问题
在之前的一篇实现中：深度学习之用CelebA_Spoof数据集搭建一个活体检测-用MNN来推理时候如何利用Conan对软件包进行管理，有的人进行了尝试，问我为什么安装了conan和根据步骤进行操作后没办法进行编译，提示的是下面图示的信息。
也就是在设置的仓库中并没有MNN这个包和对应的版本存在。安装正常的逻辑说，在编译的过程中，会优先选择本地缓存包，如果有对应的平台和指定的包，则使用，否则会到指定的私有仓库去寻找（私有仓库建立见这里），没有就再次会到Conan的官方中央仓库去寻找。但图示的问题就是没有找到，这就意味着本地也没有，私有仓库也没有。其实原因是之前并没有人将MNN这个包和源码上传到中央仓库去，当然第一次使用私有仓库的时候也并没有上传。但是像opencv的这些库就有，因为这些库都是知名度很高的库，自然就有人传了上去。
如何做
自己做上传一个，也就是将MNN封装成conan包上传到conan服务器可以保存在本地缓存或者上传到自己的私有仓库，这就需要使用conan create 和conanfile.py这两个东西了，详见大牛的博客《conan入门(十九):封装第三方开源库cpp_redis示例》。
以使用MNN-2.4.0为例（因为我本地有1.2.7了）
首先需要conanfile.py，当然可以自己使用指令 conan new 自己重新来定义一个，也可以使用下面的文件：
from conan import ConanFile
from conan.tools.cmake import CMake, CMakeDeps, CMakeToolchain
from conan.tools.env import VirtualBuildEnv
import os
class MnnConan(ConanFile):
name = "mnn"
version = "2.4.0"
# Optional metadata
url = "https://github.com/alibaba/MNN"
description = "a highly efficient and lightweight deep learning framework"
topics = ("deep learning","ai","mnn")
tool_requires = "cmake/[>=3.15.7]"
package_type = "library"
# Binary configuration
settings = "os", "compiler", "build_type", "arch"
options = {
"use_system_lib": [True, False],
"build_hard": [True, False],
"shared": [True, False],
"win_runtime_mt": [True, False],
"orbid_multi_thread": [True, False],
"openmp": [True, False],
"use_thread_pool": [True, False],
"build_train": [True, False],
"build_demo": [True, False],
"build_tools": [True, False],
"build_quantools": [True, False],
"evaluation": [True, False],
"build_converter": [True, False],
"support_tflite_quan": [True, False],
"debug_memory": [True, False],
"debug_tensor_size": [True, False],
"gpu_trace": [True, False],
"portable_build": [True, False],
"sep_build": [True, False],
"aapl_fmwk": [True, False],
"with_plugin": [True, False],
"build_mini": [True, False],
"use_sse": [True, False],
"build_codegen": [True, False],
"enable_coverage": [True, False],
"build_protobuffer": [True, False],
"build_opencv": [True, False],
"internal_features": [True, False],
"metal": [True, False],
"opencl": [True, False],
"opengl": [True, False],
"vulkan": [True, False],
"arm82": [True, False],
"onednn": [True, False],
"avx512": [True, False],
"cuda": [True, False],
"tensorrt": [True, False],
"coreml": [True, False],
"build_benchmark": [True, False],
"build_test": [True, False],
"build_for_android_command": [True, False],
"use_logcat": [True, False],
"use_cpp11": [True, False],
}
default_options = {
"use_system_lib": False,
"build_hard": False,
"shared": False,
"win_runtime_mt": False,
"orbid_multi_thread": False,
"openmp": False,
"use_thread_pool": True,
"build_train": False,
"build_demo": False,
"build_tools": True,
"build_quantools": False,
"evaluation": False,
"build_converter": False,
"support_tflite_quan": True,
"debug_memory": False,
"debug_tensor_size": False,
"gpu_trace": False,
