Unsupervised Image to Image Translation with Generative Adversarial Networks

Overview

Unsupervised Image to Image Translation with Generative Adversarial Networks

Paper: Unsupervised Image to Image Translation with Generative Adversarial Networks

Requirements

  • TensorFlow 1.0.0
  • TensorLayer 1.3.11
  • CUDA 8
  • Ubuntu

Dataset

  • Before training the network, please prepare the data
  • CelebA download
  • Cropped SVHN download
  • MNIST download, and put to data/mnist_png

Usage

Step 1: Learning shared feature

python3 train.py --train_step="ac_gan" --retrain=1

Step 2: Learning image encoder

python3 train.py --train_step="imageEncoder" --retrain=1

Step 3: Translation

python3 translate_image.py
  • Samples of all steps will be saved to data/samples/

Network

Want to use different datasets?

  • in train.py and translate_image.py modify the name of dataset flags.DEFINE_string("dataset", "celebA", "The name of dataset [celebA, obama_hillary]")
  • write your own data_loader in data_loader.py
You might also like...
The pytorch implementation of  DG-Font: Deformable Generative Networks for Unsupervised Font Generation
The pytorch implementation of DG-Font: Deformable Generative Networks for Unsupervised Font Generation

DG-Font: Deformable Generative Networks for Unsupervised Font Generation The source code for 'DG-Font: Deformable Generative Networks for Unsupervised

Minimal PyTorch implementation of Generative Latent Optimization from the paper
Minimal PyTorch implementation of Generative Latent Optimization from the paper "Optimizing the Latent Space of Generative Networks"

Minimal PyTorch implementation of Generative Latent Optimization This is a reimplementation of the paper Piotr Bojanowski, Armand Joulin, David Lopez-

Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)
Regularizing Generative Adversarial Networks under Limited Data (CVPR 2021)

Regularizing Generative Adversarial Networks under Limited Data [Project Page][Paper] Implementation for our GAN regularization method. The proposed r

NR-GAN: Noise Robust Generative Adversarial Networks
NR-GAN: Noise Robust Generative Adversarial Networks

NR-GAN: Noise Robust Generative Adversarial Networks (CVPR 2020) This repository provides PyTorch implementation for noise robust GAN (NR-GAN). NR-GAN

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis
HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis

HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis Jungil Kong, Jaehyeon Kim, Jaekyoung Bae In our paper, we p

Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper
Generating Anime Images by Implementing Deep Convolutional Generative Adversarial Networks paper

AnimeGAN - Deep Convolutional Generative Adverserial Network PyTorch implementation of DCGAN introduced in the paper: Unsupervised Representation Lear

Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch
Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) in PyTorch

alias-free-gan-pytorch Unofficial implementation of Alias-Free Generative Adversarial Networks. (https://arxiv.org/abs/2106.12423) This implementation

PyTorch implementations of Generative Adversarial Networks.
PyTorch implementations of Generative Adversarial Networks.

This repository has gone stale as I unfortunately do not have the time to maintain it anymore. If you would like to continue the development of it as

Code for the paper "TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks"

TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks This is a Python3 / Pytorch implementation of TadGAN paper. The associated

Comments
  • Where can I get “obama_hillary” dataset

    Where can I get “obama_hillary” dataset

    I’m adaping your code

    Now I’m tring to replacement faces

    Is “obama_hillary” is custom dataset? Or public dataset

    Let me know where can I get “obama_hillary”

    Thanks.

    opened by dreamegg 0
  • What is the version of tensorflow?

    What is the version of tensorflow?

    Hi,donghao, I am running this project but I find there are so many errors at the beginning of my training, e.g. Traceback (most recent call last): File "train.py", line 362, in tf.app.run() File "/home/zzw/Program/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run _sys.exit(main(_sys.argv[:1] + flags_passthrough)) File "train.py", line 355, in main train_ac_gan() File "train.py", line 98, in train_ac_gan g_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(d_logits_fake, tf.ones_like(d_logits_fake))) File "/home/zzw/Program/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_impl.py", line 149, in sigmoid_cross_entropy_with_logits labels, logits) File "/home/zzw/Program/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/nn_ops.py", line 1512, in _ensure_xent_args "named arguments (labels=..., logits=..., ...)" % name) ValueError: Only call sigmoid_cross_entropy_with_logits with named arguments (labels=..., logits=..., ...)

