使用 numpy/ctypes 公开 C 分配的内存缓冲区的更安全方法?
Posted
技术标签:
【中文标题】使用 numpy/ctypes 公开 C 分配的内存缓冲区的更安全方法?【英文标题】:Safer way to expose a C-allocated memory buffer using numpy/ctypes? 【发布时间】:2016-10-25 14:58:58 【问题描述】:我正在为 C 库编写 Python 绑定,该库使用共享内存缓冲区来存储其内部状态。这些缓冲区的分配和释放是由库本身在 Python 之外完成的,但我可以通过从 Python 中调用包装的构造函数/析构函数来间接控制何时发生这种情况。我想向 Python 公开一些缓冲区,以便我可以从中读取,并在某些情况下将值推送给它们。性能和内存使用是重要的问题,因此我希望尽可能避免复制数据。
我目前的方法是创建一个 numpy 数组,它可以直接查看 ctypes 指针:
import numpy as np
import ctypes as C
libc = C.CDLL('libc.so.6')
class MyWrapper(object):
def __init__(self, n=10):
# buffer allocated by external library
addr = libc.malloc(C.sizeof(C.c_int) * n)
self._cbuf = (C.c_int * n).from_address(addr)
def __del__(self):
# buffer freed by external library
libc.free(C.addressof(self._cbuf))
self._cbuf = None
@property
def buffer(self):
return np.ctypeslib.as_array(self._cbuf)
除了避免复制之外,这还意味着我可以使用 numpy 的索引和赋值语法,并将其直接传递给其他 numpy 函数:
wrap = MyWrapper()
buf = wrap.buffer # buf is now a writeable view of a C-allocated buffer
buf[:] = np.arange(10) # this is pretty cool!
buf[::2] += 10
print(wrap.buffer)
# [10 1 12 3 14 5 16 7 18 9]
然而,它本身也很危险:
del wrap # free the pointer
print(buf) # this is bad!
# [1852404336 1969367156 538978662 538976288 538976288 538976288
# 1752440867 1763734377 1633820787 8548]
# buf[0] = 99 # uncomment this line if you <3 segfaults
为了更安全,我需要能够在尝试读取/写入数组内容之前检查底层 C 指针是否已被释放。我对如何做到这一点有一些想法:
一种方法是生成np.ndarray
的子类,该子类包含对MyWrapper
的_cbuf
属性的引用,在对其底层内存进行任何读/写之前检查它是否为None
,并引发如果是这种情况,则例外。
我可以轻松地在同一个缓冲区上生成多个视图,例如通过.view
转换或切片,因此每个都需要继承对_cbuf
的引用和执行检查的方法。我怀疑这可以通过覆盖 __array_finalize__
来实现,但我不确定具体如何。
还需要在读取和/或写入数组内容的任何操作之前调用“指针检查”方法。我对 numpy 的内部结构知之甚少,无法提供详尽的覆盖方法列表。
如何实现np.ndarray
的子类来执行此检查?任何人都可以提出更好的方法吗?
更新:这个类做了我想要的大部分:
class SafeBufferView(np.ndarray):
def __new__(cls, get_buffer, shape=None, dtype=None):
obj = np.ctypeslib.as_array(get_buffer(), shape).view(cls)
if dtype is not None:
obj.dtype = dtype
obj._get_buffer = get_buffer
return obj
def __array_finalize__(self, obj):
if obj is None: return
self._get_buffer = getattr(obj, "_get_buffer", None)
def __array_prepare__(self, out_arr, context=None):
if not self._get_buffer(): raise Exception("Dangling pointer!")
return out_arr
# this seems very heavy-handed - surely there must be a better way?
def __getattribute__(self, name):
if name not in ["__new__", "__array_finalize__", "__array_prepare__",
"__getattribute__", "_get_buffer"]:
if not self._get_buffer(): raise Exception("Dangling pointer!")
return super(np.ndarray, self).__getattribute__(name)
例如:
wrap = MyWrapper()
sb = SafeBufferView(lambda: wrap._cbuf)
sb[:] = np.arange(10)
print(repr(sb))
# SafeBufferView([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)
print(repr(sb[::2]))
# SafeBufferView([0, 2, 4, 6, 8], dtype=int32)
sbv = sb.view(np.double)
print(repr(sbv))
# SafeBufferView([ 2.12199579e-314, 6.36598737e-314, 1.06099790e-313,
# 1.48539705e-313, 1.90979621e-313])
# we have to call the destructor method of `wrap` explicitly - `del wrap` won't
# do anything because `sb` and `sbv` both hold references to `wrap`
wrap.__del__()
print(sb) # Exception: Dangling pointer!
print(sb + 1) # Exception: Dangling pointer!
print(sbv) # Exception: Dangling pointer!
print(np.sum(sb)) # Exception: Dangling pointer!
print(sb.dot(sb)) # Exception: Dangling pointer!
print(np.dot(sb, sb)) # oops...
