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This commit is contained in:
Ricardo Montañana Gómez
2023-11-06 15:22:27 +01:00
parent 3e92372d1c
commit ed21f90b69
8 changed files with 55 additions and 69 deletions
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61
src/PyClassifier.cc
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@@ -1,5 +1,5 @@
#include "PyClassifier.h"
#include "numpy/arrayobject.h"
#include <iostream>
namespace pywrap {
@@ -16,11 +16,6 @@ namespace pywrap {
pyWrap->clean(module, className);
std::cout << "Classifier cleaned" << std::endl;
}
void print_array(np::ndarray& array)
{
std::cout << "Array: " << std::endl;
std::cout << p::extract<char const*>(p::str(array)) << std::endl;
}
np::ndarray tensor2numpy(torch::Tensor& X)
{
int m = X.size(0);
@@ -33,51 +28,63 @@ namespace pywrap {
{
int n = X.size(1);
auto yn = np::from_data(y.data_ptr(), np::dtype::get_builtin<int32_t>(), p::make_tuple(n), p::make_tuple(sizeof(y.dtype()) * 2), p::object());
//std::cout << "Printing from within tensors2numpy" << std::endl;
// print_array(yn);
return { tensor2numpy(X), yn };
}
std::string PyClassifier::version()
{
return pyWrap->version(module, className);
}
std::string PyClassifier::graph()
{
return pyWrap->graph(module, className);
}
std::string PyClassifier::callMethodString(const std::string& method)
{
return pyWrap->callMethodString(module, className, method);
}
PyClassifier& PyClassifier::fit(torch::Tensor& X, torch::Tensor& y, const std::vector<std::string>& features, const std::string& className, std::map<std::string, std::vector<int>>& states)
{
std::cout << "PyClassifier:fit:Converting X to PyObject" << std::endl;
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
std::cout << "PyClassifier:fit:Converting y to PyObject" << std::endl;
print_array(yn);
CPyObject yp = boost::python::incref(boost::python::object(yn).ptr());
std::cout << "PyClassifier:fit:Calling fit" << std::endl;
CPyObject Xp = p::incref(p::object(Xn).ptr());
CPyObject yp = p::incref(p::object(yn).ptr());
pyWrap->fit(module, this->className, Xp, yp);
return *this;
}
void print_array(np::ndarray& array)
{
std::cout << "Array: " << std::endl;
std::cout << p::extract<char const*>(p::str(array)) << std::endl;
}
torch::Tensor PyClassifier::predict(torch::Tensor& X)
{
int dimension = X.size(1);
auto Xn = tensor2numpy(X);
CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
auto PyResult = pyWrap->predict(module, className, Xp);
auto result = torch::tensor({ 1,2,3 });
CPyObject Xp = p::incref(p::object(Xn).ptr());
PyObject* incoming = pyWrap->predict(module, className, Xp);
std::cout << "Return from predict" << std::endl;
p::handle<> handle(incoming);
p::object object(handle);
np::ndarray prediction = np::from_object(object);
print_array(prediction);
// import_array();
// if (!PyArray_Check(incoming)) {
// throw std::logic_error("Returned value is not array");
// }
// std::cout << "Returned value is array" << std::endl;
// PyArrayObject* np_ret = (PyArrayObject*)incoming;
// if (PyArray_NDIM(np_ret) != dimension - 1) {
// throw std::logic_error("Returned array has wrong dimension" + std::to_string(PyArray_NDIM(np_ret)) + "!=" + std::to_string(dimension - 1));
// }
// std::cout << "Returned array has correct dimension" << PyArray_NDIM(np_ret) << std::endl;
// int len{ PyArray_SHAPE(np_ret)[0] };
// int* data = reinterpret_cast<int*>(PyArray_DATA(np_ret));
return result;
// int* data = reinterpret_cast<int*>(prediction.get_data());
// auto resultTensor = torch::tensor({ data }, torch::kInt32);
auto resultTensor = torch::zeros({ prediction.shape(0) }, torch::kInt32);
return resultTensor;
}
double PyClassifier::score(torch::Tensor& X, torch::Tensor& y)
{
std::cout << "PyClassifier::Score:Converting X to PyObject" << std::endl;
auto [Xn, yn] = tensors2numpy(X, y);
CPyObject Xp = boost::python::incref(boost::python::object(Xn).ptr());
CPyObject yp = boost::python::incref(boost::python::object(yn).ptr());
print_array(yn);
CPyObject Xp = p::incref(p::object(Xn).ptr());
CPyObject yp = p::incref(p::object(yn).ptr());
auto result = pyWrap->score(module, className, Xp, yp);
return result;
}

2
src/PyClassifier.h
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@@ -10,7 +10,6 @@
#include "PyWrap.h"
namespace pywrap {
class PyClassifier {
public:
PyClassifier(const std::string& module, const std::string& className);
@@ -19,7 +18,6 @@ namespace pywrap {
torch::Tensor predict(torch::Tensor& X);
double score(torch::Tensor& X, torch::Tensor& y);
std::string version();
std::string graph();
std::string callMethodString(const std::string& method);
private:
PyWrap* pyWrap;

