Council-GAN - Implementation for our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020)

Overview

Council-GAN

Implementation of our paper Breaking the Cycle - Colleagues are all you need (CVPR 2020)

Paper

Ori Nizan , Ayellet Tal, Breaking the Cycle - Colleagues are all you need [Project]

gan_council_teaser

gan_council_overview

male2female_gif

glasses_gif

anime_gif

Temporary Telegram Bot

Send image to this telegram bot and it will send you back its female translation using our implementation

Usage

Install requirements

conda env create -f conda_requirements.yml

Downloading the dataset

Download the selfie to anime dataset:

bash ./scripts/download.sh U_GAT_IT_selfie2anime

Download the celeba glasses removal dataset:

bash ./scripts/download.sh celeba_glasses_removal

Download the celeba male to female dataset:

bash ./scripts/download.sh celeba_male2female

use your on dataset:

├──datasets
    └──DATASET_NAME
        ├──testA
            ├──im1.png
            ├──im2.png
            └── ...
        ├──testB
            ├──im3.png
            ├──im4.png
            └── ...
        ├──trainA
            ├──im5.png
            ├──im6.png
            └── ...
        └──trainB
            ├──im7.png
            ├──im8.png
            └── ...

and change the data_root attribute to ./datasets/DATASET_NAME in the yaml file

Training:

Selfie to anime:

python train.py --config configs/anime2face_council_folder.yaml --output_path ./outputs/council_anime2face_256_256 --resume

Glasses removel:

python train.py --config configs/galsses_council_folder.yaml --output_path ./outputs/council_glasses_128_128 --resume

Male to female:

python train.py --config configs/male2female_council_folder.yaml --output_path ./outputs/male2famle_256_256 --resume

Testing:

for converting all the images in input_folder using all the members in the council:

python test_on_folder.py --config configs/anime2face_council_folder.yaml --output_folder ./outputs/council_anime2face_256_256 --checkpoint ./outputs/council_anime2face_256_256/anime2face_council_folder/checkpoints/01000000 --input_folder ./datasets/selfie2anime/testB --a2b 0

or using spsified memeber:

python test_on_folder.py --config configs/anime2face_council_folder.yaml --output_folder ./outputs/council_anime2face_256_256 --checkpoint ./outputs/council_anime2face_256_256/anime2face_council_folder/checkpoints/b2a_gen_3_01000000.pt --input_folder ./datasets/selfie2anime/testB --a2b 0

Download Pretrain Models

Download pretrain male to female model:

bash ./scripts/download.sh pretrain_male_to_female
Then to convert images in --input_folder run:
python test_on_folder.py --config pretrain/m2f/256/male2female_council_folder.yaml --output_folder ./outputs/male2famle_256_256 --checkpoint pretrain/m2f/256/01000000 --input_folder ./datasets/celeba_male2female/testA --a2b 1

Download pretrain glasses removal model:

bash ./scripts/download.sh pretrain_glasses_removal
Then to convert images in --input_folder run:
python test_on_folder.py --config pretrain/glasses_removal/128/galsses_council_folder.yaml --output_folder ./outputs/council_glasses_128_128 --checkpoint pretrain/glasses_removal/128/01000000 --input_folder ./datasets/glasses/testA --a2b 1

Download pretrain selfie to anime model:

bash ./scripts/download.sh pretrain_selfie_to_anime
Then to convert images in --input_folder run:
python test_on_folder.py --config pretrain/anime/256/anime2face_council_folder.yaml --output_folder ./outputs/council_anime2face_256_256 --checkpoint pretrain/anime/256/01000000 --input_folder ./datasets/selfie2anime/testB --a2b 0

Test GUI:

gan_council_overview

test GUI on pretrain model:

male2female
python test_gui.py --config pretrain/m2f/128/male2female_council_folder.yaml --checkpoint pretrain/m2f/128/a2b_gen_0_01000000.pt --a2b 1
glasses Removal
python test_gui.py --config pretrain/glasses_removal/128/galsses_council_folder.yaml --checkpoint pretrain/glasses_removal/128/a2b_gen_3_01000000.pt --a2b 1
selfie2anime
python test_gui.py --config pretrain/anime/256/anime2face_council_folder.yaml --checkpoint pretrain/anime/256/b2a_gen_3_01000000.pt --a2b 0

Open In Colab

Citation

@inproceedings{nizan2020council,
  title={Breaking the Cycle - Colleagues are all you need},
  author={Ori Nizan and Ayellet Tal},
  booktitle={IEEE conference on computer vision and pattern recognition (CVPR)},
  year={2020}
}

Acknowledgement

In this work we based our code on MUNIT implementation. Please cite the original MUNIT if you use their part of the code.

