This repository provides the official PyTorch implementation of our paper "Spiral Generative Network for Image Extrapolation".
Our paper can be found in https://link.springer.com/chapter/10.1007/978-3-030-58529-7_41.
- Linux
- Python 3.7
- NVIDIA GPU + CUDA CuDNN
- Clone this repo:
git clone https://github.com/zhenglab/spiralnet.git
cd spiralnet- Install PyTorch and 1.0+ and other dependencies (e.g., torchvision).
- For pip users, please type the command
pip install -r requirement.txt. - For Conda users, you can create a new Conda environment using
conda env create -f environment.yaml.
- For pip users, please type the command
- Training
python train.py --path=$configpath$
For example: python train.py --path=./checkpoints/ImagineGAN/celeba/
- Testing
python test.py --path=$configpath$
For example: python test.py --path=./checkpoints/ImagineGAN/celeba/
Put the ImagineGAN model in the corresponding directory, for example, checkpoints/SliceGAN/celeba/imagine_g.pth.
- Training
python train.py --path=$configpath$
For example: python train.py --path=./checkpoints/SliceGAN/celeba/
- Testing
python test.py --path=$configpath$
For example: python test.py --path=./checkpoints/SliceGAN/celeba/
@inproceedings{guo2020spiralnet,
author = {Guo, Dongsheng and Liu, Hongzhi and Zhao, Haoru and Cheng, Yunhao and Song, Qingwei and Gu, Zhaorui and Zheng, Haiyong and Zheng, Bing},
title = {Spiral Generative Network for Image Extrapolation},
booktitle = {The European Conference on Computer Vision (ECCV)},
pages={701--717},
year = {2020}
}