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

Forward logic of the box prediction branch. If `self.train_on_pred_boxes is True`, the function puts predicted boxes in the `prop

Args: features, targets: the same as in Same as in :meth:`ROIHeads.forward`. proposals (list[Instance

Gather feature map. Given feature map and index, return indexed feature map. Args: feat (torch.tensor): Feature map with the shape o

Use `from_config` to obtain explicit arguments. Returns: dict: arguments to be used for cls.__init__

Return the channel number of classification outputs.

Return the channel number of classification outputs.

Helper for getting the lanes connected to a given lane :param lane_token: Token for the lane. :param incoming_outgoing: Wheth

Return a process group based on gloo backend, containing all the ranks The result is cached.

Compute the equation of the plane. Args: vector1 (torch.Tensor): Parallel vector of the plane. vector2 (torch.Tensor)

Get reference point to calculate affine transform matrix. While using opencv to calculate the affine matrix, we need at least three c

Return the channel number of regression outputs.

Return the channel number of regression outputs.

Return the learning rate warmup factor at a specific iteration. See :paper:`ImageNet in 1h` for more details. Args: method (str)

A context which ignores CUDA OOM exception from pytorch.

Returns a dict of records corresponding to the layer name. :param layer_name: Name of the layer that will be loaded. :return:

Internal function used to log the usage of different detectron2 components inside facebook's infra.

["abc.zfg", "abc.zef"] -> "abc."

Make fully connect layers. Args: fc_cfg (dict): Config of fully connect. input_channels (int): Input channels for fc_

Mask head forward function used in both training and testing.

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

Prepare ._gts and ._dts for evaluation based on params :return: None

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

Randomly sample an img_scale according to ``ratio_range`` and ``multiscale_mode``. If ``ratio_range`` is specified, a ratio will be sa

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

Based on the matching between N proposals and M groundtruth, sample the proposals and set their classification labels. Args:

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

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

Randomly sample a subset of positive and negative examples, and overwrite the label vector to the ignore value (-1) for all elements

Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions.

Get indexes based on scores. Args: scores (torch.Tensor): scores with the shape of [B, N, W, H]. K (int, optional): N

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

Adds a list of boxes.

Add config for DDETRSVLUni.

Call `add_ground_truth_to_proposals_single_image` for all images. Args: gt(Union[List[Instances], List[Boxes]): list of N elements.

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.

Applies Rotary Position Embedding to the query and key tensors.

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

Test function of point cloud branch with augmentaiton.

Performs non-maximum suppression in a batched fashion. Each index value correspond to a category, and NMS will not be applied between el

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

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

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

Returns the four bottom corners. :return: <np.float: 3, 4>. Bottom corners. First two face forward, last two face backwards.