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./datasets */__pycache__/ */.idea -docs/_build/ -docs/_cache/ -docs/_templates/ -docs/_static +./outputs diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100755 index 0000000..26d3352 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml new file mode 100755 index 0000000..105ce2d --- /dev/null +++ b/.idea/inspectionProfiles/profiles_settings.xml @@ -0,0 +1,6 @@ + + + + \ No newline at end of file diff --git a/.idea/misc.xml b/.idea/misc.xml new file mode 100755 index 0000000..3e3ee3e --- /dev/null +++ b/.idea/misc.xml @@ -0,0 +1,4 @@ + + + + \ No newline at end of file diff --git a/.idea/modules.xml b/.idea/modules.xml new file mode 100755 index 0000000..8c69269 --- /dev/null +++ b/.idea/modules.xml @@ -0,0 +1,8 @@ + + + + + + + + \ No newline at end of file diff --git a/.idea/simca.iml b/.idea/simca.iml new file mode 100755 index 0000000..bbabeb1 --- /dev/null +++ b/.idea/simca.iml @@ -0,0 +1,12 @@ + + + + + + + + + + \ No newline at end of file diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100755 index 0000000..35eb1dd --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,6 @@ + + + + + + \ No newline at end of file diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb new file mode 100755 index 0000000..b01303d --- /dev/null +++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb @@ -0,0 +1,195 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "2db4fa17-8bda-4dad-8c5f-6a24ac30954f", + "metadata": {}, + "outputs": [], + "source": [ + "from opticalglass.glassfactory import get_glass_catalog\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "971c1d93-4dcb-46a6-847b-6a4170a6f9a1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "category NaN abbe number NaN \\\n", + "data item nd ne vd ve Colour code \n", + "glass \n", + "F2 1.62004 1.62408 36.37 36.11 35/32 \n", + "F2HT 1.62004 1.62408 36.37 36.11 35/32 \n", + "F5 1.60342 1.60718 38.03 37.77 35/32 \n", + "FK5HTi 1.48748 1.48913 70.47 70.29 29/25 \n", + "K10 1.50137 1.50349 56.41 56.15 33/30 \n", + "... ... ... ... ... ... \n", + "SF56A 1.7847 1.7918 26.08 25.87 42/37 \n", + "SF57 1.84666 1.85504 23.83 23.64 40/37* \n", + "SF57HTultra 1.84666 1.85504 23.83 23.64 39/36* \n", + "SF6 1.80518 1.81265 25.43 25.24 42/36 \n", + "SF6HT 1.80518 1.81265 25.43 25.24 41/36 \n", + "\n", + "category dispersion coefficients \\\n", + "data item B1 B2 B3 C1 C2 \n", + "glass \n", + "F2 1.345334 0.209073 0.937357 0.009977 0.047045 \n", + "F2HT 1.345334 0.209073 0.937357 0.009977 0.047045 \n", + "F5 1.310446 0.196034 0.96613 0.009586 0.045763 \n", + "FK5HTi 0.909362 0.279077 0.891813 0.005201 0.015894 \n", + "K10 1.156871 0.064263 0.872376 0.008094 0.038605 \n", + "... ... ... ... ... ... \n", + "SF56A 1.705793 0.344223 1.096018 0.013387 0.057956 \n", + "SF57 1.816514 0.428894 1.071863 0.01437 0.05928 \n", + "SF57HTultra 1.816514 0.428894 1.071863 0.01437 0.05928 \n", + "SF6 1.724485 0.390105 1.045729 0.013487 0.056932 \n", + "SF6HT 1.724485 0.390105 1.045729 0.013487 0.056932 \n", + "\n", + "category ... NaN \\\n", + "data item ... DP g,F DP i,g Glascode \n", + "glass ... \n", + "F2 ... 0.0002 0.0006 620364.360 \n", + "F2HT ... 0.0002 0.0006 620364.360 \n", + "F5 ... -0.0003 -0.0028 603380.347 \n", + "FK5HTi ... 0.0036 0.0321 487705.245 \n", + "K10 ... -0.0015 -0.0048 501564.252 \n", + "... ... ... ... ... \n", + "SF56A ... 0.0098 785261.492 \n", + "SF57 ... 0.0123 847238.551 \n", + "SF57HTultra ... 0.0123 847238.551 \n", + "SF6 ... 0.0092 0.0669 805254.518 \n", + "SF6HT ... 0.0092 0.0669 805254.518 \n", + "\n", + "category \\\n", + "data item Remarks Date \n", + "glass \n", + "F2 lead containing glass type 2014-02-01 \n", + "F2HT lead containing glass type 2014-02-01 \n", + "F5 lead containing glass type 2014-02-01 \n", + "FK5HTi i-line glass 2013-05-17 \n", + "K10 lead containing glass type 2014-02-01 \n", + "... ... ... \n", + "SF56A lead containing glass type 2014-02-01 \n", + "SF57 lead containing glass type, suitable for preci... 2018-02-21 \n", + "SF57HTultra lead containing glass type, suitable for preci... 2018-02-21 \n", + "SF6 lead containing glass type 2014-02-01 \n", + "SF6HT lead containing glass type 2014-02-01 \n", + "\n", + "category \n", + "data item rel. Price WR-J SR-J AT Abrasion HG-J \n", + "glass \n", + "F2 1.241862 \n", + "F2HT NaN NaN NaN NaN \n", + "F5 1.84953 \n", + "FK5HTi NaN NaN NaN NaN \n", + "K10 2.58259 \n", + "... ... ... ... ... ... \n", + "SF56A 2.693513 \n", + "SF57 2.56571 1 6 449 344 \n", + "SF57HTultra 1 6 449 344 \n", + "SF6 1.938751 NaN \n", + "SF6HT \n", + "\n", + "[123 rows x 154 columns]\n" + ] + } + ], + "source": [ + "\n", + "hoya_pd = get_glass_catalog('Schott')\n", + "\n", + "print(hoya_pd.df)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "5a4644d0-f2e9-4f4b-b162-a86a9bbd527f", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "invalid syntax (1074339294.py, line 6)", + "output_type": "error", + "traceback": [ + "\u001b[0;36m Cell \u001b[0;32mIn[6], line 6\u001b[0;36m\u001b[0m\n\u001b[0;31m B = (nd-1)/constant\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" + ] + } + ], + "source": [ + "nd = 1.6204\n", + "vd = 36.37\n", + "nm = 1*10**-9\n", + "constant = (1/(589.3*nm)**2 - 1/(656.3*nm)**2\n", + "\n", + "B = (nd-1)/constant\n", + "A = nd - B/(589.3*nm)**2" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7ee33c71-678d-49c3-aa4b-f3497b428db6", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'nm' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m lambda_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m602\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[43mnm\u001b[49m\n\u001b[1;32m 2\u001b[0m n \u001b[38;5;241m=\u001b[39m A \u001b[38;5;241m+\u001b[39m B\u001b[38;5;241m/\u001b[39mlambda_\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n", + "\u001b[0;31mNameError\u001b[0m: name 'nm' is not defined" + ] + } + ], + "source": [ + "lambda_ = 602*nm\n", + "n = A + B/lambda_**2" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86d6c502-4d3d-4186-a5c4-abb620f4e9c5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "017a93e6-a720-4d23-be45-5118ca4aabdf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/MST/LICENSE.txt b/MST/LICENSE.txt deleted file mode 100755 index 18bb3c9..0000000 --- a/MST/LICENSE.txt +++ /dev/null @@ -1,21 +0,0 @@ -MIT License - -Copyright (c) 2022 Yuanhao Cai - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. diff --git a/MST/README.md b/MST/README.md deleted file mode 100755 index e7cea45..0000000 --- a/MST/README.md +++ /dev/null @@ -1,596 +0,0 @@ -[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/mst-multi-stage-spectral-wise-transformer-for/spectral-reconstruction-on-arad-1k)](https://paperswithcode.com/sota/spectral-reconstruction-on-arad-1k?p=mst-multi-stage-spectral-wise-transformer-for) -[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/degradation-aware-unfolding-half-shuffle/spectral-reconstruction-on-cave)](https://paperswithcode.com/sota/spectral-reconstruction-on-cave?p=degradation-aware-unfolding-half-shuffle) - -[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/degradation-aware-unfolding-half-shuffle/spectral-reconstruction-on-kaist)](https://paperswithcode.com/sota/spectral-reconstruction-on-kaist?p=degradation-aware-unfolding-half-shuffle) -[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/degradation-aware-unfolding-half-shuffle/spectral-reconstruction-on-real-hsi)](https://paperswithcode.com/sota/spectral-reconstruction-on-real-hsi?p=degradation-aware-unfolding-half-shuffle) - - -# A Toolbox for Spectral Compressive Imaging -[![winner](https://img.shields.io/badge/MST++-Winner_of_NTIRE_2022_Spectral_Reconstruction_Challenge-179bd3)](https://github.com/caiyuanhao1998/MST-plus-plus/) -[![zhihu](https://img.shields.io/badge/知乎解读-MST-179bd3)](https://zhuanlan.zhihu.com/p/501101943) -[![zhihu](https://img.shields.io/badge/知乎解读-CST-179bd3)](https://zhuanlan.zhihu.com/p/544979161) -[![zhihu](https://img.shields.io/badge/知乎解读-DAUHST-179bd3)](https://zhuanlan.zhihu.com/p/576280023) - -#### Authors -Yuanhao Cai*, Jing Lin*, Xiaowan Hu, Haoqian Wang, Xin Yuan, Yulun Zhang, Radu Timofte, and Luc Van Gool - -#### Papers -- [Binarized Spectral Compressive Imaging (NeurIPS 2023)](https://arxiv.org/abs/2305.10299) -- [Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction (CVPR 2022)](https://arxiv.org/abs/2111.07910) -- [Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction (ECCV 2022)](https://arxiv.org/abs/2203.04845) -- [Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging (NeurIPS 2022)](https://arxiv.org/abs/2205.10102) -- [MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022)](https://arxiv.org/abs/2111.07910) -- [HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging (CVPR 2022)](https://arxiv.org/abs/2203.02149) - - - -#### Awards -![ntire](./figure/ntire.png) - - -#### News -- **2024.02.15 :** [NTIRE 2024 Challenge on Low Light Enhancement](https://codalab.lisn.upsaclay.fr/competitions/17640) begins. Welcome to use our [Retinexformer](https://github.com/caiyuanhao1998/Retinexformer) or [MST++](https://github.com/caiyuanhao1998/MST-plus-plus) (NTIRE 2022 Spectral Reconstruction Challenge Winner) to participate in this challenge! :trophy: -- **2023.12.02 :** Codes for real experiments have been updated. Welcome to check and use them. 🥳 -- **2023.11.24 :** Code, models, and results of [BiSRNet](https://arxiv.org/pdf/2305.10299.pdf) (NeurIPS 2023) are released at this repo. We also develop a toolbox [BiSCI](https://github.com/caiyuanhao1998/BiSCI) for binarized SCI reconstruction. Feel free to check and use them. 🌟 -- **2023.11.02 :** MST, MST++, CST, and DAUHST are added to the [Awesome-Transformer-Attention](https://github.com/cmhungsteve/Awesome-Transformer-Attention/blob/main/README_2.md#image-restoration) collection. 💫 -- **2023.09.21 :** Our new work [BiSRNet](https://arxiv.org/pdf/2305.10299.pdf) is accepted by NeurIPS 23. Code will be released at this repo and [BiSCI](https://github.com/caiyuanhao1998/BiSCI) -- **2023.02.26 :** We release the RGB images of [five real scenes](https://drive.google.com/drive/folders/1VTMgEbfX9MVpGo98XVVFKaANtQfgApAg?usp=sharing) and [ten simulation scenes](https://drive.google.com/drive/folders/1EkJsOxYKgyehZdOgKUeY75dU19GHZgE-?usp=sharing). Please feel free to check and use them. 🌟 -- **2022.11.02 :** We have provided more visual results of state-of-the-art methods and the function to evaluate the parameters and computational complexity of models. Please feel free to check and use them. :high_brightness: -- **2022.10.23 :** Code, models, and reconstructed HSI results of [DAUHST](https://arxiv.org/abs/2205.10102) have been released. 🔥 -- **2022.09.15 :** Our [DAUHST](https://arxiv.org/abs/2205.10102) has been accepted by NeurIPS 2022, code and models are coming soon. :rocket: -- **2022.07.20 :** Code, models, and reconstructed HSI results of [CST](https://arxiv.org/abs/2203.04845) have been released. 🔥 -- **2022.07.04 :** Our paper [CST](https://arxiv.org/abs/2203.04845) has been accepted by ECCV 2022, code and models are coming soon. :rocket: -- **2022.06.14 :** Code and models of [MST](https://arxiv.org/abs/2111.07910) and [MST++](https://arxiv.org/abs/2111.07910) have been released. This repo supports 12 learning-based methods to serve as toolbox for Spectral Compressive Imaging. The model zoo will be enlarged. 🔥 -- **2022.05.20 :** Our work [DAUHST](https://arxiv.org/abs/2205.10102) is on arxiv. :dizzy: -- **2022.04.02 :** Further work [MST++](https://github.com/caiyuanhao1998/MST-plus-plus/) has won the NTIRE 2022 Spectral Reconstruction Challenge. :trophy: -- **2022.03.09 :** Our work [CST](https://arxiv.org/abs/2203.04845) is on arxiv. :dizzy: -- **2022.03.02 :** Our paper MST has been accepted by CVPR 2022, code and models are coming soon. :rocket: - -| *Scene 2* | *Scene 3* | *Scene 4* | *Scene 7* | -| :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | -| | | | | - - -  - - -## 1. Comparison with State-of-the-art Methods -This repo is a baseline and toolbox containing 12 learning-based algorithms for spectral compressive imaging. - -
-Supported algorithms: - -* [x] [MST](https://arxiv.org/abs/2111.07910) (CVPR 2022) -* [x] [CST](https://arxiv.org/abs/2203.04845) (ECCV 2022) -* [x] [DAUHST](https://arxiv.org/abs/2205.10102) (NeurIPS 2022) -* [x] [BiSRNet](https://arxiv.org/abs/2305.10299) (NeurIPS 2023) -* [x] [MST++](https://arxiv.org/abs/2111.07910) (CVPRW 2022) -* [x] [HDNet](https://arxiv.org/abs/2203.02149) (CVPR 2022) -* [x] [BIRNAT](https://ieeexplore.ieee.org/abstract/document/9741335/) (TPAMI 2022) -* [x] [DGSMP](https://arxiv.org/abs/2103.07152) (CVPR 2021) -* [x] [GAP-Net](https://arxiv.org/abs/2012.08364) (Arxiv 2020) -* [x] [TSA-Net](https://link.springer.com/chapter/10.1007/978-3-030-58592-1_12) (ECCV 2020) -* [x] [ADMM-Net](https://openaccess.thecvf.com/content_ICCV_2019/html/Ma_Deep_Tensor_ADMM-Net_for_Snapshot_Compressive_Imaging_ICCV_2019_paper.html) (ICCV 2019) -* [x] [λ-Net](https://ieeexplore.ieee.org/document/9010044) (ICCV 2019) - - -
- -We are going to enlarge our model zoo in the future. - -| MST vs. SOTA | CST vs. MST | -| :----------------------------------------------: | :-----------------------------------------: | -| | | -| MST++ vs. SOTA | DAUHST vs. SOTA | -| | | - -| BiSRNet vs. SOTA BNNs | -| :----------------------------------------------: | -| | - - - - -### Quantitative Comparison on Simulation Dataset - -| Method | Params (M) | FLOPS (G) | PSNR | SSIM | Model Zoo | Simulation Result | Real Result | -| :----------------------------------------------------------: | :--------: | :-------: | :---: | :---: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | -| [λ-Net](https://ieeexplore.ieee.org/document/9010044) | 62.64 | 117.98 | 28.53 | 0.841 | [Google Drive](https://drive.google.com/drive/folders/11DwTFdgtG7sRnBwvkxxfN9rcOICsEdpC?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1xXkL2p4_mCLeTGa68wEbNQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1csOZ2Kfto_tWIiSD0hc2nzKjR4Ze7ftA?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1-0LjvHnkINW8YYaBiA4-VA?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1vVjlVLUm7Gb5zkDxH-mBwM-KnjXbgsjp?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Ah9NxFyBhKuzQaAL9WsmRw?pwd=mst1) | -| [TSA-Net](https://link.springer.com/chapter/10.1007/978-3-030-58592-1_12) | 44.25 | 110.06 | 31.46 | 0.894 | [Google Drive](https://drive.google.com/drive/folders/1f29eS8WqXu31310nD-7mRR81XfLBYKBd?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1riGZ83AXXkcjHiGVNrNeYg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1BOXBIu2Ze-L__XuLRUu4y9-lq0fw2FJd?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1icqxYnsD27zrDQ95STfRvw?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1OPZF0PpThWhC7aqNqhPEh3dX63OwGTC4?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1T3HX_Z7IqmC2imNLfgyBpg?pwd=mst1) | -| [DGSMP](https://arxiv.org/abs/2103.07152) | 3.76 | 646.65 | 32.63 | 0.917 | [Google Drive](https://drive.google.com/drive/folders/1j1k8mYKWh8FVe77Cz8hj69nI2lK2D5QC?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1v-uqYJZ5mQxOupLc6E_C1g?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1PjnTYEEDfWlTpe0jzCmImxxXLfy5Viva?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Y1AJICGUqUJEV-74Eg2FEg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1Sw5FPMCOrkF5a9IltYWA-QaYXkpKRw5-?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1BLb2w-yT25OTeeFwGFyMkQ?pwd=mst1) | -| [GAP-Net](https://arxiv.org/abs/2012.08364) | 4.27 | 78.58 | 33.26 | 0.917 | [Google Drive](https://drive.google.com/drive/folders/1AF3P42DZtBzKpWvjTVKYoLmGHsL2f_SL?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1hraGGd_HEsfCkSGv5QyOaw?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/16_kqEj4nlE_KlHzu8Q6zQCizTTBII82F?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/12azFFSEOic7iNTFGmC7wMw?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1u7MoHnZraM4NL2uAxC7U04sBhy4AoY_S?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1YDqnQ9BlqWmvdfQKx5vMQw?pwd=mst1) | -| [ADMM-Net](https://openaccess.thecvf.com/content_ICCV_2019/html/Ma_Deep_Tensor_ADMM-Net_for_Snapshot_Compressive_Imaging_ICCV_2019_paper.html) | 4.27 | 78.58 | 33.58 | 0.918 | [Google Drive](https://drive.google.com/drive/folders/1I9JqdyikulUVjXcdciHaJxfAceqfaF2G?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1ddkA9TazTq0rZReFYgGHMg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1WT0QYfC5dbigl9znD_JFNHpH0k_rTDc-?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1b2sRrJaS3PKYqqQErmtIJg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1MJKLjAo7Yzq_eF1JQK89-r40ItLUq4c8?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1aESUHFHjeMSL7shES3YmSw?pwd=mst1) | -| [BIRNAT](https://ieeexplore.ieee.org/abstract/document/9741335/) | 4.40 | 2122.66 | 37.58 | 0.960 | [Google Drive](https://drive.google.com/drive/folders/1bwhy0csM6GSNY0Qe9RS7_U3Bm9JJjHoc?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1HDMQu3jWvQ9X1yQA5dprGg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1fp4-D1-RGx9bICSmzpIiKBRAEmDOE3gD?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1pHfEKypguCLydNsmCGojpw?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1BUmusDHR-E533NdlHDLCYm_CwSCBJ0IF?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1l77HQkVbH7R6vbWNwwksTA?pwd=mst1) | -| [HDNet](https://arxiv.org/abs/2203.02149) | 2.37 | 154.76 | 34.97 | 0.943 | [Google Drive](https://drive.google.com/drive/folders/1F41BlUQulzPCf5yNo-q6V6Mdtr7bPV6-?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1mCoGHT22cw7ElVSaXgU5Lw?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1oUrgFUWIKR-96zAT2Y42UlAM6l5NVJsf?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Nb5IE02iAClMBC4OHcnePQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1vhzRWzIbhYxeZL-N2ZxaW2WqeT80Jogm?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1BXyMsecmENSoU9mCUKU2xA?pwd=mst1) | -| [MST-S](https://arxiv.org/abs/2111.07910) | 0.93 | 12.96 | 34.26 | 0.935 | [Google Drive](https://drive.google.com/drive/folders/176f_PammL0ZrIg3lVaQwd6Vr6Ui8FANs?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1ZQ08_Ec3a_-8YYAa5ms5PQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1afMG9PndlvjDTtl7UJZoElmDe5FtpCoW?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1xzJRVjnI-7A54Rj_zi3crA?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1FSwDZKAC03B8XhyAkaL9t1MWFz6q985y?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1H1pT1oKfK_o4EDXkts8VMQ?pwd=mst1) | -| [MST-M](https://arxiv.org/abs/2111.07910) | 1.50 | 18.07 | 34.94 | 0.943 | [Google Drive](https://drive.google.com/drive/folders/176f_PammL0ZrIg3lVaQwd6Vr6Ui8FANs?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1ZQ08_Ec3a_-8YYAa5ms5PQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1hnEuwYO9luwLmPeT98cUaik_zCPK6z30?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1OUuozfd3zLqzBjHnf6evCQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1FSwDZKAC03B8XhyAkaL9t1MWFz6q985y?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1H1pT1oKfK_o4EDXkts8VMQ?pwd=mst1) | -| [MST-L](https://arxiv.org/abs/2111.07910) | 2.03 | 28.15 | 35.18 | 0.948 | [Google Drive](https://drive.google.com/drive/folders/176f_PammL0ZrIg3lVaQwd6Vr6Ui8FANs?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1ZQ08_Ec3a_-8YYAa5ms5PQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/18ZF5wC1LRmqOh6VDeXD4eB8mP0Dvv6jb?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1imed0w1CWqx7IOlSpVh7qw?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1FSwDZKAC03B8XhyAkaL9t1MWFz6q985y?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1H1pT1oKfK_o4EDXkts8VMQ?pwd=mst1) | -| [MST++](https://arxiv.org/abs/2111.07910) | 1.33 | 19.42 | 35.99 | 0.951 | [Google Drive](https://drive.google.com/drive/folders/1rbV8LYD5k1RVR4usMORoXxY2szlFsr_9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1QUx_MpYCBSU4Zas5gpao2g?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/14sz-y99fEAJDQAN1itE5K-BlMHC1Tt3z?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1s3btC7QQrasW1NqFzOm8fQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/14CgUhfUp4BFalyigL4RDnTYyGiN5ojRK?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1fYsAvAjXTLLWpzlt_S2ppQ?pwd=mst1) | -| [CST-S](https://arxiv.org/abs/2203.04845) | 1.20 | 11.67 | 34.71 | 0.940 | [Google Drive](https://drive.google.com/drive/folders/1-SZDH0PuUyjLlvKfON-LL02dkr2LBuL9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Xq_YV6yO0zN6AULwU9ZPyg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1AdcUGiiPTfdt366NcBV8Texuu1Qw7_DI?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Vl6xufpmLWXSmhaVCsKkzQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/11RNDmA3VrPWdbj4H-mInGfZa60W86YEc?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1m3abRwjuaneFf1cCE85yMw?pwd=mst1) | -| [CST-M](https://arxiv.org/abs/2203.04845) | 1.36 | 16.91 | 35.31 | 0.947 | [Google Drive](https://drive.google.com/drive/folders/1-SZDH0PuUyjLlvKfON-LL02dkr2LBuL9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Xq_YV6yO0zN6AULwU9ZPyg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1FpMTtKSIN-t_natQIX2gKcu8MfPQLCG1?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1u3IjojML3H7AwSMe0CxV_Q?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/11RNDmA3VrPWdbj4H-mInGfZa60W86YEc?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1m3abRwjuaneFf1cCE85yMw?pwd=mst1) | -| [CST-L](https://arxiv.org/abs/2203.04845) | 3.00 | 27.81 | 35.85 | 0.954 | [Google Drive](https://drive.google.com/drive/folders/1-SZDH0PuUyjLlvKfON-LL02dkr2LBuL9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Xq_YV6yO0zN6AULwU9ZPyg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1MRFeMoi4JzhFrf_346USCFq98kdds9HY?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1UXwTyr-xZtDR68wzmeaCEA?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/11RNDmA3VrPWdbj4H-mInGfZa60W86YEc?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1m3abRwjuaneFf1cCE85yMw?pwd=mst1) | -| [CST-L-Plus](https://arxiv.org/abs/2203.04845) | 3.00 | 40.10 | 36.12 | 0.957 | [Google Drive](https://drive.google.com/drive/folders/1-SZDH0PuUyjLlvKfON-LL02dkr2LBuL9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1Xq_YV6yO0zN6AULwU9ZPyg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1sGHrkbYKjN3XqsduQL2mesXqO1XMeqGI?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1PsoJwVfZ7qYi6mnq_q_gDA?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/11RNDmA3VrPWdbj4H-mInGfZa60W86YEc?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1m3abRwjuaneFf1cCE85yMw?pwd=mst1) | -| [DAUHST-2stg](https://arxiv.org/abs/2205.10102) | 1.40 | 18.44 | 36.34 | 0.952 | [Google Drive](https://drive.google.com/drive/folders/1zhYRhFP8ee4YHk3-M0Nrl6KE_-n0gDLr?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1O2bxz-wEMF0mnrnOXHpC3A?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1qOrnp1crkk1z5ha56UoyOqDMFGfWlLC7?usp=sharing) /[Baidu Disk]( https://pan.baidu.com/s/1_RxqZQpCcYH50nxhSWeb0w?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1SgQhXXPYn6mYGSRMz5Ntsnab26XdjOc9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1S2MKaSKdU2v53_CZnuYkpQ?pwd=mst1) | -| [DAUHST-3stg](https://arxiv.org/abs/2205.10102) | 2.08 | 27.17 | 37.21 | 0.959 | [Google Drive](https://drive.google.com/drive/folders/1zhYRhFP8ee4YHk3-M0Nrl6KE_-n0gDLr?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1O2bxz-wEMF0mnrnOXHpC3A?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1uwXh5JrD4rnh_xYBpF4K4wI4lcTD1j4p?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1iYtxPuf1rkFWut5UdEYqtg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1SgQhXXPYn6mYGSRMz5Ntsnab26XdjOc9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1S2MKaSKdU2v53_CZnuYkpQ?pwd=mst1) | -| [DAUHST-5stg](https://arxiv.org/abs/2205.10102) | 3.44 | 44.61 | 37.75 | 0.962 | [Google Drive](https://drive.google.com/drive/folders/1zhYRhFP8ee4YHk3-M0Nrl6KE_-n0gDLr?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1O2bxz-wEMF0mnrnOXHpC3A?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1snTVZSUsbtzjJ5lxbPbaKhpTJX28Byuh?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1k1q0Y8QPgMZhThBEfzGKzQ?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1SgQhXXPYn6mYGSRMz5Ntsnab26XdjOc9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1S2MKaSKdU2v53_CZnuYkpQ?pwd=mst1) | -| [DAUHST-9stg](https://arxiv.org/abs/2205.10102) | 6.15 | 79.50 | 38.36 | 0.967 | [Google Drive](https://drive.google.com/drive/folders/1zhYRhFP8ee4YHk3-M0Nrl6KE_-n0gDLr?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1O2bxz-wEMF0mnrnOXHpC3A?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1rzZG1L-s2rYmR-wHXg9KnnGPbOIT5GaP?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/10vGcOirPk2L8sQg6uJoJkg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1SgQhXXPYn6mYGSRMz5Ntsnab26XdjOc9?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1S2MKaSKdU2v53_CZnuYkpQ?pwd=mst1) | -| [BiSRNet](https://arxiv.org/abs/2305.10299) | 0.036 | 1.18 | 29.76 | 0.837 | [Google Drive](https://drive.google.com/file/d/1MIsuIHuAaETZIRosjnKh2cvVgVDh9ZHv/view?usp=drive_link) / [Baidu Disk](https://pan.baidu.com/s/1wrHExqzl07niPS0fdMCAhg?pwd=mst1) | [Google Drive](https://drive.google.com/file/d/1QpZV6MzkijtwFI9MJp87bow4XxAok50m/view?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/11ifwb4tUDAVk7oTlFBKfUg?pwd=mst1) | [Google Drive](https://drive.google.com/drive/folders/1Hgdq43kbmHm1HG9SdLGryiIakBsBWuZp?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1I8051aIYsQEG8ybfSdPF0g?pwd=mst1) | - -The performance are reported on 10 scenes of the KAIST dataset. The test size of FLOPS is 256 x 256. - -We also provide the RGB images of [five real scenes](https://drive.google.com/drive/folders/1VTMgEbfX9MVpGo98XVVFKaANtQfgApAg?usp=sharing) and [ten simulation scenes](https://drive.google.com/drive/folders/1EkJsOxYKgyehZdOgKUeY75dU19GHZgE-?usp=sharing) for your convenience to draw a figure. - -Note: access code for `Baidu Disk` is `mst1` - - -  - - -## 2. 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 -``` - - -  - - -## 3. Prepare Dataset: -Download cave_1024_28 ([Baidu Disk](https://pan.baidu.com/s/1X_uXxgyO-mslnCTn4ioyNQ), code: `fo0q` | [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 ([Baidu Disk](https://pan.baidu.com/s/1LI9tMaSprtxT8PiAG1oETA), code: `efu8` | [One Drive](https://1drv.ms/u/s!Au_cHqZBKiu2gYFDwE-7z1fzeWCRDA?e=ofvwrD)), TSA_real_data ([Baidu Disk](https://pan.baidu.com/s/1RoOb1CKsUPFu0r01tRi5Bg), code: `eaqe` | [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: - -```shell -|--MST - |--real - |-- test_code - |-- train_code - |--simulation - |-- test_code - |-- train_code - |--visualization - |--datasets - |--cave_1024_28 - |--scene1.mat - |--scene2.mat - ： - |--scene205.mat - |--CAVE_512_28 - |--scene1.mat - |--scene2.mat - ： - |--scene30.mat - |--KAIST_CVPR2021 - |--1.mat - |--2.mat - ： - |--30.mat - |--TSA_simu_data - |--mask.mat - |--Truth - |--scene01.mat - |--scene02.mat - ： - |--scene10.mat - |--TSA_real_data - |--mask.mat - |--Measurements - |--scene1.mat - |--scene2.mat - ： - |--scene5.mat -``` - -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. - - -  - - -## 4. Simulation Experiement: - -### 4.1　Training - -```shell -cd MST/simulation/train_code/ - -# MST_S -python train.py --template mst_s --outf ./exp/mst_s/ --method mst_s - -# MST_M -python train.py --template mst_m --outf ./exp/mst_m/ --method mst_m - -# MST_L -python train.py --template mst_l --outf ./exp/mst_l/ --method mst_l - -# CST_S -python train.py --template cst_s --outf ./exp/cst_s/ --method cst_s - -# CST_M -python train.py --template cst_m --outf ./exp/cst_m/ --method cst_m - -# CST_L -python train.py --template cst_l --outf ./exp/cst_l/ --method cst_l - -# CST_L_Plus -python train.py --template cst_l_plus --outf ./exp/cst_l_plus/ --method cst_l_plus - -# GAP-Net -python train.py --template gap_net --outf ./exp/gap_net/ --method gap_net - -# ADMM-Net -python train.py --template admm_net --outf ./exp/admm_net/ --method admm_net - -# TSA-Net -python train.py --template tsa_net --outf ./exp/tsa_net/ --method tsa_net - -# HDNet -python train.py --template hdnet --outf ./exp/hdnet/ --method hdnet - -# DGSMP -python train.py --template dgsmp --outf ./exp/dgsmp/ --method dgsmp - -# BIRNAT -python train.py --template birnat --outf ./exp/birnat/ --method birnat - -# MST_Plus_Plus -python train.py --template mst_plus_plus --outf ./exp/mst_plus_plus/ --method mst_plus_plus - -# λ-Net -python train.py --template lambda_net --outf ./exp/lambda_net/ --method lambda_net - -# DAUHST-2stg -python train.py --template dauhst_2stg --outf ./exp/dauhst_2stg/ --method dauhst_2stg - -# DAUHST-3stg -python train.py --template dauhst_3stg --outf ./exp/dauhst_3stg/ --method dauhst_3stg - -# DAUHST-5stg -python train.py --template dauhst_5stg --outf ./exp/dauhst_5stg/ --method dauhst_5stg - -# DAUHST-9stg -python train.py --template dauhst_9stg --outf ./exp/dauhst_9stg/ --method dauhst_9stg - -# BiSRNet -python train.py --template bisrnet --outf ./exp/bisrnet/ --method bisrnet -``` - -- The training log, trained model, and reconstrcuted HSI will be available in `MST/simulation/train_code/exp/` - - -### 4.2　Testing - -Download the pretrained model zoo from ([Google Drive](https://drive.google.com/drive/folders/1zgB7jHqTzY1bjCSzdX4lKQEGyK3bpWIx?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1CH4uq_NZPpo5ra2tFzAdfQ?pwd=mst1), code: `mst1`) and place them to `MST/simulation/test_code/model_zoo/` - -Run the following command to test the model on the simulation dataset. - -```python -cd MST/simulation/test_code/ - -# MST_S -python test.py --template mst_s --outf ./exp/mst_s/ --method mst_s --pretrained_model_path ./model_zoo/mst/mst_s.pth - -# MST_M -python test.py --template mst_m --outf ./exp/mst_m/ --method mst_m --pretrained_model_path ./model_zoo/mst/mst_m.pth - -# MST_L -python test.py --template mst_l --outf ./exp/mst_l/ --method mst_l --pretrained_model_path ./model_zoo/mst/mst_l.pth - -# CST_S -python test.py --template cst_s --outf ./exp/cst_s/ --method cst_s --pretrained_model_path ./model_zoo/cst/cst_s.pth - -# CST_M -python test.py --template cst_m --outf ./exp/cst_m/ --method cst_m --pretrained_model_path ./model_zoo/cst/cst_m.pth - -# CST_L -python test.py --template cst_l --outf ./exp/cst_l/ --method cst_l --pretrained_model_path ./model_zoo/cst/cst_l.pth - -# CST_L_Plus -python test.py --template cst_l_plus --outf ./exp/cst_l_plus/ --method cst_l_plus --pretrained_model_path ./model_zoo/cst/cst_l_plus.pth - -# GAP_Net -python test.py --template gap_net --outf ./exp/gap_net/ --method gap_net --pretrained_model_path ./model_zoo/gap_net/gap_net.pth - -# ADMM_Net -python test.py --template admm_net --outf ./exp/admm_net/ --method admm_net --pretrained_model_path ./model_zoo/admm_net/admm_net.pth - -# TSA_Net -python test.py --template tsa_net --outf ./exp/tsa_net/ --method tsa_net --pretrained_model_path ./model_zoo/tsa_net/tsa_net.pth - -# HDNet -python test.py --template hdnet --outf ./exp/hdnet/ --method hdnet --pretrained_model_path ./model_zoo/hdnet/hdnet.pth - -# DGSMP -python test.py --template dgsmp --outf ./exp/dgsmp/ --method dgsmp --pretrained_model_path ./model_zoo/dgsmp/dgsmp.pth - -# BIRNAT -python test.py --template birnat --outf ./exp/birnat/ --method birnat --pretrained_model_path ./model_zoo/birnat/birnat.pth - -# MST_Plus_Plus -python test.py --template mst_plus_plus --outf ./exp/mst_plus_plus/ --method mst_plus_plus --pretrained_model_path ./model_zoo/mst_plus_plus/mst_plus_plus.pth - -# λ-Net -python test.py --template lambda_net --outf ./exp/lambda_net/ --method lambda_net --pretrained_model_path ./model_zoo/lambda_net/lambda_net.pth - -# DAUHST-2stg -python test.py --template dauhst_2stg --outf ./exp/dauhst_2stg/ --method dauhst_2stg --pretrained_model_path ./model_zoo/dauhst_2stg/dauhst_2stg.pth - -# DAUHST-3stg -python test.py --template dauhst_3stg --outf ./exp/dauhst_3stg/ --method dauhst_3stg --pretrained_model_path ./model_zoo/dauhst_3stg/dauhst_3stg.pth - -# DAUHST-5stg -python test.py --template dauhst_5stg --outf ./exp/dauhst_5stg/ --method dauhst_5stg --pretrained_model_path ./model_zoo/dauhst_5stg/dauhst_5stg.pth - -# DAUHST-9stg -python test.py --template dauhst_9stg --outf ./exp/dauhst_9stg/ --method dauhst_9stg --pretrained_model_path ./model_zoo/dauhst_9stg/dauhst_9stg.pth - -# BiSRNet -python test.py --template bisrnet --outf ./exp/bisrnet/ --method bisrnet --pretrained_model_path ./model_zoo/bisrnet/bisrnet.pth -``` - -- The reconstrcuted HSIs will be output into `MST/simulation/test_code/exp/`. Then place the reconstructed results into `MST/simulation/test_code/Quality_Metrics/results` and run the following MATLAB command to calculate the PSNR and SSIM of the reconstructed HSIs. - -```shell -Run cal_quality_assessment.m -``` - - - -- #### Evaluating the Params and FLOPS of models - - We provide two functions `my_summary()` and `my_summary_bnn()` in `simulation/test_code/utils.py`. Use them to evaluate the parameters and FLOPS of full-precision and binarized models - -```shell -from utils import my_summary, my_summary_bnn -my_summary(MST(), 256, 256, 28, 1) -my_summary_bnn(BiSRNet(), 256, 256, 28, 1) -``` - -### 4.3　Visualization - -- Put the reconstruted HSI in `MST/visualization/simulation_results/results` and rename it as method.mat, e.g., mst_s.mat. - -- Generate the RGB images of the reconstructed HSIs - -```shell - cd MST/visualization/ - Run show_simulation.m -``` - -- Draw the spetral density lines - -```shell -cd MST/visualization/ -Run show_line.m -``` - - -  - - -## 5. Real Experiement: - -### 5.1　Training - -```shell -cd MST/real/train_code/ - -# MST_S -python train.py --template mst_s --outf ./exp/mst_s/ --method mst_s - -# MST_M -python train.py --template mst_m --outf ./exp/mst_m/ --method mst_m - -# MST_L -python train.py --template mst_l --outf ./exp/mst_l/ --method mst_l - -# CST_S -python train.py --template cst_s --outf ./exp/cst_s/ --method cst_s - -# CST_M -python train.py --template cst_m --outf ./exp/cst_m/ --method cst_m - -# CST_L -python train.py --template cst_l --outf ./exp/cst_l/ --method cst_l - -# CST_L_Plus -python train.py --template cst_l_plus --outf ./exp/cst_l_plus/ --method cst_l_plus - -# GAP-Net -python train.py --template gap_net --outf ./exp/gap_net/ --method gap_net - -# ADMM-Net -python train.py --template admm_net --outf ./exp/admm_net/ --method admm_net - -# TSA-Net -python train.py --template tsa_net --outf ./exp/tsa_net/ --method tsa_net - -# HDNet -python train.py --template hdnet --outf ./exp/hdnet/ --method hdnet - -# DGSMP -python train.py --template dgsmp --outf ./exp/dgsmp/ --method dgsmp - -# BIRNAT -python train.py --template birnat --outf ./exp/birnat/ --method birnat - -# MST_Plus_Plus -python train.py --template mst_plus_plus --outf ./exp/mst_plus_plus/ --method mst_plus_plus - -# λ-Net -python train.py --template lambda_net --outf ./exp/lambda_net/ --method lambda_net - -# DAUHST-2stg -python train.py --template dauhst_2stg --outf ./exp/dauhst_2stg/ --method dauhst_2stg - -# DAUHST-3stg -python train.py --template dauhst_3stg --outf ./exp/dauhst_3stg/ --method dauhst_3stg - -# DAUHST-5stg -python train.py --template dauhst_5stg --outf ./exp/dauhst_5stg/ --method dauhst_5stg - -# DAUHST-9stg -python train.py --template dauhst_9stg --outf ./exp/dauhst_9stg/ --method dauhst_9stg - -# BiSRNet -python train_s.py --outf ./exp/bisrnet/ --method bisrnet -``` - -- If you do not have a large memory GPU, add `--size 128` to use a small patch size. - -- The training log, trained model, and reconstrcuted HSI will be available in `MST/real/train_code/exp/` - -- Note: you can use `train_s.py` for other methods except BiSRNet if you cannot access the mask data or you have limited GPU resources. In this case, you need to replace the `--method` paramter in the above commands and make some modifications. - - -### 5.2　Testing - -The pretrained model of BiSRNet can be download from ([Google Drive](https://drive.google.com/file/d/1zQ7PFuiaEgIpulBl8TA7S_8Am93nAKPb/view?usp=sharing) / [Baidu Disk](https://pan.baidu.com/s/1hiPbuUEBnIGQP6Ks9agfWQ?pwd=mst1), code: `mst1`) and place them to `MST/real/test_code/model_zoo/` - -```python -cd MST/real/test_code/ - -# MST_S -python test.py --outf ./exp/mst_s/ --pretrained_model_path ./model_zoo/mst/mst_s.pth - -# MST_M -python test.py --outf ./exp/mst_m/ --pretrained_model_path ./model_zoo/mst/mst_m.pth - -# MST_L -python test.py --outf ./exp/mst_l/ --pretrained_model_path ./model_zoo/mst/mst_l.pth - -# CST_S -python test.py --outf ./exp/cst_s/ --pretrained_model_path ./model_zoo/cst/cst_s.pth - -# CST_M -python test.py --outf ./exp/cst_m/ --pretrained_model_path ./model_zoo/cst/cst_m.pth - -# CST_L -python test.py --outf ./exp/cst_l/ --pretrained_model_path ./model_zoo/cst/cst_l.pth - -# CST_L_Plus -python test.py --outf ./exp/cst_l_plus/ --pretrained_model_path ./model_zoo/cst/cst_l_plus.pth - -# GAP_Net -python test.py --outf ./exp/gap_net/ --pretrained_model_path ./model_zoo/gap_net/gap_net.pth - -# ADMM_Net -python test.py --outf ./exp/admm_net/ --pretrained_model_path ./model_zoo/admm_net/admm_net.pth - -# TSA_Net -python test.py --outf ./exp/tsa_net/ --pretrained_model_path ./model_zoo/tsa_net/tsa_net.pth - -# HDNet -python test.py --template hdnet --outf ./exp/hdnet/ --method hdnet --pretrained_model_path ./model_zoo/hdnet/hdnet.pth - -# DGSMP -python test.py --outf ./exp/dgsmp/ --pretrained_model_path ./model_zoo/dgsmp/dgsmp.pth - -# BIRNAT -python test.py --outf ./exp/birnat/ --pretrained_model_path ./model_zoo/birnat/birnat.pth - -# MST_Plus_Plus -python test.py --outf ./exp/mst_plus_plus/ --pretrained_model_path ./model_zoo/mst_plus_plus/mst_plus_plus.pth - -# λ-Net -python test.py --outf ./exp/lambda_net/ --pretrained_model_path ./model_zoo/lambda_net/lambda_net.pth - -# DAUHST_2stg -python test.py --outf ./exp/dauhst_2stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_2stg.pth - -# DAUHST_3stg -python test.py --outf ./exp/dauhst_3stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_3stg.pth - -# DAUHST_5stg -python test.py --outf ./exp/dauhst_5stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_5stg.pth - -# DAUHST_9stg -python test.py --outf ./exp/dauhst_9stg/ --pretrained_model_path ./model_zoo/dauhst/dauhst_9stg.pth - -# BiSRNet -python test.py --outf ./exp/bisrnet --pretrained_model_path ./model_zoo/bisrnet/bisrnet.pth --method bisrnet -``` - -- The reconstrcuted HSI will be output into `MST/real/test_code/exp/` - -### 5.3　Visualization - -- Put the reconstruted HSI in `MST/visualization/real_results/results` and rename it as method.mat, e.g., mst_plus_plus.mat. - -- Generate the RGB images of the reconstructed HSI - -```shell -cd MST/visualization/ -Run show_real.m -``` - - -  - - -## 6. Citation -If this repo helps you, please consider citing our works: - - -```shell - - -# MST -@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} -} - - -# CST -@inproceedings{cst, - title={Coarse-to-Fine Sparse Transformer for 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={ECCV}, - year={2022} -} - - -# DAUHST -@inproceedings{dauhst, - title={Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging}, - author={Yuanhao Cai and Jing Lin and Haoqian Wang and Xin Yuan and Henghui Ding and Yulun Zhang and Radu Timofte and Luc Van Gool}, - booktitle={NeurIPS}, - year={2022} -} - - -# BiSCI -@inproceedings{bisci, - title={Binarized Spectral Compressive Imaging}, - author={Yuanhao Cai and Yuxin Zheng and Jing Lin and Xin Yuan and Yulun Zhang and Haoqian Wang}, - booktitle={NeurIPS}, - year={2023} -} - - -# MST++ -@inproceedings{mst_pp, - title={MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction}, - author={Yuanhao Cai and Jing Lin and Zudi Lin and Haoqian Wang and Yulun Zhang and Hanspeter Pfister and Radu Timofte and Luc Van Gool}, - booktitle={CVPRW}, - year={2022} -} - - -# HDNet -@inproceedings{hdnet, - title={HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging}, - author={Xiaowan Hu and Yuanhao Cai and Jing Lin and Haoqian Wang and Xin Yuan and Yulun Zhang and Radu Timofte and Luc Van Gool}, - booktitle={CVPR}, - year={2022} -} - -``` diff --git a/MST/figure/MST.png b/MST/figure/MST.png deleted file mode 100755 index c8f25eed6e3db15e4ccfa97741f4c7b95f48b72d..0000000000000000000000000000000000000000 GIT binary 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zF5`krA2S$`3kNQ3%7QA*@RxK@i<4eIZ}OG{l&ew(=at{#-f?R7rN_~}FGu+~6i zmt}=<%D6~Dy}dGr`B^LO?JXf&S#esT&#CMU+@!CPSb;D;mvc)=!WEl7Nek;FDD?4H zE`KMM)%-=8TOvWlFP8?KqBy>DJYwo=LY+_dj4QE*;1*ta1r<`i6TLUcm d0Pb0S#dm8J-}mQyuQuyX){6qes{{SkX-$eib diff --git a/MST/real/test_code/architecture/ADMM_Net.py b/MST/real/test_code/architecture/ADMM_Net.py deleted file mode 100755 index 64f61b4..0000000 --- a/MST/real/test_code/architecture/ADMM_Net.py +++ /dev/null @@ -1,201 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - x = self.d_conv(x) - return x - - -class Unet(nn.Module): - - def __init__(self, in_ch, out_ch): - super(Unet, self).__init__() - - self.dconv_down1 = double_conv(in_ch, 32) - self.dconv_down2 = double_conv(32, 64) - self.dconv_down3 = double_conv(64, 128) - - self.maxpool = nn.MaxPool2d(2) - self.upsample2 = nn.Sequential( - nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.upsample1 = nn.Sequential( - nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.dconv_up2 = double_conv(64 + 64, 64) - self.dconv_up1 = double_conv(32 + 32, 32) - - self.conv_last = nn.Conv2d(32, out_ch, 1) - self.afn_last = nn.Tanh() - - def forward(self, x): - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - inputs = x - conv1 = self.dconv_down1(x) - x = self.maxpool(conv1) - - conv2 = self.dconv_down2(x) - x = self.maxpool(conv2) - - conv3 = self.dconv_down3(x) - - x = self.upsample2(conv3) - x = torch.cat([x, conv2], dim=1) - - x = self.dconv_up2(x) - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - - x = self.dconv_up1(x) - - x = self.conv_last(x) - x = self.afn_last(x) - out = x + inputs - - return out[:, :, :h_inp, :w_inp] - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class ADMM_net(nn.Module): - - def __init__(self): - super(ADMM_net, self).__init__() - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - self.gamma1 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma2 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma3 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma4 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma5 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma6 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma7 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma8 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma9 = torch.nn.Parameter(torch.Tensor([0])) - - def forward(self, y, input_mask=None, input_mask_s=None): - if input_mask == None: - Phi = torch.rand((1, 28, 256, 310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask - x_list = [] - theta = At(y,Phi) - b = torch.zeros_like(Phi) - ### 1-3 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma1),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet1(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma2),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet2(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma3),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet3(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - ### 4-6 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma4),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet4(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma5),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet5(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma6),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet6(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - ### 7-9 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma7),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet7(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma8),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet8(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma9),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet9(x1) - theta = shift_3d(theta) - return theta[:, :, :, :384] diff --git a/MST/real/test_code/architecture/BIRNAT.py b/MST/real/test_code/architecture/BIRNAT.py deleted file mode 100755 index 3b9fce7..0000000 --- a/MST/real/test_code/architecture/BIRNAT.py +++ /dev/null @@ -1,365 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class self_attention(nn.Module): - def __init__(self, ch): - super(self_attention, self).__init__() - self.conv1 = nn.Conv2d(ch, ch // 8, 1) - self.conv2 = nn.Conv2d(ch, ch // 8, 1) - self.conv3 = nn.Conv2d(ch, ch, 1) - self.conv4 = nn.Conv2d(ch, ch, 1) - self.gamma1 = torch.nn.Parameter(torch.Tensor([0])) - self.ch = ch - - def forward(self, x): - batch_size = x.shape[0] - - f = self.conv1(x) - g = self.conv2(x) - h = self.conv3(x) - ht = h.reshape([batch_size, self.ch, -1]) - - ft = f.reshape([batch_size, self.ch // 8, -1]) - n = torch.matmul(ft.permute([0, 2, 1]), g.reshape([batch_size, self.ch // 8, -1])) - beta = F.softmax(n, dim=1) - - o = torch.matmul(ht, beta) - o = o.reshape(x.shape) # [bs, C, h, w] - - o = self.conv4(o) - - x = self.gamma1 * o + x - - return x - - -class res_part(nn.Module): - - def __init__(self, in_ch, out_ch): - super(res_part, self).__init__() - self.conv1 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - self.conv2 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - self.conv3 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - - def forward(self, x): - x1 = self.conv1(x) - x = x1 + x - x1 = self.conv2(x) - x = x1 + x - x1 = self.conv3(x) - x = x1 + x - return x - - -class down_feature(nn.Module): - - def __init__(self, in_ch, out_ch): - super(down_feature, self).__init__() - self.conv = nn.Sequential( - # nn.Conv2d(in_ch, 20, 5, stride=1, padding=2), - # nn.Conv2d(20, 40, 5, stride=2, padding=2), - # nn.Conv2d(40, out_ch, 5, stride=2, padding=2), - nn.Conv2d(in_ch, 20, 5, stride=1, padding=2), - nn.Conv2d(20, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.Conv2d(20, 40, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(40, out_ch, 3, stride=1, padding=1), - ) - - def forward(self, x): - x = self.conv(x) - return x - - -class up_feature(nn.Module): - - def __init__(self, in_ch, out_ch): - super(up_feature, self).__init__() - self.conv = nn.Sequential( - # nn.ConvTranspose2d(in_ch, 40, 3, stride=2, padding=1, output_padding=1), - # nn.ConvTranspose2d(40, 20, 3, stride=2, padding=1, output_padding=1), - nn.Conv2d(in_ch, 40, 3, stride=1, padding=1), - nn.Conv2d(40, 30, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(30, 20, 3, stride=1, padding=1), - nn.Conv2d(20, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - nn.Conv2d(20, out_ch, 1), - # nn.Sigmoid(), - ) - - def forward(self, x): - x = self.conv(x) - return x - -class cnn1(nn.Module): - # 输入meas concat mask - # 3 下采样 - - def __init__(self, B): - super(cnn1, self).__init__() - self.conv1 = nn.Conv2d(B + 1, 32, kernel_size=5, stride=1, padding=2) - self.relu1 = nn.LeakyReLU(inplace=True) - self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) - self.relu2 = nn.LeakyReLU(inplace=True) - self.conv3 = nn.Conv2d(64, 64, kernel_size=1, stride=1) - self.relu3 = nn.LeakyReLU(inplace=True) - self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) - self.relu4 = nn.LeakyReLU(inplace=True) - self.conv5 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1) - self.relu5 = nn.LeakyReLU(inplace=True) - self.conv51 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1) - self.relu51 = nn.LeakyReLU(inplace=True) - self.conv52 = nn.Conv2d(32, 16, kernel_size=1, stride=1) - self.relu52 = nn.LeakyReLU(inplace=True) - self.conv6 = nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1) - self.res_part1 = res_part(128, 128) - self.res_part2 = res_part(128, 128) - self.res_part3 = res_part(128, 128) - self.conv7 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - self.relu7 = nn.LeakyReLU(inplace=True) - self.conv8 = nn.Conv2d(128, 128, kernel_size=1, stride=1) - self.conv9 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - self.relu9 = nn.LeakyReLU(inplace=True) - self.conv10 = nn.Conv2d(128, 128, kernel_size=1, stride=1) - - self.att1 = self_attention(128) - - def forward(self, meas=None, nor_meas=None, PhiTy=None): - data = torch.cat([torch.unsqueeze(nor_meas, dim=1), PhiTy], dim=1) - out = self.conv1(data) - - out = self.relu1(out) - out = self.conv2(out) - out = self.relu2(out) - out = self.conv3(out) - out = self.relu3(out) - out = self.conv4(out) - out = self.relu4(out) - out = self.res_part1(out) - out = self.conv7(out) - out = self.relu7(out) - out = self.conv8(out) - out = self.res_part2(out) - out = self.conv9(out) - out = self.relu9(out) - out = self.conv10(out) - out = self.res_part3(out) - - # out = self.att1(out) - - out = self.conv5(out) - out = self.relu5(out) - out = self.conv51(out) - out = self.relu51(out) - out = self.conv52(out) - out = self.relu52(out) - out = self.conv6(out) - - return out - - -class forward_rnn(nn.Module): - - def __init__(self): - super(forward_rnn, self).__init__() - self.extract_feature1 = down_feature(1, 20) - self.up_feature1 = up_feature(60, 1) - self.conv_x1 = nn.Sequential( - nn.Conv2d(1, 16, 5, stride=1, padding=2), - nn.Conv2d(16, 32, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(32, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.conv_x2 = nn.Sequential( - nn.Conv2d(1, 10, 5, stride=1, padding=2), - nn.Conv2d(10, 10, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(10, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.h_h = nn.Sequential( - nn.Conv2d(60, 30, 3, padding=1), - nn.Conv2d(30, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - ) - self.res_part1 = res_part(60, 60) - self.res_part2 = res_part(60, 60) - - def forward(self, xt1, meas=None, nor_meas=None, PhiTy=None, mask3d_batch=None, h=None, cs_rate=28): - ht = h - xt = xt1 - - step = 2 - [bs, nC, row, col] = xt1.shape - - out = xt1 - x11 = self.conv_x1(torch.unsqueeze(nor_meas, 1)) - for i in range(cs_rate - 1): - d1 = torch.zeros(bs, row, col).cuda() - d2 = torch.zeros(bs, row, col).cuda() - for ii in range(i + 1): - d1 = d1 + torch.mul(mask3d_batch[:, ii, :, :], out[:, ii, :, :]) - for ii in range(i + 2, cs_rate): - d2 = d2 + torch.mul(mask3d_batch[:, ii, :, :], torch.squeeze(nor_meas)) - x12 = self.conv_x2(torch.unsqueeze(meas - d1 - d2, 1)) - - x2 = self.extract_feature1(xt) - h = torch.cat([ht, x11, x12, x2], dim=1) - h = self.res_part1(h) - h = self.res_part2(h) - ht = self.h_h(h) - xt = self.up_feature1(h) - out = torch.cat([out, xt], dim=1) - - return out, ht - - -class backrnn(nn.Module): - - def __init__(self): - super(backrnn, self).__init__() - self.extract_feature1 = down_feature(1, 20) - self.up_feature1 = up_feature(60, 1) - self.conv_x1 = nn.Sequential( - nn.Conv2d(1, 16, 5, stride=1, padding=2), - nn.Conv2d(16, 32, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(32, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.conv_x2 = nn.Sequential( - nn.Conv2d(1, 10, 5, stride=1, padding=2), - nn.Conv2d(10, 10, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(10, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.h_h = nn.Sequential( - nn.Conv2d(60, 30, 3, padding=1), - nn.Conv2d(30, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - ) - self.res_part1 = res_part(60, 60) - self.res_part2 = res_part(60, 60) - - def forward(self, xt8, meas=None, nor_meas=None, PhiTy=None, mask3d_batch=None, h=None, cs_rate=28): - ht = h - - step = 2 - [bs, nC, row, col] = xt8.shape - - xt = torch.unsqueeze(xt8[:, cs_rate - 1, :, :], 1) - - out = torch.zeros(bs, cs_rate, row, col).cuda() - out[:, cs_rate - 1, :, :] = xt[:, 0, :, :] - x11 = self.conv_x1(torch.unsqueeze(nor_meas, 1)) - for i in range(cs_rate - 1): - d1 = torch.zeros(bs, row, col).cuda() - d2 = torch.zeros(bs, row, col).cuda() - for ii in range(i + 1): - d1 = d1 + torch.mul(mask3d_batch[:, cs_rate - 1 - ii, :, :], out[:, cs_rate - 1 - ii, :, :].clone()) - for ii in range(i + 2, cs_rate): - d2 = d2 + torch.mul(mask3d_batch[:, cs_rate - 1 - ii, :, :], xt8[:, cs_rate - 1 - ii, :, :].clone()) - x12 = self.conv_x2(torch.unsqueeze(meas - d1 - d2, 1)) - - x2 = self.extract_feature1(xt) - h = torch.cat([ht, x11, x12, x2], dim=1) - h = self.res_part1(h) - h = self.res_part2(h) - ht = self.h_h(h) - xt = self.up_feature1(h) - - out[:, cs_rate - 2 - i, :, :] = xt[:, 0, :, :] - - return out - -def shift_gt_back(inputs, step=2): # input [bs,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - output = torch.zeros(bs, nC, row, col - (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, :] = inputs[:, i, :, step * i:step * i + col - (nC - 1) * step] - return output - -def shift(inputs, step=2): - [bs, nC, row, col] = inputs.shape - if inputs.is_cuda: - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).cuda().float() - else: - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).float() - for i in range(nC): - output[:, i, :, step * i:step * i + col] = inputs[:, i, :, :] - return output - -class BIRNAT(nn.Module): - - def __init__(self): - super(BIRNAT, self).__init__() - self.cs_rate = 28 - self.first_frame_net = cnn1(self.cs_rate).cuda() - self.rnn1 = forward_rnn().cuda() - self.rnn2 = backrnn().cuda() - - def gen_meas_torch(self, meas, shift_mask): - batch_size, H = meas.shape[0:2] - mask_s = torch.sum(shift_mask, 1) - nor_meas = torch.div(meas, mask_s) - temp = torch.mul(torch.unsqueeze(nor_meas, dim=1).expand([batch_size, 28, H, shift_mask.shape[3]]), shift_mask) - return nor_meas, temp - - def forward(self, meas, shift_mask=None, input_mask_s=None): - if shift_mask==None: - shift_mask = torch.zeros(1, 28, 256, 310).cuda() - H, W = meas.shape[-2:] - nor_meas, PhiTy = self.gen_meas_torch(meas, shift_mask) - h0 = torch.zeros(meas.shape[0], 20, H, W).cuda() - xt1 = self.first_frame_net(meas, nor_meas, PhiTy) - model_out1, h1 = self.rnn1(xt1, meas, nor_meas, PhiTy, shift_mask, h0, self.cs_rate) - model_out2 = self.rnn2(model_out1, meas, nor_meas, PhiTy, shift_mask, h1, self.cs_rate) - model_out2 = shift_gt_back(model_out2) - - return model_out2 diff --git a/MST/real/test_code/architecture/BiSRNet.py b/MST/real/test_code/architecture/BiSRNet.py deleted file mode 100755 index 2986689..0000000 --- a/MST/real/test_code/architecture/BiSRNet.py +++ /dev/null @@ -1,426 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -import os -from pdb import set_trace as stx - - -# --------------------------------------------- Binarized Basic Units ----------------------------------------------------------------- - - -class LearnableBias(nn.Module): - def __init__(self, out_chn): - super(LearnableBias, self).__init__() - self.bias = nn.Parameter(torch.zeros(1, out_chn, 1, 1), requires_grad=True) - - def forward(self, x): - # stx() - out = x + self.bias.expand_as(x) - return out - - -class ReDistribution(nn.Module): - def __init__(self, out_chn): - super(ReDistribution, self).__init__() - self.b = nn.Parameter(torch.zeros(1, out_chn, 1, 1), requires_grad=True) - self.k = nn.Parameter(torch.ones(1, out_chn, 1, 1), requires_grad=True) - - def forward(self, x): - out = x * self.k.expand_as(x) + self.b.expand_as(x) - return out - - -class RPReLU(nn.Module): - def __init__(self, inplanes): - super(RPReLU, self).__init__() - self.pr_bias0 = LearnableBias(inplanes) - self.pr_prelu = nn.PReLU(inplanes) - self.pr_bias1 = LearnableBias(inplanes) - - def forward(self, x): - x = self.pr_bias1(self.pr_prelu(self.pr_bias0(x))) - return x - - -class Spectral_Binary_Activation(nn.Module): - def __init__(self): - super(Spectral_Binary_Activation, self).__init__() - self.beta = nn.Parameter(torch.ones(1), requires_grad=True) - - def forward(self, x): - binary_activation_no_grad = torch.sign(x) - tanh_activation = torch.tanh(x * self.beta) - - out = binary_activation_no_grad.detach() - tanh_activation.detach() + tanh_activation - return out - - -class HardBinaryConv(nn.Conv2d): - def __init__(self, in_chn, out_chn, kernel_size=3, stride=1, padding=1, groups=1, bias=True): - super(HardBinaryConv, self).__init__( - in_chn, - out_chn, - kernel_size, - stride=stride, - padding=padding, - groups=groups, - bias=bias - ) - - def forward(self, x): - real_weights = self.weight - scaling_factor = torch.mean(torch.mean(torch.mean(abs(real_weights), dim=3, keepdim=True), dim=2, keepdim=True), - dim=1, keepdim=True) - scaling_factor = scaling_factor.detach() - # stx() - binary_weights_no_grad = scaling_factor * torch.sign(real_weights) - # stx() - cliped_weights = torch.clamp(real_weights, -1.0, 1.0) - binary_weights = binary_weights_no_grad.detach() - cliped_weights.detach() + cliped_weights - y = F.conv2d(x, binary_weights, self.bias, stride=self.stride, padding=self.padding, groups=self.groups) - - return y - - -class BinaryConv2d(nn.Module): - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d, self).__init__() - - self.move0 = ReDistribution(in_channels) - self.binary_activation = Spectral_Binary_Activation() - self.binary_conv = HardBinaryConv(in_chn=in_channels, - out_chn=in_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=bias, - groups=groups) - self.relu = RPReLU(in_channels) - - def forward(self, x): - out = self.move0(x) - out = self.binary_activation(out) - out = self.binary_conv(out) - out = self.relu(out) - out = out + x - return out - - -class BinaryConv2d_Down(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,2h,2w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Down, self).__init__() - - self.biconv_1 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,2c,h/2,w/2 - ''' - out = self.avg_pool(x) - out_1 = out - out_2 = out_1.clone() - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - out = torch.cat([out_1, out_2], dim=1) - - return out - - -class BinaryConv2d_Up(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,2h,2w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Up, self).__init__() - - self.biconv_1 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,c/2,2h,2w - ''' - b, c, h, w = x.shape - out = F.interpolate(x, scale_factor=2, mode='bilinear') - - out_1 = out[:, :c // 2, :, :] - out_2 = out[:, c // 2:, :, :] - - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - - out = (out_1 + out_2) / 2 - - return out - - -class BinaryConv2d_Fusion_Decrease(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,h,w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Fusion_Decrease, self).__init__() - - self.biconv_1 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - # self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,c/2,h,w - ''' - b, c, h, w = x.shape - out = x - - out_1 = out[:, :c // 2, :, :] - out_2 = out[:, c // 2:, :, :] - - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - - out = (out_1 + out_2) / 2 - - return out - - -class BinaryConv2d_Fusion_Increase(nn.Module): - ''' - input: b,c,h,w - output: b,2c,h,w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Fusion_Increase, self).__init__() - - self.biconv_1 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - # self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,2c,h,w - ''' - # stx() - out_1 = x - out_2 = out_1.clone() - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - out = torch.cat([out_1, out_2], dim=1) - - return out - - -# ---------------------------------------------------------- Binarized UNet------------------------------------------------------ - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -def shift_back(inputs, step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256 // row - step = float(step) / float(down_sample * down_sample) - out_col = row - for i in range(nC): - inputs[:, i, :, :out_col] = \ - inputs[:, i, :, int(step * i):int(step * i) + out_col] - return inputs[:, :, :, :out_col] - - -class FeedForward(nn.Module): - def __init__(self, dim, mult=2): - super().__init__() - self.net = nn.Sequential( - BinaryConv2d_Fusion_Increase(dim, dim * mult, 1, 1, bias=False), - BinaryConv2d_Fusion_Increase(dim * mult, dim * mult * mult, 1, 1, bias=False), - RPReLU(dim * mult * mult), - BinaryConv2d(dim * mult * mult, dim * mult * mult, 3, 1, 1, bias=False, groups=dim), - RPReLU(dim * mult * mult), - BinaryConv2d_Fusion_Decrease(dim * mult * mult, dim * mult, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - - -class BiSRNet_Block(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append( - PreNorm(dim, FeedForward(dim=dim)) - ) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for ff in self.blocks: - # x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - -class BiSRNet_body(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2, 4, 4]): - super(BiSRNet_body, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = BinaryConv2d(in_dim, self.dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - BiSRNet_Block(dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - BinaryConv2d_Down(dim_stage, dim_stage * 2, 3, 1, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = BiSRNet_Block( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - BinaryConv2d_Up(dim_stage, dim_stage // 2, 3, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim_stage, dim_stage // 2, 1, 1, bias=False), - BiSRNet_Block( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = BinaryConv2d(self.dim, out_dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (BiSRNet_Block, FeaDownSample) in self.encoder_layers: - # stx() - fea = BiSRNet_Block(fea) - fea_encoder.append(fea) - # stx() - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, BiSRNet_Block) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage - 1 - i]], dim=1)) - fea = BiSRNet_Block(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - - -class BiSRNet(nn.Module): - ''' - Only 3 layers are 32-bit conv - ''' - - def __init__(self, in_channels=28, out_channels=28, n_feat=28, stage=3, num_blocks=[1, 1, 1]): - super(BiSRNet, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2, - bias=False) # 1-bit -> 32-bit - modules_body = [BiSRNet_body(dim=n_feat, stage=2, num_blocks=num_blocks) for _ in range(stage)] - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) # 1-bit -> 32-bit - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2, - bias=False) # 1-bit -> 32-bit - - def y2x(self, y): - ## Spilt operator - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - return x - - def forward(self, y, input_mask=None, input_mask_s=None): - """ - x: [b,h,w] - return out:[b,c,h,w] - """ - - x = self.y2x(y) - - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] \ No newline at end of file diff --git a/MST/real/test_code/architecture/CST.py b/MST/real/test_code/architecture/CST.py deleted file mode 100755 index 14fda4c..0000000 --- a/MST/real/test_code/architecture/CST.py +++ /dev/null @@ -1,600 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -from collections import defaultdict, Counter -import numpy as np -from tqdm import tqdm -import random - -def uniform(a, b, shape, device='cuda'): - return (b - a) * torch.rand(shape, device=device) + a - -class AsymmetricTransform: - - def Q(self, *args, **kwargs): - raise NotImplementedError('Query transform not implemented') - - def K(self, *args, **kwargs): - raise NotImplementedError('Key transform not implemented') - -class LSH: - def __call__(self, *args, **kwargs): - raise NotImplementedError('LSH scheme not implemented') - - def compute_hash_agreement(self, q_hash, k_hash): - return (q_hash == k_hash).min(dim=-1)[0].sum(dim=-1) - -class XBOXPLUS(AsymmetricTransform): - - def set_norms(self, x): - self.x_norms = x.norm(p=2, dim=-1, keepdim=True) - self.MX = torch.amax(self.x_norms, dim=-2, keepdim=True) - - def X(self, x): - device = x.device - ext = torch.sqrt((self.MX**2).to(device) - (self.x_norms**2).to(device)) - zero = torch.tensor(0.0, device=x.device).repeat(x.shape[:-1], 1).unsqueeze(-1) - return torch.cat((x, ext, zero), -1) - -def lsh_clustering(x, n_rounds, r=1): - salsh = SALSH(n_rounds=n_rounds, dim=x.shape[-1], r=r, device=x.device) - x_hashed = salsh(x).reshape((n_rounds,) + x.shape[:-1]) - return x_hashed.argsort(dim=-1) - -class SALSH(LSH): - def __init__(self, n_rounds, dim, r, device='cuda'): - super(SALSH, self).__init__() - self.alpha = torch.normal(0, 1, (dim, n_rounds), device=device) - self.beta = uniform(0, r, shape=(1, n_rounds), device=device) - self.dim = dim - self.r = r - - def __call__(self, vecs): - projection = vecs @ self.alpha - projection_shift = projection + self.beta - projection_rescale = projection_shift / self.r - return projection_rescale.permute(2, 0, 1) - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - - variance = scale / denom - - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def batch_scatter(output, src, dim, index): - """ - :param output: [b,n,c] - :param src: [b,n,c] - :param dim: int - :param index: [b,n] - :return: output: [b,n,c] - """ - b,k,c = src.shape - index = index[:, :, None].expand(-1, -1, c) - output, src, index = map(lambda t: rearrange(t, 'b k c -> (b c) k'), (output, src, index)) - output.scatter_(dim,index,src) - output = rearrange(output, '(b c) k -> b k c', b=b) - return output - -def batch_gather(x, index, dim): - """ - :param x: [b,n,c] - :param index: [b,n//2] - :param dim: int - :return: output: [b,n//2,c] - """ - b,n,c = x.shape - index = index[:,:,None].expand(-1,-1,c) - x, index = map(lambda t: rearrange(t, 'b n c -> (b c) n'), (x, index)) - output = torch.gather(x,dim,index) - output = rearrange(output, '(b c) n -> b n c', b=b) - return output - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class SAH_MSA(nn.Module): - def __init__(self, heads=4, n_rounds=2, channels=64, patch_size=144, - r=1): - super(SAH_MSA, self).__init__() - self.heads = heads - self.n_rounds = n_rounds - inner_dim = channels*3 - self.to_q = nn.Linear(channels, inner_dim, bias=False) - self.to_k = nn.Linear(channels, inner_dim, bias=False) - self.to_v = nn.Linear(channels, inner_dim, bias=False) - self.to_out = nn.Linear(inner_dim, channels, bias=False) - - self.xbox_plus = XBOXPLUS() - self.clustering_params = { - 'r': r, - 'n_rounds': self.n_rounds - } - self.q_attn_size = patch_size[0] * patch_size[1] - self.k_attn_size = patch_size[0] * patch_size[1] - - def forward(self, input): - """ - :param input: [b,n,c] - :return: output: [b,n,c] - """ - - B, N, C_inp = input.shape - query = self.to_q(input) - key = self.to_k(input) - value = self.to_v(input) - input_hash = input.view(B, N, self.heads, C_inp//self.heads) - x_hash = rearrange(input_hash, 'b t h e -> (b h) t e') - bs, x_seqlen, dim = x_hash.shape - with torch.no_grad(): - self.xbox_plus.set_norms(x_hash) - Xs = self.xbox_plus.X(x_hash) - x_positions = lsh_clustering(Xs, **self.clustering_params) - x_positions = x_positions.reshape(self.n_rounds, bs, -1) - - del Xs - - C = query.shape[-1] - query = query.view(B, N, self.heads, C // self.heads) - key = key.view(B, N, self.heads, C // self.heads) - value = value.view(B, N, self.heads, C // self.heads) - - query = rearrange(query, 'b t h e -> (b h) t e') # [bs, q_seqlen,c] - key = rearrange(key, 'b t h e -> (b h) t e') - value = rearrange(value, 'b s h d -> (b h) s d') - - bs, q_seqlen, dim = query.shape - bs, k_seqlen, dim = key.shape - v_dim = value.shape[-1] - - x_rev_positions = torch.argsort(x_positions, dim=-1) - x_offset = torch.arange(bs, device=query.device).unsqueeze(-1) * x_seqlen - x_flat = (x_positions + x_offset).reshape(-1) - - s_queries = query.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.q_attn_size, dim) - s_keys = key.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, dim) - s_values = value.reshape(-1, v_dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, v_dim) - - inner = s_queries @ s_keys.transpose(2, 1) - norm_factor = 1 - inner = inner / norm_factor - - # free memory - del x_positions - - # softmax denominator - dots_logsumexp = torch.logsumexp(inner, dim=-1, keepdim=True) - # softmax - dots = torch.exp(inner - dots_logsumexp) - # dropout - - # n_rounds outs - bo = (dots @ s_values).reshape(self.n_rounds, bs, q_seqlen, -1) - - # undo sort - x_offset = torch.arange(bs * self.n_rounds, device=query.device).unsqueeze(-1) * x_seqlen - x_rev_flat = (x_rev_positions.reshape(-1, x_seqlen) + x_offset).reshape(-1) - o = bo.reshape(-1, v_dim).index_select(0, x_rev_flat).reshape(self.n_rounds, bs, q_seqlen, -1) - - slogits = dots_logsumexp.reshape(self.n_rounds, bs, -1) - logits = torch.gather(slogits, 2, x_rev_positions) - - # free memory - del x_rev_positions - - # weighted sum multi-round attention - probs = torch.exp(logits - torch.logsumexp(logits, dim=0, keepdim=True)) - out = torch.sum(o * probs.unsqueeze(-1), dim=0) - out = rearrange(out, '(b h) t d -> b t h d', h=self.heads) - out = out.reshape(B, N, -1) - out = self.to_out(out) - - return out - -class SAHAB(nn.Module): - def __init__( - self, - dim, - patch_size=(16, 16), - heads=8, - shift_size=0, - sparse=False - ): - super().__init__() - self.blocks = nn.ModuleList([]) - self.attn = PreNorm(dim, SAH_MSA(heads=heads, n_rounds=2, r=1, channels=dim, patch_size=patch_size)) - self.ffn = PreNorm(dim, FeedForward(dim=dim)) - self.shift_size = shift_size - self.patch_size = patch_size - self.sparse = sparse - - def forward(self, x, mask=None): - """ - x: [b,h,w,c] - mask: [b,h,w] - return out: [b,h,w,c] - """ - b,h,w,c = x.shape - if self.shift_size > 0: - x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - mask = torch.roll(mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - w_size = self.patch_size - - # Split into large patches - x = rearrange(x, 'b (nh hh) (nw ww) c-> b (nh nw) (hh ww c)', hh=w_size[0] * 2, ww=w_size[1] * 2) - mask = rearrange(mask, 'b (nh hh) (nw ww) -> b (nh nw) (hh ww)', hh=w_size[0] * 2, ww=w_size[1] * 2) - N = x.shape[1] - - mask = torch.mean(mask,dim=2,keepdim=False) # [b,nh*nw] - if self.sparse: - mask_select = mask.topk(mask.shape[1] // 2, dim=1)[1] # [b,nh*nw//2] - x_select = batch_gather(x, mask_select, 1) # [b,nh*nw//2,hh*ww*c] - x_select = x_select.reshape(b*N//2,-1,c) - x_select = self.attn(x_select)+x_select - x_select = x_select.view(b,N//2,-1) - x = batch_scatter(x.clone(), x_select, 1, mask_select) - else: - x = x.view(b*N,-1,c) - x = self.attn(x) + x - x = x.view(b, N, -1) - x = rearrange(x, 'b (nh nw) (hh ww c) -> b (nh hh) (nw ww) c', nh=h//(w_size[0] * 2), hh=w_size[0] * 2, ww=w_size[1] * 2) - - if self.shift_size > 0: - x = torch.roll(x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - - x = self.ffn(x) + x - - return x - - -class SAHABs(nn.Module): - def __init__( - self, - dim, - patch_size=(8, 8), - heads=8, - num_blocks=2, - sparse=False - ): - super().__init__() - blocks = [] - for _ in range(num_blocks): - blocks.append( - SAHAB(heads=heads, dim=dim, patch_size=patch_size,sparse=sparse, - shift_size=0 if (_ % 2 == 0) else patch_size[0])) - self.blocks = nn.Sequential(*blocks) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - mask: [b,1,h,w] - return x: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - mask = mask.squeeze(1) - for block in self.blocks: - x = block(x, mask) - x = x.permute(0, 3, 1, 2) - return x - -class ASPPConv(nn.Sequential): - def __init__(self, in_channels, out_channels, dilation): - modules = [ - nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False), - nn.ReLU() - ] - super(ASPPConv, self).__init__(*modules) - - -class ASPPPooling(nn.Sequential): - def __init__(self, in_channels, out_channels): - super(ASPPPooling, self).__init__( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(in_channels, out_channels, 1, bias=False), - nn.ReLU()) - - def forward(self, x): - size = x.shape[-2:] - for mod in self: - x = mod(x) - return F.interpolate(x, size=size, mode='bilinear', align_corners=False) - - -class ASPP(nn.Module): - def __init__(self, in_channels, atrous_rates, out_channels): - super(ASPP, self).__init__() - modules = [] - - rates = tuple(atrous_rates) - for rate in rates: - modules.append(ASPPConv(in_channels, out_channels, rate)) - - modules.append(ASPPPooling(in_channels, out_channels)) - - self.convs = nn.ModuleList(modules) - - self.project = nn.Sequential( - nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False), - nn.ReLU(), - nn.Dropout(0.5)) - - def forward(self, x): - res = [] - for conv in self.convs: - res.append(conv(x)) - res = torch.cat(res, dim=1) - return self.project(res) - -class Sparsity_Estimator(nn.Module): - def __init__(self, dim=28, expand=2, sparse=False): - super(Sparsity_Estimator, self).__init__() - self.dim = dim - self.stage = 2 - self.sparse = sparse - - # Input projection - self.in_proj = nn.Conv2d(28, dim, 1, 1, 0, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(2): - self.encoder_layers.append(nn.ModuleList([ - nn.Conv2d(dim_stage, dim_stage * expand, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage * expand, dim_stage * expand, 3, 2, 1, bias=False, groups=dim_stage * expand), - nn.Conv2d(dim_stage * expand, dim_stage*expand, 1, 1, 0, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = ASPP(dim_stage, [3,6], dim_stage) - - # Decoder: - self.decoder_layers = nn.ModuleList([]) - for i in range(2): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage // 2, dim_stage, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage, dim_stage, 3, 1, 1, bias=False, groups=dim_stage), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, 0, bias=False), - ])) - dim_stage //= 2 - - # Output projection - if sparse: - self.out_conv2 = nn.Conv2d(self.dim, self.dim+1, 3, 1, 1, bias=False) - else: - self.out_conv2 = nn.Conv2d(self.dim, self.dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - # Input projection - fea = self.lrelu(self.in_proj(x)) - # Encoder - fea_encoder = [] # [c 2c 4c 8c] - for (Conv1, Conv2, Conv3) in self.encoder_layers: - fea_encoder.append(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - # Bottleneck - fea = self.bottleneck(fea)+fea - # Decoder - for i, (FeaUpSample, Conv1, Conv2, Conv3) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - fea = fea + fea_encoder[self.stage-1-i] - # Output projection - out = self.out_conv2(fea) - if self.sparse: - error_map = out[:,-1:,:,:] - return out[:,:-1], error_map - return out - -class CST(nn.Module): - def __init__(self, dim=28, stage=2, num_blocks=[2, 2, 2], sparse=False): - super(CST, self).__init__() - self.dim = dim - self.stage = stage - self.sparse = sparse - - # Fution physical mask and shifted measurement - self.fution = nn.Conv2d(28, 28, 1, 1, 0, bias=False) - - # Sparsity Estimator - if num_blocks==[2,4,6]: - self.fe = nn.Sequential(Sparsity_Estimator(dim=28,expand=2,sparse=False), - Sparsity_Estimator(dim=28, expand=2, sparse=sparse)) - else: - self.fe = Sparsity_Estimator(dim=28, expand=2, sparse=sparse) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - SAHABs(dim=dim_stage, num_blocks=num_blocks[i], heads=dim_stage // dim, sparse=sparse), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - nn.AvgPool2d(kernel_size=2, stride=2), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = SAHABs( - dim=dim_stage, heads=dim_stage // dim, num_blocks=num_blocks[-1], sparse=sparse) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - SAHABs(dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], - heads=(dim_stage // 2) // dim, sparse=sparse), - ])) - dim_stage //= 2 - - # Output projection - self.out_proj = nn.Conv2d(self.dim, dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :return: x: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None, input_mask_s=None): - """ - x: [b,h,w] - return out:[b,c,h,w] - """ - - x = self.initial_x(x) - - # Feature Extraction - if self.sparse: - fea,mask = self.fe(x) - else: - fea = self.fe(x) - mask = torch.randn((b,1,h,w)).cuda() - - # Encoder - fea_encoder = [] - masks = [] - for (Blcok, FeaDownSample, MaskDownSample) in self.encoder_layers: - fea = Blcok(fea, mask) - masks.append(mask) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - mask = MaskDownSample(mask) - - # Bottleneck - fea = self.bottleneck(fea, mask) - - # Decoder - for i, (FeaUpSample, Blcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = fea + fea_encoder[self.stage - 1 - i] - mask = masks[self.stage - 1 - i] - fea = Blcok(fea, mask) - - # Output projection - out = self.out_proj(fea) + x - if self.sparse: - return out, mask - return out - - - - - - - - - - - - diff --git a/MST/real/test_code/architecture/DAUHST.py b/MST/real/test_code/architecture/DAUHST.py deleted file mode 100755 index bc3f420..0000000 --- a/MST/real/test_code/architecture/DAUHST.py +++ /dev/null @@ -1,364 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch import einsum - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -class HS_MSA(nn.Module): - def __init__( - self, - dim, - window_size=(8, 8), - dim_head=28, - heads=8, - only_local_branch=False - ): - super().__init__() - - self.dim = dim - self.heads = heads - self.scale = dim_head ** -0.5 - self.window_size = window_size - self.only_local_branch = only_local_branch - - # position embedding - if only_local_branch: - seq_l = window_size[0] * window_size[1] - self.pos_emb = nn.Parameter(torch.Tensor(1, heads, seq_l, seq_l)) - trunc_normal_(self.pos_emb) - else: - seq_l1 = window_size[0] * window_size[1] - self.pos_emb1 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l1, seq_l1)) - h,w = 384//self.heads, 384//self.heads - seq_l2 = h*w//seq_l1 - self.pos_emb2 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l2, seq_l2)) - trunc_normal_(self.pos_emb1) - trunc_normal_(self.pos_emb2) - - inner_dim = dim_head * heads - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) - self.to_out = nn.Linear(inner_dim, dim) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x.shape - w_size = self.window_size - assert h % w_size[0] == 0 and w % w_size[1] == 0, 'fmap dimensions must be divisible by the window size' - if self.only_local_branch: - x_inp = rearrange(x, 'b (h b0) (w b1) c -> (b h w) (b0 b1) c', b0=w_size[0], b1=w_size[1]) - q = self.to_q(x_inp) - k, v = self.to_kv(x_inp).chunk(2, dim=-1) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), (q, k, v)) - q *= self.scale - sim = einsum('b h i d, b h j d -> b h i j', q, k) - sim = sim + self.pos_emb - attn = sim.softmax(dim=-1) - out = einsum('b h i j, b h j d -> b h i d', attn, v) - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - out = rearrange(out, '(b h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - else: - q = self.to_q(x) - k, v = self.to_kv(x).chunk(2, dim=-1) - q1, q2 = q[:,:,:,:c//2], q[:,:,:,c//2:] - k1, k2 = k[:,:,:,:c//2], k[:,:,:,c//2:] - v1, v2 = v[:,:,:,:c//2], v[:,:,:,c//2:] - - # local branch - q1, k1, v1 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q1, k1, v1)) - q1, k1, v1 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q1, k1, v1)) - q1 *= self.scale - sim1 = einsum('b n h i d, b n h j d -> b n h i j', q1, k1) - sim1 = sim1 + self.pos_emb1 - attn1 = sim1.softmax(dim=-1) - out1 = einsum('b n h i j, b n h j d -> b n h i d', attn1, v1) - out1 = rearrange(out1, 'b n h mm d -> b n mm (h d)') - - # non-local branch - q2, k2, v2 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q2, k2, v2)) - q2, k2, v2 = map(lambda t: t.permute(0, 2, 1, 3), (q2.clone(), k2.clone(), v2.clone())) - q2, k2, v2 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q2, k2, v2)) - q2 *= self.scale - sim2 = einsum('b n h i d, b n h j d -> b n h i j', q2, k2) - - pos_emb2_interp = [] - for i in range(sim2.shape[0]): - pos_emb2_interp.append(torch.nn.functional.interpolate(self.pos_emb2[i], size=sim2.shape[-2:], mode='bilinear', - align_corners=False)) - pos_emb2_interp = torch.stack(pos_emb2_interp, dim=0) - sim2 = sim2 + pos_emb2_interp - attn2 = sim2.softmax(dim=-1) - out2 = einsum('b n h i j, b n h j d -> b n h i d', attn2, v2) - out2 = rearrange(out2, 'b n h mm d -> b n mm (h d)') - out2 = out2.permute(0, 2, 1, 3) - - out = torch.cat([out1,out2],dim=-1).contiguous() - out = self.to_out(out) - out = rearrange(out, 'b (h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - return out - -class HSAB(nn.Module): - def __init__( - self, - dim, - window_size=(8, 8), - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - PreNorm(dim, HS_MSA(dim=dim, window_size=window_size, dim_head=dim_head, heads=heads, only_local_branch=(heads==1))), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class HST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, num_blocks=[1,1,1]): - super(HST, self).__init__() - self.dim = dim - self.scales = len(num_blocks) - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_scale = dim - for i in range(self.scales-1): - self.encoder_layers.append(nn.ModuleList([ - HSAB(dim=dim_scale, num_blocks=num_blocks[i], dim_head=dim, heads=dim_scale // dim), - nn.Conv2d(dim_scale, dim_scale * 2, 4, 2, 1, bias=False), - ])) - dim_scale *= 2 - - # Bottleneck - self.bottleneck = HSAB(dim=dim_scale, dim_head=dim, heads=dim_scale // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(self.scales-1): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_scale, dim_scale // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_scale, dim_scale // 2, 1, 1, bias=False), - HSAB(dim=dim_scale // 2, num_blocks=num_blocks[self.scales - 2 - i], dim_head=dim, - heads=(dim_scale // 2) // dim), - ])) - dim_scale //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - b, c, h_inp, w_inp = x.shape - hb, wb = 16, 16 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - - # Embedding - fea = self.embedding(x) - x = x[:,:28,:,:] - - # Encoder - fea_encoder = [] - for (HSAB, FeaDownSample) in self.encoder_layers: - fea = HSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, HSAB) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.scales-2-i]], dim=1)) - fea = HSAB(fea) - - # Mapping - out = self.mapping(fea) + x - return out[:, :, :h_inp, :w_inp] - -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class HyPaNet(nn.Module): - def __init__(self, in_nc=29, out_nc=8, channel=64): - super(HyPaNet, self).__init__() - self.fution = nn.Conv2d(in_nc, channel, 1, 1, 0, bias=True) - self.down_sample = nn.Conv2d(channel, channel, 3, 2, 1, bias=True) - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.mlp = nn.Sequential( - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, out_nc, 1, padding=0, bias=True), - nn.Softplus()) - self.relu = nn.ReLU(inplace=True) - self.out_nc = out_nc - - def forward(self, x): - x = self.down_sample(self.relu(self.fution(x))) - x = self.avg_pool(x) - x = self.mlp(x) + 1e-6 - return x[:,:self.out_nc//2,:,:], x[:,self.out_nc//2:,:,:] - -class DAUHST(nn.Module): - - def __init__(self, num_iterations=1): - super(DAUHST, self).__init__() - self.para_estimator = HyPaNet(in_nc=28, out_nc=num_iterations*2) - self.fution = nn.Conv2d(28, 28, 1, padding=0, bias=True) - self.num_iterations = num_iterations - self.denoisers = nn.ModuleList([]) - for _ in range(num_iterations): - self.denoisers.append( - HST(in_dim=28, out_dim=28, dim=28, num_blocks=[1,1,1]), - ) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None, input_mask_s=None): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,310] - :param Phi_PhiT: [b,256,310] - :return: z_crop: [b,28,256,256] - """ - x = self.initial_x(x) - for i in range(self.num_iterations): - x = self.denoisers[i](x) - return x[:, :, :, 0:384] - diff --git a/MST/real/test_code/architecture/DGSMP.py b/MST/real/test_code/architecture/DGSMP.py deleted file mode 100755 index cf30aaf..0000000 --- a/MST/real/test_code/architecture/DGSMP.py +++ /dev/null @@ -1,322 +0,0 @@ -import torch -import torch.nn as nn -from torch.nn.parameter import Parameter -import torch.nn.functional as F - - -class Resblock(nn.Module): - def __init__(self, HBW): - super(Resblock, self).__init__() - self.block1 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1)) - self.block2 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1)) - - def forward(self, x): - tem = x - r1 = self.block1(x) - out = r1 + tem - r2 = self.block2(out) - out = r2 + out - return out - -class Encoding(nn.Module): - def __init__(self): - super(Encoding, self).__init__() - self.E1 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E2 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E3 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E4 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E5 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - - def forward(self, x): - ## encoding blocks - E1 = self.E1(x) - E2 = self.E2(F.avg_pool2d(E1, kernel_size=2, stride=2)) - E3 = self.E3(F.avg_pool2d(E2, kernel_size=2, stride=2)) - E4 = self.E4(F.avg_pool2d(E3, kernel_size=2, stride=2)) - E5 = self.E5(F.avg_pool2d(E4, kernel_size=2, stride=2)) - return E1, E2, E3, E4, E5 - -class Decoding(nn.Module): - def __init__(self, Ch=28, kernel_size=[7,7,7]): - super(Decoding, self).__init__() - self.upMode = 'bilinear' - self.Ch = Ch - out_channel1 = Ch * kernel_size[0] - out_channel2 = Ch * kernel_size[1] - out_channel3 = Ch * kernel_size[2] - self.D1 = nn.Sequential(nn.Conv2d(in_channels=128+128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.D2 = nn.Sequential(nn.Conv2d(in_channels=128+64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.D3 = nn.Sequential(nn.Conv2d(in_channels=64+64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.D4 = nn.Sequential(nn.Conv2d(in_channels=64+32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - - self.w_generator = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=32, out_channels=self.Ch, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=self.Ch, out_channels=self.Ch, kernel_size=1, stride=1, padding=0) - ) - - self.filter_g_1 = nn.Sequential(nn.Conv2d(64 + 32, out_channel1, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel1, out_channel1, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel1, out_channel1, 1, 1, 0) - ) - - self.filter_g_2 = nn.Sequential(nn.Conv2d(64 + 32, out_channel2, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel2, out_channel2, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel2, out_channel2, 1, 1, 0) - ) - - self.filter_g_3 = nn.Sequential(nn.Conv2d(64 + 32, out_channel3, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel3, out_channel3, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel3, out_channel3, 1, 1, 0) - ) - - - def forward(self, E1, E2, E3, E4, E5): - ## decoding blocks - D1 = self.D1(torch.cat([E4, F.interpolate(E5, scale_factor=2, mode=self.upMode)], dim=1)) - D2 = self.D2(torch.cat([E3, F.interpolate(D1, scale_factor=2, mode=self.upMode)], dim=1)) - D3 = self.D3(torch.cat([E2, F.interpolate(D2, scale_factor=2, mode=self.upMode)], dim=1)) - D4 = self.D4(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - - ## estimating the regularization parameters w - w = self.w_generator(D4) - - ## generate 3D filters - f1 = self.filter_g_1(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - f2 = self.filter_g_2(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - f3 = self.filter_g_3(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - return w, f1, f2, f3 - - -class HSI_CS(nn.Module): - def __init__(self, Ch, stages): - super(HSI_CS, self).__init__() - self.Ch = Ch - self.s = stages - self.filter_size = [7,7,7] ## 3D filter size - - ## The modules for learning the measurement matrix A and A^T - self.AT = nn.Sequential(nn.Conv2d(Ch, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(), - Resblock(64), Resblock(64), - nn.Conv2d(64, Ch, kernel_size=3, stride=1, padding=1), nn.LeakyReLU()) - self.A = nn.Sequential(nn.Conv2d(Ch, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(), - Resblock(64), Resblock(64), - nn.Conv2d(64, Ch, kernel_size=3, stride=1, padding=1), nn.LeakyReLU()) - - ## Encoding blocks - self.Encoding = Encoding() - - ## Decoding blocks - self.Decoding = Decoding(Ch=self.Ch, kernel_size=self.filter_size) - - ## Dense connection - self.conv = nn.Conv2d(Ch, 32, kernel_size=3, stride=1, padding=1) - self.Den_con1 = nn.Conv2d(32 , 32, kernel_size=1, stride=1, padding=0) - self.Den_con2 = nn.Conv2d(32 * 2, 32, kernel_size=1, stride=1, padding=0) - self.Den_con3 = nn.Conv2d(32 * 3, 32, kernel_size=1, stride=1, padding=0) - self.Den_con4 = nn.Conv2d(32 * 4, 32, kernel_size=1, stride=1, padding=0) - # self.Den_con5 = nn.Conv2d(32 * 5, 32, kernel_size=1, stride=1, padding=0) - # self.Den_con6 = nn.Conv2d(32 * 6, 32, kernel_size=1, stride=1, padding=0) - - - self.delta_0 = Parameter(torch.ones(1), requires_grad=True) - self.delta_1 = Parameter(torch.ones(1), requires_grad=True) - self.delta_2 = Parameter(torch.ones(1), requires_grad=True) - self.delta_3 = Parameter(torch.ones(1), requires_grad=True) - # self.delta_4 = Parameter(torch.ones(1), requires_grad=True) - # self.delta_5 = Parameter(torch.ones(1), requires_grad=True) - - self._initialize_weights() - torch.nn.init.normal_(self.delta_0, mean=0.1, std=0.01) - torch.nn.init.normal_(self.delta_1, mean=0.1, std=0.01) - torch.nn.init.normal_(self.delta_2, mean=0.1, std=0.01) - torch.nn.init.normal_(self.delta_3, mean=0.1, std=0.01) - # torch.nn.init.normal_(self.delta_4, mean=0.1, std=0.01) - # torch.nn.init.normal_(self.delta_5, mean=0.1, std=0.01) - - - def _initialize_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.xavier_normal_(m.weight.data) - nn.init.constant_(m.bias.data, 0.0) - elif isinstance(m, nn.Linear): - nn.init.xavier_normal_(m.weight.data) - nn.init.constant_(m.bias.data, 0.0) - - - def Filtering_1(self, cube, core): - batch_size, bandwidth, height, width = cube.size() - cube_pad = F.pad(cube, [self.filter_size[0] // 2, self.filter_size[0] // 2, 0, 0], mode='replicate') - img_stack = [] - for i in range(self.filter_size[0]): - img_stack.append(cube_pad[:, :, :, i:i + width]) - img_stack = torch.stack(img_stack, dim=1) - out = torch.sum(core.mul_(img_stack), dim=1, keepdim=False) - return out - - def Filtering_2(self, cube, core): - batch_size, bandwidth, height, width = cube.size() - cube_pad = F.pad(cube, [0, 0, self.filter_size[1] // 2, self.filter_size[1] // 2], mode='replicate') - img_stack = [] - for i in range(self.filter_size[1]): - img_stack.append(cube_pad[:, :, i:i + height, :]) - img_stack = torch.stack(img_stack, dim=1) - out = torch.sum(core.mul_(img_stack), dim=1, keepdim=False) - return out - - def Filtering_3(self, cube, core): - batch_size, bandwidth, height, width = cube.size() - cube_pad = F.pad(cube.unsqueeze(0).unsqueeze(0), pad=(0, 0, 0, 0, self.filter_size[2] // 2, self.filter_size[2] // 2)).squeeze(0).squeeze(0) - img_stack = [] - for i in range(self.filter_size[2]): - img_stack.append(cube_pad[:, i:i + bandwidth, :, :]) - img_stack = torch.stack(img_stack, dim=1) - out = torch.sum(core.mul_(img_stack), dim=1, keepdim=False) - return out - - def recon(self, res1, res2, Xt, i): - if i == 0 : - delta = self.delta_0 - elif i == 1: - delta = self.delta_1 - elif i == 2: - delta = self.delta_2 - elif i == 3: - delta = self.delta_3 - # elif i == 4: - # delta = self.delta_4 - # elif i == 5: - # delta = self.delta_5 - - Xt = Xt - 2 * delta * (res1 + res2) - return Xt - - def y2x(self, y): - ## Spilt operator - sz = y.size() - if len(sz) == 3: - y = y.unsqueeze(1) - bs = sz[0] - sz = y.size() - x = torch.zeros([bs, 28, sz[2], sz[2]]).cuda() - for t in range(28): - temp = y[:, :, :, 0 + 2 * t : sz[2] + 2 * t] - - x[:, t, :, :] = temp.squeeze(1) - return x - - def x2y(self, x): - ## Shift and Sum operator - sz = x.size() - if len(sz) == 3: - x = x.unsqueeze(0).unsqueeze(0) - bs = 1 - else: - bs = sz[0] - sz = x.size() - y = torch.zeros([bs, sz[2], sz[2]+2*27]).cuda() - for t in range(28): - y[:, :, 0 + 2 * t : sz[2] + 2 * t] = x[:, t, :, :] + y[:, :, 0 + 2 * t : sz[2] + 2 * t] - return y - - def forward(self, y, input_mask=None, input_mask_s=None): - ## The measurements y is split into a 3D data cube of size H × W × L to initialize x. - y = y / 28 * 2 - Xt = self.y2x(y) - feature_list = [] - - for i in range(0, self.s): - AXt = self.x2y(self.A(Xt)) # y = Ax - Res1 = self.AT(self.y2x(AXt - y)) # A^T * (Ax − y) - - fea = self.conv(Xt) - - if i == 0: - feature_list.append(fea) - fufea = self.Den_con1(fea) - elif i == 1: - feature_list.append(fea) - fufea = self.Den_con2(torch.cat(feature_list, 1)) - elif i == 2: - feature_list.append(fea) - fufea = self.Den_con3(torch.cat(feature_list, 1)) - elif i == 3: - feature_list.append(fea) - fufea = self.Den_con4(torch.cat(feature_list, 1)) - # elif i == 4: - # feature_list.append(fea) - # fufea = self.Den_con5(torch.cat(feature_list, 1)) - # elif i == 5: - # feature_list.append(fea) - # fufea = self.Den_con6(torch.cat(feature_list, 1)) - - E1, E2, E3, E4, E5 = self.Encoding(fufea) - W, f1, f2, f3 = self.Decoding(E1, E2, E3, E4, E5) - - batch_size, p, height, width = f1.size() - f1 = F.normalize(f1.view(batch_size, self.filter_size[0], self.Ch, height, width),dim=1) - batch_size, p, height, width = f2.size() - f2 = F.normalize(f2.view(batch_size, self.filter_size[1], self.Ch, height, width),dim=1) - batch_size, p, height, width = f3.size() - f3 = F.normalize(f3.view(batch_size, self.filter_size[2], self.Ch, height, width),dim=1) - - ## Estimating the local means U - u1 = self.Filtering_1(Xt, f1) - u2 = self.Filtering_2(u1, f2) - U = self.Filtering_3(u2, f3) - - ## w * (x − u) - Res2 = (Xt - U).mul(W) - - ## Reconstructing HSIs - Xt = self.recon(Res1, Res2, Xt, i) - - return Xt diff --git a/MST/real/test_code/architecture/GAP_Net.py b/MST/real/test_code/architecture/GAP_Net.py deleted file mode 100755 index 687b775..0000000 --- a/MST/real/test_code/architecture/GAP_Net.py +++ /dev/null @@ -1,189 +0,0 @@ -import torch.nn.functional as F -import torch -import torch.nn as nn - -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - x = self.d_conv(x) - return x - - -class Unet(nn.Module): - - def __init__(self, in_ch, out_ch): - super(Unet, self).__init__() - - self.dconv_down1 = double_conv(in_ch, 32) - self.dconv_down2 = double_conv(32, 64) - self.dconv_down3 = double_conv(64, 128) - - self.maxpool = nn.MaxPool2d(2) - self.upsample2 = nn.Sequential( - nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2), - # nn.Conv2d(64, 64, (1,2), padding=(0,1)), - nn.ReLU(inplace=True) - ) - self.upsample1 = nn.Sequential( - nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.dconv_up2 = double_conv(64 + 64, 64) - self.dconv_up1 = double_conv(32 + 32, 32) - - self.conv_last = nn.Conv2d(32, out_ch, 1) - self.afn_last = nn.Tanh() - - def forward(self, x): - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - inputs = x - conv1 = self.dconv_down1(x) - x = self.maxpool(conv1) - conv2 = self.dconv_down2(x) - x = self.maxpool(conv2) - conv3 = self.dconv_down3(x) - - x = self.upsample2(conv3) - x = torch.cat([x, conv2], dim=1) - - x = self.dconv_up2(x) - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - - x = self.dconv_up1(x) - - x = self.conv_last(x) - x = self.afn_last(x) - out = x + inputs - - return out[:, :, :h_inp, :w_inp] - - -class DoubleConv(nn.Module): - """(convolution => [BN] => ReLU) * 2""" - - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - if not mid_channels: - mid_channels = out_channels - self.double_conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(mid_channels), - nn.ReLU(inplace=True), - nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(out_channels), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - return self.double_conv(x) - -class GAP_net(nn.Module): - - def __init__(self): - super(GAP_net, self).__init__() - - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - - def forward(self, y, input_mask=None, input_mask_s=None): - if input_mask==None: - Phi = torch.rand((1,28,256,310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask, input_mask_s - x_list = [] - x = At(y, Phi) # v0=H^T y - ### 1-3 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet1(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet2(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet3(x) - x = shift_3d(x) - ### 4-6 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet4(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet5(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet6(x) - x = shift_3d(x) - # ### 7-9 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet7(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet8(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet9(x) - x = shift_3d(x) - return x[:, :, :, :384] \ No newline at end of file diff --git a/MST/real/test_code/architecture/HDNet.py b/MST/real/test_code/architecture/HDNet.py deleted file mode 100755 index 6820558..0000000 --- a/MST/real/test_code/architecture/HDNet.py +++ /dev/null @@ -1,390 +0,0 @@ -import torch -import torch.nn as nn -import math - -def default_conv(in_channels, out_channels, kernel_size, bias=True): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias) - -class MeanShift(nn.Conv2d): - def __init__( - self, rgb_range, - rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1): - - super(MeanShift, self).__init__(3, 3, kernel_size=1) - std = torch.Tensor(rgb_std) - self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1) - self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std - for p in self.parameters(): - p.requires_grad = False - -class BasicBlock(nn.Sequential): - def __init__( - self, conv, in_channels, out_channels, kernel_size, stride=1, bias=False, - bn=True, act=nn.ReLU(True)): - - m = [conv(in_channels, out_channels, kernel_size, bias=bias)] - if bn: - m.append(nn.BatchNorm2d(out_channels)) - if act is not None: - m.append(act) - - super(BasicBlock, self).__init__(*m) - -class ResBlock(nn.Module): - def __init__( - self, conv, n_feats, kernel_size, - bias=True, bn=False, act=nn.ReLU(True), res_scale=1): - - super(ResBlock, self).__init__() - m = [] - for i in range(2): - m.append(conv(n_feats, n_feats, kernel_size, bias=bias)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if i == 0: - m.append(act) - - self.body = nn.Sequential(*m) - self.res_scale = res_scale - - def forward(self, x): - res = self.body(x).mul(self.res_scale) # So res_scale is a scaler? just scale all elements in each feature's residual? Why? - res += x - - return res - -class Upsampler(nn.Sequential): - def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True): - - m = [] - if (scale & (scale - 1)) == 0: - for _ in range(int(math.log(scale, 2))): - m.append(conv(n_feats, 4 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(2)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - - elif scale == 3: - m.append(conv(n_feats, 9 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(3)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - else: - raise NotImplementedError - - super(Upsampler, self).__init__(*m) - -_NORM_BONE = False - -def constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def kaiming_init(module, - a=0, - mode='fan_out', - nonlinearity='relu', - bias=0, - distribution='normal'): - assert distribution in ['uniform', 'normal'] - if distribution == 'uniform': - nn.init.kaiming_uniform_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - -# depthwise-separable convolution (DSC) -class DSC(nn.Module): - - def __init__(self, nin: int) -> None: - super(DSC, self).__init__() - self.conv_dws = nn.Conv2d( - nin, nin, kernel_size=1, stride=1, padding=0, groups=nin - ) - self.bn_dws = nn.BatchNorm2d(nin, momentum=0.9) - self.relu_dws = nn.ReLU(inplace=False) - - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) - - self.conv_point = nn.Conv2d( - nin, 1, kernel_size=1, stride=1, padding=0, groups=1 - ) - self.bn_point = nn.BatchNorm2d(1, momentum=0.9) - self.relu_point = nn.ReLU(inplace=False) - - self.softmax = nn.Softmax(dim=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - out = self.conv_dws(x) - out = self.bn_dws(out) - out = self.relu_dws(out) - - out = self.maxpool(out) - - out = self.conv_point(out) - out = self.bn_point(out) - out = self.relu_point(out) - - m, n, p, q = out.shape - out = self.softmax(out.view(m, n, -1)) - out = out.view(m, n, p, q) - - out = out.expand(x.shape[0], x.shape[1], x.shape[2], x.shape[3]) - - out = torch.mul(out, x) - - out = out + x - - return out - -# Efficient Feature Fusion(EFF) -class EFF(nn.Module): - def __init__(self, nin: int, nout: int, num_splits: int) -> None: - super(EFF, self).__init__() - - assert nin % num_splits == 0 - - self.nin = nin - self.nout = nout - self.num_splits = num_splits - self.subspaces = nn.ModuleList( - [DSC(int(self.nin / self.num_splits)) for i in range(self.num_splits)] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - sub_feat = torch.chunk(x, self.num_splits, dim=1) - out = [] - for idx, l in enumerate(self.subspaces): - out.append(self.subspaces[idx](sub_feat[idx])) - out = torch.cat(out, dim=1) - - return out - - -# spatial-spectral domain attention learning(SDL) -class SDL_attention(nn.Module): - def __init__(self, inplanes, planes, kernel_size=1, stride=1): - super(SDL_attention, self).__init__() - - self.inplanes = inplanes - self.inter_planes = planes // 2 - self.planes = planes - self.kernel_size = kernel_size - self.stride = stride - self.padding = (kernel_size-1)//2 - - self.conv_q_right = nn.Conv2d(self.inplanes, 1, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_v_right = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_up = nn.Conv2d(self.inter_planes, self.planes, kernel_size=1, stride=1, padding=0, bias=False) - self.softmax_right = nn.Softmax(dim=2) - self.sigmoid = nn.Sigmoid() - - self.conv_q_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #g - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv_v_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #theta - self.softmax_left = nn.Softmax(dim=2) - - self.reset_parameters() - - def reset_parameters(self): - kaiming_init(self.conv_q_right, mode='fan_in') - kaiming_init(self.conv_v_right, mode='fan_in') - kaiming_init(self.conv_q_left, mode='fan_in') - kaiming_init(self.conv_v_left, mode='fan_in') - - self.conv_q_right.inited = True - self.conv_v_right.inited = True - self.conv_q_left.inited = True - self.conv_v_left.inited = True - # HR spatial attention - def spatial_attention(self, x): - input_x = self.conv_v_right(x) - batch, channel, height, width = input_x.size() - - input_x = input_x.view(batch, channel, height * width) - context_mask = self.conv_q_right(x) - context_mask = context_mask.view(batch, 1, height * width) - context_mask = self.softmax_right(context_mask) - - context = torch.matmul(input_x, context_mask.transpose(1,2)) - context = context.unsqueeze(-1) - context = self.conv_up(context) - - mask_ch = self.sigmoid(context) - - out = x * mask_ch - - return out - # HR spectral attention - def spectral_attention(self, x): - - g_x = self.conv_q_left(x) - batch, channel, height, width = g_x.size() - - avg_x = self.avg_pool(g_x) - batch, channel, avg_x_h, avg_x_w = avg_x.size() - - avg_x = avg_x.view(batch, channel, avg_x_h * avg_x_w).permute(0, 2, 1) - theta_x = self.conv_v_left(x).view(batch, self.inter_planes, height * width) - context = torch.matmul(avg_x, theta_x) - context = self.softmax_left(context) - context = context.view(batch, 1, height, width) - - mask_sp = self.sigmoid(context) - - out = x * mask_sp - - return out - - def forward(self, x): - context_spectral = self.spectral_attention(x) - context_spatial = self.spatial_attention(x) - out = context_spatial + context_spectral - return out - - -class HDNet(nn.Module): - - def __init__(self, in_ch=28, out_ch=28, conv=default_conv): - super(HDNet, self).__init__() - - n_resblocks = 16 - n_feats = 64 - kernel_size = 3 - act = nn.ReLU(True) - - self.fution = nn.Conv2d(28, 28, 3, 1, 1, bias=False) - - # define head module - m_head = [conv(in_ch, n_feats, kernel_size)] - - # define body module - m_body = [ - ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - ) for _ in range(n_resblocks) - ] - m_body.append(SDL_attention(inplanes = n_feats, planes = n_feats)) - m_body.append(EFF(nin=n_feats, nout=n_feats, num_splits=4)) - - for i in range(1, n_resblocks): - m_body.append(ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - )) - - m_body.append(conv(n_feats, n_feats, kernel_size)) - - m_tail = [conv(n_feats, out_ch, kernel_size)] - - self.head = nn.Sequential(*m_head) - self.body = nn.Sequential(*m_body) - self.tail = nn.Sequential(*m_tail) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None, input_mask_s=None): - - x = self.initial_x(x) - - x = self.head(x) - - res = self.body(x) - res += x - - x = self.tail(res) - - return x - -# frequency domain learning(FDL) -class FDL(nn.Module): - def __init__(self, loss_weight=1.0, alpha=1.0, patch_factor=1, ave_spectrum=False, log_matrix=False, batch_matrix=False): - super(FDL, self).__init__() - self.loss_weight = loss_weight - self.alpha = alpha - self.patch_factor = patch_factor - self.ave_spectrum = ave_spectrum - self.log_matrix = log_matrix - self.batch_matrix = batch_matrix - - def tensor2freq(self, x): - patch_factor = self.patch_factor - _, _, h, w = x.shape - assert h % patch_factor == 0 and w % patch_factor == 0, ( - 'Patch factor should be divisible by image height and width') - patch_list = [] - patch_h = h // patch_factor - patch_w = w // patch_factor - for i in range(patch_factor): - for j in range(patch_factor): - patch_list.append(x[:, :, i * patch_h:(i + 1) * patch_h, j * patch_w:(j + 1) * patch_w]) - - y = torch.stack(patch_list, 1) - - return torch.rfft(y, 2, onesided=False, normalized=True) - - def loss_formulation(self, recon_freq, real_freq, matrix=None): - if matrix is not None: - weight_matrix = matrix.detach() - else: - matrix_tmp = (recon_freq - real_freq) ** 2 - matrix_tmp = torch.sqrt(matrix_tmp[..., 0] + matrix_tmp[..., 1]) ** self.alpha - if self.log_matrix: - matrix_tmp = torch.log(matrix_tmp + 1.0) - - if self.batch_matrix: - matrix_tmp = matrix_tmp / matrix_tmp.max() - else: - matrix_tmp = matrix_tmp / matrix_tmp.max(-1).values.max(-1).values[:, :, :, None, None] - - matrix_tmp[torch.isnan(matrix_tmp)] = 0.0 - matrix_tmp = torch.clamp(matrix_tmp, min=0.0, max=1.0) - weight_matrix = matrix_tmp.clone().detach() - - assert weight_matrix.min().item() >= 0 and weight_matrix.max().item() <= 1, ( - 'The values of spectrum weight matrix should be in the range [0, 1], ' - 'but got Min: %.10f Max: %.10f' % (weight_matrix.min().item(), weight_matrix.max().item())) - - tmp = (recon_freq - real_freq) ** 2 - freq_distance = tmp[..., 0] + tmp[..., 1] - - loss = weight_matrix * freq_distance - return torch.mean(loss) - - def forward(self, pred, target, matrix=None, **kwargs): - - pred_freq = self.tensor2freq(pred) - target_freq = self.tensor2freq(target) - - if self.ave_spectrum: - pred_freq = torch.mean(pred_freq, 0, keepdim=True) - target_freq = torch.mean(target_freq, 0, keepdim=True) - - return self.loss_formulation(pred_freq, target_freq, matrix) * self.loss_weight diff --git a/MST/real/test_code/architecture/Lambda_Net.py b/MST/real/test_code/architecture/Lambda_Net.py deleted file mode 100755 index 44017bf..0000000 --- a/MST/real/test_code/architecture/Lambda_Net.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum - -class LambdaNetAttention(nn.Module): - def __init__( - self, - dim, - ): - super().__init__() - - self.dim = dim - self.to_q = nn.Linear(dim, dim//8, bias=False) - self.to_k = nn.Linear(dim, dim//8, bias=False) - self.to_v = nn.Linear(dim, dim, bias=False) - self.rescale = (dim//8)**-0.5 - self.gamma = nn.Parameter(torch.ones(1)) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0,2,3,1) - b, h, w, c = x.shape - - # Reshape to (B,N,C), where N = window_size[0]*window_size[1] is the length of sentence - x_inp = rearrange(x, 'b h w c -> b (h w) c') - - # produce query, key and value - q = self.to_q(x_inp) - k = self.to_k(x_inp) - v = self.to_v(x_inp) - - # attention - sim = einsum('b i d, b j d -> b i j', q, k)*self.rescale - attn = sim.softmax(dim=-1) - - # aggregate - out = einsum('b i j, b j d -> b i d', attn, v) - - # merge blocks back to original feature map - out = rearrange(out, 'b (h w) c -> b h w c', h=h, w=w) - out = self.gamma*out + x - return out.permute(0,3,1,2) - -class triple_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(triple_conv, self).__init__() - self.t_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.t_conv(x) - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.d_conv(x) - return x - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class Lambda_Net(nn.Module): - - def __init__(self, out_ch=28): - super(Lambda_Net, self).__init__() - self.conv_in = nn.Conv2d(1+28, 28, 3, padding=1) - - # encoder - self.conv_down1 = triple_conv(28, 32) - self.conv_down2 = triple_conv(32, 64) - self.conv_down3 = triple_conv(64, 128) - self.conv_down4 = triple_conv(128, 256) - self.conv_down5 = double_conv(256, 512) - self.conv_down6 = double_conv(512, 1024) - - self.maxpool = nn.MaxPool2d(2) - - # decoder - self.upsample5 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2) - self.upsample4 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2) - self.upsample3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2) - self.upsample2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2) - self.upsample1 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2) - - self.conv_up1 = triple_conv(32+32, 32) - self.conv_up2 = triple_conv(64+64, 64) - self.conv_up3 = triple_conv(128+128, 128) - self.conv_up4 = triple_conv(256+256, 256) - self.conv_up5 = double_conv(512+512, 512) - - # attention - self.attention = LambdaNetAttention(dim=128) - - self.conv_last1 = nn.Conv2d(32, 6, 3,1,1) - self.conv_last2 = nn.Conv2d(38, 32, 3,1,1) - self.conv_last3 = nn.Conv2d(32, 12, 3,1,1) - self.conv_last4 = nn.Conv2d(44, 32, 3,1,1) - self.conv_last5 = nn.Conv2d(32, out_ch, 1) - self.act = nn.ReLU() - - def forward(self, x, input_mask=None, input_mask_s=None): - if input_mask == None: - input_mask = torch.zeros((1,28,256,310)).cuda() - x = x/28*2 - x = self.conv_in(torch.cat([x.unsqueeze(1), input_mask], dim=1)) - b, c, h_inp, w_inp = x.shape - hb, wb = 32, 32 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - res0 = x - conv1 = self.conv_down1(x) - x = self.maxpool(conv1) - conv2 = self.conv_down2(x) - x = self.maxpool(conv2) - conv3 = self.conv_down3(x) - x = self.maxpool(conv3) - conv4 = self.conv_down4(x) - x = self.maxpool(conv4) - conv5 = self.conv_down5(x) - x = self.maxpool(conv5) - conv6 = self.conv_down6(x) - - x = self.upsample5(conv6) - x = torch.cat([x, conv5], dim=1) - x = self.conv_up5(x) - - x = self.upsample4(x) - x = torch.cat([x, conv4], dim=1) - x = self.conv_up4(x) - - x = self.upsample3(x) - x = torch.cat([x, conv3], dim=1) - x = self.conv_up3(x) - x = self.attention(x) - - x = self.upsample2(x) - x = torch.cat([x, conv2], dim=1) - x = self.conv_up2(x) - - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - x = self.conv_up1(x) - - res1 = x - out1 = self.act(self.conv_last1(x)) - x = self.conv_last2(torch.cat([res1,out1],dim=1)) - - res2 = x - out2 = self.act(self.conv_last3(x)) - out3 = self.conv_last4(torch.cat([res2, out2], dim=1)) - - out = self.conv_last5(out3)+res0 - out = out[:, :, :h_inp, :w_inp] - - return shift_back_3d(out)[:, :, :, :384] - diff --git a/MST/real/test_code/architecture/MST.py b/MST/real/test_code/architecture/MST.py deleted file mode 100755 index 07f1e8e..0000000 --- a/MST/real/test_code/architecture/MST.py +++ /dev/null @@ -1,294 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.dim = dim - - def forward(self, x_in): - """ - x_in: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp)) - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - -class MST(nn.Module): - def __init__(self, dim=28, stage=3, num_blocks=[2,2,2]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(28, self.dim, 3, 1, 1, bias=False) - self.fution = nn.Conv2d(28, 28, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, 28, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None, input_mask_s=None): - """ - x: [b,h,w] - return out:[b,c,h,w] - """ - - x = self.initial_x(x) - - # Embedding - fea = self.lrelu(self.embedding(x)) - - # Encoder - fea_encoder = [] - for (MSAB, FeaDownSample) in self.encoder_layers: - fea = MSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = LeWinBlcok(fea) - - # Mapping - out = self.mapping(fea) + x - return out - - - - - - - - - - - - - - - - - - - - - diff --git a/MST/real/test_code/architecture/MST_Plus_Plus.py b/MST/real/test_code/architecture/MST_Plus_Plus.py deleted file mode 100755 index 051c737..0000000 --- a/MST/real/test_code/architecture/MST_Plus_Plus.py +++ /dev/null @@ -1,324 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.dim = dim - - def forward(self, x_in): - """ - x_in: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp)) - v = v - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class MST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2,4,4]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (MSAB, FeaDownSample) in self.encoder_layers: - fea = MSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = LeWinBlcok(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - -class MST_Plus_Plus(nn.Module): - def __init__(self, in_channels=3, out_channels=28, n_feat=28, stage=3): - super(MST_Plus_Plus, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2,bias=False) - modules_body = [MST(dim=n_feat, stage=2, num_blocks=[1,1,1]) for _ in range(stage)] - self.fution = nn.Conv2d(28, 28, 1, padding=0, bias=True) - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2,bias=False) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None, input_mask_s=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - x = self.initial_x(x) - - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] - - - - - - - - - - - - - - diff --git a/MST/real/test_code/architecture/TSA_Net.py b/MST/real/test_code/architecture/TSA_Net.py deleted file mode 100755 index 212595b..0000000 --- a/MST/real/test_code/architecture/TSA_Net.py +++ /dev/null @@ -1,350 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -_NORM_BONE = False - - -def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding=the_padding) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_planes) - if flag_norm: - return nn.Sequential(conv,norm,activation) if flag_norm_act else nn.Sequential(conv,activation,norm) - else: - return nn.Sequential(conv,activation) - -def conv1x1_block(in_planes, out_planes, flag_norm=False): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0,bias=False) - norm = nn.BatchNorm2d(out_planes) - return nn.Sequential(conv,norm) if flag_norm else conv - -def fully_block(in_dim, out_dim, flag_norm=False, flag_norm_act=True): - fc = nn.Linear(in_dim, out_dim) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_dim) - if flag_norm: - return nn.Sequential(fc,norm,activation) if flag_norm_act else nn.Sequential(fc,activation,norm) - else: - return nn.Sequential(fc,activation) - -class Res2Net(nn.Module): - def __init__(self, inChannel, uPlane, scale=4): - super(Res2Net, self).__init__() - self.uPlane = uPlane - self.scale = scale - - self.conv_init = nn.Conv2d(inChannel, uPlane * scale, kernel_size=1, bias=False) - self.bn_init = nn.BatchNorm2d(uPlane * scale) - - convs = [] - bns = [] - for i in range(self.scale - 1): - convs.append(nn.Conv2d(self.uPlane, self.uPlane, kernel_size=3, stride=1, padding=1, bias=False)) - bns.append(nn.BatchNorm2d(self.uPlane)) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - - self.conv_end = nn.Conv2d(uPlane * scale, inChannel, kernel_size=1, bias=False) - self.bn_end = nn.BatchNorm2d(inChannel) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - - out = self.conv_init(x) - out = self.bn_init(out) - out = self.relu(out) - - spx = torch.split(out, self.uPlane, 1) - for i in range(self.scale - 1): - if i == 0: - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.convs[i](sp) - sp = self.relu(self.bns[i](sp)) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - out = torch.cat((out, spx[self.scale - 1]), 1) - - out = self.conv_end(out) - out = self.bn_end(out) - return out - -_NORM_ATTN = True -_NORM_FC = False - - -class TSA_Transform(nn.Module): - """ Spectral-Spatial Self-Attention """ - - def __init__(self, uSpace, inChannel, outChannel, nHead, uAttn, mode=[0, 1], flag_mask=False, gamma_learn=False): - super(TSA_Transform, self).