108 lines
3.1 KiB
Markdown
108 lines
3.1 KiB
Markdown
# 支持 RISC-V
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MMDeploy 选择 ncnn 作为 RISC-V 平台下的推理后端。完整的部署过程包含两个步骤：
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模型转换：在 Host 端将 PyTorch 模型转为 ncnn 模型。并将转换后的模型传到 device。
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模型部署：在 Host 端以交叉编译方式编译 ncnn 和 MMDeploy。传到 Device 端进行推理。
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## 1. 模型转换
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a) 安装 MMDeploy
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可参考 [BUILD 文档](./linux-x86_64.md)，安装 ncnn 推理引擎以及 MMDeploy。
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b) 模型转换
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以 Resnet-18 为例。先参照[文档](https://github.com/open-mmlab/mmclassification)安装 mmcls，然后使用 `tools/deploy.py` 转换模型。
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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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# 模型转换
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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. 模型部署
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a) 下载交叉编译工具链，设置环境变量
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```bash
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# 下载 Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz
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# https://occ.t-head.cn/community/download?id=4046947553902661632
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tar xf Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz
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export RISCV_ROOT_PATH=`realpath Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6`
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```
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b) 编译 ncnn & opencv
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```bash
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# ncnn
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# 可参考 https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-allwinner-d1
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# opencv
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git clone https://github.com/opencv/opencv.git
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mkdir build_riscv && cd build_riscv
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cmake .. \
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-DCMAKE_TOOLCHAIN_FILE=/path/to/mmdeploy/cmake/toolchains/riscv64-unknown-linux-gnu.cmake \
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-DCMAKE_INSTALL_PREFIX=install \
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-DBUILD_PERF_TESTS=OFF \
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-DBUILD_SHARED_LIBS=OFF \
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-DBUILD_TESTS=OFF \
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-DCMAKE_BUILD_TYPE=Release
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make -j$(nproc) && make install
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```
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c) 编译 mmdeploy SDK & demo
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```bash
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cd /path/to/mmdeploy
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mkdir build_riscv && cd build_riscv
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cmake .. \
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-DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/riscv64-unknown-linux-gnu.cmake \
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-DMMDEPLOY_BUILD_SDK=ON \
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-DMMDEPLOY_SHARED_LIBS=OFF \
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-DMMDEPLOY_BUILD_EXAMPLES=ON \
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-DMMDEPLOY_TARGET_DEVICES="cpu" \
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-DMMDEPLOY_TARGET_BACKENDS="ncnn" \
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-Dncnn_DIR=${ncnn_DIR}/build-c906/install/lib/cmake/ncnn/ \
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-DMMDEPLOY_CODEBASES=all \
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-DOpenCV_DIR=${OpenCV_DIR}/build_riscv/install/lib/cmake/opencv4
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make -j$(nproc) && make install
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```
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执行 `make install` 之后, examples的可执行文件会保存在 `install/bin`
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```
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tree -L 1 install/bin/
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.
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├── image_classification
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├── image_restorer
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├── image_segmentation
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├── object_detection
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├── ocr
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├── pose_detection
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└── rotated_object_detection
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```
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d) 运行 demo
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先确认测试模型用了 `--dump-info`，这样 `resnet18` 目录才有 `pipeline.json` 等 SDK 所需文件。
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把 dump 好的模型目录(resnet18)、可执行文件(image_classification)、测试图片(tests/data/tiger.jpeg)拷贝到设备中
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```bash
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./image_classification cpu ./resnet18 tiger.jpeg
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```
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