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需要在APP里使用tensorflow lite来运行PC端训练的model.tlite，又想apk的体积最小，尝试了如下方法：
1. 在gradle里配置
implementation("org.tensorflow:tensorflow-lite:2.16.1")
这样会引入tensorflow.jar，最终apk的size增加大约2.2M
2. 根据tensorflow官方的优化编译教程
https://www.tensorflow.org/lite/android/lite_build?spm=5176.28103460.0.0.73711db8niy7UE&hl=zh-cn
针对我们的模型,构建出针对性的TensorFlow Lite AAR，最后集成到apk里，体积增加约1.5M
分析TensorFlow Lite AAR的实现，发现其本质还是通过JNI调用了libtensorflowlite.so，
而这个libtensorflowlite.so，包含了tensorflow lite几乎所有核心framework代码，因此肯定很大。
3. 因为我们仅需要用到tensorflow lite里model 初始化，interpreter推理等基础功能，并不需要tensorflow lite里的其他功能，因此，想要最小，直接在我们的JNI文件里，集成tensorflow lite相关类的源码进行编译，应该就能使得体积增加最小化了。
把我们JNI文件依赖的类，比如
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/model.h"
等.h和.cc引入我们的JNI里，一起编译就行了。
一开始是在android studio里，导入tensorflow lite的源码， 修改CmakeLists.txt，尝试编译出可以独立运行的JNI so， 但是总是失败。
最后，把JNI文件，放到tensorflow lite的源码目录里，利用tensorflow的编译工具bazel，编译成功。然后把生成的milc_jni.so放到app的jniLibs里，成功:
a. 在tensorflow/lite/下创建milc_jni/这个目录，目录下创建BUILD，milc_jni.cc, custom_op_resolver.h和custom_op_resolver.cc
b. 根据我们的模型文件model.tflite里用到的算子，比如，我只用了FULLY_CONNECTED，RELU， LOGISTIC这3个算子，定制精简算子的Resolver类
custom_op_resolver.h
#ifndef TENSORFLOW_LITE_CUSTOM_OP_RESOLVER_H_
#define TENSORFLOW_LITE_CUSTOM_OP_RESOLVER_H_#include "tensorflow/lite/mutable_op_resolver.h"namespace tflite {class MinimalOpResolver : public MutableOpResolver {public:MinimalOpResolver();
};} // namespace tflite#endif // TENSORFLOW_LITE_CUSTOM_OP_RESOLVER_H_custom_op_resolver.cc
#include "tensorflow/lite/milc_jni/custom_op_resolver.h"
#include "tensorflow/lite/kernels/builtin_op_kernels.h"namespace tflite {
MinimalOpResolver::MinimalOpResolver() {// 使用 kernels::builtin:: 命名空间下的注册函数AddBuiltin(BuiltinOperator_FULLY_CONNECTED, tflite::ops::builtin::Register_FULLY_CONNECTED());AddBuiltin(BuiltinOperator_RELU, tflite::ops::builtin::Register_RELU());AddBuiltin(BuiltinOperator_LOGISTIC, tflite::ops::builtin::Register_LOGISTIC());
}
} // namespace tflite
c. 创建JNI文件milc_jni.cc
#include <jni.h>
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/milc_jni/custom_op_resolver.h"
#include <android/log.h>#define LOG_TAG "TensorFlowLiteJNI"
#define LOGI(...) __android_log_print(ANDROID_LOG_INFO, LOG_TAG, __VA_ARGS__)
#define LOGE(...) __android_log_print(ANDROID_LOG_ERROR, LOG_TAG, __VA_ARGS__)// 移除所有日志输出
//#define LOGI(...)
