To support a custom dataset, implement this function to receive the predicted results from the model, and then transform the unified

Calculate average precision. Args: recalls: precisions: Returns (float): average precision. Returns:

Returns 3D bounding box for a vehicle based on camera view.

Get sorted list of class names. Args: gt: Returns: Sorted list of class names.

Args: point_centers: (N, 3) max_neighbour_distance: float num_voxels: [num_x, num_y, num_z] Retu

Loads depth map for a sample Args: idx: str, Sample index Returns: depth: (H, W), Depth map

Loads depth map for a sample Args: idx: str, Sample index Returns: depth: (H, W), Depth map

Compute the precision envelope. Args: precisions: Returns:

Valid point should be in the image (and in the PC_AREA_SCOPE) :param pts_rect: :param img_shape: :return:

Loads image for a sample Args: idx: int, Sample index Returns: image: (H, W, 3), RGB Image

Loads image for a sample Args: idx: int, Sample index Returns: image: (H, W, 3), RGB Image

Generate the dataset infos dict for each sample of the dataset. For each sample, this dict contains: - the sequence index

Return two lists, one for the model parameters in FP16 and one for the master parameters in FP32.

Args: rois: (N, 7) roi_labels: (N) gt_boxes: (N, ) gt_labels: Returns:

Get model Args: constructor: function, Model constructor Returns: model: nn.Module, Model

Returns the current plan of the local planner

Returns the data path as well as all annotations related to that sample_data. Note that the boxes are transformed into the current sensor's c

Args: batch_dict: Returns: keypoints: (N1 + N2 + ..., 4), where 4 indicates [bs_idx, x, y, z]

Get vehicle speed in km/h :return: vehicle speed in km/h