This repo includes:

• Specification of dependencies.
• Evaluation code.
• Pre-trained models.
• README file.

This repo can reproduce the main results in Table (1) of our main paper. All the source code and pre-trained models will be released to the public for further research.

• Python 3 (Recommend to use Anaconda)

• PyTorch >= 1.3

• NVIDIA GPU + CUDA

• Python packages:

  pip install -r requirements.txt

To use the TSA-Net dataset, please follow the steps below:

1. Download the Dataset: Download the dataset from TSA-Net GitHub Repository.

2. Organize the Dataset: Place the downloaded dataset and camera response curve files into the 'code/data/' folder.

   The structure of the 'code/data/' folder should look like this:

   |--data
      |--mask.mat   
      |--mask_3d_shift.mat
      |--cameraSpectralResponse.mat
      |--Truth
          |--scene01.mat
          |--scene02.mat
          :
          |--scene10.mat
   

Note: The files 'cameraSpectralResponse.mat,' 'mask.mat,' and 'mask_3d_shift.mat' have already been included in this repository.

3. 1 Test our pre-trained In2SET models on the HSI dataset. The results will be saved in 'code/evaluation/testing_result/' in the MatFile format.

python test.py --gpu_id=0 --weight_path=./ckpts/In2SET_2stg.pth

python test.py --gpu_id=0 --weight_path=./ckpts/In2SET_3stg.pth

python test.py --gpu_id=0 --weight_path=./ckpts/In2SET_5stg.pth

python test.py --gpu_id=0 --weight_path=./ckpts/In2SET_9stg.pth

3. 2 Test inference time

python test_fps.py --gpu_id=0 --weight_path=./ckpts/In2SET_2stg.pth

python test_fps.py --gpu_id=0 --weight_path=./ckpts/In2SET_3stg.pth

python test_fps.py --gpu_id=0 --weight_path=./ckpts/In2SET_5stg.pth

python test_fps.py --gpu_id=0 --weight_path=./ckpts/In2SET_9stg.pth

Note: Due to size limitations for direct uploads on GitHub, our 9stg model is provided in three compressed parts: ckpts/In2SET_9stg.zip.001, ckpts/In2SET_9stg.zip.002, ckpts/In2SET_9stg.zip.003. Please use joint extraction for decompression.

(1) MST: https://github.com/caiyuanhao1998/MST

(2) rTVRA: https://github.com/zspCoder/rTVRA-Release.git

We extend our sincere appreciation and gratitude for the valuable contributions made by these repositories.