# -70104698
print(np.extract(np.ones(10), sb))
# array([251019024, 32522, 498870232, 32522, 4, 5,
# 6, 7, 48, 0], dtype=int32)
# np.copyto(sb, np.ones(10, np.int32)) # don't try this at home, kids!
我确定我还漏掉了其他边缘情况。
更新 2: 根据 @ivan_pozdeev 的建议,我玩过 weakref.proxy
。这是一个好主意,但不幸的是我看不出它如何与 numpy 数组一起工作。我可以尝试为 .buffer
返回的 numpy 数组创建一个弱引用:
wrap = MyWrapper()
wr = weakref.proxy(wrap.buffer)
print(wr)
# ReferenceError: weakly-referenced object no longer exists
# <weakproxy at 0x7f6fe715efc8 to NoneType at 0x91a870>
我认为这里的问题是wrap.buffer
返回的np.ndarray
实例立即超出范围。一种解决方法是让类在初始化时实例化数组,持有对它的强引用,并让 .buffer()
getter 将 weakref.proxy
返回到数组:
class MyWrapper2(object):
def __init__(self, n=10):
# buffer allocated by external library
addr = libc.malloc(C.sizeof(C.c_int) * n)
self._cbuf = (C.c_int * n).from_address(addr)
self._buffer = np.ctypeslib.as_array(self._cbuf)
def __del__(self):
# buffer freed by external library
libc.free(C.addressof(self._cbuf))
self._cbuf = None
self._buffer = None
@property
def buffer(self):
return weakref.proxy(self._buffer)
但是,如果我在同一个数组上创建第二个视图,而缓冲区仍处于分配状态,则会中断:
wrap2 = MyWrapper2()
buf = wrap2.buffer
buf[:] = np.arange(10)
buf2 = buf[:] # create a second view onto the contents of buf
print(repr(buf))
# <weakproxy at 0x7fec3e709b50 to numpy.ndarray at 0x210ac80>
print(repr(buf2))
# array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)
wrap2.__del__()
print(buf2[:]) # this is bad
# [1291716568 32748 1291716568 32748 0 0 0
# 0 48 0]
print(buf[:]) # WTF?!
# [34525664 0 0 0 0 0 0 0
# 0 0]
这严重坏了 - 在调用 wrap2.__del__()
之后,我不仅可以读取和写入 buf2
,这是 wrap2._cbuf
上的一个 numpy 数组视图,而且我什至可以读取和写入buf
,鉴于wrap2.__del__()
将wrap2._buffer
设置为None
,这应该是不可能的。
【问题讨论】:
您是否考虑过使用 Cython 编写包装器?它有一个更简洁(并且可能更安全)的界面,用于通过“类型化的内存视图”获取内存缓冲区的视图 @JoshAdel 在这种情况下,键入的内存视图真的有帮助吗?最终的问题是,这些缓冲区的分配和释放是由我无法直接控制的外部库在 Python 之外完成的。我能做的最好的事情就是根据我迄今为止调用的任何库函数来跟踪它们是否应该仍然被分配。我想我可以用 Cython 而不是 Python 来记账,但我还没有看到切换到 Cython 的显着优势(还有其他一些原因导致在这个阶段这会很痛苦)。 如果您保留对某些将为您调用删除器的对象的强引用(例如cffi
(您应该始终使用而不是ctypes
)内置支持使用gc
方法的删除器),那么您不必担心无效的弱引用。
@o11c gc
在这里无关紧要,因为缓冲区是由我包装的外部库在 Python 之外分配和释放的。
@ali_m:分配_buffer = None
不会释放_buffer
,因为另一个数组仍然有对它的引用。如果您在指针准备好被释放之前手动调用释放指针的函数,那么东西就会中断。
【参考方案1】:
当存在任何 numpy 数组时,您必须保留对 Wrapper 的引用。实现这一点的最简单方法是将此引用保存在 ctype-buffer 的属性中:
class MyWrapper(object):
def __init__(self, n=10):
# buffer allocated by external library
self.size = n
self.addr = libc.malloc(C.sizeof(C.c_int) * n)
def __del__(self):
# buffer freed by external library
libc.free(self.addr)
@property
def buffer(self):
buf = (C.c_int * self.size).from_address(self.addr)
buf._wrapper = self
return np.ctypeslib.as_array(buf)
当最后一个引用(例如最后一个 numpy 数组)被垃圾收集时,您的包装器会自动释放。
【讨论】:
【参考方案2】:它是由第三方编写并以二进制形式分发的专有库。我可以从 C 而不是 Python 调用相同的库函数,但这无济于事,因为我仍然无法访问实际分配和释放缓冲区的代码。例如,我不能自己分配缓冲区,然后将它们作为指针传递给库。
但是,您可以将缓冲区包装在 Python 扩展类型中。这样,您可以只公开您希望可用的接口,并让扩展类型自动处理缓冲区的释放。这样一来,Python API 就不可能进行空闲内存读/写。
mybuffer.c
#include <python3.3/Python.h>
// Hardcoded values
// N.B. Most of these are only needed for defining the view in the Python
// buffer protocol
static long external_buffer_size = 32; // Size of buffer in bytes
static long external_buffer_shape[] = 32 ; // Number of items for each dimension
static long external_buffer_strides[] = 1 ; // Size of item for each dimension
//----------------------------------------------------------------------------
// Code to simulate the third-party library
//----------------------------------------------------------------------------
// Allocate a new buffer
static void* external_buffer_allocate()
// Allocate the memory
void* ptr = malloc(external_buffer_size);
// Debug
