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Complete Example

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This commit is contained in:
Ricardo Montañana Gómez
2023-11-04 01:21:43 +01:00
parent bec04bc3a6
commit 7b6d05b5ac
6 changed files with 227 additions and 202 deletions
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18
src/PyClassifier.cc
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@@ -16,7 +16,9 @@ namespace pywrap {
PyClassifier::~PyClassifier()
{
std::cout << "Cleaning Classifier" << std::endl;
pyWrap->clean(module, className);
std::cout << "Classifier cleaned" << std::endl;
}
PyObject* PyClassifier::toPyObject(torch::Tensor& data_tensor)
{
@@ -60,23 +62,19 @@ namespace pywrap {
int n = XX.size(1);
auto data_numpy = np::from_data(XX.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(XX.dtype()) * 2 * n, sizeof(XX.dtype()) * 2), p::object());
print_array(data_numpy);
PyObject* Xp = data_numpy.ptr();
CPyObject Xp = data_numpy.ptr();
std::cout << "Converting y to PyObject" << std::endl;
auto y_numpy = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(m), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
PyObject* yp = y_numpy.ptr();
CPyObject yp = y_numpy.ptr();
std::cout << "Calling fit" << std::endl;
pyWrap->fit(module, this->className, Xp, yp);
Py_DECREF(Xp);
Py_DECREF(yp);
return *this;
}
torch::Tensor PyClassifier::predict(torch::Tensor& X)
{
PyObject* Xp = toPyObject(X);
CPyObject Xp = toPyObject(X);
auto PyResult = pyWrap->predict(module, className, Xp);
auto result = THPVariable_Unpack(PyResult);
Py_DECREF(Xp);
Py_DECREF(PyResult);
return result;
}
double PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
@@ -90,13 +88,11 @@ namespace pywrap {
int n = XX.size(1);
auto data_numpy = np::from_data(XX.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(XX.dtype()) * 2 * n, sizeof(XX.dtype()) * 2), p::object());
print_array(data_numpy);
PyObject* Xp = data_numpy.ptr();
CPyObject Xp = data_numpy.ptr();
std::cout << "Converting y to PyObject" << std::endl;
auto y_numpy = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(m), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
PyObject* yp = y_numpy.ptr();
CPyObject yp = y_numpy.ptr();
auto result = pyWrap->score(module, className, Xp, yp);
Py_DECREF(Xp);
Py_DECREF(yp);
return result;
}

9
src/PyHelper.hpp
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@@ -3,12 +3,17 @@
#pragma once
#include <Python.h>
#include <boost/python/numpy.hpp>
namespace pywrap {
namespace p = boost::python;
namespace np = boost::python::numpy;
class CPyInstance {
public:
CPyInstance()
{
Py_Initialize();
np::initialize();
}
~CPyInstance()
@@ -57,7 +62,7 @@ public:
void Release()
{
if (p) {
Py_DECREF(p);
Py_XDECREF(p);
}
p = NULL;
@@ -90,5 +95,5 @@ public:
}
};
} /* namespace pywrap */
#endif

41
src/PyWrap.cc
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@@ -5,7 +5,6 @@
#include <string>
#include <map>
#include <boost/python/numpy.hpp>
#include "PyHelper.hpp"
namespace pywrap {
namespace np = boost::python::numpy;
@@ -34,12 +33,9 @@ namespace pywrap {
PyWrap::~PyWrap()
{
for (const auto& item : moduleClassMap) {
Py_DECREF(std::get<0>(item.second));
Py_DECREF(std::get<1>(item.second));
Py_DECREF(std::get<2>(item.second));
}
std::cout << "Destruyendo PyWrap" << std::endl;
Py_Finalize();
std::cout << "PyWrap destruido" << std::endl;
}
void PyWrap::importClass(const std::string& moduleName, const std::string& className)
@@ -50,15 +46,15 @@ namespace pywrap {
return;
}
std::cout << "No estaba en el mapa" << std::endl;
PyObject* module = PyImport_ImportModule(moduleName.c_str());
CPyObject module = PyImport_ImportModule(moduleName.c_str());
if (PyErr_Occurred()) {
errorAbort("Could't import module " + moduleName);
}
