Official implementation of Densely connected normalizing flows

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Deep LearningDenseFlow
Overview

Densely connected normalizing flows

This repository is the official implementation of NeurIPS 2021 paper Densely connected normalizing flows. Poster available here.

PWC PWC

Setup

  • CUDA 11.1
  • Python 3.8
pip install -r requirements.txt
pip install -e .

Training

cd ./experiments/image

CIFAR-10:

python train.py --epochs 400 --batch_size 64 --optimizer adamax --lr 1e-3  --gamma 0.9975 --warmup 5000  --eval_every 1 --check_every 10 --dataset cifar10 --augmentation eta --block_conf 6 4 1 --layers_conf  5 6 20  --layer_mid_chnls 48 48 48 --growth_rate 10  --name DF_74_10

python train_more.py --model ./log/cifar10_8bit/densenet-flow/expdecay/DF_74_10 --new_lr 2e-5 --new_epochs 420

ImageNet32:

python train.py --epochs 20 --batch_size 64 --optimizer adamax --lr 1e-3  --gamma 0.95 --warmup 5000  --eval_every 1 --check_every 10 --dataset imagenet32 --augmentation eta --block_conf 6 4 1 --layers_conf  5 6 20  --layer_mid_chnls 48 48 48 --growth_rate 10  --name DF_74_10

python train_more.py --model ./log/imagenet32_8bit/densenet-flow/expdecay/DF_74_10 --new_lr 2e-5 --new_epochs 22

ImageNet64:

python train.py --epochs 10 --batch_size 32 --optimizer adamax --lr 1e-3  --gamma 0.95 --warmup 5000  --eval_every 1 --check_every 10 --dataset imagenet64 --augmentation eta --block_conf 6 4 1 --layers_conf  5 6 20  --layer_mid_chnls 48 48 48 --growth_rate 10  --name DF_74_10

python train_more.py --model ./log/imagenet64_8bit/densenet-flow/expdecay/DF_74_10 --new_lr 2e-5 --new_epochs 11

CelebA:

python train.py --epochs 50 --batch_size 32 --optimizer adamax --lr 1e-3  --gamma 0.95 --warmup 5000  --eval_every 1 --check_every 10 --dataset celeba --augmentation horizontal_flip --block_conf 6 4 1 --layers_conf  5 6 20  --layer_mid_chnls 48 48 48 --growth_rate 10  --name DF_74_10

python train_more.py --model ./log/celeba_8bit/densenet-flow/expdecay/DF_74_10 --new_lr 2e-5 --new_epochs 55

Note: Download instructions for ImageNet and CelebA are defined in denseflow/data/datasets/image/{dataset}.py

Evaluation

CIFAR-10:

python eval_loglik.py --model PATH_TO_MODEL --k 1000 --kbs 50

ImageNet32:

python eval_loglik.py --model PATH_TO_MODEL --k 200 --kbs 50

ImageNet64 and CelebA:

python eval_loglik.py --model PATH_TO_MODEL --k 200 --kbs 25

Model weights

Model weights are stored here.

Samples generation

Generated samples are stored in PATH_TO_MODEL/samples

python eval_sample.py --model PATH_TO_MODEL

Note: PATH_TO_MODEL has to contain check directory.

ImageNet 32x32

Alt text

ImageNet 64x64

Alt text

CelebA

Alt text

Acknowledgements

Significant part of this code benefited from SurVAE [1] code implementation, available under MIT license.

References

[1] Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, and Max Welling. Survae flows: Surjections to bridge the gap between vaes and flows. InAdvances in Neural Information Processing Systems 33. Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020.

Owner
Matej Grcić
PhD Student | Research associate focused on Computer Vision @ University of Zagreb, Faculty of Electrical Engineering and Computing
Matej Grcić
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