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10.Visual Studio动态加载_哔哩哔哩_bilibili

1.工程组织

vs2015 工程组织与动态加载,c++

① researcher.cpp

#include "nn/nn.h"

#include "nn/factory.h"
#include "nn/factory_impl/factory_impl.h"

#include <iostream>

int main()
{
    int ret = 0;

    factory_i* fct = new factory_impl();
    auto nn = fct->create_nn("dnn");

    const char* data_from_somewhere = "A B C D";
    ret = nn->train(data_from_somewhere, strlen(data_from_somewhere));
    if (0 != ret)
        std::cout << "failed to train the model." << std::endl;

    const char* model_from_somewhere = "k=10;b=200";
    ret = nn->forward(model_from_somewhere, strlen(model_from_somewhere));
    if (0 != ret)
        std::cout << "failed to forward the model." << std::endl;

    delete nn; nn = nullptr;
    delete fct; fct = nullptr;

    return 0;
}

② nn.h

#ifndef __NN_H__
#define __NN_H__

class nn_i
{
public:
    virtual ~nn_i() = default;

    virtual int train(const char* data, size_t data_size) = 0;
    virtual int forward(const char* model, size_t model_size) = 0;
};

#endif

（1）cnn.h

#ifndef __CNN_H__
#define __CNN_H__
#include "nn/nn.h"

#include <string>
#include <iostream>

class cnn : public nn_i
{
public:
    explicit cnn(const char* conf)
        : conf_(conf)
    {
    }
    virtual ~cnn() = default;

    virtual int train(const char* data, size_t data_size) override
    {
        std::cout << "train with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

    virtual int forward(const char* model, size_t model_size) override
    {
        std::cout << "forward with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

public:
    std::string conf_;
};
#endif

（2）dnn.h

#ifndef __DNN_H__
#define __DNN_H__
#include "nn/nn.h"

#include <string>
#include <iostream>

class dnn : public nn_i
{
public:
    explicit dnn(const char* conf)
        : conf_(conf)
    {
    }
    virtual ~dnn() = default;

    virtual int train(const char* data, size_t data_size) override
    {
        std::cout << "train with fully connected layer in dnn." << std::endl;
        return 0;
    }

    virtual int forward(const char* model, size_t model_size) override
    {
        std::cout << "forward with fully connected layer in dnn." << std::endl;
        return 0;
    }

public:
    std::string conf_;
};

#endif

（3）rnn.h

#ifndef __RNN_H__
#define __RNN_H__
#include "nn/nn.h"

#include <string>
#include <iostream>

class rnn : public nn_i
{
public:
    explicit rnn(const char* conf)
        : conf_(conf)
    {
    }
    virtual ~rnn() = default;

    virtual int train(const char* data, size_t data_size) override
    {
        std::cout << "train with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

    virtual int forward(const char* model, size_t model_size) override
    {
        std::cout << "forward with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

public:
    std::string conf_;
};

#endif

③ factory.h

#ifndef __FACTORY_H__
#define __FACTORY_H__

class nn_i;
class factory_i
{
public:
    virtual ~factory_i() = default;
    virtual nn_i* create_nn(const char* name) = 0;
};

#endif

（1）factory_impl.h

#ifndef __FACTORY_IMPL_H__
#define __FACTORY_IMPL_H__

#include "nn/factory.h"

class nn_i;
class factory_impl : public factory_i
{
public:
    explicit factory_impl();
    virtual ~factory_impl() = default;

    virtual nn_i* create_nn(const char* name) override;
};

#endif

（2）factory_impl.cpp

#include "factory_impl.h"

#include "nn/network_impl/rnn.h"
#include "nn/network_impl/cnn.h"
#include "nn/network_impl/dnn.h"

#include <string>

factory_impl::factory_impl()
{
}

nn_i* factory_impl::create_nn(const char* name)
{
    if (nullptr == name)
        return nullptr;

    const char* conf_from_somewhere = "Hero=Iron Man";

    auto str_name = std::string(name);
    if (str_name == "dnn")
        return new dnn(conf_from_somewhere);
    else if (str_name == "rnn")
        return new rnn(conf_from_somewhere);
    else if (str_name == "cnn")
        return new cnn(conf_from_somewhere);

    return nullptr;
}

2.动态加载

vs2015 工程组织与动态加载,c++

现在要把上面的工程改成动态加载：

vs2015 工程组织与动态加载,c++ 文章来源地址https://www.toymoban.com/news/detail-606924.html

① network_loader.cpp

#include "nn/nn_api.h"

#include <iostream>
#include <windows.h>

static const char* filename = "libnn.dll";

int main()
{
    /* 1. 加载DLL到当前的地址空间 */
    HMODULE handle =  LoadLibraryA(filename);
    if (!handle)
    {
        std::cout << "failed to load library: "
            << filename << std::endl;
        return -1;
    }

    /* 2. 获取dll导出函数地址 */
    auto func_init_network 
        = (Proc_InitNetwork)GetProcAddress(handle, "InitNetwork");
    auto func_fini_network
        = (Proc_FiniNetwork)GetProcAddress(handle, "FiniNetwork");
    auto func_train_network
        = (Proc_TrainNetwork)GetProcAddress(handle, "TrainNetwork");
    auto func_infer_network
        = (Proc_InferNetwork)GetProcAddress(handle, "InferNetwork");

    /* 3. 调用dll完成我们的功能 */
    func_init_network("dnn");

    const char* data_from_somewhere = "A B C D";
    func_train_network(data_from_somewhere, strlen(data_from_somewhere));

    const char* model_from_somewhere = "k=10;b=200";
    func_infer_network(model_from_somewhere, strlen(model_from_somewhere));

    func_fini_network();

    /* 4. 释放dll */
    FreeLibrary(handle);

    return 0;
}

② api

（1）nn_api.h

#pragma once

#ifdef __cplusplus
extern "C" {
#endif

_declspec(dllexport) int __stdcall InitNetwork(const char* which);

_declspec(dllexport) int __stdcall FiniNetwork();

_declspec(dllexport) int __stdcall TrainNetwork(const char* data, size_t size);

_declspec(dllexport) int __stdcall InferNetwork(const char* model, size_t size);


#ifdef __cplusplus
}
#endif

（2）nn_api.cpp

#include "nn_api.h"

#include "nn/factory_impl/factory_impl.h"
#include "nn/factory.h"
#include "nn/nn.h"

#include <iostream>

static nn_i* g_nn = nullptr;

int InitNetwork(const char* which)
{
    factory_i* fct = new factory_impl();
    g_nn = fct->create_nn(which);

    delete fct;

    return 0;
}

int FiniNetwork()
{
    delete g_nn; g_nn = nullptr;
    return 0;
}

int TrainNetwork(const char* data, size_t size)
{
    if (!g_nn)
        return -1;

    int ret = g_nn->train(data, size);
    if (0 != ret)
        std::cout << "failed to train the model." << std::endl;

    return 0;
}

int InferNetwork(const char* model, size_t size)
{
    if (!g_nn)
        return -1;

    int ret = g_nn->forward(model, size);
    if (0 != ret)
        std::cout << "failed to infert the network." << std::endl;

    return 0;

    return 0;
}

③ nn.h （自此及往下部分与之前的一致）

#ifndef __NN_H__
#define __NN_H__

class nn_i
{
public:
    virtual ~nn_i() = default;

    virtual int train(const char* data, size_t data_size) = 0;
    virtual int forward(const char* model, size_t model_size) = 0;
};

#endif

（1）cnn.h

#ifndef __CNN_H__
#define __CNN_H__
#include "nn/nn.h"

#include <string>
#include <iostream>

class cnn : public nn_i
{
public:
    explicit cnn(const char* conf)
        : conf_(conf)
    {
    }
    virtual ~cnn() = default;

    virtual int train(const char* data, size_t data_size) override
    {
        std::cout << "train with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

    virtual int forward(const char* model, size_t model_size) override
    {
        std::cout << "forward with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

public:
    std::string conf_;
};
#endif

（2）dnn.h

#ifndef __DNN_H__
#define __DNN_H__
#include "nn/nn.h"