__init__() - ''' ------------------------------------------ - uSpace: - uHeight: the [-2] dim of the 3D tensor - uWidth: the [-1] dim of the 3D tensor - inChannel: - the number of Channel of the input tensor - outChannel: - the number of Channel of the output tensor - nHead: - the number of Head of the input tensor - uAttn: - uSpatial: the dim of the spatial features - uSpectral: the dim of the spectral features - mask: - The Spectral Smoothness Mask - {mode} and {gamma_learn} is just for variable selection - ------------------------------------------ ''' - - self.nHead = nHead - self.uAttn = uAttn - self.outChannel = outChannel - self.uSpatial = nn.Parameter(torch.tensor(float(uAttn[0])), requires_grad=False) - self.uSpectral = nn.Parameter(torch.tensor(float(uAttn[1])), requires_grad=False) - self.mask = nn.Parameter(Spectral_Mask(outChannel), requires_grad=False) if flag_mask else None - self.attn_scale = nn.Parameter(torch.tensor(1.1), requires_grad=False) if flag_mask else None - self.gamma = nn.Parameter(torch.tensor(1.0), requires_grad=gamma_learn) - - if sum(mode) > 0: - down_sample = [] - scale = 1 - cur_channel = outChannel - for i in range(sum(mode)): - scale *= 2 - down_sample.append(conv_block(cur_channel, 2 * cur_channel, 3, 2, 1, _NORM_ATTN)) - cur_channel = 2 * cur_channel - self.cur_channel = cur_channel - self.down_sample = nn.Sequential(*down_sample) - self.up_sample = nn.ConvTranspose2d(outChannel * scale, outChannel, scale, scale) - else: - self.down_sample = None - self.up_sample = None - - spec_dim = int(uSpace[0] / 4 - 3) * int(uSpace[1] / 4 - 3) - self.preproc = conv1x1_block(inChannel, outChannel, _NORM_ATTN) - self.query_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.query_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.query_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.key_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.key_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.key_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.value = conv1x1_block(outChannel, nHead * outChannel, _NORM_ATTN) - self.aggregation = nn.Linear(nHead * outChannel, outChannel) - - def forward(self, image): - feat = self.preproc(image) - feat_qx = self.query_x(feat, 'X') - feat_qy = self.query_y(feat, 'Y') - feat_qlambda = self.query_lambda(feat) - feat_kx = self.key_x(feat, 'X') - feat_ky = self.key_y(feat, 'Y') - feat_klambda = self.key_lambda(feat) - feat_value = self.value(feat) - - feat_qx = torch.cat(torch.split(feat_qx, 1, dim=1)).squeeze(dim=1) - feat_qy = torch.cat(torch.split(feat_qy, 1, dim=1)).squeeze(dim=1) - feat_kx = torch.cat(torch.split(feat_kx, 1, dim=1)).squeeze(dim=1) - feat_ky = torch.cat(torch.split(feat_ky, 1, dim=1)).squeeze(dim=1) - feat_qlambda = torch.cat(torch.split(feat_qlambda, self.uAttn[1], dim=-1)) - feat_klambda = torch.cat(torch.split(feat_klambda, self.uAttn[1], dim=-1)) - feat_value = torch.cat(torch.split(feat_value, self.outChannel, dim=1)) - - energy_x = torch.bmm(feat_qx, feat_kx.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_y = torch.bmm(feat_qy, feat_ky.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_lambda = torch.bmm(feat_qlambda, feat_klambda.permute(0, 2, 1)) / torch.sqrt(self.uSpectral) - - attn_x = F.softmax(energy_x, dim=-1) - attn_y = F.softmax(energy_y, dim=-1) - attn_lambda = F.softmax(energy_lambda, dim=-1) - if self.mask is not None: - attn_lambda = (attn_lambda + self.mask) / torch.sqrt(self.attn_scale) - - pro_feat = feat_value if self.down_sample is None else self.down_sample(feat_value) - batchhead, dim_c, dim_x, dim_y = pro_feat.size() - attn_x_repeat = attn_x.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_x, dim_x) - attn_y_repeat = attn_y.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_y, dim_y) - pro_feat = pro_feat.view(-1, dim_x, dim_y) - pro_feat = torch.bmm(pro_feat, attn_y_repeat.permute(0, 2, 1)) - pro_feat = torch.bmm(pro_feat.permute(0, 2, 1), attn_x_repeat.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, dim_c, dim_x, dim_y) - - if self.up_sample is not None: - pro_feat = self.up_sample(pro_feat) - _, _, dim_x, dim_y = pro_feat.size() - pro_feat = pro_feat.contiguous().view(batchhead, self.outChannel, -1).permute(0, 2, 1) - pro_feat = torch.bmm(pro_feat, attn_lambda.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, self.outChannel, dim_x, dim_y) - pro_feat = torch.cat(torch.split(pro_feat, int(batchhead / self.nHead), dim=0), dim=1).permute(0, 2, 3, 1) - pro_feat = self.aggregation(pro_feat).permute(0, 3, 1, 2) - out = self.gamma * pro_feat + feat - return out, (attn_x, attn_y, attn_lambda) - - -class Feature_Spatial(nn.Module): - """ Spatial Feature Generation Component """ - - def __init__(self, inChannel, nHead, shiftDim, outDim, mode): - super(Feature_Spatial, self).__init__() - kernel = [(1, 5), (3, 5)] - stride = [(1, 2), (2, 2)] - padding = [(0, 2), (1, 2)] - self.conv1 = conv_block(inChannel, nHead, kernel[mode[0]], stride[mode[0]], padding[mode[0]], _NORM_ATTN) - self.conv2 = conv_block(nHead, nHead, kernel[mode[1]], stride[mode[1]], padding[mode[1]], _NORM_ATTN) - self.fully = fully_block(shiftDim, outDim, _NORM_FC) - - def forward(self, image, direction): - if direction == 'Y': - image = image.permute(0, 1, 3, 2) - feat = self.conv1(image) - feat = self.conv2(feat) - # import pdb; pdb.set_trace() - feat = self.fully(feat) - return feat - - -class Feature_Spectral(nn.Module): - """ Spectral Feature Generation Component """ - - def __init__(self, inChannel, nHead, viewDim, outDim): - super(Feature_Spectral, self).__init__() - self.inChannel = inChannel - self.conv1 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.conv2 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.fully = fully_block(viewDim, int(nHead * outDim), _NORM_FC) - - def forward(self, image): - bs = image.size(0) - feat = self.conv1(image) - feat = self.conv2(feat) - feat = feat.view(bs, self.inChannel, -1) - feat = self.fully(feat) - return feat - - -def Spectral_Mask(dim_lambda): - '''After put the available data into the model, we use this mask to avoid outputting the estimation of itself.''' - orig = (np.cos(np.linspace(-1, 1, num=2 * dim_lambda - 1) * np.pi) + 1.0) / 2.0 - att = np.zeros((dim_lambda, dim_lambda)) - for i in range(dim_lambda): - att[i, :] = orig[dim_lambda - 1 - i:2 * dim_lambda - 1 - i] - AM_Mask = torch.from_numpy(att.astype(np.float32)).unsqueeze(0) - return AM_Mask - -class TSA_Net(nn.Module): - - def __init__(self, in_ch=28, out_ch=28): - super(TSA_Net, self).__init__() - - self.tconv_down1 = Encoder_Triblock(in_ch, 64, False) - self.tconv_down2 = Encoder_Triblock(64, 128, False) - self.tconv_down3 = Encoder_Triblock(128, 256) - self.tconv_down4 = Encoder_Triblock(256, 512) - - self.bottom1 = conv_block(512, 1024) - self.bottom2 = conv_block(1024, 1024) - - self.tconv_up4 = Decoder_Triblock(1024, 512) - self.tconv_up3 = Decoder_Triblock(512, 256) - self.transform3 = TSA_Transform((96, 96), 256, 256, 8, (64, 80), [0, 0]) - self.tconv_up2 = Decoder_Triblock(256, 128) - self.transform2 = TSA_Transform((192, 192), 128, 128, 8, (64, 40), [1, 0]) - self.tconv_up1 = Decoder_Triblock(128, 64) - self.transform1 = TSA_Transform((384, 384), 64, 28, 8, (48, 30), [1, 1], True) - - self.conv_last = nn.Conv2d(out_ch, out_ch, 1) - self.afn_last = nn.Sigmoid() - self.fution = nn.Conv2d(28, 28, 3, 1, 1, bias=False) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None, input_mask_s=None): - - x = self.initial_x(x) - # print(x.shape) - - enc1, enc1_pre = self.tconv_down1(x) - enc2, enc2_pre = self.tconv_down2(enc1) - enc3, enc3_pre = self.tconv_down3(enc2) - enc4, enc4_pre = self.tconv_down4(enc3) - # enc5,enc5_pre = self.tconv_down5(enc4) - - bottom = self.bottom1(enc4) - bottom = self.bottom2(bottom) - - # dec5 = self.tconv_up5(bottom,enc5_pre) - dec4 = self.tconv_up4(bottom, enc4_pre) - dec3 = self.tconv_up3(dec4, enc3_pre) - dec3, _ = self.transform3(dec3) - dec2 = self.tconv_up2(dec3, enc2_pre) - dec2, _ = self.transform2(dec2) - dec1 = self.tconv_up1(dec2, enc1_pre) - dec1, _ = self.transform1(dec1) - - dec1 = self.conv_last(dec1) - output = self.afn_last(dec1) - - return output - - -class Encoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Encoder_Triblock, self).__init__() - - self.layer1 = conv_block(inChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - if flag_res: - self.layer2 = Res2Net(outChannel, int(outChannel / 4)) - else: - self.layer2 = conv_block(outChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - - self.pool = nn.MaxPool2d(nPool) if flag_Pool else None - - def forward(self, x): - feat = self.layer1(x) - feat = self.layer2(feat) - - feat_pool = self.pool(feat) if self.pool is not None else feat - return feat_pool, feat - - -class Decoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Decoder_Triblock, self).__init__() - - self.layer1 = nn.Sequential( - nn.ConvTranspose2d(inChannel, outChannel, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - if flag_res: - self.layer2 = Res2Net(int(outChannel * 2), int(outChannel / 2)) - else: - self.layer2 = conv_block(outChannel * 2, outChannel * 2, nKernal, flag_norm=_NORM_BONE) - self.layer3 = conv_block(outChannel * 2, outChannel, nKernal, flag_norm=_NORM_BONE) - - def forward(self, feat_dec, feat_enc): - feat_dec = self.layer1(feat_dec) - diffY = feat_enc.size()[2] - feat_dec.size()[2] - diffX = feat_enc.size()[3] - feat_dec.size()[3] - if diffY != 0 or diffX != 0: - print('Padding for size mismatch ( Enc:', feat_enc.size(), 'Dec:', feat_dec.size(), ')') - feat_dec = F.pad(feat_dec, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) - feat = torch.cat([feat_dec, feat_enc], dim=1) - feat = self.layer2(feat) - feat = self.layer3(feat) - return feat \ No newline at end of file diff --git a/MST/real/test_code/architecture/__init__.py b/MST/real/test_code/architecture/__init__.py deleted file mode 100755 index a66ae98..0000000 --- a/MST/real/test_code/architecture/__init__.py +++ /dev/null @@ -1,70 +0,0 @@ -import torch -from .MST import MST -from .GAP_Net import GAP_net -from .ADMM_Net import ADMM_net -from .TSA_Net import TSA_Net -from .HDNet import HDNet, FDL -from .DGSMP import HSI_CS -from .BIRNAT import BIRNAT -from .MST_Plus_Plus import MST_Plus_Plus -from .Lambda_Net import Lambda_Net -from .CST import CST -from .DAUHST import DAUHST -from .BiSRNet import BiSRNet - -def model_generator(method, pretrained_model_path=None): - if method == 'mst_s': - model = MST(dim=28, stage=2, num_blocks=[2, 2, 2]).cuda() - elif method == 'mst_m': - model = MST(dim=28, stage=2, num_blocks=[2, 4, 4]).cuda() - elif method == 'mst_l': - model = MST(dim=28, stage=2, num_blocks=[4, 7, 5]).cuda() - elif method == 'gap_net': - model = GAP_net().cuda() - elif method == 'admm_net': - model = ADMM_net().cuda() - elif method == 'tsa_net': - model = TSA_Net().cuda() - elif method == 'hdnet': - model = HDNet().cuda() - fdl_loss = FDL(loss_weight=0.7, - alpha=2.0, - patch_factor=4, - ave_spectrum=True, - log_matrix=True, - batch_matrix=True, - ).cuda() - elif method == 'dgsmp': - model = HSI_CS(Ch=28, stages=4).cuda() - elif method == 'birnat': - model = BIRNAT().cuda() - elif method == 'mst_plus_plus': - model = MST_Plus_Plus(in_channels=28, out_channels=28, n_feat=28, stage=3).cuda() - elif method == 'lambda_net': - model = Lambda_Net(out_ch=28).cuda() - elif method == 'cst_s': - model = CST(num_blocks=[1, 1, 2], sparse=True).cuda() - elif method == 'cst_m': - model = CST(num_blocks=[2, 2, 2], sparse=True).cuda() - elif method == 'cst_l': - model = CST(num_blocks=[2, 4, 6], sparse=True).cuda() - elif method == 'cst_l_plus': - model = CST(num_blocks=[2, 4, 6], sparse=False).cuda() - elif method == 'bisrnet': - model = BiSRNet(in_channels=28, out_channels=28, n_feat=28, stage=1, num_blocks=[1,1,1]).cuda() - elif 'dauhst' in method: - num_iterations = int(method.split('_')[1][0]) - model = DAUHST(num_iterations=num_iterations).cuda() - else: - print(f'Method {method} is not defined !!!!') - if pretrained_model_path is not None: - print(f'load model from {pretrained_model_path}') - try: - checkpoint = torch.load(pretrained_model_path) - model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint.items()}, - strict=True) - except: - model = torch.load(pretrained_model_path) - if method == 'hdnet': - return model, fdl_loss - return model \ No newline at end of file diff --git a/MST/real/test_code/dataset.py b/MST/real/test_code/dataset.py deleted file mode 100755 index 50838bd..0000000 --- a/MST/real/test_code/dataset.py +++ /dev/null @@ -1,96 +0,0 @@ -import torch.utils.data as tud -import random -import torch -import numpy as np -import scipy.io as sio - - -class dataset(tud.Dataset): - def __init__(self, opt, CAVE, KAIST): - super(dataset, self).__init__() - self.isTrain = opt.isTrain - self.size = opt.size - # self.path = opt.data_path - if self.isTrain == True: - self.num = opt.trainset_num - else: - self.num = opt.testset_num - self.CAVE = CAVE - self.KAIST = KAIST - ## load mask - data = sio.loadmat(opt.mask_path) - self.mask = data['mask'] - self.mask_3d = np.tile(self.mask[:, :, np.newaxis], (1, 1, 28)) - - def __getitem__(self, index): - if self.isTrain == True: - # index1 = 0 - index1 = random.randint(0, 29) - d = random.randint(0, 1) - if d == 0: - hsi = self.CAVE[:,:,:,index1] - else: - hsi = self.KAIST[:, :, :, index1] - else: - index1 = index - hsi = self.HSI[:, :, :, index1] - shape = np.shape(hsi) - - px = random.randint(0, shape[0] - self.size) - py = random.randint(0, shape[1] - self.size) - label = hsi[px:px + self.size:1, py:py + self.size:1, :] - # while np.max(label)==0: - # px = random.randint(0, shape[0] - self.size) - # py = random.randint(0, shape[1] - self.size) - # label = hsi[px:px + self.size:1, py:py + self.size:1, :] - # print(np.min(), np.max()) - - pxm = random.randint(0, 660 - self.size) - pym = random.randint(0, 660 - self.size) - mask_3d = self.mask_3d[pxm:pxm + self.size:1, pym:pym + self.size:1, :] - - mask_3d_shift = np.zeros((self.size, self.size + (28 - 1) * 2, 28)) - mask_3d_shift[:, 0:self.size, :] = mask_3d - for t in range(28): - mask_3d_shift[:, :, t] = np.roll(mask_3d_shift[:, :, t], 2 * t, axis=1) - mask_3d_shift_s = np.sum(mask_3d_shift ** 2, axis=2, keepdims=False) - mask_3d_shift_s[mask_3d_shift_s == 0] = 1 - - if self.isTrain == True: - - rotTimes = random.randint(0, 3) - vFlip = random.randint(0, 1) - hFlip = random.randint(0, 1) - - # Random rotation - for j in range(rotTimes): - label = np.rot90(label) - - # Random vertical Flip - for j in range(vFlip): - label = label[:, ::-1, :].copy() - - # Random horizontal Flip - for j in range(hFlip): - label = label[::-1, :, :].copy() - - temp = mask_3d * label - temp_shift = np.zeros((self.size, self.size + (28 - 1) * 2, 28)) - temp_shift[:, 0:self.size, :] = temp - for t in range(28): - temp_shift[:, :, t] = np.roll(temp_shift[:, :, t], 2 * t, axis=1) - meas = np.sum(temp_shift, axis=2) - input = meas / 28 * 2 * 1.2 - - QE, bit = 0.4, 2048 - input = np.random.binomial((input * bit / QE).astype(int), QE) - input = np.float32(input) / np.float32(bit) - - label = torch.FloatTensor(label.copy()).permute(2,0,1) - input = torch.FloatTensor(input.copy()) - mask_3d_shift = torch.FloatTensor(mask_3d_shift.copy()).permute(2,0,1) - mask_3d_shift_s = torch.FloatTensor(mask_3d_shift_s.copy()) - return input, label, mask_3d, mask_3d_shift, mask_3d_shift_s - - def __len__(self): - return self.num diff --git a/MST/real/test_code/option.py b/MST/real/test_code/option.py deleted file mode 100755 index 67f1ca6..0000000 --- a/MST/real/test_code/option.py +++ /dev/null @@ -1,53 +0,0 @@ -import argparse -import template - -parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox") -parser.add_argument('--template', default='mst', - help='You can set various templates in option.py') - -# Hardware specifications -parser.add_argument("--gpu_id", type=str, default='0') - -# Data specifications -parser.add_argument('--data_root', type=str, default='../../datasets/', help='dataset directory') -parser.add_argument('--data_path_CAVE', default='../../datasets/CAVE_512_28/', type=str, - help='path of data') -parser.add_argument('--data_path_KAIST', default='../../datasets/KAIST_CVPR2021/', type=str, - help='path of data') -parser.add_argument('--mask_path', default='../../datasets/TSA_real_data/mask.mat', type=str, - help='path of mask') - -# Saving specifications -parser.add_argument('--outf', type=str, default='./exp/mst_s/', help='saving_path') - -# Model specifications -parser.add_argument('--method', type=str, default='mst_s', help='method name') -parser.add_argument('--pretrained_model_path', type=str, default=None, help='pretrained model directory') -parser.add_argument("--input_setting", type=str, default='H', - help='the input measurement of the network: H, HM or Y') -parser.add_argument("--input_mask", type=str, default='Phi', - help='the input mask of the network: Phi, Phi_PhiPhiT, Mask or None') # Phi: shift_mask Mask: mask - - -# Training specifications -parser.add_argument("--size", default=384, type=int, help='cropped patch size') -parser.add_argument("--epoch_sam_num", default=5000, type=int, help='total number of trainset') -parser.add_argument("--seed", default=1, type=int, help='Random_seed') -parser.add_argument('--batch_size', type=int, default=1, help='the number of HSIs per batch') -parser.add_argument("--isTrain", default=True, type=bool, help='train or test') -parser.add_argument("--max_epoch", type=int, default=300, help='total epoch') -parser.add_argument("--scheduler", type=str, default='MultiStepLR', help='MultiStepLR or CosineAnnealingLR') -parser.add_argument("--milestones", type=int, default=[50,100,150,200,250], help='milestones for MultiStepLR') -parser.add_argument("--gamma", type=float, default=0.5, help='learning rate decay for MultiStepLR') -parser.add_argument("--learning_rate", type=float, default=0.0004) - -opt = parser.parse_args() -template.set_template(opt) - -opt.trainset_num = 20000 // ((opt.size // 96) ** 2) - -for arg in vars(opt): - if vars(opt)[arg] == 'True': - vars(opt)[arg] = True - elif vars(opt)[arg] == 'False': - vars(opt)[arg] = False \ No newline at end of file diff --git a/MST/real/test_code/template.py b/MST/real/test_code/template.py deleted file mode 100755 index cfb3891..0000000 --- a/MST/real/test_code/template.py +++ /dev/null @@ -1,55 +0,0 @@ -def set_template(args): - # Set the templates here - if args.template.find('mst') >= 0: - args.input_setting = 'Y' - args.input_mask = None - - if args.template.find('cst') >= 0: - args.input_setting = 'Y' - args.input_mask = None - - if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0 or args.template.find('dauhst') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - - if args.template.find('tsa_net') >= 0: - args.input_setting = 'HM' - args.input_mask = None - - if args.template.find('hdnet') >= 0: - args.input_setting = 'H' - args.input_mask = None - - if args.template.find('dgsmp') >= 0: - args.input_setting = 'Y' - args.input_mask = None - args.batch_size = 2 - args.milestones = [args.max_epoch] # fix the learning rate during training, following the official implementation - args.learning_rate = 1e-4 - - if args.template.find('birnat') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - args.batch_size = 1 - args.max_epoch = 100 - args.milestones = [args.max_epoch] # fix the learning rate during training, following the official implementation - args.learning_rate = 1.5e-4 - - if args.template.find('mst_plus_plus') >= 0: - args.input_setting = 'Y' - args.input_mask = None - args.scheduler = 'CosineAnnealingLR' - - if args.template.find('dnu') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.batch_size = 2 - args.max_epoch = 150 - args.milestones = range(10,args.max_epoch,10) - args.gamma = 0.9 - args.learning_rate = 4e-4 - - if args.template.find('lambda_net') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - args.learning_rate = 1.5e-4 \ No newline at end of file diff --git a/MST/real/test_code/test.py b/MST/real/test_code/test.py deleted file mode 100755 index 7c7bdd2..0000000 --- a/MST/real/test_code/test.py +++ /dev/null @@ -1,83 +0,0 @@ -import torch -import os -import argparse -from utils import dataparallel -import scipy.io as sio -import numpy as np -from torch.autograd import Variable -from option import opt -from architecture import * - -os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" -os.environ["CUDA_VISIBLE_DEVICES"] = "0" - -parser = argparse.ArgumentParser(description="PyTorch HSIFUSION") -parser.add_argument('--data_path', default='../../datasets/TSA_real_data/Measurements/', type=str,help='path of data') -parser.add_argument('--mask_path', default='../../datasets/TSA_real_data/mask.mat', type=str,help='path of mask') -parser.add_argument("--size", default=660, type=int, help='the size of trainset image') -parser.add_argument("--seed", default=1, type=int, help='Random_seed') -parser.add_argument("--batch_size", default=1, type=int, help='batch_size') -parser.add_argument("--pretrained_model_path", default=None, type=str) -parser.add_argument("--outf", default='./exp/real_test_result/', type=str) -parser.add_argument("--method", type=str, default='others') -opt = parser.parse_args() - -def prepare_data(path, file_num): - HR_HSI = np.zeros((((660,714,file_num)))) - for idx in range(file_num): - #### read HrHSI - path1 = os.path.join(path) + 'scene' + str(idx+1) + '.mat' - data = sio.loadmat(path1) - HR_HSI[:,:,idx] = data['meas_real'] - HR_HSI[HR_HSI < 0] = 0.0 - HR_HSI[HR_HSI > 1] = 1.0 - return HR_HSI - -def load_mask(path,size=660): - ## load mask - data = sio.loadmat(path) - mask = data['mask'] - mask_3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28)) - mask_3d_shift = np.zeros((size, size + (28 - 1) * 2, 28)) - mask_3d_shift[:, 0:size, :] = mask_3d - for t in range(28): - mask_3d_shift[:, :, t] = np.roll(mask_3d_shift[:, :, t], 2 * t, axis=1) - mask_3d_shift_s = np.sum(mask_3d_shift ** 2, axis=2, keepdims=False) - mask_3d_shift_s[mask_3d_shift_s == 0] = 1 - mask_3d_shift = torch.FloatTensor(mask_3d_shift.copy()).permute(2, 0, 1) - mask_3d_shift_s = torch.FloatTensor(mask_3d_shift_s.copy()) - return mask_3d_shift.unsqueeze(0), mask_3d_shift_s.unsqueeze(0) - -HR_HSI = prepare_data(opt.data_path, 5) -mask_3d_shift, mask_3d_shift_s = load_mask(opt.mask_path) - -pretrained_model_path = opt.pretrained_model_path -save_path = opt.outf -if opt.method=='bisrnet': - model = model_generator(opt.method, opt.pretrained_model_path).cuda() -else: - model = torch.load(pretrained_model_path) -model = model.eval() -model = dataparallel(model, 1) -psnr_total = 0 -k = 0 -for j in range(5): - with torch.no_grad(): - meas = HR_HSI[:,:,j] - meas = meas / meas.max() * 0.8 - meas = torch.FloatTensor(meas) - # meas = torch.FloatTensor(meas).unsqueeze(2).permute(2, 0, 1) - input = meas.unsqueeze(0) - input = Variable(input) - input = input.cuda() - mask_3d_shift = mask_3d_shift.cuda() - mask_3d_shift_s = mask_3d_shift_s.cuda() - out = model(input, mask_3d_shift, mask_3d_shift_s) - result = out - result = result.clamp(min=0., max=1.) - k = k + 1 - if not os.path.exists(save_path): # Create the model directory if it doesn't exist - os.makedirs(save_path) - res = result.cpu().permute(2,3,1,0).squeeze(3).numpy() - save_file = save_path + f'{j}.mat' - sio.savemat(save_file, {'res':res}) diff --git a/MST/real/test_code/test_s.py b/MST/real/test_code/test_s.py deleted file mode 100755 index 911bedc..0000000 --- a/MST/real/test_code/test_s.py +++ /dev/null @@ -1,69 +0,0 @@ -import torch -import os -import argparse - -import scipy.io as sio -import numpy as np -from torch.autograd import Variable -from architecture import * - - -parser = argparse.ArgumentParser(description="PyTorch HSIFUSION") -parser.add_argument('--data_path', default='../../datasets/TSA_real_data/Measurements/', type=str,help='path of data') -parser.add_argument('--mask_path', default='../../datasets/TSA_real_data/mask.mat', type=str,help='path of mask') -parser.add_argument("--size", default=660, type=int, help='the size of trainset image') -parser.add_argument("--trainset_num", default=2000, type=int, help='total number of trainset') -parser.add_argument("--testset_num", default=5, type=int, help='total number of testset') -parser.add_argument("--seed", default=1, type=int, help='Random_seed') -parser.add_argument("--batch_size", default=1, type=int, help='batch_size') -parser.add_argument("--isTrain", default=False, type=bool, help='train or test') -parser.add_argument("--gpu_id", type=str, default='0') -parser.add_argument("--pretrained_model_path", type=str, default='model_zoo/bisrnet/bisrnet.pth') -parser.add_argument("--outf", type=str, default='./exp/bisrnet/') -parser.add_argument("--method", type=str, default='bisrnet') -opt = parser.parse_args() -print(opt) - -os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID' -os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id - -def prepare_data(path, file_num): - HR_HSI = np.zeros((((660,714,file_num)))) - for idx in range(file_num): - #### read HrHSI - path1 = os.path.join(path) + 'scene' + str(idx+1) + '.mat' - data = sio.loadmat(path1) - HR_HSI[:,:,idx] = data['meas_real'] - HR_HSI[HR_HSI < 0] = 0.0 - HR_HSI[HR_HSI > 1] = 1.0 - return HR_HSI - - - -HR_HSI = prepare_data(opt.data_path, 5) - -model = model_generator(opt.method, opt.pretrained_model_path).cuda() -model = model.eval() -psnr_total = 0 -k = 0 -for j in range(5): - with torch.no_grad(): - meas = HR_HSI[:,:,j] - meas = meas / meas.max() * 0.8 - meas = torch.FloatTensor(meas).unsqueeze(2).permute(2, 0, 1) - input = meas.unsqueeze(0) - input = Variable(input) - input = input.cuda() - out = model(input) - result = out - result = result.clamp(min=0., max=1.) - k = k + 1 - model_dir = opt.outf - if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist - os.makedirs(model_dir) - res = result.cpu().permute(2,3,1,0).squeeze(3).numpy() - save_path = model_dir + '/' + str(j + 1) + '.mat' - sio.savemat(save_path, {'res':res}) - - - diff --git a/MST/real/test_code/utils.py b/MST/real/test_code/utils.py deleted file mode 100755 index 4b531a7..0000000 --- a/MST/real/test_code/utils.py +++ /dev/null @@ -1,184 +0,0 @@ -import numpy as np -import scipy.io as sio -import os -import glob -import re -import torch -import torch.nn as nn -import math -import random - -def _as_floats(im1, im2): - float_type = np.result_type(im1.dtype, im2.dtype, np.float32) - im1 = np.asarray(im1, dtype=float_type) - im2 = np.asarray(im2, dtype=float_type) - return im1, im2 - - -def compare_mse(im1, im2): - im1, im2 = _as_floats(im1, im2) - return np.mean(np.square(im1 - im2), dtype=np.float64) - - -def compare_psnr(im_true, im_test, data_range=None): - im_true, im_test = _as_floats(im_true, im_test) - - err = compare_mse(im_true, im_test) - return 10 * np.log10((data_range ** 2) / err) - - -def psnr(img1, img2): - mse = np.mean((img1/255. - img2/255.) ** 2) - if mse < 1.0e-10: - return 100 - PIXEL_MAX = 1 - return 20 * math.log10(PIXEL_MAX / math.sqrt(mse)) - - -def PSNR_GPU(im_true, im_fake): - im_true *= 255 - im_fake *= 255 - im_true = im_true.round() - im_fake = im_fake.round() - data_range = 255 - esp = 1e-12 - C = im_true.size()[0] - H = im_true.size()[1] - W = im_true.size()[2] - Itrue = im_true.clone() - Ifake = im_fake.clone() - mse = nn.MSELoss(reduce=False) - err = mse(Itrue, Ifake).sum() / (C*H*W) - psnr = 10. * np.log((data_range**2)/(err.data + esp)) / np.log(10.) - return psnr - - -def PSNR_Nssr(im_true, im_fake): - mse = ((im_true - im_fake)**2).mean() - psnr = 10. * np.log10(1/mse) - return psnr - - -def dataparallel(model, ngpus, gpu0=0): - if ngpus==0: - assert False, "only support gpu mode" - gpu_list = list(range(gpu0, gpu0+ngpus)) - assert torch.cuda.device_count() >= gpu0 + ngpus - if ngpus > 1: - if not isinstance(model, torch.nn.DataParallel): - model = torch.nn.DataParallel(model, gpu_list).cuda() - else: - - model = model.cuda() - elif ngpus == 1: - model = model.cuda() - return model - - -def findLastCheckpoint(save_dir): - file_list = glob.glob(os.path.join(save_dir, 'model_*.pth')) - if file_list: - epochs_exist = [] - for file_ in file_list: - result = re.findall(".*model_(.*).pth.*", file_) - epochs_exist.append(int(result[0])) - initial_epoch = max(epochs_exist) - else: - initial_epoch = 0 - return initial_epoch - -# load HSIs -def prepare_data(path, file_num): - HR_HSI = np.zeros((((512,512,28,file_num)))) - for idx in range(file_num): - # read HrHSI - path1 = os.path.join(path) + 'scene%02d.mat' % (idx+1) - # path1 = os.path.join(path) + HR_code + '.mat' - data = sio.loadmat(path1) - HR_HSI[:,:,:,idx] = data['data_slice'] / 65535.0 - HR_HSI[HR_HSI < 0.] = 0. - HR_HSI[HR_HSI > 1.] = 1. - return HR_HSI - - -def loadpath(pathlistfile): - fp = open(pathlistfile) - pathlist = fp.read().splitlines() - fp.close() - random.shuffle(pathlist) - return pathlist - -def time2file_name(time): - year = time[0:4] - month = time[5:7] - day = time[8:10] - hour = time[11:13] - minute = time[14:16] - second = time[17:19] - time_filename = year + '_' + month + '_' + day + '_' + hour + '_' + minute + '_' + second - return time_filename - -# def prepare_data_cave(path, file_list, file_num): -# HR_HSI = np.zeros((((512,512,28,file_num)))) -# for idx in range(file_num): -# #### read HrHSI -# HR_code = file_list[idx] -# path1 = os.path.join(path) + HR_code + '.mat' -# data = sio.loadmat(path1) -# HR_HSI[:,:,:,idx] = data['data_slice'] / 65535.0 -# HR_HSI[HR_HSI < 0] = 0 -# HR_HSI[HR_HSI > 1] = 1 -# return HR_HSI -# -# def prepare_data_KASIT(path, file_list, file_num): -# HR_HSI = np.zeros((((2704,3376,28,file_num)))) -# for idx in range(file_num): -# #### read HrHSI -# HR_code = file_list[idx] -# path1 = os.path.join(path) + HR_code + '.mat' -# data = sio.loadmat(path1) -# HR_HSI[:,:,:,idx] = data['HSI'] -# HR_HSI[HR_HSI < 0] = 0 -# HR_HSI[HR_HSI > 1] = 1 -# return HR_HSI - -def prepare_data_cave(path, file_num): - HR_HSI = np.zeros((((512,512,28,file_num)))) - file_list = os.listdir(path) - # for idx in range(1): - for idx in range(file_num): - print(f'loading CAVE {idx}') - #### read HrHSI - HR_code = file_list[idx] - path1 = os.path.join(path) + HR_code - data = sio.loadmat(path1) - HR_HSI[:,:,:,idx] = data['data_slice'] / 65535.0 - HR_HSI[HR_HSI < 0] = 0 - HR_HSI[HR_HSI > 1] = 1 - return HR_HSI - -def prepare_data_KAIST(path, file_num): - HR_HSI = np.zeros((((2704,3376,28,file_num)))) - file_list = os.listdir(path) - # for idx in range(1): - for idx in range(file_num): - print(f'loading KAIST {idx}') - #### read HrHSI - HR_code = file_list[idx] - path1 = os.path.join(path) + HR_code - data = sio.loadmat(path1) - HR_HSI[:,:,:,idx] = data['HSI'] - HR_HSI[HR_HSI < 0] = 0 - 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-1,201 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - x = self.d_conv(x) - return x - - -class Unet(nn.Module): - - def __init__(self, in_ch, out_ch): - super(Unet, self).__init__() - - self.dconv_down1 = double_conv(in_ch, 32) - self.dconv_down2 = double_conv(32, 64) - self.dconv_down3 = double_conv(64, 128) - - self.maxpool = nn.MaxPool2d(2) - self.upsample2 = nn.Sequential( - nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.upsample1 = nn.Sequential( - nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.dconv_up2 = double_conv(64 + 64, 64) - self.dconv_up1 = double_conv(32 + 32, 32) - - self.conv_last = nn.Conv2d(32, out_ch, 1) - self.afn_last = nn.Tanh() - - def forward(self, x): - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - inputs = x - conv1 = self.dconv_down1(x) - x = self.maxpool(conv1) - - conv2 = self.dconv_down2(x) - x = self.maxpool(conv2) - - conv3 = self.dconv_down3(x) - - x = self.upsample2(conv3) - x = torch.cat([x, conv2], dim=1) - - x = self.dconv_up2(x) - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - - x = self.dconv_up1(x) - - x = self.conv_last(x) - x = self.afn_last(x) - out = x + inputs - - return out[:, :, :h_inp, :w_inp] - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class ADMM_net(nn.Module): - - def __init__(self): - super(ADMM_net, self).__init__() - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - self.gamma1 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma2 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma3 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma4 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma5 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma6 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma7 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma8 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma9 = torch.nn.Parameter(torch.Tensor([0])) - - def forward(self, y, input_mask=None): - if input_mask == None: - Phi = torch.rand((1, 28, 256, 310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask - x_list = [] - theta = At(y,Phi) - b = torch.zeros_like(Phi) - ### 1-3 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma1),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet1(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma2),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet2(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma3),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet3(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - ### 4-6 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma4),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet4(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma5),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet5(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma6),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet6(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - ### 7-9 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma7),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet7(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma8),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet8(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma9),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet9(x1) - theta = shift_3d(theta) - return theta[:, :, :, :384] diff --git a/MST/real/train_code/architecture/BIRNAT.py b/MST/real/train_code/architecture/BIRNAT.py deleted file mode 100755 index e3d8123..0000000 --- a/MST/real/train_code/architecture/BIRNAT.py +++ /dev/null @@ -1,365 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class self_attention(nn.Module): - def __init__(self, ch): - super(self_attention, self).__init__() - self.conv1 = nn.Conv2d(ch, ch // 8, 1) - self.conv2 = nn.Conv2d(ch, ch // 8, 1) - self.conv3 = nn.Conv2d(ch, ch, 1) - self.conv4 = nn.Conv2d(ch, ch, 1) - self.gamma1 = torch.nn.Parameter(torch.Tensor([0])) - self.ch = ch - - def forward(self, x): - batch_size = x.shape[0] - - f = self.conv1(x) - g = self.conv2(x) - h = self.conv3(x) - ht = h.reshape([batch_size, self.ch, -1]) - - ft = f.reshape([batch_size, self.ch // 8, -1]) - n = torch.matmul(ft.permute([0, 2, 1]), g.reshape([batch_size, self.ch // 8, -1])) - beta = F.softmax(n, dim=1) - - o = torch.matmul(ht, beta) - o = o.reshape(x.shape) # [bs, C, h, w] - - o = self.conv4(o) - - x = self.gamma1 * o + x - - return x - - -class res_part(nn.Module): - - def __init__(self, in_ch, out_ch): - super(res_part, self).__init__() - self.conv1 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - self.conv2 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - self.conv3 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - - def forward(self, x): - x1 = self.conv1(x) - x = x1 + x - x1 = self.conv2(x) - x = x1 + x - x1 = self.conv3(x) - x = x1 + x - return x - - -class down_feature(nn.Module): - - def __init__(self, in_ch, out_ch): - super(down_feature, self).__init__() - self.conv = nn.Sequential( - # nn.Conv2d(in_ch, 20, 5, stride=1, padding=2), - # nn.Conv2d(20, 40, 5, stride=2, padding=2), - # nn.Conv2d(40, out_ch, 5, stride=2, padding=2), - nn.Conv2d(in_ch, 20, 5, stride=1, padding=2), - nn.Conv2d(20, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.Conv2d(20, 40, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(40, out_ch, 3, stride=1, padding=1), - ) - - def forward(self, x): - x = self.conv(x) - return x - - -class up_feature(nn.Module): - - def __init__(self, in_ch, out_ch): - super(up_feature, self).__init__() - self.conv = nn.Sequential( - # nn.ConvTranspose2d(in_ch, 40, 3, stride=2, padding=1, output_padding=1), - # nn.ConvTranspose2d(40, 20, 3, stride=2, padding=1, output_padding=1), - nn.Conv2d(in_ch, 40, 3, stride=1, padding=1), - nn.Conv2d(40, 30, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(30, 20, 3, stride=1, padding=1), - nn.Conv2d(20, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - nn.Conv2d(20, out_ch, 1), - # nn.Sigmoid(), - ) - - def forward(self, x): - x = self.conv(x) - return x - -class cnn1(nn.Module): - # 输入meas concat mask - # 3 下采样 - - def __init__(self, B): - super(cnn1, self).__init__() - self.conv1 = nn.Conv2d(B + 1, 32, kernel_size=5, stride=1, padding=2) - self.relu1 = nn.LeakyReLU(inplace=True) - self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) - self.relu2 = nn.LeakyReLU(inplace=True) - self.conv3 = nn.Conv2d(64, 64, kernel_size=1, stride=1) - self.relu3 = nn.LeakyReLU(inplace=True) - self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) - self.relu4 = nn.LeakyReLU(inplace=True) - self.conv5 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1) - self.relu5 = nn.LeakyReLU(inplace=True) - self.conv51 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1) - self.relu51 = nn.LeakyReLU(inplace=True) - self.conv52 = nn.Conv2d(32, 16, kernel_size=1, stride=1) - self.relu52 = nn.LeakyReLU(inplace=True) - self.conv6 = nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1) - self.res_part1 = res_part(128, 128) - self.res_part2 = res_part(128, 128) - self.res_part3 = res_part(128, 128) - self.conv7 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - self.relu7 = nn.LeakyReLU(inplace=True) - self.conv8 = nn.Conv2d(128, 128, kernel_size=1, stride=1) - self.conv9 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - self.relu9 = nn.LeakyReLU(inplace=True) - self.conv10 = nn.Conv2d(128, 128, kernel_size=1, stride=1) - - self.att1 = self_attention(128) - - def forward(self, meas=None, nor_meas=None, PhiTy=None): - data = torch.cat([torch.unsqueeze(nor_meas, dim=1), PhiTy], dim=1) - out = self.conv1(data) - - out = self.relu1(out) - out = self.conv2(out) - out = self.relu2(out) - out = self.conv3(out) - out = self.relu3(out) - out = self.conv4(out) - out = self.relu4(out) - out = self.res_part1(out) - out = self.conv7(out) - out = self.relu7(out) - out = self.conv8(out) - out = self.res_part2(out) - out = self.conv9(out) - out = self.relu9(out) - out = self.conv10(out) - out = self.res_part3(out) - - # out = self.att1(out) - - out = self.conv5(out) - out = self.relu5(out) - out = self.conv51(out) - out = self.relu51(out) - out = self.conv52(out) - out = self.relu52(out) - out = self.conv6(out) - - return out - - -class forward_rnn(nn.Module): - - def __init__(self): - super(forward_rnn, self).__init__() - self.extract_feature1 = down_feature(1, 20) - self.up_feature1 = up_feature(60, 1) - self.conv_x1 = nn.Sequential( - nn.Conv2d(1, 16, 5, stride=1, padding=2), - nn.Conv2d(16, 32, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(32, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.conv_x2 = nn.Sequential( - nn.Conv2d(1, 10, 5, stride=1, padding=2), - nn.Conv2d(10, 10, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(10, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.h_h = nn.Sequential( - nn.Conv2d(60, 30, 3, padding=1), - nn.Conv2d(30, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - ) - self.res_part1 = res_part(60, 60) - self.res_part2 = res_part(60, 60) - - def forward(self, xt1, meas=None, nor_meas=None, PhiTy=None, mask3d_batch=None, h=None, cs_rate=28): - ht = h - xt = xt1 - - step = 2 - [bs, nC, row, col] = xt1.shape - - out = xt1 - x11 = self.conv_x1(torch.unsqueeze(nor_meas, 1)) - for i in range(cs_rate - 1): - d1 = torch.zeros(bs, row, col).cuda() - d2 = torch.zeros(bs, row, col).cuda() - for ii in range(i + 1): - d1 = d1 + torch.mul(mask3d_batch[:, ii, :, :], out[:, ii, :, :]) - for ii in range(i + 2, cs_rate): - d2 = d2 + torch.mul(mask3d_batch[:, ii, :, :], torch.squeeze(nor_meas)) - x12 = self.conv_x2(torch.unsqueeze(meas - d1 - d2, 1)) - - x2 = self.extract_feature1(xt) - h = torch.cat([ht, x11, x12, x2], dim=1) - h = self.res_part1(h) - h = self.res_part2(h) - ht = self.h_h(h) - xt = self.up_feature1(h) - out = torch.cat([out, xt], dim=1) - - return out, ht - - -class backrnn(nn.Module): - - def __init__(self): - super(backrnn, self).__init__() - self.extract_feature1 = down_feature(1, 20) - self.up_feature1 = up_feature(60, 1) - self.conv_x1 = nn.Sequential( - nn.Conv2d(1, 16, 5, stride=1, padding=2), - nn.Conv2d(16, 32, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(32, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.conv_x2 = nn.Sequential( - nn.Conv2d(1, 10, 5, stride=1, padding=2), - nn.Conv2d(10, 10, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(10, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.h_h = nn.Sequential( - nn.Conv2d(60, 30, 3, padding=1), - nn.Conv2d(30, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - ) - self.res_part1 = res_part(60, 60) - self.res_part2 = res_part(60, 60) - - def forward(self, xt8, meas=None, nor_meas=None, PhiTy=None, mask3d_batch=None, h=None, cs_rate=28): - ht = h - - step = 2 - [bs, nC, row, col] = xt8.shape - - xt = torch.unsqueeze(xt8[:, cs_rate - 1, :, :], 1) - - out = torch.zeros(bs, cs_rate, row, col).cuda() - out[:, cs_rate - 1, :, :] = xt[:, 0, :, :] - x11 = self.conv_x1(torch.unsqueeze(nor_meas, 1)) - for i in range(cs_rate - 1): - d1 = torch.zeros(bs, row, col).cuda() - d2 = torch.zeros(bs, row, col).cuda() - for ii in range(i + 1): - d1 = d1 + torch.mul(mask3d_batch[:, cs_rate - 1 - ii, :, :], out[:, cs_rate - 1 - ii, :, :].clone()) - for ii in range(i + 2, cs_rate): - d2 = d2 + torch.mul(mask3d_batch[:, cs_rate - 1 - ii, :, :], xt8[:, cs_rate - 1 - ii, :, :].clone()) - x12 = self.conv_x2(torch.unsqueeze(meas - d1 - d2, 1)) - - x2 = self.extract_feature1(xt) - h = torch.cat([ht, x11, x12, x2], dim=1) - h = self.res_part1(h) - h = self.res_part2(h) - ht = self.h_h(h) - xt = self.up_feature1(h) - - out[:, cs_rate - 2 - i, :, :] = xt[:, 0, :, :] - - return out - -def shift_gt_back(inputs, step=2): # input [bs,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - output = torch.zeros(bs, nC, row, col - (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, :] = inputs[:, i, :, step * i:step * i + col - (nC - 1) * step] - return output - -def shift(inputs, step=2): - [bs, nC, row, col] = inputs.shape - if inputs.is_cuda: - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).cuda().float() - else: - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).float() - for i in range(nC): - output[:, i, :, step * i:step * i + col] = inputs[:, i, :, :] - return output - -class BIRNAT(nn.Module): - - def __init__(self): - super(BIRNAT, self).__init__() - self.cs_rate = 28 - self.first_frame_net = cnn1(self.cs_rate).cuda() - self.rnn1 = forward_rnn().cuda() - self.rnn2 = backrnn().cuda() - - def gen_meas_torch(self, meas, shift_mask): - batch_size, H = meas.shape[0:2] - mask_s = torch.sum(shift_mask, 1) - nor_meas = torch.div(meas, mask_s) - temp = torch.mul(torch.unsqueeze(nor_meas, dim=1).expand([batch_size, 28, H, shift_mask.shape[3]]), shift_mask) - return nor_meas, temp - - def forward(self, meas, shift_mask=None): - if shift_mask==None: - shift_mask = torch.zeros(1, 28, 256, 310).cuda() - H, W = meas.shape[-2:] - nor_meas, PhiTy = self.gen_meas_torch(meas, shift_mask) - h0 = torch.zeros(meas.shape[0], 20, H, W).cuda() - xt1 = self.first_frame_net(meas, nor_meas, PhiTy) - model_out1, h1 = self.rnn1(xt1, meas, nor_meas, PhiTy, shift_mask, h0, self.cs_rate) - model_out2 = self.rnn2(model_out1, meas, nor_meas, PhiTy, shift_mask, h1, self.cs_rate) - model_out2 = shift_gt_back(model_out2) - - return model_out2 diff --git a/MST/real/train_code/architecture/BiSRNet.py b/MST/real/train_code/architecture/BiSRNet.py deleted file mode 100755 index 41c751e..0000000 --- a/MST/real/train_code/architecture/BiSRNet.py +++ /dev/null @@ -1,426 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -import os -from pdb import set_trace as stx - - -# --------------------------------------------- Binarized Basic Units ----------------------------------------------------------------- - - -class LearnableBias(nn.Module): - def __init__(self, out_chn): - super(LearnableBias, self).__init__() - self.bias = nn.Parameter(torch.zeros(1, out_chn, 1, 1), requires_grad=True) - - def forward(self, x): - # stx() - out = x + self.bias.expand_as(x) - return out - - -class ReDistribution(nn.Module): - def __init__(self, out_chn): - super(ReDistribution, self).__init__() - self.b = nn.Parameter(torch.zeros(1, out_chn, 1, 1), requires_grad=True) - self.k = nn.Parameter(torch.ones(1, out_chn, 1, 1), requires_grad=True) - - def forward(self, x): - out = x * self.k.expand_as(x) + self.b.expand_as(x) - return out - - -class RPReLU(nn.Module): - def __init__(self, inplanes): - super(RPReLU, self).__init__() - self.pr_bias0 = LearnableBias(inplanes) - self.pr_prelu = nn.PReLU(inplanes) - self.pr_bias1 = LearnableBias(inplanes) - - def forward(self, x): - x = self.pr_bias1(self.pr_prelu(self.pr_bias0(x))) - return x - - -class Spectral_Binary_Activation(nn.Module): - def __init__(self): - super(Spectral_Binary_Activation, self).__init__() - self.beta = nn.Parameter(torch.ones(1), requires_grad=True) - - def forward(self, x): - binary_activation_no_grad = torch.sign(x) - tanh_activation = torch.tanh(x * self.beta) - - out = binary_activation_no_grad.detach() - tanh_activation.detach() + tanh_activation - return out - - -class HardBinaryConv(nn.Conv2d): - def __init__(self, in_chn, out_chn, kernel_size=3, stride=1, padding=1, groups=1, bias=True): - super(HardBinaryConv, self).__init__( - in_chn, - out_chn, - kernel_size, - stride=stride, - padding=padding, - groups=groups, - bias=bias - ) - - def forward(self, x): - real_weights = self.weight - scaling_factor = torch.mean(torch.mean(torch.mean(abs(real_weights), dim=3, keepdim=True), dim=2, keepdim=True), - dim=1, keepdim=True) - scaling_factor = scaling_factor.detach() - # stx() - binary_weights_no_grad = scaling_factor * torch.sign(real_weights) - # stx() - cliped_weights = torch.clamp(real_weights, -1.0, 1.0) - binary_weights = binary_weights_no_grad.detach() - cliped_weights.detach() + cliped_weights - y = F.conv2d(x, binary_weights, self.bias, stride=self.stride, padding=self.padding, groups=self.groups) - - return y - - -class BinaryConv2d(nn.Module): - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d, self).__init__() - - self.move0 = ReDistribution(in_channels) - self.binary_activation = Spectral_Binary_Activation() - self.binary_conv = HardBinaryConv(in_chn=in_channels, - out_chn=in_channels, - kernel_size=kernel_size, - stride=stride, - padding=padding, - bias=bias, - groups=groups) - self.relu = RPReLU(in_channels) - - def forward(self, x): - out = self.move0(x) - out = self.binary_activation(out) - out = self.binary_conv(out) - out = self.relu(out) - out = out + x - return out - - -class BinaryConv2d_Down(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,2h,2w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Down, self).__init__() - - self.biconv_1 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.avg_pool = nn.AvgPool2d(kernel_size=2, stride=2, padding=0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,2c,h/2,w/2 - ''' - out = self.avg_pool(x) - out_1 = out - out_2 = out_1.clone() - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - out = torch.cat([out_1, out_2], dim=1) - - return out - - -class BinaryConv2d_Up(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,2h,2w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Up, self).__init__() - - self.biconv_1 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,c/2,2h,2w - ''' - b, c, h, w = x.shape - out = F.interpolate(x, scale_factor=2, mode='bilinear') - - out_1 = out[:, :c // 2, :, :] - out_2 = out[:, c // 2:, :, :] - - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - - out = (out_1 + out_2) / 2 - - return out - - -class BinaryConv2d_Fusion_Decrease(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,h,w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Fusion_Decrease, self).__init__() - - self.biconv_1 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - # self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,c/2,h,w - ''' - b, c, h, w = x.shape - out = x - - out_1 = out[:, :c // 2, :, :] - out_2 = out[:, c // 2:, :, :] - - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - - out = (out_1 + out_2) / 2 - - return out - - -class BinaryConv2d_Fusion_Increase(nn.Module): - ''' - input: b,c,h,w - output: b,2c,h,w - ''' - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Fusion_Increase, self).__init__() - - self.biconv_1 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - # self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,2c,h,w - ''' - # stx() - out_1 = x - out_2 = out_1.clone() - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - out = torch.cat([out_1, out_2], dim=1) - - return out - - -# ---------------------------------------------------------- Binarized UNet------------------------------------------------------ - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -def shift_back(inputs, step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256 // row - step = float(step) / float(down_sample * down_sample) - out_col = row - for i in range(nC): - inputs[:, i, :, :out_col] = \ - inputs[:, i, :, int(step * i):int(step * i) + out_col] - return inputs[:, :, :, :out_col] - - -class FeedForward(nn.Module): - def __init__(self, dim, mult=2): - super().__init__() - self.net = nn.Sequential( - BinaryConv2d_Fusion_Increase(dim, dim * mult, 1, 1, bias=False), - BinaryConv2d_Fusion_Increase(dim * mult, dim * mult * mult, 1, 1, bias=False), - RPReLU(dim * mult * mult), - BinaryConv2d(dim * mult * mult, dim * mult * mult, 3, 1, 1, bias=False, groups=dim), - RPReLU(dim * mult * mult), - BinaryConv2d_Fusion_Decrease(dim * mult * mult, dim * mult, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - - -class BiSRNet_Block(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append( - PreNorm(dim, FeedForward(dim=dim)) - ) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for ff in self.blocks: - # x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - -class BiSRNet_body(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2, 4, 4]): - super(BiSRNet_body, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = BinaryConv2d(in_dim, self.dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - BiSRNet_Block(dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - BinaryConv2d_Down(dim_stage, dim_stage * 2, 3, 1, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = BiSRNet_Block( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - BinaryConv2d_Up(dim_stage, dim_stage // 2, 3, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim_stage, dim_stage // 2, 1, 1, bias=False), - BiSRNet_Block( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = BinaryConv2d(self.dim, out_dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (BiSRNet_Block, FeaDownSample) in self.encoder_layers: - # stx() - fea = BiSRNet_Block(fea) - fea_encoder.append(fea) - # stx() - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, BiSRNet_Block) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage - 1 - i]], dim=1)) - fea = BiSRNet_Block(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - - -class BiSRNet(nn.Module): - ''' - Only 3 layers are 32-bit conv - ''' - - def __init__(self, in_channels=28, out_channels=28, n_feat=28, stage=3, num_blocks=[1, 1, 1]): - super(BiSRNet, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2, - bias=False) # 1-bit -> 32-bit - modules_body = [BiSRNet_body(dim=n_feat, stage=2, num_blocks=num_blocks) for _ in range(stage)] - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) # 1-bit -> 32-bit - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2, - bias=False) # 1-bit -> 32-bit - - def y2x(self, y): - ## Spilt operator - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - return x - - def forward(self, y, input_mask=None): - """ - x: [b,h,w] - return out:[b,c,h,w] - """ - - x = self.y2x(y) - - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] \ No newline at end of file diff --git a/MST/real/train_code/architecture/CST.py b/MST/real/train_code/architecture/CST.py deleted file mode 100755 index dab3f10..0000000 --- a/MST/real/train_code/architecture/CST.py +++ /dev/null @@ -1,600 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -from collections import defaultdict, Counter -import numpy as np -from tqdm import tqdm -import random - -def uniform(a, b, shape, device='cuda'): - return (b - a) * torch.rand(shape, device=device) + a - -class AsymmetricTransform: - - def Q(self, *args, **kwargs): - raise NotImplementedError('Query transform not implemented') - - def K(self, *args, **kwargs): - raise NotImplementedError('Key transform not implemented') - -class LSH: - def __call__(self, *args, **kwargs): - raise NotImplementedError('LSH scheme not implemented') - - def compute_hash_agreement(self, q_hash, k_hash): - return (q_hash == k_hash).min(dim=-1)[0].sum(dim=-1) - -class XBOXPLUS(AsymmetricTransform): - - def set_norms(self, x): - self.x_norms = x.norm(p=2, dim=-1, keepdim=True) - self.MX = torch.amax(self.x_norms, dim=-2, keepdim=True) - - def X(self, x): - device = x.device - ext = torch.sqrt((self.MX**2).to(device) - (self.x_norms**2).to(device)) - zero = torch.tensor(0.0, device=x.device).repeat(x.shape[:-1], 1).unsqueeze(-1) - return torch.cat((x, ext, zero), -1) - -def lsh_clustering(x, n_rounds, r=1): - salsh = SALSH(n_rounds=n_rounds, dim=x.shape[-1], r=r, device=x.device) - x_hashed = salsh(x).reshape((n_rounds,) + x.shape[:-1]) - return x_hashed.argsort(dim=-1) - -class SALSH(LSH): - def __init__(self, n_rounds, dim, r, device='cuda'): - super(SALSH, self).__init__() - self.alpha = torch.normal(0, 1, (dim, n_rounds), device=device) - self.beta = uniform(0, r, shape=(1, n_rounds), device=device) - self.dim = dim - self.r = r - - def __call__(self, vecs): - projection = vecs @ self.alpha - projection_shift = projection + self.beta - projection_rescale = projection_shift / self.r - return projection_rescale.permute(2, 0, 1) - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - - variance = scale / denom - - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def batch_scatter(output, src, dim, index): - """ - :param output: [b,n,c] - :param src: [b,n,c] - :param dim: int - :param index: [b,n] - :return: output: [b,n,c] - """ - b,k,c = src.shape - index = index[:, :, None].expand(-1, -1, c) - output, src, index = map(lambda t: rearrange(t, 'b k c -> (b c) k'), (output, src, index)) - output.scatter_(dim,index,src) - output = rearrange(output, '(b c) k -> b k c', b=b) - return output - -def batch_gather(x, index, dim): - """ - :param x: [b,n,c] - :param index: [b,n//2] - :param dim: int - :return: output: [b,n//2,c] - """ - b,n,c = x.shape - index = index[:,:,None].expand(-1,-1,c) - x, index = map(lambda t: rearrange(t, 'b n c -> (b c) n'), (x, index)) - output = torch.gather(x,dim,index) - output = rearrange(output, '(b c) n -> b n c', b=b) - return output - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class SAH_MSA(nn.Module): - def __init__(self, heads=4, n_rounds=2, channels=64, patch_size=144, - r=1): - super(SAH_MSA, self).__init__() - self.heads = heads - self.n_rounds = n_rounds - inner_dim = channels*3 - self.to_q = nn.Linear(channels, inner_dim, bias=False) - self.to_k = nn.Linear(channels, inner_dim, bias=False) - self.to_v = nn.Linear(channels, inner_dim, bias=False) - self.to_out = nn.Linear(inner_dim, channels, bias=False) - - self.xbox_plus = XBOXPLUS() - self.clustering_params = { - 'r': r, - 'n_rounds': self.n_rounds - } - self.q_attn_size = patch_size[0] * patch_size[1] - self.k_attn_size = patch_size[0] * patch_size[1] - - def forward(self, input): - """ - :param input: [b,n,c] - :return: output: [b,n,c] - """ - - B, N, C_inp = input.shape - query = self.to_q(input) - key = self.to_k(input) - value = self.to_v(input) - input_hash = input.view(B, N, self.heads, C_inp//self.heads) - x_hash = rearrange(input_hash, 'b t h e -> (b h) t e') - bs, x_seqlen, dim = x_hash.shape - with torch.no_grad(): - self.xbox_plus.set_norms(x_hash) - Xs = self.xbox_plus.X(x_hash) - x_positions = lsh_clustering(Xs, **self.clustering_params) - x_positions = x_positions.reshape(self.n_rounds, bs, -1) - - del Xs - - C = query.shape[-1] - query = query.view(B, N, self.heads, C // self.heads) - key = key.view(B, N, self.heads, C // self.heads) - value = value.view(B, N, self.heads, C // self.heads) - - query = rearrange(query, 'b t h e -> (b h) t e') # [bs, q_seqlen,c] - key = rearrange(key, 'b t h e -> (b h) t e') - value = rearrange(value, 'b s h d -> (b h) s d') - - bs, q_seqlen, dim = query.shape - bs, k_seqlen, dim = key.shape - v_dim = value.shape[-1] - - x_rev_positions = torch.argsort(x_positions, dim=-1) - x_offset = torch.arange(bs, device=query.device).unsqueeze(-1) * x_seqlen - x_flat = (x_positions + x_offset).reshape(-1) - - s_queries = query.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.q_attn_size, dim) - s_keys = key.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, dim) - s_values = value.reshape(-1, v_dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, v_dim) - - inner = s_queries @ s_keys.transpose(2, 1) - norm_factor = 1 - inner = inner / norm_factor - - # free memory - del x_positions - - # softmax denominator - dots_logsumexp = torch.logsumexp(inner, dim=-1, keepdim=True) - # softmax - dots = torch.exp(inner - dots_logsumexp) - # dropout - - # n_rounds outs - bo = (dots @ s_values).reshape(self.n_rounds, bs, q_seqlen, -1) - - # undo sort - x_offset = torch.arange(bs * self.n_rounds, device=query.device).unsqueeze(-1) * x_seqlen - x_rev_flat = (x_rev_positions.reshape(-1, x_seqlen) + x_offset).reshape(-1) - o = bo.reshape(-1, v_dim).index_select(0, x_rev_flat).reshape(self.n_rounds, bs, q_seqlen, -1) - - slogits = dots_logsumexp.reshape(self.n_rounds, bs, -1) - logits = torch.gather(slogits, 2, x_rev_positions) - - # free memory - del x_rev_positions - - # weighted sum multi-round attention - probs = torch.exp(logits - torch.logsumexp(logits, dim=0, keepdim=True)) - out = torch.sum(o * probs.unsqueeze(-1), dim=0) - out = rearrange(out, '(b h) t d -> b t h d', h=self.heads) - out = out.reshape(B, N, -1) - out = self.to_out(out) - - return out - -class SAHAB(nn.Module): - def __init__( - self, - dim, - patch_size=(16, 16), - heads=8, - shift_size=0, - sparse=False - ): - super().__init__() - self.blocks = nn.ModuleList([]) - self.attn = PreNorm(dim, SAH_MSA(heads=heads, n_rounds=2, r=1, channels=dim, patch_size=patch_size)) - self.ffn = PreNorm(dim, FeedForward(dim=dim)) - self.shift_size = shift_size - self.patch_size = patch_size - self.sparse = sparse - - def forward(self, x, mask=None): - """ - x: [b,h,w,c] - mask: [b,h,w] - return out: [b,h,w,c] - """ - b,h,w,c = x.shape - if self.shift_size > 0: - x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - mask = torch.roll(mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - w_size = self.patch_size - - # Split into large patches - x = rearrange(x, 'b (nh hh) (nw ww) c-> b (nh nw) (hh ww c)', hh=w_size[0] * 2, ww=w_size[1] * 2) - mask = rearrange(mask, 'b (nh hh) (nw ww) -> b (nh nw) (hh ww)', hh=w_size[0] * 2, ww=w_size[1] * 2) - N = x.shape[1] - - mask = torch.mean(mask,dim=2,keepdim=False) # [b,nh*nw] - if self.sparse: - mask_select = mask.topk(mask.shape[1] // 2, dim=1)[1] # [b,nh*nw//2] - x_select = batch_gather(x, mask_select, 1) # [b,nh*nw//2,hh*ww*c] - x_select = x_select.reshape(b*N//2,-1,c) - x_select = self.attn(x_select)+x_select - x_select = x_select.view(b,N//2,-1) - x = batch_scatter(x.clone(), x_select, 1, mask_select) - else: - x = x.view(b*N,-1,c) - x = self.attn(x) + x - x = x.view(b, N, -1) - x = rearrange(x, 'b (nh nw) (hh ww c) -> b (nh hh) (nw ww) c', nh=h//(w_size[0] * 2), hh=w_size[0] * 2, ww=w_size[1] * 2) - - if self.shift_size > 0: - x = torch.roll(x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - - x = self.ffn(x) + x - - return x - - -class SAHABs(nn.Module): - def __init__( - self, - dim, - patch_size=(8, 8), - heads=8, - num_blocks=2, - sparse=False - ): - super().__init__() - blocks = [] - for _ in range(num_blocks): - blocks.append( - SAHAB(heads=heads, dim=dim, patch_size=patch_size,sparse=sparse, - shift_size=0 if (_ % 2 == 0) else patch_size[0])) - self.blocks = nn.Sequential(*blocks) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - mask: [b,1,h,w] - return x: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - mask = mask.squeeze(1) - for block in self.blocks: - x = block(x, mask) - x = x.permute(0, 3, 1, 2) - return x - -class ASPPConv(nn.Sequential): - def __init__(self, in_channels, out_channels, dilation): - modules = [ - nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False), - nn.ReLU() - ] - super(ASPPConv, self).__init__(*modules) - - -class ASPPPooling(nn.Sequential): - def __init__(self, in_channels, out_channels): - super(ASPPPooling, self).__init__( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(in_channels, out_channels, 1, bias=False), - nn.ReLU()) - - def forward(self, x): - size = x.shape[-2:] - for mod in self: - x = mod(x) - return F.interpolate(x, size=size, mode='bilinear', align_corners=False) - - -class ASPP(nn.Module): - def __init__(self, in_channels, atrous_rates, out_channels): - super(ASPP, self).__init__() - modules = [] - - rates = tuple(atrous_rates) - for rate in rates: - modules.append(ASPPConv(in_channels, out_channels, rate)) - - modules.append(ASPPPooling(in_channels, out_channels)) - - self.convs = nn.ModuleList(modules) - - self.project = nn.Sequential( - nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False), - nn.ReLU(), - nn.Dropout(0.5)) - - def forward(self, x): - res = [] - for conv in self.convs: - res.append(conv(x)) - res = torch.cat(res, dim=1) - return self.project(res) - -class Sparsity_Estimator(nn.Module): - def __init__(self, dim=28, expand=2, sparse=False): - super(Sparsity_Estimator, self).__init__() - self.dim = dim - self.stage = 2 - self.sparse = sparse - - # Input projection - self.in_proj = nn.Conv2d(28, dim, 1, 1, 0, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(2): - self.encoder_layers.append(nn.ModuleList([ - nn.Conv2d(dim_stage, dim_stage * expand, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage * expand, dim_stage * expand, 3, 2, 1, bias=False, groups=dim_stage * expand), - nn.Conv2d(dim_stage * expand, dim_stage*expand, 1, 1, 0, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = ASPP(dim_stage, [3,6], dim_stage) - - # Decoder: - self.decoder_layers = nn.ModuleList([]) - for i in range(2): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage // 2, dim_stage, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage, dim_stage, 3, 1, 1, bias=False, groups=dim_stage), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, 0, bias=False), - ])) - dim_stage //= 2 - - # Output projection - if sparse: - self.out_conv2 = nn.Conv2d(self.dim, self.dim+1, 3, 1, 1, bias=False) - else: - self.out_conv2 = nn.Conv2d(self.dim, self.dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - # Input projection - fea = self.lrelu(self.in_proj(x)) - # Encoder - fea_encoder = [] # [c 2c 4c 8c] - for (Conv1, Conv2, Conv3) in self.encoder_layers: - fea_encoder.append(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - # Bottleneck - fea = self.bottleneck(fea)+fea - # Decoder - for i, (FeaUpSample, Conv1, Conv2, Conv3) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - fea = fea + fea_encoder[self.stage-1-i] - # Output projection - out = self.out_conv2(fea) - if self.sparse: - error_map = out[:,-1:,:,:] - return out[:,:-1], error_map - return out - -class CST(nn.Module): - def __init__(self, dim=28, stage=2, num_blocks=[2, 2, 2], sparse=False): - super(CST, self).__init__() - self.dim = dim - self.stage = stage - self.sparse = sparse - - # Fution physical mask and shifted measurement - self.fution = nn.Conv2d(28, 28, 1, 1, 0, bias=False) - - # Sparsity Estimator - if num_blocks==[2,4,6]: - self.fe = nn.Sequential(Sparsity_Estimator(dim=28,expand=2,sparse=False), - Sparsity_Estimator(dim=28, expand=2, sparse=sparse)) - else: - self.fe = Sparsity_Estimator(dim=28, expand=2, sparse=sparse) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - SAHABs(dim=dim_stage, num_blocks=num_blocks[i], heads=dim_stage // dim, sparse=sparse), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - nn.AvgPool2d(kernel_size=2, stride=2), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = SAHABs( - dim=dim_stage, heads=dim_stage // dim, num_blocks=num_blocks[-1], sparse=sparse) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - SAHABs(dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], - heads=(dim_stage // 2) // dim, sparse=sparse), - ])) - dim_stage //= 2 - - # Output projection - self.out_proj = nn.Conv2d(self.dim, dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :return: x: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None): - """ - x: [b,h,w] - return out:[b,c,h,w] - """ - - x = self.initial_x(x) - - # Feature Extraction - if self.sparse: - fea,mask = self.fe(x) - else: - fea = self.fe(x) - mask = torch.randn((b,1,h,w)).cuda() - - # Encoder - fea_encoder = [] - masks = [] - for (Blcok, FeaDownSample, MaskDownSample) in self.encoder_layers: - fea = Blcok(fea, mask) - masks.append(mask) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - mask = MaskDownSample(mask) - - # Bottleneck - fea = self.bottleneck(fea, mask) - - # Decoder - for i, (FeaUpSample, Blcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = fea + fea_encoder[self.stage - 1 - i] - mask = masks[self.stage - 1 - i] - fea = Blcok(fea, mask) - - # Output projection - out = self.out_proj(fea) + x - if self.sparse: - return out, mask - return out - - - - - - - - - - - - diff --git a/MST/real/train_code/architecture/DAUHST.py b/MST/real/train_code/architecture/DAUHST.py deleted file mode 100755 index 1880f8e..0000000 --- a/MST/real/train_code/architecture/DAUHST.py +++ /dev/null @@ -1,358 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch import einsum - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -class HS_MSA(nn.Module): - def __init__( - self, - dim, - window_size=(8, 8), - dim_head=28, - heads=8, - only_local_branch=False - ): - super().__init__() - - self.dim = dim - self.heads = heads - self.scale = dim_head ** -0.5 - self.window_size = window_size - self.only_local_branch = only_local_branch - - # position embedding - if only_local_branch: - seq_l = window_size[0] * window_size[1] - self.pos_emb = nn.Parameter(torch.Tensor(1, heads, seq_l, seq_l)) - trunc_normal_(self.pos_emb) - else: - seq_l1 = window_size[0] * window_size[1] - self.pos_emb1 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l1, seq_l1)) - h,w = 384//self.heads, 384//self.heads - seq_l2 = h*w//seq_l1 - self.pos_emb2 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l2, seq_l2)) - trunc_normal_(self.pos_emb1) - trunc_normal_(self.pos_emb2) - - inner_dim = dim_head * heads - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) - self.to_out = nn.Linear(inner_dim, dim) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x.shape - w_size = self.window_size - assert h % w_size[0] == 0 and w % w_size[1] == 0, 'fmap dimensions must be divisible by the window size' - if self.only_local_branch: - x_inp = rearrange(x, 'b (h b0) (w b1) c -> (b h w) (b0 b1) c', b0=w_size[0], b1=w_size[1]) - q = self.to_q(x_inp) - k, v = self.to_kv(x_inp).chunk(2, dim=-1) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), (q, k, v)) - q *= self.scale - sim = einsum('b h i d, b h j d -> b h i j', q, k) - sim = sim + self.pos_emb - attn = sim.softmax(dim=-1) - out = einsum('b h i j, b h j d -> b h i d', attn, v) - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - out = rearrange(out, '(b h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - else: - q = self.to_q(x) - k, v = self.to_kv(x).chunk(2, dim=-1) - q1, q2 = q[:,:,:,:c//2], q[:,:,:,c//2:] - k1, k2 = k[:,:,:,:c//2], k[:,:,:,c//2:] - v1, v2 = v[:,:,:,:c//2], v[:,:,:,c//2:] - - # local branch - q1, k1, v1 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q1, k1, v1)) - q1, k1, v1 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q1, k1, v1)) - q1 *= self.scale - sim1 = einsum('b n h i d, b n h j d -> b n h i j', q1, k1) - sim1 = sim1 + self.pos_emb1 - attn1 = sim1.softmax(dim=-1) - out1 = einsum('b n h i j, b n h j d -> b n h i d', attn1, v1) - out1 = rearrange(out1, 'b n h mm d -> b n mm (h d)') - - # non-local branch - q2, k2, v2 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q2, k2, v2)) - q2, k2, v2 = map(lambda t: t.permute(0, 2, 1, 3), (q2.clone(), k2.clone(), v2.clone())) - q2, k2, v2 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q2, k2, v2)) - q2 *= self.scale - sim2 = einsum('b n h i d, b n h j d -> b n h i j', q2, k2) - sim2 = sim2 + self.pos_emb2 - attn2 = sim2.softmax(dim=-1) - out2 = einsum('b n h i j, b n h j d -> b n h i d', attn2, v2) - out2 = rearrange(out2, 'b n h mm d -> b n mm (h d)') - out2 = out2.permute(0, 2, 1, 3) - - out = torch.cat([out1,out2],dim=-1).contiguous() - out = self.to_out(out) - out = rearrange(out, 'b (h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - return out - -class HSAB(nn.Module): - def __init__( - self, - dim, - window_size=(8, 8), - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - PreNorm(dim, HS_MSA(dim=dim, window_size=window_size, dim_head=dim_head, heads=heads, only_local_branch=(heads==1))), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class HST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, num_blocks=[1,1,1]): - super(HST, self).__init__() - self.dim = dim - self.scales = len(num_blocks) - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_scale = dim - for i in range(self.scales-1): - self.encoder_layers.append(nn.ModuleList([ - HSAB(dim=dim_scale, num_blocks=num_blocks[i], dim_head=dim, heads=dim_scale // dim), - nn.Conv2d(dim_scale, dim_scale * 2, 4, 2, 1, bias=False), - ])) - dim_scale *= 2 - - # Bottleneck - self.bottleneck = HSAB(dim=dim_scale, dim_head=dim, heads=dim_scale // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(self.scales-1): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_scale, dim_scale // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_scale, dim_scale // 2, 1, 1, bias=False), - HSAB(dim=dim_scale // 2, num_blocks=num_blocks[self.scales - 2 - i], dim_head=dim, - heads=(dim_scale // 2) // dim), - ])) - dim_scale //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - b, c, h_inp, w_inp = x.shape - hb, wb = 16, 16 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - - # Embedding - fea = self.embedding(x) - x = x[:,:28,:,:] - - # Encoder - fea_encoder = [] - for (HSAB, FeaDownSample) in self.encoder_layers: - fea = HSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, HSAB) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.scales-2-i]], dim=1)) - fea = HSAB(fea) - - # Mapping - out = self.mapping(fea) + x - return out[:, :, :h_inp, :w_inp] - -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class HyPaNet(nn.Module): - def __init__(self, in_nc=29, out_nc=8, channel=64): - super(HyPaNet, self).__init__() - self.fution = nn.Conv2d(in_nc, channel, 1, 1, 0, bias=True) - self.down_sample = nn.Conv2d(channel, channel, 3, 2, 1, bias=True) - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.mlp = nn.Sequential( - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, out_nc, 1, padding=0, bias=True), - nn.Softplus()) - self.relu = nn.ReLU(inplace=True) - self.out_nc = out_nc - - def forward(self, x): - x = self.down_sample(self.relu(self.fution(x))) - x = self.avg_pool(x) - x = self.mlp(x) + 1e-6 - return x[:,:self.out_nc//2,:,:], x[:,self.out_nc//2:,:,:] - -class DAUHST(nn.Module): - - def __init__(self, num_iterations=1): - super(DAUHST, self).__init__() - self.para_estimator = HyPaNet(in_nc=28, out_nc=num_iterations*2) - self.fution = nn.Conv2d(28, 28, 1, padding=0, bias=True) - self.num_iterations = num_iterations - self.denoisers = nn.ModuleList([]) - for _ in range(num_iterations): - self.denoisers.append( - HST(in_dim=28, out_dim=28, dim=28, num_blocks=[1,1,1]), - ) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,310] - :param Phi_PhiT: [b,256,310] - :return: z_crop: [b,28,256,256] - """ - x = self.initial_x(x) - for i in range(self.num_iterations): - x = self.denoisers[i](x) - return x[:, :, :, 0:384] - diff --git a/MST/real/train_code/architecture/DGSMP.py b/MST/real/train_code/architecture/DGSMP.py deleted file mode 100755 index 026fefa..0000000 --- a/MST/real/train_code/architecture/DGSMP.py +++ /dev/null @@ -1,322 +0,0 @@ -import torch -import torch.nn as nn -from torch.nn.parameter import Parameter -import torch.nn.functional as F - - -class Resblock(nn.Module): - def __init__(self, HBW): - super(Resblock, self).__init__() - self.block1 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1)) - self.block2 = nn.Sequential(nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(HBW, HBW, kernel_size=3, stride=1, padding=1)) - - def forward(self, x): - tem = x - r1 = self.block1(x) - out = r1 + tem - r2 = self.block2(out) - out = r2 + out - return out - -class Encoding(nn.Module): - def __init__(self): - super(Encoding, self).__init__() - self.E1 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E2 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E3 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E4 = nn.Sequential(nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.E5 = nn.Sequential(nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - - def forward(self, x): - ## encoding blocks - E1 = self.E1(x) - E2 = self.E2(F.avg_pool2d(E1, kernel_size=2, stride=2)) - E3 = self.E3(F.avg_pool2d(E2, kernel_size=2, stride=2)) - E4 = self.E4(F.avg_pool2d(E3, kernel_size=2, stride=2)) - E5 = self.E5(F.avg_pool2d(E4, kernel_size=2, stride=2)) - return E1, E2, E3, E4, E5 - -class Decoding(nn.Module): - def __init__(self, Ch=28, kernel_size=[7,7,7]): - super(Decoding, self).__init__() - self.upMode = 'bilinear' - self.Ch = Ch - out_channel1 = Ch * kernel_size[0] - out_channel2 = Ch * kernel_size[1] - out_channel3 = Ch * kernel_size[2] - self.D1 = nn.Sequential(nn.Conv2d(in_channels=128+128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.D2 = nn.Sequential(nn.Conv2d(in_channels=128+64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.D3 = nn.Sequential(nn.Conv2d(in_channels=64+64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - self.D4 = nn.Sequential(nn.Conv2d(in_channels=64+32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU() - ) - - self.w_generator = nn.Sequential(nn.Conv2d(in_channels=32, out_channels=32, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=32, out_channels=self.Ch, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(in_channels=self.Ch, out_channels=self.Ch, kernel_size=1, stride=1, padding=0) - ) - - self.filter_g_1 = nn.Sequential(nn.Conv2d(64 + 32, out_channel1, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel1, out_channel1, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel1, out_channel1, 1, 1, 0) - ) - - self.filter_g_2 = nn.Sequential(nn.Conv2d(64 + 32, out_channel2, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel2, out_channel2, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel2, out_channel2, 1, 1, 0) - ) - - self.filter_g_3 = nn.Sequential(nn.Conv2d(64 + 32, out_channel3, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel3, out_channel3, kernel_size=3, stride=1, padding=1), - nn.ReLU(), - nn.Conv2d(out_channel3, out_channel3, 1, 1, 0) - ) - - - def forward(self, E1, E2, E3, E4, E5): - ## decoding blocks - D1 = self.D1(torch.cat([E4, F.interpolate(E5, scale_factor=2, mode=self.upMode)], dim=1)) - D2 = self.D2(torch.cat([E3, F.interpolate(D1, scale_factor=2, mode=self.upMode)], dim=1)) - D3 = self.D3(torch.cat([E2, F.interpolate(D2, scale_factor=2, mode=self.upMode)], dim=1)) - D4 = self.D4(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - - ## estimating the regularization parameters w - w = self.w_generator(D4) - - ## generate 3D filters - f1 = self.filter_g_1(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - f2 = self.filter_g_2(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - f3 = self.filter_g_3(torch.cat([E1, F.interpolate(D3, scale_factor=2, mode=self.upMode)], dim=1)) - return w, f1, f2, f3 - - -class HSI_CS(nn.Module): - def __init__(self, Ch, stages): - super(HSI_CS, self).__init__() - self.Ch = Ch - self.s = stages - self.filter_size = [7,7,7] ## 3D filter size - - ## The modules for learning the measurement matrix A and A^T - self.AT = nn.Sequential(nn.Conv2d(Ch, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(), - Resblock(64), Resblock(64), - nn.Conv2d(64, Ch, kernel_size=3, stride=1, padding=1), nn.LeakyReLU()) - self.A = nn.Sequential(nn.Conv2d(Ch, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(), - Resblock(64), Resblock(64), - nn.Conv2d(64, Ch, kernel_size=3, stride=1, padding=1), nn.LeakyReLU()) - - ## Encoding blocks - self.Encoding = Encoding() - - ## Decoding blocks - self.Decoding = Decoding(Ch=self.Ch, kernel_size=self.filter_size) - - ## Dense connection - self.conv = nn.Conv2d(Ch, 32, kernel_size=3, stride=1, padding=1) - self.Den_con1 = nn.Conv2d(32 , 32, kernel_size=1, stride=1, padding=0) - self.Den_con2 = nn.Conv2d(32 * 2, 32, kernel_size=1, stride=1, padding=0) - self.Den_con3 = nn.Conv2d(32 * 3, 32, kernel_size=1, stride=1, padding=0) - self.Den_con4 = nn.Conv2d(32 * 4, 32, kernel_size=1, stride=1, padding=0) - # self.Den_con5 = nn.Conv2d(32 * 5, 32, kernel_size=1, stride=1, padding=0) - # self.Den_con6 = nn.Conv2d(32 * 6, 32, kernel_size=1, stride=1, padding=0) - - - self.delta_0 = Parameter(torch.ones(1), requires_grad=True) - self.delta_1 = Parameter(torch.ones(1), requires_grad=True) - self.delta_2 = Parameter(torch.ones(1), requires_grad=True) - self.delta_3 = Parameter(torch.ones(1), requires_grad=True) - # self.delta_4 = Parameter(torch.ones(1), requires_grad=True) - # self.delta_5 = Parameter(torch.ones(1), requires_grad=True) - - self._initialize_weights() - torch.nn.init.normal_(self.delta_0, mean=0.1, std=0.01) - torch.nn.init.normal_(self.delta_1, mean=0.1, std=0.01) - torch.nn.init.normal_(self.delta_2, mean=0.1, std=0.01) - torch.nn.init.normal_(self.delta_3, mean=0.1, std=0.01) - # torch.nn.init.normal_(self.delta_4, mean=0.1, std=0.01) - # torch.nn.init.normal_(self.delta_5, mean=0.1, std=0.01) - - - def _initialize_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - nn.init.xavier_normal_(m.weight.data) - nn.init.constant_(m.bias.data, 0.0) - elif isinstance(m, nn.Linear): - nn.init.xavier_normal_(m.weight.data) - nn.init.constant_(m.bias.data, 0.0) - - - def Filtering_1(self, cube, core): - batch_size, bandwidth, height, width = cube.size() - cube_pad = F.pad(cube, [self.filter_size[0] // 2, self.filter_size[0] // 2, 0, 0], mode='replicate') - img_stack = [] - for i in range(self.filter_size[0]): - img_stack.append(cube_pad[:, :, :, i:i + width]) - img_stack = torch.stack(img_stack, dim=1) - out = torch.sum(core.mul_(img_stack), dim=1, keepdim=False) - return out - - def Filtering_2(self, cube, core): - batch_size, bandwidth, height, width = cube.size() - cube_pad = F.pad(cube, [0, 0, self.filter_size[1] // 2, self.filter_size[1] // 2], mode='replicate') - img_stack = [] - for i in range(self.filter_size[1]): - img_stack.append(cube_pad[:, :, i:i + height, :]) - img_stack = torch.stack(img_stack, dim=1) - out = torch.sum(core.mul_(img_stack), dim=1, keepdim=False) - return out - - def Filtering_3(self, cube, core): - batch_size, bandwidth, height, width = cube.size() - cube_pad = F.pad(cube.unsqueeze(0).unsqueeze(0), pad=(0, 0, 0, 0, self.filter_size[2] // 2, self.filter_size[2] // 2)).squeeze(0).squeeze(0) - img_stack = [] - for i in range(self.filter_size[2]): - img_stack.append(cube_pad[:, i:i + bandwidth, :, :]) - img_stack = torch.stack(img_stack, dim=1) - out = torch.sum(core.mul_(img_stack), dim=1, keepdim=False) - return out - - def recon(self, res1, res2, Xt, i): - if i == 0 : - delta = self.delta_0 - elif i == 1: - delta = self.delta_1 - elif i == 2: - delta = self.delta_2 - elif i == 3: - delta = self.delta_3 - # elif i == 4: - # delta = self.delta_4 - # elif i == 5: - # delta = self.delta_5 - - Xt = Xt - 2 * delta * (res1 + res2) - return Xt - - def y2x(self, y): - ## Spilt operator - sz = y.size() - if len(sz) == 3: - y = y.unsqueeze(1) - bs = sz[0] - sz = y.size() - x = torch.zeros([bs, 28, sz[2], sz[2]]).cuda() - for t in range(28): - temp = y[:, :, :, 0 + 2 * t : sz[2] + 2 * t] - - x[:, t, :, :] = temp.squeeze(1) - return x - - def x2y(self, x): - ## Shift and Sum operator - sz = x.size() - if len(sz) == 3: - x = x.unsqueeze(0).unsqueeze(0) - bs = 1 - else: - bs = sz[0] - sz = x.size() - y = torch.zeros([bs, sz[2], sz[2]+2*27]).cuda() - for t in range(28): - y[:, :, 0 + 2 * t : sz[2] + 2 * t] = x[:, t, :, :] + y[:, :, 0 + 2 * t : sz[2] + 2 * t] - return y - - def forward(self, y, input_mask=None): - ## The measurements y is split into a 3D data cube of size H × W × L to initialize x. - y = y / 28 * 2 - Xt = self.y2x(y) - feature_list = [] - - for i in range(0, self.s): - AXt = self.x2y(self.A(Xt)) # y = Ax - Res1 = self.AT(self.y2x(AXt - y)) # A^T * (Ax − y) - - fea = self.conv(Xt) - - if i == 0: - feature_list.append(fea) - fufea = self.Den_con1(fea) - elif i == 1: - feature_list.append(fea) - fufea = self.Den_con2(torch.cat(feature_list, 1)) - elif i == 2: - feature_list.append(fea) - fufea = self.Den_con3(torch.cat(feature_list, 1)) - elif i == 3: - feature_list.append(fea) - fufea = self.Den_con4(torch.cat(feature_list, 1)) - # elif i == 4: - # feature_list.append(fea) - # fufea = self.Den_con5(torch.cat(feature_list, 1)) - # elif i == 5: - # feature_list.append(fea) - # fufea = self.Den_con6(torch.cat(feature_list, 1)) - - E1, E2, E3, E4, E5 = self.Encoding(fufea) - W, f1, f2, f3 = self.Decoding(E1, E2, E3, E4, E5) - - batch_size, p, height, width = f1.size() - f1 = F.normalize(f1.view(batch_size, self.filter_size[0], self.Ch, height, width),dim=1) - batch_size, p, height, width = f2.size() - f2 = F.normalize(f2.view(batch_size, self.filter_size[1], self.Ch, height, width),dim=1) - batch_size, p, height, width = f3.size() - f3 = F.normalize(f3.view(batch_size, self.filter_size[2], self.Ch, height, width),dim=1) - - ## Estimating the local means U - u1 = self.Filtering_1(Xt, f1) - u2 = self.Filtering_2(u1, f2) - U = self.Filtering_3(u2, f3) - - ## w * (x − u) - Res2 = (Xt - U).mul(W) - - ## Reconstructing HSIs - Xt = self.recon(Res1, Res2, Xt, i) - - return Xt diff --git a/MST/real/train_code/architecture/GAP_Net.py b/MST/real/train_code/architecture/GAP_Net.py deleted file mode 100755 index 75b0b37..0000000 --- a/MST/real/train_code/architecture/GAP_Net.py +++ /dev/null @@ -1,189 +0,0 @@ -import torch.nn.functional as F -import torch -import torch.nn as nn - -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - x = self.d_conv(x) - return x - - -class Unet(nn.Module): - - def __init__(self, in_ch, out_ch): - super(Unet, self).__init__() - - self.dconv_down1 = double_conv(in_ch, 32) - self.dconv_down2 = double_conv(32, 64) - self.dconv_down3 = double_conv(64, 128) - - self.maxpool = nn.MaxPool2d(2) - self.upsample2 = nn.Sequential( - nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2), - # nn.Conv2d(64, 64, (1,2), padding=(0,1)), - nn.ReLU(inplace=True) - ) - self.upsample1 = nn.Sequential( - nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.dconv_up2 = double_conv(64 + 64, 64) - self.dconv_up1 = double_conv(32 + 32, 32) - - self.conv_last = nn.Conv2d(32, out_ch, 1) - self.afn_last = nn.Tanh() - - def forward(self, x): - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - inputs = x - conv1 = self.dconv_down1(x) - x = self.maxpool(conv1) - conv2 = self.dconv_down2(x) - x = self.maxpool(conv2) - conv3 = self.dconv_down3(x) - - x = self.upsample2(conv3) - x = torch.cat([x, conv2], dim=1) - - x = self.dconv_up2(x) - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - - x = self.dconv_up1(x) - - x = self.conv_last(x) - x = self.afn_last(x) - out = x + inputs - - return out[:, :, :h_inp, :w_inp] - - -class DoubleConv(nn.Module): - """(convolution => [BN] => ReLU) * 2""" - - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - if not mid_channels: - mid_channels = out_channels - self.double_conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(mid_channels), - nn.ReLU(inplace=True), - nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(out_channels), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - return self.double_conv(x) - -class GAP_net(nn.Module): - - def __init__(self): - super(GAP_net, self).__init__() - - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - - def forward(self, y, input_mask=None): - if input_mask==None: - Phi = torch.rand((1,28,256,310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask - x_list = [] - x = At(y, Phi) # v0=H^T y - ### 1-3 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet1(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet2(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet3(x) - x = shift_3d(x) - ### 4-6 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet4(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet5(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet6(x) - x = shift_3d(x) - # ### 7-9 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet7(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet8(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet9(x) - x = shift_3d(x) - return x[:, :, :, :384] \ No newline at end of file diff --git a/MST/real/train_code/architecture/HDNet.py b/MST/real/train_code/architecture/HDNet.py deleted file mode 100755 index 71dce8c..0000000 --- a/MST/real/train_code/architecture/HDNet.py +++ /dev/null @@ -1,390 +0,0 @@ -import torch -import torch.nn as nn -import math - -def default_conv(in_channels, out_channels, kernel_size, bias=True): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias) - -class MeanShift(nn.Conv2d): - def __init__( - self, rgb_range, - rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1): - - super(MeanShift, self).__init__(3, 3, kernel_size=1) - std = torch.Tensor(rgb_std) - self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1) - self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std - for p in self.parameters(): - p.requires_grad = False - -class BasicBlock(nn.Sequential): - def __init__( - self, conv, in_channels, out_channels, kernel_size, stride=1, bias=False, - bn=True, act=nn.ReLU(True)): - - m = [conv(in_channels, out_channels, kernel_size, bias=bias)] - if bn: - m.append(nn.BatchNorm2d(out_channels)) - if act is not None: - m.append(act) - - super(BasicBlock, self).__init__(*m) - -class ResBlock(nn.Module): - def __init__( - self, conv, n_feats, kernel_size, - bias=True, bn=False, act=nn.ReLU(True), res_scale=1): - - super(ResBlock, self).__init__() - m = [] - for i in range(2): - m.append(conv(n_feats, n_feats, kernel_size, bias=bias)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if i == 0: - m.append(act) - - self.body = nn.Sequential(*m) - self.res_scale = res_scale - - def forward(self, x): - res = self.body(x).mul(self.res_scale) # So res_scale is a scaler? just scale all elements in each feature's residual? Why? - res += x - - return res - -class Upsampler(nn.Sequential): - def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True): - - m = [] - if (scale & (scale - 1)) == 0: - for _ in range(int(math.log(scale, 2))): - m.append(conv(n_feats, 4 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(2)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - - elif scale == 3: - m.append(conv(n_feats, 9 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(3)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - else: - raise NotImplementedError - - super(Upsampler, self).__init__(*m) - -_NORM_BONE = False - -def constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def kaiming_init(module, - a=0, - mode='fan_out', - nonlinearity='relu', - bias=0, - distribution='normal'): - assert distribution in ['uniform', 'normal'] - if distribution == 'uniform': - nn.init.kaiming_uniform_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - -# depthwise-separable convolution (DSC) -class DSC(nn.Module): - - def __init__(self, nin: int) -> None: - super(DSC, self).__init__() - self.conv_dws = nn.Conv2d( - nin, nin, kernel_size=1, stride=1, padding=0, groups=nin - ) - self.bn_dws = nn.BatchNorm2d(nin, momentum=0.9) - self.relu_dws = nn.ReLU(inplace=False) - - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) - - self.conv_point = nn.Conv2d( - nin, 1, kernel_size=1, stride=1, padding=0, groups=1 - ) - self.bn_point = nn.BatchNorm2d(1, momentum=0.9) - self.relu_point = nn.ReLU(inplace=False) - - self.softmax = nn.Softmax(dim=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - out = self.conv_dws(x) - out = self.bn_dws(out) - out = self.relu_dws(out) - - out = self.maxpool(out) - - out = self.conv_point(out) - out = self.bn_point(out) - out = self.relu_point(out) - - m, n, p, q = out.shape - out = self.softmax(out.view(m, n, -1)) - out = out.view(m, n, p, q) - - out = out.expand(x.shape[0], x.shape[1], x.shape[2], x.shape[3]) - - out = torch.mul(out, x) - - out = out + x - - return out - -# Efficient Feature Fusion(EFF) -class EFF(nn.Module): - def __init__(self, nin: int, nout: int, num_splits: int) -> None: - super(EFF, self).__init__() - - assert nin % num_splits == 0 - - self.nin = nin - self.nout = nout - self.num_splits = num_splits - self.subspaces = nn.ModuleList( - [DSC(int(self.nin / self.num_splits)) for i in range(self.num_splits)] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - sub_feat = torch.chunk(x, self.num_splits, dim=1) - out = [] - for idx, l in enumerate(self.subspaces): - out.append(self.subspaces[idx](sub_feat[idx])) - out = torch.cat(out, dim=1) - - return out - - -# spatial-spectral domain attention learning(SDL) -class SDL_attention(nn.Module): - def __init__(self, inplanes, planes, kernel_size=1, stride=1): - super(SDL_attention, self).__init__() - - self.inplanes = inplanes - self.inter_planes = planes // 2 - self.planes = planes - self.kernel_size = kernel_size - self.stride = stride - self.padding = (kernel_size-1)//2 - - self.conv_q_right = nn.Conv2d(self.inplanes, 1, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_v_right = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_up = nn.Conv2d(self.inter_planes, self.planes, kernel_size=1, stride=1, padding=0, bias=False) - self.softmax_right = nn.Softmax(dim=2) - self.sigmoid = nn.Sigmoid() - - self.conv_q_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #g - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv_v_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #theta - self.softmax_left = nn.Softmax(dim=2) - - self.reset_parameters() - - def reset_parameters(self): - kaiming_init(self.conv_q_right, mode='fan_in') - kaiming_init(self.conv_v_right, mode='fan_in') - kaiming_init(self.conv_q_left, mode='fan_in') - kaiming_init(self.conv_v_left, mode='fan_in') - - self.conv_q_right.inited = True - self.conv_v_right.inited = True - self.conv_q_left.inited = True - self.conv_v_left.inited = True - # HR spatial attention - def spatial_attention(self, x): - input_x = self.conv_v_right(x) - batch, channel, height, width = input_x.size() - - input_x = input_x.view(batch, channel, height * width) - context_mask = self.conv_q_right(x) - context_mask = context_mask.view(batch, 1, height * width) - context_mask = self.softmax_right(context_mask) - - context = torch.matmul(input_x, context_mask.transpose(1,2)) - context = context.unsqueeze(-1) - context = self.conv_up(context) - - mask_ch = self.sigmoid(context) - - out = x * mask_ch - - return out - # HR spectral attention - def spectral_attention(self, x): - - g_x = self.conv_q_left(x) - batch, channel, height, width = g_x.size() - - avg_x = self.avg_pool(g_x) - batch, channel, avg_x_h, avg_x_w = avg_x.size() - - avg_x = avg_x.view(batch, channel, avg_x_h * avg_x_w).permute(0, 2, 1) - theta_x = self.conv_v_left(x).view(batch, self.inter_planes, height * width) - context = torch.matmul(avg_x, theta_x) - context = self.softmax_left(context) - context = context.view(batch, 1, height, width) - - mask_sp = self.sigmoid(context) - - out = x * mask_sp - - return out - - def forward(self, x): - context_spectral = self.spectral_attention(x) - context_spatial = self.spatial_attention(x) - out = context_spatial + context_spectral - return out - - -class HDNet(nn.Module): - - def __init__(self, in_ch=28, out_ch=28, conv=default_conv): - super(HDNet, self).__init__() - - n_resblocks = 16 - n_feats = 64 - kernel_size = 3 - act = nn.ReLU(True) - - self.fution = nn.Conv2d(28, 28, 3, 1, 1, bias=False) - - # define head module - m_head = [conv(in_ch, n_feats, kernel_size)] - - # define body module - m_body = [ - ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - ) for _ in range(n_resblocks) - ] - m_body.append(SDL_attention(inplanes = n_feats, planes = n_feats)) - m_body.append(EFF(nin=n_feats, nout=n_feats, num_splits=4)) - - for i in range(1, n_resblocks): - m_body.append(ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - )) - - m_body.append(conv(n_feats, n_feats, kernel_size)) - - m_tail = [conv(n_feats, out_ch, kernel_size)] - - self.head = nn.Sequential(*m_head) - self.body = nn.Sequential(*m_body) - self.tail = nn.Sequential(*m_tail) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None): - - x = self.initial_x(x) - - x = self.head(x) - - res = self.body(x) - res += x - - x = self.tail(res) - - return x - -# frequency domain learning(FDL) -class FDL(nn.Module): - def __init__(self, loss_weight=1.0, alpha=1.0, patch_factor=1, ave_spectrum=False, log_matrix=False, batch_matrix=False): - super(FDL, self).__init__() - self.loss_weight = loss_weight - self.alpha = alpha - self.patch_factor = patch_factor - self.ave_spectrum = ave_spectrum - self.log_matrix = log_matrix - self.batch_matrix = batch_matrix - - def tensor2freq(self, x): - patch_factor = self.patch_factor - _, _, h, w = x.shape - assert h % patch_factor == 0 and w % patch_factor == 0, ( - 'Patch factor should be divisible by image height and width') - patch_list = [] - patch_h = h // patch_factor - patch_w = w // patch_factor - for i in range(patch_factor): - for j in range(patch_factor): - patch_list.append(x[:, :, i * patch_h:(i + 1) * patch_h, j * patch_w:(j + 1) * patch_w]) - - y = torch.stack(patch_list, 1) - - return torch.rfft(y, 2, onesided=False, normalized=True) - - def loss_formulation(self, recon_freq, real_freq, matrix=None): - if matrix is not None: - weight_matrix = matrix.detach() - else: - matrix_tmp = (recon_freq - real_freq) ** 2 - matrix_tmp = torch.sqrt(matrix_tmp[..., 0] + matrix_tmp[..., 1]) ** self.alpha - if self.log_matrix: - matrix_tmp = torch.log(matrix_tmp + 1.0) - - if self.batch_matrix: - matrix_tmp = matrix_tmp / matrix_tmp.max() - else: - matrix_tmp = matrix_tmp / matrix_tmp.max(-1).values.max(-1).values[:, :, :, None, None] - - matrix_tmp[torch.isnan(matrix_tmp)] = 0.0 - matrix_tmp = torch.clamp(matrix_tmp, min=0.0, max=1.0) - weight_matrix = matrix_tmp.clone().detach() - - assert weight_matrix.min().item() >= 0 and weight_matrix.max().item() <= 1, ( - 'The values of spectrum weight matrix should be in the range [0, 1], ' - 'but got Min: %.10f Max: %.10f' % (weight_matrix.min().item(), weight_matrix.max().item())) - - tmp = (recon_freq - real_freq) ** 2 - freq_distance = tmp[..., 0] + tmp[..., 1] - - loss = weight_matrix * freq_distance - return torch.mean(loss) - - def forward(self, pred, target, matrix=None, **kwargs): - - pred_freq = self.tensor2freq(pred) - target_freq = self.tensor2freq(target) - - if self.ave_spectrum: - pred_freq = torch.mean(pred_freq, 0, keepdim=True) - target_freq = torch.mean(target_freq, 0, keepdim=True) - - return self.loss_formulation(pred_freq, target_freq, matrix) * self.loss_weight diff --git a/MST/real/train_code/architecture/Lambda_Net.py b/MST/real/train_code/architecture/Lambda_Net.py deleted file mode 100755 index f878177..0000000 --- a/MST/real/train_code/architecture/Lambda_Net.py +++ /dev/null @@ -1,180 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum - -class LambdaNetAttention(nn.Module): - def __init__( - self, - dim, - ): - super().__init__() - - self.dim = dim - self.to_q = nn.Linear(dim, dim//8, bias=False) - self.to_k = nn.Linear(dim, dim//8, bias=False) - self.to_v = nn.Linear(dim, dim, bias=False) - self.rescale = (dim//8)**-0.5 - self.gamma = nn.Parameter(torch.ones(1)) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0,2,3,1) - b, h, w, c = x.shape - - # Reshape to (B,N,C), where N = window_size[0]*window_size[1] is the length of sentence - x_inp = rearrange(x, 'b h w c -> b (h w) c') - - # produce query, key and value - q = self.to_q(x_inp) - k = self.to_k(x_inp) - v = self.to_v(x_inp) - - # attention - sim = einsum('b i d, b j d -> b i j', q, k)*self.rescale - attn = sim.softmax(dim=-1) - - # aggregate - out = einsum('b i j, b j d -> b i d', attn, v) - - # merge blocks back to original feature map - out = rearrange(out, 'b (h w) c -> b h w c', h=h, w=w) - out = self.gamma*out + x - return out.permute(0,3,1,2) - -class triple_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(triple_conv, self).__init__() - self.t_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.t_conv(x) - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.d_conv(x) - return x - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class Lambda_Net(nn.Module): - - def __init__(self, out_ch=28): - super(Lambda_Net, self).__init__() - self.conv_in = nn.Conv2d(1+28, 28, 3, padding=1) - - # encoder - self.conv_down1 = triple_conv(28, 32) - self.conv_down2 = triple_conv(32, 64) - self.conv_down3 = triple_conv(64, 128) - self.conv_down4 = triple_conv(128, 256) - self.conv_down5 = double_conv(256, 512) - self.conv_down6 = double_conv(512, 1024) - - self.maxpool = nn.MaxPool2d(2) - - # decoder - self.upsample5 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2) - self.upsample4 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2) - self.upsample3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2) - self.upsample2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2) - self.upsample1 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2) - - self.conv_up1 = triple_conv(32+32, 32) - self.conv_up2 = triple_conv(64+64, 64) - self.conv_up3 = triple_conv(128+128, 128) - self.conv_up4 = triple_conv(256+256, 256) - self.conv_up5 = double_conv(512+512, 512) - - # attention - self.attention = LambdaNetAttention(dim=128) - - self.conv_last1 = nn.Conv2d(32, 6, 3,1,1) - self.conv_last2 = nn.Conv2d(38, 32, 3,1,1) - self.conv_last3 = nn.Conv2d(32, 12, 3,1,1) - self.conv_last4 = nn.Conv2d(44, 32, 3,1,1) - self.conv_last5 = nn.Conv2d(32, out_ch, 1) - self.act = nn.ReLU() - - def forward(self, x, input_mask=None): - if input_mask == None: - input_mask = torch.zeros((1,28,256,310)).cuda() - x = x/28*2 - x = self.conv_in(torch.cat([x.unsqueeze(1), input_mask], dim=1)) - b, c, h_inp, w_inp = x.shape - hb, wb = 32, 32 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - res0 = x - conv1 = self.conv_down1(x) - x = self.maxpool(conv1) - conv2 = self.conv_down2(x) - x = self.maxpool(conv2) - conv3 = self.conv_down3(x) - x = self.maxpool(conv3) - conv4 = self.conv_down4(x) - x = self.maxpool(conv4) - conv5 = self.conv_down5(x) - x = self.maxpool(conv5) - conv6 = self.conv_down6(x) - - x = self.upsample5(conv6) - x = torch.cat([x, conv5], dim=1) - x = self.conv_up5(x) - - x = self.upsample4(x) - x = torch.cat([x, conv4], dim=1) - x = self.conv_up4(x) - - x = self.upsample3(x) - x = torch.cat([x, conv3], dim=1) - x = self.conv_up3(x) - x = self.attention(x) - - x = self.upsample2(x) - x = torch.cat([x, conv2], dim=1) - x = self.conv_up2(x) - - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - x = self.conv_up1(x) - - res1 = x - out1 = self.act(self.conv_last1(x)) - x = self.conv_last2(torch.cat([res1,out1],dim=1)) - - res2 = x - out2 = self.act(self.conv_last3(x)) - out3 = self.conv_last4(torch.cat([res2, out2], dim=1)) - - out = self.conv_last5(out3)+res0 - out = out[:, :, :h_inp, :w_inp] - - return shift_back_3d(out)[:, :, :, :384] - diff --git a/MST/real/train_code/architecture/MST.py b/MST/real/train_code/architecture/MST.py deleted file mode 100755 index e82b77c..0000000 --- a/MST/real/train_code/architecture/MST.py +++ /dev/null @@ -1,294 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.dim = dim - - def forward(self, x_in): - """ - x_in: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp)) - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - -class MST(nn.Module): - def __init__(self, dim=28, stage=3, num_blocks=[2,2,2]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(28, self.dim, 3, 1, 1, bias=False) - self.fution = nn.Conv2d(28, 28, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, 28, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None): - """ - x: [b,h,w] - return out:[b,c,h,w] - """ - - x = self.initial_x(x) - - # Embedding - fea = self.lrelu(self.embedding(x)) - - # Encoder - fea_encoder = [] - for (MSAB, FeaDownSample) in self.encoder_layers: - fea = MSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = LeWinBlcok(fea) - - # Mapping - out = self.mapping(fea) + x - return out - - - - - - - - - - - - - - - - - - - - - diff --git a/MST/real/train_code/architecture/MST_Plus_Plus.py b/MST/real/train_code/architecture/MST_Plus_Plus.py deleted file mode 100755 index 2b93eb1..0000000 --- a/MST/real/train_code/architecture/MST_Plus_Plus.py +++ /dev/null @@ -1,324 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.dim = dim - - def forward(self, x_in): - """ - x_in: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp)) - v = v - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class MST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2,4,4]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (MSAB, FeaDownSample) in self.encoder_layers: - fea = MSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = LeWinBlcok(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - -class MST_Plus_Plus(nn.Module): - def __init__(self, in_channels=3, out_channels=28, n_feat=28, stage=3): - super(MST_Plus_Plus, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2,bias=False) - modules_body = [MST(dim=n_feat, stage=2, num_blocks=[1,1,1]) for _ in range(stage)] - self.fution = nn.Conv2d(28, 28, 1, padding=0, bias=True) - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2,bias=False) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - x = self.initial_x(x) - - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] - - - - - - - - - - - - - - diff --git a/MST/real/train_code/architecture/TSA_Net.py b/MST/real/train_code/architecture/TSA_Net.py deleted file mode 100755 index 85a03d2..0000000 --- a/MST/real/train_code/architecture/TSA_Net.py +++ /dev/null @@ -1,350 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -_NORM_BONE = False - - -def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding=the_padding) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_planes) - if flag_norm: - return nn.Sequential(conv,norm,activation) if flag_norm_act else nn.Sequential(conv,activation,norm) - else: - return nn.Sequential(conv,activation) - -def conv1x1_block(in_planes, out_planes, flag_norm=False): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0,bias=False) - norm = nn.BatchNorm2d(out_planes) - return nn.Sequential(conv,norm) if flag_norm else conv - -def fully_block(in_dim, out_dim, flag_norm=False, flag_norm_act=True): - fc = nn.Linear(in_dim, out_dim) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_dim) - if flag_norm: - return nn.Sequential(fc,norm,activation) if flag_norm_act else nn.Sequential(fc,activation,norm) - else: - return nn.Sequential(fc,activation) - -class Res2Net(nn.Module): - def __init__(self, inChannel, uPlane, scale=4): - super(Res2Net, self).__init__() - self.uPlane = uPlane - self.scale = scale - - self.conv_init = nn.Conv2d(inChannel, uPlane * scale, kernel_size=1, bias=False) - self.bn_init = nn.BatchNorm2d(uPlane * scale) - - convs = [] - bns = [] - for i in range(self.scale - 1): - convs.append(nn.Conv2d(self.uPlane, self.uPlane, kernel_size=3, stride=1, padding=1, bias=False)) - bns.append(nn.BatchNorm2d(self.uPlane)) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - - self.conv_end = nn.Conv2d(uPlane * scale, inChannel, kernel_size=1, bias=False) - self.bn_end = nn.BatchNorm2d(inChannel) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - - out = self.conv_init(x) - out = self.bn_init(out) - out = self.relu(out) - - spx = torch.split(out, self.uPlane, 1) - for i in range(self.scale - 1): - if i == 0: - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.convs[i](sp) - sp = self.relu(self.bns[i](sp)) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - out = torch.cat((out, spx[self.scale - 1]), 1) - - out = self.conv_end(out) - out = self.bn_end(out) - return out - -_NORM_ATTN = True -_NORM_FC = False - - -class TSA_Transform(nn.Module): - """ Spectral-Spatial Self-Attention """ - - def __init__(self, uSpace, inChannel, outChannel, nHead, uAttn, mode=[0, 1], flag_mask=False, gamma_learn=False): - super(TSA_Transform, self).__init__() - ''' ------------------------------------------ - uSpace: - uHeight: the [-2] dim of the 3D tensor - uWidth: the [-1] dim of the 3D tensor - inChannel: - the number of Channel of the input tensor - outChannel: - the number of Channel of the output tensor - nHead: - the number of Head of the input tensor - uAttn: - uSpatial: the dim of the spatial features - uSpectral: the dim of the spectral features - mask: - The Spectral Smoothness Mask - {mode} and {gamma_learn} is just for variable selection - ------------------------------------------ ''' - - self.nHead = nHead - self.uAttn = uAttn - self.outChannel = outChannel - self.uSpatial = nn.Parameter(torch.tensor(float(uAttn[0])), requires_grad=False) - self.uSpectral = nn.Parameter(torch.tensor(float(uAttn[1])), requires_grad=False) - self.mask = nn.Parameter(Spectral_Mask(outChannel), requires_grad=False) if flag_mask else None - self.attn_scale = nn.Parameter(torch.tensor(1.1), requires_grad=False) if flag_mask else None - self.gamma = nn.Parameter(torch.tensor(1.0), requires_grad=gamma_learn) - - if sum(mode) > 0: - down_sample = [] - scale = 1 - cur_channel = outChannel - for i in range(sum(mode)): - scale *= 2 - down_sample.append(conv_block(cur_channel, 2 * cur_channel, 3, 2, 1, _NORM_ATTN)) - cur_channel = 2 * cur_channel - self.cur_channel = cur_channel - self.down_sample = nn.Sequential(*down_sample) - self.up_sample = nn.ConvTranspose2d(outChannel * scale, outChannel, scale, scale) - else: - self.down_sample = None - self.up_sample = None - - spec_dim = int(uSpace[0] / 4 - 3) * int(uSpace[1] / 4 - 3) - self.preproc = conv1x1_block(inChannel, outChannel, _NORM_ATTN) - self.query_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.query_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.query_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.key_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.key_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.key_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.value = conv1x1_block(outChannel, nHead * outChannel, _NORM_ATTN) - self.aggregation = nn.Linear(nHead * outChannel, outChannel) - - def forward(self, image): - feat = self.preproc(image) - feat_qx = self.query_x(feat, 'X') - feat_qy = self.query_y(feat, 'Y') - feat_qlambda = self.query_lambda(feat) - feat_kx = self.key_x(feat, 'X') - feat_ky = self.key_y(feat, 'Y') - feat_klambda = self.key_lambda(feat) - feat_value = self.value(feat) - - feat_qx = torch.cat(torch.split(feat_qx, 1, dim=1)).squeeze(dim=1) - feat_qy = torch.cat(torch.split(feat_qy, 1, dim=1)).squeeze(dim=1) - feat_kx = torch.cat(torch.split(feat_kx, 1, dim=1)).squeeze(dim=1) - feat_ky = torch.cat(torch.split(feat_ky, 1, dim=1)).squeeze(dim=1) - feat_qlambda = torch.cat(torch.split(feat_qlambda, self.uAttn[1], dim=-1)) - feat_klambda = torch.cat(torch.split(feat_klambda, self.uAttn[1], dim=-1)) - feat_value = torch.cat(torch.split(feat_value, self.outChannel, dim=1)) - - energy_x = torch.bmm(feat_qx, feat_kx.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_y = torch.bmm(feat_qy, feat_ky.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_lambda = torch.bmm(feat_qlambda, feat_klambda.permute(0, 2, 1)) / torch.sqrt(self.uSpectral) - - attn_x = F.softmax(energy_x, dim=-1) - attn_y = F.softmax(energy_y, dim=-1) - attn_lambda = F.softmax(energy_lambda, dim=-1) - if self.mask is not None: - attn_lambda = (attn_lambda + self.mask) / torch.sqrt(self.attn_scale) - - pro_feat = feat_value if self.down_sample is None else self.down_sample(feat_value) - batchhead, dim_c, dim_x, dim_y = pro_feat.size() - attn_x_repeat = attn_x.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_x, dim_x) - attn_y_repeat = attn_y.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_y, dim_y) - pro_feat = pro_feat.view(-1, dim_x, dim_y) - pro_feat = torch.bmm(pro_feat, attn_y_repeat.permute(0, 2, 1)) - pro_feat = torch.bmm(pro_feat.permute(0, 2, 1), attn_x_repeat.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, dim_c, dim_x, dim_y) - - if self.up_sample is not None: - pro_feat = self.up_sample(pro_feat) - _, _, dim_x, dim_y = pro_feat.size() - pro_feat = pro_feat.contiguous().view(batchhead, self.outChannel, -1).permute(0, 2, 1) - pro_feat = torch.bmm(pro_feat, attn_lambda.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, self.outChannel, dim_x, dim_y) - pro_feat = torch.cat(torch.split(pro_feat, int(batchhead / self.nHead), dim=0), dim=1).permute(0, 2, 3, 1) - pro_feat = self.aggregation(pro_feat).permute(0, 3, 1, 2) - out = self.gamma * pro_feat + feat - return out, (attn_x, attn_y, attn_lambda) - - -class Feature_Spatial(nn.Module): - """ Spatial Feature Generation Component """ - - def __init__(self, inChannel, nHead, shiftDim, outDim, mode): - super(Feature_Spatial, self).__init__() - kernel = [(1, 5), (3, 5)] - stride = [(1, 2), (2, 2)] - padding = [(0, 2), (1, 2)] - self.conv1 = conv_block(inChannel, nHead, kernel[mode[0]], stride[mode[0]], padding[mode[0]], _NORM_ATTN) - self.conv2 = conv_block(nHead, nHead, kernel[mode[1]], stride[mode[1]], padding[mode[1]], _NORM_ATTN) - self.fully = fully_block(shiftDim, outDim, _NORM_FC) - - def forward(self, image, direction): - if direction == 'Y': - image = image.permute(0, 1, 3, 2) - feat = self.conv1(image) - feat = self.conv2(feat) - # import pdb; pdb.set_trace() - feat = self.fully(feat) - return feat - - -class Feature_Spectral(nn.Module): - """ Spectral Feature Generation Component """ - - def __init__(self, inChannel, nHead, viewDim, outDim): - super(Feature_Spectral, self).__init__() - self.inChannel = inChannel - self.conv1 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.conv2 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.fully = fully_block(viewDim, int(nHead * outDim), _NORM_FC) - - def forward(self, image): - bs = image.size(0) - feat = self.conv1(image) - feat = self.conv2(feat) - feat = feat.view(bs, self.inChannel, -1) - feat = self.fully(feat) - return feat - - -def Spectral_Mask(dim_lambda): - '''After put the available data into the model, we use this mask to avoid outputting the estimation of itself.''' - orig = (np.cos(np.linspace(-1, 1, num=2 * dim_lambda - 1) * np.pi) + 1.0) / 2.0 - att = np.zeros((dim_lambda, dim_lambda)) - for i in range(dim_lambda): - att[i, :] = orig[dim_lambda - 1 - i:2 * dim_lambda - 1 - i] - AM_Mask = torch.from_numpy(att.astype(np.float32)).unsqueeze(0) - return AM_Mask - -class TSA_Net(nn.Module): - - def __init__(self, in_ch=28, out_ch=28): - super(TSA_Net, self).__init__() - - self.tconv_down1 = Encoder_Triblock(in_ch, 64, False) - self.tconv_down2 = Encoder_Triblock(64, 128, False) - self.tconv_down3 = Encoder_Triblock(128, 256) - self.tconv_down4 = Encoder_Triblock(256, 512) - - self.bottom1 = conv_block(512, 1024) - self.bottom2 = conv_block(1024, 1024) - - self.tconv_up4 = Decoder_Triblock(1024, 512) - self.tconv_up3 = Decoder_Triblock(512, 256) - self.transform3 = TSA_Transform((96, 96), 256, 256, 8, (64, 80), [0, 0]) - self.tconv_up2 = Decoder_Triblock(256, 128) - self.transform2 = TSA_Transform((192, 192), 128, 128, 8, (64, 40), [1, 0]) - self.tconv_up1 = Decoder_Triblock(128, 64) - self.transform1 = TSA_Transform((384, 384), 64, 28, 8, (48, 30), [1, 1], True) - - self.conv_last = nn.Conv2d(out_ch, out_ch, 1) - self.afn_last = nn.Sigmoid() - self.fution = nn.Conv2d(28, 28, 3, 1, 1, bias=False) - - def initial_x(self, y): - """ - :param y: [b,1,256,310] - :param Phi: [b,28,256,310] - :return: z: [b,28,256,310] - """ - nC, step = 28, 2 - bs, row, col = y.shape - x = torch.zeros(bs, nC, row, row).cuda().float() - for i in range(nC): - x[:, i, :, :] = y[:, :, step * i:step * i + col - (nC - 1) * step] - x = self.fution(x) - return x - - def forward(self, x, input_mask=None): - - x = self.initial_x(x) - # print(x.shape) - - enc1, enc1_pre = self.tconv_down1(x) - enc2, enc2_pre = self.tconv_down2(enc1) - enc3, enc3_pre = self.tconv_down3(enc2) - enc4, enc4_pre = self.tconv_down4(enc3) - # enc5,enc5_pre = self.tconv_down5(enc4) - - bottom = self.bottom1(enc4) - bottom = self.bottom2(bottom) - - # dec5 = self.tconv_up5(bottom,enc5_pre) - dec4 = self.tconv_up4(bottom, enc4_pre) - dec3 = self.tconv_up3(dec4, enc3_pre) - dec3, _ = self.transform3(dec3) - dec2 = self.tconv_up2(dec3, enc2_pre) - dec2, _ = self.transform2(dec2) - dec1 = self.tconv_up1(dec2, enc1_pre) - dec1, _ = self.transform1(dec1) - - dec1 = self.conv_last(dec1) - output = self.afn_last(dec1) - - return output - - -class Encoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Encoder_Triblock, self).__init__() - - self.layer1 = conv_block(inChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - if flag_res: - self.layer2 = Res2Net(outChannel, int(outChannel / 4)) - else: - self.layer2 = conv_block(outChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - - self.pool = nn.MaxPool2d(nPool) if flag_Pool else None - - def forward(self, x): - feat = self.layer1(x) - feat = self.layer2(feat) - - feat_pool = self.pool(feat) if self.pool is not None else feat - return feat_pool, feat - - -class Decoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Decoder_Triblock, self).