Awesome
Deep Probabilistic Imaging (DPI)
Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging, AAAI 2021
Run Examples
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The simple 2D example can be run using the ipython notebook
DPItorch/notebook/DPI toy 2D results.ipynb
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The DPI radio interferometric example can be trained using
DPItorch/DPI_interferometry.py
, and analyzed usingDPItorch/notebook/DPI interferometry results.ipynb
python DPI_interferometry.py --lr 1e-4 --clip 1e-3 --n_epoch 30000 --npix 32 --n_flow 16 --logdet 1.0 --save_path ./checkpoint/interferometry --obspath ../dataset/interferometry1/obs.uvfits
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The DPI MRI example can be trained using
DPItorch/DPI_interferometry.py
, and analyzed usingDPItorch/notebook/DPI MRI results.ipynb
python DPI_MRI.py --lr 1e-5 --clip 1e-3 --n_epoch 100000 --npix 64 --n_flow 16 --ratio 4 --logdet 1.0 --tv 1e3 --save_path ./checkpoint/mri --impath ../dataset/fastmri_sample/mri/knee/scan_0.pkl --maskpath ../dataset/fastmri_sample/mask/mask4.npy --sigma 5e-7
Arguments:
General:
* lr (float) - learning rate
* clip (float) - threshold for gradient clip
* n_epoch (int) - number of epochs
* npix (int) - size of reconstruction images (npix * npix)
* n_flow (int) - number of affine coupling blocks
* logdet (float) - weight of the entropy loss (larger means more diverse samples)
* save_path (str) - folder that saves the learned DPI normalizing flow model
For radio interferometric imaging:
* obspath (str) - observation data file
For compressed sensing MRI:
* impath (str) - fast MRI image for generating MRI measurements
* maskpath (str) - compressed sensing sampling mask
* sigma (float) - additive measurement noise
Requirements
General requirements for PyTorch release:
For radio interferometric imaging:
Please check DPI.yml
for the detailed Anaconda environment information. TensorFlow release is coming soon!
Citation
@inproceedings{sun2021deep,
author = {He Sun and Katherine L. Bouman},
title = {Deep Probabilistic Imaging: Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging},
booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
year = {2021},
}
alpha-Deep Probabilistic Imaging (alpha-DPI)
alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction, arXiv
Run Examples
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The alpha-DPI radio interferometric example can be trained using
DPItorch/DPIx_interferometry.py
python DPIx_interferometry.py --n_gaussian 2 --divergence_type alpha --alpha_divergence 0.95 --n_epoch 20000 --lr 1e-4 --fov 160 --save_path ./checkpoint/interferometry_m87_mcfe/synthetic/crescentfloornuissance2/alpha095closure --obspath ../dataset/interferometry_m87/synthetic_crescentfloorgaussian2/obs_mring_synthdata_allnoise_scanavg_sysnoise2.uvfits
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The alpha-DPI planet direct imaging orbit fitting example can be trained using
DPItorch/DPIx_orbit.py
python DPIx_orbit.py --divergence_type alpha --alpha_divergence 0.6 --coordinate_type cartesian --save_path ./checkpoint/orbit_beta_pic_b/cartesian/alpha06
Citation
@article{sun2022alpha,
title={alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction},
author={Sun, He and Bouman, Katherine L and Tiede, Paul and Wang, Jason J and Blunt, Sarah and Mawet, Dimitri},
journal={arXiv preprint arXiv:2201.08506},
year={2022}
}