differential geometric computer vision for deep learning
Project description
The PyTorch Geometry package is a geometric computer vision library for PyTorch.
It consists of a set of routines and differentiable modules to solve generic geometry computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions.
Installation
From pip:
pip install torchgeometry
From source:
python setup.py install
From source using pip:
pip install git+https://github.com/arraiyopensource/torchgeometry
Quick Usage
import torch
import torchgeometry as tgm
x_rad = tgm.pi * torch.rand(1, 3, 3)
x_deg = tgm.rad2deg(x_rad)
torch.allclose(x_rad, tgm.deg2rad(x_deg)) # True
Examples
Run our Jupyter notebooks examples to learn to use the library.
Cite
If you are using torchgeometry in your research-related documents, it is recommended that you cite the poster.
@misc{Arraiy2018,
author = {E. Riba, M. Fathollahi, W. Chaney, E. Rublee and G. Bradski}
title = {torchgeometry: when PyTorch meets geometry},
booktitle = {PyTorch Developer Conference},
year = {2018},
url = {https://drive.google.com/file/d/1xiao1Xj9WzjJ08YY_nYwsthE-wxfyfhG/view?usp=sharing}
}
Contributing
We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. Please, consider to read the CONTRIBUTING notes.
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