__init__() - - self.layer1 = nn.Sequential( - nn.ConvTranspose2d(inChannel, outChannel, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - if flag_res: - self.layer2 = Res2Net(int(outChannel * 2), int(outChannel / 2)) - else: - self.layer2 = conv_block(outChannel * 2, outChannel * 2, nKernal, flag_norm=_NORM_BONE) - self.layer3 = conv_block(outChannel * 2, outChannel, nKernal, flag_norm=_NORM_BONE) - - def forward(self, feat_dec, feat_enc): - feat_dec = self.layer1(feat_dec) - diffY = feat_enc.size()[2] - feat_dec.size()[2] - diffX = feat_enc.size()[3] - feat_dec.size()[3] - if diffY != 0 or diffX != 0: - print('Padding for size mismatch ( Enc:', feat_enc.size(), 'Dec:', feat_dec.size(), ')') - feat_dec = F.pad(feat_dec, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) - feat = torch.cat([feat_dec, feat_enc], dim=1) - feat = self.layer2(feat) - feat = self.layer3(feat) - return feat \ No newline at end of file diff --git a/MST/real/train_code/architecture/__init__.py b/MST/real/train_code/architecture/__init__.py deleted file mode 100755 index b049a2b..0000000 --- a/MST/real/train_code/architecture/__init__.py +++ /dev/null @@ -1,67 +0,0 @@ -import torch -from .MST import MST -from .GAP_Net import GAP_net -from .ADMM_Net import ADMM_net -from .TSA_Net import TSA_Net -from .HDNet import HDNet, FDL -from .DGSMP import HSI_CS -from .BIRNAT import BIRNAT -from .MST_Plus_Plus import MST_Plus_Plus -from .Lambda_Net import Lambda_Net -from .CST import CST -from .DAUHST import DAUHST -from .BiSRNet import BiSRNet - -def model_generator(method, pretrained_model_path=None): - if method == 'mst_s': - model = MST(dim=28, stage=2, num_blocks=[2, 2, 2]).cuda() - elif method == 'mst_m': - model = MST(dim=28, stage=2, num_blocks=[2, 4, 4]).cuda() - elif method == 'mst_l': - model = MST(dim=28, stage=2, num_blocks=[4, 7, 5]).cuda() - elif method == 'gap_net': - model = GAP_net().cuda() - elif method == 'admm_net': - model = ADMM_net().cuda() - elif method == 'tsa_net': - model = TSA_Net().cuda() - elif method == 'hdnet': - model = HDNet().cuda() - fdl_loss = FDL(loss_weight=0.7, - alpha=2.0, - patch_factor=4, - ave_spectrum=True, - log_matrix=True, - batch_matrix=True, - ).cuda() - elif method == 'dgsmp': - model = HSI_CS(Ch=28, stages=4).cuda() - elif method == 'birnat': - model = BIRNAT().cuda() - elif method == 'mst_plus_plus': - model = MST_Plus_Plus(in_channels=28, out_channels=28, n_feat=28, stage=3).cuda() - elif method == 'lambda_net': - model = Lambda_Net(out_ch=28).cuda() - elif method == 'cst_s': - model = CST(num_blocks=[1, 1, 2], sparse=True).cuda() - elif method == 'cst_m': - model = CST(num_blocks=[2, 2, 2], sparse=True).cuda() - elif method == 'cst_l': - model = CST(num_blocks=[2, 4, 6], sparse=True).cuda() - elif method == 'cst_l_plus': - model = CST(num_blocks=[2, 4, 6], sparse=False).cuda() - elif method == 'bisrnet': - model = BiSRNet(in_channels=28, out_channels=28, n_feat=28, stage=1, num_blocks=[1,1,1]).cuda() - elif 'dauhst' in method: - num_iterations = int(method.split('_')[1][0]) - model = DAUHST(num_iterations=num_iterations).cuda() - else: - print(f'Method {method} is not defined !!!!') - if pretrained_model_path is not None: - print(f'load model from {pretrained_model_path}') - checkpoint = torch.load(pretrained_model_path) - 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a/MST/real/train_code/dataset.py +++ /dev/null @@ -1,96 +0,0 @@ -import torch.utils.data as tud -import random -import torch -import numpy as np -import scipy.io as sio - - -class dataset(tud.Dataset): - def __init__(self, opt, CAVE, KAIST): - super(dataset, self).__init__() - self.isTrain = opt.isTrain - self.size = opt.size - # self.path = opt.data_path - if self.isTrain == True: - self.num = opt.trainset_num - else: - self.num = opt.testset_num - self.CAVE = CAVE - self.KAIST = KAIST - ## load mask - data = sio.loadmat(opt.mask_path) - self.mask = data['mask'] - self.mask_3d = np.tile(self.mask[:, :, np.newaxis], (1, 1, 28)) - - def __getitem__(self, index): - if self.isTrain == True: - # index1 = 0; d=0 - index1 = random.randint(0, 29) - d = random.randint(0, 1) - if d == 0: - hsi = self.CAVE[:,:,:,index1] - else: - hsi = self.KAIST[:, :, :, index1] - else: - index1 = index - hsi = self.HSI[:, :, :, index1] - shape = np.shape(hsi) - - px = random.randint(0, shape[0] - self.size) - py = random.randint(0, shape[1] - self.size) - label = hsi[px:px + self.size:1, py:py + self.size:1, :] - # while np.max(label)==0: - # px = random.randint(0, shape[0] - self.size) - # py = random.randint(0, shape[1] - self.size) - # label = hsi[px:px + self.size:1, py:py + self.size:1, :] - # print(np.min(), np.max()) - - pxm = random.randint(0, 660 - self.size) - pym = random.randint(0, 660 - self.size) - mask_3d = self.mask_3d[pxm:pxm + self.size:1, pym:pym + self.size:1, :] - - mask_3d_shift = np.zeros((self.size, self.size + (28 - 1) * 2, 28)) - mask_3d_shift[:, 0:self.size, :] = mask_3d - for t in range(28): - mask_3d_shift[:, :, t] = np.roll(mask_3d_shift[:, :, t], 2 * t, axis=1) - mask_3d_shift_s = np.sum(mask_3d_shift ** 2, axis=2, keepdims=False) - mask_3d_shift_s[mask_3d_shift_s == 0] = 1 - - if self.isTrain == True: - - rotTimes = random.randint(0, 3) - vFlip = random.randint(0, 1) - hFlip = random.randint(0, 1) - - # Random rotation - for j in range(rotTimes): - label = np.rot90(label) - - # Random vertical Flip - for j in range(vFlip): - label = label[:, ::-1, :].copy() - - # Random horizontal Flip - for j in range(hFlip): - label = label[::-1, :, :].copy() - - temp = mask_3d * label - temp_shift = np.zeros((self.size, self.size + (28 - 1) * 2, 28)) - temp_shift[:, 0:self.size, :] = temp - for t in range(28): - temp_shift[:, :, t] = np.roll(temp_shift[:, :, t], 2 * t, axis=1) - meas = np.sum(temp_shift, axis=2) - input = meas / 28 * 2 * 1.2 - - QE, bit = 0.4, 2048 - input = np.random.binomial((input * bit / QE).astype(int), QE) - input = np.float32(input) / np.float32(bit) - - label = torch.FloatTensor(label.copy()).permute(2,0,1) - input = torch.FloatTensor(input.copy()) - mask_3d_shift = torch.FloatTensor(mask_3d_shift.copy()).permute(2,0,1) - mask_3d_shift_s = torch.FloatTensor(mask_3d_shift_s.copy()) - return input, label, mask_3d, mask_3d_shift, mask_3d_shift_s - - def __len__(self): - return self.num diff --git a/MST/real/train_code/option.py b/MST/real/train_code/option.py deleted file mode 100755 index 67f1ca6..0000000 --- a/MST/real/train_code/option.py +++ /dev/null @@ -1,53 +0,0 @@ -import argparse -import template - -parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox") -parser.add_argument('--template', default='mst', - help='You can set various templates in option.py') - -# Hardware specifications -parser.add_argument("--gpu_id", type=str, default='0') - -# Data specifications -parser.add_argument('--data_root', type=str, default='../../datasets/', help='dataset directory') -parser.add_argument('--data_path_CAVE', default='../../datasets/CAVE_512_28/', type=str, - help='path of data') -parser.add_argument('--data_path_KAIST', default='../../datasets/KAIST_CVPR2021/', type=str, - help='path of data') -parser.add_argument('--mask_path', default='../../datasets/TSA_real_data/mask.mat', type=str, - help='path of mask') - -# Saving specifications -parser.add_argument('--outf', type=str, default='./exp/mst_s/', help='saving_path') - -# Model specifications -parser.add_argument('--method', type=str, default='mst_s', help='method name') -parser.add_argument('--pretrained_model_path', type=str, default=None, help='pretrained model directory') -parser.add_argument("--input_setting", type=str, default='H', - help='the input measurement of the network: H, HM or Y') -parser.add_argument("--input_mask", type=str, default='Phi', - help='the input mask of the network: Phi, Phi_PhiPhiT, Mask or None') # Phi: shift_mask Mask: mask - - -# Training specifications -parser.add_argument("--size", default=384, type=int, help='cropped patch size') -parser.add_argument("--epoch_sam_num", default=5000, type=int, help='total number of trainset') -parser.add_argument("--seed", default=1, type=int, help='Random_seed') -parser.add_argument('--batch_size', type=int, default=1, help='the number of HSIs per batch') -parser.add_argument("--isTrain", default=True, type=bool, help='train or test') -parser.add_argument("--max_epoch", type=int, default=300, help='total epoch') -parser.add_argument("--scheduler", type=str, default='MultiStepLR', help='MultiStepLR or CosineAnnealingLR') -parser.add_argument("--milestones", type=int, default=[50,100,150,200,250], help='milestones for MultiStepLR') -parser.add_argument("--gamma", type=float, default=0.5, help='learning rate decay for MultiStepLR') -parser.add_argument("--learning_rate", type=float, default=0.0004) - -opt = parser.parse_args() -template.set_template(opt) - -opt.trainset_num = 20000 // ((opt.size // 96) ** 2) - -for arg in vars(opt): - if vars(opt)[arg] == 'True': - vars(opt)[arg] = True - elif vars(opt)[arg] == 'False': - vars(opt)[arg] = False \ No newline at end of file diff --git a/MST/real/train_code/option_s.py b/MST/real/train_code/option_s.py deleted file mode 100755 index c2532aa..0000000 --- a/MST/real/train_code/option_s.py +++ /dev/null @@ -1,52 +0,0 @@ -import argparse - -parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox") - -# Hardware specifications -parser.add_argument("--gpu_id", type=str, default='0') - -# Data specifications -parser.add_argument('--data_root', type=str, default='../../datasets/', help='dataset directory') -parser.add_argument('--data_path_CAVE', default='../../datasets/CAVE_512_28/', type=str, - help='path of data') -parser.add_argument('--data_path_KAIST', default='../../datasets/KAIST_CVPR2021/', type=str, - help='path of data') -parser.add_argument('--mask_path', default='../../datasets/TSA_real_data/mask.mat', type=str, - help='path of mask') - -# Saving specifications -parser.add_argument('--outf', type=str, default='./exp/bisrnet/', help='saving_path') - -# Model specifications -parser.add_argument('--method', type=str, default='bisrnet', help='method name') -parser.add_argument('--pretrained_model_path', type=str, default=None, help='pretrained model directory') -parser.add_argument("--input_setting", type=str, default='H', - help='the input measurement of the network: H, HM or Y') -parser.add_argument("--input_mask", type=str, default='Phi', - help='the input mask of the network: Phi, Phi_PhiPhiT, Mask or None') # Phi: shift_mask Mask: mask - - -# Training specifications -parser.add_argument("--size", default=96, type=int, help='cropped patch size') -parser.add_argument("--epoch_sam_num", default=5000, type=int, help='total number of trainset') -parser.add_argument("--seed", default=1, type=int, help='Random_seed') -parser.add_argument('--batch_size', type=int, default=8, help='the number of HSIs per batch') -parser.add_argument("--isTrain", default=True, type=bool, help='train or test') -parser.add_argument("--max_epoch", type=int, default=500, help='total epoch') -parser.add_argument("--scheduler", type=str, default='MultiStepLR', help='MultiStepLR or CosineAnnealingLR') -parser.add_argument("--milestones", type=int, default=[50,100,150,200,250], help='milestones for MultiStepLR') -parser.add_argument("--gamma", type=float, default=0.1, help='learning rate decay for MultiStepLR') -parser.add_argument("--learning_rate", type=float, default=0.0001) - -opt = parser.parse_args() -opt.input_setting = 'H' -opt.input_mask = 'Mask' -opt.scheduler = 'CosineAnnealingLR' - -opt.trainset_num = 20000 // ((opt.size // 96) ** 2) - -for arg in vars(opt): - if vars(opt)[arg] == 'True': - vars(opt)[arg] = True - elif vars(opt)[arg] == 'False': - vars(opt)[arg] = False diff --git a/MST/real/train_code/template.py b/MST/real/train_code/template.py deleted file mode 100755 index 0b9ffef..0000000 --- a/MST/real/train_code/template.py +++ /dev/null @@ -1,66 +0,0 @@ -def set_template(args): - # Set the templates here - if args.template.find('mst') >= 0: - args.input_setting = 'Y' - args.input_mask = None - - if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.milestones = range(30,args.max_epoch,30) - args.gamma = 0.9 - args.learning_rate = 1e-3 - - if args.template.find('dauhst') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.scheduler = 'CosineAnnealingLR' - args.max_epoch = 300 - - if args.template.find('tsa_net') >= 0: - args.input_setting = 'HM' - args.input_mask = None - - if args.template.find('hdnet') >= 0: - args.input_setting = 'H' - args.input_mask = None - - if args.template.find('dgsmp') >= 0: - args.input_setting = 'Y' - args.input_mask = None - args.batch_size = 2 - args.milestones = [args.max_epoch] # fix the learning rate during training, following the official implementation - args.learning_rate = 1e-4 - - if args.template.find('birnat') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - args.batch_size = 1 - args.max_epoch = 100 - args.milestones = [args.max_epoch] # fix the learning rate during training, following the official implementation - args.learning_rate = 1.5e-4 - - if args.template.find('mst_plus_plus') >= 0: - args.input_setting = 'Y' - args.input_mask = None - args.scheduler = 'CosineAnnealingLR' - - if args.template.find('cst') >= 0: - args.input_setting = 'Y' - args.input_mask = None - args.scheduler = 'CosineAnnealingLR' - args.max_epoch = 500 - - if args.template.find('dnu') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.batch_size = 2 - args.max_epoch = 150 - args.milestones = range(10,args.max_epoch,10) - args.gamma = 0.9 - args.learning_rate = 4e-4 - - if args.template.find('lambda_net') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - args.learning_rate = 1.5e-4 \ No newline at end of file diff --git a/MST/real/train_code/train.py b/MST/real/train_code/train.py deleted file mode 100755 index e4c4f8a..0000000 --- a/MST/real/train_code/train.py +++ /dev/null @@ -1,96 +0,0 @@ -from architecture import * -from utils import * -from dataset import dataset -import torch.utils.data as tud -import torch -import torch.nn.functional as F -import time -import datetime -from torch.autograd import Variable -import os -from option import opt - -os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" -os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id -torch.backends.cudnn.enabled = True -torch.backends.cudnn.benchmark = True -if not torch.cuda.is_available(): - raise Exception('NO GPU!') - -# load training data -CAVE = prepare_data_cave(opt.data_path_CAVE, 30) -KAIST = prepare_data_KAIST(opt.data_path_KAIST, 30) - -# saving path -date_time = str(datetime.datetime.now()) -date_time = time2file_name(date_time) -opt.outf = os.path.join(opt.outf, date_time) -if not os.path.exists(opt.outf): - os.makedirs(opt.outf) - -# model -if opt.method == 'hdnet': - model, FDL_loss = model_generator(opt.method, opt.pretrained_model_path) - model = model.cuda() - FDL_loss = FDL_loss.cuda() -else: - model = model_generator(opt.method, opt.pretrained_model_path).cuda() - -# optimizing -optimizer = torch.optim.Adam(model.parameters(), lr=opt.learning_rate, betas=(0.9, 0.999)) -if opt.scheduler == 'MultiStepLR': - scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.milestones, gamma=opt.gamma) -elif opt.scheduler == 'CosineAnnealingLR': - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.max_epoch, eta_min=1e-6) -criterion = nn.L1Loss() - -if __name__ == "__main__": - - print("Random Seed: ", opt.seed) - torch.manual_seed(opt.seed) - torch.cuda.manual_seed(opt.seed) - - ## pipline of training - for epoch in range(1, opt.max_epoch): - model.train() - Dataset = dataset(opt, CAVE, KAIST) - loader_train = tud.DataLoader(Dataset, num_workers=8, batch_size=opt.batch_size, shuffle=True) - - scheduler.step(epoch) - epoch_loss = 0 - - start_time = time.time() - for i, (input, label, Mask, Phi, Phi_s) in enumerate(loader_train): - input, label, Phi, Phi_s = Variable(input), Variable(label), Variable(Phi), Variable(Phi_s) - input, label, Phi, Phi_s = input.cuda(), label.cuda(), Phi.cuda(), Phi_s.cuda() - - input_mask = init_mask(Mask, Phi, Phi_s, opt.input_mask) - - if opt.method in ['cst_s', 'cst_m', 'cst_l']: - out, diff_pred = model(input, input_mask) - loss = criterion(out, label) - diff_gt = torch.mean(torch.abs(out.detach() - label),dim=1, keepdim=True) # [b,1,h,w] - loss_sparsity = F.mse_loss(diff_gt, diff_pred) - loss = loss + 2 * loss_sparsity - else: - out = model(input, input_mask) - # import pdb; pdb.set_trace() - # print(out.shape, label.shape) - loss = criterion(out, label) - - epoch_loss += loss.item() - - optimizer.zero_grad() - loss.backward() - - optimizer.step() - - if i % (1000) == 0: - print('%4d %4d / %4d loss = %.10f time = %s' % ( - epoch + 1, i, len(Dataset) // opt.batch_size, epoch_loss / ((i + 1) * opt.batch_size), - datetime.datetime.now())) - - - elapsed_time = time.time() - start_time - print('epcoh = %4d , loss = %.10f , time = %4.2f s' % (epoch + 1, epoch_loss / len(Dataset), elapsed_time)) - torch.save(model, os.path.join(opt.outf, 'model_%03d.pth' % (epoch + 1))) diff --git a/MST/real/train_code/train_s.py b/MST/real/train_code/train_s.py deleted file mode 100755 index 9215354..0000000 --- a/MST/real/train_code/train_s.py +++ /dev/null @@ -1,106 +0,0 @@ -from architecture import * -from utils import * -from dataset import dataset -import torch.utils.data as tud -import torch -import torch.nn.functional as F -import time -import datetime -from torch.autograd import Variable -import os -from option_s import opt - -from torch import optim -from torch.optim.lr_scheduler import MultiStepLR -from pdb import set_trace as stx - -os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" -os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id - -if not torch.cuda.is_available(): - raise Exception('NO GPU!') - -# load training data -CAVE = prepare_data_cave(opt.data_path_CAVE, 30) -KAIST = prepare_data_KAIST(opt.data_path_KAIST, 30) -# KAIST = None - -# saving path -date_time = str(datetime.datetime.now()) -date_time = time2file_name(date_time) -opt.outf = os.path.join(opt.outf, date_time) -if not os.path.exists(opt.outf): - os.makedirs(opt.outf) - -model_path = opt.outf -# model -model = model_generator(opt.method, opt.pretrained_model_path).cuda() - -#optimizing -optimizer = torch.optim.Adam(model.parameters(), lr=opt.learning_rate, betas=(0.9, 0.999)) -if opt.scheduler == 'MultiStepLR': - scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.milestones, gamma=opt.gamma) -elif opt.scheduler == 'CosineAnnealingLR': - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.max_epoch, eta_min=1e-6) - -criterion = nn.L1Loss() - - -if __name__ == "__main__": - - print("Random Seed: ", opt.seed) - torch.manual_seed(opt.seed) - torch.cuda.manual_seed(opt.seed) - - ## Initialize weight - for layer in model.modules(): - if isinstance(layer, nn.Conv2d): - nn.init.xavier_uniform_(layer.weight) - # nn.init.constant_(layer.bias, 0.0) - if isinstance(layer, nn.ConvTranspose2d): - nn.init.xavier_uniform_(layer.weight) - # nn.init.constant_(layer.bias, 0.0) - - logger = gen_log(model_path) - logger.info("Learning rate:{}, batch_size:{}.\n".format(opt.learning_rate, opt.batch_size)) - - ## pipline of training - for epoch in range(0, opt.max_epoch): - model.train() - - Dataset = dataset(opt, CAVE, KAIST) - # Dataset = dataset(opt, CAVE) - loader_train = tud.DataLoader(Dataset, num_workers=8, batch_size=opt.batch_size, shuffle=True) - - scheduler.step(epoch) - epoch_loss = 0 - - start_time = time.time() - for i, (input, label, Mask, Phi, Phi_s) in enumerate(loader_train): - input, label = Variable(input), Variable(label) - input, label = input.cuda(), label.cuda() - out = model(input) - - loss = criterion(out, label) - epoch_loss += loss.item() - optimizer.zero_grad() - loss.backward() - - optimizer.step() - - if i % (1000) == 0: - print('%4d %4d / %4d loss = %.10f time = %s' % ( - epoch + 1, i, len(Dataset) // opt.batch_size, epoch_loss / ((i + 1) * opt.batch_size), - datetime.datetime.now())) - - elapsed_time = time.time() - start_time - - logger.info("===> Epoch {} Complete: Avg. Loss: {:.6f} time: {:.2f} s". - format(epoch+1 , epoch_loss / len(Dataset), elapsed_time)) - - print('epcoh = %4d , loss = %.10f , time = %4.2f s' % (epoch + 1, epoch_loss / len(Dataset), elapsed_time)) - - - torch.save(model, os.path.join(opt.outf, 'model_%03d.pth' % (epoch + 1))) - - diff --git a/MST/real/train_code/utils.py b/MST/real/train_code/utils.py deleted file mode 100755 index 0bd9a60..0000000 --- a/MST/real/train_code/utils.py +++ /dev/null @@ -1,203 +0,0 @@ -import numpy as np -import scipy.io as sio -import os -import glob -import re -import torch -import torch.nn as nn -import math -import random -import logging - -def _as_floats(im1, im2): - float_type = np.result_type(im1.dtype, im2.dtype, np.float32) - im1 = np.asarray(im1, dtype=float_type) - im2 = np.asarray(im2, dtype=float_type) - return im1, im2 - - -def compare_mse(im1, im2): - im1, im2 = _as_floats(im1, im2) - return np.mean(np.square(im1 - im2), dtype=np.float64) - - -def compare_psnr(im_true, im_test, data_range=None): - im_true, im_test = _as_floats(im_true, im_test) - - err = compare_mse(im_true, im_test) - return 10 * np.log10((data_range ** 2) / err) - - -def psnr(img1, img2): - mse = np.mean((img1/255. - img2/255.) ** 2) - if mse < 1.0e-10: - return 100 - PIXEL_MAX = 1 - return 20 * math.log10(PIXEL_MAX / math.sqrt(mse)) - - -def PSNR_GPU(im_true, im_fake): - im_true *= 255 - im_fake *= 255 - im_true = im_true.round() - im_fake = im_fake.round() - data_range = 255 - esp = 1e-12 - C = im_true.size()[0] - H = im_true.size()[1] - W = im_true.size()[2] - Itrue = im_true.clone() - Ifake = im_fake.clone() - mse = nn.MSELoss(reduce=False) - err = mse(Itrue, Ifake).sum() / (C*H*W) - psnr = 10. * np.log((data_range**2)/(err.data + esp)) / np.log(10.) - return psnr - - -def PSNR_Nssr(im_true, im_fake): - mse = ((im_true - im_fake)**2).mean() - psnr = 10. * np.log10(1/mse) - return psnr - - -def dataparallel(model, ngpus, gpu0=0): - if ngpus==0: - assert False, "only support gpu mode" - gpu_list = list(range(gpu0, gpu0+ngpus)) - assert torch.cuda.device_count() >= gpu0 + ngpus - if ngpus > 1: - if not isinstance(model, torch.nn.DataParallel): - model = torch.nn.DataParallel(model, gpu_list).cuda() - else: - - model = model.cuda() - elif ngpus == 1: - model = model.cuda() - return model - - -def findLastCheckpoint(save_dir): - file_list = glob.glob(os.path.join(save_dir, 'model_*.pth')) - if file_list: - epochs_exist = [] - for file_ in file_list: - result = re.findall(".*model_(.*).pth.*", file_) - epochs_exist.append(int(result[0])) - initial_epoch = max(epochs_exist) - else: - initial_epoch = 0 - return initial_epoch - -# load HSIs -def prepare_data(path, file_num): - HR_HSI = np.zeros((((512,512,28,file_num)))) - for idx in range(file_num): - # read HrHSI - path1 = os.path.join(path) + 'scene%02d.mat' % (idx+1) - # path1 = os.path.join(path) + HR_code + '.mat' - data = sio.loadmat(path1) - HR_HSI[:,:,:,idx] = data['data_slice'] / 65535.0 - HR_HSI[HR_HSI < 0.] = 0. - HR_HSI[HR_HSI > 1.] = 1. - return HR_HSI - - -def loadpath(pathlistfile): - fp = open(pathlistfile) - pathlist = fp.read().splitlines() - fp.close() - random.shuffle(pathlist) - return pathlist - -def time2file_name(time): - year = time[0:4] - month = time[5:7] - day = time[8:10] - hour = time[11:13] - minute = time[14:16] - second = time[17:19] - time_filename = year + '_' + month + '_' + day + '_' + hour + '_' + minute + '_' + second - return time_filename - -# def prepare_data_cave(path, file_list, file_num): -# HR_HSI = np.zeros((((512,512,28,file_num)))) -# for idx in range(file_num): -# #### read HrHSI -# HR_code = file_list[idx] -# path1 = os.path.join(path) + HR_code + '.mat' -# data = sio.loadmat(path1) -# HR_HSI[:,:,:,idx] = data['data_slice'] / 65535.0 -# HR_HSI[HR_HSI < 0] = 0 -# HR_HSI[HR_HSI > 1] = 1 -# return HR_HSI -# -# def prepare_data_KASIT(path, file_list, file_num): -# HR_HSI = np.zeros((((2704,3376,28,file_num)))) -# for idx in range(file_num): -# #### read HrHSI -# HR_code = file_list[idx] -# path1 = os.path.join(path) + HR_code + '.mat' -# data = sio.loadmat(path1) -# HR_HSI[:,:,:,idx] = data['HSI'] -# HR_HSI[HR_HSI < 0] = 0 -# HR_HSI[HR_HSI > 1] = 1 -# return HR_HSI - -def prepare_data_cave(path, file_num): - HR_HSI = np.zeros((((512,512,28,file_num)))) - file_list = os.listdir(path) - # for idx in range(1): - for idx in range(file_num): - print(f'loading CAVE {idx}') - #### read HrHSI - HR_code = file_list[idx] - path1 = os.path.join(path) + HR_code - data = sio.loadmat(path1) - HR_HSI[:,:,:,idx] = data['data_slice'] / 65535.0 - HR_HSI[HR_HSI < 0] = 0 - HR_HSI[HR_HSI > 1] = 1 - return HR_HSI - -def prepare_data_KAIST(path, file_num): - HR_HSI = np.zeros((((2704,3376,28,file_num)))) - file_list = os.listdir(path) - # for idx in range(1): - for idx in range(file_num): - print(f'loading KAIST {idx}') - #### read HrHSI - HR_code = file_list[idx] - path1 = os.path.join(path) + HR_code - data = sio.loadmat(path1) - HR_HSI[:,:,:,idx] = data['HSI'] - HR_HSI[HR_HSI < 0] = 0 - HR_HSI[HR_HSI > 1] = 1 - return HR_HSI - -def init_mask(mask, Phi, Phi_s, mask_type): - if mask_type == 'Phi': - input_mask = Phi - elif mask_type == 'Phi_PhiPhiT': - input_mask = (Phi, Phi_s) - elif mask_type == 'Mask': - input_mask = mask - elif mask_type == None: - input_mask = None - return input_mask - -def gen_log(model_path): - logger = logging.getLogger() - logger.setLevel(logging.INFO) - formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s") - - log_file = model_path + '/log.txt' - fh = logging.FileHandler(log_file, mode='a') - fh.setLevel(logging.INFO) - fh.setFormatter(formatter) - - ch = logging.StreamHandler() - ch.setLevel(logging.INFO) - ch.setFormatter(formatter) - - logger.addHandler(fh) - logger.addHandler(ch) - return logger diff --git a/MST/requirements.txt b/MST/requirements.txt deleted file mode 100755 index b5086d0..0000000 --- a/MST/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -opencv-python==4.4.0.46 -einops -torchvision==0.7.0 -torch==1.6.0 -scipy==1.0.0 -fvcore diff --git a/MST/simulation/test_code/Quality_Metrics/CC.m b/MST/simulation/test_code/Quality_Metrics/CC.m deleted file mode 100755 index eee4d50..0000000 --- a/MST/simulation/test_code/Quality_Metrics/CC.m +++ /dev/null @@ -1,41 +0,0 @@ -function out = CC(ref,tar,mask) -%-------------------------------------------------------------------------- -% Cross Correlation -% -% USAGE -% out = CC(ref,tar,mask) -% -% INPUT -% ref : reference HS data (rows,cols,bands) -% tar : target HS data (rows,cols,bands) -% mask: binary mask (rows,cols) (optional) -% -% OUTPUT -% out : cross correlations (bands) -% -%-------------------------------------------------------------------------- - -if nargin==2 - [rows,cols,bands] = size(tar); - - out = zeros(1,bands); - for i = 1:bands - tar_tmp = tar(:,:,i); - ref_tmp = ref(:,:,i); - cc = corrcoef(tar_tmp(:),ref_tmp(:)); - out(1,i) = cc(1,2); - end - -else - [rows,cols,bands] = size(tar); - - out = zeros(1,bands); - mask = find(mask~=0); - for i = 1:bands - tar_tmp = tar(:,:,i); - ref_tmp = ref(:,:,i); - cc = corrcoef(tar_tmp(mask),ref_tmp(mask)); - out(1,i) = cc(1,2); - end -end - out=mean(out); diff --git a/MST/simulation/test_code/Quality_Metrics/Cal_quality_assessment.m b/MST/simulation/test_code/Quality_Metrics/Cal_quality_assessment.m deleted file mode 100755 index bb2aca7..0000000 --- a/MST/simulation/test_code/Quality_Metrics/Cal_quality_assessment.m +++ /dev/null @@ -1,36 +0,0 @@ -clear;clc; -res_path = 'results/Test_result.mat'; -load(res_path); - -psnr_total=0.0; -ssim_total=0.0; -psnr_list = zeros(10,1); -ssim_list = zeros(10,1); -for i=1:10 - Z = squeeze(pred(i,:,:,:)); - Z = double(Z); - S = squeeze(truth(i,:,:,:)); - S = double(S); - - Z(Z>1.0) = 1.0; - Z(Z<0.0) = 0.0; - - [psnr, rmse, ergas, sam, uiqi, ssim] = quality_assessment(double(im2uint8(S)), double(im2uint8(Z)), 0, 1); - - pred(1,i) = psnr; - pred(2,i) = rmse; - pred(3,i) = ergas; - pred(4,i) = sam; - pred(5,i) = uiqi; - pred(6,i) = ssim; - psnr_list(i,1) = psnr; - ssim_list(i,1) = ssim; - - psnr_total = psnr_total+psnr; - ssim_total = ssim_total+ssim; -end -psnr = mean(psnr_list); -ssim = mean(ssim_list); -fprintf('The PNSR=%f\n',psnr); -fprintf('The SSIM=%f\n',ssim); - diff --git a/MST/simulation/test_code/Quality_Metrics/SpectAngMapper.m b/MST/simulation/test_code/Quality_Metrics/SpectAngMapper.m deleted file mode 100755 index e440f25..0000000 --- a/MST/simulation/test_code/Quality_Metrics/SpectAngMapper.m +++ /dev/null @@ -1,29 +0,0 @@ -function sam = SpectAngMapper(imagery1, imagery2) - -%========================================================================== -% Evaluates the mean Spectral Angle Mapper (SAM)[1] for two MSIs. -% -% Syntax: -% [psnr, ssim, fsim, ergas, msam ] = MSIQA(imagery1, imagery2) -% -% Input: -% imagery1 - the reference MSI data array -% imagery2 - the target MSI data array -% NOTE: MSI data array is a M*N*K array for imagery with M*N spatial -% pixels, K bands and DYNAMIC RANGE [0, 255]. If imagery1 and imagery2 -% have different size, the larger one will be truncated to fit the -% smaller one. -% -% [1] R. YUHAS, J. BOARDMAN, and A. GOETZ, "Determination of semi-arid -% landscape endmembers and seasonal trends using convex geometry -% spectral unmixing techniques", JPL, Summaries of the 4 th Annual JPL -% Airborne Geoscience Workshop. 1993. -% -% See also StructureSIM, FeatureSIM and ErrRelGlobAdimSyn -% -% by Yi Peng -%========================================================================== - -tmp = (sum(imagery1.*imagery2, 3) + eps) ... - ./ (sqrt(sum(imagery1.^2, 3)) + eps) ./ (sqrt(sum(imagery2.^2, 3)) + eps); -sam = mean2(real(acos(tmp))); \ No newline at end of file diff --git a/MST/simulation/test_code/Quality_Metrics/cal_ssim.m b/MST/simulation/test_code/Quality_Metrics/cal_ssim.m deleted file mode 100755 index 8d844b7..0000000 --- a/MST/simulation/test_code/Quality_Metrics/cal_ssim.m +++ /dev/null @@ -1,213 +0,0 @@ -function ssim = cal_ssim( im1, im2, b_row, b_col ) - -[h w ch] = size( im1 ); -ssim = 0; -if ch==1 - ssim = ssim_index( im1(b_row+1:h-b_row, b_col+1:w-b_col), im2( b_row+1:h-b_row, b_col+1:w-b_col) ); -else - for i = 1:ch - ssim = ssim + ssim_index( im1(b_row+1:h-b_row, b_col+1:w-b_col, i), im2( b_row+1:h-b_row, b_col+1:w-b_col, i) ); - end - ssim = ssim/ch; -end -return; - - - - -function [mssim, ssim_map] = ssim_index(img1, img2, K, window, L) - -%======================================================================== -%SSIM Index, Version 1.0 -%Copyright(c) 2003 Zhou Wang -%All Rights Reserved. -% -%The author was with Howard Hughes Medical Institute, and Laboratory -%for Computational Vision at Center for Neural Science and Courant -%Institute of Mathematical Sciences, New York University, USA. He is -%currently with Department of Electrical and Computer Engineering, -%University of Waterloo, Canada. -% -%---------------------------------------------------------------------- -%Permission to use, copy, or modify this software and its documentation -%for educational and research purposes only and without fee is hereby -%granted, provided that this copyright notice and the original authors' -%names appear on all copies and supporting documentation. This program -%shall not be used, rewritten, or adapted as the basis of a commercial -%software or hardware product without first obtaining permission of the -%authors. The authors make no representations about the suitability of -%this software for any purpose. It is provided "as is" without express -%or implied warranty. -%---------------------------------------------------------------------- -% -%This is an implementation of the algorithm for calculating the -%Structural SIMilarity (SSIM) index between two images. Please refer -%to the following paper: -% -%Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image -%quality assessment: From error measurement to structural similarity" -%IEEE Transactios on Image Processing, vol. 13, no. 4, Apr. 2004. -% -%Kindly report any suggestions or corrections to zhouwang@ieee.org -% -%---------------------------------------------------------------------- -% -%Input : (1) img1: the first image being compared -% (2) img2: the second image being compared -% (3) K: constants in the SSIM index formula (see the above -% reference). defualt value: K = [0.01 0.03] -% (4) window: local window for statistics (see the above -% reference). default widnow is Gaussian given by -% window = fspecial('gaussian', 11, 1.5); -% (5) L: dynamic range of the images. default: L = 255 -% -%Output: (1) mssim: the mean SSIM index value between 2 images. -% If one of the images being compared is regarded as -% perfect quality, then mssim can be considered as the -% quality measure of the other image. -% If img1 = img2, then mssim = 1. -% (2) ssim_map: the SSIM index map of the test image. The map -% has a smaller size than the input images. The actual size: -% size(img1) - size(window) + 1. -% -%Default Usage: -% Given 2 test images img1 and img2, whose dynamic range is 0-255 -% -% [mssim ssim_map] = ssim_index(img1, img2); -% -%Advanced Usage: -% User defined parameters. For example -% -% K = [0.05 0.05]; -% window = ones(8); -% L = 100; -% [mssim ssim_map] = ssim_index(img1, img2, K, window, L); -% -%See the results: -% -% mssim %Gives the mssim value -% imshow(max(0, ssim_map).^4) %Shows the SSIM index map -% -%======================================================================== - - -if (nargin < 2 | nargin > 5) - mssim = -Inf; - ssim_map = -Inf; - return; -end - -if (size(img1) ~= size(img2)) - mssim = -Inf; - ssim_map = -Inf; - return; -end - -[M N] = size(img1); - -if (nargin == 2) - if ((M < 11) | (N < 11)) - mssim = -Inf; - ssim_map = -Inf; - return - end - window = fspecial('gaussian', 11, 1.5); % - K(1) = 0.01; % default settings - K(2) = 0.03; % - L = 255; % -end - -if (nargin == 3) - if ((M < 11) | (N < 11)) - mssim = -Inf; - ssim_map = -Inf; - return - end - window = fspecial('gaussian', 11, 1.5); - L = 255; - if (length(K) == 2) - if (K(1) < 0 | K(2) < 0) - mssim = -Inf; - ssim_map = -Inf; - return; - end - else - mssim = -Inf; - ssim_map = -Inf; - return; - end -end - -if (nargin == 4) - [H W] = size(window); - if ((H*W) < 4 | (H > M) | (W > N)) - mssim = -Inf; - ssim_map = -Inf; - return - end - L = 255; - if (length(K) == 2) - if (K(1) < 0 | K(2) < 0) - mssim = -Inf; - ssim_map = -Inf; - return; - end - else - mssim = -Inf; - ssim_map = -Inf; - return; - end -end - -if (nargin == 5) - [H W] = size(window); - if ((H*W) < 4 | (H > M) | (W > N)) - mssim = -Inf; - ssim_map = -Inf; - return - end - if (length(K) == 2) - if (K(1) < 0 | K(2) < 0) - mssim = -Inf; - ssim_map = -Inf; - return; - end - else - mssim = -Inf; - ssim_map = -Inf; - return; - end -end - -C1 = (K(1)*L)^2; -C2 = (K(2)*L)^2; -window = window/sum(sum(window)); -img1 = double(img1); -img2 = double(img2); - -mu1 = filter2(window, img1, 'valid'); -mu2 = filter2(window, img2, 'valid'); -mu1_sq = mu1.*mu1; -mu2_sq = mu2.*mu2; -mu1_mu2 = mu1.*mu2; -sigma1_sq = filter2(window, img1.*img1, 'valid') - mu1_sq; -sigma2_sq = filter2(window, img2.*img2, 'valid') - mu2_sq; -sigma12 = filter2(window, img1.*img2, 'valid') - mu1_mu2; - -if (C1 > 0 & C2 > 0) - ssim_map = ((2*mu1_mu2 + C1).*(2*sigma12 + C2))./((mu1_sq + mu2_sq + C1).*(sigma1_sq + sigma2_sq + C2)); -else - numerator1 = 2*mu1_mu2 + C1; - numerator2 = 2*sigma12 + C2; - denominator1 = mu1_sq + mu2_sq + C1; - denominator2 = sigma1_sq + sigma2_sq + C2; - ssim_map = ones(size(mu1)); - index = (denominator1.*denominator2 > 0); - ssim_map(index) = (numerator1(index).*numerator2(index))./(denominator1(index).*denominator2(index)); - index = (denominator1 ~= 0) & (denominator2 == 0); - ssim_map(index) = numerator1(index)./denominator1(index); -end - -mssim = mean2(ssim_map); - -return \ No newline at end of file diff --git a/MST/simulation/test_code/Quality_Metrics/csnr.m b/MST/simulation/test_code/Quality_Metrics/csnr.m deleted file mode 100755 index 65ffe01..0000000 --- a/MST/simulation/test_code/Quality_Metrics/csnr.m +++ /dev/null @@ -1,22 +0,0 @@ -function s=csnr(A,B,row,col) - -[n,m,ch]=size(A); -summa = 0; -if ch==1 - e=A-B; - e=e(row+1:n-row,col+1:m-col); - me=mean(mean(e.^2)); - s=10*log10(255^2/me); -else - for i=1:ch - e=A-B; - e=e(row+1:n-row,col+1:m-col,i); - mse = mean(mean(e.^2)); - s = 10*log10(255^2/mse); - summa = summa + s; - end - s = summa/ch; -end - -return; - diff --git a/MST/simulation/test_code/Quality_Metrics/img_qi.m b/MST/simulation/test_code/Quality_Metrics/img_qi.m deleted file mode 100755 index 73eb0ec..0000000 --- a/MST/simulation/test_code/Quality_Metrics/img_qi.m +++ /dev/null @@ -1,118 +0,0 @@ -function [quality, quality_map] = img_qi(img1, img2, block_size) - -%======================================================================== -% -%Copyright (c) 2001 The University of Texas at Austin -%All Rights Reserved. -% -%This program is free software; you can redistribute it and/or modify -%it under the terms of the GNU General Public License as published by -%the Free Software Foundation; either version 2 of the License, or -%(at your option) any later version. -% -%This program is distributed in the hope that it will be useful, -%but WITHOUT ANY WARRANTY; without even the implied warranty of -%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the -%GNU General Public License for more details. -% -%The GNU Public License is available in the file LICENSE, or you -%can write to the Free Software Foundation, Inc., 59 Temple Place - -%Suite 330, Boston, MA 02111-1307, USA, or you can find it on the -%World Wide Web at http://www.fsf.org. -% -%Author : Zhou Wang -%Version : 1.0 -% -%The authors are with the Laboratory for Image and Video Engineering -%(LIVE), Department of Electrical and Computer Engineering, The -%University of Texas at Austin, Austin, TX. -% -%Kindly report any suggestions or corrections to zwang@ece.utexas.edu -% -%Acknowledgement: -%The author would like to thank Mr. Umesh Rajashekar, the Matlab master -%in our lab, for spending his precious time and giving his kind help -%on writing this program. Without his help, this program would not -%achieve its current efficiency. -% -%======================================================================== -% -%This is an efficient implementation of the algorithm for calculating -%the universal image quality index proposed by Zhou Wang and Alan C. -%Bovik. Please refer to the paper "A Universal Image Quality Index" -%by Zhou Wang and Alan C. Bovik, published in IEEE Signal Processing -%Letters, 2001. In order to run this function, you must have Matlab's -%Image Processing Toobox. -% -%Input : an original image and a test image of the same size -%Output: (1) an overall quality index of the test image, with a value -% range of [-1, 1]. -% (2) a quality map of the test image. The map has a smaller -% size than the input images. The actual size is -% img_size - BLOCK_SIZE + 1. -% -%Usage: -% -%1. Load the original and the test images into two matrices -% (say img1 and img2) -% -%2. Run this function in one of the two ways: -% -% % Choice 1 (suggested): -% [qi qi_map] = img_qi(img1, img2); -% -% % Choice 2: -% [qi qi_map] = img_qi(img1, img2, BLOCK_SIZE); -% -% The default BLOCK_SIZE is 8 (Choice 1). Otherwise, you can specify -% it by yourself (Choice 2). -% -%3. See the results: -% -% qi %Gives the over quality index. -% imshow((qi_map+1)/2) %Shows the quality map as an image. -% -%======================================================================== - -if (nargin == 1 | nargin > 3) - quality = -Inf; - quality_map = -1*ones(size(img1)); - return; -end - -if (size(img1) ~= size(img2)) - quality = -Inf; - quality_map = -1*ones(size(img1)); - return; -end - -if (nargin == 2) - block_size = 8; -end - -N = block_size.^2; -sum2_filter = ones(block_size); - -img1_sq = img1.*img1; -img2_sq = img2.*img2; -img12 = img1.*img2; - -img1_sum = filter2(sum2_filter, img1, 'valid'); -img2_sum = filter2(sum2_filter, img2, 'valid'); -img1_sq_sum = filter2(sum2_filter, img1_sq, 'valid'); -img2_sq_sum = filter2(sum2_filter, img2_sq, 'valid'); -img12_sum = filter2(sum2_filter, img12, 'valid'); - -img12_sum_mul = img1_sum.*img2_sum; -img12_sq_sum_mul = img1_sum.*img1_sum + img2_sum.*img2_sum; -numerator = 4*(N*img12_sum - img12_sum_mul).*img12_sum_mul; -denominator1 = N*(img1_sq_sum + img2_sq_sum) - img12_sq_sum_mul; -denominator = denominator1.*img12_sq_sum_mul; - -quality_map = ones(size(denominator)); -index = (denominator1 == 0) & (img12_sq_sum_mul ~= 0); -quality_map(index) = 2*img12_sum_mul(index)./img12_sq_sum_mul(index); -index = (denominator ~= 0); -quality_map(index) = numerator(index)./denominator(index); - -quality = mean2(quality_map); \ No newline at end of file diff --git a/MST/simulation/test_code/Quality_Metrics/quality_assessment.m b/MST/simulation/test_code/Quality_Metrics/quality_assessment.m deleted file mode 100755 index bf157a9..0000000 --- a/MST/simulation/test_code/Quality_Metrics/quality_assessment.m +++ /dev/null @@ -1,39 +0,0 @@ -function [psnr,rmse, ergas, sam, uiqi,ssim,DD,CCS] = quality_assessment(ground_truth, estimated, ignore_edges, ratio_ergas) - -% Ignore borders -y = ground_truth(ignore_edges+1:end-ignore_edges, ignore_edges+1:end-ignore_edges, :); -x = estimated(ignore_edges+1:end-ignore_edges, ignore_edges+1:end-ignore_edges, :); - -% Size, bands, samples -sz_x = size(x); -n_bands = sz_x(3); -n_samples = sz_x(1)*sz_x(2); - -% RMSE -aux = sum(sum((x - y).^2, 1), 2)/n_samples; -rmse_per_band = sqrt(aux); -rmse = sqrt(sum(aux, 3)/n_bands); - -% ERGAS -mean_y = sum(sum(y, 1), 2)/n_samples; -ergas = 100*ratio_ergas*sqrt(sum((rmse_per_band ./ mean_y).^2)/n_bands); - -% SAM -sam= SpectAngMapper( ground_truth, estimated ); -sam=sam*180/pi; -% num = sum(x .* y, 3); -% den = sqrt(sum(x.^2, 3) .* sum(y.^2, 3)); -% sam = sum(sum(acosd(num ./ den)))/(n_samples); - -% UIQI - calls the method described in "A Universal Image Quality Index" -% by Zhou Wang and Alan C. Bovik -q_band = zeros(1, n_bands); -for idx1=1:n_bands - q_band(idx1)=img_qi(ground_truth(:,:,idx1), estimated(:,:,idx1), 32); -end -uiqi = mean(q_band); -ssim=cal_ssim(ground_truth, estimated,0,0); -DD=norm(ground_truth(:)-estimated(:),1)/numel(ground_truth); -CCS = CC(ground_truth,estimated); -CCS=mean(CCS); -psnr=csnr(ground_truth, estimated,0,0); \ No newline at end of file diff --git a/MST/simulation/test_code/Quality_Metrics/results/README.md b/MST/simulation/test_code/Quality_Metrics/results/README.md deleted file mode 100755 index e36c521..0000000 --- a/MST/simulation/test_code/Quality_Metrics/results/README.md +++ /dev/null @@ -1 +0,0 @@ -Please put the reconstructed HSI in this folder. \ No newline at end of file diff --git a/MST/simulation/test_code/__pycache__/option.cpython-36.pyc b/MST/simulation/test_code/__pycache__/option.cpython-36.pyc deleted file mode 100755 index e948007741aedc664a72e2125659a1f5c8959b3a..0000000000000000000000000000000000000000 GIT binary patch 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+++ /dev/null @@ -1,201 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - x = self.d_conv(x) - return x - - -class Unet(nn.Module): - - def __init__(self, in_ch, out_ch): - super(Unet, self).__init__() - - self.dconv_down1 = double_conv(in_ch, 32) - self.dconv_down2 = double_conv(32, 64) - self.dconv_down3 = double_conv(64, 128) - - self.maxpool = nn.MaxPool2d(2) - self.upsample2 = nn.Sequential( - nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.upsample1 = nn.Sequential( - nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - self.dconv_up2 = double_conv(64 + 64, 64) - self.dconv_up1 = double_conv(32 + 32, 32) - - self.conv_last = nn.Conv2d(32, out_ch, 1) - self.afn_last = nn.Tanh() - - def forward(self, x): - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - inputs = x - conv1 = self.dconv_down1(x) - x = self.maxpool(conv1) - - conv2 = self.dconv_down2(x) - x = self.maxpool(conv2) - - conv3 = self.dconv_down3(x) - - x = self.upsample2(conv3) - x = torch.cat([x, conv2], dim=1) - - x = self.dconv_up2(x) - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - - x = self.dconv_up1(x) - - x = self.conv_last(x) - x = self.afn_last(x) - out = x + inputs - - return out[:, :, :h_inp, :w_inp] - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class ADMM_net(nn.Module): - - def __init__(self): - super(ADMM_net, self).__init__() - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - self.gamma1 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma2 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma3 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma4 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma5 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma6 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma7 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma8 = torch.nn.Parameter(torch.Tensor([0])) - self.gamma9 = torch.nn.Parameter(torch.Tensor([0])) - - def forward(self, y, input_mask=None): - if input_mask == None: - Phi = torch.rand((1, 28, 256, 310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask - x_list = [] - theta = At(y,Phi) - b = torch.zeros_like(Phi) - ### 1-3 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma1),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet1(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma2),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet2(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma3),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet3(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - ### 4-6 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma4),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet4(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma5),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet5(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma6),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet6(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - ### 7-9 - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma7),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet7(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma8),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet8(x1) - theta = shift_3d(theta) - b = b- (x-theta) - x_list.append(theta) - yb = A(theta+b,Phi) - x = theta+b + At(torch.div(y-yb,Phi_s+self.gamma9),Phi) - x1 = x-b - x1 = shift_back_3d(x1) - theta = self.unet9(x1) - theta = shift_3d(theta) - return theta[:, :, :, 0:256] diff --git a/MST/simulation/test_code/architecture/BIRNAT.py b/MST/simulation/test_code/architecture/BIRNAT.py deleted file mode 100755 index e3d8123..0000000 --- a/MST/simulation/test_code/architecture/BIRNAT.py +++ /dev/null @@ -1,365 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F - -class self_attention(nn.Module): - def __init__(self, ch): - super(self_attention, self).__init__() - self.conv1 = nn.Conv2d(ch, ch // 8, 1) - self.conv2 = nn.Conv2d(ch, ch // 8, 1) - self.conv3 = nn.Conv2d(ch, ch, 1) - self.conv4 = nn.Conv2d(ch, ch, 1) - self.gamma1 = torch.nn.Parameter(torch.Tensor([0])) - self.ch = ch - - def forward(self, x): - batch_size = x.shape[0] - - f = self.conv1(x) - g = self.conv2(x) - h = self.conv3(x) - ht = h.reshape([batch_size, self.ch, -1]) - - ft = f.reshape([batch_size, self.ch // 8, -1]) - n = torch.matmul(ft.permute([0, 2, 1]), g.reshape([batch_size, self.ch // 8, -1])) - beta = F.softmax(n, dim=1) - - o = torch.matmul(ht, beta) - o = o.reshape(x.shape) # [bs, C, h, w] - - o = self.conv4(o) - - x = self.gamma1 * o + x - - return x - - -class res_part(nn.Module): - - def __init__(self, in_ch, out_ch): - super(res_part, self).__init__() - self.conv1 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - self.conv2 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - self.conv3 = nn.Sequential( - nn.Conv2d(in_ch, in_ch, 3, padding=1), - # nn.BatchNorm2d(out_ch), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, out_ch, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(in_ch, in_ch, 3, padding=1), - ) - - def forward(self, x): - x1 = self.conv1(x) - x = x1 + x - x1 = self.conv2(x) - x = x1 + x - x1 = self.conv3(x) - x = x1 + x - return x - - -class down_feature(nn.Module): - - def __init__(self, in_ch, out_ch): - super(down_feature, self).__init__() - self.conv = nn.Sequential( - # nn.Conv2d(in_ch, 20, 5, stride=1, padding=2), - # nn.Conv2d(20, 40, 5, stride=2, padding=2), - # nn.Conv2d(40, out_ch, 5, stride=2, padding=2), - nn.Conv2d(in_ch, 20, 5, stride=1, padding=2), - nn.Conv2d(20, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.Conv2d(20, 40, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(40, out_ch, 3, stride=1, padding=1), - ) - - def forward(self, x): - x = self.conv(x) - return x - - -class up_feature(nn.Module): - - def __init__(self, in_ch, out_ch): - super(up_feature, self).__init__() - self.conv = nn.Sequential( - # nn.ConvTranspose2d(in_ch, 40, 3, stride=2, padding=1, output_padding=1), - # nn.ConvTranspose2d(40, 20, 3, stride=2, padding=1, output_padding=1), - nn.Conv2d(in_ch, 40, 3, stride=1, padding=1), - nn.Conv2d(40, 30, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(30, 20, 3, stride=1, padding=1), - nn.Conv2d(20, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - nn.Conv2d(20, out_ch, 1), - # nn.Sigmoid(), - ) - - def forward(self, x): - x = self.conv(x) - return x - -class cnn1(nn.Module): - # 输入meas concat mask - # 3 下采样 - - def __init__(self, B): - super(cnn1, self).__init__() - self.conv1 = nn.Conv2d(B + 1, 32, kernel_size=5, stride=1, padding=2) - self.relu1 = nn.LeakyReLU(inplace=True) - self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1) - self.relu2 = nn.LeakyReLU(inplace=True) - self.conv3 = nn.Conv2d(64, 64, kernel_size=1, stride=1) - self.relu3 = nn.LeakyReLU(inplace=True) - self.conv4 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1) - self.relu4 = nn.LeakyReLU(inplace=True) - self.conv5 = nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1) - self.relu5 = nn.LeakyReLU(inplace=True) - self.conv51 = nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1) - self.relu51 = nn.LeakyReLU(inplace=True) - self.conv52 = nn.Conv2d(32, 16, kernel_size=1, stride=1) - self.relu52 = nn.LeakyReLU(inplace=True) - self.conv6 = nn.Conv2d(16, 1, kernel_size=3, stride=1, padding=1) - self.res_part1 = res_part(128, 128) - self.res_part2 = res_part(128, 128) - self.res_part3 = res_part(128, 128) - self.conv7 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - self.relu7 = nn.LeakyReLU(inplace=True) - self.conv8 = nn.Conv2d(128, 128, kernel_size=1, stride=1) - self.conv9 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) - self.relu9 = nn.LeakyReLU(inplace=True) - self.conv10 = nn.Conv2d(128, 128, kernel_size=1, stride=1) - - self.att1 = self_attention(128) - - def forward(self, meas=None, nor_meas=None, PhiTy=None): - data = torch.cat([torch.unsqueeze(nor_meas, dim=1), PhiTy], dim=1) - out = self.conv1(data) - - out = self.relu1(out) - out = self.conv2(out) - out = self.relu2(out) - out = self.conv3(out) - out = self.relu3(out) - out = self.conv4(out) - out = self.relu4(out) - out = self.res_part1(out) - out = self.conv7(out) - out = self.relu7(out) - out = self.conv8(out) - out = self.res_part2(out) - out = self.conv9(out) - out = self.relu9(out) - out = self.conv10(out) - out = self.res_part3(out) - - # out = self.att1(out) - - out = self.conv5(out) - out = self.relu5(out) - out = self.conv51(out) - out = self.relu51(out) - out = self.conv52(out) - out = self.relu52(out) - out = self.conv6(out) - - return out - - -class forward_rnn(nn.Module): - - def __init__(self): - super(forward_rnn, self).__init__() - self.extract_feature1 = down_feature(1, 20) - self.up_feature1 = up_feature(60, 1) - self.conv_x1 = nn.Sequential( - nn.Conv2d(1, 16, 5, stride=1, padding=2), - nn.Conv2d(16, 32, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(32, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.conv_x2 = nn.Sequential( - nn.Conv2d(1, 10, 5, stride=1, padding=2), - nn.Conv2d(10, 10, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(10, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.h_h = nn.Sequential( - nn.Conv2d(60, 30, 3, padding=1), - nn.Conv2d(30, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - ) - self.res_part1 = res_part(60, 60) - self.res_part2 = res_part(60, 60) - - def forward(self, xt1, meas=None, nor_meas=None, PhiTy=None, mask3d_batch=None, h=None, cs_rate=28): - ht = h - xt = xt1 - - step = 2 - [bs, nC, row, col] = xt1.shape - - out = xt1 - x11 = self.conv_x1(torch.unsqueeze(nor_meas, 1)) - for i in range(cs_rate - 1): - d1 = torch.zeros(bs, row, col).cuda() - d2 = torch.zeros(bs, row, col).cuda() - for ii in range(i + 1): - d1 = d1 + torch.mul(mask3d_batch[:, ii, :, :], out[:, ii, :, :]) - for ii in range(i + 2, cs_rate): - d2 = d2 + torch.mul(mask3d_batch[:, ii, :, :], torch.squeeze(nor_meas)) - x12 = self.conv_x2(torch.unsqueeze(meas - d1 - d2, 1)) - - x2 = self.extract_feature1(xt) - h = torch.cat([ht, x11, x12, x2], dim=1) - h = self.res_part1(h) - h = self.res_part2(h) - ht = self.h_h(h) - xt = self.up_feature1(h) - out = torch.cat([out, xt], dim=1) - - return out, ht - - -class backrnn(nn.Module): - - def __init__(self): - super(backrnn, self).__init__() - self.extract_feature1 = down_feature(1, 20) - self.up_feature1 = up_feature(60, 1) - self.conv_x1 = nn.Sequential( - nn.Conv2d(1, 16, 5, stride=1, padding=2), - nn.Conv2d(16, 32, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(32, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.conv_x2 = nn.Sequential( - nn.Conv2d(1, 10, 5, stride=1, padding=2), - nn.Conv2d(10, 10, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(10, 40, 3, stride=2, padding=1), - nn.Conv2d(40, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, stride=1, padding=1), - nn.LeakyReLU(inplace=True), - nn.ConvTranspose2d(20, 10, kernel_size=3, stride=2, padding=1, output_padding=1), - ) - self.h_h = nn.Sequential( - nn.Conv2d(60, 30, 3, padding=1), - nn.Conv2d(30, 20, 1), - nn.LeakyReLU(inplace=True), - nn.Conv2d(20, 20, 3, padding=1), - ) - self.res_part1 = res_part(60, 60) - self.res_part2 = res_part(60, 60) - - def forward(self, xt8, meas=None, nor_meas=None, PhiTy=None, mask3d_batch=None, h=None, cs_rate=28): - ht = h - - step = 2 - [bs, nC, row, col] = xt8.shape - - xt = torch.unsqueeze(xt8[:, cs_rate - 1, :, :], 1) - - out = torch.zeros(bs, cs_rate, row, col).cuda() - out[:, cs_rate - 1, :, :] = xt[:, 0, :, :] - x11 = self.conv_x1(torch.unsqueeze(nor_meas, 1)) - for i in range(cs_rate - 1): - d1 = torch.zeros(bs, row, col).cuda() - d2 = torch.zeros(bs, row, col).cuda() - for ii in range(i + 1): - d1 = d1 + torch.mul(mask3d_batch[:, cs_rate - 1 - ii, :, :], out[:, cs_rate - 1 - ii, :, :].clone()) - for ii in range(i + 2, cs_rate): - d2 = d2 + torch.mul(mask3d_batch[:, cs_rate - 1 - ii, :, :], xt8[:, cs_rate - 1 - ii, :, :].clone()) - x12 = self.conv_x2(torch.unsqueeze(meas - d1 - d2, 1)) - - x2 = self.extract_feature1(xt) - h = torch.cat([ht, x11, x12, x2], dim=1) - h = self.res_part1(h) - h = self.res_part2(h) - ht = self.h_h(h) - xt = self.up_feature1(h) - - out[:, cs_rate - 2 - i, :, :] = xt[:, 0, :, :] - - return out - -def shift_gt_back(inputs, step=2): # input [bs,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - output = torch.zeros(bs, nC, row, col - (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, :] = inputs[:, i, :, step * i:step * i + col - (nC - 1) * step] - return output - -def shift(inputs, step=2): - [bs, nC, row, col] = inputs.shape - if inputs.is_cuda: - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).cuda().float() - else: - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).float() - for i in range(nC): - output[:, i, :, step * i:step * i + col] = inputs[:, i, :, :] - return output - -class BIRNAT(nn.Module): - - def __init__(self): - super(BIRNAT, self).__init__() - self.cs_rate = 28 - self.first_frame_net = cnn1(self.cs_rate).cuda() - self.rnn1 = forward_rnn().cuda() - self.rnn2 = backrnn().cuda() - - def gen_meas_torch(self, meas, shift_mask): - batch_size, H = meas.shape[0:2] - mask_s = torch.sum(shift_mask, 1) - nor_meas = torch.div(meas, mask_s) - temp = torch.mul(torch.unsqueeze(nor_meas, dim=1).expand([batch_size, 28, H, shift_mask.shape[3]]), shift_mask) - return nor_meas, temp - - def forward(self, meas, shift_mask=None): - if shift_mask==None: - shift_mask = torch.zeros(1, 28, 256, 310).cuda() - H, W = meas.shape[-2:] - nor_meas, PhiTy = self.gen_meas_torch(meas, shift_mask) - h0 = torch.zeros(meas.shape[0], 20, H, W).cuda() - xt1 = self.first_frame_net(meas, nor_meas, PhiTy) - model_out1, h1 = self.rnn1(xt1, meas, nor_meas, PhiTy, shift_mask, h0, self.cs_rate) - model_out2 = self.rnn2(model_out1, meas, nor_meas, PhiTy, shift_mask, h1, self.cs_rate) - model_out2 = shift_gt_back(model_out2) - - return model_out2 diff --git a/MST/simulation/test_code/architecture/BiSRNet.py b/MST/simulation/test_code/architecture/BiSRNet.py deleted file mode 100755 index 08014a0..0000000 --- a/MST/simulation/test_code/architecture/BiSRNet.py +++ /dev/null @@ -1,422 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -import os -from pdb import set_trace as stx - -# --------------------------------------------- Binarized Basic Units ----------------------------------------------------------------- - - -class LearnableBias(nn.Module): - def __init__(self, out_chn): - super(LearnableBias, self).__init__() - self.bias = nn.Parameter(torch.zeros(1,out_chn,1,1), requires_grad=True) - - def forward(self, x): - # stx() - out = x + self.bias.expand_as(x) - return out - -class ReDistribution(nn.Module): - def __init__(self, out_chn): - super(ReDistribution, self).__init__() - self.b = nn.Parameter(torch.zeros(1,out_chn,1,1), requires_grad=True) - self.k = nn.Parameter(torch.ones(1,out_chn,1,1), requires_grad=True) - - def forward(self, x): - out = x * self.k.expand_as(x) + self.b.expand_as(x) - return out - -class RPReLU(nn.Module): - def __init__(self, inplanes): - super(RPReLU, self).__init__() - self.pr_bias0 = LearnableBias(inplanes) - self.pr_prelu = nn.PReLU(inplanes) - self.pr_bias1 = LearnableBias(inplanes) - - def forward(self, x): - x = self.pr_bias1(self.pr_prelu(self.pr_bias0(x))) - return x - -class Spectral_Binary_Activation(nn.Module): - def __init__(self): - super(Spectral_Binary_Activation, self).__init__() - self.beta = nn.Parameter(torch.ones(1), requires_grad=True) - - def forward(self, x): - - binary_activation_no_grad = torch.sign(x) - tanh_activation = torch.tanh(x*self.beta) - - out = binary_activation_no_grad.detach() - tanh_activation.detach() + tanh_activation - return out - - -class HardBinaryConv(nn.Conv2d): - def __init__(self, in_chn, out_chn, kernel_size=3, stride=1, padding=1, groups=1, bias=True): - super(HardBinaryConv, self).__init__( - in_chn, - out_chn, - kernel_size, - stride=stride, - padding=padding, - groups=groups, - bias=bias - ) - - def forward(self, x): - real_weights = self.weight - scaling_factor = torch.mean(torch.mean(torch.mean(abs(real_weights),dim=3,keepdim=True),dim=2,keepdim=True),dim=1,keepdim=True) - scaling_factor = scaling_factor.detach() - # stx() - binary_weights_no_grad = scaling_factor * torch.sign(real_weights) - # stx() - cliped_weights = torch.clamp(real_weights, -1.0, 1.0) - binary_weights = binary_weights_no_grad.detach() - cliped_weights.detach() + cliped_weights - y = F.conv2d(x, binary_weights,self.bias, stride=self.stride, padding=self.padding, groups=self.groups) - - return y - -class BinaryConv2d(nn.Module): - - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d, self).__init__() - - self.move0 = ReDistribution(in_channels) - self.binary_activation = Spectral_Binary_Activation() - self.binary_conv = HardBinaryConv(in_chn=in_channels, - out_chn=in_channels, - kernel_size=kernel_size, - stride = stride, - padding=padding, - bias=bias, - groups=groups) - self.relu=RPReLU(in_channels) - - - def forward(self, x): - out = self.move0(x) - out = self.binary_activation(out) - out = self.binary_conv(out) - out =self.relu(out) - out = out + x - return out - - -class BinaryConv2d_Down(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,2h,2w - ''' - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Down, self).__init__() - - self.biconv_1 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,2c,h/2,w/2 - ''' - out = self.avg_pool(x) - out_1 = out - out_2 = out_1.clone() - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - out = torch.cat([out_1, out_2], dim=1) - - return out - - - -class BinaryConv2d_Up(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,2h,2w - ''' - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Up, self).__init__() - - self.biconv_1 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,c/2,2h,2w - ''' - b,c,h,w = x.shape - out = F.interpolate(x, scale_factor=2, mode='bilinear') - - out_1 = out[:,:c//2,:,:] - out_2 = out[:,c//2:,:,:] - - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - - - out = (out_1 + out_2) / 2 - - return out - - -class BinaryConv2d_Fusion_Decrease(nn.Module): - ''' - input: b,c,h,w - output: b,c/2,h,w - ''' - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Fusion_Decrease, self).__init__() - - self.biconv_1 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(out_channels, out_channels, kernel_size, stride, padding, bias, groups) - # self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,c/2,h,w - ''' - b,c,h,w = x.shape - out = x - - out_1 = out[:,:c//2,:,:] - out_2 = out[:,c//2:,:,:] - - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - - out = (out_1 + out_2) / 2 - - return out - - -class BinaryConv2d_Fusion_Increase(nn.Module): - ''' - input: b,c,h,w - output: b,2c,h,w - ''' - def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, bias=False, groups=1): - super(BinaryConv2d_Fusion_Increase, self).__init__() - - self.biconv_1 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - self.biconv_2 = BinaryConv2d(in_channels, in_channels, kernel_size, stride, padding, bias, groups) - # self.avg_pool = nn.AvgPool2d(kernel_size = 2, stride = 2, padding = 0) - - def forward(self, x): - ''' - x: b,c,h,w - out: b,2c,h,w - ''' - # stx() - out_1 = x - out_2 = out_1.clone() - out_1 = self.biconv_1(out_1) - out_2 = self.biconv_2(out_2) - out = torch.cat([out_1, out_2], dim=1) - - return out - -# ---------------------------------------------------------- Binarized UNet------------------------------------------------------ - - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - - - -class FeedForward(nn.Module): - def __init__(self, dim, mult=2): - super().__init__() - self.net = nn.Sequential( - BinaryConv2d_Fusion_Increase(dim, dim * mult, 1, 1, bias=False), - BinaryConv2d_Fusion_Increase(dim * mult, dim * mult * mult, 1, 1, bias=False), - RPReLU(dim * mult * mult), - BinaryConv2d(dim * mult * mult, dim * mult * mult, 3, 1, 1, bias=False, groups=dim), - RPReLU(dim * mult * mult), - BinaryConv2d_Fusion_Decrease(dim * mult * mult, dim * mult, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - - - -class BiSRNet_Block(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append( - PreNorm(dim, FeedForward(dim=dim)) - ) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for ff in self.blocks: - # x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - - -class BiSRNet_body(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2,4,4]): - super(BiSRNet_body, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = BinaryConv2d(in_dim, self.dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - BiSRNet_Block(dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - BinaryConv2d_Down(dim_stage, dim_stage * 2, 3, 1, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = BiSRNet_Block( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - BinaryConv2d_Up(dim_stage, dim_stage // 2, 3, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim_stage, dim_stage // 2, 1, 1, bias=False), - BiSRNet_Block( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = BinaryConv2d(self.dim, out_dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (BiSRNet_Block, FeaDownSample) in self.encoder_layers: - # stx() - fea = BiSRNet_Block(fea) - fea_encoder.append(fea) - # stx() - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, BiSRNet_Block) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = BiSRNet_Block(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - - - -class BiSRNet(nn.Module): - ''' - Only 3 layers are 32-bit conv - ''' - def __init__(self, in_channels=28, out_channels=28, n_feat=28, stage=3, num_blocks=[1,1,1]): - super(BiSRNet, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2,bias=False) # 1-bit -> 32-bit - modules_body = [BiSRNet_body(dim=n_feat, stage=2, num_blocks=num_blocks) for _ in range(stage)] - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) # 1-bit -> 32-bit - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2,bias=False) # 1-bit -> 32-bit - - def initial_x(self, y, Phi): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,256] - :return: z: [b,28,256,256] - """ - x = self.fution(torch.cat([y, Phi], dim=1)) - return x - - def forward(self, y, Phi=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - if Phi==None: - Phi = torch.rand((1,28,256,256)).cuda() - # Phi = torch.rand((1,28,256,256)).to(device) - x = self.initial_x(y, Phi) - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] \ No newline at end of file diff --git a/MST/simulation/test_code/architecture/CST.py b/MST/simulation/test_code/architecture/CST.py deleted file mode 100755 index ff5aa53..0000000 --- a/MST/simulation/test_code/architecture/CST.py +++ /dev/null @@ -1,588 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -from collections import defaultdict, Counter -import numpy as np -from tqdm import tqdm -import random - -def uniform(a, b, shape, device='cuda'): - return (b - a) * torch.rand(shape, device=device) + a - -class AsymmetricTransform: - - def Q(self, *args, **kwargs): - raise NotImplementedError('Query transform not implemented') - - def K(self, *args, **kwargs): - raise NotImplementedError('Key transform not implemented') - -class LSH: - def __call__(self, *args, **kwargs): - raise NotImplementedError('LSH scheme not implemented') - - def compute_hash_agreement(self, q_hash, k_hash): - return (q_hash == k_hash).min(dim=-1)[0].sum(dim=-1) - -class XBOXPLUS(AsymmetricTransform): - - def set_norms(self, x): - self.x_norms = x.norm(p=2, dim=-1, keepdim=True) - self.MX = torch.amax(self.x_norms, dim=-2, keepdim=True) - - def X(self, x): - device = x.device - ext = torch.sqrt((self.MX**2).to(device) - (self.x_norms**2).to(device)) - zero = torch.tensor(0.0, device=x.device).repeat(x.shape[:-1], 1).unsqueeze(-1) - return torch.cat((x, ext, zero), -1) - -def lsh_clustering(x, n_rounds, r=1): - salsh = SALSH(n_rounds=n_rounds, dim=x.shape[-1], r=r, device=x.device) - x_hashed = salsh(x).reshape((n_rounds,) + x.shape[:-1]) - return x_hashed.argsort(dim=-1) - -class SALSH(LSH): - def __init__(self, n_rounds, dim, r, device='cuda'): - super(SALSH, self).__init__() - self.alpha = torch.normal(0, 1, (dim, n_rounds), device=device) - self.beta = uniform(0, r, shape=(1, n_rounds), device=device) - self.dim = dim - self.r = r - - def __call__(self, vecs): - projection = vecs @ self.alpha - projection_shift = projection + self.beta - projection_rescale = projection_shift / self.r - return projection_rescale.permute(2, 0, 1) - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - - variance = scale / denom - - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def batch_scatter(output, src, dim, index): - """ - :param output: [b,n,c] - :param src: [b,n,c] - :param dim: int - :param index: [b,n] - :return: output: [b,n,c] - """ - b,k,c = src.shape - index = index[:, :, None].expand(-1, -1, c) - output, src, index = map(lambda t: rearrange(t, 'b k c -> (b c) k'), (output, src, index)) - output.scatter_(dim,index,src) - output = rearrange(output, '(b c) k -> b k c', b=b) - return output - -def batch_gather(x, index, dim): - """ - :param x: [b,n,c] - :param index: [b,n//2] - :param dim: int - :return: output: [b,n//2,c] - """ - b,n,c = x.shape - index = index[:,:,None].expand(-1,-1,c) - x, index = map(lambda t: rearrange(t, 'b n c -> (b c) n'), (x, index)) - output = torch.gather(x,dim,index) - output = rearrange(output, '(b c) n -> b n c', b=b) - return output - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class SAH_MSA(nn.Module): - def __init__(self, heads=4, n_rounds=2, channels=64, patch_size=144, - r=1): - super(SAH_MSA, self).__init__() - self.heads = heads - self.n_rounds = n_rounds - inner_dim = channels*3 - self.to_q = nn.Linear(channels, inner_dim, bias=False) - self.to_k = nn.Linear(channels, inner_dim, bias=False) - self.to_v = nn.Linear(channels, inner_dim, bias=False) - self.to_out = nn.Linear(inner_dim, channels, bias=False) - - self.xbox_plus = XBOXPLUS() - self.clustering_params = { - 'r': r, - 'n_rounds': self.n_rounds - } - self.q_attn_size = patch_size[0] * patch_size[1] - self.k_attn_size = patch_size[0] * patch_size[1] - - def forward(self, input): - """ - :param input: [b,n,c] - :return: output: [b,n,c] - """ - - B, N, C_inp = input.shape - query = self.to_q(input) - key = self.to_k(input) - value = self.to_v(input) - input_hash = input.view(B, N, self.heads, C_inp//self.heads) - x_hash = rearrange(input_hash, 'b t h e -> (b h) t e') - bs, x_seqlen, dim = x_hash.shape - with torch.no_grad(): - self.xbox_plus.set_norms(x_hash) - Xs = self.xbox_plus.X(x_hash) - x_positions = lsh_clustering(Xs, **self.clustering_params) - x_positions = x_positions.reshape(self.n_rounds, bs, -1) - - del Xs - - C = query.shape[-1] - query = query.view(B, N, self.heads, C // self.heads) - key = key.view(B, N, self.heads, C // self.heads) - value = value.view(B, N, self.heads, C // self.heads) - - query = rearrange(query, 'b t h e -> (b h) t e') # [bs, q_seqlen,c] - key = rearrange(key, 'b t h e -> (b h) t e') - value = rearrange(value, 'b s h d -> (b h) s d') - - bs, q_seqlen, dim = query.shape - bs, k_seqlen, dim = key.shape - v_dim = value.shape[-1] - - x_rev_positions = torch.argsort(x_positions, dim=-1) - x_offset = torch.arange(bs, device=query.device).unsqueeze(-1) * x_seqlen - x_flat = (x_positions + x_offset).reshape(-1) - - s_queries = query.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.q_attn_size, dim) - s_keys = key.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, dim) - s_values = value.reshape(-1, v_dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, v_dim) - - inner = s_queries @ s_keys.transpose(2, 1) - norm_factor = 1 - inner = inner / norm_factor - - # free memory - del x_positions - - # softmax denominator - dots_logsumexp = torch.logsumexp(inner, dim=-1, keepdim=True) - # softmax - dots = torch.exp(inner - dots_logsumexp) - # dropout - - # n_rounds outs - bo = (dots @ s_values).reshape(self.n_rounds, bs, q_seqlen, -1) - - # undo sort - x_offset = torch.arange(bs * self.n_rounds, device=query.device).unsqueeze(-1) * x_seqlen - x_rev_flat = (x_rev_positions.reshape(-1, x_seqlen) + x_offset).reshape(-1) - o = bo.reshape(-1, v_dim).index_select(0, x_rev_flat).reshape(self.n_rounds, bs, q_seqlen, -1) - - slogits = dots_logsumexp.reshape(self.n_rounds, bs, -1) - logits = torch.gather(slogits, 2, x_rev_positions) - - # free memory - del x_rev_positions - - # weighted sum multi-round attention - probs = torch.exp(logits - torch.logsumexp(logits, dim=0, keepdim=True)) - out = torch.sum(o * probs.unsqueeze(-1), dim=0) - out = rearrange(out, '(b h) t d -> b t h d', h=self.heads) - out = out.reshape(B, N, -1) - out = self.to_out(out) - - return out - -class SAHAB(nn.Module): - def __init__( - self, - dim, - patch_size=(16, 16), - heads=8, - shift_size=0, - sparse=False - ): - super().__init__() - self.blocks = nn.ModuleList([]) - self.attn = PreNorm(dim, SAH_MSA(heads=heads, n_rounds=2, r=1, channels=dim, patch_size=patch_size)) - self.ffn = PreNorm(dim, FeedForward(dim=dim)) - self.shift_size = shift_size - self.patch_size = patch_size - self.sparse = sparse - - def forward(self, x, mask=None): - """ - x: [b,h,w,c] - mask: [b,h,w] - return out: [b,h,w,c] - """ - b,h,w,c = x.shape - if self.shift_size > 0: - x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - mask = torch.roll(mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - w_size = self.patch_size - - # Split into large patches - x = rearrange(x, 'b (nh hh) (nw ww) c-> b (nh nw) (hh ww c)', hh=w_size[0] * 2, ww=w_size[1] * 2) - mask = rearrange(mask, 'b (nh hh) (nw ww) -> b (nh nw) (hh ww)', hh=w_size[0] * 2, ww=w_size[1] * 2) - N = x.shape[1] - - mask = torch.mean(mask,dim=2,keepdim=False) # [b,nh*nw] - if self.sparse: - mask_select = mask.topk(mask.shape[1] // 2, dim=1)[1] # [b,nh*nw//2] - x_select = batch_gather(x, mask_select, 1) # [b,nh*nw//2,hh*ww*c] - x_select = x_select.reshape(b*N//2,-1,c) - x_select = self.attn(x_select)+x_select - x_select = x_select.view(b,N//2,-1) - x = batch_scatter(x.clone(), x_select, 1, mask_select) - else: - x = x.view(b*N,-1,c) - x = self.attn(x) + x - x = x.view(b, N, -1) - x = rearrange(x, 'b (nh nw) (hh ww c) -> b (nh hh) (nw ww) c', nh=h//(w_size[0] * 2), hh=w_size[0] * 2, ww=w_size[1] * 2) - - if self.shift_size > 0: - x = torch.roll(x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - - x = self.ffn(x) + x - - return x - - -class SAHABs(nn.Module): - def __init__( - self, - dim, - patch_size=(8, 8), - heads=8, - num_blocks=2, - sparse=False - ): - super().__init__() - blocks = [] - for _ in range(num_blocks): - blocks.append( - SAHAB(heads=heads, dim=dim, patch_size=patch_size,sparse=sparse, - shift_size=0 if (_ % 2 == 0) else patch_size[0])) - self.blocks = nn.Sequential(*blocks) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - mask: [b,1,h,w] - return x: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - mask = mask.squeeze(1) - for block in self.blocks: - x = block(x, mask) - x = x.permute(0, 3, 1, 2) - return x - -class ASPPConv(nn.Sequential): - def __init__(self, in_channels, out_channels, dilation): - modules = [ - nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False), - nn.ReLU() - ] - super(ASPPConv, self).__init__(*modules) - - -class ASPPPooling(nn.Sequential): - def __init__(self, in_channels, out_channels): - super(ASPPPooling, self).__init__( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(in_channels, out_channels, 1, bias=False), - nn.ReLU()) - - def forward(self, x): - size = x.shape[-2:] - for mod in self: - x = mod(x) - return F.interpolate(x, size=size, mode='bilinear', align_corners=False) - - -class ASPP(nn.Module): - def __init__(self, in_channels, atrous_rates, out_channels): - super(ASPP, self).__init__() - modules = [] - - rates = tuple(atrous_rates) - for rate in rates: - modules.append(ASPPConv(in_channels, out_channels, rate)) - - modules.append(ASPPPooling(in_channels, out_channels)) - - self.convs = nn.ModuleList(modules) - - self.project = nn.Sequential( - nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False), - nn.ReLU(), - nn.Dropout(0.5)) - - def forward(self, x): - res = [] - for conv in self.convs: - res.append(conv(x)) - res = torch.cat(res, dim=1) - return self.project(res) - -class Sparsity_Estimator(nn.Module): - def __init__(self, dim=28, expand=2, sparse=False): - super(Sparsity_Estimator, self).__init__() - self.dim = dim - self.stage = 2 - self.sparse = sparse - - # Input projection - self.in_proj = nn.Conv2d(28, dim, 1, 1, 0, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(2): - self.encoder_layers.append(nn.ModuleList([ - nn.Conv2d(dim_stage, dim_stage * expand, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage * expand, dim_stage * expand, 3, 2, 1, bias=False, groups=dim_stage * expand), - nn.Conv2d(dim_stage * expand, dim_stage*expand, 1, 1, 0, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = ASPP(dim_stage, [3,6], dim_stage) - - # Decoder: - self.decoder_layers = nn.ModuleList([]) - for i in range(2): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage // 2, dim_stage, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage, dim_stage, 3, 1, 1, bias=False, groups=dim_stage), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, 0, bias=False), - ])) - dim_stage //= 2 - - # Output projection - if sparse: - self.out_conv2 = nn.Conv2d(self.dim, self.dim+1, 3, 1, 1, bias=False) - else: - self.out_conv2 = nn.Conv2d(self.dim, self.dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - # Input projection - fea = self.lrelu(self.in_proj(x)) - # Encoder - fea_encoder = [] # [c 2c 4c 8c] - for (Conv1, Conv2, Conv3) in self.encoder_layers: - fea_encoder.append(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - # Bottleneck - fea = self.bottleneck(fea)+fea - # Decoder - for i, (FeaUpSample, Conv1, Conv2, Conv3) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - fea = fea + fea_encoder[self.stage-1-i] - # Output projection - out = self.out_conv2(fea) - if self.sparse: - error_map = out[:,-1:,:,:] - return out[:,:-1], error_map - return out - -class CST(nn.Module): - def __init__(self, dim=28, stage=2, num_blocks=[2, 2, 2], sparse=False): - super(CST, self).__init__() - self.dim = dim - self.stage = stage - self.sparse = sparse - - # Fution physical mask and shifted measurement - self.fution = nn.Conv2d(56, 28, 1, 1, 0, bias=False) - - # Sparsity Estimator - if num_blocks==[2,4,6]: - self.fe = nn.Sequential(Sparsity_Estimator(dim=28,expand=2,sparse=False), - Sparsity_Estimator(dim=28, expand=2, sparse=sparse)) - else: - self.fe = Sparsity_Estimator(dim=28, expand=2, sparse=sparse) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - SAHABs(dim=dim_stage, num_blocks=num_blocks[i], heads=dim_stage // dim, sparse=sparse), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - nn.AvgPool2d(kernel_size=2, stride=2), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = SAHABs( - dim=dim_stage, heads=dim_stage // dim, num_blocks=num_blocks[-1], sparse=sparse) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - SAHABs(dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], - heads=(dim_stage // 2) // dim, sparse=sparse), - ])) - dim_stage //= 2 - - # Output projection - self.out_proj = nn.Conv2d(self.dim, dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - mask: [b,c,h,w] - return out:[b,c,h,w] - """ - b, c, h, w = x.shape - - # Fution - x = self.fution(torch.cat([x,mask],dim=1)) - - # Feature Extraction - if self.sparse: - fea,mask = self.fe(x) - else: - fea = self.fe(x) - mask = torch.randn((b,1,h,w)).cuda() - - # Encoder - fea_encoder = [] - masks = [] - for (Blcok, FeaDownSample, MaskDownSample) in self.encoder_layers: - fea = Blcok(fea, mask) - masks.append(mask) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - mask = MaskDownSample(mask) - - # Bottleneck - fea = self.bottleneck(fea, mask) - - # Decoder - for i, (FeaUpSample, Blcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = fea + fea_encoder[self.stage - 1 - i] - mask = masks[self.stage - 1 - i] - fea = Blcok(fea, mask) - - # Output projection - out = self.out_proj(fea) + x - return out - - - - - - - - - - - - diff --git a/MST/simulation/test_code/architecture/DAUHST.py b/MST/simulation/test_code/architecture/DAUHST.py deleted file mode 100755 index 07f8461..0000000 --- a/MST/simulation/test_code/architecture/DAUHST.py +++ /dev/null @@ -1,367 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch import einsum - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -class HS_MSA(nn.Module): - def __init__( - self, - dim, - window_size=(8, 8), - dim_head=28, - heads=8, - only_local_branch=False - ): - super().__init__() - - self.dim = dim - self.heads = heads - self.scale = dim_head ** -0.5 - self.window_size = window_size - self.only_local_branch = only_local_branch - - # position embedding - if only_local_branch: - seq_l = window_size[0] * window_size[1] - self.pos_emb = nn.Parameter(torch.Tensor(1, heads, seq_l, seq_l)) - trunc_normal_(self.pos_emb) - else: - seq_l1 = window_size[0] * window_size[1] - self.pos_emb1 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l1, seq_l1)) - h,w = 256//self.heads,320//self.heads - seq_l2 = h*w//seq_l1 - self.pos_emb2 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l2, seq_l2)) - trunc_normal_(self.pos_emb1) - trunc_normal_(self.pos_emb2) - - inner_dim = dim_head * heads - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) - self.to_out = nn.Linear(inner_dim, dim) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x.shape - w_size = self.window_size - assert h % w_size[0] == 0 and w % w_size[1] == 0, 'fmap dimensions must be divisible by the window size' - if self.only_local_branch: - x_inp = rearrange(x, 'b (h b0) (w b1) c -> (b h w) (b0 b1) c', b0=w_size[0], b1=w_size[1]) - q = self.to_q(x_inp) - k, v = self.to_kv(x_inp).chunk(2, dim=-1) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), (q, k, v)) - q *= self.scale - sim = einsum('b h i d, b h j d -> b h i j', q, k) - sim = sim + self.pos_emb - attn = sim.softmax(dim=-1) - out = einsum('b h i j, b h j d -> b h i d', attn, v) - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - out = rearrange(out, '(b h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - else: - q = self.to_q(x) - k, v = self.to_kv(x).chunk(2, dim=-1) - q1, q2 = q[:,:,:,:c//2], q[:,:,:,c//2:] - k1, k2 = k[:,:,:,:c//2], k[:,:,:,c//2:] - v1, v2 = v[:,:,:,:c//2], v[:,:,:,c//2:] - - # local branch - q1, k1, v1 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q1, k1, v1)) - q1, k1, v1 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q1, k1, v1)) - q1 *= self.scale - sim1 = einsum('b n h i d, b n h j d -> b n h i j', q1, k1) - sim1 = sim1 + self.pos_emb1 - attn1 = sim1.softmax(dim=-1) - out1 = einsum('b n h i j, b n h j d -> b n h i d', attn1, v1) - out1 = rearrange(out1, 'b n h mm d -> b n mm (h d)') - - # non-local branch - q2, k2, v2 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q2, k2, v2)) - q2, k2, v2 = map(lambda t: t.permute(0, 2, 1, 3), (q2.clone(), k2.clone(), v2.clone())) - q2, k2, v2 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q2, k2, v2)) - q2 *= self.scale - sim2 = einsum('b n h i d, b n h j d -> b n h i j', q2, k2) - sim2 = sim2 + self.pos_emb2 - attn2 = sim2.softmax(dim=-1) - out2 = einsum('b n h i j, b n h j d -> b n h i d', attn2, v2) - out2 = rearrange(out2, 'b n h mm d -> b n mm (h d)') - out2 = out2.permute(0, 2, 1, 3) - - out = torch.cat([out1,out2],dim=-1).contiguous() - out = self.to_out(out) - out = rearrange(out, 'b (h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - return out - -class HSAB(nn.Module): - def __init__( - self, - dim, - window_size=(8, 8), - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - PreNorm(dim, HS_MSA(dim=dim, window_size=window_size, dim_head=dim_head, heads=heads, only_local_branch=(heads==1))), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class HST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, num_blocks=[1,1,1]): - super(HST, self).__init__() - self.dim = dim - self.scales = len(num_blocks) - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_scale = dim - for i in range(self.scales-1): - self.encoder_layers.append(nn.ModuleList([ - HSAB(dim=dim_scale, num_blocks=num_blocks[i], dim_head=dim, heads=dim_scale // dim), - nn.Conv2d(dim_scale, dim_scale * 2, 4, 2, 1, bias=False), - ])) - dim_scale *= 2 - - # Bottleneck - self.bottleneck = HSAB(dim=dim_scale, dim_head=dim, heads=dim_scale // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(self.scales-1): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_scale, dim_scale // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_scale, dim_scale // 2, 1, 1, bias=False), - HSAB(dim=dim_scale // 2, num_blocks=num_blocks[self.scales - 2 - i], dim_head=dim, - heads=(dim_scale // 2) // dim), - ])) - dim_scale //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - b, c, h_inp, w_inp = x.shape - hb, wb = 16, 16 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - - # Embedding - fea = self.embedding(x) - x = x[:,:28,:,:] - - # Encoder - fea_encoder = [] - for (HSAB, FeaDownSample) in self.encoder_layers: - fea = HSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, HSAB) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.scales-2-i]], dim=1)) - fea = HSAB(fea) - - # Mapping - out = self.mapping(fea) + x - return out[:, :, :h_inp, :w_inp] - -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class HyPaNet(nn.Module): - def __init__(self, in_nc=29, out_nc=8, channel=64): - super(HyPaNet, self).__init__() - self.fution = nn.Conv2d(in_nc, channel, 1, 1, 0, bias=True) - self.down_sample = nn.Conv2d(channel, channel, 3, 2, 1, bias=True) - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.mlp = nn.Sequential( - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, out_nc, 1, padding=0, bias=True), - nn.Softplus()) - self.relu = nn.ReLU(inplace=True) - self.out_nc = out_nc - - def forward(self, x): - x = self.down_sample(self.relu(self.fution(x))) - x = self.avg_pool(x) - x = self.mlp(x) + 1e-6 - return x[:,:self.out_nc//2,:,:], x[:,self.out_nc//2:,:,:] - -class DAUHST(nn.Module): - - def __init__(self, num_iterations=1): - super(DAUHST, self).__init__() - self.para_estimator = HyPaNet(in_nc=28, out_nc=num_iterations*2) - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) - self.num_iterations = num_iterations - self.denoisers = nn.ModuleList([]) - for _ in range(num_iterations): - self.denoisers.append( - HST(in_dim=29, out_dim=28, dim=28, num_blocks=[1,1,1]), - ) - def initial(self, y, Phi): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,310] - :return: temp: [b,28,256,310]; alpha: [b, num_iterations]; beta: [b, num_iterations] - """ - nC, step = 28, 2 - y = y / nC * 2 - bs,row,col = y.shape - y_shift = torch.zeros(bs, nC, row, col).cuda().float() - for i in range(nC): - y_shift[:, i, :, step * i:step * i + col - (nC - 1) * step] = y[:, :, step * i:step * i + col - (nC - 1) * step] - z = self.fution(torch.cat([y_shift, Phi], dim=1)) - alpha, beta = self.para_estimator(self.fution(torch.cat([y_shift, Phi], dim=1))) - return z, alpha, beta - - def forward(self, y, input_mask=None): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,310] - :param Phi_PhiT: [b,256,310] - :return: z_crop: [b,28,256,256] - """ - Phi, Phi_s = input_mask - z, alphas, betas = self.initial(y, Phi) - for i in range(self.num_iterations): - alpha, beta = alphas[:,i,:,:], betas[:,i:i+1,:,:] - Phi_z = A(z, Phi) - x = z + At(torch.div(y-Phi_z,alpha+Phi_s), Phi) - x = shift_back_3d(x) - beta_repeat = beta.repeat(1,1,x.shape[2], x.shape[3]) - z = self.denoisers[i](torch.cat([x, beta_repeat],dim=1)) - if i [BN] => ReLU) * 2""" - - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - if not mid_channels: - mid_channels = out_channels - self.double_conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(mid_channels), - nn.ReLU(inplace=True), - nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(out_channels), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - return self.double_conv(x) - -class GAP_net(nn.Module): - - def __init__(self): - super(GAP_net, self).__init__() - - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - - def forward(self, y, input_mask=None): - if input_mask==None: - Phi = torch.rand((1,28,256,310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask - x_list = [] - x = At(y, Phi) # v0=H^T y - ### 1-3 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet1(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet2(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet3(x) - x = shift_3d(x) - ### 4-6 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet4(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet5(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet6(x) - x = shift_3d(x) - # ### 7-9 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet7(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet8(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet9(x) - x = shift_3d(x) - return x[:, :, :, 0:256] diff --git a/MST/simulation/test_code/architecture/HDNet.py b/MST/simulation/test_code/architecture/HDNet.py deleted file mode 100755 index 442acc8..0000000 --- a/MST/simulation/test_code/architecture/HDNet.py +++ /dev/null @@ -1,371 +0,0 @@ -import torch -import torch.nn as nn -import math - -def default_conv(in_channels, out_channels, kernel_size, bias=True): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias) - -class MeanShift(nn.Conv2d): - def __init__( - self, rgb_range, - rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1): - - super(MeanShift, self).__init__(3, 3, kernel_size=1) - std = torch.Tensor(rgb_std) - self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1) - self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std - for p in self.parameters(): - p.requires_grad = False - -class BasicBlock(nn.Sequential): - def __init__( - self, conv, in_channels, out_channels, kernel_size, stride=1, bias=False, - bn=True, act=nn.ReLU(True)): - - m = [conv(in_channels, out_channels, kernel_size, bias=bias)] - if bn: - m.append(nn.BatchNorm2d(out_channels)) - if act is not None: - m.append(act) - - super(BasicBlock, self).__init__(*m) - -class ResBlock(nn.Module): - def __init__( - self, conv, n_feats, kernel_size, - bias=True, bn=False, act=nn.ReLU(True), res_scale=1): - - super(ResBlock, self).__init__() - m = [] - for i in range(2): - m.append(conv(n_feats, n_feats, kernel_size, bias=bias)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if i == 0: - m.append(act) - - self.body = nn.Sequential(*m) - self.res_scale = res_scale - - def forward(self, x): - res = self.body(x).mul(self.res_scale) # So res_scale is a scaler? just scale all elements in each feature's residual? Why? - res += x - - return res - -class Upsampler(nn.Sequential): - def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True): - - m = [] - if (scale & (scale - 1)) == 0: - for _ in range(int(math.log(scale, 2))): - m.append(conv(n_feats, 4 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(2)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - - elif scale == 3: - m.append(conv(n_feats, 9 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(3)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - else: - raise NotImplementedError - - super(Upsampler, self).__init__(*m) - -_NORM_BONE = False - -def constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def kaiming_init(module, - a=0, - mode='fan_out', - nonlinearity='relu', - bias=0, - distribution='normal'): - assert distribution in ['uniform', 'normal'] - if distribution == 'uniform': - nn.init.kaiming_uniform_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - -# depthwise-separable convolution (DSC) -class DSC(nn.Module): - - def __init__(self, nin: int) -> None: - super(DSC, self).__init__() - self.conv_dws = nn.Conv2d( - nin, nin, kernel_size=1, stride=1, padding=0, groups=nin - ) - self.bn_dws = nn.BatchNorm2d(nin, momentum=0.9) - self.relu_dws = nn.ReLU(inplace=False) - - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) - - self.conv_point = nn.Conv2d( - nin, 1, kernel_size=1, stride=1, padding=0, groups=1 - ) - self.bn_point = nn.BatchNorm2d(1, momentum=0.9) - self.relu_point = nn.ReLU(inplace=False) - - self.softmax = nn.Softmax(dim=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - out = self.conv_dws(x) - out = self.bn_dws(out) - out = self.relu_dws(out) - - out = self.maxpool(out) - - out = self.conv_point(out) - out = self.bn_point(out) - out = self.relu_point(out) - - m, n, p, q = out.shape - out = self.softmax(out.view(m, n, -1)) - out = out.view(m, n, p, q) - - out = out.expand(x.shape[0], x.shape[1], x.shape[2], x.shape[3]) - - out = torch.mul(out, x) - - out = out + x - - return out - -# Efficient Feature Fusion(EFF) -class EFF(nn.Module): - def __init__(self, nin: int, nout: int, num_splits: int) -> None: - super(EFF, self).__init__() - - assert nin % num_splits == 0 - - self.nin = nin - self.nout = nout - self.num_splits = num_splits - self.subspaces = nn.ModuleList( - [DSC(int(self.nin / self.num_splits)) for i in range(self.num_splits)] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - sub_feat = torch.chunk(x, self.num_splits, dim=1) - out = [] - for idx, l in enumerate(self.subspaces): - out.append(self.subspaces[idx](sub_feat[idx])) - out = torch.cat(out, dim=1) - - return out - - -# spatial-spectral domain attention learning(SDL) -class SDL_attention(nn.Module): - def __init__(self, inplanes, planes, kernel_size=1, stride=1): - super(SDL_attention, self).__init__() - - self.inplanes = inplanes - self.inter_planes = planes // 2 - self.planes = planes - self.kernel_size = kernel_size - self.stride = stride - self.padding = (kernel_size-1)//2 - - self.conv_q_right = nn.Conv2d(self.inplanes, 1, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_v_right = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_up = nn.Conv2d(self.inter_planes, self.planes, kernel_size=1, stride=1, padding=0, bias=False) - self.softmax_right = nn.Softmax(dim=2) - self.sigmoid = nn.Sigmoid() - - self.conv_q_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #g - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv_v_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #theta - self.softmax_left = nn.Softmax(dim=2) - - self.reset_parameters() - - def reset_parameters(self): - kaiming_init(self.conv_q_right, mode='fan_in') - kaiming_init(self.conv_v_right, mode='fan_in') - kaiming_init(self.conv_q_left, mode='fan_in') - kaiming_init(self.conv_v_left, mode='fan_in') - - self.conv_q_right.inited = True - self.conv_v_right.inited = True - self.conv_q_left.inited = True - self.conv_v_left.inited = True - # HR spatial attention - def spatial_attention(self, x): - input_x = self.conv_v_right(x) - batch, channel, height, width = input_x.size() - - input_x = input_x.view(batch, channel, height * width) - context_mask = self.conv_q_right(x) - context_mask = context_mask.view(batch, 1, height * width) - context_mask = self.softmax_right(context_mask) - - context = torch.matmul(input_x, context_mask.transpose(1,2)) - context = context.unsqueeze(-1) - context = self.conv_up(context) - - mask_ch = self.sigmoid(context) - - out = x * mask_ch - - return out - # HR spectral attention - def spectral_attention(self, x): - - g_x = self.conv_q_left(x) - batch, channel, height, width = g_x.size() - - avg_x = self.avg_pool(g_x) - batch, channel, avg_x_h, avg_x_w = avg_x.size() - - avg_x = avg_x.view(batch, channel, avg_x_h * avg_x_w).permute(0, 2, 1) - theta_x = self.conv_v_left(x).view(batch, self.inter_planes, height * width) - context = torch.matmul(avg_x, theta_x) - context = self.softmax_left(context) - context = context.view(batch, 1, height, width) - - mask_sp = self.sigmoid(context) - - out = x * mask_sp - - return out - - def forward(self, x): - context_spectral = self.spectral_attention(x) - context_spatial = self.spatial_attention(x) - out = context_spatial + context_spectral - return out - - -class HDNet(nn.Module): - - def __init__(self, in_ch=28, out_ch=28, conv=default_conv): - super(HDNet, self).__init__() - - n_resblocks = 16 - n_feats = 64 - kernel_size = 3 - act = nn.ReLU(True) - - # define head module - m_head = [conv(in_ch, n_feats, kernel_size)] - - # define body module - m_body = [ - ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - ) for _ in range(n_resblocks) - ] - m_body.append(SDL_attention(inplanes = n_feats, planes = n_feats)) - m_body.append(EFF(nin=n_feats, nout=n_feats, num_splits=4)) - - for i in range(1, n_resblocks): - m_body.append(ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - )) - - m_body.append(conv(n_feats, n_feats, kernel_size)) - - m_tail = [conv(n_feats, out_ch, kernel_size)] - - self.head = nn.Sequential(*m_head) - self.body = nn.Sequential(*m_body) - self.tail = nn.Sequential(*m_tail) - - def forward(self, x, input_mask=None): - x = self.head(x) - - res = self.body(x) - res += x - - x = self.tail(res) - - return x - -# frequency domain learning(FDL) -class FDL(nn.Module): - def __init__(self, loss_weight=1.0, alpha=1.0, patch_factor=1, ave_spectrum=False, log_matrix=False, batch_matrix=False): - super(FDL, self).__init__() - self.loss_weight = loss_weight - self.alpha = alpha - self.patch_factor = patch_factor - self.ave_spectrum = ave_spectrum - self.log_matrix = log_matrix - self.batch_matrix = batch_matrix - - def tensor2freq(self, x): - patch_factor = self.patch_factor - _, _, h, w = x.shape - assert h % patch_factor == 0 and w % patch_factor == 0, ( - 'Patch factor should be divisible by image height and width') - patch_list = [] - patch_h = h // patch_factor - patch_w = w // patch_factor - for i in range(patch_factor): - for j in range(patch_factor): - patch_list.append(x[:, :, i * patch_h:(i + 1) * patch_h, j * patch_w:(j + 1) * patch_w]) - - y = torch.stack(patch_list, 1) - - return torch.rfft(y, 2, onesided=False, normalized=True) - - def loss_formulation(self, recon_freq, real_freq, matrix=None): - if matrix is not None: - weight_matrix = matrix.detach() - else: - matrix_tmp = (recon_freq - real_freq) ** 2 - matrix_tmp = torch.sqrt(matrix_tmp[..., 0] + matrix_tmp[..., 1]) ** self.alpha - if self.log_matrix: - matrix_tmp = torch.log(matrix_tmp + 1.0) - - if self.batch_matrix: - matrix_tmp = matrix_tmp / matrix_tmp.max() - else: - matrix_tmp = matrix_tmp / matrix_tmp.max(-1).values.max(-1).values[:, :, :, None, None] - - matrix_tmp[torch.isnan(matrix_tmp)] = 0.0 - matrix_tmp = torch.clamp(matrix_tmp, min=0.0, max=1.0) - weight_matrix = matrix_tmp.clone().detach() - - assert weight_matrix.min().item() >= 0 and weight_matrix.max().item() <= 1, ( - 'The values of spectrum weight matrix should be in the range [0, 1], ' - 'but got Min: %.10f Max: %.10f' % (weight_matrix.min().item(), weight_matrix.max().item())) - - tmp = (recon_freq - real_freq) ** 2 - freq_distance = tmp[..., 0] + tmp[..., 1] - - loss = weight_matrix * freq_distance - return torch.mean(loss) - - def forward(self, pred, target, matrix=None, **kwargs): - - pred_freq = self.tensor2freq(pred) - target_freq = self.tensor2freq(target) - - if self.ave_spectrum: - pred_freq = torch.mean(pred_freq, 0, keepdim=True) - target_freq = torch.mean(target_freq, 0, keepdim=True) - - return self.loss_formulation(pred_freq, target_freq, matrix) * self.loss_weight diff --git a/MST/simulation/test_code/architecture/Lambda_Net.py b/MST/simulation/test_code/architecture/Lambda_Net.py deleted file mode 100755 index ddac0aa..0000000 --- a/MST/simulation/test_code/architecture/Lambda_Net.py +++ /dev/null @@ -1,179 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum - -class LambdaNetAttention(nn.Module): - def __init__( - self, - dim, - ): - super().__init__() - - self.dim = dim - self.to_q = nn.Linear(dim, dim//8, bias=False) - self.to_k = nn.Linear(dim, dim//8, bias=False) - self.to_v = nn.Linear(dim, dim, bias=False) - self.rescale = (dim//8)**-0.5 - self.gamma = nn.Parameter(torch.ones(1)) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0,2,3,1) - b, h, w, c = x.shape - - # Reshape to (B,N,C), where N = window_size[0]*window_size[1] is the length of sentence - x_inp = rearrange(x, 'b h w c -> b (h w) c') - - # produce query, key and value - q = self.to_q(x_inp) - k = self.to_k(x_inp) - v = self.to_v(x_inp) - - # attention - sim = einsum('b i d, b j d -> b i j', q, k)*self.rescale - attn = sim.softmax(dim=-1) - - # aggregate - out = einsum('b i j, b j d -> b i d', attn, v) - - # merge blocks back to original feature map - out = rearrange(out, 'b (h w) c -> b h w c', h=h, w=w) - out = self.gamma*out + x - return out.permute(0,3,1,2) - -class triple_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(triple_conv, self).__init__() - self.t_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.t_conv(x) - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.d_conv(x) - return x - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class Lambda_Net(nn.Module): - - def __init__(self, out_ch=28): - super(Lambda_Net, self).__init__() - self.conv_in = nn.Conv2d(1+28, 28, 3, padding=1) - - # encoder - self.conv_down1 = triple_conv(28, 32) - self.conv_down2 = triple_conv(32, 64) - self.conv_down3 = triple_conv(64, 128) - self.conv_down4 = triple_conv(128, 256) - self.conv_down5 = double_conv(256, 512) - self.conv_down6 = double_conv(512, 1024) - - self.maxpool = nn.MaxPool2d(2) - - # decoder - self.upsample5 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2) - self.upsample4 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2) - self.upsample3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2) - self.upsample2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2) - self.upsample1 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2) - - self.conv_up1 = triple_conv(32+32, 32) - self.conv_up2 = triple_conv(64+64, 64) - self.conv_up3 = triple_conv(128+128, 128) - self.conv_up4 = triple_conv(256+256, 256) - self.conv_up5 = double_conv(512+512, 512) - - # attention - self.attention = LambdaNetAttention(dim=128) - - self.conv_last1 = nn.Conv2d(32, 6, 3,1,1) - self.conv_last2 = nn.Conv2d(38, 32, 3,1,1) - self.conv_last3 = nn.Conv2d(32, 12, 3,1,1) - self.conv_last4 = nn.Conv2d(44, 32, 3,1,1) - self.conv_last5 = nn.Conv2d(32, out_ch, 1) - self.act = nn.ReLU() - - def forward(self, x, input_mask=None): - if input_mask == None: - input_mask = torch.zeros((1,28,256,310)).cuda() - x = x/28*2 - x = self.conv_in(torch.cat([x.unsqueeze(1), input_mask], dim=1)) - b, c, h_inp, w_inp = x.shape - hb, wb = 32, 32 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - res0 = x - conv1 = self.conv_down1(x) - x = self.maxpool(conv1) - conv2 = self.conv_down2(x) - x = self.maxpool(conv2) - conv3 = self.conv_down3(x) - x = self.maxpool(conv3) - conv4 = self.conv_down4(x) - x = self.maxpool(conv4) - conv5 = self.conv_down5(x) - x = self.maxpool(conv5) - conv6 = self.conv_down6(x) - - x = self.upsample5(conv6) - x = torch.cat([x, conv5], dim=1) - x = self.conv_up5(x) - - x = self.upsample4(x) - x = torch.cat([x, conv4], dim=1) - x = self.conv_up4(x) - - x = self.upsample3(x) - x = torch.cat([x, conv3], dim=1) - x = self.conv_up3(x) - x = self.attention(x) - - x = self.upsample2(x) - x = torch.cat([x, conv2], dim=1) - x = self.conv_up2(x) - - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - x = self.conv_up1(x) - - res1 = x - out1 = self.act(self.conv_last1(x)) - x = self.conv_last2(torch.cat([res1,out1],dim=1)) - - res2 = x - out2 = self.act(self.conv_last3(x)) - out3 = self.conv_last4(torch.cat([res2, out2], dim=1)) - - out = self.conv_last5(out3)+res0 - out = out[:, :, :h_inp, :w_inp] - - return shift_back_3d(out)[:, :, :, :256] diff --git a/MST/simulation/test_code/architecture/MST.py b/MST/simulation/test_code/architecture/MST.py deleted file mode 100755 index 3686dfc..0000000 --- a/MST/simulation/test_code/architecture/MST.py +++ /dev/null @@ -1,311 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MaskGuidedMechanism(nn.Module): - def __init__( - self, n_feat): - super(MaskGuidedMechanism, self).__init__() - - self.conv1 = nn.Conv2d(n_feat, n_feat, kernel_size=1, bias=True) - self.conv2 = nn.Conv2d(n_feat, n_feat, kernel_size=1, bias=True) - self.depth_conv = nn.Conv2d(n_feat, n_feat, kernel_size=5, padding=2, bias=True, groups=n_feat) - - def forward(self, mask_shift): - # x: b,c,h,w - [bs, nC, row, col] = mask_shift.shape - mask_shift = self.conv1(mask_shift) - attn_map = torch.sigmoid(self.depth_conv(self.conv2(mask_shift))) - res = mask_shift * attn_map - mask_shift = res + mask_shift - mask_emb = shift_back(mask_shift) - return mask_emb - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.mm = MaskGuidedMechanism(dim) - self.dim = dim - - def forward(self, x_in, mask=None): - """ - x_in: [b,h,w,c] - mask: [1,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - mask_attn = self.mm(mask.permute(0,3,1,2)).permute(0,2,3,1) - if b != 0: - mask_attn = (mask_attn[0, :, :, :]).expand([b, h, w, c]) - q, k, v, mask_attn = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp, mask_attn.flatten(1, 2))) - v = v * mask_attn - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x, mask): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x, mask=mask.permute(0, 2, 3, 1)) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - -class MST(nn.Module): - def __init__(self, dim=28, stage=3, num_blocks=[2,2,2]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(28, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False) - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, 28, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - if mask == None: - mask = torch.zeros((1,28,256,310)).cuda() - - # Embedding - fea = self.lrelu(self.embedding(x)) - - # Encoder - fea_encoder = [] - masks = [] - for (MSAB, FeaDownSample, MaskDownSample) in self.encoder_layers: - fea = MSAB(fea, mask) - masks.append(mask) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - mask = MaskDownSample(mask) - - # Bottleneck - fea = self.bottleneck(fea, mask) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - mask = masks[self.stage - 1 - i] - fea = LeWinBlcok(fea, mask) - - # Mapping - out = self.mapping(fea) + x - - return out - - - - - - - - - - - - - - - - - - - - - - diff --git a/MST/simulation/test_code/architecture/MST_Plus_Plus.py b/MST/simulation/test_code/architecture/MST_Plus_Plus.py deleted file mode 100755 index e7a4826..0000000 --- a/MST/simulation/test_code/architecture/MST_Plus_Plus.py +++ /dev/null @@ -1,320 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.dim = dim - - def forward(self, x_in): - """ - x_in: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp)) - v = v - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class MST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2,4,4]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (MSAB, FeaDownSample) in self.encoder_layers: - fea = MSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = LeWinBlcok(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - -class MST_Plus_Plus(nn.Module): - def __init__(self, in_channels=3, out_channels=28, n_feat=28, stage=3): - super(MST_Plus_Plus, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2,bias=False) - modules_body = [MST(dim=n_feat, stage=2, num_blocks=[1,1,1]) for _ in range(stage)] - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2,bias=False) - - def initial_x(self, y, Phi): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,256] - :return: z: [b,28,256,256] - """ - x = self.fution(torch.cat([y, Phi], dim=1)) - return x - - def forward(self, y, Phi=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - if Phi==None: - Phi = torch.rand((1,28,256,256)).cuda() - x = self.initial_x(y, Phi) - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] - - - - - - - - - - - - - - diff --git a/MST/simulation/test_code/architecture/TSA_Net.py b/MST/simulation/test_code/architecture/TSA_Net.py deleted file mode 100755 index 1c9dc42..0000000 --- a/MST/simulation/test_code/architecture/TSA_Net.py +++ /dev/null @@ -1,330 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -_NORM_BONE = False - - -def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding=the_padding) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_planes) - if flag_norm: - return nn.Sequential(conv,norm,activation) if flag_norm_act else nn.Sequential(conv,activation,norm) - else: - return nn.Sequential(conv,activation) - -def conv1x1_block(in_planes, out_planes, flag_norm=False): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0,bias=False) - norm = nn.BatchNorm2d(out_planes) - return nn.Sequential(conv,norm) if flag_norm else conv - -def fully_block(in_dim, out_dim, flag_norm=False, flag_norm_act=True): - fc = nn.Linear(in_dim, out_dim) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_dim) - if flag_norm: - return nn.Sequential(fc,norm,activation) if flag_norm_act else nn.Sequential(fc,activation,norm) - else: - return nn.Sequential(fc,activation) - -class Res2Net(nn.Module): - def __init__(self, inChannel, uPlane, scale=4): - super(Res2Net, self).__init__() - self.uPlane = uPlane - self.scale = scale - - self.conv_init = nn.Conv2d(inChannel, uPlane * scale, kernel_size=1, bias=False) - self.bn_init = nn.BatchNorm2d(uPlane * scale) - - convs = [] - bns = [] - for i in range(self.scale - 1): - convs.append(nn.Conv2d(self.uPlane, self.uPlane, kernel_size=3, stride=1, padding=1, bias=False)) - bns.append(nn.BatchNorm2d(self.uPlane)) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - - self.conv_end = nn.Conv2d(uPlane * scale, inChannel, kernel_size=1, bias=False) - self.bn_end = nn.BatchNorm2d(inChannel) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - - out = self.conv_init(x) - out = self.bn_init(out) - out = self.relu(out) - - spx = torch.split(out, self.uPlane, 1) - for i in range(self.scale - 1): - if i == 0: - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.convs[i](sp) - sp = self.relu(self.bns[i](sp)) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - out = torch.cat((out, spx[self.scale - 1]), 1) - - out = self.conv_end(out) - out = self.bn_end(out) - return out - -_NORM_ATTN = True -_NORM_FC = False - - -class TSA_Transform(nn.Module): - """ Spectral-Spatial Self-Attention """ - - def __init__(self, uSpace, inChannel, outChannel, nHead, uAttn, mode=[0, 1], flag_mask=False, gamma_learn=False): - super(TSA_Transform, self).__init__() - ''' ------------------------------------------ - uSpace: - uHeight: the [-2] dim of the 3D tensor - uWidth: the [-1] dim of the 3D tensor - inChannel: - the number of Channel of the input tensor - outChannel: - the number of Channel of the output tensor - nHead: - the number of Head of the input tensor - uAttn: - uSpatial: the dim of the spatial features - uSpectral: the dim of the spectral features - mask: - The Spectral Smoothness Mask - {mode} and {gamma_learn} is just for variable selection - ------------------------------------------ ''' - - self.nHead = nHead - self.uAttn = uAttn - self.outChannel = outChannel - self.uSpatial = nn.Parameter(torch.tensor(float(uAttn[0])), requires_grad=False) - self.uSpectral = nn.Parameter(torch.tensor(float(uAttn[1])), requires_grad=False) - self.mask = nn.Parameter(Spectral_Mask(outChannel), requires_grad=False) if flag_mask else None - self.attn_scale = nn.Parameter(torch.tensor(1.1), requires_grad=False) if flag_mask else None - self.gamma = nn.Parameter(torch.tensor(1.0), requires_grad=gamma_learn) - - if sum(mode) > 0: - down_sample = [] - scale = 1 - cur_channel = outChannel - for i in range(sum(mode)): - scale *= 2 - down_sample.append(conv_block(cur_channel, 2 * cur_channel, 3, 2, 1, _NORM_ATTN)) - cur_channel = 2 * cur_channel - self.cur_channel = cur_channel - self.down_sample = nn.Sequential(*down_sample) - self.up_sample = nn.ConvTranspose2d(outChannel * scale, outChannel, scale, scale) - else: - self.down_sample = None - self.up_sample = None - - spec_dim = int(uSpace[0] / 4 - 3) * int(uSpace[1] / 4 - 3) - self.preproc = conv1x1_block(inChannel, outChannel, _NORM_ATTN) - self.query_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.query_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.query_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.key_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.key_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.key_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.value = conv1x1_block(outChannel, nHead * outChannel, _NORM_ATTN) - self.aggregation = nn.Linear(nHead * outChannel, outChannel) - - def forward(self, image): - feat = self.preproc(image) - feat_qx = self.query_x(feat, 'X') - feat_qy = self.query_y(feat, 'Y') - feat_qlambda = self.query_lambda(feat) - feat_kx = self.key_x(feat, 'X') - feat_ky = self.key_y(feat, 'Y') - feat_klambda = self.key_lambda(feat) - feat_value = self.value(feat) - - feat_qx = torch.cat(torch.split(feat_qx, 1, dim=1)).squeeze(dim=1) - feat_qy = torch.cat(torch.split(feat_qy, 1, dim=1)).squeeze(dim=1) - feat_kx = torch.cat(torch.split(feat_kx, 1, dim=1)).squeeze(dim=1) - feat_ky = torch.cat(torch.split(feat_ky, 1, dim=1)).squeeze(dim=1) - feat_qlambda = torch.cat(torch.split(feat_qlambda, self.uAttn[1], dim=-1)) - feat_klambda = torch.cat(torch.split(feat_klambda, self.uAttn[1], dim=-1)) - feat_value = torch.cat(torch.split(feat_value, self.outChannel, dim=1)) - - energy_x = torch.bmm(feat_qx, feat_kx.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_y = torch.bmm(feat_qy, feat_ky.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_lambda = torch.bmm(feat_qlambda, feat_klambda.permute(0, 2, 1)) / torch.sqrt(self.uSpectral) - - attn_x = F.softmax(energy_x, dim=-1) - attn_y = F.softmax(energy_y, dim=-1) - attn_lambda = F.softmax(energy_lambda, dim=-1) - if self.mask is not None: - attn_lambda = (attn_lambda + self.mask) / torch.sqrt(self.attn_scale) - - pro_feat = feat_value if self.down_sample is None else self.down_sample(feat_value) - batchhead, dim_c, dim_x, dim_y = pro_feat.size() - attn_x_repeat = attn_x.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_x, dim_x) - attn_y_repeat = attn_y.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_y, dim_y) - pro_feat = pro_feat.view(-1, dim_x, dim_y) - pro_feat = torch.bmm(pro_feat, attn_y_repeat.permute(0, 2, 1)) - pro_feat = torch.bmm(pro_feat.permute(0, 2, 1), attn_x_repeat.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, dim_c, dim_x, dim_y) - - if self.up_sample is not None: - pro_feat = self.up_sample(pro_feat) - _, _, dim_x, dim_y = pro_feat.size() - pro_feat = pro_feat.contiguous().view(batchhead, self.outChannel, -1).permute(0, 2, 1) - pro_feat = torch.bmm(pro_feat, attn_lambda.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, self.outChannel, dim_x, dim_y) - pro_feat = torch.cat(torch.split(pro_feat, int(batchhead / self.nHead), dim=0), dim=1).permute(0, 2, 3, 1) - pro_feat = self.aggregation(pro_feat).permute(0, 3, 1, 2) - out = self.gamma * pro_feat + feat - return out, (attn_x, attn_y, attn_lambda) - - -class Feature_Spatial(nn.Module): - """ Spatial Feature Generation Component """ - - def __init__(self, inChannel, nHead, shiftDim, outDim, mode): - super(Feature_Spatial, self).__init__() - kernel = [(1, 5), (3, 5)] - stride = [(1, 2), (2, 2)] - padding = [(0, 2), (1, 2)] - self.conv1 = conv_block(inChannel, nHead, kernel[mode[0]], stride[mode[0]], padding[mode[0]], _NORM_ATTN) - self.conv2 = conv_block(nHead, nHead, kernel[mode[1]], stride[mode[1]], padding[mode[1]], _NORM_ATTN) - self.fully = fully_block(shiftDim, outDim, _NORM_FC) - - def forward(self, image, direction): - if direction == 'Y': - image = image.permute(0, 1, 3, 2) - feat = self.conv1(image) - feat = self.conv2(feat) - feat = self.fully(feat) - return feat - - -class Feature_Spectral(nn.Module): - """ Spectral Feature Generation Component """ - - def __init__(self, inChannel, nHead, viewDim, outDim): - super(Feature_Spectral, self).__init__() - self.inChannel = inChannel - self.conv1 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.conv2 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.fully = fully_block(viewDim, int(nHead * outDim), _NORM_FC) - - def forward(self, image): - bs = image.size(0) - feat = self.conv1(image) - feat = self.conv2(feat) - feat = feat.view(bs, self.inChannel, -1) - feat = self.fully(feat) - return feat - - -def Spectral_Mask(dim_lambda): - '''After put the available data into the model, we use this mask to avoid outputting the estimation of itself.''' - orig = (np.cos(np.linspace(-1, 1, num=2 * dim_lambda - 1) * np.pi) + 1.0) / 2.0 - att = np.zeros((dim_lambda, dim_lambda)) - for i in range(dim_lambda): - att[i, :] = orig[dim_lambda - 1 - i:2 * dim_lambda - 1 - i] - AM_Mask = torch.from_numpy(att.astype(np.float32)).unsqueeze(0) - return AM_Mask - -class TSA_Net(nn.Module): - - def __init__(self, in_ch=28, out_ch=28): - super(TSA_Net, self).