Question

我是Cython的新手，并试图学习如何使用numpy来加速代码。我一直在关注此link中的教程。

我在这里复制了他们的代码：

from __future__ import division
import numpy as np


# "cimport" is used to import special compile-time information
# about the numpy module (this is stored in a file numpy.pxd which is
# currently part of the Cython distribution).
cimport numpy as np
# We now need to fix a datatype for our arrays. I've used the variable
# DTYPE for this, which is assigned to the usual NumPy runtime
# type info object.
DTYPE = np.int
# "ctypedef" assigns a corresponding compile-time type to DTYPE_t. For
# every type in the numpy module there's a corresponding compile-time
# type with a _t-suffix.
ctypedef np.int_t DTYPE_t
# The builtin min and max functions works with Python objects, and are
# so very slow. So we create our own.
#  - "cdef" declares a function which has much less overhead than a normal
#    def function (but it is not Python-callable)
#  - "inline" is passed on to the C compiler which may inline the functions
#  - The C type "int" is chosen as return type and argument types
#  - Cython allows some newer Python constructs like "a if x else b", but
#    the resulting C file compiles with Python 2.3 through to Python 3.0 beta.
cdef inline int int_max(int a, int b): return a if a >= b else b
cdef inline int int_min(int a, int b): return a if a <= b else b
# "def" can type its arguments but not have a return type. The type of the
# arguments for a "def" function is checked at run-time when entering the
# function.
#
# The arrays f, g and h is typed as "np.ndarray" instances. The only effect
# this has is to a) insert checks that the function arguments really are
# NumPy arrays, and b) make some attribute access like f.shape[0] much
# more efficient. (In this example this doesn't matter though.)
cimport cython
@cython.boundscheck(False)
def naive_convolve(np.ndarray[DTYPE_t, ndim=2] f, np.ndarray[DTYPE_t, ndim=2] g):
    if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1:
        raise ValueError("Only odd dimensions on filter supported")
    assert f.dtype == DTYPE and g.dtype == DTYPE
    # The "cdef" keyword is also used within functions to type variables. It
    # can only be used at the top indendation level (there are non-trivial
    # problems with allowing them in other places, though we'd love to see
    # good and thought out proposals for it).
    #
    # For the indices, the "int" type is used. This corresponds to a C int,
    # other C types (like "unsigned int") could have been used instead.
    # Purists could use "Py_ssize_t" which is the proper Python type for
    # array indices.
    cdef int vmax = f.shape[0]
    cdef int wmax = f.shape[1]
    cdef int smax = g.shape[0]
    cdef int tmax = g.shape[1]
    cdef int smid = smax // 2
    cdef int tmid = tmax // 2
    cdef int xmax = vmax + 2*smid
    cdef int ymax = wmax + 2*tmid
    cdef np.ndarray[DTYPE_t, ndim=2] h = np.zeros([xmax, ymax], dtype=DTYPE)
    cdef int s, t
    cdef unsigned int x, y, v, w
    # It is very important to type ALL your variables. You do not get any
    # warnings if not, only much slower code (they are implicitly typed as
    # Python objects).
    cdef int s_from, s_to, t_from, t_to
    # For the value variable, we want to use the same data type as is
    # stored in the array, so we use "DTYPE_t" as defined above.
    # NB! An important side-effect of this is that if "value" overflows its
    # datatype size, it will simply wrap around like in C, rather than raise
    # an error like in Python.
    cdef DTYPE_t value
    for x in range(xmax):
        for y in range(ymax):
            s_from = int_max(smid - x, -smid)
            s_to = int_min((xmax - x) - smid, smid + 1)
            t_from = int_max(tmid - y, -tmid)
            t_to = int_min((ymax - y) - tmid, tmid + 1)
            value = 0
            for s in range(s_from, s_to):
                for t in range(t_from, t_to):
                    v = <unsigned int>(x - smid + s)
                    w = <unsigned int>(y - tmid + t)
                    value += g[<unsigned int>(smid - s), <unsigned int>(tmid - t)] * f[v, w]
            h[x, y] = value
    return h

有一件事我不明白。我知道cdef定义了一个关于Cython语言基础的link的C类型。但是，上面的示例还定义了一个名为np.int_t的编译时类型，例如，在cdef DTYPE_t value所在的行中，其中DTYPE_t实际上是np.int_t。

我的问题是：np.int和np.int_t之间有什么区别？它类似于python int与ctypes.c_int，但更具体针对numpy？在这种情况下，如果我只使用cdef int代替cdef np.int_t，会不会相同？

另外，我测试了如果我用cdef DTYPE_t value替换cdef int value会发生什么。结果显示两者之间没有差异。

这是原始的cdef DTYPE_t value 1个循环，最好为10：每循环93.9 ms

这是针对修改后的cdef int value 1个循环，最好为10：每循环93.8 ms

任何帮助将不胜感激。谢谢！

Answer 1

np.int是一个Python对象，它引用Python代码中的整数dtype。 np.int_t是仅存在于Cython中的C typedef。（它相当于C long，我相信，不是int。）

Cython中C类型和编译时类型的区别

1 个答案: