This repository is a pytorch implementation for the paper Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching

Checkout the original tensorflow implementation here

• Python3
• Pytorch >=0.4.0
• Opencv
• numpy
• tensorflow-gpu (if you are trying to get inception score or FID)

Clone the repository

$ git clone https://github.com/FANG-Xiaolin/pytorch-CoopNets.git

You can simply install the requirements via pip. (A virtualenv is recommended)

$ pip install opencv-python
$ pip install torch==0.4.0 torchvision
$ pip install numpy

Pretrained models for cifar has been provided in ./test

Load the pretrained model and generate sample images.

Specify the path to checkpoint in the command line. By default, the result will be saved to ./result_images, or you can change it by -output_dir /path/to/test-result.

E.g. Generate 10 examples, each with 20x20 images by the following line.

$ python main.py -test -output_dir ./test_res -ckpt_gen ./test/ckpt_gen_cifar.pth -ckpt_des ./test/ckpt_des_cifar.pth -test_size 4000 -nRow 20 -nCol 20 -langevin_step_num_des 8

Load the pretrained model and generate sample images, then evaluate by inception_score or FID. The code to evaluate scores are from corresponding authors' original implementation.

Specify the path to checkpoint in the command line. By default, the result will be saved to ./result_images, or you can change it by -output_dir /path/to/test-result.

Tips

If you are encountered error like

Cannot feed value of shape (50, 32, 32, 3) for Tensor 'FID_Inception _Net/ExpandDims:0', which has shape '(1, ?, ?, 3)'

try upgrade your tf version.

From test directory,

$ bash test-cifar-fid.sh

From test directory

$ bash test-cifar-inception.sh

Generation and evaluation process are seperated to avoid conflictions between pytorch and tf.

1. Download the dataset

To train on scene subset of ImageNet, run the following command at the root directory of the project.

$ python download.py scene

The ImageNet-scene dataset will be downloaded and saved to ./data directory. (approximately 3.8G)

Download the original tar.gz file, unzip it, and run

$ python convert_cifar.py

to convert it to seperated images.

2. Train a model by

   $ python main.py

The training images will be read from [specified_data_path]/[specified_category]

E.g. Train the model on alp dataset by

$ python main.py -category alp -num_epoch 300 -lr_des 0.01 --lr_gen 0.0001

E.g. Train the model on cifar10 dataset by

$ python main.py -set cifar -category cifar -img_size 32 -lr_des 0.003 -langevin_step_size_des 0.001 -langevin_step_num_des 10 -sigma_des 0.016 -num_epoch 500 -log_epoch 50 -batch_size 300 -nRow 30 -nCol 30 -data_path ./data/scene/

By default, the result will be save to ./result_images, the checkpoints and log will be saved into ./checkpoint

Details about the flags can be seen by

$ python main.py -h

Below is the result_image after training on alp(about 2k images) within hundreds of epochs.

@inproceedings{coopnets,
    author = {Xie, Jianwen and Lu, Yang and Gao, Ruiqi and Wu, Ying Nian},
    title = {Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching},
    booktitle = {The 32nd AAAI Conference on Artitifical Intelligence},
    year = {2018}
}

Thanks to @Jianwen-Xie and @Zilong-Zheng for their tensorflow implementation