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GlowIP

Code to reproduce results from the paper, "Invertible generative models for inverse problems: mitigating representation error and dataset bias" by M. Asim, Max Daniels, Ali Ahmed, Oscar Leong, and Paul Hand.

In this paper, we demonstrate that an invertible generative model, specifically GLOW, which have zero representation error by design, can be effective natural signal priors at inverse problems such as denoising, compressive sensing, and inpainting.

image
Denoising: PSNR for 64px image denoising at the noise level .
image
Compressive Sensing: PSNR for 64px compressive sensing recoveries for in- and out-of-distribution images.
Denoising: qualitative results for 64px in-distribution denoising. Compressive Sensing: qualitative results for 128px in-distribution compressive sensing, here with approximately 25% measurements.



Prepare Training Datasets

To prepare training and validation datasets for CelebA, Caltech-UCSD Birds 200, and the Oxford Flowers dataset, move into ./data directory and run setup_{data}.sh followed by process_{data}.py as shown below. setup_{data}.sh will download and extract compressed files in the desired folders. The python script then process_{data}.py will then pre-process and split each data into training and validation sets.

cd data/
# prepare celeba
./setup_celeba.sh
python process_celeba.py

# prepare birds
./setup_birds.sh
python process_birds.py

# prepare flowers
./setup_flowers.sh
python process_flowers.py

The processed datasets will be placed in celeba_processed, birds_processed and flowers_processed directories.

We present results mainly for images from the CelebA validation set and from Flickr-Faces-HQ, along with additional results for a random selection of out-of-distribution qualitative images "in the wild." All test sets used in our experiments are available in the directory test_images.




Training Generative Models

To train generative models, simply run the train_{model}.py file from the root directory of the project.

# train glow on celeba
python train_glow.py -dataset celeba

# train dcgan on celeba
python train_dcgan.py -dataset celeba

# train glow on birds
python train_glow.py -dataset birds

# train dcgan on birds
python train_dcgan.py -dataset birds

# train glow on flowers
python train_glow.py -dataset flowers

# train dcgan on flowers
python train_dcgan.py -dataset flowers

The weights of Glow and DCGAN will be saved in trained_models/{data}/glow/ and trained_models/{data}/dcgan/.




Pre-trained models, used in the paper, can be downloaded from here. Additionally, to run lasso-wavelet experiments for compressive sensing, generate the wavelet basis vectors by running solvers/lasso_utils/wavelet_basis.py.




Solving Inverse Problems

For each inverse problem, there is an all purpose python script solve_{ip}.py that will be used. See below code snippet for details.

# run denoising for celeba using dcgan as prior at noise level 0.1 and gamma 0 
python solve_denoising.py -experiment exp1 -dataset celeba -prior dcgan -model celeba \
-noise_std 0.1 -gamma 0 -init_std 0.1 -device cuda

# run cs for out-of-distribution (ood) images using glow as prior at m=5000 with gamma=0 
python solve_cs.py -experiment exp2 -dataset ood -prior glow -model celeba \
-m 5000 -gamma 0 -init_std 0 -device cuda

# run inpainting for celeba using glow as prior with gamma set to 0
python solve_inpainintg.py -experiment exp3 -dataset celeba -prior glow -model celeba \
-gamma 0 -init_std 0 -device cuda

The results of each experiment will be saved in the results directory.

Please Note: By default, inverse problems are solved in batches of 6 images. This may require more memory than available on certain hardware. With batch size set to 1, the model may crash early while solving the inverse problem, due to numerical instability. In this case, please try reducing the learning rate and increasing the number of training iterations, or try setting the batch size to 2-3 images. See the following examples:

# compressive sensing
python solve_cs.py -experiment exp1 -dataset celeba -prior glow -model celeba -m 12288 \
-lr 0.05 -gamma 0 -init_std 0 -batchsize 1 -steps 50

# denoising - with gamma > 0, the latent norm is encouraged not to blow up, and the default parameters are stable.
python solve_denoising.py -prior glow -experiment exp2 -dataset celeba -model celeba \
-gamma 0.75 -batchsize 1

# inpainting
python solve_inpainting.py -experiment exp3 -dataset celeba -prior glow -model celeba \
-lr 0.01 -gamma 0 -init_std 0 -batchsize 1 -steps 50



Run all experiments from paper

To reproduce all experiments from paper, run all the shell scripts in the folder run/ from the root directory of the project. For example,

# reproduce results of cs on out-of-distribution (ood) using glow prior
./run/run_ood_cs_glow_main.sh

# reproduce results of denoising on celeba using glow prior at noise level 0.1
./run/run_celeba_denoising_glow_noisestd_0.10.sh



Generate Plots from paper

All experiments from paper have been compiled as *.csv files in plot/csv/ to reproduce plots from paper. Plots and their generating codes have been provided in plots.ipynb notebook.




Custom Datasets

To run experiments on any custom set of images, simply place the new set of images in the test_images folder with directory hierarchy matching the other image folders. The folder structure should be like test_images/{custom}/imgs/. See example below.

# run cs on custom images using glow trained on celeba for m=5000 and gamma=0
python solve_cs.py -experiment exp4 -dataset {custom} -prior glow -model celeba \
-m 5000 -gamma 0 -init_std 0 -device cuda



It is preferred that you align face images for best results.




Align Face Images

First download this file and place in align_faces/ directory. To align face images, simply run align_faces/align_face.py as shown below.

python align_faces/align_faces.py -input {path_to_image} -output {path_to_output}



Dependencies

  1. ubuntu==18.04
  2. python==3.6
  3. pytorch==1.0
  4. torchvision==0.2.1
  5. tensorflow==1.12
  6. numpy
  7. matplotlib
  8. scikit-image
  9. pillow
  10. scikit-learn
  11. easydict
  12. scipy
  13. glob
  14. pandas
  15. tqdm



Citation

If you find our work useful in your research or publication, please cite it:

@incollection{icml2020_2655,
 abstract = {Trained generative models have shown remarkable performance as priors for inverse problems in imaging.  For example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors.  Unfortunately, these models may be unable to represent any particular image because of architectural choices, mode collapse, and bias in the training dataset. In this paper, we demonstrate that invertible neural networks, which have zero representation error by design, can be effective natural signal priors at inverse problems such as denoising, compressive sensing, and inpainting.  Given a trained generative model, we study the empirical risk formulation of the desired inverse problem under a regularization that promotes high likelihood images, either directly by penalization or algorithmically by initialization. For compressive sensing, invertible priors can yield higher accuracy than sparsity priors across almost all undersampling ratios.  For the same accuracy on test images, they can use 10-20x fewer measurements.  We demonstrate that invertible priors can yield better reconstructions than GAN priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images.  We additionally compare performance for compressive sensing to unlearned methods, such as the deep decoder, and we establish theoretical bounds on expected recovery error in the case of a linear invertible model.},
 author = {Asim, Muhammad and Daniels, Max and Leong, Oscar and Hand, Paul and Ahmed, Ali},
 booktitle = {Proceedings of Machine Learning and Systems 2020},
 pages = {4577--4587},
 title = {Invertible generative models for inverse problems: mitigating representation error and dataset bias},
 year = {2020}
}

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Code to reproduce results from "Invertible generative models for inverse problems: mitigating representation error and dataset bias"

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