Pybind11：使用 Pybind11 在 C++ 中转换 numpy 数组的问题

Question

我正在尝试从 C++ 中获取一个 Python 数组，并使用 C++ with Pybind11 将其放回 Python 函数中。首先，我访问一个 numpy 数组字典，然后将每个 numpy 数组放入一个 Python 函数中，该函数只计算数组中元素的总和。给我带来麻烦的代码的主要部分是：

        py::array item = list_items.attr("__getitem__")(i); 
    //equivalent to dict.__getitem__[i]

    //code runs if removing the following line
        boost_mod.attr("sum_of_dict")(item).cast<int>(); 
    //calls the function sum_of_dict in the module boost_mod having as argument a 1D numpy array
    //this function computes the sum of the elements in the numpy array, which are all integers 

我认为我遇到了转换问题，我不知道如何克服它。我看过this documentation link，但没有帮助。我还查看了不同的cast 可能性，以及this one 或这个other one 等其他帖子，但它们不适用于我的问题，因为它们不使用pybind。对于转换和使用我的 numpy 数组对象的任何帮助或任何提示将不胜感激。非常感谢

---

完整代码如下：

C++ 代码

#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <chrono>
#include <thread>
#include <Python.h>
namespace py = pybind11;
py::module boost_mod = py::module::import("boost_mod");

int parallelize(py::dict list_items, py::object q_in, py::object q_out){

    unsigned int sum = 0;
    unsigned int len = list_items.attr("__len__")().cast<int>();
    for( unsigned int i = 0; i < len ; i++){

        //PROBLEM IS HERE//
        py::array item = list_items.attr("__getitem__")(i);
        sum+= boost_mod.attr("sum_of_dict")(item).cast<int>();
        std::this_thread::sleep_for(std::chrono::milliseconds(5));

    }
    return sum;
}

PYBIND11_MODULE(cpp_parallel, m) {

    m.doc() = "pybind11 plugin";
    m.def("parallelize", &parallelize,
      "a function");
}

包含函数的 Python 模块

import numpy as np
import cpp_parallel as cpp

class child(object):
    def __init__(self, index):
        self.nb = index
        self.total = None

    def sum(self, i, j):
        return self.nb + self.nb

    def create_dict(self):
        self.list_items = {}
        for i in range(self.nb):
            lth = np.random.randint(1,10)
            a = np.random.binomial(size=lth, n=1, p =0.6)
            self.list_items[i] = a
        return self.list_items

    def sum_of_dict(self, element):  # fonction comme eval function
        a = np.sum(element)
        return a

    def sub(self, q_in, q_out):
        //FUNCTION CALLED FROM C++//
        return cpp.parallelize(self.list_items, q_in, q_out)

Python 代码

from multiprocessing import Process, Queue
import time
import boost_mod as test
import cpp_parallel as cpp

q_in = Queue()
q_out = Queue()

q_in.put(True)

dict_evaluated = test.child(1000)
dict_evaluated.create_dict()
dict_evaluated.sub(q_in, q_out)

Answer 1

实际上，我没有正确实例化我从中调用函数的模块。归功于 Wim Lavrijsen。答案如下：

#include <pybind11/pybind11.h>
#include <pybind11/numpy.h>
#include <omp.h>
#include <chrono>
#include <thread>
#include <Python.h>
namespace py = pybind11;
py::module boost_mod = py::module::import("boost_mod");


int parallelize(py::object child, py::object q_in, py::object q_out){

    unsigned int sum = 0;
    py::object items = child.attr("list_items");
    unsigned int len = items.attr("__len__")().cast<int>();
    #pragma omp simd reduction (+:sum)
    for( unsigned int i = 0; i < len ; i++){

        py::array item = items.attr("__getitem__")(i);
        sum += child.attr("sum_of_dict")(item).cast<int>();
        bool accord = q_in.attr("get")().cast<bool>();
        if (accord == true){
          q_out.attr("put")(sum);
        }
        accord = false;
        std::this_thread::sleep_for(std::chrono::milliseconds(5));
    }
    return sum;
}

PYBIND11_MODULE(cpp_parallel, m) {

    m.doc() = "pybind11 example plugin";

    m.def("parallelize", &parallelize,
      "the function which parallelizes the evaluation");
}

还有子类：

from multiprocessing import Process, Event, Lock, Queue, Pipe
import time
import numpy as np
import cpp_parallel as cpp

class child(object):
    def __init__(self, index):
        self.nb = index
        self.total = None

    def sum(self, i, j):
        return self.nb + self.nb

    def create_dict(self):
        self.list_items = {}
        for i in range(self.nb):
            lth = np.random.randint(1,10)
            a = np.random.binomial(size=lth, n=1, p =0.6)
            self.list_items[i] = a

    def sum_of_dict(self, element):  # fonction comme eval function
        a = np.sum(element)
        return a

    def sub(self, q_in, q_out):
        return cpp.parallelize(self, q_in, q_out)