.. _ops: Operators ========= .. currentmodule:: torchvision.ops :mod:`torchvision.ops` implements operators, losses and layers that are specific for Computer Vision. .. note:: All operators have native support for TorchScript. Detection and Segmentation Operators ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The below operators perform pre-processing as well as post-processing required in object detection and segmentation models. .. autosummary:: :toctree: generated/ :template: function.rst batched_nms masks_to_boxes nms roi_align roi_pool ps_roi_align ps_roi_pool .. autosummary:: :toctree: generated/ :template: class.rst FeaturePyramidNetwork MultiScaleRoIAlign RoIAlign RoIPool PSRoIAlign PSRoIPool Box Operators ~~~~~~~~~~~~~ These utility functions perform various operations on bounding boxes. .. autosummary:: :toctree: generated/ :template: function.rst box_area box_convert box_iou clip_boxes_to_image complete_box_iou distance_box_iou generalized_box_iou remove_small_boxes Losses ~~~~~~ The following vision-specific loss functions are implemented: .. autosummary:: :toctree: generated/ :template: function.rst complete_box_iou_loss distance_box_iou_loss generalized_box_iou_loss sigmoid_focal_loss Layers ~~~~~~ TorchVision provides commonly used building blocks as layers: .. autosummary:: :toctree: generated/ :template: class.rst Conv2dNormActivation Conv3dNormActivation DeformConv2d DropBlock2d DropBlock3d FrozenBatchNorm2d MLP Permute SqueezeExcitation StochasticDepth .. autosummary:: :toctree: generated/ :template: function.rst deform_conv2d drop_block2d drop_block3d stochastic_depth