Benchmarks generally should go to pypy/benchmarks directory in the main source tree (that is svn+ssh://codespeak.net/svn/pypy/benchmarks) On Sat, Jul 17, 2010 at 12:41 AM, <dan@codespeak.net> wrote:

Author: dan Date: Sat Jul 17 00:41:02 2010 New Revision: 76268

Added:   pypy/branch/micronumpy/pypy/tool/convolve.py Modified:   pypy/branch/micronumpy/pypy/tool/numpybench.py Log: Oops, I forgot the most important part of the benchmark!

Added: pypy/branch/micronumpy/pypy/tool/convolve.py ============================================================================== --- (empty file) +++ pypy/branch/micronumpy/pypy/tool/convolve.py        Sat Jul 17 00:41:02 2010 @@ -0,0 +1,43 @@ +from __future__ import division +from __main__ import numpy as np + +def naive_convolve(f, g): +    # f is an image and is indexed by (v, w) +    # g is a filter kernel and is indexed by (s, t), +    #   it needs odd dimensions +    # h is the output image and is indexed by (x, y), +    #   it is not cropped +    if g.shape[0] % 2 != 1 or g.shape[1] % 2 != 1: +        raise ValueError("Only odd dimensions on filter supported") +    # smid and tmid are number of pixels between the center pixel +    # and the edge, ie for a 5x5 filter they will be 2. +    # +    # The output size is calculated by adding smid, tmid to each +    # side of the dimensions of the input image. +    vmax = f.shape[0] +    wmax = f.shape[1] +    smax = g.shape[0] +    tmax = g.shape[1] +    smid = smax // 2 +    tmid = tmax // 2 +    xmax = vmax + 2*smid +    ymax = wmax + 2*tmid +    # Allocate result image. +    h = np.zeros([xmax, ymax], dtype=f.dtype) +    # Do convolution +    for x in range(xmax): +        for y in range(ymax): +            # Calculate pixel value for h at (x,y). Sum one component +            # for each pixel (s, t) of the filter g. +            s_from = max(smid - x, -smid) +            s_to = min((xmax - x) - smid, smid + 1) +            t_from = max(tmid - y, -tmid) +            t_to = 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 = x - smid + s +                    w = y - tmid + t +                    value += g[smid - s, tmid - t] * f[v, w] +            h[x, y] = value +    return h

Modified: pypy/branch/micronumpy/pypy/tool/numpybench.py ============================================================================== --- pypy/branch/micronumpy/pypy/tool/numpybench.py      (original) +++ pypy/branch/micronumpy/pypy/tool/numpybench.py      Sat Jul 17 00:41:02 2010 @@ -21,13 +21,29 @@     return numpy.array(kernel)

 if __name__ == '__main__': -    from sys import argv as args -    width, height, kwidth, kheight = [int(x) for x in args[1:]] +    from optparse import OptionParser + +    option_parser = OptionParser() +    option_parser.add_option('--kernel-size', dest='kernel', default='3x3', +                             help="The size of the convolution kernel, given as WxH. ie 3x3" +                                  "Note that both dimensions must be odd.") +    option_parser.add_option('--image-size', dest='image', default='256x256', +                             help="The size of the image, given as WxH. ie. 256x256") +    option_parser.add_option('--runs', '--count', dest='count', default=1000, +                             help="The number of times to run the convolution filter") + +    options, args = option_parser.parse_args() + +    def parse_dimension(arg): +        return [int(s.strip()) for s in arg.split('x')] + +    width, height = parse_dimension(options.image) +    kwidth, kheight = parse_dimension(options.kernel) +    count = int(options.count)

    image = generate_image(width, height)     kernel = generate_kernel(kwidth, kheight)

    from timeit import Timer     convolve_timer = Timer('naive_convolve(image, kernel)', 'from convolve import naive_convolve; from __main__ import image, kernel; gc.enable()') -    count = 100     print "%.5f sec/pass" % (convolve_timer.timeit(number=count)/count) _______________________________________________ pypy-svn mailing list pypy-svn@codespeak.net http://codespeak.net/mailman/listinfo/pypy-svn