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https://github.com/svsamsonov/ex2mcmc_new

Last synced: 11 days ago
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Host: GitHub
URL: https://github.com/svsamsonov/ex2mcmc_new
Owner: svsamsonov
License: mit
Created: 2022-04-26T13:09:30.000Z (over 2 years ago)
Default Branch: master
Last Pushed: 2023-05-04T08:15:04.000Z (over 1 year ago)
Last Synced: 2024-09-15T19:01:39.509Z (about 2 months ago)
Language: Jupyter Notebook
Size: 1.25 GB
Stars: 6
Watchers: 3
Forks: 4
Open Issues: 0
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project

README

        

# Ex2MCMC: Local-Global MCMC kernels: the best of both worlds (NeurIPS 2022) [[Paper]](https://proceedings.neurips.cc/paper_files/paper/2022/hash/21c86d5b10cdc28664ccdadf0a29065a-Abstract-Conference.html)

## Implementation of Ex2MCMC, FlEx2MCMC and experiments

[![build](https://github.com/svsamsonov/ex2mcmc_new/actions/workflows/main.yml/badge.svg)](https://github.com/svsamsonov/ex2mcmc_new/actions/workflows/main.yml) 

[![pypi](http://img.shields.io/pypi/v/ex2mcmc)](https://pypi.org/project/ex2mcmc/)

[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](https://github.com/svsamsonov/ex2mcmc_new/blob/master/LICENSE)

[![CodeFactor](https://www.codefactor.io/repository/github/svsamsonov/ex2mcmc_new/badge)](https://www.codefactor.io/repository/github/svsamsonov/ex2mcmc_new)

[[ArXiv]](https://arxiv.org/abs/2111.02702)

Authors: Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines.

> **Abstract:** *In the present paper we study an Explore-Exploit Markov chain Monte Carlo strategy (Ex2MCMC) that combines local and global samplers showing that it enjoys the advantages of both approaches. We prove V-uniform geometric ergodicity of Ex2MCMC without requiring a uniform adaptation of the global sampler to the target distribution. We also compute explicit bounds on the mixing rate of the Explore-Exploit strategy under realistic conditions. Moreover, we also analyze an adaptive version of the strategy (FlEx2MCMC) where a normalizing flow is trained while sampling to serve as a proposal for global moves. We illustrate the efficiency of Ex2MCMC and its adaptive version on classical sampling benchmarks as well as in sampling high-dimensional distributions defined by Generative Adversarial Networks seen as Energy Based Models.*

> 

- [Ex2MCMC: Local-Global MCMC kernels: the best of both worlds (NeurIPS 2022) \[Paper\]](#ex2mcmc-local-global-mcmc-kernels-the-best-of-both-worlds-neurips-2022-paper)

  - [Implementation of Ex2MCMC, FlEx2MCMC and experiments](#implementation-of-ex2mcmc-flex2mcmc-and-experiments)

    - [Sampling from GAN as Energy-Based Model with MCMC](#sampling-from-gan-as-energy-based-model-with-mcmc)

    - [Algorithms](#algorithms)

    - [Quick start](#quick-start)

    - [Requirements](#requirements)

    - [Installation](#installation)

    - [Checkpoints](#checkpoints)

    - [Usage](#usage)

      - [Experiments with synthetic distributions:](#experiments-with-synthetic-distributions)

      - [Experiments with GANs on MNIST dataset](#experiments-with-gans-on-mnist-dataset)

      - [Experiments with GANs on CIFAR10 dataset](#experiments-with-gans-on-cifar10-dataset)

      - [Sampling and FID computation](#sampling-and-fid-computation)

    - [Results](#results)

      - [FID and Inception Score (CIFAR10)](#fid-and-inception-score-cifar10)

      - [Energy landscape approximation (MNIST)](#energy-landscape-approximation-mnist)

      - [Sampling trajectories (CIFAR10)](#sampling-trajectories-cifar10)

    - [Citation](#citation)

### Sampling from GAN as Energy-Based Model with MCMC

Metrics:

  

Samples from SNGAN with FlEx2MCMC: 



### Algorithms 

**Ex²MCMC:**



**FlEx²MCMC:**



### Quick start

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Cy3Ion97F1kIWMNkF6wl-XODnfP9VQ5u?usp=sharing)

```bash

pip install ex2mcmc

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

```

### Requirements

* Python >= 3.8

* PyTorch >= 1.8.0

* torchvision

* pyro-ppl

* Jax

* POT

### Installation

Create environment:

```bash

conda create -n ex2mcmc python=3.8

conda activate ex2mcmc

```

Install poetry (if absent):

```bash

curl -sSL https://install.python-poetry.org | python3 -

poetry config virtualenvs.create false

```

Install the project:

```bash

poetry install --with dev

poetry add git+https://github.com/kwotsin/mimicry.git@a7fda06c4aff1e6af8dc4c4a35ed6636e434c766

