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SST-ReversibleNet

// repository documentation

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# SST

##Create Environment:

- Python 3 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux))

- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)

- Python packages:

```shell
pip install -r requirements.txt
```

##Prepare Dataset:

Download cave_1024_28 ([One Drive](https://bupteducn-my.sharepoint.com/:f:/g/personal/mengziyi_bupt_edu_cn/EmNAsycFKNNNgHfV9Kib4osB7OD4OSu-Gu6Qnyy5PweG0A?e=5NrM6S)), CAVE_512_28 ([Baidu Disk](https://pan.baidu.com/s/1ue26weBAbn61a7hyT9CDkg), code: `ixoe` | [One Drive](https://mailstsinghuaeducn-my.sharepoint.com/:f:/g/personal/lin-j21_mails_tsinghua_edu_cn/EjhS1U_F7I1PjjjtjKNtUF8BJdsqZ6BSMag_grUfzsTABA?e=sOpwm4)), KAIST_CVPR2021 ([Baidu Disk](https://pan.baidu.com/s/1LfPqGe0R_tuQjCXC_fALZA), code: `5mmn` | [One Drive](https://mailstsinghuaeducn-my.sharepoint.com/:f:/g/personal/lin-j21_mails_tsinghua_edu_cn/EkA4B4GU8AdDu0ZkKXdewPwBd64adYGsMPB8PNCuYnpGlA?e=VFb3xP)), TSA_simu_data ([One Drive](https://1drv.ms/u/s!Au_cHqZBKiu2gYFDwE-7z1fzeWCRDA?e=ofvwrD)), TSA_real_data ([One Drive](https://1drv.ms/u/s!Au_cHqZBKiu2gYFTpCwLdTi_eSw6ww?e=uiEToT)), and then put them into the corresponding folders of `datasets/` and recollect them as the following form:

Following TSA-Net and DGSMP, we use the CAVE dataset (cave_1024_28) as the simulation training set. Both the CAVE (CAVE_512_28) and KAIST (KAIST_CVPR2021) datasets are used as the real training set.

##Prepare Pretrained ckpt:

Download pretrained (Simulation and real ([Baidu Disk](https://pan.baidu.com/s/1T2Kz1rQ8Lo_SzpPajJB3qA?pwd=2ndf), code: `2ndf` | [One Drive](https://1drv.ms/f/s!AgPCk81UUoyYoBB8ebE22P65wrx3?e=FvWHnS)), and then put them into the corresponding folders of `pretrained/` .

## Simulation Experiement:

### Training

```shell
cd SST/simulation/train_code/

# SST_S
python train.py  --outf ./exp/SST_S/ --method SST_S

# SST_M
python train.py --outf ./exp/SST_M/ --method SST_M

# SST_L
python train.py --outf ./exp/SST_L/ --method SST_L

# SST_LPlus
python train.py --outf ./exp/SST_LPlus/ --method SST_LPlus

```

The training log, trained model, and reconstrcuted HSI will be available in `SST/simulation/train_code/exp/` .

### Testing

Run the following command to test the model on the simulation dataset.

```shell
cd SST/simulation/test_code/

# SST_S
python test.py  --outf ./exp/SST_S/ --method SST_S --pretrained_model_path ./SST_S.pth

# SST_M
python test.py --outf ./exp/SST_M/ --method SST_M  --pretrained_model_path ./SST_M.pth

# SST_L
python test.py --outf ./exp/SST_L/ --method SST_L --pretrained_model_path ./SST_L.pth

# SST_LPlus
python test.py --outf ./exp/SST_LPlus/ --method SST_LPlus --pretrained_model_path ./SST_LPlus.pth

```

- The reconstrcuted HSIs will be output into `SST/simulation/test_code/exp/`

## Real Experiement:

### Training

```shell
cd SST/real/train_code/

# SST_S
python train.py  --outf ./exp/SST_S/ --method SST_S

# SST_M
python train.py --outf ./exp/SST_M/ --method SST_M

The training log, trained model, and reconstrcuted HSI will be available in `SST/real/train_code/exp/` . 
```

### Testing

```shell
cd SST/real/test_code/

# SST_S
python train.py  --outf ./exp/SST_S/ --method SST_S  --pretrained_model_path ./SST_S.pth

# SST_M
python train.py --outf ./exp/SST_M/ --method SST_M   --pretrained_model_path ./SST_M.pth

The reconstrcuted HSI will be output into `SST/real/test_code/exp/`  
```

# Acknowledgement:

Many thanks for the excellent work that has gone before and for their dedication. 
The code structure and datasets are borrowed from MST and DAUHST(https://github.com/caiyuanhao1998/MST).

please consider citing their works:

@inproceedings{mst,
  title={Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction},
  author={Yuanhao Cai and Jing Lin and Xiaowan Hu and Haoqian Wang and Xin Yuan and Yulun Zhang and Radu Timofte and Luc Van Gool},
  booktitle={CVPR},
  year={2022}
}

@inproceedings{dauhst,
  title={Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging},
  author={Cai, Yuanhao and Lin, Jing and Wang, Haoqian and Yuan, Xin and Ding, Henghui and Zhang, Yulun and Timofte, Radu and Van Gool, Luc},
  booktitle={NeurIPS}, 
  year={2022}
}

SST

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