An implementation of the pix2pix paper using Keras to build models and Tensorflow to train.

The model is trained on the façades dataset. In this setting the model is provided with a diagram of a buildings' facade, showing the layout of windows, doors, balconies, mantels, with the objective being to generate a photo-realistic rendering.

A webpage is updated during training so that you can watch the model learn. Notice the development of concepts such as reflective windows, dampness and mildew on render, stonework detail, and shadows under balconies. Here's a few examples from the end of training.

Here the columns are:

• Input: the diagram provided as input to the model as reference
• Authors' Pytorch: Generated output of model provided by the authors of the original paper
• This Implementation: Generated output of this implementation
• Target: Real photograph of the building
• Patchgan: A heatmap visualisation showing which parts of the generated image (in 3rd column) the discriminator classifies as real (white) and fake (grey).

See the full training results by downloading this repo and opening results/index.html in your browser. Or train the model yourself by following the steps below.

Tensorflow 1.13.1 requires CUDA 10 drivers if running on GPU, installation steps here. If running on CPU, change tensorflow-gpu to tensorflow in requirements.txt.

Setup python environment:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Download facades dataset:

bash download_dataset.sh façades

Preprocess data:

python preprocess.py

Train:

python train.py --experiment_title my_experiment

To view results as the model trains open results/index.html in your browser to view training progress visualisations, including training plots and checkpoint images for each epoch.

• The 'pix2pix' paper on which this implementation is based: P. Isola, J. Zhu, T. Zhou, A. Efros. Image-to-Image Translation with Conditional Adversarial Networks. (https://arxiv.org/pdf/1611.07004.pdf)
  • Authors' PyTorch implementation
  • Authors' original Lua implementation
• The original GAN paper: I. Goodfellow et al. Generative Adversarial Networks (https://arxiv.org/abs/1406.2661)
• U-net architecture used by generator: O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. In MIC- CAI, pages 234–241. Springer, 2015. 2, 3, 4 (https://arxiv.org/abs/1505.04597)
• Insights on receptive field theory exploited by Patchgan discriminator: W. Luo, Y. Li, R. Urtasun, R. Zemel. Understanding the Effective Receptive Field in Deep Convolutional Neural Networks (https://arxiv.org/abs/1701.04128)
• Useful overview of theory challenges and tricks when training GANS: T. Salimans et al. Improved Techniques for Training GANs (https://arxiv.org/pdf/1606.03498.pdf)