将std::vector转换为NumPy数组，而不复制数据

I have a C++ library which currently has some methods inside which return a std::vector defined like

我有一个c++库，它现在有一些方法可以返回std::向量定义的。

public:
  const std::vector<uint32_t>& getValues() const;

I'm currently working on wrapping the whole library for Python using SWIG and this is working well so far.

我目前正在使用SWIG为Python包装整个库，到目前为止效果很好。

SWIG wraps this getValues() function fine such that it returns a Python tuple. The issue is in my Python-side code I want to convert this to a NumPy array. Of course I can do this by:

SWIG将getValues()函数包装得很好，这样它就会返回一个Python元组。问题是在我的python代码中，我想把它转换成一个NumPy数组。当然我可以这样做:

my_array = np.array(my_object.getValues(), dtype='uint32')

but this causes all the entries in the original vector to be first copied into a Python tuple by SWIG and then again into a numpy array by me. Since this vector could be very large, I'd rather avoid making these two copies and would like for a way to have SWIG create a numpy.array wrapper around the original vector data in memory.

但是这会导致原始向量中的所有条目首先被SWIG复制到一个Python元组中，然后再被我复制到一个numpy数组中。由于这个向量可能很大，所以我宁愿避免做这两个拷贝，并希望有一种方法可以让SWIG创建一个numpy。在内存中围绕原始向量数据的数组包装器。

I've read the documentation for numpy.i but that explicitly mentions that output arrays are not supported since they seem to be working under the assumption of C-style arrays rather than C++ vectors.

我读过numpy的文档。但是，这明确地提到了不支持输出数组，因为它们似乎是在假设C样式数组而不是c++向量的情况下工作的。

numpy.array's underlying data structure is just a C-style array as is a C++ std::vector so I would hope that it is feasible to have then access the same data in memory.

numpy。数组的底层数据结构只是一个C风格的数组，它是一个c++ std::vector，所以我希望在内存中访问相同的数据是可行的。

Is there any way to make SWIG return a numpy.array which doesn't copy the original data?

有什么办法可以让SWIG返回麻木。不复制原始数据的数组?

2 个解决方案

#1

Apparently it is trivial to "cast" a C++ vector to (C) array, see answer on this question: How to convert vector to array in C++

显然，将一个c++向量“转换”到(C)数组是很简单的，请参阅这个问题的答案:如何在c++中将向量转换为数组

Next you can create a numpy array which will use that C array without copying, see discussion here, or google for PyArray_SimpleNewFromData.

接下来，您可以创建一个numpy数组，该数组将使用该C数组而无需复制，请参阅这里的讨论，或者PyArray_SimpleNewFromData的谷歌。

I wouldn't expect SWIG to do all these for you automatically, instead you should probably write a wrapper for your function getValues yourself, something like getValuesAsNumPyArray.

我不希望SWIG自动为你做这些，相反，你应该为自己的函数编写一个包装器，比如getValuesAsNumPyArray。

#2

It seems like PyArray_SimpleNewFromData would require you to do your own memory management; if memory management is already handled on the C++ side, that is, Python is not responsible for the memory, you can just use np.asarray to get a numpy array that shares memory with the C++ vector, like so:

看起来PyArray_SimpleNewFromData需要您自己进行内存管理;如果在c++方面已经处理了内存管理，也就是说，Python不负责内存，您可以使用np。asarray获取与c++向量共享内存的numpy数组，如下所示:

from libcpp.vector cimport vector
import numpy as np
cdef vector[double] vec
vec.push_back(1)
vec.push_back(2)
cdef double *vec_ptr = &vec[0]    # get hold of data array underlying vec; also vec.data() if you have C++11
cdef double[::1] vec_view = <double[:vec.size()]>vec_ptr    # cast to typed memory view
vec_npr = np.asarray(vec_view)    # get numpy array from memory view
print(vec_npr)    # array([1.0, 2.0])

The "Wrapping C and C++ Arrays" section in chapter 10 of Kurt Smith's Cython book provides good examples on this. Also see Coercion to Numpy from official user guide.

库尔特·史密斯的Cython书第10章中的“包装C和c++数组”一节提供了很好的例子。还可以从官方用户指南中看到对Numpy的强制。

#1