WassersteinGAN_DIV-PyTorch
Overview
This repository contains an op-for-op PyTorch reimplementation of Wasserstein Divergence for GANs.
Table of contents
About Wasserstein Divergence for GANs
If you're new to WassersteinGAN-DIV, here's an abstract straight from the paper:
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the fam- ily of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance. However, it is very challenging to approximate the k-Lipschitz constraint required by the Wasserstein-1 metric (W-met). In this paper, we propose a novel Wasserstein divergence (W-div), which is a relaxed version of W-met and does not require the k-Lipschitz constraint.As a concrete application, we introduce a Wasserstein divergence objective for GANs (WGAN-div), which can faithfully approximate W-div through optimization. Under various settings, including progressive growing training, we demonstrate the stability of the proposed WGAN-div owing to its theoretical and practical advantages over WGANs. Also, we study the quantitative and visual performance of WGAN-div on standard image synthesis benchmarks, showing the superior performance of WGAN-div compared to the state-of-the-art methods.
Model Description
We have two networks, G (Generator) and D (Discriminator).The Generator is a network for generating images. It receives a random noise z and generates images from this noise, which is called G(z).Discriminator is a discriminant network that discriminates whether an image is real. The input is x, x is a picture, and the output is D of x is the probability that x is a real picture, and if it's 1, it's 100% real, and if it's 0, it's not real.
Installation
Clone and install requirements
$ git clone https://github.com/Lornatang/WassersteinGAN_DIV-PyTorch.git
$ cd WassersteinGAN_DIV-PyTorch/
$ pip3 install -r requirements.txtDownload pretrained weights (e.g. LSUN)
$ cd weights/
$ python3 download_weights.pyDownload cartoon faces
baiduclouddisk access: 68rn
Test
Torch hub call
# Using Torch Hub library.
import torch
import torchvision.utils as vutils
# Choose to use the device.
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# Load the model into the specified device.
model = torch.hub.load("Lornatang/WassersteinGAN_DIV-PyTorch", "lsun", pretrained=True, progress=True, verbose=False)
model.eval()
model = model.to(device)
# Create random noise image.
num_images = 64
noise = torch.randn(num_images, 100, 1, 1, device=device)
# The noise is input into the generator model to generate the image.
with torch.no_grad():
generated_images = model(noise)
# Save generate image.
vutils.save_image(generated_images, "lsun.png", normalize=True)Base call
Using pre training model to generate pictures.
usage: test.py [-h] [-a ARCH] [-n NUM_IMAGES] [--outf PATH] [--device DEVICE]
An implementation of WassersteinGAN-DIV algorithm using PyTorch framework.
optional arguments:
-h, --help show this help message and exit
-a ARCH, --arch ARCH model architecture: _gan | discriminator |
load_state_dict_from_url | lsun (default: cifar10)
-n NUM_IMAGES, --num-images NUM_IMAGES
How many samples are generated at one time. (default:
64).
--outf PATH The location of the image in the evaluation process.
(default: ``test``).
--device DEVICE device id i.e. `0` or `0,1` or `cpu`. (default:
``cpu``).
# Example (e.g. LSUN)
$ python3 test.py -a lsun --device cpu
Train (e.g. LSUN)
usage: train.py [-h] --dataset DATASET [-a ARCH] [-j N] [--start-iter N]
[--iters N] [-b N] [--lr LR] [--n_critic N_CRITIC]
[--clip_value CLIP_VALUE] [--image-size IMAGE_SIZE]
[--classes CLASSES] [--pretrained] [--netD PATH] [--netG PATH]
[--manualSeed MANUALSEED] [--device DEVICE]
DIR
An implementation of WassersteinGAN-DIV algorithm using PyTorch framework.
positional arguments:
DIR path to dataset
optional arguments:
-h, --help show this help message and exit
--dataset DATASET | lsun |.
-a ARCH, --arch ARCH model architecture: _gan | discriminator |
load_state_dict_from_url | lsun (default: lsun)
-j N, --workers N Number of data loading workers. (default:8)
--start-iter N manual iter number (useful on restarts)
--iters N The number of iterations is needed in the training of
model. (default: 500000)
-b N, --batch-size N mini-batch size (default: 64), this is the total batch
size of all GPUs on the current node when using Data
Parallel or Distributed Data Parallel.
--lr LR Learning rate. (default:0.00005)
--n_critic N_CRITIC Number of training steps for discriminator per iter.
(Default: 5).
--clip_value CLIP_VALUE
Lower and upper clip value for disc. weights.
(Default: 0.01).
--image-size IMAGE_SIZE
The height / width of the input image to network.
(default: 64).
--classes CLASSES comma separated list of classes for the lsun data set.
(default: ``church_outdoor``).
--pretrained Use pre-trained model.
--netD PATH Path to latest discriminator checkpoint. (default:
````).
--netG PATH Path to latest generator checkpoint. (default: ````).
--manualSeed MANUALSEED
Seed for initializing training. (default:1111)
--device DEVICE device id i.e. `0` or `0,1` or `cpu`. (default:
``0``).
# Example (e.g. LSUN)
$ python3 train.py data -a lsun --dataset lsun --image-size 64 --classes church_outdoor --pretrained --device 0If you want to load weights that you've trained before, run the following command.
$ python3 train.py data \
-a lsun \
--dataset lsun \
--image-size 64 \
--classes church_outdoor \
--start-iter 10000 \
--netG weights/lsun_G_iter_10000.pth \
--netD weights/lsun_D_iter_10000.pthContributing
If you find a bug, create a GitHub issue, or even better, submit a pull request. Similarly, if you have questions, simply post them as GitHub issues.
I look forward to seeing what the community does with these models!
Credit
Wasserstein Divergence for GANs
Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, Luc Van Gool
Abstract
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the
family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and
competitive qualitative performance. However, it is very challenging to approximate the k-Lipschitz constraint required
by the Wasserstein-1 metric~(W-met). In this paper, we propose a novel Wasserstein divergence~(W-div), which is a
relaxed version of W-met and does not require the k-Lipschitz constraint. As a concrete application, we introduce a
Wasserstein divergence objective for GANs~(WGAN-div), which can faithfully approximate W-div through optimization. Under
various settings, including progressive growing training, we demonstrate the stability of the proposed WGAN-div owing to
its theoretical and practical advantages over WGANs. Also, we study the quantitative and visual performance of WGAN-div
on standard image synthesis benchmarks of computer vision, showing the superior performance of WGAN-div compared to the
state-of-the-art methods.
@article{DBLP:journals/corr/abs-1712-01026,
author = {Jiqing Wu and
Zhiwu Huang and
Janine Thoma and
Luc Van Gool},
title = {Energy-relaxed Wasserstein GANs(EnergyWGAN): Towards More Stable and
High Resolution Image Generation},
journal = {CoRR},
volume = {abs/1712.01026},
year = {2017},
url = {http://arxiv.org/abs/1712.01026},
archivePrefix = {arXiv},
eprint = {1712.01026},
timestamp = {Mon, 13 Aug 2018 16:48:51 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1712-01026.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}