"portable_build": False,
"sep_build": False,
"aapl_fmwk": False,
"with_plugin": False,
"build_mini": False,
"use_sse": True,
"build_codegen": False,
"enable_coverage": False,
"build_protobuffer": True,
"build_opencv": False,
"internal_features": False,
"metal": False,
"opencl": False,
"opengl": False,
"vulkan": False,
"arm82": False,
"onednn": False,
"avx512": False,
"cuda": False,
"tensorrt": False,
"coreml": False,
"build_benchmark": False,
"build_test": False,
"build_for_android_command": False,
"use_logcat": True,
"use_cpp11": True,
}
# Sources are located in the same place as this recipe, copy them to the recipe
exports_sources = "CMakeLists.txt", "*/*"
def generate(self):
tc = CMakeToolchain(self)
tc.variables["MNN_USE_SYSTEM_LIB"] = self.options.use_system_lib
tc.variables["MNN_BUILD_HARD"] = self.options.build_hard
tc.variables["MNN_BUILD_SHARED_LIBS"] = self.options.shared
tc.variables["MNN_WIN_RUNTIME_MT"] = self.options.win_runtime_mt
tc.variables["MNN_FORBID_MULTI_THREAD"] = self.options.orbid_multi_thread
tc.variables["MNN_OPENMP"] = self.options.openmp
tc.variables["MNN_USE_THREAD_POOL"] = self.options.use_thread_pool
tc.variables["MNN_BUILD_TRAIN"] = self.options.build_train
tc.variables["MNN_BUILD_DEMO"] = self.options.build_demo
tc.variables["MNN_BUILD_TOOLS"] = self.options.build_tools
tc.variables["MNN_BUILD_QUANTOOLS"] = self.options.build_quantools
tc.variables["MNN_EVALUATION"] = self.options.evaluation
tc.variables["MNN_BUILD_CONVERTER"] = self.options.build_converter
tc.variables["MNN_SUPPORT_TFLITE_QUAN"] = self.options.support_tflite_quan
tc.variables["MNN_DEBUG_MEMORY"] = self.options.debug_memory
tc.variables["MNN_DEBUG_TENSOR_SIZE"] = self.options.debug_tensor_size
tc.variables["MNN_GPU_TRACE"] = self.options.gpu_trace
tc.variables["MNN_PORTABLE_BUILD"] = self.options.portable_build
tc.variables["MNN_SEP_BUILD"] = self.options.sep_build
tc.variables["MNN_AAPL_FMWK"] = self.options.aapl_fmwk
tc.variables["MNN_WITH_PLUGIN"] = self.options.with_plugin
tc.variables["MNN_BUILD_MINI"] = self.options.build_mini
tc.variables["MNN_USE_SSE"] = self.options.use_sse
tc.variables["MNN_BUILD_CODEGEN"] = self.options.build_codegen
tc.variables["MNN_ENABLE_COVERAGE"] = self.options.enable_coverage
tc.variables["MNN_BUILD_PROTOBUFFER"] = self.options.build_protobuffer
tc.variables["MNN_BUILD_OPENCV"] = self.options.build_opencv
tc.variables["MNN_INTERNAL"] = self.options.internal_features
# backend options
tc.variables["MNN_METAL"] = self.options.metal
tc.variables["MNN_OPENCL"] = self.options.opencl
tc.variables["MNN_OPENGL"] = self.options.opengl
tc.variables["MNN_VULKAN"] = self.options.vulkan
tc.variables["MNN_ARM82"] = self.options.arm82
tc.variables["MNN_ONEDNN"] = self.options.onednn
tc.variables["MNN_AVX512"] = self.options.avx512
tc.variables["MNN_CUDA"] = self.options.cuda
