Returns the bounding box corners. :param wlh_factor: Multiply w, l, h by a factor to scale the box. :return: <np.float: 3, 8>

Crop each bitmask by the given box, and resize results to (mask_size, mask_size). This can be used to prepare training targets for Ma

Decode probabilistic depth map. Args: depth_cls_preds (torch.Tensor): Depth probabilistic map in shape [..., self

Decode yaw angle and change it from local to global.i. Args: bbox (torch.Tensor): Bounding box predictions in shape

Initialize from serialized content.

Args: binary_mask (ndarray): numpy array of shape (H, W), where H is the image height and W is the image width. E

Args: text (str): class label position (tuple): a tuple of the x and y coordinates to place text on image.

Encode points with backbone and decode into a semantic segmentation map of the same size as input. Args: points (torch.Te

Enlarge the length, width and height boxes. Args: extra_width (float | torch.Tensor): Extra width to enlarge the box. Re

Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP. Args: gt_annos (dict): Must from get_label_annos() in kitti_common

Kitti eval. support 2d/bev/3d/aos eval. support 0.5:0.05:0.95 coco AP. Args: gt_annotations: dict, must from get_label_annotations() in ev

Extract bounding boxes from 2d detector. Args: img (torch.Tensor): of shape (N, C, H, W) encoding input images. T

Extract features from images and points.

Extract features from images and points.

Extract features from points.

Construct a shapely LineString object out of a line token. :param line_token: The token of the line record. :return: The line

Extract features of points.

Fix the __qualname__ of module members to be their exported api name, so when they are referenced in docs, sphinx can find them. Reference:

Forward training function. Args: points (list[torch.Tensor], optional): Points of each sample. Defaults to None.

Use the given boxes in `instances` to produce other (non-box) per-ROI outputs. Args: features: same as in `forward()`

Make this block not trainable. This method sets all parameters to `requires_grad=False`, and convert all BatchNorm layers to

During inference, the functionary of batch norm layers is turned off but only the mean and var alone channels are used, which exposes the chance t

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

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

Generate bboxes from vote head predictions. Args: points (torch.Tensor): Input points. bbox_preds (dict): Predictions

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

obtain bev features.

obtain bev features.

Args: points: (N, 3 + (f)) [bxyz + (f)] voxel_idxs: (N, 4) [bxyz] num_points_in_voxel: (N) Return

Get a copy of the default config. Returns: a detectron2 CfgNode instance.

Return dict from versioned blob to list of (i, j), where i is index of consumer op, j is the index of input of that op.

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

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

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

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

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

Only keep loading image, points and annotations related configuration. Args: pipeline (list[dict] | list[:obj:`Pipeline`]): D

Return dict from versioned blob to (i, j), where i is index of producer op, j is the index of output of that op.

Get the position embedding of proposal.

Compute surface and line center of bounding boxes. Returns: torch.Tensor: Surface and line center of bounding boxes.

Return the device type ("cpu" or "gpu"/"cuda") of each (versioned) blob

Run inference on the given inputs. Args: batched_inputs (list[dict]): same as in :meth:`forward` detected_in

Initialize model weights.

Initialize the weights in backbone. Args: pretrained (str, optional): Path to pre-trained weights. Defaults to Non

Prepare some proposals to be used to train the ROI heads. It performs box matching between `proposals` and `targets`, and assigns

Neural network layers that makes predictions from input features.

Limit the yaw to a given period and offset. Args: offset (float, optional): The offset of the yaw. Defaults to 0.5. p

Locate and return an object ``x`` using an input string ``{x.__module__}.{x.__qualname__}``, such as "module.submodule.class_name". Rais

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features

Make auxiliary points for loss computation. Args: source_tensor (torch.Tensor): (M, C) features to be propigated. tar

make plugins for block. Args: in_channels (int): Input channels of plugin. plugins (list[dict]): List of plugins cfg

make plugins for block. Args: in_channels (int): Input channels of plugin. plugins (list[dict]): List of plugins cfg

If gradient clipping is enabled through config options, wraps the existing optimizer type to become a new dynamically created class Optimizer

Load content from the given config file and merge it into self. Args: cfg_filename: config filename allow_un

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

Return intersection-over-union (Jaccard index) of boxes. Both sets of boxes are expected to be in (x_center, y_center, width, height, an

Plot the boundary lines of 3D rectangular on 2D images. Args: img (numpy.array): The numpy array of image. num_rects (int): Numbe

Initialization before data preparation. Args: results (dict): Dict before data preprocessing. - img_fields (list

Initialization before data preparation. Args: results (dict): Dict before data preprocessing. - img_fields (list

Args: predictions: return values of :meth:`forward()`. proposals (list[Instances]): proposals that match the features