使用一个遵循buffer protocol的对象就可以和numpy交互了.
这个buffer_protocol要有哪些东西呢? 要有如下接口:
struct buffer_info { void *ptr; ssize_t itemsize; std::string format; ssize_t ndim; std::vector<ssize_t> shape; std::vector<ssize_t> strides; };
其实就是一个指向数组的指针+各个维度的信息就可以了. 然后我们就可以用指针+偏移来访问数字中的任意位置上的数字了.
下面是一个可以跑的例子:
1 #include <pybind11/pybind11.h>
2 #include <pybind11/numpy.h>
3
4 namespace py = pybind11; 5
6 py::array_t<double> add_arrays(py::array_t<double> input1, py::array_t<double> input2) { 7 py::buffer_info buf1 = input1.request(), buf2 = input2.request(); 8
9 if (buf1.ndim != 1 || buf2.ndim != 1) 10 throw std::runtime_error("Number of dimensions must be one"); 11
12 if (buf1.size != buf2.size) 13 throw std::runtime_error("Input shapes must match"); 14
15 /* No pointer is passed, so NumPy will allocate the buffer */
16 auto result = py::array_t<double>(buf1.size); 17
18 py::buffer_info buf3 = result.request(); 19
20 double *ptr1 = (double *) buf1.ptr, 21 *ptr2 = (double *) buf2.ptr, 22 *ptr3 = (double *) buf3.ptr; 23
24 for (size_t idx = 0; idx < buf1.shape[0]; idx++) 25 ptr3[idx] = ptr1[idx] + ptr2[idx]; 26
27 return result; 28 } 29
30 PYBIND11_MODULE(test, m) { 31 m.def("add_arrays", &add_arrays, "Add two NumPy arrays"); 32 }
array_t里的buf就是一个兼容的接口.
buf中可以得到指针和对应数字的维度信息.
为了方便我们甚至可以使用Eigen当作我们兼容numpy的接口:
1 #include <pybind11/pybind11.h> 2 #include <pybind11/eigen.h> 3 4 #include <Eigen/LU> 5 6 // N.B. this would equally work with Eigen-types that are not predefined. For example replacing 7 // all occurrences of "Eigen::MatrixXd" with "MatD", with the following definition: 8 // 9 // typedef Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor> MatD; 10 11 Eigen::MatrixXd inv(const Eigen::MatrixXd &xs) 12 { 13 return xs.inverse(); 14 } 15 16 double det(const Eigen::MatrixXd &xs) 17 { 18 return xs.determinant(); 19 } 20 21 namespace py = pybind11; 22 23 PYBIND11_MODULE(example,m) 24 { 25 m.doc() = "pybind11 example plugin"; 26 27 m.def("inv", &inv); 28 29 m.def("det", &det); 30 }
更多参考: