Binary Lovasz hinge loss logits: [P] Variable, logits at each prediction (between -\infty and +\infty) labels: [P] Tensor, binary gro

Multi-class Lovasz-Softmax loss probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1) labels: [P] Tensor,

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

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

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

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

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

iou = TP / (TP + FP + FN).

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

Prepare data for testing. Args: index (int): Index for accessing the target data. Returns: dict: Test

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

:param points: :param colors: false # do not show the color of point :param points_colors: :param bbox3d: voxel boundary Nx7 (center

Test function of point cloud branch.

Convert the model into training mode while keep normalization layer freezed.

Data preparation for voxel pooling. Args: coor (torch.tensor): Coordinate of points in the lidar space in shape (

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

Build the default pipeline for this dataset.

Convert class_id in loaded segmentation mask to label.

Convert class_id in loaded segmentation mask to label.

Load data using input pipeline and extract data according to key. Args: index (int): Index for accessing the target data.

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: 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

Forward function for feature maps before classifying each pixel with ``self.cls_seg`` fc. Args: inputs (list[Tensor]): Lis

Get data loading pipeline in self.show/evaluate function. Args: pipeline (list[dict] | None): Input pipeline. If None is given,

Check and initialize input transforms. The in_channels, in_index and input_transform must match. Specifically, when input_transfor

Pad images according to `self.size`.

Test function without augmentaiton.

Test function of point cloud branch with augmentaiton. The function implementation process is as follows: - step 1: map features

Build data config for loading visualization data.

Convert transformation matrix in Cartesian coordinates to homogeneous format. Args: mat (np.ndarray): Transformation matr

Check if a box is visible inside an image without accounting for occlusions. :param box: The box to be checked. :param intrinsic: <float:

Plot the true positive curve for the specified class. :param md_list: DetectionMetricDataList instance. :param metrics: DetectionMetrics

Combine predictions in waymo format for each sample together. Args: pathnames (str): Paths to save predictions. Returns:

Calculate count matrix. Args: voxel_semantics (torch.Tensor): semantic occpuancy ground truth. voxel_sema

Calculate count matrix. Args: voxel_semantics (torch.Tensor): semantic occpuancy ground truth. voxel_sema

Calculate count matrix. Args: voxel_semantics (torch.Tensor): semantic occpuancy ground truth. voxel_semantic

Convert keys in pycls pretrained RegNet models to mmdet style.

Convert action.

Convert the boxes into valid format. Args: box_dict (dict): Bounding boxes to be converted. - boxes_3d (:obj:``L

Create folder for data preprocessing.

Create folder for data conversion.

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

Generate the grid information including the lower bound, interval, and size. Args: x (tuple(float)): Config of grid alone

Encode bitmap mask to RLE code. Semantic Masks only Args: mask_results (list | tuple[list]): bitmap mask results. In mask s

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

Initialize from serialized content.

pcl: ego frame

3x3 convolution with padding

Extract features of images. Args: img (torch.Tensor): Image tensor with shape (bs, n_views, C, H, W).

Extract image featrues from the input image and some image metas. Args: img_inputs (List[torch.Tensor]): With input image

Extract features of images. Args: img (torch.Tensor): Image tensor with shape (bs, n_views, C, H, W).

Extract features of images.