Awesome

QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models

This repo shows code of an advanced variant of QCS-SGM, namely QCS-SGM+.

QCS-SGM+: Improved Quantized Compressed Sensing with Score-Based Generative Models

A comparison of QCS-SGM+ with QCS-SGM and other algorithms on MNIST and CelebA datasets in the case of 1-bit CS for ill-conditioned sensing matrix A.

Running Experiments

Dependencies

Create a new environment and run the following to install all necessary python packages for our code.

pip install -r requirements.txt

Project structure

main.py` is the file that you should run for quantized CS. Execute python main.py --help to get its usage description:

usage: main.py [-h] --config CONFIG [--seed SEED] [--exp EXP] --model_dir DIR
               [--comment COMMENT] [--verbose VERBOSE] [--test] [--sample]
               [--fast_fid] [--resume_training] [-i IMAGE_FOLDER] [--ni]

optional arguments:
  -h, --help            show this help message and exit
  --config CONFIG       Path to the config file
  --seed SEED           Random seed
  --exp EXP             Path for saving running related data.
  --model_dir DIR       Path for putting the checkpoint file.
  --comment COMMENT     A string for experiment comment
  --verbose VERBOSE     Verbose level: info | debug | warning | critical
  --test                Whether to test the model
  --sample              Whether to produce samples from the model
  --fast_fid            Whether to do fast fid test
  --resume_training     Whether to resume training
  -i IMAGE_FOLDER, --image_folder IMAGE_FOLDER
                        The folder name of samples (saved results)
  --ni                  No interaction. Suitable for Slurm Job launcher

Configuration files are in config/. You don't need to include the prefix config/ when specifying --config . All files generated when running the code is under the directory specified by --exp. They are structured as:

<exp> # a folder named by the argument `--exp` given to main.py
├── datasets # all dataset files
├── logs # contains checkpoints and samples produced during training
│   └── <model_dir> # a folder named by the argument `--model_dir` specified to main.py
│      ├── checkpoint_x.pth # the checkpoint file saved at the x-th training iteration
├── saved_results # contains original/recovered images
│   └── DATA_Type # Name of datasets, e.g., MNIST, CIFAR10, CELEBA
│       └── 1-bit # recovered images from 1-bit CS       
```       └── image_x.png # samples generated from checkpoint_x.pth 
│       └── 2-bit # recovered images from 2-bit CS         
```       └── image_x.png # samples generated from checkpoint_x.pth
│       └── 3-bit # recovered images from 3-bit CS        
```       └── image_x.png # samples generated from checkpoint_x.pth
│       └── linear # recovered images from linear (un-quantized) CS        
```       └── image_x.png # samples generated from checkpoint_x.pth

Getting Started

To reconstruct images from the Q-bit quantized noisy measurements using NCSNv2, one can run the code as follows: (Take CelebA dataset for an example)

Step 1: Edit celeba_ill_1bit.yml in ./configs/ to specify the simulated setting, e.g.,

In the sampling group of **.yml checkpoint id: ckpt_id learning rate: step_lr whether the CS problem is considered: linear_inverse (True or False, set true for CS) True: perform conditional sampling based on observations y False: perform unconditional sampling

In the measurements group: Number of measurements M : measure_size additive noise variance sigma^2 : noise_variance
Whether or not quantization is used: quantization (True or False) Number of quantization bits Q: quantize_bits matrix: type of sensing matrix A (eg., 'correlated','ill-conditioned','Gaussian')

Step 2: Run the following command

python python main.py  --config celeba.yml --model_dir celeba --i ./celeba_results

Reconstructed will be saved in <exp>/celeba_results/.

Pretrained Checkpoints

Please download the existing open-sourced pretrained checkpoints from the following link for MNIST, Cifar10, CelebA, and put them in the ./exp/logs/mnist, ./exp/logs/cifar10, ./exp/logs/celeba, respectively (Or, you can simply download the whole exp.zip file, unzip it in the root folder of this project). Please select the pre-trained models with the specified ckpt_id in the config files MNIST Link: https://1drv.ms/u/s!AksoPUl-6AQRhOceyE4DTvfCTd7b3Q?e=zrJYoi Link: https://drive.google.com/drive/folders/1217uhIvLg9ZrYNKOR3XTRFSurt4miQrd?usp=sharing

It assumes the --exp argument is set to exp

References

This repo is built on top of the open-sourced QCS-SGM code https://github.com/mengxiangming/QCS-SGM and ncsnv2 code: https://github.com/ermongroup/ncsnv2

@article{meng2022quantized,
  title={Quantized Compressed Sensing with Score-Based Generative Models},
  author={Meng, Xiangming and Kabashima, Yoshiyuki},
  journal={arXiv preprint arXiv:2211.13006},
  year={2022}
}

@inproceedings{song2020improved,
  author    = {Yang Song and Stefano Ermon},
  editor    = {Hugo Larochelle and
               Marc'Aurelio Ranzato and
               Raia Hadsell and
               Maria{-}Florina Balcan and
               Hsuan{-}Tien Lin},
  title     = {Improved Techniques for Training Score-Based Generative Models},
  booktitle = {Advances in Neural Information Processing Systems 33: Annual Conference
               on Neural Information Processing Systems 2020, NeurIPS 2020, December
               6-12, 2020, virtual},
  year      = {2020}
}