Shift-Net

Getting Started

We expect you have an nvidia GPU and have installed CUDA. The code does not support running on CPU for now.

Installation

Install torch and dependencies from https://github.com/torch/distro
Install torch packages nngraph, cudnn, display

luarocks install nngraph
luarocks install cudnn
luarocks install https://raw.githubusercontent.com/szym/display/master/display-scm-0.rockspec

Clone this repo:

git clone https://github.com/Zhaoyi-Yan/Shift-Net
cd Shift-Net

Download pre-trained model

bash  scripts/download_models.sh

The model will be downloaded and unzipped.

Train

Download your own dataset.
Change the options in train.lua according to your path of dataset. Normally, you should at least specify three options. They are DATA_ROOT, phase and name.

For example:

DATA_ROOT: ./datasets/Paris_StreetView_Dataset/

phase: paris_train

name: paris_train_shiftNet

This means that the training images are under the folder of ./datasets/Paris_StreetView_Dataset/paris_train/. As for name, it gives your experiment a name, e.g., paris_train_shiftNet. When training, the checkpoints are stored under the folder ./checkpoints/paris_train_shiftNet/.

Train a model:

th train.lua

Display the temporary results on the browser. Set display = 1, and then open another console,

th -ldisplay.start

Open this URL in your browser: http://localhost:8000

Test

Before test, you should change DATA_ROOT, phase, name, checkpoint_dir and which_epoch. For example, if you want to test the 30-th epoch of your trained model, then DATA_ROOT: ./datasets/Paris_StreetView_Dataset/

phase: paris_train

name: paris_train_shiftNet

checkpoint_dir:./checkpoints/

which_epoch: '30'

The first two options determine where the dataset is, and the rest define the folder where the model is stored.

Finally, test the model:

th test.lua

Acknowledgments

We benefit a lot from pix2pix and DCGAN. The data loader is modified from pix2pix and the implemetation of Instance Normalization borrows from Instance Normalization. The shift operation is inspired by style-swap.