• 我从 Keras 构建了一个 Tensorflow (TF) 模型并将其转换为 Tensorflow-Lite (TFL)
• 我在 Android Studio 中构建了一个 Android 应用，并使用 Java API 来运行 TFL 模型
• 在 Java 应用程序中，我使用了 TFL 支持库(参见 此处)，以及来自 JCenter 的 TensorFlow Lite AAR，方法是在我的 build.gradle 下包含 implementation 'org.tensorflow:tensorflow-lite:+' 依赖

• I built a Tensorflow (TF) model from Keras and converted it to Tensorflow-Lite (TFL)
• I built an Android app in Android Studio and used the Java API to run the TFL model
• In the Java app, I used the TFL Support Library (see here), and the TensorFlow Lite AAR from JCenter by including implementation 'org.tensorflow:tensorflow-lite:+' under my build.gradle dependencies

推理时间不是很好，所以现在我想在 Android 的 NDK 中使用 TFL.

Inference times are not so great, so now I want to use TFL in Android's NDK.

所以我在 Android Studio 的 NDK 中构建了 Java 应用程序的精确副本，现在我尝试在项目中包含 TFL 库.我遵循 TensorFlow-Lite 的 Android 指南 并在本地构建了 TFL 库(并获得了一个AAR 文件)，并将该库包含在我在 Android Studio 中的 NDK 项目中.

So I built an exact copy of the Java app in Android Studio's NDK, and now I'm trying to include the TFL libs in the project. I followed TensorFlow-Lite's Android guide and built the TFL library locally (and got an AAR file), and included the library in my NDK project in Android Studio.

现在我试图在我的 C++ 文件中使用 TFL 库，尝试在代码中 #include 它，但我收到一条错误消息:cannot find tensorflow(或我尝试使用的任何其他名称，根据我在 CMakeLists.txt 文件中给它的名称).

Now I'm trying to use the TFL library in my C++ file, by trying to #include it in code, but I get an error message: cannot find tensorflow (or any other name I'm trying to use, according to the name I give it in my CMakeLists.txt file).

应用build.gradle:

apply plugin: 'com.android.application'

android {
    compileSdkVersion 29
    buildToolsVersion "29.0.3"

    defaultConfig {
        applicationId "com.ndk.tflite"
        minSdkVersion 28
        targetSdkVersion 29
        versionCode 1
        versionName "1.0"

        testInstrumentationRunner "androidx.test.runner.AndroidJUnitRunner"

        externalNativeBuild {
            cmake {
                cppFlags ""
            }
        }

        ndk {
            abiFilters 'arm64-v8a'
        }

    }

    buildTypes {
        release {
            minifyEnabled false
            proguardFiles getDefaultProguardFile('proguard-android-optimize.txt'), 'proguard-rules.pro'
        }
    }

    // tf lite
    aaptOptions {
        noCompress "tflite"
    }

    externalNativeBuild {
        cmake {
            path "src/main/cpp/CMakeLists.txt"
            version "3.10.2"
        }
    }
}

dependencies {
    implementation fileTree(dir: 'libs', include: ['*.jar'])

    implementation 'androidx.appcompat:appcompat:1.1.0'
    implementation 'androidx.constraintlayout:constraintlayout:1.1.3'
    testImplementation 'junit:junit:4.12'
    androidTestImplementation 'androidx.test.ext:junit:1.1.1'
    androidTestImplementation 'androidx.test.espresso:espresso-core:3.2.0'

    // tflite build
    compile(name:'tensorflow-lite', ext:'aar')

}

项目build.gradle:

buildscript {

    repositories {
        google()
        jcenter()

    }
    dependencies {
        classpath 'com.android.tools.build:gradle:3.6.2'

    }
}

allprojects {
    repositories {
        google()
        jcenter()

        // native tflite
        flatDir {
            dirs 'libs'
        }

    }

}


task clean(type: Delete) {
    delete rootProject.buildDir
}

CMakeLists.txt:

cmake_minimum_required(VERSION 3.4.1)

add_library( # Sets the name of the library.
             native-lib

             # Sets the library as a shared library.
             SHARED

             # Provides a relative path to your source file(s).
             native-lib.cpp )

add_library( # Sets the name of the library.
        tensorflow-lite

        # Sets the library as a shared library.
        SHARED

        # Provides a relative path to your source file(s).
        native-lib.cpp )

find_library( # Sets the name of the path variable.
              log-lib

              # Specifies the name of the NDK library that
              # you want CMake to locate.
              log )


target_link_libraries( # Specifies the target library.
                       native-lib tensorflow-lite

                       # Links the target library to the log library
                       # included in the NDK.
                       ${log-lib} )

native-lib.cpp:

#include <jni.h>
#include <string>

#include "tensorflow"

extern "C" JNIEXPORT jstring JNICALL
Java_com_xvu_f32c_1jni_MainActivity_stringFromJNI(
        JNIEnv* env,
        jobject /* this */) {
    std::string hello = "Hello from C++";
    return env->NewStringUTF(hello.c_str());
}

class FlatBufferModel {
    // Build a model based on a file. Return a nullptr in case of failure.
    static std::unique_ptr<FlatBufferModel> BuildFromFile(
            const char* filename,
            ErrorReporter* error_reporter);

    // Build a model based on a pre-loaded flatbuffer. The caller retains
    // ownership of the buffer and should keep it alive until the returned object
    // is destroyed. Return a nullptr in case of failure.
    static std::unique_ptr<FlatBufferModel> BuildFromBuffer(
            const char* buffer,
            size_t buffer_size,
            ErrorReporter* error_reporter);
};

进展

我也尝试遵循这些:

Progress

I also tried to follow these:

• 使用问题Android Studio 项目中的 tensorflow lite C++ API
• Android C++ NDK:一些共享库拒绝在运行时链接
• 如何将 TensorFlow Lite 构建为静态库并从单独的 (CMake) 项目链接到它?
• 如何设置Tensorflow Lite C++的输入
• 我该怎么做只构建 TensorFlow lite 而不是从源代码构建所有 TensorFlow?

但就我而言，我使用 Bazel 来构建 TFL 库.

but in my case I used Bazel to build the TFL libs.

尝试构建(label_image)，我设法构建它并将 adb push 发送到我的设备，但是在尝试运行时出现以下错误:

Trying to build the classification demo of (label_image), I managed to build it and adb push to my device, but when trying to run I got the following error:

ERROR: Could not open './mobilenet_quant_v1_224.tflite'.
Failed to mmap model ./mobilenet_quant_v1_224.tflite

• 我关注了zimenglyu 的帖子:试图在 WORKSPACE 中设置 android_sdk_repository/android_ndk_repository 给我一个错误:WORKSPACE:149:1:无法在 WORKSPACE 文件中的任何加载语句之后重新定义存储库(对于存储库androidsdk")，并且将这些语句放置在不同的位置会导致相同的错误.
• 我删除了对 WORKSPACE 的这些更改并继续 zimenglyu 的帖子:我编译了 libtensorflowLite.so，并编辑了 CMakeLists.txt 所以引用了 libtensorflowLite.so 文件，但保留了 FlatBuffer 部分.Android 项目编译成功，但没有明显变化，我仍然无法包含任何 TFLite 库.
• I followed zimenglyu's post: trying to set android_sdk_repository / android_ndk_repository in WORKSPACE got me an error: WORKSPACE:149:1: Cannot redefine repository after any load statement in the WORKSPACE file (for repository 'androidsdk'), and locating these statements at different places resulted in the same error.
• I deleted these changes to WORKSPACE and continued with zimenglyu's post: I've compiled libtensorflowLite.so, and edited CMakeLists.txt so that the libtensorflowLite.so file was referenced, but left the FlatBuffer part out. The Android project compiled successfully, but there was no evident change, I still can't include any TFLite libraries.

尝试编译 TFL，我添加了一个 cc_binary 到 tensorflow/tensorflow/lite/BUILD(遵循 label_image 示例):

Trying to compile TFL, I added a cc_binary to tensorflow/tensorflow/lite/BUILD (following the label_image example):

cc_binary(
    name = "native-lib",
    srcs = [
        "native-lib.cpp",
    ],
    linkopts = tflite_experimental_runtime_linkopts() + select({
        "//tensorflow:android": [
            "-pie",
            "-lm",
        ],
        "//conditions:default": [],
    }),
    deps = [
        "//tensorflow/lite/c:common",
        "//tensorflow/lite:framework",
        "//tensorflow/lite:string_util",
        "//tensorflow/lite/delegates/nnapi:nnapi_delegate",
        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/profiling:profiler",
        "//tensorflow/lite/tools/evaluation:utils",
    ] + select({
        "//tensorflow:android": [
            "//tensorflow/lite/delegates/gpu:delegate",
        ],
        "//tensorflow:android_arm64": [
            "//tensorflow/lite/delegates/gpu:delegate",
        ],
        "//conditions:default": [],
    }),
)

并尝试为 x86_64 和 arm64-v8a 构建它，我收到一个错误:cc_toolchain_suite rule @local_config_cc//:toolchain: cc_toolchain_suite '@local_config_cc//:toolchain' 不包含 cpu 'x86_64' 的工具链.

and trying to build it for x86_64, and arm64-v8a I get an error: cc_toolchain_suite rule @local_config_cc//:toolchain: cc_toolchain_suite '@local_config_cc//:toolchain' does not contain a toolchain for cpu 'x86_64'.

在第 47 行检查 external/local_config_cc/BUILD(提供错误):

Checking external/local_config_cc/BUILD (which provided the error) in line 47:

cc_toolchain_suite(
    name = "toolchain",
    toolchains = {
        "k8|compiler": ":cc-compiler-k8",
        "k8": ":cc-compiler-k8",
        "armeabi-v7a|compiler": ":cc-compiler-armeabi-v7a",
        "armeabi-v7a": ":cc-compiler-armeabi-v7a",
    },
)

这些是唯一找到的 2 个 cc_toolchain.在存储库中搜索cc-compiler-"，我只找到了aarch64"，我认为它适用于 64 位 ARM，但没有找到x86_64".不过有x64_windows" - 我在 Linux 上.

and these are the only 2 cc_toolchains found. Searching the repository for "cc-compiler-" I only found "aarch64", which I assumed is for the 64-bit ARM, but nothing with "x86_64". There are "x64_windows", though - and I'm on Linux.

