# ncnn Support

MMDeploy now supports ncnn version == 1.0.20220216

## Installation

### Install ncnn

- Download VulkanTools for the compilation of ncnn.

  ```bash
  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:

  ```bash
  apt-get install libprotobuf-dev protobuf-compiler
  ```

- Prepare ncnn Framework

  - Download ncnn source code

    ```bash
    git clone -b 20220216 git@github.com:Tencent/ncnn.git
    ```

  - <font color=red>Make install</font> ncnn library

    ```bash
    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

    ```bash
    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:

```bash
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.

```bash
 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](../02-how-to-run/convert_model.md).
- 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:

   ```bash
   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`