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Build MMDeploy

Preparation

  • Download MMDeploy

    git clone -b master git@github.com:open-mmlab/mmdeploy.git MMDeploy
cd MMDeploy
git submodule update --init --recursive

    Note:

    • If fetching submodule fails, you could get submodule manually by following instructions:

      git clone git@github.com:NVIDIA/cub.git third_party/cub
cd third_party/cub
git checkout c3cceac115

# go back to third_party directory and git clone pybind11
cd ..
git clone git@github.com:pybind/pybind11.git pybind11
cd pybind11
git checkout 70a58c5

  • Install cmake

    Install cmake>=3.14.0, you could refer to cmake website for more detailed info.

    sudo apt-get install -y libssl-dev
wget https://github.com/Kitware/CMake/releases/download/v3.20.0/cmake-3.20.0.tar.gz
tar -zxvf cmake-3.20.0.tar.gz
cd cmake-3.20.0
./bootstrap
make
sudo make install

  • GCC 7+

    MMDeploy requires compilers that support C++17.

    # Add repository if ubuntu < 18.04
sudo add-apt-repository ppa:ubuntu-toolchain-r/test

sudo apt-get install gcc-7
sudo apt-get install g++-7

Create Environment

  • Create a conda virtual environment and activate it

    conda create -n mmdeploy python=3.7 -y
conda activate mmdeploy

  • Install PyTorch>=1.8.0, following the official instructions

    # CUDA 11.1
conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge

  • Install mmcv-full. Refer to the guide for details.

    export cu_version=cu111 # cuda 11.1
export torch_version=torch1.8.0
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/${cu_version}/${torch_version}/index.html

Build backend support

Build the inference engine extension libraries you need.

Install mmdeploy

cd ${MMDEPLOY_DIR} # To mmdeploy root directory
pip install -e .

Note

  • Some dependencies are optional. Simply running pip install -e . will only install the minimum runtime requirements. To use optional dependencies, install them manually with pip install -r requirements/optional.txt or specify desired extras when calling pip (e.g. pip install -e . [optional]). Valid keys for the extras field are: all, tests, build, optional.

Build SDK

Readers can skip this chapter if you are only interested in model converter.

Dependencies

Currently, SDK is tested on Linux x86-64, more platforms will be added in the future. The following packages are required to build MMDeploy SDK.

Each package's installation command is given based on Ubuntu 18.04.

  • OpenCV 3+

    sudo apt-get install libopencv-dev

  • spdlog 0.16+

    sudo apt-get install libspdlog-dev

    On Ubuntu 16.04, please use the following command

    wget http://archive.ubuntu.com/ubuntu/pool/universe/s/spdlog/libspdlog-dev_0.16.3-1_amd64.deb
sudo dpkg -i libspdlog-dev_0.16.3-1_amd64.deb

    You can also build spdlog from its source to enjoy its latest features. But be sure to add -fPIC compilation flags at first.

  • pplcv

    A high-performance image processing library of openPPL supporting x86 and cuda platforms.
    It is OPTIONAL which only be needed if cuda platform is required.

    git clone git@github.com:openppl-public/ppl.cv.git
cd ppl.cv
./build.sh cuda

  • backend engines

    SDK uses the same backends as model converter does. Please follow build backend guide to install your interested backend.

Set Build Option

  • Turn on SDK build switch

    -DMMDEPLOY_BUILD_SDK=ON

  • Enabling Devices

    By default, only CPU device is included in the target devices. You can enable device support for other devices by passing a semicolon separated list of device names to MMDEPLOY_TARGET_DEVICES variable, e.g. -DMMDEPLOY_TARGET_DEVICES="cpu;cuda".
    Currently, the following devices are supported.

    device name path setter
    Host cpu N/A
    CUDA cuda CUDA_TOOLKIT_ROOT_DIR & pplcv_DIR

    If you have multiple CUDA versions installed on your system, you will need to pass CUDA_TOOLKIT_ROOT_DIR to cmake to specify the version.
    Meanwhile, pplcv_DIR has to be provided in order to build image processing operators on cuda platform.

  • Enabling inference engines

    By default, no target inference engines are set, since it's highly dependent on the use case. MMDEPLOY_TARGET_BACKENDS must be set to a semicolon separated list of inference engine names, e.g. -DMMDEPLOY_TARGET_BACKENDS="trt;ort;pplnn;ncnn;openvino" A path to the inference engine library is also needed. The following backends are currently supported

    library name path setter
    PPL.nn pplnn pplnn_DIR
    ncnn ncnn ncnn_DIR
    ONNXRuntime ort ONNXRUNTIME_DIR
    TensorRT trt TENSORRT_DIR & CUDNN_DIR
    OpenVINO openvino InferenceEngine_DIR

  • Enabling codebase's postprocess components

    MMDEPLOY_CODEBASES MUST be specified by a semicolon separated list of codebase names. The currently supported codebases are 'mmcls', 'mmdet', 'mmedit', 'mmseg', 'mmocr'. Instead of listing them one by one in MMDEPLOY_CODEBASES, user can also pass all to enable all of them, i.e., -DMMDEPLOY_CODEBASES=all

  • Put it all together

    The following is a recipe for building MMDeploy SDK with cpu device and ONNXRuntime support

    mkdir build && cd build
cmake .. \
    -DMMDEPLOY_BUILD_SDK=ON \
    -DCMAKE_CXX_COMPILER=g++-7 \
    -DONNXRUNTIME_DIR=/path/to/onnxruntime \
    -DMMDEPLOY_TARGET_DEVICES=cpu \
    -DMMDEPLOY_TARGET_BACKENDS=ort \
    -DMMDEPLOY_CODEBASES=all
cmake --build . -- -j$(nproc) && cmake --install .

    Here is another example to build MMDeploy SDK with cuda device and TensorRT backend

     mkdir build && cd build
 cmake .. \
   -DMMDEPLOY_BUILD_SDK=ON \
   -DCMAKE_CXX_COMPILER=g++-7 \
   -Dpplcv_DIR=/path/to/ppl.cv/install/lib/cmake/ppl \
   -DTENSORRT_DIR=/path/to/tensorrt \
   -DCUDNN_DIR=/path/to/cudnn \
   -DMMDEPLOY_TARGET_DEVICES="cuda;cpu" \
   -DMMDEPLOY_TARGET_BACKENDS=trt \
   -DMMDEPLOY_CODEBASES=all
 cmake --build . -- -j$(nproc) && cmake --install .