New version of backend and more extensive examples to match paper's results coming soon
Official PyTorch implementation of the mapped convolution operation
This repository contains the "Mapped Convolutions" library. It is written to be a Python extension to PyTorch and can run on either GPU (needs CUDA) or CPU.
I highly recommend using some kind of virtual environment, like Conda, virtualenv, or Docker.
To install the Python dependencies, you can either use the provided Conda YML file (for a Conda environment) or use the requirements.txt file for a pip installation.
For Conda, first install Conda and then call the command:
conda env create -f mapped-conv.yml
For pip, navigate to the top-level of this project and install the dependencies using:
pip install -r requirements.txt
This code has been tested with Python 3.7, PyTorch 1.0, and CUDA 10.0. I cannot help you with driver incompatibility issues.
Note: I know that PyTorch has been updated since I originally wrote this package. I will try to improve compatibility with the more recent PyTorch version as I find time.*
The library has been set up as a PyTorch extension. All you need to do to install is navigate to the package directory and run:
python setup install
This should take care of all the compilation and installation to your Python environment
To use mapped convolutions, simply import the desired subpackage. For example:
# my_file.py
import mapped_convolutions.nn as mcnn
import mapped_convolutions.util as mcutil
After you've installed the package, I highly recommend you run the unit test suite to make sure everything has installed correctly. It only take a minute. To do this, navigate to the package directory and then use the command:
python -m pytest
This will trigger all unit tests to run. All 52 should pass successfully. If you do not have a CUDA-enabled machine, 26 should pass and 26 should be skipped.
Please also read our related papers:
If you find this code useful for your research, please cite:
@article{eder2019mapped,
Author = {Marc Eder and True Price and Thanh Vu and Akash Bapat and Jan-Michael Frahm},
Title = {Mapped Convolutions},
Year = {2019},
Eprint = {arXiv:1906.11096}}
@inproceedings{eder2019convolutions,
title={Convolutions on Spherical Images},
author={Eder, Marc and Frahm, Jan-Michael},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops},
pages={1--5},
year={2019}
}
Things I intend to do in the next few weeks
- Push package to public GitHub
- Write some example scripts for resampling to sphere
- Update backend to PyTorch 1.2 compatibility
- Upload network example
- Layer docs
- Clean up some code and improve comments