VMamba+MMRotate+DOTA-v1.0旋转目标检测

写在前面

本项目仅为中文区的CVers提供快速实装VMamba视觉表征模型的参考例。

本项目以代码差分方式提交，请务必参考：

MMRotate: main dev-1.x
DOTA_devkit: code
VMamba: paper code
VHeat: paper code

本项目基于2024/05/30版本的VMamba代码和VHeat代码编写。

本项目组织方式：

把"VMamba/classification/models/"文件夹作为"mmrotate/models/backbones/vmamba_models/"文件夹，把"vHeat/detection/vHeat/"文件夹作为"mmrotate/models/backbones/vheat_models/"文件夹
把"VMamba/detection/model.py"文件作为"mmrotate/models/backbones/vmamba_model.py"文件，把"vheat/detection/model.py"文件作为"mmrotate/models/backbones/vheat_model.py"文件，并修改"__init__.py"
制作vmamba和VHeat的config文件
迁移"VMamba/kernels/"文件夹

mmrotate-0.3.3/0.3.4/dev-1.x安装

【说明】我们推荐使用mmrotate-0.3.3/0.3.4版本，它是一个较为稳定的版本。mmrotate-dev-1.x版本是基于mmcv-2与mmdet-3编写的未来主流版本，但它的多尺度测试可能存在一些问题。

# 受到 https://github.com/state-spaces/mamba 要求：PyTorch 1.12+ CUDA 11.6+
# wget https://developer.download.nvidia.com/compute/cuda/11.7.1/local_installers/cuda_11.7.1_515.65.01_linux.run
# chmod +x ./cuda_11.7.1_515.65.01_linux.run
# sudo sh cuda_11.7.1_515.65.01_linux.run
wget https://developer.download.nvidia.com/compute/cuda/11.6.2/local_installers/cuda_11.6.2_510.47.03_linux.run
chmod +x ./cuda_11.6.2_510.47.03_linux.run
sudo ./cuda_11.6.2_510.47.03_linux.run
# 
# vi ~/.bashrc
# Add CUDA path
# export PATH=/usr/local/cuda-11.7/bin:$PATH
# export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64:$LD_LIBRARY_PATH
export PATH=/usr/local/cuda-11.6/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.6/lib64:$LD_LIBRARY_PATH
export NCCL_P2P_DISABLE="1"
export NCCL_IB_DISABLE="1"
# 
source ~/.bashrc
nvcc -V
# 
# NO sudo when install anaconda
# chmod +x ./Anaconda3-2023.09-0-Linux-x86_64.sh
# ./Anaconda3-2023.09-0-Linux-x86_64.sh
# 
# conda create -n openmmlab1131 python=3.9 -y
# conda activate openmmlab1131
# # ref: https://pytorch.org/get-started/previous-versions/#v1131
# conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda create -n openmmlab1121 python=3.8 -y
conda activate openmmlab1121
# ref: https://pytorch.org/get-started/previous-versions/#v1121
conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge
# 
pip install shapely tqdm timm
# 
# # if mmrotate-dev-1.x
# pip install -U openmim
# mim install mmengine
# # 受到 dev-1.x 要求，可以安装 mmcv==2.0.0rc2 和 mmdet==3.0.0rc6 之后的那个版本
# mim install mmcv==2.0.1
# mim install mmdet==3.1.0
# # 我们所使用mmrotate-dev-1.x版本的提交码是fd60beff130a54e284a73651903de29fe728f97b，请注意核对
# git clone https://github.com/open-mmlab/mmrotate.git -b dev-1.x
# cd mmrotate
# pip install -r requirements/build.txt
# pip install -v -e .
# 
# if mmrotate-0.3.3/0.3.4
pip install openmim
mim install mmcv-full==1.6.1
mim install mmdet==2.25.1
git clone https://github.com/open-mmlab/mmrotate.git
cd mmrotate
pip install -r requirements/build.txt
pip install -v -e .
# 
# 为mmrotate-0.3.3/0.3.4降低部分包的版本
pip install numpy==1.21.5
pip install yapf==0.40.1
# 
# 安装必要的vmamba依赖
pip install einops fvcore triton ninja
cd kernels/selective_scan/ && pip install . && cd ../../

