|
/*
|
||
|
|
* Licensed to the Apache Software Foundation (ASF) under one
|
||
|
|
* or more contributor license agreements. See the NOTICE file
|
||
|
|
* distributed with this work for additional information
|
||
|
|
* regarding copyright ownership. The ASF licenses this file
|
||
|
|
* to you under the Apache License, Version 2.0 (the
|
||
|
|
* "License"); you may not use this file except in compliance
|
||
|
|
* with the License. You may obtain a copy of the License at
|
||
|
|
*
|
||
|
|
* http://www.apache.org/licenses/LICENSE-2.0
|
||
|
|
*
|
||
|
|
* Unless required by applicable law or agreed to in writing,
|
||
|
|
* software distributed under the License is distributed on an
|
||
|
|
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
|
||
|
|
* KIND, either express or implied. See the License for the
|
||
|
|
* specific language governing permissions and limitations
|
||
|
|
* under the License.
|
||
|
|
*/
|
||
|
|
|
||
|
|
/*!
|
||
|
|
*/
|
||
|
|
#include <iostream>
|
||
|
|
#include <fstream>
|
||
|
|
#include <map>
|
||
|
|
#include <string>
|
||
|
|
#include <vector>
|
||
|
|
#include "mxnet-cpp/MxNetCpp.h"
|
||
|
|
using namespace std;
|
||
|
|
using namespace mxnet::cpp;
|
||
|
|
|
||
|
|
/*
|
||
|
|
* This example shows how to extract features with a pretrained model.
|
||
|
|
* Get the model here:
|
||
|
|
* https://github.com/dmlc/mxnet-model-gallery
|
||
|
|
* */
|
||
|
|
|
||
|
|
/*The global context, change them if necessary*/
|
||
|
|
Context global_ctx(kGPU, 0);
|
||
|
|
// Context global_ctx(kCPU,0);
|
||
|
|
|
||
|
|
class FeatureExtractor {
|
||
|
|
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;
|
||
|
|
/*Get the feature layer we want to extract*/
|
||
|
|
void GetFeatureSymbol() {
|
||
|
|
/*
|
||
|
|
* use the following to check all the layers' names:
|
||
|
|
* */
|
||
|
|
/*
|
||
|
|
net=Symbol::Load("./model/Inception_BN-symbol.json").GetInternals();
|
||
|
|
for(const auto & layer_name:net.ListOutputs()){
|
||
|
|
LG<<layer_name;
|
||
|
|
}
|
||
|
|
*/
|
||
|
|
net = Symbol::Load("./model/Inception-BN-symbol.json")
|
||
|
|
.GetInternals()["global_pool_output"];
|
||
|
|
}
|
||
|
|
/*Fill the trained paramters into the model, a.k.a. net, executor*/
|
||
|
|
void LoadParameters() {
|
||
|
|
map<string, NDArray> paramters;
|
||
|
|
NDArray::Load("./model/Inception-BN-0126.params", 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(global_ctx);
|
||
|
|
}
|
||
|
|
if (k.first.substr(0, 4) == "arg:") {
|
||
|
|
auto name = k.first.substr(4, k.first.size() - 4);
|
||
|
|
args_map[name] = k.second.Copy(global_ctx);
|
||
|
|
}
|
||
|
|
}
|
||
|
|
/*WaitAll is need when we copy data between GPU and the main memory*/
|
||
|
|
NDArray::WaitAll();
|
||
|
|
}
|
||
|
|
void GetMeanImg() {
|
||
|
|
mean_img = NDArray(Shape(1, 3, 224, 224), global_ctx, false);
|
||
|
|
mean_img.SyncCopyFromCPU(
|
||
|
|
NDArray::LoadToMap("./model/mean_224.nd")["mean_img"].GetData(),
|
||
|
|
1 * 3 * 224 * 224);
|
||
|
|
NDArray::WaitAll();
|
||
|
|
}
|
||
|
|
|
||
|
|
public:
|
||
|
|
FeatureExtractor() {
|
||
|
|
/*prepare the model, fill the pretrained parameters, get the mean image*/
|
||
|
|
GetFeatureSymbol();
|
||
|
|
LoadParameters();
|
||
|
|
GetMeanImg();
|
||
|
|
}
|
||
|
|
|
||
|
|
void Extract(NDArray data) {
|
||
|
|
/*Normalize the pictures*/
|
||
|
|
data.Slice(0, 1) -= mean_img;
|
||
|
|
data.Slice(1, 2) -= mean_img;
|
||
|
|
args_map["data"] = data;
|
||
|
|
/*bind the executor*/
|
||
|
|
executor = net.SimpleBind(global_ctx, args_map, map<string, NDArray>(),
|
||
|
|
map<string, OpReqType>(), aux_map);
|
||
|
|
executor->Forward(false);
|
||
|
|
/*print out the features*/
|
||
|
|
auto array = executor->outputs[0].Copy(Context(kCPU, 0));
|
||
|
|
NDArray::WaitAll();
|
||
|
|
array = array.Reshape({2, 1024});
|
||
|
|
for (int i = 0; i < 1024; ++i) {
|
||
|
|
cout << array.At(0, i) << ",";
|
||
|
|
}
|
||
|
|
cout << endl;
|
||
|
|
}
|
||
|
|
};
|
||
|
|
|
||
|
|
NDArray Data2NDArray() {
|
||
|
|
NDArray ret(Shape(2, 3, 224, 224), global_ctx, false);
|
||
|
|
ifstream inf("./img.dat", ios::binary);
|
||
|
|
vector<float> data(2 * 3 * 224 * 224);
|
||
|
|
inf.read(reinterpret_cast<char *>(data.data()), 2 * 3 * 224 * 224 * sizeof(float));
|
||
|
|
inf.close();
|
||
|
|
ret.SyncCopyFromCPU(data.data(), 2 * 3 * 224 * 224);
|
||
|
|
NDArray::WaitAll();
|
||
|
|
return ret;
|
||
|
|
}
|
||
|
|
|
||
|
|
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
|
||
|
|
*
|
||
|
|
*/
|
||
|
|
auto data = Data2NDArray();
|
||
|
|
FeatureExtractor fe;
|
||
|
|
fe.Extract(data);
|
||
|
|
return 0;
|
||
|
|
}
|