Cython: Transpose a memoryview

问题:

Some background for the question:

I'm trying to optimise a custom neural network code. It relies heavily on loops and I decided to use cython to speed up the calculation.

I followed the usual online tips: Declare all local variables with appropriate cdefs and switch off boundscheck and nonecheck. This barely gave me 10% performance.

Well, my code relies on lots of class members. Therefore I decided to convert the entire class into a cdef class. Turns out that cython doesn't allow numpy ndarrays as types for class members. Instead one has to use memoryviews. Unfortunately the two types seem to be vastly incompatible.

I already ran into this problem: Cython memoryview transpose: Typeerror

To sum it up: You can store an np.ndarray in a memoryview. You can transpose it and store the returned array in a memview. But not if that memview is a class member. Then you have to create an intermediate memview, store the result in that and assign the intermediate memview to the class member.

Here's the code ( many thanks to DavidW)

def double[:,:,:,:] temporary_view_of_transpose

# temporary_view_of_transpose now "looks at" the memory allocated by transpose
# no square brackets!
temporary_view_of_transpose = out_image.transpose(1, 0, 2, 3)

# data is copied from temporary_view_of_transpose to self.y
self.y[...] = temporary_view_of_transpose # (remembering that self.y must be the correct shape before this assignment).

Now I've got a new problem. The code above is from the so-called "forward-pass". There is also a corresponding backward-pass, which does all the calculations backward (for analytical gradients).

This means that for the backward pass, I have to transpose the memoryview and store it in a numpy array:

cdef np.ndarray[DTYPE_t, ndim=4] d_out_image = self.d_y.transpose(1, 0, 2,3)

d_y has to be a class member, therefore it has to be a memoryview. Memoryviews don't allow transposing. They have a .T method, but that doesn't help me.

Actual Question:

• How do I correctly store a numpy array as a class member of a cdef class?
• If the answer is :"as a memoryview", how do I transpose a memoryview ?

回答1:

I think the best answer is "you store the numpy as an untyped python object"

cdef class C:
    cdef object array

    def example_function(self):
        # if you want to use the fast Cython array indexing in a function
        # you can do:
        cdef np.ndarray[np.float64_t,ndim=4] self_array = self.array
        # or
        cdef np.float64_t[:,:,:,:] self_array2 = self.array

        # note that neither of these are copies - they're references
        # to exactly the same array and so if you modify one it'll modify
        # self.array too

    def function2(self):
        return self.array.transpose(1,0,2,3) # works fine!

The small cost to doing it this way is that there's a bit of type-checking at the start of example_function to check that it is actually a 4D numpy array with the correct dtype. Provided you do a decent amount of work in the function that shouldn't matter.

---

As an alternative (if you decide you really want to store them as memoryviews) you could use np.asarray to convert it back to a numpy array without making a copy (i.e. they share data).

e.g.

cdef np.ndarray[DTYPE_t, ndim=4] d_out_image = np.asarray(self.d_y).transpose(1, 0, 2,3)