A Tensorflow implementation of Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks using Eager Execution, tf.keras.layers, and tf.data.
Requirements:
- Tensorflow 1.11
Thanks to the original authors PyTorch implementation for inspiration: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
├── README.md
├── requirements.txt <- Use `pip install -r requirements.txt`
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── LICENSE
└── src <- Source code for use in this project
├── __init__.py <- Makes src a Python module
│
├── train.py <- Run this to train
│
├── test.py <- Run this to test
│
├── data <- Code for downloading or loading data
│ ├── data.py <- Dataset class
│ └── download_data.py
│
├── models <- Code for defining the network structure and loss functions
│ ├── cyclegan.py <- CycleGAN model class
│ ├── networks.py
│ └── losses.py
│
└── utils <- Utility files
├── options.py <- Class for command line options
└── image_history_buffer.py
Project organisation based on the cookiecutter data science project template. #cookiecutterdatascience