[Numpy-discussion] Efficient numpy slicing for a "sliding window approach".
Nicolas Pinto
pinto at mit.edu
Sat Feb 21 01:08:15 EST 2009
Thanks Josef.
I'm not sure how I could use correlate2d because of the 'stride' parameter
on the y and x axes, but I may be able to do something on the z axis.
On Sat, Feb 21, 2009 at 12:56 AM, <josef.pktd at gmail.com> wrote:
> On Sat, Feb 21, 2009 at 12:36 AM, Nicolas Pinto <pinto at mit.edu> wrote:
> > Thanks a lot for the pointer to segmentaxis. I'm trying to use it "as is"
> > and it seems that I need to a big reshape before the matrix
> multiplication.
> > Am I missing something ?
> >
> >
> > ========================================
> >
> > import numpy as np
> > from numpy import dot, transpose
> >
> > arrh, arrw, arrd = 480,640,96
> > arr = np.random.randn(arrh, arrw, arrd).astype("float32")
> >
> > stride = 16
> > winh, winw, wind = 128,64,96
> >
> > limit = 100
> >
> > clas_w = np.random.randn(8,4,96).astype("float32").ravel()
> >
> > from segmentaxis import segment_axis
> >
> > nh, nw = arrh-winh+1, arrw-winw+1
> >
> > @profile
> > def func_loop(arr):
> >
> > resps = np.empty((nh,nw), dtype="float32")
> > for j in xrange(nh):
> > for i in xrange(nw):
> > win = arr[j:j+winh:stride, i:i+winw:stride]
> > win = win.ravel()
> > resp = dot(win, clas_w)
> > resps[j,i] = resp
> >
> > resps = resps.ravel()
> > print resps.mean()
> >
> >
> > @profile
> > def func_segment(arr):
> >
> > arr = segment_axis(arr, winh, winh-1, axis=0)
> > arr = arr[:, ::stride]
> >
> > arr = segment_axis(arr, winw, winw-1, axis=2)
> > arr = arr[:, :, :, ::stride]
> >
> > arr = transpose(arr, [0, 2, 1, 3, 4])
> > arr = arr.reshape(-1, clas_w.size)
> >
> > resps2 = dot(arr, clas_w)
> >
> > resps2 = resps2.ravel()
> > print resps2.mean()
> >
> > # ...
> >
> > func_loop(arr)
> >
> > func_segment(arr)
> >
> >
> > ========================================
> >
> >
> > % python kernprof.py -l -v sliding_win_all.py
> > w-0.0500485824034
> > segmentaxis.py:94: UserWarning: Problem with ndarray creation forces
> copy.
> > warnings.warn("Problem with ndarray creation forces copy.")
> > -0.0500485824034
> > Wrote profile results to sliding_win_all.py.lprof
> > Timer unit: 1e-06 s
> >
> > File: sliding_win_all.py
> > Function: func_loop at line 18
> > Total time: 3.9785 s
> >
> > Line # Hits Time Per Hit % Time Line Contents
> > ==============================================================
> > 18 @profile
> > 19 def func_loop(arr):
> > 20
> > 21 1 28 28.0 0.0 resps =
> > np.empty((nh,nw), dtype="float32")
> > 22 354 308 0.9 0.0 for j in xrange(nh):
> > 23 204034 199753 1.0 5.0 for i in
> > xrange(nw):
> > 24 203681 691915 3.4 17.4 win =
> > arr[j:j+winh:stride, i:i+winw:stride]
> > 25 203681 1341174 6.6 33.7 win =
> > win.ravel()
> > 26 203681 1417998 7.0 35.6 resp =
> dot(win,
> > clas_w)
> > 27 203681 326520 1.6 8.2 resps[j,i] =
> > resp
> > 28
> > 29 1 2 2.0 0.0 resps =
> resps.ravel()
> > 30 1 805 805.0 0.0 print resps.mean()
> >
> > File: sliding_win_all.py
> > Function: func_segment at line 33
> > Total time: 3.82026 s
> >
> > Line # Hits Time Per Hit % Time Line Contents
> > ==============================================================
> > 33 @profile
> > 34 def func_segment(arr):
> > 35
> > 36 1 43 43.0 0.0 arr =
> segment_axis(arr,
> > winh, winh-1, axis=0)
> > 37 1 4 4.0 0.0 arr = arr[:,
> ::stride]
> > 38
> > 39 1 546505 546505.0 14.3 arr =
> segment_axis(arr,
> > winw, winw-1, axis=2)
> > 40 1 12 12.0 0.0 arr = arr[:, :, :,
> > ::stride]
> > 41
> > 42 1 12 12.0 0.0 arr = transpose(arr,
> > [0, 2, 1, 3, 4])
> > 43 1 2435047 2435047.0 63.7 arr =
> arr.reshape(-1,
> > clas_w.size)
> > 44
> > 45 1 837700 837700.0 21.9 resps2 = dot(arr,
> > clas_w)
> > 46
> > 47 1 41 41.0 0.0 resps2 =
> resps2.ravel()
> > 48 1 892 892.0 0.0 print resps2.mean()
> >
> >
> > On Fri, Feb 20, 2009 at 11:55 PM, David Cournapeau <cournape at gmail.com>
> > wrote:
> >>
> >> On Sat, Feb 21, 2009 at 1:46 PM, Nicolas Pinto <pinto at mit.edu> wrote:
> >> > Dear all,
> >> >
> >> > I'm trying to optimize the code below and I was wondering if there is
> an
> >> > efficient method that could reduce the numpy slicing overheard without
> >> > going
> >> > with cython. Is there anyway I could use mgrid to get a matrix with
> all
> >> > my
> >> > "windows" and then do a large matrix multiply instead?
> >>
> >> If you only care about removing the two loops for the per-window
> >> processing, Anne Archibald and Robert Kern wrote a very useful
> >> function, segment_axis, which is like the matlab buffer function on
> >> steroids, using numpy stride tricks (to avoid copies in many cases). I
> >> think that would do everything you want, right ? I use it a lot in my
> >> own code, it may be worth being included in numpy or scipy proper.
> >>
> >>
> >>
> http://projects.scipy.org/scipy/scikits/browser/trunk/talkbox/scikits/talkbox/tools/segmentaxis.py
> >>
> >> David
> >> _______________________________________________
> >> Numpy-discussion mailing list
> >> Numpy-discussion at scipy.org
> >> http://projects.scipy.org/mailman/listinfo/numpy-discussion
> >
> >
> > Thanks again!
> >
> > --
> > Nicolas Pinto
> > Ph.D. Candidate, Brain & Computer Sciences
> > Massachusetts Institute of Technology, USA
> > http://web.mit.edu/pinto
> >
> > _______________________________________________
> > Numpy-discussion mailing list
> > Numpy-discussion at scipy.org
> > http://projects.scipy.org/mailman/listinfo/numpy-discussion
> >
> >
>
> Hi,
>
> I don't really understand your array dimension, but to me it seems
> that you could use ndimage.correlate2d for this.
>
> Josef
> _______________________________________________
> Numpy-discussion mailing list
> Numpy-discussion at scipy.org
> http://projects.scipy.org/mailman/listinfo/numpy-discussion
>
--
Nicolas Pinto
Ph.D. Candidate, Brain & Computer Sciences
Massachusetts Institute of Technology, USA
http://web.mit.edu/pinto
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