cc4.0介绍 · CC框架深度学习机器学习 caffe

# 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 https://github.com/dlunion/CC4.0 # 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 8.接口查看[cc.h](https://github.com/dlunion/CC4.0/blob/master/include/caffe/cc/core/cc.h)和[cc_utils.h](https://github.com/dlunion/CC4.0/blob/master/include/caffe/cc/core/cc_utils.h) # 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 <del>[CC4.0.3.rar-百度网盘](https://pan.baidu.com/s/1OQDmxWwVpVohER2YMqGbZQ)</del>，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 <cc_utils.h> #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> solver = loadSolverFromPrototxt("solver-gpu.prototxt"); //solver->Restore("models/blstmctc_iter_12111.solverstate"); solver->Solve(); } ``` # 前向运算 ``` c++ void test(){ //... WPtr<Net> 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 <cc_utils.h> 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> 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 } ```