109 lines
2.8 KiB
Markdown
109 lines
2.8 KiB
Markdown
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# Ubuntu Cross Build aarch64
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mmdeploy chose ncnn as the inference backend for aarch64 embedded linux devices. There are two parts:
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Host
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- model conversion
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- cross build SDK and demo for embedded devices
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Device
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- Run converted model
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## 1. Model Convert on Host
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Refer to the doc to install [mmdeploy](../01-how-to-build/) and [mmcls](https://github.com/open-mmlab/mmclassification), and convert resnet18 for model package
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```bash
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export MODEL_CONFIG=/path/to/mmclassification/configs/resnet/resnet18_8xb32_in1k.py
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export MODEL_PATH=https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth
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# Convert resnet18
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cd /path/to/mmdeploy
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python tools/deploy.py \
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configs/mmcls/classification_ncnn_static.py \
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$MODEL_CONFIG \
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$MODEL_PATH \
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tests/data/tiger.jpeg \
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--work-dir resnet18 \
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--device cpu \
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--dump-info
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```
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## 2. Cross Build on Host
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It is recommended to compile directly with the script
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```bash
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sh -x tools/scripts/ubuntu_cross_build_aarch64.sh
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```
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The following is the manual process corresponding to the script:
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a) Install aarch64 build tools
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```bash
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sudo apt install -y gcc-aarch64-linux-gnu g++-aarch64-linux-gnu
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```
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b) Cross build opencv and install to /tmp/ocv-aarch64
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```bash
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git clone https://github.com/opencv/opencv --depth=1 --branch=4.x --recursive
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cd opencv/platforms/linux/
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mkdir build && cd build
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cmake ../../.. \
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-DCMAKE_INSTALL_PREFIX=/tmp/ocv-aarch64 \
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-DCMAKE_TOOLCHAIN_FILE=../aarch64-gnu.toolchain.cmake
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make -j && make install
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ls -alh /tmp/ocv-aarch64
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..
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```
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c) Cross build ncnn and install to /tmp/ncnn-aarch64
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```bash
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git clone https://github.com/tencent/ncnn --branch 20220729 --depth=1
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mkdir build && cd build
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cmake .. \
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-DCMAKE_TOOLCHAIN_FILE=../toolchains/aarch64-linux-gnu.toolchain.cmake \
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-DCMAKE_INSTALL_PREFIX=/tmp/ncnn-aarch64
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make -j && make install
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ls -alh /tmp/ncnn-aarch64
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..
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```
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d) Cross build mmdeploy
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```bash
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git submodule init
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git submodule update
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mkdir build && cd build
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cmake .. \
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-DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/aarch64-linux-gnu.cmake \
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-DMMDEPLOY_TARGET_DEVICES="cpu" \
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-DMMDEPLOY_TARGET_BACKENDS="ncnn" \
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-Dncnn_DIR=/tmp/ncnn-aarch64/lib/cmake/ncnn \
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-DOpenCV_DIR=/tmp/ocv-aarch64/lib/cmake/opencv4
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make install
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ls -lah install/bin/*
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..
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```
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## 3. Execute on Device
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Make sure that `--dump-info` is used during model conversion, so that the `resnet18` directory contains the files required by the SDK such as `pipeline.json`.
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Copy the model folder(resnet18), executable(image_classification) file, test image(tests/data/tiger.jpeg) and prebuilt OpenCV(/tmp/ocv-aarch64) to the device.
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```bash
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./image_classification cpu ./resnet18 tiger.jpeg
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..
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label: 292, score: 0.9261
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label: 282, score: 0.0726
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label: 290, score: 0.0008
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label: 281, score: 0.0002
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label: 340, score: 0.0001
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```
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