我正在尝试与TensorFlow库的静态C API版本链接。我使用以下命令构建了静态库：
// get the sources
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
// create a build directory
mkdir builddir
cd builddir
// build the lib using CMake
cmake -S ../tensorflow_src/tensorflow/lite/c -DTFLITE_C_BUILD_SHARED_LIBS:BOOL=OFF
cmake --build . -j
这构建了libtensorflow-lite.a。然而，libtensorflow-lite.a不是自包含的，它有自己的一组10个依赖项，如CMake文件中所述
# TensorFlow Lite dependencies.
find_package(absl REQUIRED)
find_package(eigen REQUIRED)
find_package(farmhash REQUIRED)
find_package(fft2d REQUIRED)
find_package(flatbuffers REQUIRED)
find_package(gemmlowp REQUIRED)
find_package(neon2sse REQUIRED)
find_package(clog REQUIRED)
find_package(cpuinfo REQUIRED) #CPUINFO is used by XNNPACK and RUY library
find_package(ruy REQUIRED)
问题是，我如何找到所需子库的.a名称？
我使用find ./builddir -type f -name "*.a"列出了CMake构建的库，预计大约有10个库，但实际列表太长：
./_deps/xnnpack-build/libXNNPACK.a
./_deps/ruy-build/ruy/libruy_pack_avx2_fma.a
./_deps/ruy-build/ruy/libruy_have_built_path_for_avx2_fma.a
./_deps/ruy-build/ruy/libruy_block_map.a
./_deps/ruy-build/ruy/libruy_system_aligned_alloc.a
./_deps/ruy-build/ruy/libruy_have_built_path_for_avx512.a
./_deps/ruy-build/ruy/profiler/libruy_profiler_instrumentation.a
./_deps/ruy-build/ruy/libruy_trmul.a
./_deps/ruy-build/ruy/libruy_cpuinfo.a
./_deps/ruy-build/ruy/libruy_blocking_counter.a
./_deps/ruy-build/ruy/libruy_pack_arm.a
./_deps/ruy-build/ruy/libruy_apply_multiplier.a
./_deps/ruy-build/ruy/libruy_kernel_avx2_fma.a
./_deps/ruy-build/ruy/libruy_prepacked_cache.a
./_deps/ruy-build/ruy/libruy_tune.a
./_deps/ruy-build/ruy/libruy_context_get_ctx.a
./_deps/ruy-build/ruy/libruy_have_built_path_for_avx.a
./_deps/ruy-build/ruy/libruy_ctx.a
./_deps/ruy-build/ruy/libruy_wait.a
./_deps/ruy-build/ruy/libruy_allocator.a
./_deps/ruy-build/ruy/libruy_context.a
./_deps/ruy-build/ruy/libruy_kernel_avx.a
./_deps/ruy-build/ruy/libruy_prepare_packed_matrices.a
./_deps/ruy-build/ruy/libruy_pack_avx512.a
./_deps/ruy-build/ruy/libruy_kernel_arm.a
./_deps/ruy-build/ruy/libruy_denormal.a
./_deps/ruy-build/ruy/libruy_kernel_avx512.a
./_deps/ruy-build/ruy/libruy_frontend.a
./_deps/ruy-build/ruy/libruy_pack_avx.a
./_deps/ruy-build/ruy/libruy_thread_pool.a
./_deps/flatbuffers-build/libflatbuffers.a
./_deps/fft2d-build/libfft2d_fftsg2d.a
./_deps/fft2d-build/libfft2d_fftsg.a
./_deps/farmhash-build/libfarmhash.a
./_deps/clog-build/libclog.a
./_deps/abseil-cpp-build/absl/synchronization/libabsl_graphcycles_internal.a
./_deps/abseil-cpp-build/absl/synchronization/libabsl_synchronization.a
./_deps/abseil-cpp-build/absl/strings/libabsl_strings.a
./_deps/abseil-cpp-build/absl/strings/libabsl_str_format_internal.a
./_deps/abseil-cpp-build/absl/strings/libabsl_cord.a
./_deps/abseil-cpp-build/absl/strings/libabsl_strings_internal.a
./_deps/abseil-cpp-build/absl/status/libabsl_status.a
./_deps/abseil-cpp-build/absl/hash/libabsl_city.a
./_deps/abseil-cpp-build/absl/hash/libabsl_wyhash.a
./_deps/abseil-cpp-build/absl/hash/libabsl_hash.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_reflection.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_program_name.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_internal.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_private_handle_accessor.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_marshalling.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_commandlineflag_internal.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_commandlineflag.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags_config.a
./_deps/abseil-cpp-build/absl/flags/libabsl_flags.a
./_deps/abseil-cpp-build/absl/numeric/libabsl_int128.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_symbolize.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_debugging_internal.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_demangle_internal.a
./_deps/abseil-cpp-build/absl/debugging/libabsl_stacktrace.a
./_deps/abseil-cpp-build/absl/base/libabsl_spinlock_wait.a
./_deps/abseil-cpp-build/absl/base/libabsl_raw_logging_internal.a
./_deps/abseil-cpp-build/absl/base/libabsl_malloc_internal.a
./_deps/abseil-cpp-build/absl/base/libabsl_throw_delegate.a
./_deps/abseil-cpp-build/absl/base/libabsl_exponential_biased.a
./_deps/abseil-cpp-build/absl/base/libabsl_base.a
./_deps/abseil-cpp-build/absl/base/libabsl_log_severity.a
./_deps/abseil-cpp-build/absl/time/libabsl_time_zone.a
./_deps/abseil-cpp-build/absl/time/libabsl_civil_time.a
./_deps/abseil-cpp-build/absl/time/libabsl_time.a
./_deps/abseil-cpp-build/absl/container/libabsl_hashtablez_sampler.a
./_deps/abseil-cpp-build/absl/container/libabsl_raw_hash_set.a
./_deps/abseil-cpp-build/absl/types/libabsl_bad_variant_access.a
./_deps/abseil-cpp-build/absl/types/libabsl_bad_optional_access.a
./_deps/cpuinfo-build/libcpuinfo.a
libs的状态似乎如下：
- absl:找到30个库
- eigen：好的，在标题中定义的模板库
- farmhash:好，找到1个库
- fft2d:好，找到2个库
- 平板缓冲区：正常，找到1个库
- gemmlowp:好的，仅限于标题
- neon2se:好，仅限标题
- 阻塞：好，找到1个库
- cpuinfo:好，找到1个库
- ruy:找到30个库
总而言之，大多数库都是可以的，要么有一个库可以链接，要么这些库只是头。仍然存在的问题是：
- absl
- ruy
因为它们包含大约30个.a库。不确定我是否必须与所有这些联系起来？这将非常麻烦，因为我的构建系统是Meson，并且我正在使用custom_target((与TensorFlow链接。
一年后，但我只是自己经历了这一切，所以我的答案如下
根据我的经验(使用makefile，不使用-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和ruy_pack_arm，假设您在x64平台上运行程序。(令人恼火的是，-DTFLITE_ENABLE_RUY=OFF在构建TfLite时不受尊重，所以你只能使用Ruy(
详细步骤：
生成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libs:
$ 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 "Donen";
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
希望这能帮助你或其他可怜的家伙让TfLite C++发挥作用。