https://github.com/bayesiains/nsf

Code for Neural Spline Flows paper
https://github.com/bayesiains/nsf

Last synced: about 2 months ago
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Code for Neural Spline Flows paper

• Host: GitHub
• URL: https://github.com/bayesiains/nsf
• Owner: bayesiains
• License: mit
• Created: 2019-06-10T13:21:58.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-06-20T08:49:06.000Z (over 4 years ago)
• Last Synced: 2024-07-04T02:16:06.963Z (3 months ago)
• Language: Python
• Size: 142 KB
• Stars: 253
• Watchers: 12
• Forks: 42
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# Neural Spline Flows

A record of the code and experiments for the paper:

> C. Durkan, A. Bekasov, I. Murray, G. Papamakarios, _Neural Spline Flows_, NeurIPS 2019.

> [[arXiv]](https://arxiv.org/abs/1906.04032) [[bibtex]](https://gpapamak.github.io/bibtex/neural_spline_flows.bib)

Work in this repository has now stopped. Please go to [nflows](https://github.com/bayesiains/nflows) for an updated and pip-installable normalizing flows framework for PyTorch.

## Dependencies

See `environment.yml` for required Conda/pip packages, or use this to create a Conda environment with 

all dependencies:

```bash

conda env create -f environment.yml

```

Tested with Python 3.5 and PyTorch 1.1.

## Data

Data for density-estimation experiments is available at https://zenodo.org/record/1161203#.Wmtf_XVl8eN.

Data for VAE and image-modeling experiments is downloaded automatically using either `torchvision` or custom 

data providers.

## Usage

`DATAROOT` environment variable needs to be set before running experiments.

### 2D toy density experiments

Use `experiments/face.py` or `experiments/plane.py`.

### Density-estimation experiments

Use `experiments/uci.py`.

### VAE experiments

Use `experiments/vae_.py`.

### Image-modeling experiments

Use `experiments/images.py`.

[Sacred](https://github.com/IDSIA/sacred) is used to organize image experiments. See the 

[documentation](http://sacred.readthedocs.org) for more information.

`experiments/image_configs` contains .json configurations used for RQ-NSF (C) experiments. For baseline experiments use `coupling_layer_type='affine'`.

For example, to run RQ-NSF (C) on CIFAR-10 8-bit:

```bash

python experiments/images.py with experiments/image_configs/cifar-10-8bit.json

```

Corresponding affine baseline run:

```bash

python experiments/images.py with experiments/image_configs/cifar-10-8bit.json coupling_layer_type='affine'

```

To evaluate on the test set:

```bash

python experiments/images.py eval_on_test with experiments/image_configs/cifar-10-8bit.json flow_checkpoint=''

```

To sample:

```bash

python experiments/images.py sample with experiments/image_configs/cifar-10-8bit.json flow_checkpoint=''

```