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.. _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
|
