如何解决Boost 序列化：在不破坏兼容性的情况下添加新的 register_type
我使用 Boost 用这样的代码序列化一个 NeuralNetwork 对象
template <class Archive>
void NeuralNetwork::serialize(Archive& ar,unsigned version)
{
boost::serialization::void_cast_register<NeuralNetwork,StatisticAnalysis>();
ar & boost::serialization::base_object<StatisticAnalysis>(*this);
ar.template register_type<FullyConnected>(); // derived from Layer object
ar.template register_type<Recurrence>();
ar.template register_type<Convolution>();
ar.template register_type<MaxPooling>();
ar & layers; // vector<unique_ptr<Layer>>
}
我的问题是我已经序列化了对象，当我添加一个从 Layer 继承的新类时，出现以下错误：unkNown file: error: C++ exception with description "unregistered class" thrown in the test body.
如何添加新的 register_type<T>
不破坏与已经序列化和保存的对象的兼容性？
解决方法
当我添加一个从 Layer 继承的新类时，出现以下错误：unknown file: error: C++ exception with description "unregistered class" throw in the test body.
我认为这是由于其他原因造成的。
参考点：“自动”类型注册
典型的模式是不使用 register_type。相反，您将使用自动注册机制：https://www.boost.org/doc/libs/1_32_0/libs/serialization/doc/special.html#registration
#include <boost/archive/text_oarchive.hpp>
#include <boost/archive/text_iarchive.hpp>
#include <boost/serialization/base_object.hpp>
#include <boost/serialization/export.hpp>
#include <boost/serialization/unique_ptr.hpp>
#include <boost/serialization/access.hpp>
#include <boost/serialization/vector.hpp>
#include <sstream>
#include <iostream>
#include <iomanip>
#include <boost/core/demangle.hpp>
using boost::serialization::base_object;
using boost::core::demangle;
struct StatisticAnalysis {
virtual ~StatisticAnalysis() = default;
virtual void report(std::ostream&) const = 0;
std::vector<int> base_data {1,2,3};
void serialize(auto& ar,unsigned) { ar & base_data; }
friend std::ostream& operator<<(std::ostream& os,StatisticAnalysis const& sa) {
sa.report(os);
return os;
}
};
BOOST_SERIALIZATION_ASSUME_ABSTRACT(StatisticAnalysis)
BOOST_CLASS_EXPORT(StatisticAnalysis)
struct Layer {
virtual ~Layer() = default;
void serialize(auto&,unsigned) { }
};
BOOST_SERIALIZATION_ASSUME_ABSTRACT(Layer)
BOOST_CLASS_EXPORT(Layer)
struct FullyConnected : Layer { void serialize(auto &ar,unsigned) { ar &base_object<Layer>(*this); } };
struct Recurrence : Layer { void serialize(auto &ar,unsigned) { ar &base_object<Layer>(*this); } };
struct Convolution : Layer { void serialize(auto &ar,unsigned) { ar &base_object<Layer>(*this); } };
struct MaxPooling : Layer { void serialize(auto &ar,unsigned) { ar &base_object<Layer>(*this); } };
BOOST_CLASS_EXPORT(FullyConnected)
BOOST_CLASS_EXPORT(Recurrence)
BOOST_CLASS_EXPORT(Convolution)
BOOST_CLASS_EXPORT(MaxPooling)
#if defined(VERSION2)
struct NewLayer : Layer {
void serialize(auto &ar,unsigned) { ar &base_object<Layer>(*this); }
};
BOOST_CLASS_EXPORT(NewLayer)
#endif
struct NeuralNetwork : StatisticAnalysis {
virtual void report(std::ostream& os) const override {
os << layers.size() << " layers: {";
for (auto& layer : layers) {
os << " " << demangle(typeid(*layer).name());
}
os << " }\n";
}
std::vector<std::unique_ptr<Layer> > layers;
void serialize(auto& ar,unsigned) {
ar &base_object<StatisticAnalysis>(*this);
ar &layers;
}
};
BOOST_CLASS_EXPORT(NeuralNetwork)
int main()
{
std::unique_ptr<StatisticAnalysis> analysis;
std::stringstream ss;
{
boost::archive::text_oarchive oa(ss);
analysis = [] {
auto nn = std::make_unique<NeuralNetwork>();
nn->layers.emplace_back(std::make_unique<FullyConnected>());
nn->layers.emplace_back( std::make_unique<Recurrence>());
nn->layers.emplace_back(std::make_unique<Convolution>());
nn->layers.emplace_back(std::make_unique<FullyConnected>());
nn->layers.emplace_back(std::make_unique<FullyConnected>());
nn->layers.emplace_back(std::make_unique<MaxPooling>());
return nn;
}();
oa << analysis;
}
std::cout << "Data: " << std::quoted(ss.str()) << "\n";
{
