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Author
Last Commit
Apr. 9, 2018
Created
Dec. 18, 2017

GibbsNet

PyTorch implementation of GibbsNet: Iterative Adversarial Inference for Deep Graphical Models.

Requirements

  • Python 3
  • Pytorch
  • visdom

Usage

SVHN, CIFAR10 dataset are currently supported.

Train

$ python train.py --model=GibbsNet --batch_size=100 --lr=1e-5 --dataset=SVHN --sampling_count=20
$ python train.py --model=GibbsNet --batch_size=100 --lr=1e-5 --dataset=SVHN --gpu_ids=0,1 --sampling_count=20

Visualize

  • To visualize intermediate results and loss plots, run python -m visdom.server and go to the URL http://localhost:8097

Test

$ python test.py --test_count=20 --model=GibbsNet --repeat_generation=10
  • Test result will generate in ./[opt.test_dir]/[opt.model/, of which default value is ./test/GibbsNet/
  • Test result consists of real_[i].png files and fake_[i]_[j].png files. real_[i].png files are sampled from real dataset, and fake_[i]_[j].png files are generated from sampled latent variable of real_[i].png

Results

1. SVHN

  • epoch 100, lr 1e-5, sampling_count 20
  • generated results


2. CIFAR10

  • Working in Progress

Implementation detail

  • Original implementation of discriminator network for CIFAR10 dataset uses maxout activation layer, but this implementation uses leaky ReLU rather than maxout layer because of lack of GPU memory.
  • all hyper parameters references to paper Adversarially Learned Inference.
  • To train GibbsNet, appropriate learning rate is 1e-5 for sampling count 20. You can increase learning rate when you sample less than 20 times.

TODOs

  • Custom dataset support
  • Visualize test results

Code reference

Visualization code(visualizer.py, utils.py) references to pytorch-CycleGAN-and-pix2pix(https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) by Jun-Yan Zhu

Author

Tony Kim