Auto-encoder Tutorials

Setup

Virtual Environment

If you don't operate inside a virtual environment, or only have access to an incompatible python version (<3.8), it is recommended you create a virtual environment using conda:

conda env create -f environment.yml
conda activate deep-learning-tutorials

Creating the environment this way also takes care of installing the dependencies for you, so you can skip the rest of the setup and dive straight into one of the tutorials.

Installing Dependencies

If you already have a python environment set aside for this project and just want to install the dependencies, you can do that using the following command:

pip install -e .

How to Run

Once you've went through the setup instructions above, you can start exploring the tutorial's notebooks. We recommend using JupyterLab to run the notebooks, which can be launched by running (from within your environment):

jupyter-lab

When you've launched JupyterLab's web interface, you can simply navigate to any of the tutorials listed below, and follow the instructions in there!

Available Tutorials

Representation Learning

• Basic Variational Autoencoders
• Variational Autoencoders Applied to Cardiac MRI

You may download the MNIST and ACDC datasets here. Once downloaded, you may untar the file and copy the data/ folder in the root of your code, at the same level than the src/ and the tutorials/ folders.

tar -xvzf data.tar.gz

How to Contribute

If you want to contribute to the project, then you have to install development dependencies and pre-commit hooks, on top of the basic setup for using the project, detailed above. The pre-commit hooks are there to ensure that any code committed to the repository meets the project's format and quality standards.

# Install development dependencies
pip install -e .[dev]

# Install pre-commit hooks
pre-commit install