__init__() - - self.tconv_down1 = Encoder_Triblock(in_ch, 64, False) - self.tconv_down2 = Encoder_Triblock(64, 128, False) - self.tconv_down3 = Encoder_Triblock(128, 256) - self.tconv_down4 = Encoder_Triblock(256, 512) - - self.bottom1 = conv_block(512, 1024) - self.bottom2 = conv_block(1024, 1024) - - self.tconv_up4 = Decoder_Triblock(1024, 512) - self.tconv_up3 = Decoder_Triblock(512, 256) - self.transform3 = TSA_Transform((64, 64), 256, 256, 8, (64, 80), [0, 0]) - self.tconv_up2 = Decoder_Triblock(256, 128) - self.transform2 = TSA_Transform((128, 128), 128, 128, 8, (64, 40), [1, 0]) - self.tconv_up1 = Decoder_Triblock(128, 64) - self.transform1 = TSA_Transform((256, 256), 64, 28, 8, (48, 30), [1, 1], True) - - self.conv_last = nn.Conv2d(out_ch, out_ch, 1) - self.afn_last = nn.Sigmoid() - - def forward(self, x, input_mask=None): - enc1, enc1_pre = self.tconv_down1(x) - enc2, enc2_pre = self.tconv_down2(enc1) - enc3, enc3_pre = self.tconv_down3(enc2) - enc4, enc4_pre = self.tconv_down4(enc3) - # enc5,enc5_pre = self.tconv_down5(enc4) - - bottom = self.bottom1(enc4) - bottom = self.bottom2(bottom) - - # dec5 = self.tconv_up5(bottom,enc5_pre) - dec4 = self.tconv_up4(bottom, enc4_pre) - dec3 = self.tconv_up3(dec4, enc3_pre) - dec3, _ = self.transform3(dec3) - dec2 = self.tconv_up2(dec3, enc2_pre) - dec2, _ = self.transform2(dec2) - dec1 = self.tconv_up1(dec2, enc1_pre) - dec1, _ = self.transform1(dec1) - - dec1 = self.conv_last(dec1) - output = self.afn_last(dec1) - - return output - - -class Encoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Encoder_Triblock, self).__init__() - - self.layer1 = conv_block(inChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - if flag_res: - self.layer2 = Res2Net(outChannel, int(outChannel / 4)) - else: - self.layer2 = conv_block(outChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - - self.pool = nn.MaxPool2d(nPool) if flag_Pool else None - - def forward(self, x): - feat = self.layer1(x) - feat = self.layer2(feat) - - feat_pool = self.pool(feat) if self.pool is not None else feat - return feat_pool, feat - - -class Decoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Decoder_Triblock, self).__init__() - - self.layer1 = nn.Sequential( - nn.ConvTranspose2d(inChannel, outChannel, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - if flag_res: - self.layer2 = Res2Net(int(outChannel * 2), int(outChannel / 2)) - else: - self.layer2 = conv_block(outChannel * 2, outChannel * 2, nKernal, flag_norm=_NORM_BONE) - self.layer3 = conv_block(outChannel * 2, outChannel, nKernal, flag_norm=_NORM_BONE) - - def forward(self, feat_dec, feat_enc): - feat_dec = self.layer1(feat_dec) - diffY = feat_enc.size()[2] - feat_dec.size()[2] - diffX = feat_enc.size()[3] - feat_dec.size()[3] - if diffY != 0 or diffX != 0: - print('Padding for size mismatch ( Enc:', feat_enc.size(), 'Dec:', feat_dec.size(), ')') - feat_dec = F.pad(feat_dec, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) - feat = torch.cat([feat_dec, feat_enc], dim=1) - feat = self.layer2(feat) - feat = self.layer3(feat) - return feat \ No newline at end of file diff --git a/MST/simulation/test_code/architecture/__init__.py b/MST/simulation/test_code/architecture/__init__.py deleted file mode 100755 index fdb77ea..0000000 --- a/MST/simulation/test_code/architecture/__init__.py +++ /dev/null @@ -1,67 +0,0 @@ -import torch -from .MST import MST -from .GAP_Net import GAP_net -from .ADMM_Net import ADMM_net -from .TSA_Net import TSA_Net -from .HDNet import HDNet, FDL -from .DGSMP import HSI_CS -from .BIRNAT import BIRNAT -from .MST_Plus_Plus import MST_Plus_Plus -from .Lambda_Net import Lambda_Net -from .CST import CST -from .DAUHST import DAUHST -from .BiSRNet import BiSRNet - -def model_generator(method, pretrained_model_path=None): - if method == 'mst_s': - model = MST(dim=28, stage=2, num_blocks=[2, 2, 2]).cuda() - elif method == 'mst_m': - model = MST(dim=28, stage=2, num_blocks=[2, 4, 4]).cuda() - elif method == 'mst_l': - model = MST(dim=28, stage=2, num_blocks=[4, 7, 5]).cuda() - elif method == 'gap_net': - model = GAP_net().cuda() - elif method == 'admm_net': - model = ADMM_net().cuda() - elif method == 'tsa_net': - model = TSA_Net().cuda() - elif method == 'hdnet': - model = HDNet().cuda() - fdl_loss = FDL(loss_weight=0.7, - alpha=2.0, - patch_factor=4, - ave_spectrum=True, - log_matrix=True, - batch_matrix=True, - ).cuda() - elif method == 'dgsmp': - model = HSI_CS(Ch=28, stages=4).cuda() - elif method == 'birnat': - model = BIRNAT().cuda() - elif method == 'mst_plus_plus': - model = MST_Plus_Plus(in_channels=28, out_channels=28, n_feat=28, stage=3).cuda() - elif method == 'lambda_net': - model = Lambda_Net(out_ch=28).cuda() - elif method == 'cst_s': - model = CST(num_blocks=[1, 1, 2], sparse=True).cuda() - elif method == 'cst_m': - model = CST(num_blocks=[2, 2, 2], sparse=True).cuda() - elif method == 'cst_l': - model = CST(num_blocks=[2, 4, 6], sparse=True).cuda() - elif method == 'cst_l_plus': - model = CST(num_blocks=[2, 4, 6], sparse=False).cuda() - elif 'dauhst' in method: - num_iterations = int(method.split('_')[1][0]) - model = DAUHST(num_iterations=num_iterations).cuda() - elif method == 'bisrnet': - model = BiSRNet(in_channels=28, out_channels=28, n_feat=28, stage=1, num_blocks=[1,1,1]).cuda() - else: - print(f'Method {method} is not defined !!!!') - if pretrained_model_path is not None: - print(f'load model from {pretrained_model_path}') - checkpoint = torch.load(pretrained_model_path) - model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint.items()}, - strict=True) - if method == 'hdnet': - return model,fdl_loss - return model \ No newline at end of file diff --git a/MST/simulation/test_code/architecture/__pycache__/ADMM_Net.cpython-36.pyc b/MST/simulation/test_code/architecture/__pycache__/ADMM_Net.cpython-36.pyc deleted file mode 100755 index a9b0feaba6b7c7a4019b3f0b2ef78297045afbaf..0000000000000000000000000000000000000000 GIT binary patch literal 0 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Reconstruction Toolbox") -parser.add_argument('--template', default='mst', - help='You can set various templates in option.py') - -# Hardware specifications -parser.add_argument("--gpu_id", type=str, default='0') - -# Data specifications -parser.add_argument('--data_root', type=str, default='../../datasets/', help='dataset directory') - -# Saving specifications -parser.add_argument('--outf', type=str, default='./exp/mst_s/', help='saving_path') - -# Model specifications -parser.add_argument('--method', type=str, default='mst_s', help='method name') -parser.add_argument('--pretrained_model_path', type=str, default=None, help='pretrained model directory') -parser.add_argument("--input_setting", type=str, default='H', - help='the input measurement of the network: H, HM or Y') -parser.add_argument("--input_mask", type=str, default='Phi', - help='the input mask of the network: Phi, Phi_PhiPhiT or None') - -opt = parser.parse_args() -template.set_template(opt) - -opt.mask_path = f"{opt.data_root}/TSA_simu_data/" -opt.test_path = f"{opt.data_root}/TSA_simu_data/Truth/" - -for arg in vars(opt): - if vars(opt)[arg] == 'True': - vars(opt)[arg] = True - elif vars(opt)[arg] == 'False': - vars(opt)[arg] = False diff --git a/MST/simulation/test_code/template.py b/MST/simulation/test_code/template.py deleted file mode 100755 index 7559ca4..0000000 --- a/MST/simulation/test_code/template.py +++ /dev/null @@ -1,42 +0,0 @@ -def set_template(args): - # Set the templates here - if args.template.find('mst') >= 0: - args.input_setting = 'H' - args.input_mask = 'Phi' - - if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0 or args.template.find('dnu')>= 0 \ - or args.template.find('dauhst')>= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - - if args.template.find('tsa_net') >= 0: - args.input_setting = 'HM' - args.input_mask = None - - if args.template.find('hdnet') >= 0: - args.input_setting = 'H' - args.input_mask = None - - if args.template.find('dgsmp') >= 0: - args.input_setting = 'Y' - args.input_mask = None - - if args.template.find('birnat') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - - if args.template.find('mst_plus_plus') >= 0: - args.input_setting = 'H' - args.input_mask = 'Mask' - - if args.template.find('bisrnet') >= 0: - args.input_setting = 'H' - args.input_mask = 'Mask' - - if args.template.find('cst') >= 0: - args.input_setting = 'H' - args.input_mask = 'Mask' - - if args.template.find('lambda_net') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' diff --git a/MST/simulation/test_code/test.py b/MST/simulation/test_code/test.py deleted file mode 100755 index 46267be..0000000 --- a/MST/simulation/test_code/test.py +++ /dev/null @@ -1,47 +0,0 @@ -from architecture import * -from utils import * -import scipy.io as scio -import torch -import os -import numpy as np -from option import opt - -os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID' -os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id -torch.backends.cudnn.enabled = True -torch.backends.cudnn.benchmark = True -if not torch.cuda.is_available(): - raise Exception('NO GPU!') - -# Intialize mask -mask3d_batch, input_mask = init_mask(opt.mask_path, opt.input_mask, 10) - -if not os.path.exists(opt.outf): - os.makedirs(opt.outf) - -def test(model): - test_data = LoadTest(opt.test_path) - test_gt = test_data.cuda().float() - input_meas = init_meas(test_gt, mask3d_batch, opt.input_setting) - model.eval() - with torch.no_grad(): - model_out = model(input_meas, input_mask) - pred = np.transpose(model_out.detach().cpu().numpy(), (0, 2, 3, 1)).astype(np.float32) - truth = np.transpose(test_gt.cpu().numpy(), (0, 2, 3, 1)).astype(np.float32) - model.train() - return pred, truth - -def main(): - # model - if opt.method == 'hdnet': - model, FDL_loss = model_generator(opt.method, opt.pretrained_model_path) - model = model.cuda() - else: - model = model_generator(opt.method, opt.pretrained_model_path).cuda() - pred, truth = test(model) - name = opt.outf + 'Test_result.mat' - print(f'Save reconstructed HSIs as {name}.') - scio.savemat(name, {'truth': truth, 'pred': pred}) - -if __name__ == '__main__': - main() \ No newline at end of file diff --git a/MST/simulation/test_code/utils.py b/MST/simulation/test_code/utils.py deleted file mode 100755 index 329fb92..0000000 --- a/MST/simulation/test_code/utils.py +++ /dev/null @@ -1,155 +0,0 @@ -import scipy.io as sio -import os -import numpy as np -import torch -import logging -from fvcore.nn import FlopCountAnalysis - - -def generate_masks(mask_path, batch_size): - mask = sio.loadmat(mask_path + '/mask.mat') - mask = mask['mask'] - mask3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28)) - mask3d = np.transpose(mask3d, [2, 0, 1]) - mask3d = torch.from_numpy(mask3d) - [nC, H, W] = mask3d.shape - mask3d_batch = mask3d.expand([batch_size, nC, H, W]).cuda().float() - return mask3d_batch - -def generate_shift_masks(mask_path, batch_size): - mask = sio.loadmat(mask_path + '/mask_3d_shift.mat') - mask_3d_shift = mask['mask_3d_shift'] - mask_3d_shift = np.transpose(mask_3d_shift, [2, 0, 1]) - mask_3d_shift = torch.from_numpy(mask_3d_shift) - [nC, H, W] = mask_3d_shift.shape - Phi_batch = mask_3d_shift.expand([batch_size, nC, H, W]).cuda().float() - Phi_s_batch = torch.sum(Phi_batch**2,1) - Phi_s_batch[Phi_s_batch==0] = 1 - # print(Phi_batch.shape, Phi_s_batch.shape) - return Phi_batch, Phi_s_batch - -def LoadTest(path_test): - scene_list = os.listdir(path_test) - scene_list.sort() - test_data = np.zeros((len(scene_list), 256, 256, 28)) - for i in range(len(scene_list)): - scene_path = path_test + scene_list[i] - img = sio.loadmat(scene_path)['img'] - test_data[i, :, :, :] = img - test_data = torch.from_numpy(np.transpose(test_data, (0, 3, 1, 2))) - return test_data - -def LoadMeasurement(path_test_meas): - img = sio.loadmat(path_test_meas)['simulation_test'] - test_data = img - test_data = torch.from_numpy(test_data) - return test_data - -def time2file_name(time): - year = time[0:4] - month = time[5:7] - day = time[8:10] - hour = time[11:13] - minute = time[14:16] - second = time[17:19] - time_filename = year + '_' + month + '_' + day + '_' + hour + '_' + minute + '_' + second - return time_filename - -def shuffle_crop(train_data, batch_size, crop_size=256): - index = np.random.choice(range(len(train_data)), batch_size) - processed_data = np.zeros((batch_size, crop_size, crop_size, 28), dtype=np.float32) - for i in range(batch_size): - h, w, _ = train_data[index[i]].shape - x_index = np.random.randint(0, h - crop_size) - y_index = np.random.randint(0, w - crop_size) - processed_data[i, :, :, :] = train_data[index[i]][x_index:x_index + crop_size, y_index:y_index + crop_size, :] - gt_batch = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))) - return gt_batch - -def gen_meas_torch(data_batch, mask3d_batch, Y2H=True, mul_mask=False): - [batch_size, nC, H, W] = data_batch.shape - mask3d_batch = (mask3d_batch[0, :, :, :]).expand([batch_size, nC, H, W]).cuda().float() # [10,28,256,256] - temp = shift(mask3d_batch * data_batch, 2) - meas = torch.sum(temp, 1) - if Y2H: - meas = meas / nC * 2 - H = shift_back(meas) - if mul_mask: - HM = torch.mul(H, mask3d_batch) - return HM - return H - return meas - -def shift(inputs, step=2): - [bs, nC, row, col] = inputs.shape - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, step * i:step * i + col] = inputs[:, i, :, :] - return output - -def shift_back(inputs, step=2): # input [bs,256,310] output [bs, 28, 256, 256] - [bs, row, col] = inputs.shape - nC = 28 - output = torch.zeros(bs, nC, row, col - (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, :] = inputs[:, :, step * i:step * i + col - (nC - 1) * step] - return output - -def gen_log(model_path): - logger = logging.getLogger() - logger.setLevel(logging.INFO) - formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s") - - log_file = model_path + '/log.txt' - fh = logging.FileHandler(log_file, mode='a') - fh.setLevel(logging.INFO) - fh.setFormatter(formatter) - - ch = logging.StreamHandler() - ch.setLevel(logging.INFO) - ch.setFormatter(formatter) - - logger.addHandler(fh) - logger.addHandler(ch) - return logger - -def init_mask(mask_path, mask_type, batch_size): - mask3d_batch = generate_masks(mask_path, batch_size) - if mask_type == 'Phi': - shift_mask3d_batch = shift(mask3d_batch) - input_mask = shift_mask3d_batch - elif mask_type == 'Phi_PhiPhiT': - Phi_batch, Phi_s_batch = generate_shift_masks(mask_path, batch_size) - input_mask = (Phi_batch, Phi_s_batch) - elif mask_type == 'Mask': - input_mask = mask3d_batch - elif mask_type == None: - input_mask = None - return mask3d_batch, input_mask - -def init_meas(gt, mask, input_setting): - if input_setting == 'H': - input_meas = gen_meas_torch(gt, mask, Y2H=True, mul_mask=False) - elif input_setting == 'HM': - input_meas = gen_meas_torch(gt, mask, Y2H=True, mul_mask=True) - elif input_setting == 'Y': - input_meas = gen_meas_torch(gt, mask, Y2H=False, mul_mask=True) - return input_meas - -def my_summary(test_model, H = 256, W = 256, C = 28, N = 1): - model = test_model.cuda() - print(model) - inputs = torch.randn((N, C, H, W)).cuda() - flops = FlopCountAnalysis(model,inputs) - n_param = sum([p.nelement() for p in model.parameters()]) - print(f'GMac:{flops.total()/(1024*1024*1024)}') - print(f'Params:{n_param}') - -def my_summary_bnn(test_model, H = 256, W = 256, C = 28, N = 1): - model = test_model.cuda() - print(model) - inputs = torch.randn((N, C, H, W)).cuda() - flops = FlopCountAnalysis(model,inputs) - n_param = sum([p.nelement() for p in model.parameters()]) - print(f'GMac:{flops.total()/(1024*1024*1024*58)}') - print(f'Params:{n_param / 32}') \ No newline at end of file diff --git a/MST/simulation/train_code/__pycache__/option.cpython-36.pyc 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zfF=^VHMK?t`rLK@0KH0N{*VKolKc_LACqwLdHL#3*!w2QIjQ^(#{LdhQU-CvkSLdX z%8)Y`=@{i^-u4l0m`7j}Qbhn9LIDt!9@q)|jAwlK7x2Fa4v%*@;N{Q>uy`^TFbm!| zd8&Xn;qZ5c8%(*VQb?^qLC)v}d_rnRr~zMl?OHu)5Xe`O&DS?yyVi*x#vQ`>O0t7n z#QmhckDH@t8wbIy^fAI!oz?9wYNqf|l!Ev7QD(8l{W!gcer#-%pv(7I|J~g?zh;Yf zq|%duwQvW=g}Xp49o&>L@07K8KQ(c^J7t#YT2J~op_ce_oxC(0@+a|lF)twDwbcki za!#>svt`{z>ztl!0r9qmN&f)3CyXQx+#MAkm-V2$B`xt-{}XT<9_LIxv}WE*ocs`- z6gG|A4!zlmtty8^xk{%(eh)+^)A z!Ch5TgNU$83>A~AQy11QNc|7Rj zUmrglZ4J`a(fZr1xbZ;Xz-Cf^80*cn&(dsQ-oy_{xJN(-1Nk%MKP^}mwSK$T>;u5z zj1RxDfxOcE1)umaNOdXyCB%F2sq-=@HD*70g6Ro$$(WDL{|r6I{M%^uDlX(dgO!rp zUxN2H+Jh%s_&1T;?&n3g3MSh?{sWZ%M?vt=pJG%%Xtl&rOrDwZV$Mrsy+X1>a+~BW zl6Og1sL6{8DSMhECXuwwge1{d99e8rG$t!Qvr$nyVXKX9K^ntO{2hh6 32-bit - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - BiSRNet_Block(dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - BinaryConv2d_Down(dim_stage, dim_stage * 2, 3, 1, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = BiSRNet_Block( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - BinaryConv2d_Up(dim_stage, dim_stage // 2, 3, 1, 1, bias=False), - BinaryConv2d_Fusion_Decrease(dim_stage, dim_stage // 2, 1, 1, bias=False), - BiSRNet_Block( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = BinaryConv2d(self.dim, out_dim, 3, 1, 1, bias=False) # 1-bit -> 32-bit - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (BiSRNet_Block, FeaDownSample) in self.encoder_layers: - # stx() - fea = BiSRNet_Block(fea) - fea_encoder.append(fea) - # stx() - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, BiSRNet_Block) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = BiSRNet_Block(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - - - -class BiSRNet(nn.Module): - ''' - Only 3 layers are 32-bit conv - ''' - def __init__(self, in_channels=28, out_channels=28, n_feat=28, stage=3, num_blocks=[1,1,1]): - super(BiSRNet, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2,bias=False) # 1-bit -> 32-bit - modules_body = [BiSRNet_body(dim=n_feat, stage=2, num_blocks=num_blocks) for _ in range(stage)] - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) # 1-bit -> 32-bit - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2,bias=False) # 1-bit -> 32-bit - - def initial_x(self, y, Phi): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,256] - :return: z: [b,28,256,256] - """ - x = self.fution(torch.cat([y, Phi], dim=1)) - return x - - def forward(self, y, Phi=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - if Phi==None: - Phi = torch.rand((1,28,256,256)).cuda() - # Phi = torch.rand((1,28,256,256)).to(device) - x = self.initial_x(y, Phi) - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] \ No newline at end of file diff --git a/MST/simulation/train_code/architecture/CST.py b/MST/simulation/train_code/architecture/CST.py deleted file mode 100755 index 8e58c50..0000000 --- a/MST/simulation/train_code/architecture/CST.py +++ /dev/null @@ -1,591 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out -from collections import defaultdict, Counter -import numpy as np -from tqdm import tqdm -import random - -def uniform(a, b, shape, device='cuda'): - return (b - a) * torch.rand(shape, device=device) + a - -class AsymmetricTransform: - - def Q(self, *args, **kwargs): - raise NotImplementedError('Query transform not implemented') - - def K(self, *args, **kwargs): - raise NotImplementedError('Key transform not implemented') - -class LSH: - def __call__(self, *args, **kwargs): - raise NotImplementedError('LSH scheme not implemented') - - def compute_hash_agreement(self, q_hash, k_hash): - return (q_hash == k_hash).min(dim=-1)[0].sum(dim=-1) - -class XBOXPLUS(AsymmetricTransform): - - def set_norms(self, x): - self.x_norms = x.norm(p=2, dim=-1, keepdim=True) - self.MX = torch.amax(self.x_norms, dim=-2, keepdim=True) - - def X(self, x): - device = x.device - ext = torch.sqrt((self.MX**2).to(device) - (self.x_norms**2).to(device)) - zero = torch.tensor(0.0, device=x.device).repeat(x.shape[:-1], 1).unsqueeze(-1) - return torch.cat((x, ext, zero), -1) - -def lsh_clustering(x, n_rounds, r=1): - salsh = SALSH(n_rounds=n_rounds, dim=x.shape[-1], r=r, device=x.device) - x_hashed = salsh(x).reshape((n_rounds,) + x.shape[:-1]) - return x_hashed.argsort(dim=-1) - -class SALSH(LSH): - def __init__(self, n_rounds, dim, r, device='cuda'): - super(SALSH, self).__init__() - self.alpha = torch.normal(0, 1, (dim, n_rounds), device=device) - self.beta = uniform(0, r, shape=(1, n_rounds), device=device) - self.dim = dim - self.r = r - - def __call__(self, vecs): - projection = vecs @ self.alpha - projection_shift = projection + self.beta - projection_rescale = projection_shift / self.r - return projection_rescale.permute(2, 0, 1) - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - - variance = scale / denom - - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def batch_scatter(output, src, dim, index): - """ - :param output: [b,n,c] - :param src: [b,n,c] - :param dim: int - :param index: [b,n] - :return: output: [b,n,c] - """ - b,k,c = src.shape - index = index[:, :, None].expand(-1, -1, c) - output, src, index = map(lambda t: rearrange(t, 'b k c -> (b c) k'), (output, src, index)) - output.scatter_(dim,index,src) - output = rearrange(output, '(b c) k -> b k c', b=b) - return output - -def batch_gather(x, index, dim): - """ - :param x: [b,n,c] - :param index: [b,n//2] - :param dim: int - :return: output: [b,n//2,c] - """ - b,n,c = x.shape - index = index[:,:,None].expand(-1,-1,c) - x, index = map(lambda t: rearrange(t, 'b n c -> (b c) n'), (x, index)) - output = torch.gather(x,dim,index) - output = rearrange(output, '(b c) n -> b n c', b=b) - return output - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class SAH_MSA(nn.Module): - def __init__(self, heads=4, n_rounds=2, channels=64, patch_size=144, - r=1): - super(SAH_MSA, self).__init__() - self.heads = heads - self.n_rounds = n_rounds - inner_dim = channels*3 - self.to_q = nn.Linear(channels, inner_dim, bias=False) - self.to_k = nn.Linear(channels, inner_dim, bias=False) - self.to_v = nn.Linear(channels, inner_dim, bias=False) - self.to_out = nn.Linear(inner_dim, channels, bias=False) - - self.xbox_plus = XBOXPLUS() - self.clustering_params = { - 'r': r, - 'n_rounds': self.n_rounds - } - self.q_attn_size = patch_size[0] * patch_size[1] - self.k_attn_size = patch_size[0] * patch_size[1] - - def forward(self, input): - """ - :param input: [b,n,c] - :return: output: [b,n,c] - """ - - B, N, C_inp = input.shape - query = self.to_q(input) - key = self.to_k(input) - value = self.to_v(input) - input_hash = input.view(B, N, self.heads, C_inp//self.heads) - x_hash = rearrange(input_hash, 'b t h e -> (b h) t e') - bs, x_seqlen, dim = x_hash.shape - with torch.no_grad(): - self.xbox_plus.set_norms(x_hash) - Xs = self.xbox_plus.X(x_hash) - x_positions = lsh_clustering(Xs, **self.clustering_params) - x_positions = x_positions.reshape(self.n_rounds, bs, -1) - - del Xs - - C = query.shape[-1] - query = query.view(B, N, self.heads, C // self.heads) - key = key.view(B, N, self.heads, C // self.heads) - value = value.view(B, N, self.heads, C // self.heads) - - query = rearrange(query, 'b t h e -> (b h) t e') # [bs, q_seqlen,c] - key = rearrange(key, 'b t h e -> (b h) t e') - value = rearrange(value, 'b s h d -> (b h) s d') - - bs, q_seqlen, dim = query.shape - bs, k_seqlen, dim = key.shape - v_dim = value.shape[-1] - - x_rev_positions = torch.argsort(x_positions, dim=-1) - x_offset = torch.arange(bs, device=query.device).unsqueeze(-1) * x_seqlen - x_flat = (x_positions + x_offset).reshape(-1) - - s_queries = query.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.q_attn_size, dim) - s_keys = key.reshape(-1, dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, dim) - s_values = value.reshape(-1, v_dim).index_select(0, x_flat).reshape(-1, self.k_attn_size, v_dim) - - inner = s_queries @ s_keys.transpose(2, 1) - norm_factor = 1 - inner = inner / norm_factor - - # free memory - del x_positions - - # softmax denominator - dots_logsumexp = torch.logsumexp(inner, dim=-1, keepdim=True) - # softmax - dots = torch.exp(inner - dots_logsumexp) - # dropout - - # n_rounds outs - bo = (dots @ s_values).reshape(self.n_rounds, bs, q_seqlen, -1) - - # undo sort - x_offset = torch.arange(bs * self.n_rounds, device=query.device).unsqueeze(-1) * x_seqlen - x_rev_flat = (x_rev_positions.reshape(-1, x_seqlen) + x_offset).reshape(-1) - o = bo.reshape(-1, v_dim).index_select(0, x_rev_flat).reshape(self.n_rounds, bs, q_seqlen, -1) - - slogits = dots_logsumexp.reshape(self.n_rounds, bs, -1) - logits = torch.gather(slogits, 2, x_rev_positions) - - # free memory - del x_rev_positions - - # weighted sum multi-round attention - probs = torch.exp(logits - torch.logsumexp(logits, dim=0, keepdim=True)) - out = torch.sum(o * probs.unsqueeze(-1), dim=0) - out = rearrange(out, '(b h) t d -> b t h d', h=self.heads) - out = out.reshape(B, N, -1) - out = self.to_out(out) - - return out - -class SAHAB(nn.Module): - def __init__( - self, - dim, - patch_size=(16, 16), - heads=8, - shift_size=0, - sparse=False - ): - super().__init__() - self.blocks = nn.ModuleList([]) - self.attn = PreNorm(dim, SAH_MSA(heads=heads, n_rounds=2, r=1, channels=dim, patch_size=patch_size)) - self.ffn = PreNorm(dim, FeedForward(dim=dim)) - self.shift_size = shift_size - self.patch_size = patch_size - self.sparse = sparse - - def forward(self, x, mask=None): - """ - x: [b,h,w,c] - mask: [b,h,w] - return out: [b,h,w,c] - """ - b,h,w,c = x.shape - if self.shift_size > 0: - x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - mask = torch.roll(mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)) - w_size = self.patch_size - - # Split into large patches - x = rearrange(x, 'b (nh hh) (nw ww) c-> b (nh nw) (hh ww c)', hh=w_size[0] * 2, ww=w_size[1] * 2) - mask = rearrange(mask, 'b (nh hh) (nw ww) -> b (nh nw) (hh ww)', hh=w_size[0] * 2, ww=w_size[1] * 2) - N = x.shape[1] - - mask = torch.mean(mask,dim=2,keepdim=False) # [b,nh*nw] - if self.sparse: - mask_select = mask.topk(mask.shape[1] // 2, dim=1)[1] # [b,nh*nw//2] - x_select = batch_gather(x, mask_select, 1) # [b,nh*nw//2,hh*ww*c] - x_select = x_select.reshape(b*N//2,-1,c) - x_select = self.attn(x_select)+x_select - x_select = x_select.view(b,N//2,-1) - x = batch_scatter(x.clone(), x_select, 1, mask_select) - else: - x = x.view(b*N,-1,c) - x = self.attn(x) + x - x = x.view(b, N, -1) - x = rearrange(x, 'b (nh nw) (hh ww c) -> b (nh hh) (nw ww) c', nh=h//(w_size[0] * 2), hh=w_size[0] * 2, ww=w_size[1] * 2) - - if self.shift_size > 0: - x = torch.roll(x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)) - - x = self.ffn(x) + x - - return x - - -class SAHABs(nn.Module): - def __init__( - self, - dim, - patch_size=(8, 8), - heads=8, - num_blocks=2, - sparse=False - ): - super().__init__() - blocks = [] - for _ in range(num_blocks): - blocks.append( - SAHAB(heads=heads, dim=dim, patch_size=patch_size,sparse=sparse, - shift_size=0 if (_ % 2 == 0) else patch_size[0])) - self.blocks = nn.Sequential(*blocks) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - mask: [b,1,h,w] - return x: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - mask = mask.squeeze(1) - for block in self.blocks: - x = block(x, mask) - x = x.permute(0, 3, 1, 2) - return x - -class ASPPConv(nn.Sequential): - def __init__(self, in_channels, out_channels, dilation): - modules = [ - nn.Conv2d(in_channels, out_channels, 3, padding=dilation, dilation=dilation, bias=False), - nn.ReLU() - ] - super(ASPPConv, self).__init__(*modules) - - -class ASPPPooling(nn.Sequential): - def __init__(self, in_channels, out_channels): - super(ASPPPooling, self).__init__( - nn.AdaptiveAvgPool2d(1), - nn.Conv2d(in_channels, out_channels, 1, bias=False), - nn.ReLU()) - - def forward(self, x): - size = x.shape[-2:] - for mod in self: - x = mod(x) - return F.interpolate(x, size=size, mode='bilinear', align_corners=False) - - -class ASPP(nn.Module): - def __init__(self, in_channels, atrous_rates, out_channels): - super(ASPP, self).__init__() - modules = [] - - rates = tuple(atrous_rates) - for rate in rates: - modules.append(ASPPConv(in_channels, out_channels, rate)) - - modules.append(ASPPPooling(in_channels, out_channels)) - - self.convs = nn.ModuleList(modules) - - self.project = nn.Sequential( - nn.Conv2d(len(self.convs) * out_channels, out_channels, 1, bias=False), - nn.ReLU(), - nn.Dropout(0.5)) - - def forward(self, x): - res = [] - for conv in self.convs: - res.append(conv(x)) - res = torch.cat(res, dim=1) - return self.project(res) - -class Sparsity_Estimator(nn.Module): - def __init__(self, dim=28, expand=2, sparse=False): - super(Sparsity_Estimator, self).__init__() - self.dim = dim - self.stage = 2 - self.sparse = sparse - - # Input projection - self.in_proj = nn.Conv2d(28, dim, 1, 1, 0, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(2): - self.encoder_layers.append(nn.ModuleList([ - nn.Conv2d(dim_stage, dim_stage * expand, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage * expand, dim_stage * expand, 3, 2, 1, bias=False, groups=dim_stage * expand), - nn.Conv2d(dim_stage * expand, dim_stage*expand, 1, 1, 0, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = ASPP(dim_stage, [3,6], dim_stage) - - # Decoder: - self.decoder_layers = nn.ModuleList([]) - for i in range(2): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage // 2, dim_stage, 1, 1, 0, bias=False), - nn.Conv2d(dim_stage, dim_stage, 3, 1, 1, bias=False, groups=dim_stage), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, 0, bias=False), - ])) - dim_stage //= 2 - - # Output projection - if sparse: - self.out_conv2 = nn.Conv2d(self.dim, self.dim+1, 3, 1, 1, bias=False) - else: - self.out_conv2 = nn.Conv2d(self.dim, self.dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - # Input projection - fea = self.lrelu(self.in_proj(x)) - # Encoder - fea_encoder = [] # [c 2c 4c 8c] - for (Conv1, Conv2, Conv3) in self.encoder_layers: - fea_encoder.append(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - # Bottleneck - fea = self.bottleneck(fea)+fea - # Decoder - for i, (FeaUpSample, Conv1, Conv2, Conv3) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Conv3(self.lrelu(Conv2(self.lrelu(Conv1(fea))))) - fea = fea + fea_encoder[self.stage-1-i] - # Output projection - out = self.out_conv2(fea) - if self.sparse: - error_map = out[:,-1:,:,:] - return out[:,:-1], error_map - return out - -class CST(nn.Module): - def __init__(self, dim=28, stage=2, num_blocks=[2, 2, 2], sparse=False): - super(CST, self).__init__() - self.dim = dim - self.stage = stage - self.sparse = sparse - - # Fution physical mask and shifted measurement - self.fution = nn.Conv2d(56, 28, 1, 1, 0, bias=False) - - # Sparsity Estimator - if num_blocks==[2,4,6]: - self.fe = nn.Sequential(Sparsity_Estimator(dim=28,expand=2,sparse=False), - Sparsity_Estimator(dim=28, expand=2, sparse=sparse)) - else: - self.fe = Sparsity_Estimator(dim=28, expand=2, sparse=sparse) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - SAHABs(dim=dim_stage, num_blocks=num_blocks[i], heads=dim_stage // dim, sparse=sparse), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - nn.AvgPool2d(kernel_size=2, stride=2), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = SAHABs( - dim=dim_stage, heads=dim_stage // dim, num_blocks=num_blocks[-1], sparse=sparse) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - SAHABs(dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], - heads=(dim_stage // 2) // dim, sparse=sparse), - ])) - dim_stage //= 2 - - # Output projection - self.out_proj = nn.Conv2d(self.dim, dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - mask: [b,c,h,w] - return out:[b,c,h,w] - """ - b, c, h, w = x.shape - - # Fution - x = self.fution(torch.cat([x,mask],dim=1)) - - # Feature Extraction - if self.sparse: - fea,mask = self.fe(x) - else: - fea = self.fe(x) - mask = torch.randn((b,1,h,w)).cuda() - - # Encoder - fea_encoder = [] - masks = [] - for (Blcok, FeaDownSample, MaskDownSample) in self.encoder_layers: - fea = Blcok(fea, mask) - masks.append(mask) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - mask = MaskDownSample(mask) - - # Bottleneck - fea = self.bottleneck(fea, mask) - - # Decoder - for i, (FeaUpSample, Blcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = fea + fea_encoder[self.stage - 1 - i] - mask = masks[self.stage - 1 - i] - fea = Blcok(fea, mask) - - # Output projection - out = self.out_proj(fea) + x - if self.sparse: - return out, mask - else: - return out - - - - - - - - - - - - diff --git a/MST/simulation/train_code/architecture/DAUHST.py b/MST/simulation/train_code/architecture/DAUHST.py deleted file mode 100755 index d7c9bf3..0000000 --- a/MST/simulation/train_code/architecture/DAUHST.py +++ /dev/null @@ -1,372 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch import einsum - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - - -class HS_MSA(nn.Module): - def __init__( - self, - dim, - #window_size=(8, 8), - window_size=(7, 7), - dim_head=28, - heads=8, - only_local_branch=False - ): - super().__init__() - - self.dim = dim - self.heads = heads - self.scale = dim_head ** -0.5 - self.window_size = window_size - self.only_local_branch = only_local_branch - - # position embedding - if only_local_branch: - seq_l = window_size[0] * window_size[1] - self.pos_emb = nn.Parameter(torch.Tensor(1, heads, seq_l, seq_l)) - trunc_normal_(self.pos_emb) - else: - seq_l1 = window_size[0] * window_size[1] - self.pos_emb1 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l1, seq_l1)) - #h,w = 256//self.heads,320//self.heads - h,w = 112//self.heads,112//self.heads - seq_l2 = h*w//seq_l1 - self.pos_emb2 = nn.Parameter(torch.Tensor(1, 1, heads//2, seq_l2, seq_l2)) - trunc_normal_(self.pos_emb1) - trunc_normal_(self.pos_emb2) - - inner_dim = dim_head * heads - self.to_q = nn.Linear(dim, inner_dim, bias=False) - self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False) - self.to_out = nn.Linear(inner_dim, dim) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x.shape - w_size = self.window_size - - assert h % w_size[0] == 0 and w % w_size[1] == 0, 'fmap dimensions must be divisible by the window size' - if self.only_local_branch: - x_inp = rearrange(x, 'b (h b0) (w b1) c -> (b h w) (b0 b1) c', b0=w_size[0], b1=w_size[1]) - q = self.to_q(x_inp) - k, v = self.to_kv(x_inp).chunk(2, dim=-1) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.heads), (q, k, v)) - q *= self.scale - sim = einsum('b h i d, b h j d -> b h i j', q, k) - sim = sim + self.pos_emb - attn = sim.softmax(dim=-1) - out = einsum('b h i j, b h j d -> b h i d', attn, v) - out = rearrange(out, 'b h n d -> b n (h d)') - out = self.to_out(out) - out = rearrange(out, '(b h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - else: - q = self.to_q(x) - k, v = self.to_kv(x).chunk(2, dim=-1) - q1, q2 = q[:,:,:,:c//2], q[:,:,:,c//2:] - k1, k2 = k[:,:,:,:c//2], k[:,:,:,c//2:] - v1, v2 = v[:,:,:,:c//2], v[:,:,:,c//2:] - - # local branch - q1, k1, v1 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q1, k1, v1)) - q1, k1, v1 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q1, k1, v1)) - q1 *= self.scale - sim1 = einsum('b n h i d, b n h j d -> b n h i j', q1, k1) - sim1 = sim1 + self.pos_emb1 - attn1 = sim1.softmax(dim=-1) - out1 = einsum('b n h i j, b n h j d -> b n h i d', attn1, v1) - out1 = rearrange(out1, 'b n h mm d -> b n mm (h d)') - - # non-local branch - q2, k2, v2 = map(lambda t: rearrange(t, 'b (h b0) (w b1) c -> b (h w) (b0 b1) c', - b0=w_size[0], b1=w_size[1]), (q2, k2, v2)) - q2, k2, v2 = map(lambda t: t.permute(0, 2, 1, 3), (q2.clone(), k2.clone(), v2.clone())) - q2, k2, v2 = map(lambda t: rearrange(t, 'b n mm (h d) -> b n h mm d', h=self.heads//2), (q2, k2, v2)) - q2 *= self.scale - sim2 = einsum('b n h i d, b n h j d -> b n h i j', q2, k2) - sim2 = sim2 + self.pos_emb2 - attn2 = sim2.softmax(dim=-1) - out2 = einsum('b n h i j, b n h j d -> b n h i d', attn2, v2) - out2 = rearrange(out2, 'b n h mm d -> b n mm (h d)') - out2 = out2.permute(0, 2, 1, 3) - - out = torch.cat([out1,out2],dim=-1).contiguous() - out = self.to_out(out) - out = rearrange(out, 'b (h w) (b0 b1) c -> b (h b0) (w b1) c', h=h // w_size[0], w=w // w_size[1], - b0=w_size[0]) - return out - -class HSAB(nn.Module): - def __init__( - self, - dim, - #window_size=(8, 8), - window_size=(7, 7), - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - PreNorm(dim, HS_MSA(dim=dim, window_size=window_size, dim_head=dim_head, heads=heads, only_local_branch=(heads==1))), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class HST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, num_blocks=[1,1,1]): - super(HST, self).__init__() - self.dim = dim - self.scales = len(num_blocks) - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_scale = dim - for i in range(self.scales-1): - self.encoder_layers.append(nn.ModuleList([ - HSAB(dim=dim_scale, num_blocks=num_blocks[i], dim_head=dim, heads=dim_scale // dim), - nn.Conv2d(dim_scale, dim_scale * 2, 4, 2, 1, bias=False), - ])) - dim_scale *= 2 - - # Bottleneck - self.bottleneck = HSAB(dim=dim_scale, dim_head=dim, heads=dim_scale // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(self.scales-1): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_scale, dim_scale // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_scale, dim_scale // 2, 1, 1, bias=False), - HSAB(dim=dim_scale // 2, num_blocks=num_blocks[self.scales - 2 - i], dim_head=dim, - heads=(dim_scale // 2) // dim), - ])) - dim_scale //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - b, c, h_inp, w_inp = x.shape - hb, wb = 16, 16 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - - # Embedding - fea = self.embedding(x) - x = x[:,:28,:,:] - - # Encoder - fea_encoder = [] - for (HSAB, FeaDownSample) in self.encoder_layers: - fea = HSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, HSAB) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.scales-2-i]], dim=1)) - fea = HSAB(fea) - - # Mapping - out = self.mapping(fea) + x - return out[:, :, :h_inp, :w_inp] - -def A(x,Phi): - temp = x*Phi - y = torch.sum(temp,1) - return y - -def At(y,Phi): - temp = torch.unsqueeze(y, 1).repeat(1,Phi.shape[1],1,1) - x = temp*Phi - return x - -def shift_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=step*i, dims=2) - return inputs - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class HyPaNet(nn.Module): - def __init__(self, in_nc=29, out_nc=8, channel=64): - super(HyPaNet, self).__init__() - self.fution = nn.Conv2d(in_nc, channel, 1, 1, 0, bias=True) - self.down_sample = nn.Conv2d(channel, channel, 3, 2, 1, bias=True) - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.mlp = nn.Sequential( - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, channel, 1, padding=0, bias=True), - nn.ReLU(inplace=True), - nn.Conv2d(channel, out_nc, 1, padding=0, bias=True), - nn.Softplus()) - self.relu = nn.ReLU(inplace=True) - self.out_nc = out_nc - - def forward(self, x): - x = self.down_sample(self.relu(self.fution(x))) - x = self.avg_pool(x) - x = self.mlp(x) + 1e-6 - return x[:,:self.out_nc//2,:,:], x[:,self.out_nc//2:,:,:] - -class DAUHST(nn.Module): - - def __init__(self, num_iterations=1): - super(DAUHST, self).__init__() - self.para_estimator = HyPaNet(in_nc=28, out_nc=num_iterations*2) - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) - self.num_iterations = num_iterations - self.denoisers = nn.ModuleList([]) - for _ in range(num_iterations): - self.denoisers.append( - HST(in_dim=29, out_dim=28, dim=28, num_blocks=[1,1,1]), - ) - def initial(self, y, Phi): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,310] - :return: temp: [b,28,256,310]; alpha: [b, num_iterations]; beta: [b, num_iterations] - """ - nC, step = 28, 2 - y = y / nC * 2 - bs,row,col = y.shape - """ y_shift = torch.zeros(bs, nC, row, col).cuda().float() - for i in range(nC): - y_shift[:, i, :, step * i:step * i + col - (nC - 1) * step] = y[:, :, step * i:step * i + col - (nC - 1) * step] """ - y_shift = y.unsqueeze(1).repeat((1, 28, 1, 1)).float() - z = self.fution(torch.cat([y_shift, Phi], dim=1)) - alpha, beta = self.para_estimator(self.fution(torch.cat([y_shift, Phi], dim=1))) - return z, alpha, beta - - def forward(self, y, input_mask=None): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,310] - :param Phi_PhiT: [b,256,310] - :return: z_crop: [b,28,256,256] - """ - Phi, Phi_s = input_mask - z, alphas, betas = self.initial(y, Phi) - for i in range(self.num_iterations): - alpha, beta = alphas[:,i,:,:], betas[:,i:i+1,:,:] - Phi_z = A(z, Phi) - x = z + At(torch.div(y-Phi_z,alpha+Phi_s), Phi) - x = shift_back_3d(x) - beta_repeat = beta.repeat(1,1,x.shape[2], x.shape[3]) - z = self.denoisers[i](torch.cat([x, beta_repeat],dim=1)) - if i [BN] => ReLU) * 2""" - - def __init__(self, in_channels, out_channels, mid_channels=None): - super().__init__() - if not mid_channels: - mid_channels = out_channels - self.double_conv = nn.Sequential( - nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(mid_channels), - nn.ReLU(inplace=True), - nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1), - nn.BatchNorm2d(out_channels), - nn.ReLU(inplace=True) - ) - - def forward(self, x): - return self.double_conv(x) - -class GAP_net(nn.Module): - - def __init__(self): - super(GAP_net, self).__init__() - - self.unet1 = Unet(28, 28) - self.unet2 = Unet(28, 28) - self.unet3 = Unet(28, 28) - self.unet4 = Unet(28, 28) - self.unet5 = Unet(28, 28) - self.unet6 = Unet(28, 28) - self.unet7 = Unet(28, 28) - self.unet8 = Unet(28, 28) - self.unet9 = Unet(28, 28) - - def forward(self, y, input_mask=None): - if input_mask==None: - Phi = torch.rand((1,28,256,310)).cuda() - Phi_s = torch.rand((1, 256, 310)).cuda() - else: - Phi, Phi_s = input_mask - x_list = [] - x = At(y, Phi) # v0=H^T y - ### 1-3 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet1(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet2(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet3(x) - x = shift_3d(x) - ### 4-6 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet4(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet5(x) - x = shift_3d(x) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet6(x) - x = shift_3d(x) - # ### 7-9 - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet7(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet8(x) - x = shift_3d(x) - x_list.append(x[:, :, :, 0:256]) - yb = A(x, Phi) - x = x + At(torch.div(y - yb, Phi_s), Phi) - x = shift_back_3d(x) - x = self.unet9(x) - x = shift_3d(x) - return x[:, :, :, 0:256] diff --git a/MST/simulation/train_code/architecture/HDNet.py b/MST/simulation/train_code/architecture/HDNet.py deleted file mode 100755 index 442acc8..0000000 --- a/MST/simulation/train_code/architecture/HDNet.py +++ /dev/null @@ -1,371 +0,0 @@ -import torch -import torch.nn as nn -import math - -def default_conv(in_channels, out_channels, kernel_size, bias=True): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias) - -class MeanShift(nn.Conv2d): - def __init__( - self, rgb_range, - rgb_mean=(0.4488, 0.4371, 0.4040), rgb_std=(1.0, 1.0, 1.0), sign=-1): - - super(MeanShift, self).__init__(3, 3, kernel_size=1) - std = torch.Tensor(rgb_std) - self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1) - self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std - for p in self.parameters(): - p.requires_grad = False - -class BasicBlock(nn.Sequential): - def __init__( - self, conv, in_channels, out_channels, kernel_size, stride=1, bias=False, - bn=True, act=nn.ReLU(True)): - - m = [conv(in_channels, out_channels, kernel_size, bias=bias)] - if bn: - m.append(nn.BatchNorm2d(out_channels)) - if act is not None: - m.append(act) - - super(BasicBlock, self).__init__(*m) - -class ResBlock(nn.Module): - def __init__( - self, conv, n_feats, kernel_size, - bias=True, bn=False, act=nn.ReLU(True), res_scale=1): - - super(ResBlock, self).__init__() - m = [] - for i in range(2): - m.append(conv(n_feats, n_feats, kernel_size, bias=bias)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if i == 0: - m.append(act) - - self.body = nn.Sequential(*m) - self.res_scale = res_scale - - def forward(self, x): - res = self.body(x).mul(self.res_scale) # So res_scale is a scaler? just scale all elements in each feature's residual? Why? - res += x - - return res - -class Upsampler(nn.Sequential): - def __init__(self, conv, scale, n_feats, bn=False, act=False, bias=True): - - m = [] - if (scale & (scale - 1)) == 0: - for _ in range(int(math.log(scale, 2))): - m.append(conv(n_feats, 4 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(2)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - - elif scale == 3: - m.append(conv(n_feats, 9 * n_feats, 3, bias)) - m.append(nn.PixelShuffle(3)) - if bn: - m.append(nn.BatchNorm2d(n_feats)) - if act == 'relu': - m.append(nn.ReLU(True)) - elif act == 'prelu': - m.append(nn.PReLU(n_feats)) - else: - raise NotImplementedError - - super(Upsampler, self).__init__(*m) - -_NORM_BONE = False - -def constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def kaiming_init(module, - a=0, - mode='fan_out', - nonlinearity='relu', - bias=0, - distribution='normal'): - assert distribution in ['uniform', 'normal'] - if distribution == 'uniform': - nn.init.kaiming_uniform_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - -# depthwise-separable convolution (DSC) -class DSC(nn.Module): - - def __init__(self, nin: int) -> None: - super(DSC, self).__init__() - self.conv_dws = nn.Conv2d( - nin, nin, kernel_size=1, stride=1, padding=0, groups=nin - ) - self.bn_dws = nn.BatchNorm2d(nin, momentum=0.9) - self.relu_dws = nn.ReLU(inplace=False) - - self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) - - self.conv_point = nn.Conv2d( - nin, 1, kernel_size=1, stride=1, padding=0, groups=1 - ) - self.bn_point = nn.BatchNorm2d(1, momentum=0.9) - self.relu_point = nn.ReLU(inplace=False) - - self.softmax = nn.Softmax(dim=2) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - out = self.conv_dws(x) - out = self.bn_dws(out) - out = self.relu_dws(out) - - out = self.maxpool(out) - - out = self.conv_point(out) - out = self.bn_point(out) - out = self.relu_point(out) - - m, n, p, q = out.shape - out = self.softmax(out.view(m, n, -1)) - out = out.view(m, n, p, q) - - out = out.expand(x.shape[0], x.shape[1], x.shape[2], x.shape[3]) - - out = torch.mul(out, x) - - out = out + x - - return out - -# Efficient Feature Fusion(EFF) -class EFF(nn.Module): - def __init__(self, nin: int, nout: int, num_splits: int) -> None: - super(EFF, self).__init__() - - assert nin % num_splits == 0 - - self.nin = nin - self.nout = nout - self.num_splits = num_splits - self.subspaces = nn.ModuleList( - [DSC(int(self.nin / self.num_splits)) for i in range(self.num_splits)] - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - sub_feat = torch.chunk(x, self.num_splits, dim=1) - out = [] - for idx, l in enumerate(self.subspaces): - out.append(self.subspaces[idx](sub_feat[idx])) - out = torch.cat(out, dim=1) - - return out - - -# spatial-spectral domain attention learning(SDL) -class SDL_attention(nn.Module): - def __init__(self, inplanes, planes, kernel_size=1, stride=1): - super(SDL_attention, self).__init__() - - self.inplanes = inplanes - self.inter_planes = planes // 2 - self.planes = planes - self.kernel_size = kernel_size - self.stride = stride - self.padding = (kernel_size-1)//2 - - self.conv_q_right = nn.Conv2d(self.inplanes, 1, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_v_right = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) - self.conv_up = nn.Conv2d(self.inter_planes, self.planes, kernel_size=1, stride=1, padding=0, bias=False) - self.softmax_right = nn.Softmax(dim=2) - self.sigmoid = nn.Sigmoid() - - self.conv_q_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #g - self.avg_pool = nn.AdaptiveAvgPool2d(1) - self.conv_v_left = nn.Conv2d(self.inplanes, self.inter_planes, kernel_size=1, stride=stride, padding=0, bias=False) #theta - self.softmax_left = nn.Softmax(dim=2) - - self.reset_parameters() - - def reset_parameters(self): - kaiming_init(self.conv_q_right, mode='fan_in') - kaiming_init(self.conv_v_right, mode='fan_in') - kaiming_init(self.conv_q_left, mode='fan_in') - kaiming_init(self.conv_v_left, mode='fan_in') - - self.conv_q_right.inited = True - self.conv_v_right.inited = True - self.conv_q_left.inited = True - self.conv_v_left.inited = True - # HR spatial attention - def spatial_attention(self, x): - input_x = self.conv_v_right(x) - batch, channel, height, width = input_x.size() - - input_x = input_x.view(batch, channel, height * width) - context_mask = self.conv_q_right(x) - context_mask = context_mask.view(batch, 1, height * width) - context_mask = self.softmax_right(context_mask) - - context = torch.matmul(input_x, context_mask.transpose(1,2)) - context = context.unsqueeze(-1) - context = self.conv_up(context) - - mask_ch = self.sigmoid(context) - - out = x * mask_ch - - return out - # HR spectral attention - def spectral_attention(self, x): - - g_x = self.conv_q_left(x) - batch, channel, height, width = g_x.size() - - avg_x = self.avg_pool(g_x) - batch, channel, avg_x_h, avg_x_w = avg_x.size() - - avg_x = avg_x.view(batch, channel, avg_x_h * avg_x_w).permute(0, 2, 1) - theta_x = self.conv_v_left(x).view(batch, self.inter_planes, height * width) - context = torch.matmul(avg_x, theta_x) - context = self.softmax_left(context) - context = context.view(batch, 1, height, width) - - mask_sp = self.sigmoid(context) - - out = x * mask_sp - - return out - - def forward(self, x): - context_spectral = self.spectral_attention(x) - context_spatial = self.spatial_attention(x) - out = context_spatial + context_spectral - return out - - -class HDNet(nn.Module): - - def __init__(self, in_ch=28, out_ch=28, conv=default_conv): - super(HDNet, self).__init__() - - n_resblocks = 16 - n_feats = 64 - kernel_size = 3 - act = nn.ReLU(True) - - # define head module - m_head = [conv(in_ch, n_feats, kernel_size)] - - # define body module - m_body = [ - ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - ) for _ in range(n_resblocks) - ] - m_body.append(SDL_attention(inplanes = n_feats, planes = n_feats)) - m_body.append(EFF(nin=n_feats, nout=n_feats, num_splits=4)) - - for i in range(1, n_resblocks): - m_body.append(ResBlock( - conv, n_feats, kernel_size, act=act, res_scale= 1 - )) - - m_body.append(conv(n_feats, n_feats, kernel_size)) - - m_tail = [conv(n_feats, out_ch, kernel_size)] - - self.head = nn.Sequential(*m_head) - self.body = nn.Sequential(*m_body) - self.tail = nn.Sequential(*m_tail) - - def forward(self, x, input_mask=None): - x = self.head(x) - - res = self.body(x) - res += x - - x = self.tail(res) - - return x - -# frequency domain learning(FDL) -class FDL(nn.Module): - def __init__(self, loss_weight=1.0, alpha=1.0, patch_factor=1, ave_spectrum=False, log_matrix=False, batch_matrix=False): - super(FDL, self).__init__() - self.loss_weight = loss_weight - self.alpha = alpha - self.patch_factor = patch_factor - self.ave_spectrum = ave_spectrum - self.log_matrix = log_matrix - self.batch_matrix = batch_matrix - - def tensor2freq(self, x): - patch_factor = self.patch_factor - _, _, h, w = x.shape - assert h % patch_factor == 0 and w % patch_factor == 0, ( - 'Patch factor should be divisible by image height and width') - patch_list = [] - patch_h = h // patch_factor - patch_w = w // patch_factor - for i in range(patch_factor): - for j in range(patch_factor): - patch_list.append(x[:, :, i * patch_h:(i + 1) * patch_h, j * patch_w:(j + 1) * patch_w]) - - y = torch.stack(patch_list, 1) - - return torch.rfft(y, 2, onesided=False, normalized=True) - - def loss_formulation(self, recon_freq, real_freq, matrix=None): - if matrix is not None: - weight_matrix = matrix.detach() - else: - matrix_tmp = (recon_freq - real_freq) ** 2 - matrix_tmp = torch.sqrt(matrix_tmp[..., 0] + matrix_tmp[..., 1]) ** self.alpha - if self.log_matrix: - matrix_tmp = torch.log(matrix_tmp + 1.0) - - if self.batch_matrix: - matrix_tmp = matrix_tmp / matrix_tmp.max() - else: - matrix_tmp = matrix_tmp / matrix_tmp.max(-1).values.max(-1).values[:, :, :, None, None] - - matrix_tmp[torch.isnan(matrix_tmp)] = 0.0 - matrix_tmp = torch.clamp(matrix_tmp, min=0.0, max=1.0) - weight_matrix = matrix_tmp.clone().detach() - - assert weight_matrix.min().item() >= 0 and weight_matrix.max().item() <= 1, ( - 'The values of spectrum weight matrix should be in the range [0, 1], ' - 'but got Min: %.10f Max: %.10f' % (weight_matrix.min().item(), weight_matrix.max().item())) - - tmp = (recon_freq - real_freq) ** 2 - freq_distance = tmp[..., 0] + tmp[..., 1] - - loss = weight_matrix * freq_distance - return torch.mean(loss) - - def forward(self, pred, target, matrix=None, **kwargs): - - pred_freq = self.tensor2freq(pred) - target_freq = self.tensor2freq(target) - - if self.ave_spectrum: - pred_freq = torch.mean(pred_freq, 0, keepdim=True) - target_freq = torch.mean(target_freq, 0, keepdim=True) - - return self.loss_formulation(pred_freq, target_freq, matrix) * self.loss_weight diff --git a/MST/simulation/train_code/architecture/Lambda_Net.py b/MST/simulation/train_code/architecture/Lambda_Net.py deleted file mode 100755 index ddac0aa..0000000 --- a/MST/simulation/train_code/architecture/Lambda_Net.py +++ /dev/null @@ -1,179 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -from torch import einsum - -class LambdaNetAttention(nn.Module): - def __init__( - self, - dim, - ): - super().__init__() - - self.dim = dim - self.to_q = nn.Linear(dim, dim//8, bias=False) - self.to_k = nn.Linear(dim, dim//8, bias=False) - self.to_v = nn.Linear(dim, dim, bias=False) - self.rescale = (dim//8)**-0.5 - self.gamma = nn.Parameter(torch.ones(1)) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0,2,3,1) - b, h, w, c = x.shape - - # Reshape to (B,N,C), where N = window_size[0]*window_size[1] is the length of sentence - x_inp = rearrange(x, 'b h w c -> b (h w) c') - - # produce query, key and value - q = self.to_q(x_inp) - k = self.to_k(x_inp) - v = self.to_v(x_inp) - - # attention - sim = einsum('b i d, b j d -> b i j', q, k)*self.rescale - attn = sim.softmax(dim=-1) - - # aggregate - out = einsum('b i j, b j d -> b i d', attn, v) - - # merge blocks back to original feature map - out = rearrange(out, 'b (h w) c -> b h w c', h=h, w=w) - out = self.gamma*out + x - return out.permute(0,3,1,2) - -class triple_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(triple_conv, self).__init__() - self.t_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.t_conv(x) - return x - -class double_conv(nn.Module): - - def __init__(self, in_channels, out_channels): - super(double_conv, self).__init__() - self.d_conv = nn.Sequential( - nn.Conv2d(in_channels, out_channels, 3, padding=1), - nn.ReLU(), - nn.Conv2d(out_channels, out_channels, 3, padding=1), - ) - - def forward(self, x): - x = self.d_conv(x) - return x - -def shift_back_3d(inputs,step=2): - [bs, nC, row, col] = inputs.shape - for i in range(nC): - inputs[:,i,:,:] = torch.roll(inputs[:,i,:,:], shifts=(-1)*step*i, dims=2) - return inputs - -class Lambda_Net(nn.Module): - - def __init__(self, out_ch=28): - super(Lambda_Net, self).__init__() - self.conv_in = nn.Conv2d(1+28, 28, 3, padding=1) - - # encoder - self.conv_down1 = triple_conv(28, 32) - self.conv_down2 = triple_conv(32, 64) - self.conv_down3 = triple_conv(64, 128) - self.conv_down4 = triple_conv(128, 256) - self.conv_down5 = double_conv(256, 512) - self.conv_down6 = double_conv(512, 1024) - - self.maxpool = nn.MaxPool2d(2) - - # decoder - self.upsample5 = nn.ConvTranspose2d(1024, 512, kernel_size=2, stride=2) - self.upsample4 = nn.ConvTranspose2d(512, 256, kernel_size=2, stride=2) - self.upsample3 = nn.ConvTranspose2d(256, 128, kernel_size=2, stride=2) - self.upsample2 = nn.ConvTranspose2d(128, 64, kernel_size=2, stride=2) - self.upsample1 = nn.ConvTranspose2d(64, 32, kernel_size=2, stride=2) - - self.conv_up1 = triple_conv(32+32, 32) - self.conv_up2 = triple_conv(64+64, 64) - self.conv_up3 = triple_conv(128+128, 128) - self.conv_up4 = triple_conv(256+256, 256) - self.conv_up5 = double_conv(512+512, 512) - - # attention - self.attention = LambdaNetAttention(dim=128) - - self.conv_last1 = nn.Conv2d(32, 6, 3,1,1) - self.conv_last2 = nn.Conv2d(38, 32, 3,1,1) - self.conv_last3 = nn.Conv2d(32, 12, 3,1,1) - self.conv_last4 = nn.Conv2d(44, 32, 3,1,1) - self.conv_last5 = nn.Conv2d(32, out_ch, 1) - self.act = nn.ReLU() - - def forward(self, x, input_mask=None): - if input_mask == None: - input_mask = torch.zeros((1,28,256,310)).cuda() - x = x/28*2 - x = self.conv_in(torch.cat([x.unsqueeze(1), input_mask], dim=1)) - b, c, h_inp, w_inp = x.shape - hb, wb = 32, 32 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - res0 = x - conv1 = self.conv_down1(x) - x = self.maxpool(conv1) - conv2 = self.conv_down2(x) - x = self.maxpool(conv2) - conv3 = self.conv_down3(x) - x = self.maxpool(conv3) - conv4 = self.conv_down4(x) - x = self.maxpool(conv4) - conv5 = self.conv_down5(x) - x = self.maxpool(conv5) - conv6 = self.conv_down6(x) - - x = self.upsample5(conv6) - x = torch.cat([x, conv5], dim=1) - x = self.conv_up5(x) - - x = self.upsample4(x) - x = torch.cat([x, conv4], dim=1) - x = self.conv_up4(x) - - x = self.upsample3(x) - x = torch.cat([x, conv3], dim=1) - x = self.conv_up3(x) - x = self.attention(x) - - x = self.upsample2(x) - x = torch.cat([x, conv2], dim=1) - x = self.conv_up2(x) - - x = self.upsample1(x) - x = torch.cat([x, conv1], dim=1) - x = self.conv_up1(x) - - res1 = x - out1 = self.act(self.conv_last1(x)) - x = self.conv_last2(torch.cat([res1,out1],dim=1)) - - res2 = x - out2 = self.act(self.conv_last3(x)) - out3 = self.conv_last4(torch.cat([res2, out2], dim=1)) - - out = self.conv_last5(out3)+res0 - out = out[:, :, :h_inp, :w_inp] - - return shift_back_3d(out)[:, :, :, :256] diff --git a/MST/simulation/train_code/architecture/MST.py b/MST/simulation/train_code/architecture/MST.py deleted file mode 100755 index 3686dfc..0000000 --- a/MST/simulation/train_code/architecture/MST.py +++ /dev/null @@ -1,311 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MaskGuidedMechanism(nn.Module): - def __init__( - self, n_feat): - super(MaskGuidedMechanism, self).__init__() - - self.conv1 = nn.Conv2d(n_feat, n_feat, kernel_size=1, bias=True) - self.conv2 = nn.Conv2d(n_feat, n_feat, kernel_size=1, bias=True) - self.depth_conv = nn.Conv2d(n_feat, n_feat, kernel_size=5, padding=2, bias=True, groups=n_feat) - - def forward(self, mask_shift): - # x: b,c,h,w - [bs, nC, row, col] = mask_shift.shape - mask_shift = self.conv1(mask_shift) - attn_map = torch.sigmoid(self.depth_conv(self.conv2(mask_shift))) - res = mask_shift * attn_map - mask_shift = res + mask_shift - mask_emb = shift_back(mask_shift) - return mask_emb - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.mm = MaskGuidedMechanism(dim) - self.dim = dim - - def forward(self, x_in, mask=None): - """ - x_in: [b,h,w,c] - mask: [1,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - mask_attn = self.mm(mask.permute(0,3,1,2)).permute(0,2,3,1) - if b != 0: - mask_attn = (mask_attn[0, :, :, :]).expand([b, h, w, c]) - q, k, v, mask_attn = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp, mask_attn.flatten(1, 2))) - v = v * mask_attn - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head=64, - heads=8, - num_blocks=2, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x, mask): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x, mask=mask.permute(0, 2, 3, 1)) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - - -class MST(nn.Module): - def __init__(self, dim=28, stage=3, num_blocks=[2,2,2]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(28, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False) - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, 28, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - - def forward(self, x, mask=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - if mask == None: - mask = torch.zeros((1,28,256,310)).cuda() - - # Embedding - fea = self.lrelu(self.embedding(x)) - - # Encoder - fea_encoder = [] - masks = [] - for (MSAB, FeaDownSample, MaskDownSample) in self.encoder_layers: - fea = MSAB(fea, mask) - masks.append(mask) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - mask = MaskDownSample(mask) - - # Bottleneck - fea = self.bottleneck(fea, mask) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - mask = masks[self.stage - 1 - i] - fea = LeWinBlcok(fea, mask) - - # Mapping - out = self.mapping(fea) + x - - return out - - - - - - - - - - - - - - - - - - - - - - diff --git a/MST/simulation/train_code/architecture/MST_Plus_Plus.py b/MST/simulation/train_code/architecture/MST_Plus_Plus.py deleted file mode 100755 index a5bdc8a..0000000 --- a/MST/simulation/train_code/architecture/MST_Plus_Plus.py +++ /dev/null @@ -1,321 +0,0 @@ -import torch.nn as nn -import torch -import torch.nn.functional as F -from einops import rearrange -import math -import warnings -from torch.nn.init import _calculate_fan_in_and_fan_out - -def _no_grad_trunc_normal_(tensor, mean, std, a, b): - def norm_cdf(x): - return (1. + math.erf(x / math.sqrt(2.))) / 2. - - if (mean < a - 2 * std) or (mean > b + 2 * std): - warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " - "The distribution of values may be incorrect.", - stacklevel=2) - with torch.no_grad(): - l = norm_cdf((a - mean) / std) - u = norm_cdf((b - mean) / std) - tensor.uniform_(2 * l - 1, 2 * u - 1) - tensor.erfinv_() - tensor.mul_(std * math.sqrt(2.)) - tensor.add_(mean) - tensor.clamp_(min=a, max=b) - return tensor - - -def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.): - # type: (Tensor, float, float, float, float) -> Tensor - return _no_grad_trunc_normal_(tensor, mean, std, a, b) - - -def variance_scaling_(tensor, scale=1.0, mode='fan_in', distribution='normal'): - fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) - if mode == 'fan_in': - denom = fan_in - elif mode == 'fan_out': - denom = fan_out - elif mode == 'fan_avg': - denom = (fan_in + fan_out) / 2 - variance = scale / denom - if distribution == "truncated_normal": - trunc_normal_(tensor, std=math.sqrt(variance) / .87962566103423978) - elif distribution == "normal": - tensor.normal_(std=math.sqrt(variance)) - elif distribution == "uniform": - bound = math.sqrt(3 * variance) - tensor.uniform_(-bound, bound) - else: - raise ValueError(f"invalid distribution {distribution}") - - -def lecun_normal_(tensor): - variance_scaling_(tensor, mode='fan_in', distribution='truncated_normal') - - -class PreNorm(nn.Module): - def __init__(self, dim, fn): - super().__init__() - self.fn = fn - self.norm = nn.LayerNorm(dim) - - def forward(self, x, *args, **kwargs): - x = self.norm(x) - return self.fn(x, *args, **kwargs) - - -class GELU(nn.Module): - def forward(self, x): - return F.gelu(x) - -def conv(in_channels, out_channels, kernel_size, bias=False, padding = 1, stride = 1): - return nn.Conv2d( - in_channels, out_channels, kernel_size, - padding=(kernel_size//2), bias=bias, stride=stride) - - -def shift_back(inputs,step=2): # input [bs,28,256,310] output [bs, 28, 256, 256] - [bs, nC, row, col] = inputs.shape - down_sample = 256//row - step = float(step)/float(down_sample*down_sample) - out_col = row - for i in range(nC): - inputs[:,i,:,:out_col] = \ - inputs[:,i,:,int(step*i):int(step*i)+out_col] - return inputs[:, :, :, :out_col] - -class MS_MSA(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - ): - super().__init__() - self.num_heads = heads - self.dim_head = dim_head - self.to_q = nn.Linear(dim, dim_head * heads, bias=False) - self.to_k = nn.Linear(dim, dim_head * heads, bias=False) - self.to_v = nn.Linear(dim, dim_head * heads, bias=False) - self.rescale = nn.Parameter(torch.ones(heads, 1, 1)) - self.proj = nn.Linear(dim_head * heads, dim, bias=True) - self.pos_emb = nn.Sequential( - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - GELU(), - nn.Conv2d(dim, dim, 3, 1, 1, bias=False, groups=dim), - ) - self.dim = dim - - def forward(self, x_in): - """ - x_in: [b,h,w,c] - return out: [b,h,w,c] - """ - b, h, w, c = x_in.shape - x = x_in.reshape(b,h*w,c) - q_inp = self.to_q(x) - k_inp = self.to_k(x) - v_inp = self.to_v(x) - q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=self.num_heads), - (q_inp, k_inp, v_inp)) - v = v - # q: b,heads,hw,c - q = q.transpose(-2, -1) - k = k.transpose(-2, -1) - v = v.transpose(-2, -1) - q = F.normalize(q, dim=-1, p=2) - k = F.normalize(k, dim=-1, p=2) - attn = (k @ q.transpose(-2, -1)) # A = K^T*Q - attn = attn * self.rescale - attn = attn.softmax(dim=-1) - x = attn @ v # b,heads,d,hw - x = x.permute(0, 3, 1, 2) # Transpose - x = x.reshape(b, h * w, self.num_heads * self.dim_head) - out_c = self.proj(x).view(b, h, w, c) - out_p = self.pos_emb(v_inp.reshape(b,h,w,c).permute(0, 3, 1, 2)).permute(0, 2, 3, 1) - out = out_c + out_p - - return out - -class FeedForward(nn.Module): - def __init__(self, dim, mult=4): - super().__init__() - self.net = nn.Sequential( - nn.Conv2d(dim, dim * mult, 1, 1, bias=False), - GELU(), - nn.Conv2d(dim * mult, dim * mult, 3, 1, 1, bias=False, groups=dim * mult), - GELU(), - nn.Conv2d(dim * mult, dim, 1, 1, bias=False), - ) - - def forward(self, x): - """ - x: [b,h,w,c] - return out: [b,h,w,c] - """ - out = self.net(x.permute(0, 3, 1, 2)) - return out.permute(0, 2, 3, 1) - -class MSAB(nn.Module): - def __init__( - self, - dim, - dim_head, - heads, - num_blocks, - ): - super().__init__() - self.blocks = nn.ModuleList([]) - for _ in range(num_blocks): - self.blocks.append(nn.ModuleList([ - MS_MSA(dim=dim, dim_head=dim_head, heads=heads), - PreNorm(dim, FeedForward(dim=dim)) - ])) - - def forward(self, x): - """ - x: [b,c,h,w] - return out: [b,c,h,w] - """ - x = x.permute(0, 2, 3, 1) - for (attn, ff) in self.blocks: - x = attn(x) + x - x = ff(x) + x - out = x.permute(0, 3, 1, 2) - return out - -class MST(nn.Module): - def __init__(self, in_dim=28, out_dim=28, dim=28, stage=2, num_blocks=[2,4,4]): - super(MST, self).__init__() - self.dim = dim - self.stage = stage - - # Input projection - self.embedding = nn.Conv2d(in_dim, self.dim, 3, 1, 1, bias=False) - - # Encoder - self.encoder_layers = nn.ModuleList([]) - dim_stage = dim - for i in range(stage): - self.encoder_layers.append(nn.ModuleList([ - MSAB( - dim=dim_stage, num_blocks=num_blocks[i], dim_head=dim, heads=dim_stage // dim), - nn.Conv2d(dim_stage, dim_stage * 2, 4, 2, 1, bias=False), - ])) - dim_stage *= 2 - - # Bottleneck - self.bottleneck = MSAB( - dim=dim_stage, dim_head=dim, heads=dim_stage // dim, num_blocks=num_blocks[-1]) - - # Decoder - self.decoder_layers = nn.ModuleList([]) - for i in range(stage): - self.decoder_layers.append(nn.ModuleList([ - nn.ConvTranspose2d(dim_stage, dim_stage // 2, stride=2, kernel_size=2, padding=0, output_padding=0), - nn.Conv2d(dim_stage, dim_stage // 2, 1, 1, bias=False), - MSAB( - dim=dim_stage // 2, num_blocks=num_blocks[stage - 1 - i], dim_head=dim, - heads=(dim_stage // 2) // dim), - ])) - dim_stage //= 2 - - # Output projection - self.mapping = nn.Conv2d(self.dim, out_dim, 3, 1, 1, bias=False) - - #### activation function - self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True) - self.apply(self._init_weights) - - def _init_weights(self, m): - if isinstance(m, nn.Linear): - trunc_normal_(m.weight, std=.02) - if isinstance(m, nn.Linear) and m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - def forward(self, x): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - - # Embedding - fea = self.embedding(x) - - # Encoder - fea_encoder = [] - for (MSAB, FeaDownSample) in self.encoder_layers: - fea = MSAB(fea) - fea_encoder.append(fea) - fea = FeaDownSample(fea) - - # Bottleneck - fea = self.bottleneck(fea) - - # Decoder - for i, (FeaUpSample, Fution, LeWinBlcok) in enumerate(self.decoder_layers): - fea = FeaUpSample(fea) - fea = Fution(torch.cat([fea, fea_encoder[self.stage-1-i]], dim=1)) - fea = LeWinBlcok(fea) - - # Mapping - out = self.mapping(fea) + x - - return out - -class MST_Plus_Plus(nn.Module): - def __init__(self, in_channels=3, out_channels=28, n_feat=28, stage=3): - super(MST_Plus_Plus, self).__init__() - self.stage = stage - self.conv_in = nn.Conv2d(in_channels, n_feat, kernel_size=3, padding=(3 - 1) // 2,bias=False) - modules_body = [MST(dim=n_feat, stage=2, num_blocks=[1,1,1]) for _ in range(stage)] - self.fution = nn.Conv2d(56, 28, 1, padding=0, bias=True) - self.body = nn.Sequential(*modules_body) - self.conv_out = nn.Conv2d(n_feat, out_channels, kernel_size=3, padding=(3 - 1) // 2,bias=False) - - def initial_x(self, y, Phi): - """ - :param y: [b,256,310] - :param Phi: [b,28,256,256] - :return: z: [b,28,256,256] - """ - x = self.fution(torch.cat([y, Phi], dim=1)) - return x - - def forward(self, y, Phi=None): - """ - x: [b,c,h,w] - return out:[b,c,h,w] - """ - if Phi==None: - #Phi = torch.rand((1,28,256,256))#.cuda() - Phi = torch.rand((1,28,256,256)) - x = self.initial_x(y, Phi) - b, c, h_inp, w_inp = x.shape - hb, wb = 8, 8 - pad_h = (hb - h_inp % hb) % hb - pad_w = (wb - w_inp % wb) % wb - x = F.pad(x, [0, pad_w, 0, pad_h], mode='reflect') - x = self.conv_in(x) - h = self.body(x) - h = self.conv_out(h) - h += x - return h[:, :, :h_inp, :w_inp] - - - - - - - - - - - - - - diff --git a/MST/simulation/train_code/architecture/TSA_Net.py b/MST/simulation/train_code/architecture/TSA_Net.py deleted file mode 100755 index bb7a77e..0000000 --- a/MST/simulation/train_code/architecture/TSA_Net.py +++ /dev/null @@ -1,331 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -import numpy as np -_NORM_BONE = False - - -def conv_block(in_planes, out_planes, the_kernel=3, the_stride=1, the_padding=1, flag_norm=False, flag_norm_act=True): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=the_kernel, stride=the_stride, padding=the_padding) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_planes) - if flag_norm: - return nn.Sequential(conv,norm,activation) if flag_norm_act else nn.Sequential(conv,activation,norm) - else: - return nn.Sequential(conv,activation) - -def conv1x1_block(in_planes, out_planes, flag_norm=False): - conv = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0,bias=False) - norm = nn.BatchNorm2d(out_planes) - return nn.Sequential(conv,norm) if flag_norm else conv - -def fully_block(in_dim, out_dim, flag_norm=False, flag_norm_act=True): - fc = nn.Linear(in_dim, out_dim) - activation = nn.ReLU(inplace=True) - norm = nn.BatchNorm2d(out_dim) - if flag_norm: - return nn.Sequential(fc,norm,activation) if flag_norm_act else nn.Sequential(fc,activation,norm) - else: - return nn.Sequential(fc,activation) - -class Res2Net(nn.Module): - def __init__(self, inChannel, uPlane, scale=4): - super(Res2Net, self).__init__() - self.uPlane = uPlane - self.scale = scale - - self.conv_init = nn.Conv2d(inChannel, uPlane * scale, kernel_size=1, bias=False) - self.bn_init = nn.BatchNorm2d(uPlane * scale) - - convs = [] - bns = [] - for i in range(self.scale - 1): - convs.append(nn.Conv2d(self.uPlane, self.uPlane, kernel_size=3, stride=1, padding=1, bias=False)) - bns.append(nn.BatchNorm2d(self.uPlane)) - self.convs = nn.ModuleList(convs) - self.bns = nn.ModuleList(bns) - - self.conv_end = nn.Conv2d(uPlane * scale, inChannel, kernel_size=1, bias=False) - self.bn_end = nn.BatchNorm2d(inChannel) - - self.relu = nn.ReLU(inplace=True) - - def forward(self, x): - - out = self.conv_init(x) - out = self.bn_init(out) - out = self.relu(out) - - spx = torch.split(out, self.uPlane, 1) - for i in range(self.scale - 1): - if i == 0: - sp = spx[i] - else: - sp = sp + spx[i] - sp = self.convs[i](sp) - sp = self.relu(self.bns[i](sp)) - if i == 0: - out = sp - else: - out = torch.cat((out, sp), 1) - out = torch.cat((out, spx[self.scale - 1]), 1) - - out = self.conv_end(out) - out = self.bn_end(out) - return out - -_NORM_ATTN = True -_NORM_FC = False - - -class TSA_Transform(nn.Module): - """ Spectral-Spatial Self-Attention """ - - def __init__(self, uSpace, inChannel, outChannel, nHead, uAttn, mode=[0, 1], flag_mask=False, gamma_learn=False): - super(TSA_Transform, self).__init__() - ''' ------------------------------------------ - uSpace: - uHeight: the [-2] dim of the 3D tensor - uWidth: the [-1] dim of the 3D tensor - inChannel: - the number of Channel of the input tensor - outChannel: - the number of Channel of the output tensor - nHead: - the number of Head of the input tensor - uAttn: - uSpatial: the dim of the spatial features - uSpectral: the dim of the spectral features - mask: - The Spectral Smoothness Mask - {mode} and {gamma_learn} is just for variable selection - ------------------------------------------ ''' - - self.nHead = nHead - self.uAttn = uAttn - self.outChannel = outChannel - self.uSpatial = nn.Parameter(torch.tensor(float(uAttn[0])), requires_grad=False) - self.uSpectral = nn.Parameter(torch.tensor(float(uAttn[1])), requires_grad=False) - self.mask = nn.Parameter(Spectral_Mask(outChannel), requires_grad=False) if flag_mask else None - self.attn_scale = nn.Parameter(torch.tensor(1.1), requires_grad=False) if flag_mask else None - self.gamma = nn.Parameter(torch.tensor(1.0), requires_grad=gamma_learn) - - if sum(mode) > 0: - down_sample = [] - scale = 1 - cur_channel = outChannel - for i in range(sum(mode)): - scale *= 2 - down_sample.append(conv_block(cur_channel, 2 * cur_channel, 3, 2, 1, _NORM_ATTN)) - cur_channel = 2 * cur_channel - self.cur_channel = cur_channel - self.down_sample = nn.Sequential(*down_sample) - self.up_sample = nn.ConvTranspose2d(outChannel * scale, outChannel, scale, scale) - else: - self.down_sample = None - self.up_sample = None - - spec_dim = int(uSpace[0] / 4 - 3) * int(uSpace[1] / 4 - 3) - self.preproc = conv1x1_block(inChannel, outChannel, _NORM_ATTN) - self.query_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.query_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.query_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.key_x = Feature_Spatial(outChannel, nHead, int(uSpace[1] / 4), uAttn[0], mode) - self.key_y = Feature_Spatial(outChannel, nHead, int(uSpace[0] / 4), uAttn[0], mode) - self.key_lambda = Feature_Spectral(outChannel, nHead, spec_dim, uAttn[1]) - self.value = conv1x1_block(outChannel, nHead * outChannel, _NORM_ATTN) - self.aggregation = nn.Linear(nHead * outChannel, outChannel) - - def forward(self, image): - feat = self.preproc(image) - feat_qx = self.query_x(feat, 'X') - feat_qy = self.query_y(feat, 'Y') - feat_qlambda = self.query_lambda(feat) - feat_kx = self.key_x(feat, 'X') - feat_ky = self.key_y(feat, 'Y') - feat_klambda = self.key_lambda(feat) - feat_value = self.value(feat) - - feat_qx = torch.cat(torch.split(feat_qx, 1, dim=1)).squeeze(dim=1) - feat_qy = torch.cat(torch.split(feat_qy, 1, dim=1)).squeeze(dim=1) - feat_kx = torch.cat(torch.split(feat_kx, 1, dim=1)).squeeze(dim=1) - feat_ky = torch.cat(torch.split(feat_ky, 1, dim=1)).squeeze(dim=1) - feat_qlambda = torch.cat(torch.split(feat_qlambda, self.uAttn[1], dim=-1)) - feat_klambda = torch.cat(torch.split(feat_klambda, self.uAttn[1], dim=-1)) - feat_value = torch.cat(torch.split(feat_value, self.outChannel, dim=1)) - - energy_x = torch.bmm(feat_qx, feat_kx.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_y = torch.bmm(feat_qy, feat_ky.permute(0, 2, 1)) / torch.sqrt(self.uSpatial) - energy_lambda = torch.bmm(feat_qlambda, feat_klambda.permute(0, 2, 1)) / torch.sqrt(self.uSpectral) - - attn_x = F.softmax(energy_x, dim=-1) - attn_y = F.softmax(energy_y, dim=-1) - attn_lambda = F.softmax(energy_lambda, dim=-1) - if self.mask is not None: - attn_lambda = (attn_lambda + self.mask) / torch.sqrt(self.attn_scale) - - pro_feat = feat_value if self.down_sample is None else self.down_sample(feat_value) - batchhead, dim_c, dim_x, dim_y = pro_feat.size() - attn_x_repeat = attn_x.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_x, dim_x) - attn_y_repeat = attn_y.unsqueeze(dim=1).repeat(1, dim_c, 1, 1).view(-1, dim_y, dim_y) - pro_feat = pro_feat.view(-1, dim_x, dim_y) - pro_feat = torch.bmm(pro_feat, attn_y_repeat.permute(0, 2, 1)) - pro_feat = torch.bmm(pro_feat.permute(0, 2, 1), attn_x_repeat.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, dim_c, dim_x, dim_y) - - if self.up_sample is not None: - pro_feat = self.up_sample(pro_feat) - _, _, dim_x, dim_y = pro_feat.size() - pro_feat = pro_feat.contiguous().view(batchhead, self.outChannel, -1).permute(0, 2, 1) - pro_feat = torch.bmm(pro_feat, attn_lambda.permute(0, 2, 1)).permute(0, 2, 1) - pro_feat = pro_feat.view(batchhead, self.outChannel, dim_x, dim_y) - pro_feat = torch.cat(torch.split(pro_feat, int(batchhead / self.nHead), dim=0), dim=1).permute(0, 2, 3, 1) - pro_feat = self.aggregation(pro_feat).permute(0, 3, 1, 2) - out = self.gamma * pro_feat + feat - return out, (attn_x, attn_y, attn_lambda) - - -class Feature_Spatial(nn.Module): - """ Spatial Feature Generation Component """ - - def __init__(self, inChannel, nHead, shiftDim, outDim, mode): - super(Feature_Spatial, self).__init__() - kernel = [(1, 5), (3, 5)] - stride = [(1, 2), (2, 2)] - padding = [(0, 2), (1, 2)] - self.conv1 = conv_block(inChannel, nHead, kernel[mode[0]], stride[mode[0]], padding[mode[0]], _NORM_ATTN) - self.conv2 = conv_block(nHead, nHead, kernel[mode[1]], stride[mode[1]], padding[mode[1]], _NORM_ATTN) - self.fully = fully_block(shiftDim, outDim, _NORM_FC) - - def forward(self, image, direction): - if direction == 'Y': - image = image.permute(0, 1, 3, 2) - feat = self.conv1(image) - feat = self.conv2(feat) - feat = self.fully(feat) - return feat - - -class Feature_Spectral(nn.Module): - """ Spectral Feature Generation Component """ - - def __init__(self, inChannel, nHead, viewDim, outDim): - super(Feature_Spectral, self).__init__() - self.inChannel = inChannel - self.conv1 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.conv2 = conv_block(inChannel, inChannel, 5, 2, 0, _NORM_ATTN) - self.fully = fully_block(viewDim, int(nHead * outDim), _NORM_FC) - - def forward(self, image): - bs = image.size(0) - feat = self.conv1(image) - feat = self.conv2(feat) - feat = feat.view(bs, self.inChannel, -1) - feat = self.fully(feat) - return feat - - -def Spectral_Mask(dim_lambda): - '''After put the available data into the model, we use this mask to avoid outputting the estimation of itself.''' - orig = (np.cos(np.linspace(-1, 1, num=2 * dim_lambda - 1) * np.pi) + 1.0) / 2.0 - att = np.zeros((dim_lambda, dim_lambda)) - for i in range(dim_lambda): - att[i, :] = orig[dim_lambda - 1 - i:2 * dim_lambda - 1 - i] - AM_Mask = torch.from_numpy(att.astype(np.float32)).unsqueeze(0) - return AM_Mask - -class TSA_Net(nn.Module): - - def __init__(self, in_ch=28, out_ch=28): - super(TSA_Net, self).__init__() - - self.tconv_down1 = Encoder_Triblock(in_ch, 64, False) - self.tconv_down2 = Encoder_Triblock(64, 128, False) - self.tconv_down3 = Encoder_Triblock(128, 256) - self.tconv_down4 = Encoder_Triblock(256, 512) - - self.bottom1 = conv_block(512, 1024) - self.bottom2 = conv_block(1024, 1024) - - self.tconv_up4 = Decoder_Triblock(1024, 512) - self.tconv_up3 = Decoder_Triblock(512, 256) - self.transform3 = TSA_Transform((64, 64), 256, 256, 8, (64, 80), [0, 0]) - self.tconv_up2 = Decoder_Triblock(256, 128) - self.transform2 = TSA_Transform((128, 128), 128, 128, 8, (64, 40), [1, 0]) - self.tconv_up1 = Decoder_Triblock(128, 64) - self.transform1 = TSA_Transform((256, 256), 64, 28, 8, (48, 30), [1, 1], True) - - self.conv_last = nn.Conv2d(out_ch, out_ch, 1) - self.afn_last = nn.Sigmoid() - - def forward(self, x, input_mask=None): - - enc1, enc1_pre = self.tconv_down1(x) - enc2, enc2_pre = self.tconv_down2(enc1) - enc3, enc3_pre = self.tconv_down3(enc2) - enc4, enc4_pre = self.tconv_down4(enc3) - # enc5,enc5_pre = self.tconv_down5(enc4) - - bottom = self.bottom1(enc4) - bottom = self.bottom2(bottom) - - # dec5 = self.tconv_up5(bottom,enc5_pre) - dec4 = self.tconv_up4(bottom, enc4_pre) - dec3 = self.tconv_up3(dec4, enc3_pre) - dec3, _ = self.transform3(dec3) - dec2 = self.tconv_up2(dec3, enc2_pre) - dec2, _ = self.transform2(dec2) - dec1 = self.tconv_up1(dec2, enc1_pre) - dec1, _ = self.transform1(dec1) - - dec1 = self.conv_last(dec1) - output = self.afn_last(dec1) - - return output - - -class Encoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Encoder_Triblock, self).__init__() - - self.layer1 = conv_block(inChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - if flag_res: - self.layer2 = Res2Net(outChannel, int(outChannel / 4)) - else: - self.layer2 = conv_block(outChannel, outChannel, nKernal, flag_norm=_NORM_BONE) - - self.pool = nn.MaxPool2d(nPool) if flag_Pool else None - - def forward(self, x): - feat = self.layer1(x) - feat = self.layer2(feat) - - feat_pool = self.pool(feat) if self.pool is not None else feat - return feat_pool, feat - - -class Decoder_Triblock(nn.Module): - def __init__(self, inChannel, outChannel, flag_res=True, nKernal=3, nPool=2, flag_Pool=True): - super(Decoder_Triblock, self).__init__() - - self.layer1 = nn.Sequential( - nn.ConvTranspose2d(inChannel, outChannel, kernel_size=2, stride=2), - nn.ReLU(inplace=True) - ) - if flag_res: - self.layer2 = Res2Net(int(outChannel * 2), int(outChannel / 2)) - else: - self.layer2 = conv_block(outChannel * 2, outChannel * 2, nKernal, flag_norm=_NORM_BONE) - self.layer3 = conv_block(outChannel * 2, outChannel, nKernal, flag_norm=_NORM_BONE) - - def forward(self, feat_dec, feat_enc): - feat_dec = self.layer1(feat_dec) - diffY = feat_enc.size()[2] - feat_dec.size()[2] - diffX = feat_enc.size()[3] - feat_dec.size()[3] - if diffY != 0 or diffX != 0: - print('Padding for size mismatch ( Enc:', feat_enc.size(), 'Dec:', feat_dec.size(), ')') - feat_dec = F.pad(feat_dec, [diffX // 2, diffX - diffX // 2, diffY // 2, diffY - diffY // 2]) - feat = torch.cat([feat_dec, feat_enc], dim=1) - feat = self.layer2(feat) - feat = self.layer3(feat) - return feat \ No newline at end of file diff --git a/MST/simulation/train_code/architecture/__init__.py b/MST/simulation/train_code/architecture/__init__.py deleted file mode 100755 index 3fa9eb2..0000000 --- a/MST/simulation/train_code/architecture/__init__.py +++ /dev/null @@ -1,67 +0,0 @@ -import torch -from .MST import MST -from .GAP_Net import GAP_net -from .ADMM_Net import ADMM_net -from .TSA_Net import TSA_Net -from .HDNet import HDNet, FDL -from .DGSMP import HSI_CS -from .BIRNAT import BIRNAT -from .MST_Plus_Plus import MST_Plus_Plus -from .Lambda_Net import Lambda_Net -from .CST import CST -from .DAUHST import DAUHST -from .BiSRNet import BiSRNet - -def model_generator(method, pretrained_model_path=None): - if method == 'mst_s': - model = MST(dim=28, stage=2, num_blocks=[2, 2, 2]).cuda() - elif method == 'mst_m': - model = MST(dim=28, stage=2, num_blocks=[2, 4, 4]).cuda() - elif method == 'mst_l': - model = MST(dim=28, stage=2, num_blocks=[4, 7, 5]).cuda() - elif method == 'gap_net': - model = GAP_net().cuda() - elif method == 'admm_net': - model = ADMM_net().cuda() - elif method == 'tsa_net': - model = TSA_Net().cuda() - elif method == 'hdnet': - model = HDNet().cuda() - fdl_loss = FDL(loss_weight=0.7, - alpha=2.0, - patch_factor=4, - ave_spectrum=True, - log_matrix=True, - batch_matrix=True, - ).cuda() - elif method == 'dgsmp': - model = HSI_CS(Ch=28, stages=4).cuda() - elif method == 'birnat': - model = BIRNAT().cuda() - elif method == 'mst_plus_plus': - model = MST_Plus_Plus(in_channels=28, out_channels=28, n_feat=28, stage=3) - elif method == 'lambda_net': - model = Lambda_Net(out_ch=28).cuda() - elif method == 'cst_s': - model = CST(num_blocks=[1, 1, 2], sparse=True).cuda() - elif method == 'cst_m': - model = CST(num_blocks=[2, 2, 2], sparse=True).cuda() - elif method == 'cst_l': - model = CST(num_blocks=[2, 4, 6], sparse=True).cuda() - elif method == 'cst_l_plus': - model = CST(num_blocks=[2, 4, 6], sparse=False).cuda() - elif 'dauhst' in method: - num_iterations = int(method.split('_')[1][0]) - model = DAUHST(num_iterations=num_iterations).cuda() - elif method == 'bisrnet': - model = BiSRNet(in_channels=28, out_channels=28, n_feat=28, stage=1, num_blocks=[1,1,1]).cuda() - else: - print(f'Method {method} is not defined !!!!') - if pretrained_model_path is not None: - print(f'load model from {pretrained_model_path}') - checkpoint = torch.load(pretrained_model_path) - model.load_state_dict({k.replace('module.', ''): v for k, v in checkpoint.items()}, - strict=False) - if method == 'hdnet': - return model,fdl_loss - return model \ No newline at end of file diff --git a/MST/simulation/train_code/architecture/__pycache__/ADMM_Net.cpython-36.pyc b/MST/simulation/train_code/architecture/__pycache__/ADMM_Net.cpython-36.pyc deleted file mode 100755 index 7823a549f5f356c10e1fda452ef6468843f4e451..0000000000000000000000000000000000000000 GIT binary patch literal 0 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20c36e1..0000000 --- a/MST/simulation/train_code/option.py +++ /dev/null @@ -1,46 +0,0 @@ -import argparse -import template - -parser = argparse.ArgumentParser(description="HyperSpectral Image Reconstruction Toolbox") -parser.add_argument('--template', default='mst', - help='You can set various templates in option.py') - -# Hardware specifications -parser.add_argument("--gpu_id", type=str, default='0') - -# Data specifications -parser.add_argument('--data_root', type=str, default='../../datasets/', help='dataset directory') - -# Saving specifications -parser.add_argument('--outf', type=str, default='./exp/mst_s/', help='saving_path') - -# Model specifications -parser.add_argument('--method', type=str, default='mst_s', help='method name') -parser.add_argument('--pretrained_model_path', type=str, default=None, help='pretrained model directory') -parser.add_argument("--input_setting", type=str, default='H', - help='the input measurement of the network: H, HM or Y') -parser.add_argument("--input_mask", type=str, default='Phi', - help='the input mask of the network: Phi, Phi_PhiPhiT, Mask or None') # Phi: shift_mask Mask: mask - -# Training specifications -parser.add_argument('--batch_size', type=int, default=5, help='the number of HSIs per batch') -parser.add_argument("--max_epoch", type=int, default=300, help='total epoch') -parser.add_argument("--scheduler", type=str, default='MultiStepLR', help='MultiStepLR or CosineAnnealingLR') -parser.add_argument("--milestones", type=int, default=[50,100,150,200,250], help='milestones for MultiStepLR') -parser.add_argument("--gamma", type=float, default=0.5, help='learning rate decay for MultiStepLR') -parser.add_argument("--epoch_sam_num", type=int, default=5000, help='the number of samples per epoch') -parser.add_argument("--learning_rate", type=float, default=0.0004) - -opt = parser.parse_args() -template.set_template(opt) - -# dataset -opt.data_path = f"{opt.data_root}/cave_1024_28/" -opt.mask_path = f"{opt.data_root}/TSA_simu_data/" -opt.test_path = f"{opt.data_root}/TSA_simu_data/Truth/" - -for arg in vars(opt): - if vars(opt)[arg] == 'True': - vars(opt)[arg] = True - elif vars(opt)[arg] == 'False': - vars(opt)[arg] = False \ No newline at end of file diff --git a/MST/simulation/train_code/ssim_torch.py b/MST/simulation/train_code/ssim_torch.py deleted file mode 100755 index 0787fa2..0000000 --- a/MST/simulation/train_code/ssim_torch.py +++ /dev/null @@ -1,77 +0,0 @@ -import torch -import torch.nn.functional as F -from torch.autograd import Variable -import numpy as np -from math import exp - - -def gaussian(window_size, sigma): - gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)]) - return gauss / gauss.sum() - - -def create_window(window_size, channel): - _1D_window = gaussian(window_size, 1.5).unsqueeze(1) - _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0) - window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous()) - return window - - -def _ssim(img1, img2, window, window_size, channel, size_average=True): - mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel) - mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel) - - mu1_sq = mu1.pow(2) - mu2_sq = mu2.pow(2) - mu1_mu2 = mu1 * mu2 - - sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq - sigma2_sq = F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq - sigma12 = F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2 - - C1 = 0.01 ** 2 - C2 = 0.03 ** 2 - - ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)) - - if size_average: - return ssim_map.mean() - else: - return ssim_map.mean(1).mean(1).mean(1) - - -class SSIM(torch.nn.Module): - def __init__(self, window_size=11, size_average=True): - super(SSIM, self).__init__() - self.window_size = window_size - self.size_average = size_average - self.channel = 1 - self.window = create_window(window_size, self.channel) - - def forward(self, img1, img2): - (_, channel, _, _) = img1.size() - - if channel == self.channel and self.window.data.type() == img1.data.type(): - window = self.window - else: - window = create_window(self.window_size, channel) - - if img1.is_cuda: - window = window.cuda(img1.get_device()) - window = window.type_as(img1) - - self.window = window - self.channel = channel - - return _ssim(img1, img2, window, self.window_size, channel, self.size_average) - - -def ssim(img1, img2, window_size=11, size_average=True): - (_, channel, _, _) = img1.size() - window = create_window(window_size, channel) - - if img1.is_cuda: - window = window.cuda(img1.get_device()) - window = window.type_as(img1) - - return _ssim(img1, img2, window, window_size, channel, size_average) diff --git a/MST/simulation/train_code/template.py b/MST/simulation/train_code/template.py deleted file mode 100755 index 6104ef2..0000000 --- a/MST/simulation/train_code/template.py +++ /dev/null @@ -1,71 +0,0 @@ -def set_template(args): - # Set the templates here - if args.template.find('mst') >= 0: - args.input_setting = 'H' - args.input_mask = 'Phi' - - if args.template.find('gap_net') >= 0 or args.template.find('admm_net') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.milestones = range(30,args.max_epoch,30) - args.gamma = 0.9 - args.learning_rate = 1e-3 - - if args.template.find('dauhst') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.scheduler = 'CosineAnnealingLR' - args.max_epoch = 300 - - if args.template.find('tsa_net') >= 0: - args.input_setting = 'HM' - args.input_mask = None - - if args.template.find('hdnet') >= 0: - args.input_setting = 'H' - args.input_mask = None - - if args.template.find('dgsmp') >= 0: - args.input_setting = 'Y' - args.input_mask = None - args.batch_size = 2 - args.milestones = [args.max_epoch] # fix the learning rate during training, following the official implementation - args.learning_rate = 1e-4 - - if args.template.find('birnat') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - args.batch_size = 1 - args.max_epoch = 100 - args.milestones = [args.max_epoch] # fix the learning rate during training, following the official implementation - args.learning_rate = 1.5e-4 - - if args.template.find('mst_plus_plus') >= 0: - args.input_setting = 'H' - args.input_mask = 'Mask' - args.scheduler = 'CosineAnnealingLR' - - if args.template.find('bisrnet') >= 0: - args.input_setting = 'H' - args.input_mask = 'Mask' - args.scheduler = 'CosineAnnealingLR' - - if args.template.find('cst') >= 0: - args.input_setting = 'H' - args.input_mask = 'Mask' - args.scheduler = 'CosineAnnealingLR' - args.max_epoch = 500 - - if args.template.find('dnu') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi_PhiPhiT' - args.batch_size = 2 - args.max_epoch = 150 - args.milestones = range(10,args.max_epoch,10) - args.gamma = 0.9 - args.learning_rate = 4e-4 - - if args.template.find('lambda_net') >= 0: - args.input_setting = 'Y' - args.input_mask = 'Phi' - args.learning_rate = 1.5e-4 \ No newline at end of file diff --git a/MST/simulation/train_code/train.py b/MST/simulation/train_code/train.py deleted file mode 100755 index 3f27c59..0000000 --- a/MST/simulation/train_code/train.py +++ /dev/null @@ -1,130 +0,0 @@ -from architecture import * -from utils import * -import torch -import scipy.io as scio -import time -import os -import numpy as np -from torch.autograd import Variable -import datetime -from option import opt -import torch.nn.functional as F - -os.environ["CUDA_DEVICE_ORDER"] = 'PCI_BUS_ID' -os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpu_id -torch.backends.cudnn.enabled = True -torch.backends.cudnn.benchmark = True -if not torch.cuda.is_available(): - raise Exception('NO GPU!') - -# init mask -mask3d_batch_train, input_mask_train = init_mask(opt.mask_path, opt.input_mask, opt.batch_size) -mask3d_batch_test, input_mask_test = init_mask(opt.mask_path, opt.input_mask, 10) - -# dataset -train_set = LoadTraining(opt.data_path) -test_data = LoadTest(opt.test_path) - -# saving path -date_time = str(datetime.datetime.now()) -date_time = time2file_name(date_time) -result_path = opt.outf + date_time + '/result/' -model_path = opt.outf + date_time + '/model/' -if not os.path.exists(result_path): - os.makedirs(result_path) -if not os.path.exists(model_path): - os.makedirs(model_path) - -# model -if opt.method=='hdnet': - model, FDL_loss = model_generator(opt.method, opt.pretrained_model_path).cuda() -else: - model = model_generator(opt.method, opt.pretrained_model_path).cuda() - -# optimizing -optimizer = torch.optim.Adam(model.parameters(), lr=opt.learning_rate, betas=(0.9, 0.999)) -if opt.scheduler=='MultiStepLR': - scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=opt.milestones, gamma=opt.gamma) -elif opt.scheduler=='CosineAnnealingLR': - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.max_epoch, eta_min=1e-6) -mse = torch.nn.MSELoss().cuda() - -def train(epoch, logger): - epoch_loss = 0 - begin = time.time() - batch_num = int(np.floor(opt.epoch_sam_num / opt.batch_size)) - for i in range(batch_num): - gt_batch = shuffle_crop(train_set, opt.batch_size) - gt = Variable(gt_batch).cuda().float() - input_meas = init_meas(gt, mask3d_batch_train, opt.input_setting) - optimizer.zero_grad() - - if opt.method in ['cst_s', 'cst_m', 'cst_l']: - model_out, diff_pred = model(input_meas, input_mask_train) - loss = torch.sqrt(mse(model_out, gt)) - diff_gt = torch.mean(torch.abs(model_out.detach() - gt),dim=1, keepdim=True) # [b,1,h,w] - loss_sparsity = F.mse_loss(diff_gt, diff_pred) - loss = loss + 2 * loss_sparsity - else: - model_out = model(input_meas, input_mask_train) - loss = torch.sqrt(mse(model_out, gt)) - - if opt.method=='hdnet': - fdl_loss = FDL_loss(model_out, gt) - loss = loss + 0.7 * fdl_loss - epoch_loss += loss.data - loss.backward() - optimizer.step() - end = time.time() - logger.info("===> Epoch {} Complete: Avg. Loss: {:.6f} time: {:.2f}". - format(epoch, epoch_loss / batch_num, (end - begin))) - return 0 - -def test(epoch, logger): - psnr_list, ssim_list = [], [] - test_gt = test_data.cuda().float() - input_meas = init_meas(test_gt, mask3d_batch_test, opt.input_setting) - model.eval() - begin = time.time() - with torch.no_grad(): - if opt.method in ['cst_s', 'cst_m', 'cst_l']: - model_out, _ = model(input_meas, input_mask_test) - else: - model_out = model(input_meas, input_mask_test) - - end = time.time() - for k in range(test_gt.shape[0]): - psnr_val = torch_psnr(model_out[k, :, :, :], test_gt[k, :, :, :]) - ssim_val = torch_ssim(model_out[k, :, :, :], test_gt[k, :, :, :]) - psnr_list.append(psnr_val.detach().cpu().numpy()) - ssim_list.append(ssim_val.detach().cpu().numpy()) - pred = np.transpose(model_out.detach().cpu().numpy(), (0, 2, 3, 1)).astype(np.float32) - truth = np.transpose(test_gt.cpu().numpy(), (0, 2, 3, 1)).astype(np.float32) - psnr_mean = np.mean(np.asarray(psnr_list)) - ssim_mean = np.mean(np.asarray(ssim_list)) - logger.info('===> Epoch {}: testing psnr = {:.2f}, ssim = {:.3f}, time: {:.2f}' - .format(epoch, psnr_mean, ssim_mean,(end - begin))) - model.train() - return pred, truth, psnr_list, ssim_list, psnr_mean, ssim_mean - -def main(): - logger = gen_log(model_path) - logger.info("Learning rate:{}, batch_size:{}.\n".format(opt.learning_rate, opt.batch_size)) - psnr_max = 0 - for epoch in range(1, opt.max_epoch + 1): - train(epoch, logger) - (pred, truth, psnr_all, ssim_all, psnr_mean, ssim_mean) = test(epoch, logger) - scheduler.step() - if psnr_mean > psnr_max: - psnr_max = psnr_mean - if psnr_mean > 28: - name = result_path + '/' + 'Test_{}_{:.2f}_{:.3f}'.format(epoch, psnr_max, ssim_mean) + '.mat' - scio.savemat(name, {'truth': truth, 'pred': pred, 'psnr_list': psnr_all, 'ssim_list': ssim_all}) - checkpoint(model, epoch, model_path, logger) - -if __name__ == '__main__': - torch.backends.cudnn.enabled = True - torch.backends.cudnn.benchmark = True - main() - - diff --git a/MST/simulation/train_code/utils.py b/MST/simulation/train_code/utils.py deleted file mode 100755 index 477a3eb..0000000 --- a/MST/simulation/train_code/utils.py +++ /dev/null @@ -1,235 +0,0 @@ -import scipy.io as sio -import os -import numpy as np -import torch -import logging -import random -from ssim_torch import ssim - -def generate_masks(mask_path, batch_size): - mask = sio.loadmat(mask_path + '/mask.mat') - mask = mask['mask'] - mask3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28)) - mask3d = np.transpose(mask3d, [2, 0, 1]) - mask3d = torch.from_numpy(mask3d) - [nC, H, W] = mask3d.shape - mask3d_batch = mask3d.expand([batch_size, nC, H, W]).cuda().float() - return mask3d_batch - -def generate_shift_masks(mask_path, batch_size): - mask = sio.loadmat(mask_path + '/mask_3d_shift.mat') - mask_3d_shift = mask['mask_3d_shift'] - mask_3d_shift = np.transpose(mask_3d_shift, [2, 0, 1]) - mask_3d_shift = torch.from_numpy(mask_3d_shift) - [nC, H, W] = mask_3d_shift.shape - Phi_batch = mask_3d_shift.expand([batch_size, nC, H, W]).cuda().float() - Phi_s_batch = torch.sum(Phi_batch**2,1) - Phi_s_batch[Phi_s_batch==0] = 1 - return Phi_batch, Phi_s_batch - -def LoadTraining(path): - imgs = [] - scene_list = os.listdir(path) - scene_list.sort() - print('training sences:', len(scene_list)) - for i in range(len(scene_list)): - # for i in range(5): - scene_path = path + scene_list[i] - scene_num = int(scene_list[i].split('.')[0][5:]) - if scene_num<=205: - if 'mat' not in scene_path: - continue - img_dict = sio.loadmat(scene_path) - if "img_expand" in img_dict: - img = img_dict['img_expand'] / 65536. - elif "img" in img_dict: - img = img_dict['img'] / 65536. - img = img.astype(np.float32) - imgs.append(img) - print('Sence {} is loaded. {}'.format(i, scene_list[i])) - return imgs - -def LoadTest(path_test): - scene_list = os.listdir(path_test) - scene_list.sort() - test_data = np.zeros((len(scene_list), 256, 256, 28)) - for i in range(len(scene_list)): - scene_path = path_test + scene_list[i] - img = sio.loadmat(scene_path)['img'] - test_data[i, :, :, :] = img - test_data = torch.from_numpy(np.transpose(test_data, (0, 3, 1, 2))) - return test_data - -def LoadMeasurement(path_test_meas): - img = sio.loadmat(path_test_meas)['simulation_test'] - test_data = img - test_data = torch.from_numpy(test_data) - return test_data - -# We find that this calculation method is more close to DGSMP's. -def torch_psnr(img, ref): # input [28,256,256] - img = (img*256).round() - ref = (ref*256).round() - nC = img.shape[0] - psnr = 0 - for i in range(nC): - mse = torch.mean((img[i, :, :] - ref[i, :, :]) ** 2) - psnr += 10 * torch.log10((255*255)/mse) - return psnr / nC - -def torch_ssim(img, ref): # input [28,256,256] - return ssim(torch.unsqueeze(img, 0), torch.unsqueeze(ref, 0)) - -def time2file_name(time): - year = time[0:4] - month = time[5:7] - day = time[8:10] - hour = time[11:13] - minute = time[14:16] - second = time[17:19] - time_filename = year + '_' + month + '_' + day + '_' + hour + '_' + minute + '_' + second - return time_filename - -def shuffle_crop(train_data, batch_size, crop_size=256, argument=True): - if argument: - gt_batch = [] - # The first half data use the original data. - index = np.random.choice(range(len(train_data)), batch_size//2) - processed_data = np.zeros((batch_size//2, crop_size, crop_size, 28), dtype=np.float32) - for i in range(batch_size//2): - img = train_data[index[i]] - h, w, _ = img.shape - x_index = np.random.randint(0, h - crop_size) - y_index = np.random.randint(0, w - crop_size) - processed_data[i, :, :, :] = img[x_index:x_index + crop_size, y_index:y_index + crop_size, :] - processed_data = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))).cuda().float() - for i in range(processed_data.shape[0]): - gt_batch.append(arguement_1(processed_data[i])) - - # The other half data use splicing. - processed_data = np.zeros((4, 128, 128, 28), dtype=np.float32) - for i in range(batch_size - batch_size // 2): - sample_list = np.random.randint(0, len(train_data), 4) - for j in range(4): - x_index = np.random.randint(0, h-crop_size//2) - y_index = np.random.randint(0, w-crop_size//2) - processed_data[j] = train_data[sample_list[j]][x_index:x_index+crop_size//2,y_index:y_index+crop_size//2,:] - gt_batch_2 = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))).cuda() # [4,28,128,128] - gt_batch.append(arguement_2(gt_batch_2)) - gt_batch = torch.stack(gt_batch, dim=0) - return gt_batch - else: - index = np.random.choice(range(len(train_data)), batch_size) - processed_data = np.zeros((batch_size, crop_size, crop_size, 28), dtype=np.float32) - for i in range(batch_size): - h, w, _ = train_data[index[i]].shape - x_index = np.random.randint(0, h - crop_size) - y_index = np.random.randint(0, w - crop_size) - processed_data[i, :, :, :] = train_data[index[i]][x_index:x_index + crop_size, y_index:y_index + crop_size, :] - gt_batch = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))) - return gt_batch - -def arguement_1(x): - """ - :param x: c,h,w - :return: c,h,w - """ - rotTimes = random.randint(0, 3) - vFlip = random.randint(0, 1) - hFlip = random.randint(0, 1) - # Random rotation - for j in range(rotTimes): - x = torch.rot90(x, dims=(1, 2)) - # Random vertical Flip - for j in range(vFlip): - x = torch.flip(x, dims=(2,)) - # Random horizontal Flip - for j in range(hFlip): - x = torch.flip(x, dims=(1,)) - return x - -def arguement_2(generate_gt): - c, h, w = generate_gt.shape[1],256,256 - divid_point_h = 128 - divid_point_w = 128 - output_img = torch.zeros(c,h,w).cuda() - output_img[:, :divid_point_h, :divid_point_w] = generate_gt[0] - output_img[:, :divid_point_h, divid_point_w:] = generate_gt[1] - output_img[:, divid_point_h:, :divid_point_w] = generate_gt[2] - output_img[:, divid_point_h:, divid_point_w:] = generate_gt[3] - return output_img - - -def gen_meas_torch(data_batch, mask3d_batch, Y2H=True, mul_mask=False): - nC = data_batch.shape[1] - temp = shift(mask3d_batch * data_batch, 2) - meas = torch.sum(temp, 1) - if Y2H: - meas = meas / nC * 2 - H = shift_back(meas) - if mul_mask: - HM = torch.mul(H, mask3d_batch) - return HM - return H - return meas - -def shift(inputs, step=2): - [bs, nC, row, col] = inputs.shape - output = torch.zeros(bs, nC, row, col + (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, step * i:step * i + col] = inputs[:, i, :, :] - return output - -def shift_back(inputs, step=2): # input [bs,256,310] output [bs, 28, 256, 256] - [bs, row, col] = inputs.shape - nC = 28 - output = torch.zeros(bs, nC, row, col - (nC - 1) * step).cuda().float() - for i in range(nC): - output[:, i, :, :] = inputs[:, :, step * i:step * i + col - (nC - 1) * step] - return output - -def gen_log(model_path): - logger = logging.getLogger() - logger.setLevel(logging.INFO) - formatter = logging.Formatter("%(asctime)s - %(levelname)s: %(message)s") - - log_file = model_path + '/log.txt' - fh = logging.FileHandler(log_file, mode='a') - fh.setLevel(logging.INFO) - fh.setFormatter(formatter) - - ch = logging.StreamHandler() - ch.setLevel(logging.INFO) - ch.setFormatter(formatter) - - logger.addHandler(fh) - logger.addHandler(ch) - return logger - -def init_mask(mask_path, mask_type, batch_size): - mask3d_batch = generate_masks(mask_path, batch_size) - if mask_type == 'Phi': - shift_mask3d_batch = shift(mask3d_batch) - input_mask = shift_mask3d_batch - elif mask_type == 'Phi_PhiPhiT': - Phi_batch, Phi_s_batch = generate_shift_masks(mask_path, batch_size) - input_mask = (Phi_batch, Phi_s_batch) - elif mask_type == 'Mask': - input_mask = mask3d_batch - elif mask_type == None: - input_mask = None - return mask3d_batch, input_mask - -def init_meas(gt, mask, input_setting): - if input_setting == 'H': - input_meas = gen_meas_torch(gt, mask, Y2H=True, mul_mask=False) - elif input_setting == 'HM': - input_meas = gen_meas_torch(gt, mask, Y2H=True, mul_mask=True) - elif input_setting == 'Y': - input_meas = gen_meas_torch(gt, mask, Y2H=False, mul_mask=True) - return input_meas - -def checkpoint(model, epoch, model_path, logger): - model_out_path = model_path + "/model_epoch_{}.pth".format(epoch) - torch.save(model.state_dict(), model_out_path) - logger.info("Checkpoint saved to {}".format(model_out_path)) diff --git a/MST/visualization/createfigure.m b/MST/visualization/createfigure.m deleted file mode 100755 index 15ea8af..0000000 --- a/MST/visualization/createfigure.m +++ /dev/null @@ -1,47 +0,0 @@ -function createfigure(X1, YMatrix1, Corr) -%CREATEFIGURE(X1, YMatrix1) -% X1: x 数据的向量 -% YMATRIX1: y 数据的矩阵 - -% 由 MATLAB 于 19-Feb-2022 11:12:35 自动生成 - -% 创建 figure -figure1 = figure('PaperOrientation','landscape',... - 'PaperSize',[29.69999902 20.99999864]); - -% 创建 axes -axes1 = axes('Parent',figure1,'Position',[0.13 0.11 0.385625 0.815]); -hold(axes1,'on'); - -% 使用 plot 的矩阵输入创建多行 -plot1 = plot(X1,YMatrix1,'MarkerSize',16,'Marker','.','LineWidth',2.5,... - 'Parent',axes1); -set(plot1(1),'DisplayName',' Ground Truth','Color',[1 0 0]); -set(plot1(2),'DisplayName',' HDNet, corr: '+string(roundn(Corr(1),-4)),'Color',[0.01 0.66 0.62]); -set(plot1(3),'DisplayName',' MST-S, corr: '+string(roundn(Corr(2),-4)),'Color',[0.66 0.01 0.62]); -set(plot1(4),'DisplayName',' MST-M, corr: '+string(roundn(Corr(3),-4)),'Color',[1 1 0]); -set(plot1(5),'DisplayName',' MST-L, corr: '+string(roundn(Corr(4),-4)),'Color',[0 0.5 0.5]); -set(plot1(6),'DisplayName',' MST++, corr: '+string(roundn(Corr(5),-4)),'Color',[0 1 0]); - - - - -% 取消以下行的注释以保留坐标区的 Y 范围 -ylim(axes1,[0 1]); -box(axes1,'on'); -hold(axes1,'off'); -% 设置其余坐标区属性 -set(axes1,'FontName','Arial','FontSize',22,'LineWidth',3.5); - -% 创建 ylabel -ylabel('Density','FontSize',28,'FontName','Arial'); - -% 创建 xlabel -xlabel('Wavelength (nm)','FontSize',28,'FontName','Arial'); -% 创建 legend -legend1 = legend(axes1,'show'); -set(legend1,... - 'Position',[0.320670220276361 0.124725505873052 0.187369795342287 0.36915888702758],... - 'FontSize',22,... - 'EdgeColor',[1 1 1]); - diff --git a/MST/visualization/dispCubeAshwin.m b/MST/visualization/dispCubeAshwin.m deleted file mode 100755 index 8b65196..0000000 --- a/MST/visualization/dispCubeAshwin.m +++ /dev/null @@ -1,2124 +0,0 @@ -function [] = dispCubeAshwin(datacube,brightness,mywl,labelss,cols,rows,writefile,grayc,resultname) -%dispCubeAshwin(x_twist16,80,linspace(450,650,23),[],4,6,0,0) -h = figure; -set(gcf,'color','white'); -% fig_xpos = 10; % 50 -% fig_ypos = -30; % 50 -% fig_width = 820; % 820 -% fig_height = 860; % 860 -% set(gcf,'position',[fig_xpos fig_ypos fig_width fig_height]); - -% wltextposition = [0 size(datacube,2)-0]; -wltextposition = [12 12]; %[12 40]; -m=size(datacube,3); -for ind=1:m - datacube(:,:,ind)=flipud(datacube(:,:,ind)); % up side dowm -end -imagedatacube2(h,double(datacube),brightness,mywl,labelss,wltextposition,cols,rows,writefile,grayc,resultname); - - - - -function imagedatacube2(h, data,mymax,wavelengths,labelss,textposition,cols,rows,writefile,grayc,resultname) -%data = datacube to plot -%mymax = scaling factor to increase brightness of each slice -%wavelengths = wavelengths of each spectral slice in nm -%textposition = where the wavelengths text will be placed - -n = size(data,1); -% data=mymax*data; -temp=sort(data(:)); -temp=mean(temp(end-50:end)); -data=data*(50/temp); -if mymax > 1 - data=data*mymax; -end - -totalfigs = size(data,3); - -% cols = ceil(sqrt(totalfigs)); -% rows = ceil(totalfigs/cols); - -figscount = 1; -set(gcf,'color','white'); - -subplot1(rows,cols,'Gap',[0.005 0.005]); -for r = 1:rows - for c = 1:cols - if figscount>totalfigs - subplot1(figscount);axis off; - else - currentwl = wavelengths(figscount); - - cmM=(gray*kron(ones(3,1),spectrumRGB(wavelengths(figscount)))); - cmM=cmM/max(max(cmM)); - - if figscount<=totalfigs - subplot1((r-1)*cols+c); - if isempty(labelss) - subimage2(squeeze(data(:,:,figscount)),colormap(cmM)); - %text(textposition(1),textposition(2),['\bf' num2str(currentwl,4) ' nm'],'color','w') - else - if grayc==1 - subimage2(make01(squeeze(data(:,:,figscount)))); - else - subimage2(squeeze(data(:,:,figscount)),colormap(cmM)); - end - if r*c==1 - text(textposition(1),textposition(2),['\bf' num2str(labelss(r*c),4) ' frame'],'color','w') - else - text(textposition(1),textposition(2),['\bf' num2str(labelss((r-1)*cols+c),4) ' frames'],'color','w') - end - end - if writefile - imwrite(squeeze(rot90(rot90(data(:,:,figscount)))),colormap(cmM),['file' num2str(figscount) '.png']) - end - axis off; - end - end - figscount = figscount+1; - end -end -% h -% rect1 = [181 37 553 553]; -% h_crop = imcrop(h,rect1); -% saveas(h_crop, [resultname '.png']); -saveas(h, [resultname, '.png']); -% print(gcf,'-dpng',resultname); - - -function sRGB = spectrumRGB(lambdaIn, varargin) -%spectrumRGB Converts a spectral wavelength to RGB. -% -% sRGB = spectrumRGB(LAMBDA) converts the spectral color with wavelength -% LAMBDA in the visible light spectrum to its RGB values in the sRGB -% color space. -% -% sRGB = spectrumRGB(LAMBDA, MATCH) converts LAMBDA to sRGB using the -% color matching function MATCH, a string. See colorMatchFcn for a list -% of supported matching functions. -% -% Note: LAMBDA must be a scalar value or a vector of wavelengths. -% -% See also colorMatchFcn, createSpectrum, spectrumLabel. - -% Copyright 1993-2005 The MathWorks, Inc. - -if (numel(lambdaIn) ~= length(lambdaIn)) - - error('spectrumRGB:unsupportedDimension', ... - 'Input must be a scalar or vector of wavelengths.') - -end - -% Get the color matching functions. -if (nargin == 1) - - matchingFcn = '1964_full'; - -elseif (nargin == 2) - - matchingFcn = varargin{1}; - -else - - error(nargchk(1, 2, nargin, 'struct')) - -end - -[lambdaMatch, xFcn, yFcn, zFcn] = colorMatchFcn(matchingFcn); - -% Interpolate the input wavelength in the color matching functions. -XYZ = interp1(lambdaMatch', [xFcn; yFcn; zFcn]', lambdaIn, 'pchip', 0); - -% Reshape interpolated values to match standard image representation. -if (numel(lambdaIn) > 1) - - XYZ = permute(XYZ', [3 2 1]); - -end - -% Convert the XYZ values to sRGB. -XYZ2sRGB = makecform('xyz2srgb'); -sRGB = applycform(XYZ, XYZ2sRGB); - - -function hout = subimage2(varargin) -%SUBIMAGE Display multiple images in single figure. -% You can use SUBIMAGE in conjunction with SUBPLOT to create -% figures with multiple images, even if the images have -% different colormaps. SUBIMAGE works by converting images to -% truecolor for display purposes, thus avoiding colormap -% conflicts. -% -% SUBIMAGE(X,MAP) displays the indexed image X with colormap -% MAP in the current axes. -% -% SUBIMAGE(I) displays the intensity image I in the current -% axes. -% -% SUBIMAGE(BW) displays the binary image BW in the current -% axes. -% -% SUBIMAGE(RGB) displays the truecolor image RGB in the current -% axes. -% -% SUBIMAGE(x,y,...) displays an image with nondefault spatial -% coordinates. -% -% H = SUBIMAGE(...) returns a handle to the image object. -% -% Class Support -% ------------- -% The input image can be of class logical, uint8, uint16, -% or double. -% -% Example -% ------- -% load trees -% [X2,map2] = imread('forest.tif'); -% subplot(1,2,1), subimage(X,map) -% subplot(1,2,2), subimage(X2,map2) -% -% See also IMSHOW, SUBPLOT. - -% Copyright 1993-2005 The MathWorks, Inc. -% $Revision: 1.1.8.3 $ $Date: 2005/05/27 14:07:39 $ - -[x,y,cdata] = parse_inputs(varargin{:}); - -ax = newplot; -fig = ancestor(ax,'figure'); -cm = get(fig,'Colormap'); - -% Go change all the existing image and texture-mapped surface -% objects to truecolor. -h = [findobj(fig,'Type','image') ; - findobj(fig,'Type','surface','FaceColor','texturemap')]; -for k = 1:length(h) - if (ndims(get(h(k), 'CData')) < 3) - if (isequal(get(h(k), 'CDataMapping'), 'direct')) - if (strcmp(get(h(k),'Type'),'image')) - set(h(k), 'CData', ... - iptgate('ind2rgb8',get(h(k),'CData'), cm)); - else - set(h(k), 'CData', ind2rgb(get(h(k),'CData'), cm)); - end - else - clim = get(ancestor(h(k),'axes'), 'CLim'); - data = get(h(k), 'CData'); - if (isa(data,'uint8')) - data = double(data) / 255; - clim = clim / 255; - elseif (isa(data,'uint16')) - data = double(data) / 65535; - clim = clim / 65535; - end - data = (data - clim(1)) / (clim(2) - clim(1)); - if (strcmp(get(h(k),'Type'),'image')) - data = im2uint8(data); - set(h(k), 'CData', cat(3, data, data, data)); - else - data = min(max(data,0),1); - set(h(k), 'CData', cat(3, data, data, data)); - end - end - end -end - -h = image(x, y, cdata); -axis image; - -if nargout==1, - hout = h; -end - -%-------------------------------------------------------- -% Subfunction PARSE_INPUTS -%-------------------------------------------------------- -function [x,y,cdata] = parse_inputs(varargin) - -x = []; -y = []; -cdata = []; -msg = ''; % empty string if no error encountered - -scaled = 0; -binary = 0; - -msg_aEqualsb = 'a cannot equal b in subimage(I,[a b])'; -eid_aEqualsb = sprintf('Images:%s:aEqualsB',mfilename); - -switch nargin -case 0 - msg = 'Not enough input arguments.'; - eid = sprintf('Images:%s:notEnoughInputs',mfilename); - error(eid,'%s',msg); - -case 1 - % subimage(I) - % subimage(RGB) - - if ((ndims(varargin{1}) == 3) && (size(varargin{1},3) == 3)) - % subimage(RGB) - cdata = varargin{1}; - - else - % subimage(I) - binary = islogical(varargin{1}); - cdata = cat(3, varargin{1}, varargin{1}, varargin{1}); - - end - -case 2 - % subimage(I,[a b]) - % subimage(I,N) - % subimage(X,map) - - if (numel(varargin{2}) == 1) - % subimage(I,N) - binary = islogical(varargin{1}); - cdata = cat(3, varargin{1}, varargin{1}, varargin{1}); - - elseif (isequal(size(varargin{2}), [1 2])) - % subimage(I,[a b]) - clim = varargin{2}; - if (clim(1) == clim(2)) - error(eid_aEqualsb,'%s',msg_aEqualsb); - - else - cdata = cat(3, varargin{1}, varargin{1}, varargin{1}); - end - scaled = 1; - - elseif (size(varargin{2},2) == 3) - % subimage(X,map); - cdata = iptgate('ind2rgb8',varargin{1},varargin{2}); - - else - msg = 'Invalid input arguments.'; - eid = sprintf('Images:%s:invalidInputs',mfilename); - error(eid,'%s',msg); - - end - -case 3 - % subimage(x,y,I) - % subimage(x,y,RGB) - - if ((ndims(varargin{3}) == 3) && (size(varargin{3},3) == 3)) - % subimage(x,y,RGB) - x = varargin{1}; - y = varargin{2}; - cdata = varargin{3}; - - else - % subimage(x,y,I) - x = varargin{1}; - y = varargin{2}; - binary = islogical(varargin{3}); - cdata = cat(3, varargin{3}, varargin{3}, varargin{3}); - - end - -case 4 - % subimage(x,y,I,[a b]) - % subimage(x,y,I,N) - % subimage(x,y,X,map) - - if (numel(varargin{4}) == 1) - % subimage(x,y,I,N) - x = varargin{1}; - y = varargin{2}; - binary = islogical(varargin{3}); - cdata = cat(3, varargin{3}, varargin{3}, varargin{3}); - - elseif (isequal(size(varargin{4}), [1 2])) - % subimage(x,y,I,[a b]) - scaled = 1; - clim = varargin{4}; - if (clim(1) == clim(2)) - error(eid_aEqualsb,'%s',msg_aEqualsb); - else - x = varargin{1}; - y = varargin{2}; - cdata = cat(3, varargin{3}, varargin{3}, varargin{3}); - end - - elseif (size(varargin{4},2) == 3) - % subimage(x,y,X,map); - x = varargin{1}; - y = varargin{2}; - cdata = iptgate('ind2rgb8',varargin{3},varargin{4}); - - else - msg = 'Invalid input arguments'; - eid = sprintf('Images:%s:invalidInputs',mfilename); - error(eid,'%s',msg); - - end - -otherwise - msg = 'Too many input arguments'; - eid = sprintf('Images:%s:tooManyInputs',mfilename); - error(eid,'%s',msg); - -end - -if (isempty(msg)) - if (scaled) - if (isa(cdata,'double')) - cdata = (cdata - clim(1)) / (clim(2) - clim(1)); - cdata = min(max(cdata,0),1); - - elseif (isa(cdata,'uint8')) - cdata = im2double(cdata); - clim = clim / 255; - cdata = (cdata - clim(1)) / (clim(2) - clim(1)); - cdata = im2uint8(cdata); - - elseif (isa(cdata,'uint16')) - cdata = im2double(cdata); - clim = clim / 65535; - cdata = (cdata - clim(1)) / (clim(2) - clim(1)); - cdata = im2uint8(cdata); - - else - msg = 'Class of input image must be uint8, uint16, or double.'; - eid = sprintf('Images:%s:invalidClass',mfilename); - error(eid,'%s',msg); - - end - - elseif (binary) - cdata = uint8(cdata); - cdata(cdata ~= 0) = 255; - end - - if (isempty(x)) - x = [1 size(cdata,2)]; - y = [1 size(cdata,1)]; - end -end - -% Regardless of the input type, at this point in the code, -% cdata represents an RGB image; atomatically clip double RGB images -% to [0 1] range -if isa(cdata, 'double') - - cdata(cdata > 1) = 1; - cdata(cdata < 0) = 0; -end - - -function subplot1(M,N,varargin); -%------------------------------------------------------------------------- -% subplot1 function An mproved subplot function -% Input : - If more than one input argumenst are given, -% then the first parameter is the number of rows. -% If single input argument is given, then this is the -% subplot-number for which to set focus. -% This could a scalar or two element vector (I,J). -% - Number of columns. -% * variable number of parameters -% (in pairs: ...,Keywoard, Value,...) -% - 'Min' : X, Y lower position of lowest subplot, -% default is [0.10 0.10]. -% - 'Max' : X, Y largest position of highest subplot, -% default is [0.95 0.95]. -% - 'Gap' : X,Y gaps between subplots, -% default is [0.01 0.01]. -% - 'XTickL' : x ticks labels option, -% 'Margin' : plot only XTickLabels in the -% subplot of the lowest row (default). -% 'All' : plot XTickLabels in all subplots. -% 'None' : don't plot XTickLabels in subplots. -% - 'YTickL' : y ticks labels option, -% 'Margin' : plot only YTickLabels in the -% subplot of the lowest row (defailt). -% 'All' : plot YTickLabels in all subplots. -% 'None' : don't plot YTickLabels in subplots. -% - 'FontS' : axis font size, default is 10. -% 'XScale' : scale of x axis: -% 'linear', default. -% 'log' -% - 'YScale' : scale of y axis: -% 'linear', default. -% 'log' -% Example: subplot1(2,2,'Gap',[0.02 0.02]); -% subplot1(2,3,'Gap',[0.02 0.02],'XTickL','None','YTickL','All','FontS',16); -% See also : subplot1c.m -% Tested : Matlab 5.3 -% By : Eran O. Ofek June 2002 -% URL : http://wise-obs.tau.ac.il/~eran/matlab.html -%------------------------------------------------------------------------- -MinDef = [0.10 0.10]; -MaxDef = [0.95 0.95]; -GapDef = [0.01 0.01]; -XTickLDef = 'Margin'; -YTickLDef = 'Margin'; -FontSDef = 10; -XScaleDef = 'linear'; -YScaleDef = 'linear'; - -% set default parameters -Min = MinDef; -Max = MaxDef; -Gap = GapDef; -XTickL = XTickLDef; -YTickL = YTickLDef; -FontS = FontSDef; -XScale = XScaleDef; -YScale = YScaleDef; - - -MoveFoc = 0; -if (nargin==1), - %--- move focus to subplot # --- - MoveFoc = 1; -elseif (nargin==2), - % do nothing -elseif (nargin>2), - Narg = length(varargin); - if (0.5.