Transform outputs for a single batch item into bbox predictions. Args: cls_scores (list[Tensor]): Box scores for a single scale l

Retrieve the centerline of a particular layer within the specified patch. :param patch_box: Patch box defined as [x_center, y_cente

Return the channel number of classification outputs.

Find the predefined detector config directory.

Find the predefined detector config directory.

Find the predefined detector config directory.

Find the predefined detector config directory.

Find the predefined detector config directory.

Load a configuration as a python module.

Load a configuration as a python module.

Grab configs necessary to create a model. These are deep copied to allow for safe modification of parameters without influencing other tests.

Grab configs necessary to create a parta2_bbox_head. These are deep copied to allow for safe modification of parameters without influencing o

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

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

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

Get points of a single scale level.

Get point cloud filename according to the given index. Args: index (int): Index of the point cloud file to get. Returns:

Return the channel number of regression outputs.

Grab configs necessary to create a rpn_head. These are deep copied to allow for safe modification of parameters without influencing other tes

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

Initialize the target assigner and sampler of the head.

Initialize ``auxiliary_head``

Initialize classification conv layers of the head.

Initialize ``decode_head``

Initialize layers of the head.

Initialize neural network layers of the head.

Initialize classification branch and regression branch of head.

Initialize classification branch and regression branch of head.

Initialize ``loss_regularization``

Initialize predictor layers of the head.

Initialize bbox regression conv layers of the head.

Generating model input. Generate input by subtracting patch center and adding additional \ features. Currently support colors and

Generating model input. Generate input by subtracting patch center and adding additional \ features. Currently support colors and

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Returns

Private function to load 3D bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

Private function to load 2.5D bounding box annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`.

Private function to load label annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Returns

Private function to load 3D mask annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset`. Retur

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Private function to load point clouds data. Args: pts_filename (str): Filename of point clouds data. Returns:

Private function to load point clouds data. Args: lidar_path (str): Filename of point clouds data. Returns:

Private function to load point clouds data. Args: lidar_path (str): Filename of point clouds data. Returns:

Private function to load 3D semantic segmentation annotations. Args: results (dict): Result dict from :obj:`mmdet3d.CustomDataset

Calculate regularization loss for model weight in training.

Normalizes the confusion matrix to probability

Pad images according to ``self.size``.

Pad images according to ``self.size``.

Patch points sampling. First sample a valid patch. Then sample points within that patch to a certain number. Args:

Args: points (np.ndarray): Input points with shape of (num_points, 3). polygon_surfaces (np.ndarray): Polygon surfaces with shape

Points random sampling. Sample points to a certain number. Args: points (np.ndarray | :obj:`BasePoints`): 3D Points.

convert kitti points(N, >=3) to voxels. Args: points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \ points[:

convert kitti points(N, >=3) to voxels. Args: points (np.ndarray): [N, ndim]. points[:, :3] contain xyz points and \ points[:

Randomly get another item with the same flag. Returns: int: Another index of item with the same flag.

Private function to randomly set the scale factor. Args: input_dict (dict): Result dict from loading pipeline. Returns:

Reset the index of batchsampler to zero.

Private function to rotate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Return

Private function to rotate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. rot

Get the 3D rotation matrix. Args: rot_mat_T (np.ndarray): Transposed rotation matrix. angle (float): Rotation angle. axis

Sample specific number of ground truths and return indices. Args: num (int): Sampled number. Returns: list[i

Randomly sample some negative samples.

Randomly sample some positive samples.

Private function to scale bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Returns

Train semantic head. Args: x (torch.Tensor): Point-wise semantic features for segmentation voxels_dict (dict): Contai

Set flag according to image aspect ratio. Images with aspect ratio greater than 1 will be set as group 1, otherwise group 0. In 3D da

Sampling points in a sliding window fashion. First sample patches to cover all the input points. Then sample points in each patch to

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

Private function to translate bounding boxes and points. Args: input_dict (dict): Result dict from loading pipeline. Ret

Private function to translate points. Args: input_dict (dict): Result dict from loading pipeline. trans_factor (np.nd

Add extra files that are required to support MIM into the package. These files will be added by creating a symlink to the originals if the pa

Convert the rotation difference to difference in sine function. Args: boxes1 (torch.Tensor): Original Boxes in shape (NxC), where

Pre-compute features with weight matrices in weight bank. This function is used before cuda op assign_score_withk in CUDA version PAConv. Arg

Perform weighted sum to aggregate output features according to scores. This function is used in non-CUDA version of PAConv. Compared to the c

Test with augmentations. Args: points (list[torch.Tensor]): List of points of shape [B, N, 3+C]. img_metas (list[list

r"""Test function with augmentation, image network pretrain. May refer to `<https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models

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

Test function of point cloud branch with augmentaiton.

Calculate average precision (for single or multiple scales). Args: recalls (ndarray): shape (num_scales, num_dets) or (num_dets, )

Calculate overlap between two set of axis aligned 3D bboxes. If ``is_aligned`` is ``False``, then calculate the overlaps between each bbox of

Calculate the IoU loss (1-IoU) of two set of axis aligned bounding boxes. Note that predictions and targets are one-to-one corresponded. Args

Transform 2d bbox according to img_meta. Args: img_meta(dict): Meta info regarding data transformation. bbox_2d (torch.Tensor): S