尝试像这样使用 aarch64 构建:

Trying to build with aarch64 like so:

bazel build -c opt --fat_apk_cpu=aarch64 --cpu=aarch64 --host_crosstool_top=@bazel_tools//tools/cpp:toolchain //tensorflow/lite/java:tensorflow-lite

导致错误:

ERROR: /.../external/local_config_cc/BUILD:47:1: in cc_toolchain_suite rule @local_config_cc//:toolchain: cc_toolchain_suite '@local_config_cc//:toolchain' does not contain a toolchain for cpu 'aarch64'

在 Android Studio 中使用库:

通过更改构建配置中的 soname 并使用 CMakeLists.txt 中的完整路径，我能够为 x86_64 架构构建库.这导致了 .so 共享库.此外 - 通过调整 aarch64_makefile.inc 文件，我能够使用 TFLite Docker 容器为 arm64-v8a 构建库，但我没有更改任何构建选项，并让 build_aarch64_lib.sh 无论它构建什么.这导致了 .a 静态库.

Using the libraries in Android Studio:

I was able to build the library for x86_64 architecture by changing the soname in build config and using full paths in CMakeLists.txt. This resulted in a .so shared library. Also - I was able to build the library for arm64-v8a using the TFLite Docker container, by adjusting the aarch64_makefile.inc file, but I did not change any build options, and let build_aarch64_lib.sh whatever it builds. This resulted in a .a static library.

所以现在我有两个 TFLite 库，但我仍然无法使用它们(例如，我不能#include "..." 任何东西).

So now I have two TFLite libs, but I'm still unable to use them (I can't #include "..." anything for example).

尝试构建项目时，仅使用 x86_64 工作正常，但尝试包含 arm64-v8a 库会导致 ninja 错误:'.../libtensorflow-lite.a'，需要'.../app/build/intermediates/cmake/debug/obj/armeabi-v7a/libnative-lib.so'，缺少并且没有已知的规则来制作它.

When trying to build the project, using only x86_64 works fine, but trying to include the arm64-v8a library results in ninja error: '.../libtensorflow-lite.a', needed by '.../app/build/intermediates/cmake/debug/obj/armeabi-v7a/libnative-lib.so', missing and no known rule to make it.

1. 我在 Android Studio 中创建了一个 Native C++ 项目
2. 我从 Tensorflow 的 lite 目录中获取了基本的 C/C++ 源文件和头文件，并在 app/src/main/cpp 中创建了一个类似的结构，其中我包括 (A) tensorflow、(B) absl 和 (C) flatbuffers 文件
3. 我将所有 tensorflow 头文件中的 #include "tensorflow/... 行更改为相对路径，以便编译器可以找到它们.
4. 在应用程序的 build.gradle 中，我为 .tflite 文件添加了一个无压缩行:aaptOptions { noCompress "tflite" }
5. 我在应用中添加了一个 assets 目录
6. 在 native-lib.cpp 中，我添加了 一些示例代码来自 TFLite 网站
7. 尝试构建包含源文件的项目(构建目标是 arm64-v8a).

1. I created a Native C++ project in Android Studio
2. I took the basic C/C++ source files and headers from Tensorflow's lite directory, and created a similar structure in app/src/main/cpp, in which I include the (A) tensorflow, (B) absl and (C) flatbuffers files
3. I changed the #include "tensorflow/... lines in all of tensorflow's header files to relative paths so the compiler can find them.
4. In the app's build.gradle I added a no-compression line for the .tflite file: aaptOptions { noCompress "tflite" }
5. I added an assets directory to the app
6. In native-lib.cpp I added some example code from the TFLite website
7. Tried to build the project with the source files included (build target is arm64-v8a).

我收到一个错误:

/path/to/Android/Sdk/ndk/20.0.5594570/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include/c++/v1/memory:2339: error: undefined reference to 'tflite::impl::Interpreter::~Interpreter()'

在中，第2339行是"delete __ptr;"行:

in <memory>, line 2339 is the "delete __ptr;" line:

_LIBCPP_INLINE_VISIBILITY void operator()(_Tp* __ptr) const _NOEXCEPT {
    static_assert(sizeof(_Tp) > 0,
                  "default_delete can not delete incomplete type");
    static_assert(!is_void<_Tp>::value,
                  "default_delete can not delete incomplete type");
    delete __ptr;
  }

问题

如何在 Android Studio 中包含 TFLite 库，以便从 NDK 运行 TFL 推理?

Question

How can I include the TFLite libraries in Android Studio, so I can run a TFL inference from the NDK?

或者 - 我如何使用 gradle(目前使用 cmake)来构建和编译源文件?

Alternatively - how can I use gradle (currently with cmake) to build and compile the source files?