*Narg==floor(0.5.*Narg)), - - for I=1:2:Narg-1, - switch varargin{I}, - case 'Min' - Min = varargin{I+1}; - case 'Max' - Max = varargin{I+1}; - case 'Gap' - Gap = varargin{I+1}; - case 'XTickL' - XTickL = varargin{I+1}; - case 'YTickL' - YTickL = varargin{I+1}; - case 'FontS' - FontS = varargin{I+1}; - case 'XScale' - XScale = varargin{I+1}; - case 'YScale' - YScale = varargin{I+1}; - otherwise - error('Unknown keyword'); - end - end - else - error('Optional arguments should given as keyword, value'); - end -else - error('Illegal number of input arguments'); -end - - - - - - - - -switch MoveFoc - case 1 - %--- move focus to subplot # --- - H = get(gcf,'Children'); - Ptot = length(H); - if (length(M)==1), - M = Ptot - M + 1; - elseif (length(M)==2), - %--- check for subplot size --- - Pos1 = get(H(1),'Position'); - Pos1x = Pos1(1); - for Icheck=2:1:Ptot, - PosN = get(H(Icheck),'Position'); - PosNx = PosN(1); - if (PosNx==Pos1x), - NumberOfCol = Icheck - 1; - break; - end - end - NumberOfRow = Ptot./NumberOfCol; - - Row = M(1); - Col = M(2); - - M = (Row-1).*NumberOfCol + Col; - M = Ptot - M + 1; - else - error('Unknown option, undefined subplot index'); - end - - set(gcf,'CurrentAxes',H(M)); - - case 0 - %--- open subplots --- - - Xmin = Min(1); - Ymin = Min(2); - Xmax = Max(1); - Ymax = Max(2); - Xgap = Gap(1); - Ygap = Gap(2); - - - Xsize = (Xmax - Xmin)./N; - Ysize = (Ymax - Ymin)./M; - - Xbox = Xsize - Xgap; - Ybox = Ysize - Ygap; - - - Ptot = M.*N; - - Hgcf = gcf; - clf; - figure(Hgcf); - for Pi=1:1:Ptot, - Row = ceil(Pi./N); - Col = Pi - (Row - 1)*N; - - Xstart = Xmin + Xsize.*(Col - 1); - Ystart = Ymax - Ysize.*Row; - -% subplot(M,N,Pi); -% hold on; - axes('position',[Xstart,Ystart,Xbox,Ybox]); - %set(gca,'position',[Xstart,Ystart,Xbox,Ybox]); - set(gca,'FontSize',FontS); - box on; - hold on; - - switch XTickL - case 'Margin' - if (Row~=M), - %--- erase XTickLabel --- - set(gca,'XTickLabel',[]); - end - case 'All' - % do nothing - case 'None' - set(gca,'XTickLabel',[]); - otherwise - error('Unknown XTickL option'); - end - - switch YTickL - case 'Margin' - if (Col~=1), - %--- erase YTickLabel --- - set(gca,'YTickLabel',[]); - end - case 'All' - % do nothing - case 'None' - set(gca,'YTickLabel',[]); - otherwise - error('Unknown XTickL option'); - end - - switch XScale - case 'linear' - set(gca,'XScale','linear'); - case 'log' - set(gca,'XScale','log'); - otherwise - error('Unknown XScale option'); - end - - switch YScale - case 'linear' - set(gca,'YScale','linear'); - case 'log' - set(gca,'YScale','log'); - otherwise - error('Unknown YScale option'); - end - - end - - otherwise - error('Unknown MoveFoc option'); -end - - -function [lambda, xFcn, yFcn, zFcn] = colorMatchFcn(formulary) -%colorMatchFcn Popular color matching functions. -% -% [LAMBDA, XFCN, YFCN, ZFCN] = colorMatchFcn(FORMULARY) returns the -% color matching functions XFCN, YFCN, and ZFCN at each wavelength in -% the vector LAMBDA. FORMULARY is a string specifying which set of -% color matching functions to return. Supported color matching -% functions are given below: -% -% CIE_1931 CIE 1931 2-degree, XYZ -% 1931_FULL CIE 1931 2-degree, XYZ (at 1nm resolution) -% CIE_1964 CIE 1964 10-degree, XYZ -% 1964_FULL CIE 1964 10-degree, XYZ (at 1nm resolution) -% Judd CIE 1931 2-degree, XYZ modified by Judd (1951) -% Judd_Vos CIE 1931 2-degree, XYZ modified by Judd (1951) and Vos (1978) -% Stiles_2 Stiles and Burch 2-degree, RGB (1955) -% Stiles_10 Stiles and Burch 10-degree, RGB (1959) -% -% Reference: http://cvrl.ioo.ucl.ac.uk/cmfs.htm -% -% See also illuminant. - -% Copyright 1993-2005 The MathWorks, Inc. - -switch (lower(formulary)) - case 'judd_vos' - - cmf = [380, 2.689900e-003, 2.000000e-004, 1.226000e-002 - 385, 5.310500e-003, 3.955600e-004, 2.422200e-002 - 390, 1.078100e-002, 8.000000e-004, 4.925000e-002 - 395, 2.079200e-002, 1.545700e-003, 9.513500e-002 - 400, 3.798100e-002, 2.800000e-003, 1.740900e-001 - 405, 6.315700e-002, 4.656200e-003, 2.901300e-001 - 410, 9.994100e-002, 7.400000e-003, 4.605300e-001 - 415, 1.582400e-001, 1.177900e-002, 7.316600e-001 - 420, 2.294800e-001, 1.750000e-002, 1.065800e+000 - 425, 2.810800e-001, 2.267800e-002, 1.314600e+000 - 430, 3.109500e-001, 2.730000e-002, 1.467200e+000 - 435, 3.307200e-001, 3.258400e-002, 1.579600e+000 - 440, 3.333600e-001, 3.790000e-002, 1.616600e+000 - 445, 3.167200e-001, 4.239100e-002, 1.568200e+000 - 450, 2.888200e-001, 4.680000e-002, 1.471700e+000 - 455, 2.596900e-001, 5.212200e-002, 1.374000e+000 - 460, 2.327600e-001, 6.000000e-002, 1.291700e+000 - 465, 2.099900e-001, 7.294200e-002, 1.235600e+000 - 470, 1.747600e-001, 9.098000e-002, 1.113800e+000 - 475, 1.328700e-001, 1.128400e-001, 9.422000e-001 - 480, 9.194400e-002, 1.390200e-001, 7.559600e-001 - 485, 5.698500e-002, 1.698700e-001, 5.864000e-001 - 490, 3.173100e-002, 2.080200e-001, 4.466900e-001 - 495, 1.461300e-002, 2.580800e-001, 3.411600e-001 - 500, 4.849100e-003, 3.230000e-001, 2.643700e-001 - 505, 2.321500e-003, 4.054000e-001, 2.059400e-001 - 510, 9.289900e-003, 5.030000e-001, 1.544500e-001 - 515, 2.927800e-002, 6.081100e-001, 1.091800e-001 - 520, 6.379100e-002, 7.100000e-001, 7.658500e-002 - 525, 1.108100e-001, 7.951000e-001, 5.622700e-002 - 530, 1.669200e-001, 8.620000e-001, 4.136600e-002 - 535, 2.276800e-001, 9.150500e-001, 2.935300e-002 - 540, 2.926900e-001, 9.540000e-001, 2.004200e-002 - 545, 3.622500e-001, 9.800400e-001, 1.331200e-002 - 550, 4.363500e-001, 9.949500e-001, 8.782300e-003 - 555, 5.151300e-001, 1.000100e+000, 5.857300e-003 - 560, 5.974800e-001, 9.950000e-001, 4.049300e-003 - 565, 6.812100e-001, 9.787500e-001, 2.921700e-003 - 570, 7.642500e-001, 9.520000e-001, 2.277100e-003 - 575, 8.439400e-001, 9.155800e-001, 1.970600e-003 - 580, 9.163500e-001, 8.700000e-001, 1.806600e-003 - 585, 9.770300e-001, 8.162300e-001, 1.544900e-003 - 590, 1.023000e+000, 7.570000e-001, 1.234800e-003 - 595, 1.051300e+000, 6.948300e-001, 1.117700e-003 - 600, 1.055000e+000, 6.310000e-001, 9.056400e-004 - 605, 1.036200e+000, 5.665400e-001, 6.946700e-004 - 610, 9.923900e-001, 5.030000e-001, 4.288500e-004 - 615, 9.286100e-001, 4.417200e-001, 3.181700e-004 - 620, 8.434600e-001, 3.810000e-001, 2.559800e-004 - 625, 7.398300e-001, 3.205200e-001, 1.567900e-004 - 630, 6.328900e-001, 2.650000e-001, 9.769400e-005 - 635, 5.335100e-001, 2.170200e-001, 6.894400e-005 - 640, 4.406200e-001, 1.750000e-001, 5.116500e-005 - 645, 3.545300e-001, 1.381200e-001, 3.601600e-005 - 650, 2.786200e-001, 1.070000e-001, 2.423800e-005 - 655, 2.148500e-001, 8.165200e-002, 1.691500e-005 - 660, 1.616100e-001, 6.100000e-002, 1.190600e-005 - 665, 1.182000e-001, 4.432700e-002, 8.148900e-006 - 670, 8.575300e-002, 3.200000e-002, 5.600600e-006 - 675, 6.307700e-002, 2.345400e-002, 3.954400e-006 - 680, 4.583400e-002, 1.700000e-002, 2.791200e-006 - 685, 3.205700e-002, 1.187200e-002, 1.917600e-006 - 690, 2.218700e-002, 8.210000e-003, 1.313500e-006 - 695, 1.561200e-002, 5.772300e-003, 9.151900e-007 - 700, 1.109800e-002, 4.102000e-003, 6.476700e-007 - 705, 7.923300e-003, 2.929100e-003, 4.635200e-007 - 710, 5.653100e-003, 2.091000e-003, 3.330400e-007 - 715, 4.003900e-003, 1.482200e-003, 2.382300e-007 - 720, 2.825300e-003, 1.047000e-003, 1.702600e-007 - 725, 1.994700e-003, 7.401500e-004, 1.220700e-007 - 730, 1.399400e-003, 5.200000e-004, 8.710700e-008 - 735, 9.698000e-004, 3.609300e-004, 6.145500e-008 - 740, 6.684700e-004, 2.492000e-004, 4.316200e-008 - 745, 4.614100e-004, 1.723100e-004, 3.037900e-008 - 750, 3.207300e-004, 1.200000e-004, 2.155400e-008 - 755, 2.257300e-004, 8.462000e-005, 1.549300e-008 - 760, 1.597300e-004, 6.000000e-005, 1.120400e-008 - 765, 1.127500e-004, 4.244600e-005, 8.087300e-009 - 770, 7.951300e-005, 3.000000e-005, 5.834000e-009 - 775, 5.608700e-005, 2.121000e-005, 4.211000e-009 - 780, 3.954100e-005, 1.498900e-005, 3.038300e-009 - 785, 2.785200e-005, 1.058400e-005, 2.190700e-009 - 790, 1.959700e-005, 7.465600e-006, 1.577800e-009 - 795, 1.377000e-005, 5.259200e-006, 1.134800e-009 - 800, 9.670000e-006, 3.702800e-006, 8.156500e-010 - 805, 6.791800e-006, 2.607600e-006, 5.862600e-010 - 810, 4.770600e-006, 1.836500e-006, 4.213800e-010 - 815, 3.355000e-006, 1.295000e-006, 3.031900e-010 - 820, 2.353400e-006, 9.109200e-007, 2.175300e-010 - 825, 1.637700e-006, 6.356400e-007, 1.547600e-010]; - - case 'judd' - - cmf = [370, 0.0008, 0.0001, 0.0046 - 380, 0.0045, 0.0004, 0.0224 - 390, 0.0201, 0.0015, 0.0925 - 400, 0.0611, 0.0045, 0.2799 - 410, 0.1267, 0.0093, 0.5835 - 420, 0.2285, 0.0175, 1.0622 - 430, 0.3081, 0.0273, 1.4526 - 440, 0.3312, 0.0379, 1.6064 - 450, 0.2888, 0.0468, 1.4717 - 460, 0.2323, 0.0600, 1.2880 - 470, 0.1745, 0.0910, 1.1133 - 480, 0.0920, 0.1390, 0.7552 - 490, 0.0318, 0.2080, 0.4461 - 500, 0.0048, 0.3230, 0.2644 - 510, 0.0093, 0.5030, 0.1541 - 520, 0.0636, 0.7100, 0.0763 - 530, 0.1668, 0.8620, 0.0412 - 540, 0.2926, 0.9540, 0.0200 - 550, 0.4364, 0.9950, 0.0088 - 560, 0.5970, 0.9950, 0.0039 - 570, 0.7642, 0.9520, 0.0020 - 580, 0.9159, 0.8700, 0.0016 - 590, 1.0225, 0.7570, 0.0011 - 600, 1.0544, 0.6310, 0.0007 - 610, 0.9922, 0.5030, 0.0003 - 620, 0.8432, 0.3810, 0.0002 - 630, 0.6327, 0.2650, 0.0001 - 640, 0.4404, 0.1750, 0.0000 - 650, 0.2787, 0.1070, 0.0000 - 660, 0.1619, 0.0610, 0.0000 - 670, 0.0858, 0.0320, 0.0000 - 680, 0.0459, 0.0170, 0.0000 - 690, 0.0222, 0.0082, 0.0000 - 700, 0.0113, 0.0041, 0.0000 - 710, 0.0057, 0.0021, 0.0000 - 720, 0.0028, 0.0011, 0.0000 - 730, 0.0015, 0.0005, 0.0000 - 740, 0.0005, 0.0002, 0.0000 - 750, 0.0003, 0.0001, 0.0000 - 760, 0.0002, 0.0001, 0.0000 - 770, 0.0001, 0.0000, 0.0000]; - - case 'cie_1931' - - cmf = [360, 0.000129900000, 0.000003917000, 0.000606100000 - 365, 0.000232100000, 0.000006965000, 0.001086000000 - 370, 0.000414900000, 0.000012390000, 0.001946000000 - 375, 0.000741600000, 0.000022020000, 0.003486000000 - 380, 0.001368000000, 0.000039000000, 0.006450001000 - 385, 0.002236000000, 0.000064000000, 0.010549990000 - 390, 0.004243000000, 0.000120000000, 0.020050010000 - 395, 0.007650000000, 0.000217000000, 0.036210000000 - 400, 0.014310000000, 0.000396000000, 0.067850010000 - 405, 0.023190000000, 0.000640000000, 0.110200000000 - 410, 0.043510000000, 0.001210000000, 0.207400000000 - 415, 0.077630000000, 0.002180000000, 0.371300000000 - 420, 0.134380000000, 0.004000000000, 0.645600000000 - 425, 0.214770000000, 0.007300000000, 1.039050100000 - 430, 0.283900000000, 0.011600000000, 1.385600000000 - 435, 0.328500000000, 0.016840000000, 1.622960000000 - 440, 0.348280000000, 0.023000000000, 1.747060000000 - 445, 0.348060000000, 0.029800000000, 1.782600000000 - 450, 0.336200000000, 0.038000000000, 1.772110000000 - 455, 0.318700000000, 0.048000000000, 1.744100000000 - 460, 0.290800000000, 0.060000000000, 1.669200000000 - 465, 0.251100000000, 0.073900000000, 1.528100000000 - 470, 0.195360000000, 0.090980000000, 1.287640000000 - 475, 0.142100000000, 0.112600000000, 1.041900000000 - 480, 0.095640000000, 0.139020000000, 0.812950100000 - 485, 0.057950010000, 0.169300000000, 0.616200000000 - 490, 0.032010000000, 0.208020000000, 0.465180000000 - 495, 0.014700000000, 0.258600000000, 0.353300000000 - 500, 0.004900000000, 0.323000000000, 0.272000000000 - 505, 0.002400000000, 0.407300000000, 0.212300000000 - 510, 0.009300000000, 0.503000000000, 0.158200000000 - 515, 0.029100000000, 0.608200000000, 0.111700000000 - 520, 0.063270000000, 0.710000000000, 0.078249990000 - 525, 0.109600000000, 0.793200000000, 0.057250010000 - 530, 0.165500000000, 0.862000000000, 0.042160000000 - 535, 0.225749900000, 0.914850100000, 0.029840000000 - 540, 0.290400000000, 0.954000000000, 0.020300000000 - 545, 0.359700000000, 0.980300000000, 0.013400000000 - 550, 0.433449900000, 0.994950100000, 0.008749999000 - 555, 0.512050100000, 1.000000000000, 0.005749999000 - 560, 0.594500000000, 0.995000000000, 0.003900000000 - 565, 0.678400000000, 0.978600000000, 0.002749999000 - 570, 0.762100000000, 0.952000000000, 0.002100000000 - 575, 0.842500000000, 0.915400000000, 0.001800000000 - 580, 0.916300000000, 0.870000000000, 0.001650001000 - 585, 0.978600000000, 0.816300000000, 0.001400000000 - 590, 1.026300000000, 0.757000000000, 0.001100000000 - 595, 1.056700000000, 0.694900000000, 0.001000000000 - 600, 1.062200000000, 0.631000000000, 0.000800000000 - 605, 1.045600000000, 0.566800000000, 0.000600000000 - 610, 1.002600000000, 0.503000000000, 0.000340000000 - 615, 0.938400000000, 0.441200000000, 0.000240000000 - 620, 0.854449900000, 0.381000000000, 0.000190000000 - 625, 0.751400000000, 0.321000000000, 0.000100000000 - 630, 0.642400000000, 0.265000000000, 0.000049999990 - 635, 0.541900000000, 0.217000000000, 0.000030000000 - 640, 0.447900000000, 0.175000000000, 0.000020000000 - 645, 0.360800000000, 0.138200000000, 0.000010000000 - 650, 0.283500000000, 0.107000000000, 0.000000000000 - 655, 0.218700000000, 0.081600000000, 0.000000000000 - 660, 0.164900000000, 0.061000000000, 0.000000000000 - 665, 0.121200000000, 0.044580000000, 0.000000000000 - 670, 0.087400000000, 0.032000000000, 0.000000000000 - 675, 0.063600000000, 0.023200000000, 0.000000000000 - 680, 0.046770000000, 0.017000000000, 0.000000000000 - 685, 0.032900000000, 0.011920000000, 0.000000000000 - 690, 0.022700000000, 0.008210000000, 0.000000000000 - 695, 0.015840000000, 0.005723000000, 0.000000000000 - 700, 0.011359160000, 0.004102000000, 0.000000000000 - 705, 0.008110916000, 0.002929000000, 0.000000000000 - 710, 0.005790346000, 0.002091000000, 0.000000000000 - 715, 0.004109457000, 0.001484000000, 0.000000000000 - 720, 0.002899327000, 0.001047000000, 0.000000000000 - 725, 0.002049190000, 0.000740000000, 0.000000000000 - 730, 0.001439971000, 0.000520000000, 0.000000000000 - 735, 0.000999949300, 0.000361100000, 0.000000000000 - 740, 0.000690078600, 0.000249200000, 0.000000000000 - 745, 0.000476021300, 0.000171900000, 0.000000000000 - 750, 0.000332301100, 0.000120000000, 0.000000000000 - 755, 0.000234826100, 0.000084800000, 0.000000000000 - 760, 0.000166150500, 0.000060000000, 0.000000000000 - 765, 0.000117413000, 0.000042400000, 0.000000000000 - 770, 0.000083075270, 0.000030000000, 0.000000000000 - 775, 0.000058706520, 0.000021200000, 0.000000000000 - 780, 0.000041509940, 0.000014990000, 0.000000000000 - 785, 0.000029353260, 0.000010600000, 0.000000000000 - 790, 0.000020673830, 0.000007465700, 0.000000000000 - 795, 0.000014559770, 0.000005257800, 0.000000000000 - 800, 0.000010253980, 0.000003702900, 0.000000000000 - 805, 0.000007221456, 0.000002607800, 0.000000000000 - 810, 0.000005085868, 0.000001836600, 0.000000000000 - 815, 0.000003581652, 0.000001293400, 0.000000000000 - 820, 0.000002522525, 0.000000910930, 0.000000000000 - 825, 0.000001776509, 0.000000641530, 0.000000000000 - 830, 0.000001251141, 0.000000451810, 0.000000000000]; - - case 'stiles_2' - - cmf = [390, 1.83970e-003, -4.53930e-004, 1.21520e-002 - 395, 4.61530e-003, -1.04640e-003, 3.11100e-002 - 400, 9.62640e-003, -2.16890e-003, 6.23710e-002 - 405, 1.89790e-002, -4.43040e-003, 1.31610e-001 - 410, 3.08030e-002, -7.20480e-003, 2.27500e-001 - 415, 4.24590e-002, -1.25790e-002, 3.58970e-001 - 420, 5.16620e-002, -1.66510e-002, 5.23960e-001 - 425, 5.28370e-002, -2.12400e-002, 6.85860e-001 - 430, 4.42870e-002, -1.99360e-002, 7.96040e-001 - 435, 3.22200e-002, -1.60970e-002, 8.94590e-001 - 440, 1.47630e-002, -7.34570e-003, 9.63950e-001 - 445, -2.33920e-003, 1.36900e-003, 9.98140e-001 - 450, -2.91300e-002, 1.96100e-002, 9.18750e-001 - 455, -6.06770e-002, 4.34640e-002, 8.24870e-001 - 460, -9.62240e-002, 7.09540e-002, 7.85540e-001 - 465, -1.37590e-001, 1.10220e-001, 6.67230e-001 - 470, -1.74860e-001, 1.50880e-001, 6.10980e-001 - 475, -2.12600e-001, 1.97940e-001, 4.88290e-001 - 480, -2.37800e-001, 2.40420e-001, 3.61950e-001 - 485, -2.56740e-001, 2.79930e-001, 2.66340e-001 - 490, -2.77270e-001, 3.33530e-001, 1.95930e-001 - 495, -2.91250e-001, 4.05210e-001, 1.47300e-001 - 500, -2.95000e-001, 4.90600e-001, 1.07490e-001 - 505, -2.97060e-001, 5.96730e-001, 7.67140e-002 - 510, -2.67590e-001, 7.01840e-001, 5.02480e-002 - 515, -2.17250e-001, 8.08520e-001, 2.87810e-002 - 520, -1.47680e-001, 9.10760e-001, 1.33090e-002 - 525, -3.51840e-002, 9.84820e-001, 2.11700e-003 - 530, 1.06140e-001, 1.03390e+000, -4.15740e-003 - 535, 2.59810e-001, 1.05380e+000, -8.30320e-003 - 540, 4.19760e-001, 1.05120e+000, -1.21910e-002 - 545, 5.92590e-001, 1.04980e+000, -1.40390e-002 - 550, 7.90040e-001, 1.03680e+000, -1.46810e-002 - 555, 1.00780e+000, 9.98260e-001, -1.49470e-002 - 560, 1.22830e+000, 9.37830e-001, -1.46130e-002 - 565, 1.47270e+000, 8.80390e-001, -1.37820e-002 - 570, 1.74760e+000, 8.28350e-001, -1.26500e-002 - 575, 2.02140e+000, 7.46860e-001, -1.13560e-002 - 580, 2.27240e+000, 6.49300e-001, -9.93170e-003 - 585, 2.48960e+000, 5.63170e-001, -8.41480e-003 - 590, 2.67250e+000, 4.76750e-001, -7.02100e-003 - 595, 2.80930e+000, 3.84840e-001, -5.74370e-003 - 600, 2.87170e+000, 3.00690e-001, -4.27430e-003 - 605, 2.85250e+000, 2.28530e-001, -2.91320e-003 - 610, 2.76010e+000, 1.65750e-001, -2.26930e-003 - 615, 2.59890e+000, 1.13730e-001, -1.99660e-003 - 620, 2.37430e+000, 7.46820e-002, -1.50690e-003 - 625, 2.10540e+000, 4.65040e-002, -9.38220e-004 - 630, 1.81450e+000, 2.63330e-002, -5.53160e-004 - 635, 1.52470e+000, 1.27240e-002, -3.16680e-004 - 640, 1.25430e+000, 4.50330e-003, -1.43190e-004 - 645, 1.00760e+000, 9.66110e-005, -4.08310e-006 - 650, 7.86420e-001, -1.96450e-003, 1.10810e-004 - 655, 5.96590e-001, -2.63270e-003, 1.91750e-004 - 660, 4.43200e-001, -2.62620e-003, 2.26560e-004 - 665, 3.24100e-001, -2.30270e-003, 2.15200e-004 - 670, 2.34550e-001, -1.87000e-003, 1.63610e-004 - 675, 1.68840e-001, -1.44240e-003, 9.71640e-005 - 680, 1.20860e-001, -1.07550e-003, 5.10330e-005 - 685, 8.58110e-002, -7.90040e-004, 3.52710e-005 - 690, 6.02600e-002, -5.67650e-004, 3.12110e-005 - 695, 4.14800e-002, -3.92740e-004, 2.45080e-005 - 700, 2.81140e-002, -2.62310e-004, 1.65210e-005 - 705, 1.91170e-002, -1.75120e-004, 1.11240e-005 - 710, 1.33050e-002, -1.21400e-004, 8.69650e-006 - 715, 9.40920e-003, -8.57600e-005, 7.43510e-006 - 720, 6.51770e-003, -5.76770e-005, 6.10570e-006 - 725, 4.53770e-003, -3.90030e-005, 5.02770e-006 - 730, 3.17420e-003, -2.65110e-005, 4.12510e-006]; - - case 'stiles_10' - - cmf = [390, 1.5000E-03, -4.0000E-04, 6.2000E-03 - 395, 3.8000E-03, -1.0000E-03, 1.6100E-02 - 400, 8.9000E-03, -2.5000E-03, 4.0000E-02 - 405, 1.8800E-02, -5.9000E-03, 9.0600E-02 - 410, 3.5000E-02, -1.1900E-02, 1.8020E-01 - 415, 5.3100E-02, -2.0100E-02, 3.0880E-01 - 420, 7.0200E-02, -2.8900E-02, 4.6700E-01 - 425, 7.6300E-02, -3.3800E-02, 6.1520E-01 - 430, 7.4500E-02, -3.4900E-02, 7.6380E-01 - 435, 5.6100E-02, -2.7600E-02, 8.7780E-01 - 440, 3.2300E-02, -1.6900E-02, 9.7550E-01 - 445, -4.4000E-03, 2.4000E-03, 1.0019E+00 - 450, -4.7800E-02, 2.8300E-02, 9.9960E-01 - 455, -9.7000E-02, 6.3600E-02, 9.1390E-01 - 460, -1.5860E-01, 1.0820E-01, 8.2970E-01 - 465, -2.2350E-01, 1.6170E-01, 7.4170E-01 - 470, -2.8480E-01, 2.2010E-01, 6.1340E-01 - 475, -3.3460E-01, 2.7960E-01, 4.7200E-01 - 480, -3.7760E-01, 3.4280E-01, 3.4950E-01 - 485, -4.1360E-01, 4.0860E-01, 2.5640E-01 - 490, -4.3170E-01, 4.7160E-01, 1.8190E-01 - 495, -4.4520E-01, 5.4910E-01, 1.3070E-01 - 500, -4.3500E-01, 6.2600E-01, 9.1000E-02 - 505, -4.1400E-01, 7.0970E-01, 5.8000E-02 - 510, -3.6730E-01, 7.9350E-01, 3.5700E-02 - 515, -2.8450E-01, 8.7150E-01, 2.0000E-02 - 520, -1.8550E-01, 9.4770E-01, 9.5000E-03 - 525, -4.3500E-02, 9.9450E-01, 7.0000E-04 - 530, 1.2700E-01, 1.0203E+00, -4.3000E-03 - 535, 3.1290E-01, 1.0375E+00, -6.4000E-03 - 540, 5.3620E-01, 1.0517E+00, -8.2000E-03 - 545, 7.7220E-01, 1.0390E+00, -9.4000E-03 - 550, 1.0059E+00, 1.0029E+00, -9.7000E-03 - 555, 1.2710E+00, 9.6980E-01, -9.7000E-03 - 560, 1.5574E+00, 9.1620E-01, -9.3000E-03 - 565, 1.8465E+00, 8.5710E-01, -8.7000E-03 - 570, 2.1511E+00, 7.8230E-01, -8.0000E-03 - 575, 2.4250E+00, 6.9530E-01, -7.3000E-03 - 580, 2.6574E+00, 5.9660E-01, -6.3000E-03 - 585, 2.9151E+00, 5.0630E-01, -5.3700E-03 - 590, 3.0779E+00, 4.2030E-01, -4.4500E-03 - 595, 3.1613E+00, 3.3600E-01, -3.5700E-03 - 600, 3.1673E+00, 2.5910E-01, -2.7700E-03 - 605, 3.1048E+00, 1.9170E-01, -2.0800E-03 - 610, 2.9462E+00, 1.3670E-01, -1.5000E-03 - 615, 2.7194E+00, 9.3800E-02, -1.0300E-03 - 620, 2.4526E+00, 6.1100E-02, -6.8000E-04 - 625, 2.1700E+00, 3.7100E-02, -4.4200E-04 - 630, 1.8358E+00, 2.1500E-02, -2.7200E-04 - 635, 1.5179E+00, 1.1200E-02, -1.4100E-04 - 640, 1.2428E+00, 4.4000E-03, -5.4900E-05 - 645, 1.0070E+00, 7.8000E-05, -2.2000E-06 - 650, 7.8270E-01, -1.3680E-03, 2.3700E-05 - 655, 5.9340E-01, -1.9880E-03, 2.8600E-05 - 660, 4.4420E-01, -2.1680E-03, 2.6100E-05 - 665, 3.2830E-01, -2.0060E-03, 2.2500E-05 - 670, 2.3940E-01, -1.6420E-03, 1.8200E-05 - 675, 1.7220E-01, -1.2720E-03, 1.3900E-05 - 680, 1.2210E-01, -9.4700E-04, 1.0300E-05 - 685, 8.5300E-02, -6.8300E-04, 7.3800E-06 - 690, 5.8600E-02, -4.7800E-04, 5.2200E-06 - 695, 4.0800E-02, -3.3700E-04, 3.6700E-06 - 700, 2.8400E-02, -2.3500E-04, 2.5600E-06 - 705, 1.9700E-02, -1.6300E-04, 1.7600E-06 - 710, 1.3500E-02, -1.1100E-04, 1.2000E-06 - 715, 9.2400E-03, -7.4800E-05, 8.1700E-07 - 720, 6.3800E-03, -5.0800E-05, 5.5500E-07 - 725, 4.4100E-03, -3.4400E-05, 3.7500E-07 - 730, 3.0700E-03, -2.3400E-05, 2.5400E-07 - 735, 2.1400E-03, -1.5900E-05, 1.7100E-07 - 740, 1.4900E-03, -1.0700E-05, 1.1600E-07 - 745, 1.0500E-03, -7.2300E-06, 7.8500E-08 - 750, 7.3900E-04, -4.8700E-06, 5.3100E-08 - 755, 5.2300E-04, -3.2900E-06, 3.6000E-08 - 760, 3.7200E-04, -2.2200E-06, 2.4400E-08 - 765, 2.6500E-04, -1.5000E-06, 1.6500E-08 - 770, 1.9000E-04, -1.0200E-06, 1.1200E-08 - 775, 1.3600E-04, -6.8800E-07, 7.5300E-09 - 780, 9.8400E-05, -4.6500E-07, 5.0700E-09 - 785, 7.1300E-05, -3.1200E-07, 3.4000E-09 - 790, 5.1800E-05, -2.0800E-07, 2.2700E-09 - 795, 3.7700E-05, -1.3700E-07, 1.5000E-09 - 800, 2.7600E-05, -8.8000E-08, 9.8600E-10 - 805, 2.0300E-05, -5.5300E-08, 6.3900E-10 - 810, 1.4900E-05, -3.3600E-08, 4.0700E-10 - 815, 1.1000E-05, -1.9600E-08, 2.5300E-10 - 820, 8.1800E-06, -1.0900E-08, 1.5200E-10 - 825, 6.0900E-06, -5.7000E-09, 8.6400E-11 - 830, 4.5500E-06, -2.7700E-09, 4.4200E-11]; - - case 'cie_1964' - - cmf = [360, 0.000000122200, 0.000000013398, 0.000000535027 - 365, 0.000000919270, 0.000000100650, 0.000004028300 - 370, 0.000005958600, 0.000000651100, 0.000026143700 - 375, 0.000033266000, 0.000003625000, 0.000146220000 - 380, 0.000159952000, 0.000017364000, 0.000704776000 - 385, 0.000662440000, 0.000071560000, 0.002927800000 - 390, 0.002361600000, 0.000253400000, 0.010482200000 - 395, 0.007242300000, 0.000768500000, 0.032344000000 - 400, 0.019109700000, 0.002004400000, 0.086010900000 - 405, 0.043400000000, 0.004509000000, 0.197120000000 - 410, 0.084736000000, 0.008756000000, 0.389366000000 - 415, 0.140638000000, 0.014456000000, 0.656760000000 - 420, 0.204492000000, 0.021391000000, 0.972542000000 - 425, 0.264737000000, 0.029497000000, 1.282500000000 - 430, 0.314679000000, 0.038676000000, 1.553480000000 - 435, 0.357719000000, 0.049602000000, 1.798500000000 - 440, 0.383734000000, 0.062077000000, 1.967280000000 - 445, 0.386726000000, 0.074704000000, 2.027300000000 - 450, 0.370702000000, 0.089456000000, 1.994800000000 - 455, 0.342957000000, 0.106256000000, 1.900700000000 - 460, 0.302273000000, 0.128201000000, 1.745370000000 - 465, 0.254085000000, 0.152761000000, 1.554900000000 - 470, 0.195618000000, 0.185190000000, 1.317560000000 - 475, 0.132349000000, 0.219940000000, 1.030200000000 - 480, 0.080507000000, 0.253589000000, 0.772125000000 - 485, 0.041072000000, 0.297665000000, 0.570060000000 - 490, 0.016172000000, 0.339133000000, 0.415254000000 - 495, 0.005132000000, 0.395379000000, 0.302356000000 - 500, 0.003816000000, 0.460777000000, 0.218502000000 - 505, 0.015444000000, 0.531360000000, 0.159249000000 - 510, 0.037465000000, 0.606741000000, 0.112044000000 - 515, 0.071358000000, 0.685660000000, 0.082248000000 - 520, 0.117749000000, 0.761757000000, 0.060709000000 - 525, 0.172953000000, 0.823330000000, 0.043050000000 - 530, 0.236491000000, 0.875211000000, 0.030451000000 - 535, 0.304213000000, 0.923810000000, 0.020584000000 - 540, 0.376772000000, 0.961988000000, 0.013676000000 - 545, 0.451584000000, 0.982200000000, 0.007918000000 - 550, 0.529826000000, 0.991761000000, 0.003988000000 - 555, 0.616053000000, 0.999110000000, 0.001091000000 - 560, 0.705224000000, 0.997340000000, 0 - 565, 0.793832000000, 0.982380000000, 0 - 570, 0.878655000000, 0.955552000000, 0 - 575, 0.951162000000, 0.915175000000, 0 - 580, 1.014160000000, 0.868934000000, 0 - 585, 1.074300000000, 0.825623000000, 0 - 590, 1.118520000000, 0.777405000000, 0 - 595, 1.134300000000, 0.720353000000, 0 - 600, 1.123990000000, 0.658341000000, 0 - 605, 1.089100000000, 0.593878000000, 0 - 610, 1.030480000000, 0.527963000000, 0 - 615, 0.950740000000, 0.461834000000, 0 - 620, 0.856297000000, 0.398057000000, 0 - 625, 0.754930000000, 0.339554000000, 0 - 630, 0.647467000000, 0.283493000000, 0 - 635, 0.535110000000, 0.228254000000, 0 - 640, 0.431567000000, 0.179828000000, 0 - 645, 0.343690000000, 0.140211000000, 0 - 650, 0.268329000000, 0.107633000000, 0 - 655, 0.204300000000, 0.081187000000, 0 - 660, 0.152568000000, 0.060281000000, 0 - 665, 0.112210000000, 0.044096000000, 0 - 670, 0.081260600000, 0.031800400000, 0 - 675, 0.057930000000, 0.022601700000, 0 - 680, 0.040850800000, 0.015905100000, 0 - 685, 0.028623000000, 0.011130300000, 0 - 690, 0.019941300000, 0.007748800000, 0 - 695, 0.013842000000, 0.005375100000, 0 - 700, 0.009576880000, 0.003717740000, 0 - 705, 0.006605200000, 0.002564560000, 0 - 710, 0.004552630000, 0.001768470000, 0 - 715, 0.003144700000, 0.001222390000, 0 - 720, 0.002174960000, 0.000846190000, 0 - 725, 0.001505700000, 0.000586440000, 0 - 730, 0.001044760000, 0.000407410000, 0 - 735, 0.000727450000, 0.000284041000, 0 - 740, 0.000508258000, 0.000198730000, 0 - 745, 0.000356380000, 0.000139550000, 0 - 750, 0.000250969000, 0.000098428000, 0 - 755, 0.000177730000, 0.000069819000, 0 - 760, 0.000126390000, 0.000049737000, 0 - 765, 0.000090151000, 0.000035540500, 0 - 770, 0.000064525800, 0.000025486000, 0 - 775, 0.000046339000, 0.000018338400, 0 - 780, 0.000033411700, 0.000013249000, 0 - 785, 0.000024209000, 0.000009619600, 0 - 790, 0.000017611500, 0.000007012800, 0 - 795, 0.000012855000, 0.000005129800, 0 - 800, 0.000009413630, 0.000003764730, 0 - 805, 0.000006913000, 0.000002770810, 0 - 810, 0.000005093470, 0.000002046130, 0 - 815, 0.000003767100, 0.000001516770, 0 - 820, 0.000002795310, 0.000001128090, 0 - 825, 0.000002082000, 0.000000842160, 0 - 830, 0.000001553140, 0.000000629700, 0]; - - case '1931_full' - - cmf = [360, 0.000129900000, 0.000003917000, 0.000606100000 - 361, 0.000145847000, 0.000004393581, 0.000680879200 - 362, 0.000163802100, 0.000004929604, 0.000765145600 - 363, 0.000184003700, 0.000005532136, 0.000860012400 - 364, 0.000206690200, 0.000006208245, 0.000966592800 - 365, 0.000232100000, 0.000006965000, 0.001086000000 - 366, 0.000260728000, 0.000007813219, 0.001220586000 - 367, 0.000293075000, 0.000008767336, 0.001372729000 - 368, 0.000329388000, 0.000009839844, 0.001543579000 - 369, 0.000369914000, 0.000011043230, 0.001734286000 - 370, 0.000414900000, 0.000012390000, 0.001946000000 - 371, 0.000464158700, 0.000013886410, 0.002177777000 - 372, 0.000518986000, 0.000015557280, 0.002435809000 - 373, 0.000581854000, 0.000017442960, 0.002731953000 - 374, 0.000655234700, 0.000019583750, 0.003078064000 - 375, 0.000741600000, 0.000022020000, 0.003486000000 - 376, 0.000845029600, 0.000024839650, 0.003975227000 - 377, 0.000964526800, 0.000028041260, 0.004540880000 - 378, 0.001094949000, 0.000031531040, 0.005158320000 - 379, 0.001231154000, 0.000035215210, 0.005802907000 - 380, 0.001368000000, 0.000039000000, 0.006450001000 - 381, 0.001502050000, 0.000042826400, 0.007083216000 - 382, 0.001642328000, 0.000046914600, 0.007745488000 - 383, 0.001802382000, 0.000051589600, 0.008501152000 - 384, 0.001995757000, 0.000057176400, 0.009414544000 - 385, 0.002236000000, 0.000064000000, 0.010549990000 - 386, 0.002535385000, 0.000072344210, 0.011965800000 - 387, 0.002892603000, 0.000082212240, 0.013655870000 - 388, 0.003300829000, 0.000093508160, 0.015588050000 - 389, 0.003753236000, 0.000106136100, 0.017730150000 - 390, 0.004243000000, 0.000120000000, 0.020050010000 - 391, 0.004762389000, 0.000134984000, 0.022511360000 - 392, 0.005330048000, 0.000151492000, 0.025202880000 - 393, 0.005978712000, 0.000170208000, 0.028279720000 - 394, 0.006741117000, 0.000191816000, 0.031897040000 - 395, 0.007650000000, 0.000217000000, 0.036210000000 - 396, 0.008751373000, 0.000246906700, 0.041437710000 - 397, 0.010028880000, 0.000281240000, 0.047503720000 - 398, 0.011421700000, 0.000318520000, 0.054119880000 - 399, 0.012869010000, 0.000357266700, 0.060998030000 - 400, 0.014310000000, 0.000396000000, 0.067850010000 - 401, 0.015704430000, 0.000433714700, 0.074486320000 - 402, 0.017147440000, 0.000473024000, 0.081361560000 - 403, 0.018781220000, 0.000517876000, 0.089153640000 - 404, 0.020748010000, 0.000572218700, 0.098540480000 - 405, 0.023190000000, 0.000640000000, 0.110200000000 - 406, 0.026207360000, 0.000724560000, 0.124613300000 - 407, 0.029782480000, 0.000825500000, 0.141701700000 - 408, 0.033880920000, 0.000941160000, 0.161303500000 - 409, 0.038468240000, 0.001069880000, 0.183256800000 - 410, 0.043510000000, 0.001210000000, 0.207400000000 - 411, 0.048995600000, 0.001362091000, 0.233692100000 - 412, 0.055022600000, 0.001530752000, 0.262611400000 - 413, 0.061718800000, 0.001720368000, 0.294774600000 - 414, 0.069212000000, 0.001935323000, 0.330798500000 - 415, 0.077630000000, 0.002180000000, 0.371300000000 - 416, 0.086958110000, 0.002454800000, 0.416209100000 - 417, 0.097176720000, 0.002764000000, 0.465464200000 - 418, 0.108406300000, 0.003117800000, 0.519694800000 - 419, 0.120767200000, 0.003526400000, 0.579530300000 - 420, 0.134380000000, 0.004000000000, 0.645600000000 - 421, 0.149358200000, 0.004546240000, 0.718483800000 - 422, 0.165395700000, 0.005159320000, 0.796713300000 - 423, 0.181983100000, 0.005829280000, 0.877845900000 - 424, 0.198611000000, 0.006546160000, 0.959439000000 - 425, 0.214770000000, 0.007300000000, 1.039050100000 - 426, 0.230186800000, 0.008086507000, 1.115367300000 - 427, 0.244879700000, 0.008908720000, 1.188497100000 - 428, 0.258777300000, 0.009767680000, 1.258123300000 - 429, 0.271807900000, 0.010664430000, 1.323929600000 - 430, 0.283900000000, 0.011600000000, 1.385600000000 - 431, 0.294943800000, 0.012573170000, 1.442635200000 - 432, 0.304896500000, 0.013582720000, 1.494803500000 - 433, 0.313787300000, 0.014629680000, 1.542190300000 - 434, 0.321645400000, 0.015715090000, 1.584880700000 - 435, 0.328500000000, 0.016840000000, 1.622960000000 - 436, 0.334351300000, 0.018007360000, 1.656404800000 - 437, 0.339210100000, 0.019214480000, 1.685295900000 - 438, 0.343121300000, 0.020453920000, 1.709874500000 - 439, 0.346129600000, 0.021718240000, 1.730382100000 - 440, 0.348280000000, 0.023000000000, 1.747060000000 - 441, 0.349599900000, 0.024294610000, 1.760044600000 - 442, 0.350147400000, 0.025610240000, 1.769623300000 - 443, 0.350013000000, 0.026958570000, 1.776263700000 - 444, 0.349287000000, 0.028351250000, 1.780433400000 - 445, 0.348060000000, 0.029800000000, 1.782600000000 - 446, 0.346373300000, 0.031310830000, 1.782968200000 - 447, 0.344262400000, 0.032883680000, 1.781699800000 - 448, 0.341808800000, 0.034521120000, 1.779198200000 - 449, 0.339094100000, 0.036225710000, 1.775867100000 - 450, 0.336200000000, 0.038000000000, 1.772110000000 - 451, 0.333197700000, 0.039846670000, 1.768258900000 - 452, 0.330041100000, 0.041768000000, 1.764039000000 - 453, 0.326635700000, 0.043766000000, 1.758943800000 - 454, 0.322886800000, 0.045842670000, 1.752466300000 - 455, 0.318700000000, 0.048000000000, 1.744100000000 - 456, 0.314025100000, 0.050243680000, 1.733559500000 - 457, 0.308884000000, 0.052573040000, 1.720858100000 - 458, 0.303290400000, 0.054980560000, 1.705936900000 - 459, 0.297257900000, 0.057458720000, 1.688737200000 - 460, 0.290800000000, 0.060000000000, 1.669200000000 - 461, 0.283970100000, 0.062601970000, 1.647528700000 - 462, 0.276721400000, 0.065277520000, 1.623412700000 - 463, 0.268917800000, 0.068042080000, 1.596022300000 - 464, 0.260422700000, 0.070911090000, 1.564528000000 - 465, 0.251100000000, 0.073900000000, 1.528100000000 - 466, 0.240847500000, 0.077016000000, 1.486111400000 - 467, 0.229851200000, 0.080266400000, 1.439521500000 - 468, 0.218407200000, 0.083666800000, 1.389879900000 - 469, 0.206811500000, 0.087232800000, 1.338736200000 - 470, 0.195360000000, 0.090980000000, 1.287640000000 - 471, 0.184213600000, 0.094917550000, 1.237422300000 - 472, 0.173327300000, 0.099045840000, 1.187824300000 - 473, 0.162688100000, 0.103367400000, 1.138761100000 - 474, 0.152283300000, 0.107884600000, 1.090148000000 - 475, 0.142100000000, 0.112600000000, 1.041900000000 - 476, 0.132178600000, 0.117532000000, 0.994197600000 - 477, 0.122569600000, 0.122674400000, 0.947347300000 - 478, 0.113275200000, 0.127992800000, 0.901453100000 - 479, 0.104297900000, 0.133452800000, 0.856619300000 - 480, 0.095640000000, 0.139020000000, 0.812950100000 - 481, 0.087299550000, 0.144676400000, 0.770517300000 - 482, 0.079308040000, 0.150469300000, 0.729444800000 - 483, 0.071717760000, 0.156461900000, 0.689913600000 - 484, 0.064580990000, 0.162717700000, 0.652104900000 - 485, 0.057950010000, 0.169300000000, 0.616200000000 - 486, 0.051862110000, 0.176243100000, 0.582328600000 - 487, 0.046281520000, 0.183558100000, 0.550416200000 - 488, 0.041150880000, 0.191273500000, 0.520337600000 - 489, 0.036412830000, 0.199418000000, 0.491967300000 - 490, 0.032010000000, 0.208020000000, 0.465180000000 - 491, 0.027917200000, 0.217119900000, 0.439924600000 - 492, 0.024144400000, 0.226734500000, 0.416183600000 - 493, 0.020687000000, 0.236857100000, 0.393882200000 - 494, 0.017540400000, 0.247481200000, 0.372945900000 - 495, 0.014700000000, 0.258600000000, 0.353300000000 - 496, 0.012161790000, 0.270184900000, 0.334857800000 - 497, 0.009919960000, 0.282293900000, 0.317552100000 - 498, 0.007967240000, 0.295050500000, 0.301337500000 - 499, 0.006296346000, 0.308578000000, 0.286168600000 - 500, 0.004900000000, 0.323000000000, 0.272000000000 - 501, 0.003777173000, 0.338402100000, 0.258817100000 - 502, 0.002945320000, 0.354685800000, 0.246483800000 - 503, 0.002424880000, 0.371698600000, 0.234771800000 - 504, 0.002236293000, 0.389287500000, 0.223453300000 - 505, 0.002400000000, 0.407300000000, 0.212300000000 - 506, 0.002925520000, 0.425629900000, 0.201169200000 - 507, 0.003836560000, 0.444309600000, 0.190119600000 - 508, 0.005174840000, 0.463394400000, 0.179225400000 - 509, 0.006982080000, 0.482939500000, 0.168560800000 - 510, 0.009300000000, 0.503000000000, 0.158200000000 - 511, 0.012149490000, 0.523569300000, 0.148138300000 - 512, 0.015535880000, 0.544512000000, 0.138375800000 - 513, 0.019477520000, 0.565690000000, 0.128994200000 - 514, 0.023992770000, 0.586965300000, 0.120075100000 - 515, 0.029100000000, 0.608200000000, 0.111700000000 - 516, 0.034814850000, 0.629345600000, 0.103904800000 - 517, 0.041120160000, 0.650306800000, 0.096667480000 - 518, 0.047985040000, 0.670875200000, 0.089982720000 - 519, 0.055378610000, 0.690842400000, 0.083845310000 - 520, 0.063270000000, 0.710000000000, 0.078249990000 - 521, 0.071635010000, 0.728185200000, 0.073208990000 - 522, 0.080462240000, 0.745463600000, 0.068678160000 - 523, 0.089739960000, 0.761969400000, 0.064567840000 - 524, 0.099456450000, 0.777836800000, 0.060788350000 - 525, 0.109600000000, 0.793200000000, 0.057250010000 - 526, 0.120167400000, 0.808110400000, 0.053904350000 - 527, 0.131114500000, 0.822496200000, 0.050746640000 - 528, 0.142367900000, 0.836306800000, 0.047752760000 - 529, 0.153854200000, 0.849491600000, 0.044898590000 - 530, 0.165500000000, 0.862000000000, 0.042160000000 - 531, 0.177257100000, 0.873810800000, 0.039507280000 - 532, 0.189140000000, 0.884962400000, 0.036935640000 - 533, 0.201169400000, 0.895493600000, 0.034458360000 - 534, 0.213365800000, 0.905443200000, 0.032088720000 - 535, 0.225749900000, 0.914850100000, 0.029840000000 - 536, 0.238320900000, 0.923734800000, 0.027711810000 - 537, 0.251066800000, 0.932092400000, 0.025694440000 - 538, 0.263992200000, 0.939922600000, 0.023787160000 - 539, 0.277101700000, 0.947225200000, 0.021989250000 - 540, 0.290400000000, 0.954000000000, 0.020300000000 - 541, 0.303891200000, 0.960256100000, 0.018718050000 - 542, 0.317572600000, 0.966007400000, 0.017240360000 - 543, 0.331438400000, 0.971260600000, 0.015863640000 - 544, 0.345482800000, 0.976022500000, 0.014584610000 - 545, 0.359700000000, 0.980300000000, 0.013400000000 - 546, 0.374083900000, 0.984092400000, 0.012307230000 - 547, 0.388639600000, 0.987418200000, 0.011301880000 - 548, 0.403378400000, 0.990312800000, 0.010377920000 - 549, 0.418311500000, 0.992811600000, 0.009529306000 - 550, 0.433449900000, 0.994950100000, 0.008749999000 - 551, 0.448795300000, 0.996710800000, 0.008035200000 - 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745, 0.000356380000, 0.000139550000, 0.000000000000 - 746, 0.000332110000, 0.000130086000, 0.000000000000 - 747, 0.000309550000, 0.000121290000, 0.000000000000 - 748, 0.000288580000, 0.000113106000, 0.000000000000 - 749, 0.000269090000, 0.000105501000, 0.000000000000 - 750, 0.000250969000, 0.000098428000, 0.000000000000 - 751, 0.000234130000, 0.000091853000, 0.000000000000 - 752, 0.000218470000, 0.000085738000, 0.000000000000 - 753, 0.000203910000, 0.000080048000, 0.000000000000 - 754, 0.000190350000, 0.000074751000, 0.000000000000 - 755, 0.000177730000, 0.000069819000, 0.000000000000 - 756, 0.000165970000, 0.000065222000, 0.000000000000 - 757, 0.000155020000, 0.000060939000, 0.000000000000 - 758, 0.000144800000, 0.000056942000, 0.000000000000 - 759, 0.000135280000, 0.000053217000, 0.000000000000 - 760, 0.000126390000, 0.000049737000, 0.000000000000 - 761, 0.000118100000, 0.000046491000, 0.000000000000 - 762, 0.000110370000, 0.000043464000, 0.000000000000 - 763, 0.000103150000, 0.000040635000, 0.000000000000 - 764, 0.000096427000, 0.000038000000, 0.000000000000 - 765, 0.000090151000, 0.000035540500, 0.000000000000 - 766, 0.000084294000, 0.000033244800, 0.000000000000 - 767, 0.000078830000, 0.000031100600, 0.000000000000 - 768, 0.000073729000, 0.000029099000, 0.000000000000 - 769, 0.000068969000, 0.000027230700, 0.000000000000 - 770, 0.000064525800, 0.000025486000, 0.000000000000 - 771, 0.000060376000, 0.000023856100, 0.000000000000 - 772, 0.000056500000, 0.000022333200, 0.000000000000 - 773, 0.000052880000, 0.000020910400, 0.000000000000 - 774, 0.000049498000, 0.000019580800, 0.000000000000 - 775, 0.000046339000, 0.000018338400, 0.000000000000 - 776, 0.000043389000, 0.000017177700, 0.000000000000 - 777, 0.000040634000, 0.000016093400, 0.000000000000 - 778, 0.000038060000, 0.000015080000, 0.000000000000 - 779, 0.000035657000, 0.000014133600, 0.000000000000 - 780, 0.000033411700, 0.000013249000, 0.000000000000 - 781, 0.000031315000, 0.000012422600, 0.000000000000 - 782, 0.000029355000, 0.000011649900, 0.000000000000 - 783, 0.000027524000, 0.000010927700, 0.000000000000 - 784, 0.000025811000, 0.000010251900, 0.000000000000 - 785, 0.000024209000, 0.000009619600, 0.000000000000 - 786, 0.000022711000, 0.000009028100, 0.000000000000 - 787, 0.000021308000, 0.000008474000, 0.000000000000 - 788, 0.000019994000, 0.000007954800, 0.000000000000 - 789, 0.000018764000, 0.000007468600, 0.000000000000 - 790, 0.000017611500, 0.000007012800, 0.000000000000 - 791, 0.000016532000, 0.000006585800, 0.000000000000 - 792, 0.000015521000, 0.000006185700, 0.000000000000 - 793, 0.000014574000, 0.000005810700, 0.000000000000 - 794, 0.000013686000, 0.000005459000, 0.000000000000 - 795, 0.000012855000, 0.000005129800, 0.000000000000 - 796, 0.000012075000, 0.000004820600, 0.000000000000 - 797, 0.000011345000, 0.000004531200, 0.000000000000 - 798, 0.000010659000, 0.000004259100, 0.000000000000 - 799, 0.000010017000, 0.000004004200, 0.000000000000 - 800, 0.000009413630, 0.000003764730, 0.000000000000 - 801, 0.000008847900, 0.000003539950, 0.000000000000 - 802, 0.000008317100, 0.000003329140, 0.000000000000 - 803, 0.000007819000, 0.000003131150, 0.000000000000 - 804, 0.000007351600, 0.000002945290, 0.000000000000 - 805, 0.000006913000, 0.000002770810, 0.000000000000 - 806, 0.000006501500, 0.000002607050, 0.000000000000 - 807, 0.000006115300, 0.000002453290, 0.000000000000 - 808, 0.000005752900, 0.000002308940, 0.000000000000 - 809, 0.000005412700, 0.000002173380, 0.000000000000 - 810, 0.000005093470, 0.000002046130, 0.000000000000 - 811, 0.000004793800, 0.000001926620, 0.000000000000 - 812, 0.000004512500, 0.000001814400, 0.000000000000 - 813, 0.000004248300, 0.000001708950, 0.000000000000 - 814, 0.000004000200, 0.000001609880, 0.000000000000 - 815, 0.000003767100, 0.000001516770, 0.000000000000 - 816, 0.000003548000, 0.000001429210, 0.000000000000 - 817, 0.000003342100, 0.000001346860, 0.000000000000 - 818, 0.000003148500, 0.000001269450, 0.000000000000 - 819, 0.000002966500, 0.000001196620, 0.000000000000 - 820, 0.000002795310, 0.000001128090, 0.000000000000 - 821, 0.000002634500, 0.000001063680, 0.000000000000 - 822, 0.000002483400, 0.000001003130, 0.000000000000 - 823, 0.000002341400, 0.000000946220, 0.000000000000 - 824, 0.000002207800, 0.000000892630, 0.000000000000 - 825, 0.000002082000, 0.000000842160, 0.000000000000 - 826, 0.000001963600, 0.000000794640, 0.000000000000 - 827, 0.000001851900, 0.000000749780, 0.000000000000 - 828, 0.000001746500, 0.000000707440, 0.000000000000 - 829, 0.000001647100, 0.000000667480, 0.000000000000 - 830, 0.000001553140, 0.000000629700, 0.000000000000]; - - otherwise - - error('colorMatchFcn:unrecognizedMatchFcn', ... - 'Unrecognized color match function.') - -end - -lambda = cmf(:, 1)'; -xFcn = cmf(:, 2)'; -yFcn = cmf(:, 3)'; -zFcn = cmf(:, 4)'; diff --git a/MST/visualization/real_results/results/README.md b/MST/visualization/real_results/results/README.md deleted file mode 100755 index 8eaedaa..0000000 --- a/MST/visualization/real_results/results/README.md +++ /dev/null @@ -1 +0,0 @@ -Please put the reconstructed HSI here and rename it as method.mat, e.g., mst_plus_plus.mat. diff --git a/MST/visualization/show_line.m b/MST/visualization/show_line.m deleted file mode 100755 index a429935..0000000 --- a/MST/visualization/show_line.m +++ /dev/null @@ -1,72 +0,0 @@ -%% plot color pics -clear; clc; -load(['simulation_results\results\','truth','.mat']); - -load(['simulation_results\results\','hdnet','.mat']); -pred_block_hdnet = pred; - -load(['simulation_results\results\','mst_s','.mat']); -pred_block_mst_s = pred; - -load(['simulation_results\results\','mst_m','.mat']); -pred_block_mst_m = pred; - -load(['simulation_results\results\','mst_l','.mat']); -pred_block_mst_l = pred; - -load(['simulation_results\results\','mst_plus_plus','.mat']); -pred_block_mst_plus_plus = pred; - -lam28 = [453.5 457.5 462.0 466.0 471.5 476.5 481.5 487.0 492.5 498.0 504.0 510.0... - 516.0 522.5 529.5 536.5 544.0 551.5 558.5 567.5 575.5 584.5 594.5 604.0... - 614.5 625.0 636.5 648.0]; - -truth(find(truth>0.7))=0.7; -pred_block_hdnet(find(pred_block_hdnet>0.7))=0.7; -pred_block_mst_s(find(pred_block_mst_s>0.7))=0.7; -pred_block_mst_m(find(pred_block_mst_m>0.7))=0.7; -pred_block_mst_l(find(pred_block_mst_l>0.7))=0.7; -pred_block_mst_plus_plus(find(pred_block_mst_plus_plus>0.7))=0.7; - -f = 2; - -%% plot spectrum -figure(123); -[yx, rect2crop]=imcrop(sum(squeeze(truth(f, :, :, :)), 3)); -rect2crop=round(rect2crop) -close(123); - -figure; - -spec_mean_truth = mean(mean(squeeze(truth(f,rect2crop(2):rect2crop(2)+rect2crop(4) , rect2crop(1):rect2crop(1)+rect2crop(3),:)),1),2); -spec_mean_hdnet = mean(mean(squeeze(pred_block_hdnet(f,rect2crop(2):rect2crop(2)+rect2crop(4) , rect2crop(1):rect2crop(1)+rect2crop(3),:)),1),2); -spec_mean_mst_s = mean(mean(squeeze(pred_block_mst_s(f,rect2crop(2):rect2crop(2)+rect2crop(4) , rect2crop(1):rect2crop(1)+rect2crop(3),:)),1),2); -spec_mean_mst_m = mean(mean(squeeze(pred_block_mst_m(f,rect2crop(2):rect2crop(2)+rect2crop(4) , rect2crop(1):rect2crop(1)+rect2crop(3),:)),1),2); -spec_mean_mst_l = mean(mean(squeeze(pred_block_mst_l(f,rect2crop(2):rect2crop(2)+rect2crop(4) , rect2crop(1):rect2crop(1)+rect2crop(3),:)),1),2); -spec_mean_mst_plus_plus = mean(mean(squeeze(pred_block_mst_plus_plus(f,rect2crop(2):rect2crop(2)+rect2crop(4) , rect2crop(1):rect2crop(1)+rect2crop(3),:)),1),2); - -spec_mean_truth = spec_mean_truth./max(spec_mean_truth); -spec_mean_hdnet = spec_mean_hdnet./max(spec_mean_hdnet); -spec_mean_mst_s = spec_mean_mst_s./max(spec_mean_mst_s); -spec_mean_mst_m = spec_mean_mst_m./max(spec_mean_mst_m); -spec_mean_mst_l = spec_mean_mst_l./max(spec_mean_mst_l); -spec_mean_mst_plus_plus = spec_mean_mst_plus_plus./max(spec_mean_mst_plus_plus); - -corr_hdnet = roundn(corr(spec_mean_truth(:),spec_mean_hdnet(:)),-4); -corr_mst_s = roundn(corr(spec_mean_truth(:),spec_mean_mst_s(:)),-4); -corr_mst_m = roundn(corr(spec_mean_truth(:),spec_mean_mst_m(:)),-4); -corr_mst_l = roundn(corr(spec_mean_truth(:),spec_mean_mst_l(:)),-4); -corr_mst_plus_plus = roundn(corr(spec_mean_truth(:),spec_mean_mst_plus_plus(:)),-4); - -X = lam28; - -Y(1,:) = spec_mean_truth(:); -Y(2,:) = spec_mean_hdnet(:); Corr(1)=corr_hdnet; -Y(3,:) = spec_mean_mst_s(:); Corr(2)=corr_mst_s; -Y(4,:) = spec_mean_mst_m(:); Corr(3)=corr_mst_m; -Y(5,:) = spec_mean_mst_l(:); Corr(4)=corr_mst_l; -Y(6,:) = spec_mean_mst_plus_plus(:); Corr(5)=corr_mst_plus_plus; - -createfigure(X,Y,Corr) - - diff --git a/MST/visualization/show_real.m b/MST/visualization/show_real.m deleted file mode 100755 index 9c921c7..0000000 --- a/MST/visualization/show_real.m +++ /dev/null @@ -1,28 +0,0 @@ -%% plot color pics -clear; clc; -close all - -load('real_results\results\mst.mat'); -x_result_1 = flip(flip(squeeze(pred(1, :, :, :)),1),2); -x_result_2 = flip(flip(squeeze(pred(2, :, :, :)),1),2); -x_result_3 = flip(flip(squeeze(pred(3, :, :, :)),1),2); -x_result_4 = flip(flip(squeeze(pred(4, :, :, :)),1),2); -x_result_5 = flip(flip(squeeze(pred(5, :, :, :)),1),2); - -save_file = 'real_results\rgb_results\mst\'; -mkdir(save_file); - -frame = 1; -for recon = {x_result_1,x_result_2,x_result_3,x_result_4,x_result_5} - recon = cell2mat(recon); - intensity = 5; - for channel=1:28 - img_nb = [channel]; % channel number - row_num = 1; col_num = 1; - lam28 = [453.5 457.5 462.0 466.0 471.5 476.5 481.5 487.0 492.5 498.0 504.0 510.0 516.0 522.5 529.5 536.5 544.0 551.5 558.5 567.5 575.5 584.5 594.5 604.0 614.5 625.0 636.5 648.0]; - recon(find(recon>1))=1; - name = [save_file 'frame' num2str(frame) 'channel' num2str(channel)]; - dispCubeAshwin(recon(:,:,img_nb),intensity,lam28(img_nb), [] ,col_num,row_num,0,1,name); - end - frame = frame+1; -end \ No newline at end of file diff --git a/MST/visualization/show_simulation.m b/MST/visualization/show_simulation.m deleted file mode 100755 index a6497df..0000000 --- a/MST/visualization/show_simulation.m +++ /dev/null @@ -1,26 +0,0 @@ -%% plot color pics -clear; clc; -load('simulation_results\results\mst_s.mat'); -save_file = 'simulation_results\rgb_results\mst_s\'; -mkdir(save_file); - -close all; -frame = 1; -for i = 1:10 - recon = squeeze(pred(i,:,:,:)); - intensity = 5; - for channel=1:28 - img_nb = [channel]; % channel number - row_num = 1; col_num = 1; - lam28 = [453.5 457.5 462.0 466.0 471.5 476.5 481.5 487.0 492.5 498.0 504.0 510.0... - 516.0 522.5 529.5 536.5 544.0 551.5 558.5 567.5 575.5 584.5 594.5 604.0... - 614.5 625.0 636.5 648.0]; - recon(find(recon>1))=1; - name = [save_file 'frame' num2str(frame) 'channel' num2str(channel)]; - dispCubeAshwin(recon(:,:,img_nb),intensity,lam28(img_nb), [] ,col_num,row_num,0,1,name); - end - frame = frame+1; -end -close all; - - diff --git a/MST/visualization/simulation_results/results/README.md b/MST/visualization/simulation_results/results/README.md deleted file mode 100755 index afd8c6d..0000000 --- a/MST/visualization/simulation_results/results/README.md +++ /dev/null @@ -1 +0,0 @@ -Please put the reconstructed HSI here and rename it as method.mat, e.g., mst_s.mat. diff --git a/Resnet_only.py b/Resnet_only.py deleted file mode 100644 index 1365597..0000000 --- a/Resnet_only.py +++ /dev/null @@ -1,270 +0,0 @@ -import pytorch_lightning as pl -import torch -import torch.nn as nn -from simca.CassiSystem_lightning import CassiSystemOptim -from MST.simulation.train_code.architecture import * -from simca import load_yaml_config -import matplotlib.pyplot as plt -import torchvision -import numpy as np -from simca.functions_acquisition import * -from piqa import SSIM -from torch.utils.tensorboard import SummaryWriter -import io -import torchvision.transforms as transforms -from PIL import Image -from optimization_modules_with_resnet import UnetModel - -class ResnetOnly(pl.LightningModule): - - def __init__(self,log_dir="tb_logs"): - super().__init__() - - self.mask_generation = UnetModel(classes=1,encoder_weights=None,in_channels=1) - self.writer = SummaryWriter(log_dir) - - - def _normalize_data_by_itself(self, data): - # Calculate the mean and std for each batch individually - # Keep dimensions for broadcasting - mean = torch.mean(data, dim=[1, 2], keepdim=True) - std = torch.std(data, dim=[1, 2], keepdim=True) - - # Normalize each batch by its mean and std - normalized_data = (data - mean) / std - return normalized_data - - - def forward(self, x, pattern=None): - print("---FORWARD---") - - hyperspectral_cube, wavelengths = x - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - batch_size, H, W, C = hyperspectral_cube.shape - - #generate stupid acq - self.acquisition = torch.sum(hyperspectral_cube, dim=-1) - self.acquisition = self.acquisition.flip(1) - self.acquisition = self.acquisition.flip(2) - self.acquisition = self.acquisition.unsqueeze(1).float() - - # print("acquisition shape: ", self.acquisition.shape) - # plt.imshow(self.acquisition[0,0,:,:].cpu().numpy()) - # plt.show() - - self.pattern = self.mask_generation(self.acquisition) - self.pattern = BinarizeFunction.apply(self.pattern) - - # print("pattern shape: ", self.pattern.shape) - # plt.imshow(self.pattern[0,0,:,:].detach().cpu().numpy()) - # plt.show() - - return self.pattern - - - def training_step(self, batch, batch_idx): - print("Training step") - - loss = self._common_step(batch, batch_idx) - - input_images = self._convert_output_to_images(self._normalize_image_tensor(self.acquisition[:,0,:,:])) - patterns = self._convert_output_to_images(self._normalize_image_tensor(self.pattern[:,0,:,:])) - - if self.global_step % 30 == 0: - self._log_images('train/input_images', input_images, self.global_step) - self._log_images('train/patterns', patterns, self.global_step) - - self.log_dict( - { "train_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - - return {"loss": loss} - - def _normalize_image_tensor(self, tensor): - # Normalize the tensor to the range [0, 1] - min_val = tensor.min() - max_val = tensor.max() - normalized_tensor = (tensor - min_val) / (max_val - min_val) - return normalized_tensor - - def validation_step(self, batch, batch_idx): - - print("Validation step") - loss = self._common_step(batch, batch_idx) - - self.log_dict( - { "val_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def test_step(self, batch, batch_idx): - print("Test step") - loss = self._common_step(batch, batch_idx) - self.log_dict( - { "test_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - return {"loss": loss} - - def predict_step(self, batch, batch_idx): - print("Predict step") - loss = self._common_step(batch, batch_idx) - - return loss - - def _common_step(self, batch, batch_idx): - - output_pattern = self.forward(batch) - - sum_result = torch.mean(output_pattern,dim=(2,3)) - print("sum_result: ", sum_result) - sum_final = torch.sum(torch.abs(sum_result - 0.5)) - loss1 = sum_final - - loss2 = calculate_spectral_flatness(output_pattern) - loss2 = torch.sum(1 - loss2) - - print("mean loss1: ", loss1) - print("spectral flatness loss 2: ", loss2) - - loss = loss1 + loss2 - - return loss - - def configure_optimizers(self): - optimizer = torch.optim.Adam(self.parameters(), lr=4e-4) - return { "optimizer":optimizer, - "lr_scheduler":{ - "scheduler":torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 500, eta_min=1e-6), - "interval": "epoch" - } - } - - def _log_images(self, tag, images, global_step): - # Convert model output to image grid and log to TensorBoard - img_grid = torchvision.utils.make_grid(images) - self.writer.add_image(tag, img_grid, global_step) - - def _convert_output_to_images(self, acquired_images): - - acquired_images = acquired_images.unsqueeze(1) - - # Create a grid of images for visualization - img_grid = torchvision.utils.make_grid(acquired_images) - return img_grid - - def plot_spectral_filter(self,ref_hyperspectral_cube,recontructed_hyperspectral_cube): - - - batch_size, y,x, lmabda_ = ref_hyperspectral_cube.shape - - # Create a figure with subplots arranged horizontally - fig, axs = plt.subplots(1, batch_size, figsize=(batch_size * 5, 4)) # Adjust figure size as needed - - # Check if batch_size is 1, axs might not be iterable - if batch_size == 1: - axs = [axs] - - # Plot each spectral filter in its own subplot - for i in range(batch_size): - colors = ['b', 'g', 'r'] - for j in range(3): - pix_j_row_value = np.random.randint(0,y) - pix_j_col_value = np.random.randint(0,x) - - pix_j_ref = ref_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - pix_j_reconstructed = recontructed_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - axs[i].plot(pix_j_reconstructed, label="pix reconstructed" + str(j),c=colors[j]) - axs[i].plot(pix_j_ref, label="pix" + str(j), linestyle='--',c=colors[j]) - - axs[i].set_title(f"Reconstruction quality") - - axs[i].set_xlabel("Wavelength index") - axs[i].set_ylabel("pix values") - axs[i].grid(True) - - plt.legend() - # Adjust layout - plt.tight_layout() - - # Create a buffer to save plot - buf = io.BytesIO() - plt.savefig(buf, format='png') - plt.close(fig) - buf.seek(0) - - # Convert PNG buffer to PIL Image - image = Image.open(buf) - - # Convert PIL Image to Tensor - image_tensor = transforms.ToTensor()(image) - return image_tensor - - -def subsample(input, origin_sampling, target_sampling): - [bs, row, col, nC] = input.shape - indices = torch.zeros(len(target_sampling), dtype=torch.int) - for i in range(len(target_sampling)): - sample = target_sampling[i] - idx = torch.abs(origin_sampling-sample).argmin() - indices[i] = idx - return input[:,:,:,indices] - -def expand_mask_3d(mask_batch): - if len(mask_batch.shape)==3: - mask3d = mask_batch.unsqueeze(-1).repeat((1, 1, 1, 28)) - else: - mask3d = mask_batch.repeat((1, 1, 1, 28)) - mask3d = torch.permute(mask3d, (0, 3, 1, 2)) - return mask3d - -class EmptyModule(nn.Module): - def __init__(self): - super().__init__() - self.useless_linear = nn.Linear(1, 1) - def forward(self, x): - return x - -def calculate_spectral_flatness(pattern): - - fft_result = torch.fft.fft2(pattern) - - # Calculate the Power Spectrum - power_spectrum = torch.abs(fft_result)**2 - - # Calculate the Geometric Mean of the power spectrum - # Use torch.log and torch.exp for differentiability, adding a small epsilon to avoid log(0) - epsilon = 1e-10 - geometric_mean = torch.exp(torch.mean(torch.log(power_spectrum + epsilon))) - - # Calculate the Arithmetic Mean of the power spectrum - arithmetic_mean = torch.mean(power_spectrum) - - # Compute the Spectral Flatness - spectral_flatness = geometric_mean / arithmetic_mean - - return spectral_flatness - -class BinarizeFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, input): - # Forward pass is the binary threshold operation - return (input > 0.5).float() - - @staticmethod - def backward(ctx, grad_output): - # For backward pass, just pass the gradients through unchanged - return grad_output diff --git a/analyze_cassi.py b/analyze_cassi.py new file mode 100755 index 0000000..55ccf7b --- /dev/null +++ b/analyze_cassi.py @@ -0,0 +1,26 @@ +import argparse +from pprint import pprint +import os + +from simca.cost_functions_optics import test_cassi_system + +def analyze_system(input_config_dir): + + final_config_path = os.path.join(input_config_dir, "config_system.yml") + config_system, performances = test_cassi_system(final_config_path, save_fig_dir=input_config_dir,save_fig=True) + + print(f"\n---- Optical System Configuration for {final_config_path} ----") + pprint(config_system) + + print(f"\n---- Optical Performances for {final_config_path} ----") + pprint(performances) + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Analyze existing SD-CASSI system") + parser.add_argument("--input_dir", required=True, help="Path to the configuration file to analyze") + args = parser.parse_args() + + analyze_system(args.input_config_dir) + diff --git a/data_handler.py b/data_handler.py deleted file mode 100755 index e33b896..0000000 --- a/data_handler.py +++ /dev/null @@ -1,212 +0,0 @@ -import os -from pytorch_lightning.utilities.types import EVAL_DATALOADERS -import torch -import scipy.io as sio -from torch.utils.data import Dataset, DataLoader -from torch.utils.data import random_split -from torchvision import datasets -from torchvision.transforms import ToTensor -import matplotlib.pyplot as plt -import numpy as np -import random -from pytorch_lightning import LightningDataModule - - -class CubesDataset(Dataset): - def __init__(self, data_dir, augment=True): - self.data_dir = data_dir - self.augment_ = augment - self.data_file_names = sorted(os.listdir(self.data_dir)) - - def __len__(self): - return len(self.data_file_names) - - def __getitem__(self, idx): - - cube, wavelengths = self.load_hyperspectral_data(idx) # H x W x lambda - - if self.augment_: - cube = self.augment(cube, 128) # lambda x H x W - else: - cube = torch.from_numpy(np.transpose(cube, (2, 0, 1))).float()[:,:128,:128] # lambda x H x W - - return cube, wavelengths - - def load_hyperspectral_data(self, idx): - file_path = os.path.join(self.data_dir, self.data_file_names[idx]) - data = sio.loadmat(file_path) - if "img_expand" in data: - cube = data['img_expand'] / 65536. - elif "img" in data: - cube = data['img'] / 65536. - cube = cube.astype(np.float32) # H x W x lambda - wavelengths = torch.tensor(np.linspace(450, 650, 28)) - - return cube, wavelengths - - def augment(self, img, crop_size = 128): - h, w, _ = img.shape - x_index = np.random.randint(0, h - crop_size) - y_index = np.random.randint(0, w - crop_size) - processed_data = np.zeros((crop_size, crop_size, 28), dtype=np.float32) - processed_data = img[x_index:x_index + crop_size, y_index:y_index + crop_size, :] - processed_data = torch.from_numpy(np.transpose(processed_data, (2, 0, 1))).float() - processed_data = arguement_1(processed_data) - - """ # The other half data use splicing. - processed_data = np.zeros((4, crop_size//2, crop_size//2, 28), dtype=np.float32) - for i in range(batch_size - batch_size // 2): - sample_list = np.random.randint(0, len(train_data), 4) - for j in range(4): - x_index = np.random.randint(0, h-crop_size//2) - y_index = np.random.randint(0, w-crop_size//2) - processed_data[j] = train_data[sample_list[j]][x_index:x_index+crop_size//2,y_index:y_index+crop_size//2,:] - gt_batch_2 = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))).cuda() # [4,28,128,128] - gt_batch.append(arguement_2(gt_batch_2)) - gt_batch = torch.stack(gt_batch, dim=0) """ - return processed_data - - -class CubesDataModule(LightningDataModule): - def __init__(self, data_dir, batch_size, num_workers=1): - super().__init__() - self.data_dir = data_dir - self.batch_size = batch_size - self.num_workers = num_workers - self.dataset = CubesDataset(self.data_dir,augment=True) - - def setup(self, stage=None): - dataset_size = len(self.dataset) - train_size = int(0.7 * dataset_size) - val_size = int(0.2 * dataset_size) - test_size = dataset_size - train_size - val_size - - self.train_ds, self.val_ds, self.test_ds = random_split(self.dataset, [train_size, val_size, test_size]) - - def train_dataloader(self): - return DataLoader(self.train_ds, - batch_size=self.batch_size, - num_workers=self.num_workers, - shuffle=True) - - def val_dataloader(self): - return DataLoader(self.val_ds, - batch_size=self.batch_size, - num_workers=self.num_workers, - shuffle=False) - - def test_dataloader(self): - return DataLoader(self.test_ds, - batch_size=self.batch_size, - num_workers=self.num_workers, - shuffle=False) - - def predict_dataloader(self): - return DataLoader(self.train_ds, - batch_size=self.batch_size, - num_workers=self.num_workers, - shuffle=False) - - -def arguement_1(x): - """ - :param x: c,h,w - :return: c,h,w - """ - rotTimes = random.randint(0, 3) - vFlip = random.randint(0, 1) - hFlip = random.randint(0, 1) - # Random rotation - for j in range(rotTimes): - x = torch.rot90(x, dims=(1, 2)) - # Random vertical Flip - for j in range(vFlip): - x = torch.flip(x, dims=(2,)) - # Random horizontal Flip - for j in range(hFlip): - x = torch.flip(x, dims=(1,)) - return x - - -def shuffle_crop(train_data, batch_size, crop_size=256, argument=True): - if argument: - gt_batch = [] - # The first half data use the original data. - index = np.random.choice(range(len(train_data)), batch_size//2) - processed_data = np.zeros((batch_size//2, crop_size, crop_size, 28), dtype=np.float32) - for i in range(batch_size//2): - img = train_data[index[i]] - h, w, _ = img.shape - x_index = np.random.randint(0, h - crop_size) - y_index = np.random.randint(0, w - crop_size) - processed_data[i, :, :, :] = img[x_index:x_index + crop_size, y_index:y_index + crop_size, :] - processed_data = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))).cuda().float() - for i in range(processed_data.shape[0]): - gt_batch.append(arguement_1(processed_data[i])) - - # The other half data use splicing. - processed_data = np.zeros((4, crop_size//2, crop_size//2, 28), dtype=np.float32) - for i in range(batch_size - batch_size // 2): - sample_list = np.random.randint(0, len(train_data), 4) - for j in range(4): - x_index = np.random.randint(0, h-crop_size//2) - y_index = np.random.randint(0, w-crop_size//2) - processed_data[j] = train_data[sample_list[j]][x_index:x_index+crop_size//2,y_index:y_index+crop_size//2,:] - gt_batch_2 = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))).cuda() # [4,28,128,128] - gt_batch.append(arguement_2(gt_batch_2)) - gt_batch = torch.stack(gt_batch, dim=0) - return gt_batch - else: - index = np.random.choice(range(len(train_data)), batch_size) - processed_data = np.zeros((batch_size, crop_size, crop_size, 28), dtype=np.float32) - for i in range(batch_size): - h, w, _ = train_data[index[i]].shape - x_index = np.random.randint(0, h - crop_size) - y_index = np.random.randint(0, w - crop_size) - processed_data[i, :, :, :] = train_data[index[i]][x_index:x_index + crop_size, y_index:y_index + crop_size, :] - gt_batch = torch.from_numpy(np.transpose(processed_data, (0, 3, 1, 2))) - return gt_batch - -def arguement_2(generate_gt): - c, h, w = generate_gt.shape[1],generate_gt.shape[2],generate_gt.shape[3] - divid_point_h = h//2 - divid_point_w = w//2 - output_img = torch.zeros(c,h,w).cuda() - output_img[:, :divid_point_h, :divid_point_w] = generate_gt[0] - output_img[:, :divid_point_h, divid_point_w:] = generate_gt[1] - output_img[:, divid_point_h:, :divid_point_w] = generate_gt[2] - output_img[:, divid_point_h:, divid_point_w:] = generate_gt[3] - return output_img - -# class AcquisitionDataset(Dataset): -# def __init__(self, input, hs_cubes, transform=None, target_transform=None): -# """_summary_ - -# Args: -# input (_type_): List of size 2 with each element being a list: -# - First list: List of n torch.tensor acquisitions (2D) -# - Second list: List of n int labels -# hs_cubes (_type_): List of size m, hs_cubes[m] being the m-th hs cube -# transform (_type_, optional): _description_. Defaults to None. -# target_transform (_type_, optional): _description_. Defaults to None. -# """ -# self.data = input # list of size 2, first elem is a list of n torch.tensor acquisitions (input), second elem is a list of size n with the index of corresponding hs cubes (output) -# self.labels = self.data[1] - -# self.cubes = hs_cubes # list of cubes, number of cubes must be >= max(self.labels) - -# self.transform = transform -# self.target_transform = target_transform - -# def __len__(self): -# return len(self.data[1]) - -# def __getitem__(self, index): -# acq = self.data[0][index] # torch tensor of size x*y -# cube = self.cubes[self.labels[index]] # torch tensor of size x*y*w - -# return acq, cube - -if __name__ == "__main__": - data_dir = "/local/users/ademaio/lpaillet/mst_datasets/cave_1024_28/" - datamodule = CubesDataModule(data_dir, batch_size=5, num_workers=2) \ No newline at end of file diff --git a/design_cassi.py b/design_cassi.py new file mode 100755 index 0000000..9456349 --- /dev/null +++ b/design_cassi.py @@ -0,0 +1,116 @@ +import argparse +from pprint import pprint +import os +import json +import os +import matplotlib.pyplot as plt + +from simca.cost_functions_optics import ( + optimize_cassi_system, + test_cassi_system, + plot_optimization_process +) +from simca.functions_optim import save_config_system + +def optimize_step(params_to_optimize, cost_weights, init_config_path, target_dispersion, iterations, patience, device, index_estimation_method, step_name, output_dir): + final_config, latest_optical_params = optimize_cassi_system( + params_to_optimize, + target_dispersion, + cost_weights, + init_config_path, + iterations, + patience, + output_dir, + step_name, + index_estimation_method=index_estimation_method, + device=device + ) + + # Plot the optimization process after each step + plot_optimization_process(output_dir, step_name) + + return final_config, latest_optical_params + + +def main(prism_type, output_dir): + target_dispersion = 1000 # in [µm] ---> modify to spectral spreading + iterations = 2000 + patience = 500 + device = "cuda" + + # Ensure the output directory exists + os.makedirs(output_dir, exist_ok=True) + + # Set the final config path + final_config_path = os.path.join(output_dir, "config_system.yml") + + if prism_type == "amici": + # STEP 1: Optimize everything (including prism materials) + params_step1 = ['lba_c', 'alpha_c', 'A1', 'A2', 'nd1', 'nd2', 'vd1', 'vd2'] + init_config_path_step1 = "./init_configs/system_config_amici.yml" + + # STEP 2: Optimize everything (excluding prism materials) + params_step2 = ['lba_c', 'alpha_c', 'A1', 'A2'] + + cost_weights = { + 'cost_dispersion': 1.0, + 'cost_distance_glasses': 1.0, + 'cost_deviation': 1, + 'cost_distorsion': 1.0, + 'cost_thickness': 1.0, + 'cost_beam_compression': 1.0, + 'cost_non_linearity': 1.0, + 'cost_distance_total_intern_reflection': 1.0 + } + elif prism_type == "single": # single prism + # STEP 1: Optimize everything (including prism material) + params_step1 = ['lba_c', 'alpha_c', 'A1', 'nd1', 'vd1'] + init_config_path_step1 = "./init_configs/system_config_single.yml" + + # STEP 2: Optimize everything (excluding prism material) + params_step2 = ['lba_c', 'alpha_c', 'A1'] + + cost_weights = { + 'cost_dispersion': 20.0, + 'cost_distance_glasses': 1.0, + 'cost_deviation': 1e-5, + 'cost_distorsion': 1.0, + 'cost_thickness': 1.0, + 'cost_beam_compression': 1.0, + 'cost_non_linearity': 1.0, + 'cost_distance_total_intern_reflection': 1.0 + } + else: + raise ValueError(f"Invalid prism type: {prism_type}") + + # STEP 1 + config_step1, params_step1 = optimize_step(params_step1, cost_weights, init_config_path_step1, target_dispersion, iterations, patience, device, "cauchy", "step1", output_dir) + + temp_config_path = os.path.join(output_dir, "temp.yml") + save_config_system(temp_config_path, os.path.join(output_dir, "optimization_results", "step1"), init_config_path_step1) + + # STEP 2 + if prism_type == "amici": + cost_weights['cost_dispersion'] = 2.0 # Increase weight for dispersion in step 2 for Amici + config_step2, params_step2 = optimize_step(params_step2, cost_weights, temp_config_path, target_dispersion, iterations, patience, device, "sellmeier", "step2", output_dir) + + save_config_system(final_config_path, os.path.join(output_dir, "optimization_results", "step2"), temp_config_path, index_estimation_method="sellmeier") + + # Testing + config_system, performances = test_cassi_system(final_config_path, index_estimation_method="sellmeier") + + print("\n---- Optical System Configuration----") + pprint(config_system) + + print("\n---- Optical Performances ----") + pprint(performances) + + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Design SD-CASSI system with Amici or single prism") + parser.add_argument("--prism_type", choices=["amici", "single"], required=True, help="Type of prism system to design") + parser.add_argument("--output_dir", required=True, help="Directory for the final configuration file") + args = parser.parse_args() + + main(args.prism_type, args.output_dir) \ No newline at end of file diff --git a/gradient_descent_optics_optim_triplet.py b/gradient_descent_optics_optim_triplet.py deleted file mode 100755 index c176903..0000000 --- a/gradient_descent_optics_optim_triplet.py +++ /dev/null @@ -1,349 +0,0 @@ -from simca.cost_functions_optics import evaluate_spectral_dispersion_values -from simca import load_yaml_config -from simca.CassiSystemOptim import CassiSystemOptim -from torch.optim import Adam, LBFGS -from opticalglass.glassfactory import get_glass_catalog -from simca.cost_functions_optics import * -import matplotlib.pyplot as plt -import pandas as pd -import torch -import os -import datetime -import json -# from torchviz import make_dot - -def format_score_details(**kwargs): - """ - Format the score details into a dictionary. - - Args: - **kwargs: Keyword arguments of score components and their values. - - Returns: - dict: A dictionary containing the score formula and component values. - """ - score_details = { - 'formula': "score = cost_dispersion + cost_distance_glasses + cost_deviation + cost_distorsion + cost_thickness + cost_beam_compression", - 'components': kwargs # This will include all the passed score components and their values. - } - return score_details - -target_dispersion = 2200 - -import math -#matplotlib.use('Agg') -config_system = load_yaml_config("simca/configs/cassi_system_optim_optics_full_triplet_starting_point.yml") - -hoya_pd = get_glass_catalog('Schott') -list_of_glasses1 = hoya_pd.df.iloc[:, 0].index.tolist() -# Access the 'nd' values where the first level is NaN -idx = pd.IndexSlice - -nd_values = hoya_pd.df.loc[:, idx[pd.NA, 'nd']] -vd_values = hoya_pd.df.loc[:, idx["abbe number", 'vd']] - -nd_values = torch.tensor(nd_values.values) -vd_values = torch.tensor(vd_values.values) - -cassi_system = CassiSystemOptim(system_config=config_system) - -# Assuming 'device' is defined (e.g., 'cuda' or 'cpu') -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -# cassi_system.to(device) # Move your model to the specified device - -optim_params = [cassi_system.optical_model.lba_c, - cassi_system.optical_model.alpha_c, - cassi_system.optical_model.A1, - cassi_system.optical_model.A2, - # cassi_system.optical_model.A3, - cassi_system.optical_model.nd1, - cassi_system.optical_model.nd2, - # cassi_system.optical_model.nd3, - cassi_system.optical_model.vd1, - cassi_system.optical_model.vd2, - # cassi_system.optical_model.vd3, - ] - -optimizer = Adam(optim_params, lr=0.005) -iterations = 10000 - - -# Initialize tracking variables -best_loss = float('inf') # Set the initial best loss to infinity -patience = 500 # How many iterations to wait after last improvement -non_improvement_count = 0 # Counter for iterations without improvement - - -### create a new dir for the results - -if not os.path.exists('results'): - os.makedirs('results') - -# list_scores = [] -# numpy_alpha_c = np.linspace(1, 90, 80) -# for alpha_c in numpy_alpha_c: -# config_system["system architecture"]["dispersive element"]["alpha_c"] = alpha_c -# cassi_system = CassiSystemOptim(system_config=config_system) -# -# score = testing_model(cassi_system,nd_values, vd_values) -# is_nan = torch.isnan(score).item() -# list_scores.append(score.detach().numpy()) -# print(score) -# -# # idx_min = list_scores.index(min(list_scores)) -# min_value = np.nanmin(list_scores) # Returns the min of the array, ignoring any NaNs -# idx_min = list_scores.index(min_value) -# # print("min score", min(idx_min), min_value) -# config_system["system architecture"]["dispersive element"]["alpha_c"] = numpy_alpha_c[idx_min] -# cassi_system = CassiSystemOptim(system_config=config_system) - -# create a new dir for the results - -run_id = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S') -results_dir = os.path.join('results', run_id) -os.makedirs(results_dir, exist_ok=True) - - -for i in range(iterations): - # Zero the parameter gradients - optimizer.zero_grad() - - cassi_system.optical_model.A3 = cassi_system.optical_model.A1 - cassi_system.optical_model.nd3 = cassi_system.optical_model.nd1 - cassi_system.optical_model.vd3 = cassi_system.optical_model.vd1 - - alpha_c_out = cassi_system.optical_model.rerun_central_dispersion() - # Forward pass - x_vec_out, y_vec_out = cassi_system.propagate_coded_aperture_grid() - - alpha_c = cassi_system.optical_model.alpha_c - alpha_c_out = cassi_system.optical_model.alpha_c_transmis - list_apex_angles = [cassi_system.optical_model.A1, cassi_system.optical_model.A2, cassi_system.optical_model.A3] - list_theta_in = cassi_system.optical_model.list_theta_in - list_theta_out = cassi_system.optical_model.list_theta_out - - # evaluate spectral dispersion - dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(x_vec_out, y_vec_out) - - y_second_derivative = calculate_second_derivative(cassi_system.wavelengths, central_coordinates_X) - - # evaluate direct_view - deviation = evaluate_direct_view(alpha_c, alpha_c_out, list_apex_angles) - # print("deviation in degrees", deviation * 180 / np.pi) - - # evaluate beam compression - beam_compression = evaluate_beam_compression(list_theta_in, list_theta_out) - # print("beam compression", beam_compression) - - # evaluate thickness - thickness = evaluate_thickness(list_apex_angles) - # print("thickness", thickness) - - # evaluate distortions - x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions(cassi_system.X_coded_aper_coordinates, - cassi_system.Y_coded_aper_coordinates, - central_coordinates_X) - - # plt.scatter(cassi_system.X_coordinates_propagated_coded_aperture.detach().numpy()[..., 0], - # cassi_system.Y_coordinates_propagated_coded_aperture.detach().numpy()[..., 0], color='blue', label="distor") - # plt.scatter(cassi_system.X_coordinates_propagated_coded_aperture.detach().numpy()[..., -1], - # cassi_system.Y_coordinates_propagated_coded_aperture.detach().numpy()[..., -1], color='red', label="distor") - # plt.scatter(x_vec_no_dist.detach().numpy()[..., 0], y_vec_no_dist.detach().numpy()[..., 0], label="no dist", - # color='green') - # plt.scatter(x_vec_no_dist.detach().numpy()[..., -1], y_vec_no_dist.detach().numpy()[..., -1], label="no dist", - # color='green') - # plt.show() - distortion_metric = evaluate_distortions(x_vec_out, y_vec_out, x_vec_no_dist, y_vec_no_dist) - - # print("distortion metric", distortion_metric) - - # distance from closest point - current_value1 = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] - current_value2 = [cassi_system.optical_model.nd2, cassi_system.optical_model.vd2] - current_value3 = [cassi_system.optical_model.nd3, cassi_system.optical_model.vd3] - - distance_closest_point1, min_idx1 = evaluate_distance(current_value1, nd_values, vd_values) - distance_closest_point2, min_idx2 = evaluate_distance(current_value2, nd_values, vd_values) - distance_closest_point3, min_idx3 = evaluate_distance(current_value3, nd_values, vd_values) - - # # cost functions - cost_dispersion = (target_dispersion/5- dispersion/5)**2 - cost_distance_glasses = (10**10*(distance_closest_point1**2 + distance_closest_point2**2 + distance_closest_point3**2))**2 - cost_deviation = (deviation*10)**2 - cost_distorsion = (distortion_metric/1000)**2 - cost_thickness = thickness*10 - cost_beam_compression = torch.abs(beam_compression - 1)*10 - cost_non_linearity = 0.001* y_second_derivative**2 - # cost_distance_total_intern_reflection = 0.1*softplus(cassi_system.optical_model.min_distance_from_total_intern_reflection)**2 - cost_block = ((-1*list_apex_angles[0] + list_apex_angles[1] - list_apex_angles[2])*100)**2 - - - score = cost_dispersion + cost_distance_glasses + cost_deviation + cost_thickness + cost_block - # score = (alpha_c_out - 10)**2 - # Assume `score` is your loss metric that you want to minimize - - current_loss = score.item() - - is_nan = math.isnan(current_loss) - if is_nan: - print("nan value") - break - - - latest_optical_params = {"alpha_c":cassi_system.optical_model.alpha_c.clone(), - "lba_c":cassi_system.optical_model.lba_c.clone(), - "A1":cassi_system.optical_model.A1.clone(), - "A2":cassi_system.optical_model.A2.clone(), - "A3":cassi_system.optical_model.A3.clone(), - "nd1":cassi_system.optical_model.nd1.clone(), - "nd2":cassi_system.optical_model.nd2.clone(), - "nd3":cassi_system.optical_model.nd3.clone(), - "vd1":cassi_system.optical_model.vd1.clone(), - "vd2":cassi_system.optical_model.vd2.clone(), - "vd3":cassi_system.optical_model.vd3.clone(), - } - - # Backward pass - score.backward() - optimizer.step() - - - - if i %100 == 0: - print(f'Iteration {i}, Loss: {current_loss}') - print("dispersion in microns", dispersion) - print("cost_dispersion", cost_dispersion) - print("cost_distance_glasses", cost_distance_glasses) - print("cost_deviation", cost_deviation) - print("cost_distorsion", cost_distorsion) - print("cost_thickness", cost_thickness) - # print("cost_beam_compression", cost_beam_compression) - print("cost_block", cost_block) - print(cost_block) - # print("cost_block", cost_block) - # print("cost_distance_total_intern_reflection", cost_distance_total_intern_reflection) - - # Saving optimization details at the end of optimization - optimization_details = { - 'reason_for_stopping': 'no improvement' if non_improvement_count >= patience else 'completed', - 'iterations': i + 1, - 'end_parameters': {param_name: param.item() for param_name, param in latest_optical_params.items()}, - 'current_loss': current_loss, - # Add other details as needed - } - - details_path = os.path.join(results_dir, 'optimization_details.json') - with open(details_path, 'a') as f: - json.dump(optimization_details, f, indent=4) - - score_details = format_score_details( - cost_dispersion=cost_dispersion.item(), - cost_distance_glasses=cost_distance_glasses.item(), - cost_deviation=cost_deviation.item(), - cost_distorsion=cost_distorsion.item(), - cost_thickness=cost_thickness.item(), - cost_beam_compression=cost_beam_compression.item(), - ) - - # Save the score details to a file - details_path = os.path.join(results_dir, f'score_details_iteration.json') - with open(details_path, 'a') as f: - json.dump(score_details, f, indent=4) - - # Check for improvement - if current_loss < best_loss: - best_loss = current_loss - non_improvement_count = 0 # Reset counter - else: - non_improvement_count += 1 - - # Check if it's time to stop - if non_improvement_count >= patience: - print(f'Stopping early at iteration {i} due to no improvement.') - break # Exit the loop - - # input("Press Enter to continue...") - - - -# Testing with closest points - -current_glass_values_1 = [latest_optical_params["nd1"], latest_optical_params["vd1"]] -current_glass_values_2 = [latest_optical_params["nd2"], latest_optical_params["vd2"]] -current_glass_values_3 = [latest_optical_params["nd3"], latest_optical_params["vd3"]] - - -distance_closest_point1, min_idx_1 = evaluate_distance(current_glass_values_1, nd_values, vd_values) -distance_closest_point2, min_idx_2 = evaluate_distance(current_glass_values_2, nd_values, vd_values) -distance_closest_point3, min_idx_3 = evaluate_distance(current_glass_values_3, nd_values, vd_values) - -plt.scatter(nd_values, vd_values, c='b', label='all glasses') -plt.scatter(current_glass_values_1[0].detach().numpy(), current_glass_values_1[1].detach().numpy(), c='r', label='current glass 1') -plt.scatter(current_glass_values_2[0].detach().numpy(), current_glass_values_2[1].detach().numpy(), c='g', label='current glass 2') -plt.scatter(current_glass_values_3[0].detach().numpy(), current_glass_values_3[1].detach().numpy(), c='y', label='current glass 3') -plt.scatter(nd_values[min_idx_1], vd_values[min_idx_1], c='r', label='closest glass 1',marker='x') -plt.scatter(nd_values[min_idx_2], vd_values[min_idx_2], c='g', label='closest glass 2',marker='x') -plt.scatter(nd_values[min_idx_3], vd_values[min_idx_3], c='y', label='closest glass 3',marker='x') -plot_path = os.path.join(results_dir, 'glasses_plot.png') -plt.savefig(plot_path) -# plt.close() -plt.show() - -glass_1 = list_of_glasses1[min_idx_1] -glass_2 = list_of_glasses1[min_idx_2] -glass_3 = list_of_glasses1[min_idx_3] - -# print(glass_1, glass_2, glass_3) - - - -print(latest_optical_params) - -cassi_system_renewal = CassiSystemOptim(system_config=cassi_system.system_config) - - -print(cassi_system_renewal.system_config) -# set the closest glasses -cassi_system_renewal.system_config["system architecture"]["dispersive element"]["wavelength center"] = latest_optical_params["lba_c"] -cassi_system_renewal.system_config["system architecture"]["dispersive element"]["alpha_c"] = latest_optical_params["alpha_c"] * 180 / np.pi -cassi_system_renewal.system_config["system architecture"]["dispersive element"]["A1"] = latest_optical_params["A1"] * 180 / np.pi -cassi_system_renewal.system_config["system architecture"]["dispersive element"]["A2"] = latest_optical_params["A2"]* 180 / np.pi -cassi_system_renewal.system_config["system architecture"]["dispersive element"]["A3"] = latest_optical_params["A3"] * 180 / np.pi -cassi_system_renewal.system_config["system architecture"]["dispersive element"]['continuous glass materials 1'] = False -cassi_system_renewal.system_config["system architecture"]["dispersive element"]['continuous glass materials 2']= False -cassi_system_renewal.system_config["system architecture"]["dispersive element"]['continuous glass materials 3'] = False -cassi_system_renewal.system_config["system architecture"]["dispersive element"]['glass1'] = glass_1 -cassi_system_renewal.system_config["system architecture"]["dispersive element"]['glass2'] = glass_2 -cassi_system_renewal.system_config["system architecture"]["dispersive element"]['glass3'] = glass_3 - -cassi_system_renewal.update_optical_model(cassi_system.system_config) - -# pattern : Generate the coded aperture pattern -# cassi_system_renewal.generate_2D_pattern(config_patterns) - -X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture = cassi_system_renewal.propagate_coded_aperture_grid() - -# evaluate spectral dispersion -dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture) - -x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions(cassi_system_renewal.X_coded_aper_coordinates, - cassi_system_renewal.Y_coded_aper_coordinates, - central_coordinates_X) -print(cassi_system_renewal.system_config) -print("dispersion in microns", dispersion) - -plt.scatter(X_coordinates_propagated_coded_aperture.detach().numpy()[..., 0], Y_coordinates_propagated_coded_aperture.detach().numpy()[..., 0], color='blue', label="distor") -plt.scatter(X_coordinates_propagated_coded_aperture.detach().numpy()[..., -1], Y_coordinates_propagated_coded_aperture.detach().numpy()[..., -1], color='red', label="distor") -# -plt.scatter(x_vec_no_dist.detach().numpy()[..., 0], y_vec_no_dist.detach().numpy()[..., 0], label="no dist", - color='green') -plt.scatter(x_vec_no_dist.detach().numpy()[..., -1], y_vec_no_dist.detach().numpy()[..., -1], label="no dist", - color='green') - -plot_path = os.path.join(results_dir, 'grids.png') -plt.savefig(plot_path) -plt.show() - - - - diff --git a/init_configs/system_config_amici.yml b/init_configs/system_config_amici.yml new file mode 100755 index 0000000..30a98fb --- /dev/null +++ b/init_configs/system_config_amici.yml @@ -0,0 +1,38 @@ +##### Configuration file for the chosen optical system + +infos: + system name: HYACAMEO + +system architecture: + system type: DD-CASSI + focal lens: 75000 + dispersive element: + # dispersive element caracteristics + catalog: Schott + index evaluation method: cauchy + type: amici # name of the dispersive element + glass1: N-SK2 # glass type of the dispersive element (only used if type == 'prism') + glass2: N-SF4 # glass type of the dispersive element (only used if type == 'prism') + glass3: N-SK2 # glass type of the dispersive element (only used if type == 'prism') + A1: 19.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees + A2: 39.4 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees + A3: 19.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees + alpha_c: 0 + delta alpha c: 0 + delta beta c: 0 + wavelength center: 520 # central wavelength -- in nm +detector: + number of pixels along X: 112 # number of pixels along X axis -- no units + number of pixels along Y: 112 # number of pixels along Y axis -- no units + pixel size along X: 71 # pixel size along X -- in um + pixel size along Y: 71 # pixel size along Y -- in um + +coded aperture: + number of pixels along X: 25 # number of pixels along X axis -- no units + number of pixels along Y: 25 # number of pixels along Y axis -- no units + pixel size along X: 210 # pixel size along X -- in um + pixel size along Y: 210 # pixel size along Y -- in um +spectral range: + wavelength min: 450 # minimum wavelength -- in nm + wavelength max: 650 # maximum wavelength -- in nm + number of spectral samples: 10 diff --git a/init_configs/system_config_single.yml b/init_configs/system_config_single.yml new file mode 100755 index 0000000..7154424 --- /dev/null +++ b/init_configs/system_config_single.yml @@ -0,0 +1,34 @@ +##### Configuration file for the chosen optical system + +infos: + system name: HYACAMEO + +system architecture: + system type: DD-CASSI + focal lens: 75000 + dispersive element: + # dispersive element caracteristics + catalog: Schott + type: prism # name of the dispersive element + glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') + A1: 50 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees + alpha_c: 40.9 + delta alpha c: 0 + delta beta c: 0 + wavelength 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zC^n9~oEojwcls9}d-+q3-Sy;5pGAorrQ19QuzAxr#{cLwD=uBs+x0=bQryythNGJP#{)Zm{)hyf zW{FvclDu;iFC7?Mvgi4+A!BepMU@_=2o?&^%GucTv`%$ZlJWha~uN$1M!o4@yG3U z`x8R(@3Pj!N`s-G2oN?9VXzn{@em6I0jH^xKU!nz)yi%4?&?SOoPX}UvZ1Wq&J~p8 zJqkcWLu$oM|9Sd>vBtHc_3we=I5iB4l-rO%1kwVr2Er*K1|&@(#EnW6{rlp+g;(x; z;@&6A^`3JJC3%k`tUB$?6Zeldu4yOf**dCpSz(`r1|STr1!=npvJefmFRoWMo^aao zYqpGxl$}6x5M|7J6k*5KJGU?E?mfSv<)3VO;$Tb$WZKY@0;UN`T8IEdO8tC6Pw$5Z zPCM`FlDKmhrR6;eapK@$ZF0w+w~dcYUQIe$?iz>;lolgIKUutJ(Ld~d;=%h%H+gQO zB=1p*!NI}k;T@xInruv51S_uq^D`iI>xvUrT>tn3KigdrckZJk?@^95YhJ%-*YnRF z*H^3TyWoPiJ-4CECLc8@$y=ht97UP1Dp6vN;{O0%<5mYx9*+nB0000100].flatten(), bins=100) - - # Run optimization - cassi_system = optim_width(cassi_system, start_position, target, cassi_system.system_config["coded aperture"]["number of pixels along Y"], start_width, device, algo, lr, num_iterations, max_iter_cnt, plot_frequency = None) - - pattern = cassi_system.pattern.detach().to('cpu').numpy() - cube = cassi_system.filtering_cube.detach().to('cpu').numpy() - acquisition = cassi_system.measurement.detach().to('cpu').numpy() - - patterns2.append(pattern) - cubes2.append(cube) - acquisitions.append(acquisition) - width_values.append(cassi_system.array_x_positions.detach().to('cpu').numpy()) - - - - #print(torch.std(cassi_system.measurement.detach())) - - print(f"Exec time: {time.time() - time_start}s") - - fig1 = plt.figure() - im1 = plt.imshow(patterns1[0], animated = True, aspect=aspect) - plt.colorbar() - - fig1bis = plt.figure() - im1bis = plt.imshow(corrected_patterns1[0], animated = True, aspect=aspect) - plt.colorbar() - - fig2 = plt.figure() - im2 = plt.imshow(cubes1[0][:,:,cubes1[0].shape[2]//2], animated = True, aspect=aspect) - plt.colorbar() - - fig2bis = plt.figure() - im2bis = plt.imshow(corrected_cubes1[0][:,:,corrected_cubes1[0].shape[2]//2], animated = True, aspect=aspect) - plt.colorbar() - - fig3 = plt.figure() - im3 = plt.imshow(patterns2[0], animated = True, aspect=aspect) - plt.colorbar() - - fig4 = plt.figure() - im4 = plt.imshow(cubes2[0][:,:,cubes2[0].shape[2]//2], animated = True, aspect=aspect) - plt.colorbar() - - fig5 = plt.figure() - im5 = plt.imshow(np.clip(acquisitions[0], 1, None), animated = True, aspect=aspect, cmap="gray", norm="log") - plt.colorbar() - - def update1(i): - im1.set_array(patterns1[i]) - return im1, - def update1bis(i): - im1bis.set_array(corrected_patterns1[i]) - return im1bis, - def update2(i): - im2.set_array(cubes1[i][:,:,cubes1[0].shape[2]//2]) - return im2, - def update2bis(i): - im2bis.set_array(corrected_cubes1[i][:,:,corrected_cubes1[0].shape[2]//2]) - return im2bis, - def update3(i): - im3.set_array(patterns2[i]) - return im3, - def update4(i): - im4.set_array(cubes2[i][:,:,cubes2[0].shape[2]//2]) - return im4, - def update5(i): - im5.set_array(acquisitions[i]) - return im5, - - animation_fig1 = anim.FuncAnimation(fig1, update1, frames=len(patterns1), interval = 1000, repeat=True) - animation_fig1bis = anim.FuncAnimation(fig1bis, update1bis, frames=len(corrected_patterns1), interval = 1000, repeat=True) - animation_fig2 = anim.FuncAnimation(fig2, update2, frames=len(cubes1), interval = 1000, repeat=True) - animation_fig2bis = anim.FuncAnimation(fig2bis, update2bis, frames=len(corrected_cubes1), interval = 1000, repeat=True) - animation_fig3 = anim.FuncAnimation(fig3, update3, frames=len(patterns2), interval = 1000, repeat=True) - animation_fig4 = anim.FuncAnimation(fig4, update4, frames=len(cubes2), interval = 1000, repeat=True) - animation_fig5 = anim.FuncAnimation(fig5, update5, frames=len(acquisitions), interval = 1000, repeat=True) - - #print("Var: ", np.var(acquisitions[0][int(pos_slit_detector*145)-2:int(pos_slit_detector*145)+2].flatten())) - print("Var: ", np.var(acquisitions[0][acquisitions[0]>100].flatten())) - - - fig6 = plt.figure() - plt.hist(acquisitions[0][acquisitions[0]>100].flatten(), bins=100) - - plt.show() - - - - - folder = datetime.datetime.now().strftime('%y-%m-%d_%Hh%M') - os.makedirs(f"./results/{folder}") - - fig6.savefig(f"./results/{folder}/histo.png") - fig_first_histo.savefig(f"./results/{folder}/first_histo.png") - fig1.savefig(f"./results/{folder}/patterns_smile.png") - fig1bis.savefig(f"./results/{folder}/corrected_patterns_smile.png") - fig2.savefig(f"./results/{folder}/cubes_smile.png") - fig2bis.savefig(f"./results/{folder}/corrected_cubes_smile.png") - fig3.savefig(f"./results/{folder}/patterns_width.png") - fig4.savefig(f"./results/{folder}/cubes_width.png") - fig5.savefig(f"./results/{folder}/acquisitions.png") - #animation_fig1.save(f"./results/{folder}/patterns_smile.gif") - #animation_fig1bis.save(f"./results/{folder}/corrected_patterns_smile.gif") - #animation_fig2.save(f"./results/{folder}/cubes_smile.gif") - #animation_fig2bis.save(f"./results/{folder}/corrected_cubes_smile.gif") - #animation_fig3.save(f"./results/{folder}/patterns_width.gif") - #animation_fig4.save(f"./results/{folder}/cubes_width.gif") - #animation_fig5.save(f"./results/{folder}/acquisitions.gif") - - """np.savez(f"./results/{folder}/results.npz", smile_positions=np.stack(smile_positions, axis=0), patterns_smile=np.stack(patterns1, axis=0), cubes_smile = np.stack(cubes1, axis=0), - corrected_smile_positions=np.stack(corrected_smile_positions, axis=0), corrected_patterns_smile=np.stack(corrected_patterns1, axis=0), corrected_cubes_smile=np.stack(corrected_cubes1, axis=0), - start_width_values=np.stack(start_width_values, axis=0), width_values=np.stack(width_values, axis=0), patterns_width=np.stack(patterns2, axis=0), cubes_width = np.stack(cubes2, axis=0), - acquisitions=np.stack(acquisitions, axis=0), - variance=np.var(acquisitions[0][acquisitions[0]>500].flatten()))""" - np.savez(f"./results/{folder}/results.npz", smile_positions=np.stack(smile_positions, axis=0), patterns_smile=np.stack(patterns1, axis=0), - corrected_smile_positions=np.stack(corrected_smile_positions, axis=0), corrected_patterns_smile=np.stack(corrected_patterns1, axis=0), - start_width_values=np.stack(start_width_values, axis=0), width_values=np.stack(width_values, axis=0), patterns_width=np.stack(patterns2, axis=0), - acquisitions=np.stack(acquisitions, axis=0), - variance=np.var(acquisitions[0][acquisitions[0]>500].flatten())) - #np.savez(f"./results/{folder}/results.npz", patterns_smile=np.stack(patterns1, axis=0), cubes_smile = np.stack(cubes1, axis=0)) diff --git a/mask_optim_recon.py b/mask_optim_recon.py deleted file mode 100755 index ce030a9..0000000 --- a/mask_optim_recon.py +++ /dev/null @@ -1,175 +0,0 @@ -from simca import load_yaml_config -from simca.CassiSystemOptim import CassiSystemOptim -from simca.CassiSystem import CassiSystem -from data_handler import CubesDataModule -from MST.simulation.train_code.architecture import * -from MST.simulation.train_code.utils import * -import numpy as np -import snoop -import matplotlib.pyplot as plt -import matplotlib.animation as anim -#import matplotlib -import torch -import time, datetime -import os -import tqdm - -config_dataset = load_yaml_config("simca/configs/dataset.yml") -config_patterns = load_yaml_config("simca/configs/pattern.yml") -config_acquisition = load_yaml_config("simca/configs/acquisition.yml") -config_system = load_yaml_config() - -# Load datacubes -# Generate random mask -# Run SIMCA to make acquisition 1 -# ResNET -> mask -# Run SIMCA to make acquisition 2 -# Reconstruction MST/CST -> out_cube -# Compare out_cube with datacube to compute loss - -data_dir = "/local/users/ademaio/lpaillet/mst_datasets" -model_name = 'mst_plus_plus' -device = 'cuda' -lr = 0.001 -nb_epochs = 100 - -def expand_mask_3d(mask): - mask3d = np.tile(mask[:, :, np.newaxis], (1, 1, 28)) - mask3d = np.transpose(mask3d, [2, 0, 1]) - mask3d = torch.from_numpy(mask3d) - return mask3d - -def train_recons(model, cassi_system, optimizer, mse, nb_epochs, display_iter=10): - train_losses = [] - test_losses = [] - for e in range(1, nb_epochs + 1): - avg_loss = 0.0 - mean_losses = [] - # Run the training loop for one epoch - model.train() - for batch_idx, (cube, wavelengths) in tqdm( - enumerate(datamodule.train_dataloader), total=len(datamodule.train_dataloader)): - optimizer.zero_grad() - #for i in range(cube.shape[0]): - # cube_i = cube[i,...] - # wavelengths_i = wavelengths[i,...] - - - # cassi_system.dataset = cube # b x 28 x H x W - # cassi_system.wavelengths = wavelengths - input_mask = np.random.randint(0,1,size=(128,128)) - cassi_system.pattern = torch.tensor(input_mask) - cassi_system.generate_filtering_cube() - - input_acq = cassi_system.image_acquisition(use_psf=False) # b x H x (W + d*(28-1)) - d=2 - input_acq = shift_back(input_acq, step=d) - - input_mask_3d = expand_mask_3d(input_mask) #TODO, like in train_method/utils.py - output = model(input_acq, input_mask_3d) - loss = torch.sqrt(mse(output, cube)) - - loss.backward() - optimizer.step() - avg_loss += loss.item() - mean_losses.append(np.mean(np.array(mean_losses))) - if display_iter and batch_idx % display_iter == 0: - string = "Train (epoch {}/{}) [{}/{} ({:.0f}%)]\tLoss: {:.6f}" - string = string.format( - e, - nb_epochs, - batch_idx * len(cube), - len(cube) * len(datamodule.train_dataloader), - 100.0 * batch_idx / len(datamodule.train_dataloader), - mean_losses[batch_idx], - ) - tqdm.write(string) - - avg_loss /= len(datamodule.train_dataloader) - train_losses.append(avg_loss) - - # Run the test loop for one epoch - avg_loss = 0 - model.eval() - for batch_idx, (cube, wavelengths) in enumerate(datamodule.val_dataloader): - with torch.no_grad(): - input_mask = np.random.randint(0,1,size=(128,128)) - cassi_system.pattern = torch.tensor(input_mask) - # cassi_system.dataset = cube - # cassi_system.wavelengths = wavelengths - cassi_system.generate_filtering_cube() - - input_acq = cassi_system.image_acquisition(use_psf=False) - d=2 - input_acq = shift_back(input_acq, step=d) - input_mask_3d = expand_mask_3d(input_mask) #TODO, like in train_method/utils.py - - output = model(input_acq, input_mask_3d) - loss = torch.sqrt(mse(output, cube)) - avg_loss += loss.item() - - avg_loss /= len(datamodule.val_dataloader) - string = "Test (epoch {}/{})\tLoss:{:.6f}".format(e, nb_epochs, avg_loss) - tqdm.write(string) - test_losses.append(avg_loss) - -def test_recons(model, mse, cassi_system): - model.eval() - avg_loss = 0 - - for batch_idx, (cube, wavelengths) in enumerate(datamodule.test_dataloader): - with torch.no_grad(): - input_mask = np.random.randint(0,1,size=(128,128)) - cassi_system.pattern = torch.tensor(input_mask) - # cassi_system.dataset = cube - # cassi_system.wavelengths = wavelengths - cassi_system.generate_filtering_cube() - - input_acq = cassi_system.image_acquisition(use_psf=False) - d=2 - input_acq = shift_back(input_acq, step=d) - input_mask_3d = expand_mask_3d(input_mask) #TODO, like in train_method/utils.py - - output = model(input_acq, input_mask_3d) - loss = torch.sqrt(mse(output, cube)) - avg_loss += loss.item() - avg_loss /= len(datamodule.test_dataloader) - print(f"\nTest loss: {avg_loss:.6f}") - -def full_flow(cassi_system, scene, wavelengths, resnet, recons_model, mse): - input_mask = np.random.randint(0,1,size=(128,128)) - cassi_system.dataset = scene - cassi_system.wavelengths = wavelengths - cassi_system.pattern = torch.tensor(input_mask) - cassi_system.generate_filtering_cube() - - acq1 = cassi_system.image_acquisition(use_psf=False) - new_mask = resnet(acq1) - - cassi_system.pattern = new_mask - cassi_system.generate_filtering_cube() - - acq2 = cassi_system.image_acquisition(use_psf=False) - - input_mask_3d = expand_mask_3d(new_mask.numpy()) - input_acq = shift_back(acq2, step=d) - out_cube = recons_model(input_acq, input_mask_3d) - loss = torch.sqrt(mse(out_cube, scene)) - print(f"\nTest loss: {loss:.6f}") - - -if __name__ == '__main__': - datamodule = CubesDataModule(data_dir, batch_size=5, num_workers=2) - cassi_system = CassiSystemOptim(system_config=config_system) - cassi_system.device = device - - cassi_system.update_optical_model(system_config=config_system) - X_vec_out, Y_vec_out = cassi_system.propagate_coded_aperture_grid() - - recons_model = model_generator(model_name, None) - optimizer = torch.optim.Adam(recons_model.parameters(), lr=lr, betas=(0.9, 0.999)) - mse = torch.nn.MSELoss() - - recons_model = train_recons(recons_model, optimizer, mse, cassi_system, nb_epochs) - test_recons(recons_model, mse, cassi_system) - torch.save(recons_model.state_dict(), "./recons_model.pt") \ No newline at end of file diff --git a/multi_acq_script.py b/multi_acq_script.py deleted file mode 100644 index d082216..0000000 --- a/multi_acq_script.py +++ /dev/null @@ -1,61 +0,0 @@ -import matplotlib.pyplot as plt - -from simca import CassiSystem -from simca.functions_general_purpose import * -import os - -config_dataset = load_yaml_config("simca/configs/dataset.yml") -config_system = load_yaml_config("simca/configs/cassi_system.yml") -config_patterns = load_yaml_config("simca/configs/pattern.yml") -config_acquisition = load_yaml_config("simca/configs/acquisition.yml") - -dataset_name = "indian_pines" -results_directory = "./data/results/lego_test_1" -nb_of_acq = 10 - -if __name__ == '__main__': - - # Initialize the CASSI system - cassi_system = CassiSystem(system_config=config_system) - - # DATASET : Load the hyperspectral dataset - cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"]) - - - cassi_system.generate_multiple_patterns(config_patterns,nb_of_acq) - - # PROPAGATION : Propagate the pattern grid to the detector plane - cassi_system.propagate_coded_aperture_grid() - - # FILTERING CUBE : Generate the filtering cubes - cassi_system.generate_multiple_filtering_cubes(nb_of_acq) - - # ACQUISITION : Simulate the acquisition with psf (use_psf is optional) - cassi_system.multiple_image_acquisitions(use_psf=False,nb_of_filtering_cubes=nb_of_acq,chunck_size=50) - - # Save the acquisition - cassi_system.result_directory =results_directory - os.makedirs(results_directory, exist_ok=True) - - save_config_file("config_system", cassi_system.system_config, cassi_system.result_directory) - save_config_file("config_pattern", config_patterns,cassi_system.result_directory) - save_config_file("config_acquisition", config_acquisition, cassi_system.result_directory) - - save_data_in_hdf5("interpolated_scene",cassi_system.interpolated_scene, cassi_system.result_directory) - save_data_in_hdf5("panchro",cassi_system.panchro,cassi_system.result_directory) - - save_data_in_hdf5("wavelengths",cassi_system.optical_model.system_wavelengths,cassi_system.result_directory) - save_data_in_hdf5("list_of_compressed_measurements",cassi_system.list_of_measurements,cassi_system.result_directory) - save_data_in_hdf5("list_of_filtering_cubes",cassi_system.list_of_filtering_cubes,cassi_system.result_directory) - save_data_in_hdf5("list_of_patterns",cassi_system.list_of_patterns,cassi_system.result_directory) - - - - - - - - - - - diff --git a/optimization_modules.py b/optimization_modules.py deleted file mode 100755 index 225d3cc..0000000 --- a/optimization_modules.py +++ /dev/null @@ -1,315 +0,0 @@ -import pytorch_lightning as pl -import torch -import torch.nn as nn -from simca.CassiSystem_lightning import CassiSystemOptim -from MST.simulation.train_code.architecture import * -from simca import load_yaml_config -import matplotlib.pyplot as plt -import torchvision -import numpy as np -from simca.functions_acquisition import * -from piqa import SSIM -from torch.utils.tensorboard import SummaryWriter -import io -import torchvision.transforms as transforms -from PIL import Image - -class JointReconstructionModule_V1(pl.LightningModule): - - def __init__(self, model_name,log_dir="tb_logs", reconstruction_checkpoint=None): - super().__init__() - - self.model_name = model_name - self.reconstruction_model = model_generator(self.model_name, reconstruction_checkpoint) - - self.loss_fn = nn.MSELoss() - #self.ssim_loss = SSIM(window_size=11, n_channels=28) - self.ssim_loss = SSIM(window_size=11, n_channels=3) - - self.writer = SummaryWriter(log_dir) - - def on_validation_start(self,stage=None): - print("---VALIDATION START---") - self.config = "simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml" - - config_system = load_yaml_config(self.config) - self.config_patterns = load_yaml_config("simca/configs/pattern.yml") - self.cassi_system = CassiSystemOptim(system_config=config_system) - self.cassi_system.propagate_coded_aperture_grid() - - def on_predict_start(self,stage=None): - print("---PREDICT START---") - self.config = "simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml" - - config_system = load_yaml_config(self.config) - self.config_patterns = load_yaml_config("simca/configs/pattern.yml") - self.cassi_system = CassiSystemOptim(system_config=config_system) - self.cassi_system.propagate_coded_aperture_grid() - - def _normalize_data_by_itself(self, data): - # Calculate the mean and std for each batch individually - # Keep dimensions for broadcasting - mean = torch.mean(data, dim=[1, 2], keepdim=True) - std = torch.std(data, dim=[1, 2], keepdim=True) - - # Normalize each batch by its mean and std - normalized_data = (data - mean) / std - return normalized_data - - - def forward(self, x, pattern=None): - print("---FORWARD---") - - hyperspectral_cube, wavelengths = x - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - batch_size, H, W, C = hyperspectral_cube.shape - - # fig, ax = plt.subplots(1, 1) - # plt.title(f"entry cube") - # ax.imshow(hyperspectral_cube[0, :, :, 0].cpu().detach().numpy()) - # plt.show() - # print(f"batch size:{batch_size}") - # generate pattern - - if pattern is None: - self.pattern = self.cassi_system.generate_2D_pattern(self.config_patterns,nb_of_patterns=batch_size) - self.pattern = self.pattern.to(self.device) - else: - self.pattern = pattern.to(self.device) - self.cassi_system.pattern = pattern.to(self.device) - - # plt.imshow(pattern[0, :, :].cpu().detach().numpy()) - # plt.show() - - # print(f"pattern_size: {pattern.shape}") - - # generate first acquisition with simca - - filtering_cube = self.cassi_system.generate_filtering_cube().to(self.device) - self.acquired_image1 = self.cassi_system.image_acquisition(hyperspectral_cube, self.pattern, wavelengths).to(self.device) - - - # self.acquired_image1 = self._normalize_data_by_itself(self.acquired_image1) - # acquired_cubes = self.acquired_image1.unsqueeze(1).repeat((1, 28, 1, 1)).float().to(self.device) # b x W x R x C - - filtering_cubes = subsample(filtering_cube, torch.linspace(450, 650, filtering_cube.shape[-1]), torch.linspace(450, 650, 28)).permute((0, 3, 1, 2)).float().to(self.device) - - if self.model_name == "birnat": - # acquisition = self.acquired_image1.unsqueeze(1) - acquisition = self.acquired_image1.float() - filtering_cubes = filtering_cubes.float() - elif "dauhst" in self.model_name: - acquisition = self.acquired_image1.float() - - filtering_cubes_s = torch.sum(filtering_cubes**2,1) - filtering_cubes_s[filtering_cubes_s==0] = 1 - filtering_cubes = (filtering_cubes.float(), filtering_cubes_s.float()) - - elif self.model_name == "mst_plus_plus": - acquisition = self.acquired_image1.unsqueeze(1).repeat((1, 28, 1, 1)).float().to(self.device) - #print(f"acquisition shape: {acquisition.shape}") - #print(f"filtering_cubes shape: {filtering_cubes.shape}") - - reconstructed_cube = self.reconstruction_model(acquisition, filtering_cubes) - - - return reconstructed_cube - - - def training_step(self, batch, batch_idx): - print("Training step") - - loss, ssim_loss, reconstructed_cube, ref_cube = self._common_step(batch, batch_idx) - - - output_images = self._convert_output_to_images(self._normalize_image_tensor(self.acquired_image1)) - patterns = self._convert_output_to_images(self._normalize_image_tensor(self.pattern)) - input_images = self._convert_output_to_images(self._normalize_image_tensor(ref_cube[:,:,:,0])) - reconstructed_image = self._convert_output_to_images(self._normalize_image_tensor(reconstructed_cube[:,:,:,0])) - - if self.global_step % 30 == 0: - self._log_images('train/acquisition', output_images, self.global_step) - self._log_images('train/ground_truth', input_images, self.global_step) - self._log_images('train/reconstructed', reconstructed_image, self.global_step) - self._log_images('train/patterns', patterns, self.global_step) - - spectral_filter_plot = self.plot_spectral_filter(ref_cube,reconstructed_cube) - - self.writer.add_image('Spectral Filter', spectral_filter_plot, self.global_step) - - self.log_dict( - { "train_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "train_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def _normalize_image_tensor(self, tensor): - # Normalize the tensor to the range [0, 1] - min_val = tensor.min() - max_val = tensor.max() - normalized_tensor = (tensor - min_val) / (max_val - min_val) - return normalized_tensor - - def validation_step(self, batch, batch_idx): - - print("Validation step") - loss, ssim_loss, reconstructed_cube, ref_cube = self._common_step(batch, batch_idx) - - self.log_dict( - { "val_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def test_step(self, batch, batch_idx): - print("Test step") - loss, ssim_loss, reconstructed_cube, ref_cube = self._common_step(batch, batch_idx) - self.log_dict( - { "test_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - return {"loss": loss} - - def predict_step(self, batch, batch_idx): - print("Predict step") - loss, ssim_loss, reconstructed_cube, ref_cube = self._common_step(batch, batch_idx) - print("Predict loss: ", loss.item()) - print("Predict ssim loss: ", ssim_loss) - #self.log('predict_step', loss,on_step=True, on_epoch=True, prog_bar=True, logger=True) - return loss - - def _common_step(self, batch, batch_idx): - - - reconstructed_cube = self.forward(batch) - hyperspectral_cube, wavelengths = batch - - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - reconstructed_cube = reconstructed_cube.permute(0, 2, 3, 1).to(self.device) - ref_cube = match_dataset_to_instrument(hyperspectral_cube, reconstructed_cube[0, :, :,0]) - - # fig, ax = plt.subplots(1, 2) - # plt.title(f"true cube vs reconstructed cube") - # ax[0].imshow(hyperspectral_cube[0, :, :, 0].cpu().detach().numpy()) - # ax[1].imshow(reconstructed_cube[0, :, :, 0].cpu().detach().numpy()) - # plt.show() - - - loss = torch.sqrt(self.loss_fn(reconstructed_cube, ref_cube)) - ssim_loss = self.ssim_loss(torch.clamp(reconstructed_cube.permute(0, 3, 1, 2), 0, 1), ref_cube.permute(0, 3, 1, 2)) - #ssim_loss = 0 - - return loss, ssim_loss, reconstructed_cube, ref_cube - - def configure_optimizers(self): - optimizer = torch.optim.Adam(self.parameters(), lr=4e-4) - return { "optimizer":optimizer, - "lr_scheduler":{ - "scheduler":torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 500, eta_min=1e-6), - "interval": "epoch" - } - } - - def _log_images(self, tag, images, global_step): - # Convert model output to image grid and log to TensorBoard - img_grid = torchvision.utils.make_grid(images) - self.writer.add_image(tag, img_grid, global_step) - - def _convert_output_to_images(self, acquired_images): - - acquired_images = acquired_images.unsqueeze(1) - - # Create a grid of images for visualization - img_grid = torchvision.utils.make_grid(acquired_images) - return img_grid - - def plot_spectral_filter(self,ref_hyperspectral_cube,recontructed_hyperspectral_cube): - - - batch_size, y,x, lmabda_ = ref_hyperspectral_cube.shape - - # Create a figure with subplots arranged horizontally - fig, axs = plt.subplots(1, batch_size, figsize=(batch_size * 5, 4)) # Adjust figure size as needed - - # Check if batch_size is 1, axs might not be iterable - if batch_size == 1: - axs = [axs] - - # Plot each spectral filter in its own subplot - for i in range(batch_size): - colors = ['b', 'g', 'r'] - for j in range(3): - pix_j_row_value = np.random.randint(0,y) - pix_j_col_value = np.random.randint(0,x) - - pix_j_ref = ref_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - pix_j_reconstructed = recontructed_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - axs[i].plot(pix_j_reconstructed, label="pix reconstructed" + str(j),c=colors[j]) - axs[i].plot(pix_j_ref, label="pix" + str(j), linestyle='--',c=colors[j]) - - axs[i].set_title(f"Reconstruction quality") - - axs[i].set_xlabel("Wavelength index") - axs[i].set_ylabel("pix values") - axs[i].grid(True) - - plt.legend() - # Adjust layout - plt.tight_layout() - - # Create a buffer to save plot - buf = io.BytesIO() - plt.savefig(buf, format='png') - plt.close(fig) - buf.seek(0) - - # Convert PNG buffer to PIL Image - image = Image.open(buf) - - # Convert PIL Image to Tensor - image_tensor = transforms.ToTensor()(image) - return image_tensor - - -def subsample(input, origin_sampling, target_sampling): - [bs, row, col, nC] = input.shape - indices = torch.zeros(len(target_sampling), dtype=torch.int) - for i in range(len(target_sampling)): - sample = target_sampling[i] - idx = torch.abs(origin_sampling-sample).argmin() - indices[i] = idx - return input[:,:,:,indices] - -def expand_mask_3d(mask_batch): - if len(mask_batch.shape)==3: - mask3d = mask_batch.unsqueeze(-1).repeat((1, 1, 1, 28)) - else: - mask3d = mask_batch.repeat((1, 1, 1, 28)) - mask3d = torch.permute(mask3d, (0, 3, 1, 2)) - return mask3d - -class EmptyModule(nn.Module): - def __init__(self): - super().__init__() - self.useless_linear = nn.Linear(1, 1) - def forward(self, x): - return x diff --git a/optimization_modules_with_resnet.py b/optimization_modules_with_resnet.py deleted file mode 100755 index 510a7f8..0000000 --- a/optimization_modules_with_resnet.py +++ /dev/null @@ -1,407 +0,0 @@ -import pytorch_lightning as pl -import torch -import torch.nn as nn -from simca.CassiSystem_lightning import CassiSystemOptim -from MST.simulation.train_code.architecture import * -from simca import load_yaml_config -import matplotlib.pyplot as plt -import torchvision -import numpy as np -from simca.functions_acquisition import * -from piqa import SSIM -from torch.utils.tensorboard import SummaryWriter -import io -import torchvision.transforms as transforms -from PIL import Image -import segmentation_models_pytorch as smp -import torch.nn.functional as F - -class UnetModel(nn.Module): - def __init__(self,encoder_name="resnet18",encoder_weights="",in_channels=1,classes=2,index=0): - super().__init__() - self.i= index - self.model= smp.Unet(encoder_name= encoder_name, in_channels=in_channels,encoder_weights=encoder_weights,classes=classes,activation='sigmoid') - def forward(self,x): - x= self.model(x) - return x - - -class JointReconstructionModule_V2(pl.LightningModule): - - def __init__(self, model_name,log_dir="tb_logs",reconstruction_checkpoint=None): - super().