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README.md

DiffAugment for BigGAN (CIFAR)

This repo is implemented upon the BigGAN-PyTorch repo. The main dependencies are:

  • PyTorch version >= 1.0.1. Code has been tested with PyTorch 1.4.0.

  • TensorFlow 1.14 or 1.15 with GPU support (for IS and FID calculation).

  • We recommend using 2 GPUs with at least 12 GB of DRAM for training and evaluation.

Pre-Trained Models and Evaluation

To evaluate a model on CIFAR-10 or CIFAR-100, run the following command:

CUDA_VISIBLE_DEVICES=0,1 python eval.py --dataset=WHICH_DATASET --network=WHICH_MODEL

Here, WHICH_DATASET specifies either C10 (CIFAR-10, default) or C100 (CIFAR-100); WHICH_MODEL specifies the path of a checkpoint containing the generator's weights (typically this should be the path of a file named G_ema_best.pth in the weights folder), or a pre-trained model in the following list, which will be automatically downloaded:

Model name Dataset is10k fid10k
mit-han-lab:biggan-cifar10.pth C10 9.06 9.59
mit-han-lab:DiffAugment-biggan-cifar10.pth C10 9.16 8.70
mit-han-lab:cr-biggan-cifar10.pth C10 9.20 9.06
mit-han-lab:DiffAugment-cr-biggan-cifar10.pth C10 9.17 8.49
mit-han-lab:biggan-cifar10-0.2.pth C10 (20% data) 8.41 21.58
mit-han-lab:DiffAugment-biggan-cifar10-0.2.pth C10 (20% data) 8.65 14.04
mit-han-lab:cr-biggan-cifar10-0.2.pth C10 (20% data) 8.43 20.62
mit-han-lab:DiffAugment-cr-biggan-cifar10-0.2.pth C10 (20% data) 8.61 12.84
mit-han-lab:biggan-cifar10-0.1.pth C10 (10% data) 7.62 39.78
mit-han-lab:DiffAugment-biggan-cifar10-0.1.pth C10 (10% data) 8.09 22.40
mit-han-lab:cr-biggan-cifar10-0.1.pth C10 (10% data) 7.66 37.45
mit-han-lab:DiffAugment-cr-biggan-cifar10-0.1.pth C10 (10% data) 8.49 18.70
mit-han-lab:biggan-cifar100.pth C100 10.92 12.87
mit-han-lab:DiffAugment-biggan-cifar10.pth C100 10.66 12.00
mit-han-lab:cr-biggan-cifar100.pth C100 10.95 11.26
mit-han-lab:DiffAugment-cr-biggan-cifar10.pth C100 10.81 11.25
mit-han-lab:biggan-cifar100-0.2.pth C100 (20% data) 9.11 33.11
mit-han-lab:DiffAugment-biggan-cifar100-0.2.pth C100 (20% data) 9.47 22.14
mit-han-lab:cr-biggan-cifar100-0.2.pth C100 (20% data) 8.44 36.91
mit-han-lab:DiffAugment-cr-biggan-cifar100-0.2.pth C100 (20% data) 9.12 20.28
mit-han-lab:biggan-cifar100-0.1.pth C100 (10% data) 5.94 66.71
mit-han-lab:DiffAugment-biggan-cifar100-0.1.pth C100 (10% data) 8.38 33.70
mit-han-lab:cr-biggan-cifar100-0.1.pth C100 (10% data) 7.91 47.16
mit-han-lab:DiffAugment-cr-biggan-cifar100-0.1.pth C100 (10% data) 8.70 26.90

The evaluation results of the pre-trained models should be close to these numbers. Specify --repeat=NUM_REPEATS to compute means and standard deviations over multiple evaluation runs. A standard deviation of less than 1% relatively is expected.

Training

We provide a complete set of training scripts in the scripts folder to facilitate replicating our results. The scripts have the same naming format as the pre-trained models listed above. For example, the following command will run BigGAN + DiffAugment on CIFAR-10 with 10% training data:

CUDA_VISIBLE_DEVICES=0,1 bash scripts/DiffAugment-biggan-cifar10-0.1.sh

The training typically takes around 1 day on 2 GPUs.

Acknowledgements

The official TensorFlow implementation of the Inception v3 model for IS and FID calculation is borrowed from the StyleGAN2 repo.