DOTA数据集创建

使用官网下载的数据集解压创建

# 请先准备好与mmrotate主目录并列的mmrotate-data和mmrotate-tools文件夹
# 以下命令均在mmrotate主目录下执行
# 
# ln -s /Workspace/Dataset/DOTA/ ../mmrotate-data/data/
# 
ln -s ../mmrotate-data/data/ ./
ln -s ../mmrotate-data/work_dirs/ ./
# 
python ../mmrotate-tools/md5_calc.py --path ./data/DOTA/train.tar.gz
# cfb5007ada913241e02c24484e12d5d2
python ../mmrotate-tools/md5_calc.py --path ./data/DOTA/val.tar.gz
# a53e74b0d69dacf3ffcb438accd60c45
python ../mmrotate-tools/md5_calc.py --path ./data/DOTA/test/part1.zip
# d3028e48da64b37ad2f2f5f31059e0da
python ../mmrotate-tools/md5_calc.py --path ./data/DOTA/test/part2.zip
# 99f779850cc44b8f8b28d348494c6b41
# 
tar -xzf ./data/DOTA/train.tar.gz -C ./data/DOTA/
tar -xzf ./data/DOTA/val.tar.gz -C ./data/DOTA/
unzip ./data/DOTA/test/part1.zip -d ./data/DOTA/test/
unzip ./data/DOTA/test/part2.zip -d ./data/DOTA/test/
# 
python ../mmrotate-tools/dir_list.py --path ./data/DOTA/train/images/ --output ./data/DOTA/train/trainset.txt
# 1411
python ../mmrotate-tools/dir_list.py --path ./data/DOTA/val/images/ --output ./data/DOTA/val/valset.txt
# 458
python ../mmrotate-tools/dir_list.py --path ./data/DOTA/test/images/ --output ./data/DOTA/test/testset.txt
# 937

mmrotate分割处理

python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ss_train.json
# Total images number: 15749
python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ss_val.json
# Total images number: 5297
python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ss_trainval.json
# Total images number: 21046
python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ss_test.json
# Total images number: 10833
python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ms_trainval.json
# Total images number: 138883
python tools/data/dota/split/img_split.py --base-json tools/data/dota/split/split_configs/ms_test.json
# Total images number: 71888

预训练模型目录为./data/pretrained/

DOTA_devkit安装

sudo apt install swig
swig -c++ -python polyiou.i
python setup.py build_ext --inplace

DOTA数据集训练与合并测试（示例）

多卡训练：

# if mmrotate-0.3.3/0.3.4
CUDA_VISIBLE_DEVICES=0,1 ./tools/dist_train.sh ./configs/_rotated_faster_rcnn_/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py 2
CUDA_VISIBLE_DEVICES=0,1 nohup ./tools/dist_train.sh ./configs/_rotated_faster_rcnn_/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py 2 > nohup.log 2>&1 &
# 
# if mmrotate-dev-1.x
CUDA_VISIBLE_DEVICES=0,1 ./tools/dist_train.sh ./configs/_rotated_faster_rcnn_/rotated-faster-rcnn-le90_r50_fpn_1x_dota.py 2
CUDA_VISIBLE_DEVICES=0,1 nohup ./tools/dist_train.sh ./configs/_rotated_faster_rcnn_/rotated-faster-rcnn-le90_r50_fpn_1x_dota.py 2 > nohup.log 2>&1 &

多卡合并测试：

# if mmrotate-0.3.3/0.3.4
CUDA_VISIBLE_DEVICES=0,1 ./tools/dist_test.sh ./configs/_rotated_faster_rcnn_/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py ./work_dirs/rotated_faster_rcnn_r50_fpn_1x_dota_le90/rotated_faster_rcnn_r50_fpn_1x_dota_le90-0393aa5c.pth 2 --format-only --eval-options submission_dir="./work_dirs/Task1_r50_033"
python "../DOTA_devkit-master/dota_evaluation_task1.py" --mergedir "./work_dirs/Task1_r50_033/" --imagesetdir "./data/DOTA/val/" --use_07_metric True
# map: 0.820117064577964
# 
# if mmrotate-dev-1.x
CUDA_VISIBLE_DEVICES=0,1 ./tools/dist_test.sh ./configs/_rotated_faster_rcnn_/rotated-faster-rcnn-le90_r50_fpn_1x_dota.py ./work_dirs/rotated_faster_rcnn_r50_fpn_1x_dota_le90/rotated_faster_rcnn_r50_fpn_1x_dota_le90-0393aa5c.pth 2
python "../DOTA_devkit-master/dota_evaluation_task1.py" --mergedir "./work_dirs/Task1_rotated-faster-rcnn-le90_r50_fpn_1x_dota/" --imagesetdir "./data/DOTA/val/" --use_07_metric True
# map: 0.8193743727960783

Params&FLOPs计算：

python ./tools/analysis_tools/get_flops.py ./configs/rotated_faster_rcnn/rotated_faster_rcnn_r50_fpn_1x_dota_le90.py

Performance

所有的训练和测试均在4×A100卡上进行。

表中split mAP是对ss-val的评测；merge mAP是对ss-val或ms-test的评测；
表中VMamba的FLOPs暂无计算。
表中VHeat的性能暂不发布。

