Pass array of structs from C++ to GO - c++

I'm trying to get an array of tensorflow box predictions from C++ to golang, but I'm not able to do it no matter what I do. I have a GO program that calls a function that does tensorflow detections in C++ using cgo. This all works and I'm able to get the predictions in C++. The problem is to transfer these predictions into GO as an array of 100 structs that each hold one prediction.
I'm able to set a pointer in GO and use this pointer address to set one struct in C++. The code for this is seen below.
I want to set an array of structs in C++ and retreive this array in GO. I thought it should be easy to just use the same pointer address as earlier and use this as the address for my C++ array. Then I could restore the struct from the pointer in GO. Does anyone have a solution for this?
GO
type PredictResult struct {
Loc [4]float32
Score int
Label int
}
var predictions PredictResult
predictions_ptr := unsafe.Pointer(&predictions)
C.LIB_predict(predictions_ptr)
fmt.Println("GO predictions; ", predictions)
bridge.hpp
struct PredictResult{
float Loc[4];
int64_t Score;
int64_t Label;
};
void LIB_predict(void* predictions);
bridge.cpp
void LIB_predict(void* predictions){
PredictResult *p = (PredictResult*)predictions;
p->Score = 6;
p->Label = 95;
}
Prints:
GO predictions; {[0 0 0 0] 6 95}

Assuming your C function returns the array as PredictResult* and assuming you know the length of the returned array (in the example below I assume 10, but you can replace it by whatever works), this approach should work:
// #include <stdio.h>
// #include <stdlib.h>
//
// typedef struct PredictResult {
// float Loc[4];
// int64_t Score;
// int64_t Label;
// } PredictResult;
//
// PredictResult* getOneResult() {
// PredictResult* p = (PredictResult*)calloc(1, sizeof(PredictResult));
// p->Score = 10;
// p->Label = 99;
// p->Loc[1] = 2.5;
// p->Loc[3] = 3.5;
// return p;
// }
//
// PredictResult* getTenResults() {
// PredictResult* parr = (PredictResult*)calloc(10, sizeof(PredictResult));
// parr[0].Score = 10;
// parr[0].Label = 99;
// parr[0].Loc[1] = 2.5;
// parr[0].Loc[3] = 3.5;
//
// parr[4].Score = 44;
// parr[4].Label = 123;
// parr[4].Loc[1] = 12.25;
// parr[4].Loc[3] = -40.5;
// return parr;
// }
//
//
import "C"
type PredictResult C.struct_PredictResult
func main() {
p := C.getOneResult()
if p == nil {
log.Fatal("got nil")
}
pp := (*PredictResult)(p)
fmt.Println(pp)
parr := C.getTenResults()
if parr == nil {
log.Fatal("got nil")
}
pslice := (*[1 << 28]PredictResult)(unsafe.Pointer(parr))[:10:10]
fmt.Println(pslice)
}
What you'll be most interested in is how the result of getTenResults is converted to a Go slice of the appropriate struct type. This is employing the technique recommended on the Go wiki.
Depending on the exact signature of your C function you may need to write a "bridge" function in the import "C" part to provide the data as convenient to Go, but this is the basic gist of it.
As an alternative, if you wish to allocate the slice on the Go side and pass in a pointer to C to populate, you can do this:
// void PopulateTenResults(void* arr) {
// PredictResult* parr = (PredictResult*)arr;
// parr[1].Score = 210;
// parr[1].Label = 299;
// parr[1].Loc[1] = 22.5;
// parr[1].Loc[3] = 23.5;
//
// parr[8].Score = 344;
// parr[8].Label = 3123;
// parr[8].Loc[1] = 312.25;
// parr[8].Loc[3] = -340.5;
// }
//
//
import "C"
And then in Go do:
prslice := make([]PredictResult, 10)
C.PopulateTenResults(unsafe.Pointer(&prslice[0]))
fmt.Println(prslice)
Of course the hard-coded 10 is just for simplicity here; you could pass the length of arr as a parameter to C.

You can pass a pointer to the first element in a slice and the length of the slice to C++ and treat it like a C-style array.

Related

Creating an Array C++

I have two int values:
v_y
v_x
That I would like to convert to a size 2 char array then write to my serial port.
I currently have this code that isn't working:
void Array2 (char charArray[], int sizeOfArray);
........
}
{
char one[] = { 'v_x', 'v_y' };
Array2(one, 2);
Serial::WriteData(one, 2);
}
}
I currently get two errors:
a nonstatic member reference must be relative to a specific object
and
'Serial::WriteData':illegal call of non-static member function.
Any help, hint or idea on what I'm doing wrong would be great!
Edit: I'm using this code to communicate with my serialport CODE
// data.h or similar, you need this for both the arduino and desktop machine
struct Data {
double v_x;
double v_y;
}
On your arduino: To write
Data data = {
.v_x = 1.0, // Example put your values here
.v_y = 2.0,
};
// This should write from your Arduino to the computer
Serial::WriteData((const char*)data, sizeof(data));
On your computer
Data data;
// This reads from the serial port, and put the data in the struct
auto len = SP->ReadData((const char*)data, sizeof(data));
// check that data is the right size
// use the data from data.v_x and data.v_y
auto v_x = data.v_x;
auto v_y = data.v_y; // etc

std::map pass by reference Pointer to Object

I'm coding a plugin for XPLANE10 which gets a MSG from ROS.
My IDE is QTcreator 4.1.0 based QT 5.7.0 for Ubuntu 64 Bit. I would like to use C++11 Standards
My code explained
The main initializes ROS and creates a map -> container.
ROS spins in a loop till my GUI sends a MSG where my AirPlane should fly.
The MSG contains 3 floats(phi, theta, psi) where "phi" is the AirPlane ID, theta contains the ID for my ETA(Estimated Time of Arrival)
and psi contains the ID for my pose All of the IDs are saved in the ParameterServer(lookuptable).
So at the beginning i look up the activeAirplanes which returns a vector . I would like to store them in a map where the key is the AirCraft ID and the second param is an instance of the Object.
So i have initialized the for example(looked in container while debugging):
[0] first = 1 // Airplane ID1
[0] second = new CObject(freq)
[1] first = 2 // Airplane ID2
[1] second = new CObject(freq)
If i get a MSG from GUI
phi = 1
theta=2
psi=3
,
ROS will callback
MSG(....std::map<i32, CObject> &container)
// if phi is 1 so use the mapkey 1 and trigger the method do_stuff from CObject
do_stuff(phi, theta, psi,freq)
I would like to call the in a function from main
int getPlanes(std::map<i32,CObject>& container)
{
...
getActiveAirplanesFromServer(activePlanes);
}
First Question:
How do i pass the container to my callback?
Second Question:
How do i parallelize do_stuff() so my callback will return to main and i'm able to command more aircrafts while the others are calculated?
Third Question:
How would be the correct syntax for getPlanes to pass the container by reference so getPlanes() can edit it?
Fourth Question:
Is there a difference between
std::map<i32,CObject*> map
std::map<i32,CObject>* map
and
std::map<i32,CObject*>::iterator it=container->begin();
std::map<i32,CObject*>::iterator* it=container->begin();
If yes, what do i want ? #4Solved
// I have to edit stuff 'cause of some restrictions in my company.
#include "Header.h"
int main()
{
f64 freq = 10;
std::map<i32, CObject>* container;
std::map<i32,CObject>::iterator* it=container->begin();
// ROS
if(!ros::isInitialized())
{
int rosargc = 0;
char** rosargv = NULL;
ros::init(rosargc, rosargv, "MainNode");//), ros::init_options::AnonymousName);
}
else
{
printf("Ros has already been initialized.....\n");
}
ros::NodeHandle* mainNodeHandle=new ros::NodeHandle;
ros::AsyncSpinner spinner(2);
ParameterServer * ptrParam= new ParameterServer(mainNodeHandle);
ros::Subscriber airSub=mainNodeHandle->subscribe<own_msgs::ownStruct>("/MSG",
1000,
boost::bind(MSG,
_1,
freq,
container));
std::vector<i32> activePlanes;
i32 retVal=0;
retVal += ptrParam-> ParameterServer::getActiveAirplanesFromServer(activePlanes);
if (retVal == 0 && activePlanes.size()>0)
{
for (u32 j =0; j <activePlanes.size(); j++)
{
container->insert (std::pair<i32,CObject> (activePlanes[j] , new CObject(freq)));
}
}
while (ros::ok())
{
spinner.start(); //spinnt sehr viel :-)
ros::waitForShutdown ();
}
std::cout<<"ENDE"<<std::endl;
int retval = 1;
return retval;
}
void MSG(const own_msgs::ownStruct<std::allocator<void> >::ConstPtr &guiMSG,
f64 freq,
std::map<i32, CObject> &container)
{
if ((guiMSG->phi != 0) && (guiMSG->theta != 0) && (guiMSG->psi != 0))
{
std::string alpha = std::to_string(guiMSG->phi)+std::to_string(guiMSG->theta)+to_string(guiMSG->psi);
container.at(guiMSG->phi) -> do_stuff(guiMSG->phi,guiMSG->theta,guiMSG->psi, freq);
}
else
{
std::cout<<" Did not receive anything\n"<<endl;
}
}
void do_stuff(...)
{
//copy the IDs to private Member of this single Object
//setROS() for this single Object
//callback the current AC pose via ID from XPLANE
//callback the wished AC pose via ID from ParamServer
// do some calculations for optimum flight path
// publish the Route to XPlane
}
EDIT::
Problem is i get it to compile now and if debug it and set a breakpoint at :
void MSG(const own_msgs::ownStruct<std::allocator<void> >::ConstPtr &guiMSG,f64 freq,std::map<i32, CObject*> &container)
{
..
/*->*/ container.at(guiMSG->)...
}
The Container remains empty.
So i read some stuff about pointers and i saw my errors..
I confused * and &
if i want to pass the adress of a variable i have to write like
int main()
{
int a = 0;
AddTwo(&a)
cout<<a<<endl; // Output: 2
}
void AddTwo(int* a)
{
a+=2;
}

std::list copy to std::vector skipping elements

I've run across a rather bizarre exception while running C++ code in my objective-C application. I'm using libxml2 to read an XSD file. I then store the relevant tags as instances of the Tag class in an std::list. I then copy this list into an std::vector using an iterator on the list. However, every now and then some elements of the list aren't copied to the vector. Any help would be greatly appreciated.
printf("\n length list = %lu, length vector = %lu\n",XSDFile::tagsList.size(), XSDFile::tags.size() );
std::list<Tag>::iterator it = XSDFile::tagsList.begin();
//result: length list = 94, length vector = 0
/*
for(;it!=XSDFile::tagsList.end();++it)
{
XSDFile::tags.push_back(*it); //BAD_ACCESS code 1 . . very bizarre . . . . 25
}
*/
std::copy (XSDFile::tagsList.begin(), XSDFile::tagsList.end(), std::back_inserter (XSDFile::tags));
printf("\n Num tags in vector = %lu\n", XSDFile::tags.size());
if (XSDFile::tagsList.size() != XSDFile::tags.size())
{
printf("\n length list = %lu, length vector = %lu\n",XSDFile::tagsList.size(), XSDFile::tags.size() );
//result: length list = 94, length vector = 83
}
I've found the problem. The memory was corrupted causing the std::list to become corrupted during the parsing of the XSD. I parse the XSD using a function start_element.
xmlSAXHandler handler = {0};
handler.startElement = start_element;
I used malloc guard in xcode to locate the use of freed memory. It pointed to the line:
std::strcpy(message, (char*)name);
So I removed the malloc (actually commented in the code) and it worked. The std::vector now consistently copies all 94 entries of the list. If anyone has an explanation as to why this worked that would be great.
static void start_element(void * ctx, const xmlChar *name, const xmlChar **atts)
{
// int len = strlen((char*)name);
// char *message = (char*)malloc(len*sizeof(char));
// std::strcpy(message, (char*)name);
if (atts != NULL)
{
// atts[0] = type
// atts[1] = value
// len = strlen((char*)atts[1]);
// char *firstAttr = (char*)malloc(len*sizeof(char));
// std::strcpy(firstAttr, (char*)atts[1]);
if(strcmp((char*)name, "xs:include")==0)
{
XSDFile xsd;
xsd.ReadXSDTypes((char*)atts[1]);
}
else if(strcmp((char*)name, "xs:element")==0)
{
doElement(atts);
}
else if(strcmp((char*)name, "xs:sequence")==0)
{
//set the default values
XSDFile::sequenceMin = XSDFile::sequenceMax = 1;
if (sizeof(atts) == 4)
{
if(strcmp((char*)atts[3],"unbounded")==0)
XSDFile::sequenceMax = -1;
int i = 0;
while(atts[i] != NULL)
{
//atts[i] = name
//atts[i+i] = value
std::string name((char*)atts[i]);
std::string value((char*)atts[i+1]);
if(name=="minOccurs")
XSDFile::sequenceMin = (atoi(value.c_str()));
else if(name=="maxOccurs")
XSDFile::sequenceMax = (atoi(value.c_str()));
i += 2;
}
}
}
}
//free(message);
}

How to train in Matlab a model, save it to disk, and load in C++ program?

