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https://github.com/dlunion/CC4.0

Caffe for CC4.0-Windows，简单的Caffe C++接口，方便简单
https://github.com/dlunion/CC4.0

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Caffe for CC4.0-Windows，简单的Caffe C++接口，方便简单

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README 

        
# 提示

建议不要使用这个项目，更建议学习pytorch+tensorRT技术栈。这样收益会很大，谢谢



# CC4.0

Caffe for CC4.0-Windows，简单的Caffe C++接口，方便简单而更深入的研究深度学习



# 特性

1.只需要一个头文件和一个依赖项libcaffe.lib


2.能够轻易使用C++写训练过程或调用过程


3.能够轻易自定义layer（不用编译caffe也不用修改caffe.proto，只修改代码即可使用）、自己实现数据层，不需要lmdb也能高效率训练


4.能够在训练过程中对自定义layer进行调试查看中间结果


5.支持LSTM不定长OCR（有案例），支持SSD更轻易的训练起来


6.有了4.0的支持，很轻易的能够实现任何新的网络结构


7.可以允许通过自定义层，训练中查看训练效果，更加容易理解CNN在干嘛，学的效果怎么样，不再盲目了



# 编译

编译环境：VS2013


CUDA版本：8.0


CUDNN版本：5.0


只需要下载3rd目录下的下载地址，解压出来后。安装完cuda8.0即可编译


如果不想自己编译可以下载下面已经编译好的库文件即可，库文件里面包含了CUDA8.0的下载地址

和所有要用到的工具等的下载地址或文件，直接vs打开后即可编译。编译时请选择ReleaseDLL



# 下载编译好的库文件和案例等数据

推荐使用VS2013，下载后压缩包已经配置好环境和带好了OpenCV2.4.10静态库


[CC4.0.3.rar-百度网盘](https://pan.baidu.com/s/1OQDmxWwVpVohER2YMqGbZQ)，里面的依赖可以用，但是头文件和libcaffe.dll不可用（因为有几个bug），等待重新编译并上传



# 案例

非常容易在C++里面实现自己的datalayer、losslayer等，自定义数据的输入等

在prototxt中定义如下：

``` protobuf

layer {

  name: "data"

  type: "CPP"

  top: "data"

  top: "label"

  include {

    phase: TRAIN

  }

  cpp_param {

    type: "LstmDataLayer"

    param_str: "batch_size: 16; width: 150; height: 60; num: 6" 

  }

}

```



# cpp代码训练：

``` c++

#include 

#pragma comment(lib, "libcaffe.lib")



//define my LstmDataLayer

class LstmDataLayer : public DataLayer{

public:

    SETUP_LAYERFUNC(LstmDataLayer);



    virtual int getBatchCacheSize(){

        return 3;

    }



    virtual void loadBatch(Blob** top, int numTop){



        Blob* image = top[0];

        Blob* label = top[1];



        float* image_ptr = image->mutable_cpu_data();

        float* label_ptr = label->mutable_cpu_data();

        int batch_size = image->num();

        int w = image->width();

        int h = image->height();



        for (int i = 0; i < batch_size; ++i){

           //...

        }

    }



    virtual void setup(

        const char* name, const char* type, const char* param_str, int phase, 

        Blob** bottom, int numBottom, Blob** top, int numTop){

        //...

    }

};



void main(){

    installRegister();



    //register LstmDataLayer

    INSTALL_LAYER(LstmDataLayer);



    WPtr solver = loadSolverFromPrototxt("solver-gpu.prototxt");

    //solver->Restore("models/blstmctc_iter_12111.solverstate");

    solver->Solve();

}

```



# 前向运算

``` c++

void test(){

    //...

    WPtr net = loadNetFromPrototxt("deploy.prototxt");

    net->copyTrainedParamFromFile("models/blstmctc_iter_6044.caffemodel");



    im.convertTo(im, CV_32F, 1/127.5, -1);

    Blob* input = net->input_blob(0);

    input->Reshape(1, 3, im.rows, im.cols);

    net->Reshape();



    Mat ms[3];

    float* ptr = input->mutable_cpu_data();

    for (int i = 0; i < 3; ++i){

        ms[i] = Mat(input->height(), input->width(), CV_32F, ptr);

        ptr += input->width()*input->height();

    }

    split(im, ms);

    net->Forward();



    Blob* out = net->output_blob(0);

    //...

    //out就是结果

}

```



# SSD的一步训练

``` c++

#include 

using namespace cc;



class SSDMyDataLayer : public SSDDataLayer{

public:

    SETUP_LAYERFUNC(SSDMyDataLayer);



    SSDMyDataLayer(){

        this->datum_ = createAnnDatum();

        this->label_map_ = loadLabelMap("labelmap_voc.prototxt");

    }



    virtual ~SSDMyDataLayer(){

        releaseAnnDatum(this->datum_);

    }



    virtual int getBatchCacheSize(){

        return 3;

    }



    virtual void* getAnnDatum(){

        if (!loadAnnDatum("00001.jpg", "00001.xml", 0, 0, 0, 0, true, "jpg", "xml", this->label_map_, this->datum_)){

            printf("无法加载.\n");

            exit(0);

        }

        return this->datum_;

    }



    virtual void releaseAnnDatum(void* datum){

    }



private:

    void* datum_;

    void* label_map_;

};



void main(){

    installRegister();

    INSTALL_LAYER(SSDMyDataLayer);



    WPtr solver = loadSolverFromPrototxt("solver.prototxt");

    solver->net()->copyTrainedParamFromFile("VGG_ILSVRC_16_layers_fc_reduced.caffemodel");

    //solver->Restore("models/blstmctc_iter_12111.solverstate");

    solver->Solve();

}

```



# SSD的train.prototxt的data层：

``` protobuf

layer {

  name: "data"

  type: "CPP"

  top: "data"

  top: "label"

  include {

    phase: TRAIN

  }

  cpp_param{

    type: "SSDMyDataLayer"

  }

  transform_param {

    mirror: true

    mean_value: 104

    mean_value: 117

    mean_value: 123

    resize_param {

      prob: 1

      resize_mode: WARP

      height: 300

      width: 300   

    }

    emit_constraint {

      emit_type: CENTER

    }

  }

  #... 参考标准SSD的数据层部分即可，主要修改了type和cpp_param

}

```