mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-06-02 23:39:58 +00:00
[Android NNAPI EP] Remove dependency on external JD/DNNLibrary (#4576)
* remove dependency of external jd-dnnlibrary * remove extra variables not used any more * update /cgmanifest.json
This commit is contained in:
gwang-msft
GitHub
parent
f0edd074fb
commit
c2ec3b734b
23 changed files with 40 additions and 613 deletions
3
.gitmodules
vendored
3
.gitmodules
vendored
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@ -25,9 +25,6 @@
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[submodule "cmake/external/eigen"]
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path = cmake/external/eigen
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url = https://gitlab.com/libeigen/eigen.git
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[submodule "cmake/external/DNNLibrary"]
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path = cmake/external/DNNLibrary
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url = https://github.com/JDAI-CV/DNNLibrary
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[submodule "cmake/external/horovod"]
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path = cmake/external/horovod
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url = https://github.com/horovod/horovod.git
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48
BUILD.md
48
BUILD.md
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@ -257,13 +257,13 @@ DNNL: `./build.sh --use_dnnl`
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#### Deprecation Notice
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| | |
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| --- | --- |
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| --- | --- |
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| Deprecation Begins | June 1, 2020 |
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| Removal Date | December 1, 2020 |
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Starting with the OpenVINO™ toolkit 2020.2 release, all of the features previously available through nGraph have been merged into the OpenVINO™ toolkit. As a result, all the features previously available through ONNX RT Execution Provider for nGraph have been merged with ONNX RT Execution Provider for OpenVINO™ toolkit.
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Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.
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Therefore, ONNX RT Execution Provider for **nGraph** will be deprecated starting June 1, 2020 and will be completely removed on December 1, 2020. Users are recommended to migrate to the ONNX RT Execution Provider for OpenVINO™ toolkit as the unified solution for all AI inferencing on Intel® hardware.
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See more information on the nGraph Execution Provider [here](./docs/execution_providers/nGraph-ExecutionProvider.md).
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@ -345,31 +345,6 @@ For more information on OpenVINO Execution Provider's ONNX Layer support, To
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---
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### Android NNAPI
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See more information on the NNAPI Execution Provider [here](./docs/execution_providers/NNAPI-ExecutionProvider.md).
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#### Prerequisites
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To build ONNX Runtime with the NN API EP, first install Android NDK (see [Android Build instructions](#android))
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#### Build Instructions
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The basic build commands are below. There are also some other parameters for building the Android version. See [Android Build instructions](#android) for more details.
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##### Cross compiling on Windows
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```bash
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./build.bat --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --use_dnnlibrary
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```
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##### Cross compiling on Linux
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```bash
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./build.sh --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --use_dnnlibrary
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```
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---
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### NUPHAR
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See more information on the Nuphar Execution Provider [here](./docs/execution_providers/Nuphar-ExecutionProvider.md).
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@ -983,7 +958,7 @@ Install an NDK version
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- NDK path in our example with this install would be `.../Android/ndk/21.1.6352462`
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- NOTE: If you install the ndk-bundle package the path will be `.../Android/ndk-bundle` as there's no version number
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#### Build Instructions
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#### Android Build Instructions
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##### Cross compiling on Windows
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@ -998,14 +973,23 @@ e.g. using the paths from our example
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./build.bat --android --android_sdk_path .../Android --android_ndk_path .../Android/ndk/21.1.6352462 --android_abi arm64-v8a --android_api 27 --cmake_generator Ninja
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```
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##### Cross compiling on Linux
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##### Cross compiling on Linux and macOS
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```
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./build.sh --android --android_sdk_path <android sdk path> --android_ndk_path <android ndk path> --android_abi <android abi, e.g., arm64-v8a (default) or armeabi-v7a> --android_api <android api level, e.g., 27 (default)>
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```
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Android Archive (AAR) files, which can be imported directly in Android Studio, will be generated in your_build_dir/java/build/outputs/aar.
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If you want to use NNAPI Execution Provider on Android, see [docs/execution_providers/NNAPI-ExecutionProvider.md](/docs/execution_providers/NNAPI-ExecutionProvider.md).
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##### Build Android Archive (AAR)
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Android Archive (AAR) files, which can be imported directly in Android Studio, will be generated in your_build_dir/java/build/outputs/aar, by using the above building commands with `--build_java`
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#### Android NNAPI Execution Provider
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If you want to use NNAPI Execution Provider on Android, see [NNAPI Execution Provider](/docs/execution_providers/NNAPI-ExecutionProvider.md).
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##### Build Instructions
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Android NNAPI Execution Provider can be built using building commands in [Android Build instructions](#android-build-instructions) with `--use_nnapi`
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---
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@ -1014,7 +998,7 @@ If you want to use NNAPI Execution Provider on Android, see [docs/execution_prov
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See more information on the MIGraphX Execution Provider [here](./docs/execution_providers/MIGraphX-ExecutionProvider.md).
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#### Prerequisites
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* Install [ROCM](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html)
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* Install [ROCM](https://rocmdocs.amd.com/en/latest/Installation_Guide/Installation-Guide.html)
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* The MIGraphX execution provider for ONNX Runtime is built and tested with ROCM3.3
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* Install [MIGraphX](https://github.com/ROCmSoftwarePlatform/AMDMIGraphX)
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* The path to MIGraphX installation must be provided via the `--migraphx_home parameter`.
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@ -173,15 +173,6 @@
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}
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "647d4c3f4d47d9cf63fb90ec175c414a005adea7",
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"repositoryUrl": "https://github.com/JDAI-CV/DNNLibrary.git"
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}
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}
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},
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{
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"component": {
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"Type": "other",
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@ -308,4 +299,4 @@
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}
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],
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"Version": 1
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}
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}
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@ -1,126 +1,6 @@
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{
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"Version": 1,
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"Registrations": [
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "e17f11e966b2cce7d747799b76bb9843813d4b01",
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"repositoryUrl": "https://github.com/JDAI-CV/DNNLibrary"
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},
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"comments": "git submodule at cmake/external/DNNLibrary"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "9e7e8cbe9f675123dd41b7c62868acad39188cae",
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"repositoryUrl": "https://github.com/google/flatbuffers"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/flatbuffers"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "8d7a107d68c127f3f494bb7807b796c8c5a97a82",
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"repositoryUrl": "https://github.com/google/glog"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/glog"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "94d238d96e3fb3a7ba34f03c284b9ad3516163be",
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"repositoryUrl": "https://github.com/onnx/onnx"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/onnx"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "e776aa0275e293707b6a0901e0e8d8a8a3679508",
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"repositoryUrl": "https://github.com/google/benchmark.git"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/onnx/third_party/benchmark"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "a1041190c8b8ff0cd9e2f0752248ad5e3789ea0c",
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"repositoryUrl": "https://github.com/pybind/pybind11.git"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/onnx/third_party/pybind11"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5",
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"repositoryUrl": "https://github.com/wjakob/clang-cindex-python3"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/onnx/third_party/pybind11/tools/clang"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "6973c3a5041636c1d8dc5f7f6c8c1f3c15bc63d6",
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"repositoryUrl": "https://github.com/google/protobuf/"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/protobuf"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "5b7683f49e1e9223cf9927b24f6fd3d6bd82e3f8",
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"repositoryUrl": "https://github.com/google/benchmark.git"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/protobuf/third_party/benchmark"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "c3bb0ee2a63279a803aaad956b9b26d74bf9e6e2",
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"repositoryUrl": "https://github.com/google/googletest.git"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/protobuf/third_party/googletest"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "9bb3313162c0b856125e481ceece9d8faa567716",
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"repositoryUrl": "https://github.com/pybind/pybind11"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/pybind11"
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}
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},
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{
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"component": {
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"type": "git",
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"git": {
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"commitHash": "6a00cbc4a9b8e68b71caf7f774b3f9c753ae84d5",
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"repositoryUrl": "https://github.com/wjakob/clang-cindex-python3"
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},
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"comments": "git submodule at cmake/external/DNNLibrary/third_party/pybind11/tools/clang"
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}
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},
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{
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"component": {
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"type": "git",
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@ -50,7 +50,6 @@ option(onnxruntime_ENABLE_MEMLEAK_CHECKER "Experimental: Enable memory leak chec
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option(onnxruntime_USE_CUDA "Build with CUDA support" OFF)
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option(onnxruntime_USE_OPENVINO "Build with OpenVINO support" OFF)
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option(onnxruntime_USE_EIGEN_FOR_BLAS "Use eign for blas" ON)
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option(onnxruntime_USE_NNAPI_DNNLIBRARY "Build with DNNLibrary for Android NNAPI support" OFF)
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option(onnxruntime_USE_NNAPI_BUILTIN "Build with builtin NNAPI lib for Android NNAPI support" OFF)
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option(onnxruntime_USE_RKNPU "Build with RKNPU support" OFF)
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option(onnxruntime_USE_DNNL "Build with DNNL support" OFF)
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@ -199,10 +198,6 @@ if(onnxruntime_ENABLE_LTO)
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endif()
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endif()
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if(onnxruntime_USE_NNAPI_BUILTIN AND onnxruntime_USE_NNAPI_DNNLIBRARY)
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message(FATAL_ERROR "Please use only one of onnxruntime_USE_NNAPI_BUILTIN, onnxruntime_USE_NNAPI_DNNLIBRARY")
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endif()
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if(onnxruntime_DISABLE_RTTI)
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add_compile_definitions(ORT_NO_RTTI GOOGLE_PROTOBUF_NO_RTTI)
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if(MSVC)
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@ -924,7 +919,7 @@ if (onnxruntime_USE_TENSORRT)
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# needs to link with stdc++fs in Linux
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if (NOT APPLE)
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list(APPEND onnxruntime_EXTERNAL_LIBRARIES stdc++fs)
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endif()
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endif()
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endif()
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endif()
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@ -998,7 +993,7 @@ if (onnxruntime_ENABLE_TRAINING)
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if(NOT DEFINED onnxruntime_MPI_HOME)
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execute_process(COMMAND mpirun --version OUTPUT_VARIABLE MPIRUN_OUTPUT)
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else()
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execute_process(COMMAND ${onnxruntime_MPI_HOME}/bin/mpirun --version OUTPUT_VARIABLE MPIRUN_OUTPUT)
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execute_process(COMMAND ${onnxruntime_MPI_HOME}/bin/mpirun --version OUTPUT_VARIABLE MPIRUN_OUTPUT)
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endif(NOT DEFINED onnxruntime_MPI_HOME)
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string( REGEX MATCH "[0-9]+.[0-9]+.[0-9]" MPI_VERSION ${MPIRUN_OUTPUT})
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message( STATUS "MPI Version: ${MPI_VERSION}")
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1
cmake/external/DNNLibrary
vendored
1
cmake/external/DNNLibrary
vendored
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@ -1 +0,0 @@
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Subproject commit e17f11e966b2cce7d747799b76bb9843813d4b01
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@ -76,7 +76,7 @@ endif()
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if (onnxruntime_USE_TENSORRT)
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target_compile_definitions(onnxruntime4j_jni PRIVATE USE_TENSORRT=1)
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endif()
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if (onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
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if (onnxruntime_USE_NNAPI_BUILTIN)
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target_compile_definitions(onnxruntime4j_jni PRIVATE USE_NNAPI=1)
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endif()
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if (onnxruntime_USE_NUPHAR)
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@ -59,7 +59,7 @@ if(onnxruntime_USE_TENSORRT)
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set(PROVIDERS_TENSORRT onnxruntime_providers_tensorrt)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES tensorrt)
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endif()
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if(onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
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if(onnxruntime_USE_NNAPI_BUILTIN)
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set(PROVIDERS_NNAPI onnxruntime_providers_nnapi)
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list(APPEND ONNXRUNTIME_PROVIDER_NAMES nnapi)
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endif()
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@ -159,7 +159,7 @@ if (onnxruntime_ENABLE_TRAINING)
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if (onnxruntime_USE_HOROVOD)
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target_include_directories(onnxruntime_providers PRIVATE ${HOROVOD_INCLUDE_DIRS})
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endif()
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if (onnxruntime_USE_NCCL OR onnxruntime_USE_HOROVOD)
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if (onnxruntime_USE_NCCL OR onnxruntime_USE_HOROVOD)
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target_include_directories(onnxruntime_providers PUBLIC ${MPI_INCLUDE_DIRS})
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endif()
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endif()
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@ -509,36 +509,8 @@ if (onnxruntime_USE_OPENVINO)
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endif()
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if (onnxruntime_USE_NNAPI_DNNLIBRARY)
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if (onnxruntime_USE_NNAPI_BUILTIN)
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add_definitions(-DUSE_NNAPI=1)
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add_definitions(-DUSE_NNAPI_DNNLIBRARY=1)
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option(DNN_READ_ONNX "" ON)
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set(DNN_CUSTOM_PROTOC_EXECUTABLE ${ONNX_CUSTOM_PROTOC_EXECUTABLE})
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option(DNN_CMAKE_INSTALL "" OFF)
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option(DNN_BUILD_BIN "" OFF)
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add_subdirectory(${REPO_ROOT}/cmake/external/DNNLibrary)
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file(GLOB
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onnxruntime_providers_nnapi_cc_srcs CONFIGURE_DEPENDS
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"${ONNXRUNTIME_ROOT}/core/providers/nnapi/*.cc"
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"${ONNXRUNTIME_ROOT}/core/providers/nnapi/nnapi_dnnlibrary/*.h"
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"${ONNXRUNTIME_ROOT}/core/providers/nnapi/nnapi_dnnlibrary/*.cc"
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)
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source_group(TREE ${ONNXRUNTIME_ROOT}/core FILES ${onnxruntime_providers_nnapi_cc_srcs})
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add_library(onnxruntime_providers_nnapi ${onnxruntime_providers_nnapi_cc_srcs})
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onnxruntime_add_include_to_target(onnxruntime_providers_nnapi onnxruntime_common onnxruntime_framework onnx onnx_proto protobuf::libprotobuf-lite dnnlibrary::dnnlibrary)
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target_link_libraries(onnxruntime_providers_nnapi dnnlibrary::dnnlibrary)
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add_dependencies(onnxruntime_providers_nnapi
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dnnlibrary::dnnlibrary
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onnx ${onnxruntime_EXTERNAL_DEPENDENCIES})
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# Header files of DNNLibrary requires C++17, fortunately, all modern Android NDKs support C++17
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set_target_properties(onnxruntime_providers_nnapi PROPERTIES CXX_STANDARD 17)
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set_target_properties(onnxruntime_providers_nnapi PROPERTIES CXX_STANDARD_REQUIRED ON)
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set_target_properties(onnxruntime_providers_nnapi PROPERTIES FOLDER "ONNXRuntime")
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target_include_directories(onnxruntime_providers_nnapi PRIVATE ${ONNXRUNTIME_ROOT} ${nnapi_INCLUDE_DIRS})
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set_target_properties(onnxruntime_providers_nnapi PROPERTIES LINKER_LANGUAGE CXX)
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elseif (onnxruntime_USE_NNAPI_BUILTIN)
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add_definitions(-DUSE_NNAPI=1)
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add_definitions(-DUSE_NNAPI_BUILTIN=1)
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file(GLOB
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onnxruntime_providers_nnapi_cc_srcs_top CONFIGURE_DEPENDS
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"${ONNXRUNTIME_ROOT}/core/providers/nnapi/*.cc"
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@ -207,7 +207,7 @@ if (onnxruntime_USE_NGRAPH)
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list(APPEND onnxruntime_test_providers_src ${onnxruntime_test_providers_ngraph_src})
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endif()
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if (onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
|
||||
if (onnxruntime_USE_NNAPI_BUILTIN)
|
||||
file(GLOB_RECURSE onnxruntime_test_providers_nnapi_src CONFIGURE_DEPENDS
|
||||
"${TEST_SRC_DIR}/providers/nnapi/*"
|
||||
)
|
||||
|
|
@ -303,7 +303,7 @@ if(onnxruntime_USE_OPENVINO)
|
|||
list(APPEND onnxruntime_test_providers_dependencies onnxruntime_providers_openvino)
|
||||
endif()
|
||||
|
||||
if(onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
|
||||
if(onnxruntime_USE_NNAPI_BUILTIN)
|
||||
list(APPEND onnxruntime_test_providers_dependencies onnxruntime_providers_nnapi)
|
||||
endif()
|
||||
|
||||
|
|
@ -393,7 +393,7 @@ if(onnxruntime_USE_TENSORRT)
|
|||
list(APPEND onnxruntime_test_providers_libs onnxruntime_providers_tensorrt)
|
||||
endif()
|
||||
|
||||
if(onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
|
||||
if(onnxruntime_USE_NNAPI_BUILTIN)
|
||||
list(APPEND onnxruntime_test_framework_src_patterns ${TEST_SRC_DIR}/providers/nnapi/*)
|
||||
list(APPEND onnxruntime_test_framework_libs onnxruntime_providers_nnapi)
|
||||
list(APPEND onnxruntime_test_providers_dependencies onnxruntime_providers_nnapi)
|
||||
|
|
@ -692,9 +692,6 @@ add_test(NAME onnx_test_pytorch_operator
|
|||
|
||||
if (CMAKE_SYSTEM_NAME STREQUAL "Android")
|
||||
list(APPEND android_shared_libs log android)
|
||||
if (onnxruntime_USE_NNAPI_DNNLIBRARY)
|
||||
list(APPEND android_shared_libs neuralnetworks)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
#perf test runner
|
||||
|
|
|
|||
|
|
@ -1,14 +1,14 @@
|
|||
# NNAPI Execution Provider
|
||||
|
||||
[Android Neural Networks API (NNAPI)](https://developer.android.com/ndk/guides/neuralnetworks) is a unified interface to CPU, GPU, and NN accelerators on Android. It is supported by onnxruntime via [DNNLibrary](https://github.com/JDAI-CV/DNNLibrary).
|
||||
[Android Neural Networks API (NNAPI)](https://developer.android.com/ndk/guides/neuralnetworks) is a unified interface to CPU, GPU, and NN accelerators on Android.
|
||||
|
||||
## Minimum requirements
|
||||
|
||||
The NNAPI EP requires Android devices with Android 8.1 or higher.
|
||||
The NNAPI EP requires Android devices with Android 8.1 or higher, it is recommended to use Android devices with Android 9 or higher to achieve optimal performance.
|
||||
|
||||
## Build NNAPI EP
|
||||
|
||||
For build instructions, please see the [BUILD page](../../BUILD.md#Android-NNAPI).
|
||||
For build instructions, please see the [BUILD page](../../BUILD.md#Android-NNAPI-Execution-Provider).
|
||||
|
||||
## Using NNAPI EP in C/C++
|
||||
|
||||
|
|
@ -27,11 +27,3 @@ session_object.RegisterExecutionProvider(std::make_unique<::onnxruntime::NnapiEx
|
|||
status = session_object.Load(model_file_name);
|
||||
```
|
||||
The C API details are [here](../C_API.md#c-api).
|
||||
|
||||
## Performance
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
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|
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|
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|
Before Width: | Height: | Size: 39 KiB |
|
|
@ -1,328 +0,0 @@
|
|||
// Copyright 2019 JD.com Inc. JD AI
|
||||
|
||||
#include "nnapi_execution_provider.h"
|
||||
#include "core/framework/allocatormgr.h"
|
||||
#include "core/framework/compute_capability.h"
|
||||
#include "core/session/onnxruntime_cxx_api.h"
|
||||
#include "core/session/inference_session.h"
|
||||
#include "core/graph/model.h"
|
||||
#include "dnnlibrary/ModelBuilder.h"
|
||||
#include "dnnlibrary/OnnxReader.h"
|
||||
#include "tools/onnx2daq/OnnxConverter.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
|
||||
constexpr const char* NNAPI = "Nnapi";
|
||||
|
||||
NnapiExecutionProvider::NnapiExecutionProvider()
|
||||
: IExecutionProvider{onnxruntime::kNnapiExecutionProvider} {
|
||||
DeviceAllocatorRegistrationInfo device_info(
|
||||
{OrtMemTypeDefault,
|
||||
[](int) {
|
||||
return onnxruntime::make_unique<CPUAllocator>(OrtMemoryInfo(NNAPI, OrtAllocatorType::OrtDeviceAllocator));
|
||||
},
|
||||
std::numeric_limits<size_t>::max()});
|
||||
|
||||
InsertAllocator(CreateAllocator(device_info));
|
||||
|
||||
DeviceAllocatorRegistrationInfo cpu_memory_info(
|
||||
{OrtMemTypeCPUOutput,
|
||||
[](int) {
|
||||
return onnxruntime::make_unique<CPUAllocator>(
|
||||
OrtMemoryInfo(NNAPI, OrtAllocatorType::OrtDeviceAllocator, OrtDevice(), 0, OrtMemTypeCPUOutput));
|
||||
},
|
||||
std::numeric_limits<size_t>::max()});
|
||||
|
||||
InsertAllocator(CreateAllocator(cpu_memory_info));
|
||||
}
|
||||
|
||||
NnapiExecutionProvider::~NnapiExecutionProvider() {}
|
||||
|
||||
std::vector<std::vector<int>> NnapiExecutionProvider::GetSupportedNodes(const ONNX_NAMESPACE::ModelProto& model_proto) const {
|
||||
dnn::OnnxConverter converter;
|
||||
const auto nodes = converter.GetSupportedNodes(model_proto);
|
||||
if (!nodes) {
|
||||
return {{}};
|
||||
}
|
||||
return nodes.value();
|
||||
}
|
||||
|
||||
std::vector<std::unique_ptr<ComputeCapability>>
|
||||
NnapiExecutionProvider::GetCapability(const onnxruntime::GraphViewer& graph,
|
||||
const std::vector<const KernelRegistry*>& /*kernel_registries*/) const {
|
||||
// This method is based on that of TRT EP
|
||||
// Construct modelproto from graph
|
||||
onnxruntime::Model model(graph.Name(), true, ModelMetaData(), PathString(), IOnnxRuntimeOpSchemaRegistryList(),
|
||||
graph.DomainToVersionMap(), std::vector<ONNX_NAMESPACE::FunctionProto>(), *GetLogger());
|
||||
onnxruntime::Graph& graph_build = model.MainGraph();
|
||||
const std::vector<NodeIndex>& node_index = graph.GetNodesInTopologicalOrder();
|
||||
std::set<NodeArg*> all_node_inputs;
|
||||
for (const auto& node : graph.Nodes()) {
|
||||
std::vector<onnxruntime::NodeArg*> inputs, outputs;
|
||||
for (auto input : node.InputDefs()) {
|
||||
auto& n_input = graph_build.GetOrCreateNodeArg(input->Name(), input->TypeAsProto());
|
||||
inputs.push_back(&n_input);
|
||||
all_node_inputs.insert(&n_input);
|
||||
}
|
||||
for (auto output : node.OutputDefs()) {
|
||||
auto& n_output = graph_build.GetOrCreateNodeArg(output->Name(), output->TypeAsProto());
|
||||
outputs.push_back(&n_output);
|
||||
}
|
||||
graph_build.AddNode(node.Name(), node.OpType(), node.Description(), inputs, outputs, &node.GetAttributes(), node.Domain());
|
||||
}
|
||||
const auto graph_outputs = graph.GetOutputs();
|
||||
//Add initializer to graph
|
||||
const auto& init_tensors = graph.GetAllInitializedTensors();
|
||||
for (const auto& tensor : init_tensors) {
|
||||
graph_build.AddInitializedTensor(*(tensor.second));
|
||||
}
|
||||
|
||||
ORT_ENFORCE(graph_build.Resolve().IsOK());
|
||||
ONNX_NAMESPACE::ModelProto model_proto = model.ToProto();
|
||||
model_proto.set_ir_version(ONNX_NAMESPACE::Version::IR_VERSION);
|
||||
|
||||
const auto supported_nodes_vector = GetSupportedNodes(model_proto);
|
||||
|
||||
std::unique_ptr<IndexedSubGraph> sub_graph = onnxruntime::make_unique<IndexedSubGraph>();
|
||||
|
||||
// Find inputs, initializers and outputs for each supported subgraph
|
||||
std::vector<std::unique_ptr<ComputeCapability>> result;
|
||||
|
||||
int counter = 0;
|
||||
|
||||
for (const auto& group : supported_nodes_vector) {
|
||||
if (!group.empty()) {
|
||||
std::unordered_set<size_t> node_set;
|
||||
node_set.reserve(group.size());
|
||||
for (const auto& index : group) {
|
||||
node_set.insert(node_index[index]);
|
||||
}
|
||||
std::unique_ptr<IndexedSubGraph> sub_graph = onnxruntime::make_unique<IndexedSubGraph>();
|
||||
// Find inputs and outputs of the subgraph
|
||||
std::unordered_map<const NodeArg*, int> fused_inputs, fused_outputs, fused_outputs_to_add;
|
||||
std::unordered_set<const NodeArg*> erased;
|
||||
int input_order = 0;
|
||||
int output_order = 0;
|
||||
|
||||
for (const auto& index : group) {
|
||||
sub_graph->nodes.push_back(node_index[index]);
|
||||
const auto& node = graph.GetNode(node_index[index]);
|
||||
|
||||
for (const auto& input : node->InputDefs()) {
|
||||
const auto& it = fused_outputs.find(input);
|
||||
|
||||
if (it != fused_outputs.end()) {
|
||||
fused_outputs.erase(it);
|
||||
erased.insert(input);
|
||||
}
|
||||
//only when input is neither in output list nor erased list, add the input to input list
|
||||
else if (erased.find(input) == erased.end()) {
|
||||
fused_inputs[input] = input_order++;
|
||||
}
|
||||
}
|
||||
|
||||
// For output searching, there is a special case:
|
||||
// If node's OutputEdges are more than its outputs, meaning certain output is used more than once,
|
||||
// if the output is connected to nodes that don't belong to the subgraph, the output need to be added
|
||||
// to the output list
|
||||
if (node->GetOutputEdgesCount() > node->OutputDefs().size()) {
|
||||
for (auto it = node->OutputEdgesBegin(), end = node->OutputEdgesEnd(); it != end; ++it) {
|
||||
const auto& node_idx = it->GetNode().Index();
|
||||
const auto& output = (it->GetNode()).InputDefs()[it->GetDstArgIndex()];
|
||||
|
||||
if (node_set.find(node_idx) != node_set.end()) {
|
||||
const auto& iter = fused_inputs.find(output);
|
||||
|
||||
if (iter != fused_inputs.end()) {
|
||||
fused_inputs.erase(iter);
|
||||
erased.insert(output);
|
||||
} else if (erased.find(output) == erased.end()) {
|
||||
fused_outputs[output] = output_order++;
|
||||
}
|
||||
} else {
|
||||
fused_outputs_to_add[output] = output_order++;
|
||||
}
|
||||
}
|
||||
} else {
|
||||
for (const auto& output : node->OutputDefs()) {
|
||||
const auto& it = fused_inputs.find(output);
|
||||
|
||||
if (it != fused_inputs.end()) {
|
||||
fused_inputs.erase(it);
|
||||
erased.insert(output);
|
||||
}
|
||||
// only when output is neither in input list nor erased list, add the output to output list
|
||||
else if (erased.find(output) == erased.end()) {
|
||||
fused_outputs[output] = output_order++;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
fused_outputs.insert(fused_outputs_to_add.begin(), fused_outputs_to_add.end());
|
||||
|
||||
// Sort inputs and outputs by the order they were added
|
||||
std::multimap<int, const NodeArg*> inputs, outputs;
|
||||
|
||||
for (auto it = fused_inputs.begin(), end = fused_inputs.end(); it != end; ++it) {
|
||||
inputs.insert(std::pair<int, const NodeArg*>(it->second, it->first));
|
||||
}
|
||||
|
||||
for (auto it = fused_outputs.begin(), end = fused_outputs.end(); it != end; ++it) {
|
||||
for (const auto& x : all_node_inputs) {
|
||||
if (x->Name() == it->first->Name()) {
|
||||
outputs.insert(std::pair<int, const NodeArg*>(it->second, it->first));
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (std::find(graph_outputs.begin(), graph_outputs.end(), it->first) != graph_outputs.end()) {
|
||||
outputs.insert(std::pair<int, const NodeArg*>(it->second, it->first));
|
||||
}
|
||||
}
|
||||
|
||||
// Assign inputs and outputs to subgraph's meta_def
|
||||
auto meta_def = onnxruntime::make_unique<::onnxruntime::IndexedSubGraph::MetaDef>();
|
||||
meta_def->name = "NNAPI_" + std::to_string(counter++);
|
||||
meta_def->domain = kMSDomain;
|
||||
|
||||
for (const auto& input : inputs) {
|
||||
meta_def->inputs.push_back(input.second->Name());
|
||||
}
|
||||
|
||||
for (const auto& output : outputs) {
|
||||
meta_def->outputs.push_back(output.second->Name());
|
||||
}
|
||||
|
||||
meta_def->since_version = 1;
|
||||
sub_graph->SetMetaDef(meta_def);
|
||||
|
||||
result.push_back(onnxruntime::make_unique<ComputeCapability>(std::move(sub_graph)));