PyObject* classObject = PyObject_GetAttrString(module, className.c_str());
CPyObject classObject = PyObject_GetAttrString(module, className.c_str());
if (PyErr_Occurred()) {
errorAbort("Couldn't find class " + className);
}
PyObject* instance = PyObject_CallObject(classObject, NULL);
CPyObject instance = PyObject_CallObject(classObject, NULL);
if (PyErr_Occurred()) {
errorAbort("Couldn't create instance of class " + className);
}
@@ -74,9 +70,6 @@ namespace pywrap {
return;
}
std::cout << "--> Limpiando" << std::endl;
Py_DECREF(std::get<0>(result->second));
Py_DECREF(std::get<1>(result->second));
Py_DECREF(std::get<2>(result->second));
moduleClassMap.erase(result);
std::cout << "Limpieza terminada" << std::endl;
}
@@ -100,14 +93,13 @@ namespace pywrap {
std::string PyWrap::callMethodString(const std::string& moduleName, const std::string& className, const std::string& method)
{
std::cout << "Llamando método " << method << std::endl;
PyObject* instance = getClass(moduleName, className);
PyObject* result;
CPyObject instance = getClass(moduleName, className);
CPyObject result;
if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
errorAbort("Couldn't call method " + method);
std::string value = PyUnicode_AsUTF8(result);
std::cout << "Result: " << value << std::endl;
Py_DECREF(result);
return value;
}
std::string PyWrap::version(const std::string& moduleName, const std::string& className)
@@ -115,31 +107,30 @@ namespace pywrap {
return callMethodString(moduleName, className, "version");
}
void PyWrap::fit(const std::string& moduleName, const std::string& className, PyObject* X, PyObject* y)
void PyWrap::fit(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y)
{
std::cout << "Llamando método fit" << std::endl;
PyObject* instance = getClass(moduleName, className);
PyObject* result;
CPyObject instance = getClass(moduleName, className);
CPyObject result;
std::string method = "fit";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X, y, NULL)))
errorAbort("Couldn't call method fit");
Py_DECREF(result);
}
PyObject* PyWrap::predict(const std::string& moduleName, const std::string& className, PyObject* X)
CPyObject PyWrap::predict(const std::string& moduleName, const std::string& className, CPyObject& X)
{
std::cout << "Llamando método predict" << std::endl;
PyObject* instance = getClass(moduleName, className);
PyObject* result;
CPyObject instance = getClass(moduleName, className);
CPyObject result;
std::string method = "predict";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X, NULL)))
errorAbort("Couldn't call method predict");
return result; // The caller has to decref the result
}
double PyWrap::score(const std::string& moduleName, const std::string& className, PyObject* X, PyObject* y)
double PyWrap::score(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y)
{
std::cout << "Llamando método score" << std::endl;
PyObject* instance = getClass(moduleName, className);
PyObject* result;
CPyObject instance = getClass(moduleName, className);
CPyObject result;
std::string method = "score";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X, y, NULL)))
errorAbort("Couldn't call method score");

10
src/PyWrap.h
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@@ -5,6 +5,8 @@
#include <map>
#include <tuple>
#include <mutex>
#include "PyHelper.hpp"
namespace pywrap {
/*
@@ -27,11 +29,11 @@ namespace pywrap {
// return THPVariable_Unpack(result);
// };
// PyObject* callMethodArgs(const std::string& moduleName, const std::string& className, const std::string& method, PyObject* args);
void fit(const std::string& moduleName, const std::string& className, PyObject* X, PyObject* y);
PyObject* predict(const std::string& moduleName, const std::string& className, PyObject* X);
void fit(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y);
