笔记--Nvidia Jetson利用Anaconda安装MMDeploy及API测试

目录

前言

1--安装Anaconda

2--创建conda环境及安装pytorch

3--配置环境依赖

4--安装mmdeploy

5--实例测试

6--参考

---

前言

建议结合官方文档阅读。

1--安装Anaconda

①下载Anaconda

cd ~/mmdeploy_test # 进入一个新的文件夹

# 下载anaconda包
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge-pypy3-Linux-aarch64.sh"
bash Miniforge-pypy3-Linux-aarch64.sh # 执行安装

source ~/.bashrc # 更新Path

②设置国内源

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/peterjc123/
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/pytorch/
conda config --add channels http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/peterjc123/
conda config --set show_channel_urls yes

③测试

conda config --show channels # 查看源路径

conda --version # 查看版本

2--创建conda环境及安装pytorch

①创建mmdeploy环境，python版本为3.6

# 创建mmdeploy环境
conda create -n mmdeploy python=3.6 -y
conda activate mmdeploy

python版本建议安装3.6和3.8，以与nvidia jetson提供的pytorch安装包适配。

②下载pytorch安装包

国内无pvn的情况下，建议从nvidia jetson提供的pytorch官网下载pytorch安装包。

# 在windows或服务器上下载pytorch安装包
# 通过sftp传到nvidia jetson中
# 以代替命令：wget https://nvidia.box.com/shared/static/fjtbno0vpo676a25cgvuqc1wty0fkkg6.whl -O torch-1.10.0-cp36-cp36m-linux_aarch64.whl

③安装pytorch

# 安装依赖库
sudo apt-get install libopenblas-base libopenmpi-dev libomp-dev
pip3 install Cython numpy

# 运行pytorch安装包
pip3 install torch-1.10.0-cp36-cp36m-linux_aarch64.whl

④添加路径

# 添加路径
vim ~/.bashrc
export OPENBLAS_CORETYPE=ARMV8 # 增加这行内容
:wq # 保存退出
source ~/.bashrc

⑤安装torchvision（这个过程可能会卡顿或者持续时间较长）

# 安装torchvision
sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev -y
git clone https://github.com/pytorch/vision torchvision
cd torchvision
git checkout tags/v0.11.1 -b v0.11.1
export BUILD_VERSION=0.11.1
pip install -e .

3--配置环境依赖

①配置cmake

## 更新cmake
# 卸载已安装的版本
sudo apt-get purge cmake
sudo snap remove cmake

# 安装新版本
export CMAKE_VER=3.23.1
export ARCH=aarch64
wget https://github.com/Kitware/CMake/releases/download/v${CMAKE_VER}/cmake-${CMAKE_VER}-linux-${ARCH}.sh
chmod +x cmake-${CMAKE_VER}-linux-${ARCH}.sh
sudo ./cmake-${CMAKE_VER}-linux-${ARCH}.sh --prefix=/usr --skip-license
cmake --version

②配置Tensorrt

配置Tensorrt
cp -r /usr/lib/python3.6/dist-packages/tensorrt* /home/nvidia/miniforge-pypy3/envs/mmdeploy/lib/python3.6/site-packages/

conda deactivate
conda activate mmdeploy
python -c "import tensorrt; print(tensorrt.__version__)"  # 测试TensorRT版本

# 设置路径 利用vim ~/.bashrc进行
export TENSORRT_DIR=/usr/include/aarch64-linux-gnu
export PATH=$PATH:/usr/local/cuda/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64

③安装mmcv-full （这个过程编译可能会卡顿或者持续时间较长）

# 安装mmcv-full
sudo apt-get install -y libssl-dev
git clone https://github.com/open-mmlab/mmcv.git
cd mmcv
git checkout -b v1.4.0
MMCV_WITH_OPS=1 pip install -e .

④安装onnx

pip install onnx

⑤安装h5py

sudo apt-get install -y pkg-config libhdf5-100 libhdf5-dev # 安装依赖

pip install versioned-hdf5 # 安装

⑥安装spdlog

sudo apt-get install -y libspdlog-dev

⑦安装ppl.cv

git clone https://github.com/openppl-public/ppl.cv.git  # 下载
cd ppl.cv
export PPLCV_DIR=$(pwd)

echo -e '\n# set environment variable for ppl.cv' >> ~/.bashrc  # 设置path
echo "export PPLCV_DIR=$(pwd)" >> ~/.bashrc
git checkout tags/v0.6.2 -b v0.6.2
./build.sh cuda

⑧安装mmdetecttion

4--安装mmdeploy

①下载mmdeploy

git clone --recursive https://github.com/open-mmlab/mmdeploy.git
cd mmdeploy
export MMDEPLOY_DIR=$(pwd)

②编译TensorRT自定义算子

# 编译tensorRT自定义算子
mkdir -p build && cd build
cmake .. -DMMDEPLOY_TARGET_BACKENDS="trt"
make -j$(nproc)

③安装mmdeploy依赖库

# 安装MMDeploy依赖库
cd ${MMDEPLOY_DIR}
pip install -v -e .

④编译C++ SDK

mkdir -p build && cd build
cmake .. \
    -DMMDEPLOY_BUILD_SDK=ON \
    -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
    -DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
    -DMMDEPLOY_TARGET_BACKENDS="trt" \
    -DMMDEPLOY_CODEBASES=all \
    -Dpplcv_DIR=${PPLCV_DIR}/cuda-build/install/lib/cmake/ppl
make -j$(nproc) && make install

⑤编译demo sdk（六个.cpp文件）

# 编译demo sdk
cd ${MMDEPLOY_DIR}/build/install/example
mkdir -p build && cd build
cmake .. -DMMDeploy_DIR=${MMDEPLOY_DIR}/build/install/lib/cmake/MMDeploy
make -j$(nproc)

5--实例测试

①mask_RCNN模型，python API模型转换

## mask_RCNN 实例
# 调用pythonAPI 转换模型： pytorch→onnx→engine
python tools/deploy.py \
configs/mmdet/instance-seg/instance-seg_tensorrt_dynamic-320x320-1344x1344.py \
/home/nvidia/mmdeploy_test/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py \      # 这个要在mmdetection源码中引用
checkpoints/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth \
maskrcnn_file/demo.jpg --work-dir work_dirs/maskrcnn/ \
--device cuda --show --dump-info

引用“/home/nvidia/mmdeploy_test/mmdetection/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py”文件时，需要在mmdetection源码中引用，所以要先git clone 下载mmdetection

②C++推理实例

# 测试C++实例
# 运行实例分割程序（参数1：gpu加速； 参数2：推理模型的地址； 参数3：推理测试图片的地址）
./object_detection cuda /home/nvidia/mmdeploy_test/mmdeploy/work_dirs/maskrcnn/ /home/nvidia/mmdeploy_test/mmdeploy/maskrcnn_file/demo.jpg

6--参考

官方安装文档