Skip to content

Latest commit

 

History

History
299 lines (237 loc) · 19.7 KB

README.md

File metadata and controls

299 lines (237 loc) · 19.7 KB
Raw

alt text

CircleCI codecov PyPI version Documentation Status License: MIT

About | Documentation | Tutorial | Gallery | Paper

Mimicry is a lightweight PyTorch library aimed towards the reproducibility of GAN research.

Comparing GANs is often difficult - mild differences in implementations and evaluation methodologies can result in huge performance differences. Mimicry aims to resolve this by providing: (a) Standardized implementations of popular GANs that closely reproduce reported scores; (b) Baseline scores of GANs trained and evaluated under the same conditions; (c) A framework for researchers to focus on implementation of GANs without rewriting most of GAN training boilerplate code, with support for multiple GAN evaluation metrics.

We provide a model zoo and set of baselines to benchmark different GANs of the same model size trained under the same conditions, using multiple metrics. To ensure reproducibility, we verify scores of our implemented models against reported scores in literature.

Installation

The library can be installed with:

pip install git+https://github.com/kwotsin/mimicry.git

See also setup information for more.

Example Usage

Training a popular GAN like SNGAN that reproduces reported scores can be done as simply as:

import torch
import torch.optim as optim
import torch_mimicry as mmc
from torch_mimicry.nets import sngan

# Data handling objects
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
dataset = mmc.datasets.load_dataset(root='./datasets', name='cifar10')
dataloader = torch.utils.data.DataLoader(
    dataset, batch_size=64, shuffle=True, num_workers=4)

# Define models and optimizers
netG = sngan.SNGANGenerator32().to(device)
netD = sngan.SNGANDiscriminator32().to(device)
optD = optim.Adam(netD.parameters(), 2e-4, betas=(0.0, 0.9))
optG = optim.Adam(netG.parameters(), 2e-4, betas=(0.0, 0.9))

# Start training
trainer = mmc.training.Trainer(
    netD=netD,
    netG=netG,
    optD=optD,
    optG=optG,
    n_dis=5,
    num_steps=100000,
    lr_decay='linear',
    dataloader=dataloader,
    log_dir='./log/example',
    device=device)
trainer.train()

# Evaluate fid
mmc.metrics.evaluate(
    metric='fid',
    log_dir='./log/example',
    netG=netG,
    dataset='cifar10',
    num_real_samples=50000,
    num_fake_samples=50000,
    evaluate_step=100000,
    device=device)

Example outputs:

>>> INFO: [Epoch 1/127][Global Step: 10/100000]
| D(G(z)): 0.5941
| D(x): 0.9303
| errD: 1.4052
| errG: -0.6671
| lr_D: 0.0002
| lr_G: 0.0002
| (0.4550 sec/idx)
^CINFO: Saving checkpoints from keyboard interrupt...
INFO: Training Ended

Tensorboard visualizations:

tensorboard --logdir=./log/example

See further details in example script, as well as a detailed tutorial on implementing a custom GAN from scratch.

Further Guides

Baselines | Model Zoo

For a fair comparison, we train all models under the same training conditions for each dataset, each implemented using ResNet backbones of the same architectural capacity. We train our models with the Adam optimizer using the popular hyperparameters (β1, β2) = (0.0, 0.9). ndis represents the number of discriminator update steps per generator update step, and niter is simply the number of training iterations.

Models

Abbrev. Name Type*
DCGAN Deep Convolutional GAN Unconditional
WGAN-GP Wasserstein GAN with Gradient Penalty Unconditional
SNGAN Spectral Normalization GAN Unconditional
cGAN-PD Conditional GAN with Projection Discriminator Conditional
SSGAN Self-supervised GAN Unconditional
InfoMax-GAN Infomax-GAN Unconditional

*Conditional GAN scores are only reported for labelled datasets.

Metrics

Metric Method
Inception Score (IS)* 50K samples at 10 splits
Fréchet Inception Distance (FID) 50K real/generated samples
Kernel Inception Distance (KID) 50K real/generated samples, averaged over 10 splits.

*Inception Score can be a poor indicator of GAN performance, as it does not measure diversity and is not domain agnostic. This is why certain datasets with only a single class (e.g. CelebA and LSUN-Bedroom) will perform poorly when using this metric.

