SalienceNet

Prerequisites

• Linux or macOS
• Python 3
• CPU or NVIDIA GPU + CUDA CuDNN

Getting Started

Installation

• Clone this repo:

git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
cd pytorch-CycleGAN-and-pix2pix

• Install PyTorch and 0.4+ and other dependencies (e.g., torchvision, visdom and dominate).
  • 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.

CycleGAN train/test

• 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 model:

#!./scripts/train_cyclegan.sh
python train.py  --gpu_ids x --dataroot datasets/dataset_example/ --n_epochs xxx  --model cycle_gan --gan_mode LSSSIMGRAD --input_nc 1 --output_nc 1 --name modelname --wcrit1 0.2 --wcrit2 0.2 --wcrit3 0.6

To see more intermediate results, check out ./checkpoints/maps_cyclegan/web/index.html.

• Test the model:

#!./scripts/test_cyclegan.sh
python test.py --gpu_ids x --dataroot datasets/dataset_example/ --model cycle_gan --input_nc 1 --output_nc 1 --name modelname

• The test results will be saved to a html file here: ./results/maps_cyclegan/latest_test/index.html.

Pretrained model

A pretrained model is available, to use it for prediction use the model name salienceNet :

#!./scripts/test_cyclegan.sh
python test.py --gpu_ids x --dataroot datasets/dataset_example/ --model cycle_gan --input_nc 1 --output_nc 1 --name salienceNet

Acknowledgments

Our code is inspired by pytorch-cycleGAN.