93 lines
2.5 KiB
Markdown
93 lines
2.5 KiB
Markdown
# ncnn Support
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MMDeploy now supports ncnn version == 1.0.20220216
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## Installation
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### Install ncnn
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- Download VulkanTools for the compilation of ncnn.
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```bash
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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
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tar -xf vulkansdk-linux-x86_64-1.2.176.1.tar.gz
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export VULKAN_SDK=$(pwd)/1.2.176.1/x86_64
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export LD_LIBRARY_PATH=$VULKAN_SDK/lib:$LD_LIBRARY_PATH
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```
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- Check your gcc version.
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You should ensure your gcc satisfies `gcc >= 6`.
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- Install Protocol Buffers through:
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```bash
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apt-get install libprotobuf-dev protobuf-compiler
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```
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- Prepare ncnn Framework
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- Download ncnn source code
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```bash
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git clone -b 20220216 git@github.com:Tencent/ncnn.git
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```
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- <font color=red>Make install</font> ncnn library
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```bash
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cd ncnn
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export NCNN_DIR=$(pwd)
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git submodule update --init
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mkdir -p build && cd build
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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 ..
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make install
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```
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- Install pyncnn module
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```bash
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cd ${NCNN_DIR} # To ncnn root directory
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cd python
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pip install -e .
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```
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### Build custom ops
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Some custom ops are created to support models in OpenMMLab, the custom ops can be built as follows:
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```bash
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cd ${MMDEPLOY_DIR}
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mkdir -p build && cd build
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cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn ..
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make -j$(nproc)
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```
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If you haven't installed ncnn in the default path, please add `-Dncnn_DIR` flag in cmake.
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```bash
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cmake -DMMDEPLOY_TARGET_BACKENDS=ncnn -Dncnn_DIR=${NCNN_DIR}/build/install/lib/cmake/ncnn ..
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make -j$(nproc)
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```
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## Convert model
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- This follows the tutorial on [How to convert model](../02-how-to-run/convert_model.md).
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- The converted model has two files: `.param` and `.bin`, as model structure file and weight file respectively.
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## Reminder
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- In ncnn version >= 1.0.20220216, the dimension of ncnn.Mat should be no more than 4.
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## FAQs
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1. When running ncnn models for inference with custom ops, it fails and shows the error message like:
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```bash
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TypeError: register mm custom layers(): incompatible function arguments. The following argument types are supported:
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1.(ar0: ncnn:Net) -> int
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Invoked with: <ncnn.ncnn.Net object at 0x7f7fc4038bb0>
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```
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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`
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