The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

Related tags

Deep LearningASMAGAN
Overview

ASMA-GAN

Anisotropic Stroke Control for Multiple Artists Style Transfer

Proceedings of the 28th ACM International Conference on Multimedia

The official repository with Pytorch

[Arxiv paper]

logo

title

Methodology

Framework

Dependencies

  • python3.6+
  • pytorch1.5+
  • torchvision
  • pyyaml
  • paramiko
  • pandas
  • requests
  • tensorboard
  • tensorboardX
  • tqdm

Installation

We highly recommend you to use Anaconda for installation

conda create -n ASMA python=3.6
conda activate ASMA
conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.1 -c pytorch
pip install pyyaml paramiko pandas requests tensorboard tensorboardX tqdm

Preparation

  • Traning dataset
    • Coming soon
  • pre-trained model
    • Download the model from Github Releases, and unzip the files to ./train_logs/

Usage

To test with pretrained model

The command line below will generate 1088*1920 HD style migration pictures of 11 painters for each picture of testImgRoot (11 painters include: Berthe Moriso , Edvard Munch, Ernst Ludwig Kirchner, Jackson Pollock, Wassily Kandinsky, Oscar-Claude Monet, Nicholas Roerich, Paul Cézanne, Pablo Picasso ,Samuel Colman, Vincent Willem van Gogh. The output image(s) can be found in ./test_logs/ASMAfinal/

  • Example of style transfer with all 11 artists style

    python main.py --mode test --cuda 0 --version ASMAfinal  --dataloader_workers 8   --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle -1 
  • Example of style transfer with Pablo Picasso style

    python main.py --mode test --cuda 0 --version ASMAfinal  --dataloader_workers 8   --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 8 
  • Example of style transfer with Wassily Kandinsky style

    python main.py --mode test --cuda 0 --version ASMAfinal  --dataloader_workers 8   --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 4

--version refers to the ASMAGAN training logs name.

--testImgRoot can be a folder with images or the path of a single picture.You can assign the image(s) you want to perform style transfer to this argument.

--specify_sytle is used to specify which painter's style is used for style transfer. When the value is -1, 11 painters' styles are used for image(s) respectively for style transfer. The values corresponding to each painter's style are as follows [0: Berthe Moriso, 1: Edvard Munch, 2: Ernst Ludwig Kirchner, 3: Jackson Pollock, 4: Wassily Kandinsky, 5: Oscar-Claude Monet, 6: Nicholas Roerich, 7: Paul Cézanne, 8: Pablo Picasso, 9 : Samuel Colman, 10: Vincent Willem van Gogh]

Training

Coming soon

To cite our paper

@inproceedings{DBLP:conf/mm/ChenYLQN20,
  author    = {Xuanhong Chen and
               Xirui Yan and
               Naiyuan Liu and
               Ting Qiu and
               Bingbing Ni},
  title     = {Anisotropic Stroke Control for Multiple Artists Style Transfer},
  booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia, 2020},
  publisher = {{ACM}},
  year      = {2020},
  url       = {https://doi.org/10.1145/3394171.3413770},
  doi       = {10.1145/3394171.3413770},
  timestamp = {Thu, 15 Oct 2020 16:32:08 +0200},
  biburl    = {https://dblp.org/rec/conf/mm/ChenYLQN20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Some Results

Results1

Related Projects

Learn about our other projects [RainNet], [Sketch Generation], [CooGAN], [Knowledge Style Transfer], [SimSwap],[ASMA-GAN],[Pretrained_VGG19].

High Resolution Results

Comments
  • Can't download pre-trained model

    Can't download pre-trained model

    Hi! Could you please check your pre-trained model. The follow links is no found. Thank you https://github.com/neuralchen/ASMAGAN/releases/download/v.1.0/ASMAfinal.zip

    opened by namdn 5
  • Thank you for your great project. When will the training code be released

    Thank you for your great project. When will the training code be released

    Thank you for your great project.

    1. When will the training code be released.
    2. I want to get more painters how do I do that, how do I make the training datasets, how much data do I need
    3. Looking forward to your reply
    opened by zhanghongyong123456 5
  • Fine Tuning for single class

    Fine Tuning for single class

    Hello team, I would like to finetune your pretrained model for just five new class (total output will be five), how should I use the finetune? Thank you!

    opened by minhtcai 0
  • KeyError 1920

    KeyError 1920

    using the official command: python main.py --mode test --cuda 0 --version ASMAfinal --dataloader_workers 8 --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 8

    then error happened Generator Script Name: Conditional_Generator_asm 11 classes Finished preprocessing the test dataset, total image number: 25... /home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/transforms.py:332: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. warnings.warn( Traceback (most recent call last): File "/home/ama/ASMAGAN/main.py", line 266, in tester.test() File "/home/ama/ASMAGAN/test_scripts/tester_common_useage.py", line 50, in test test_data = TestDataset(test_img,batch_size) File "/home/ama/ASMAGAN/data_tools/test_data_loader_resize.py", line 36, in init transform.append(T.Resize(1088,1920)) File "/home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/transforms.py", line 336, in init interpolation = _interpolation_modes_from_int(interpolation) File "/home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/functional.py", line 47, in _interpolation_modes_from_int return inverse_modes_mapping[i] KeyError: 1920

    opened by Kayce001 1
  • Change aspect ratio of images

    Change aspect ratio of images

    test code change aspect ratio of input images so output images are deformed to fix this i make some correction at "test_data_loader_resize.py"

    image

    opened by birolkuyumcu 0
  • RuntimeError: cuDNN

    RuntimeError: cuDNN

    Hi I get the following error when running the code:

    RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED when calling backward()

    I would appreciate your help on how to resolve this.

