Generate targets for primitive z and primitive xy. Args: point_mask (torch.Tensor): Tensor to store the ground tr

Create reduced point clouds for given info. Args: data_path (str): Path of original data. info_path (str): Path of data info.

Returns all EvalBoxes in a list.

Calculated average precision.

Calculates true positive errors.

Convert action.

Resize the output instances. The input images are often resized when entering an object detector. As a result, we often need the outputs

Build normalization layer specially designed for DLANet. Args: cfg (dict): The norm layer config, which should contain: - ty

Extract features from images and points.

Directly extract features from the backbone+neck.

Extract and return the data corresponding to key in result dict. ``results`` is a dict output from `pipeline(input_dict)`, which is the l

Applies filtering to boxes. Distance, bike-racks and points per box. :param nusc: An instance of the NuScenes class. :param eval_boxes: A

like `get_fields` in the Instances object, but return each field in tensor representations

Calculate the locations of the frustum points in the lidar coordinate system. Args: rots (torch.Tensor): Rotation from ca

Get targets for ATSS head. This method is almost the same as `AnchorHead.get_targets()`. Besides returning the targets as the parent

Get the valid radios of feature maps of all level.

Match points to plane. Args: plane (torch.Tensor): Equation of the plane. points (torch.Tensor): Points of input.

A 'as_tuple=True' version of torch.nonzero to support torchscript. because of https://github.com/pytorch/pytorch/issues/38718

Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. senso

Obtain the info with RT matric from general sensor to Top LiDAR. Args: nusc (class): Dataset class in the nuScenes dataset. senso

Parse the package dependencies listed in a requirements file but strips specific versioning information. Args: fname (str): path to r

This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, num_key_value_heads, seqlen, he

Scale the points with horizontal and vertical scaling factors. Args: scale_factors (float): Scale factors to scale the points.

Convert results into format that is directly readable for meshlab. Args: points (np.ndarray): Points. gt_seg (np.ndarray): Ground

Args: start_iter, max_iter (int): See docs above

Args: img_size (int): Input image size. patch_size (int): Patch size. in_chans (int): Number of input ima

Args: dim (int): Number of input channels. num_heads (int): Number of attention heads in each ViT block.

Args: img_size (int): Input image size. patch_size (int): Patch size. in_chans (int): Number of input ima

If one size (or aspect ratio) is specified and there are multiple feature maps, we "broadcast" anchors of that single size (or aspect ratio)

Depending on the brightness_factor, gives a lighter or darker color i.e. a color with less or more saturation than the original color

Inverse of ``locate()``. Args: t: any object with ``__module__`` and ``__qualname__``

Load checkpoint from somewhere (modelzoo, file, url). Args: filename (str): Accept local filepath, URL, ``torchvision://xxx``,

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

Accumulate per image evaluation results and store the result in self.eval :param p: input params for evaluation :return: None

Assign anchors and encode positive anchors. Args: bbox_assigner (BaseAssigner): assign positive and negative boxes. a

Build a backbone from `cfg.MODEL.BACKBONE.NAME`. Returns: an instance of :class:`Backbone`

Calculate Chamfer Distance of two sets. Args: src (torch.Tensor): Source set with shape [B, N, C] to calculate Chamfer Distan

Given the raw data, generate the ground truth database. Args: dataset_class_name (str): Name of the input dataset. data_path (str

Create a small table using the keys of small_dict as headers. This is only suitable for small dictionaries. Args: small_dict (di

Initialize from serialized content.

Get gaussian masked heatmap. Args: heatmap (torch.Tensor): Heatmap to be masked. center (torch.Tensor): Center coord of the heatm

Extract features from images and points.

Extract features from images and points.

Args: feat: shape=[N, C], N is the voxel num in the batch. voxel_drop_lvl: shape=[N, ]. Indicates drop_level of the window the vo

Forward function for `Transformer`. Args: x (Tensor): Input query with shape [bs, c, h, w] where c = embed_dims.

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch pts_fea

Forward function for point cloud branch. Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxe

Return list of map mask layers of the specified patch. :param patch_box: Patch box defined as [x_center, y_center, height, width]. If

Create boolean mask by actually number of a padded tensor. Args: actual_num ([type]): [description] max_num ([type]): [description

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