EDIT: It works now, I do not know why. Don't think I changed anything
I want to pass in and modify a large numpy array with pybind11. Because it's large I want to avoid copying it and returning a new one.
Here's the code:
#include <pybind11/pybind11.h>
#include <pybind11/stl.h>
#include <pybind11/numpy.h>
#include <vector>
// C++ code
void calc_sum_cost(float* ptr, int N, int M, float* ptr_cost) {
for(int32_t i = 1; i < N; i++) {
for(int32_t j = 1; j < M; j++) {
float upc = ptr[(i-1) * M + j];
float leftc = ptr[i * M + j - 1];
float diagc = ptr[(i-1) * M + j - 1];
float transition_cost = std::min(upc, std::min(leftc, diagc));
if (transition_cost == diagc) {
transition_cost += 2 * ptr_cost[i*M + j];
} else {
transition_cost += ptr_cost[i*M + j];
}
std::cout << transition_cost << std::endl;
ptr[i * M + j] = transition_cost;
}
}
}
// Interface
namespace py = pybind11;
// wrap C++ function with NumPy array IO
py::object wrapper(py::array_t<float> array,
py::array_t<float> arrayb) {
// check input dimensions
if ( array.ndim() != 2 )
throw std::runtime_error("Input should be 2-D NumPy array");
auto buf = array.request();
auto buf2 = arrayb.request();
if (buf.size != buf2.size) throw std::runtime_error("sizes do not match!");
int N = array.shape()[0], M = array.shape()[1];
float* ptr = (float*) buf.ptr;
float* ptr_cost = (float*) buf2.ptr;
// call pure C++ function
calc_sum_cost(ptr, N, M, ptr_cost);
return py::cast<py::none>(Py_None);
}
PYBIND11_MODULE(fast,m) {
m.doc() = "pybind11 plugin";
m.def("calc_sum_cost", &wrapper, "Calculate the length of an array of vectors");
}
I think the py::array::forcecast
is causing a conversion and so leaving the input matrix unmodified (in python). When I remove that though I get a runtime error, when I remove ::c_style
it runs but again in python the numpy array is the same.
Basically my question is how can one pass and modify a numpy array with pybind11?
I just had the same problem. If, from Python, you pass a numpy array of the type matching the C++ argument then no conversion happens, and you can modify the data in-place i.e. for py::array_t<float>
argument pass in a numpy np.float32
array. If you happen to pass in a np.float64
array (the default type) then pybind11 does the conversion due to the py::array::forcecast
template parameter (default on py::array_t<T>
), so your C++ function only gets a converted copy of a numpy array, and any changes are lost after returning.
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