Datasets

Dataset Split Resolution
CIFAR-10 Train 32 x 32
CIFAR-100 Train 32 x 32
ImageNet Train 32 x 32
STL-10 Unlabeled 48 x 48
CelebA All 64 x 64
CelebA All 128 x 128
LSUN-Bedroom Train 128 x 128
ImageNet Train 128 x 128

CelebA

Paper | Dataset

Training Parameters

Resolution Batch Size Learning Rate β1 β2 Decay Policy ndis niter
128 x 128 64 2e-4 0.0 0.9 None 2 100K
64 x 64 64 2e-4 0.0 0.9 Linear 5 100K

Results

Resolution Model IS FID KID Checkpoint Code
128 x 128 SNGAN 2.72 ± 0.01 12.93 ± 0.04 0.0076 ± 0.0001 netG.pth sngan_128.py
128 x 128 SSGAN 2.63 ± 0.01 15.18 ± 0.10 0.0101 ± 0.0001 netG.pth ssgan_128.py
128 x 128 InfoMax-GAN 2.84 ± 0.01 9.50 ± 0.04 0.0063 ± 0.0001 netG.pth infomax_gan_128.py
64 x 64 SNGAN 2.68 ± 0.01 5.71 ± 0.02 0.0033 ± 0.0001 netG.pth sngan_64.py
64 x 64 SSGAN 2.67 ± 0.01 6.03 ± 0.04 0.0036 ± 0.0001 netG.pth ssgan_64.py
64 x 64 InfoMax-GAN 2.68 ± 0.01 5.71 ± 0.06 0.0033 ± 0.0001 netG.pth infomax_gan_64.py

LSUN-Bedroom

Paper | Dataset

Training Parameters

Resolution Batch Size Learning Rate β1 β2 Decay Policy ndis niter
128 x 128 64 2e-4 0.0 0.9 Linear 2 100K

Results

Resolution Model IS FID KID Checkpoint Code
128 x 128 SNGAN 2.30 ± 0.01 25.87 ± 0.03 0.0141 ± 0.0001 netG.pth sngan_128.py
128 x 128 SSGAN 2.12 ± 0.01 12.02 ± 0.07 0.0077 ± 0.0001 netG.pth ssgan_128.py
128 x 128 InfoMax-GAN 2.22 ± 0.01 12.13 ± 0.16 0.0080 ± 0.0001 netG.pth infomax_gan_128.py

STL-10

Paper | Dataset

Training Parameters

Resolution Batch Size Learning Rate β1 β2 Decay Policy ndis niter
48 x 48 64 2e-4 0.0 0.9 Linear 5 100K

Results

Resolution Model IS FID KID Checkpoint Code
48 x 48 WGAN-GP 8.55 ± 0.02 43.01 ± 0.19 0.0440 ± 0.0003 netG.pth wgan_gp_48.py
48 x 48 SNGAN 8.04 ± 0.07 39.56 ± 0.10 0.0369 ± 0.0002 netG.pth sngan_48.py
48 x 48 SSGAN 8.25 ± 0.06 37.06 ± 0.19 0.0332 ± 0.0004 netG.pth ssgan_48.py
48 x 48 InfoMax-GAN 8.54 ± 0.12 35.52 ± 0.10 0.0326 ± 0.0002 netG.pth infomax_gan_48.py

ImageNet

Paper | Dataset

Training Parameters

Resolution Batch Size Learning Rate β1 β2 Decay Policy ndis niter
32 x 32 64 2e-4 0.0 0.9 Linear 5 100K
128 x 128 64 2e-4 0.0 0.9 None 5 450k

Results

Resolution Model IS FID KID Checkpoint Code
128 x 128 SNGAN 13.05 ± 0.05 65.74 ± 0.31 0.0663 ± 0.0004 netG.pth sngan_128.py
128 x 128 SSGAN 13.30 ± 0.03 62.48 ± 0.31 0.0616 ± 0.0004 netG.pth ssgan_128.py
128 x 128 InfoMax-GAN 13.68 ± 0.06 58.91 ± 0.14 0.0579 ± 0.0004 netG.pth infomax_gan_128.py
32 x 32 SNGAN 8.97 ± 0.12 23.04 ± 0.06 0.0157 ± 0.0002 netG.pth sngan_32.py
32 x 32 cGAN-PD 9.08 ± 0.17 21.17 ± 0.05 0.0145 ± 0.0002 netG.pth cgan_pd_32.py
32 x 32 SSGAN 9.11 ± 0.12 21.79 ± 0.09 0.0152 ± 0.0002 netG.pth ssgan_32.py
32 x 32 InfoMax-GAN 9.04 ± 0.10 20.68 ± 0.02 0.0149 ± 0.0001 netG.pth infomax_gan_32.py

