Args: in_channels (int): Number of input channels. out_channels (int): Number of output channels. bottlen

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

Args: net (Backbone): module representing the subnetwork backbone. Must be a subclass of :class:`Backbone`.

NOTE: this interface is experimental. Args: is_train: whether it's used in training or inference augmentatio

A helper class to initialize, evaluate and summarize Omni3D metrics. The evaluator relies on the detectron2 MetadataCatalog for kee

Args: dataset_name (str): name of the dataset to be evaluated. It must have either the following corresponding me

Args: iouType (str): defines 2D or 3D evaluation parameters. One of {"2D", "3D"}

Initialize COCOeval using coco APIs for Gt and Dt Args: cocoGt: COCO object with ground truth annotations coc

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

str: a string that describes the module

str: a string that describes the module

Evaluate predictions. Fill self._results with the metrics of the tasks.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Args: cfg: a detectron2 CfgNode Returns: backbone (Backbone): backbone module, must be a subclass of :class:`Backbone`.

Computes estimated time left for an iterative function to finish. Args: start_time (int): the time the function started at (e.g from

Computes priors via simple averaging or a custom K-Means clustering.

3x3 convolution with padding