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Variational Autoencoder

in Disentangled Representation Learning

Clemens Kubach, Joanina Oltersdorff, Lukas Beinlich and Yilei Chen

Tutor: Monika

Available Models

  • Simple Residual VAE (SimpleResVAE)
  • VAE with normalizing flows (NF-VAE)
  • Very Deep VAE (VDVAE)
  • Residual VAE (ResVAE - inspired by VDVAE)
  • Advanced VAE (Adv-VAE)
  • Sequential VAE (SeqVAE)
  • Sequential VDVAE (SeqVDVAE)

Prerequisites

  • Install git lfs for downloading the saved checkpoints in selected-checkpoints directory

Getting Started

  1. Clone the repository
  2. Install Python 3.10
  3. Install Dependencies
    • Run pip install -r requirements.txt to install all dependencies
    • WandB account is optional but recommended
  4. Goto repo root directory
  5. Setup the dataset
    • Make sure you have the SIAR dataset available in datafiles/.
    • For the time of reviewing, you can download it from SIAR and extract it to datafiles/.
    • An example path from the repository root to one image would look like the following: datafiles/1/1.png.
    • See section "Dataset" for further notes.
  6. Make packages available to python
    • Set env export PYTHONPATH=$PYTHONPATH:$(pwd)
  7. Run experiments
    • Under siar/experiments/ you can find scripts that you can use for doing experiments.
    • For getting started easily, you can just run python3 siar/experiments/simple_run_script.py to train a predefined simple VAE.
    • You can simply modify the configuration to your needs, like selecting different batch sizes, architectures, etc.
    • Alternatively, you can also run our other run scripts in the folder for the different model types.

Repository Structure

Unitorchplate

The unitorchplate package contains a try of implementing a unified template for Pytorch experiments in the Computer Vision Domain. The idea is to offer a baseline structure and functionality for your deep learning projects as Gradle is offering for software projects. Every run is configurable via config classes/file to increase easy usability and reproducibility. It is based on the framework Pytorch, Lightning and some ideas of mlflow.

While offering a standardized implementation for Lightning DataModules and Modules for Models (here named: ModelModules), it is saves time for getting started with a new project but also easily customizable via subclassing. This project is a result of this semester's project and will be further developed in the future.

Siar

The siar package implements this template for our specific use case and the SIAR dataset of this project. For the model implementations, we built upon Pythae, a library for different autoencoder architectures for reconstructing input images.

Dataset

You can also have the somewhere else, but then you have to change the path in the config in every run. To make it easier, you can also make a softlink in the datafiles folder in the repository root directory to the folder where the dataset is located.

Windows:

mklink /D "C:\<your-path-to-repo>\cvp2\datafiles" "C:\<your-path-to-dataset>\SIAR"

Unix:

ln -s <your-path-to-dataset>/SIAR <your-path-to-repo>/cvp2/datafiles

Development

Experiment Configs

In siar/experiments/ you can find scripts that you can use for doing experiments. Just change the parameters offered in the run config to your needs.

Adding further models

You can orientate yourself on the already implemented models. The following files are relevant:

In siar/models/architectures/

  • Define YourModelName in your_model_name.py for defining Encoder, Decoder, Architecture and Config. For an example take a look at simple_res_vae.py.
  • ModelTypes in model_types.py for registering your new architecture
  • In siar/experiments/ you can add a new experiment or just change the parameter in an already created run-script.

Optional:

  • Look under base_architectures for some base architectures that you can use for your model or create a new one to customize the forward pass between encoder and decoder.

Note: The Pythae models only support reconstruction of the input image (self-reconstruction task). That's why we had to override the forward function of their model classes (see VAE, NF-VAE).

ML System

Papers

The references can be found in the project report.