https://github.com/ermongroup/sparse_gen
Code for "Modeling Sparse Deviations for Compressed Sensing using Generative Models", ICML 2018
https://github.com/ermongroup/sparse_gen
Last synced: 5 months ago
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Code for "Modeling Sparse Deviations for Compressed Sensing using Generative Models", ICML 2018
- Host: GitHub
- URL: https://github.com/ermongroup/sparse_gen
- Owner: ermongroup
- License: mit
- Created: 2018-07-03T00:59:30.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-05T06:37:34.000Z (over 7 years ago)
- Last Synced: 2025-03-31T16:13:20.349Z (6 months ago)
- Language: Python
- Homepage:
- Size: 73.2 KB
- Stars: 24
- Watchers: 8
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Sparse-Gen
============================================
This repository provides a reference implementation for learning Sparse-Gen models as described in the paper:
> Modeling Sparse Deviations for Compressed Sensing using Generative Models
Manik Dhar, Aditya Grover, Stefano Ermon
International Conference on Machine Learning (ICML), 2018
Paper: https://arxiv.org/abs/1807.01442
## Requirements
The codebase is implemented in Python 2.7. To install the necessary requirements, run the following commands:
```
pip install -r requirements.txt
```
## Setup
The following command will download the CelebA, OMNIGLOT, and MNIST datasets:
```
bash ./setup/download_data.sh
```
The following command will unzip the trained model weights for the experiments:
```
unzip models.zip
```
The following command will create wavelet basis for the celebA experiments
```
python ./src/wavelet_basis.py
```
## Options
Learning and inference of Sparse-Gen models is handled by the `main.py` script which provides the following command line arguments.
```
--pretrained-model-dir PRETRAINED_MODEL_DIR
Directory containing pretrained model
--dataset DATASET Dataset to use
--input-type INPUT_TYPE
Where to take input from
--input-path-pattern INPUT_PATH_PATTERN
Pattern to match to get images
--num-input-images NUM_INPUT_IMAGES
number of input images
--batch-size BATCH_SIZE
How many examples are processed together
--measurement-type MEASUREMENT_TYPE
measurement type
--noise-std NOISE_STD
std dev of noise
--num-measurements NUM_MEASUREMENTS
number of gaussian measurements
--model-types MODEL_TYPES [MODEL_TYPES ...]
model(s) used for estimation
--mloss1_weight MLOSS1_WEIGHT
L1 measurement loss weight
--mloss2_weight MLOSS2_WEIGHT
L2 measurement loss weight
--zprior_weight ZPRIOR_WEIGHT
weight on z prior
--dloss1_weight DLOSS1_WEIGHT
-log(D(G(z))
--dloss2_weight DLOSS2_WEIGHT
log(1-D(G(z))
--sparse_gen_weight SPARSE_GEN_WEIGHT
weight for sparse deviations
--optimizer-type OPTIMIZER_TYPE
Optimizer type
--learning-rate LEARNING_RATE
learning rate
--momentum MOMENTUM momentum value
--max-update-iter MAX_UPDATE_ITER
maximum updates to z
--num-random-restarts NUM_RANDOM_RESTARTS
number of random restarts
--decay-lr whether to decay learning rate
--lmbd LMBD lambda : regularization parameter for LASSO
--lasso-solver LASSO_SOLVER
Solver for LASSO
--const_dummy CONST_DUMMY
dummy hack
--save-images whether to save estimated images
--save-stats whether to save estimated images
--print-stats whether to print statistics
--checkpoint-iter CHECKPOINT_ITER
checkpoint every x batches
--image-matrix IMAGE_MATRIX
0 = 00 = no image matrix, 1 = 01 = show image matrix 2
= 10 = save image matrix 3 = 11 = save and show image
matrix
```
## Examples
### You will need to download the datasets to run the experiments. To run the quantitative experiments as given in the paper, run the scripts in the quant_scripts directory:
```
bash ./quant_scripts/celebA_reconstruction.sh
bash ./quant_scripts/omniglot_reconstruction.sh
bash ./quant_scripts/mnist_reconstruction.sh
```
This will generate the scripts in multiple directories for the required experiments which can be run using the utils/run_sequentially.sh script. The exact commands are as follows:
```
bash ./utils/run_sequentially.sh scripts_mnist
bash ./utils/run_sequentially.sh scripts_mnist2omni
bash ./utils/run_sequentially.sh scritps_omni
bash ./utils/run_sequentially.sh scritps_omni2mnist
bash ./utils/run_sequentially.sh scritps_celebA
```
When all experiments have finished running the graphs can be generated using:
```
bash ./setup/make_graphs.py
```
Portions of the codebase in this repository uses code originally provided in the open-source Compressed Sensing with Generative Model (https://github.com/AshishBora/csgm) repositories.
## Citing
If you find Sparse-Gen useful in your research, please consider citing the following paper:
>@inproceedings{dhar2018modeling,
title={Modeling Sparse Deviations for Compressed Sensing using Generative Models},
author={Dhar, Manik and Grover, Aditya and Ermon, Stefano},
booktitle={International Conference on Machine Learning},
year={2018}}