```

### Checkpoints

CIFAR10 checkpoints:

| GAN   |     Steps     |  Path, G |  Path, D |

|:----------|:-------------:|:------:|:------:|

| DCGAN NS  | 100k      |   [netG_100000_steps.pth](https://drive.google.com/file/d/1gv8_qr_xa8hJzdJpBXiKr8v922EqcE-E/view?usp=share_link) |   [netD_100000_steps.pth](https://drive.google.com/file/d/1u1sPUmlvyhcbNDX2DVsR-mGOzqQ6U8sh/view?usp=share_link) |

| SNGAN, Hinge  | 100k      |   [netG.pth](https://drive.google.com/file/d/118zC_iEkN27jGLVNmDuQpMeyw7BKOUra/view?usp=share_link) |   [netD.pth](https://drive.google.com/file/d/1xU5FV59TLhAlkFubJGmJVS87HnZZ2xHT/view?usp=share_link) |

MNIST checkpoints:

| GAN      |  Path |

|:----------|:-------------:|

| Vanilla  |   [vanilla_gan.pth](https://drive.google.com/file/d/1xa1v4hPQQdU2RkhjMn5sFZCITxTJ5Dhj/view?usp=share_link) |

| WGAN CP  |   [wgan.pth](https://drive.google.com/file/d/17nQJnfs2_T6kyahnkW3fu8AVY54kmRmw/view?usp=share_link) |

You also can run script to download checkpoints:

```bash

chmod +x get_ckpts.sh

./get_ckpts.sh

```

Download statistics for FID cimputation for CIFAR10 dataset:

```bash

mkdir -p stats & gdown 1jjgB_iuvmoVAXPRvVTI_hBfuIz7mQgOg -O stats/fid_stats_cifar10.npz

```

### Usage

Demonstration on SNGAN:  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1EQQ_OdwCLn5MsOzlG-GS7yNcjTBU-KMp?usp=sharing)

#### Experiments with synthetic distributions:

FlEx²MCMC  vs     NUTS:

 

  

| Experiment | Path | Colab |

|:----------|:-------|:-----:|

| Toyish Gaussian   |     ```experiments/exp_synthetic/toyish_gaussian.ipynb``` | [TBD]() |

| Ex2MCMC for Mixture of Gaussians  |     ```experiments/exp_synthetic/ex2mcmc_mog.ipynb``` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1xmBOZr1YhN8E7Y8GuwjgdM7hqaCgE6ik?usp=sharing) |

| FlEx2MCMC for Mixture of Gaussians          |   ```experiments/exp_synthetic/flex_mog.ipynb```    | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1Cy3Ion97F1kIWMNkF6wl-XODnfP9VQ5u?usp=sharing) |

| FlEx2MCMC for banana-shaped distribution   |     ```experiments/exp_synthetic/flex_banana.ipynb``` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/16ZjJH8id__6aPPeCPFEQXO86kvFBO1wb?usp=sharing) |

| FlEx2MCMC for Neal's funnel distribution   |     ```experiments/exp_synthetic/flex_funnel.ipynb``` | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/15MwmA3kY7sSPmk2i3mm1TUc_Vg-AJC8g?usp=sharing) |

To reproduce the experimets on banana-shaped and funnel distributions:

```bash

python experiments/exp_synthetic/banana_funnel_metrics.py --distribution {banana,funnel} --device cuda:0

```

 #### Experiments with GANs on MNIST dataset

 

 ```experiments/exp_mnist/JSGAN_samples.ipynb``` [TBD]()

 ```experiments/exp_mnist/WGAN_samples.ipynb``` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1aarHEdILfnR-vB0j7NN4uBurti08BtZk?usp=sharing)

 #### Experiments with GANs on CIFAR10 dataset

```experiments/exp_cifar10_demo/DCGAN_samples.ipynb``` 

```experiments/exp_cifar10_demo/SNGAN_samples.ipynb``` 

#### Sampling and FID computation

```bash

python experiments/exp_cifar10_fid/run.py configs/mcmc_configs/{ula,mala,isir,ex2mcmc,flex2mcmc}.yml configs/mmc_dcgan.yml

```

To run a full experiment:

```bash

chmod +x experiments/exp_cifar10_fid/run.sh & ./experiments/exp_cifar10_fid/run.sh

```

### Results

#### FID and Inception Score (CIFAR10)

| GAN | MCMC | Steps | Inception Score | FID  |

|:----|:-----|:------:|:---------------:|:----:|

|DCGAN| none | 0     |       6.3          |  28.4    |

|DCGAN| i-SIR  | 1k     |    6.96          |  22.7    |

|DCGAN| MALA  | 1k      |    6.95           |   23.4   |

|DCGAN| Ex²MCMC (our)  | 1k   |    7.56          |   19.0   |

|DCGAN| FlEx²MCMC (our)  | 1k |    **7.92**          |  19.2    |

|DCGAN| FlEx²MCMC (our) | 180 |   7.62      |  **17.1**    |

#### Energy landscape approximation (MNIST)

Projection of GAN samples onto the energy landsape when trained on MNIST dataset:

 

#### Sampling trajectories (CIFAR10)

Generation trajectories for DCGAN.

* ULA:

 

* MALA:

 

* i-SIR:

 

* Ex²MCMC:

 

* FlEx²MCMC:

 

### Citation

```bibtex

@article{samsonov2022local,

  title={Local-Global MCMC kernels: the best of both worlds},

  author={Samsonov, Sergey and Lagutin, Evgeny and Gabri{\'e}, Marylou and Durmus, Alain and Naumov, Alexey and Moulines, Eric},

  journal={Advances in Neural Information Processing Systems},

  volume={35},

  pages={5178--5193},

  year={2022}

}

```