Corresponding code to WSA/SCC 2023 contribution
Before you start, the figure folder has to be created. Run:
cd Autoencoder
mkdir figures
If you are planning on running the code on cpu, run:
pip install -r requirements.txt
If you want to run on gpu, cupy is installed additionally. Run:
pip install -r requirements_gpu.txt
Additionally, install pytorch according to their website.
Training and plotting can be done by running split_test.py. We recommend running this file to get accustomed to the code.
Main Components are:
- autoencoder_compare_cpr: evaluation script for sweeping number of samples
- set folder: pickled versions of trained autoencoders
- Folder Autoencoder:
- training_routine has training function which is described below
- NN_classes has the following functions and classes:
- Encoder
- Decoder (Comm Receiver)
- custom binary loss function (equivalent to BCE loss)
- Beamformer
- Presence_Detector
- Angle_Estimator