Metadata-Version: 2.2
Name: kuat
Version: 0.17.0
Summary: Quaternions in PyTorch (part of NVIDIA Kaolin)
Home-page: https://github.com/emaballarin/kuat
Author: ['Emanuele Ballarin', 'NVIDIA']
Author-email: emanuele@ballarin.cc
License: Apache License, Version 2.0
Keywords: Deep Learning,Machine Learning,Computer Vision,Differentiable Programming,Mathematics,Computer Graphics,Robotics
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Environment :: Console
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: torch>=2
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Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
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# Quaternions in PyTorch

Quaternions are handy mathematical tools for working with rotations and 3D transformations, and since v0.16.0 the [Kaolin Library](https://github.com/NVIDIAGameWorks/kaolin) bundles a module of low-level convenience operations with quaternions, targeting common use cases and exposing a plain PyTorch interface to all functions.

See [`kaolin.math.quat`documentation](https://kaolin.readthedocs.io/en/latest/modules/kaolin.math.quat.html) for a full list of functions and a short python recipe in [examples/recipes/math/quaternions.py](https://github.com/NVIDIAGameWorks/kaolin/blob/master/examples/recipes/math/quaternions.py).
