This is the support repository for the blog post How to Trust Your Deep Learning Code. It contains code for training a Variational Autoencoder (VAE) and the associated unit tests. The unit tests illustrate useful concepts to test in deep learning projects. The focus lay on writing tests that are readable and reusable.
For more information check out the blog post.
The project uses Python 3.7.
First, install the packages specified in the requirements.txt file.
conda create -n unittest_dl python=3.7
conda activate unittest_dl
conda install --file requirements.txt -c pytorch
## or
virtualenv -p python3.7 unittest_dl
source unittest_dl/bin/activate
pip install -r requirements.txt
This project was developed in PyCharm, so it is the easiest to use that way.
Open it in the IDE and mark the src directory as Sources Root (right-click the folder > Mark directory as > Sources Root).
Everything should work out of the box now.
To run all tests, right-click the tests directory and select "Run 'Unittests in tests'".
As an alternative, you can manually add src to your PYTHON_PATH environment variable.