CIFAR-10

Paper | Dataset

Training Parameters

Resolution Batch Size Learning Rate β1 β2 Decay Policy ndis niter
32 x 32 64 2e-4 0.0 0.9 Linear 5 100K

Results

Resolution Model IS FID KID Checkpoint Code
32 x 32 WGAN-GP 7.33 ± 0.02 22.29 ± 0.06 0.0204± 0.0004 netG.pth wgan_gp_32.py
32 x 32 SNGAN 7.97 ± 0.06 16.77 ± 0.04 0.0125 ± 0.0001 netG.pth sngan_32.py
32 x 32 cGAN-PD 8.25 ± 0.13 10.84 ± 0.03 0.0070 ± 0.0001 netG.pth cgan_pd_32.py
32 x 32 SSGAN 8.17 ± 0.06 14.65 ± 0.04 0.0101 ± 0.0002 netG.pth ssgan_32.py
32 x 32 InfoMax-GAN 8.08± 0.08 15.12 ± 0.10 0.0112 ± 0.0001 netG.pth infomax_gan_32.py

CIFAR-100

Paper | Dataset

Training Parameters

Resolution Batch Size Learning Rate β1 β2 Decay Policy ndis niter
32 x 32 64 2e-4 0.0 0.9 Linear 5 100K

Results

Resolution Model IS FID KID Checkpoint Code
32 x 32 SNGAN 7.57 ± 0.11 22.61 ± 0.06 0.0156 ± 0.0003 netG.pth sngan_32.py
32 x 32 cGAN-PD 8.92 ± 0.07 14.16 ± 0.01 0.0085 ± 0.0002 netG.pth cgan_pd_32.py
32 x 32 SSGAN 7.56 ± 0.07 22.18 ± 0.10 0.0161 ± 0.0002 netG.pth ssgan_32.py
32 x 32 InfoMax-GAN 7.86 ± 0.10 18.94 ± 0.13 0.0135 ± 0.0004 netG.pth infomax_gan_32.py

Reproducibility

To verify our implementations, we reproduce reported scores in literature by re-implementing the models with the same architecture, training them under the same conditions and evaluate them on CIFAR-10 using the exact same methodology for computing FID.

As FID produces highly biased estimates (where using larger samples lead to a lower score), we reproduce the scores using the same sample sizes, where nreal and nfake refers to the number of real and fake images used respectively for computing FID.

Metric Model Score Reported Score nreal nfake Checkpoint Code
FID DCGAN 28.95 ± 0.42 28.12 [4] 10K 10K netG.pth dcgan_cifar.py
FID WGAN-GP 26.08 ± 0.12 29.3 † [6] 50K 50K netG.pth wgan_gp_32.py
FID SNGAN 23.90 ± 0.20 21.7 ± 0.21 [1] 10K 5K netG.pth sngan_32.py
FID cGAN-PD 17.84 ± 0.17 17.5 [2] 10K 5K netG.pth cgan_pd_32.py
FID SSGAN 17.61 ± 0.14 17.88 ± 0.64 [3] 10K 10K netG.pth ssgan_32.py
FID InfoMax-GAN 17.14 ± 0.20 17.14 ± 0.20 [5] 50K 10K netG.pth infomax_gan_32.py

† Best FID was reported at 53K steps, but we find our score can improve till 100K steps to achieve 23.13 ± 0.13.

Citation

If you have found this work useful, please consider citing our work:

@article{lee2020mimicry,
    title={Mimicry: Towards the Reproducibility of GAN Research},
    author={Kwot Sin Lee and Christopher Town},
    booktitle={CVPR Workshop on AI for Content Creation},
    year={2020},
}

For citing InfoMax-GAN:

@InProceedings{Lee_2021_WACV,
    author    = {Lee, Kwot Sin and Tran, Ngoc-Trung and Cheung, Ngai-Man},
    title     = {InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2021},
    pages     = {3942-3952}
}

References

[1] Spectral Normalization for Generative Adversarial Networks

[2] cGANs with Projection Discriminator

[3] Self-Supervised GANs via Auxiliary Rotation Loss

[4] A Large-Scale Study on Regularization and Normalization in GANs

[5] InfoMax-GAN: Improved Adversarial Image Generation via Information Maximization and Contrastive Learning

[6] GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium