def test_convolution(self):
# print '\n\n*************************************************'
# print ' TEST CONVOLUTION'
# print '*************************************************'
# fixed parameters
bsize = 10 # batch size
imshp = (28, 28)
kshp = (5, 5)
nkern = 5
ssizes = ((1, 1), (2, 2), (3, 3), (4, 4))
convmodes = ('full', 'valid')
# symbolic stuff
bias = tensor.dvector()
kerns = tensor.dmatrix()
input = tensor.dmatrix()
rng = numpy.random.RandomState(3423489)
filters = rng.randn(nkern, numpy.prod(kshp))
biasvals = rng.randn(nkern)
for mode in ('FAST_COMPILE', 'FAST_RUN'):
ttot, ntot = 0, 0
for conv_mode in convmodes:
for ss in ssizes:
output, outshp = sp.convolve(kerns, kshp, nkern, input,\
imshp, ss, bias=bias, mode=conv_mode)
f = function([kerns, bias, input], output, mode=mode)
# now test with real values
img2d = numpy.arange(bsize * numpy.prod(imshp)).reshape(( \
bsize,) + imshp)
img1d = img2d.reshape(bsize, -1)
# create filters (need to be flipped to use convolve2d)
filtersflipped = numpy.zeros((nkern, ) + kshp)
for k in range(nkern):
it = reversed(filters[k, :])
for i in range(kshp[0]):
for j in range(kshp[1]):
filtersflipped[k, i, j] = next(it)
# compute output with convolve2d
if conv_mode == 'valid':
fulloutshp = numpy.array(imshp) - numpy.array(kshp) + 1
else:
fulloutshp = numpy.array(imshp) + numpy.array(kshp) - 1
ntime1 = time.time()
refout = numpy.zeros((bsize, ) + tuple(fulloutshp) +
(nkern, ))
for b in range(bsize):
for n in range(nkern):
refout[b, ...,
n] = convolve2d(img2d[b, :, :],
filtersflipped[n, ...],
conv_mode)
ntot += time.time() - ntime1
# need to flatten images
bench1 = refout[:, 0::ss[0],
0::ss[1], :].reshape(bsize, -1, nkern)
bench1 += biasvals.reshape(1, 1, nkern)
# swap the last two dimensions (output needs to be nkern x outshp)
bench1 = numpy.swapaxes(bench1, 1, 2)
ttime1 = time.time()
out1 = f(filters, biasvals, img1d)
ttot += time.time() - ttime1
temp = bench1.flatten() - out1.flatten()
assert (temp < 1e-5).all()
def test_convolution(self):
# print '\n\n*************************************************'
# print ' TEST CONVOLUTION'
# print '*************************************************'
# fixed parameters
bsize = 10 # batch size
imshp = (28, 28)
kshp = (5, 5)
nkern = 5
ssizes = ((1, 1), (2, 2), (3, 3), (4, 4))
convmodes = ('full', 'valid')
# symbolic stuff
bias = tensor.dvector()
kerns = tensor.dmatrix()
input = tensor.dmatrix()
rng = numpy.random.RandomState(3423489)
filters = rng.randn(nkern, numpy.prod(kshp))
biasvals = rng.randn(nkern)
for mode in ('FAST_COMPILE', 'FAST_RUN'): # , profmode):
ttot, ntot = 0, 0
for conv_mode in convmodes:
for ss in ssizes:
output, outshp = sp.convolve(kerns, kshp, nkern, input,\
imshp, ss, bias=bias, mode=conv_mode)
f = function([kerns, bias, input], output, mode=mode)
# now test with real values
img2d = numpy.arange(bsize * numpy.prod(imshp)).reshape(( \
bsize,) + imshp)
img1d = img2d.reshape(bsize, -1)
# create filters (need to be flipped to use convolve2d)
filtersflipped = numpy.zeros((nkern,) + kshp)
for k in range(nkern):
it = reversed(filters[k, :])
for i in range(kshp[0]):
for j in range(kshp[1]):
filtersflipped[k,i,j] = next(it)
# compute output with convolve2d
if conv_mode == 'valid':
fulloutshp = numpy.array(imshp) - numpy.array(kshp) + 1
else:
fulloutshp = numpy.array(imshp) + numpy.array(kshp) - 1
ntime1 = time.time()
refout = numpy.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
for b in range(bsize):
for n in range(nkern):
refout[b,...,n] = convolve2d(img2d[b,:,:],
filtersflipped[n,...],
conv_mode)
ntot += time.time() - ntime1
# need to flatten images
bench1 = refout[:,0::ss[0],0::ss[1],:].reshape(bsize,-1,nkern)
bench1 += biasvals.reshape(1,1,nkern)
# swap the last two dimensions (output needs to be nkern x outshp)
bench1 = numpy.swapaxes(bench1,1,2)
ttime1 = time.time()
out1 = f(filters, biasvals, img1d)
ttot += time.time() - ttime1
temp = bench1.flatten() - out1.flatten()
assert (temp < 1e-5).all()