Owner
ori nizan
Computer Vision & Deep Learning PhD student
ori nizan
Official Chainer implementation of GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral)

GP-GAN: Towards Realistic High-Resolution Image Blending (ACMMM 2019, oral) [Project] [Paper] [Demo] [Related Work: A2RL (for Auto Image Cropping)] [C

Wu Huikai 402 Dec 27, 2022
Atif Hassan 103 Dec 14, 2022
Codes of paper "Unseen Object Amodal Instance Segmentation via Hierarchical Occlusion Modeling"

Unseen Object Amodal Instance Segmentation (UOAIS) Seunghyeok Back, Joosoon Lee, Taewon Kim, Sangjun Noh, Raeyoung Kang, Seongho Bak, Kyoobin Lee This

GIST-AILAB 92 Dec 13, 2022
RLDS stands for Reinforcement Learning Datasets

RLDS RLDS stands for Reinforcement Learning Datasets and it is an ecosystem of tools to store, retrieve and manipulate episodic data in the context of

Google Research 135 Jan 01, 2023
HDMapNet: A Local Semantic Map Learning and Evaluation Framework

HDMapNet_devkit Devkit for HDMapNet. HDMapNet: A Local Semantic Map Learning and Evaluation Framework Qi Li, Yue Wang, Yilun Wang, Hang Zhao [Paper] [

Tsinghua MARS Lab 421 Jan 04, 2023
Code for Multiple Instance Active Learning for Object Detection, CVPR 2021

Language: 简体中文 | English Introduction This is the code for Multiple Instance Active Learning for Object Detection, CVPR 2021. Installation A Linux pla

Tianning Yuan 269 Dec 21, 2022
Dilated RNNs in pytorch

PyTorch Dilated Recurrent Neural Networks PyTorch implementation of Dilated Recurrent Neural Networks (DilatedRNN). Getting Started Installation: $ pi

Zalando Research 200 Nov 17, 2022
DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe.

DeepLab Introduction DeepLab is a state-of-art deep learning system for semantic image segmentation built on top of Caffe. It combines densely-compute

Ali 234 Nov 14, 2022
[arXiv22] Disentangled Representation Learning for Text-Video Retrieval

Disentangled Representation Learning for Text-Video Retrieval This is a PyTorch implementation of the paper Disentangled Representation Learning for T

Qiang Wang 49 Dec 18, 2022
Official implementation of "A Shared Representation for Photorealistic Driving Simulators" in PyTorch.

A Shared Representation for Photorealistic Driving Simulators The official code for the paper: "A Shared Representation for Photorealistic Driving Sim

VITA lab at EPFL 7 Oct 13, 2022
official code for dynamic convolution decomposition

Revisiting Dynamic Convolution via Matrix Decomposition (ICLR 2021) A pytorch implementation of DCD. If you use this code in your research please cons

Yunsheng Li 110 Nov 23, 2022
A Lightweight Experiment & Resource Monitoring Tool 📺

Lightweight Experiment & Resource Monitoring 📺 "Did I already run this experiment before? How many resources are currently available on my cluster?"

170 Dec 28, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

Korbinian Pöppel 47 Nov 28, 2022
Training a Resilient Q-Network against Observational Interference, Causal Inference Q-Networks

Obs-Causal-Q-Network AAAI 2022 - Training a Resilient Q-Network against Observational Interference Preprint | Slides | Colab Demo | Environment Setup

23 Nov 21, 2022
Source Code for ICSE 2022 Paper - ``Can We Achieve Fairness Using Semi-Supervised Learning?''

Fair-SSL Source Code for ICSE 2022 Paper - Can We Achieve Fairness Using Semi-Supervised Learning? Ethical bias in machine learning models has become

1 Dec 18, 2021
Code for generating a single image pretraining dataset

Single Image Pretraining of Visual Representations As shown in the paper A critical analysis of self-supervision, or what we can learn from a single i

Yuki M. Asano 12 Dec 19, 2022
Nsdf: A mesh SDF with just some code we can directly paste into our raymarcher

nsdf Representing SDFs of arbitrary meshes has been a bit tricky so far. Express

Jan Ivanecky 5 Feb 18, 2022
This repository contains the official MATLAB implementation of the TDA method for reverse image filtering

ReverseFilter TDA This repository contains the official MATLAB implementation of the TDA method for reverse image filtering proposed in the paper: "Re

Fergaletto 2 Dec 13, 2021
Process JSON files for neural recording sessions using Medtronic's BrainSense Percept PC neurostimulator

percept_processing This code processes JSON files for streamed neural data using Medtronic's Percept PC neurostimulator with BrainSense Technology for

Maria Olaru 3 Jun 06, 2022