mmdeploy在Jetson Xavier AGX/NX Jetpack 4.6.1上的部署

pip3 install torch-1.10.0-cp36-cp36m-linux_aarch64.whl

sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev libopenblas-base libopenmpi-dev libssl-dev  -y
pip3 install pillow==8.4.0

wget https://github.com/pytorch/vision/archive/refs/tags/v0.11.1.zip
unzip vision-0.11.1.zip
cd torchvision
export BUILD_VERSION=0.11.1
pip install -e .

sudo apt-get purge cmake -y  # 移除历史cmake
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  # 查看是否安装成功

echo -e '\n# set environment variable for TensorRT' >> ~/.bashrc
echo 'export TENSORRT_DIR=/usr/include/aarch64-linux-gnu' >> ~/.bashrc
echo -e '\n# set environment variable for CUDA' >> ~/.bashrc
echo 'export PATH=$PATH:/usr/local/cuda/bin' >> ~/.bashrc
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64' >> ~/.bashrc
source ~/.bashrc

git clone --branch v1.4.0 https://github.com/open-mmlab/mmcv.git
cd mmcv
MMCV_WITH_OPS=1 pip3 install -e .

【注意，3.20.0太新，编译时会报错，所以将版本见到3.19.4】
git clone -b v3.19.4 https://github.com/protocolbuffers/protobuf.git
cd protobuf
git submodule update --init --recursive
./autogen.sh
./configure
make -j$(nproc) && make install
ldconfig
protoc --version 

pip3 install protobuf==3.19.4

pip3 install cython onnx==1.11.0

sudo apt-get install -y pkg-config libhdf5-100 libhdf5-dev
pip3 install pkgconfig
pip3 install versioned-hdf5

sudo apt-get install -y libspdlog-dev

git clone https://github.com/openppl-public/ppl.cv.git
cd ppl.cv
export PPLCV_DIR=$(pwd)
echo -e '\n# set environment variable for ppl.cv' >> ~/.bashrc
echo "export PPLCV_DIR=$(pwd)" >> ~/.bashrc
./build.sh cuda

pip3 install pycuda

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

mkdir -p build && cd build
cmake .. -DMMDEPLOY_TARGET_BACKENDS="trt"
make -j$(nproc) && make install

cd ${MMDEPLOY_DIR}
pip3 install -v -e . 或者 python3 -m pip install -v -e .

mkdir -p build && cd build
cmake .. \
    -DMMDEPLOY_BUILD_SDK=ON \
    -DMMDEPLOY_BUILD_SDK_PYTHON_API=ON \
    -DMMDEPLOY_BUILD_EXAMPLES=ON \
    -DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
    -DMMDEPLOY_TARGET_BACKENDS="trt" \
    -DMMDEPLOY_CODEBASES=all \
    -Dpplcv_DIR=${PPLCV_DIR}/cuda-build/install/lib/cmake/ppl  \
    -DPYTHON_EXECUTABLE=/usr/bin/python3.6
make -j$(nproc) && make install

git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
pip3 install -r requirements/build.txt
pip3 install -v -e .  # or "python3 setup.py develop"

python3 tools/deploy.py /data/models/spaie_132_5997/132_5997/0/model_deploy_config.py \
    /data/models/spaie_132_5997/132_5997/0/mmdet_model_config.py \
    /data/models/spaie_132_5997/132_5997/0/latest.pth \
    /data/models/spaie_132_5997/132_5997/0/demo.jpg \
    --test-img /model/demo.jpg \
    --work-dir /model/convert \
    --device cuda:0 \
    --log-level INFO --dump-info

./object_detection cuda ${directory/to/the/converted/models} ${path/to/an/image}

export PYTHONPATH=$PYTHONPATH:"/root/george/mmdeploy/build/lib"  # 设置环境变量
python3 -c  "import mmdeploy_python;print(dir(mmdeploy_python))"    # 测试是否可以引入mmdeploy_python

    ./csrc/mmdeploy/backend_ops/torchscript/optimizer/bind.cpp: In function ‘void mmdeploy::torch_jit::pybind11_init_ts_optimizer(pybind11::module&)’:
    ./csrc/mmdeploy/backend_ops/torchscript/optimizer/bind.cpp:36:13: error: ‘module_’ is not a member of ‘pybind11’
       pybind11::module_ onnx_module = m.def_submodule("onnx");
                 ^~~~~~~
    ./csrc/mmdeploy/backend_ops/torchscript/optimizer/bind.cpp:36:13: note: suggested alternative: ‘module’
       pybind11::module_ onnx_module = m.def_submodule("onnx");
                 ^~~~~~~
                 module
    ./csrc/mmdeploy/backend_ops/torchscript/optimizer/bind.cpp:37:3: error: ‘onnx_module’ was not declared in this scope
       onnx_module.def("_jit_pass_merge_shape_concate", MergeShapeConcate, pybind11::arg("graph"));