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提交 6c74e56c authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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theano/sparse/sandbox/test_sp.py
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......@@ -19,7 +19,7 @@ import time
class TestSP(unittest.TestCase):
def test_convolution(self):
print '\n\n*************************************************'
print ' TEST CONVOLUTION'
print ' TEST CONVOLUTION'
print '*************************************************'
# fixed parameters
......@@ -29,7 +29,7 @@ class TestSP(unittest.TestCase):
nkern = 5
ssizes = ((1,1),(2,2),(3,3),(4,4))
convmodes = ('full','valid')
# symbolic stuff
bias = T.dvector()
kerns = T.dmatrix()
......@@ -46,11 +46,11 @@ class TestSP(unittest.TestCase):
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 = N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize,-1)
# create filters (need to be flipped to use convolve2d)
filtersflipped = N.zeros((nkern,)+kshp)
for k in range(nkern):
......@@ -81,9 +81,9 @@ class TestSP(unittest.TestCase):
out1 = f(filters, biasvals, img1d)
ttot += time.time() - ttime1
temp = bench1.flatten() - out1.flatten()
assert (temp < 1e-5).all()
# test downward propagation -- symbolic stuff
#vis = T.grad(output, input, output)
#downprop = function([kerns,input], vis, mode=mode)
......@@ -93,8 +93,8 @@ class TestSP(unittest.TestCase):
#patchstack = N.zeros(pshape)
#for bi in N.arange(pshape[0]): # batch index
#abspos = 0
#for outy in N.arange(outshp[1]):
#for outx in N.arange(outshp[2]):
#for outy in N.arange(outshp[1]):
#for outx in N.arange(outshp[2]):
#for ni in N.arange(nkern):
#print 'filters[n,:].shape = ', filters[n,:].shape
#print 'out1[bi,abspos].shape =',out1[bi,abspos].shape
......@@ -108,7 +108,7 @@ class TestSP(unittest.TestCase):
#print 'visval = ', visval
#print 'visref = ', visref
#assert N.all(visref==visval)
......@@ -122,7 +122,7 @@ class TestSP(unittest.TestCase):
def test_sparse(self):
print '\n\n*************************************************'
print ' TEST SPARSE'
print ' TEST SPARSE'
print '*************************************************'
# fixed parameters
......@@ -132,7 +132,7 @@ class TestSP(unittest.TestCase):
nkern = 1 # per output pixel
ssizes = ((1,1),(2,2))
convmodes = ('full','valid',)
# symbolic stuff
bias = T.dvector()
kerns = T.dvector()
......@@ -140,7 +140,7 @@ class TestSP(unittest.TestCase):
rng = N.random.RandomState(3423489)
import theano.gof as gof
#Mode(optimizer='fast_run', linker=gof.OpWiseCLinker(allow_gc=False)),):
#Mode(optimizer='fast_run', linker=gof.OpWiseCLinker(allow_gc=False)),):
for mode in ('FAST_COMPILE','FAST_RUN'): #,profmode):
ntot, ttot = 0,0
for conv_mode in convmodes:
......@@ -182,15 +182,15 @@ class TestSP(unittest.TestCase):
pixi += 1
refout = refout.reshape(bsize,-1) + biasvals
ntot += time.time() - ntime1
# need to flatten images
ttime1 = time.time()
out1 = f(spfilt, biasvals, img1d)
ttot += time.time() - ttime1
temp = refout - out1
assert (temp < 1e-10).all()
# test downward propagation
vis = T.grad(0.5*T.sqr(output).sum(), input)
downprop = function([kerns,output], vis)
......@@ -217,7 +217,7 @@ class TestSP(unittest.TestCase):
nkerns = (10,20) # per output pixel
ssizes = ((1,1),(2,2))
convmodes = ('full','valid',)
# symbolic stuff
kerns = [T.dvector(),T.dvector()]
input = T.dmatrix()
......@@ -235,10 +235,10 @@ class TestSP(unittest.TestCase):
nkerns[0], input, imshp, ss, mode=conv_mode)
l2hid, l2outshp = sp.applySparseFilter(kerns[1], kshp[1],\
nkerns[1], l1hid, l1outshp, ss, mode=conv_mode)
l1propup = function([kerns[0], input], l1hid, mode=mode)
l2propup = function([kerns[1], l1hid], l2hid, mode=mode)
# actual values
l1kernvals = N.arange(N.prod(l1outshp)*N.prod(kshp[0]))
l2kernvals = N.arange(N.prod(l2outshp)*N.prod(kshp[1])*nkerns[0])
......@@ -255,7 +255,7 @@ class TestSP(unittest.TestCase):
nkerns = (3,6) # per output pixel
ssizes = (((1,1),(2,2)),)
convmodes = ('full',)#'valid',)
# symbolic stuff
kerns = [T.dmatrix(),T.dmatrix()]
input = T.dmatrix()
......@@ -272,7 +272,7 @@ class TestSP(unittest.TestCase):
l1hid, l1shp = sp.convolve(kerns[0], kshp[0],\
nkerns[0], input, imshp, ss[0], mode=conv_mode)
l1propup = function([kerns[0], input], l1hid, mode=mode)
#l1kernvals = N.random.rand(nkerns[0],N.prod(kshp[0]))
l1kernvals = N.arange(nkerns[0]*N.prod(kshp[0])).reshape(nkerns[0],N.prod(kshp[0]))
l1hidval = l1propup(l1kernvals,img1d)
......@@ -298,18 +298,18 @@ class TestSP(unittest.TestCase):
images = T.dmatrix()
for maxpoolshp in maxpoolshps:
# symbolic stuff
output, outshp = sp.max_pool(images, imval.shape[1:], maxpoolshp)
f = function([images,],[output,])
output_val = f(imval.reshape(imval.shape[0],-1))
# numeric verification
my_output_val = N.zeros((imval.shape[0], imval.shape[1],
imval.shape[2]/maxpoolshp[0],
imval.shape[3]/maxpoolshp[1]))
assert N.prod(my_output_val.shape[1:]) == N.prod(N.r_[imval.shape[1],outshp])
for n in range(imval.shape[0]):
for k in range(imval.shape[1]):
for i in range(imval.shape[2]/maxpoolshp[0]):
......@@ -351,7 +351,7 @@ class TestSP(unittest.TestCase):
return theano.sparse.dense_from_sparse(
theano.sparse.CSM(sptype,kmap)(
kerns, indvals, indptrvals, spshapevals))
# symbolic stuff
utt.verify_grad(d, [kvals])
......
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