105 lines
3.0 KiB
Markdown
105 lines
3.0 KiB
Markdown
# Build for RISC-V
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MMDeploy chooses ncnn as the inference backend on RISC-V platform. The deployment process consists of two steps:
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Model conversion: Convert the PyTorch model to the ncnn model on the host side, and then upload the converted model to the device.
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Model deployment: Compile ncnn and MMDeploy in cross-compilation mode on the host side, and then upload the executable for inference.
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## 1. Model conversion
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a) Install MMDeploy
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You can refer to [Build document](./linux-x86_64.md) to install ncnn inference engine and MMDeploy
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b) Convert model
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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 the model
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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. Model deployment
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a) Download the compiler toolchain and set environment
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```bash
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# download 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) Compile ncnn & opencv
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```bash
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# ncnn
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# refer to 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$(nproc) && make install
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```
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c) Compile mmdeploy SDK & demo
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```bash
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cd /path/to/mmdeploy
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mkdir build_riscv && cd build_riscv
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cmake .. \
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-DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/riscv64-unknown-linux-gnu.cmake \
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-DMMDEPLOY_BUILD_SDK=ON \
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-DMMDEPLOY_SHARED_LIBS=OFF \
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-DMMDEPLOY_BUILD_EXAMPLES=ON \
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-DMMDEPLOY_TARGET_DEVICES="cpu" \
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-DMMDEPLOY_TARGET_BACKENDS="ncnn" \
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-Dncnn_DIR=${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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After `make install`, the examples will locate in `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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### 4) Run the demo
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First make sure that `--dump-info` is used during convert model, so that the `resnet18` directory has the files required by the SDK such as `pipeline.json`.
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Copy the model folder(resnet18), executable(image_classification) file and test image(tests/data/tiger.jpeg) 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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