gridl/pytorch-flows

PyTorch implementations of algorithms for density estimation

pytorch-flows

A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invertible 1x1 Convolutions and Density estimation using Real NVP.

For MAF, I'm getting results similar to ones reported in the paper. GLOW requires some work.

Run

python main.py --dataset POWER

Available datasets are POWER, GAS, HEPMASS, MINIBONE and BSDS300. For the moment, I removed MNIST and CIFAR10 because I have plans to add pixel-based models later.

Datasets

The datasets are taken from the original MAF repository. Follow the instructions to get them.

Tests

Tests check invertibility, you can run them as

pytest flow_test.py