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
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和cc_utils.h
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
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,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
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:
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"
}
}
#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();
}
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
}
#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();
}
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
}