This repository contains an op-for-op PyTorch reimplementation of Generative Adversarial Networks.
If you're new to GANs, here's an abstract straight from the paper:
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
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.
$ git clone https://github.com/Lornatang/GAN-PyTorch.git
$ cd GAN-PyTorch/
$ pip3 install -r requirements.txt$ cd weights/
$ python3 download_weights.py# 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/GAN-PyTorch", "gan", 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, 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, "mnist.png", normalize=True)usage: test.py [-h] [-a ARCH] [--num-images NUM_IMAGES] [--model-path PATH] [--pretrained] [--seed SEED] [--gpu GPU]
optional arguments:
-h, --help show this help message and exit
-a ARCH, --arch ARCH model architecture: gan. (Default: `gan`)
--num-images NUM_IMAGES
How many samples are generated at one time. (Default: 64)
--model-path PATH Path to latest checkpoint for model.
--pretrained Use pre-trained model.
--seed SEED Seed for initializing training. (Default: 666)
--gpu GPU GPU id to use.
# Example (e.g. MNIST)
$ python3 test.py --arch gan --pretrained --gpu 0
usage: train.py [-h] [-a ARCH] [-j N] [--epochs N] [--start-epoch N] [-b N] [--lr LR] [--image-size IMAGE_SIZE] [--channels CHANNELS] [--netD PATH] [--netG PATH] [--pretrained] [--world-size WORLD_SIZE] [--rank RANK] [--dist-url DIST_URL]
[--dist-backend DIST_BACKEND] [--seed SEED] [--gpu GPU] [--multiprocessing-distributed]
DIR
positional arguments:
DIR Path to dataset.
optional arguments:
-h, --help show this help message and exit
-a ARCH, --arch ARCH Model architecture: gan. (Default: gan)
-j N, --workers N Number of data loading workers. (Default: 4)
--epochs N Number of total epochs to run. (Default: 128)
--start-epoch N Manual epoch number (useful on restarts). (Default: 0)
-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: 0.0002)
--image-size IMAGE_SIZE
Image size of high resolution image. (Default: 28)
--channels CHANNELS The number of channels of the image. (Default: 1)
--netD PATH Path to Discriminator checkpoint.
--netG PATH Path to Generator checkpoint.
--pretrained Use pre-trained model.
--world-size WORLD_SIZE
Number of nodes for distributed training.
--rank RANK Node rank for distributed training. (Default: -1)
--dist-url DIST_URL url used to set up distributed training. (Default: `tcp://59.110.31.55:12345`)
--dist-backend DIST_BACKEND
Distributed backend. (Default: `nccl`)
--seed SEED Seed for initializing training.
--gpu GPU GPU id to use.
--multiprocessing-distributed
Use multi-processing distributed training to launch N processes per node, which has N GPUs. This is the fastest way to use PyTorch for either single node or multi node data parallel training.
# Example (e.g. MNIST)
$ python3 train.py --arch gan --gpu 0 data
If you want to load weights that you've trained before, run the following command.
$ python3 train.py --arch gan --netD weights/Discriminator_epoch8.pth --netG weights/Generator_epoch8.pth --start-epoch 8 --gpu 0 dataIf 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!
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
Abstract
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions G and D, a unique solution exists, with G recovering the training data distribution and D equal to 1/2 everywhere. In the case where G and D are defined by multilayer perceptrons, the entire system can be trained with backpropagation. There is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of the generated samples.
[Paper] [Authors' Implementation]
@article{adversarial,
title={Generative Adversarial Networks},
author={Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio},
journal={nips},
year={2014}
}