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differential geometric computer vision for deep learning

Project description

https://travis-ci.com/arraiyopensource/torchgeometry.svg?branch=master https://codecov.io/github/arraiyopensource/torchgeometry/branch/master/graph/badge.svg https://badge.fury.io/py/torchgeometry.svg Documentation Status

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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