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Build for RISC-V

MMDeploy chooses ncnn as the inference backend on RISC-V platform. The deployment process consists of two steps:

Model conversion: Convert the PyTorch model to the ncnn model on the host side, and then upload the converted model to the device.

Model deployment: Compile ncnn and MMDeploy in cross-compilation mode on the host side, and then upload the executable for inference.

1. Model conversion

a) Install MMDeploy

You can refer to Build document to install ncnn inference engine and MMDeploy

b) Convert model

export MODEL_CONFIG=/path/to/mmclassification/configs/resnet/resnet18_8xb32_in1k.py
export MODEL_PATH=https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_8xb32_in1k_20210831-fbbb1da6.pth

# Convert the model
cd /path/to/mmdeploy
python tools/deploy.py \
  configs/mmcls/classification_ncnn_static.py \
  $MODEL_CONFIG \
  $MODEL_PATH \
  tests/data/tiger.jpeg \
  --work-dir resnet18 \
  --device cpu \
  --dump-info

2. Model deployment

a) Download the compiler toolchain and set environment

# download Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz
# https://occ.t-head.cn/community/download?id=4046947553902661632
tar xf Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6-20220516.tar.gz
export RISCV_ROOT_PATH=`realpath Xuantie-900-gcc-linux-5.10.4-glibc-x86_64-V2.2.6`

b) Compile ncnn & opencv

# ncnn
# refer to https://github.com/Tencent/ncnn/wiki/how-to-build#build-for-allwinner-d1

# opencv
git clone https://github.com/opencv/opencv.git
mkdir build_riscv && cd build_riscv
cmake .. \
 -DCMAKE_TOOLCHAIN_FILE=/path/to/mmdeploy/cmake/toolchains/riscv64-unknown-linux-gnu.cmake \
 -DCMAKE_INSTALL_PREFIX=install \
 -DBUILD_PERF_TESTS=OFF \
 -DBUILD_SHARED_LIBS=OFF \
 -DBUILD_TESTS=OFF \
 -DCMAKE_BUILD_TYPE=Release
make -j$(nproc) && make install

c) Compile mmdeploy SDK & demo

cd /path/to/mmdeploy
mkdir build_riscv && cd build_riscv
cmake .. \
  -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/riscv64-unknown-linux-gnu.cmake \
  -DMMDEPLOY_BUILD_SDK=ON \
  -DMMDEPLOY_SHARED_LIBS=OFF \
  -DMMDEPLOY_BUILD_EXAMPLES=ON \
  -DMMDEPLOY_TARGET_DEVICES="cpu" \
  -DMMDEPLOY_TARGET_BACKENDS="ncnn" \
  -Dncnn_DIR=${ncnn_DIR}/build-c906/install/lib/cmake/ncnn/ \
  -DMMDEPLOY_CODEBASES=all \
  -DOpenCV_DIR=${OpenCV_DIR}/build_riscv/install/lib/cmake/opencv4

make -j$(nproc) && make install

After make install, the examples will locate in install\bin

tree -L 1 install/bin/
.
├── image_classification
├── image_restorer
├── image_segmentation
├── object_detection
├── ocr
├── pose_detection
└── rotated_object_detection

4) Run the demo

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.

Copy the model folder(resnet18), executable(image_classification) file and test image(tests/data/tiger.jpeg) to the device.

./image_classification cpu ./resnet18  tiger.jpeg