rotation 2d points based on origin point clockwise when angle positive. Args: points (float array, shape=[N, point_size, 2]): points to

Sampling specific categories of bounding boxes. Args: name (str): Class of objects to be sampled. num (int): Number

Results visualization. Args: results (list[dict]): List of bounding boxes results. out_dir (str): Output directory

Test function of point cloud branch.

Test function of point cloud branch.

Test function of point cloud branch.

Test function of point cloud branch.

Apply dynamic voxelization to points. Args: points (list[torch.Tensor]): Points of each sample. Returns: tuple

Apply dynamic voxelization to points. Args: points (list[torch.Tensor]): Points of each sample. Returns: tuple

Args: bbox_pred (Tensor): Predicted boxes with normalized coordinates (cx, cy, w, h), which are all in range [

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict:

Call function to perform photometric distortion on images. Args: results (dict): Result dict from loading pipeline. Ret

Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to pad images, masks, semantic segmentation maps. Args: results (dict): Result dict from loading pipeline.

Call function to flip points, values in the ``bbox3d_fields`` and \ also flip 2D image and its annotations. Args: inp

Private function to rotate, scale and translate bounding boxes and \ points. Args: input_dict (dict): Result dict fro

Call function to load points data from file. Args: results (dict): Result dict containing point clouds data. Returns:

Call function to load multi-sweep point clouds from files. Args: results (dict): Result dict containing multi-sweep point cloud

Call function to load multi-sweep point clouds from files. Args: results (dict): Result dict containing multi-sweep point cloud

Call function to load multi-sweep point clouds from files. Args: results (dict): Result dict containing multi-sweep point cloud

Call function to load multi-sweep point clouds from files. Args: results (dict): Result dict containing multi-sweep point cloud

Call function to load multi-sweep point clouds from files. Args: results (dict): Result dict containing multi-sweep point cloud

Private function to rotate, scale and translate bounding boxes and points. Args: input_dict (dict): Result dict from load

Call function to flip points, values in the ``bbox3d_fields`` and also flip 2D image and its annotations. Args: input_dic

Call function to augment common fields in results. Args: results (dict): Result dict contains the data to augment. Retur

Initialize a batch normalization block. If network does not use batch normalization, replace with biases. :param in_dim: dimension in

Initialize a standard unary block with its ReLU and BatchNorm. :param in_dim: dimension input features :param out_dim: dimens

Initialize a simple convolution block with its ReLU and BatchNorm. :param in_dim: dimension input features :param out_dim: di

Initialize a resnet bottleneck block. :param in_dim: dimension input features :param out_dim: dimension input features

Initialize a global average block with its ReLU and BatchNorm.

Initialize a nearest upsampling block with its ReLU and BatchNorm.

Initialize a max pooling block with its ReLU and BatchNorm.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

str: Return a string that describes the module.

Build the default pipeline for this dataset.

Compute regression and classification targets for one image. Outputs from a single decoder layer of a single feature level are used.

Initialize classification branch and regression branch of head.

points: [N, 18]

Test function with augmentaiton.

Test function with augmentaiton.

Test function with augmentaiton.

Test function with augmentaiton.

Close ObjectSample.