printf("external_buffer_allocate() = 0x%lx\n", (long) ptr);
// Fill buffer with a recognizable pattern
int i;
for (i = 0; i < external_buffer_size; ++i)
*((char*) ptr + i) = i;
// Done
return ptr;
// Free an existing buffer
static void external_buffer_free(void* ptr)
// Debug
printf("external_buffer_free(0x%lx)\n", (long) ptr);
// Release the memory
free(ptr);
//----------------------------------------------------------------------------
// Define a new Python instance object for the external buffer
// See: https://docs.python.org/3/extending/newtypes.html
//----------------------------------------------------------------------------
typedef struct
// Python macro to include standard members, like reference count
PyObject_HEAD
// Base address of allocated memory
void* ptr;
BufferObject;
//----------------------------------------------------------------------------
// Define the instance methods for the new object
//----------------------------------------------------------------------------
// Called when there are no more references to the object
static void BufferObject_dealloc(BufferObject* self)
external_buffer_free(self->ptr);
// Called when we want a new view of the buffer, using the buffer protocol
// See: https://docs.python.org/3/c-api/buffer.html
static int BufferObject_getbuffer(BufferObject *self, Py_buffer *view, int flags)
// Set the view info
view->obj = (PyObject*) self;
view->buf = self->ptr; // Base pointer
view->len = external_buffer_size; // Length
view->readonly = 0;
view->itemsize = 1;
view->format = "B"; // unsigned byte
view->ndim = 1;
view->shape = external_buffer_shape;
view->strides = external_buffer_strides;
view->suboffsets = NULL;
view->internal = NULL;
// We need to increase the reference count of our buffer object here, but
// Python will automatically decrease it when the view goes out of scope
Py_INCREF(self);
// Done
return 0;
//----------------------------------------------------------------------------
// Define the struct required to implement the buffer protocol
//----------------------------------------------------------------------------
static PyBufferProcs BufferObject_as_buffer =
// Create new view
(getbufferproc) BufferObject_getbuffer,
// Release an existing view
(releasebufferproc) 0,
;
//----------------------------------------------------------------------------
// Define a new Python type object for the external buffer
//----------------------------------------------------------------------------
static PyTypeObject BufferType =
PyVarObject_HEAD_INIT(NULL, 0)
"external buffer", /* tp_name */
sizeof(BufferObject), /* tp_basicsize */
0, /* tp_itemsize */
(destructor) BufferObject_dealloc, /* tp_dealloc */
0, /* tp_print */
0, /* tp_getattr */
0, /* tp_setattr */
0, /* tp_reserved */
0, /* tp_repr */
0, /* tp_as_number */
0, /* tp_as_sequence */
0, /* tp_as_mapping */
0, /* tp_hash */
0, /* tp_call */
0, /* tp_str */
0, /* tp_getattro */
0, /* tp_setattro */
&BufferObject_as_buffer, /* tp_as_buffer */
Py_TPFLAGS_DEFAULT, /* tp_flags */
"External buffer", /* tp_doc */
0, /* tp_traverse */
0, /* tp_clear */
0, /* tp_richcompare */
0, /* tp_weaklistoffset */
0, /* tp_iter */
0, /* tp_iternext */
0, /* tp_methods */
0, /* tp_members */
0, /* tp_getset */
0, /* tp_base */
0, /* tp_dict */
0, /* tp_descr_get */
0, /* tp_descr_set */
0, /* tp_dictoffset */
(initproc) 0, /* tp_init */
0, /* tp_alloc */
0, /* tp_new */
;
//----------------------------------------------------------------------------
// Define a Python function to put in the module which creates a new buffer