//#define LOGE(...)extern "C" JNIEXPORT jfloat JNICALL
Java_com_xm_j_milc_predictJNI(JNIEnv* env, jobject /* this */, jstring modelPath, jfloatArray inputArray) {const char* modelPathStr = env->GetStringUTFChars(modelPath, nullptr);// 获取输入数组jfloat* inputElements = env->GetFloatArrayElements(inputArray, nullptr);jsize inputLength = env->GetArrayLength(inputArray);if (inputLength != 31) {LOGE("Input array length must be 31");env->ReleaseStringUTFChars(modelPath, modelPathStr);env->ReleaseFloatArrayElements(inputArray, inputElements, JNI_ABORT);return -1.0;}// 加载 TensorFlow Lite 模型std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile(modelPathStr);if (!model) {LOGE("Failed to load model from %s", modelPathStr);env->ReleaseStringUTFChars(modelPath, modelPathStr);env->ReleaseFloatArrayElements(inputArray, inputElements, JNI_ABORT);return -1.0;}// 创建解释器//tflite::ops::builtin::BuiltinOpResolver resolver;tflite::MinimalOpResolver resolver;std::unique_ptr<tflite::Interpreter> interpreter;tflite::InterpreterBuilder(*model, resolver)(&interpreter);if (!interpreter) {LOGE("Failed to create interpreter");env->ReleaseStringUTFChars(modelPath, modelPathStr);env->ReleaseFloatArrayElements(inputArray, inputElements, JNI_ABORT);return -1.0;}// 分配张量if (interpreter->AllocateTensors() != kTfLiteOk) {LOGE("Failed to allocate tensors");env->ReleaseStringUTFChars(modelPath, modelPathStr);env->ReleaseFloatArrayElements(inputArray, inputElements, JNI_ABORT);return -1.0;}// 设置输入float* input = interpreter->typed_input_tensor<float>(0);for (int i = 0; i < inputLength; ++i) {input[i] = inputElements[i];}// 运行推理if (interpreter->Invoke() != kTfLiteOk) {LOGE("Failed to invoke interpreter");env->ReleaseStringUTFChars(modelPath, modelPathStr);env->ReleaseFloatArrayElements(inputArray, inputElements, JNI_ABORT);return -1.0;}// 获取输出// 5. 获取输出结果float* outputTensor = interpreter->typed_output_tensor<float>(0);// 释放资源env->ReleaseStringUTFChars(modelPath, modelPathStr);env->ReleaseFloatArrayElements(inputArray, inputElements, JNI_ABORT);return outputTensor[0]; // 直接返回标量值
}
d. 创建BUILD文件
# 自定义操作解析器（仅包含必要算子）
cc_library(name = "custom_op_resolver",srcs = ["custom_op_resolver.cc"],hdrs = ["custom_op_resolver.h"],deps = ["//tensorflow/lite/kernels:builtin_ops",],
)cc_binary(name = "milc_jni.so",srcs = ["milc_jni.cc"],linkshared = True,linkstatic = True, # 静态链接所有依赖deps = [":custom_op_resolver","//tensorflow/lite:framework","//tensorflow/lite/kernels:builtin_ops","@flatbuffers//:flatbuffers",],copts = ["-Oz","-flto=thin","-ffunction-sections","-fdata-sections","-fvisibility=hidden","-fvisibility-inlines-hidden","-DFLATBUFFERS_RELEASE","-DTF_LITE_STRIP_ERROR_STRINGS=1","-DNDEBUG","-DFORCE_MINIMAL_LOGGING","-fno-exceptions","-fno-rtti","-fno-unwind-tables","-fno-asynchronous-unwind-tables","-ffreestanding",],linkopts = ["-flto=thin","-Wl,--gc-sections","-Wl,--exclude-libs,ALL","-s", "-Wl,--as-needed","-Wl,-z,norelro","-Wl,--build-id=none", # 移除构建ID"-Wl,--strip-all", # 彻底去除符号"-nostdlib","-lc","-Wl,--hash-style=gnu", # 更小的哈希表"-Wl,--compress-debug-sections=zlib", # 压缩调试节],features = ["-layering_check",],
)
e. 在tensorflow的源码目录里，初始化好环境，AndroidNDK之类的，然后执行编译
bazel build -c opt --config=android_arm64 --copt="-DFORCE_DISABLE_ALL_OPS" --linkopt="-Wl,--gc-sections" --linkopt="-Wl,--exclude-libs,ALL" --linkopt="-s" --define=tflite_with_xnnpack=false --copt="-Os" --copt="-fomit-frame-pointer" --copt="-ffunction-sections" --copt="-fdata-sections" --copt="-fvisibility=hidden" --copt="-g0" --copt="-DFLATBUFFERS_RELEASE" //tensorflow/lite/milc_jni:milc_jni.so然后，就会生成一个milc_jni.so，大约500K，它是可以独立运行的，不用依赖libtensorflowlite.so，因此，APK的size，也就只会增加约500K。
f.针对生成的milc_jni.so，进一步压缩优化
sudo apt-get install upx
upx --android-shlib --best --lzma milc_jni.so -o milc_jni_upx.so最终的milc_jni_upx.so大约200K，因此，APK的size，也就只会增加约200K。