Integrating C++ Machine Learning Model with Python API â Facing Data Type Mismatch
I'm trying to figure out I keep running into I'm relatively new to this, so bear with me... Integrating a C++ machine learning model with a Python API using pybind11 has been quite the challenge. I've successfully built my model and wrapped it using pybind11, but I'm running into some data type mismatches when passing data from Python to C++. The model expects a `std::vector<float>` but receives a `numpy.ndarray`. Trying to convert this in my binding code is where the hiccup occurs. Here's a snippet of my binding code: ```cpp #include <pybind11/pybind11.h> #include <pybind11/numpy.h> #include <vector> namespace py = pybind11; std::vector<float> process_data(const py::array_t<float>& input) { auto buf = input.unchecked(); std::vector<float> output; output.reserve(buf.size()); for (ssize_t i = 0; i < buf.size(); ++i) { output.push_back(buf(i)); } return output; } PYBIND11_MODULE(my_model, m) { m.def("process_data", &process_data); } ``` In Python, I'm calling this function like so: ```python import my_model import numpy as np data = np.array([1.0, 2.0, 3.0], dtype=np.float32) result = my_model.process_data(data) ``` The function call seems fine, but occasionally, I get a segmentation fault when the input array is modified after being passed. I tried the following: 1. Ensured the data is contiguous using `numpy.ascontiguousarray(data)` before passing it to my C++ function. 2. Added rigorous checks for input size to avoid out-of-bounds access. 3. Used `py::array_t<double>` and `py::array_t<float>` interchangeably, but it didn't resolve the crashes. Curious if there are better practices or common pitfalls with data type handling between C++ and Python that I might be overlooking. Any insights on improving the robustness of this integration would be greatly appreciated. For context: I'm using C++ on Windows. I'm open to any suggestions. My development environment is Windows 10.