Extern-C-Opr with MACE

Build MegEngine `load_and_run` for arm64-v8a

cd $MEGENGINE_HOME
./scripts/cmake-build/cross_build_android_arm_inference.sh -a arm64-v8a

After successfully built, load_and_run should be in $MEGENGINE_HOME/build_dir/android/arm64-v8a/Release/install/bin

Build MACE libraries for arm64-v8a with GPU runtime

cd $MACE_HOME
RUNTIME=GPU bash tools/cmake/cmake-build-arm64-v8a.sh

cp -r $MACE_HOME/build/cmake-build/arm64-v8a/install $MEGENGINE_HOME/sdk/c-opr-loaders/mace/arm64-v8a

Build MACE loader for MegEngine

SDK_PATH=/path/to/mace-sdk make

If SDK_PATH is not set, by default it's ./arm64-v8a

You can run with debug mode(by adding DEBUG=1 to make command), which will show more running information

Prepare a MACE model(for example: resnet_50), wrap it with MegEngine extern c opr

python3 dump_model.py path/to/resnet_50.pb path/to/resnet_50.data path/to/resnet_50.mdl path/to/resnet_50.yml

*.pb file denotes the model structure, *.data denotes the model parameters

Check here to learn how to write yml files for MACE

Run with load-and-run

First of all, send all files to the executed device:

load_and_run
resnet_50.mdl
libmace_loader.so
opencl library(something like libOpenCL.so, libmali.so or libEGL.so ...) if you want to run it on GPU

RUNTIME=GPU OPENCPATH=/path/to/opencl DATAFORMAT=NCHW /path/to/load_and_run /path/to/resnet_50.mdl --c-opr-lib /path/to/libmace_loader.so

RUNTIME candidates:

CPU
GPU

Running with GPU runtime on android needs opencl library, one can set OPENCLPATH by using environment variable

We mainly use NCHW data format, if you have NHWC model, use environment DATAFORMAT=NHWC

if you want to run with HEXAGON runtime, more efforts should be made, please check here.

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