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load_and_run
for arm64-v8acd $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
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
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
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
First of all, send all files to the executed device:
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:
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.
MegEngine 安装包中集成了使用 GPU 运行代码所需的 CUDA 环境,不用区分 CPU 和 GPU 版。 如果想要运行 GPU 程序,请确保机器本身配有 GPU 硬件设备并安装好驱动。 如果你想体验在云端 GPU 算力平台进行深度学习开发的感觉,欢迎访问 MegStudio 平台
C++ Cuda Python C SVG other