28
src/PyWrap.cc
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@@ -47,12 +47,10 @@ namespace pywrap {
}
void PyWrap::importClass(const std::string& moduleName, const std::string& className)
{
std::cout << "Importando clase" << std::endl;
auto result = moduleClassMap.find({ moduleName, className });
if (result != moduleClassMap.end()) {
return;
}
std::cout << "No estaba en el mapa" << std::endl;
CPyObject module = PyImport_ImportModule(moduleName.c_str());
if (PyErr_Occurred()) {
errorAbort("Could't import module " + moduleName);
@@ -75,12 +73,12 @@ namespace pywrap {
void PyWrap::clean(const std::string& moduleName, const std::string& className)
{
std::lock_guard<std::mutex> lock(mutex);
std::cout << "Limpiando" << std::endl;
std::cout << "Start cleaning " << moduleName << "." << className << std::endl;
auto result = moduleClassMap.find({ moduleName, className });
if (result == moduleClassMap.end()) {
return;
}
std::cout << "--> Limpiando" << std::endl;
std::cout << "--> Cleaning PyObject" << std::endl;
Py_DECREF(std::get<0>(result->second));
Py_DECREF(std::get<1>(result->second));
Py_DECREF(std::get<2>(result->second));
@@ -92,7 +90,7 @@ namespace pywrap {
if (moduleClassMap.empty()) {
RemoveInstance();
}
std::cout << "Limpieza terminada" << std::endl;
std::cout << "End Cleaning " << moduleName << "." << className << std::endl;
}
void PyWrap::errorAbort(const std::string& message)
{
@@ -107,12 +105,10 @@ namespace pywrap {
if (item == moduleClassMap.end()) {
errorAbort("Module " + moduleName + " and class " + className + " not found");
}
std::cout << "Clase encontrada" << std::endl;
return std::get<2>(item->second);
}
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;
try {
@@ -125,21 +121,15 @@ namespace pywrap {
exit(1);
}
std::string value = PyUnicode_AsUTF8(result);
std::cout << "Result: " << value << std::endl;
Py_DECREF(result);
Py_XDECREF(result);
return value;
}
std::string PyWrap::version(const std::string& moduleName, const std::string& className)
{
return callMethodString(moduleName, className, "version");
}
std::string PyWrap::graph(const std::string& moduleName, const std::string& className)
{
return callMethodString(moduleName, className, "graph");
}
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);
CPyObject result;
std::string method = "fit";
@@ -153,11 +143,12 @@ namespace pywrap {
exit(1);
}
}
CPyObject PyWrap::predict(const std::string& moduleName, const std::string& className, CPyObject& X)
PyObject* PyWrap::predict(const std::string& moduleName, const std::string& className, CPyObject& X)
{
std::cout << "Llamando método predict" << std::endl;
CPyObject instance = getClass(moduleName, className);
CPyObject result;
PyObject* instance = getClass(moduleName, className);
PyObject* result;
std::string method = "predict";
try {
if (!(result = PyObject_CallMethodObjArgs(instance, PyUnicode_FromString(method.c_str()), X.getObject(), NULL)))
@@ -168,11 +159,10 @@ namespace pywrap {
RemoveInstance();
exit(1);
}
return result;
return result; // Caller must free this object
}
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);
CPyObject result;
std::string method = "score";

3
src/PyWrap.h
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@@ -22,9 +22,8 @@ namespace pywrap {
~PyWrap() = default;
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);
std::string graph(const std::string& moduleName, const std::string& className);
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);
PyObject* predict(const std::string& moduleName, const std::string& className, CPyObject& X);
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);

4
src/STree.cc
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@@ -1,10 +1,8 @@
#include "STree.h"
namespace pywrap {
std::string STree::graph()
{
// return callMethodString("graph");
return PyClassifier::graph();
return callMethodString("graph");
}
} /* namespace pywrap */

1
src/STree.h
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@@ -9,6 +9,5 @@ namespace pywrap {
~STree() = default;
std::string graph();
};
} /* namespace pywrap */
#endif /* STREE_H */

2
src/SVC.cc
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@@ -1,10 +1,8 @@
#include "SVC.h"
namespace pywrap {
std::string SVC::version()
{
return callMethodString("_repr_html_");
}
} /* namespace pywrap */

23
src/main.cc
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@@ -47,21 +47,18 @@ int main(int argc, char* argv[])
auto [X, y, features, className, states] = loadDataset("iris", true);
cout << "X: " << X.sizes() << endl;
cout << "y: " << y.sizes() << endl;
auto clf = pywrap::PyClassifier("stree", "Stree");
auto clf = pywrap::STree();
cout << "STree Version: " << clf.version() << endl;
if (true) {
auto svc = pywrap::PyClassifier("sklearn.svm", "SVC");
cout << "SVC Version: " << svc.callMethodString("_repr_html_") << endl;
cout << "Calling fit" << endl;
svc.fit(X, y, features, className, states);
cout << "Calling score" << endl;
cout << "SVC Score: " << svc.score(X, y) << endl;
}
cout << "Graph: " << clf.graph() << endl;
cout << "Calling fit" << endl;
// if (true) {
// auto svc = pywrap::SVC();
// svc.fit(X, y, features, className, states);
// cout << "SVC Score: " << svc.score(X, y) << endl;
// }
// cout << "Graph: " << endl << clf.graph() << endl;
clf.fit(X, y, features, className, states);
cout << "Calling score" << endl;
cout << "STree Score: " << clf.score(X, y) << endl;
// cout << "STree Score: " << clf.score(X, y) << endl;
auto prediction = clf.predict(X);
cout << "Prediction: " << prediction << endl;
}
cout << "* End." << endl;
}