    I guess these errors are due to differences between mine and yours,so could you please tell me what is your version of tensorflow?

    opened by zzw1123 3
  • Is the output image size of 256 x 256 an option – or is just 64 x 64 px possible?

    Is the output image size of 256 x 256 an option – or is just 64 x 64 px possible?

    Hey it's me again, browsing through your other repos i found this gem – seems fun! A few months ago i've tested another gender swap network written in TF, but the output resolution was hardcoded and i couldn't figure out how to change it (with my limited knowledge of TF). Your version again seems a lot easier to read – due to the usage of the Tensorlayer library?

    I'm using the celebA dataset and have left all thetf.flags by default. So the default image size is 64 x 64px but i've seen that you've also written quite a few lines in train.py and model.py for a 256 x 256px option.

    if FLAGS.image_size == 64:
        generator = model.generator
        discriminator = model.discriminator
        imageEncoder = model.imageEncoder
    # elif FLAGS.image_size == 256:
    #     generator = model.generator_256
    #     discriminator = model.discriminator_256
    #     imageEncoder = model.imageEncoder_256
    else:
        raise Exception("image_size should be 64 or 256")
    
    ################## 256x256x3
    def generator_256(inputs, is_train=True, reuse=False):
    (...)
    def discriminator_256(inputs, is_train=True, reuse=False):
    (...)
    

    Since the second if-statement (elif FLAGS.image_size == 256:) is commented out and never changes the default 64x64px model generator and encoder, setting flags.DEFINE_integer("image_size", ...) in train.py to 256 doesn't really change the size - is this correct?

    I've tried to uncomment the code and enable the elif line but then ran into this error: ValueError: Shapes (64, 64, 64, 256) and (64, 32, 32, 256) are not compatible

    You've added generator_256, discriminator_256 and imageEncoder_256 to model.py so i'm wondering if you just have just experimented with this image size and then discarded the option (and just left the 64x64 image_size option) or if i'm missing something here...

    There is also a commented out flag for output_size – but this variable doesn't show up anywhere else so i guess it's from a previous version of your code: # flags.DEFINE_integer("output_size", 64, "The size of the output images to produce [64]")

    And this one is also non-functional: # flags.DEFINE_integer("train_size", np.inf, "The size of train images [np.inf]")


    I just wondered if it's possible to crank up the training and output resolution to 256x256px (and maybe finish the training process this year – when i get my 1080 Ti 😎).

    Will try to finish the 64x64px first and save the model-.npz files for later, but it would be interesting to know if the mentioned portions of your code are still functional.

    Thanks!

    opened by subzerofun 1
Releases(0.3)
Owner
Hao
Assistant Professor @ Peking University
Hao
CR-Fill: Generative Image Inpainting with Auxiliary Contextual Reconstruction. ICCV 2021

crfill Usage | Web App | | Paper | Supplementary Material | More results | code for paper ``CR-Fill: Generative Image Inpainting with Auxiliary Contex

182 Dec 20, 2022
Good Semi-Supervised Learning That Requires a Bad GAN

Good Semi-Supervised Learning that Requires a Bad GAN This is the code we used in our paper Good Semi-supervised Learning that Requires a Bad GAN Ziha

Zhilin Yang 177 Dec 12, 2022
OpenDILab Multi-Agent Environment

Go-Bigger: Multi-Agent Decision Intelligence Environment GoBigger Doc (中文版) Ongoing 2021.11.13 We are holding a competition —— Go-Bigger: Multi-Agent

OpenDILab 441 Jan 05, 2023
an implementation of Video Frame Interpolation via Adaptive Separable Convolution using PyTorch

This work has now been superseded by: https://github.com/sniklaus/revisiting-sepconv sepconv-slomo This is a reference implementation of Video Frame I

Simon Niklaus 985 Jan 08, 2023
An implementation for the ICCV 2021 paper Deep Permutation Equivariant Structure from Motion.

