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Add more docs on using TFLite through C++

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PiperOrigin-RevId: 315378099
Change-Id: I1ab60a8a2bbed768c2714444f48f173b006fe540
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A. Unique TensorFlower 2020-06-08 16:32:25 -07:00 committed by TensorFlower Gardener
parent bb08729daa
commit 56c7861736
1 changed files with 23 additions and 2 deletions
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tensorflow/lite/g3doc/guide/android.md
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@ -262,9 +262,24 @@ Note that the `0.1.100` version here is purely for the sake of
testing/development. With the local AAR installed, you can use the standard
[TensorFlow Lite Java inference APIs](../guide/inference.md) in your app code.
##### Build C++ libraries
## Build Android app using C++
If you want to use TFLite through C++ libraries, you can build the shared
There are two ways to use TFLite through C++ if you build your app with the NDK:
### Use TFLite C API
This is the *recommended* approach. Download the
[TensorFlow Lite AAR hosted at JCenter](https://bintray.com/google/tensorflow/tensorflow-lite),
rename it to `tensorflow-lite-*.zip`, and unzip it. You must include the three
header files in `headers/tensorflow/lite/c/` folder and the relevant
`libtensorflowlite_jni.so` dynamic library in `jni/` folder in your NDK project.
The `c_api.h` header file contains basic documentation about using the TFLite C
API.
### Use TFLite C++ API
If you want to use TFLite through C++ API, you can build the C++ shared
libraries:
32bit armeabi-v7a:
@ -278,3 +293,9 @@ bazel build -c opt --config=android_arm //tensorflow/lite:libtensorflowlite.so
```sh
bazel build -c opt --config=android_arm64 //tensorflow/lite:libtensorflowlite.so
```
Currently, there is no straightforward way to extract all header files needed,
so you must include all header files in `tensorflow/lite/` from the TensorFlow
repository. Additionally, you will need header files from
[FlatBUffers](https://github.com/google/flatbuffers) and
[Abseil](https://github.com/abseil/abseil-cpp).