Remove the points in the sampled bounding boxes. Args: points (:obj:`BasePoints`): Input point cloud array. boxes (np

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

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

Test function without augmentaiton.

Test function without augmentaiton.

Test function without augmentaiton.

Test function without augmentaiton.

Test function of point cloud branch.

Test function of point cloud branch.

Test function of point cloud branch.

Test function of point cloud branch.

Args: bbox_pred (Tensor): Predicted boxes with normalized coordinates (cx, cy, w, h), which are all in range [0,

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

Call function to normalize images. Args: results (dict): Result dict from loading pipeline. Returns: dict: Nor

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

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

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

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

Call function to sample ground truth objects to the data. Args: input_dict (dict): Result dict from loading pipeline. Re

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

Call function to collect keys in results. The keys in ``meta_keys`` will be converted to :obj:`mmcv.DataContainer`. Args:

Call function to load multi-view image from files. Args: results (dict): Result dict containing multi-view image filenames.

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. A

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

Initialize classification branch and regression branch of head.

Initialize classification branch and regression branch of head.

Create info file of nuscene dataset. Given the raw data, generate its related info file in pkl format. Args: root_path (str): Path o

Export 2d annotation from the info file and raw data. Args: root_path (str): Root path of the raw data. info_path (str): Path of

Filter ground truths by difficulties. Args: db_infos (dict): Info of groundtruth database. removed_difficulty (list):

Filter ground truths by number of points in the bbox. Args: db_infos (dict): Info of groundtruth database. min_gt_poi

Forward function. Args: x (torch.Tensor): 4D Tensor in (N, C, H, W) shape. Returns: list[torch.Tensor]: Mult

Forward function. Args: x (torch.Tensor): Input with shape (N, C, H, W). Returns: tuple[torch.Tensor]: Multi

Forward function. Args: mlvl_feats (tuple[Tensor]): Features from the upstream network, each is a 5D-tensor with s

Forward function. Args: mlvl_feats (tuple[Tensor]): Features from the upstream network, each is a 5D-tensor with s

Forward function for `UniTransformerDecoder`. Args: query (Tensor): Input query with shape `(num_query, b

Forward Function of UniCrossAtten. Args: query (Tensor): Query of Transformer with shape (num_query, bs, embed_dim

Forward function for `Uni3DViewTrans`. Args: mlvl_feats (list(Tensor)): Input queries from different level. Each e

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. W

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. W

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. W

Calls either forward_train or forward_test depending on whether return_loss=True. Note this setting will change the expected inputs. W

Forward of SparseEncoder. Args: voxel_features (torch.float32): Voxel features in shape (N, C). coors (torch.int32):