pytorch-glow 0.0.1

PyTorch implementation of Glow

Glow: Generative Flow with Invertible 1x1 Convolutions [Work in Progress]

Unofficial PyTorch implementation of "Glow: Generative Flow with Invertible 1x1 Convolutions"

The original paper can be found here.

The code is based off another implementation found here.

This repository contain the Glow model code and associated training / sampling scripts.

This repository is a work in progress. Default parameters may not be optimal!

Usage

Glow Training

Run Glow training using config file cfg.toml. Defaults to config/cifar10.toml

python main.py --cfg-path cfg.toml --no-amp

currently recommend NOT using automatic mixed precision (AMP)

Other useful flags:

--nb-samples            # number of samples to generate when evaluating [16]
--resume                # resume training from specified checkpoint 
--seed                  # set RNG seed 
--no-save               # disable saving of checkpoints [False]
--no-cuda               # disable the use of CUDA device [False]
--no-amp                # disable the use of automatic mixed precision [False]
--nb-workers            # set number of dataloader workers. [4]
--no-grad-checkpoint    # don't checkpoint gradients [False]
--temperature           # set temperature when sampling at evaluation [0.7]

Glow Sampling

Run Glow sampling using config file cfg.toml from checkpoint checkpoint.pt using sample mode mode:

python main.py --sample --sample-mode mode --resume checkpoint.pt --cfg-path cfg.toml --no-amp

Other flags from training will also work during sampling.

The sampling modes are:

• normal: samples random latent and displays corresponding samples, saving to sample.jpg
• vtemp: samples random latent and varies temperature, dumping samples samples-vtemp/
• interpolate: computes latent of dataset items, then linearly interpolates between them, dumping samples in samples-interpolate/

Samples

TODO: add (nice) sample outputs

Checkpoints

TODO: add pretrained checkpoints

TODO:

• Glow Model
• Training script
• Sampling script
• Gradient checkpoints
• PyPi library
• Add pretrained models / nice samples

Citations:

Glow: Generative Flow with Invertible 1x1 Convolutions

Diederik P. Kingma, Prafulla Dhariwal

@misc{kingma2018glow,
      title={Glow: Generative Flow with Invertible 1x1 Convolutions}, 
      author={Diederik P. Kingma and Prafulla Dhariwal},
      year={2018},
      eprint={1807.03039},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}