I am using libsvm version 3.16. I have done some training in Matlab, and created a model. Now I would like to save this model to disk and load this model in my C++ program. So far I have found the following alternatives:
This answer explains how to save a model from C++, which is based on this website. Not exactly what I need, but could be adapted. (This requires development time).
I could find the best training parameters (kernel,C) in Matlab and re-train everything in C++. (Will require doing the training in C++ each time I change a parameter. It's not scalable).
Thus, both of these options are not satisfactory,
Does anyone have an idea?
My solution was to retrain in C++ because I couldn't find a nice way to directly save the model. Here's my code. You'll need to adapt it and clean it up a bit. The biggest change you'll have to make it not hard coding the svm_parameter values like I did. You'll also have to replace FilePath with std::string. I'm copying, pasting and making small edits here in SO so the formatting won't e perfect:
Used like this:
auto targetsPath = FilePath("targets.txt");
auto observationsPath = FilePath("observations.txt");
auto targetsMat = MatlabMatrixFileReader::Read(targetsPath, ',');
auto observationsMat = MatlabMatrixFileReader::Read(observationsPath, ',');
auto v = MiscVector::ConvertVecOfVecToVec(targetsMat);
auto model = SupportVectorRegressionModel{ observationsMat, v };
std::vector<double> observation{ { // 32 feature observation
0.883575729725847,0.919446119013878,0.95359403450317,
0.968233630936732,0.91891307107125,0.887897763183844,
0.937588566544751,0.920582702918882,0.888864454119387,
0.890066735260163,0.87911085669864,0.903745573664995,
0.861069296586979,0.838606194934074,0.856376230548304,
0.863011311537075,0.807688936997926,0.740434984165146,
0.738498042748759,0.736410940165691,0.697228384912424,
0.608527698289016,0.632994967880269,0.66935784966765,
0.647761430696238,0.745961037635717,0.560761134660957,
0.545498063585615,0.590854855113663,0.486827902942118,
0.187128866890822,- 0.0746523069562551
} };
double prediction = model.Predict(observation);
miscvector.h
static vector<double> ConvertVecOfVecToVec(const vector<vector<double>> &mat)
{
vector<double> targetsVec;
targetsVec.reserve(mat.size());
for (size_t i = 0; i < mat.size(); i++)
{
targetsVec.push_back(mat[i][0]);
}
return targetsVec;
}
libsvmtargetobjectconvertor.h
#pragma once
#include "machinelearning.h"
struct svm_node;
class LibSvmTargetObservationConvertor
{
public:
svm_node ** LibSvmTargetObservationConvertor::ConvertObservations(const vector<MlObservation> &observations, size_t numFeatures) const
{
svm_node **svmObservations = (svm_node **)malloc(sizeof(svm_node *) * observations.size());
for (size_t rowI = 0; rowI < observations.size(); rowI++)
{
svm_node *row = (svm_node *)malloc(sizeof(svm_node) * numFeatures);
for (size_t colI = 0; colI < numFeatures; colI++)
{
row[colI].index = colI;
row[colI].value = observations[rowI][colI];
}
row[numFeatures].index = -1; // apparently needed
svmObservations[rowI] = row;
}
return svmObservations;
}
svm_node* LibSvmTargetObservationConvertor::ConvertMatToSvmNode(const MlObservation &observation) const
{
size_t numFeatures = observation.size();
svm_node *obsNode = (svm_node *)malloc(sizeof(svm_node) * numFeatures);
for (size_t rowI = 0; rowI < numFeatures; rowI++)
{
obsNode[rowI].index = rowI;
obsNode[rowI].value = observation[rowI];
}
obsNode[numFeatures].index = -1; // apparently needed
return obsNode;
}
};
machinelearning.h
#pragma once
#include <vector>
using std::vector;
using MlObservation = vector<double>;
using MlTarget = double;
//machinelearningmodel.h
#pragma once
#include <vector>
#include "machinelearning.h"
class MachineLearningModel
{
public:
virtual ~MachineLearningModel() {}
virtual double Predict(const MlObservation &observation) const = 0;
};
matlabmatrixfilereader.h
#pragma once
#include <vector>
using std::vector;
class FilePath;
// Matrix created with command:
// dlmwrite('my_matrix.txt', somematrix, 'delimiter', ',', 'precision', 15);
// In these files, each row is a matrix row. Commas separate elements on a row.
// There is no space at the end of a row. There is a blank line at the bottom of the file.
