scikit-surgerytorch 0.2.6

Description:

scikitsurgerytorch 0.2.6

Author: Thomas Dowrick
scikit-surgerytorch is part of the scikit-surgery software project, developed at the Wellcome EPSRC Centre for Interventional and Surgical Sciences, part of University College London (UCL).
The aim of scikit-surgery torch is to provide a home for various pytorch models/examples/utilities that may be useful for Image Guided Surgery.

Features
Implemented models:

High Resolution Stereo network Inference only, see author’s repo for pre trained weights. As at commit aae0b9b.
Volume2SurfaceCNN Inferencece only, see author’s repo for pre trained weights. As at commit 5a656381.
Models can run on GPU or CPU.
Example usage in tests/.

scikit-surgerytorch is NOT meant to be a layer on-top of pytorch
or provide a new kind-of platform. The aim is that researchers can learn from examples,
and importantly, learn how to deliver an algorithm that can be used by other people
out of the box, with just a `pip install`, rather than a new user having to
re-implement stuff, or struggle to get someone else’s code running.

Cloning
You can clone the repository using the following command:
git clone https://github.com/UCL/scikit-surgerytorch


Running tests
Pytest is used for running unit tests:
pip install pytest
python -m pytest


Linting
This code conforms to the PEP8 standard. Pylint can be used to analyse the code:
pip install pylint
pylint --rcfile=tests/pylintrc sksurgerytorch



Installing
You can pip install directly from the repository as follows:
pip install git+https://github.com/UCL/scikit-surgerytorch

Contributing
Please see the contributing guidelines.


Useful links

Source code repository
Documentation




Licensing and copyright
Copyright 2020 University College London.
scikit-surgerytorch is released under the BSD-3 license. Please see the license file for details.


Acknowledgements
Supported by Wellcome and EPSRC.