//----------------------------------------------------------------------------
static PyObject* mybuffer_create(PyObject *self, PyObject *args)
BufferObject* buf = (BufferObject*)(&BufferType)->tp_alloc(&BufferType, 0);
buf->ptr = external_buffer_allocate();
return (PyObject*) buf;
//----------------------------------------------------------------------------
// Define the set of all methods which will be exposed in the module
//----------------------------------------------------------------------------
static PyMethodDef mybufferMethods[] =
"create", mybuffer_create, METH_VARARGS, "Create a buffer",
NULL, NULL, 0, NULL /* Sentinel */
;
//----------------------------------------------------------------------------
// Define the module
//----------------------------------------------------------------------------
static PyModuleDef mybuffermodule =
PyModuleDef_HEAD_INIT,
"mybuffer",
"Example module that creates an extension type.",
-1,
mybufferMethods
//NULL, NULL, NULL, NULL, NULL
;
//----------------------------------------------------------------------------
// Define the module's entry point
//----------------------------------------------------------------------------
PyMODINIT_FUNC PyInit_mybuffer(void)
PyObject* m;
if (PyType_Ready(&BufferType) < 0)
return NULL;
m = PyModule_Create(&mybuffermodule);
if (m == NULL)
return NULL;
return m;
test.py
#!/usr/bin/env python3
import numpy as np
import mybuffer
def test():
print('Create buffer')
b = mybuffer.create()
print('Print buffer')
print(b)
print('Create memoryview')
m = memoryview(b)
print('Print memoryview shape')
print(m.shape)
print('Print memoryview format')
print(m.format)
print('Create numpy array')
a = np.asarray(b)
print('Print numpy array')
print(repr(a))
print('Change every other byte in numpy')
a[::2] += 10
print('Print numpy array')
print(repr(a))
print('Change first byte in memory view')
m[0] = 42
print('Print numpy array')
print(repr(a))
print('Delete buffer')
del b
print('Delete memoryview')
del m
print('Delete numpy array - this is the last ref, so should free memory')
del a
print('Memory should be free before this line')
if __name__ == '__main__':
test()
示例
$ gcc -fPIC -shared -o mybuffer.so mybuffer.c -lpython3.3m
$ ./test.py
Create buffer
external_buffer_allocate() = 0x290fae0
Print buffer
<external buffer object at 0x7f7231a2cc60>
Create memoryview
Print memoryview shape
(32,)
Print memoryview format
B
Create numpy array
Print numpy array
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31], dtype=uint8)
Change every other byte in numpy
Print numpy array
array([10, 1, 12, 3, 14, 5, 16, 7, 18, 9, 20, 11, 22, 13, 24, 15, 26,
17, 28, 19, 30, 21, 32, 23, 34, 25, 36, 27, 38, 29, 40, 31], dtype=uint8)
Change first byte in memory view
Print numpy array
array([42, 1, 12, 3, 14, 5, 16, 7, 18, 9, 20, 11, 22, 13, 24, 15, 26,
17, 28, 19, 30, 21, 32, 23, 34, 25, 36, 27, 38, 29, 40, 31], dtype=uint8)
Delete buffer
Delete memoryview
Delete numpy array - this is the last ref, so should free memory
external_buffer_free(0x290fae0)
Memory should be free before this line
【讨论】:
【参考方案3】:我喜欢@Vikas 的方法,但是当我尝试它时,我只得到了一个包含单个FreeOnDel
对象的 Numpy 对象数组。以下更简单且有效:
class FreeOnDel(object):
def __init__(self, data, shape, dtype, readonly=False):
self.__array_interface__ = "version": 3,
"typestr": numpy.dtype(dtype).str,
"data": (data, readonly),
"shape": shape
def __del__(self):
data = self.__array_interface__["data"][0] # integer ptr
print("do what you want with the data at ".format(data))
view = numpy.array(FreeOnDel(ptr, shape, dtype), copy=False)
其中ptr
是指向整数数据的指针(例如ctypesptr.addressof(...)
