- Pytorch
- torchvision
- opencv for video generation
All code tested on Ubuntu 16.04, pytorch 0.4.1, and opencv 3.4.2
- Clone from github:
git clone https://github.com/sunshineatnoon/LinearStyleTransfer - Download pre-trained models from google drive.
- Uncompress to root folder :
cd LinearStyleTransfer
unzip models.zip
rm models.zip
python TestArtistic.py
or conduct style transfer on relu_31 features
python TestArtistic.py --vgg_dir models/vgg_r31.pth --decoder_dir models/dec_r31.pth --matrixPath models/r31.pth --layer r31
For photo-realistic style transfer, we need first compile the pytorch_spn repository.
cd libs/pytorch_spn
sh make.sh
cd ../..
Then:
python TestPhotoReal.py
Note: images with _filtered.png as postfix are images filtered by the SPN after style transfer, images with _smooth.png as postfix are images post process by a smooth filter.
python TestVideo.py
python real-time-demo.py --vgg_dir models/vgg_r31.pth --decoder_dir models/dec_r31.pth --matrixPath models/r31.pth --layer r31
- MSCOCO
wget http://msvocds.blob.core.windows.net/coco2014/train2014.zip
- WikiArt
- Either manually download from kaggle.
- Or install kaggle-cli and download by running:
kg download -u <username> -p <password> -c painter-by-numbers -f train.zip
To train a model that transfers relu4_1 features, run:
python Train.py --vgg_dir models/vgg_r41.pth --decoder_dir models/dec_r41.pth --layer r41 --contentPath PATH_TO_MSCOCO --stylePath PATH_TO_WikiArt --outf OUTPUT_DIR
or train a model that transfers relu3_1 features:
python Train.py --vgg_dir models/vgg_r31.pth --decoder_dir models/dec_r31.pth --layer r31 --contentPath PATH_TO_MSCOCO --stylePath PATH_TO_WikiArt --outf OUTPUT_DIR
Key hyper-parameters:
- style_layers: which features to compute style loss.
- style_weight: larger style weight leads to heavier style in transferred images.
Intermediate results and weight will be stored in OUTPUT_DIR
Run:
python TrainSPN.py --contentPath PATH_TO_MSCOCO
- We use the smooth filter by LouieYang in the photo-realistic style transfer.
@inproceedings{li2018learning,
author = {Li, Xueting and Liu, Sifei and Kautz, Jan and Yang, Ming-Hsuan},
title = {Learning Linear Transformations for Fast Arbitrary Style Transfer},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2019}
}
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