Format prediction result to submission format. Args: results (list[Tensor]): List of prediction results. denormalize

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

r""" Retuens traffic element annotations of the current frame. Returns ------- list [{'id': int, 'categor

Transform network outputs for a batch into bbox predictions. Args: all_cls_scores (Tensor): Classification score of all

Provides the default colors based on the category names. This method works for the general nuScenes categories, as well as the nuScenes detec

r""" Retuens the extrinsic given a camera. Parameters ---------- camera : str Returns -------

Inverse function of sigmoid. Args: x (Tensor): The tensor to do the inverse. eps (float): EPS avoid numerical

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

Training data preparation. Args: index (int): Index for accessing the target data. Returns: dict: Tra

Renders various PR and TP curves. :param metrics: DetectionMetrics instance. :param md_list: DetectionMetricDataList instance

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

Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO evaluation. Args: bbox (numpy.ndarray): The bounding boxe

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

DCNv3 Module :param channels :param kernel_size :param stride :param pad :param dilation :par

r""" Calculate mAP over distance thresholds. Parameters ---------- gts : dict Dict storing ground truth for all samples.

Assign boxes to either a ground truth boxes or a negative boxes.

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

Encode images with backbone and decode into a semantic segmentation map of the same size as input.

Encode images with backbone and decode into a semantic segmentation map of the same size as input.

Encode bitmap mask to RLE code. Args: mask_results (list | tuple[list]): bitmap mask results. In mask scoring rcnn, mask_resu

Extract features from images and points.

Extract features from images and points.

Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict wher

Forward function for training. Args: img (Tensor): Input images. img_metas (list[dict]): List of image info dict wher

r""" Retuens a list of camera names. Returns ------- list A list of str.

r""" Returns a frame with the given identifier (split, segment_id, timestamp). Parameters ---------- identifier : tup

Get data infos. This method gets information from the raw data. Args: num_workers (int, optional): Number of threads to

KITTI annotation format version 2: { [optional]points: [N, 3+] point cloud [optional, for kitti]image: { image_id

Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image i

Inference with slide/whole style. Args: img (Tensor): The input image of shape (N, 3, H, W). img_meta (dict): Image i

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

r''' Linearly interpolate equally-spaced points along a polyline, either in 2d or 3d. Parameters ---------- points : List Lis

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

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

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

r""" Calculate pairwise distance. Parameters ---------- xs : list List of data in shape (X, ). ys : list List of

r""" Parameters ---------- path : str Returns ------- object

Returns: pred_dist: Categorical predictive distribution with batch shape [batch_size, seq_length].

Transform outputs from the last decoder layer into bbox predictions for each image. Args: cls_score (Tensor): Box score l