sGAN

source code of Enhanced Flow Visualization Using Image Processing and Deep Learning Techniques 这个是我用来保存和分享上述论文中所需要的sGAN代码部分的内容，至于XX部分，请访问XXX

为了让代码成功运行起来，你可能需要如下环境：

GAN是一种非常强大的神经网络

《Intro of sGAN》 一些效果图

Note: The current software works well with PyTorch-cuda 12.1. It may have some trouble if using older version.

Prerequisites

• Linux or Windows
• Python 3.8
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Clone this repo:

git clone https://github.com/Dehakaa/sGAN
cd sGAN

• Install PyTorch and 0.4+ and other.
  • For pip users, please type the command pip install -r requirements.txt.
  • For Conda users, you can create a new Conda environment using conda env create -f environment.yml.

to train/test sGAN

• Download a sGAN dataset (you may find temp files follow the guidance ):

bash ./datasets/download_sgan_dataset.sh temp

• To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097.
• To log training progress and test images to W&B dashboard, set the --use_wandb flag with train and test script
• Train a temp model:

#!./scripts/train_sgan.sh
python train.py --dataroot ./datasets/temps --name temps_train --model s_gan

If you want to get more information about the training process, please refer to Wandb.

• Test the model:

#!./scripts/test_sgan.sh
python test.py --dataroot ./datasets/temps --name temps_train --model s_gan

• Test the results

python test.py --dataroot datasets/temps/testA --name cascade_pretrained --model test --no_dropout

Acknowledgments

Our code is inspired by pytorch-CycleGAN.