一年后，但我自己刚刚经历过这个，所以这是我的答案。
根据我的经验（使用 makefile 并且没有你的-DTFLITE_C_BUILD_SHARED_LIBS:BOOL=OFF）执行推理的程序不需要链接到 Abseil。
您需要链接到您提到的所有其他库，除了ruy_kernel_arm and ruy_pack_arm，假设您在 x64 平台上运行程序。 （烦人的是-DTFLITE_ENABLE_RUY=OFF在构建 TfLite 时不受尊重，所以你只能选择 Ruy）
详细步骤：
Build TfLite:
mkdir ~/my_tflite_project
cd ~/my_tflite_project/
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
mkdir tflite_build_x64
cd tflite_build_x64/
cmake ../tensorflow_src/tensorflow/lite/
/* You may encounter two CMake messages:
-- The Fortran compiler identification is unknown
I believe a Fortran compiler is only necessary to build Fortran bindings for TfLite.
-- Could NOT find CLANG_FORMAT: Found unsuitable version "0.0", but required is exact version "9" (found CLANG_FORMAT_EXECUTABLE-NOTFOUND)
sudo apt install clang-format-9
Annoyingly you need clang-format-9, plain clang-format (version 13, the newest) won't do. */
cmake --build . -j 4
相关库：
$ cd ~/my_tflite_project/tflite_build_x64
$ ls *.a
libtensorflow-lite.a
$ ls pthreadpool/*.a
pthreadpool/libpthreadpool.a
$ ls _deps/*/*.a
_deps/clog-build/libclog.a _deps/farmhash-build/libfarmhash.a _deps/fft2d-build/libfft2d_fftsg2d.a _deps/xnnpack-build/libXNNPACK.a
_deps/cpuinfo-build/libcpuinfo.a _deps/fft2d-build/libfft2d_fftsg.a _deps/flatbuffers-build/libflatbuffers.a
$ ls _deps/ruy-build/ruy/*.a
_deps/ruy-build/ruy/libruy_allocator.a _deps/ruy-build/ruy/libruy_ctx.a _deps/ruy-build/ruy/libruy_kernel_avx.a _deps/ruy-build/ruy/libruy_prepacked_cache.a
_deps/ruy-build/ruy/libruy_apply_multiplier.a _deps/ruy-build/ruy/libruy_denormal.a _deps/ruy-build/ruy/libruy_kernel_avx2_fma.a _deps/ruy-build/ruy/libruy_prepare_packed_matrices.a
_deps/ruy-build/ruy/libruy_block_map.a _deps/ruy-build/ruy/libruy_frontend.a _deps/ruy-build/ruy/libruy_kernel_avx512.a _deps/ruy-build/ruy/libruy_system_aligned_alloc.a
_deps/ruy-build/ruy/libruy_blocking_counter.a _deps/ruy-build/ruy/libruy_have_built_path_for_avx.a _deps/ruy-build/ruy/libruy_pack_arm.a _deps/ruy-build/ruy/libruy_thread_pool.a
_deps/ruy-build/ruy/libruy_context.a _deps/ruy-build/ruy/libruy_have_built_path_for_avx2_fma.a _deps/ruy-build/ruy/libruy_pack_avx.a _deps/ruy-build/ruy/libruy_trmul.a
_deps/ruy-build/ruy/libruy_context_get_ctx.a _deps/ruy-build/ruy/libruy_have_built_path_for_avx512.a _deps/ruy-build/ruy/libruy_pack_avx2_fma.a _deps/ruy-build/ruy/libruy_tune.a
_deps/ruy-build/ruy/libruy_cpuinfo.a _deps/ruy-build/ruy/libruy_kernel_arm.a _deps/ruy-build/ruy/libruy_pack_avx512.a _deps/ruy-build/ruy/libruy_wait.a
使用 TfLite 构建 MWE：
$ cd ~/my_tflite_project
$ mkdir my_dev_x64
$ cd my_dev_x64/
/* Construct minimal.cpp and makefile below */
$ cat minimal.cpp
#include "tensorflow/lite/model.h"
#include "tensorflow/lite/interpreter.h"
#include "tensorflow/lite/kernels/register.h"
#include <iostream>
int main() {
std::unique_ptr<tflite::FlatBufferModel> model = tflite::FlatBufferModel::BuildFromFile("your_network_here.tflite");
tflite::ops::builtin::BuiltinOpResolver resolver;
std::cout "Done\n";
return EXIT_SUCCESS;
}
$ cat makefile
COMPILER := g++
LINKER := g++
CXX_FILES := minimal.cpp
OBJ_FILES := $(CXX_FILES:.cpp=.o)
EXE_FILE := app
INCLUDE_DIRS := -I../tensorflow_src -I../tflite_build_x64/flatbuffers/include
LIB_DIRS := \
-L../tflite_build_x64 \
-L../tflite_build_x64/_deps/fft2d-build \
-L../tflite_build_x64/_deps/flatbuffers-build \
-L../tflite_build_x64/_deps/ruy-build/ruy \
-L../tflite_build_x64/_deps/farmhash-build \
-L../tflite_build_x64/_deps/xnnpack-build \
-L../tflite_build_x64/_deps/cpuinfo-build \
-L../tflite_build_x64/_deps/clog-build \
-L../tflite_build_x64/pthreadpool
LIBS := \
-ltensorflow-lite \
-lfft2d_fftsg \
-lfft2d_fftsg2d \
-lflatbuffers \
-lruy_ctx \
-lruy_allocator \
-lruy_frontend \
-lruy_context_get_ctx \
-lruy_context \
-lruy_apply_multiplier \
-lruy_prepacked_cache \
-lruy_tune \
-lruy_cpuinfo \
-lruy_system_aligned_alloc \
-lruy_prepare_packed_matrices \
-lruy_trmul \
-lruy_block_map \
-lruy_denormal \
-lruy_thread_pool \
-lruy_blocking_counter \
-lruy_wait \
-lruy_kernel_avx \
-lruy_kernel_avx2_fma \
-lruy_kernel_avx512 \
-lruy_pack_avx \
-lruy_pack_avx2_fma \
-lruy_pack_avx512 \
-lruy_have_built_path_for_avx \
-lruy_have_built_path_for_avx2_fma \
-lruy_have_built_path_for_avx512 \
-lfarmhash \
-lXNNPACK \
-lpthreadpool \
-lcpuinfo \
-lclog
CXX_FLAGS := -Wall #-pedantic
LINK_FLAGS :=
#Do not print the output of the commands
.SILENT:
#Phony targets do not represent actual files, so files with the following names are ignored
.PHONY: clean depend
#Link object files to form an executable file
$(EXE_FILE): $(OBJ_FILES)
$(LINKER) $(LINK_FLAGS) $(OBJ_FILES) -o $(EXE_FILE) $(LIB_DIRS) $(LIBS)
#Compile cpp files to object files
%.o: %.cpp
$(COMPILER) $(CXX_FLAGS) $(INCLUDE_DIRS) -c $<
#Remove object files, executable, and possible linkinfo files
clean:
-rm -f $(OBJ_FILES) $(EXE_FILE)
#Generate dependency file
depend:
$(COMPILER) $(CXX_FLAGS) $(INCLUDE_DIRS) -MM $(CXX_FILES) > make.dep
#Include dependency file
-include make.dep
构建并运行 MWE：
$ cd ~/my_tflite_project/my_dev_x64
$ make
$ ./app
Done
希望这可以帮助您或其他一些可怜的家伙让 TfLite C++ 工作。