CPyObject predict(const std::string& moduleName, const std::string& className, CPyObject& X);
std::string callMethodString(const std::string& moduleName, const std::string& className, const std::string& method);
std::string version(const std::string& moduleName, const std::string& className);
double score(const std::string& moduleName, const std::string& className, PyObject* X, PyObject* y);
double score(const std::string& moduleName, const std::string& className, CPyObject& X, CPyObject& y);
void clean(const std::string& moduleName, const std::string& className);
void importClass(const std::string& moduleName, const std::string& className);
// void doCommand2();
@@ -42,7 +44,7 @@ namespace pywrap {
PyStatus initPython();
static PyWrap* wrapper;
static std::mutex mutex;
std::map<std::pair<std::string, std::string>, std::tuple<PyObject*, PyObject*, PyObject*>> moduleClassMap;
std::map<std::pair<std::string, std::string>, std::tuple<CPyObject, CPyObject, CPyObject>> moduleClassMap;
};
} /* namespace python */
#endif /* PYWRAP_H */

83
src/example.cpp
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@@ -1,10 +1,10 @@
#include <boost/python/numpy.hpp>
#include <string>
#include <iostream>
#include "ArffFiles.h"
#include <torch/torch.h>
#include "PyHelper.hpp"
namespace p = boost::python;
namespace np = boost::python::numpy;
namespace pywrap {
using namespace std;
void errorAbort(const std::string& message)
@@ -57,67 +57,98 @@ void flat()
// auto data_numpy = np::from_data(data, np::dtype::get_builtin<double>(), p::make_tuple(m, n), p::make_tuple(sizeof(double) * n, sizeof(double)), p::object());
// auto y_numpy = np::from_data(labels, np::dtype::get_builtin<int>(), p::make_tuple(m), p::make_tuple(sizeof(int)), p::object());
}
class Paths {
public:
static string datasets()
{
return "/home/rmontanana/Code/discretizbench/datasets/";
}
};
tuple<torch::Tensor, torch::Tensor, vector<string>, string, map<string, vector<int>>> loadDataset(const string& name, bool class_last)
{
auto handler = ArffFiles();
handler.load(Paths::datasets() + static_cast<string>(name) + ".arff", class_last);
// Get Dataset X, y
vector<vector<float>> X = handler.getX();
vector<int> y = handler.getY();
// Get className & Features
auto className = handler.getClassName();
vector<string> features;
auto attributes = handler.getAttributes();
transform(attributes.begin(), attributes.end(), back_inserter(features), [](const auto& pair) { return pair.first; });
torch::Tensor Xd;
auto states = map<string, vector<int>>();
Xd = torch::zeros({ static_cast<int>(X.size()), static_cast<int>(X[0].size()) }, torch::kFloat32);
for (int i = 0; i < features.size(); ++i) {
Xd.index_put_({ i, "..." }, torch::tensor(X[i], torch::kFloat32));
}
return { Xd, torch::tensor(y, torch::kInt32), features, className, states };
}
} /* namespace pywrap */
using namespace pywrap;
int main(int argc, char** argv)
{
Py_Initialize();
np::initialize();
int m = 7;
int n = 4;
auto [data_tensor, y_label, featuresx, classNamex, statesx] = loadDataset("iris", true);
//data_tensor = data_tensor.transpose(0, 1);
CPyInstance pInstance;
int m = data_tensor.size(0);
int n = data_tensor.size(1);
// int m = 7;
// int n = 4;
// torch::Tensor data_tensor = torch::rand({ m, n }, torch::kFloat64);
torch::Tensor data_tensor = torch::tensor({ {0.1, 0.2, 0.3, 0.4} , { 0.5, 0.6, 0.7, 0.8 }, { 0.9, 0.11, 0.12, 0.13 }, { 0.14, 0.15, 0.16, 0.17 }, { 0.18, 0.19, 0.21, 0.22 }, { 0.23, 0.24, 0.25, 0.26 }, { 0.27, 0.28, 0.29, 0.31 } }, torch::kFloat32);
//torch::Tensor data_tensor = torch::tensor({ {0.1, 0.2, 0.3, 0.4} , { 0.5, 0.6, 0.7, 0.8 }, { 0.9, 0.11, 0.12, 0.13 }, { 0.14, 0.15, 0.16, 0.17 }, { 0.18, 0.19, 0.21, 0.22 }, { 0.23, 0.24, 0.25, 0.26 }, { 0.27, 0.28, 0.29, 0.31 } }, torch::kFloat32);
// torch::Tensor y_label = torch::randint(0, 2, { m }, torch::kInt16);
