Metadata-Version: 2.1
Name: disent
Version: 0.0.1.dev1
Summary: Vae disentanglement framework built with pytorch lightning.
Home-page: https://github.com/nmichlo/eunomia
Author: Nathan Juraj Michlo
Author-email: NathanJMichlo@gmail.com
License: UNKNOWN
Description: # 🧶 Disent ⚠️ [W.I.P] 
        
        Disentanglement Library for pytorch and pytorch-lightning. With an easy to use configuration based on Hydra.
        
        **Documentation**: Check out the [docs and examples](https://docs.disent.dontpanic.sh/en/latest/)!
        
        ## Another disentanglement library?
          
        - I needed to become more familiar with VAE's (Currently working on my masters)
        
        - **DISCLAIMER:** This project has its roots in the tensorflow [disentanglement_lib](https://github.com/google-research/disentanglement_lib) library.
        
        - [Weakly-Supervised Disentanglement Without Compromises](https://arxiv.org/abs/2002.02886) stated they would release
          their code as part of disentanglement_lib... I didn't have time to wait... As of September it has been released.
          
        - The disentanglement_lib still uses Tensorflow 1.0 and [Gin Config](https://github.com/google/gin-config) controls execution, **hiding** the flow of data in the library (I am not a fan).
        
        
        
        ## Features
        
        ### Frameworks
        - **Unsupervised**:
          - <ins>VAE</ins>:
          - <ins>BetaVAE</ins>:
          - <ins>DFCVAE</ins>:
        - **Weakly Supervised**:
            - <ins>Ada-GVAE</ins>: *`AdaVae(..., average_mode='gvae')`*
            - <ins>Ada-ML-VAE</ins>: *`AdaVae(..., average_mode='ml-vae')`*
        - **Supervised**:
            - <ins>TVAE</ins>:
        
        ### Metrics
        - **Disentanglement**:
            - <ins>FactorVAE score</ins>:
            - <ins>DCI</ins>:
        
        ### Datasets:
        - **Ground Truth**:
            - <ins>Cars3D</ins>:
            - <ins>dSprites</ins>:
            - <ins>MPI3D</ins>:
            - <ins>SmallNORB</ins>:
            - <ins>Shapes3D</ins>:
        - **Ground Truth Non-Overlapping (Synthetic)**:
            - <ins>XYBlocks</ins>: *3 blocks of decreasing size that move across a grid. Blocks can be one of three colors R, G, B. if a smaller block overlaps a larger one and is the same color, the block is xor'd to black.*
            - <ins>XYSquares</ins>: *3 squares (R, G, B) that move across a non-overlapping grid. Obervations have no channel-wise loss overlap.*
            - <ins>XYObject</ins>: *A simplistic version of dSprites with a single square.*
        
        
        
        ## Usage
        
        Disent is still under active development (I an sorry there are no tests yet).
        
        The easiest way to use this library is by running `experiements/hydra_system.py` and changing the config in `experiements/config/config.yaml`. Configurations are managed by [Hydra Config](https://github.com/facebookresearch/hydra)
        
Platform: UNKNOWN
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3.8
Classifier: Intended Audience :: Science/Research
Requires-Python: ==3.8
Description-Content-Type: text/markdown