// File format:
// 0.4,0.7,0.8
// 0.9,0.3,0.5
// etc.
static class MatlabMatrixFileReader
{
public:
static vector<vector<double>> Read(const FilePath &asciiFilePath, char delimiter)
{
vector<vector<double>> values;
vector<double> valueline;
std::ifstream fin(asciiFilePath.Path());
string item, line;
while (getline(fin, line))
{
std::istringstream in(line);
while (getline(in, item, delimiter))
{
valueline.push_back(atof(item.c_str()));
}
values.push_back(valueline);
valueline.clear();
}
fin.close();
return values;
}
};
supportvectorregressionmodel.h
#pragma once
#include <vector>
using std::vector;
#include "machinelearningmodel.h"
#include "svm.h" // libsvm
class FilePath;
class SupportVectorRegressionModel : public MachineLearningModel
{
public:
SupportVectorRegressionModel::~SupportVectorRegressionModel()
{
svm_free_model_content(model_);
svm_destroy_param(&param_);
svm_free_and_destroy_model(&model_);
}
SupportVectorRegressionModel::SupportVectorRegressionModel(const vector<MlObservation>& observations, const vector<MlTarget>& targets)
{
// assumes all observations have same number of features
size_t numFeatures = observations[0].size();
//setup targets
//auto v = ConvertVecOfVecToVec(targetsMat);
double *targetsPtr = const_cast<double *>(&targets[0]); // why aren't the targets const?
LibSvmTargetObservationConvertor conv;
svm_node **observationsPtr = conv.ConvertObservations(observations, numFeatures);
// setup observations
//svm_node **observations = BuildObservations(observationsMat, numFeatures);
// setup problem
svm_problem problem;
problem.l = targets.size();
problem.y = targetsPtr;
problem.x = observationsPtr;
// specific to out training sets
// TODO: This is hard coded.
// Bust out these values for use in constructor
param_.C = 0.4; // cost
param_.svm_type = 4; // SVR
param_.kernel_type = 2; // radial
param_.nu = 0.6; // SVR nu
// These values are the defaults used in the Matlab version
// as found in svm_model_matlab.c
param_.gamma = 1.0 / (double)numFeatures;
param_.coef0 = 0;
param_.cache_size = 100; // in MB
param_.shrinking = 1;
param_.probability = 0;
param_.degree = 3;
param_.eps = 1e-3;
param_.p = 0.1;
param_.shrinking = 1;
param_.probability = 0;
param_.nr_weight = 0;
param_.weight_label = NULL;
param_.weight = NULL;
// suppress command line output
svm_set_print_string_function([](auto c) {});
model_ = svm_train(&problem, &param_);
}
double SupportVectorRegressionModel::Predict(const vector<double>& observation) const
{
LibSvmTargetObservationConvertor conv;
svm_node *obsNode = conv.ConvertMatToSvmNode(observation);
double prediction = svm_predict(model_, obsNode);
return prediction;
}
SupportVectorRegressionModel::SupportVectorRegressionModel(const FilePath & modelFile)
{
model_ = svm_load_model(modelFile.Path().c_str());
}
private:
svm_model *model_;
svm_parameter param_;
};
Option 1 is actually pretty reasonable. If you save the model in libsvm's C format through matlab, then it is straightforward to work with the model in C/C++ using functions provided by libsvm. Trying to work with matlab-formatted data in C++ will probably be much more difficult.
The main function in "svm-predict.c" (located in the root directory of the libsvm package) probably has most of what you need:
if((model=svm_load_model(argv[i+1]))==0)
{
fprintf(stderr,"can't open model file %s\n",argv[i+1]);
exit(1);
}
To predict a label for example x using the model, you can run
int predict_label = svm_predict(model,x);
The trickiest part of this will be to transfer your data into the libsvm format (unless your data is in the libsvm text file format, in which case you can just use the predict function in "svm-predict.c").
A libsvm vector, x, is an array of struct svm_node that represents a sparse array of data. Each svm_node has an index and a value, and the vector must be terminated by an index that is set to -1. For instance, to encode the vector [0,1,0,5], you could do the following:
struct svm_node *x = (struct svm_node *) malloc(3*sizeof(struct svm_node));
x[0].index=2; //NOTE: libsvm indices start at 1
x[0].value=1.0;
x[1].index=4;
x[1].value=5.0;
x[2].index=-1;
For SVM types other than the classifier (C_SVC), look at the predict function in "svm-predict.c".

