rish-16 / CycleGANsformer

Unpaired Image-to-Image Translation with Transformer-based GANs in PyTorch [WIP]

CycleGANsformer

Unpaired Image-to-Image Translation using Transformer-based GANs.

About

This is an independent research project to build a Convolution-free GAN using Transformers for unpaired image-to-image translation between two domains (eg: horse and zebra, painting and photograph, seasons, etc.). It's fully implemented with pytorch and torchvision, and was inspired by the GANsformer, TransGAN, and CycleGAN papers.

Usage [WIP]

I've prepared a CycleGANsformer wrapper over the entire model. You can install it via pip like so:

$ pip install pytorch-cyclegansformer

You can use the wrapper like so:

import torch
from cyclegansformer import CycleGANsformer

x = torch.rand(1, 256, 256, 3) # your input image
cgf = CycleGANsformer()

output_img = cgf(x) # can be viewed using matplotlib

Training [WIP]

You can even train your own CycleGANsformer from scratch using the provided ImageDatasetLoader. Here, path_to_x and path_to_y represent the canonical filepaths to your training dataset comprising of two disjoint sets of images from two domains (eg: horses and zebras). Ensure you have the following directory structure:

my_image_dataset/
    |- train/
        |- HORSES
            |- horse_1.jpg
            |- horse_2.jpg
            |- ...
            |- horse_n.jpg
        |- ZEBRAS
            |- zebra_1.jpg
            |- zebra_2.jpg
            |- ...
            |- zebra_m.jpg
    |- test/
        |- HORSES
            |- horse_1.jpg
            |- horse_2.jpg
            |- ...
            |- horse_n.jpg
        |- ZEBRAS
            |- zebra_1.jpg
            |- zebra_2.jpg
            |- ...
            |- zebra_m.jpg

Here, n is the number of horse images (X) and m is the number of zebra images (Y).

Once ready, you can start the training process (ideally on some acceleration hardware) like so:

import torch
from cyclegansformer import CycleGANsformer, ImageDatasetLoader

img_ds = ImageDatasetLoader(path_to_x, path_to_y)
cgf = CycleGANsformer()

cgf.fit(img_ds, epochs=200, alpha_decay=True) # proceeds to train – ideally use GPU, not CPU

Credits

Credits to Aladdin Persson for the CycleGAN tutorial found here, to Phil Wang for his implementation of the Vision Transformer by Dosovitskiy et al., and TransGAN by Jiang et al.

License

MIT