torch::Tensor y_label = torch::tensor({ 17, 18, 19, 20 , 21, 22, 23 }, torch::kInt32);
//torch::Tensor y_label = torch::tensor({ 17, 18, 19, 20 , 21, 22, 23 }, torch::kInt32);
cout << "Tensor data: (" << data_tensor.size(0) << ", " << data_tensor.size(1) << ") " << endl << data_tensor << endl;
cout << "Tensor data sizes: " << data_tensor.sizes() << endl;
cout << "Tensor labels: " << y_label << endl;
// cout << "Tensor labels: " << y_label << endl;
cout << "Tensor labels sizes: " << y_label.sizes() << endl;
auto data_numpy = np::from_data(data_tensor.data_ptr(), np::dtype::get_builtin<float>(), p::make_tuple(m, n), p::make_tuple(sizeof(data_tensor.dtype()) * 2 * n, sizeof(data_tensor.dtype()) * 2), p::object());
auto y_numpy = np::from_data(y_label.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(m), p::make_tuple(sizeof(y_label.dtype()) * 2), p::object());
data_numpy = data_numpy.transpose();
auto y_numpy = np::from_data(y_label.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(n), p::make_tuple(sizeof(y_label.dtype()) * 2), p::object());
//auto y_numpy = np::from_data(y_label.data_ptr(), np::dtype::get_builtin<int64_t>(), p::make_tuple(m), p::make_tuple(sizeof(y_label.dtype()) * 4), p::object());
cout << "Numpy array data: " << endl;
print_array(data_numpy);
cout << "Numpy array labels: " << endl;
print_array(y_numpy);
cout << "primero" << endl;
PyObject* p = data_numpy.ptr();
PyObject* yp = y_numpy.ptr();
CPyObject p = data_numpy.ptr();
CPyObject yp = y_numpy.ptr();
cout << "segundo" << endl;
string moduleName = "stree";
string className = "Stree";
string method = "version";
PyObject* module = PyImport_ImportModule(moduleName.c_str());
CPyObject module = PyImport_ImportModule(moduleName.c_str());
if (PyErr_Occurred()) {
errorAbort("Could't import module " + moduleName);
}
PyObject* classObject = PyObject_GetAttrString(module, className.c_str());
CPyObject classObject = PyObject_GetAttrString(module, className.c_str());
if (PyErr_Occurred()) {
errorAbort("Couldn't find class " + className);
}
PyObject* instance = PyObject_CallObject(classObject, NULL);
CPyObject instance = PyObject_CallObject(classObject, NULL);
if (PyErr_Occurred()) {
errorAbort("Couldn't create instance of class " + className);
}
PyObject* result;
CPyObject result;
if (!(result = PyObject_CallMethod(instance, method.c_str(), NULL)))
errorAbort("Couldn't call method " + method);
std::string value = PyUnicode_AsUTF8(result);
cout << "Version: " << value << endl;
cout << "Calling fit" << endl;
p.AddRef();
yp.AddRef();
method = "fit";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p, yp, NULL)))
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
errorAbort("Couldn't call method fit");
cout << "Calling score" << endl;
method = "score";
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p, yp, NULL)))
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), p.getObject(), yp.getObject(), NULL)))
errorAbort("Couldn't call method score");
float score = PyFloat_AsDouble(result);
cout << "Score: " << score << endl;
Py_DECREF(result);
Py_DECREF(instance);
Py_DECREF(module);
Py_DECREF(p);
Py_DECREF(yp);
cout << "tercero" << endl;
return 0;
}

8
src/main.cc
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@@ -51,9 +51,9 @@ int main(int argc, char* argv[])
cout << string(80, '-') << endl;
cout << "X: " << X.sizes() << endl;
cout << "y: " << y.sizes() << endl;
auto result = stree.fit(X, y, features, className, states);
cout << "Now calling score" << endl;
auto result2 = stree.score(X, y);
cout << "SVC score " << result2 << endl;
// auto result = stree.fit(X, y, features, className, states);
// cout << "Now calling score" << endl;
// auto result2 = stree.score(X, y);
// cout << "SVC score " << result2 << endl;
return 0;
}