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ncnn Support

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 [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.

List of supported custom ops

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

Reminder

  • If ncnn version >= 1.0.20201208, the dimension of ncnn.Mat should be no more than 4, or the dimension of the ncnn.Mat should be no more than 3.

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