Deep Permutation Equivariant Structure from Motion Paper | Poster This repository contains an implementation for the ICCV 2021 paper Deep Permutation

72 Dec 27, 2022
Unimodal Face Classification with Multimodal Training

Unimodal Face Classification with Multimodal Training This is a PyTorch implementation of the following paper: Unimodal Face Classification with Multi

Wenbin Teng 3 Jul 06, 2022
The official PyTorch implementation for NCSNv2 (NeurIPS 2020)

Improved Techniques for Training Score-Based Generative Models This repo contains the official implementation for the paper Improved Techniques for Tr

174 Dec 26, 2022
PyTorch implementation of Pointnet2/Pointnet++

Pointnet2/Pointnet++ PyTorch Project Status: Unmaintained. Due to finite time, I have no plans to update this code and I will not be responding to iss

Erik Wijmans 1.2k Dec 29, 2022
Learning Time-Critical Responses for Interactive Character Control

Learning Time-Critical Responses for Interactive Character Control Abstract This code implements the paper Learning Time-Critical Responses for Intera

Movement Research Lab 227 Dec 31, 2022
This repository contains all data used for writing a research paper Multiple Object Trackers in OpenCV: A Benchmark, presented in ISIE 2021 conference in Kyoto, Japan.

OpenCV-Multiple-Object-Tracking Python is version 3.6.7 to install opencv: pip uninstall opecv-python pip uninstall opencv-contrib-python pip install

6 Dec 19, 2021
Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral]

Learning to Disambiguate Strongly Interacting Hands via Probabilistic Per-Pixel Part Segmentation [3DV 2021 Oral] Learning to Disambiguate Strongly In

Zicong Fan 40 Dec 22, 2022
[CVPR 2021] Forecasting the panoptic segmentation of future video frames

Panoptic Segmentation Forecasting Colin Graber, Grace Tsai, Michael Firman, Gabriel Brostow, Alexander Schwing - CVPR 2021 [Link to paper] We propose

Niantic Labs 44 Nov 29, 2022
Implementation of Online Label Smoothing in PyTorch

Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst

83 Dec 14, 2022
This is Unofficial Repo. Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection (CVPR 2021)

Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection This is a PyTorch implementation of the LipForensics paper. This is an U

Minha Kim 2 May 11, 2022
For IBM Quantum Challenge Africa 2021, 9 September (07:00 UTC) - 20 September (23:00 UTC).

IBM Quantum Challenge Africa 2021 To ensure Africa is able to apply quantum computing to solve problems relevant to the continent, the IBM Research La

Qiskit Community 48 Dec 25, 2022
The code for the NSDI'21 paper "BMC: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing".

BMC The code for the NSDI'21 paper "BMC: Accelerating Memcached using Safe In-kernel Caching and Pre-stack Processing". BibTex entry available here. B

Orange 383 Dec 16, 2022
UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning

UniMoCo: Unsupervised, Semi-Supervised and Full-Supervised Visual Representation Learning This is the official PyTorch implementation for UniMoCo pape

dddzg 49 Jan 02, 2023
Tensorflow Tutorials using Jupyter Notebook

Tensorflow Tutorials using Jupyter Notebook TensorFlow tutorials written in Python (of course) with Jupyter Notebook. Tried to explain as kindly as po

Sungjoon 2.6k Dec 22, 2022
A 3D Dense mapping backend library of SLAM based on taichi-Lang designed for the aerial swarm.

TaichiSLAM This project is a 3D Dense mapping backend library of SLAM based Taichi-Lang, designed for the aerial swarm. Intro Taichi is an efficient d

XuHao 230 Dec 19, 2022
HuSpaCy: industrial-strength Hungarian natural language processing

HuSpaCy: Industrial-strength Hungarian NLP HuSpaCy is a spaCy model and a library providing industrial-strength Hungarian language processing faciliti

HuSpaCy 120 Dec 14, 2022