tc.variables["MNN_TENSORRT"] = self.options.tensorrt
tc.variables["MNN_COREML"] = self.options.coreml
# target options
tc.variables["MNN_BUILD_BENCHMARK"] = self.options.build_benchmark
tc.variables["MNN_BUILD_TEST"] = self.options.build_test
tc.variables["MNN_BUILD_FOR_ANDROID_COMMAND"] = self.options.build_for_android_command
tc.variables["MNN_USE_LOGCAT"] = self.options.use_logcat
tc.variables["MNN_USE_CPP11"] = self.options.use_cpp11
tc.generate()
cd = CMakeDeps(self)
cd.generate()
env = VirtualBuildEnv(self)
env.generate()
def build(self):
cmake = CMake(self)
cmake.configure()
cmake.build()
def package(self):
cmake = CMake(self)
cmake.install()
@property
def _mnn_lib(self):
_name = "MNN.lib" if self.settings.compiler == "msvc" else "libMNN.a"
return os.path.join(self.package_folder,"lib","",_name)
def package_info(self):
self.cpp_info.libs = ["MNN"]
if not self.options.shared :
if self.settings.compiler == "msvc":
self.cpp_info.sharedlinkflags.extend(["/WHOLEARCHIVE:MNN"])
self.cpp_info.exelinkflags = self.cpp_info.sharedlinkflags
elif self.settings.compiler == "gcc":
# about LINKER: ,see also https://cmake.org/cmake/help/latest/command/target_link_options.html#handling-compiler-driver-differences
self.cpp_info.sharedlinkflags.extend(["LINKER:--whole-archive",self._mnn_lib,"LINKER:--no-whole-archive"])
self.cpp_info.exelinkflags = self.cpp_info.sharedlinkflags
elif self.settings.compiler == "clang":
self.cpp_info.sharedlinkflags.extend(["LINKER:--whole-archive",self._mnn_lib,"LINKER:--no-whole-archive"])
self.cpp_info.exelinkflags = self.cpp_info.sharedlinkflags
这个文件来自《conan入门(二十九):对阿里mnn进行Conan封装塈conans.CMake和conan.tools.cmake.CMake的区别》，这个文件主要是针对CMakeLists.txt的编译选项进行conan的option定义，换句话说，这就是conan的CMakeLists.txt文件。
从MNN的官方中获取源码，并切换到指定版本的源码
git clone https://github.com/alibaba/MNN.git -b 2.4.0
将上面的conanfile.py保存到MNN的源码根目录下，进行交叉编译，这里我是用之前提供的msvc编译windows的版本。如果需要其他平台的交叉编译工具链文件，更换这个地方就行。
[settings]
os=Windows
compiler=msvc
#Visual Studio 2015 对应 compiler.version=190
#Visual Studio 2017 对应 compiler.version=191
#Visual Studio 2019 对应 compiler.version=192
#Visual Studio 2022 对应 compiler.version=193
compiler.version=192
#对于 Visual Studio 2015，您可以使用：v140或者v140_xp（如果您需要支持 Windows XP）
#compiler.toolset=v190_xp
# static ：表示使用静态链接的运行时库。dynamic：表示使用动态链接的运行时库。
compiler.runtime=static
compiler.cppstd=14
build_type=Release
arch=x86_64
[options]
boost/*:without_stacktrace=False
#True
#[build_requires]
#[env]
cd MNN
conan create . -pr:h msvc -pr:b default
开始进行配置，并保存在当地的缓存，接下来变会根据提供的工具链文件进行编译。
编译好的库将保存在conan的本地缓存指定目录下：
其实到了这一步，文章开头的问题就已经解决了，此时此刻在你的源码编译中，就能直接编译啦，因为会优先从缓存中进行获取。当然，为了后面更换环境，或者你的团队中其他人想要用到这个版本的库，最好是上传到私有仓库中。
conan upload mnn/2.4.0 -r conan
指令中的conan是我这边的一个私有仓库。
这样，你的私有仓库和本地缓存就存在了原来没有的MNN库。这时候编译自己的源码就能顺利进行了
这里已经找到了缓存的版本，编译正常进行。
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