boost::archive::text_iarchive ia(ss);
analysis.reset();
ia >> analysis;
std::cerr << *analysis << "\n";
}
}
两个版本都有相同的存档：
Data: "22 serialization::archive 17 0 0 1 13 NeuralNetwork 1 0
0 0 0 3 0 1 2 3 0 0 6 0 0 0 7 14 FullyConnected 1 0
1 1 0
2 8 10 Recurrence 1 0
3
4 9 11 Convolution 1 0
5
6 7
7
8 7
9
10 10 10 MaxPooling 1 0
11
12
"
6 layers: { FullyConnected Recurrence Convolution FullyConnected FullyConnected MaxPooling }
与 register_type 比较
只要确保 register_type 实际上不会造成兼容性问题 - 因为文档可能是 implying indeed：
请注意，如果序列化功能在保存和加载之间拆分，则两个功能都必须包含注册。这是保持同步保存和相应加载所必需的。
注意：在看到输出与文本档案的预期相同后，我还修改了写入二进制档案，以防万一有一些实现差异。
Live Demo（同时包含 v1 和 v2 版本）：
//#define VERSION2
#include <boost/archive/binary_oarchive.hpp>
#include <boost/archive/binary_iarchive.hpp>
#include <boost/serialization/base_object.hpp>
#include <boost/serialization/export.hpp>
#include <boost/serialization/unique_ptr.hpp>
#include <boost/serialization/access.hpp>
#include <boost/serialization/vector.hpp>
#include <fstream>
#include <iostream>
#include <iomanip>
#include <boost/core/demangle.hpp>
using boost::serialization::base_object;
using boost::core::demangle;
struct StatisticAnalysis {
virtual ~StatisticAnalysis() = default;
virtual void report(std::ostream&) const = 0;
std::vector<int> base_data {1,unsigned) { ar &base_object<Layer>(*this); } };
//BOOST_CLASS_EXPORT(FullyConnected)
//BOOST_CLASS_EXPORT(Recurrence)
//BOOST_CLASS_EXPORT(Convolution)
//BOOST_CLASS_EXPORT(MaxPooling)
#if defined(VERSION2)
struct NewLayer : Layer {
void serialize(auto &ar,unsigned) { ar &base_object<Layer>(*this); }
};
//BOOST_CLASS_EXPORT(NewLayer)
#endif
struct NeuralNetwork : StatisticAnalysis {
virtual void report(std::ostream& os) const override {
os << layers.size() << " layers: {";
for (auto& layer : layers) {
os << " " << demangle(typeid(*layer).name());
}
os << " }\n";
}
std::vector<std::unique_ptr<Layer> > layers;
void serialize(auto& ar,unsigned) {
ar &base_object<StatisticAnalysis>(*this);
ar.template register_type<FullyConnected>(); // derived from Layer object
ar.template register_type<Recurrence>();
ar.template register_type<Convolution>();
ar.template register_type<MaxPooling>();
#if defined(VERSION2)
ar.template register_type<NewLayer>();
#endif
ar &layers;
}
};
BOOST_CLASS_EXPORT(NeuralNetwork)
int main(int,char **argv) {
std::string program_name(*argv);
std::unique_ptr<StatisticAnalysis> analysis;
{
std::ofstream ofs(program_name + ".bin",std::ios::binary);
boost::archive::binary_oarchive oa(ofs);
analysis = [] {
auto nn = std::make_unique<NeuralNetwork>();
nn->layers.emplace_back(std::make_unique<FullyConnected>());
nn->layers.emplace_back( std::make_unique<Recurrence>());
nn->layers.emplace_back(std::make_unique<Convolution>());
nn->layers.emplace_back(std::make_unique<FullyConnected>());
nn->layers.emplace_back(std::make_unique<FullyConnected>());
nn->layers.emplace_back(std::make_unique<MaxPooling>());
return nn;
}();
oa << analysis;
}
{
std::ifstream ifs(program_name + ".bin",std::ios::binary);
boost::archive::binary_iarchive ia(ifs);
analysis.reset();
ia >> analysis;
std::cerr << *analysis << "\n";
}
}
测试命令
g++ -std=c++20 -Os -DVERSION1 -lboost_serialization main.cpp -o v1
g++ -std=c++20 -Os -DVERSION2 -lboost_serialization main.cpp -o v2
./v1 && ./v2 && md5sum v1.bin v2.bin
成功完成，写入相同的存档 v1.bin 和 v2.bin，如它们的 md5sum 所示：
5bba3ef7d8a25bd50d0768fed5dfed64 v1.bin
5bba3ef7d8a25bd50d0768fed5dfed64 v2.bin
总结 - 从这里开始
我认为原则上添加子类不应该破坏存档兼容性。如果看起来确实如此，
-
从以上两个选项中寻找实现方式的不同
-
寻找其他同时发生的变化
- 使用 git-bisect 之类的东西来检查仍然可以反序列化的最后一个“工作”版本，隔离破坏性更改
- 检查例如boost 库版本兼容
- 确保您没有使用不可移植的档案（特别是
binary_[io]archiveis not portable；另见 boost text deserialization crashing on 32bit windows machine）
如果您想了解更多信息，我会在这里。如果问题变得足够不同，请考虑开一个新问题。
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