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Please see https://github.com/jrmcornish/cif for code to reproduce our updated version of this paper, Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows.

README

Code release for Localised Generative Flows (LGFs).

Usage

Setup

First, install submodules:

$ git submodule init
$ git submodule update

Next, install dependencies. If you use conda, the following will create an environment called lgf:

conda env create -f environment.yml

Activate this with

conda activate lgf

before running any code or tests.

If you don't use conda, then please see environment.yml for a list of required packages, which will need to be installed manually via pip etc.

Datasets

Our code runs on several types of datasets, including synthetic 2-D data, UCI data, and image data. For a full list run

pipenv run ./main.py --help

The 2-D datasets are automatically generated, and the image datasets are downloaded automatically. However the UCI datasets will need to be manually downloaded from this location. The following should do the trick:

mkdir -p DATA_ROOT && wget -O - https://zenodo.org/record/1161203/files/data.tar.gz | tar --strip-components=1 -C DATA_ROOT -xvzf - data/{gas,hepmass,miniboone,power}

Replace DATA_ROOT with the desired path to the data files. This path then needs to be specified as an argument to main.py.

Training

To train our model on a simple 2D dataset, run:

pipenv run ./main.py --dataset 2uniforms

By default, this will create a directory runs/, which will contain Tensorboard logs giving various information about the training run, including 2-D density plots in this case. To inspect this, ensure you have tensorboard installed (e.g. pip install tensorboard), and run in a new terminal:

tensorboard --logdir runs/ --port=8008

Keep this running, and navigate to http://localhost:8008, where the results should be visible.

Each dataset has a default configuration set up for it that is described in the paper. For comparison purposes, for each model we also provide a standard baseline flow with roughly the same number of parameters. To run these, simply add the --baseline option when running main.py.

To inspect the model (either LGF or baseline) used for a given dataset, add the --print-model argument. To try out alternative configurations, simply modify the relevant options in config.py.

Example results

2-D data

When trained on the 2uniforms dataset, the baseline MAF will produce something like the following:

Notice that the MAF is unable to transform the support of the prior distribution (which is Gaussian) into the support of the target, which has two disconnected components and hence a different topology.

In contrast, LGF-MAF can properly separate the two components:

We also obtain improved performance when using LGFs on various UCI and image datasets. Although harder to visualise, we conjecture that an analogous story holds here as for the simple 2-D case.

Generated images

Below are some samples generated by the models in this repository.

Baseline RealNVP

FashionMNIST

CIFAR10

LGF-RealNVP

FashionMNIST

CIFAR10

Bibtex

@misc{cornish2019localised,
    title={Localised Generative Flows},
    author={Rob Cornish and Anthony L. Caterini and George Deligiannidis and Arnaud Doucet},
    year={2019},
    eprint={1909.13833},
    archivePrefix={arXiv},
    primaryClass={stat.ML}
}