Tensorflow 2.x implementation of Image-to-Image Translation with Conditional Adversarial Net paper, better known as pix2pix.
Model was trained for 200 epochs with a batch size of 1 and a learning schedule that linearly decayed learining rate after half the epochs passed. Both training and validation data was used to train the generator. Subjectively results are somewhat worse than what I was able to reproduce with reference implementation.
From left to right, images below show:
- input satelite image
- generated google maps image
- ground truth google maps image
Model was trained for 1000 epochs with a batch of 8 and a fixed learning rate. There aren't really any obvious failures in predictions.
This project can be run in a docker container.
You can build the docke rimage manually with docker, but there is also an invoke task provided: invoke host.build-app-container. You'll need invoke on your host system to run it.
You can start the container with invoke host.run {/path/to/configuration.yaml} command that takes care of mounting paths for data volume, using GPU if available, etc.
Once inside container, following key invoke commands are available:
- train.train-facades-gan - Train GAN model on facades dataset.
- train.train-maps-gan - Train GAN model on maps dataset.
- visualize.facades-model-predictions - Visualize facades model predictions
- visualize.maps-model-predictions - Visualize maps model predictions
- visualize.visualize-facades-data - Visualize facades data
- visualize.visualize-maps-data - Visualize maps data
Most commands accept --config-path argument that accepts a path pointing to a configuration file. Sample configuration file is provided at ./config.yaml.