Get ``num_points`` most uncertain points with random points during train. Sample points in [0, 1] x [0, 1] coordinate space based on their

Estimate uncertainty based on pred logits. We estimate uncertainty as L1 distance between 0.0 and the logits prediction in 'mask_pred' for th

Get the valid radios of feature maps of all level.

Vectorize map elements. Iterate over the input dict and apply the specified sample funcion. Args: line (LineString): lin

Checks if a file is an allowed extension. Args: filename (string): path to a file Returns: bool: True if the filename ends wit

build confusion matrix # empty classes:0 non-empty class: 0-16 free voxel class: 17 Args: n_cl (

Sample from face model using caching. Args: context: Dictionary of context, including 'vertices' and 'vertices_mask'.

num_samples_batch: number of sample per batch (batch size)

Initialize layers of the DeformableDetrTransformer.

Initialize layers of the DeformableDetrTransformer.

Initialize layers of the Detr3DTransformer.

Initialize layers of the Detr3DTransformer.

Initialize layers of the Detr3DTransformer.

Initialize model weights.

Initialize the weights in detector. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to No

Initialize weights of the DeformDETR head.

Initialize weights of the DeformDETR head.

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Compute whether detected lines are true positive or false positive. Args: pred_lines (List): Detected lines of a sample, each line has sh

Interpolate a line at fixed interval. Args: line (LineString): line sample_dist (float): sample interval Returns: p

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

inverse project Args: cam_feat: B*ncam, C, cH, cW img_shape: tuple(H, W) Returns:

judge if this is a zip path

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

Load annotations from ann_file. Args: ann_file (str): Path of the annotation file. Returns: list[dict]: List

Loads ground truth boxes from DB. :param nusc: A NuScenes instance. :param eval_split: The evaluation split for which we load GT boxes.

Loss function. Only outputs from the last feature level are used for computing losses by default. Args: all_cls_

Loss function. Args: all_cls_scores (Tensor): Classification scores for all decoder layers with shape [num_decode

Loss function. Args: all_cls_scores (Tensor): Classification scores for all decoder layers with shape [num_decode

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 log

Convert keys in checkpoints for VoteNet. There can be some breaking changes during the development of mmdetection3d, and this tool is used fo

Convert keys in checkpoints for VoteNet. There can be some breaking changes during the development of mmdetection3d, and this tool is used fo