我目前正在从事有关神经网络的项目。
为此，我想构建一个Android应用程序，该应用程序应使用tensorflow [lite]解决一些对象检测/识别问题。

因为我希望代码尽可能地可移植，所以我想用C++编写大多数代码，从而在Java API/包装器上使用tensorflow lite的C++ API。
因此，我修改了tensorflow/contrib/lite/BUILD并添加了以下内容以能够创建共享的tensorflow库。

cc_binary(
name = "libtensorflowLite.so",

linkopts=["-shared", "-Wl"],
linkshared=1,

copts = tflite_copts(),
deps = [
    ":framework",
    "//tensorflow/contrib/lite/kernels:builtin_ops",
],

)

bazel build //tensorflow/contrib/lite:libtensorflowLite.so --crosstool_top=//external:android/crosstool --cpu=arm64-v8a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cxxopt="-std=c++11"

04-14 20:09:59.084 9623-9623/com.example.hellolibs E/AndroidRuntime: FATAL EXCEPTION: main
    Process: com.example.hellolibs, PID: 9623
    java.lang.UnsatisfiedLinkError: dlopen failed: library "/home/User/tensorflowtest/app/src/main/cpp/../../../../distribution/tensorflow/lib/x86/libtensorflowLite.so" not found
        at java.lang.Runtime.loadLibrary0(Runtime.java:1016)
        at java.lang.System.loadLibrary(System.java:1657)
        at com.example.hellolibs.MainActivity.<clinit>(MainActivity.java:36)
        at java.lang.Class.newInstance(Native Method)
        at android.app.Instrumentation.newActivity(Instrumentation.java:1174)
        at android.app.ActivityThread.performLaunchActivity(ActivityThread.java:2669)
        at android.app.ActivityThread.handleLaunchActivity(ActivityThread.java:2856)
        at android.app.ActivityThread.-wrap11(Unknown Source:0)
        at android.app.ActivityThread$H.handleMessage(ActivityThread.java:1589)
        at android.os.Handler.dispatchMessage(Handler.java:106)
        at android.os.Looper.loop(Looper.java:164)
        at android.app.ActivityThread.main(ActivityThread.java:6494)
        at java.lang.reflect.Method.invoke(Native Method)
        at com.android.internal.os.RuntimeInit$MethodAndArgsCaller.run(RuntimeInit.java:438)
        at com.android.internal.os.ZygoteInit.main(ZygoteInit.java:807)

sourceSets {
        main {
            // let gradle pack the shared library into apk
            jniLibs.srcDirs = ['../distribution/tensorflow/lib']
        }
})

最佳答案

我只是记得几周前我问过这个问题。
同时，我找到了解决问题的方法，并且TensorflowLite现在可以很好地嵌入到我的Android项目中，在那里我可以使用C++ API进行所有编程!

问题是我构建的Tensorflow共享库不包含soname。因此，在构建过程中，该库被剥离，并且由于未找到名称，因此将该路径用作“名称”。我注意到，在我进一步研究使用Linux“字符串”工具的native-lib.so(NDK C++库，然后由App加载)时。在这里，我发现确实是从以下位置加载库的路径:“/home/User/tensorflowtest/app/src/main/cpp/../../../../distribution/tensorflow/lib/x86/libtensorflowLite .so”已设置。
在构建文件的构建选项中添加“-Wl，-soname = libtensorflowLite.so”可解决此问题!您可以在下面找到我使用的整个规则。

由于缺乏解释(所有内容都很难设置(似乎TensorflowLite主要通过Android上的Java API使用？)，因此很难进行所有设置)，因此，我想就如何在Android Studio中使用TensorflowLite的C++ API给出简短的指导(从Android NDK项目中)。

1. 为您的体系结构构建库

要使用C++ API，您首先需要构建TensorflowLite库。为此，将以下规则添加到tensorflow/contrib/lite中的BUILD文件中:

cc_binary(

name = "libtensorflowLite.so",
linkopts=[
    "-shared", 
    "-Wl,-soname=libtensorflowLite.so",
],
linkshared = 1,
copts = tflite_copts(),
deps = [
    ":framework",
    "//tensorflow/contrib/lite/kernels:builtin_ops",
],

bazel build //tensorflow/contrib/lite:libtensorflowLite.so --crosstool_top=//external:android/crosstool --cpu=arm64-v8a --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cxxopt="-std=c++11"

    # This will create a new "variable" holding the path to a directory
    # where we will put our library and header files.
    # Change this to your needs
    set(distribution_DIR ${CMAKE_SOURCE_DIR}/distribution)

    # This states that there exists a shared library called libtensorflowLite
    # which will be imported (means it is not built with the rest of the project!)
    add_library(libtensorflowLite SHARED IMPORTED)

    # This indicates where the libtensorflowLite.so for each architecture is found relative to our distribution directory
    set_target_properties(libtensorflowLite PROPERTIES IMPORTED_LOCATION
        ${distribution_DIR}/lib/${ANDROID_ABI}/libtensorflowLite.so)

    # This indicates where the header files are found relative to our distribution dir
    target_include_directories(native-lib PRIVATE
                       ${distribution_DIR}/include)

    # Finally, we make sure our libtensorflowLite.so is linked to our native-lib and loaded during runtime 
    target_link_libraries( # Specifies the target library.
                   native-lib
                   libtensorflowLite
                   # Links the target library to the log library
                   # included in the NDK.
                   ${log-lib} )

    sourceSets {
        main {
            // let gradle pack the shared library into apk
            jni.srcDirs = []
            jniLibs.srcDirs = ['distribution/lib']
        }
    }

关于android - 在Android Studio Project中使用Tensorflow Lite C++ API的问题，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49834875/