    Thank you!

    Gero

    opened by Limbicnation 8
Releases(v.1.1)
Owner
Six_God
Six_God
Experiments for Neural Flows paper

Neural Flows: Efficient Alternative to Neural ODEs [arxiv] TL;DR: We directly model the neural ODE solutions with neural flows, which is much faster a

54 Dec 07, 2022
Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Context Terms

LESA Introduction This repository contains the official implementation of Locally Enhanced Self-Attention: Rethinking Self-Attention as Local and Cont

Chenglin Yang 20 Dec 31, 2021
[BMVC 2021] Official PyTorch Implementation of Self-supervised learning of Image Scale and Orientation Estimation

Self-Supervised Learning of Image Scale and Orientation Estimation (BMVC 2021) This is the official implementation of the paper "Self-Supervised Learn

Jongmin Lee 17 Nov 10, 2022
Probabilistic Gradient Boosting Machines

PGBM Probabilistic Gradient Boosting Machines (PGBM) is a probabilistic gradient boosting framework in Python based on PyTorch/Numba, developed by Air

Olivier Sprangers 112 Dec 28, 2022
Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics".

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics This repository is the official PyTorch implementation of "Physics-aware Differ

USC-Melady 46 Nov 20, 2022
YOLOPのPythonでのONNX推論サンプル

YOLOP-ONNX-Video-Inference-Sample YOLOPのPythonでのONNX推論サンプルです。 ONNXモデルは、hustvl/YOLOP/weights を使用しています。 Requirement OpenCV 3.4.2 or later onnxruntime 1.

KazuhitoTakahashi 8 Sep 05, 2022
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.

News December 27: v1.1.0 New loss functions: CentroidTripletLoss and VICRegLoss Mean reciprocal rank + per-class accuracies See the release notes Than

Kevin Musgrave 5k Jan 05, 2023
A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

Deep Reinforcement Learning Agents This repository contains a collection of reinforcement learning algorithms written in Tensorflow. The ipython noteb

Arthur Juliani 2.2k Jan 01, 2023
Unified file system operation experience for different backend

megfile - Megvii FILE library Docs: http://megvii-research.github.io/megfile megfile provides a silky operation experience with different backends (cu

MEGVII Research 76 Dec 14, 2022
PyTorch code for our paper "Attention in Attention Network for Image Super-Resolution"

Under construction... Attention in Attention Network for Image Super-Resolution (A2N) This repository is an PyTorch implementation of the paper "Atten

Haoyu Chen 71 Dec 30, 2022
Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER 🦌 🦒 Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEE

33 Dec 23, 2022
Python Classes: Medical Insurance Project using Object Oriented Programming Concepts

Medical-Insurance-Project-OOP Python Classes: Medical Insurance Project using Object Oriented Programming Concepts Classes are an incredibly useful pr

Hugo B. 0 Feb 04, 2022
This repository implements Douzero's interface to IGCA.

douzero-interface-for-ICGA This repository implements Douzero's interface to ICGA. ./douzero: This directory stores Doudizhu AI projects. ./interface:

zhanggenjin 4 Aug 07, 2022
Google Landmark Recogntion and Retrieval 2021 Solutions

Google Landmark Recogntion and Retrieval 2021 Solutions In this repository you can find solution and code for Google Landmark Recognition 2021 and Goo

Vadim Timakin 5 Nov 25, 2022
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021

DVG-Face: Dual Variational Generation for HFR This repo is a PyTorch implementation of DVG-Face: Dual Variational Generation for Heterogeneous Face Re

52 Dec 30, 2022
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection

Deep learning for time series forecasting Flow forecast is an open-source deep learning for time series forecasting framework. It provides all the lat

AIStream 1.2k Jan 04, 2023
Official implementation of the ICCV 2021 paper "Joint Inductive and Transductive Learning for Video Object Segmentation"

JOINT This is the official implementation of Joint Inductive and Transductive learning for Video Object Segmentation, to appear in ICCV 2021. @inproce

Yunyao 35 Oct 16, 2022
This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive Selective Coding)

HCSC: Hierarchical Contrastive Selective Coding This repository provides a PyTorch implementation and model weights for HCSC (Hierarchical Contrastive

YUANFAN GUO 111 Dec 20, 2022
This is the official implementation of the paper "Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation".

[CVPRW 2021] - Object Propagation via Inter-Frame Attentions for Temporally Stable Video Instance Segmentation

Anirudh S Chakravarthy 6 May 03, 2022
This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

This is an unofficial implementation of the paper “Student-Teacher Feature Pyramid Matching for Unsupervised Anomaly Detection”.

haifeng xia 32 Oct 26, 2022