Improving Numpy Performance

I'd like to improve the performance of convolution using python, and was hoping for some insight on how to best go about improving performance.

I am currently using scipy to perform the convolution, using code somewhat like the snippet below:

import numpy
import scipy
import scipy.signal
import timeit

a=numpy.array ( [ range(1000000) ] )
a.reshape(1000,1000)
filt=numpy.array( [ [ 1, 1, 1 ], [1, -8, 1], [1,1,1] ] )

def convolve():
  global a, filt
  scipy.signal.convolve2d ( a, filt, mode="same" )

t=timeit.Timer("convolve()", "from __main__ import convolve")
print "%.2f sec/pass" % (10 * t.timeit(number=10)/100)

I am processing image data, using grayscale (integer values between 0 and 255), and I currently开发者_StackOverflow中文版 get about a quarter of a second per convolution. My thinking was to do one of the following:

Use corepy, preferably with some optimizations Recompile numpy with icc & ikml. Use python-cuda.

I was wondering if anyone had any experience with any of these approaches ( what sort of gain would be typical, and if it is worth the time ), or if anyone is aware of a better library to perform convolution with Numpy.

Thanks!

EDIT:

Speed up of about 10x by re-writing python loop in C over using Numpy.

The code in scipy for doing 2d convolutions is a bit messy and unoptimized. See http://svn.scipy.org/svn/scipy/trunk/scipy/signal/firfilter.c if you want a glimpse into the low-level functioning of scipy.

If all you want is to process with a small, constant kernel like the one you showed, a function like this might work:

def specialconvolve(a):
    # sorry, you must pad the input yourself
    rowconvol = a[1:-1,:] + a[:-2,:] + a[2:,:]
    colconvol = rowconvol[:,1:-1] + rowconvol[:,:-2] + rowconvol[:,2:] - 9*a[1:-1,1:-1]
    return colconvol

This function takes advantage of the separability of the kernel like DarenW suggested above, as well as taking advantage of the more optimized numpy arithmetic routines. It's over 1000 times faster than the convolve2d function by my measurements.

For the particular example 3x3 kernel, I'd observe that

1  1  1
1 -8  1
1  1  1

  1  1  1     0  0  0
= 1  1  1  +  0 -9  0
  1  1  1     0  0  0

and that the first of these is factorable - it can be convoluted by convolving (1 1 1) for each row, and then again for each column. Then subtract nine times the original data. This may or may not be faster, depending on whether the scipy programmers made it smart enough to automatically do this. (I haven't checked in a while.)

You probably want to do more interesting convolutions, where factoring may or may not be possible.

Before going to say C with ctypes, I'd suggest running a standalone convolve in C, to see where the limit is.
Similarly for CUDA, cython, scipy.weave ...

Added 7feb: convolve33 8-bit data with clipping takes ~ 20 clock cycles per point, 2 clock cycles per mem access, on my mac g4 pcc with gcc 4.2. Your mileage will vary.

A couple of subtleties:

• do you care about correct clipping to 0..255 ? np.clip() is slow, cython etc. don't know.
• Numpy/scipy may need memory for temps the size of A (so keep 2*sizeof(A) < cache size).
  If your C code, though, does a running update inplace, that's half the mem but a different algorithm.

By the way, google theano convolve => "A convolution op that should mimic scipy.signal.convolve2d, but faster! In development"

A typical optimization for convolution is to use the FFT of your signal. The reason is: the convolution in real space is a product in FFT space. It is often faster to compute the FFT, then the product, and the iFFT of the result than convolve the usual way.

As of 2018, seems like SciPy/Numpy combo has been sped up a lot. This is what I saw on my laptop (Dell Inspiron 13, i5). OpenCV did the best but you don't have any control on modes.

>>> img= np.random.rand(1000,1000)
>>> kernel = np.ones((3,3), dtype=np.float)/9.0
>>> t1= time.time();dst1 = cv2.filter2D(img,-1,kernel);print(time.time()-t1)
0.0235188007355
>>> t1= time.time();dst2 = signal.correlate(img,kernel,mode='valid',method='fft');print(time.time()-t1)
0.140458106995
>>> t1= time.time();dst3 = signal.convolve2d(img,kernel,mode='valid');print(time.time()-t1)
0.0548939704895
>>> t1= time.time();dst4 = signal.correlate2d(img,kernel,mode='valid');print(time.time()-t1)
0.0518119335175
>>> t1= time.time();dst5 = signal.fftconvolve(img,kernel,mode='valid');print(time.time()-t1)
0.13204407692