Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
create_scripts moving around Feb 1, 2018
images
run_scripts
src adding requirements Feb 14, 2018
.gitignore
LICENSE
README.md
plotting_cifar.ipynb
plotting_mnist.ipynb
requirements.txt adding requirements Feb 14, 2018
setup.png using png instead Feb 13, 2018
test_celebA.sh
test_cifar.sh
test_mnist.sh

README.md

AmbientGAN: Generative models from lossy measurements

This repository provides code to reproduce results from the paper AmbientGAN: Generative models from lossy measurements.

The training setup is as in the following diagram:

Here are a few example results:

Measured Baseline AmbientGAN (ours)

Few more samples from AmbientGAN models trained with 1-D projections:

Pad-Rotate-Project Pad-Rotate-Project-theta

The rest of the README describes how to reproduce the results.

Requirements

  • Python 2.7
  • Tensorflow >= 1.4.0
  • matplotlib
  • scipy
  • numpy
  • cvxpy
  • scikit-learn
  • tqdm
  • opencv-python
  • pandas

For pip installation, use $ pip install -r requirements.txt

Get the data

  • MNIST data is automatically downloaded
  • Get the celebA dataset here and put the jpeg files in ./data/celebA/
  • Get the CIFAR-10 python data from here and put it in ./data/cifar10/cifar-10-batches-py/*

Get inference models

We need inference models for computing the inception score.

  • For MNIST, you can train your own by

    cd ./src/mnist/inf
    python train.py
    

    [TODO]: Provide a pretrained model.

  • Inception model for use with CIFAR-10 is automatically downloaded.

Create experiment scripts

Run ./create_scripts/create_scripts.sh

This will create scripts for all the experiments in the paper.

[Optional] If you want to run only a subset of experiments you can define the grid in ./create_scripts/DATASET_NAME/grid_*.sh or if you wish to tweak a lot of parameters, you can change ./create_scripts/DATASET_NAME/base_script.sh. Then run ./create_scripts/create_scripts.sh as above to create the corresponding scripts (remember to remove any previous files from ./scripts/)

Run experiments

We provide scripts to train on multiple GPUs in parallel. For example, if you wish to use 4 GPUs, you can run: ./run_scripts/run_sequentially_parallel.sh "0 1 2 3"

This will start 4 GNU screens. Each program within the screen will attempt to acquire and run experiments from ./scripts/, one at a time. Each experiment run will save samples, checkpoints, etc. to ./results/.

See results as you train

Samples

You can see samples for each experiment in ./results/samples/EXPT_DIR/

EXPT_DIR is defined based on the hyperparameters of the experiment. See ./src/commons/dir_def.py to see how this is done.

Quantitative plots

Run

python src/aggregator_mnist.py
python src/aggregator_cifar.py

This will create pickle files in ./results/ with the relevant data in a Pandas dataframe.

Now use the ipython notebooks ./plotting_mnist.ipynb and ./plotting_cifar.ipynb to get the relevant plots. The generated plots are also saved to ./results/plots/ (make sure this directory exists)