__init__() - - self.model_name = model_name - self.reconstruction_model = model_generator(self.model_name, pretrained_model_path=None) - if reconstruction_checkpoint is not None: - #self.reconstruction_model = model_generator(self.model_name, pretrained_model_path=reconstruction_checkpoint) - self.reconstruction_model.load_state_dict(torch.load(reconstruction_checkpoint), strict=False) - self.mask_generation = UnetModel(classes=1,encoder_weights=None,in_channels=1) - - self.loss_fn = nn.MSELoss() - self.ssim_loss = SSIM(window_size=11, n_channels=28) - - self.writer = SummaryWriter(log_dir) - - # for param in self.reconstruction_model.parameters(): - # param.requires_grad = False - - def on_validation_start(self,stage=None): - print("---VALIDATION START---") - self.config = "simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml" - config_system = load_yaml_config(self.config) - self.config_patterns = load_yaml_config("simca/configs/pattern.yml") - self.cassi_system = CassiSystemOptim(system_config=config_system) - self.cassi_system.propagate_coded_aperture_grid() - - def _normalize_data_by_itself(self, data): - # Calculate the mean and std for each batch individually - # Keep dimensions for broadcasting - mean = torch.mean(data, dim=[1, 2], keepdim=True) - std = torch.std(data, dim=[1, 2], keepdim=True) - - # Normalize each batch by its mean and std - normalized_data = (data - mean) / std - return normalized_data - - - def forward(self, x): - print("---FORWARD---") - - hyperspectral_cube, wavelengths = x - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - batch_size, H, W, C = hyperspectral_cube.shape - - - self.pattern = self.cassi_system.generate_2D_pattern(self.config_patterns,nb_of_patterns=batch_size) - self.pattern = self.pattern.to(self.device) - filtering_cube = self.cassi_system.generate_filtering_cube().to(self.device) - self.acquired_image1 = self.cassi_system.image_acquisition(hyperspectral_cube, self.pattern, wavelengths).to(self.device) - - self.acquired_image1 = pad_tensor(self.acquired_image1, (128, 128)) - - # flip along second and thrid axis - self.acquired_image1 = self.acquired_image1.flip(1) - self.acquired_image1 = self.acquired_image1.flip(2) - self.acquired_image1 = self.acquired_image1.unsqueeze(1).float() - #self.acquired_image1 = self._normalize_data_by_itself(self.acquired_image1) - - - self.pattern = self.mask_generation(self.acquired_image1).squeeze(1) - self.pattern = BinarizeFunction.apply(self.pattern) - self.pattern = pad_tensor(self.pattern, (131, 131)) - self.cassi_system.pattern = self.pattern.to(self.device) - - filtering_cube = self.cassi_system.generate_filtering_cube().to(self.device) - self.acquired_image2 = self.cassi_system.image_acquisition(hyperspectral_cube, self.pattern, wavelengths).to(self.device) - - - # self.acquired_image1 = self._normalize_data_by_itself(self.acquired_image1) - # acquired_cubes = self.acquired_image1.unsqueeze(1).repeat((1, 28, 1, 1)).float().to(self.device) # b x W x R x C - - filtering_cubes = subsample(filtering_cube, torch.linspace(450, 650, filtering_cube.shape[-1]), torch.linspace(450, 650, 28)).permute((0, 3, 1, 2)).float().to(self.device) - - if self.model_name == "birnat": - # acquisition = self.acquired_image1.unsqueeze(1) - acquisition = self.acquired_image2.float() - filtering_cubes = filtering_cubes.float() - elif "dauhst" in self.model_name: - acquisition = self.acquired_image2.float() - filtering_cubes_s = torch.sum(filtering_cubes**2,1) - filtering_cubes_s[filtering_cubes_s==0] = 1 - filtering_cubes = (filtering_cubes.float(), filtering_cubes_s.float()) - - elif self.model_name == "mst_plus_plus": - acquisition = self.acquired_image2.unsqueeze(1).repeat((1, 28, 1, 1)).float().to(self.device) - - - reconstructed_cube = self.reconstruction_model(acquisition, filtering_cubes) - - - return reconstructed_cube - - - def training_step(self, batch, batch_idx): - print("Training step") - - loss, ssim_loss, reconstructed_cube, ref_cube = self._common_step(batch, batch_idx) - - - - output_images = self._convert_output_to_images(self._normalize_image_tensor(self.acquired_image2)) - patterns = self._convert_output_to_images(self._normalize_image_tensor(self.pattern)) - input_images = self._convert_output_to_images(self._normalize_image_tensor(ref_cube[:,:,:,0])) - reconstructed_image = self._convert_output_to_images(self._normalize_image_tensor(reconstructed_cube[:,:,:,0])) - - if self.global_step % 30 == 0: - self._log_images('train/acquisition2', output_images, self.global_step) - self._log_images('train/ground_truth', input_images, self.global_step) - self._log_images('train/reconstructed', reconstructed_image, self.global_step) - self._log_images('train/patterns', patterns, self.global_step) - - plt.imshow(self.pattern[0,:,:].cpu().detach().numpy()) - plt.colorbar() - plt.savefig('./pattern.png') - - spectral_filter_plot = self.plot_spectral_filter(ref_cube,reconstructed_cube) - self.log_gradients(self.global_step) - self.writer.add_image('Spectral Filter', spectral_filter_plot, self.global_step) - - self.log_dict( - { "train_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "train_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def _normalize_image_tensor(self, tensor): - # Normalize the tensor to the range [0, 1] - min_val = tensor.min() - max_val = tensor.max() - normalized_tensor = (tensor - min_val) / (max_val - min_val) - return normalized_tensor - - def validation_step(self, batch, batch_idx): - - print("Validation step") - loss, ssim_loss, reconstructed_cube, ref_cube= self._common_step(batch, batch_idx) - - self.log_dict( - { "val_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "val_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def test_step(self, batch, batch_idx): - print("Test step") - loss, ssim_loss, reconstructed_cube, ref_cube= self._common_step(batch, batch_idx) - self.log_dict( - { "test_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "test_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def predict_step(self, batch, batch_idx): - print("Predict step") - loss, ssim_loss, reconstructed_cube, ref_cube= self._common_step(batch, batch_idx) - print("Predict loss: ", loss.item()) - print("Predict ssim loss: ", ssim_loss) - #self.log('predict_step', loss,on_step=True, on_epoch=True, prog_bar=True, logger=True) - return loss - - def _common_step(self, batch, batch_idx): - - - reconstructed_cube = self.forward(batch) - hyperspectral_cube, wavelengths = batch - - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - reconstructed_cube = reconstructed_cube.permute(0, 2, 3, 1).to(self.device) - ref_cube = match_dataset_to_instrument(hyperspectral_cube, reconstructed_cube[0, :, :,0]) - - # fig, ax = plt.subplots(1, 2) - # plt.title(f"true cube vs reconstructed cube") - # ax[0].imshow(hyperspectral_cube[0, :, :, 0].cpu().detach().numpy()) - # ax[1].imshow(reconstructed_cube[0, :, :, 0].cpu().detach().numpy()) - # plt.show() - total_sum_pattern = torch.sum(self.pattern, dim=(1, 2)) - total_half_pattern_equal_1 = torch.sum(torch.ones_like(self.pattern), dim=(1, 2)) / 2 - - print(f"total_sum_pattern {total_sum_pattern}") - print(f"total_half_pattern_equal_1 {total_half_pattern_equal_1}") - - - - loss1 = torch.sqrt(self.loss_fn(reconstructed_cube, ref_cube)) - loss2 = torch.sum(torch.abs((total_sum_pattern - total_half_pattern_equal_1)/(self.pattern.shape[1]*self.pattern.shape[2]))**2) - loss = loss1 + loss2 - - ssim_loss = self.ssim_loss(torch.clamp(reconstructed_cube.permute(0, 3, 1, 2), 0, 1), ref_cube.permute(0, 3, 1, 2)) - print(f"loss1 {loss1}") - print(f"loss2 {loss2}") - return loss, ssim_loss, reconstructed_cube, ref_cube - - def configure_optimizers(self): - optimizer = torch.optim.Adam(self.parameters(), lr=4e-4) - return { "optimizer":optimizer, - "lr_scheduler":{ - "scheduler":torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 500, eta_min=1e-6), - "interval": "epoch" - } - } - - def _log_images(self, tag, images, global_step): - # Convert model output to image grid and log to TensorBoard - img_grid = torchvision.utils.make_grid(images) - self.writer.add_image(tag, img_grid, global_step) - - def _convert_output_to_images(self, acquired_images): - - acquired_images = acquired_images.unsqueeze(1) - - # Create a grid of images for visualization - img_grid = torchvision.utils.make_grid(acquired_images) - return img_grid - - def log_gradients(self, step): - for name, param in self.mask_generation.named_parameters(): - if param.grad is not None: - self.writer.add_scalar(f"Gradients/{name}", param.grad.norm(), step) - - - def plot_spectral_filter(self,ref_hyperspectral_cube,recontructed_hyperspectral_cube): - - - batch_size, y,x, lmabda_ = ref_hyperspectral_cube.shape - - # Create a figure with subplots arranged horizontally - fig, axs = plt.subplots(1, batch_size, figsize=(batch_size * 5, 4)) # Adjust figure size as needed - - # Check if batch_size is 1, axs might not be iterable - if batch_size == 1: - axs = [axs] - - # Plot each spectral filter in its own subplot - for i in range(batch_size): - colors = ['b', 'g', 'r'] - for j in range(3): - pix_j_row_value = np.random.randint(0,y) - pix_j_col_value = np.random.randint(0,x) - - pix_j_ref = ref_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - pix_j_reconstructed = recontructed_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - axs[i].plot(pix_j_reconstructed, label="pix reconstructed" + str(j),c=colors[j]) - axs[i].plot(pix_j_ref, label="pix" + str(j), linestyle='--',c=colors[j]) - - axs[i].set_title(f"Reconstruction quality") - - axs[i].set_xlabel("Wavelength index") - axs[i].set_ylabel("pix values") - axs[i].grid(True) - - plt.legend() - # Adjust layout - plt.tight_layout() - - # Create a buffer to save plot - buf = io.BytesIO() - plt.savefig(buf, format='png') - plt.close(fig) - buf.seek(0) - - # Convert PNG buffer to PIL Image - image = Image.open(buf) - - # Convert PIL Image to Tensor - image_tensor = transforms.ToTensor()(image) - return image_tensor - - -def subsample(input, origin_sampling, target_sampling): - [bs, row, col, nC] = input.shape - indices = torch.zeros(len(target_sampling), dtype=torch.int) - for i in range(len(target_sampling)): - sample = target_sampling[i] - idx = torch.abs(origin_sampling-sample).argmin() - indices[i] = idx - return input[:,:,:,indices] - -def expand_mask_3d(mask_batch): - if len(mask_batch.shape)==3: - mask3d = mask_batch.unsqueeze(-1).repeat((1, 1, 1, 28)) - else: - mask3d = mask_batch.repeat((1, 1, 1, 28)) - mask3d = torch.permute(mask3d, (0, 3, 1, 2)) - return mask3d - -class EmptyModule(nn.Module): - def __init__(self): - super().__init__() - self.useless_linear = nn.Linear(1, 1) - def forward(self, x): - return x - - -def pad_tensor(input_tensor, target_shape): - [bs, row, col] = input_tensor.shape - [target_row, target_col] = target_shape - - # Calculate padding for rows - pad_row_total = max(target_row - row, 0) - pad_row_top = pad_row_total // 2 - pad_row_bottom = pad_row_total - pad_row_top - - # Calculate padding for columns - pad_col_total = max(target_col - col, 0) - pad_col_left = pad_col_total // 2 - pad_col_right = pad_col_total - pad_col_left - - # Apply padding - padded_tensor = F.pad(input_tensor, (pad_col_left, pad_col_right, pad_row_top, pad_row_bottom), 'constant', 0) - return padded_tensor - -def crop_tensor(input_tensor, target_shape): - [bs, row, col] = input_tensor.shape - [target_row, target_col] = target_shape - crop_row = (row-target_row)//2 - crop_col = (col-target_col)//2 - return input_tensor[:,crop_row:crop_row+target_row,crop_col:crop_col+target_col] - -# img = torch.randn(3, 112, 112) # Example tensor -# target_height = 128 -# target_width = 128 -# -# plt.imshow(img[0,...].cpu().detach().numpy()) -# plt.show() -# -# zda = pad_tensor(img, (target_height, target_width)) -# -# plt.imshow(zda[0,...].cpu().detach().numpy()) -# plt.show() - - -class BinarizeFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, input): - # Forward pass is the binary threshold operation - return (input > 0.5).float() - - @staticmethod - def backward(ctx, grad_output): - # For backward pass, just pass the gradients through unchanged - return grad_output \ No newline at end of file diff --git a/optimization_modules_with_resnet_v2.py b/optimization_modules_with_resnet_v2.py deleted file mode 100755 index 62081bf..0000000 --- a/optimization_modules_with_resnet_v2.py +++ /dev/null @@ -1,385 +0,0 @@ -import pytorch_lightning as pl -import torch -import torch.nn as nn -from simca.CassiSystem_lightning import CassiSystemOptim -from MST.simulation.train_code.architecture import * -from simca import load_yaml_config -import matplotlib.pyplot as plt -import torchvision -import numpy as np -from simca.functions_acquisition import * -from piqa import SSIM -from torch.utils.tensorboard import SummaryWriter -import io -import torchvision.transforms as transforms -from PIL import Image -import segmentation_models_pytorch as smp -import torch.nn.functional as F - -class UnetModel(nn.Module): - def __init__(self,encoder_name="resnet18",encoder_weights="",in_channels=1,classes=2,index=0): - super().__init__() - self.i= index - self.model= smp.Unet(encoder_name= encoder_name, in_channels=in_channels,encoder_weights=encoder_weights,classes=classes,activation='sigmoid') - def forward(self,x): - x= self.model(x) - return x - -class JointReconstructionModule_V3(pl.LightningModule): - def __init__(self, recon_lightning_module, log_dir="tb_logs",resnet_checkpoint=None): - super().__init__() - - self.reconstruction_module = recon_lightning_module - self.mask_generation = UnetModel(classes=1,encoder_weights=None,in_channels=1) - - if resnet_checkpoint is not None: - # Load the weights from the checkpoint into self.seg_model - checkpoint = torch.load(resnet_checkpoint, map_location=self.device) - # Adjust the keys - adjusted_state_dict = {key.replace('mask_generation.', ''): value - for key, value in checkpoint['state_dict'].items()} - # Filter out unexpected keys - model_keys = set(self.mask_generation.state_dict().keys()) - filtered_state_dict = {k: v for k, v in adjusted_state_dict.items() if k in model_keys} - self.mask_generation.load_state_dict(filtered_state_dict) - - # Freeze the seg_model parameters - # for param in self.mask_generation.parameters(): - # param.requires_grad = False - - self.loss_fn = nn.MSELoss() - self.ssim_loss = SSIM(window_size=11, n_channels=28) - self.reconstruction_module.ssim_loss = SSIM(window_size=11, n_channels=28) - - self.writer = SummaryWriter(log_dir) - - # for param in self.reconstruction_model.parameters(): - # param.requires_grad = False - - def on_validation_start(self,stage=None): - print("---VALIDATION START---") - self.config = "simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml" - config_system = load_yaml_config(self.config) - self.reconstruction_module.config_patterns = load_yaml_config("simca/configs/pattern.yml") - self.reconstruction_module.cassi_system = CassiSystemOptim(system_config=config_system) - self.reconstruction_module.cassi_system.propagate_coded_aperture_grid() - - self.first_config_patterns = load_yaml_config("simca/configs/pattern.yml") - self.first_cassi_system = CassiSystemOptim(system_config=config_system) - self.first_cassi_system.propagate_coded_aperture_grid() - - - def _normalize_data_by_itself(self, data): - # Calculate the mean and std for each batch individually - # Keep dimensions for broadcasting - mean = torch.mean(data, dim=[1, 2], keepdim=True) - std = torch.std(data, dim=[1, 2], keepdim=True) - - # Normalize each batch by its mean and std - normalized_data = (data - mean) / std - return normalized_data - - def forward(self, x): - print("---FORWARD---") - - hyperspectral_cube, wavelengths = x - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - batch_size, H, W, C = hyperspectral_cube.shape - - - self.pattern = self.first_cassi_system.generate_2D_pattern(self.first_config_patterns, nb_of_patterns=batch_size) - self.pattern = self.pattern.to(self.device) - filtering_cube = self.first_cassi_system.generate_filtering_cube().to(self.device) - self.acquired_image1 = self.first_cassi_system.image_acquisition(hyperspectral_cube, self.pattern, wavelengths).to(self.device) - - self.acquired_image1 = pad_tensor(self.acquired_image1, (128, 128)) - - # flip along second and thrid axis - self.acquired_image1 = self.acquired_image1.flip(1) - self.acquired_image1 = self.acquired_image1.flip(2) - self.acquired_image1 = self.acquired_image1.unsqueeze(1).float() - #self.acquired_image1 = self._normalize_data_by_itself(self.acquired_image1) - - self.pattern = self.mask_generation(self.acquired_image1).squeeze(1) - self.pattern = BinarizeFunction.apply(self.pattern) - self.pattern = pad_tensor(self.pattern, (131, 131)) - #self.reconstruction_module.pattern = self.pattern.to(self.device) - #self.reconstruction_module.cassi_system.pattern = self.pattern.to(self.device) - - reconstructed_cube = self.reconstruction_module.forward(x, self.pattern) - - self.acquired_image2 = self.reconstruction_module.acquired_image1 - - return reconstructed_cube - - def training_step(self, batch, batch_idx): - print("Training step") - - loss, ssim_loss, reconstructed_cube, ref_cube = self._common_step(batch, batch_idx) - - - - output_images = self._convert_output_to_images(self._normalize_image_tensor(self.acquired_image2)) - patterns = self._convert_output_to_images(self._normalize_image_tensor(self.pattern)) - input_images = self._convert_output_to_images(self._normalize_image_tensor(ref_cube[:,:,:,0])) - reconstructed_image = self._convert_output_to_images(self._normalize_image_tensor(reconstructed_cube[:,:,:,0])) - - if self.global_step % 30 == 0: - self._log_images('train/acquisition2', output_images, self.global_step) - self._log_images('train/ground_truth', input_images, self.global_step) - self._log_images('train/reconstructed', reconstructed_image, self.global_step) - self._log_images('train/patterns', patterns, self.global_step) - - plt.imshow(self.pattern[0,:,:].cpu().detach().numpy()) - plt.colorbar() - plt.savefig('./pattern.png') - - spectral_filter_plot = self.plot_spectral_filter(ref_cube,reconstructed_cube) - self.log_gradients(self.global_step) - self.writer.add_image('Spectral Filter', spectral_filter_plot, self.global_step) - - self.log_dict( - { "train_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "train_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def _normalize_image_tensor(self, tensor): - # Normalize the tensor to the range [0, 1] - min_val = tensor.min() - max_val = tensor.max() - normalized_tensor = (tensor - min_val) / (max_val - min_val) - return normalized_tensor - - def validation_step(self, batch, batch_idx): - - print("Validation step") - loss, ssim_loss, reconstructed_cube, ref_cube= self._common_step(batch, batch_idx) - - self.log_dict( - { "val_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "val_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def test_step(self, batch, batch_idx): - print("Test step") - loss, ssim_loss, reconstructed_cube, ref_cube= self._common_step(batch, batch_idx) - self.log_dict( - { "test_loss": loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - self.log_dict( - { "test_ssim_loss": ssim_loss, - }, - on_step=True, - on_epoch=True, - prog_bar=True, - ) - - return {"loss": loss} - - def predict_step(self, batch, batch_idx): - print("Predict step") - loss, ssim_loss, reconstructed_cube, ref_cube= self._common_step(batch, batch_idx) - print("Predict loss: ", loss.item()) - print("Predict ssim loss: ", ssim_loss) - #self.log('predict_step', loss,on_step=True, on_epoch=True, prog_bar=True, logger=True) - return loss - - def _common_step(self, batch, batch_idx): - - reconstructed_cube = self.forward(batch) - hyperspectral_cube, wavelengths = batch - - hyperspectral_cube = hyperspectral_cube.permute(0, 2, 3, 1).to(self.device) - reconstructed_cube = reconstructed_cube.permute(0, 2, 3, 1).to(self.device) - ref_cube = match_dataset_to_instrument(hyperspectral_cube, reconstructed_cube[0, :, :,0]) - - # fig, ax = plt.subplots(1, 2) - # plt.title(f"true cube vs reconstructed cube") - # ax[0].imshow(hyperspectral_cube[0, :, :, 0].cpu().detach().numpy()) - # ax[1].imshow(reconstructed_cube[0, :, :, 0].cpu().detach().numpy()) - # plt.show() - total_sum_pattern = torch.sum(self.pattern, dim=(1, 2)) - total_half_pattern_equal_1 = torch.sum(torch.ones_like(self.pattern), dim=(1, 2)) / 2 - - print(f"total_sum_pattern {total_sum_pattern}") - print(f"total_half_pattern_equal_1 {total_half_pattern_equal_1}") - - - - loss1 = torch.sqrt(self.loss_fn(reconstructed_cube, ref_cube)) - loss2 = torch.sum(torch.abs((total_sum_pattern - total_half_pattern_equal_1)/(self.pattern.shape[1]*self.pattern.shape[2]))**2) - loss = loss1 - - ssim_loss = self.ssim_loss(torch.clamp(reconstructed_cube.permute(0, 3, 1, 2), 0, 1), ref_cube.permute(0, 3, 1, 2)) - print(f"loss1 {loss1}") - print(f"loss2 {loss2}") - return loss, ssim_loss, reconstructed_cube, ref_cube - - def configure_optimizers(self): - optimizer = torch.optim.Adam(self.parameters(), lr=1e-4) - return { "optimizer":optimizer, - "lr_scheduler":{ - "scheduler":torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, 500, eta_min=1e-6), - "interval": "epoch" - } - } - - def _log_images(self, tag, images, global_step): - # Convert model output to image grid and log to TensorBoard - img_grid = torchvision.utils.make_grid(images) - self.writer.add_image(tag, img_grid, global_step) - - def _convert_output_to_images(self, acquired_images): - - acquired_images = acquired_images.unsqueeze(1) - - # Create a grid of images for visualization - img_grid = torchvision.utils.make_grid(acquired_images) - return img_grid - - def log_gradients(self, step): - for name, param in self.mask_generation.named_parameters(): - if param.grad is not None: - self.writer.add_scalar(f"Gradients/{name}", param.grad.norm(), step) - - - def plot_spectral_filter(self,ref_hyperspectral_cube,recontructed_hyperspectral_cube): - - - batch_size, y,x, lmabda_ = ref_hyperspectral_cube.shape - - # Create a figure with subplots arranged horizontally - fig, axs = plt.subplots(1, batch_size, figsize=(batch_size * 5, 4)) # Adjust figure size as needed - - # Check if batch_size is 1, axs might not be iterable - if batch_size == 1: - axs = [axs] - - # Plot each spectral filter in its own subplot - for i in range(batch_size): - colors = ['b', 'g', 'r'] - for j in range(3): - pix_j_row_value = np.random.randint(0,y) - pix_j_col_value = np.random.randint(0,x) - - pix_j_ref = ref_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - pix_j_reconstructed = recontructed_hyperspectral_cube[i, pix_j_row_value,pix_j_col_value,:].cpu().detach().numpy() - axs[i].plot(pix_j_reconstructed, label="pix reconstructed" + str(j),c=colors[j]) - axs[i].plot(pix_j_ref, label="pix" + str(j), linestyle='--',c=colors[j]) - - axs[i].set_title(f"Reconstruction quality") - - axs[i].set_xlabel("Wavelength index") - axs[i].set_ylabel("pix values") - axs[i].grid(True) - - plt.legend() - # Adjust layout - plt.tight_layout() - - # Create a buffer to save plot - buf = io.BytesIO() - plt.savefig(buf, format='png') - plt.close(fig) - buf.seek(0) - - # Convert PNG buffer to PIL Image - image = Image.open(buf) - - # Convert PIL Image to Tensor - image_tensor = transforms.ToTensor()(image) - return image_tensor - - -def subsample(input, origin_sampling, target_sampling): - [bs, row, col, nC] = input.shape - indices = torch.zeros(len(target_sampling), dtype=torch.int) - for i in range(len(target_sampling)): - sample = target_sampling[i] - idx = torch.abs(origin_sampling-sample).argmin() - indices[i] = idx - return input[:,:,:,indices] - -def expand_mask_3d(mask_batch): - if len(mask_batch.shape)==3: - mask3d = mask_batch.unsqueeze(-1).repeat((1, 1, 1, 28)) - else: - mask3d = mask_batch.repeat((1, 1, 1, 28)) - mask3d = torch.permute(mask3d, (0, 3, 1, 2)) - return mask3d - -class EmptyModule(nn.Module): - def __init__(self): - super().__init__() - self.useless_linear = nn.Linear(1, 1) - def forward(self, x): - return x - - -def pad_tensor(input_tensor, target_shape): - [bs, row, col] = input_tensor.shape - [target_row, target_col] = target_shape - - # Calculate padding for rows - pad_row_total = max(target_row - row, 0) - pad_row_top = pad_row_total // 2 - pad_row_bottom = pad_row_total - pad_row_top - - # Calculate padding for columns - pad_col_total = max(target_col - col, 0) - pad_col_left = pad_col_total // 2 - pad_col_right = pad_col_total - pad_col_left - - # Apply padding - padded_tensor = F.pad(input_tensor, (pad_col_left, pad_col_right, pad_row_top, pad_row_bottom), 'constant', 0) - return padded_tensor - -def crop_tensor(input_tensor, target_shape): - [bs, row, col] = input_tensor.shape - [target_row, target_col] = target_shape - crop_row = (row-target_row)//2 - crop_col = (col-target_col)//2 - return input_tensor[:,crop_row:crop_row+target_row,crop_col:crop_col+target_col] - - -class BinarizeFunction(torch.autograd.Function): - @staticmethod - def forward(ctx, input): - # Forward pass is the binary threshold operation - return (input > 0.5).float() - - @staticmethod - def backward(ctx, grad_output): - # For backward pass, just pass the gradients through unchanged - return grad_output diff --git a/outputs/amici/config_system.yml b/outputs/amici/config_system.yml new file mode 100755 index 0000000..9c06d18 --- /dev/null +++ b/outputs/amici/config_system.yml @@ -0,0 +1,33 @@ +coded aperture: + number of pixels along X: 25 + number of pixels along Y: 25 + pixel size along X: 210 + pixel size along Y: 210 +detector: + number of pixels along X: 112 + number of pixels along Y: 112 + pixel size along X: 71 + pixel size along Y: 71 +infos: + system name: HYACAMEO +spectral range: + number of spectral samples: 10 + wavelength max: 650 + wavelength min: 450 +system architecture: + dispersive element: + A1: 22.990923887808794 + A2: 33.78312991797481 + A3: 22.990923887808794 + alpha_c: 5.892854562568398 + catalog: Schott + delta alpha c: 0 + delta beta c: 0 + glass1: N-BAK4 + glass2: N-SF14 + glass3: N-BAK4 + index evaluation method: cauchy + type: amici + wavelength center: 520.0 + focal lens: 75000 + system type: DD-CASSI diff --git a/outputs/amici/optimization_plot_individual_step1.png b/outputs/amici/optimization_plot_individual_step1.png new file mode 100755 index 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a/outputs/single_prism/optimization_results/step2/score_details_iteration_570.json b/outputs/single_prism/optimization_results/step2/score_details_iteration_570.json new file mode 100755 index 0000000..59da867 --- /dev/null +++ b/outputs/single_prism/optimization_results/step2/score_details_iteration_570.json @@ -0,0 +1,32 @@ +{ + "components non weights": { + "cost_dispersion": 20.0, + "cost_distance_glasses": 1.0, + "cost_deviation": 1e-05, + "cost_distorsion": 1.0, + "cost_thickness": 1.0, + "cost_beam_compression": 1.0, + "cost_non_linearity": 1.0, + "cost_distance_total_intern_reflection": 1.0 + }, + "components with weights": { + "cost_dispersion": 23.82278823852539, + "cost_distance_glasses": 0.0, + "cost_deviation": 9094829.0, + "cost_distorsion": 17092.1796875, + "cost_thickness": 4202.1923828125, + "cost_beam_compression": 1.9052726030349731, + "cost_non_linearity": 904.7328491210938, + "cost_distance_total_intern_reflection": 17281.021484375 + }, + "weighted components": { + 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+ "cost_non_linearity": 894.0025024414062, + "cost_distance_total_intern_reflection": 17139.728515625 + } +} \ No newline at end of file diff --git a/outputs/single_prism/optimization_results/step2/score_details_iteration_70.json b/outputs/single_prism/optimization_results/step2/score_details_iteration_70.json new file mode 100755 index 0000000..533d6c1 --- /dev/null +++ b/outputs/single_prism/optimization_results/step2/score_details_iteration_70.json @@ -0,0 +1,32 @@ +{ + "components non weights": { + "cost_dispersion": 20.0, + "cost_distance_glasses": 1.0, + "cost_deviation": 1e-05, + "cost_distorsion": 1.0, + "cost_thickness": 1.0, + "cost_beam_compression": 1.0, + "cost_non_linearity": 1.0, + "cost_distance_total_intern_reflection": 1.0 + }, + "components with weights": { + "cost_dispersion": 0.6563926935195923, + "cost_distance_glasses": 0.0, + "cost_deviation": 9048709.0, + "cost_distorsion": 13849.0830078125, + "cost_thickness": 4187.044921875, + "cost_beam_compression": 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b/outputs/single_prism/optimization_results/step2/score_details_iteration_90.json new file mode 100755 index 0000000..5e3f279 --- /dev/null +++ b/outputs/single_prism/optimization_results/step2/score_details_iteration_90.json @@ -0,0 +1,32 @@ +{ + "components non weights": { + "cost_dispersion": 20.0, + "cost_distance_glasses": 1.0, + "cost_deviation": 1e-05, + "cost_distorsion": 1.0, + "cost_thickness": 1.0, + "cost_beam_compression": 1.0, + "cost_non_linearity": 1.0, + "cost_distance_total_intern_reflection": 1.0 + }, + "components with weights": { + "cost_dispersion": 0.8665900826454163, + "cost_distance_glasses": 0.0, + "cost_deviation": 9041868.0, + "cost_distorsion": 14072.1455078125, + "cost_thickness": 4184.80224609375, + "cost_beam_compression": 1.9123202562332153, + "cost_non_linearity": 894.4546508789062, + "cost_distance_total_intern_reflection": 17165.693359375 + }, + "weighted components": { + "cost_dispersion": 17.331802368164062, + "cost_distance_glasses": 0.0, + "cost_deviation": 90.4186782836914, + "cost_distorsion": 14072.1455078125, + "cost_thickness": 4184.80224609375, + "cost_beam_compression": 1.9123202562332153, + "cost_non_linearity": 894.4546508789062, + "cost_distance_total_intern_reflection": 17165.693359375 + } +} \ No newline at end of file diff --git a/outputs/single_prism/temp.yml b/outputs/single_prism/temp.yml new file mode 100755 index 0000000..88e8b83 --- /dev/null +++ b/outputs/single_prism/temp.yml @@ -0,0 +1,28 @@ +coded aperture: + number of pixels along X: 25 + number of pixels along Y: 25 + pixel size along X: 210 + pixel size along Y: 210 +detector: + number of pixels along X: 112 + number of pixels along Y: 112 + pixel size along X: 71 + pixel size along Y: 71 +infos: + system name: HYACAMEO +spectral range: + number of spectral samples: 10 + wavelength max: 650 + wavelength min: 450 +system architecture: + dispersive element: + A1: 50.294261877928975 + alpha_c: 41.57785791200926 + catalog: Schott + delta alpha c: 0 + delta beta c: 0 + glass1: N-PSK3 + type: prism + wavelength center: 520.0 + focal lens: 75000 + system type: DD-CASSI diff --git a/playing_with_TF.py b/playing_with_TF.py deleted file mode 100755 index b873dd9..0000000 --- a/playing_with_TF.py +++ /dev/null @@ -1,34 +0,0 @@ -import matplotlib.pyplot as plt - -from simca.functions_general_purpose import load_yaml_config -from simca.CassiSystemOptim import CassiSystemOptim -import torch - -config = "simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml" -config_patterns = load_yaml_config("simca/configs/pattern.yml") - -config_system = load_yaml_config(config) -cassi_system = CassiSystemOptim(system_config=config_system) -cassi_system.propagate_coded_aperture_grid() - -pattern = cassi_system.generate_2D_pattern(config_patterns,nb_of_patterns=1) - - -# Compute the 2D FFT of the tensor -fft_result = torch.fft.fft2(pattern) - -# Calculate the Power Spectrum -power_spectrum = torch.abs(fft_result)**2 - -# Calculate the Geometric Mean of the power spectrum -# Use torch.log and torch.exp for differentiability, adding a small epsilon to avoid log(0) -epsilon = 1e-10 -geometric_mean = torch.exp(torch.mean(torch.log(power_spectrum + epsilon))) - -# Calculate the Arithmetic Mean of the power spectrum -arithmetic_mean = torch.mean(power_spectrum) - -# Compute the Spectral Flatness -spectral_flatness = geometric_mean / arithmetic_mean - -print("spectral_flatness: ", spectral_flatness) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index 612ed2d..fe19143 100755 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,4 @@ h5py -pyqt5 -pyqtgraph pyyaml scipy seaborn @@ -12,6 +10,6 @@ matplotlib opticalglass pyyaml snoop -#torch -#torch-geometric -#torch-cluster \ No newline at end of file +torch +torch-geometric +torch-cluster diff --git a/simca/CassiSystem.py b/simca/CassiSystem.py old mode 100644 new mode 100755 index f88b4ce..e0a30fe --- a/simca/CassiSystem.py +++ b/simca/CassiSystem.py @@ -1,656 +1,49 @@ from simca.OpticalModel import OpticalModel -from simca.functions_acquisition import * -from simca.functions_patterns_generation import * -from simca.functions_scenes import * -from simca.functions_general_purpose import * -from scipy.signal import convolve +from simca.helper import * +import pytorch_lightning as pl -class CassiSystem(): - """Class that contains the cassi system main attributes and methods""" - - def __init__(self, system_config=None, system_config_path=None): - - """ - - Args: - system_config_path (str): path to the configs file - system_config (dict): system configuration - - """ - - self.set_up_system(system_config=system_config, system_config_path=system_config_path) - def update_config(self, system_config_path=None, system_config=None): +class CassiSystem(pl.LightningModule): - """ - Update the system configuration file and re-initialize the grids for the coded aperture and the detector + """Class that contains the cassi system main attributes and methods""" - Args: - system_config_path (str): path to the configs file - system_config (dict): system configuration - Returns: - dict: updated system configuration + def __init__(self, system_config=None,index_estimation_method="cauchy",device="cuda"): - """ + # Initialize LightningModule + pl.LightningModule.__init__(self) + self.to(device) - self.set_up_system(system_config_path=system_config_path, system_config=system_config) + self.system_config = system_config - return self.system_config + self.X_coded_aper_coordinates, self.Y_coded_aper_coordinates = self.generate_grid_coordinates(grid_name="coded aperture") + self.X_detector_coordinates_grid, self.Y_detector_coordinates_grid = self.generate_grid_coordinates(grid_name="detector") + + self.wavelengths, self.nb_of_spectral_samples = self.generate_wavelengths() - def set_up_system(self, system_config_path=None, system_config=None): + self.optical_model = OpticalModel(self.system_config,index_estimation_method=index_estimation_method,device=self.device) - """ - Loading system config & initializing the grids coordinates for the coded aperture and the detector - Args: - system_config_path (str): path to the configs file - system_config (dict): system configuration - """ + def generate_grid_coordinates(self, grid_name): + + X_coordinates, Y_coordinates = self.create_coordinates_grid( + self.system_config[f"{grid_name}"]["number of pixels along X"], + self.system_config[f"{grid_name}"]["number of pixels along Y"], + self.system_config[f"{grid_name}"]["pixel size along X"], + self.system_config[f"{grid_name}"]["pixel size along Y"]) - if system_config_path is not None: - self.system_config = load_yaml_config(system_config_path) - elif system_config is not None: - self.system_config = system_config - self.optical_model = OpticalModel(self.system_config) - self.X_coded_aper_coordinates, self.Y_coded_aper_coordinates = self.create_coordinates_grid( - self.system_config["coded aperture"]["number of pixels along X"], - self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["pixel size along X"], - self.system_config["coded aperture"]["pixel size along Y"]) - - self.X_detector_coordinates_grid, self.Y_detector_coordinates_grid = self.create_coordinates_grid( - self.system_config["detector"]["number of pixels along X"], - self.system_config["detector"]["number of pixels along Y"], - self.system_config["detector"]["pixel size along X"], - self.system_config["detector"]["pixel size along Y"]) + return X_coordinates, Y_coordinates + + def generate_wavelengths(self): + wavelengths = torch.linspace(self.system_config["spectral range"]["wavelength min"], + self.system_config["spectral range"]["wavelength max"], + self.system_config["spectral range"]["number of spectral samples"]) + wavelengths = wavelengths.to(self.device) + nb_of_spectral_samples = wavelengths.shape[0] - def set_wavelengths(self, wavelength_min, wavelength_max, nb_of_spectral_samples): - """ - Set the wavelengths range of the optical system - - Args: - wavelength_min (float): minimum wavelength of the system - wavelength_max (float): maximum wavelength of the system - nb_of_spectral_samples (int): number of spectral samples of the system - Returns: - - """ - self.wavelength_min = wavelength_min - self.wavelength_max = wavelength_max - self.nb_of_spectral_samples = nb_of_spectral_samples - - self.system_wavelengths = np.linspace(self.wavelength_min,self.wavelength_max,self.nb_of_spectral_samples) - - return self.system_wavelengths - - def load_dataset(self, directory, dataset_name): - """ - Loading the dataset and related attributes - - Args: - directory (str): name of the directory containing the dataset - dataset_name (str): dataset name - - Returns: - list: a list containing the dataset (shape= R_dts x C_dts x W_dts), the corresponding wavelengths (shape= W_dts), the labeled dataset, the label names and the ignored labels - """ - - dataset, wavelengths_vec, dataset_labels, label_names, ignored_labels = get_dataset(directory, dataset_name) - - self.dataset = dataset - - ### MODIFICATIONS TO TEST - #self.dataset[20:50,:] /= 10 - #self.dataset[60:120, :] *= 10 - ################################# - - self.dataset_labels = dataset_labels - self.dataset_wavelengths = wavelengths_vec - - # additional attributes - self.dataset_label_names = label_names - self.dataset_ignored_labels = ignored_labels - self.dataset_palette = palette_init(label_names) - - - list_dataset_data = [self.dataset, self.dataset_labels, self.dataset_wavelengths, self.dataset_label_names, - self.dataset_ignored_labels, self.dataset_palette] - - return list_dataset_data - - def interpolate_dataset_along_wavelengths(self, new_wavelengths_sampling, chunk_size): - """ - Interpolate the dataset cube along the wavelength axis to match the system sampling - - Args: - new_wavelengths_sampling (numpy.ndarray): new wavelengths on which to interpolate the dataset (shape = W) - chunk_size (int): chunk size for the multiprocessing - - Returns: - numpy.ndarray : interpolated dataset cube along the wavelength axis (shape = R_dts x C_dts x W) - - """ - try: - self.dataset - except : - raise ValueError("The dataset must be loaded first") - - if self.dataset_wavelengths[0] <= new_wavelengths_sampling[0] and self.dataset_wavelengths[-1] >= new_wavelengths_sampling[-1]: - - self.dataset_interpolated = interpolate_data_along_wavelength(self.dataset,self.dataset_wavelengths,new_wavelengths_sampling, chunk_size) - return self.dataset_interpolated - else: - raise ValueError("The new wavelengths sampling must be inside the dataset wavelengths range") - - def generate_2D_pattern(self, config_pattern, nb_of_patterns=1): - """ - Generate multiple coded aperture 2D patterns based on the "pattern" configuration file - and stack them to match the desired number of patterns. - - Args: - config_pattern (dict): coded-aperture pattern configuration. - nb_of_patterns (int): Number of patterns to generate. - - Returns: - torch.Tensor: Stacked coded-aperture 2D patterns (shape = nb_of_patterns x H x L). - """ - - pattern_list = [] # List to hold individual patterns - - for _ in range(nb_of_patterns): - pattern_type = config_pattern['pattern']['type'] - - if pattern_type == "random": - pattern = generate_random_pattern((self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["number of pixels along X"]), - config_pattern['pattern']['ROM']) - elif pattern_type == "slit": - pattern = generate_slit_pattern((self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["number of pixels along X"]), - config_pattern['pattern']['slit position'], - config_pattern['pattern']['slit width']) - elif pattern_type == "blue-noise type 1": - pattern = generate_blue_noise_type_1_pattern((self.system_config["coded aperture"][ - "number of pixels along Y"], - self.system_config["coded aperture"][ - "number of pixels along X"])) - elif pattern_type == "blue-noise type 2": - pattern = generate_blue_noise_type_2_pattern((self.system_config["coded aperture"][ - "number of pixels along Y"], - self.system_config["coded aperture"][ - "number of pixels along X"])) - elif pattern_type == "custom h5 pattern": - pattern = load_custom_pattern((self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["number of pixels along X"]), - config_pattern['pattern']['file path']) - else: - raise ValueError("Pattern type is not supported, change it in the 'pattern.yml' config file") - - # Assume pattern is a numpy array; convert to tensor - pattern_tensor = torch.from_numpy(pattern) - pattern_list.append(pattern_tensor) - - # Stack all generated pattern tensors along a new dimension - stacked_patterns = torch.stack(pattern_list) # Shape: (nb_of_patterns, y, x) - - self.pattern = stacked_patterns - return stacked_patterns - - def generate_multiple_patterns(self, config_pattern, number_of_patterns): - """ - Generate a list of coded aperture patterns based on the "pattern" configuration file - - Args: - config_pattern (dict): pattern configuration - number_of_patterns (int): number of patterns to generate - - Returns: - list: coded aperture patterns (numpy.ndarray) generated according to the configuration file - """ - list_of_patterns = list() - pattern_type = config_pattern['pattern']['type'] - - if pattern_type == "random": - for i in range(number_of_patterns): - pattern= generate_random_pattern((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"]),config_pattern['pattern']['ROM']) - list_of_patterns.append(pattern) - - elif pattern_type == "slit": - # mmmmh you are weird, why would you want to do that ? - for i in range(number_of_patterns): - pattern= generate_slit_pattern((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"]), - config_pattern['pattern']['slit position'], - config_pattern['pattern']['slit width']) - list_of_patterns.append(pattern) - - elif pattern_type == "LN-random": - list_of_patterns = generate_ln_orthogonal_pattern(size=(self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["number of pixels along X"]), - W=self.system_config["spectral range"]["number of spectral samples"], - N=number_of_patterns) - - elif pattern_type == "blue-noise type 1": - - for i in range(number_of_patterns): - pattern= generate_blue_noise_type_1_pattern((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"])) - list_of_patterns.append(pattern) - - elif pattern_type == "blue-noise type 2": - - for i in range(number_of_patterns): - pattern= generate_blue_noise_type_2_pattern((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"])) - list_of_patterns.append(pattern) - - elif pattern_type == "custom h5": - list_of_patterns = load_custom_pattern((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"]), - config_pattern['pattern']['file path']) - - else: - print("pattern type is not supported") - list_of_patterns = None - - self.list_of_patterns = list_of_patterns - - return self.list_of_patterns - - - - def generate_filtering_cube(self): - """ - Generate filtering cube : each slice of the cube is a propagated pattern interpolated on the detector grid - - Returns: - numpy.ndarray: filtering cube generated according to the optical system & the pattern configuration (R x C x W) - - """ - - self.filtering_cube = interpolate_data_on_grid_positions(data=self.pattern, - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) + return wavelengths, nb_of_spectral_samples - def generate_filtering_cube_torch(self): - """ - Generate filtering cube : each slice of the cube is a propagated pattern interpolated on the detector grid - - Returns: - numpy.ndarray: filtering cube generated according to the optical system & the pattern configuration (R x C x W) - - """ - - self.filtering_cube = interpolate_data_on_grid_positions_torch(data=self.pattern, - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) - - - return self.filtering_cube - - def generate_multiple_filtering_cubes(self, number_of_patterns): - """ - Generate multiple filtering cubes, each cube corresponds to a pattern, and for each pattern, each slice is a propagated coded apertureinterpolated on the detector grid - - Args: - number_of_patterns (int): number of patterns to generate - Returns: - list: filtering cubes generated according to the current optical system and the pattern configuration - - """ - self.list_of_filtering_cubes = [] - - for idx in range(number_of_patterns): - - self.filtering_cube = interpolate_data_on_grid_positions(data=self.list_of_patterns[idx], - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) - - self.list_of_filtering_cubes.append(self.filtering_cube) - - return self.list_of_filtering_cubes - - def generate_multiple_filtering_cubes_torch(self, number_of_patterns): - """ - Generate multiple filtering cubes, each cube corresponds to a pattern, and for each pattern, each slice is a propagated coded apertureinterpolated on the detector grid - - Args: - number_of_patterns (int): number of patterns to generate - Returns: - list: filtering cubes generated according to the current optical system and the pattern configuration - - """ - self.list_of_filtering_cubes = [] - - for idx in range(number_of_patterns): - - self.filtering_cube = interpolate_data_on_grid_positions_torch(data=self.list_of_patterns[idx], - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) - - self.list_of_filtering_cubes.append(self.filtering_cube) - - return self.list_of_filtering_cubes - - def image_acquisition(self, use_psf=False, chunck_size=50): - """ - Run the acquisition/measurement process depending on the cassi system type - - Args: - chunck_size (int): default block size for the interpolation - - Returns: - numpy.ndarray: compressed measurement (R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths(self.optical_model.system_wavelengths, chunck_size) - - if dataset is None: - return None - dataset_labels = self.dataset_labels - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - - try: - self.filtering_cube - except: - return print("Please generate filtering cube first") - - scene = match_dataset_to_instrument(dataset, self.filtering_cube) - - measurement_in_3D = generate_dd_measurement(scene, self.filtering_cube, chunck_size) - - self.last_filtered_interpolated_scene = measurement_in_3D - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube) - self.scene_labels = scene_labels - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - scene = match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop) - - pattern_crop = crop_center(self.pattern, scene.shape[1], scene.shape[0]) - - filtered_scene = scene * np.tile(pattern_crop[..., np.newaxis], (1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop) - - sd_measurement = interpolate_data_on_grid_positions(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - - self.last_filtered_interpolated_scene = sd_measurement - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = match_dataset_labels_to_instrument(dataset_labels, self.last_filtered_interpolated_scene) - self.scene_labels = scene_labels - - self.panchro = np.sum(self.interpolated_scene, axis=2) - - if use_psf: - self.apply_psf() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.measurement = np.sum(self.last_filtered_interpolated_scene, axis=2) - - - return self.measurement - - def image_acquisition_torch(self, use_psf=False, chunck_size=50): - """ - Run the acquisition/measurement process depending on the cassi system type - - Args: - chunck_size (int): default block size for the interpolation - - Returns: - numpy.ndarray: compressed measurement (R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths_torch(self.optical_model.system_wavelengths, chunck_size) - - if dataset is None: - return None - dataset_labels = self.dataset_labels - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - - try: - self.filtering_cube - except: - return print("Please generate filtering cube first") - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, self.filtering_cube)) - - measurement_in_3D = generate_dd_measurement_torch(scene, self.filtering_cube, chunck_size) - - self.last_filtered_interpolated_scene = measurement_in_3D - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop)) - - pattern_crop = crop_center(self.pattern, scene.shape[1], scene.shape[0]) - - filtered_scene = scene * pattern_crop[..., None].repeat((1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop, use_torch = True) - - sd_measurement = interpolate_data_on_grid_positions_torch(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - - self.last_filtered_interpolated_scene = sd_measurement - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.last_filtered_interpolated_scene)) - self.scene_labels = scene_labels - - self.panchro = torch.sum(self.interpolated_scene, dim=2) - - if use_psf: - self.apply_psf_torch() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.measurement = torch.sum(self.last_filtered_interpolated_scene, dim=2) - - - return self.measurement - - def multiple_image_acquisitions(self, use_psf=False, nb_of_filtering_cubes=1,chunck_size=50): - """ - Run the acquisition process depending on the cassi system type - - Args: - chunck_size (int): default block size for the dataset - - Returns: - list: list of compressed measurements (list of numpy.ndarray of size R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths(self.optical_model.system_wavelengths, chunck_size) - if dataset is None: - return None - dataset_labels = self.dataset_labels - - self.list_of_filtered_scenes = [] - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - try: - self.list_of_filtering_cubes - except: - return print("Please generate list of filtering cubes first") - - scene = match_dataset_to_instrument(dataset, self.list_of_filtering_cubes[0]) - - if dataset_labels is not None: - scene_labels = match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube) - self.scene_labels = scene_labels - - self.interpolated_scene = scene - - for i in range(nb_of_filtering_cubes): - - filtered_scene = generate_dd_measurement(scene, self.list_of_filtering_cubes[i], chunck_size) - self.list_of_filtered_scenes.append(filtered_scene) - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - - scene = match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop) - - if dataset_labels is not None: - scene_labels = match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube) - self.scene_labels = scene_labels - - self.interpolated_scene = scene - - for i in range(nb_of_filtering_cubes): - - mask_crop = crop_center(self.list_of_patterns[i], scene.shape[1], scene.shape[0]) - - filtered_scene = scene * np.tile(mask_crop[..., np.newaxis], (1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop) - - sd_measurement_cube = interpolate_data_on_grid_positions(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - self.list_of_filtered_scenes.append(sd_measurement_cube) - - self.panchro = np.sum(self.interpolated_scene, axis=2) - - if use_psf: - self.apply_psf() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.list_of_measurements = [] - for i in range(nb_of_filtering_cubes): - self.list_of_measurements.append(np.sum(self.list_of_filtered_scenes[i], axis=2)) - - return self.list_of_measurements - - def multiple_image_acquisitions_torch(self, use_psf=False, nb_of_filtering_cubes=1,chunck_size=50): - """ - Run the acquisition process depending on the cassi system type - - Args: - chunck_size (int): default block size for the dataset - - Returns: - list: list of compressed measurements (list of numpy.ndarray of size R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths_torch(self.optical_model.system_wavelengths, chunck_size) - if dataset is None: - return None - dataset_labels = self.dataset_labels - - self.list_of_filtered_scenes = [] - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - try: - self.list_of_filtering_cubes - except: - return print("Please generate list of filtering cubes first") - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, self.list_of_filtering_cubes[0])) - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - self.interpolated_scene = scene - - for i in range(nb_of_filtering_cubes): - - filtered_scene = generate_dd_measurement_torch(scene, self.list_of_filtering_cubes[i], chunck_size) - self.list_of_filtered_scenes.append(filtered_scene) - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop)) - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - self.interpolated_scene = scene - - for i in range(nb_of_filtering_cubes): - - mask_crop = crop_center(self.list_of_patterns[i], scene.shape[1], scene.shape[0]) - - filtered_scene = scene * mask_crop[..., None].repeat((1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop, use_torch = True) - - sd_measurement_cube = interpolate_data_on_grid_positions_torch(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - self.list_of_filtered_scenes.append(sd_measurement_cube) - - self.panchro = torch.sum(self.interpolated_scene, dim=2) - - if use_psf: - self.apply_psf_torch() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.list_of_measurements = [] - for i in range(nb_of_filtering_cubes): - self.list_of_measurements.append(torch.sum(self.list_of_filtered_scenes[i], dim=2)) - - return self.list_of_measurements - - def create_coordinates_grid(self, nb_of_pixels_along_x, nb_of_pixels_along_y, delta_x, delta_y): """ @@ -672,125 +65,94 @@ def create_coordinates_grid(self, nb_of_pixels_along_x, nb_of_pixels_along_y, de # Create a two-dimensional grid of coordinates X_input_grid, Y_input_grid = np.meshgrid(x, y) - return X_input_grid, Y_input_grid + return torch.from_numpy(X_input_grid).float().to(self.device), torch.from_numpy(Y_input_grid).float().to(self.device) + - def propagate_coded_aperture_grid(self, X_input_grid=None, Y_input_grid=None,use_torch=False): + + def update_optical_model(self, system_config=None): """ - Propagate the coded_aperture pattern through one CASSI system + Update the optical model of the system Args: - X_input_grid (numpy.ndarray): x coordinates grid - Y_input_grid (numpy.ndarray): y coordinates grid + system_config (dict): configuration of the system Returns: - tuple: propagated coded aperture x coordinates grid in the detector plane (3D numpy.ndarray), propagated coded aperture y coordinates grid in the detector plane (3D numpy.ndarray), 1D array of propagated coded aperture x coordinates (numpy.ndarray), 1D array of system wavelengths (numpy.ndarray) + None """ + if system_config is not None: + self.system_config = system_config + self.optical_model = OpticalModel(self.system_config) - if X_input_grid is None: - X_input_grid = self.X_coded_aper_coordinates - if Y_input_grid is None: - Y_input_grid = self.Y_coded_aper_coordinates - - propagation_type = self.system_config["system architecture"]["propagation type"] + def propagate_coded_aperture_grid(self): + + # if self.device == "cuda": + # wavelengths = self.wavelengths.cpu() + # else: + # wavelengths = self.wavelengths + if self.optical_model.index_estimation_method == "cauchy": + n1 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths,self.optical_model.nd1,self.optical_model.vd1) + n2 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths,self.optical_model.nd2,self.optical_model.vd2) + n3 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths,self.optical_model.nd3,self.optical_model.vd3) + if self.optical_model.index_estimation_method == "sellmeier": + n1 = self.optical_model.sellmeier(self.wavelengths,self.optical_model.glass1).clone().detach().requires_grad_(True).to(device=self.device) + try: + n2 = self.optical_model.sellmeier(self.wavelengths,self.optical_model.glass2).clone().detach().requires_grad_(True).to(device=self.device) + except: + n2 = torch.tensor(1,device=self.device).expand_as(self.wavelengths) + try: + n3 = self.optical_model.sellmeier(self.wavelengths,self.optical_model.glass3).clone().detach().requires_grad_(True).to(device=self.device) + except: + n3 = torch.tensor(1,device=self.device).expand_as(self.wavelengths) - if propagation_type == "simca" and use_torch == False: - self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture = self.optical_model.propagation_with_distorsions(X_input_grid, Y_input_grid) - if propagation_type == "simca" and use_torch == True: - self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture = self.optical_model.propagation_with_distorsions_torch(X_input_grid, Y_input_grid) + n1 = n1.to(self.device) + n2 = n2.to(self.device) + n3 = n3.to(self.device) - if propagation_type == "higher-order": - self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture = self.optical_model.propagation_with_no_distorsions(X_input_grid, Y_input_grid) - self.optical_model.check_if_sampling_is_sufficiant() + X_input_grid = torch.from_numpy(self.X_coded_aper_coordinates) if isinstance(self.X_coded_aper_coordinates, np.ndarray) else self.X_coded_aper_coordinates + Y_input_grid = torch.from_numpy(self.Y_coded_aper_coordinates) if isinstance(self.Y_coded_aper_coordinates, np.ndarray) else self.Y_coded_aper_coordinates + wavelength_vec = torch.from_numpy(self.wavelengths) if isinstance(self.wavelengths, np.ndarray) else self.wavelengths + n1_vec = torch.from_numpy(n1) if isinstance(n1, np.ndarray) else n1 + n2_vec = torch.from_numpy(n2) if isinstance(n2, np.ndarray) else n2 + n3_vec = torch.from_numpy(n3) if isinstance(n3, np.ndarray) else n3 - if use_torch == False: - self.X_coordinates_propagated_coded_aperture = np.nan_to_num(self.X_coordinates_propagated_coded_aperture) - self.Y_coordinates_propagated_coded_aperture = np.nan_to_num(self.Y_coordinates_propagated_coded_aperture) - else: - self.X_coordinates_propagated_coded_aperture = torch.nan_to_num(self.X_coordinates_propagated_coded_aperture) - self.Y_coordinates_propagated_coded_aperture = torch.nan_to_num(self.Y_coordinates_propagated_coded_aperture) + X_input_grid_3D = X_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples).to(self.device) + Y_input_grid_3D = Y_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples).to(self.device) + lba_3D = wavelength_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1).to(self.device) + n1_3D = n1_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) + n2_3D = n2_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) + n3_3D = n3_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) + - return self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture, self.optical_model.system_wavelengths + self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture = self.optical_model.propagate(X_input_grid_3D, Y_input_grid_3D, lba_3D, n1_3D, n2_3D, n3_3D) + return self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture - def apply_psf(self): - """ - Apply the PSF to the last measurement + def retropropagate_coded_aperture_grid(self): - Returns: - numpy.ndarray: last measurement cube convolved with by PSF (shape= R x C x W). Each slice of the 3D filtered scene is convolved with the PSF - """ - if (self.optical_model.psf is not None) and (self.last_filtered_interpolated_scene is not None): - # Expand the dimensions of the 2D matrix to match the 3D matrix - psf_3D = np.expand_dims(self.optical_model.psf, axis=-1) + n1 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths ,self.optical_model.nd1,self.optical_model.vd1).to(self.device) + n2 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths ,self.optical_model.nd2,self.optical_model.vd2).to(self.device) + n3 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths ,self.optical_model.nd3,self.optical_model.vd3).to(self.device) - # Perform the convolution using convolve - result = convolve(self.last_filtered_interpolated_scene, psf_3D, mode='same') - result_panchro = convolve(self.panchro, self.optical_model.psf, mode='same') + X_input_grid = torch.from_numpy(self.X_detector_coordinates_grid) if isinstance(self.X_detector_coordinates_grid, np.ndarray) else self.X_detector_coordinates_grid + Y_input_grid = torch.from_numpy(self.Y_detector_coordinates_grid) if isinstance(self.Y_detector_coordinates_grid, np.ndarray) else self.Y_detector_coordinates_grid + wavelength_vec = torch.from_numpy(self.wavelengths) if isinstance(self.wavelengths, np.ndarray) else self.wavelengths + n1_vec = torch.from_numpy(n1) if isinstance(n1, np.ndarray) else n1 + n2_vec = torch.from_numpy(n2) if isinstance(n2, np.ndarray) else n2 + n3_vec = torch.from_numpy(n3) if isinstance(n3, np.ndarray) else n3 - else: - print("No PSF or last measurement to apply PSF") - result = self.last_filtered_interpolated_scene - result_panchro = self.panchro + X_input_grid_3D = X_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples).to(self.device) + Y_input_grid_3D = Y_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples).to(self.device) + lba_3D = wavelength_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1).to(self.device) + n1_3D = n1_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) + n2_3D = n2_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) + n3_3D = n3_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) + - self.last_filtered_interpolated_scene = result - self.panchro = result_panchro + self.retro_X_detect_coords, self.retro_Y_detect_coords = self.optical_model.repropagate(X_input_grid_3D, Y_input_grid_3D, lba_3D, n1_3D, n2_3D, n3_3D) - return self.last_filtered_interpolated_scene + return self.retro_X_detect_coords, self.retro_Y_detect_coords + + - def apply_psf_torch(self): - """ - Apply the PSF to the last measurement - - Returns: - numpy.ndarray: last measurement cube convolved with by PSF (shape= R x C x W). Each slice of the 3D filtered scene is convolved with the PSF - """ - if (self.optical_model.psf is not None) and (self.last_filtered_interpolated_scene is not None): - # Expand the dimensions of the 2D matrix to match the 3D matrix - psf_3D = self.optical_model.psf[..., None] - - # Perform the convolution using convolve - result = torch.nn.functional.conv3d(self.last_filtered_interpolated_scene[None, None, ...], torch.flip(psf_3D, (0,1,2))[None, None, ...], padding = tuple((np.array(psf_3D.shape)-1)//2)).squeeze(0,1) - result_panchro = torch.nn.functional.conv2d(self.panchro[None, None, ...], torch.flip(self.optical_model.psf, (0,1))[None, None, ...], padding = tuple((np.array(self.optical_model.psf.shape)-1)//2)).squeeze(0,1) - - else: - print("No PSF or last measurement to apply PSF") - result = self.last_filtered_interpolated_scene - result_panchro = self.panchro - - self.last_filtered_interpolated_scene = result - self.panchro = result_panchro - - return self.last_filtered_interpolated_scene - - - - def save_acquisition(self, config_pattern, config_acquisition): - """ - Save the all data related to an acquisition - - Args: - config_pattern (dict): configuration dictionary related to pattern generation - config_acquisition (dict): configuration dictionary related to acquisition parameters - - """ - - self.result_directory = initialize_acquisitions_directory(config_acquisition) - - save_config_file("config_system",self.system_config,self.result_directory) - save_config_file("config_pattern",config_pattern,self.result_directory) - save_config_file("config_acquisition",config_acquisition,self.result_directory) - save_data_in_hdf5("interpolated_scene",self.interpolated_scene, self.result_directory) - try: - save_data_in_hdf5("scene_labels",self.scene_labels,self.result_directory) - except : - pass - save_data_in_hdf5("filtered_interpolated_scene",self.last_filtered_interpolated_scene, self.result_directory) - save_data_in_hdf5("measurement",self.measurement,self.result_directory) - save_data_in_hdf5("panchro",self.panchro,self.result_directory) - save_data_in_hdf5("filtering_cube",self.filtering_cube,self.result_directory) - save_data_in_hdf5("pattern",self.pattern,self.result_directory) - save_data_in_hdf5("wavelengths",self.optical_model.system_wavelengths,self.result_directory) - - print("Acquisition saved in " + self.result_directory) diff --git a/simca/CassiSystemOptim.py b/simca/CassiSystemOptim.py deleted file mode 100755 index 2ec8f8d..0000000 --- a/simca/CassiSystemOptim.py +++ /dev/null @@ -1,483 +0,0 @@ -from simca.OpticalModelTorch import OpticalModelTorch -from simca.functions_acquisition import * -from simca.functions_patterns_generation import * -from simca.functions_scenes import * -from simca.functions_scenes_torch import * -from simca.functions_general_purpose import * -from simca.CassiSystem import CassiSystem -from simca.functions_acquisition_torch import * -import time - - -class CassiSystemOptim(CassiSystem): - """Class that contains the cassi system main attributes and methods""" - - def __init__(self, system_config=None): - - super().__init__(system_config=system_config) - - self.system_config = system_config - - self.X_coded_aper_coordinates, self.Y_coded_aper_coordinates = self.create_coordinates_grid( - self.system_config["coded aperture"]["number of pixels along X"], - self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["pixel size along X"], - self.system_config["coded aperture"]["pixel size along Y"]) - - self.X_detector_coordinates_grid, self.Y_detector_coordinates_grid = self.create_coordinates_grid( - self.system_config["detector"]["number of pixels along X"], - self.system_config["detector"]["number of pixels along Y"], - self.system_config["detector"]["pixel size along X"], - self.system_config["detector"]["pixel size along Y"]) - - - self.wavelengths = self.set_wavelengths(self.system_config["spectral range"]["wavelength min"], - self.system_config["spectral range"]["wavelength max"], - self.system_config["spectral range"]["number of spectral samples"]) - self.optical_model = OpticalModelTorch(self.system_config) - - - self.empty_grid = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["number of pixels along X"])) - # self.array_x_positions = torch.rand(-1,1,self.system_config["coded aperture"]["number of pixels along X"]) - self.array_x_positions = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"]))+ 0.5 - - def create_coordinates_grid(self, nb_of_pixels_along_x, nb_of_pixels_along_y, delta_x, delta_y): - """ - Create a coordinates grid for a given number of samples along X and Y axis and a given pixel size - - Args: - nb_of_pixels_along_x (int): number of samples along X axis - nb_of_pixels_along_y (int): number of samples along Y axis - delta_x (float): pixel size along X axis - delta_y (float): pixel size along Y axis - - Returns: - tuple: X coordinates grid (numpy.ndarray) and Y coordinates grid (numpy.ndarray) - """ - x = np.arange(-(nb_of_pixels_along_x-1) * delta_x / 2, (nb_of_pixels_along_x+1) * delta_x / 2,delta_x) - y = np.arange(-(nb_of_pixels_along_y-1) * delta_y / 2, (nb_of_pixels_along_y+1) * delta_y / 2, delta_y) - - - # Create a two-dimensional grid of coordinates - X_input_grid, Y_input_grid = np.meshgrid(x, y) - - return torch.from_numpy(X_input_grid).float(), torch.from_numpy(Y_input_grid).float() - - def set_wavelengths(self, wavelength_min, wavelength_max, nb_of_spectral_samples): - """ - Set the wavelengths range of the optical system - - Args: - wavelength_min (float): minimum wavelength of the system - wavelength_max (float): maximum wavelength of the system - nb_of_spectral_samples (int): number of spectral samples of the system - Returns: - - """ - self.wavelength_min = wavelength_min - self.wavelength_max = wavelength_max - self.nb_of_spectral_samples = nb_of_spectral_samples - - self.system_wavelengths = torch.linspace(self.wavelength_min,self.wavelength_max,self.nb_of_spectral_samples) - - return self.system_wavelengths - - def update_optical_model(self, system_config=None): - """ - Update the optical model of the system - - Args: - system_config (dict): configuration of the system - - Returns: - None - """ - - if system_config is not None: - self.system_config = system_config - - self.optical_model = OpticalModelTorch(self.system_config) - - def propagate_coded_aperture_grid(self): - - if self.optical_model.continuous_glass_materials1 == True: - n1 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths,self.optical_model.nd1,self.optical_model.vd1) - else: - n1 = self.optical_model.glass1.calc_rindex(self.wavelengths) - if self.optical_model.continuous_glass_materials2 == True: - n2 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths,self.optical_model.nd2,self.optical_model.vd2) - else: - n2 = self.optical_model.glass2.calc_rindex(self.wavelengths) - if self.optical_model.continuous_glass_materials3 == True: - n3 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths,self.optical_model.nd3,self.optical_model.vd3) - else: - n3 = self.optical_model.glass3.calc_rindex(self.wavelengths) - - # n1 = np.repeat(1.5, self.wavelengths.shape[0]) - # n2 = np.repeat(1.8, self.wavelengths.shape[0]) - # n3 = np.repeat(1.5, self.wavelengths.shape[0]) - - - - X_input_grid = torch.from_numpy(self.X_coded_aper_coordinates) if isinstance(self.X_coded_aper_coordinates, np.ndarray) else self.X_coded_aper_coordinates - Y_input_grid = torch.from_numpy(self.Y_coded_aper_coordinates) if isinstance(self.Y_coded_aper_coordinates, np.ndarray) else self.Y_coded_aper_coordinates - wavelength_vec = torch.from_numpy(self.wavelengths) if isinstance(self.wavelengths, np.ndarray) else self.wavelengths - n1_vec = torch.from_numpy(n1) if isinstance(n1, np.ndarray) else n1 - n2_vec = torch.from_numpy(n2) if isinstance(n2, np.ndarray) else n2 - n3_vec = torch.from_numpy(n3) if isinstance(n3, np.ndarray) else n3 - - X_input_grid_3D = X_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples) - Y_input_grid_3D = Y_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples) - lba_3D = wavelength_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n1_3D = n1_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n2_3D = n2_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n3_3D = n3_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - - starting_time = time.time() - - self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture = self.optical_model.propagate(X_input_grid_3D, Y_input_grid_3D, lba_3D, n1_3D, n2_3D, n3_3D) - - return self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture - - def generate_filtering_cube(self): - """ - Generate filtering cube : each slice of the cube is a propagated pattern interpolated on the detector grid - - Returns: - numpy.ndarray: filtering cube generated according to the optical system & the pattern configuration (R x C x W) - - """ - - self.filtering_cube = interpolate_data_on_grid_positions_torch(data=self.pattern, - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) - - return self.filtering_cube - - - def image_acquisition(self, hyperspectral_cube, wavelengths,use_psf=False, chunck_size=50): - """ - Run the acquisition/measurement process depending on the cassi system type - - Args: - chunck_size (int): default block size for the interpolation - - Returns: - numpy.ndarray: compressed measurement (R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths_torch(hyperspectral_cube, wavelengths,self.wavelengths, chunck_size) - - if dataset is None: - return None - dataset_labels = self.dataset_labels - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - - try: - self.filtering_cube - except: - return print("Please generate filtering cube first") - - scene = match_dataset_to_instrument(dataset, self.filtering_cube) - scene = torch.from_numpy(match_dataset_to_instrument(dataset, self.filtering_cube)).to(self.device) if isinstance(scene, np.ndarray) else scene.to(self.device) - - measurement_in_3D = generate_dd_measurement_torch(scene, self.filtering_cube, chunck_size) - - self.last_filtered_interpolated_scene = measurement_in_3D - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop)) - - pattern_crop = crop_center(self.pattern, scene.shape[1], scene.shape[0]) - - filtered_scene = scene * pattern_crop[..., None].repeat((1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop, use_torch = True) - - sd_measurement = interpolate_data_on_grid_positions_torch(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - - self.last_filtered_interpolated_scene = sd_measurement - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.last_filtered_interpolated_scene)) - self.scene_labels = scene_labels - - self.panchro = torch.sum(self.interpolated_scene, dim=2) - - if use_psf: - self.apply_psf_torch() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.measurement = torch.sum(self.last_filtered_interpolated_scene, dim=2) - - return self.measurement - - def generate_custom_pattern_parameters_slit(self, position=0.5): - # Position is a float: 0 means slit is on the left edge, 1 means the slit is on the right edge - self.array_x_positions = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"]))+ position - self.array_x_positions = self.array_x_positions.to(self.device) - return self.array_x_positions - - def generate_custom_pattern_parameters_slit_width(self, nb_slits=1, nb_rows=1, start_width=1): - # Situation where we have nb_slits per row, and nb_rows rows of slits - # Values in self.array_x_positions correspond to the width of the slit - # self.array_x_positions is of shape (self.system_config["coded aperture"]["number of pixels along Y"], nb_rows) - - self.array_x_positions = torch.zeros((nb_slits, nb_rows))+start_width # Every slit starts with the same width - self.array_x_positions = self.array_x_positions.to(self.device) - self.array_x_positions_normalized = torch.zeros((nb_slits, nb_rows))+start_width - return self.array_x_positions - - def generate_custom_slit_pattern_width(self, start_pattern = "line", start_position = 0): - nb_slits, nb_rows = self.array_x_positions.shape - pos_slits = self.system_config["coded aperture"]["number of pixels along X"]//(nb_slits+1) # Equally spaced slits - height_slits = self.system_config["coded aperture"]["number of pixels along Y"]//nb_rows # Same length slits - - self.pattern = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"])).to(self.device) # Pattern of correct size - if start_pattern == "line": - if start_position != 0: - start_position = start_position - pos_slits/self.system_config["coded aperture"]["number of pixels along X"] - for j in range(nb_slits): - for i in range(nb_rows): - top_pad = i*height_slits # Padding necessary above slit (j,i) - if i == nb_rows-1: - bottom_pad = 0 # Padding necessary below slit (j,i) - # In that case, the last slit might be longer than the other ones in case size_X isn't divisible by nb_rows - array_x_pos = torch.zeros((height_slits+self.system_config["coded aperture"]["number of pixels along Y"] % nb_rows)) + start_position + (j+1)*pos_slits/self.system_config["coded aperture"]["number of pixels along X"] - else: - # Set the position of the slit (j,i) - array_x_pos = torch.zeros((height_slits)) + start_position + (j+1)*pos_slits/self.system_config["coded aperture"]["number of pixels along X"] - bottom_pad = (nb_rows - i-1)*height_slits + self.system_config["coded aperture"]["number of pixels along Y"] % nb_rows # Padding necessary below slit (j,i) - top_pad = i*height_slits - - # Create a grid to represent positions - grid_positions = torch.arange(self.empty_grid.shape[1], dtype=torch.float32) - # Expand dimensions for broadcasting - expanded_x_positions = (array_x_pos.unsqueeze(-1)) * (self.empty_grid.shape[1]-1) - expanded_grid_positions = grid_positions.unsqueeze(0) - - # Apply Gaussian-like function - sigma = (self.array_x_positions[j,i]+1)/2 - gaussian = torch.exp(-(((expanded_grid_positions - expanded_x_positions)) ** 2) / (2 * sigma ** 2)) - - padded = torch.nn.functional.pad(gaussian, (0,0,top_pad,bottom_pad)) # padding: left - right - top - bottom - - # Normalize to make sure the maximum value is 1 - self.pattern = self.pattern + padded/padded.max() - elif start_pattern == "corrected": - for j in range(nb_slits): - for i in range(nb_rows): - top_pad = i*height_slits # Padding necessary above slit (j,i) - if i == nb_rows-1: - bottom_pad = 0 # Padding necessary below slit (j,i) - # In that case, the last slit might be longer than the other ones in case size_X isn't divisible by nb_rows - else: - # Set the position of the slit (j,i) - bottom_pad = (nb_rows - i-1)*height_slits + self.system_config["coded aperture"]["number of pixels along Y"] % nb_rows # Padding necessary below slit (j,i) - top_pad = i*height_slits - """ array_x_pos = torch.tensor(start_position[i]) - - # Create a grid to represent positions - grid_positions = torch.arange(self.empty_grid.shape[1], dtype=torch.float32) - # Expand dimensions for broadcasting - expanded_x_positions = (array_x_pos.unsqueeze(-1)) * (self.empty_grid.shape[1]-1) - expanded_grid_positions = grid_positions.unsqueeze(0) - - # Apply Gaussian-like function - sigma = (self.array_x_positions[j,i]+1)/2 - gaussian = torch.exp(-(((expanded_grid_positions - expanded_x_positions)) ** 2) / (2 * sigma ** 2)) - - padded = torch.nn.functional.pad(gaussian, (0,0,top_pad,bottom_pad)) # padding: left - right - top - bottom - - # Normalize to make sure the maximum value is 1 - self.pattern = self.pattern + padded/padded.max() """ - - c = start_position[i].clone().detach() # center of the slit - #d = ((torch.tanh(1.1*self.array_x_positions[j,i])+1)/2)/2 # width of the slit at pos - d = self.array_x_positions[j,i]/2 # width of the slit at pos - m = (c-d)*(self.system_config["coded aperture"]["number of pixels along X"]-1) # left bound - M = (c+d)*(self.system_config["coded aperture"]["number of pixels along X"]-1) # right bound - rect = torch.arange(self.system_config["coded aperture"]["number of pixels along X"]).to(self.device) - clamp_M = torch.clamp(M-rect, 0, 1) - - clamp_m = torch.clamp(rect-m, 0, 1) - diff = 1-clamp_m - reg = torch.where(diff < 1, diff, -1) - clamp_m = torch.where(reg!=0, reg, 1) - clamp_m = torch.where(clamp_m!=-1, clamp_m, 0) - clamp_m = torch.roll(clamp_m, -1) - clamp_m[-1]=1 - - rect = clamp_M - clamp_m +1 - rect = torch.where(rect!=2, rect, 0) - rect = torch.where(rect <= 1, rect, rect-1) - #rect = torch.clamp(-(rect-m)*(rect-M)+1,0,1).to(self.device) - - gaussian_range = torch.arange(self.system_config["coded aperture"]["number of pixels along X"], dtype=torch.float32) - center_pos = 0.5*(len(gaussian_range)-1) - sigma = 1.5 - gaussian_peaks = torch.exp(-((center_pos - gaussian_range) ** 2) / (2 * sigma ** 2)).to(self.device) - gaussian = gaussian_peaks /gaussian_peaks.max() - res = torch.nn.functional.conv1d(rect.unsqueeze(0), gaussian.unsqueeze(0).unsqueeze(0), padding = (len(gaussian_range)-1)//2).squeeze().to(self.device) - - res = res/res.max() - self.pattern[i, :] = self.pattern[i, :] + res - - # Normalize to make sure the maximum value is 1 - self.pattern = self.pattern / self.pattern.max(dim=1).values.unsqueeze(-1) - - return self.pattern - - def generate_custom_slit_pattern(self): - - # Create a grid to represent positions - grid_positions = torch.arange(self.empty_grid.shape[1], dtype=torch.float32).to(self.device) - # Expand dimensions for broadcasting - expanded_x_positions = ((self.array_x_positions.unsqueeze(-1)) * (self.empty_grid.shape[1]-1)).to(self.device) - expanded_grid_positions = grid_positions.unsqueeze(0).to(self.device) - - # Apply Gaussian-like function - # Adjust 'sigma' to control the sharpness - sigma = 1.5 - gaussian_peaks = torch.exp(-((expanded_grid_positions - expanded_x_positions) ** 2) / (2 * sigma ** 2)).to(self.device) - - # Normalize to make sure the maximum value is 1 - self.pattern = gaussian_peaks / gaussian_peaks.max() - return self.pattern - - - - # def generate_custom_slit_pattern(self): - # """ - # Generate a custom slit pattern - - # Args: - # array_x_positions (numpy.ndarray): array of the x positions of the slits between -1 and 1 - - # Returns: - # numpy.ndarray: generated slit pattern - # """ - # pattern = torch.clone(self.empty_grid) - # self.array_x_positions += 1 - # self.array_x_positions *= self.empty_grid.shape[1] // 2 - # self.array_x_positions = self.array_x_positions.type(torch.int32) - # for i in range(self.array_x_positions.shape[0]): - # pattern[0, self.array_x_positions[i]] = 1 - - # return self.pattern - - def interpolate_dataset_along_wavelengths_torch(self, hyperspectral_cube, wavelengths,new_wavelengths_sampling, chunk_size): - """ - Interpolate the dataset cube along the wavelength axis to match the system sampling - - Args: - new_wavelengths_sampling (numpy.ndarray): new wavelengths on which to interpolate the dataset (shape = W) - chunk_size (int): chunk size for the multiprocessing - - Returns: - numpy.ndarray : interpolated dataset cube along the wavelength axis (shape = R_dts x C_dts x W) - - """ - try: - self.dataset - except : - self.dataset = hyperspectral_cube - self.dataset_wavelengths = wavelengths - - #print(self.dataset.shape) - #print(self.dataset_wavelengths.shape) - - self.dataset_wavelengths = torch.from_numpy(self.dataset_wavelengths) if isinstance(self.dataset_wavelengths, np.ndarray) else self.dataset_wavelengths - new_wavelengths_sampling = torch.from_numpy(new_wavelengths_sampling).float() if isinstance(new_wavelengths_sampling, np.ndarray) else new_wavelengths_sampling - self.dataset = torch.from_numpy(self.dataset).float() if isinstance(self.dataset, np.ndarray) else self.dataset - - self.dataset_interpolated = self.interpolate_data_along_wavelength_torch(self.dataset,self.dataset_wavelengths,new_wavelengths_sampling,chunk_size) - return self.dataset_interpolated - - def interpolate_data_along_wavelength_torch(self,data, current_sampling, new_sampling, chunk_size=50): - """Interpolate the input 3D data along a new sampling in the third axis. - - Args: - data (numpy.ndarray): 3D data to interpolate - current_sampling (numpy.ndarray): current sampling for the 3rd axis - new_sampling (numpy.ndarray): new sampling for the 3rd axis - chunk_size (int): size of the chunks to use for the interpolation - """ - - # Generate the coordinates for the original grid - x = torch.arange(data.shape[0]).float() - y = torch.arange(data.shape[1]).float() - z = current_sampling - - # Initialize an empty array for the result - interpolated_data = torch.empty((data.shape[0], data.shape[1], len(new_sampling))) - - # Perform the interpolation in chunks - for i in range(0, data.shape[0], chunk_size): - for j in range(0, data.shape[1], chunk_size): - new_coordinates = torch.meshgrid(x[i:i + chunk_size], y[j:j + chunk_size], new_sampling, indexing='ij') - new_grid = torch.stack(new_coordinates, axis=-1) - - min_bound = torch.tensor([torch.min(x[i:i + chunk_size]), torch.min(y[j:j + chunk_size]), torch.min(z)]) - max_bound = torch.tensor([torch.max(x[i:i + chunk_size]), torch.max(y[j:j + chunk_size]), torch.max(z)]) - - new_grid = (2 * ((new_grid - min_bound) / (max_bound - min_bound)) - 1).unsqueeze(0).flip( - -1).float() # Normalize between -1 and 1 - - """new_coordinates = np.meshgrid(x[i:i+chunk_size], y[j:j+chunk_size], new_sampling, indexing='ij') - new_grid = np.stack(new_coordinates, axis=-1) - - new_grid = torch.from_numpy(2*((new_grid-min_bound)/(max_bound - min_bound))-1).unsqueeze(0).flip(-1).double()""" - interpolated_data[i:i + chunk_size, j:j + chunk_size, :] = torch.nn.functional.grid_sample( - input=data[i:i + chunk_size, j:j + chunk_size, :][None, None, ...], - grid=new_grid, - padding_mode="zeros", - mode="bilinear", - align_corners=True).squeeze(0, 1) - - return interpolated_data - def apply_psf(self): - """ - Apply the PSF to the last measurement - - Returns: - numpy.ndarray: last measurement cube convolved with by PSF (shape= R x C x W). Each slice of the 3D filtered scene is convolved with the PSF - """ - if (self.optical_model.psf is not None) and (self.last_filtered_interpolated_scene is not None): - # Expand the dimensions of the 2D matrix to match the 3D matrix - psf_3D = self.optical_model.psf[..., None] - - # Perform the convolution using convolve - result = torch.nn.functional.conv3d(self.last_filtered_interpolated_scene[None, None, ...], torch.flip(psf_3D, (0,1,2))[None, None, ...], padding = tuple((np.array(psf_3D.shape)-1)//2)).squeeze(0,1) - result_panchro = torch.nn.functional.conv2d(self.panchro[None, None, ...], torch.flip(self.optical_model.psf, (0,1))[None, None, ...], padding = tuple((np.array(self.optical_model.psf.shape)-1)//2)).squeeze(0,1) - - else: - print("No PSF or last measurement to apply PSF") - result = self.last_filtered_interpolated_scene - result_panchro = self.panchro - - self.last_filtered_interpolated_scene = result - self.panchro = result_panchro - - return self.last_filtered_interpolated_scene \ No newline at end of file diff --git a/simca/CassiSystemTorch.py b/simca/CassiSystemTorch.py deleted file mode 100644 index b85330e..0000000 --- a/simca/CassiSystemTorch.py +++ /dev/null @@ -1,288 +0,0 @@ -from simca.OpticalModel import OpticalModelTorch -from simca.functions_acquisition import * -from simca.functions_patterns_generation import * -from simca.functions_scenes import * -from simca.functions_general_purpose import * -from CassiSystem import CassiSystem -from functions_acquisition_torch import * - -class CassiSystemTorch(CassiSystem): - """Class that contains the cassi system main attributes and methods""" - - def __init__(self, system_config=None, system_config_path=None): - - """ - - Args: - system_config_path (str): path to the configs file - system_config (dict): system configuration - - """ - super().__init__(system_config=system_config, system_config_path=system_config_path) - self.set_up_system(system_config_path=system_config_path, system_config=system_config) - - def set_up_system(self, system_config_path=None, system_config=None): - """ - Loading system config & initializing the grids coordinates for the coded aperture and the detector - - Args: - system_config_path (str): path to the configs file - system_config (dict): system configuration - - """ - - if system_config_path is not None: - self.system_config = load_yaml_config(system_config_path) - elif system_config is not None: - self.system_config = system_config - - self.optical_model = OpticalModelTorch(self.system_config) - - self.X_coded_aper_coordinates, self.Y_coded_aper_coordinates = self.create_coordinates_grid( - self.system_config["coded aperture"]["number of pixels along X"], - self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["pixel size along X"], - self.system_config["coded aperture"]["pixel size along Y"]) - - self.X_detector_coordinates_grid, self.Y_detector_coordinates_grid = self.create_coordinates_grid( - self.system_config["detector"]["number of pixels along X"], - self.system_config["detector"]["number of pixels along Y"], - self.system_config["detector"]["pixel size along X"], - self.system_config["detector"]["pixel size along Y"]) - - def update_config(self, system_config_path=None, system_config=None): - - """ - Update the system configuration file and re-initialize the grids for the coded aperture and the detector - - Args: - system_config_path (str): path to the configs file - system_config (dict): system configuration - Returns: - dict: updated system configuration - - """ - - self.set_up_system(system_config_path=system_config_path, system_config=system_config) - - return self.system_config - - def generate_filtering_cube(self): - """ - Generate filtering cube : each slice of the cube is a propagated pattern interpolated on the detector grid - - Returns: - numpy.ndarray: filtering cube generated according to the optical system & the pattern configuration (R x C x W) - - """ - - self.filtering_cube = interpolate_data_on_grid_positions_torch(data=self.pattern, - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) - - - return self.filtering_cube - - def generate_multiple_filtering_cubes(self, number_of_patterns): - """ - Generate multiple filtering cubes, each cube corresponds to a pattern, and for each pattern, each slice is a propagated coded apertureinterpolated on the detector grid - - Args: - number_of_patterns (int): number of patterns to generate - Returns: - list: filtering cubes generated according to the current optical system and the pattern configuration - - """ - self.list_of_filtering_cubes = [] - - for idx in range(number_of_patterns): - - self.filtering_cube = interpolate_data_on_grid_positions_torch(data=self.list_of_patterns[idx], - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid) - - self.list_of_filtering_cubes.append(self.filtering_cube) - - return self.list_of_filtering_cubes - - def image_acquisition(self, use_psf=False, chunck_size=50): - """ - Run the acquisition/measurement process depending on the cassi system type - - Args: - chunck_size (int): default block size for the interpolation - - Returns: - numpy.ndarray: compressed measurement (R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths_torch(self.optical_model.system_wavelengths, chunck_size) - - if dataset is None: - return None - dataset_labels = self.dataset_labels - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - - try: - self.filtering_cube - except: - return print("Please generate filtering cube first") - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, self.filtering_cube)) - - measurement_in_3D = generate_dd_measurement_torch(scene, self.filtering_cube, chunck_size) - - self.last_filtered_interpolated_scene = measurement_in_3D - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop)) - - pattern_crop = crop_center(self.pattern, scene.shape[1], scene.shape[0]) - - filtered_scene = scene * pattern_crop[..., None].repeat((1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop, use_torch = True) - - sd_measurement = interpolate_data_on_grid_positions_torch(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - - self.last_filtered_interpolated_scene = sd_measurement - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.last_filtered_interpolated_scene)) - self.scene_labels = scene_labels - - self.panchro = torch.sum(self.interpolated_scene, dim=2) - - if use_psf: - self.apply_psf_torch() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.measurement = torch.sum(self.last_filtered_interpolated_scene, dim=2) - - return self.measurement - - def multiple_image_acquisitions(self, use_psf=False, nb_of_filtering_cubes=1,chunck_size=50): - """ - Run the acquisition process depending on the cassi system type - - Args: - chunck_size (int): default block size for the dataset - - Returns: - list: list of compressed measurements (list of numpy.ndarray of size R x C) - """ - - dataset = self.interpolate_dataset_along_wavelengths_torch(self.optical_model.system_wavelengths, chunck_size) - if dataset is None: - return None - dataset_labels = self.dataset_labels - - self.list_of_filtered_scenes = [] - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - try: - self.list_of_filtering_cubes - except: - return print("Please generate list of filtering cubes first") - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, self.list_of_filtering_cubes[0])) - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - self.interpolated_scene = scene - - for i in range(nb_of_filtering_cubes): - - filtered_scene = generate_dd_measurement_torch(scene, self.list_of_filtering_cubes[i], chunck_size) - self.list_of_filtered_scenes.append(filtered_scene) - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[0]) - - scene = torch.from_numpy(match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop)) - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - self.interpolated_scene = scene - - for i in range(nb_of_filtering_cubes): - - mask_crop = crop_center(self.list_of_patterns[i], scene.shape[1], scene.shape[0]) - - filtered_scene = scene * mask_crop[..., None].repeat((1, 1, scene.shape[2])) - - self.propagate_coded_aperture_grid(X_input_grid=X_coded_aper_coordinates_crop, Y_input_grid=Y_coded_aper_coordinates_crop, use_torch = True) - - sd_measurement_cube = interpolate_data_on_grid_positions_torch(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - self.list_of_filtered_scenes.append(sd_measurement_cube) - - self.panchro = torch.sum(self.interpolated_scene, dim=2) - - if use_psf: - self.apply_psf_torch() - else: - print("No PSF was applied") - - # Calculate the other two arrays - self.list_of_measurements = [] - for i in range(nb_of_filtering_cubes): - self.list_of_measurements.append(torch.sum(self.list_of_filtered_scenes[i], dim=2)) - - return self.list_of_measurements - - def apply_psf(self): - """ - Apply the PSF to the last measurement - - Returns: - numpy.ndarray: last measurement cube convolved with by PSF (shape= R x C x W). Each slice of the 3D filtered scene is convolved with the PSF - """ - if (self.optical_model.psf is not None) and (self.last_filtered_interpolated_scene is not None): - # Expand the dimensions of the 2D matrix to match the 3D matrix - psf_3D = self.optical_model.psf[..., None] - - # Perform the convolution using convolve - result = torch.nn.functional.conv3d(self.last_filtered_interpolated_scene[None, None, ...], torch.flip(psf_3D, (0,1,2))[None, None, ...], padding = tuple((np.array(psf_3D.shape)-1)//2)).squeeze(0,1) - result_panchro = torch.nn.functional.conv2d(self.panchro[None, None, ...], torch.flip(self.optical_model.psf, (0,1))[None, None, ...], padding = tuple((np.array(self.optical_model.psf.shape)-1)//2)).squeeze(0,1) - - else: - print("No PSF or last measurement to apply PSF") - result = self.last_filtered_interpolated_scene - result_panchro = self.panchro - - self.last_filtered_interpolated_scene = result - self.panchro = result_panchro - - return self.last_filtered_interpolated_scene \ No newline at end of file diff --git a/simca/CassiSystem_lightning.py b/simca/CassiSystem_lightning.py deleted file mode 100755 index ff1e1ce..0000000 --- a/simca/CassiSystem_lightning.py +++ /dev/null @@ -1,598 +0,0 @@ -from simca.OpticalModelTorch import OpticalModelTorch -from simca.functions_acquisition import * -from simca.functions_patterns_generation import * -from simca.functions_scenes import * -from simca.functions_scenes_torch import * -from simca.functions_general_purpose import * -from simca.CassiSystem import CassiSystem -from simca.functions_acquisition_torch import * -import pytorch_lightning as pl -import time -import torch.nn.functional as F -import matplotlib.pyplot as plt - - -class CassiSystemOptim(pl.LightningModule, CassiSystem): - """Class that contains the cassi system main attributes and methods""" - - def __init__(self, system_config=None): - - # Initialize LightningModule - pl.LightningModule.__init__(self) - # Initialize CassiSystem with system_config - CassiSystem.__init__(self, system_config=system_config) - - self.system_config = system_config - self.X_coded_aper_coordinates, self.Y_coded_aper_coordinates = self.create_coordinates_grid( - self.system_config["coded aperture"]["number of pixels along X"], - self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["pixel size along X"], - self.system_config["coded aperture"]["pixel size along Y"]) - - self.X_detector_coordinates_grid, self.Y_detector_coordinates_grid = self.create_coordinates_grid( - self.system_config["detector"]["number of pixels along X"], - self.system_config["detector"]["number of pixels along Y"], - self.system_config["detector"]["pixel size along X"], - self.system_config["detector"]["pixel size along Y"]) - - - self.wavelengths = self.set_wavelengths(self.system_config["spectral range"]["wavelength min"], - self.system_config["spectral range"]["wavelength max"], - self.system_config["spectral range"]["number of spectral samples"]) - self.optical_model = OpticalModelTorch(self.system_config) - - - self.empty_grid = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"], - self.system_config["coded aperture"]["number of pixels along X"])) - # self.array_x_positions = torch.rand(-1,1,self.system_config["coded aperture"]["number of pixels along X"]) - self.array_x_positions = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"]))+ 0.5 - - def create_coordinates_grid(self, nb_of_pixels_along_x, nb_of_pixels_along_y, delta_x, delta_y): - """ - Create a coordinates grid for a given number of samples along X and Y axis and a given pixel size - - Args: - nb_of_pixels_along_x (int): number of samples along X axis - nb_of_pixels_along_y (int): number of samples along Y axis - delta_x (float): pixel size along X axis - delta_y (float): pixel size along Y axis - - Returns: - tuple: X coordinates grid (numpy.ndarray) and Y coordinates grid (numpy.ndarray) - """ - x = np.arange(-(nb_of_pixels_along_x-1) * delta_x / 2, (nb_of_pixels_along_x+1) * delta_x / 2,delta_x) - y = np.arange(-(nb_of_pixels_along_y-1) * delta_y / 2, (nb_of_pixels_along_y+1) * delta_y / 2, delta_y) - - - # Create a two-dimensional grid of coordinates - X_input_grid, Y_input_grid = np.meshgrid(x, y) - - print( "My DEVICE IS: ", self.device) - return torch.from_numpy(X_input_grid).float().to(self.device), torch.from_numpy(Y_input_grid).float().to(self.device) - - def set_wavelengths(self, wavelength_min, wavelength_max, nb_of_spectral_samples): - """ - Set the wavelengths range of the optical system - - Args: - wavelength_min (float): minimum wavelength of the system - wavelength_max (float): maximum wavelength of the system - nb_of_spectral_samples (int): number of spectral samples of the system - Returns: - - """ - self.wavelength_min = wavelength_min - self.wavelength_max = wavelength_max - self.nb_of_spectral_samples = nb_of_spectral_samples - - self.system_wavelengths = torch.linspace(self.wavelength_min,self.wavelength_max,self.nb_of_spectral_samples) - - return self.system_wavelengths - - def update_optical_model(self, system_config=None): - """ - Update the optical model of the system - - Args: - system_config (dict): configuration of the system - - Returns: - None - """ - - if system_config is not None: - self.system_config = system_config - - self.optical_model = OpticalModelTorch(self.system_config) - - def propagate_coded_aperture_grid(self): - - if self.optical_model.continuous_glass_materials1 == True: - n1 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths.cpu() ,self.optical_model.nd1,self.optical_model.vd1) - else: - n1 = self.optical_model.glass1.calc_rindex(self.wavelengths.cpu() ) - if self.optical_model.continuous_glass_materials2 == True: - n2 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths.cpu() ,self.optical_model.nd2,self.optical_model.vd2) - else: - n2 = self.optical_model.glass2.calc_rindex(self.wavelengths.cpu() ) - if self.optical_model.continuous_glass_materials3 == True: - n3 = self.optical_model.calculate_dispersion_with_cauchy(self.wavelengths.cpu() ,self.optical_model.nd3,self.optical_model.vd3) - else: - n3 = self.optical_model.glass3.calc_rindex(self.wavelengths.cpu() ) - - n1 = n1.to(self.device) - n2 = n2.to(self.device) - n3 = n3.to(self.device) - - #print("New X coded aperture_coordinates: ", self.X_coded_aper_coordinates.shape) - #print("New Y coded aperture_coordinates: ", self.Y_coded_aper_coordinates.shape) - - X_input_grid = torch.from_numpy(self.X_coded_aper_coordinates) if isinstance(self.X_coded_aper_coordinates, np.ndarray) else self.X_coded_aper_coordinates - Y_input_grid = torch.from_numpy(self.Y_coded_aper_coordinates) if isinstance(self.Y_coded_aper_coordinates, np.ndarray) else self.Y_coded_aper_coordinates - wavelength_vec = torch.from_numpy(self.wavelengths) if isinstance(self.wavelengths, np.ndarray) else self.wavelengths - n1_vec = torch.from_numpy(n1) if isinstance(n1, np.ndarray) else n1 - n2_vec = torch.from_numpy(n2) if isinstance(n2, np.ndarray) else n2 - n3_vec = torch.from_numpy(n3) if isinstance(n3, np.ndarray) else n3 - - X_input_grid_3D = X_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples).to(self.device) - Y_input_grid_3D = Y_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples).to(self.device) - lba_3D = wavelength_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n1_3D = n1_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n2_3D = n2_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n3_3D = n3_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - - starting_time = time.time() - #print('Propagating the coded aperture grid...') - #print(X_input_grid_3D.get_device()) - #print(Y_input_grid_3D.get_device()) - #print(lba_3D.get_device()) - #print(n1_3D.get_device()) - #print(n2_3D.get_device()) - - self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture = self.optical_model.propagate(X_input_grid_3D, Y_input_grid_3D, lba_3D, n1_3D, n2_3D, n3_3D) - - return self.X_coordinates_propagated_coded_aperture, self.Y_coordinates_propagated_coded_aperture - - def generate_filtering_cube(self): - """ - Generate filtering cube : each slice of the cube is a propagated pattern interpolated on the detector grid - - Returns: - numpy.ndarray: filtering cube generated according to the optical system & the pattern configuration (R x C x W) - - """ - - self.filtering_cube = interpolate_data_on_grid_positions_torch(data=self.pattern.unsqueeze(-1).repeat(1, 1, 1, self.wavelengths.shape[0]).to(self.device), - X_init=self.X_coordinates_propagated_coded_aperture, - Y_init=self.Y_coordinates_propagated_coded_aperture, - X_target=self.X_detector_coordinates_grid, - Y_target=self.Y_detector_coordinates_grid).to(self.device) - - return self.filtering_cube - - def get_displacement_in_pixels(self,dataset_wavelengths): - - central_coordinates_X = self.X_coordinates_propagated_coded_aperture[0,self.X_coordinates_propagated_coded_aperture.shape[1] // 2, self.X_coordinates_propagated_coded_aperture.shape[2] // 2, :] - displacement_in_pix = [float(coord/self.system_config["detector"]["pixel size along X"]) for coord in list(central_coordinates_X)] - - current_wavelengths = self.wavelengths.cpu().numpy() - # Interpolate displacement values onto dataset_wavelengths - interpolated_displacement_in_pix = np.interp(dataset_wavelengths.cpu().numpy(), current_wavelengths, displacement_in_pix) - - return interpolated_displacement_in_pix - - - def image_acquisition(self, hyperspectral_cube, pattern,wavelengths,use_psf=False, chunck_size=50): - """ - Run the acquisition/measurement process depending on the cassi system type - - Args: - chunck_size (int): default block size for the interpolation - - Returns: - numpy.ndarray: compressed measurement (R x C) - """ - self.wavelengths= self.wavelengths.to(self.device) - - dataset = self.interpolate_dataset_along_wavelengths_torch(hyperspectral_cube, wavelengths,self.wavelengths, chunck_size) - - if dataset is None: - return None - - try: - dataset_labels = self.dataset_labels - except: - dataset_labels = None - - if self.system_config["system architecture"]["system type"] == "DD-CASSI": - - try: - self.filtering_cube - except: - return print("Please generate filtering cube first") - - - scene = match_dataset_to_instrument(dataset, self.filtering_cube[0,:,:,0]) - - # scene = torch.from_numpy(match_dataset_to_instrument(dataset, self.filtering_cube)).to(self.device) if isinstance(scene, np.ndarray) else scene.to(self.device) - - measurement_in_3D = generate_dd_measurement_torch(scene, self.filtering_cube, chunck_size) - - self.last_filtered_interpolated_scene = measurement_in_3D - self.interpolated_scene = scene - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.filtering_cube)) - self.scene_labels = scene_labels - - - elif self.system_config["system architecture"]["system type"] == "SD-CASSI": - - X_coded_aper_coordinates_crop = crop_center(self.X_coded_aper_coordinates,dataset.shape[1], dataset.shape[2]) - Y_coded_aper_coordinates_crop = crop_center(self.Y_coded_aper_coordinates,dataset.shape[1], dataset.shape[2]) - - self.X_coded_aper_coordinates = X_coded_aper_coordinates_crop - self.Y_coded_aper_coordinates = Y_coded_aper_coordinates_crop - - # print("dataset shape: ", dataset.shape) - # print("X coded shape: ", X_coded_aper_coordinates_crop.shape) - - - scene = match_dataset_to_instrument(dataset, X_coded_aper_coordinates_crop) - - pattern_crop = crop_center_3D(pattern, scene.shape[2], scene.shape[1]).to(self.device) - - self.pattern_crop = pattern_crop - - pattern_crop = pattern_crop.unsqueeze(-1).repeat(1, 1, 1, scene.size(-1)) - - #print(scene.get_device()) - #print(pattern_crop.get_device()) - - plt.imshow(scene[0,:,:,0].cpu().numpy()) - plt.title("scene") - plt.show() - - # filtered_scene = scene * pattern_crop[..., None].repeat((1, 1, scene.shape[2])) - # print(f"scene: {scene.shape}") - # print(f"pattern_crop: {pattern_crop.shape}") - filtered_scene = scene * pattern_crop - - # print(f"filtered_scene: {filtered_scene.shape}") - - - plt.imshow(pattern_crop[0,:,:,0].cpu().numpy()) - plt.title("Pattern crop1") - plt.show() - - - - plt.imshow(filtered_scene[0,:,:,0].cpu().numpy()) - plt.title("Filtered scene") - plt.show() - - #print("filtered_scene",filtered_scene.shape) - - self.propagate_coded_aperture_grid() - - - sd_measurement = interpolate_data_on_grid_positions_torch(filtered_scene, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - - self.filtering_cube = interpolate_data_on_grid_positions_torch(pattern_crop, - self.X_coordinates_propagated_coded_aperture, - self.Y_coordinates_propagated_coded_aperture, - self.X_detector_coordinates_grid, - self.Y_detector_coordinates_grid) - - self.last_filtered_interpolated_scene = sd_measurement - self.interpolated_scene = scene - - - if dataset_labels is not None: - scene_labels = torch.from_numpy(match_dataset_labels_to_instrument(dataset_labels, self.last_filtered_interpolated_scene)) - self.scene_labels = scene_labels - - self.panchro = torch.sum(self.interpolated_scene, dim=3) - - if use_psf: - self.apply_psf_torch() - else: - print("") - #print("No PSF was applied") - - # Calculate the other two arrays - self.measurement = torch.sum(self.last_filtered_interpolated_scene, dim=3) - - return self.measurement - - def generate_custom_pattern_parameters_slit(self, position=0.5): - # Position is a float: 0 means slit is on the left edge, 1 means the slit is on the right edge - self.array_x_positions = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"]))+ position - self.array_x_positions = self.array_x_positions.to(self.device) - return self.array_x_positions - - def generate_custom_pattern_parameters_slit_width(self, nb_slits=1, nb_rows=1, start_width=1): - # Situation where we have nb_slits per row, and nb_rows rows of slits - # Values in self.array_x_positions correspond to the width of the slit - # self.array_x_positions is of shape (self.system_config["coded aperture"]["number of pixels along Y"], nb_rows) - - self.array_x_positions = torch.zeros((nb_slits, nb_rows))+start_width # Every slit starts with the same width - self.array_x_positions = self.array_x_positions.to(self.device) - self.array_x_positions_normalized = torch.zeros((nb_slits, nb_rows))+start_width - return self.array_x_positions - - def generate_custom_slit_pattern_width(self, start_pattern = "line", start_position = 0): - nb_slits, nb_rows = self.array_x_positions.shape - pos_slits = self.system_config["coded aperture"]["number of pixels along X"]//(nb_slits+1) # Equally spaced slits - height_slits = self.system_config["coded aperture"]["number of pixels along Y"]//nb_rows # Same length slits - - self.pattern = torch.zeros((self.system_config["coded aperture"]["number of pixels along Y"], self.system_config["coded aperture"]["number of pixels along X"])).to(self.device) # Pattern of correct size - if start_pattern == "line": - if start_position != 0: - start_position = start_position - pos_slits/self.system_config["coded aperture"]["number of pixels along X"] - for j in range(nb_slits): - for i in range(nb_rows): - top_pad = i*height_slits # Padding necessary above slit (j,i) - if i == nb_rows-1: - bottom_pad = 0 # Padding necessary below slit (j,i) - # In that case, the last slit might be longer than the other ones in case size_X isn't divisible by nb_rows - array_x_pos = torch.zeros((height_slits+self.system_config["coded aperture"]["number of pixels along Y"] % nb_rows)) + start_position + (j+1)*pos_slits/self.system_config["coded aperture"]["number of pixels along X"] - else: - # Set the position of the slit (j,i) - array_x_pos = torch.zeros((height_slits)) + start_position + (j+1)*pos_slits/self.system_config["coded aperture"]["number of pixels along X"] - bottom_pad = (nb_rows - i-1)*height_slits + self.system_config["coded aperture"]["number of pixels along Y"] % nb_rows # Padding necessary below slit (j,i) - top_pad = i*height_slits - - # Create a grid to represent positions - grid_positions = torch.arange(self.empty_grid.shape[1], dtype=torch.float32) - # Expand dimensions for broadcasting - expanded_x_positions = (array_x_pos.unsqueeze(-1)) * (self.empty_grid.shape[1]-1) - expanded_grid_positions = grid_positions.unsqueeze(0) - - # Apply Gaussian-like function - sigma = (self.array_x_positions[j,i]+1)/2 - gaussian = torch.exp(-(((expanded_grid_positions - expanded_x_positions)) ** 2) / (2 * sigma ** 2)) - - padded = torch.nn.functional.pad(gaussian, (0,0,top_pad,bottom_pad)) # padding: left - right - top - bottom - - # Normalize to make sure the maximum value is 1 - self.pattern = self.pattern + padded/padded.max() - elif start_pattern == "corrected": - for j in range(nb_slits): - for i in range(nb_rows): - top_pad = i*height_slits # Padding necessary above slit (j,i) - if i == nb_rows-1: - bottom_pad = 0 # Padding necessary below slit (j,i) - # In that case, the last slit might be longer than the other ones in case size_X isn't divisible by nb_rows - else: - # Set the position of the slit (j,i) - bottom_pad = (nb_rows - i-1)*height_slits + self.system_config["coded aperture"]["number of pixels along Y"] % nb_rows # Padding necessary below slit (j,i) - top_pad = i*height_slits - """ array_x_pos = torch.tensor(start_position[i]) - - # Create a grid to represent positions - grid_positions = torch.arange(self.empty_grid.shape[1], dtype=torch.float32) - # Expand dimensions for broadcasting - expanded_x_positions = (array_x_pos.unsqueeze(-1)) * (self.empty_grid.shape[1]-1) - expanded_grid_positions = grid_positions.unsqueeze(0) - - # Apply Gaussian-like function - sigma = (self.array_x_positions[j,i]+1)/2 - gaussian = torch.exp(-(((expanded_grid_positions - expanded_x_positions)) ** 2) / (2 * sigma ** 2)) - - padded = torch.nn.functional.pad(gaussian, (0,0,top_pad,bottom_pad)) # padding: left - right - top - bottom - - # Normalize to make sure the maximum value is 1 - self.pattern = self.pattern + padded/padded.max() """ - - c = start_position[i].clone().detach() # center of the slit - #d = ((torch.tanh(1.1*self.array_x_positions[j,i])+1)/2)/2 # width of the slit at pos - d = self.array_x_positions[j,i]/2 # width of the slit at pos - m = (c-d)*(self.system_config["coded aperture"]["number of pixels along X"]-1) # left bound - M = (c+d)*(self.system_config["coded aperture"]["number of pixels along X"]-1) # right bound - rect = torch.arange(self.system_config["coded aperture"]["number of pixels along X"]).to(self.device) - clamp_M = torch.clamp(M-rect, 0, 1) - - clamp_m = torch.clamp(rect-m, 0, 1) - diff = 1-clamp_m - reg = torch.where(diff < 1, diff, -1) - clamp_m = torch.where(reg!=0, reg, 1) - clamp_m = torch.where(clamp_m!=-1, clamp_m, 0) - clamp_m = torch.roll(clamp_m, -1) - clamp_m[-1]=1 - - rect = clamp_M - clamp_m +1 - rect = torch.where(rect!=2, rect, 0) - rect = torch.where(rect <= 1, rect, rect-1) - #rect = torch.clamp(-(rect-m)*(rect-M)+1,0,1).to(self.device) - - gaussian_range = torch.arange(self.system_config["coded aperture"]["number of pixels along X"], dtype=torch.float32) - center_pos = 0.5*(len(gaussian_range)-1) - sigma = 1.5 - gaussian_peaks = torch.exp(-((center_pos - gaussian_range) ** 2) / (2 * sigma ** 2)).to(self.device) - gaussian = gaussian_peaks /gaussian_peaks.max() - res = torch.nn.functional.conv1d(rect.unsqueeze(0), gaussian.unsqueeze(0).unsqueeze(0), padding = (len(gaussian_range)-1)//2).squeeze().to(self.device) - - res = res/res.max() - self.pattern[i, :] = self.pattern[i, :] + res - - # Normalize to make sure the maximum value is 1 - self.pattern = self.pattern / self.pattern.max(dim=1).values.unsqueeze(-1) - - return self.pattern - - def generate_custom_slit_pattern(self): - - # Create a grid to represent positions - grid_positions = torch.arange(self.empty_grid.shape[1], dtype=torch.float32).to(self.device) - # Expand dimensions for broadcasting - expanded_x_positions = ((self.array_x_positions.unsqueeze(-1)) * (self.empty_grid.shape[1]-1)).to(self.device) - expanded_grid_positions = grid_positions.unsqueeze(0).to(self.device) - - # Apply Gaussian-like function - # Adjust 'sigma' to control the sharpness - sigma = 1.5 - gaussian_peaks = torch.exp(-((expanded_grid_positions - expanded_x_positions) ** 2) / (2 * sigma ** 2)).to(self.device) - - # Normalize to make sure the maximum value is 1 - self.pattern = gaussian_peaks / gaussian_peaks.max() - return self.pattern - - - - # def generate_custom_slit_pattern(self): - # """ - # Generate a custom slit pattern - - # Args: - # array_x_positions (numpy.ndarray): array of the x positions of the slits between -1 and 1 - - # Returns: - # numpy.ndarray: generated slit pattern - # """ - # pattern = torch.clone(self.empty_grid) - # self.array_x_positions += 1 - # self.array_x_positions *= self.empty_grid.shape[1] // 2 - # self.array_x_positions = self.array_x_positions.type(torch.int32) - # for i in range(self.array_x_positions.shape[0]): - # pattern[0, self.array_x_positions[i]] = 1 - - # return self.pattern - - def interpolate_dataset_along_wavelengths_torch(self, hyperspectral_cube, wavelengths, new_wavelengths_sampling, - chunk_size): - """ - Interpolate the dataset cube along the wavelength axis to match the system sampling - - Args: - new_wavelengths_sampling (numpy.ndarray): new wavelengths on which to interpolate the dataset (shape = W) - chunk_size (int): chunk size for the multiprocessing - - Returns: - numpy.ndarray : interpolated dataset cube along the wavelength axis (shape = R_dts x C_dts x W) - - """ - try: - self.dataset - except: - self.dataset = hyperspectral_cube - self.dataset_wavelengths = wavelengths - #print(self.dataset.shape) - #print(self.dataset_wavelengths.shape) - - self.dataset = hyperspectral_cube - self.dataset_wavelengths = wavelengths - - - - self.dataset_wavelengths = torch.from_numpy(self.dataset_wavelengths) if isinstance(self.dataset_wavelengths, - np.ndarray) else self.dataset_wavelengths - new_wavelengths_sampling = torch.from_numpy(new_wavelengths_sampling).float() if isinstance( - new_wavelengths_sampling, np.ndarray) else new_wavelengths_sampling - self.dataset = torch.from_numpy(self.dataset).float() if isinstance(self.dataset, np.ndarray) else self.dataset - - self.dataset_interpolated = self.interpolate_data_along_wavelength_torch(self.dataset, self.dataset_wavelengths[0], - new_wavelengths_sampling) - return self.dataset_interpolated - - def interpolate_data_along_wavelength_torch(self, data, current_sampling, new_sampling): - """Interpolate the input 3D data along a new sampling in the third axis (lambda). - - Args: - data (torch.Tensor): 4D data to interpolate with shape (batch_size, lambda, x, y). - current_sampling (torch.Tensor): 1D tensor of current sampling for the lambda axis. - new_sampling (torch.Tensor): 1D tensor of new sampling for the lambda axis. - """ - # Assuming data is (batch, y, x, lambda) - data = data.to(self.device) - - # Reshape data to treat lambda as a separate dimension, e.g., combining batch, y, x into one dimension - original_shape = data.shape - data_reshaped = data.reshape(-1, original_shape[-1]) # Shape: (batch*y*x, lambda) - - # Calculate the scale factor or directly use the new size - scale_factor = len(new_sampling) / original_shape[-1] - - # Interpolate using 1D interpolation along the last dimension - interpolated_data_reshaped = F.interpolate(data_reshaped.unsqueeze(1), # Add a fake channel dimension - size=len(new_sampling), - mode='linear', - align_corners=True).squeeze(1) # Remove the fake channel dimension - - # Reshape back to the original batch, y, x format but with new lambda size - interpolated_data = interpolated_data_reshaped.reshape(original_shape[0], original_shape[1], original_shape[2], - -1) - - return interpolated_data - - def interpolate_data_along_wavelength_torch_old(self, data, current_sampling, new_sampling, chunk_size=50): - """Interpolate the input 3D data along a new sampling in the third axis. - - Args: - data (numpy.ndarray): 3D data to interpolate - current_sampling (numpy.ndarray): current sampling for the 3rd axis - new_sampling (numpy.ndarray): new sampling for the 3rd axis - chunk_size (int): size of the chunks to use for the interpolation - """ - # print(self.device) - # Generate the coordinates for the original grid - x = torch.arange(data.shape[0]).float() - y = torch.arange(data.shape[1]).float() - z = current_sampling - - # Initialize an empty array for the result - interpolated_data = torch.empty((data.shape[0], data.shape[1], len(new_sampling))) - - # Perform the interpolation in chunks - for i in range(0, data.shape[0], chunk_size): - for j in range(0, data.shape[1], chunk_size): - new_coordinates = torch.meshgrid(x[i:i + chunk_size], y[j:j + chunk_size], new_sampling, indexing='ij') - new_grid = torch.stack(new_coordinates, axis=-1).to(self.device) - - min_bound = torch.tensor([torch.min(x[i:i + chunk_size]), torch.min(y[j:j + chunk_size]), torch.min(z)]).to(self.device) - max_bound = torch.tensor([torch.max(x[i:i + chunk_size]), torch.max(y[j:j + chunk_size]), torch.max(z)]).to(self.device) - - new_grid = (2 * ((new_grid - min_bound) / (max_bound - min_bound)) - 1).unsqueeze(0).flip( - -1).float() # Normalize between -1 and 1 - - """new_coordinates = np.meshgrid(x[i:i+chunk_size], y[j:j+chunk_size], new_sampling, indexing='ij') - new_grid = np.stack(new_coordinates, axis=-1) - - new_grid = torch.from_numpy(2*((new_grid-min_bound)/(max_bound - min_bound))-1).unsqueeze(0).flip(-1).double()""" - interpolated_data[i:i + chunk_size, j:j + chunk_size, :] = torch.nn.functional.grid_sample( - input=data[i:i + chunk_size, j:j + chunk_size, :][None, None, ...], - grid=new_grid, - padding_mode="zeros", - mode="bilinear", - align_corners=True).squeeze(0, 1) - - return interpolated_data - - def apply_psf(self): - """ - Apply the PSF to the last measurement - - Returns: - numpy.ndarray: last measurement cube convolved with by PSF (shape= R x C x W). Each slice of the 3D filtered scene is convolved with the PSF - """ - if (self.optical_model.psf is not None) and (self.last_filtered_interpolated_scene is not None): - # Expand the dimensions of the 2D matrix to match the 3D matrix - psf_3D = self.optical_model.psf[..., None] - - # Perform the convolution using convolve - result = torch.nn.functional.conv3d(self.last_filtered_interpolated_scene[None, None, ...], torch.flip(psf_3D, (0,1,2))[None, None, ...], padding = tuple((np.array(psf_3D.shape)-1)//2)).squeeze(0,1) - result_panchro = torch.nn.functional.conv2d(self.panchro[None, None, ...], torch.flip(self.optical_model.psf, (0,1))[None, None, ...], padding = tuple((np.array(self.optical_model.psf.shape)-1)//2)).squeeze(0,1) - - else: - print("No PSF or last measurement to apply PSF") - result = self.last_filtered_interpolated_scene - result_panchro = self.panchro - - self.last_filtered_interpolated_scene = result - self.panchro = result_panchro - - return self.last_filtered_interpolated_scene \ No newline at end of file diff --git a/simca/OpticalModel.py b/simca/OpticalModel.py index 7efc452..b638d3f 100644 --- a/simca/OpticalModel.py +++ b/simca/OpticalModel.py @@ -1,380 +1,316 @@ -from simca.functions_general_purpose import * -from opticalglass.glassfactory import create_glass +import torch import snoop +import opticalglass as og +from simca.helper import * +# import snoop +import torch +from opticalglass.glassfactory import create_glass +import pytorch_lightning as pl +from torch import nn +from opticalglass.glassfactory import get_glass_catalog -class OpticalModel: +class OpticalModel(pl.LightningModule): """ Class that contains the optical model caracteristics and propagation models """ - def __init__(self, system_config): - self.set_optical_config(system_config) + def __init__(self, config,index_estimation_method="cauchy",device="cpu"): + super().__init__() - def update_config(self, new_config): - """ - Update the optical model configuration + self.to(device) + self.system_config = config + self.index_estimation_method = index_estimation_method - Args: - new_config (dict): new configuration + self.lba_c = nn.Parameter(torch.tensor(config["system architecture"]["dispersive element"]["wavelength center"], dtype=torch.float,device=self.device,requires_grad=True)) - """ - self.set_optical_config(new_config) + self.define_lenses_carac() + self.define_dispersive_element_carac() - def set_optical_config(self, config): - """ - Set the optical model configuration - Args: - config (dict): configuration file + def define_lenses_carac(self): - """ + config = self.system_config + self.F = torch.tensor(config["system architecture"]["focal lens"], + dtype=torch.float,device=self.device) # If F needs to be optimized, wrap with nn.Parameter as well + + return self.F - self.system_config = config + def define_dispersive_element_carac(self): + + config = self.system_config self.dispersive_element_type = config["system architecture"]["dispersive element"]["type"] - self.glass1 = config["system architecture"]["dispersive element"]["glass1"] - self.A1 = math.radians(config["system architecture"]["dispersive element"]["A1"]) - self.glass2 = config["system architecture"]["dispersive element"]["glass2"] - self.A2 = math.radians(config["system architecture"]["dispersive element"]["A2"]) - self.glass3 = config["system architecture"]["dispersive element"]["glass3"] - self.A3 = math.radians(config["system architecture"]["dispersive element"]["A3"]) - self.G = config["system architecture"]["dispersive element"]["G"] - self.lba_c = config["system architecture"]["dispersive element"]["wavelength center"] - self.m = config["system architecture"]["dispersive element"]["m"] - self.F = config["system architecture"]["focal lens"] - self.delta_alpha_c = math.radians(config["system architecture"]["dispersive element"]["delta alpha c"]) - self.delta_beta_c = math.radians(config["system architecture"]["dispersive element"]["delta beta c"]) - - self.nb_of_det_pixels_X = config["detector"]["number of pixels along X"] - self.nb_of_det_pixels_Y = config["detector"]["number of pixels along Y"] - self.nb_of_coded_apert_pixels_X = config["coded aperture"]["number of pixels along X"] - self.nb_of_coded_apert_pixels_Y = config["coded aperture"]["number of pixels along Y"] - - self.set_wavelengths(config["spectral range"]["wavelength min"], - config["spectral range"]["wavelength max"], - config["spectral range"]["number of spectral samples"]) - - - def propagation_with_distorsions(self, X_input_grid, Y_input_grid): - """ - Propagate the coded aperture coded_aperture through one CASSI system - Args: - X_input_grid (numpy.ndarray): x coordinates grid - Y_input_grid (numpy.ndarray): y coordinates grid + if self.dispersive_element_type == "grating": + self.G = torch.tensor(config["system architecture"]["dispersive element"]["G"], dtype=torch.float,device=self.device) + self.m = torch.tensor(config["system architecture"]["dispersive element"]["m"], dtype=torch.float,device=self.device) + if self.index_estimation_method == "cauchy": + self.nd1, self.vd1 = (nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device)),nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device))) + self.nd2, self.vd2 = (nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device)),nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device))) + self.nd3, self.vd3 = (nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device)),nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device))) + self.delta_beta_c = nn.Parameter(torch.tensor(math.radians(config["system architecture"]["dispersive element"]["delta beta c"]), dtype=torch.float,device=self.device)) + + + elif self.dispersive_element_type in ["prism", "doubleprism", "amici", "tripleprism"]: + self.catalog = config["system architecture"]["dispersive element"]["catalog"] + self.glass1 = config["system architecture"]["dispersive element"]["glass1"] + self.A1 = nn.Parameter(torch.tensor(math.radians(config["system architecture"]["dispersive element"]["A1"]), dtype=torch.float,device=self.device)) + self.A2 = nn.Parameter(torch.tensor(0, dtype=torch.float,device=self.device)) + self.A3 = nn.Parameter(torch.tensor(0, dtype=torch.float,device=self.device)) + if self.index_estimation_method == "cauchy": + self.nd1, self.vd1 = self.get_corresponding_nd_vd(self.glass1,self.catalog) + self.nd2, self.vd2 = (nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device)),nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device))) + self.nd3, self.vd3 = (nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device)),nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device))) + # only used for prisms + self.delta_beta_c = nn.Parameter(torch.tensor(math.radians(config["system architecture"]["dispersive element"]["delta beta c"]), dtype=torch.float,device=self.device)) + + if self.dispersive_element_type in ["doubleprism", "amici", "tripleprism"]: + + self.glass2 = config["system architecture"]["dispersive element"]["glass2"] + self.A2 = nn.Parameter(torch.tensor(math.radians(config["system architecture"]["dispersive element"]["A2"]), dtype=torch.float,device=self.device)) + if self.index_estimation_method == "cauchy": + self.nd2, self.vd2 = self.get_corresponding_nd_vd(self.glass2,self.catalog) + self.nd3, self.vd3 = (nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device)),nn.Parameter(torch.tensor(1, dtype=torch.float,device=self.device))) - Returns: - tuple: X coordinates of the propagated coded aperture grids, Y coordinates of the propagated coded aperture grids - """ + + if self.dispersive_element_type in ["amici", "tripleprism"]: + + self.glass3 = config["system architecture"]["dispersive element"]["glass3"] + self.A3 = nn.Parameter(torch.tensor(math.radians(config["system architecture"]["dispersive element"]["A3"]), dtype=torch.float,device=self.device)) + if self.index_estimation_method == "cauchy": + self.nd3, self.vd3 = self.get_corresponding_nd_vd(self.glass3,self.catalog) + + if self.dispersive_element_type =="amici": + self.A3 = self.A1 + self.glass3 = self.glass1 + if self.index_estimation_method == "cauchy": + self.nd3 = self.nd1 + self.vd3 = self.vd1 + + self.alpha_c = nn.Parameter( + torch.tensor(math.radians(config["system architecture"]["dispersive element"]["alpha_c"]), + dtype=torch.float,device=self.device)) + self.delta_alpha_c = nn.Parameter(torch.tensor( + math.radians(config["system architecture"]["dispersive element"]["delta alpha c"]), dtype=torch.float,device=self.device)) + + # Calculation based on the above parameters + alpha_c_transmis = -1 * self.propagate_central_microm_through_disperser(self.lba_c) + self.alpha_c_transmis = alpha_c_transmis[0,0,0] + # print(self.alpha_c_transmis.detach().cpu().numpy()*180/np.pi) - self.calculate_central_dispersion() - - # X_coordinates_propagated_coded_aperture = np.zeros((X_input_grid.shape[0],X_input_grid.shape[1], - # self.nb_of_spectral_samples)) - # Y_coordinates_propagated_coded_aperture = np.zeros((X_input_grid.shape[0],X_input_grid.shape[1], - # self.nb_of_spectral_samples)) - # - # X_input_grid_flatten = X_input_grid.flatten() - # Y_input_grid_flatten = Y_input_grid.flatten() - # - # glass = create_glass(self.glass1, 'Schott') - # print(glass) - # - # - # n1 = 1.5 - # n2 = 1.8 - # n3 = 1.5 - # - # for idx,lba in enumerate(np.linspace(self.system_wavelengths[0], self.system_wavelengths[-1],self.nb_of_spectral_samples)): - # - # prism_index = glass.calc_rindex(lba) - # # n_array_flatten = np.full(X_input_grid_flatten.shape, prism_index) - # # n_array_flatten = np.full(X_input_grid_flatten.shape, self.sellmeier(lba)) - # n1_flatten = np.full(X_input_grid_flatten.shape, n1) - # n2_flatten = np.full(X_input_grid_flatten.shape, n2) - # n3_flatten = np.full(X_input_grid_flatten.shape, n3) - # - # lba_array_flatten = np.full(X_input_grid_flatten.shape, lba) - # - # X_propagated_coded_aperture, Y_propagated_coded_aperture = self.propagate_through_arm(X_input_grid_flatten,Y_input_grid_flatten, - # n1=n1_flatten, - # n2=n2_flatten, - # n3=n3_flatten, - # lba=lba_array_flatten) - # - # X_coordinates_propagated_coded_aperture[:,:,idx] = X_propagated_coded_aperture.reshape(X_input_grid.shape) - # Y_coordinates_propagated_coded_aperture[:,:,idx] = Y_propagated_coded_aperture.reshape(Y_input_grid.shape) - # - # return X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture - - def propagation_with_no_distorsions(self, X_input_grid, Y_input_grid): - """ - Vanilla Propagation model used in most cassi acquisitions simulation. + + def get_corresponding_nd_vd(self,glass_name,catalog): + glass_pd = get_glass_catalog(catalog) + list_of_glasses = glass_pd.df.iloc[:, 0].index.tolist() - Args: - X_input_grid (numpy.ndarray): X coordinates of the grid to be propagated (2D) - Y_input_grid (numpy.ndarray): Y coordinates of the grid to be propagated (2D) + idx_glass = list_of_glasses.index(glass_name) + tuple_glass_data = glass_pd.glass_map_data("d") - Returns: - tuple: X coordinates grids of the propagated coded apertures, Y coordinates grids of the propagated coded apertures - """ + nd = tuple_glass_data[0][idx_glass] + vd = tuple_glass_data[1][idx_glass] - self.calculate_central_dispersion() + nd = nn.Parameter(torch.tensor(nd, dtype=torch.float,device=self.device)) + vd = nn.Parameter(torch.tensor(vd, dtype=torch.float,device=self.device)) - X_coordinates_propagated_coded_aperture = np.zeros((X_input_grid.shape[0],X_input_grid.shape[1], - self.nb_of_spectral_samples)) - Y_coordinates_propagated_coded_aperture = np.zeros((X_input_grid.shape[0],X_input_grid.shape[1], - self.nb_of_spectral_samples)) + # print(f"nd = {nd}, vd = {vd}") - for idx, wav in enumerate(self.system_wavelengths): + return nd, vd - X_ref = -1 * X_input_grid + self.X0_propagated[idx] - Y_ref = -1 * Y_input_grid + self.Y0_propagated[idx] + def rerun_central_dispersion(self): + alpha_c_transmis = -1 * self.propagate_central_microm_through_disperser(self.lba_c.detach().cpu().numpy()) + self.alpha_c_transmis = alpha_c_transmis - X_coordinates_propagated_coded_aperture[:,:,idx] = X_ref - Y_coordinates_propagated_coded_aperture[:,:,idx] = Y_ref + return alpha_c_transmis + - return X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture + def propagate(self,X,Y,lba,n1,n2,n3): - def set_wavelengths(self, wavelength_min, wavelength_max, nb_of_spectral_samples): - """ - Set the wavelengths range of the optical system + k = self.model_Lens_pos_to_angle(X, Y, self.F) - Args: - wavelength_min (float): minimum wavelength of the system - wavelength_max (float): maximum wavelength of the system - nb_of_spectral_samples (int): number of spectral samples of the system - Returns: + alpha_1 = self.define_alpha_1() - """ - self.wavelength_min = wavelength_min - self.wavelength_max = wavelength_max - self.nb_of_spectral_samples = nb_of_spectral_samples + k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - self.system_wavelengths = np.linspace(self.wavelength_min,self.wavelength_max,self.nb_of_spectral_samples) + if self.dispersive_element_type in ["prism", "doubleprism", "amici", "tripleprism"]: + k, list_theta_in, list_theta_out = self.propagate_through_triple_prism(k,n1,n2,n3,self.A1,self.A2,self.A3) + elif self.dispersive_element_type == "grating": + k = self.model_Grating_angle_to_angle(k, lba, self.m, self.G) - @snoop - def calculate_central_dispersion(self): - """ - Calculate the dispersion related to the central pixel of the coded aperture + k = self.rotate_from_dispersive_element_to_lens(k,self.alpha_c_transmis,self.delta_alpha_c,self.delta_beta_c,alpha_1) - Returns: - numpy.float: spectral dispersion of the central pixel of the coded aperture - """ + X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k, self.F) - # self.alpha_c = self.calculate_alpha_c() - self.alpha_c = 55*np.pi/180 - X0_coordinates_array_flatten = np.zeros(self.system_wavelengths.shape[0]) - Y0_coordinates_array_flatten = np.zeros(self.system_wavelengths.shape[0]) - lba_array_flatten = self.system_wavelengths + return X_vec_out, Y_vec_out + + + def repropagate(self,X,Y,lba,n1,n2,n3): - n1_array_flatten = np.full(lba_array_flatten.shape, 1.5) - n2_array_flatten = np.full(lba_array_flatten.shape, 1.8) - n3_array_flatten = np.full(lba_array_flatten.shape, 1.5) + k = self.model_Lens_pos_to_angle(X, Y, self.F) - # n_array_flatten = np.full(lba_array_flatten.shape, self.sellmeier(lba_array_flatten)) + alpha_1 = self.define_alpha_1() - X0_propagated, Y0_propagated = self.propagate_through_arm(X_vec_in=X0_coordinates_array_flatten,Y_vec_in=Y0_coordinates_array_flatten, - n1=n1_array_flatten, - n2=n2_array_flatten, - n3=n3_array_flatten, - lba=lba_array_flatten) + k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c_transmis,-1*self.delta_alpha_c,-1*self.delta_beta_c,alpha_1) - self.X0_propagated, self.Y0_propagated = X0_propagated, Y0_propagated + if self.dispersive_element_type in ["prism", "doubleprism", "amici", "tripleprism"]: + k, list_theta_in, list_theta_out = self.propagate_through_triple_prism(k,n1,n2,n3,self.A1,self.A2,self.A3) + elif self.dispersive_element_type == "grating": + k = self.model_Grating_angle_to_angle(k, lba, self.m, self.G) - self.central_distorsion_in_X = np.abs(self.X0_propagated[-1] - self.X0_propagated[0]) + + k = self.rotate_from_dispersive_element_to_lens(k,self.alpha_c,-1*self.delta_alpha_c,-1*self.delta_beta_c,alpha_1) - return self.central_distorsion_in_X - - @snoop - def propagate_through_simple_prism(self,k,n,A): + # print(k) + X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k, self.F) - norm_k = np.sqrt(k[0] ** 2 + k[1] ** 2 + k[2] ** 2) - k /= norm_k + return X_vec_out, Y_vec_out - k_out = self.model_Prism_angle_to_angle(k, n, A) - k_out = k_out * norm_k + def calculate_dispersion_with_cauchy(self,lambda_vec, nD, V): - return k_out + lambda_vec = lambda_vec * 1e-9 # Convert nm to meters for calculation + lambda_D = 589.3e-9 # D line wavelength in meters + lambda_F = 486.1e-9 # F line wavelength in meters + lambda_C = 656.3e-9 # C line wavelength in meters + + # Calculate B using the given formula + B = (nD - 1) / (V * (1 / lambda_F ** 2 - 1 / lambda_C ** 2)) + + # Calculate A using the given formula + A = nD - B / lambda_D ** 2 + + # Calculate n for each wavelength in lambda_vec + n = A + B / lambda_vec ** 2 # Note: lambda_vec is already in meters + + # print("lambda_vec = ",lambda_vec) + # print("n_cauchy = ",n) + # print("n sellemeir = ",self.sellmeir_NSF4(lambda_vec)) + + + # return self.sellmeir_NSF4(lambda_vec) + return n - def propagate_through_double_prism(self,k,n1,n2,A1,A2): + def sellmeier(self,lambda_vec,glass_name): - norm_k = np.sqrt(k[0] ** 2 + k[1] ** 2 + k[2] ** 2) - k /= norm_k + glass_pd = get_glass_catalog(self.catalog) + df = glass_pd.df["dispersion coefficients"] - k = self.model_Prism_angle_to_angle(k, n1, A1) - k = k * norm_k + B1 = df.loc[glass_name]["B1"] + B2 = df.loc[glass_name]["B2"] + B3 = df.loc[glass_name]["B3"] + C1 = df.loc[glass_name]["C1"] + C2 = df.loc[glass_name]["C2"] + C3 = df.loc[glass_name]["C3"] - k = rotation_z(np.pi) @ k + # print(glass_name) + # print("B1,B2,B3,C1,C2,C3 = ",B1,B2,B3,C1,C2,C3) - k_out = self.model_Prism_angle_to_angle(k, n2, A2) - k_out = k_out * norm_k + lambda_vec = lambda_vec * 1e-3 - return k_out + index_square = 1 + B1*lambda_vec**2/(lambda_vec**2-C1) + B2*lambda_vec**2/(lambda_vec**2-C2) - @snoop - def propagate_through_triple_prism(self,k,n1,n2,n3,A1,A2,A3): + index_square = torch.tensor(index_square,dtype=torch.float32) - norm_k = np.sqrt(k[0] ** 2 + k[1] ** 2 + k[2] ** 2) + index = torch.sqrt(index_square) - k /= norm_k - k = self.model_Prism_angle_to_angle(k, n1, A1) - k *= norm_k - k = rotation_z(np.pi) @ k - k = self.model_Prism_angle_to_angle(k, n2, A2) - k *= norm_k - k = rotation_z(np.pi) @ k - k_out = self.model_Prism_angle_to_angle(k, n3, A3) - k_out *= norm_k + # print(lambda_vec) + # print("index = ",index) - print('alpha', np.arctan(k_out[0, 0] / k_out[2, 0]) * 180 / np.pi) - print('beta', np.arctan(k_out[1, 0] / k_out[2, 0]) * 180 / np.pi) + return index - return k_out - @snoop def rotate_from_lens_to_dispersive_element(self,k,alpha_c,delta_alpha_c,delta_beta_c,alpha_1): - angle_with_P1 = alpha_c - alpha_1 + delta_alpha_c - - print('angle_with_P1',angle_with_P1*180/np.pi) - - k = rotation_y(angle_with_P1) @ k[:, 0, :] + #TODO : check the type of k + k = k.to(dtype=torch.float32) + # k_1 = rotation_y(angle_with_P1) @ k + k = torch.matmul(k,rotation_y(angle_with_P1).T) # Rotation in relation to P1 around the X axis - k = rotation_x(delta_beta_c) @ k + k = k.to(dtype=torch.float32) + + k = torch.matmul(k,rotation_x(delta_beta_c).T) # Rotation of P1 in relation to frame_in along the new Y axis - k = rotation_y(alpha_1) @ k + k = k.to(dtype=torch.float32) - print('alpha', np.arctan(k[0, 0] / k[2, 0]) * 180 / np.pi) - # print('beta', np.arctan(k[0, 0] / k[2, 0]) * 180 / np.pi) + k = torch.matmul(k,rotation_y(alpha_1).T) return k def rotate_from_dispersive_element_to_lens(self,k,alpha_c_transmis,delta_alpha_c,delta_beta_c,alpha_1): angle_with_P2 = alpha_c_transmis - alpha_1 - delta_alpha_c - - k = np.dot(rotation_y(alpha_1), k) + k = torch.matmul(k,rotation_y(alpha_1).T) # Rotation in relation to P2 around the X axis - k = np.dot(rotation_x(-delta_beta_c), k) + k = torch.matmul(k,rotation_x(-delta_beta_c).T) # Rotation in relation to P2 around the Y axis - k = np.dot(rotation_y(angle_with_P2), k) - + k = torch.matmul(k,rotation_y(angle_with_P2).T) + #print(k.shape) return k - def propagate_through_arm(self, X_vec_in, Y_vec_in, n1, n2,n3,lba): - """ - Propagate the light through one system arm : (lens + dispersive element + lens) - Args: - X_vec_in (numpy.ndarray) : X coordinates of the coded aperture pixels (1D array) - Y_vec_in (numpy.ndarray) : Y coordinates of the coded aperture pixels (1D array) - n (numpy.ndarray) : refractive indexes of the system (at the corresponding wavelength) - lba (numpy.ndarray) : wavelengths + def propagate_through_triple_prism(self,k,n1,n2,n3,A1,A2,A3): - Returns: - tuple: flatten arrays corresponding to the propagated X and Y coordinates - """ + norm_k = torch.sqrt(k[...,0] ** 2 + k[...,1] ** 2 + k[...,2] ** 2) + norm_k = norm_k.unsqueeze(-1) + norm_k = norm_k.repeat(1, 1, 1,3) + k_normalized = k / norm_k - dispersive_element_type = self.dispersive_element_type - A1 = self.A1 - A2 = self.A2 - A3 = self.A3 - G = self.G - m = self.m - F = self.F - delta_alpha_c = self.delta_alpha_c - delta_beta_c = self.delta_beta_c - alpha_c = self.alpha_c - # alpha_c_transmis = self.alpha_c_transmis - - # alpha_c = 50*np.pi/180 - - - k = self.model_Lens_pos_to_angle(X_vec_in, Y_vec_in, F) - - if dispersive_element_type == "prism": - alpha_1 = A1 - A1/2 - k = self.rotate_from_lens_to_dispersive_element(k,alpha_c,delta_alpha_c,delta_beta_c,alpha_1) - k = self.propagate_through_simple_prism(k,n1,A1) - k = self.rotate_from_dispersive_element_to_lens(k,alpha_c_transmis,delta_alpha_c,delta_beta_c,alpha_1) - - elif dispersive_element_type == "doubleprism": - alpha_1 = A1 - A2/2 - k = self.rotate_from_lens_to_dispersive_element(k,alpha_c,delta_alpha_c,delta_beta_c,alpha_1) - k = self.propagate_through_double_prism(k,n1,n2,A1,A2) - k = self.rotate_from_dispersive_element_to_lens(k,alpha_c_transmis,delta_alpha_c,delta_beta_c,alpha_1) - - elif dispersive_element_type == "tripleprism": - alpha_1 = A1 - A2/2 - k = self.rotate_from_lens_to_dispersive_element(k,alpha_c,delta_alpha_c,delta_beta_c,alpha_1) - k = self.propagate_through_triple_prism(k,n1,n2,n3,A1,A2,A3) - k = self.rotate_from_dispersive_element_to_lens(k,alpha_c_transmis,delta_alpha_c,delta_beta_c,alpha_1) - - elif dispersive_element_type == "grating": - alpha_1 = 0 - k = self.rotate_from_lens_to_dispersive_element(k,alpha_c,delta_alpha_c,delta_beta_c,alpha_1) - k = self.model_Grating_angle_to_angle(k, lba, m, G) - k = self.rotate_from_dispersive_element_to_lens(k,alpha_c_transmis,delta_alpha_c,delta_beta_c,alpha_1) + k,theta_in_1, theta_out_1,distance_from_total_intern_reflection1 = self.model_Prism_angle_to_angle_torch(k_normalized, n1, A1) + k = k * norm_k + k = torch.matmul(k,rotation_z(torch.tensor(np.pi,device=self.device)).T) + k,theta_in_2, theta_out_2,distance_from_total_intern_reflection2 = self.model_Prism_angle_to_angle_torch(k, n2, A2) + k = k * norm_k + k = torch.matmul(k,rotation_z(torch.tensor(np.pi,device=self.device)).T) + k,theta_in_3, theta_out_3,distance_from_total_intern_reflection3 = self.model_Prism_angle_to_angle_torch(k, n3, A3) + k = k * norm_k - X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k, F) + list_theta_in = [theta_in_1,theta_in_2,theta_in_3] + list_theta_out = [theta_out_1,theta_out_2,theta_out_3] + + self.min_distance_from_total_intern_reflection = min(torch.min(distance_from_total_intern_reflection1), + torch.min(distance_from_total_intern_reflection2), + torch.min(distance_from_total_intern_reflection3)) + + return k, list_theta_in, list_theta_out - return X_vec_out, Y_vec_out - def model_Grating_angle_to_angle(self,k_in, lba, m, G): + + + def model_Grating_angle_to_angle(self, k_in, lba, m, G): """ Model of the grating Args: - k_in (numpy.ndarray) : wave vector of the incident ray (shape = 3 x N) - lba (numpy.ndarray) : wavelengths (shape = N) -- in nm + k_in (torch.Tensor) : wave vector of the incident ray (shape = 3 x N) + lba (torch.Tensor) : wavelengths (shape = N) -- in nm m (float) : diffraction order of the grating -- no units G (float) : lines density of the grating -- in lines/mm Returns: - numpy.ndarray: wave vector of the outgoing ray (shape = 3 x N) - + torch.Tensor: wave vector of the outgoing ray (shape = 3 x N) """ - alpha_in = np.arctan(k_in[0]) * np.sqrt(1 + np.tan(k_in[0])**2 + np.tan(k_in[1])**2) - beta_in = np.arctan(k_in[1]) * np.sqrt(1 + np.tan(k_in[0])**2 + np.tan(k_in[1])**2) - alpha_out = -1*np.arcsin(m * lba*10**-9 * G * 10**3 - np.sin(alpha_in)) - beta_out = beta_in + alpha_in = torch.atan(k_in[...,0]) * torch.sqrt(1 + torch.tan(k_in[...,0])**2 + torch.tan(k_in[...,1])**2) + beta_in = torch.atan(k_in[...,1]) * torch.sqrt(1 + torch.tan(k_in[...,0])**2 + torch.tan(k_in[...,1])**2) + alpha_out = -1 * torch.asin(m * lba * 10**-9 * G * 10**3 - torch.sin(alpha_in)) + beta_out = beta_in - k_out = [np.sin(alpha_out) * np.cos(beta_out), - np.sin(beta_out)*np.cos(alpha_out), - np.cos(alpha_out) * np.cos(beta_out)] + k_out = torch.stack([ + torch.sin(alpha_out) * torch.cos(beta_out), + torch.sin(beta_out) * torch.cos(alpha_out), + torch.cos(alpha_out) * torch.cos(beta_out) + ], dim=-1) return k_out - def simplified_grating_in_out(self, alpha,lba,m,G): - """ - Model 1D of the grating in the dispersion direction - - Args: - alpha (numpy.ndarray or float) : angle of the incident ray (shape = N) -- in radians - lba (numpy.ndarray or float) : wavelengths (shape = N) -- in nm - m (float) : diffraction order of the grating -- no units - G (float) : lines density of the grating -- in lines/mm - - Returns: - numpy.ndarray: angle of the outgoing ray (shape = N) -- in radians - - """ - - alpha_out = np.arcsin(m * lba * 10 ** -9 * G * 10 ** 3 - np.sin(alpha)) - - return alpha_out def model_Lens_angle_to_position(self,k_in,F): """ Model of the lens : angle to position Args: - k_in (numpy.ndarray) : wave vector of the incident ray (shape = 3 x N) + k_in (torch.tensor) : wave vector of the incident ray (shape = 3 x N) F (float) : focal length of the lens -- in um Returns: @@ -382,15 +318,14 @@ def model_Lens_angle_to_position(self,k_in,F): """ - alpha = np.arctan(k_in[0] / k_in[2]) - beta = np.arctan(k_in[1] / k_in[2]) + alpha = torch.arctan(k_in[...,0] / k_in[...,2]) + beta = torch.arctan(k_in[...,1] / k_in[...,2]) - x = F * np.tan(alpha) - y = F * np.tan(beta) + x = F * torch.tan(alpha) + y = F * torch.tan(beta) return x, y - @snoop def model_Prism_angle_to_angle(self,k0, n,A): """ Ray tracing through the prism @@ -413,109 +348,74 @@ def model_Prism_angle_to_angle(self,k0, n,A): return kout - def model_Lens_pos_to_angle(self,x_obj, y_obj, F): + + def model_Prism_angle_to_angle_torch(self,k0, n,A): """ - Model of the lens : position to angle + Ray tracing through the prism Args: - x_obj (numpy.ndarray) : position X in the image plane (shape = N) -- in um - y_obj (numpy.ndarray) : position Y in the image plane (shape = N) -- in um - F (float) : focal length of the lens -- in um + k0 (torch.tensor) : wave vector of the incident ray + n (torch.tensor) : refractive index of the prism + A (float) : angle of the prism -- in radians Returns: - numpy.ndarray: wave vector of the outgoing ray (shape = 3 x N) + torch.tensor: wave vector of the outgoing ray """ + kp = torch.zeros_like(k0,device=k0.device) + kout = torch.zeros_like(k0,device=k0.device) - alpha = -1*np.arctan(x_obj / F) - beta = -1*np.arctan(y_obj / F) + theta_in = torch.atan2(k0[...,0], k0[...,2]) - k_out = np.array([[np.sin(alpha) * np.cos(beta)], - [np.sin(beta)*np.cos(alpha)], - [np.cos(alpha) * np.cos(beta)]]) + kp[...,0] = k0[...,0] + kp[...,1] = k0[...,1] - return k_out + # compare = n ** 2 - k0[...,0] ** 2 - k0[...,1] ** 2 - def calculate_alpha_c(self): - """ - Calculate the relative angle of incidence between the lenses and the dispersive element - Returns: - float: angle of incidence - """ - if self.dispersive_element_type == "prism": - self.Dm = self.calculate_minimum_deviation(self.sellmeier(self.lba_c), self.A1) - self.alpha_c = self.get_incident_angle_min_dev(self.A1,self.Dm) - self.alpha_c_transmis = self.alpha_c - - if self.dispersive_element_type == "doubletprism": - self.Dm = self.calculate_minimum_deviation(self.sellmeier(self.lba_c), self.A1) - self.alpha_c = self.get_incident_angle_min_dev(self.A1,self.Dm) - self.alpha_c_transmis = self.alpha_c + kp[...,2] = torch.sqrt(n ** 2 - k0[...,0] ** 2 - k0[...,1] ** 2) - if self.dispersive_element_type == "grating": - self.alpha_c = 0 - self.alpha_c_transmis = self.simplified_grating_in_out(self.alpha_c,self.lba_c,self.m,self.G) + theta_out = torch.atan2(kp[...,0], kp[...,2]) - return self.alpha_c + kp_r = torch.matmul(kp, rotation_y(-A).T) + kout[...,0] = kp_r[...,0] + kout[...,1] = kp_r[...,1] + kout[...,2] = torch.sqrt(1 - kp_r[...,0] ** 2 - kp_r[...,1] ** 2) - def calculate_minimum_deviation(self,n, A): - """ - minimum deviation angle of a prism of index n and apex angle A - - Args: - n (float or numpy.ndarray): index of the prism -- no units - A (float): apex angle of the prism -- in radians - - Returns: - float or numpy.ndarray: minimum deviation angle -- in radians - """ - return 2 * np.arcsin(n * np.sin(A / 2)) - A - - def get_incident_angle_min_dev(self,A, D_m): - """ - Calculate the angle of incidence corresponding to the minimum deviation angle - - Args: - A (float): apex angle of the prism -- in radians - D_m (float): minimum deviation angle -- in radians - - Returns: - float: angle of incidence corresponding to minimum of deviation -- in radians - """ - return (A + D_m) / 2 + distance_from_total_intern_reflection = 1 - kp_r[...,0] ** 2 - kp_r[...,1] ** 2 + return kout, theta_in, theta_out,distance_from_total_intern_reflection + - def sellmeier(self,lambda_, glass_type="BK7"): + def model_Lens_pos_to_angle(self,x_obj, y_obj, F): """ - Evaluating the refractive index value of a prism for a given lambda based on Sellmeier equation + Model of the lens : position to angle Args: - lambda_ (numpy.ndarray of float) : wavelength in nm + x_obj (torch.tensor) : position X in the image plane -- in um + y_obj (torch.tensor) : position Y in the image plane -- in um + F (torch.tensor) : focal length of the lens -- in um Returns: - numpy.ndarray of float: index value corresponding to the input wavelength + torch.tensor: wave vector of the outgoing ray """ + alpha = -1 * torch.atan(x_obj / F) + beta = -1 * torch.atan(y_obj / F) - if glass_type == "BK7": - B1 = 1.03961212 - B2 = 0.231792344 - B3 = 1.01046945 - C1 = 6.00069867 * (10 ** -3) - C2 = 2.00179144 * (10 ** -2) - C3 = 1.03560653 * (10 ** 2) + # Adjusting the dimension for k_out to make it a 4D tensor + #TODO : verify that float64 is the right type + k_out = torch.zeros(size=(x_obj.shape[0], x_obj.shape[1], x_obj.shape[2], 3),dtype=torch.float64,device=x_obj.device) - else : - raise Exception("glass_type is Unknown") + # the fourth dimension should have 3 components + k_out[...,0] = torch.sin(alpha) * torch.cos(beta) + k_out[...,1] = torch.sin(beta) * torch.cos(alpha) + k_out[...,2] = torch.cos(alpha) * torch.cos(beta) - lambda_in_mm = lambda_ / 1000 + return k_out - n = np.sqrt(1 + B1 * lambda_in_mm ** 2 / (lambda_in_mm ** 2 - C1) + B2 * lambda_in_mm ** 2 / ( - lambda_in_mm ** 2 - C2) + B3 * lambda_in_mm ** 2 / (lambda_in_mm ** 2 - C3)) - return n def generate_2D_gaussian(self,radius, sample_size_x, sample_size_y, nb_of_samples): """ @@ -582,3 +482,165 @@ def check_if_sampling_is_sufficiant(self): print("The 'number of spectral samples' (cf. system config) is not sufficiant to avoid weird sampling effect( aliasing ?). RAISE IT !") return nb_of_sample_points_per_pix + + + + def propagate_central_microm_through_disperser(self,lambda_): + + if self.index_estimation_method == "cauchy": + n1 = self.calculate_dispersion_with_cauchy(lambda_,self.nd1,self.vd1).clone().detach().requires_grad_(True).to(device=self.device) + n2 = self.calculate_dispersion_with_cauchy(lambda_,self.nd2,self.vd2).clone().detach().requires_grad_(True).to(device=self.device) + n3 = self.calculate_dispersion_with_cauchy(lambda_,self.nd3,self.vd3).clone().detach().requires_grad_(True).to(device=self.device) + if self.index_estimation_method == "sellmeier": + n1 = self.sellmeier(lambda_,self.glass1).clone().detach().requires_grad_(True).to(device=self.device) + try: + n2 = self.sellmeier(lambda_,self.glass2).clone().detach().requires_grad_(True).to(device=self.device) + except: + n2 = torch.tensor(1,device=self.device) + try: + n3 = self.sellmeier(lambda_,self.glass3).clone().detach().requires_grad_(True).to(device=self.device) + except: + n3 = torch.tensor(1,device=self.device) + + x0 = torch.zeros((1,1,1,1)).to(device=self.device) + y0 = torch.zeros((1,1,1,1)).to(device=self.device) + + k = self.model_Lens_pos_to_angle(x0, y0, self.F) + + alpha_1 = self.define_alpha_1() + + k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) + + if self.dispersive_element_type in ["prism", "doubleprism", "amici", "tripleprism"]: + k, list_theta_in, list_theta_out = self.propagate_through_triple_prism(k,n1,n2,n3,self.A1,self.A2,self.A3) + self.list_theta_in = list_theta_in + self.list_theta_out = list_theta_out + elif self.dispersive_element_type == "grating": + k = self.model_Grating_angle_to_angle(k, lambda_, self.m, self.G) + + alpha = torch.arctan(k[...,0] / k[...,2]) + beta = torch.arctan(k[...,1] / k[...,2]) + + + + return alpha + + + + def define_alpha_1(self): + """ + Define the angle alpha_1 depending on the type of dispersive element + + Returns: + float: angle alpha_1 + """ + + if self.dispersive_element_type == "prism": + alpha_1 = self.A1 / 2 + elif self.dispersive_element_type in ["doubleprism", "amici", "tripleprism"]: + alpha_1 = self.A1 - self.A2 / 2 + elif self.dispersive_element_type == "grating": + alpha_1 = torch.tensor(0,device=self.device) + else: + raise Exception("dispersive_element_type should be prism or grating") + + return alpha_1 + + + + + + +def rotation_z(theta): + """ + Rotate 3D matrix around the Z axis using PyTorch + + Args: + theta (torch.Tensor): Input angle (in rad) + + Returns: + torch.Tensor: 3D rotation matrix + """ + # Ensure theta is a tensor with requires_grad=True + if not isinstance(theta, torch.Tensor): + theta = torch.tensor(theta, requires_grad=True) + + cos_theta = torch.cos(theta) + sin_theta = torch.sin(theta) + + # Construct the rotation matrix using torch.stack to support gradient computation + # For rotation around the Z axis, the changes affect the first two rows + row1 = torch.stack([cos_theta, -sin_theta, torch.zeros_like(theta)]) + row2 = torch.stack([sin_theta, cos_theta, torch.zeros_like(theta)]) + row3 = torch.stack([torch.zeros_like(theta), torch.zeros_like(theta), torch.ones_like(theta)]) + + # Concatenate the rows to form the rotation matrix + r = torch.stack([row1, row2, row3], dim=0) + + # Adjust the matrix to have the correct shape (3, 3) for each theta + r = r.transpose(0, 1) # This may need adjustment based on how you intend to use r + + return r + + +def rotation_y(theta): + """ + Rotate 3D matrix around the Y axis using PyTorch + + Args: + theta (torch.Tensor): Input angle (in rad) + + Returns: + torch.Tensor: 3D rotation matrix + """ + # Ensure theta is a tensor with requires_grad=True + if not isinstance(theta, torch.Tensor): + theta = torch.tensor(theta, requires_grad=True, dtype=torch.float32) + + cos_theta = torch.cos(theta) + sin_theta = torch.sin(theta) + + # Construct the rotation matrix using torch.stack to support gradient computation + # For rotation around the Y axis, the changes affect the first and third rows + row1 = torch.stack([cos_theta, torch.zeros_like(theta), -sin_theta]) # Note the change to -sin_theta for correct Y-axis rotation + row2 = torch.stack([torch.zeros_like(theta), torch.ones_like(theta), torch.zeros_like(theta)]) + row3 = torch.stack([sin_theta, torch.zeros_like(theta), cos_theta]) + + # Concatenate the rows to form the rotation matrix + r = torch.stack([row1, row2, row3], dim=0) + + # Adjust the matrix to have the correct shape (3, 3) for each theta + r = r.transpose(0, 1) # Adjust transpose for consistency with your requirements + + return r + + +def rotation_x(theta): + """ + Rotate 3D matrix around the X axis using PyTorch + + Args: + theta (tensor): Input angle (in rad) + + Returns: + torch.Tensor: 3D rotation matrix + """ + # Ensure theta is a tensor with requires_grad=True + if not isinstance(theta, torch.Tensor): + theta = torch.tensor(theta, requires_grad=True, dtype=torch.float32) + + cos_theta = torch.cos(theta) + sin_theta = torch.sin(theta) + + # Use torch.stack and torch.cat to construct the rotation matrix + row1 = torch.stack([torch.ones_like(theta), torch.zeros_like(theta), torch.zeros_like(theta)]) + row2 = torch.stack([torch.zeros_like(theta), cos_theta, -sin_theta]) + row3 = torch.stack([torch.zeros_like(theta), sin_theta, cos_theta]) + + # Concatenate the rows to form the rotation matrix + r = torch.stack([row1, row2, row3], dim=0) + + # Transpose the matrix to match the expected shape + r = r.transpose(0, 1) + + return r diff --git a/simca/OpticalModelTorch.py b/simca/OpticalModelTorch.py deleted file mode 100644 index 79256c1..0000000 --- a/simca/OpticalModelTorch.py +++ /dev/null @@ -1,944 +0,0 @@ -import torch -import snoop - - -def rotation_z_torch(theta): - """ - Rotate 3D matrix around the Z axis using PyTorch - - Args: - theta (torch.Tensor): Input angle (in rad) - - Returns: - torch.Tensor: 3D rotation matrix - """ - # Ensure theta is a tensor with requires_grad=True - if not isinstance(theta, torch.Tensor): - theta = torch.tensor(theta, requires_grad=True) - - cos_theta = torch.cos(theta) - sin_theta = torch.sin(theta) - - # Construct the rotation matrix using torch.stack to support gradient computation - # For rotation around the Z axis, the changes affect the first two rows - row1 = torch.stack([cos_theta, -sin_theta, torch.zeros_like(theta)]) - row2 = torch.stack([sin_theta, cos_theta, torch.zeros_like(theta)]) - row3 = torch.stack([torch.zeros_like(theta), torch.zeros_like(theta), torch.ones_like(theta)]) - - # Concatenate the rows to form the rotation matrix - r = torch.stack([row1, row2, row3], dim=0) - - # Adjust the matrix to have the correct shape (3, 3) for each theta - r = r.transpose(0, 1) # This may need adjustment based on how you intend to use r - - return r - - -def rotation_y_torch(theta): - """ - Rotate 3D matrix around the Y axis using PyTorch - - Args: - theta (torch.Tensor): Input angle (in rad) - - Returns: - torch.Tensor: 3D rotation matrix - """ - # Ensure theta is a tensor with requires_grad=True - if not isinstance(theta, torch.Tensor): - theta = torch.tensor(theta, requires_grad=True, dtype=torch.float32) - - cos_theta = torch.cos(theta) - sin_theta = torch.sin(theta) - - # Construct the rotation matrix using torch.stack to support gradient computation - # For rotation around the Y axis, the changes affect the first and third rows - row1 = torch.stack([cos_theta, torch.zeros_like(theta), -sin_theta]) # Note the change to -sin_theta for correct Y-axis rotation - row2 = torch.stack([torch.zeros_like(theta), torch.ones_like(theta), torch.zeros_like(theta)]) - row3 = torch.stack([sin_theta, torch.zeros_like(theta), cos_theta]) - - # Concatenate the rows to form the rotation matrix - r = torch.stack([row1, row2, row3], dim=0) - - # Adjust the matrix to have the correct shape (3, 3) for each theta - r = r.transpose(0, 1) # Adjust transpose for consistency with your requirements - - return r - - -def rotation_x_torch(theta): - """ - Rotate 3D matrix around the X axis using PyTorch - - Args: - theta (tensor): Input angle (in rad) - - Returns: - torch.Tensor: 3D rotation matrix - """ - # Ensure theta is a tensor with requires_grad=True - if not isinstance(theta, torch.Tensor): - theta = torch.tensor(theta, requires_grad=True, dtype=torch.float32) - - cos_theta = torch.cos(theta) - sin_theta = torch.sin(theta) - - # Use torch.stack and torch.cat to construct the rotation matrix - row1 = torch.stack([torch.ones_like(theta), torch.zeros_like(theta), torch.zeros_like(theta)]) - row2 = torch.stack([torch.zeros_like(theta), cos_theta, -sin_theta]) - row3 = torch.stack([torch.zeros_like(theta), sin_theta, cos_theta]) - - # Concatenate the rows to form the rotation matrix - r = torch.stack([row1, row2, row3], dim=0) - - # Transpose the matrix to match the expected shape - r = r.transpose(0, 1) - - return r - -from simca.functions_general_purpose import * -# import snoop -import torch -from opticalglass.glassfactory import create_glass -import pytorch_lightning as pl - -class OpticalModelTorch(pl.LightningModule): - """ - Class that contains the optical model caracteristics and propagation models - """ - def __init__(self, config): - super().__init__() - self.system_config = config - self.dispersive_element_type = config["system architecture"]["dispersive element"]["type"] - self.lba_c = torch.tensor(config["system architecture"]["dispersive element"]["wavelength center"]) - - # Can be optimized - # 1 - with DE - self.glass1 = create_glass(config["system architecture"]["dispersive element"]["glass1"], 'Schott') - self.glass2 = create_glass(config["system architecture"]["dispersive element"]["glass2"], 'Schott') - self.glass3 = create_glass(config["system architecture"]["dispersive element"]["glass3"], 'Schott') - - # - with any optimizer - self.A1 = torch.tensor(math.radians(config["system architecture"]["dispersive element"]["A1"]),requires_grad=True) - self.A2 = torch.tensor(math.radians(config["system architecture"]["dispersive element"]["A2"]),requires_grad=True) - self.A3 = torch.tensor(math.radians(config["system architecture"]["dispersive element"]["A3"]),requires_grad=True) - self.F = torch.tensor(config["system architecture"]["focal lens"]) - - - self.G = torch.tensor(config["system architecture"]["dispersive element"]["G"]) - self.m = torch.tensor(config["system architecture"]["dispersive element"]["m"]) - - self.alpha_c = torch.tensor(math.radians(config["system architecture"]["dispersive element"]["alpha_c"]),requires_grad=True) - self.delta_alpha_c = torch.tensor(math.radians(config["system architecture"]["dispersive element"]["delta alpha c"])) - self.delta_beta_c = torch.tensor(math.radians(config["system architecture"]["dispersive element"]["delta beta c"])) - - self.continuous_glass_materials1 = config["system architecture"]["dispersive element"]["continuous glass materials 1"] - self.continuous_glass_materials2 = config["system architecture"]["dispersive element"]["continuous glass materials 2"] - self.continuous_glass_materials3 = config["system architecture"]["dispersive element"]["continuous glass materials 3"] - - self.nd1 = torch.tensor(config["system architecture"]["dispersive element"]["nd1"],requires_grad=True) - self.vd1 = torch.tensor(config["system architecture"]["dispersive element"]["vd1"],requires_grad=True) - self.nd2 = torch.tensor(config["system architecture"]["dispersive element"]["nd2"],requires_grad=True) - self.vd2 = torch.tensor(config["system architecture"]["dispersive element"]["vd2"],requires_grad=True) - self.nd3 = torch.tensor(config["system architecture"]["dispersive element"]["nd3"],requires_grad=True) - self.vd3 = torch.tensor(config["system architecture"]["dispersive element"]["vd3"],requires_grad=True) - - alpha_c_transmis = -1*self.propagate_central_microm_through_disperser(self.lba_c) - self.alpha_c_transmis = alpha_c_transmis - - print("Optica lmodel device : ",self.device) - - - - def rerun_central_dispersion(self): - alpha_c_transmis = -1 * self.propagate_central_microm_through_disperser(self.lba_c) - self.alpha_c_transmis = alpha_c_transmis - - return alpha_c_transmis - - def propagate(self,X,Y,lba,n1,n2,n3): - - k = self.model_Lens_pos_to_angle(X, Y, self.F) - - - if self.dispersive_element_type == "prism": - alpha_1 = self.A1 - self.A1/2 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k, list_theta_in, list_theta_out = self.propagate_through_simple_prism(k,n1,self.A1) - k = self.rotate_from_dispersive_element_to_lens(k,self.alpha_c_transmis,self.delta_alpha_c,self.delta_beta_c,alpha_1) - - elif self.dispersive_element_type == "doubleprism": - alpha_1 = self.A1 - self.A2/2 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k, list_theta_in, list_theta_out = self.propagate_through_double_prism(k,n1,n2,self.A1,self.A2) - k = self.rotate_from_dispersive_element_to_lens(k,self.alpha_c_transmis,self.delta_alpha_c,self.delta_beta_c,alpha_1) - - elif self.dispersive_element_type == "tripleprism": - alpha_1 = self.A1 - self.A2/2 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k, list_theta_in, list_theta_out = self.propagate_through_triple_prism(k,n1,n2,n3,self.A1,self.A2,self.A3) - k = self.rotate_from_dispersive_element_to_lens(k,self.alpha_c_transmis,self.delta_alpha_c,self.delta_beta_c,alpha_1) - - elif self.dispersive_element_type == "grating": - alpha_1 = 0 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k = self.model_Grating_angle_to_angle(k, lba, self.m, self.G) - k = self.rotate_from_dispersive_element_to_lens(k,self.alpha_c_transmis,self.delta_alpha_c,self.delta_beta_c,alpha_1) - - # print(k) - X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k, self.F) - - return X_vec_out, Y_vec_out - - def calculate_dispersion_with_cauchy(self,lambda_vec, nD, V): - lambda_vec = lambda_vec * 1e-9 # Convert nm to meters for calculation - lambda_D = 589.3e-9 # D line wavelength in meters - lambda_F = 486.1e-9 # F line wavelength in meters - lambda_C = 656.3e-9 # C line wavelength in meters - - # Calculate B using the given formula - B = (nD - 1) / (V * (1 / lambda_F ** 2 - 1 / lambda_C ** 2)) - - # Calculate A using the given formula - A = nD - B / lambda_D ** 2 - - # Calculate n for each wavelength in lambda_vec - n = A + B / lambda_vec ** 2 # Note: lambda_vec is already in meters - - return n - - def rotate_from_lens_to_dispersive_element(self,k,alpha_c,delta_alpha_c,delta_beta_c,alpha_1): - - - angle_with_P1 = alpha_c - alpha_1 + delta_alpha_c - k = k.to(dtype=torch.float32) - # k_1 = rotation_y_torch(angle_with_P1) @ k - k = torch.matmul(k,rotation_y_torch(angle_with_P1).T) - # Rotation in relation to P1 around the X axis - k = k.to(dtype=torch.float32) - k = torch.matmul(k,rotation_x_torch(delta_beta_c).T) - # Rotation of P1 in relation to frame_in along the new Y axis - k = k.to(dtype=torch.float32) - - k = torch.matmul(k,rotation_y_torch(alpha_1).T) - - - - return k - - def rotate_from_dispersive_element_to_lens(self,k,alpha_c_transmis,delta_alpha_c,delta_beta_c,alpha_1): - - angle_with_P2 = alpha_c_transmis - alpha_1 - delta_alpha_c - k = torch.matmul(k,rotation_y_torch(alpha_1).T) - # Rotation in relation to P2 around the X axis - k = torch.matmul(k,rotation_x_torch(-delta_beta_c).T) - # Rotation in relation to P2 around the Y axis - k = torch.matmul(k,rotation_y_torch(angle_with_P2).T) - #print(k.shape) - return k - - def propagate_through_simple_prism(self, k, n, A): - norm_k = torch.sqrt(k[..., 0] ** 2 + k[..., 1] ** 2 + k[..., 2] ** 2) - norm_k = norm_k.unsqueeze(-1) - norm_k = norm_k.repeat(1, 1, 1, 3) - # Use out-of-place operation instead of in-place - k_normalized = k / norm_k - - k_updated, theta_in, theta_out,distance_from_total_intern_reflection = self.model_Prism_angle_to_angle_torch(k_normalized, n, A) - # Apply the normalization factor to k_updated if necessary - k_updated = k_updated * norm_k - - list_theta_in = [theta_in] - list_theta_out = [theta_out] - - return k_updated, list_theta_in, list_theta_out - - def propagate_through_double_prism(self,k,n1,n2,A1,A2): - - norm_k = torch.sqrt(k[...,0] ** 2 + k[...,1] ** 2 + k[...,2] ** 2) - norm_k = norm_k.unsqueeze(-1) - norm_k = norm_k.repeat(1, 1, 1,3) - k_normalized = k / norm_k - - k,theta_in_1, theta_out_1,distance_from_total_intern_reflection = self.model_Prism_angle_to_angle_torch(k_normalized, n1, A1) - k = k * norm_k - k = torch.matmul(k,rotation_z_torch(torch.tensor(np.pi)).T) - k,theta_in_2, theta_out_2,distance_from_total_intern_reflection = self.model_Prism_angle_to_angle_torch(k, n2, A2) - k = k * norm_k - k = torch.matmul(k, rotation_z_torch(torch.tensor(np.pi)).T) - - list_theta_in = [theta_in_1,theta_in_2] - list_theta_out = [theta_out_1,theta_out_2] - - return k,list_theta_in, list_theta_out - - - - def propagate_through_triple_prism(self,k,n1,n2,n3,A1,A2,A3): - - norm_k = torch.sqrt(k[...,0] ** 2 + k[...,1] ** 2 + k[...,2] ** 2) - norm_k = norm_k.unsqueeze(-1) - norm_k = norm_k.repeat(1, 1, 1,3) - k_normalized = k / norm_k - - - k,theta_in_1, theta_out_1,distance_from_total_intern_reflection1 = self.model_Prism_angle_to_angle_torch(k_normalized, n1, A1) - k = k * norm_k - k = torch.matmul(k,rotation_z_torch(torch.tensor(np.pi)).T) - k,theta_in_2, theta_out_2,distance_from_total_intern_reflection2 = self.model_Prism_angle_to_angle_torch(k, n2, A2) - k = k * norm_k - k = torch.matmul(k,rotation_z_torch(torch.tensor(np.pi)).T) - k,theta_in_3, theta_out_3,distance_from_total_intern_reflection3 = self.model_Prism_angle_to_angle_torch(k, n3, A3) - k = k * norm_k - - list_theta_in = [theta_in_1,theta_in_2,theta_in_3] - list_theta_out = [theta_out_1,theta_out_2,theta_out_3] - - self.min_distance_from_total_intern_reflection = min(torch.min(distance_from_total_intern_reflection1), - torch.min(distance_from_total_intern_reflection2), - torch.min(distance_from_total_intern_reflection3)) - - return k, list_theta_in, list_theta_out - - - - def propagation_with_distorsions_torch(self, X_input_grid, Y_input_grid): - """ - Propagate the coded aperture coded_aperture through one CASSI system - - Args: - X_input_grid (numpy.ndarray): x coordinates grid - Y_input_grid (numpy.ndarray): y coordinates grid - - Returns: - tuple: X coordinates of the propagated coded aperture grids, Y coordinates of the propagated coded aperture grids - """ - - self.calculate_central_dispersion() - - X_input_grid = torch.from_numpy(X_input_grid) if isinstance(X_input_grid, np.ndarray) else X_input_grid - Y_input_grid = torch.from_numpy(Y_input_grid) if isinstance(Y_input_grid, np.ndarray) else Y_input_grid - wavelength_vec = torch.from_numpy(self.system_wavelengths) if isinstance(self.system_wavelengths, np.ndarray) else self.system_wavelengths - - X_input_grid_3D = X_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples) - Y_input_grid_3D = Y_input_grid[:,:,None].repeat(1, 1,self.nb_of_spectral_samples) - lba_3D = wavelength_vec[None,None,:].repeat(X_input_grid.shape[0], X_input_grid.shape[1],1) - n_tensor = self.sellmeier_torch(lba_3D) - - X, Y = self.propagate_through_arm_torch(X_input_grid_3D,Y_input_grid_3D,n=n_tensor,lba=lba_3D) - - return X, Y - - def calculate_central_dispersion(self): - """ - Calculate the dispersion related to the central pixel of the coded aperture - - Returns: - numpy.float: spectral dispersion of the central pixel of the coded aperture - """ - - self.alpha_c = self.calculate_alpha_c() - - X0_coordinates_array_flatten = np.zeros(self.system_wavelengths.shape[0]) - Y0_coordinates_array_flatten = np.zeros(self.system_wavelengths.shape[0]) - lba_array_flatten = self.system_wavelengths - - n_array_flatten = np.full(lba_array_flatten.shape, self.sellmeier(lba_array_flatten)) - - X0_propagated, Y0_propagated = self.propagate_through_arm(X_vec_in=X0_coordinates_array_flatten,Y_vec_in=Y0_coordinates_array_flatten,n=n_array_flatten,lba=lba_array_flatten) - - self.X0_propagated, self.Y0_propagated = X0_propagated, Y0_propagated - - self.central_distorsion_in_X = np.abs(self.X0_propagated[-1] - self.X0_propagated[0]) - - return self.central_distorsion_in_X - - def propagate_through_arm(self, X_vec_in, Y_vec_in, n, lba): - - """ - Propagate the light through one system arm : (lens + dispersive element + lens) - - Args: - X_vec_in (numpy.ndarray) : X coordinates of the coded aperture pixels (1D array) - Y_vec_in (numpy.ndarray) : Y coordinates of the coded aperture pixels (1D array) - n (numpy.ndarray) : refractive indexes of the system (at the corresponding wavelength) - lba (numpy.ndarray) : wavelengths - - Returns: - tuple: flatten arrays corresponding to the propagated X and Y coordinates - """ - - dispersive_element_type = self.dispersive_element_type - A = self.A - G = self.G - m = self.m - F = self.F - delta_alpha_c = self.delta_alpha_c - delta_beta_c = self.delta_beta_c - alpha_c = self.alpha_c - alpha_c_transmis = self.alpha_c_transmis - - - if dispersive_element_type == "prism": - - angle_with_P1 = alpha_c - A / 2 + delta_alpha_c - angle_with_P2 = alpha_c_transmis - A / 2 - delta_alpha_c - - k = self.model_Lens_pos_to_angle(X_vec_in, Y_vec_in, F) - # Rotation in relation to P1 around the Y axis - - k_1 = rotation_y(angle_with_P1) @ k[:, 0, :] - # Rotation in relation to P1 around the X axis - k_2 = rotation_x(delta_beta_c) @ k_1 - # Rotation of P1 in relation to frame_in along the new Y axis - k_3 = rotation_y(A / 2) @ k_2 - - norm_k = np.sqrt(k_3[0] ** 2 + k_3[1] ** 2 + k_3[2] ** 2) - k_3 /= norm_k - - k_out_p = self.model_Prism_angle_to_angle(k_3, n, A) - k_out_p = k_out_p * norm_k - - k_3_bis = np.dot(rotation_y(A / 2), k_out_p) - - # Rotation in relation to P2 around the X axis - k_2_bis = np.dot(rotation_x(-delta_beta_c), k_3_bis) - # Rotation in relation to P2 around the Y axis - k_1_bis = np.dot(rotation_y(angle_with_P2), k_2_bis) - - X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k_1_bis, F) - - - elif dispersive_element_type == "grating": - - angle_with_P1 = alpha_c - delta_alpha_c - angle_with_P2 = alpha_c_transmis + delta_alpha_c - - k = self.model_Lens_pos_to_angle(X_vec_in, Y_vec_in, F) - # Rotation in relation to P1 around the Y axis - - k_1 = rotation_y(angle_with_P1) @ k[:, 0, :] - # Rotation in relation to P1 around the X axis - k_2 = rotation_x(delta_beta_c) @ k_1 - - k_3 = rotation_y(0) @ k_2 - norm_k = np.sqrt(k_3[0] ** 2 + k_3[1] ** 2 + k_3[2] ** 2) - k_3 /= norm_k - - k_out_p = self.model_Grating_angle_to_angle(k_3, lba, m, G) - k_out_p = k_out_p * norm_k - - k_3_bis = np.dot(rotation_y(0), k_out_p) - - # Rotation in relation to P2 around the X axis - k_2_bis = np.dot(rotation_x(-delta_beta_c), k_3_bis) - # Rotation in relation to P2 around the Y axis - k_1_bis = np.dot(rotation_y(angle_with_P2), k_2_bis) - - X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k_1_bis, F) - - else: - raise Exception("dispersive_element_type should be prism or grating") - - return X_vec_out, Y_vec_out - - - def propagate_through_arm_torch(self, X_tensor_in, Y_tensor_in, n, lba): - - """ - Propagate the light through one system arm : (lens + dispersive element + lens) - - Args: - X_tensor_in (torch.tensor) : X coordinates of the coded aperture pixels (3D array) - Y_tensor_in (torch.tensor) : Y coordinates of the coded aperture pixels (3D array) - n (torch.tensor) : refractive indexes of the system (at the corresponding wavelength) - lba (torch.tensor) : wavelengths - - Returns: - tuple: tensors corresponding to the propagated X and Y coordinates - """ - - dispersive_element_type = self.dispersive_element_type - A = self.A - G = self.G - m = self.m - F = self.F - delta_alpha_c = self.delta_alpha_c - delta_beta_c = self.delta_beta_c - alpha_c = self.alpha_c - alpha_c_transmis = self.alpha_c_transmis - - alpha_c = torch.tensor(alpha_c, dtype=torch.float64) - A = torch.tensor(A, dtype=torch.float64) - delta_alpha_c = torch.tensor(delta_alpha_c, dtype=torch.float64) - alpha_c_transmis = torch.tensor(alpha_c_transmis, dtype=torch.float64) - delta_beta_c = torch.tensor(delta_beta_c, dtype=torch.float64) - - - - - if dispersive_element_type == "prism": - - angle_with_P1 = alpha_c - A / 2 + delta_alpha_c - angle_with_P2 = alpha_c_transmis - A / 2 - delta_alpha_c - - # THERE IS A PROBLEM HERE ??? - k = self.model_Lens_pos_to_angle_torch(X_tensor_in, Y_tensor_in, F) - # Rotation in relation to P1 around the Y axis - - # k_1 = rotation_y_torch(angle_with_P1) @ k - k_1 = torch.matmul(k,rotation_y_torch(angle_with_P1).T) - # Rotation in relation to P1 around the X axis - k_2 = torch.matmul(k_1,rotation_x_torch(delta_beta_c).T) - # Rotation of P1 in relation to frame_in along the new Y axis - k_3 = torch.matmul(k_2,rotation_y_torch(A / 2).T) - - norm_k = torch.sqrt(k_3[...,0] ** 2 + k_3[...,1] ** 2 + k_3[...,2] ** 2) - norm_k = norm_k.unsqueeze(-1) - norm_k = norm_k.repeat(1, 1, 1,3) - k_3 /= norm_k - - k_out_p = self.model_Prism_angle_to_angle_torch(k_3, n, A) - k_out_p = k_out_p * norm_k - - k_3_bis = torch.matmul(k_out_p,rotation_y_torch(A / 2).T) - - # Rotation in relation to P2 around the X axis - k_2_bis = torch.matmul(k_3_bis,rotation_x_torch(-delta_beta_c).T) - # Rotation in relation to P2 around the Y axis - k_1_bis = torch.matmul(k_2_bis,rotation_y_torch(angle_with_P2).T) - - X_vec_out, Y_vec_out = self.model_Lens_angle_to_position_torch(k_1_bis, F) - - - # elif dispersive_element_type == "grating": - # - # angle_with_P1 = alpha_c - delta_alpha_c - # angle_with_P2 = alpha_c_transmis + delta_alpha_c - # - # k = self.model_Lens_pos_to_angle(X_vec_in, Y_vec_in, F) - # # Rotation in relation to P1 around the Y axis - # - # k_1 = rotation_y(angle_with_P1) @ k[:, 0, :] - # # Rotation in relation to P1 around the X axis - # k_2 = rotation_x(delta_beta_c) @ k_1 - # - # k_3 = rotation_y(0) @ k_2 - # norm_k = np.sqrt(k_3[0] ** 2 + k_3[1] ** 2 + k_3[2] ** 2) - # k_3 /= norm_k - # - # k_out_p = self.model_Grating_angle_to_angle(k_3, lba, m, G) - # k_out_p = k_out_p * norm_k - # - # k_3_bis = np.dot(rotation_y(0), k_out_p) - # - # # Rotation in relation to P2 around the X axis - # k_2_bis = np.dot(rotation_x(-delta_beta_c), k_3_bis) - # # Rotation in relation to P2 around the Y axis - # k_1_bis = np.dot(rotation_y(angle_with_P2), k_2_bis) - # - # X_vec_out, Y_vec_out = self.model_Lens_angle_to_position(k_1_bis, F) - # - # else: - # raise Exception("dispersive_element_type should be prism or grating") - # - return X_vec_out, Y_vec_out - - def model_Grating_angle_to_angle(self,k_in, lba, m, G): - """ - Model of the grating - - Args: - k_in (numpy.ndarray) : wave vector of the incident ray (shape = 3 x N) - lba (numpy.ndarray) : wavelengths (shape = N) -- in nm - m (float) : diffraction order of the grating -- no units - G (float) : lines density of the grating -- in lines/mm - - Returns: - numpy.ndarray: wave vector of the outgoing ray (shape = 3 x N) - - """ - - alpha_in = np.arctan(k_in[0]) * np.sqrt(1 + np.tan(k_in[0])**2 + np.tan(k_in[1])**2) - beta_in = np.arctan(k_in[1]) * np.sqrt(1 + np.tan(k_in[0])**2 + np.tan(k_in[1])**2) - - alpha_out = -1*np.arcsin(m * lba*10**-9 * G * 10**3 - np.sin(alpha_in)) - beta_out = beta_in - - - k_out = [np.sin(alpha_out) * np.cos(beta_out), - np.sin(beta_out)*np.cos(alpha_out), - np.cos(alpha_out) * np.cos(beta_out)] - - return k_out - - def simplified_grating_in_out(self, alpha,lba,m,G): - """ - Model 1D of the grating in the dispersion direction - - Args: - alpha (numpy.ndarray or float) : angle of the incident ray (shape = N) -- in radians - lba (numpy.ndarray or float) : wavelengths (shape = N) -- in nm - m (float) : diffraction order of the grating -- no units - G (float) : lines density of the grating -- in lines/mm - - Returns: - numpy.ndarray: angle of the outgoing ray (shape = N) -- in radians - - """ - - alpha_out = np.arcsin(m * lba * 10 ** -9 * G * 10 ** 3 - np.sin(alpha)) - - return alpha_out - - def model_Lens_angle_to_position(self,k_in,F): - """ - Model of the lens : angle to position - - Args: - k_in (torch.tensor) : wave vector of the incident ray (shape = 3 x N) - F (float) : focal length of the lens -- in um - - Returns: - tuple: position in the image plane (X,Y) -- in um - - """ - - alpha = torch.arctan(k_in[...,0] / k_in[...,2]) - beta = torch.arctan(k_in[...,1] / k_in[...,2]) - - x = F * torch.tan(alpha) - y = F * torch.tan(beta) - - return x, y - - def model_Prism_angle_to_angle(self,k0, n,A): - """ - Ray tracing through the prism - - Args: - k0 (numpy.ndarray) : wave vector of the incident ray (shape = 3 x N) - n (numpy.ndarray) : refractive index of the prism (shape = N) - A (float) : angle of the prism -- in radians - - Returns: - numpy.ndarray: wave vector of the outgoing ray (shape = 3 x N) - - """ - - kp = np.array([k0[0], k0[1], np.sqrt(n ** 2 - k0[0] ** 2 - k0[1] ** 2)]) - - kp_r = np.matmul(rotation_y(-A), kp) - - kout = [kp_r[0], kp_r[1], np.sqrt(1 - kp_r[0] ** 2 - kp_r[1] ** 2)] - - return kout - - - def model_Prism_angle_to_angle_torch(self,k0, n,A): - """ - Ray tracing through the prism - - Args: - k0 (torch.tensor) : wave vector of the incident ray - n (torch.tensor) : refractive index of the prism - A (float) : angle of the prism -- in radians - - Returns: - torch.tensor: wave vector of the outgoing ray - - """ - kp = torch.zeros_like(k0,device=k0.device) - kout = torch.zeros_like(k0,device=k0.device) - - theta_in = torch.atan2(k0[...,0], k0[...,2]) - - kp[...,0] = k0[...,0] - kp[...,1] = k0[...,1] - - # compare = n ** 2 - k0[...,0] ** 2 - k0[...,1] ** 2 - - - kp[...,2] = torch.sqrt(n ** 2 - k0[...,0] ** 2 - k0[...,1] ** 2) - - theta_out = torch.atan2(kp[...,0], kp[...,2]) - - kp_r = torch.matmul(kp, rotation_y_torch(-A).T) - - kout[...,0] = kp_r[...,0] - kout[...,1] = kp_r[...,1] - kout[...,2] = torch.sqrt(1 - kp_r[...,0] ** 2 - kp_r[...,1] ** 2) - - distance_from_total_intern_reflection = 1 - kp_r[...,0] ** 2 - kp_r[...,1] ** 2 - - return kout, theta_in, theta_out,distance_from_total_intern_reflection - - def model_Lens_pos_to_angle(self,x_obj, y_obj, F): - """ - Model of the lens : position to angle - - Args: - x_obj (torch.tensor) : position X in the image plane -- in um - y_obj (torch.tensor) : position Y in the image plane -- in um - F (torch.tensor) : focal length of the lens -- in um - - Returns: - torch.tensor: wave vector of the outgoing ray - - """ - - alpha = -1 * torch.atan(x_obj / F) - beta = -1 * torch.atan(y_obj / F) - - # Adjusting the dimension for k_out to make it a 4D tensor - k_out = torch.zeros(size=(x_obj.shape[0], x_obj.shape[1], x_obj.shape[2], 3),dtype=torch.float64,device=x_obj.device) - - # the fourth dimension should have 3 components - k_out[...,0] = torch.sin(alpha) * torch.cos(beta) - k_out[...,1] = torch.sin(beta) * torch.cos(alpha) - k_out[...,2] = torch.cos(alpha) * torch.cos(beta) - - # k_out = torch.cat([ - # sin_alpha * cos_beta, - # sin_beta * cos_alpha, - # cos_alpha * cos_beta - # ], dim=-1) - - return k_out - - def calculate_alpha_c(self): - """ - Calculate the relative angle of incidence between the lenses and the dispersive element - - Returns: - float: angle of incidence - """ - if self.dispersive_element_type == "prism": - self.Dm = self.calculate_minimum_deviation(self.sellmeier(self.lba_c), self.A) - self.alpha_c = self.get_incident_angle_min_dev(self.A,self.Dm) - self.alpha_c_transmis = self.alpha_c - - if self.dispersive_element_type == "grating": - self.alpha_c = 0 - self.alpha_c_transmis = self.simplified_grating_in_out(self.alpha_c,self.lba_c,self.m,self.G) - - return self.alpha_c - - - def calculate_minimum_deviation(self,n, A): - """ - minimum deviation angle of a prism of index n and apex angle A - - Args: - n (float or numpy.ndarray): index of the prism -- no units - A (float): apex angle of the prism -- in radians - - Returns: - float or numpy.ndarray: minimum deviation angle -- in radians - """ - return 2 * np.arcsin(n * np.sin(A / 2)) - A - - def get_incident_angle_min_dev(self,A, D_m): - """ - Calculate the angle of incidence corresponding to the minimum deviation angle - - Args: - A (float): apex angle of the prism -- in radians - D_m (float): minimum deviation angle -- in radians - - Returns: - float: angle of incidence corresponding to minimum of deviation -- in radians - """ - return (A + D_m) / 2 - - - def sellmeier(self,lambda_, glass_type="BK7"): - """ - Evaluating the refractive index value of a prism for a given lambda based on Sellmeier equation - - Args: - lambda_ (numpy.ndarray of float) : wavelength in nm - - Returns: - numpy.ndarray of float: index value corresponding to the input wavelength - - """ - - if glass_type == "BK7": - B1 = 1.03961212 - B2 = 0.231792344 - B3 = 1.01046945 - C1 = 6.00069867 * (10 ** -3) - C2 = 2.00179144 * (10 ** -2) - C3 = 1.03560653 * (10 ** 2) - - else : - raise Exception("glass_type is Unknown") - - lambda_in_mm = lambda_ / 1000 - - n = np.sqrt(1 + B1 * lambda_in_mm ** 2 / (lambda_in_mm ** 2 - C1) + B2 * lambda_in_mm ** 2 / ( - lambda_in_mm ** 2 - C2) + B3 * lambda_in_mm ** 2 / (lambda_in_mm ** 2 - C3)) - - return n - - def sellmeier_torch(self,lambda_, glass_type="BK7"): - """ - Evaluating the refractive index value of a prism for a given lambda based on Sellmeier equation - - Args: - lambda_ (numpy.ndarray of float) : wavelength in nm - - Returns: - numpy.ndarray of float: index value corresponding to the input wavelength - - """ - - if glass_type == "BK7": - B1 = 1.03961212 - B2 = 0.231792344 - B3 = 1.01046945 - C1 = 6.00069867 * (10 ** -3) - C2 = 2.00179144 * (10 ** -2) - C3 = 1.03560653 * (10 ** 2) - - else : - raise Exception("glass_type is Unknown") - - lambda_in_mm = lambda_ / 1000 - - n = torch.sqrt(1 + B1 * lambda_in_mm ** 2 / (lambda_in_mm ** 2 - C1) + B2 * lambda_in_mm ** 2 / ( - lambda_in_mm ** 2 - C2) + B3 * lambda_in_mm ** 2 / (lambda_in_mm ** 2 - C3)) - - return n - - def generate_2D_gaussian(self,radius, sample_size_x, sample_size_y, nb_of_samples): - """ - Generate a 2D Gaussian of a given radius - - Args: - radius (float): radius of the Gaussian - sample_size_x (float): size of each sample along the X axis - sample_size_y (float): size of each sample along the Y axis - nb_of_samples (int): number of samples along each axis - - Returns: - numpy.ndarray: 2D Gaussian shape array - """ - - # Define the grid - grid_size_x = sample_size_x * (nb_of_samples - 1) - grid_size_y = sample_size_y * (nb_of_samples - 1) - x = np.linspace(-grid_size_x / 2, grid_size_x / 2, nb_of_samples) - y = np.linspace(-grid_size_y / 2, grid_size_y / 2, nb_of_samples) - X, Y = np.meshgrid(x, y) - - # Compute the 2D Gaussian function - gaussian_2d = np.exp(-(X ** 2 + Y ** 2) / (2 * radius ** 2)) - - return gaussian_2d - - def generate_psf(self, type, radius): - """ - Generate a PSF - - Args: - type (str): type of PSF to generate - radius (float): radius of the PSF - - Returns: - numpy.ndarray: PSF generated (shape = R x C) - """ - - if type == "Gaussian": - PSF = self.generate_2D_gaussian(radius, self.system_config["detector"]["pixel size along X"], - self.system_config["detector"]["pixel size along Y"], 10) - self.psf = PSF - - return self.psf - - def check_if_sampling_is_sufficiant(self): - """ - Check if the sampling is sufficiant to avoid aliasing. - - Returns: - float: number of sample points per pixel - - """ - - pix_size = self.system_config["detector"]["pixel size along X"] - nb_of_system_wavelengths = self.system_wavelengths.shape[0] - - nb_of_sample_points_per_pix = pix_size / (self.central_distorsion_in_X / nb_of_system_wavelengths ) - print("number of spectral sample points per pixel =",nb_of_sample_points_per_pix) - - if nb_of_sample_points_per_pix < 2: - - print("The 'number of spectral samples' (cf. system config) is not sufficiant to avoid weird sampling effect( aliasing ?). RAISE IT !") - - return nb_of_sample_points_per_pix - - - def propagate_central_microm_through_disperser(self,lambda_): - - if self.continuous_glass_materials1: - n1 = self.calculate_dispersion_with_cauchy(lambda_,self.nd1,self.vd1) - else: - n1 = self.glass1.calc_rindex(lambda_) - if self.continuous_glass_materials2: - n2 = self.calculate_dispersion_with_cauchy(lambda_,self.nd2,self.vd2) - else: - n2 = self.glass2.calc_rindex(lambda_) - if self.continuous_glass_materials3: - n3 = self.calculate_dispersion_with_cauchy(lambda_,self.nd3,self.vd3) - else: - n3 = self.glass3.calc_rindex(lambda_) - - # n1 = 1.5 - # n2 = 1.8 - # n3 = 1.5 - - x0 = torch.zeros((1,1,1,1)) - y0 = torch.zeros((1,1,1,1)) - - k = self.model_Lens_pos_to_angle(x0, y0, self.F) - - # torch.autograd.set_detect_anomaly(True) - - if self.dispersive_element_type == "prism": - - alpha_1 = self.A1 - self.A1/2 - # alpha_1.backward(retain_graph=True) # This requires alpha_1 to be a scalar - # print("A1 requires grd",self.A1.grad) - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - # k.backward(torch.ones_like(k), retain_graph=True) - # print("A1 requires grd", self.A1.grad) - k, list_theta_in, list_theta_out = self.propagate_through_simple_prism(k,n1,self.A1) - # k.backward(torch.ones_like(k), retain_graph=True) - # print("A1 requires grd", self.A1.grad) - - elif self.dispersive_element_type == "doubleprism": - alpha_1 = self.A1 - self.A2/2 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k, list_theta_in, list_theta_out = self.propagate_through_double_prism(k,n1,n2,self.A1,self.A2) - - elif self.dispersive_element_type == "tripleprism": - alpha_1 = self.A1 - self.A2/2 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k, list_theta_in, list_theta_out = self.propagate_through_triple_prism(k,n1,n2,n3,self.A1,self.A2,self.A3) - - elif self.dispersive_element_type == "grating": - alpha_1 = 0 - k = self.rotate_from_lens_to_dispersive_element(k,self.alpha_c,self.delta_alpha_c,self.delta_beta_c,alpha_1) - k = self.model_Grating_angle_to_angle(k, lambda_, self.m, self.G) - - alpha = torch.arctan(k[...,0] / k[...,2]) - beta = torch.arctan(k[...,1] / k[...,2]) - # - # alpha.backward(retain_graph=True) # This requires alpha_1 to be a scalar - # print("A1 requires grd", self.A1.grad) - - self.list_theta_in = list_theta_in - self.list_theta_out = list_theta_out - - return alpha diff --git a/simca/__init__.py b/simca/__init__.py index 0b47a3f..d41900b 100644 --- a/simca/__init__.py +++ b/simca/__init__.py @@ -1 +1,3 @@ -from .CassiSystem import * \ No newline at end of file +from .CassiSystem import * +from .cost_functions_optics import * +from .helper import * \ No newline at end of file diff --git a/simca/configs/acquisition.yml b/simca/configs/acquisition.yml deleted file mode 100644 index 7975f47..0000000 --- a/simca/configs/acquisition.yml +++ /dev/null @@ -1,8 +0,0 @@ -acquisition name : test_acquisition -acquisition type: single acq. -results directory: ./data/results/ -psf: - use_psf: False - type: Gaussian - radius: 100 - diff --git a/simca/configs/cassi_system.yml b/simca/configs/cassi_system.yml deleted file mode 100644 index e7bffc2..0000000 --- a/simca/configs/cassi_system.yml +++ /dev/null @@ -1,34 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 201 # number of pixels along X axis -- no units - number of pixels along Y: 201 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 253 # number of pixels along X axis -- no units - number of pixels along Y: 201 # number of pixels along Y axis -- no units - pixel size along X: 55 # pixel size along X -- in um - pixel size along Y: 55 # pixel size along Y -- in um - -spectral range: - wavelength min: 400 # minimum wavelength -- in nm - wavelength max: 600 # maximum wavelength -- in nm - number of spectral samples: 3 diff --git a/simca/configs/cassi_system_bk7prism.yml b/simca/configs/cassi_system_bk7prism.yml deleted file mode 100644 index f71373d..0000000 --- a/simca/configs/cassi_system_bk7prism.yml +++ /dev/null @@ -1,38 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 543 # central wavelength -- in nm -detector: - number of pixels along X: 201 # number of pixels along X axis -- no units - number of pixels along Y: 201 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 111 # number of pixels along X axis -- no units - number of pixels along Y: 111 # number of pixels along Y axis -- no units - pixel size along X: 101 # pixel size along X -- in um - pixel size along Y: 101 # pixel size along Y -- in um - -spectral range: - wavelength min: 542 # minimum wavelength -- in nm - wavelength max: 544 # maximum wavelength -- in nm - number of spectral samples: 50 diff --git a/simca/configs/cassi_system_optim_optics.yml b/simca/configs/cassi_system_optim_optics.yml deleted file mode 100755 index 91456d1..0000000 --- a/simca/configs/cassi_system_optim_optics.yml +++ /dev/null @@ -1,39 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-SF10 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 70 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 70 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 30 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 543 # central wavelength -- in nm -detector: - number of pixels along X: 201 # number of pixels along X axis -- no units - number of pixels along Y: 201 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 11 # number of pixels along X axis -- no units - number of pixels along Y: 13 # number of pixels along Y axis -- no units - pixel size along X: 300 # pixel size along X -- in um - pixel size along Y: 300 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 750 # maximum wavelength -- in nm - number of spectral samples: 15 diff --git a/simca/configs/cassi_system_optim_optics_doublet.yml b/simca/configs/cassi_system_optim_optics_doublet.yml deleted file mode 100755 index b52a749..0000000 --- a/simca/configs/cassi_system_optim_optics_doublet.yml +++ /dev/null @@ -1,48 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 50000 - dispersive element: - # dispersive element caracteristics - type: doubleprism # name of the dispersive element - glass1: N-LAK34 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-SF66 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 40 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 20.4 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - nd1: 1.7292 - nd2: 1.7847 - nd3: 1.55 - vd1: 54.50 - vd2: 26.09 - vd3: 54.50 - continuous glass materials 1: True - continuous glass materials 2: True - continuous glass materials 3: True - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 80 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 600 # central wavelength -- in nm -detector: - number of pixels along X: 201 # number of pixels along X axis -- no units - number of pixels along Y: 201 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 11 # number of pixels along X axis -- no units - number of pixels along Y: 13 # number of pixels along Y axis -- no units - pixel size along X: 1500 # pixel size along X -- in um - pixel size along Y: 1500 # pixel size along Y -- in um - -spectral range: - wavelength min: 400 # minimum wavelength -- in nm - wavelength max: 1100 # maximum wavelength -- in nm - number of spectral samples: 15 diff --git a/simca/configs/cassi_system_optim_optics_full_triplet.yml b/simca/configs/cassi_system_optim_optics_full_triplet.yml deleted file mode 100755 index c09e29a..0000000 --- a/simca/configs/cassi_system_optim_optics_full_triplet.yml +++ /dev/null @@ -1,48 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 50000 - dispersive element: - # dispersive element caracteristics - type: tripleprism # name of the dispersive element - glass1: P-SK60 # glass type of the dispersive element (only used if type == 'prism') - glass2: SF4 # glass type of the dispersive element (only used if type == 'prism') - glass3: P-SK60 # glass type of the dispersive element (only used if type == 'prism') - A1: 21.5 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 43.0 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 21.5 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - nd1: 1.6074 - nd2: 1.7552 - nd3: 1.6074 - vd1: 56.65 - vd2: 27.58 - vd3: 56.65 - continuous glass materials 1: False - continuous glass materials 2: False - continuous glass materials 3: False - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 2.6 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 600 # central wavelength -- in nm -detector: - number of pixels along X: 145 # number of pixels along X axis -- no units - number of pixels along Y: 901 # number of pixels along Y axis -- no units - pixel size along X: 15 # pixel size along X -- in um - pixel size along Y: 15 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 145 # 151 # number of pixels along X axis -- no units - number of pixels along Y: 901 # 151 # number of pixels along Y axis -- no units - pixel size along X: 15 # pixel size along X -- in um - pixel size along Y: 15 # pixel size along Y -- in um - -spectral range: - wavelength min: 410 # minimum wavelength -- in nm - wavelength max: 1050 # maximum wavelength -- in nm - number of spectral samples: 151 diff --git a/simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml b/simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml deleted file mode 100755 index e60a715..0000000 --- a/simca/configs/cassi_system_optim_optics_full_triplet_dd_cassi.yml +++ /dev/null @@ -1,48 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 75000 - dispersive element: - # dispersive element caracteristics - type: tripleprism # name of the dispersive element - glass1: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-SF4 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - A1: 26.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 53.4 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 26.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - nd1: 1.6074 - nd2: 1.7552 - nd3: 1.6074 - vd1: 56.65 - vd2: 27.58 - vd3: 56.65 - continuous glass materials 1: False - continuous glass materials 2: False - continuous glass materials 3: False - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 0 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 112 # number of pixels along X axis -- no units - number of pixels along Y: 112 # number of pixels along Y axis -- no units - pixel size along X: 70 # pixel size along X -- in um - pixel size along Y: 70 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 131 # number of pixels along X axis -- no units - number of pixels along Y: 131 # number of pixels along Y axis -- no units - pixel size along X: 80 # pixel size along X -- in um - pixel size along Y: 80 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 650 # maximum wavelength -- in nm - number of spectral samples: 55 diff --git a/simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi.yml b/simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi.yml deleted file mode 100755 index a7dedbf..0000000 --- a/simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi.yml +++ /dev/null @@ -1,48 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: SD-CASSI - propagation type: simca - focal lens: 75000 - dispersive element: - # dispersive element caracteristics - type: tripleprism # name of the dispersive element - glass1: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-SF4 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - A1: 26.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 53.4 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 26.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - nd1: 1.6074 - nd2: 1.7552 - nd3: 1.6074 - vd1: 56.65 - vd2: 27.58 - vd3: 56.65 - continuous glass materials 1: False - continuous glass materials 2: False - continuous glass materials 3: False - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 0 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 112 # number of pixels along X axis -- no units - number of pixels along Y: 112 # number of pixels along Y axis -- no units - pixel size along X: 70 # pixel size along X -- in um - pixel size along Y: 70 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 131 # number of pixels along X axis -- no units - number of pixels along Y: 131 # number of pixels along Y axis -- no units - pixel size along X: 80 # pixel size along X -- in um - pixel size along Y: 80 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 650 # maximum wavelength -- in nm - number of spectral samples: 55 diff --git a/simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi_shifted.yml b/simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi_shifted.yml deleted file mode 100755 index 2156ab4..0000000 --- a/simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi_shifted.yml +++ /dev/null @@ -1,48 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: SD-CASSI - propagation type: simca - focal lens: 75000 - dispersive element: - # dispersive element caracteristics - type: tripleprism # name of the dispersive element - glass1: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-SF4 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - A1: 26.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 53.4 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 26.7 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - nd1: 1.6074 - nd2: 1.7552 - nd3: 1.6074 - vd1: 56.65 - vd2: 27.58 - vd3: 56.65 - continuous glass materials 1: False - continuous glass materials 2: False - continuous glass materials 3: False - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 0 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 139 # number of pixels along X axis -- no units - number of pixels along Y: 112 # number of pixels along Y axis -- no units - pixel size along X: 70 # pixel size along X -- in um - pixel size along Y: 70 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 112 # number of pixels along X axis -- no units - number of pixels along Y: 112 # number of pixels along Y axis -- no units - pixel size along X: 80 # pixel size along X -- in um - pixel size along Y: 80 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 650 # maximum wavelength -- in nm - number of spectral samples: 55 diff --git a/simca/configs/cassi_system_optim_optics_full_triplet_starting_point.yml b/simca/configs/cassi_system_optim_optics_full_triplet_starting_point.yml deleted file mode 100755 index 29d9aba..0000000 --- a/simca/configs/cassi_system_optim_optics_full_triplet_starting_point.yml +++ /dev/null @@ -1,48 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 50000 - dispersive element: - # dispersive element caracteristics - type: tripleprism # name of the dispersive element - glass1: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - glass2: SF4 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-SK2 # glass type of the dispersive element (only used if type == 'prism') - A1: 24 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 48 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 24 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - nd1: 1.6074 - nd2: 1.7552 - nd3: 1.6074 - vd1: 56.65 - vd2: 27.58 - vd3: 56.65 - continuous glass materials 1: True - continuous glass materials 2: True - continuous glass materials 3: True - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 0.5 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 600 # central wavelength -- in nm -detector: - number of pixels along X: 201 # number of pixels along X axis -- no units - number of pixels along Y: 201 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 11 # number of pixels along X axis -- no units - number of pixels along Y: 13 # number of pixels along Y axis -- no units - pixel size along X: 1000 # pixel size along X -- in um - pixel size along Y: 1000 # pixel size along Y -- in um - -spectral range: - wavelength min: 400 # minimum wavelength -- in nm - wavelength max: 1100 # maximum wavelength -- in nm - number of spectral samples: 15 diff --git a/simca/configs/cassi_system_simple_optim.yml b/simca/configs/cassi_system_simple_optim.yml deleted file mode 100644 index 142a7da..0000000 --- a/simca/configs/cassi_system_simple_optim.yml +++ /dev/null @@ -1,39 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 100000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - alpha_c: 50 - delta alpha c: 0 - delta beta c: 0 - wavelength center: 620 # central wavelength -- in nm -detector: - number of pixels along X: 145 - number of pixels along Y: 145 - pixel size along X: 240 # pixel size along X -- in um - pixel size along Y: 240 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 145 - number of pixels along Y: 145 - pixel size along X: 320 # pixel size along X -- in um - pixel size along Y: 320 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 700 # maximum wavelength -- in nm - number of spectral samples: 61 #61 diff --git a/simca/configs/cassi_system_simple_optim_max_center.yml b/simca/configs/cassi_system_simple_optim_max_center.yml deleted file mode 100644 index 9fe740a..0000000 --- a/simca/configs/cassi_system_simple_optim_max_center.yml +++ /dev/null @@ -1,38 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 100000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 575 # central wavelength -- in nm -detector: - number of pixels along X: 145 # number of pixels along X axis -- no units - number of pixels along Y: 145 # number of pixels along Y axis -- no units - pixel size along X: 8 # pixel size along X -- in um - pixel size along Y: 8 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 145 # number of pixels along X axis -- no units - number of pixels along Y: 145 # number of pixels along Y axis -- no units - pixel size along X: 11 # pixel size along X -- in um - pixel size along Y: 11 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 700 # maximum wavelength -- in nm - number of spectral samples: 3 diff --git a/simca/configs/cassi_system_simple_optim_max_lighting.yml b/simca/configs/cassi_system_simple_optim_max_lighting.yml deleted file mode 100644 index 45791c7..0000000 --- a/simca/configs/cassi_system_simple_optim_max_lighting.yml +++ /dev/null @@ -1,38 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 100000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 600 # central wavelength -- in nm -detector: - number of pixels along X: 145 # number of pixels along X axis -- no units - number of pixels along Y: 145 # number of pixels along Y axis -- no units - pixel size along X: 6 # pixel size along X -- in um - pixel size along Y: 6 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 145 # number of pixels along X axis -- no units - number of pixels along Y: 145 # number of pixels along Y axis -- no units - pixel size along X: 12 # pixel size along X -- in um - pixel size along Y: 12 # pixel size along Y -- in um - -spectral range: - wavelength min: 400 # minimum wavelength -- in nm - wavelength max: 800 # maximum wavelength -- in nm - number of spectral samples: 5 diff --git a/simca/configs/cassi_system_simple_optim_smile.yml b/simca/configs/cassi_system_simple_optim_smile.yml deleted file mode 100644 index 52b2158..0000000 --- a/simca/configs/cassi_system_simple_optim_smile.yml +++ /dev/null @@ -1,38 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 100000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 620 # central wavelength -- in nm -detector: - number of pixels along X: 400 # number of pixels along X axis -- no units - number of pixels along Y: 401 # number of pixels along Y axis -- no units - pixel size along X: 30 # pixel size along X -- in um - pixel size along Y: 30 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 100 # number of pixels along X axis -- no units - number of pixels along Y: 401 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 700 # maximum wavelength -- in nm - number of spectral samples: 9 diff --git a/simca/configs/cassi_system_simple_optim_smile_mono.yml b/simca/configs/cassi_system_simple_optim_smile_mono.yml deleted file mode 100644 index c5503cc..0000000 --- a/simca/configs/cassi_system_simple_optim_smile_mono.yml +++ /dev/null @@ -1,38 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 4000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - glass1: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass2: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - glass3: N-BK7 # glass type of the dispersive element (only used if type == 'prism') - A1: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A2: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - A3: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 600 # central wavelength -- in nm -detector: - number of pixels along X: 167 # number of pixels along X axis -- no units -> dataset size /0.6 - number of pixels along Y: 100 # number of pixels along Y axis -- no units - pixel size along X: 8 # pixel size along X -- in um - pixel size along Y: 8 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 167 # number of pixels along X axis -- no units -> dataset size /0.6 - number of pixels along Y: 100 # number of pixels along Y axis -- no units - pixel size along X: 11 # pixel size along X -- in um - pixel size along Y: 11 # pixel size along Y -- in um - -spectral range: - wavelength min: 500 # minimum wavelength -- in nm - wavelength max: 700 # maximum wavelength -- in nm - number of spectral samples: 3 diff --git a/simca/configs/cassi_system_test.yml b/simca/configs/cassi_system_test.yml deleted file mode 100755 index f80aec6..0000000 --- a/simca/configs/cassi_system_test.yml +++ /dev/null @@ -1,33 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - A: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 30 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 41 # number of pixels along X axis -- no units - number of pixels along Y: 41 # number of pixels along Y axis -- no units - pixel size along X: 40 # pixel size along X -- in um - pixel size along Y: 40 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 43 # number of pixels along X axis -- no units - number of pixels along Y: 37 # number of pixels along Y axis -- no units - pixel size along X: 55 # pixel size along X -- in um - pixel size along Y: 55 # pixel size along Y -- in um - -spectral range: - wavelength min: 400 # minimum wavelength -- in nm - wavelength max: 600 # maximum wavelength -- in nm - number of spectral samples: 3 diff --git a/simca/configs/dataset.yml b/simca/configs/dataset.yml deleted file mode 100644 index 52835c7..0000000 --- a/simca/configs/dataset.yml +++ /dev/null @@ -1 +0,0 @@ -datasets directory: ./datasets/ diff --git a/simca/configs/pattern.yml b/simca/configs/pattern.yml deleted file mode 100644 index 0013ea4..0000000 --- a/simca/configs/pattern.yml +++ /dev/null @@ -1,11 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - results directory: ./data/patterns/ - -pattern: - type: random - slit position: 0 - slit width: 1 - ROM : 0.5 - diff --git a/simca/cost_functions.py b/simca/cost_functions.py deleted file mode 100644 index 07ac8e6..0000000 --- a/simca/cost_functions.py +++ /dev/null @@ -1,191 +0,0 @@ -import torch -from matplotlib import pyplot as plt -import math - -def evaluate_slit_scanning_straightness(filtering_cube, device, sigma = 0.75, pos_slit=0.5): - """ - Evaluate the straightness of the slit scanning. - working cost function for up to focal >100000 - """ - - pos_cube = round(filtering_cube.shape[1]*pos_slit) - cost_value = torch.tensor(0.0, requires_grad=True).to(device) - - gaussian = (torch.arange(filtering_cube.shape[1]) - pos_cube).to(device) - gaussian = torch.exp(-torch.square(gaussian)/(2*sigma**2)).unsqueeze(0).to(device) - gaussian = gaussian - w = filtering_cube.shape[2]//2 - """for i in range(filtering_cube.shape[2]): - vertical_binning = torch.sum(filtering_cube[:, :, i], axis=0) - #max_value = torch.max(vertical_binning) - std_deviation_vertical = torch.std(vertical_binning) - #std_deviation_horizontal = torch.std(torch.sum(filtering_cube[:,:,i], axis=1)) - # Reward the max value and penalize based on the standard deviation - - # Calculate the differences between consecutive rows (vectorized) - row_diffs = filtering_cube[1:, :, i] - filtering_cube[:-1, :, i] - #cost_value = cost_value + max_value / std_deviation - torch.sum(torch.sum(torch.abs(row_diffs))) - cost_value = cost_value + std_deviation_vertical - 0.2*torch.sum(torch.sum(torch.square(row_diffs))) #- 0.2*torch.sum(torch.sum(torch.abs(row_diffs)))""" - row_diffs = filtering_cube[1:, :, w] - filtering_cube[:-1, :, w] - cost_value = cost_value - torch.sum((torch.abs(filtering_cube[:, :, w] - gaussian)+1e-8)**0.4) + 0.6*torch.sum(filtering_cube[:, pos_cube,w]) - 0.8*torch.sum(torch.sum(torch.abs(row_diffs))) #- 2*torch.sum(torch.var(filtering_cube[:, :, 0], dim=0)) - #delta = 2 - #cost_value = cost_value - (delta**2)*torch.sum((torch.sqrt(1+((filtering_cube[:, :, 0] - gaussian)/delta)**2)-1)) # pseudo-huber loss - # Minimizing the negative of cost_value to maximize the original objective - return -cost_value - -def evaluate_center(acquisition): - """ - Evaluate the value of the central pixel of each line - """ - cost_value = torch.sum(acquisition[:, acquisition.shape[1]//2], axis=0) - - return -cost_value - -def evaluate_mean_lighting(acquisition): - """ - Evaluate the mean and std values of the acquisition, maximizing mean and minimizing std - """ - #cost_value = torch.mean(acquisition)/(torch.std(acquisition)+1e-9) - cost_value = 2*torch.mean(acquisition) - 8*torch.std(acquisition) - - return -cost_value - -def evaluate_max_lighting(widths, acquisition, target): - cost_value = 0 - - #col = acquisition[:, pos_cube] - #col = acquisition[:, pos_cube-2:pos_cube+2] - col = acquisition[acquisition>100].flatten() - #col = acquisition[93:208, 30].unsqueeze(1) - """for i in range(1, 5): - col = torch.cat((col, acquisition[93:208, 30+i*15].unsqueeze(1)), 1) """ - - #cost_value = 2*torch.mean(col)**2 - 25*torch.var(col) - #cost_value = - torch.var(col) - """ cost_value = 15*torch.mean(col)**2 - 25*torch.var(col) - cost_value = 8000*torch.mean(col)**2 - torch.sum((col-10000)**2) - cost_value = 0.75*torch.mean(col) - torch.mean(torch.abs(col-6000)) - cost_value = torch.mean(col) - 2*torch.std(col) - - lines = torch.mean(acquisition, axis=1) - - #cost_value = - torch.var(torch.log(col)) - - cost_value = - torch.var((torch.log(col)- torch.log(torch.tensor([40000])))**2) - cost_value = - torch.var(torch.log(col)) - torch.log(torch.var(col))# - torch.mean((torch.log(col)- torch.log(torch.tensor([14000])))**2) - #cost_value = - torch.var(torch.log(col)**2) - torch.var((torch.log(col)- torch.log(torch.tensor([20000])))**2) - cost_value = - torch.var(torch.log(col)**2) - 2*torch.var((torch.log(col)- torch.log(torch.tensor([6000])))**2) - #cost_value = - torch.sum((2000*10000*((col-2000)+(col-10000)) - (10000-2000))/2) - #cost_value = -torch.var(torch.log(col)) """ - #cost_value = -torch.var(torch.exp(col/11000)) - row_diffs = torch.abs(widths[0,1:] - widths[0,:-1]) - #cost_value = - torch.var(torch.exp(col/18100)) - torch.sum(-torch.log(1+row_diffs)) - #print(torch.var(torch.exp(col/20000))) - #print(torch.mean(torch.log(col))) - #print(torch.sum(-torch.log(1+row_diffs))) - - - def saturation(scene, target_, margin=0.05): - cost = 0 - for elem in scene: - if elem <= target_*(1+margin): - cost += (target_-elem) - else: - cost += (elem-target_)**3 - return cost - - def bowl(scene, target_, saturation=None): - if saturation is None: - saturation = target_*1.2 - s = saturation - t = target_ - cost = 0 - for x in scene: - if x <= t: - cost += ((t-x)/t)**2 - elif x < s: - A = -1/((s-t)**2) + 2/(t**2) - B = -1/(s-t) + t/((s-t)**2) - 2/t - C = - 1/2*A*t - B*t - cost += - math.log((s-x)/(s-t)) + 1/2*A*x**2 + B*x + C - #cost += - math.exp(80*(1-(saturation-elem)/(saturation-target_))) - #cost += (elem-target_)**4 - else: - cost += 1e18*x**2 - return cost - - def bowl_inverse(scene, target_, saturation=None): - if saturation is None: - saturation = target_*1.2 - s = saturation - t = target_ - cost = 0 - for x in scene: - if x <= t: - cost += ((t-x)/t)**2 - elif x < s: - A = -2/((s-t)**3) + 2/(t**2) - B = -1/((s-t)**2) - A*t - C = -1/(s-t) - 1/2*A*t - B*t - cost += - 1/(s-x) + 1/2*A*x**2 + B*x + C - #cost += - math.exp(80*(1-(saturation-elem)/(saturation-target_))) - #cost += (elem-target_)**4 - else: - cost += 1e18*x**2 - return cost - - - #cost_value = - torch.var(col) - torch.sum(-torch.log(1+row_diffs)) - #cost_value = - torch.var(torch.exp(col/18000)) - 10000*torch.sum(torch.log(1/(1+row_diffs))) - #cost_value = - torch.var(torch.exp(col/30000))# - 2*torch.count_nonzero(row_diffs) - #cost_value = - torch.var(torch.exp(col/20000)) - 40*torch.count_nonzero(row_diffs) - #cost_value = torch.mean(col) - #cost_value = - 2*torch.var((torch.exp(col/20000)- torch.exp(torch.tensor([9000])/20000))**2) - #cost_value = - saturation(col, 45000, margin=0.1) - - #print("Jumps: ", torch.count_nonzero(row_diffs)) - print("Var: ", torch.var(col)) - #print("Saturation: ", - saturation(col.flatten(), 120000, margin=0.1)) - print("Min: ", torch.min(col)) - print("Mean: ", torch.mean(col)) - print("Max: ", torch.max(col)) - - #cost_value = - torch.var(torch.exp(col/100000)) #- 1e-5*saturation(col.flatten(), 200000, margin=0.1) - #cost_value = - torch.var((torch.log(col.squeeze())- torch.log(torch.tensor([200000]).to('cuda')))**2) - #cost_value = - torch.var(col) #- saturation(col.flatten(), 120000, margin=0.1) - cost_value = - bowl(col, target, saturation=2.2e6) - print("Cost: ", - cost_value) - return - cost_value - -# def evalute_slit_scanning_straightness(filtering_cube,threshold): -# """ -# Evaluate the straightness of the slit scanning. -# working cost function for up to focal >100000 -# """ -# cost_value = torch.tensor(0.0, requires_grad=True) - -# for i in range(filtering_cube.shape[2]): -# vertical_binning = torch.sum(filtering_cube[:, :, i], axis=0) -# max_value = torch.max(vertical_binning) -# values_above_threshold = vertical_binning > threshold -# number_of_values_above_threshold = torch.sum(values_above_threshold) -# cost_value = cost_value + max_value / number_of_values_above_threshold -# return -cost_value - - -# def evalute_slit_scanning_straightness(filtering_cube,threshold): -# """ -# Evaluate the straightness of the slit scanning. -# """ -# cost_value =0 - -# for i in range(filtering_cube.shape[2]): -# vertical_binning = torch.sum(filtering_cube[:,:,i],axis=0) -# max_value = torch.max(vertical_binning) -# # Differentiable way to count values above threshold -# values_above_threshold = vertical_binning > threshold -# number_of_values_above_threshold = torch.sum(values_above_threshold) -# cost_value += 1 / (number_of_values_above_threshold ** 2) - -# cost_value = -1*(cost_value) -# return cost_value \ No newline at end of file diff --git a/simca/cost_functions_optics.py b/simca/cost_functions_optics.py index 89eb7e2..f0af0e0 100755 --- a/simca/cost_functions_optics.py +++ b/simca/cost_functions_optics.py @@ -3,14 +3,122 @@ import numpy as np from sklearn.linear_model import LinearRegression from sklearn.metrics import r2_score +import os +import math +import json +from torch.optim import Adam +from opticalglass.glassfactory import get_glass_catalog -def testing_model(cassi_system,nd_values, vd_values): - cassi_system.optical_model.A3 = cassi_system.optical_model.A1 - cassi_system.optical_model.nd3 = cassi_system.optical_model.nd1 - cassi_system.optical_model.vd3 = cassi_system.optical_model.vd1 +import numpy as np +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap - alpha_c_out = cassi_system.optical_model.rerun_central_dispersion() - # Forward pass +from simca.helper import * +import matplotlib + +from simca import load_yaml_config +from simca.CassiSystem import CassiSystem +from opticalglass.glassfactory import get_glass_catalog + + +from torch.optim import Adam +from torch.optim.lr_scheduler import CosineAnnealingLR + +def get_catalog_glass_infos(catalog, device="cpu"): + glass_pd = get_glass_catalog(catalog) + list_of_glasses = glass_pd.df.iloc[:, 0].index.tolist() + + tuple_glass_data = glass_pd.glass_map_data("d") + + nd = tuple_glass_data[0][:] + vd = tuple_glass_data[1][:] + nd_values = torch.tensor(nd, device=device) + vd_values = torch.tensor(vd, device=device) + + return list_of_glasses, nd_values, vd_values + + +def plot_grids_coordinates(cassi_system,test_name='test',save_fig_dir=None,save_fig=False): + + X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture = cassi_system.propagate_coded_aperture_grid() + + + dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(X_coordinates_propagated_coded_aperture.unsqueeze(0), Y_coordinates_propagated_coded_aperture.unsqueeze(0)) + x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions( + cassi_system.X_coded_aper_coordinates, + cassi_system.Y_coded_aper_coordinates, + central_coordinates_X + ) + plt.figure() + + # plot distorsion grids + plt.scatter(x_vec_no_dist.detach().cpu().numpy()[..., 0], y_vec_no_dist.detach().cpu().numpy()[..., 0], label="no dist", color='grey',s=70) + plt.scatter(x_vec_no_dist.detach().cpu().numpy()[..., 3], y_vec_no_dist.detach().cpu().numpy()[..., 3], label="no dist", color='grey',s=70) + plt.scatter(x_vec_no_dist.detach().cpu().numpy()[..., -1], y_vec_no_dist.detach().cpu().numpy()[..., -1], label="no dist", color='grey',s=70) + plt.scatter(X_coordinates_propagated_coded_aperture.detach().cpu().numpy()[..., 0], Y_coordinates_propagated_coded_aperture.detach().cpu().numpy()[..., 0], color='blue', label="distor") + plt.scatter(X_coordinates_propagated_coded_aperture.detach().cpu().numpy()[..., 3], Y_coordinates_propagated_coded_aperture.detach().cpu().numpy()[..., 3], color='green', label="distor") + plt.scatter(X_coordinates_propagated_coded_aperture.detach().cpu().numpy()[..., -1], Y_coordinates_propagated_coded_aperture.detach().cpu().numpy()[..., -1], color='red', label="distor") + + if save_fig: + plt.savefig(save_fig_dir +f"grids_coordinates_{test_name}.svg") + + plt.show() + + if save_fig: + np.save(save_fig_dir + f"x_coordinates_{test_name}",x_vec_no_dist.detach().cpu().numpy()) + np.save(save_fig_dir + f"y_coordinates_{test_name}",y_vec_no_dist.detach().cpu().numpy()) + np.save(save_fig_dir + f"wavelengths_{test_name}",cassi_system.wavelengths.detach().cpu().numpy()) + + + +def format_score_details(cost_details,weighted_cost_components,cost_weights): + score_details = { + 'components non weights': {key: value for key, value in cost_weights.items()}, + 'components with weights': {key : float(value) for key, value in cost_details.items()}, + 'weighted components': {key : float(value) for key, value in weighted_cost_components.items()}, + } + return score_details + + + +def initialize_optimizer(cassi_system, params_to_optimize, num_iterations): + base_params = [] + glass_params = [] + + for name, param in cassi_system.optical_model.named_parameters(): + if name in params_to_optimize: + if name in ['nd1', 'vd1', 'nd2', "vd2", "nd3", "vd3"]: + glass_params.append(param) + else: + base_params.append(param) + + # Define parameter groups with different initial learning rates + param_groups = [ + {'params': base_params, 'lr': 0.01}, # Base initial learning rate + {'params': glass_params, 'lr': 0.01} # Higher initial learning rate for glass parameters + ] + + optimizer = Adam(param_groups) + + # Create a cosine annealing scheduler + scheduler = CosineAnnealingLR(optimizer, T_max=num_iterations, eta_min=1e-6) + + return optimizer, scheduler + + +def setup_results_directory(output_dir, step_name): + # Create the optimization_results directory within the output_dir + optimization_results_dir = os.path.join(output_dir, "optimization_results") + os.makedirs(optimization_results_dir, exist_ok=True) + + # Create a subdirectory for the current step + step_dir = os.path.join(optimization_results_dir, step_name) + os.makedirs(step_dir, exist_ok=True) + + return step_dir + +def evaluate_optical_performances(cassi_system,save_fig_dir=None): + cassi_system.optical_model.rerun_central_dispersion() x_vec_out, y_vec_out = cassi_system.propagate_coded_aperture_grid() alpha_c = cassi_system.optical_model.alpha_c @@ -19,53 +127,112 @@ def testing_model(cassi_system,nd_values, vd_values): list_theta_in = cassi_system.optical_model.list_theta_in list_theta_out = cassi_system.optical_model.list_theta_out - # evaluate spectral dispersion dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(x_vec_out, y_vec_out) - y_second_derivative = calculate_second_derivative(cassi_system.wavelengths, central_coordinates_X) - - # evaluate direct_view deviation = evaluate_direct_view(alpha_c, alpha_c_out, list_apex_angles) - # print("deviation in degrees", deviation * 180 / np.pi) - - # evaluate beam compression beam_compression = evaluate_beam_compression(list_theta_in, list_theta_out) - # print("beam compression", beam_compression) - - # evaluate thickness thickness = evaluate_thickness(list_apex_angles) - # print("thickness", thickness) + x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions( + cassi_system.X_coded_aper_coordinates, + cassi_system.Y_coded_aper_coordinates, + central_coordinates_X + ) + distortion_metric, _,mean_distortion = evaluate_distortions(x_vec_out, y_vec_out, x_vec_no_dist, y_vec_no_dist,save_fig_dir) - # evaluate distortions - x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions(cassi_system.X_coded_aper_coordinates, - cassi_system.Y_coded_aper_coordinates, - central_coordinates_X) - distortion_metric = evaluate_distortions(x_vec_out, y_vec_out, x_vec_no_dist, y_vec_no_dist) + performances = { + 'dispersion [um]': int(dispersion), + 'deviation [deg]': float(deviation)*180/np.pi, + 'max distortion [um]': float(distortion_metric), + 'mean distortion [um]': float(mean_distortion), + 'beam_compression [no units]': float(beam_compression),} - # print("distortion metric", distortion_metric) - # distance from closest point - current_value1 = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] - current_value2 = [cassi_system.optical_model.nd2, cassi_system.optical_model.vd2] - current_value3 = [cassi_system.optical_model.nd3, cassi_system.optical_model.vd3] + return performances - distance_closest_point1, min_idx1 = evaluate_distance(current_value1, nd_values, vd_values) - distance_closest_point2, min_idx2 = evaluate_distance(current_value2, nd_values, vd_values) - distance_closest_point3, min_idx3 = evaluate_distance(current_value3, nd_values, vd_values) - # # cost functions - cost_distance_glasses = (10**4*(distance_closest_point1**2 + distance_closest_point2**2 + distance_closest_point3**2))**2 - cost_deviation = deviation*10 - cost_distorsion = distortion_metric**2 - cost_thickness = thickness*10 - cost_beam_compression = torch.abs(beam_compression - 1)*10 - cost_non_linearity = 0.001* y_second_derivative**2 - # cost_distance_total_intern_reflection = 0.1*softplus(cassi_system.optical_model.min_distance_from_total_intern_reflection)**2 +def evaluate_cost_functions(cassi_system, cost_weights, target_dispersion, nd_values, vd_values, iteration_number, list_of_glasses): + cassi_system.optical_model.rerun_central_dispersion() + x_vec_out, y_vec_out = cassi_system.propagate_coded_aperture_grid() - score = cost_distance_glasses + cost_deviation + cost_distorsion + cost_thickness + cost_beam_compression + cost_non_linearity + alpha_c = cassi_system.optical_model.alpha_c + alpha_c_out = cassi_system.optical_model.alpha_c_transmis + list_apex_angles = [cassi_system.optical_model.A1, cassi_system.optical_model.A2, cassi_system.optical_model.A3] + list_theta_in = cassi_system.optical_model.list_theta_in + list_theta_out = cassi_system.optical_model.list_theta_out + + dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(x_vec_out, y_vec_out) + y_second_derivative = calculate_second_derivative(cassi_system.wavelengths, central_coordinates_X) + deviation = evaluate_direct_view(alpha_c, alpha_c_out, list_apex_angles) + beam_compression = evaluate_beam_compression(list_theta_in, list_theta_out) + thickness = evaluate_thickness(list_apex_angles) + x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions( + cassi_system.X_coded_aper_coordinates, + cassi_system.Y_coded_aper_coordinates, + central_coordinates_X + ) + distortion_metric, _,mean_distortion = evaluate_distortions(x_vec_out, y_vec_out, x_vec_no_dist, y_vec_no_dist) + if cassi_system.optical_model.index_estimation_method == "cauchy": + distance_glasses_metric = evaluate_distance_glasses(cassi_system, nd_values, vd_values, list_of_glasses) + else : + distance_glasses_metric = 0 + + + + cost_components = { + 'cost_dispersion': (target_dispersion - dispersion) ** 2, + 'cost_distance_glasses': (10 ** 10 * distance_glasses_metric) * iteration_number, + 'cost_deviation': (deviation * 5000) ** 2, + 'cost_distorsion': distortion_metric ** 2, + 'cost_thickness': thickness * 5000, + 'cost_beam_compression': torch.abs(beam_compression - 1) * 10, + 'cost_non_linearity': 0.001 * y_second_derivative ** 2, + 'cost_distance_total_intern_reflection': 2 * softplus(cassi_system.optical_model.min_distance_from_total_intern_reflection) ** 2 + } + + interesting_values = { + 'dispersion [um]': dispersion, + 'deviation [deg]': deviation*180/np.pi, + 'max distortion [um]': distortion_metric, + 'mean distortion [um]': mean_distortion, + 'beam_compression [no units]': beam_compression,} + + + weighted_cost_components = {key: cost_weights[key] * value for key, value in cost_components.items()} + + + score = sum(cost_weights.get(key, 0) * value for key, value in cost_components.items()) + + + return score, cost_components,weighted_cost_components,interesting_values + + +def save_optimization_details(results_dir, non_improvement_count, patience, i, latest_optical_params, current_loss,interesting_values): + optimization_details = { + 'reason_for_stopping': 'no improvement' if non_improvement_count >= patience else 'completed', + 'iterations': i, + 'end_parameters': { + param_name: (param.item() if 'A' not in param_name and 'alpha_c' not in param_name and 'delta' not in param_name else math.degrees(param.item())) + for param_name, param in latest_optical_params.items() + }, + 'optical system values': {param_name: (param.item() if 'deviation' not in param_name else math.degrees(param.item())) for param_name, param in interesting_values.items()}, + 'current_loss': current_loss, + } + + details_path = os.path.join(results_dir, 'optimization_details.json') + with open(details_path, 'a') as f: + json.dump(optimization_details, f, indent=4) + + +def plot_glass_selection(nd_values, vd_values, current_glass_values_1, current_glass_values_2, min_idx_1, min_idx_2): + plt.scatter(nd_values.detach().cpu().numpy(), vd_values.detach().cpu().numpy(), c='b', label='all glasses') + plt.scatter(current_glass_values_1[0].detach().cpu().numpy(), current_glass_values_1[1].detach().cpu().numpy(), c='r', label='current glass 1') + plt.scatter(current_glass_values_2[0].detach().cpu().numpy(), current_glass_values_2[1].detach().cpu().numpy(), c='g', label='current glass 2') + plt.scatter(nd_values[min_idx_1].detach().cpu().numpy(), vd_values[min_idx_1].detach().cpu().numpy(), c='r', label='closest glass 1', marker='x') + plt.scatter(nd_values[min_idx_2].detach().cpu().numpy(), vd_values[min_idx_2].detach().cpu().numpy(), c='g', label='closest glass 2', marker='x') + plt.show() + - return score def calculate_second_derivative(x, y): """ Calculate the second derivative of y with respect to x using central differences. @@ -97,8 +264,8 @@ def get_cassi_system_no_spectial_distorsions(X, Y, central_coordinates_X): central_coordinates_X = [coord for coord in list(central_coordinates_X)] - X_vec_out = torch.zeros((1,X.shape[0],X.shape[1],len(central_coordinates_X))) - Y_vec_out = torch.zeros((1,X.shape[0],X.shape[1],len(central_coordinates_X))) + X_vec_out = torch.zeros((1,X.shape[0],X.shape[1],len(central_coordinates_X)),device=X.device) + Y_vec_out = torch.zeros((1,X.shape[0],X.shape[1],len(central_coordinates_X)),device=Y.device) for i in range(X_vec_out.shape[-1]): @@ -110,14 +277,12 @@ def get_cassi_system_no_spectial_distorsions(X, Y, central_coordinates_X): def evaluate_spectral_dispersion_values(x_vec_out, y_vec_out): # calculate spectral dispersion in um - - + central_coordinates_X = x_vec_out[0,x_vec_out.shape[1]//2, x_vec_out.shape[2]//2,:] spectral_dispersion = torch.abs(central_coordinates_X[0] - central_coordinates_X[-1]) - return spectral_dispersion, central_coordinates_X @@ -126,7 +291,6 @@ def evaluate_direct_view(alpha_c,alpha_c_out,list_apex_angles): total_dev = alpha_c + alpha_c_out for idx,angle in enumerate(list_apex_angles): total_dev += ((-1)**(idx+1))*angle - return total_dev def softplus(x): return torch.log(1 + torch.exp(-x+5))**3 @@ -146,15 +310,65 @@ def evaluate_thickness(list_angles): return sum_angles -def evaluate_distortions(X_vec_out_distor, Y_vec_out_distors, X_vec_out, Y_vec_out): +def evaluate_distance_glasses(cassi_system, nd_values, vd_values, list_of_glasses): - distance_map = torch.sqrt((X_vec_out_distor - X_vec_out)**2 + (Y_vec_out_distors - Y_vec_out)**2) + if cassi_system.system_config["system architecture"]["dispersive element"]["type"] == 'prism': + current_value1 = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] + distance_closest_point1, min_idx_1 = evaluate_distance(current_value1, nd_values, vd_values) + cost_distance_glass = distance_closest_point1**2 + + elif cassi_system.system_config["system architecture"]["dispersive element"]["type"] == 'doubleprism' or cassi_system.system_config["system architecture"]["dispersive element"]["type"] == 'amici': + current_value1 = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] + current_value2 = [cassi_system.optical_model.nd2, cassi_system.optical_model.vd2] + distance_closest_point1, min_idx_1 = evaluate_distance(current_value1, nd_values, vd_values) + distance_closest_point2, min_idx_2 = evaluate_distance(current_value2, nd_values, vd_values) + cost_distance_glass = distance_closest_point1**2 + distance_closest_point2**2 + + elif cassi_system.system_config["system architecture"]["dispersive element"]["type"] == 'tripleprism': + current_value1 = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] + current_value2 = [cassi_system.optical_model.nd2, cassi_system.optical_model.vd2] + current_value3 = [cassi_system.optical_model.nd3, cassi_system.optical_model.vd3] + distance_closest_point1, min_idx_1 = evaluate_distance(current_value1, nd_values, vd_values) + distance_closest_point2, min_idx_2 = evaluate_distance(current_value2, nd_values, vd_values) + distance_closest_point3, min_idx_3 = evaluate_distance(current_value3, nd_values, vd_values) + cost_distance_glass = distance_closest_point1**2 + distance_closest_point2**2 + distance_closest_point3**2 + return cost_distance_glass + +def evaluate_distortions(X_vec_out_distor, Y_vec_out_distors, X_vec_out, Y_vec_out,save_fig_dir=None): + + #print gradient value + parula_map = LinearSegmentedColormap.from_list('parula', cm_data) + distance_map = torch.sqrt((X_vec_out_distor - X_vec_out)**2 + (Y_vec_out_distors - Y_vec_out)**2) + # mean distance distortion_metric = torch.max(torch.max(torch.max(distance_map))) + mean_distortion = torch.mean(torch.mean(torch.mean(distance_map))) # distortion_metric = np.sum(np.sum(distance_map)) # distortion_metric = torch.sum(torch.sum(torch.sum(distance_map))) - return distortion_metric + + if save_fig_dir is not None: + print(X_vec_out[0,13,13,:]) + + for i in [0,5,-1]: + + max_x = X_vec_out[0,0,0,i].detach().cpu().numpy() + min_x = X_vec_out[0,0,-1,i].detach().cpu().numpy() + max_y = Y_vec_out[0,0,0,i].detach().cpu().numpy() + min_y = Y_vec_out[0,-1,0,i].detach().cpu().numpy() + # print(min_x, max_x, min_y, max_y) + + plt.figure() + norm = matplotlib.colors.LogNorm(vmin=0.1, vmax=125) + plt.imshow(distance_map[0,:,:,i].detach().cpu().numpy(),cmap=parula_map,extent=[min_x, max_x, min_y, max_y],norm=norm) + # plt.imshow(distance_map[0,:,:,0].detach().cpu().numpy(),cmap=parula_map) + plt.xlabel("x [$\mu$m]",fontsize=18) + plt.ylabel("y [$\mu$m]",fontsize=18) + plt.title(f"Distortion map at $\lambda$ = {i}",fontsize=14) + plt.colorbar() + plt.savefig(save_fig_dir + f"distor_map_{i}.svg") + # plt.show() + return distortion_metric, distance_map,mean_distortion def evaluate_distance(current_value_pair,nd_values,vd_values): @@ -198,4 +412,258 @@ def evaluate_approximation_thickness(list_angles): sum_angles = np.sum(list_angles) return sum_angles +def testing_model(cassi_system,nd_values, vd_values): + cassi_system.optical_model.A3 = cassi_system.optical_model.A1 + cassi_system.optical_model.nd3 = cassi_system.optical_model.nd1 + cassi_system.optical_model.vd3 = cassi_system.optical_model.vd1 + + alpha_c_out = cassi_system.optical_model.rerun_central_dispersion() + # Forward pass + x_vec_out, y_vec_out = cassi_system.propagate_coded_aperture_grid() + + alpha_c = cassi_system.optical_model.alpha_c + alpha_c_out = cassi_system.optical_model.alpha_c_transmis + list_apex_angles = [cassi_system.optical_model.A1, cassi_system.optical_model.A2, cassi_system.optical_model.A3] + list_theta_in = cassi_system.optical_model.list_theta_in + list_theta_out = cassi_system.optical_model.list_theta_out + + # evaluate spectral dispersion + dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(x_vec_out, y_vec_out) + + y_second_derivative = calculate_second_derivative(cassi_system.wavelengths, central_coordinates_X) + + # evaluate direct_view + deviation = evaluate_direct_view(alpha_c, alpha_c_out, list_apex_angles) + # print("deviation in degrees", deviation * 180 / np.pi) + + # evaluate beam compression + beam_compression = evaluate_beam_compression(list_theta_in, list_theta_out) + # print("beam compression", beam_compression) + + # evaluate thickness + thickness = evaluate_thickness(list_apex_angles) + # print("thickness", thickness) + + # evaluate distortions + x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions(cassi_system.X_coded_aper_coordinates, + cassi_system.Y_coded_aper_coordinates, + central_coordinates_X) + distortion_metric,_ = evaluate_distortions(x_vec_out, y_vec_out, x_vec_no_dist, y_vec_no_dist) + + # print("distortion metric", distortion_metric) + + # distance from closest point + current_value1 = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] + current_value2 = [cassi_system.optical_model.nd2, cassi_system.optical_model.vd2] + current_value3 = [cassi_system.optical_model.nd3, cassi_system.optical_model.vd3] + + distance_closest_point1, min_idx1 = evaluate_distance(current_value1, nd_values, vd_values) + distance_closest_point2, min_idx2 = evaluate_distance(current_value2, nd_values, vd_values) + distance_closest_point3, min_idx3 = evaluate_distance(current_value3, nd_values, vd_values) + + # # cost functions + cost_distance_glasses = (10**4*(distance_closest_point1**2 + distance_closest_point2**2 + distance_closest_point3**2))**2 + cost_deviation = deviation*10 + cost_distorsion = distortion_metric**2 + cost_thickness = thickness*10 + cost_beam_compression = torch.abs(beam_compression - 1)*10 + cost_non_linearity = 0.001* y_second_derivative**2 + # cost_distance_total_intern_reflection = 0.1*softplus(cassi_system.optical_model.min_distance_from_total_intern_reflection)**2 + + + score = cost_distance_glasses + cost_deviation + cost_distorsion + cost_thickness + cost_beam_compression + cost_non_linearity + + return score + + +def optimize_cassi_system(params_to_optimize, target_dispersion, cost_weights, config_path, iterations, patience, output_dir, step_name, index_estimation_method="cauchy", device='cpu'): + results_dir = setup_results_directory(output_dir, step_name) + + config_system = load_yaml_config(config_path) + cassi_system = CassiSystem(system_config=config_system, device=device, index_estimation_method=index_estimation_method) + device = cassi_system.device + catalog = config_system["system architecture"]["dispersive element"]["catalog"] + + list_of_glasses, nd_values, vd_values = get_catalog_glass_infos(catalog=catalog, device=device) + + optimizer, scheduler = initialize_optimizer(cassi_system, params_to_optimize, 300) + non_improvement_count = 0 + best_loss = float('inf') + + optimization_details = [] + + for i in range(iterations): + optimizer.zero_grad() + + if cassi_system.system_config['system architecture']['dispersive element']['type'] == 'amici': + + if cassi_system.optical_model.index_estimation_method == "cauchy": + cassi_system.optical_model.nd3 = cassi_system.optical_model.nd1 + cassi_system.optical_model.vd3 = cassi_system.optical_model.vd1 + if cassi_system.optical_model.index_estimation_method == "sellmeier": + cassi_system.optical_model.glass3 = cassi_system.optical_model.glass1 + + score, cost_details, weighted_cost_components, interesting_values = evaluate_cost_functions(cassi_system, cost_weights, target_dispersion, nd_values, vd_values, i, list_of_glasses) + current_loss = score.item() + + latest_optical_params = {param_name: param.clone().detach() for param_name, param in cassi_system.optical_model.named_parameters()} + + if cassi_system.system_config['system architecture']['dispersive element']['type'] == 'amici': + latest_optical_params['A3'] = latest_optical_params['A1'] + if cassi_system.optical_model.index_estimation_method == "cauchy": + latest_optical_params['nd3'] = latest_optical_params['nd1'] + latest_optical_params['vd3'] = latest_optical_params['vd1'] + + + if i % 10 == 0: + print(f'Iteration {i}, Loss: {current_loss}') + + + details = { + 'reason_for_stopping': 'no improvement' if non_improvement_count >= patience else 'completed', + 'iterations': i, + 'end_parameters': { + param_name: (param.item() if 'A' not in param_name and 'alpha_c' not in param_name and 'delta' not in param_name else math.degrees(param.item())) + for param_name, param in latest_optical_params.items() + }, + 'optical system values': {param_name: (param.item() if 'deviation' not in param_name else math.degrees(param.item())) for param_name, param in interesting_values.items()}, + 'current_loss': current_loss, + } + + if math.isnan(current_loss): + print("nan value") + break + + optimization_details.append(details) + + # Save optical characteristics corresponding to the various iterations + details_path = os.path.join(results_dir, 'optimization_details.json') + with open(details_path, 'w') as f: + json.dump(optimization_details, f, indent=4) + + score_details = format_score_details(cost_details, weighted_cost_components, cost_weights) + # Save the cost functions score details + details_path = os.path.join(results_dir, f'score_details_iteration_{i}.json') + with open(details_path, 'a') as f: + json.dump(score_details, f, indent=4) + + + + score.backward() + optimizer.step() + scheduler.step() # Step the scheduler + + if current_loss < best_loss: + best_loss = current_loss + non_improvement_count = 0 + else: + non_improvement_count += 1 + + if non_improvement_count >= patience: + print(f'Stopping early at iteration {i} due to no improvement.') + break + + # After the optimization loop + final_config = cassi_system.system_config + return final_config, latest_optical_params + +def test_cassi_system(config_path, index_estimation_method="sellmeier", save_fig_dir=None, save_fig=False): + + config_system = load_yaml_config(config_path) + cassi_system = CassiSystem(system_config=config_system, index_estimation_method=index_estimation_method, device="cpu") + + plot_grids_coordinates(cassi_system, save_fig_dir=save_fig_dir, save_fig=save_fig) + performances = evaluate_optical_performances(cassi_system, save_fig_dir=save_fig_dir) + + return cassi_system.system_config, performances + +import json +import os +import matplotlib.pyplot as plt +def plot_optimization_process(output_dir, step_name): + step_dir = os.path.join(output_dir, "optimization_results", step_name) + iterations = [] + weighted_costs = {} + total_costs = [] + + def parse_json_objects(content): + decoder = json.JSONDecoder() + pos = 0 + while True: + try: + obj, pos = decoder.raw_decode(content, pos) + yield obj + except json.JSONDecodeError: + if pos == len(content): + break + pos += 1 + + # Read all score details files + for filename in os.listdir(step_dir): + if filename.startswith("score_details_iteration_") and filename.endswith(".json"): + file_path = os.path.join(step_dir, filename) + try: + with open(file_path, 'r') as f: + content = f.read() + # Parse multiple JSON objects in the file + for data in parse_json_objects(content): + iteration = int(filename.split('_')[-1].split('.')[0]) + iterations.append(iteration) + total_cost = 0 + for cost_name, cost_value in data['weighted components'].items(): + if cost_name not in weighted_costs: + weighted_costs[cost_name] = [] + weighted_costs[cost_name].append(cost_value) + total_cost += cost_value + total_costs.append(total_cost) + except Exception as e: + print(f"Error processing file {filename}: {str(e)}") + continue # Skip this file and continue with the next one + + if not iterations: + print(f"No valid data found for step {step_name}. Skipping plot creation.") + return + + # Sort data by iteration + sorted_indices = sorted(range(len(iterations)), key=lambda k: iterations[k]) + iterations = [iterations[i] for i in sorted_indices] + for cost_name in weighted_costs: + weighted_costs[cost_name] = [weighted_costs[cost_name][i] for i in sorted_indices] + total_costs = [total_costs[i] for i in sorted_indices] + + # Plot individual weighted costs + plt.figure(figsize=(12, 8)) + for cost_name, cost_values in weighted_costs.items(): + plt.plot(iterations, cost_values, label=cost_name) + + plt.xlabel('Iteration') + plt.ylabel('Weighted Cost') + plt.title(f'Individual Weighted Costs over Iterations - {step_name}') + plt.legend() + plt.yscale('log') # Use log scale for y-axis + plt.grid(True) + + # Save the individual weighted costs plot + individual_plot_path = os.path.join(output_dir, f"optimization_plot_individual_{step_name}.png") + plt.savefig(individual_plot_path) + plt.close() + + # Plot total cost function + plt.figure(figsize=(12, 8)) + plt.plot(iterations, total_costs, label='Total Cost') + + plt.xlabel('Iteration') + plt.ylabel('Total Cost') + plt.title(f'Total Cost Function over Iterations - {step_name}') + plt.legend() + plt.yscale('log') # Use log scale for y-axis + plt.grid(True) + + # Save the total cost plot + total_plot_path = os.path.join(output_dir, f"optimization_plot_total_{step_name}.png") + plt.savefig(total_plot_path) + plt.close() + + print(f"Individual costs plot saved as {individual_plot_path}") + print(f"Total cost plot saved as {total_plot_path}") \ No newline at end of file diff --git a/simca/functions_acquisition.py b/simca/functions_acquisition.py deleted file mode 100644 index d0d7e4b..0000000 --- a/simca/functions_acquisition.py +++ /dev/null @@ -1,318 +0,0 @@ -import numpy as np -from scipy.interpolate import griddata -from tqdm import tqdm -import multiprocessing as mp -from multiprocessing import Pool - -import torch - -def generate_sd_measurement_cube(filtered_scene,X_input, Y_input, X_target, Y_target,grid_type,interp_method): - """ - Generate SD measurement cube from the coded aperture and the scene. - For Single-Disperser CASSI systems, the scene is filtered then propagated in the detector plane. - - Args: - filtered_scene (numpy.ndarray): filtered scene (shape = R x C x W) - - Returns: - numpy.ndarray: SD measurement cube (shape = R x C x W) - """ - - print("--- Generating SD measurement cube ---- ") - - measurement_sd = interpolate_data_on_grid_positions(filtered_scene, - X_input, - Y_input, - X_target, - Y_target, - grid_type=grid_type, - interp_method=interp_method) - return measurement_sd - -def generate_dd_measurement(scene, filtering_cube,chunk_size): - """ - Generate DD-CASSI type system measurement from a scene and a filtering cube. ref : "Single-shot compressive spectral imaging with a dual-disperser architecture", M.Gehm et al., Optics Express, 2007 - - Args: - scene (numpy.ndarray): observed scene (shape = R x C x W) - filtering_cube (numpy.ndarray): filtering cube of the instrument for a given pattern (shape = R x C x W) - chunk_size (int) : size of the spatial chunks in which the Hadamard product is performed - - Returns: - numpy.ndarray: filtered scene (shape = R x C x W) - """ - - # Initialize an empty array for the result - filtered_scene = np.empty_like(filtering_cube) - - # Calculate total iterations for tqdm - total_iterations = (filtering_cube.shape[0] // chunk_size + 1) * (filtering_cube.shape[1] // chunk_size + 1) - - with tqdm(total=total_iterations) as pbar: - # Perform the multiplication in chunks - for i in range(0, filtering_cube.shape[0], chunk_size): - for j in range(0, filtering_cube.shape[1], chunk_size): - filtered_scene[i:i + chunk_size, j:j + chunk_size, :] = filtering_cube[i:i + chunk_size, - j:j + chunk_size, :] * scene[ - i:i + chunk_size, - j:j + chunk_size, - :] - pbar.update() - - filtered_scene = np.nan_to_num(filtered_scene) - - return filtered_scene - -def match_dataset_to_instrument(dataset, filtering_cube): - """ - Match the size of the dataset to the size of the filtering cube. Either by padding or by cropping - - Args: - dataset (torch.Tensor): dataset - filtering_cube (torch.Tensor): filtering cube of the instrument - - Returns: - torch.Tensor: observed scene (shape = R x C x W) - """ - - #print("filtering_cube : ", filtering_cube.shape) - #print("dataset : ", dataset.shape) - - # Initialize scene as dataset for simplicity - scene = dataset.clone() - - # Match spatial dimensions (R and C) - if filtering_cube.shape[0] != dataset.shape[1] or filtering_cube.shape[1] != dataset.shape[2]: - # Calculate padding - pad_height = (filtering_cube.shape[0] - dataset.shape[1]) / 2 - pad_width = (filtering_cube.shape[1] - dataset.shape[2]) / 2 - - # Apply padding if necessary - if pad_height > 0 or pad_width > 0: - pad_height_low = int(torch.floor(pad_height)) if isinstance(pad_height, torch.Tensor) else int( - pad_height // 1) - pad_height_high = int(torch.ceil(pad_height)) if isinstance(pad_height, torch.Tensor) else int( - -(-pad_height // 1)) # Equivalent to ceil for positive numbers - pad_width_low = int(torch.floor(pad_width)) if isinstance(pad_width, torch.Tensor) else int(pad_width // 1) - pad_width_high = int(torch.ceil(pad_width)) if isinstance(pad_width, torch.Tensor) else int( - -(-pad_width // 1)) - - padding = ( - pad_width_low, pad_width_high, pad_height_low, pad_height_high, 0, 0) # Padding for W, H, and dummy D - scene = torch.nn.functional.pad(scene, padding, "constant", 0) - - # Crop if necessary (this adjusts if the padding made the scene too large) - scene = scene[:, :filtering_cube.shape[0], :filtering_cube.shape[1], :] - - print("Dataset Spatial Adjustment: Filtering cube and scene must have the same number of rows and columns.") - - # Match spectral dimensions (W) - if len(filtering_cube.shape) == 3 and filtering_cube.shape[2] != dataset.shape[2]: - scene = scene[:, :, :filtering_cube.shape[2]] - print("Dataset Spectral Cropping: Filtering cube and scene must have the same number of wavelengths") - - return scene - -def match_dataset_to_instrument_old(dataset, filtering_cube): - """ - Match the size of the dataset to the size of the filtering cube. Either by padding or by cropping - - Args: - dataset (numpy.ndarray): dataset - filtering_cube (numpy.ndarray): filtering cube of the instrument - - Returns: - numpy.ndarray: observed scene (shape = R x C x W) - """ - - print("filtering_cube : ", filtering_cube.shape) - print("dataset : ", dataset.shape) - - if filtering_cube.shape[0] != dataset.shape[0] or filtering_cube.shape[1] != dataset.shape[1]: - if dataset.shape[0] < filtering_cube.shape[0]: - dataset = np.pad(dataset, ((np.floor((filtering_cube.shape[0] - dataset.shape[0])/2).astype('int'), np.ceil((filtering_cube.shape[0] - dataset.shape[0])/2).astype('int')), (0, 0), (0, 0)), mode="constant") - if dataset.shape[1] < filtering_cube.shape[1]: - dataset = np.pad(dataset, ((0, 0), (np.floor((filtering_cube.shape[1] - dataset.shape[1])/2).astype('int'), np.ceil((filtering_cube.shape[1] - dataset.shape[1])/2).astype('int')), (0, 0)), mode="constant") - scene = dataset[0:filtering_cube.shape[0], 0:filtering_cube.shape[1], :] - print("Dataset Spatial Cropping : Filtering cube and scene must have the same number of lines and columns") - - else: - scene = dataset - - if len(filtering_cube.shape) == 3 and filtering_cube.shape[2] != dataset.shape[2]: - scene = dataset[:, :, 0:filtering_cube.shape[2]] - print("Dataset Spectral Cropping : Filtering cube and scene must have the same number of wavelengths") - else: - scene = dataset - - try: - scene - except: - print("Please load a scene first") - - return scene - -def match_dataset_labels_to_instrument(dataset_labels, filtering_cube): - """ - Match the size of the dataset labels to the size of the filtering cube. Either by padding or by cropping - - Args: - dataset_labels (numpy.ndarray): dataset labels (shape = R_dts x C_dts) - filtering_cube (numpy.ndarray): filtering cube of the instrument - - Returns: - numpy.ndarray: scene labels (shape = R x C) - """ - - if filtering_cube.shape[0] != dataset_labels.shape[0] or filtering_cube.shape[1] != dataset_labels.shape[1]: - if dataset_labels.shape[0] < filtering_cube.shape[0]: - dataset_labels = np.pad(dataset_labels, ((0, filtering_cube.shape[0] - dataset_labels.shape[0])), mode="constant") - if dataset_labels.shape[1] < filtering_cube.shape[1]: - dataset_labels = np.pad(dataset_labels, ((0, 0), (0, filtering_cube.shape[1] - dataset_labels.shape[1])), mode="constant") - dataset_labels = dataset_labels[0:filtering_cube.shape[0], 0:filtering_cube.shape[1]] - print("Filtering cube and scene must have the same lines and columns") - - return dataset_labels - -import torch - -def crop_center_3D(tensor, nb_of_pixels_along_x, nb_of_pixels_along_y): - """ - Crop the given tensor to the given size, centered on the tensor, for each item in the batch. - - Args: - tensor (torch.Tensor): 3D tensor to be cropped with shape (batch, y, x). - nb_of_pixels_along_x (int): Number of samples to keep along the X axis. - nb_of_pixels_along_y (int): Number of samples to keep along the Y axis. - - Returns: - torch.Tensor: Cropped tensor with shape (batch, nb_of_pixels_along_y, nb_of_pixels_along_x). - """ - - _, y_len, x_len = tensor.shape # Ignore batch dimension for the cropping calculations - - x_start = x_len // 2 - nb_of_pixels_along_x // 2 - x_end = x_start + nb_of_pixels_along_x - - y_start = y_len // 2 - nb_of_pixels_along_y // 2 - y_end = y_start + nb_of_pixels_along_y - - # Perform cropping for each item in the batch - cropped_tensor = tensor[:, y_start:y_end, x_start:x_end] - - return cropped_tensor - - -def crop_center(array, nb_of_pixels_along_x, nb_of_pixels_along_y): - """ - Crop the given array to the given size, centered on the array - - Args: - array (numpy.ndarray): 2D array to be cropped - nb_of_pixels_along_x (int): number of samples to keep along the X axis - nb_of_pixels_along_y (int): number of samples to keep along the Y axis - - Returns: - numpy.ndarray: cropped array - """ - - y_len, x_len = array.shape - - x_start = x_len//2 - nb_of_pixels_along_x//2 - x_end = x_start + nb_of_pixels_along_x - - y_start = y_len//2 - nb_of_pixels_along_y//2 - y_end = y_start + nb_of_pixels_along_y - - if nb_of_pixels_along_x= 50) and (convergence_counter >= max_iter_cnv): # If loss didn't decrease in max_iter_cnv steps, break - break - - cassi_system.array_x_positions.data = torch.relu(cassi_system.array_x_positions.data) # Prevent the parameters to be negative +from simca import load_yaml_config +from simca.CassiSystem import CassiSystem +from simca.cost_functions_optics import evaluate_distance, get_catalog_glass_infos - # Optional: Print cost_value every few iterations to monitor progress - if iteration % 5 == 0: # Adjust printing frequency as needed - print(f"\nIteration {iteration}, Cost: {cost_value.item()}") - if plot_frequency is not None: - if iteration % plot_frequency == 0: - print(f"Exec time: {time.time() - time_start:.3f}s") - plt.imshow(cassi_system.pattern.detach().numpy(), aspect=aspect_plot) - plt.show() - plt.imshow(cassi_system.filtering_cube[:, :, cassi_system.filtering_cube.shape[2]//2].detach().numpy(), aspect=aspect_plot) - plt.show() - plt.plot(np.sum(cassi_system.filtering_cube[:, :, cassi_system.filtering_cube.shape[2]//2].detach().numpy(),axis=0)) - plt.show() - - cassi_system.array_x_positions.data = torch.relu(best_x.data) - cassi_system.generate_custom_slit_pattern() - cassi_system.pattern = cassi_system.pattern.to(device) - cassi_system.generate_filtering_cube() - cassi_system.filtering_cube = cassi_system.filtering_cube.to(device) - - return cassi_system -def optim_width(cassi_system, position, target, nb_rows, start_width, device, - algo, lr, num_iterations, max_iter_cnv, threshold = 0, - plot_frequency = None, aspect_plot = 1): +def save_config_system(output_config_path, results_dir, config_path, index_estimation_method='cauchy', iteration_nb=None): - #start_width = torch.rand(size=(1,nb_rows), generator=gen)*1.5-0.75 - #start_width = 0.005 - cassi_system.generate_custom_pattern_parameters_slit_width(nb_slits=1, nb_rows=nb_rows, start_width = start_width) #0.005 - - cassi_system.array_x_positions = cassi_system.array_x_positions.to(device) - # Ensure array_x_positions is a tensor with gradient tracking - cassi_system.array_x_positions = cassi_system.array_x_positions.clone().detach().requires_grad_(True) - - best_x = cassi_system.array_x_positions.clone().detach() - - convergence_counter = 0 # Counter to check convergence - min_cost_value = np.inf - - # Define the optimizer - if algo == "LBFGS": - optimizer = torch.optim.LBFGS([cassi_system.array_x_positions], lr=lr) # Adjust the learning rate as needed - elif algo == "ADAM": - optimizer = torch.optim.Adam([cassi_system.array_x_positions], lr=lr) # Adjust the learning rate as needed - time_start = time.time() - # Main optimization loop - for iteration in range(num_iterations): # Define num_iterations as needed - - if algo == "LBFGS": - def closure(): - optimizer.zero_grad() - cassi_system.generate_custom_slit_pattern_width(start_pattern = "corrected", start_position = position) - - cassi_system.pattern = cassi_system.pattern.to(device) - cassi_system.generate_filtering_cube() - cassi_system.filtering_cube = cassi_system.filtering_cube.to(device) - - cassi_system.image_acquisition(use_psf=False, chunck_size=cassi_system.system_config["detector"]["number of pixels along Y"]) - cost_value = evaluate_max_lighting(cassi_system.array_x_positions.detach(), cassi_system.measurement, target) - cost_value.backward() - return cost_value - - optimizer.step(closure) - - cost_value = evaluate_max_lighting(cassi_system.array_x_positions.detach(), cassi_system.measurement, target) - - elif algo == "ADAM": - optimizer.zero_grad() # Clear previous gradients - cassi_system.generate_custom_slit_pattern_width(start_pattern = "corrected", start_position = position) - - cassi_system.pattern = cassi_system.pattern.to(device) - cassi_system.generate_filtering_cube() - cassi_system.filtering_cube = cassi_system.filtering_cube.to(device) - cassi_system.image_acquisition(use_psf=False, chunck_size=cassi_system.system_config["detector"]["number of pixels along Y"]) - cost_value = evaluate_max_lighting(cassi_system.array_x_positions.detach(), cassi_system.measurement, target) - cost_value.backward() - optimizer.step() - - if (cost_value - min_cost_value) < threshold: - min_cost_value = cost_value - convergence_counter = 0 - best_x = cassi_system.array_x_positions.clone().detach() - else: - convergence_counter+=1 + config_system = load_yaml_config(config_path) + cassi_system = CassiSystem(system_config=config_system, index_estimation_method=index_estimation_method) + device = cassi_system.device + catalog = config_system["system architecture"]["dispersive element"]["catalog"] + + + with open(os.path.join(results_dir, 'optimization_details.json'), 'r') as f: + optimization_details = json.load(f) + + if iteration_nb is None: + iteration_nb = max(detail['iterations'] for detail in optimization_details) + + + iteration_details = next(detail for detail in optimization_details if detail['iterations'] == iteration_nb) + latest_optical_params = iteration_details['end_parameters'] + + # test a given configuration + list_of_glasses, nd_values, vd_values = get_catalog_glass_infos(catalog=catalog, device=device) + + if cassi_system.optical_model.index_estimation_method == "cauchy": + current_glass_values_1 = [latest_optical_params["nd1"], latest_optical_params["vd1"]] + distance_closest_point1, min_idx_1 = evaluate_distance(current_glass_values_1, nd_values, vd_values) + glass_1 = list_of_glasses[min_idx_1] + + try: + current_glass_values_2 = [latest_optical_params["nd2"], latest_optical_params["vd2"]] + distance_closest_point2, min_idx_2 = evaluate_distance(current_glass_values_2, nd_values, vd_values) + glass_2 = list_of_glasses[min_idx_2] + except: + pass + try: + current_glass_values_3 = [latest_optical_params["nd3"], latest_optical_params["vd3"]] + distance_closest_point3, min_idx_3 = evaluate_distance(current_glass_values_3, nd_values, vd_values) + glass_3 = list_of_glasses[min_idx_3] + except: + pass - if (iteration >= 50) and (convergence_counter >= max_iter_cnv): # If loss didn't decrease in max_iter_cnv steps, break - break - - cassi_system.array_x_positions.data = torch.relu(cassi_system.array_x_positions.data) # Prevent the parameters to be negative - - # Optional: Print cost_value every few iterations to monitor progress - if iteration % 5 == 0: # Adjust printing frequency as needed - print(f"\nIteration {iteration}, Cost: {cost_value.item()}") + else: + glass_1 = cassi_system.optical_model.glass1 + try: + glass_2 = cassi_system.optical_model.glass2 + except: + pass + try: + glass_3 = cassi_system.optical_model.glass3 + except: + pass + + cassi_system.system_config["system architecture"]["dispersive element"]["wavelength center"] = latest_optical_params["lba_c"] + cassi_system.system_config["system architecture"]["dispersive element"]["alpha_c"] = latest_optical_params["alpha_c"] + cassi_system.system_config["system architecture"]["dispersive element"]["A1"] = latest_optical_params["A1"] + cassi_system.system_config["system architecture"]["dispersive element"]['glass1'] = glass_1 + + + if cassi_system.system_config['system architecture']['dispersive element']['type'] in ["doubleprism", "amici", "tripleprism"]: + cassi_system.system_config["system architecture"]["dispersive element"]["A2"] = latest_optical_params["A2"] + cassi_system.system_config["system architecture"]["dispersive element"]['glass2'] = glass_2 - if plot_frequency is not None: - if iteration % plot_frequency == 0: - print(f"Exec time: {time.time() - time_start:.3f}s") - plt.figure() - plt.imshow(cassi_system.pattern.detach().numpy(), aspect=aspect_plot) + + if cassi_system.system_config['system architecture']['dispersive element']['type'] in ["amici", "tripleprism"]: + cassi_system.system_config["system architecture"]["dispersive element"]["A3"] = latest_optical_params["A3"] + cassi_system.system_config["system architecture"]["dispersive element"]['glass3'] = glass_3 - plt.figure() - plt.imshow(cassi_system.filtering_cube[:, :, cassi_system.filtering_cube.shape[2]//2].detach().numpy(), aspect=aspect_plot) + if cassi_system.system_config['system architecture']['dispersive element']['type'] in ["amici"]: + cassi_system.system_config["system architecture"]["dispersive element"]["A3"] = latest_optical_params["A1"] + cassi_system.system_config["system architecture"]["dispersive element"]['glass3'] = glass_1 - #plt.figure() - #plt.plot(np.sum(cassi_system.filtering_cube[:, :, cassi_system.filtering_cube.shape[2]//2].detach().numpy(),axis=0)) + - plt.figure() - plt.imshow(cassi_system.measurement.detach().numpy(), cmap="gray") - plt.colorbar() - plt.show() + cassi_system.update_optical_model(cassi_system.system_config) - cassi_system.array_x_positions.data = torch.relu(best_x.data) - #cassi_system.array_x_positions.data = best_x.data - cassi_system.generate_custom_slit_pattern_width(start_pattern = "corrected", start_position = position) - cassi_system.pattern = cassi_system.pattern.to(device) - cassi_system.generate_filtering_cube() + with open(output_config_path, 'w') as yaml_file: + yaml.dump(config_system, yaml_file, default_flow_style=False) - return cassi_system diff --git a/simca/functions_patterns_generation.py b/simca/functions_patterns_generation.py deleted file mode 100644 index bfe9468..0000000 --- a/simca/functions_patterns_generation.py +++ /dev/null @@ -1,344 +0,0 @@ -import numpy as np -from scipy import fftpack -from scipy import ndimage -import h5py -def generate_blue_noise_type_1_pattern(shape): - """ - Generate blue noise (high frequency pseudo-random) type pattern - - Args: - shape (tuple of int): shape of the pattern - - Returns: - numpy.ndarray: binary blue noise type pattern - - """ - - N = shape[0] * shape[1] - rng = np.random.default_rng() - noise = rng.standard_normal(N) - noise = np.reshape(noise, shape) - - f_x = fftpack.fftfreq(shape[1]) - f_y = fftpack.fftfreq(shape[0]) - f_x_shift = fftpack.fftshift(f_x) - f_y_shift = fftpack.fftshift(f_y) - f_matrix = np.sqrt(f_x_shift[None, :] ** 2 + f_y_shift[:, None] ** 2) - - spectrum = fftpack.fftshift(fftpack.fft2(noise)) - filtered_spectrum = spectrum * f_matrix - filtered_noise = fftpack.ifft2(fftpack.ifftshift(filtered_spectrum)).real - - # Make the pattern binary - threshold = np.median(filtered_noise) - binary_pattern = np.where(filtered_noise > threshold, 1, 0) - - return binary_pattern - -def generate_orthogonal_pattern(size, W, N): - """ - Generate an orthogonal pattern according to https://hal.laas.fr/hal-02993037 - - Args: - size (list of int): size of the pattern - W (int): number of wavelengths in the scene - N (int): number of acquisitions - - Returns: - numpy.ndarray: orthogonal pattern : shape = size[0] x (size[1]+W-1) x N): - """ - - C, R = size[0], size[1] # Number of columns, number of rows - - K = C + W - 1 # Number of columns in H - M = int(np.ceil(W/N)) # Number of open mirrors - H_model = np.zeros((R, W, N)) # Periodic model - for line in range(R): - available_pos = list(range(W)) # Positions of possible mirrors to open - for n in range(N): - if available_pos: - if (len(available_pos)>=M): - ind = np.random.choice(len(available_pos), M, replace=False) # Indices of the N positions among the available ones - pos = np.array(available_pos)[ind] # N mirrors to open - else: - ind = list(range(len(available_pos))) - pos = np.array(available_pos) - H_model[line, pos, n] = 1 # Open the mirrors - for i in sorted(ind, reverse=True): - available_pos.pop(i) # Remove the positions of already opened mirrors - - pattern = np.tile(H_model, [1, int(np.ceil(K/W)), 1])[:, :K, :] # Repeat the model periodically - - return pattern - -def generate_ln_orthogonal_pattern(size, W, N): - """ - Generate a Length-N orthogonal pattern according to https://hal.laas.fr/hal-02993037 - - Args: - size (tuple): size of the pattern - W (int): number of wavelengths in the scene - N (int): number of acquisitions - - Returns: - numpy.ndarray : length-N orthogonal pattern of shape = size[0] x (size[1]+W-1) x N): - """ - - C, R = size[1], size[0] # Number of columns, number of rows - - K = C + W - 1 # Number of columns in H - M = int(np.floor(W/N)) # Number of open mirrors - H_model = np.zeros((R, W, N)) # Periodic model - for line in range(R): - for m in range(M): - available = list(range(m*N, m*N+N)) # Positions of possible mirrors to open - for n in range(N): - ind = np.random.randint(len(available)) # Randomly choose a mirror among the possible ones - H_model[line, available[ind], n] = 1 # Open the mirror - available.pop(ind) # Remove the mirror from the list of possible ones - available = list(range(M*N, W)) # List of positions where we can't apply the Length-N method (if W%N != 0) - for n in range(W-(M*N)): - ind = np.random.randint(len(available)) # Randomly open those mirrors among the remaining positions - H_model[line, available[ind], n] = 1 - available.pop(ind) - pattern = np.tile(H_model, [1, int(np.ceil(K/W)), 1])[:, :K, :] - - list_of_patterns = [] - for i in range(pattern.shape[2]): - m = pattern[:, :-(W-1), i] - list_of_patterns.append(m) - - return list_of_patterns - -def generate_random_pattern(shape, ROM): - """ - Generate a random pattern with a given rate of open/close mirrors - - Args: - shape (tuple of int): shape of the pattern - ROM (float): ratio of open mirrors - - Returns: - numpy.ndarray: random pattern - """ - - pattern = np.random.choice([0, 1], size=shape, p=[1 - ROM, ROM]) - - return pattern - -def generate_slit_pattern(shape, slit_position,slit_width): - """ - Generate a slit pattern that starts at the center of the image and goes to the right as slit position increases. - - Args: - shape (tuple): shape of the pattern - slit_position (int): position of the slit in relation to the central column - slit_width (int): width of the slit in pixels - - Returns: - numpy.ndarray: slit pattern - - """ - size_y, size_x = shape[0], shape[1] - slit_position = size_x // 2 + slit_position - slit_width = slit_width - pattern = np.zeros((size_y,size_x)) - - pattern[:, slit_position - slit_width // 2:slit_position + slit_width] = 1 - - return pattern - -# Source of blue noise codes: https://momentsingraphics.de/BlueNoise.html -def FindLargestVoid(BinaryPattern,StandardDeviation): - """This function returns the indices of the largest void in the given binary - pattern as defined by Ulichney. - \param BinaryPattern A boolean array (should be two-dimensional although the - implementation works in arbitrary dimensions). - \param StandardDeviation The standard deviation used for the Gaussian filter - in pixels. This can be a single float for an isotropic Gaussian or a - tuple with one float per dimension for an anisotropic Gaussian. - \return A flat index i such that BinaryPattern.flat[i] corresponds to the - largest void. By definition this is a majority pixel. - \sa GetVoidAndClusterBlueNoise""" - # The minority value is always True for convenience - if(np.count_nonzero(BinaryPattern)*2>=np.size(BinaryPattern)): - BinaryPattern=np.logical_not(BinaryPattern) - # Apply the Gaussian. We do not want to cut off the Gaussian at all because even - # the tiniest difference can change the ranking. Therefore we apply the Gaussian - # through a fast Fourier transform by means of the convolution theorem. - FilteredArray=np.fft.ifftn(ndimage.fourier_gaussian(np.fft.fftn(np.where(BinaryPattern,1.0,0.0)),StandardDeviation)).real - # Find the largest void - return np.argmin(np.where(BinaryPattern,2.0,FilteredArray)) - -# Source of blue noise codes: https://momentsingraphics.de/BlueNoise.html -def FindTightestCluster(BinaryPattern,StandardDeviation): - """Like FindLargestVoid() but finds the tightest cluster which is a minority - pixel by definition. - \sa GetVoidAndClusterBlueNoise""" - if(np.count_nonzero(BinaryPattern)*2>=np.size(BinaryPattern)): - BinaryPattern=np.logical_not(BinaryPattern) - FilteredArray=np.fft.ifftn(ndimage.fourier_gaussian(np.fft.fftn(np.where(BinaryPattern,1.0,0.0)),StandardDeviation)).real - return np.argmax(np.where(BinaryPattern,FilteredArray,-1.0)) - -# Source of blue noise codes: https://momentsingraphics.de/BlueNoise.html -def GetVoidAndClusterBlueNoise(OutputShape,StandardDeviation=1.5,InitialSeedFraction=0.1): - """Generates a blue noise dither array of the given shape using the method - proposed by Ulichney [1993] in "The void-and-cluster method for dither array - generation" published in Proc. SPIE 1913. - \param OutputShape The shape of the output array. This function works in - arbitrary dimension, i.e. OutputShape can have arbitrary length. Though - it is only tested for the 2D case where you should pass a tuple - (Height,Width). - \param StandardDeviation The standard deviation in pixels used for the - Gaussian filter defining largest voids and tightest clusters. Larger - values lead to more low-frequency content but better isotropy. Small - values lead to more ordered patterns with less low-frequency content. - Ulichney proposes to use a value of 1.5. If you want an anisotropic - Gaussian, you can pass a tuple of length len(OutputShape) with one - standard deviation per dimension. - \param InitialSeedFraction The only non-deterministic step in the algorithm - marks a small number of pixels in the grid randomly. This parameter - defines the fraction of such points. It has to be positive but less - than 0.5. Very small values lead to ordered patterns, beyond that there - is little change. - \return An integer array of shape OutputShape containing each integer from 0 - to np.prod(OutputShape)-1 exactly once.""" - nRank=np.prod(OutputShape) - # Generate the initial binary pattern with a prescribed number of ones - nInitialOne=max(1,min(int((nRank-1)/2),int(nRank*InitialSeedFraction))) - # Start from white noise (this is the only randomized step) - InitialBinaryPattern=np.zeros(OutputShape,dtype=bool) - InitialBinaryPattern.flat=np.random.permutation(np.arange(nRank)) 0 - pca = PCA(n_dim_pca) - pca.fit(img[mask]) - - for c in np.unique(complete_gt): - if c in ignored_labels: - continue - mask = complete_gt == c - class_spectrums = img[mask] - # pca = PCA(n_dim_pca) - class_spectrums_pca = pca.transform(class_spectrums) - - mean_spectrum = np.mean(class_spectrums, axis=0) - std_spectrum = np.std(class_spectrums, axis=0) - - stats_per_class['mean_spectrums'][class_names[c]] = mean_spectrum - stats_per_class['std_spectrums'][class_names[c]] = std_spectrum - stats_per_class['non_degenerate_mean_spectrums'][class_names[c]] = np.mean(class_spectrums_pca, axis=0) - stats_per_class['non_degenerate_covariance'][class_names[c]] = np.cov(np.transpose(class_spectrums_pca)) - - return stats_per_class - - - -def interpolate_data_along_wavelength(data, current_sampling, new_sampling, chunk_size=50): - """Interpolate the input 3D data along a new sampling in the third axis. - - Args: - data (numpy.ndarray): 3D data to interpolate - current_sampling (numpy.ndarray): current sampling for the 3rd axis - new_sampling (numpy.ndarray): new sampling for the 3rd axis - chunk_size (int): size of the chunks to use for the interpolation - """ - - # Generate the coordinates for the original grid - x = np.arange(data.shape[0]) - y = np.arange(data.shape[1]) - z = current_sampling - - # Initialize an empty array for the result - interpolated_data = np.empty((data.shape[0], data.shape[1], len(new_sampling))) - - # Perform the interpolation in chunks - for i in range(0, data.shape[0], chunk_size): - for j in range(0, data.shape[1], chunk_size): - new_coordinates = np.meshgrid(x[i:i+chunk_size], y[j:j+chunk_size], new_sampling, indexing='ij') - interpolated_data[i:i+chunk_size, j:j+chunk_size, :] = interpn((x[i:i+chunk_size], y[j:j+chunk_size], z), data[i:i+chunk_size, j:j+chunk_size, :], tuple(new_coordinates)) - - return interpolated_data \ No newline at end of file diff --git a/simca/functions_scenes_torch.py b/simca/functions_scenes_torch.py deleted file mode 100644 index 2a8bd52..0000000 --- a/simca/functions_scenes_torch.py +++ /dev/null @@ -1,3 +0,0 @@ -import torch -import snoop - diff --git a/simca/helper.py b/simca/helper.py new file mode 100644 index 0000000..1271e3f --- /dev/null +++ b/simca/helper.py @@ -0,0 +1,71 @@ +import yaml +import math +import os +import numpy as np +from datetime import datetime +import h5py +import torch + +def load_yaml_config(file_path): + """ + Load a YAML configuration file as a dictionary + + Args: + file_path (str): path to the YAML configuration file + + Returns: + dict: configuration dictionary + """ + with open(file_path, "r") as file: + config = yaml.safe_load(file) + return config + + + + + + + +cm_data = [[0.2081, 0.1663, 0.5292], [0.2116238095, 0.1897809524, 0.5776761905], + [0.212252381, 0.2137714286, 0.6269714286], [0.2081, 0.2386, 0.6770857143], + [0.1959047619, 0.2644571429, 0.7279], [0.1707285714, 0.2919380952, + 0.779247619], [0.1252714286, 0.3242428571, 0.8302714286], + [0.0591333333, 0.3598333333, 0.8683333333], [0.0116952381, 0.3875095238, + 0.8819571429], [0.0059571429, 0.4086142857, 0.8828428571], + [0.0165142857, 0.4266, 0.8786333333], [0.032852381, 0.4430428571, + 0.8719571429], [0.0498142857, 0.4585714286, 0.8640571429], + [0.0629333333, 0.4736904762, 0.8554380952], [0.0722666667, 0.4886666667, + 0.8467], [0.0779428571, 0.5039857143, 0.8383714286], + [0.079347619, 0.5200238095, 0.8311809524], [0.0749428571, 0.5375428571, + 0.8262714286], [0.0640571429, 0.5569857143, 0.8239571429], + [0.0487714286, 0.5772238095, 0.8228285714], [0.0343428571, 0.5965809524, + 0.819852381], [0.0265, 0.6137, 0.8135], [0.0238904762, 0.6286619048, + 0.8037619048], [0.0230904762, 0.6417857143, 0.7912666667], + [0.0227714286, 0.6534857143, 0.7767571429], [0.0266619048, 0.6641952381, + 0.7607190476], [0.0383714286, 0.6742714286, 0.743552381], + [0.0589714286, 0.6837571429, 0.7253857143], + [0.0843, 0.6928333333, 0.7061666667], [0.1132952381, 0.7015, 0.6858571429], + [0.1452714286, 0.7097571429, 0.6646285714], [0.1801333333, 0.7176571429, + 0.6424333333], [0.2178285714, 0.7250428571, 0.6192619048], + [0.2586428571, 0.7317142857, 0.5954285714], [0.3021714286, 0.7376047619, + 0.5711857143], [0.3481666667, 0.7424333333, 0.5472666667], + [0.3952571429, 0.7459, 0.5244428571], [0.4420095238, 0.7480809524, + 0.5033142857], [0.4871238095, 0.7490619048, 0.4839761905], + [0.5300285714, 0.7491142857, 0.4661142857], [0.5708571429, 0.7485190476, + 0.4493904762], [0.609852381, 0.7473142857, 0.4336857143], + [0.6473, 0.7456, 0.4188], [0.6834190476, 0.7434761905, 0.4044333333], + [0.7184095238, 0.7411333333, 0.3904761905], + [0.7524857143, 0.7384, 0.3768142857], [0.7858428571, 0.7355666667, + 0.3632714286], [0.8185047619, 0.7327333333, 0.3497904762], + [0.8506571429, 0.7299, 0.3360285714], [0.8824333333, 0.7274333333, 0.3217], + [0.9139333333, 0.7257857143, 0.3062761905], [0.9449571429, 0.7261142857, + 0.2886428571], [0.9738952381, 0.7313952381, 0.266647619], + [0.9937714286, 0.7454571429, 0.240347619], [0.9990428571, 0.7653142857, + 0.2164142857], [0.9955333333, 0.7860571429, 0.196652381], + [0.988, 0.8066, 0.1793666667], [0.9788571429, 0.8271428571, 0.1633142857], + [0.9697, 0.8481380952, 0.147452381], [0.9625857143, 0.8705142857, 0.1309], + [0.9588714286, 0.8949, 0.1132428571], [0.9598238095, 0.9218333333, + 0.0948380952], [0.9661, 0.9514428571, 0.0755333333], + [0.9763, 0.9831, 0.0538]] + + diff --git a/simca/tests/__init__.py b/simca/tests/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/simca/tests/test_cassisystem_initialization.py b/simca/tests/test_cassisystem_initialization.py deleted file mode 100644 index 0513648..0000000 --- a/simca/tests/test_cassisystem_initialization.py +++ /dev/null @@ -1,315 +0,0 @@ -import unittest -import numpy as np -import random -import glob -from simca.functions_general_purpose import load_yaml_config -from simca.CassiSystem import CassiSystem -import h5py as h5 -import os - -class TestCassiSystemInitialization(unittest.TestCase): - - def setUp(self): - self.config_system = load_yaml_config('./simca/tests/test_configs/cassi_system.yml') - self.config_dataset = load_yaml_config('./simca/tests/test_configs/dataset.yml') - def test_config_system_loading(self): - - config_files = glob.glob('./simca/tests/test_configs/*.yml') - - for config_file in config_files: - config_system = load_yaml_config(config_file) - self.assertIsInstance(config_system, dict) - - self.config_system = load_yaml_config('./simca/tests/test_configs/cassi_system.yml') - self.config_dataset = load_yaml_config('./simca/tests/test_configs/dataset.yml') - def test_coded_aper_grid_generation(self): - - config_files = glob.glob('./simca/tests/test_configs/cassi_system*.yml') - - for config_file in config_files: - config_system = load_yaml_config(config_file) - - cassi_system = CassiSystem(system_config=config_system) - - self.assertEqual(cassi_system.X_coded_aper_coordinates.shape, (config_system["coded aperture"]["number of pixels along Y"], config_system["coded aperture"]["number of pixels along X"])) - self.assertEqual(cassi_system.Y_coded_aper_coordinates.shape, (config_system["coded aperture"]["number of pixels along Y"], config_system["coded aperture"]["number of pixels along X"])) - - def test_loading_dataset(self): - - # load all datasets in the datasets directory - - for dataset in glob.glob('./datasets/*'): - - dataset_name = os.path.basename(os.path.normpath(dataset)) - # dataset_name = dataset.split('\\')[-1].split('\\')[0] - - - cassi_system = CassiSystem(system_config=self.config_system) - - cassi_system.load_dataset(dataset_name, self.config_dataset["datasets directory"]) - - self.assertIsInstance(cassi_system.dataset, np.ndarray) - self.assertEqual(cassi_system.dataset.ndim, 3) - - self.assertIsInstance(cassi_system.dataset_wavelengths, np.ndarray) - self.assertEqual(cassi_system.dataset_wavelengths.ndim, 1) - self.assertIs(cassi_system.dataset_wavelengths.shape[0] > 0, True) - self.assertEqual(cassi_system.dataset_wavelengths.shape[0], cassi_system.dataset.shape[2]) - - if cassi_system.dataset_labels is not None : - self.assertIsInstance(cassi_system.dataset_labels,np.ndarray) - self.assertEqual(cassi_system.dataset_labels.ndim, 2) - self.assertEqual(cassi_system.dataset_labels.shape[0], cassi_system.dataset.shape[0]) - self.assertEqual(cassi_system.dataset_labels.shape[1], cassi_system.dataset.shape[1]) - - self.assertIsInstance(cassi_system.dataset_label_names, list) - self.assertIs(len(cassi_system.dataset_label_names),len(list(np.unique(cassi_system.dataset_labels)))) - - # ignored labels not tested - - self.assertIsInstance(cassi_system.dataset_palette, dict) - - def test_generate_pattern(self): - - cassi_system = CassiSystem(system_config=self.config_system) - - config_files = glob.glob('./simca/tests/test_configs/filtering_simple*.yml') - - for config_file in config_files: - - config_pattern = load_yaml_config(config_file) - pattern = cassi_system.generate_2D_pattern(config_pattern) - - self.assertIsInstance(pattern, np.ndarray) - self.assertEqual(pattern.shape, (self.config_system["coded aperture"]["number of pixels along Y"], self.config_system["coded aperture"]["number of pixels along X"])) - - def test_generate_multiple_patterns(self,num_tests=4,number_of_pattern=3): - - config_system_files = glob.glob('./simca/tests/test_configs/cassi_system*.yml') - config_patterns_files = glob.glob('./simca/tests/test_configs/filtering_multiple*.yml') - - # Create all possible combinations of system and pattern configs - all_combinations = [(system, coded_aperture) for system in config_system_files for coded_aperture in config_patterns_files] - - # Randomly choose 'num_tests' combinations to test - selected_combinations = random.sample(all_combinations, num_tests) - - for config_system_file, config_patterns_file in selected_combinations: - config_system = load_yaml_config(config_system_file) - config_patterns = load_yaml_config(config_patterns_file) - - cassi_system = CassiSystem(system_config=config_system) - cassi_system.generate_multiple_patterns(config_patterns,number_of_patterns=number_of_pattern) - cassi_system.propagate_coded_aperture_grid() - list_of_filtering_cubes = cassi_system.generate_multiple_filtering_cubes(number_of_patterns=number_of_pattern) - - self.assertIsInstance(list_of_filtering_cubes,list) - self.assertEqual(len(list_of_filtering_cubes),number_of_pattern) - self.assertEqual(list_of_filtering_cubes[0].ndim, 3) - self.assertEqual(list_of_filtering_cubes[0].shape[0], cassi_system.Y_detector_coordinates_grid.shape[0]) - self.assertEqual(list_of_filtering_cubes[0].shape[1], cassi_system.X_detector_coordinates_grid.shape[1]) - self.assertEqual(list_of_filtering_cubes[0].shape[2], len(cassi_system.optical_model.system_wavelengths)) - - self.assertEqual(list_of_filtering_cubes[-1].ndim, 3) - self.assertEqual(list_of_filtering_cubes[-1].shape[0], cassi_system.Y_detector_coordinates_grid.shape[0]) - self.assertEqual(list_of_filtering_cubes[-1].shape[1], cassi_system.X_detector_coordinates_grid.shape[1]) - self.assertEqual(list_of_filtering_cubes[-1].shape[2], len(cassi_system.optical_model.system_wavelengths)) - def test_values_pattern_h5(self): - - config_folders = glob.glob('./simca/tests/test_configs/mask_*') - - for config_folder in config_folders: - config_system = load_yaml_config(os.path.join(config_folder, "config_system.yml")) - cassi_system = CassiSystem(system_config=config_system) - - config_file = os.path.join(config_folder, "config_pattern_gen.yml") - config_pattern = load_yaml_config(config_file) - pattern = cassi_system.generate_2D_pattern(config_pattern) - - with h5.File(os.path.join(config_folder, "pattern.h5"), 'r') as f: - true_pattern = np.array(f['pattern']) - - self.assertIsNone(np.testing.assert_array_equal(pattern, true_pattern)) - - def test_propagate_coded_aperture_grid(self): - - cassi_system = CassiSystem(system_config=self.config_system) - - config_files = glob.glob('./simca/tests/test_configs/filtering_simple*.yml') - - for config_file in config_files: - - config_pattern = load_yaml_config(config_file) - cassi_system.generate_2D_pattern(config_pattern) - X_grid, Y_grid, W_list = cassi_system.propagate_coded_aperture_grid() - - self.assertIsInstance(X_grid, np.ndarray) - self.assertEqual(X_grid.ndim, 3) - self.assertEqual(X_grid.shape[0], self.config_system["coded aperture"]["number of pixels along Y"]) - self.assertEqual(X_grid.shape[1], self.config_system["coded aperture"]["number of pixels along X"]) - self.assertEqual(X_grid.shape[2], W_list.shape[0]) - - self.assertIsInstance(Y_grid, np.ndarray) - self.assertEqual(Y_grid.ndim, 3) - self.assertEqual(Y_grid.shape[0], self.config_system["coded aperture"]["number of pixels along Y"]) - self.assertEqual(Y_grid.shape[1], self.config_system["coded aperture"]["number of pixels along X"]) - self.assertEqual(Y_grid.shape[2], W_list.shape[0]) - - - def test_generate_filtering_cube(self, num_tests=5): - - config_system_files = glob.glob('./simca/tests/test_configs/cassi_system*.yml') - config_patterns_files = glob.glob('./simca/tests/test_configs/filtering_simple*.yml') - - # Create all possible combinations of system and coded_aperture configs - all_combinations = [(system, pattern) for system in config_system_files for pattern in config_patterns_files] - - # Randomly choose 'num_tests' combinations to test - selected_combinations = random.sample(all_combinations, num_tests) - - for config_system_file, config_patterns_file in selected_combinations: - config_system = load_yaml_config(config_system_file) - config_patterns = load_yaml_config(config_patterns_file) - - cassi_system = CassiSystem(system_config=config_system) - cassi_system.generate_2D_pattern(config_patterns) - cassi_system.propagate_coded_aperture_grid() - filtering_cube = cassi_system.generate_filtering_cube() - - self.assertIsInstance(filtering_cube, np.ndarray) - self.assertEqual(filtering_cube.ndim, 3) - self.assertEqual(filtering_cube.shape[0], cassi_system.Y_detector_coordinates_grid.shape[0]) - self.assertEqual(filtering_cube.shape[1], cassi_system.X_detector_coordinates_grid.shape[1]) - self.assertEqual(filtering_cube.shape[2], len(cassi_system.optical_model.system_wavelengths)) - - def test_values_filtering_cube(self): - config_folders = glob.glob('./simca/tests/test_configs/mask_*') - - for config_folder in config_folders: - config_system = load_yaml_config(os.path.join(config_folder, "config_system.yml")) - cassi_system = CassiSystem(system_config = config_system) - - config_file = os.path.join(config_folder, "config_pattern_gen.yml") - config_pattern = load_yaml_config(config_file) - cassi_system.generate_2D_pattern(config_pattern) - cassi_system.propagate_coded_aperture_grid() - - filtering_cube = cassi_system.generate_filtering_cube() - - config_values = load_yaml_config(os.path.join(config_folder, "config_test_values.yml"))["filtering_cube"] - - res_values = load_yaml_config(os.path.join(config_folder, "test_values.yml"))["filtering_cube"] - - for i in range(1, 9): - self.assertEqual(filtering_cube[tuple(config_values[f"pos{i}"])], res_values[i-1]) - - self.assertEqual(np.sum(filtering_cube), res_values[8]) - self.assertEqual(np.sum(np.trace(filtering_cube, axis1=0, axis2=1)), res_values[9]) - self.assertEqual(np.sum(filtering_cube[:][1][:]), res_values[10]) - - - - - def test_acquisition(self, num_tests=5, num_acq=1): - config_system_files = glob.glob('./simca/tests/test_configs/cassi_system*.yml') - if num_acq > 1: - config_patterns_files = glob.glob('./simca/tests/test_configs/filtering_multiple*.yml') - else: - config_patterns_files = glob.glob('./simca/tests/test_configs/filtering_simple*.yml') - - config_datasets = os.listdir(self.config_dataset["datasets directory"]) - - # Create all possible combinations of system and pattern configs - all_combinations = [(system, dataset, coded_aperture) for system in config_system_files - for coded_aperture in config_patterns_files - for dataset in config_datasets] - - # Randomly choose 'num_tests' combinations to test - selected_combinations = random.sample(all_combinations, num_tests) - - for config_system_file, dataset_name, config_patterns_file in selected_combinations: - config_system = load_yaml_config(config_system_file) - config_patterns = load_yaml_config(config_patterns_file) - - cassi_system = CassiSystem(system_config=config_system) - - cassi_system.load_dataset(dataset_name, self.config_dataset["datasets directory"]) - - if num_acq > 1: - cassi_system.generate_multiple_patterns(config_patterns, number_of_patterns=num_acq) - else: - cassi_system.generate_2D_pattern(config_patterns) - cassi_system.propagate_coded_aperture_grid() - - if num_acq > 1: - cassi_system.generate_multiple_filtering_cubes(number_of_patterns=num_acq) - list_of_measurements = cassi_system.multiple_image_acquisitions(nb_of_filtering_cubes=num_acq) - - self.assertIsInstance(list_of_measurements, list) - self.assertEqual(len(list_of_measurements), num_acq) - self.assertEqual(list_of_measurements[0].ndim, 2) - self.assertEqual(list_of_measurements[0].shape[0], cassi_system.Y_detector_coordinates_grid.shape[0]) - self.assertEqual(list_of_measurements[0].shape[1], cassi_system.X_detector_coordinates_grid.shape[1]) - - self.assertEqual(list_of_measurements[-1].ndim, 2) - self.assertEqual(list_of_measurements[-1].shape[0], cassi_system.Y_detector_coordinates_grid.shape[0]) - self.assertEqual(list_of_measurements[-1].shape[1], cassi_system.X_detector_coordinates_grid.shape[1]) - else: - cassi_system.generate_filtering_cube() - measurement = cassi_system.image_acquisition() - - self.assertIsInstance(measurement, np.ndarray) - self.assertEqual(measurement.ndim, 2) - self.assertEqual(measurement.shape[0], cassi_system.Y_detector_coordinates_grid.shape[0]) - self.assertEqual(measurement.shape[1], cassi_system.X_detector_coordinates_grid.shape[1]) - - - def test_values_acquisition(self): - config_folders = glob.glob('./simca/tests/test_configs/mask_*') - - for config_folder in config_folders: - config_system = load_yaml_config(os.path.join(config_folder, "config_system.yml")) - - cassi_system = CassiSystem(system_config=config_system) - - cassi_system.load_dataset("lego" if config_folder[-4:] == "lego" else "paviaU", - self.config_dataset["datasets directory"]) - - config_file = os.path.join(config_folder, "config_pattern_gen.yml") - config_pattern = load_yaml_config(config_file) - cassi_system.generate_2D_pattern(config_pattern) - cassi_system.propagate_coded_aperture_grid() - - cassi_system.generate_filtering_cube() - - measurement = cassi_system.image_acquisition() - - for item in ["filtered_interpolated_scene", "interpolated_scene", "measurement"]: - config_values = load_yaml_config(os.path.join(config_folder, "config_test_values.yml"))[item] - res_values = load_yaml_config(os.path.join(config_folder, "test_values.yml"))[item] - - tested_item = measurement - if item == "filtered_interpolated_scene": - tested_item = cassi_system.last_filtered_interpolated_scene - - self.assertEqual(np.sum(tested_item), res_values[8]) - self.assertEqual(np.sum(np.trace(tested_item, axis1=0, axis2=1)), res_values[9]) - self.assertEqual(np.sum(tested_item[:][2][:]), res_values[10]) - elif item == "interpolated_scene": - tested_item = cassi_system.interpolated_scene - self.assertEqual(np.sum(tested_item), res_values[8]) - self.assertEqual(np.sum(np.trace(tested_item, axis1=0, axis2=1)), res_values[9]) - self.assertEqual(np.sum(tested_item[:][3][:]), res_values[10]) - else: - self.assertEqual(np.trace(tested_item), res_values[8]) - self.assertEqual(np.sum(tested_item[:][4]), res_values[9]) - self.assertEqual(np.sum(tested_item[7][:]), res_values[10]) - - for i in range(1, 9): - self.assertEqual(tested_item[tuple(config_values[f"pos{i}"])], res_values[i - 1]) - - -if __name__ == '__main__': - unittest.main() - - diff --git a/simca/tests/test_configs/cassi_system.yml b/simca/tests/test_configs/cassi_system.yml deleted file mode 100755 index 3fe0670..0000000 --- a/simca/tests/test_configs/cassi_system.yml +++ /dev/null @@ -1,33 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: grating # name of the dispersive element - A: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 80 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 23 # number of pixels along X axis -- no units - number of pixels along Y: 21 # number of pixels along Y axis -- no units - pixel size along X: 400 # pixel size along X -- in um - pixel size along Y: 400 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 31 # number of pixels along X axis -- no units - number of pixels along Y: 22 # number of pixels along Y axis -- no units - pixel size along X: 550 # pixel size along X -- in um - pixel size along Y: 550 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 610 # maximum wavelength -- in nm - number of spectral samples: 11 diff --git a/simca/tests/test_configs/cassi_system_grating.yml b/simca/tests/test_configs/cassi_system_grating.yml deleted file mode 100755 index 3fe0670..0000000 --- a/simca/tests/test_configs/cassi_system_grating.yml +++ /dev/null @@ -1,33 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: grating # name of the dispersive element - A: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 80 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 23 # number of pixels along X axis -- no units - number of pixels along Y: 21 # number of pixels along Y axis -- no units - pixel size along X: 400 # pixel size along X -- in um - pixel size along Y: 400 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 31 # number of pixels along X axis -- no units - number of pixels along Y: 22 # number of pixels along Y axis -- no units - pixel size along X: 550 # pixel size along X -- in um - pixel size along Y: 550 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 610 # maximum wavelength -- in nm - number of spectral samples: 11 diff --git a/simca/tests/test_configs/cassi_system_grating_even.yml b/simca/tests/test_configs/cassi_system_grating_even.yml deleted file mode 100755 index c6251ff..0000000 --- a/simca/tests/test_configs/cassi_system_grating_even.yml +++ /dev/null @@ -1,33 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: grating # name of the dispersive element - A: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 80 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 22 # number of pixels along X axis -- no units - number of pixels along Y: 10 # number of pixels along Y axis -- no units - pixel size along X: 400 # pixel size along X -- in um - pixel size along Y: 400 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 32 # number of pixels along X axis -- no units - number of pixels along Y: 12 # number of pixels along Y axis -- no units - pixel size along X: 550 # pixel size along X -- in um - pixel size along Y: 550 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 610 # maximum wavelength -- in nm - number of spectral samples: 11 diff --git a/simca/tests/test_configs/cassi_system_prism.yml b/simca/tests/test_configs/cassi_system_prism.yml deleted file mode 100755 index 14c18a1..0000000 --- a/simca/tests/test_configs/cassi_system_prism.yml +++ /dev/null @@ -1,33 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - system name: HYACAMEO - -system architecture: - system type: DD-CASSI - propagation type: simca - focal lens: 200000 - dispersive element: - # dispersive element caracteristics - type: prism # name of the dispersive element - A: 60 # apex angle of the prism in degrees (only used if type == 'prism') -- in degrees - m: 1 # grating order to consider (only used if type == 'grating') -- no units - G: 150 # grating density (only used if type == 'grating') -- in lines/mm - delta alpha c: 0 - delta beta c: 0 - wavelength center: 550 # central wavelength -- in nm -detector: - number of pixels along X: 23 # number of pixels along X axis -- no units - number of pixels along Y: 21 # number of pixels along Y axis -- no units - pixel size along X: 400 # pixel size along X -- in um - pixel size along Y: 400 # pixel size along Y -- in um -coded aperture: - number of pixels along X: 31 # number of pixels along X axis -- no units - number of pixels along Y: 22 # number of pixels along Y axis -- no units - pixel size along X: 550 # pixel size along X -- in um - pixel size along Y: 550 # pixel size along Y -- in um - -spectral range: - wavelength min: 450 # minimum wavelength -- in nm - wavelength max: 610 # maximum wavelength -- in nm - number of spectral samples: 11 diff --git a/simca/tests/test_configs/dataset.yml b/simca/tests/test_configs/dataset.yml deleted file mode 100755 index 52835c7..0000000 --- a/simca/tests/test_configs/dataset.yml +++ /dev/null @@ -1 +0,0 @@ -datasets directory: ./datasets/ diff --git a/simca/tests/test_configs/filtering_multiple_LN_random.yml b/simca/tests/test_configs/filtering_multiple_LN_random.yml deleted file mode 100755 index 117e09d..0000000 --- a/simca/tests/test_configs/filtering_multiple_LN_random.yml +++ /dev/null @@ -1,11 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - results directory: ./data/patterns/ - -pattern: - type: LN-random - slit position: -120 - slit width: 1 - ROM : 0.2 - diff --git a/simca/tests/test_configs/filtering_multiple_blue_noise_type_1.yml b/simca/tests/test_configs/filtering_multiple_blue_noise_type_1.yml deleted file mode 100755 index 21503d4..0000000 --- a/simca/tests/test_configs/filtering_multiple_blue_noise_type_1.yml +++ /dev/null @@ -1,11 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - results directory: ./data/patterns/ - -pattern: - type: blue-noise type 1 - slit position: -120 - slit width: 1 - ROM : 0.5 - diff --git a/simca/tests/test_configs/filtering_simple_random.yml b/simca/tests/test_configs/filtering_simple_random.yml deleted file mode 100755 index 827ca20..0000000 --- a/simca/tests/test_configs/filtering_simple_random.yml +++ /dev/null @@ -1,11 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - results directory: ./data/patterns/ - -pattern: - type: random - slit position: -120 - slit width: 1 - ROM : 0.5 - diff --git a/simca/tests/test_configs/filtering_simple_slit.yml b/simca/tests/test_configs/filtering_simple_slit.yml deleted file mode 100755 index 362d04d..0000000 --- a/simca/tests/test_configs/filtering_simple_slit.yml +++ /dev/null @@ -1,11 +0,0 @@ -##### Configuration file for the chosen optical system - -infos: - results directory: ./data/patterns/ - -pattern: - type: slit - slit position: -120 - slit width: 1 - ROM : 0.5 - diff --git a/simca/tests/test_configs/mask_1_paviaU/config_acquisition.yml b/simca/tests/test_configs/mask_1_paviaU/config_acquisition.yml deleted file mode 100755 index ebe6986..0000000 --- a/simca/tests/test_configs/mask_1_paviaU/config_acquisition.yml +++ /dev/null @@ -1,7 +0,0 @@ -acquisition name: mask_1 -acquisition type: single acq. -psf: - radius: 100.0 - type: Gaussian - use_psf: false -results directory: ./data/results/ diff --git a/simca/tests/test_configs/mask_1_paviaU/config_pattern.yml b/simca/tests/test_configs/mask_1_paviaU/config_pattern.yml deleted file mode 100755 index 998e352..0000000 --- a/simca/tests/test_configs/mask_1_paviaU/config_pattern.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - ROM: 0.2 - type: random diff --git a/simca/tests/test_configs/mask_1_paviaU/config_pattern_gen.yml b/simca/tests/test_configs/mask_1_paviaU/config_pattern_gen.yml deleted file mode 100755 index f4f9813..0000000 --- a/simca/tests/test_configs/mask_1_paviaU/config_pattern_gen.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - type: custom h5 pattern - file path: "./simca/tests/test_configs/mask_1_paviaU/pattern.h5" diff --git a/simca/tests/test_configs/mask_1_paviaU/config_system.yml b/simca/tests/test_configs/mask_1_paviaU/config_system.yml deleted file mode 100755 index 6a61b89..0000000 --- a/simca/tests/test_configs/mask_1_paviaU/config_system.yml +++ /dev/null @@ -1,28 +0,0 @@ -coded aperture: - number of pixels along X: 253 - number of pixels along Y: 201 - pixel size along X: 55 - pixel size along Y: 55 -detector: - number of pixels along X: 201 - number of pixels along Y: 201 - pixel size along X: 40 - pixel size along Y: 40 -infos: - system name: HYACAMEO -spectral range: - number of spectral samples: 50 - wavelength max: 640 - wavelength min: 450 -system architecture: - dispersive element: - A: 15 - G: 30 - delta alpha c: 0 - delta beta c: 0 - m: 1 - type: prism - wavelength center: 550 - focal lens: 200000 - propagation type: simca - system type: DD-CASSI diff --git a/simca/tests/test_configs/mask_1_paviaU/config_test_values.yml b/simca/tests/test_configs/mask_1_paviaU/config_test_values.yml deleted file mode 100755 index b88b0ac..0000000 --- a/simca/tests/test_configs/mask_1_paviaU/config_test_values.yml +++ /dev/null @@ -1,49 +0,0 @@ -filtering_cube: - pos1: [13, 15, 5] - pos2: [2, 17, 9] - pos3: [23, 47, 4] - pos4: [42, 6, 7] - pos5: [35, 15, 2] - pos6: [26, 26, 0] - pos7: [10, 29, 5] - pos8: [38, 34, 6] - -pattern: - pos1: [22, 13] - pos2: [15, 29] - pos3: [0, 12] - pos4: [2, 3] - pos5: [43, 49] - pos6: [12, 38] - pos7: [30, 30] - pos8: [19, 5] - -filtered_interpolated_scene: - pos1: [19, 4, 4] - pos2: [8, 3, 6] - pos3: [23, 46, 2] - pos4: [23, 46, 6] - pos5: [10, 27, 0] - pos6: [34, 26, 1] - pos7: [12, 17, 9] - pos8: [36, 0, 5] - -interpolated_scene: - 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zhy1d6_0@lyw@>%oYQ1myS9g8ay3a44?yjEvuerXz@3u#`SKVIq>R-LJt53VP*dBe+ zy{mWS>(27sTlu%X{i63#k1pkl`tnP!-do>0Z@J#-o%Wj3?N#e7{?~o)qwlO<{_5#} z%BOc%uYUFQUG39l@5t-Za^#m+-ag$Oje2GCYW=5vdo;SY_-$|P`HSm&>f6^}S|8h6 zetW;x*SjmPU%kHb>Rq|JtH&?xKIO0Pt?%&8%K3VD`OTkdJ?wk@Mg4O1`T8_2|H`kO z{o>R8)1G&({>qo{?xG!j+v|SaU4Hk!*5AIn_I&Jjf9u<0@6#)P>#uw3j{g5DefOg8 hmeX(9{afw5?)Z4+Z+lPseYe`3#rEm4dwG3Y{(s6md{h7c diff --git a/simca/tests/test_configs/mask_3_paviaU/test_values.yml b/simca/tests/test_configs/mask_3_paviaU/test_values.yml deleted file mode 100755 index 8ea7913..0000000 --- a/simca/tests/test_configs/mask_3_paviaU/test_values.yml +++ /dev/null @@ -1,48 +0,0 @@ -filtering_cube: - - 0.5019137995044721 - - 0.4343108759386766 - - 0.498919083507629 - - 0.5012110821158251 - - 0.8005539787639803 - - 0.4980924263828779 - - 0.0 - - 0.0 - - 779158.1875528373 - - 3817.191559136262 - - 4353.563925028386 -filtered_interpolated_scene: - - 127.71775505857346 - - 94.00100747835882 - - 409.86673434422755 - - 0.0 - - 222.3718821167046 - - 106.39520263725046 - - 222.4101631893931 - - 323.1086725354025 - - 912218474.764462 - - 4741071.782112292 - - 4406946.781538208 -interpolated_scene: - - 682.6933139534884 - - 651.5566860465117 - - 782.5000000000001 - - 610.2906976744187 - - 460.2180232558141 - - 592.9534883720929 - - 808.5450581395347 - - 644.7441860465118 - - 3052977499.545059 - - 15910900.296511626 - - 12631115.042151162 -measurement: - - 11418.438332962376 - - 13366.988450811368 - - 6609.483602812603 - - 23322.530910444148 - - 18205.08371650114 - - 2908.2515451353565 - - 28847.001182023727 - - 23297.688387114125 - - 4741071.782112291 - - 3623902.192840429 - - 3956084.724631695 diff --git a/simca/tests/test_configs/mask_4_paviaU/config_acquisition.yml b/simca/tests/test_configs/mask_4_paviaU/config_acquisition.yml deleted file mode 100755 index 4902a52..0000000 --- a/simca/tests/test_configs/mask_4_paviaU/config_acquisition.yml +++ /dev/null @@ -1,7 +0,0 @@ -acquisition name: mask_4 -acquisition type: single acq. -psf: - radius: 100.0 - type: Gaussian - use_psf: false -results directory: ./data/results/ diff --git a/simca/tests/test_configs/mask_4_paviaU/config_pattern.yml b/simca/tests/test_configs/mask_4_paviaU/config_pattern.yml deleted file mode 100755 index 29964a4..0000000 --- a/simca/tests/test_configs/mask_4_paviaU/config_pattern.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - ROM: 0.1 - type: random diff --git a/simca/tests/test_configs/mask_4_paviaU/config_pattern_gen.yml b/simca/tests/test_configs/mask_4_paviaU/config_pattern_gen.yml deleted file mode 100755 index b0aaa11..0000000 --- a/simca/tests/test_configs/mask_4_paviaU/config_pattern_gen.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - type: custom h5 pattern - file path: "./simca/tests/test_configs/mask_4_paviaU/pattern.h5" diff --git a/simca/tests/test_configs/mask_4_paviaU/config_system.yml b/simca/tests/test_configs/mask_4_paviaU/config_system.yml deleted file mode 100755 index b76083c..0000000 --- a/simca/tests/test_configs/mask_4_paviaU/config_system.yml +++ /dev/null @@ -1,28 +0,0 @@ -coded aperture: - number of pixels along X: 260 - number of pixels along Y: 290 - pixel size along X: 35 - pixel size along Y: 35 -detector: - number of pixels along X: 240 - number of pixels along Y: 230 - pixel size along X: 60 - pixel size along Y: 60 -infos: - system name: HYACAMEO -spectral range: - number of spectral samples: 100 - wavelength max: 750 - wavelength min: 450 -system architecture: - dispersive element: - A: 15 - G: 30 - delta alpha c: 0 - delta beta c: 0 - m: 1 - type: grating - wavelength center: 550 - focal lens: 200000 - propagation type: simca - system type: DD-CASSI diff --git a/simca/tests/test_configs/mask_4_paviaU/config_test_values.yml b/simca/tests/test_configs/mask_4_paviaU/config_test_values.yml deleted file mode 100755 index b88b0ac..0000000 --- a/simca/tests/test_configs/mask_4_paviaU/config_test_values.yml +++ /dev/null @@ -1,49 +0,0 @@ -filtering_cube: - pos1: [13, 15, 5] - pos2: [2, 17, 9] - pos3: [23, 47, 4] - pos4: [42, 6, 7] - pos5: [35, 15, 2] - pos6: [26, 26, 0] - pos7: [10, 29, 5] - pos8: [38, 34, 6] - -pattern: - pos1: [22, 13] - pos2: [15, 29] - pos3: [0, 12] - pos4: [2, 3] - pos5: [43, 49] - pos6: [12, 38] - pos7: [30, 30] - pos8: [19, 5] - -filtered_interpolated_scene: - pos1: [19, 4, 4] - pos2: [8, 3, 6] - pos3: [23, 46, 2] - pos4: [23, 46, 6] - pos5: [10, 27, 0] - pos6: [34, 26, 1] - pos7: [12, 17, 9] - pos8: [36, 0, 5] - -interpolated_scene: - pos1: [14, 5, 7] - pos2: [24, 41, 5] - pos3: [15, 17, 8] - pos4: [39, 45, 6] - pos5: [0, 29, 2] - pos6: [24, 5, 4] - pos7: [45, 29, 5] - pos8: [14, 46, 0] - -measurement: - pos1: [35, 14] - pos2: [16, 6] - pos3: [29, 12] - pos4: [7, 7] - pos5: [26, 47] - pos6: [1, 9] - pos7: [16, 18] - pos8: [19, 22] diff --git a/simca/tests/test_configs/mask_4_paviaU/pattern.h5 b/simca/tests/test_configs/mask_4_paviaU/pattern.h5 deleted file mode 100755 index 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z59X-Q-Y8Te+fs)3~jBwsh6~ zYQB|2=}zZhJ?>Qw%(w7P*X@xnTBr4#@0MRzm-*bgyLjK_v)=9f*1c8TDo?$$(o=J# z^PSgT0PlJJ?1sdu0Do7PkBu;sI* z%XggeVc)m({65^fJFq0c~d?6b3f(t)p_Oxp8lR)f5lhxXI}Xp*IVBOxlrAU KMR86C`~M$jB~2Ir diff --git a/simca/tests/test_configs/mask_4_paviaU/test_values.yml b/simca/tests/test_configs/mask_4_paviaU/test_values.yml deleted file mode 100755 index f25083d..0000000 --- a/simca/tests/test_configs/mask_4_paviaU/test_values.yml +++ /dev/null @@ -1,48 +0,0 @@ -filtering_cube: - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 254016.8039554461 - - 1519.539015578192 - - 0.0 -filtered_interpolated_scene: - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 309387719.0194986 - - 1994322.886341379 - - 0.0 -interpolated_scene: - - 698.2262156448204 - - 637.3086680761099 - - 654.2304439746302 - - 600.9154334038054 - - 391.81818181818164 - - 600.291754756871 - - 832.059196617336 - - 644.7441860465118 - - 6659755236.000004 - - 26383217.103594076 - - 24917267.507399578 -measurement: - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 1994322.886341379 - - 0.0 - - 0.0 diff --git a/simca/tests/test_configs/mask_5_paviaU/config_acquisition.yml b/simca/tests/test_configs/mask_5_paviaU/config_acquisition.yml deleted file mode 100755 index f450e66..0000000 --- a/simca/tests/test_configs/mask_5_paviaU/config_acquisition.yml +++ /dev/null @@ -1,7 +0,0 @@ -acquisition name: mask_5 -acquisition type: single acq. -psf: - radius: 100.0 - type: Gaussian - use_psf: false -results directory: ./data/results/ diff --git a/simca/tests/test_configs/mask_5_paviaU/config_pattern.yml b/simca/tests/test_configs/mask_5_paviaU/config_pattern.yml deleted file mode 100755 index 901fcb1..0000000 --- a/simca/tests/test_configs/mask_5_paviaU/config_pattern.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - ROM: 0.5 - type: random diff --git a/simca/tests/test_configs/mask_5_paviaU/config_pattern_gen.yml b/simca/tests/test_configs/mask_5_paviaU/config_pattern_gen.yml deleted file mode 100755 index 10638dc..0000000 --- a/simca/tests/test_configs/mask_5_paviaU/config_pattern_gen.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - type: custom h5 pattern - file path: "./simca/tests/test_configs/mask_5_paviaU/pattern.h5" diff --git a/simca/tests/test_configs/mask_5_paviaU/config_system.yml b/simca/tests/test_configs/mask_5_paviaU/config_system.yml deleted file mode 100755 index 165222f..0000000 --- a/simca/tests/test_configs/mask_5_paviaU/config_system.yml +++ /dev/null @@ -1,28 +0,0 @@ -coded aperture: - number of pixels along X: 120 - number of pixels along Y: 120 - pixel size along X: 30 - pixel size along Y: 30 -detector: - number of pixels along X: 240 - number of pixels along Y: 230 - pixel size along X: 20 - pixel size along Y: 20 -infos: - system name: HYACAMEO -spectral range: - number of spectral samples: 82 - wavelength max: 540 - wavelength min: 440 -system architecture: - dispersive element: - A: 15 - G: 30 - delta alpha c: 0 - delta beta c: 0 - m: 1 - type: grating - wavelength center: 550 - focal lens: 200000 - propagation type: simca - system type: DD-CASSI diff --git a/simca/tests/test_configs/mask_5_paviaU/config_test_values.yml b/simca/tests/test_configs/mask_5_paviaU/config_test_values.yml deleted file mode 100755 index b88b0ac..0000000 --- a/simca/tests/test_configs/mask_5_paviaU/config_test_values.yml +++ /dev/null @@ -1,49 +0,0 @@ -filtering_cube: - pos1: [13, 15, 5] - pos2: [2, 17, 9] - pos3: [23, 47, 4] - pos4: [42, 6, 7] - pos5: [35, 15, 2] - pos6: [26, 26, 0] - pos7: [10, 29, 5] - pos8: [38, 34, 6] - -pattern: - pos1: [22, 13] - pos2: [15, 29] - pos3: [0, 12] - pos4: [2, 3] - pos5: [43, 49] - pos6: [12, 38] - pos7: [30, 30] - pos8: [19, 5] - -filtered_interpolated_scene: - pos1: [19, 4, 4] - pos2: [8, 3, 6] - pos3: [23, 46, 2] - pos4: [23, 46, 6] - pos5: [10, 27, 0] - pos6: [34, 26, 1] - pos7: [12, 17, 9] - pos8: [36, 0, 5] - -interpolated_scene: - pos1: [14, 5, 7] - pos2: [24, 41, 5] - pos3: [15, 17, 8] - pos4: [39, 45, 6] - pos5: [0, 29, 2] - pos6: [24, 5, 4] - pos7: [45, 29, 5] - pos8: [14, 46, 0] - -measurement: - pos1: [35, 14] - pos2: [16, 6] - pos3: [29, 12] - pos4: [7, 7] - pos5: [26, 47] - pos6: [1, 9] - pos7: [16, 18] - pos8: [19, 22] diff --git a/simca/tests/test_configs/mask_5_paviaU/pattern.h5 b/simca/tests/test_configs/mask_5_paviaU/pattern.h5 deleted file mode 100755 index e7b80b07015ea2f9160207748e6a0b08258d753c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 117248 zcmeIyzpCeJnwIhEo*8HcgfSB%LqiWiVMZoWQ3FE_Ohse@Lqk0ikHbhjDedlEAA<{S zp7*z^+6nC4&{y66p7&dewfBGf>%aV`f9KEt{-1yQ-9Nwi&9^`O_7}hZqyPKg-~ajc zzkc<5>;LNSg@5~Avu-|sH|$!~w(f7kqr-~QtM^1J(w zf4}&%->TpFzy9?vhko<@-;h7~)%V)BZ~x=_lfUnO`}SYH^KTdb>Yx4dzx>_5(ZBp% zzJ2=#fAY8cJ%4=tk3WGw{seyfC*Z#|=py~I=t@#G*E7ZcqM7d+e@0UD{o~-s0=NJ=)z%>!JSQr#<~w_FX$ZjeO)^)W6s}`pcIucDMAk zm)p~y(tdH>TYdhuTRH#rjvjVjzd3!;ozm_{{q8^2zN2)fe)Vh5u6_Dq_xj8KG 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597.4780361757105 - - 706.1464254952624 - - 533.6046511627906 - - 4637010843.51163 - - 16945779.057708874 - - 16701104.793281656 -measurement: - - 0.0 - - 0.0 - - 0.0 - - 0.0 - - 13322.931476876352 - - 0.0 - - 0.0 - - 0.0 - - 6606316.70915661 - - 0.0 - - 0.0 diff --git a/simca/tests/test_configs/mask_6_lego/config_acquisition.yml b/simca/tests/test_configs/mask_6_lego/config_acquisition.yml deleted file mode 100755 index 5ad47d4..0000000 --- a/simca/tests/test_configs/mask_6_lego/config_acquisition.yml +++ /dev/null @@ -1,7 +0,0 @@ -acquisition name: mask_6 -acquisition type: single acq. -psf: - radius: 100.0 - type: Gaussian - use_psf: false -results directory: ./data/results/ diff --git a/simca/tests/test_configs/mask_6_lego/config_pattern.yml b/simca/tests/test_configs/mask_6_lego/config_pattern.yml deleted file mode 100755 index bd6f1b1..0000000 --- a/simca/tests/test_configs/mask_6_lego/config_pattern.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - ROM: 0.25 - type: random diff --git a/simca/tests/test_configs/mask_6_lego/config_pattern_gen.yml b/simca/tests/test_configs/mask_6_lego/config_pattern_gen.yml deleted file mode 100755 index 344e131..0000000 --- a/simca/tests/test_configs/mask_6_lego/config_pattern_gen.yml +++ /dev/null @@ -1,3 +0,0 @@ -pattern: - type: custom h5 pattern - file path: "./simca/tests/test_configs/mask_6_lego/pattern.h5" diff --git a/simca/tests/test_configs/mask_6_lego/config_system.yml b/simca/tests/test_configs/mask_6_lego/config_system.yml deleted file mode 100755 index 9f3c33a..0000000 --- a/simca/tests/test_configs/mask_6_lego/config_system.yml +++ /dev/null @@ -1,28 +0,0 @@ -coded aperture: - number of pixels along X: 140 - number of pixels along Y: 140 - pixel size along X: 40 - pixel size along Y: 40 -detector: - number of pixels along X: 240 - number of pixels along Y: 230 - pixel size along X: 20 - pixel size along Y: 20 -infos: - system name: HYACAMEO -spectral range: - number of spectral samples: 72 - wavelength max: 540 - wavelength min: 440 -system architecture: - dispersive element: - A: 15 - G: 30 - delta alpha c: 0 - delta beta c: 0 - m: 1 - type: prism - wavelength center: 550 - focal lens: 200000 - propagation type: simca - system type: DD-CASSI diff --git a/simca/tests/test_configs/mask_6_lego/config_test_values.yml b/simca/tests/test_configs/mask_6_lego/config_test_values.yml deleted file mode 100755 index b88b0ac..0000000 --- a/simca/tests/test_configs/mask_6_lego/config_test_values.yml +++ /dev/null @@ -1,49 +0,0 @@ -filtering_cube: - pos1: [13, 15, 5] - pos2: [2, 17, 9] - pos3: [23, 47, 4] - pos4: [42, 6, 7] - pos5: [35, 15, 2] - pos6: [26, 26, 0] - pos7: [10, 29, 5] - pos8: [38, 34, 6] - -pattern: - pos1: [22, 13] - pos2: [15, 29] - pos3: [0, 12] - pos4: [2, 3] - pos5: [43, 49] - pos6: [12, 38] - pos7: [30, 30] - pos8: [19, 5] - -filtered_interpolated_scene: - pos1: [19, 4, 4] - pos2: [8, 3, 6] - pos3: [23, 46, 2] - pos4: [23, 46, 6] - pos5: [10, 27, 0] - pos6: [34, 26, 1] - pos7: [12, 17, 9] - pos8: [36, 0, 5] - -interpolated_scene: - pos1: [14, 5, 7] - pos2: [24, 41, 5] - pos3: [15, 17, 8] - pos4: [39, 45, 6] - pos5: [0, 29, 2] - pos6: [24, 5, 4] - pos7: [45, 29, 5] - pos8: [14, 46, 0] - -measurement: - pos1: [35, 14] - pos2: [16, 6] - pos3: [29, 12] - pos4: [7, 7] - pos5: [26, 47] - pos6: [1, 9] - pos7: [16, 18] - pos8: [19, 22] diff --git a/simca/tests/test_configs/mask_6_lego/pattern.h5 b/simca/tests/test_configs/mask_6_lego/pattern.h5 deleted file mode 100755 index c78a6c11cffaf67aef5a9e88c7f57573ac7b7c87..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 158848 zcmeH{!K${|Sw#QtB+vweI1@(>9eN20iDaar4jd}zOhhCXkfDMX;-z?3+TH)Z2Ch)l z^Xzwj=bXlFAuCl+RqgjXbo`HB|MJg&@8^H`^H1OX^UXJ(e){Pb-~U(sDw&(@JsE}pZxUm zfBOdGNB+P13;g<5fA&}3{oud*@~io`^uNFSd&VE*U;p7Zzxk(s`VQ}%WmRNzWw+8yW}sv{TKK1zq^0>edA}}s_*=-zy3V* z&6od${N$@Iu}{DK$N&4&e*1mzz5nBH{^Box`Q87fKmV3bpZ@44@BKwTzW?K2;K#qf 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zbL}1P_SU1{Nw2-Xwa4B}@6t>8rTk01%UyHy&#`vb?x(x>-rGl;ZRb2`?mAC?>v@NU z-dnfc)|~g!9s5nUd*smf@L|j8jgouNyX4c`nx{LyYt0UlH`0gh<3n;Z9Q9L=J9}~M zJ8jo;zMmenk2c+RZtcD`eSC-y?OVI&ylL;Py?358KI9I>H_4%M>(QQfX!a0)lxuhC z{@(Y}JNTpTxrc`B-r62_6MZ-J@ZCp4dJx|vPoz)2RD0(=c_Mw7dZYf*JE@<1zJnav zPZ~eb_foI<+<~pXH6MM)Jv4l*@8Dg32O6fH{nW=d*_-s1`s@QC!f1Z$vwy(4M%-?Yw7p6V;>FanJLGI-l3ga(=+Ko=Sio2@_px0y`^{QLw{$} P-Zkxe^kK>`<@@e`#KJQI diff --git a/simple_script.py b/simple_script.py deleted file mode 100644 index 3cb59ae..0000000 --- a/simple_script.py +++ /dev/null @@ -1,34 +0,0 @@ -from simca import CassiSystem, load_yaml_config - -config_dataset = load_yaml_config("simca/configs/dataset.yml") -config_system = load_yaml_config("simca/configs/cassi_system.yml") -config_patterns = load_yaml_config("simca/configs/pattern.yml") -config_acquisition = load_yaml_config("simca/configs/acquisition.yml") - -dataset_name = "indian_pines" - -if __name__ == '__main__': - - # Initialize the CASSI system - cassi_system = CassiSystem(system_config=config_system) - - # DATASET : Load the hyperspectral dataset - cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"]) - - # pattern : Generate the coded aperture pattern - cassi_system.generate_2D_pattern(config_patterns) - - # PROPAGATION : Propagate the pattern grid to the detector plane - cassi_system.propagate_coded_aperture_grid() - - # FILTERING CUBE : Generate the filtering cube - cassi_system.generate_filtering_cube() - - # PSF (optional) : Generate the psf - cassi_system.optical_model.generate_psf(type="Gaussian",radius=100) - - # ACQUISITION : Simulate the acquisition with psf (use_psf is optional) - cassi_system.image_acquisition(use_psf=True,chunck_size=50) - - # Save the acquisition - cassi_system.save_acquisition(config_patterns, config_acquisition) diff --git a/simple_script_optim_testing.py b/simple_script_optim_testing.py deleted file mode 100644 index dbf8e1f..0000000 --- a/simple_script_optim_testing.py +++ /dev/null @@ -1,221 +0,0 @@ -from simca import load_yaml_config -from simca.CassiSystemOptim import CassiSystemOptim -from simca.CassiSystem import CassiSystem -import numpy as np -import snoop -import matplotlib.pyplot as plt -#import matplotlib -import torch -import time -from pprint import pprint -from simca.cost_functions import evaluate_slit_scanning_straightness, evaluate_center, evaluate_mean_lighting, evaluate_max_lighting -from simca.functions_optim import optim_smile, optim_width - -#matplotlib.use('Agg') -config_dataset = load_yaml_config("simca/configs/dataset.yml") -config_system = load_yaml_config("simca/configs/cassi_system_simple_optim_max_center.yml") -config_patterns = load_yaml_config("simca/configs/pattern.yml") -config_acquisition = load_yaml_config("simca/configs/acquisition.yml") - -dataset_name = "indian_pines" - -test = "SMILE" - -algo = "LBFGS" - -if test=="SMILE": - config_system = load_yaml_config("simca/configs/cassi_system_simple_optim.yml") - aspect = 1 -elif test=="EQUAL_LIGHT" or test=="MAX_CENTER": - config_system = load_yaml_config("simca/configs/cassi_system_simple_optim_max_center.yml") - aspect = 1 -elif test == "MAX_LIGHT": - config_system = load_yaml_config("simca/configs/cassi_system_simple_optim.yml") - aspect = 1 -elif test == "SMILE_mono": - config_system = load_yaml_config("simca/configs/cassi_system_simple_optim_smile_mono.yml") - aspect = 1 - - -if __name__ == '__main__': - time_start = time.time() - # Initialize the CASSI system - cassi_system = CassiSystemOptim(system_config=config_system) - - # time0 = time.time() - # DATASET : Load the hyperspectral dataset - cassi_system.load_dataset(dataset_name, config_dataset["datasets directory"]) - - # Loop beginning if optics optim. - cassi_system.update_optical_model(system_config=config_system) - X_vec_out, Y_vec_out = cassi_system.propagate_coded_aperture_grid() - sigma = 0.75 - - num_iterations = 3000 # Define num_iterations as needed - - """ convergence_counter = 0 # Counter to check convergence - max_cnt = 100 - min_cost_value = np.inf - - if test == "EQUAL_LIGHT": - cassi_system.generate_custom_pattern_parameters_slit_width(nb_slits=2, nb_rows=2, start_width = 1) - elif test == "MAX_LIGHT": - cassi_system.generate_custom_pattern_parameters_slit_width(nb_slits=1, nb_rows=cassi_system.system_config["detector"]["number of pixels along Y"], start_width = sigma) - elif (test == "SMILE") or (test == "SMILE_mono"): - cassi_system.generate_custom_pattern_parameters_slit(position=0.5) - - # Ensure array_x_positions is a tensor with gradient tracking - cassi_system.array_x_positions = cassi_system.array_x_positions.clone().detach().requires_grad_(True) - - # Define the optimizer - lr = 0.005 # default: 0.005 - optimizer = torch.optim.Adam([cassi_system.array_x_positions], lr=lr) # Adjust the learning rate as needed - - # Main optimization loop - for iteration in range(num_iterations): # Define num_iterations as needed - optimizer.zero_grad() # Clear previous gradients - if test == "EQUAL_LIGHT": - pattern = cassi_system.generate_custom_slit_pattern_width(start_pattern = "line", start_position = 0) - elif test == "MAX_LIGHT": - start_position = torch.tensor([0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, - 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.4923, - 0.4859, 0.4915, 0.4934, 0.4972, 0.4996, 0.5003, 0.5009, 0.5013, 0.5033, - 0.5041, 0.5064, 0.5068, 0.5078, 0.5070, 0.5099, 0.5103, 0.5146, 0.5145, - 0.5146, 0.5152, 0.5173, 0.5195, 0.5215, 0.5208, 0.5208, 0.5222, 0.5247, - 0.5272, 0.5287, 0.5285, 0.5240, 0.5283, 0.5282, 0.5288, 0.5288, 0.5291, - 0.5289, 0.5282, 0.5334, 0.5314, 0.5324, 0.5370, 0.5323, 0.5322, 0.5341, - 0.5329, 0.5361, 0.5364, 0.5346, 0.5333, 0.5340, 0.5333, 0.5339, 0.5345, - 0.5359, 0.5349, 0.5364, 0.5344, 0.5341, 0.5346, 0.5353, 0.5345, 0.5347, - 0.5346, 0.5362, 0.5363, 0.5330, 0.5321, 0.5323, 0.5299, 0.5315, 0.5318, - 0.5298, 0.5291, 0.5291, 0.5298, 0.5292, 0.5256, 0.5270, 0.5283, 0.5268, - 0.5255, 0.5245, 0.5200, 0.5205, 0.5207, 0.5190, 0.5188, 0.5144, 0.5122, - 0.5138, 0.5133, 0.5131, 0.5122, 0.5111, 0.5156, 0.5118, 0.5091, 0.5077, - 0.5068, 0.5030, 0.5003, 0.5000, 0.4992, 0.4970, 0.4968, 0.4947, 0.4949, - 0.4935, 0.4983, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, - 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, - 0.5000]) - pattern = cassi_system.generate_custom_slit_pattern_width(start_pattern = "corrected", start_position = start_position) - else: - pattern = cassi_system.generate_custom_slit_pattern() - #print(pattern[:, pattern.shape[1]//2-4:pattern.shape[1]//2+4]) - cassi_system.generate_filtering_cube() - pos_slit = 0.4625 - pos_slit = 0.41 - if (test == "SMILE"): - cost_value = evaluate_slit_scanning_straightness(cassi_system.filtering_cube, sigma = sigma, pos_slit = pos_slit) - elif (test == "SMILE_mono"): - cost_value = evaluate_slit_scanning_straightness(cassi_system.filtering_cube, sigma = sigma, pos_slit = pos_slit) - cassi_system.image_acquisition(use_psf = False, chunck_size = 50) - elif test == "MAX_CENTER": - cassi_system.image_acquisition(use_psf=False, chunck_size=50) - cost_value = evaluate_center(cassi_system.measurement) - elif test == "EQUAL_LIGHT": - cassi_system.image_acquisition(use_psf=False, chunck_size=50) - cost_value = evaluate_mean_lighting(cassi_system.measurement) - elif test == "MAX_LIGHT": - cassi_system.image_acquisition(use_psf=False, chunck_size=50) - cost_value = evaluate_max_lighting(cassi_system.measurement, pos_slit) - - if cost_value < min_cost_value: - min_cost_value = cost_value - convergence_counter = 0 - best_x = cassi_system.array_x_positions.clone().detach() - else: - convergence_counter+=1 - - if (iteration >= 50) and (convergence_counter >= max_cnt): # If loss didn't decrease in 25 steps, break - break - - cost_value.backward() # Perform backpropagation - # print("Gradients after backward:", cassi_system.array_x_positions.grad) - optimizer.step() # Update x positions - cassi_system.array_x_positions.data = torch.relu(cassi_system.array_x_positions.data) # Prevent the parameters to be negative - # Optional: Print cost_value every few iterations to monitor progress - if iteration % 5 == 0: # Adjust printing frequency as needed - print(f"Iteration {iteration}, Cost: {cost_value.item()}") - - if iteration % 200 == 0: - print(f"Exec time: {time.time() - time_start}s") - plt.imshow(pattern.detach().numpy(), aspect=aspect) - plt.show() - - plt.imshow(cassi_system.filtering_cube[:, :, 0].detach().numpy(), aspect=aspect) - plt.show() - - plt.plot(np.sum(cassi_system.filtering_cube[:, :, 0].detach().numpy(),axis=0)) - plt.show() - - if (test=="MAX_CENTER") or (test == "EQUAL_LIGHT") or (test == "SMILE_mono") or (test == "MAX_LIGHT"): - #plt.imshow(cassi_system.panchro.detach().numpy(), zorder=3) - plt.imshow(cassi_system.measurement.detach().numpy(), zorder=5) - plt.show() - #plt.imshow(cassi_system.panchro.detach().numpy()) - #plt.show() - cassi_system.array_x_positions.data = torch.relu(best_x.data) - - if test == "EQUAL_LIGHT": - pattern = cassi_system.generate_custom_slit_pattern_width(start_pattern = "line", start_position = 0) - elif test == "MAX_LIGHT": - pattern = cassi_system.generate_custom_slit_pattern_width(start_pattern = "corrected", start_position = start_position) - else: - pattern = cassi_system.generate_custom_slit_pattern() - cassi_system.generate_filtering_cube() - print(cassi_system.array_x_positions) - #print(torch.std(cassi_system.measurement.detach())) - - print(f"Min cost: {min_cost_value}") - print(f"Exec time: {time.time() - time_start}s") - plt.imshow(pattern.detach().numpy(), aspect=aspect) - plt.show() - - plt.imshow(cassi_system.filtering_cube[:, :, 0].detach().numpy(), aspect=aspect) - plt.show() - - plt.plot(np.sum(cassi_system.filtering_cube[:, :, 0].detach().numpy(),axis=0)) - plt.show() - - if (test=="MAX_CENTER") or (test == "EQUAL_LIGHT") or (test == "SMILE_mono") or (test == "MAX_LIGHT"): - plt.imshow(cassi_system.measurement.detach().numpy()) - plt.show()""" - algo = "ADAM" - if algo == "LBFGS": - lr = 0.002 # default: 0.05 - elif algo == "ADAM": - lr = 0.005 # default: 0.005 - - max_cnt = 25 - - #cassi_system = optim_smile(cassi_system, 0.5, 0.41, sigma, 'cpu', algo, lr, num_iterations, max_cnt, plot_frequency=200) - start_position = torch.tensor([0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, - 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.4923, - 0.4859, 0.4915, 0.4934, 0.4972, 0.4996, 0.5003, 0.5009, 0.5013, 0.5033, - 0.5041, 0.5064, 0.5068, 0.5078, 0.5070, 0.5099, 0.5103, 0.5146, 0.5145, - 0.5146, 0.5152, 0.5173, 0.5195, 0.5215, 0.5208, 0.5208, 0.5222, 0.5247, - 0.5272, 0.5287, 0.5285, 0.5240, 0.5283, 0.5282, 0.5288, 0.5288, 0.5291, - 0.5289, 0.5282, 0.5334, 0.5314, 0.5324, 0.5370, 0.5323, 0.5322, 0.5341, - 0.5329, 0.5361, 0.5364, 0.5346, 0.5333, 0.5340, 0.5333, 0.5339, 0.5345, - 0.5359, 0.5349, 0.5364, 0.5344, 0.5341, 0.5346, 0.5353, 0.5345, 0.5347, - 0.5346, 0.5362, 0.5363, 0.5330, 0.5321, 0.5323, 0.5299, 0.5315, 0.5318, - 0.5298, 0.5291, 0.5291, 0.5298, 0.5292, 0.5256, 0.5270, 0.5283, 0.5268, - 0.5255, 0.5245, 0.5200, 0.5205, 0.5207, 0.5190, 0.5188, 0.5144, 0.5122, - 0.5138, 0.5133, 0.5131, 0.5122, 0.5111, 0.5156, 0.5118, 0.5091, 0.5077, - 0.5068, 0.5030, 0.5003, 0.5000, 0.4992, 0.4970, 0.4968, 0.4947, 0.4949, - 0.4935, 0.4983, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, - 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, 0.5000, - 0.5000]) - cassi_system = optim_width(cassi_system, start_position, 0.41, cassi_system.system_config["detector"]["number of pixels along Y"], sigma, 'cpu', algo, lr, num_iterations, max_cnt, plot_frequency = 200) - - print(f"Exec time: {time.time() - time_start:.3f}s") - print(cassi_system.array_x_positions) - plt.imshow(cassi_system.pattern.detach().numpy(), aspect=aspect) - plt.show() - - plt.imshow(cassi_system.filtering_cube[:, :, 0].detach().numpy(), aspect=aspect) - plt.show() - - plt.plot(np.sum(cassi_system.filtering_cube[:, :, 0].detach().numpy(),axis=0)) - plt.show() - - if (test=="MAX_CENTER") or (test == "EQUAL_LIGHT") or (test == "SMILE_mono") or (test == "MAX_LIGHT"): - plt.imshow(cassi_system.measurement.detach().numpy()) - plt.show() diff --git a/testing_optim_proposition.py b/testing_optim_proposition.py deleted file mode 100755 index d13a96f..0000000 --- a/testing_optim_proposition.py +++ /dev/null @@ -1,137 +0,0 @@ -from simca import load_yaml_config -import matplotlib.pyplot as plt -from simca.CassiSystemOptim import CassiSystemOptim -from simca.cost_functions_optics import * -from opticalglass.glassfactory import get_glass_catalog -import pandas as pd - - -config_dataset = load_yaml_config("simca/configs/dataset.yml") -config_system = load_yaml_config("simca/configs/cassi_system_optim_optics_full_triplet_sd_cassi.yml") -config_patterns = load_yaml_config("simca/configs/pattern.yml") -config_acquisition = load_yaml_config("simca/configs/acquisition.yml") - -hoya_pd = get_glass_catalog('Schott') - -# Access the 'nd' values where the first level is NaN -idx = pd.IndexSlice - -nd_values = hoya_pd.df.loc[:, idx[pd.NA, 'nd']] -vd_values = hoya_pd.df.loc[:, idx["abbe number", 'vd']] - -if __name__ == '__main__': - - # Initialize the CASSI system - cassi_system = CassiSystemOptim(system_config=config_system) - # - # config_system["system architecture"]["dispersive element"]["glass1"] = "N-BK7" - # # config_system["system architecture"]["dispersive element"]["glass2"] = "N-SF11" - # config_system["system architecture"]["dispersive element"]["glass3"] = "N-BK7" - # config_system["system architecture"]["dispersive element"]["A1"] = 70 - # # config_system["system architecture"]["dispersive element"]["A2"] = 60 - # config_system["system architecture"]["dispersive element"]["A3"] = 70 - # config_system["system architecture"]["dispersive element"]["alpha_c"] = 55 - - - cassi_system.update_optical_model(system_config=config_system) - - x_vec_out, y_vec_out = cassi_system.propagate_coded_aperture_grid() - - # - alpha_c = cassi_system.optical_model.alpha_c - alpha_c_out = cassi_system.optical_model.alpha_c_transmis - list_apex_angles = [cassi_system.optical_model.A1, cassi_system.optical_model.A2, cassi_system.optical_model.A3] - list_theta_in = cassi_system.optical_model.list_theta_in - list_theta_out = cassi_system.optical_model.list_theta_out - - # evaluate spectral dispersion - dispersion, central_coordinates_X = evaluate_spectral_dispersion_values(x_vec_out, y_vec_out) - print("dispersion in microns", dispersion) - - # evaluate direct_view - deviation = evaluate_direct_view(alpha_c,alpha_c_out,list_apex_angles) - print("deviation in degrees", deviation*180/np.pi) - - # evaluate beam compression - beam_compression = evaluate_beam_compression(list_theta_in, list_theta_out) - print("beam compression", beam_compression) - - #evaluate thickness - thickness = evaluate_thickness(list_apex_angles) - print("thickness", thickness) - - # evaluate distortions - x_vec_no_dist, y_vec_no_dist = get_cassi_system_no_spectial_distorsions(cassi_system.X_coded_aper_coordinates, - cassi_system.Y_coded_aper_coordinates, - central_coordinates_X) - - # print(x_vec_no_dist) - - print(x_vec_no_dist.shape, y_vec_no_dist.shape) - distortion_metric = evaluate_distortions(x_vec_out, y_vec_out, x_vec_no_dist, y_vec_no_dist) - - print("distortion metric", distortion_metric) - - displacement_in_pix = central_coordinates_X.detach().numpy()/cassi_system.system_config["detector"]["pixel size along X"] - - plt.plot(cassi_system.wavelengths.detach().numpy(), displacement_in_pix) - plt.title("central coordinates in pix") - plt.show() - # y_second_derivative = calculate_second_derivative(cassi_system.wavelengths, central_coordinates_X) - # - # plt.plot(y_second_derivative.detach().numpy()) - # plt.show() - print(x_vec_out.detach().numpy().shape) - - plt.scatter(x_vec_out.detach().numpy()[...,0], y_vec_out.detach().numpy()[...,0],color='blue',label="distor") - plt.scatter(x_vec_out.detach().numpy()[...,3], y_vec_out.detach().numpy()[...,0],color='green',label="distor") - plt.scatter(x_vec_out.detach().numpy()[...,-1], y_vec_out.detach().numpy()[...,-1],color='red',label="distor") - plt.scatter(cassi_system.X_detector_coordinates_grid.detach().numpy(),cassi_system.Y_detector_coordinates_grid.detach().numpy(),color='black',label="distor") - # - # plt.scatter(x_vec_no_dist.detach().numpy()[...,0], y_vec_no_dist.detach().numpy()[...,0],label="no dist",color='green') - # plt.scatter(x_vec_no_dist.detach().numpy()[...,-1], y_vec_no_dist.detach().numpy()[...,-1],label="no dist",color='green') - - plt.legend() - plt.show() - - - # distance from closest point - current_value = [cassi_system.optical_model.nd1, cassi_system.optical_model.vd1] - distance_closest_point = evaluate_distance(current_value,nd_values, vd_values) - - - - - - - - - - - # print(cassi_system.optical_model.list_theta_in, cassi_system.optical_model.list_theta_out) - # - # dispersion,central_coordinates_X = evaluate_spectral_dispersion_values(x_vec_out, y_vec_out) - # r2, y_pred = evaluate_linearity(central_coordinates_X) - # - # y_second_derivative = calculate_second_derivative(cassi_system.wavelengths,central_coordinates_X) - # - # plt.plot(y_second_derivative) - # plt.show() - # - # #no distorsions X_vec_out, Y_vec_out - # x_vec_out_no_dist, y_vec_out_no_dist = get_cassi_system_no_spectial_distorsions(cassi_system.X_coded_aper_coordinates, - # cassi_system.Y_coded_aper_coordinates, - # central_coordinates_X) - # - # print(r2) - # - # plt.plot(central_coordinates_X) - # plt.plot(y_pred) - # plt.show() - # - # plt.scatter(x_vec_out[...,0], y_vec_out[...,0],color='blue') - # plt.scatter(x_vec_out[:,:,-1], y_vec_out[:,:,-1],color='red') - # plt.scatter(x_vec_out_no_dist[...,0], y_vec_out_no_dist[...,0]) - # plt.scatter(x_vec_out_no_dist[:,:,-1], y_vec_out_no_dist[:,:,-1]) - # - # plt.show() \ No newline at end of file diff --git a/testing_pytorch_propagation.py b/testing_pytorch_propagation.py deleted file mode 100755 index 9d71f69..0000000 --- a/testing_pytorch_propagation.py +++ /dev/null @@ -1,32 +0,0 @@ -from simca import CassiSystem, load_yaml_config -import matplotlib.pyplot as plt - -config_dataset = load_yaml_config("simca/configs/dataset.yml") -config_system = load_yaml_config("simca/configs/cassi_system_test.yml") -config_patterns = load_yaml_config("simca/configs/pattern.yml") -config_acquisition = load_yaml_config("simca/configs/acquisition.yml") - -if __name__ == '__main__': - - # Initialize the CASSI system - cassi_system = CassiSystem(system_config=config_system) - - - # pattern : Generate the coded aperture pattern - cassi_system.generate_2D_pattern(config_patterns) - - # PROPAGATION : Propagate the pattern grid to the detector plane - # X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture, system_wavelengths = cassi_system.propagate_coded_aperture_grid(use_torch=False) - - # print(system_wavelengths) - # plt.scatter(X_coordinates_propagated_coded_aperture[:,:,0], Y_coordinates_propagated_coded_aperture[:,:,0],color='blue') - # plt.scatter(X_coordinates_propagated_coded_aperture[:,:,45], Y_coordinates_propagated_coded_aperture[:,:,45],color='green') - # plt.scatter(X_coordinates_propagated_coded_aperture[:,:,90], Y_coordinates_propagated_coded_aperture[:,:,90],color='red') - # plt.show() - - X_coordinates_propagated_coded_aperture, Y_coordinates_propagated_coded_aperture, system_wavelengths = cassi_system.propagate_coded_aperture_grid(use_torch=True) - - plt.scatter(X_coordinates_propagated_coded_aperture[...,0], Y_coordinates_propagated_coded_aperture[...,0],color='blue') - plt.scatter(X_coordinates_propagated_coded_aperture[:,:,1], Y_coordinates_propagated_coded_aperture[:,:,1],color='green') - plt.scatter(X_coordinates_propagated_coded_aperture[:,:,2], Y_coordinates_propagated_coded_aperture[:,:,2],color='red') - plt.show() diff --git a/tests.py b/tests.py deleted file mode 100755 index 5945ad2..0000000 --- a/tests.py +++ /dev/null @@ -1,5 +0,0 @@ -from simca.tests import test_cassisystem_initialization -import unittest - -if __name__ == '__main__': - unittest.main(test_cassisystem_initialization, exit=False) diff --git a/train_resnet_for_noise_generation.py b/train_resnet_for_noise_generation.py deleted file mode 100644 index 05afcd6..0000000 --- a/train_resnet_for_noise_generation.py +++ /dev/null @@ -1,58 +0,0 @@ -import pytorch_lightning as pl -from data_handler import CubesDataModule -from Resnet_only import ResnetOnly -from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint -from pytorch_lightning.loggers import TensorBoardLogger -import torch -import datetime - - -data_dir = "/local/users/ademaio/lpaillet/mst_datasets/cave_1024_28/" -datamodule = CubesDataModule(data_dir, batch_size=4, num_workers=11) - -datetime_ = datetime.datetime.now().strftime('%y-%m-%d_%Hh%M') - -name = "testing_resnet_only" -model_name = "resnet_only" - -log_dir = 'tb_logs' - -train = True - -logger = TensorBoardLogger(log_dir, name=name) - -early_stop_callback = EarlyStopping( - monitor='val_loss', # Metric to monitor - patience=40, # Number of epochs to wait for improvement - verbose=True, - mode='min' # 'min' for metrics where lower is better, 'max' for vice versa - ) - -checkpoint_callback = ModelCheckpoint( - monitor='val_loss', # Metric to monitor - dirpath='checkpoints/', # Directory path for saving checkpoints - filename=f'best-checkpoint_{model_name}_{datetime_}', # Checkpoint file name - save_top_k=1, # Save the top k models - mode='min', # 'min' for metrics where lower is better, 'max' for vice versa - save_last=True # Additionally, save the last checkpoint to a file named 'last.ckpt' -) - -reconstruction_module = ResnetOnly(log_dir=log_dir+'/'+ name) - - -if torch.cuda.is_available(): - trainer = pl.Trainer( logger=logger, - accelerator="gpu", - max_epochs=500, - log_every_n_steps=1, - callbacks=[early_stop_callback, checkpoint_callback]) -else: - trainer = pl.Trainer( logger=logger, - accelerator="cpu", - max_epochs=500, - log_every_n_steps=1, - callbacks=[early_stop_callback, checkpoint_callback]) - - -trainer.fit(reconstruction_module, datamodule) - diff --git a/training_simca_reconstruction.py b/training_simca_reconstruction.py deleted file mode 100755 index 500ca70..0000000 --- a/training_simca_reconstruction.py +++ /dev/null @@ -1,63 +0,0 @@ -import pytorch_lightning as pl -from data_handler import CubesDataModule -from optimization_modules import JointReconstructionModule_V1 -from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint -from pytorch_lightning.loggers import TensorBoardLogger -import torch -import datetime - - -data_dir = "./datasets_reconstruction/mst_datasets/cave_1024_28" -datamodule = CubesDataModule(data_dir, batch_size=4, num_workers=11) - -datetime_ = datetime.datetime.now().strftime('%y-%m-%d_%Hh%M') - -name = "testing_simca_reconstruction" -model_name = "dauhst_9" -reconstruction_checkpoint = "/home/lpaillet/Documents/simca/tb_logs/testing_simca_reconstruction/version_24/checkpoints/epoch=499-step=18000.ckpt" -reconstruction_checkpoint = None - -log_dir = 'tb_logs' - -train = True - -logger = TensorBoardLogger(log_dir, name=name) - -early_stop_callback = EarlyStopping( - monitor='val_loss', # Metric to monitor - patience=40, # Number of epochs to wait for improvement - verbose=True, - mode='min' # 'min' for metrics where lower is better, 'max' for vice versa - ) - -checkpoint_callback = ModelCheckpoint( - monitor='val_loss', # Metric to monitor - dirpath='checkpoints/', # Directory path for saving checkpoints - filename=f'best-checkpoint_{model_name}_{datetime_}', # Checkpoint file name - save_top_k=1, # Save the top k models - mode='min', # 'min' for metrics where lower is better, 'max' for vice versa - save_last=True # Additionally, save the last checkpoint to a file named 'last.ckpt' -) - -reconstruction_module = JointReconstructionModule_V1(model_name,log_dir=log_dir+'/'+ name, - reconstruction_checkpoint=reconstruction_checkpoint) - - -if torch.cuda.is_available(): - trainer = pl.Trainer( logger=logger, - accelerator="gpu", - max_epochs=500, - log_every_n_steps=1, - callbacks=[early_stop_callback, checkpoint_callback]) -else: - trainer = pl.Trainer( logger=logger, - accelerator="cpu", - max_epochs=500, - log_every_n_steps=1, - callbacks=[early_stop_callback, checkpoint_callback]) - -if train: - trainer.fit(reconstruction_module, datamodule) -else: - #trainer.predict(reconstruction_module, datamodule) - trainer.predict(reconstruction_module, datamodule, ckpt_path=reconstruction_checkpoint) diff --git a/training_simca_reconstruction_with_resnet.py b/training_simca_reconstruction_with_resnet.py deleted file mode 100755 index cf1fda3..0000000 --- a/training_simca_reconstruction_with_resnet.py +++ /dev/null @@ -1,66 +0,0 @@ -import pytorch_lightning as pl -from data_handler import CubesDataModule -from optimization_modules_with_resnet import JointReconstructionModule_V2 -from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint -from pytorch_lightning.loggers import TensorBoardLogger -import torch -import datetime - - -# data_dir = "./datasets_reconstruction/" -data_dir = "/local/users/ademaio/lpaillet/mst_datasets/cave_1024_28" -data_dir = "./datasets_reconstruction/mst_datasets/cave_1024_28" -datamodule = CubesDataModule(data_dir, batch_size=4, num_workers=5) - -datetime_ = datetime.datetime.now().strftime('%y-%m-%d_%Hh%M') - -name = "testing_simca_reconstruction_full" -model_name = "dauhst_9" -reconstruction_checkpoint = "/home/lpaillet/Documents/simca/tb_logs/testing_simca_reconstruction/version_24/checkpoints/epoch=499-step=18000.ckpt" - -log_dir = 'tb_logs' - -train = True - -logger = TensorBoardLogger(log_dir, name=name) - -early_stop_callback = EarlyStopping( - monitor='val_loss', # Metric to monitor - patience=40, # Number of epochs to wait for improvement - verbose=True, - mode='min' # 'min' for metrics where lower is better, 'max' for vice versa - ) - -checkpoint_callback = ModelCheckpoint( - monitor='val_loss', # Metric to monitor - dirpath='checkpoints_with_resnet/', # Directory path for saving checkpoints - filename=f'best-checkpoint_{model_name}_{datetime_}', # Checkpoint file name - save_top_k=1, # Save the top k models - mode='min', # 'min' for metrics where lower is better, 'max' for vice versa - save_last=True # Additionally, save the last checkpoint to a file named 'last.ckpt' -) - -reconstruction_module = JointReconstructionModule_V2(model_name, - log_dir=log_dir+'/'+ name, - reconstruction_checkpoint = reconstruction_checkpoint) - - -if torch.cuda.is_available(): - trainer = pl.Trainer( logger=logger, - accelerator="gpu", - max_epochs=500, - log_every_n_steps=30, - callbacks=[early_stop_callback, checkpoint_callback]) -else: - trainer = pl.Trainer( logger=logger, - accelerator="gpu", - max_epochs=500, - log_every_n_steps=30, - callbacks=[early_stop_callback, checkpoint_callback]) - -if train: - #reconstruction_checkpoint = None - #trainer.fit(reconstruction_module, datamodule, ckpt_path = reconstruction_checkpoint) - trainer.fit(reconstruction_module, datamodule) -else: - trainer.predict(reconstruction_module, datamodule, ckpt_path=reconstruction_checkpoint) \ No newline at end of file diff --git a/training_simca_reconstruction_with_resnet_v2.py b/training_simca_reconstruction_with_resnet_v2.py deleted file mode 100755 index 22148eb..0000000 --- a/training_simca_reconstruction_with_resnet_v2.py +++ /dev/null @@ -1,79 +0,0 @@ -import pytorch_lightning as pl -from data_handler import CubesDataModule -from optimization_modules import JointReconstructionModule_V1 -from optimization_modules_with_resnet import JointReconstructionModule_V2 -from optimization_modules_with_resnet_v2 import JointReconstructionModule_V3 -from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint -from pytorch_lightning.loggers import TensorBoardLogger -import torch -import datetime - - -# data_dir = "./datasets_reconstruction/" -data_dir = "/local/users/ademaio/lpaillet/mst_datasets/cave_1024_28" -# data_dir = "./datasets_reconstruction/mst_datasets/cave_1024_28" -datamodule = CubesDataModule(data_dir, batch_size=4, num_workers=5) - -datetime_ = datetime.datetime.now().strftime('%y-%m-%d_%Hh%M') - -name = "testing_simca_reconstruction_full" -model_name = "dauhst_9" -reconstruction_checkpoint = "./checkpoints/epoch=499-step=18000.ckpt" -resnet_checkpoint = None - -log_dir = 'tb_logs' - -train = True -retrain_recons = True - -logger = TensorBoardLogger(log_dir, name=name) - -early_stop_callback = EarlyStopping( - monitor='val_loss', # Metric to monitor - patience=40, # Number of epochs to wait for improvement - verbose=True, - mode='min' # 'min' for metrics where lower is better, 'max' for vice versa - ) - -checkpoint_callback = ModelCheckpoint( - monitor='val_loss', # Metric to monitor - dirpath='checkpoints_with_resnet/', # Directory path for saving checkpoints - filename=f'best-checkpoint_{model_name}_{datetime_}', # Checkpoint file name - save_top_k=1, # Save the top k models - mode='min', # 'min' for metrics where lower is better, 'max' for vice versa - save_last=True # Additionally, save the last checkpoint to a file named 'last.ckpt' -) - -#sub_module = JointReconstructionModule_V1.load_from_checkpoint(reconstruction_checkpoint) -checkpoint = torch.load(reconstruction_checkpoint) -sub_module = JointReconstructionModule_V1(model_name, log_dir) -sub_module.load_state_dict(checkpoint["state_dict"]) - - -resnet_checkpoint = "./checkpoints/best-checkpoint_resnet_only_24-03-09_18h05.ckpt" - -if not retrain_recons or not train: - sub_module.eval() - -reconstruction_module = JointReconstructionModule_V3(sub_module, - log_dir=log_dir+'/'+ name, - resnet_checkpoint=resnet_checkpoint) - - -if torch.cuda.is_available(): - trainer = pl.Trainer( logger=logger, - accelerator="gpu", - max_epochs=500, - log_every_n_steps=30, - callbacks=[early_stop_callback, checkpoint_callback]) -else: - trainer = pl.Trainer( logger=logger, - accelerator="gpu", - max_epochs=500, - log_every_n_steps=30, - callbacks=[early_stop_callback, checkpoint_callback]) - -if train: - trainer.fit(reconstruction_module, datamodule) -else: - trainer.predict(reconstruction_module, datamodule, ckpt_path=resnet_checkpoint) diff --git a/utils/__init__.py b/utils/__init__.py index fd8ee60..89f17b4 100644 --- a/utils/__init__.py +++ b/utils/__init__.py @@ -1,2 +1,2 @@ -from .helpers import * +from .helper import * from .functions_hyperspectral_analysis import * diff --git a/utils/functions_hyperspectral_analysis.py b/utils/functions_hyperspectral_analysis.py deleted file mode 100644 index 17dbb6f..0000000 --- a/utils/functions_hyperspectral_analysis.py +++ /dev/null @@ -1,49 +0,0 @@ -import numpy as np -from sklearn.decomposition import PCA - -def explore_spectrums(img, complete_gt, class_names, - ignored_labels=None): - """Plot sampled spectrums with mean + std for each class. - - Args: - img: 3D hyperspectral image - complete_gt: 2D array of labels - class_names: list of class names - ignored_labels (optional): list of labels to ignore - vis : Visdom display - Returns: - mean_spectrums: dict of mean spectrum by class - - """ - - stats_per_class = {"mean_spectrums": {}, 'std_spectrums': {}, 'non_degenerate_mean_spectrums': {}, - 'non_degenerate_covariance': {}} - n_samples_per_class = np.array([np.sum([complete_gt == i]) for i in np.unique(complete_gt) if i not in ignored_labels]) - - """if delta_lambda is not None: - n_dim_pca = int(np.min([0.25 * img.shape[-1] * np.log10(delta_lambda), 0.75 * np.min(n_samples_per_class)])) - else: - n_dim_pca = int(np.min([0.25*img.shape[-1], 0.75*np.min(n_samples_per_class)]))""" - # n_dim_pca = int(np.min([0. * img.shape[-1], 0.5 * np.min(n_samples_per_class)])) - n_dim_pca = 5 - mask = complete_gt > 0 - pca = PCA(n_dim_pca) - pca.fit(img[mask]) - - for c in np.unique(complete_gt): - if c in ignored_labels: - continue - mask = complete_gt == c - class_spectrums = img[mask] - # pca = PCA(n_dim_pca) - class_spectrums_pca = pca.transform(class_spectrums) - - mean_spectrum = np.mean(class_spectrums, axis=0) - std_spectrum = np.std(class_spectrums, axis=0) - - stats_per_class['mean_spectrums'][class_names[c]] = mean_spectrum - stats_per_class['std_spectrums'][class_names[c]] = std_spectrum - stats_per_class['non_degenerate_mean_spectrums'][class_names[c]] = np.mean(class_spectrums_pca, axis=0) - stats_per_class['non_degenerate_covariance'][class_names[c]] = np.cov(np.transpose(class_spectrums_pca)) - - return stats_per_class \ No newline at end of file diff --git a/utils/helpers.py b/utils/helpers.py index 70412be..7763872 100644 --- a/utils/helpers.py +++ b/utils/helpers.py @@ -1,31 +1,6 @@ -import logging -import os import yaml from datetime import datetime -def configure_logging(result_directory, log_directory="logs"): - log_directory = os.path.join(result_directory, log_directory) - os.makedirs(log_directory, exist_ok=True) - - log_formatter = logging.Formatter("%(asctime)s [%(levelname)s] %(message)s") - - root_logger = logging.getLogger() - root_logger.setLevel(logging.DEBUG) - - console_handler = logging.StreamHandler() - console_handler.setFormatter(log_formatter) - console_handler.setLevel(logging.DEBUG) - root_logger.addHandler(console_handler) - - timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S-%f")[:-3] - log_file_name = f"experiment_{timestamp}.log" - - file_handler = logging.FileHandler(os.path.join(log_directory, log_file_name)) - file_handler.setFormatter(log_formatter) - file_handler.setLevel(logging.DEBUG) - root_logger.addHandler(file_handler) - - def load_yaml_config(file_path): """ @@ -38,8 +13,3 @@ def load_yaml_config(file_path): config = yaml.safe_load(file) return config -def undersample_grid(grid, target_size=40): - factor = grid.shape[0] // target_size - if factor == 0: - return grid[::1,::1] - return grid[::factor,::factor] \ No newline at end of file

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