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
common::Status NnapiExecutionProvider::Compile(const std::vector<onnxruntime::Node*>& fused_nodes,
|
||||
std::vector<NodeComputeInfo>& node_compute_funcs) {
|
||||
for (const auto* fused_node : fused_nodes) {
|
||||
// Reconstruct graph proto from fused node's function body
|
||||
const auto* func_body = fused_node->GetFunctionBody();
|
||||
if (!func_body) {
|
||||
return common::Status(common::ONNXRUNTIME, common::INVALID_ARGUMENT, "Function body is empty");
|
||||
}
|
||||
const Graph& graph_body = func_body->Body();
|
||||
onnxruntime::Model model(graph_body.Name(), true, ModelMetaData(), PathString(),
|
||||
IOnnxRuntimeOpSchemaRegistryList(), graph_body.DomainToVersionMap(),
|
||||
std::vector<ONNX_NAMESPACE::FunctionProto>(), *GetLogger());
|
||||
ONNX_NAMESPACE::ModelProto model_proto = model.ToProto();
|
||||
*(model_proto.mutable_graph()) = graph_body.ToGraphProto();
|
||||
model_proto.set_ir_version(ONNX_NAMESPACE::Version::IR_VERSION);
|
||||
|
||||
dnn::OnnxReader onnx_reader;
|
||||
dnn::ModelBuilder model_builder;
|
||||
onnx_reader.ReadOnnx(model_proto, model_builder);
|
||||
model_builder.AllowFp16(true);
|
||||
auto dnn_model = model_builder.Compile(model_builder.PREFERENCE_SUSTAINED_SPEED);
|
||||
dnn_models_.emplace(fused_node->Name(), std::move(dnn_model));
|
||||
|
||||
NodeComputeInfo compute_info;
|
||||
compute_info.create_state_func = [&](ComputeContext* context, FunctionState* state) {
|
||||
*state = dnn_models_[context->node_name].get();
|
||||
return 0;
|
||||
};
|
||||
|
||||
compute_info.release_state_func = [](FunctionState state) {
|
||||
// the `state` is a dnn::model managed by unique_ptr
|
||||
ORT_UNUSED_PARAMETER(state);
|
||||
};
|
||||
|
||||
compute_info.compute_func = [](FunctionState state, const OrtCustomOpApi* api, OrtKernelContext* context) {
|
||||
Ort::CustomOpApi ort{*api};
|
||||
dnn::Model* model = reinterpret_cast<dnn::Model*>(state);
|
||||
const size_t num_inputs = ort.KernelContext_GetInputCount(context);
|
||||
const size_t num_outputs = ort.KernelContext_GetOutputCount(context);
|
||||
ORT_ENFORCE(model->GetInputs().size() <= num_inputs, "Inconsistent input sizes");
|
||||
ORT_ENFORCE(model->GetOutputs().size() == num_outputs, "Inconsistent output sizes");
|
||||
// Maintain the created nhwc buffers so that they can be deleted after inferencing
|
||||
std::vector<float*> nhwc_inputs;
|
||||
std::vector<std::tuple<size_t, float*, std::vector<int64_t>>> nhwc_outputs;
|
||||
for (size_t i = 0; i < num_outputs; i++) {
|
||||
const auto output_name = model->GetOutputs()[i];
|
||||
const auto output_shape = model->GetShape(output_name);
|
||||
std::vector<int64_t> int64_output_shape(output_shape.begin(), output_shape.end());
|
||||
if (int64_output_shape.size() == 4) {
|
||||
// NHWC to NCHW
|
||||
std::swap(int64_output_shape[1], int64_output_shape[3]);
|
||||
std::swap(int64_output_shape[2], int64_output_shape[3]);
|
||||
float* nhwc_output = new float[model->GetSize(output_name)];
|
||||
model->SetOutputBuffer(i, nhwc_output);
|
||||
nhwc_outputs.push_back(std::make_tuple(i, nhwc_output, int64_output_shape));
|
||||
} else {
|
||||
auto* output_tensor = ort.KernelContext_GetOutput(context, i, int64_output_shape.data(), int64_output_shape.size());
|
||||
model->SetOutputBuffer(i, ort.GetTensorMutableData<float>(output_tensor));
|
||||
}
|
||||
}
|
||||
std::vector<float*> inputs;
|
||||
for (size_t i = 0; i < model->GetInputs().size(); i++) {
|
||||
const OrtValue* input_tensor = ort.KernelContext_GetInput(context, i);
|
||||
float* input = const_cast<float*>(ort.GetTensorData<float>(input_tensor));
|
||||
|
||||
const auto tensor_info = ort.GetTensorTypeAndShape(input_tensor);
|
||||
const auto& tensor_shape = ort.GetTensorShape(tensor_info);
|
||||
|
||||
if (tensor_shape.size() == 4) {
|
||||
// Transpose nchw -> nhwc manually
|
||||
const int N = tensor_shape[0], C = tensor_shape[1], H = tensor_shape[2], W = tensor_shape[3];
|
||||
float* nhwc_input = new float[N * C * H * W];
|
||||
for (int n = 0; n < N; n++) {
|
||||
for (int c = 0; c < C; c++) {
|
||||
for (int h = 0; h < H; h++) {
|
||||
for (int w = 0; w < W; w++) {
|
||||
nhwc_input[n * H * W * C + h * W * C + w * C + c] = input[n * C * H * W + c * H * W + h * W + w];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
inputs.push_back(nhwc_input);
|
||||
nhwc_inputs.push_back(nhwc_input);
|
||||
} else {
|
||||
inputs.push_back(input);
|
||||
}
|
||||
ort.ReleaseTensorTypeAndShapeInfo(tensor_info);
|
||||
}
|
||||
model->Predict(inputs);
|
||||
// Transpose nhwc -> nchw manually
|
||||
for (size_t i = 0; i < nhwc_outputs.size(); i++) {
|
||||
const auto output = nhwc_outputs[i];
|
||||
size_t index;
|
||||
float* nhwc_data;
|
||||
std::vector<int64_t> nchw_shape;
|
||||
std::tie(index, nhwc_data, nchw_shape) = output;
|
||||
auto* output_tensor = ort.KernelContext_GetOutput(context, index, nchw_shape.data(), nchw_shape.size());
|
||||
const int N = nchw_shape[0], C = nchw_shape[1], H = nchw_shape[2], W = nchw_shape[3];
|
||||
float* nchw_output = ort.GetTensorMutableData<float>(output_tensor);
|
||||
for (int n = 0; n < N; n++) {
|
||||
for (int c = 0; c < C; c++) {
|
||||
for (int h = 0; h < H; h++) {
|
||||
for (int w = 0; w < W; w++) {
|
||||
nchw_output[n * C * H * W + c * H * W + h * W + w] = nhwc_data[n * H * W * C + h * W * C + w * C + c];
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
for (auto nhwc_input : nhwc_inputs) {
|
||||
delete[] nhwc_input;
|
||||
}
|
||||
for (auto nhwc_output : nhwc_outputs) {
|
||||
delete[] std::get<1>(nhwc_output);
|
||||
}
|
||||
return Status::OK();
|
||||
};
|
||||
|
||||
node_compute_funcs.push_back(compute_info);
|
||||
}
|
||||
return Status::OK();
|
||||
}
|
||||
} // namespace onnxruntime
|
||||
|
|
@ -1,25 +0,0 @@
|
|||
// Copyright 2019 JD.com Inc. JD AI
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "core/framework/execution_provider.h"
|
||||
#include "core/graph/onnx_protobuf.h"
|
||||
#include "dnnlibrary/Model.h"
|
||||
|
||||
namespace onnxruntime {
|
||||
class NnapiExecutionProvider : public IExecutionProvider {
|
||||
public:
|
||||
NnapiExecutionProvider();
|
||||
virtual ~NnapiExecutionProvider();
|
||||
|
||||
std::vector<std::unique_ptr<ComputeCapability>>
|
||||
GetCapability(const onnxruntime::GraphViewer& graph,
|
||||
const std::vector<const KernelRegistry*>& /*kernel_registries*/) const override;
|
||||
common::Status Compile(const std::vector<onnxruntime::Node*>& fused_nodes,
|
||||
std::vector<NodeComputeInfo>& node_compute_funcs) override;
|
||||
|
||||
private:
|
||||
std::unordered_map<std::string, std::unique_ptr<dnn::Model>> dnn_models_;
|
||||
std::vector<std::vector<int>> GetSupportedNodes(const ONNX_NAMESPACE::ModelProto& model_proto) const;
|
||||
};
|
||||
} // namespace onnxruntime
|
||||
|
|
@ -2,12 +2,7 @@
|
|||
|
||||
#include "core/providers/nnapi/nnapi_provider_factory.h"
|
||||
#include "core/session/abi_session_options_impl.h"
|
||||
|
||||
#ifdef USE_NNAPI_DNNLIBRARY
|
||||
#include "nnapi_dnnlibrary/nnapi_execution_provider.h"
|
||||
#elif USE_NNAPI_BUILTIN
|
||||
#include "nnapi_builtin/nnapi_execution_provider.h"
|
||||
#endif
|
||||
|
||||
using namespace onnxruntime;
|
||||
|
||||
|
|
@ -33,5 +28,3 @@ ORT_API_STATUS_IMPL(OrtSessionOptionsAppendExecutionProvider_Nnapi, _In_ OrtSess
|
|||
options->provider_factories.push_back(onnxruntime::CreateExecutionProviderFactory_Nnapi());
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -22,11 +22,7 @@
|
|||
#include "core/providers/openvino/openvino_execution_provider.h"
|
||||
#endif
|
||||
#ifdef USE_NNAPI
|
||||
# ifdef USE_NNAPI_DNNLIBRARY
|
||||
# include "core/providers/nnapi/nnapi_dnnlibrary/nnapi_execution_provider.h"
|
||||
# elif USE_NNAPI_BUILTIN
|
||||
# include "core/providers/nnapi/nnapi_builtin/nnapi_execution_provider.h"
|
||||
# endif
|
||||
#include "core/providers/nnapi/nnapi_builtin/nnapi_execution_provider.h"
|
||||
#endif
|
||||
#ifdef USE_RKNPU
|
||||
#include "core/providers/rknpu/rknpu_execution_provider.h"
|
||||
|
|
|
|||
|
|
@ -344,7 +344,7 @@ int real_main(int argc, char* argv[], Ort::Env& env) {
|
|||
#ifdef USE_NNAPI
|
||||
Ort::ThrowOnError(OrtSessionOptionsAppendExecutionProvider_Nnapi(sf));
|
||||
#else
|
||||
fprintf(stderr, "DNNLibrary/NNAPI is not supported in this build");
|
||||
fprintf(stderr, "NNAPI is not supported in this build");
|
||||
return -1;
|
||||
#endif
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,14 +1,9 @@
|
|||
#include "core/common/logging/logging.h"
|
||||
#include "core/providers/nnapi/nnapi_builtin/nnapi_execution_provider.h"
|
||||
#include "core/session/inference_session.h"
|
||||
#include "gtest/gtest.h"
|
||||
#include "test/providers/provider_test_utils.h"
|
||||
#include "test/framework/test_utils.h"
|
||||
#include "gtest/gtest.h"
|
||||
#include "core/common/logging/logging.h"
|
||||
|
||||
#ifdef USE_NNAPI_DNNLIBRARY
|
||||
#include "core/providers/nnapi/nnapi_dnnlibrary/nnapi_execution_provider.h"
|
||||
#elif USE_NNAPI_BUILTIN
|
||||
#include "core/providers/nnapi/nnapi_builtin/nnapi_execution_provider.h"
|
||||
#endif
|
||||
|
||||
using namespace std;
|
||||
using namespace ONNX_NAMESPACE;
|
||||
|
|
|
|||
|
|
@ -15,7 +15,6 @@ endif()
|
|||
#onnxruntime providers
|
||||
option(onnxruntime_USE_CUDA "Build with CUDA support" OFF)
|
||||
option(onnxruntime_USE_OPENVINO "Build with OpenVINO support" OFF)
|
||||
