone -> fpn -> 3d projection. Args: img (torch.Tensor): Input images of shape (N, C_in, H, W).

Directly extract features from the backbone+neck. Args: points (torch.Tensor): Input points.

Applies filtering to boxes. basedon overlap . :param nusc: An instance of the NuScenes class. :param eval_boxes: An instance of the EvalB

Flip the boxes in BEV along given BEV direction. In Depth coordinates, it flips x (horizontal) or y (vertical) axis. Args:

Flip the boxes in BEV along given BEV direction.

Flip the boxes in BEV along given BEV direction.

Flip the boxes in BEV along given BEV direction.

Flip the boxes in BEV along given BEV direction.

Format the results to pkl file. Args: outputs (list[dict]): Testing results of the dataset. pklfile_prefix (str | Non

Format the results to json (standard format for COCO evaluation). Args: results (list[tuple | numpy.ndarray]): Testing results of