DCGAN LSGAN WGAN-GP DRAGAN PyTorch

Last update: Nov 30, 2022

Overview

Recommendation

Our GAN based work for facial attribute editing - AttGAN.

News

8 April 2019: We re-implement these GANs by Tensorflow 2! The old version is here: v1 or in the "v1" directory.
PyTorch Version

GANs - Tensorflow 2

Tensorflow 2 implementations of DCGAN, LSGAN, WGAN-GP and DRAGAN.

Exemplar results

Fashion-MNIST

DCGAN	LSGAN	WGAN-GP	DRAGAN

CelebA

DCGAN	LSGAN

WGAN-GP	DRAGAN

Anime

WGAN-GP	DRAGAN

Usage

Environment
- Python 3.6
- TensorFlow 2.2, TensorFlow Addons 0.10.0
- OpenCV, scikit-image, tqdm, oyaml
- we recommend Anaconda or Miniconda, then you can create the TensorFlow 2.2 environment with commands below
```
conda create -n tensorflow-2.2 python=3.6

source activate tensorflow-2.2

conda install scikit-image tqdm tensorflow-gpu=2.2

conda install -c conda-forge oyaml

pip install tensorflow-addons==0.10.0
```
- NOTICE: if you create a new conda environment, remember to activate it before any other command
```
source activate tensorflow-2.2
```
Datasets
- Fashion-MNIST will be automatically downloaded
- CelebA should be prepared by yourself in ./data/img_align_celeba/*.jpg
  - dataset link (find "img_align_celeba.zip"):
    - Baidu Netdisk (password rp0s) or
    - Google Drive
- the Anime dataset should be prepared by yourself in ./data/faces/*.jpg
  - dataset link: https://www.kaggle.com/splcher/animefacedataset

Examples of training

Fashion-MNIST DCGAN

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=fashion_mnist --epoch=25 --adversarial_loss_mode=gan

CelebA DRAGAN

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=celeba --epoch=25 --adversarial_loss_mode=gan --gradient_penalty_mode=dragan

Anime WGAN-GP

CUDA_VISIBLE_DEVICES=0 python train.py --dataset=anime --epoch=200 --adversarial_loss_mode=wgan --gradient_penalty_mode=wgan-gp --n_d=5

see more training exampls in commands.sh

tensorboard for loss visualization

tensorboard --logdir ./output/fashion_mnist_gan/summaries --port 6006

Comments

GPU is full

Hello, when the code runs, the memory is full. What happened? My python version is 3.6, tensorflow version is 1.11, my GPU is 1080ti, thanks! the error is as follow: An error ocurred while starting the kernel 2019󈚥󈚦 08:40:45.229298: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 2019󈚥󈚦 08:40:45.601632: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties: name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.582 pciBusID: 0000:65:00.0 totalMemory: 11.00GiB freeMemory: 9.10GiB 2019󈚥󈚦 08:40:45.603929: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0 2019󈚥󈚦 08:40:46.556261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix: 2019󈚥󈚦 08:40:46.558110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] 0 2019󈚥󈚦 08:40:46.558407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0: N 2019󈚥󈚦 08:40:46.558836: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8789 MB memory) ‑> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:65:00.0, compute capability: 6.1)

opened by lixingbao 9
About problem with generating image size

Hello,can your code only generate 64×64 images? Can I generate an image of the specified size? For example: 256 × 256, if you can, what parameters need to be modified?thank you!

opened by lixingbao 6
WGAN-GP does not work!!!

I have updated the code from TensorFlow 2.0-alpha to TensorFlow 2.0, everything works well except for WGAN-GP (it works in tf2.0-alpha). In tf2.0, The gradient penalty seems very unstable, but I cannot find out the problem. Does anybody help? I will be grateful.
help wanted

opened by LynnHo 3
how about 3D data?

Hi!

cartoon faces original size is [96, 96, 3],the number 3 means 3 channel RGB data. But if I have grayscale data with 3 slices, i.e the size is [121,145,3], Can I simply use this code? If not, what should I change based on this code?

Thanks for your work! Look forward to your response.

opened by KakaVlasic 3
c_iter isn't used

c_iter is defined but not used in all of the WGAN files. What is the correct behaviour? i.e should the critic be optimised heavily initially or not?

Also, can you confirm that you use a learning rate parameter of 0.0002, regardless of whether RMSProp or Adam is used as the optimiser?

opened by davidADSP 3
About n_critic = 5

i use the code whitch be used to train cartoon pictures with WGAN-GP. i don't know what the mean of n_critic = 5 , and why do you to set it. thanks.

opened by tuoniaoren 3
Error while using celeba dataset

I am getting this error while running train.py TypeError: Input 'filename' of 'ReadFile' Op has type float32 that does not match expected type of string. Please help with this. Thanks in advance

opened by yksolanki9 2
Where do you freeze the gradient descent?

Hello, I am confused about how do you freeze the gradient descent to the other model. When training d_step, I suppose the generator should be freezed, as f_logit is based on generator and used in d_loss; similarly, when training g_step, I suppose the discriminator should be freezed, as f_logit depens on discriminator.

However, I do not see where you stop those gradients flowing to the unwanted part, either generator or discriminator. Would you please provide some hints for me? Thank you.

opened by ybsave 2
do you try to use Resnet in wgan-gp?

