/NPGD_linear_inverse_prob

GAN-based Projector for Faster Recovery in Linear Inverse Problems

Primary LanguagePython

GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems.

This repository provides code for this paper: GAN-based Projector for Faster Recovery with Convergence Guarantees in Linear Inverse Problems.

Requirements

Linux system, a GPU

  1. Python 3.4
  2. Pytorch 1.2.0
  3. numpy
  4. matplotlib
  5. imageio
  6. scikit-image
  7. scikit-learn
  8. requests
  9. torchvision

Preliminaries

  1. Create a conda virtual environment and install packages
conda create --name NPGD python=3.4
conda activate NPGD
conda install pytorch=1.2.0 imageio=2.6.1 matplotlib=3.1.1 numpy=1.17.2 scikit-image=0.15.0 scikit-learn=0.21.3 requests=2.22.0 torchvision=0.4.0
  1. Clone this repo
git clone https://github.com/yuqili3/NPGD_linear_inverse_prob.git
cd NPGD_linear_inverse_prob
  1. Download the dataset as well as the test images, we need to crop and resize the faces from the original images.
unzip celebAtest.zip -d data/celebA/testing_images
unzip mnisttest.zip -d data/mnist/
python download_data.py celebA
rm -rf ./data/celebA/celebA_data_raw
rm ./data/celebA/celebA_data.zip

this may take several minutes to download the large file

  1. Train the network projector models.
    1. for mnist dataset, train a DCGAN first and then a concatenated network projector:
    python MNIST_DCGAN.py --k=100 --epochs=40 --lr=1e-4
python MNIST_invDCGAN_multi_task_loss.py --k=100 --epochs=100 --lr=1e-4 --lamda=0.1 --sigma=1
    1. for celebA dataset:
    python CELEBA_DCGAN.py --k=100 --epochs=40 --lr=1.5e-4
python CELEBA_invDCGAN_multi_task_loss.py --k=100 --epochs=100 --lr=1e-4 --lamda=0.1 --sigma=1

Demos

  1. Compressed sensing using random Gaussian matrix

    • mnist
    python MNIST_projection_using_inv_gen.py --k=100 --task=cs --m=100 --designed=0  --lr=1 --ite=30 --info=1
    • celebA
    python CELEBA_projection_using_inv_gen.py --k=100 --task=cs --m=1000 --designed=0  --lr=0.5 --ite=30 --info=1

  2. Image inpainting

    • mnist
    python MNIST_projection_using_inv_gen.py --k=100 --task=ip --mask_size=8 --lr=2 --ite=30 --info=1
    • celebA
    python CELEBA_projection_using_inv_gen.py --k=100 --task=ip --mask_size=32 --lr=1 --ite=30 --info=1

  3. Image super resolution

    • mnist
    python MNIST_projection_using_inv_gen.py --k=100 --task=sr --sr_ratio=2 --lr=6 --ite=30 --info=1
    • celebA
    python CELEBA_projection_using_inv_gen.py --k=100 --task=sr --sr_ratio=2 --lr=6 --ite=30 --info=1

  4. Use a designed matrix in compressed sensing

    • mnist
    python design_measurement_matrix.py --dataset=MNIST --k=100
python MNIST_projection_using_inv_gen.py --k=100 --task=cs --m=200 --designed=1 --lr=1 --ite=30 --info=1
    • celebA
    python design_measurement_matrix.py --dataset=CELEBA --k=100
python CELEBA_projection_using_inv_gen.py --k=100 --task=cs --m=1000 --designed=1 --lr=1 --ite=30 --info=1