Metadata-Version: 2.1
Name: dsntnn
Version: 0.5.1
Summary: PyTorch implementation of DSNT
Home-page: UNKNOWN
Author: Aiden Nibali
License: Apache Software License 2.0
Description: :warning: **I have helped integrate DSNT into [Kornia](https://github.com/arraiyopensource/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.
        
        ```bash
        $ pip install dsntnn
        ```
        
        ## Usage
        
        Please refer to the [basic usage guide](examples/basic_usage.md).
        
        ## Scripts
        
        ### Running examples
        
        ```bash
        $ 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.
        
        ### Building documentation
        
        ```bash
        $ mkdocs build
        ```
        
        ### Running tests
        
        Note: The dsntnn package must be installed before running tests.
        
        ```bash
        $ pytest                                 # Run tests.
        $ pytest --cov=dsntnn --cov-report=html  # Run tests and generate a code coverage report.
        ```
        
        ## Other implementations
        
        * Tensorflow: [ashwhall/dsnt](https://github.com/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.
        
        ## License and citation
        
        (C) 2017 Aiden Nibali
        
        This project is open source under the terms of the
        [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.html).
        
        If you use any part of this work in a research project, please cite the following paper:
        
        ```bibtex
        @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}
        }
        ```
        
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Topic :: Software Development :: Libraries
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Description-Content-Type: text/markdown
