Rescale the output instances to the target size.

Rescale the output instances to the target size.

r"""Initiate parameters in the transformer model.

Rescale a size by a ratio. Args: size (tuple[int]): (w, h). scale (float): Scaling factor. Returns: tuple[int]: scaled

This function is used to transpose image first training targets to level first ones :return: level first training targets

This function is used to transpose image first training targets to level first ones :return: level first training targets

This function is used to transpose image first training targets to level first ones :return: level first training targets

With option to don't use hflip Returns: list[Augmentation]

Create evaluator(s) for a given dataset. This uses the special metadata "evaluator_type" associated with each builtin dataset.

Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance.

Create a ResNet instance from config. Returns: ResNet: a :class:`ResNet` instance.

1x1 convolution with padding

Create semantic segmentation annotations from panoptic segmentation annotations, to be used by PanopticFPN. It maps all thing categories

Create semantic segmentation annotations from panoptic segmentation annotations, to be used by PanopticFPN. It maps all thing categories

In addition to the post processing of detectron2, we add scalign for bezier control points.

Apply the sigmoid operation.

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

evaluation_imports: Dictionary ( key = module name , value = alias ) with python modules used in the evaluation.