option(onnxruntime_USE_NNAPI_DNNLIBRARY "Build with DNNLibrary for Android NNAPI support" OFF)
|
||||
option(onnxruntime_USE_NNAPI_BUILTIN "Build with builtin NNAPI lib for Android NNAPI support" OFF)
|
||||
option(onnxruntime_USE_DNNL "Build with DNNL support" OFF)
|
||||
option(onnxruntime_USE_NGRAPH "Build with nGraph support" OFF)
|
||||
|
|
@ -56,7 +55,7 @@ endif()
|
|||
if(onnxruntime_USE_OPENVINO)
|
||||
add_definitions(-DUSE_OPENVINO)
|
||||
endif()
|
||||
if(onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
|
||||
if(onnxruntime_USE_NNAPI_BUILTIN)
|
||||
add_definitions(-DUSE_NNAPI)
|
||||
endif()
|
||||
if(onnxruntime_USE_DNNL)
|
||||
|
|
|
|||
|
|
@ -8,7 +8,6 @@ project(onnxruntime C CXX)
|
|||
|
||||
option(onnxruntime_USE_CUDA "Build with CUDA support" OFF)
|
||||
option(onnxruntime_USE_OPENVINO "Build with OpenVINO support" OFF)
|
||||
option(onnxruntime_USE_NNAPI_DNNLIBRARY "Build with DNNLibrary for Android NNAPI support" OFF)
|
||||
option(onnxruntime_USE_NNAPI_BUILTIN "Build with builtin NNAPI lib for Android NNAPI support" OFF)
|
||||
option(onnxruntime_USE_DNNL "Build with DNNL support" OFF)
|
||||
option(onnxruntime_USE_NGRAPH "Build with nGraph support" OFF)
|
||||
|
|
@ -24,7 +23,7 @@ endif()
|
|||
if(onnxruntime_USE_OPENVINO)
|
||||
add_definitions(-DUSE_OPENVINO=1)
|
||||
endif()
|
||||
if(onnxruntime_USE_NNAPI_DNNLIBRARY OR onnxruntime_USE_NNAPI_BUILTIN)
|
||||
if(onnxruntime_USE_NNAPI_BUILTIN)
|
||||
add_definitions(-DUSE_NNAPI=1)
|
||||
endif()
|
||||
if(onnxruntime_USE_DNNL)
|
||||
|
|
|
|||
|
|
@ -257,8 +257,6 @@ def parse_arguments():
|
|||
choices=["CPU_FP32", "GPU_FP32", "GPU_FP16", "VAD-M_FP16",
|
||||
"MYRIAD_FP16", "VAD-F_FP32"],
|
||||
help="Build with OpenVINO for specific hardware.")
|
||||
parser.add_argument(
|
||||
"--use_dnnlibrary", action='store_true', help="Build with DNNLibrary.")
|
||||
parser.add_argument(
|
||||
"--use_nnapi", action='store_true', help="Build with NNAPI support.")
|
||||
parser.add_argument(
|
||||
|
|
@ -601,14 +599,13 @@ def generate_build_tree(cmake_path, source_dir, build_dir, cuda_home, cudnn_home
|
|||
"ON" if args.use_openvino == "VAD-F_FP32" else "OFF"),
|
||||
"-Donnxruntime_USE_OPENVINO_BINARY=" + (
|
||||
"ON" if args.use_openvino else "OFF"),
|
||||
"-Donnxruntime_USE_NNAPI_DNNLIBRARY=" + ("ON" if args.use_dnnlibrary else "OFF"),
|
||||
"-Donnxruntime_USE_NNAPI_BUILTIN=" + ("ON" if args.use_nnapi else "OFF"),
|
||||
"-Donnxruntime_USE_RKNPU=" + ("ON" if args.use_rknpu else "OFF"),
|
||||
"-Donnxruntime_USE_OPENMP=" + (
|
||||
"ON" if args.use_openmp and not (
|
||||
args.use_dnnlibrary or args.use_mklml or args.use_ngraph or
|
||||
args.use_nnapi or args.use_mklml or args.use_ngraph or
|
||||
args.android or (args.ios and is_macOS())
|
||||
or args.use_rknpu or args.use_nnapi)
|
||||
or args.use_rknpu)
|
||||
else "OFF"),
|
||||
"-Donnxruntime_USE_TVM=" + ("ON" if args.use_tvm else "OFF"),
|
||||
"-Donnxruntime_USE_LLVM=" + ("ON" if args.use_llvm else "OFF"),
|
||||
|
|
@ -707,12 +704,6 @@ def generate_build_tree(cmake_path, source_dir, build_dir, cuda_home, cudnn_home
|
|||
"-Deigen_SOURCE_PATH=" + args.eigen_path]
|
||||
|
||||
if args.android:
|
||||
if args.use_dnnlibrary and args.use_nnapi:
|
||||
raise BuildError(
|
||||
"Only one of --use_dnnlibrary and --use_nnapi " +
|
||||
"can be enabled"
|
||||
)
|
||||
|
||||
cmake_args += [
|
||||
"-DCMAKE_TOOLCHAIN_FILE=" + args.android_ndk_path +
|
||||
"/build/cmake/android.toolchain.cmake",
|
||||
|
|
@ -1176,7 +1167,7 @@ def run_onnxruntime_tests(args, source_dir, ctest_path, build_dir, configs):
|
|||
adb_push('onnx_test_runner', '/data/local/tmp/', cwd=cwd)
|
||||
adb_shell(
|
||||
'cd /data/local/tmp && /data/local/tmp/onnxruntime_test_all')
|
||||
if args.use_dnnlibrary or args.use_nnapi:
|
||||
if args.use_nnapi:
|
||||
adb_shell(
|
||||
'cd /data/local/tmp && /data/local/tmp/onnx_test_runner -e nnapi /data/local/tmp/test') # noqa
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ if [ $BUILD_OS = "android" ]; then
|
|||
pushd /onnxruntime_src
|
||||
mkdir build-android && cd build-android
|
||||
if [ $BUILD_DEVICE = "nnapi" ]; then
|
||||
cmake -DCMAKE_TOOLCHAIN_FILE=/android-ndk/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DONNX_CUSTOM_PROTOC_EXECUTABLE=/usr/bin/protoc -Donnxruntime_USE_NNAPI=ON ../cmake
|
||||
cmake -DCMAKE_TOOLCHAIN_FILE=/android-ndk/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DONNX_CUSTOM_PROTOC_EXECUTABLE=/usr/bin/protoc -Donnxruntime_USE_NNAPI_BUILTIN=ON ../cmake
|
||||
else
|
||||
cmake -DCMAKE_TOOLCHAIN_FILE=/android-ndk/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DONNX_CUSTOM_PROTOC_EXECUTABLE=/usr/bin/protoc ../cmake
|
||||
fi
|
||||
|
|
|
|||
Loading…
Reference in a new issue