@ankkhedia the example is here;
#include <iostream>
#include <fstream>
#include <map>
#include <string>
#include <vector>
#include "mxnet-cpp/MxNetCpp.h"
#include <opencv2\opencv.hpp>
using namespace std;
using namespace mxnet::cpp;
/*The global context, change them if necessary*/
enum Mode
{
GPU,
CPU
};
// Context global_ctx(kCPU,0);
namespace MxSpace{
class FeatureExtractor {
public:
FeatureExtractor(std::string symbol_file, std::string params_file,
std::string layer_name)
{
GetFeatureSymbol(layer_name, symbol_file);
LoadParameters(params_file);
}
//FeatureExtractor() {
// /*prepare the model, fill the pretrained parameters, get the
mean image*/
// GetFeatureSymbol();
// LoadParameters();
// //GetMeanImg();
//}
NDArray Forward(cv::InputArray _img)
{
cv::Mat img = _img.getMat();
img.convertTo(img, CV_32F);
img = (img - 127.5) / 128;
//NDArray input(Shape(1, img.channels(), img.rows, img.cols),
ctx, false); //
NDArray input(Shape(1, img.channels(), img.rows, img.cols),
Context(kGPU, 0), false);
NDArray temp(Shape(1, img.channels(), img.rows, img.cols),
Context(kCPU, 0),false);
//NDArray::WaitAll();
auto ptr = temp.GetData();
std::vector<cv::Mat> chs(3);
for (int i = img.channels() - 1; i >= 0; i--)
{
chs[img.channels() - i - 1] = cv::Mat(img.size(),
CV_32FC1, (float*)ptr + i*img.size().area());
}
cv::split(img, chs);
//input = temp.Copy(ctx);
input.SyncCopyFromCPU(temp.GetData(), temp.Size());
NDArray::WaitAll();
return Extract(input);
}
private:
/*the mean image, get from the pretrained model*/
NDArray mean_img;
/*the following two maps store all the paramters need by the model*/
map<string, NDArray> args_map;
map<string, NDArray> aux_map;
Symbol net;
Executor *executor;
Context ctx = Context(kGPU, 0);
bool isbind = false;
/*Get the feature layer we want to extract*/
void GetFeatureSymbol() {
/*
* use the following to check all the layers' names:
* */
net=Symbol::Load("./model/resnet50-symbol.json").GetInternals();
for(const auto & layer_name:net.ListOutputs()){
LG<<layer_name;
}
//net = Symbol::Load("./model/resnet50-symbol.json")
// .GetInternals()["fc1_output"]; //
}
void SetMode(Mode mode , int device_id=0)
{
switch (mode)
{
case CPU:
ctx = Context(kCPU, device_id);
break;
case GPU:
ctx = Context(kGPU, device_id);
break;
default:
LG << "Unknown mode.";
break;
}
}
void GetFeatureSymbol(std::string layer_name , std::string symbol_file
= "./model/resnet50-symbol.json")
{
net = Symbol::Load(symbol_file).GetInternals(); // return all
output
net = layer_name.empty() ? net : net[layer_name];
}
/*Fill the trained paramters into the model, a.k.a. net, executor*/
void LoadParameters(std::string params_file =
"./model/resnet50-0050.params")
{
map<string, NDArray> paramters;
NDArray::Load(params_file, 0, ¶mters);
for (const auto &k : paramters) {
if (k.first.substr(0, 4) == "aux:") {
auto name = k.first.substr(4, k.first.size() -
4);
aux_map[name] = k.second.Copy(ctx);
}
if (k.first.substr(0, 4) == "arg:") {
auto name = k.first.substr(4, k.first.size() -
4);
args_map[name] = k.second.Copy(ctx);
}
}
/*WaitAll is need when we copy data between GPU and the main
memory*/
NDArray::WaitAll();
}
void GetMeanImg() {
mean_img = NDArray(Shape(1, 3, 112, 112), ctx, false);
mean_img.SyncCopyFromCPU(
NDArray::LoadToMap("./model/mean_224.nd")["mean_img"].GetData(),
1 * 3 * 224 * 224);
NDArray::WaitAll();
}
NDArray Extract(NDArray data)
{
args_map["data"] = data;
if (!isbind)
{
executor = net.SimpleBind(ctx, args_map, map<string,
NDArray>(), map<string, OpReqType>(), aux_map);
isbind = true;
}
executor->Forward(false);
/*print out the features*/
NDArray array = executor->outputs[0].Copy(ctx);
std::cout << array.Size() << std::endl;
NDArray::WaitAll();
for (int i = 0; i < 1024; ++i) {
cout << array.At(0, i) << ",";
}
//cout << endl;
return array;
}
public:
};
};
int main() {
/*
* get the data from a binary file ./img.data
* this file is generated by ./prepare_data_with_opencv
* it stores 2 pictures in NDArray format
*
*/
cv::Mat img = cv::imread("d:\\1.jpg");
cv::Mat mtest;
cv::resize(img, mtest, cv::Size(112, 112));
MxSpace::FeatureExtractor
fe("./model/resnet50-symbol.json","./model/resnet50-0050.params","fc1_output");
fe.Forward(mtest);
//getchar();
system("pause");
return 0;
}
[ Full content available at:
https://github.com/apache/incubator-mxnet/issues/12267 ]
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