openai/vdvae openai/vdvae

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    Python
  • License
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  • Created about 4 years ago
  • Updated over 1 year ago
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Repository Details

Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"

Very Deep VAEs

Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images" (https://arxiv.org/abs/2011.10650)

Some model samples and a visualization of how it generates them: image

This repository is tested with PyTorch 1.6, CUDA 10.1, Numpy 1.16, Ubuntu 18.04, and V100 GPUs.

Setup

Several additional packages are required, including NVIDIA Apex:

pip install imageio
pip install mpi4py
pip install sklearn
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ..

Also, you'll have to download the data, depending on which one you want to run:

./setup_cifar10.sh
./setup_imagenet.sh imagenet32
./setup_imagenet.sh imagenet64
./setup_ffhq256.sh
./setup_ffhq1024.sh  /path/to/images1024x1024  # this one depends on you first downloading the subfolder `images_1024x1024` from https://github.com/NVlabs/ffhq-dataset on your own

Training models

Hyperparameters all reside in hps.py. We use 2 gpus for our CIFAR-10 runs, and 32 for the rest of the models. (Using a lower batch size is also possible and results in slower learning, and may also require a lower learning rate).

The mpiexec arguments you use for runs with more than 1 node depend on the configuration of your system, so please adapt accordingly.

mpiexec -n 2 python train.py --hps cifar10
mpiexec -n 32 python train.py --hps imagenet32
mpiexec -n 32 python train.py --hps imagenet64
mpiexec -n 32 python train.py --hps ffhq256
mpiexec -n 32 python train.py --hps ffhq1024

Restoring saved models

For convenience, we have included training checkpoints which can be restored in order to confirm performance, continue training, or generate samples.

ImageNet 32

# 119M parameter model, trained for 1.7M iters (about 2.5 weeks on 32 V100)
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/imagenet32-iter-1700000-log.jsonl
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/imagenet32-iter-1700000-model.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/imagenet32-iter-1700000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/imagenet32-iter-1700000-opt.th
python train.py --hps imagenet32 --restore_path imagenet32-iter-1700000-model.th --restore_ema_path imagenet32-iter-1700000-model-ema.th --restore_log_path imagenet32-iter-1700000-log.jsonl --restore_optimizer_path imagenet32-iter-1700000-opt.th --test_eval
# should give 2.6364 nats per dim, which is 3.80 bpd

ImageNet 64

# 125M parameter model, trained for 1.6M iters (about 2.5 weeks on 32 V100)
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-log.jsonl
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-opt.th
python train.py --hps imagenet64 --restore_path imagenet64-iter-1600000-model.th --restore_ema_path imagenet64-iter-1600000-model-ema.th --restore_log_path imagenet64-iter-1600000-log.jsonl --restore_optimizer_path imagenet64-iter-1600000-opt.th --test_eval
# should be 2.44 nats, or 3.52 bits per dim

FFHQ-256

# 115M parameters, trained for 1.7M iterations (or about 2.5 weeks) on 32 V100
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq256-iter-1700000-log.jsonl
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq256-iter-1700000-model.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq256-iter-1700000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq256-iter-1700000-opt.th
python train.py --hps ffhq256 --restore_path ffhq256-iter-1700000-model.th --restore_ema_path ffhq256-iter-1700000-model-ema.th --restore_log_path ffhq256-iter-1700000-log.jsonl --restore_optimizer_path ffhq256-iter-1700000-opt.th --test_eval
# should be 0.4232 nats, or 0.61 bits per dim

FFHQ-1024

# 115M parameters, trained for 1.7M iterations (or about 2.5 weeks) on 32 V100
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq1024-iter-1700000-log.jsonl
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq1024-iter-1700000-model.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq1024-iter-1700000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets/ffhq1024-iter-1700000-opt.th
python train.py --hps ffhq1024 --restore_path ffhq1024-iter-1700000-model.th --restore_ema_path ffhq1024-iter-1700000-model-ema.th --restore_log_path ffhq1024-iter-1700000-log.jsonl --restore_optimizer_path ffhq1024-iter-1700000-opt.th --test_eval
# should be 1.678 nats, or 2.42 bits per dim

CIFAR-10

# 39M parameters, trained for ~1M iterations with early stopping (a little less than a week on 2 GPUs)
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/cifar10-seed0-iter-900000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/cifar10-seed1-iter-1050000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/cifar10-seed2-iter-650000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/cifar10-seed3-iter-1050000-model-ema.th
python train.py --hps cifar10 --restore_ema_path cifar10-seed0-iter-900000-model-ema.th --test_eval
python train.py --hps cifar10 --restore_ema_path cifar10-seed1-iter-1050000-model-ema.th --test_eval
python train.py --hps cifar10 --restore_ema_path cifar10-seed2-iter-650000-model-ema.th --test_eval
python train.py --hps cifar10 --restore_ema_path cifar10-seed3-iter-1050000-model-ema.th --test_eval
# seeds 0, 1, 2, 3 should give 2.879, 2.842, 2.898, 2.864 bits per dim, for an average of 2.87 bits per dim.

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