)。
__array_interface__
属性足以告诉 Numpy 如何将内存区域转换为数组,然后FreeOnDel
对象变成该数组的base
。当数组被删除时,删除被传播到FreeOnDel
对象,你可以在其中调用libc.free
。
我什至可以将此FreeOnDel
类称为“BufferOwner
”,因为这就是它的作用:跟踪所有权。
【讨论】:
【参考方案4】:weakref
是您提议的功能的内置机制。
具体来说,weakref.proxy
是一个与引用对象具有相同接口的对象。在引用对象的处置后,代理上的任何操作都会引发weakref.ReferenceError
。你甚至不需要numpy
:
In [2]: buffer=(c.c_int*100)() #acts as an example for an externally allocated buffer
In [3]: voidp=c.addressof(buffer)
In [10]: a=(c.c_int*100).from_address(voidp) # python object accessing the buffer.
# Here it's created from raw address value. It's better to use function
# prototypes instead for some type safety.
In [14]: ra=weakref.proxy(a)
In [15]: a[1]=1
In [16]: ra[1]
Out[16]: 1
In [17]: del a
In [18]: ra[1]
ReferenceError: weakly-referenced object no longer exists
In [20]: buffer[1]
Out[20]: 1
正如您所见,在任何情况下,您都需要一个普通的 Python 对象来覆盖 C 缓冲区。如果外部库拥有内存,则必须在 C 级别释放缓冲区之前删除对象。如果您自己拥有该内存,您只需按正常方式创建一个ctypes
对象,然后在删除时将其释放。
因此,如果您的外部库拥有内存并且可以随时释放(您的规范对此含糊不清),它必须以某种方式告诉您它即将这样做 - 否则,您无法知道要采取的措施必要的行动。
【讨论】:
感谢您的建议。不幸的是,我认为weakref.proxy
不能正确处理多个 numpy 数组由同一个缓冲区支持的情况(请参阅我的更新)。
我对这个问题做了一些澄清。尽管缓冲区的分配/释放发生在 Python 之外,但我可以通过调用包装的构造函数/析构函数来间接控制何时发生。
问题不在于weakref
,而在于view
。不拥有其内存的 Numpy 对象似乎本质上是不安全的。您所能做的就是创建挂钩视图以同样存储强引用并返回弱引用。或者,例如,根本不使用视图(正如我所展示的,您甚至不需要 ndarray
s、ctypes
数组也能正常工作)。
ctypes
数组如果我想要的只是一个容器就可以了,但我实际上想使用缓冲区的内容执行矢量化计算。我认为@Daniel 的方法可能就足够了,因为视图和切片将保存对通过它们的.base
属性派生它们的父数组的引用。只要从指针创建的初始数组包含对其父对象的引用,那么就足以确保在该内存上的所有其他视图也消失之前不会删除父对象。
@ali_m 但您告诉我们缓冲区已被库“从”对象“下方”删除 - 因此它必须有一种方法可以主动向所有相关人员发出信号,表明它已变得无效,但有许多 Python 引用有它。【参考方案5】:
在将其传递给numpy.ctypeslib.as_array
方法之前,您只需要一个带有额外__del__
函数的包装器。
class FreeOnDel(object):
def __init__(self, ctypes_ptr):
# This is not needed if you are dealing with ctypes.POINTER() objects
# Start of hack for ctypes ARRAY type;
if not hasattr(ctypes_ptr, 'contents'):
# For static ctypes arrays, the length and type are stored
# in the type() rather than object. numpy queries these
# properties to find out the shape and type, hence needs to be
# copied. I wish type() properties could be automated by
# __getattr__ too
type(self)._length_ = type(ctypes_ptr)._length_
type(self)._type_ = type(ctypes_ptr)._type_
# End of hack for ctypes ARRAY type;
# cannot call self._ctypes_ptr = ctypes_ptr because of recursion
super(FreeOnDel, self).__setattr__('_ctypes_ptr', ctypes_ptr)