#include <string>
#include <iostream>

class dnn : public nn_i
{
public:
    explicit dnn(const char* conf)
        : conf_(conf)
    {
    }
    virtual ~dnn() = default;

    virtual int train(const char* data, size_t data_size) override
    {
        std::cout << "train with fully connected layer in dnn." << std::endl;
        return 0;
    }

    virtual int forward(const char* model, size_t model_size) override
    {
        std::cout << "forward with fully connected layer in dnn." << std::endl;
        return 0;
    }

public:
    std::string conf_;
};

#endif

（3）rnn.h

#ifndef __RNN_H__
#define __RNN_H__
#include "nn/nn.h"

#include <string>
#include <iostream>

class rnn : public nn_i
{
public:
    explicit rnn(const char* conf)
        : conf_(conf)
    {
    }
    virtual ~rnn() = default;

    virtual int train(const char* data, size_t data_size) override
    {
        std::cout << "train with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

    virtual int forward(const char* model, size_t model_size) override
    {
        std::cout << "forward with Convolution and Pooling layer in cnn." << std::endl;
        return 0;
    }

public:
    std::string conf_;
};

#endif

④ factory.h

#ifndef __FACTORY_H__
#define __FACTORY_H__

class nn_i;
class factory_i
{
public:
    virtual ~factory_i() = default;
    virtual nn_i* create_nn(const char* name) = 0;
};

#endif

（1）factory_impl.h

#ifndef __FACTORY_IMPL_H__
#define __FACTORY_IMPL_H__

#include "nn/factory.h"

class nn_i;
class factory_impl : public factory_i
{
public:
    explicit factory_impl();
    virtual ~factory_impl() = default;

    virtual nn_i* create_nn(const char* name) override;
};

#endif

（2）factory_impl.cpp

#include "factory_impl.h"

#include "nn/network_impl/rnn.h"
#include "nn/network_impl/cnn.h"
#include "nn/network_impl/dnn.h"

#include <string>

factory_impl::factory_impl()
{
}

nn_i* factory_impl::create_nn(const char* name)
{
    if (nullptr == name)
        return nullptr;

    const char* conf_from_somewhere = "Hero=Iron Man";

    auto str_name = std::string(name);
    if (str_name == "dnn")
        return new dnn(conf_from_somewhere);
    else if (str_name == "rnn")
        return new rnn(conf_from_somewhere);
    else if (str_name == "cnn")
        return new cnn(conf_from_somewhere);

    return nullptr;
}

到了这里，关于vs2015 工程组织与动态加载的文章就介绍完了。如果您还想了解更多内容，请在右上角搜索TOY模板网以前的文章或继续浏览下面的相关文章，希望大家以后多多支持TOY模板网！

vs2015 工程组织与动态加载

1.工程组织

① researcher.cpp

② nn.h

（1）cnn.h

（2）dnn.h

（3）rnn.h

③ factory.h

（1）factory_impl.h

（2）factory_impl.cpp

2.动态加载

① network_loader.cpp

② api

（1）nn_api.h

（2）nn_api.cpp

③ nn.h （自此及往下部分与之前的一致）

（1）cnn.h

（2）dnn.h

（3）rnn.h

④ factory.h

（1）factory_impl.h

（2）factory_impl.cpp

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vs2015 工程组织与动态加载

1.工程组织

① researcher.cpp

② nn.h

（1）cnn.h

（2）dnn.h

（3）rnn.h

③ factory.h

（1）factory_impl.h

（2）factory_impl.cpp

2.动态加载

① network_loader.cpp

② api

（1）nn_api.h

（2）nn_api.cpp

③ nn.h （自此及往下部分 与之前的一致）

（1）cnn.h

（2）dnn.h

（3）rnn.h

④ factory.h

（1）factory_impl.h

（2）factory_impl.cpp

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③ nn.h （自此及往下部分与之前的一致）