This repository contains the codes and experiments for the paper, Solving Inverse Problems in Snapshot Compressive Imaging with Score Generative Models. Implementation of the score model was forked from Yang Song's repository and modified accordingly for our experiments, which was carried out in the environment defined in score_sci/environment.yml
.
Note that, you may encounter troubles setting up the exact environment and dependencies specified in the configuration file depending on your GPU model and CUDA version. If so, please refer to the original installation guides for relevant packages.
If you have any doubts or queries, please feel free to reach out at my email. Thank you :)
- Download model checkpoints provided by Yang Song et al.
- Store checkpoints under
score_sci/checkpoints/
. E.g.,| score_sci/checkpoints/ |---- ve/ |-------- cifar10_ncsnpp_continuous/ |------------ checkpoint_24.pth |-------- ffhq_256_ncsnpp_continuous/ |------------ checkpoint_48.pth
- Setup Conda environment.
conda env create --name envname --file=score_sci/environment.yml
- From the dataset is available at Google Drive, download the
matlab.zip
andtest_gray.zip
under theSCI
folder. - Load the dataset by running the
load_dataset_mat_example.py
.
- Navigate to the main folder,
cd ./score_sci
- Run demo either through
main.py
ormain.ipynb
- Note that, if
main.ipynb
is used, you may have to restart your kernel to clear the GPU memory if CUDA memory limit errors are encountered.
- Note that, if
- Generated samples will be saved under
assets/{scene}_{sampler}
by default