Args: input_ch(int) : the number of input channel out_features (list[str]): name of the layers whose outputs should

Build PyTorch DataLoader. In distributed training, each GPU/process has a dataloader. In non-distributed training, there is only one dataloade

Extract features from images and points.

Default initialization for Parameters of Module.

1x1 convolution with padding

3x3 convolution with padding

Test model with multiple gpus. This method tests model with multiple gpus and collects the results under two different modes: gpu and cpu mo

obtain bev features.

Get the reference points used in SCA and TSA. Args: H, W: spatial shape of bev. Z: hight of pillar. D:

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

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

A function wrapper for launching model training according to cfg. Because we need different eval_hook in runner. Should be deprecated in the

Decode bboxes. Args: all_cls_scores (Tensor): Outputs from the classification head, \ shape [nb_dec, bs, num_query

Decode bboxes. Args: cls_scores (Tensor): Outputs from the classification head, \ shape [num_query, cls_out_channe

3x3 convolution with padding

Extract features of images.

Forward function' Args: pts_feats (list[torch.Tensor]): Features of point cloud branch gt_bboxes_3d (list[:obj:`Bas

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

obtain bev features.

Generate bboxes from bbox head predictions. Args: predss : occ results. img_metas (list[dict]): Point cloud and ima

Initialize layers of the Detr3DTransformer.

Initialize layers of the Detr3DTransformer.

Default initialization for Parameters of Module.

Default initialization for Parameters of Module.

Initialize the transformer weights.

Default initialization for Parameters of Module.

Test function without augmentaiton.

Test function

Convert the model into training mode while keep normalization layer freezed.

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

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

Call function to transform and format common fields in results. Args: results (dict): Result dict contains the data to convert.

Get item from infos according to the given index. Returns: dict: Data dictionary of the corresponding index.