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Code for reproducing Flow ++ experiments

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최종 버전 다운로드 (.zip)

- - abl_noattn.py
  - abl_nodequant.py
  - abl_nomixlog.py
- flow_training.py
- flows.py
- logistic.py
- run_cifar.py
- utils.py
- - celeba128_5bit_official.py
  - celeba64_3bit_official.py
  - celeba64_5bit_official.py
- flow_training_celeba.py
- README.md
- - imagenet32_official.py
  - imagenet64_5bit_official.py
  - imagenet64_official.py
- create_imagenet_benchmark_datasets.py
- flow_training_imagenet.py
- logistic.py
- utils.py
LICENSE.md
README.md

// repository documentation

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# Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

This repository contains Tensorflow implementation of experiments from the paper [Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design](https://arxiv.org/abs/1902.00275) - [Jonathan Ho](http://www.jonathanho.me/), [Xi Chen](http://peterchen.io/), [Aravind Srinivas](https://people.eecs.berkeley.edu/~aravind/), [Yan Duan](http://rockyduan.com/), [Pieter Abbeel](https://people.eecs.berkeley.edu/~pabbeel/)

# Dependencies

* python3.6 
* Tensorflow v1.10.1 
* [horovod v0.14.1](https://github.com/uber/horovod)

[Horovod GPU setup instructions](https://github.com/uber/horovod/blob/master/docs/gpus.md)

# Usage Instructions

We trained our models using 8 GPUs with data-parallelism using Horovod.

# CIFAR 10 
```
mpiexec -n 8 python3.6 run_cifar.py
```
# Imagenet

## Data for Imagenet Experiments: 
Script to create dataset [here](https://github.com/aravind0706/flowpp/blob/master/flows_imagenet/create_imagenet_benchmark_datasets.py)

## Imagenet 32x32

```
mpiexec -n 8 python3.6 -m flows_imagenet.launchers.imagenet32_official
```
# Imagenet 64x64
```
mpiexec -n 6 python3.6 -m flows_imagenet.launchers.imagenet64_official
mpiexec -n 6 python3.6 -m flows_imagenet.launchers.imagenet64_5bit_official

```
# CelebA-HQ 64x64

## Data: 
Download links in [README](https://github.com/aravind0706/flowpp/tree/master/flows_celeba)

```
mpiexec -n 8 python3.6 -m flows_celeba.launchers.celeba64_5bit_official
mpiexec -n 8 python3.6 -m flows_celeba.launchers.celeba64_3bit_official

```
# Contact

Please open an issue

# Credits

flowpp was originally developed by Jonathan Ho (UC Berkeley), Peter Chen (UC Berkeley / covariant.ai), Aravind Srinivas (UC Berkeley), Yan Duan (covariant.ai), and Pieter Abbeel (UC Berkeley / covariant.ai).

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