Prerequisites

• Linux or OSX.
• Python 2 or Python 3.
• CPU or NVIDIA GPU + CUDA CuDNN.

Getting Started

Installation

• Install PyTorch and dependencies from http://pytorch.org/
• Install Torch vision from the source.

git clone https://github.com/pytorch/vision
cd vision
python setup.py install

• Install python libraries visdom and dominate.

pip install visdom
pip install dominate

• Clone this repo:

https://github.com/GANGREEK/TVA-GAN.git
cd TVA-GAN

• Train a model:

#!./jscript2.sh
python3 train.py --dataroot ./datasets/WHU  --name WHU --model TVAGANModel --no_dropout --gpu_ids 1  --display_id 0 --dataset_mode aligned 

• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/maps_cyclegan/web/index.html
• Test the model:

## Acknowledgments
Code is inspired by [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix).