reference paper:
- [1711.06025] Learning to Compare: Relation Network for Few-Shot Learning (arxiv.org)
- Remote Sensing | Free Full-Text | Deep Relation Network for Hyperspectral Image Few-Shot Classification (mdpi.com)
reference code:
- floodsung/LearningToCompare_FSL: PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part) (github.com)
- gokling1219/RN-FSC: Deep Relation Network for Hyperspectral Image Few-Shot Classification (github.com)
- EnayatAria/ICA-based-band-selection-HSI: Independent component analysis for dimensionality reduction of hyperspectral images (github.com)
- nshaud/DeepHyperX: Deep learning toolbox based on PyTorch for hyperspectral data classification. (github.com)
env:Miniconda / Python 3.9.6 / Cuda 11.6
GPU:NVIDIA GeForce GTX 1650 4GB
CPU:lntel(R) Core(TM) i5-9300H CPU @ 2.40GHz
memory:32GB
Python 3.9.16 (main, Mar 8 2023, 10:39:24) [MSC v.1916 64 bit (AMD64)] on win32
torch 1.12.0+cu116
torchvision 0.13.0+cu116
scikit-learn 1.2.2
numpy 1.24.2
visdom 0.2.4
h5py 3.8.0
scipy 1.10.1
spectral 0.23.1
mat73 0.60
jupyter 1.0.0
ipykernel 6.22.0
ipython 8.12.0
Image: PyTorch 1.11.0 / Python 3.8(ubuntu20.04) / Cuda 11.3
GPU:RTX 3090(24GB) * 1
CPU:24 vCPU AMD EPYC 7642 48-Core Processor
memory:80GB
.
|-- DeepHyperX
|-- HSI_FSC_0_basic
|-- HSI_FSC_result
|-- ICA-based-band-selection-HSI
|-- LICENSE
|-- README.md
|-- RN_FSC_modify
`-- requirements.txt
python -m visdom.server
# 步骤一:在服务器上install visdom
pip install visdom
# 步骤二:服务器上启动visdom
python -m visdom.server
# 步骤三:本地ssh连接服务器并映射端口
ssh -L <本地端口>:localhost:8097 -p <ssh访问服务器的端口> <服务器用户名>@<ssh访问服务器的ip>
# 本地端口号可以随便设置,服务器用户名和ssh访问服务器的ip都可以在AutoDL中查看到
ssh -L 8080:localhost:8097 -p 23844 root@region-11.autodl.com
# 步骤四:全部完成,在本地网页中输入" localhost:8080 "
> [服务器visdom的本地显示_autodl vis_江南綿雨的博客-CSDN博客](https://blog.csdn.net/weixin_43702653/article/details/127273564)
cd ICA-based-band-selection-HSI
# band select
python ICA-based_for_BS_all.py
# selected bands sorted
python bandselect_name_bands_sorted.py
cd HSI_FSC
python .\generate_source_dataset.py --datasetname HS
python .\generate_source_dataset.py --datasetname BO
python .\generate_source_dataset.py --datasetname KSC
python .\generate_source_dataset.py --datasetname CH
python .\generate_meta_dataset.py
python .\generate_target_dataset --dataset XX
# XX: SA/IP/UP/PC/XZ
python .\meta_train_EM_RN.py
python .\fewshot_train.py --datasetname XX
python .\test.py --datasetname XX
Step Merge use 'auto.sh' can merge the step 4, 5 and 6
python .\display_result_with_visdom.py
cd .\DeepHyperX\
# windows
.\auto.bat
# linux
bash auto.sh
├─HSI_FSC_1_learningrate
├─HSI_FSC_0_basic
├─HSI_FSC_2_Dropout
├─HSI_FSC_0_basic
├─HSI_FSC_3_BatchNorm
├─HSI_FSC_4_5way
├─HSI_FSC_4_10way
├─HSI_FSC_4_15way
├─HSI_FSC_0_basic
├─HSI_FSC_4_25way
├─HSI_FSC_4_30way
├─HSI_FSC_0_basic
├─HSI_FSC_5_support5_test15
├─HSI_FSC_5_support10_test10
├─HSI_FSC_5_support15_test5
# 设置代理
(base) PS D:\Document\DevelopProject\Develop_DeepLearning\HSI\HSI-FSC> git config --global http.proxy http://127.0.0.1:7890
(base) PS D:\Document\DevelopProject\Develop_DeepLearning\HSI\HSI-FSC> git config --global https.proxy http://127.0.0.1:7890
# 查看代理
(base) PS D:\Document\DevelopProject\Develop_DeepLearning\HSI\HSI-FSC> git config --global https.proxy
http://127.0.0.1:7890
(base) PS D:\Document\DevelopProject\Develop_DeepLearning\HSI\HSI-FSC> git config --global http.proxy
http://127.0.0.1:7890
# 取消代理
git config --global --unset http.proxy
git config --global --unset https.proxy