PyTorch implementation of DSNT

Project description

:warning: I have helped integrate DSNT into Kornia (from v0.1.4). New users are advised to use that implementation instead of this one. Existing users should note that the "normalised" coordinate system differs between the two implementations (see https://github.com/anibali/dsntnn/issues/15).

PyTorch DSNT

This repository contains the official implementation of the differentiable spatial to numerical (DSNT) layer and related operations.

$pip install dsntnn  Usage Please refer to the basic usage guide. Scripts Running examples $ python3 setup.py examples


HTML reports will be saved in the examples/ directory. Please note that the dsntnn package must be installed with pip install for the examples to run correctly.

$mkdocs build  Running tests Note: The dsntnn package must be installed before running tests. $ pytest                                 # Run tests.
\$ pytest --cov=dsntnn --cov-report=html  # Run tests and generate a code coverage report.


Other implementations

• Tensorflow: ashwhall/dsnt
• Be aware that this particular implementation represents coordinates in the (0, 1) range, as opposed to the (-1, 1) range used here and in the paper.

If you write your own implementation of DSNT, please let me know so that I can add it to the list. I would also greatly appreciate it if you could add the following notice to your implementation's README:

Code in this project implements ideas presented in the research paper "Numerical Coordinate Regression with Convolutional Neural Networks" by Nibali et al. If you use it in your own research project, please be sure to cite the original paper appropriately.

(C) 2017 Aiden Nibali

This project is open source under the terms of the Apache License 2.0.

If you use any part of this work in a research project, please cite the following paper:

@article{nibali2018numerical,
title={Numerical Coordinate Regression with Convolutional Neural Networks},
author={Nibali, Aiden and He, Zhen and Morgan, Stuart and Prendergast, Luke},
journal={arXiv preprint arXiv:1801.07372},
year={2018}
}


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