# numpy.ctypeslib.as_array function sets the __array_interface__
# on type(ctypes_ptr) which is not called by __getattr__ wrapper
# Hence this additional wrapper.
@property
def __array_interface__(self):
return self._ctypes_ptr.__array_interface__
@__array_interface__.setter
def __array_interface__(self, value):
self._ctypes_ptr.__array_interface__ = value
# This is the onlly additional function we need rest all is overhead
def __del__(self):
addr = ctypes.addressof(self._ctypes_ptr)
print("freeing address %x" % addr)
libc.free(addr)
# Need to be called on all object members
# object.__del__(self) does not work
del self._ctypes_ptr
def __getattr__(self, attr):
return getattr(self._ctypes_ptr, attr)
def __setattr__(self, attr, val):
setattr(self._ctypes_ptr, attr, val)
测试
In [32]: import ctypes as C
In [33]: n = 10
In [34]: libc = C.CDLL("libc.so.6")
In [35]: addr = libc.malloc(C.sizeof(C.c_int) * n)
In [36]: cbuf = (C.c_int * n).from_address(addr)
In [37]: wrap = FreeOnDel(cbuf)
In [38]: sb = np.ctypeslib.as_array(wrap, (10,))
In [39]: sb[:] = np.arange(10)
In [40]: print(repr(sb))
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)
In [41]: print(repr(sb[::2]))
array([0, 2, 4, 6, 8], dtype=int32)
In [42]: sbv = sb.view(np.double)
In [43]: print(repr(sbv))
array([ 2.12199579e-314, 6.36598737e-314, 1.06099790e-313,
1.48539705e-313, 1.90979621e-313])
In [45]: buf2 = sb[:8]
In [46]: sb[::2] += 10
In [47]: del cbuf # Memory not freed because this does not have __del__
In [48]: del wrap # Memory not freed because sb, sbv, buf2 have references
In [49]: del sb # Memory not freed because sbv, buf have references
In [50]: del buf2 # Memory not freed because sbv has reference
In [51]: del sbv # Memory freed because no more references
freeing address 2bc6bc0
其实更简单的解决方案是覆盖__del__
函数
In [7]: olddel = getattr(cbuf, '__del__', lambda: 0)
In [8]: cbuf.__del__ = lambda self : libc.free(C.addressof(self)), olddel
In [10]: import numpy as np
In [12]: sb = np.ctypeslib.as_array(cbuf, (10,))
In [13]: sb[:] = np.arange(10)
In [14]: print(repr(sb))
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)
In [15]: print(repr(sb))
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)
In [16]: print(repr(sb[::2]))
array([0, 2, 4, 6, 8], dtype=int32)
In [17]: sbv = sb.view(np.double)
In [18]: print(repr(sbv))
array([ 2.12199579e-314, 6.36598737e-314, 1.06099790e-313,
1.48539705e-313, 1.90979621e-313])
In [19]: buf2 = sb[:8]
In [20]: sb[::2] += 10
In [22]: del cbuf # Memory not freed
In [23]: del sb # Memory not freed because sbv, buf have references
In [24]: del buf2 # Memory not freed because sbv has reference
In [25]: del sbv # Memory freed because no more references
【讨论】:
【参考方案6】:如果您可以通过 Python 完全控制 C 缓冲区的生命周期,那么您基本上拥有的是一个 ndarray
应该使用的 Python“缓冲区”对象。
因此,
有两种基本的连接方式: 缓冲区-> ndarray ndarray -> 缓冲区 还有一个问题是如何实现缓冲区本身缓冲区 -> ndarray
>不安全:在ndarray
的生命周期内,没有任何东西自动持有对buffer
的引用。引入第 3 个对象来保存对两者的引用并没有更好:那么您只需要跟踪第 3 个对象而不是 buffer
。
ndarray -> 缓冲区
“现在你在说话!”因为手头的任务是“ndarray
应该使用的缓冲区”?这是自然的方式。
事实上,numpy
有一个内置机制:任何不拥有其内存的 ndarray
都持有对其 base
属性中的对象的引用(从而防止后者被垃圾收集)。对于视图,属性会相应地自动分配(如果其base
是None
,则分配给父对象,或者分配给父对象的base
)。
问题是你不能在那里放置任何旧物体。相反,该属性由构造函数填充,并且建议的对象首先通过其检查。
所以,如果我们可以构造一些 numpy.array
接受并认为符合内存重用条件的自定义对象(numpy.ctypeslib.as_array
实际上是 numpy.array(copy=False)
的包装器,并带有一些健全性检查)...
<...>
【讨论】:
以上是关于使用 numpy/ctypes 公开 C 分配的内存缓冲区的更安全方法?的主要内容,如果未能解决你的问题,请参考以下文章