Metadata-Version: 2.1
Name: torch_pdegraph
Version: 1.1.2
Summary: Running paritial difference equations (PDEs) on graphs
Home-page: https://github.com/aGIToz/Pytorch_pdegraph
Author: Amitoz AZAD
Author-email: amitoz.sudo@gmail.com
License: MIT
Description: # Torch_pdegraph
        Torch_pdegraph is a proof of concept that how one can solve PDEs (partial difference equations)  on graphs using the Message Passing class of torch_geometric and hence also benefits from the hardware acceleration. See the [video-presentation](https://www.youtube.com/watch?v=egUjPrJa2l4)
        
        ## What is a PDE on a graph?
        The basic idea is that one can define the operators like, derivatives, gradients, laplacians on graphs and construct a PDE inspired from nature on graphs.
        To know more about PDEs on graph.
        - See the publications of [Elmoataz](https://elmoatazbill.users.greyc.fr/pub.html). 
        - See also their [applications on pointclouds](https://elmoatazbill.users.greyc.fr/point_cloud/index.html)
        - Classical PDEs on images by [Guillermo Sapiro](https://www.youtube.com/watch?v=ZAmig8cw7H8&list=PLEE9b2lRB-R0vii_n3A_3ec9F_Bp_U9Fh&index=2&t=3s)
        - Be sure to play with the **jupyter-notebooks in the applications/** folder which presents few of their applications. Download the [data](https://drive.google.com/file/d/1I3IRe1HSoOyh5gBU-cGtLtATWboRMT3-/view?usp=sharing)
        - Ref to operator_calculus.md for a brisk intro to calculus on graphs.
        
        ## Installation 
        First [install](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) the torch_geometric. Then one can clone this project and install it locally:
        
        ```shell
        pip install --upgrade pip
        pip install .
        ```
        
        Or do:
        
        
        ```shell
        pip install --upgrade pip
        pip install torch_pdegraph
        ```
        
        ## Running the notebooks
        In the notebooks I am demonstrating few applications of pdes on images and pcd by creating simple knn-graphs on gpu. One will need [faiss](https://github.com/facebookresearch/faiss/blob/master/INSTALL.md) library to create the graphs.
        
        To display the pcds inside the notebook I am using jupyter visualization feature in open3d which uses a jupyter widget, notebooks must be running to for the widget to function.
        
        ## To do:
        - Add an interpolation application.
        - Add a segmentation predefined pde. 
        
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
