Compressed Sensing using Generative Models

This repository provides code to reproduce results from the paper: Compressed Sensing using Generative Models.

Here are a few example results:

Reconstruction	Super-resolution	Inpainting

Here we show the evolution of the reconstructed image for different number of iterations:

Steps to reproduce the results

NOTE: Please run all commands from the root directory of the repository, i.e from csgm/

Requirements:

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1. Python 2.7
2. Tensorflow 1.0.1
3. Scipy
4. PyPNG
5. (Optional : for lasso-wavelet) PyWavelets
6. (Optional) CVXOPT

Pip installation can be done by $ pip install -r requirements.txt

Preliminaries

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1. Clone the repository and dependencies

   $ git clone https://github.com/AshishBora/csgm.git
   $ cd csgm
   $ git submodule update --init --recursive

2. Download the datasets:

   $ ./setup/download_data.sh 

3. Download pretrained models or train your own!

   • To download pretrained models: $ ./setup/download_models.sh
   • To train your own
     • VAE on MNIST: $ ./setup/train_mnist_vae.sh
     • DCGAN on celebA, see https://github.com/carpedm20/DCGAN-tensorflow

4. To use wavelet based estimators, you need to create the basis matrix:

   $ python ./src/wavelet_basis.py

Demos

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The following are the supported experiments and example commands to run the demos:

[Note: For celebA experiments, we run very few iterations per experiment (30) to give a quick demo. To get results with better quality, increase the number of iterations to at least 500 and use at least 2 random restarts.]

1. Reconstruction from Gaussian measurements
   • $ ./quick_scripts/mnist_reconstr.sh
   • $ ./quick_scripts/celebA_reconstr.sh "./images/182659.jpg"
2. Super-resolution
   • $ ./quick_scripts/mnist_superres.sh
   • $ ./quick_scripts/celebA_superres.sh "./images/182659.jpg"
3. Reconstruction for images in the span of the generator
   • $ ./quick_scripts/mnist_genspan.sh
   • $ ./quick_scripts/celebA_genspan.sh
4. Quantifying representation error
   • $ ./quick_scripts/mnist_projection.sh
   • $ ./quick_scripts/celebA_projection.sh "./images/182659.jpg"
5. Inpainting
   • $ ./quick_scripts/mnist_inpaint.sh
   • $ ./quick_scripts/celebA_inpaint.sh "./images/182659.jpg"

Reproducing quantitative results

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1. Create a scripts directory $ mkdir scripts

2. Identfy a dataset you would like to get the quantitative results on. Locate the file ./quant_scripts/{dataset}_reconstr.sh.

3. Change BASE_SCRIPT in src/create_scripts.py to be the same as given at the top of ./quant_scripts/{dataset}_reconstr.sh.

4. Optionally, comment out the parts of ./quant_scripts/{dataset}_reconstr.sh that you don't want to run.

5. Run ./quant_scripts/{dataset}_reconstr.sh. This will create a bunch of .sh files in the ./scripts/ directory, each one of them for a different parameter setting.

6. Start running these scripts.

   • You can run $ ./utils/run_sequentially.sh to run them one by one.
   • Alternatively use $ ./utils/run_all_by_number.sh to create screens and start proccessing them in parallel. [REQUIRES: gnu screen][WARNING: This may overwhelm the computer]. You can use $ ./utils/stop_all_by_number.sh to stop the running processes, and clear up the screens started this way.

7. Create a results directory : $ mkdir results. To get the plots, see src/metrics.ipynb. To get matrix of images (as in the paper), run $ python src/view_estimated_{dataset}.py.

8. You can also manually access the results saved by the scripts. These can be found in appropriately named directories in estimated/. Directory name conventions are defined in get_checkpoint_dir() in src/utils.py

Miscellaneous

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For a complete list of images not used while training on celebA, see here.