CycleGAN-TensorFlow

An implementation of CycleGan using TensorFlow (work in progress).

Original paper: https://arxiv.org/abs/1703.10593

Results on test data

apple -> orange

Input	Output		Input	Output		Input	Output

orange -> apple

Input	Output		Input	Output		Input	Output

Environment

TensorFlow 1.0.0
Python 3.6.0

Data preparing

First, download a dataset, e.g. apple2orange

$ bash download_dataset.sh apple2orange

Write the dataset to tfrecords

$ python3 build_data.py

Check $ python3 build_data.py --help for more details.

Training

$ python3 train.py

If you want to change some default settings, you can pass those to the command line, such as:

$ python3 train.py  \
    --X=data/tfrecords/horse.tfrecords \
    --Y=data/tfrecords/zebra.tfrecords

Here is the list of arguments:

usage: train.py [-h] [--batch_size BATCH_SIZE] [--image_size IMAGE_SIZE]
                [--use_lsgan [USE_LSGAN]] [--nouse_lsgan]
                [--norm NORM] [--lambda1 LAMBDA1] [--lambda2 LAMBDA2]
                [--learning_rate LEARNING_RATE] [--beta1 BETA1]
                [--pool_size POOL_SIZE] [--ngf NGF] [--X X] [--Y Y]
                [--load_model LOAD_MODEL]

optional arguments:
  -h, --help            show this help message and exit
  --batch_size BATCH_SIZE
                        batch size, default: 1
  --image_size IMAGE_SIZE
                        image size, default: 256
  --use_lsgan [USE_LSGAN]
                        use lsgan (mean squared error) or cross entropy loss,
                        default: True
  --nouse_lsgan
  --norm NORM           [instance, batch] use instance norm or batch norm,
                        default: instance
  --lambda1 LAMBDA1     weight for forward cycle loss (X->Y->X), default: 10.0
  --lambda2 LAMBDA2     weight for backward cycle loss (Y->X->Y), default:
                        10.0
  --learning_rate LEARNING_RATE
                        initial learning rate for Adam, default: 0.0002
  --beta1 BETA1         momentum term of Adam, default: 0.5
  --pool_size POOL_SIZE
                        size of image buffer that stores previously generated
                        images, default: 50
  --ngf NGF             number of gen filters in first conv layer, default: 64
  --X X                 X tfrecords file for training, default:
                        data/tfrecords/apple.tfrecords
  --Y Y                 Y tfrecords file for training, default:
                        data/tfrecords/orange.tfrecords
  --load_model LOAD_MODEL
                        folder of saved model that you wish to continue
                        training (e.g. 20170602-1936), default: None

Check TensorBoard to see training progress and generated images.

$ tensorboard --logdir checkpoints/${datetime}

If you halted the training process and want to continue training, then you can set the load_model parameter like this.

$ python3 train.py  \
    --load_model 20170602-1936

Here are some funny screenshots from TensorBoard when training orange -> apple:

Notes

If high constrast background colors between input and generated images are observed (e.g. black becomes white), you should restart your training!
Train several times to get the best models.

Export model

You can export from a checkpoint to a standalone GraphDef file as follow:

$ python3 export_graph.py --checkpoint_dir checkpoints/${datetime} \
                          --XtoY_model apple2orange.pb \
                          --YtoX_model orange2apple.pb \
                          --image_size 256

Inference

After exporting model, you can use it for inference. For example:

python3 inference.py --model pretrained/apple2orange.pb \
                     --input input_sample.jpg \
                     --output output_sample.jpg \
                     --image_size 256

Pretrained models

My pretrained models are available at https://github.com/vanhuyz/CycleGAN-TensorFlow/releases

Contributing

Please open an issue if you have any trouble or found anything incorrect in my code :)

License

This project is licensed under the MIT License - see the LICENSE file for details.

References

CycleGAN paper: https://arxiv.org/abs/1703.10593
Official source code in Torch: https://github.com/junyanz/CycleGAN

vanhuyz/CycleGAN-TensorFlow

vanhuyz

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