Have you compared the difference between the network structure of DCGAN and the structure of Resnet in WGAN-GP?Is the effect of Resnet will be better than the structure of DCGAN.

opened by tuoniaoren 2
running question

do you meet the program stop without mistakes when the code running for some time ,and the GPU stops work.I changed the value of num_threads。（from 16 to 10）。i run it again.i don't know Is it because the value is too high。

opened by tuoniaoren 2
License

Hi Zhenliang He, I wonder whether you would be willing to please license this code under an open source license? If so please add a license, or if not please just close this request. Thanks, Connelly

opened by connellybarnes 2
A problem for your DCGAN architecture

Hi, - Your work is really interesting. But I have found there is a problem for your DCGAN that I didn't understand. You generate noise twice when train discriminator and generator for each iteration, like the blue lines in the following picture. In soumith code (includes some official DCGAN code), he only generate noise once: https://github.com/soumith/dcgan.torch. Could you please tell me the reason?

opened by RayGuo-C 1
NameError: name 'shape' is not defined

Traceback (most recent call last): File "D:/github/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2-master/DCGAN-LSGAN-WGAN-GP-DRAGAN-Tensorflow-2-master/train.py", line 91, in G = module.ConvGenerator(input_shape=(1, 1, args.z_dim), output_channels=shape[-1], n_upsamplings=n_G_upsamplings, name='G_%s' % args.dataset) NameError: name 'shape' is not defined please tell me why

opened by Tonyztj 0

Releases(v1)

v1(Apr 6, 2019)
DCGAN-LSGAN-WGAN-WGAN-GP-Tensorflow-r1.2

tensorflow r1.2

python 2.7

Source code(tar.gz)
Source code(zip)

Owner

Zhenliang He

GitHub Repository

Official PyTorch implementation of Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, Yu

63 Jan 03, 2023

Official PyTorch implementation of "Improving Face Recognition with Large AgeGaps by Learning to Distinguish Children" (BMVC 2021)

Inter-Prototype (BMVC 2021): Official Project Webpage This repository provides the official PyTorch implementation of the following paper: Improving F

16 Jun 30, 2022

PyTorch implementation of ''Background Activation Suppression for Weakly Supervised Object Localization''.

Background Activation Suppression for Weakly Supervised Object Localization PyTorch implementation of ''Background Activation Suppression for Weakly S

35 Jan 06, 2023

ObsPy: A Python Toolbox for seismology/seismological observatories.

ObsPy is an open-source project dedicated to provide a Python framework for processing seismological data. It provides parsers for common file formats

979 Jan 07, 2023

Permeability Prediction Via Multi Scale 3D CNN

Permeability-Prediction-Via-Multi-Scale-3D-CNN Data: The raw CT rock cores are obtained from the Imperial Colloge portal. The CT rock cores are sub-sa

2 Jul 06, 2022

AVD Quickstart Containerlab

AVD Quickstart Containerlab WARNING This repository is still under construction. It's fully functional, but has number of limitations. For example: RE

3 Apr 10, 2022

Fast and simple implementation of RL algorithms, designed to run fully on GPU.

RSL RL Fast and simple implementation of RL algorithms, designed to run fully on GPU. This code is an evolution of rl-pytorch provided with NVIDIA's I

68 Dec 29, 2022

Final project code: Implementing MAE with downscaled encoders and datasets, for ESE546 FA21 at University of Pennsylvania

546 Final Project: Masked Autoencoder Haoran Tang, Qirui Wu 1. Training To train the network, please run mae_pretraining.py. Please modify folder path

0 Apr 22, 2022

ConformalLayers: A non-linear sequential neural network with associative layers

ConformalLayers: A non-linear sequential neural network with associative layers ConformalLayers is a conformal embedding of sequential layers of Convo

5 Sep 28, 2022

Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

17 Sep 23, 2021

Stacked Generative Adversarial Networks

Stacked Generative Adversarial Networks This repository contains code for the paper "Stacked Generative Adversarial Networks", CVPR 2017. Part of the

241 May 07, 2022

Chinese clinical named entity recognition using pre-trained BERT model

Chinese clinical named entity recognition (CNER) using pre-trained BERT model Introduction Code for paper Chinese clinical named entity recognition wi

109 Dec 14, 2022

An automated facial recognition based attendance system (desktop application)

Facial_Recognition_based_Attendance_System An automated facial recognition based attendance system (desktop application) Made using Python, Tkinter an

1 Jun 21, 2022

Simple torch.nn.module implementation of Alias-Free-GAN style filter and resample

Alias-Free-Torch Simple torch module implementation of Alias-Free GAN. This repository including Alias-Free GAN style lowpass sinc filter @filter.py A

64 Dec 22, 2022

code for TCL: Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022

Vision-Language Pre-Training with Triple Contrastive Learning, CVPR 2022 News (03/16/2022) upload retrieval checkpoints finetuned on COCO and Flickr T

187 Jan 02, 2023

Dynamic Environments with Deformable Objects (DEDO)

DEDO - Dynamic Environments with Deformable Objects DEDO is a lightweight and customizable suite of environments with deformable objects. It is aimed

32 Dec 22, 2022

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

7 Jun 22, 2022

Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domains.

Neural Spatio-Temporal Point Processes [arxiv] Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel Abstract. We propose a new class of parameterizations

75 Dec 19, 2022

Milano is a tool for automating hyper-parameters search for your models on a backend of your choice.

Milano (This is a research project, not an official NVIDIA product.) Documentation https://nvidia.github.io/Milano Milano (Machine learning autotuner

147 Dec 17, 2022

Experiments and examples converting Transformers to ONNX

Experiments and examples converting Transformers to ONNX This repository containes experiments and examples on converting different Transformers to ON

4 Dec 24, 2022