Cycle GAN

Yet another Cycle GAN implementation in PyTorch.

The purpose of this implementation is Well-structured, reusable and easily understandable.

• CycleGAN Paper
• Download datasets

Prerequisites

• System

  • Linux or macOS
  • CPU or (NVIDIA GPU + CUDA CuDNN)
    • It can run on Single CPU/GPU or Multi GPUs.
  • Python 3

• Libraries

  • PyTorch >= 0.3.0
  • Torchvision >= 0.2.0
  • scipy >= 1.0.0
  • Pillow >= 0.2.0

Training

python train.py \
    --data_A_dir=./datasets/apple2orange/trainA \
    --data_B_dir=./datasets/apple2orange/trainB \
    --output_dir=./outputs

If you set test_data_A_dir and test_data_B_dir then generate A->B and B->A when end of every epoch.

python train.py \
    --data_A_dir=./datasets/apple2orange/trainA \
    --data_B_dir=./datasets/apple2orange/trainB \
    --test_data_A_dir=./datasets/apple2orange/testA \
    --test_data_B_dir=./datasets/apple2orange/testB \
    --output_dir=./outputs

Use python train.py --help to see more options.

Transferring

For single file

python transfer.py \
    --model=./outputs/model \
    --src=./datasets/apple2orange/testA/n07740461_41.jpg \
    --out=./outputs/apple2orange.png

For directory

python transfer.py \
    --src_dir=./datasets/apple2orange/testA \
    --out_dir=./outputs/testA

Use python transfer.py --help to see more options.

File structures

network.py and model.py is main implementations.

• cyclegan
  • config.py : Training options
  • network.py : The neural network architecture of Cycle GAN
  • model.py : Calculate loss and optimizing
  • utils
    • data.py : Utilities for loading data
    • logger.py : Utilities for logging
    • ops.py : Utilities for tensor operations
    • tester.py : Utility functions especially for testing
• train.py : A script for CycleGAN training
• transfer.py : A script for transferring with pre-trained model

TODO

• [ ] Visualizing training progress with Visdom
• [ ] Add some nice generated images and videos :-)

References

• https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
• https://hardikbansal.github.io/CycleGANBlog
• https://github.com/togheppi/CycleGAN
• https://github.com/znxlwm/pytorch-CycleGAN