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ncnn Support

MMDeploy now supports ncnn version == 1.0.20220216

Installation

Install ncnn

  • Download VulkanTools for the compilation of ncnn.

    wget https://sdk.lunarg.com/sdk/download/1.2.176.1/linux/vulkansdk-linux-x86_64-1.2.176.1.tar.gz?Human=true -O vulkansdk-linux-x86_64-1.2.176.1.tar.gz
tar -xf vulkansdk-linux-x86_64-1.2.176.1.tar.gz
export VULKAN_SDK=$(pwd)/1.2.176.1/x86_64
export LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH

  • Check your gcc version. You should ensure your gcc satisfies gcc >= 6.

  • Install Protocol Buffers through:

    apt-get install libprotobuf-dev protobuf-compiler

  • Prepare ncnn Framework

    • Download ncnn source code

      git clone -b 20220216 [email protected]:Tencent/ncnn.git

    • Make install ncnn library

      cd ncnn
export NCNN_DIR=$(pwd)
git submodule update --init
mkdir -p build && cd build
cmake -DNCNN_VULKAN=ON -DNCNN_SYSTEM_GLSLANG=ON -DNCNN_BUILD_EXAMPLES=ON -DNCNN_PYTHON=ON -DNCNN_BUILD_TOOLS=ON -DNCNN_BUILD_BENCHMARK=ON -DNCNN_BUILD_TESTS=ON ..
make install

    • Install pyncnn module

      cd ${NCNN_DIR} # To ncnn root directory
cd python
pip install -e .

Build custom ops

Some custom ops are created to support models in OpenMMLab, the custom ops can be built as follows:

cd ${MMDEPLOY_DIR}
mkdir -p build && cd build
cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn ..
make -j$(nproc)

If you haven't installed ncnn in the default path, please add -Dncnn_DIR flag in cmake.

 cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn -Dncnn_DIR=${NCNN_DIR}/build/install/lib/cmake/ncnn ..
 make -j$(nproc)

Convert model

  • This follows the tutorial on How to convert model.
  • The converted model has two files: .param and .bin, as model structure file and weight file respectively.

Reminder

  • In ncnn version >= 1.0.20220216, the dimension of ncnn.Mat should be no more than 4.

FAQs

  1. When running ncnn models for inference with custom ops, it fails and shows the error message like:

    TypeError: register mm custom layers(): incompatible function arguments. The following argument types are supported:
    1.(ar0: ncnn:Net) -> int

Invoked with: <ncnn.ncnn.Net object at 0x7f7fc4038bb0>

    This is because of the failure to bind ncnn C++ library to pyncnn. You should build pyncnn from C++ ncnn source code, but not by pip install