Function has corrupt return value

I have a situation in Visual C++ 2008 that I have not seen before. I have a class with 4 STL objects (list and vector to be precise) and integers.
It has a method:
inline int id() { return m_id; }
The return value from this method is corrupt, and I have no idea why.
debugger screenshot http://img687.imageshack.us/img687/6728/returnvalue.png
I'd like to believe its a stack smash, but as far as I know, I have no buffer over-runs or allocation issues.
Some more observations
Here's something that puts me off. The debugger prints right values in the place mentioned // wrong ID.
m_header = new DnsHeader();
assert(_CrtCheckMemory());
if (m_header->init(bytes, size))
{
eprintf("0The header ID is %d\n", m_header->id()); // wrong ID!!!
inside m_header->init()
m_qdcount = ntohs(h->qdcount);
m_ancount = ntohs(h->ancount);
m_nscount = ntohs(h->nscount);
m_arcount = ntohs(h->arcount);
eprintf("The details are %d,%d,%d,%d\n", m_qdcount, m_ancount, m_nscount, m_arcount);
// copy the flags
// this doesn't work with a bitfield struct :(
// memcpy(&m_flags, bytes + 2, sizeof(m_flags));
//unpack_flags(bytes + 2); //TODO
m_init = true;
}
eprintf("Assigning an id of %d\n", m_id); // Correct ID.
return
m_header->id() is an inline function in the header file
inline int id() { return m_id; }
I don't really know how best to post the code snippets I have , but here's my best shot at it. Please do let me know if they are insufficient:
Class DnsHeader has an object m_header inside DnsPacket.
Main body:
DnsPacket *p ;
p = new DnsPacket(r);
assert (_CrtCheckMemory());
p->add_bytes(buf, r); // add bytes to a vector m_bytes inside DnsPacket
if (p->parse())
{
read_packet(sin, *p);
}
p->parse:
size_t size = m_bytes.size(); // m_bytes is a vector
unsigned char *bytes = new u_char[m_bytes.size()];
copy(m_bytes.begin(), m_bytes.end(), bytes);
m_header = new DnsHeader();
eprintf("m_header allocated at %x\n", m_header);
assert(_CrtCheckMemory());
if (m_header->init(bytes, size)) // just set the ID and a bunch of other ints here.
{
size_t pos = DnsHeader::SIZE; // const int
if (pos != size)
; // XXX perhaps generate a warning about extraneous data?
if (ok)
m_parsed = true;
}
else
{
m_parsed = false;
}
if (!ok) {
m_parsed = false;
}
return m_parsed;
}
read_packet:
DnsHeader& h = p.header();
eprintf("The header ID is %d\n", h.id()); // ID is wrong here
...
DnsHeader constructor:
m_id = -1;
m_qdcount = m_ancount = m_nscount = m_arcount = 0;
memset(&m_flags, 0, sizeof(m_flags)); // m_flags is a struct
m_flags.rd = 1;
p.header():
return *m_header;
m_header->init: (u_char* bytes, int size)
header_fmt *h = (header_fmt *)bytes;
m_id = ntohs(h->id);
eprintf("Assigning an id of %d/%d\n", ntohs(h->id), m_id); // ID is correct here
m_qdcount = ntohs(h->qdcount);
m_ancount = ntohs(h->ancount);
m_nscount = ntohs(h->nscount);
m_arcount = ntohs(h->arcount);
You seem to be using a pointer to an invalid class somehow. The return value shown is the value that VS usually uses to initialize memory with:
2^32 - 842150451 = 0xCDCDCDCD
You probably have not initialized the class that this function is a member of.
Without seeing more of the code in context.. it might be that the m_id is out of the scope you expect it to be in.
Reinstalled VC++. That fixed everything.
Thank you for your time and support everybody! :) Appreciate it!