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 -r

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

MGB_MACE_RUNTIME=GPU MGB_MACE_OPENCL_PATH=/path/to/opencl MGB_MACE_LOADER_FORMAT=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 MGB_MACE_OPENCL_PATH by using environment variable

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

For CPU runtime, default running thread is 1, could be specified with MGB_MACE_NR_THREADS=n

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

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