Detector	backbone_size	batch_size	init_lr	split mAP	merge mAP	Training Cost	Testing FPS	Params	FLOPs	Configs
Rotated RetinaNet (1x ss)	Swin-T	4*4	1e-4	67.14	68.68	0.7h	106.6	37.13M	222.08G	cfg
	Swin-S	4*4	1e-4	67.54	69.66	1.9h	61.1	58.45M	314.82G	cfg
	Swin-B	4*4	1e-4	68.48	70.56	2.7h	45.3	97.06M	461.50G	cfg
	VHeat-T	4*4	1e-4	-	-	-	-	-	-	cfg
	VHeat-S	4*4	1e-4	-	-	-	-	-	-	cfg
	VHeat-B	4*4	1e-4	-	-	-	-	-	-	cfg
	VMamba-T	4*4	1e-4	69.15	71.11	1.0h	91.9	?	?	cfg
	VMamba-S	4*4	1e-4	69.78	72.17	1.8h	69.7	?	?	cfg
	VMamba-B	4*4	1e-4	69.70	71.77	2.2h	58.8	?	?	cfg
Rotated Faster RCNN (1x ss)	Swin-T	4*4	1e-4	70.11	72.62	0.7h	106.1	44.76M	215.54G	cfg
	Swin-S	4*4	1e-4	70.39	73.22	1.9h	58.7	66.08M	308.28G	cfg
	Swin-B	4*4	1e-4	71.73	73.91	2.7h	44.1	104.11M	455.35G	cfg
	VHeat-T	4*4	1e-4	-	73.16	-	-	49.71M	219.11G	cfg
	VHeat-S	4*4	1e-4	72.86	73.65	-	-	71.08M	3.6.28G	cfg
	VHeat-B	4*4	1e-4	-	73.56	-	-	111.65M	451.47G	cfg
	VMamba-T	4*4	1e-4	73.13	74.04	1.1h	84.0	?	?	cfg
	VMamba-S	4*4	1e-4	73.14	74.16	1.9h	63.3	?	?	cfg
	VMamba-B	4*4	1e-4	73.30	73.50	2.3h	56.1	?	?	cfg
Oriented RCNN (1x ss)	Swin-T	4*4	1e-4	73.88	75.92	0.8h	105.0	44.76M	215.68G	cfg
	Swin-S	4*4	1e-4	74.49	76.07	2.0h	58.7	66.08M	308.42G	cfg
	Swin-B	4*4	1e-4	74.88	76.16	2.8h	44.1	104.11M	455.49G	cfg
	VHeat-T	4*4	1e-4	74.85	76.56	-	-	49.71M	219.11G	cfg
	VHeat-S	4*4	1e-4	74.96	76.20	-	-	71.08M	306.42G	cfg
	VHeat-B	4*4	1e-4	74.58	76.54	-	-	111.65	451.60G	cfg
	VMamba-T	4*4	1e-4	75.95	76.59	1.1h	79.9	?	?	cfg
	VMamba-S	4*4	1e-4	76.10	76.70	1.9h	62.4	?	?	cfg
	VMamba-B	4*4	1e-4	76.27	76.23	2.3h	54.7	?	?	cfg
Oriented RCNN (1x msrr)	Swin-T	4*4	1e-4	88.45	81.36	4.6h				cfg
	Swin-S	4*4	1e-4	89.58	81.08	12.5h				cfg
	Swin-B	4*4	1e-4	89.36	81.04	17.5h				cfg
	VHeat-T	4*4	1e-4	89.73	81.50	-				cfg
	VHeat-S	4*4	1e-4	89.57	81.37	-				cfg
	VHeat-B	4*4	1e-4	90.71	81.16	-				cfg
	VMamba-T	4*4	1e-4	89.78	80.70	6.8h				cfg
	VMamba-S	4*4	1e-4	90.56	80.62	12.2h				cfg
	VMamba-B	4*4	1e-4	90.48	80.97	15.1h				cfg

写在后面

有错误请及时指出！虽然不会经常来看issue非常抱歉但是看到就一定会回复的。

更新代码好麻烦呜呜呜

Date modified: 2024/06/03

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
DOTA_devkit-master		DOTA_devkit-master
mmrotate-0.3.3		mmrotate-0.3.3
mmrotate-dev-1.x		mmrotate-dev-1.x
mmrotate-tools		mmrotate-tools
.gitattributes		.gitattributes
.gitignore		.gitignore
README.md		README.md
README_datasets.md		README_datasets.md

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VMamba+MMRotate+DOTA-v1.0旋转目标检测

写在前面

mmrotate-0.3.3/0.3.4/dev-1.x安装

DOTA数据集创建

DOTA_devkit安装

DOTA数据集训练与合并测试（示例）

Performance

写在后面

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VMamba+MMRotate+DOTA-v1.0旋转目标检测

写在前面

mmrotate-0.3.3/0.3.4/dev-1.x安装

DOTA数据集创建

DOTA_devkit安装

DOTA数据集训练与合并测试（示例）

Performance

写在后面

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