mmdeploy/docs/en/backends/ncnn.md

ncnn Support

MMDeploy now supports ncnn version == 1.0.20211208

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 20211208 git@github.com: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.

List of supported custom ops

Operator	CPU	MMDeploy Releases
Expand	Y	master
Gather	Y	master
Shape	Y	master
TopK	Y	master

Reminder

• In ncnn version >= 1.0.20201208, 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