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提交 b879aa80 authored 作者: Frederic Bastien's avatar Frederic Bastien
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Make numpy import follow our standard.

上级 fb964cdb
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正在显示 包含 47 行增加 和 48 行删除
theano/sparse/sandbox/test_sp.py
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......@@ -11,7 +11,6 @@ import scipy.sparse
from scipy.signal import convolve2d
import scipy.sparse as sparse
import numpy
import numpy as N
from theano import function
import theano
......@@ -38,8 +37,8 @@ class TestSP(unittest.TestCase):
bias = T.dvector()
kerns = T.dmatrix()
input = T.dmatrix()
rng = N.random.RandomState(3423489)
filters = rng.randn(nkern,N.prod(kshp))
rng = numpy.random.RandomState(3423489)
filters = rng.randn(nkern,numpy.prod(kshp))
biasvals = rng.randn(nkern)
for mode in ('FAST_COMPILE','FAST_RUN'): #, profmode):
......@@ -52,11 +51,11 @@ class TestSP(unittest.TestCase):
f = function([kerns, bias, input], output, mode=mode)
# now test with real values
img2d = N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
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 = N.zeros((nkern,)+kshp)
filtersflipped = numpy.zeros((nkern,)+kshp)
for k in range(nkern):
it = reversed(filters[k,:])
for i in range(kshp[0]):
......@@ -65,11 +64,11 @@ class TestSP(unittest.TestCase):
# compute output with convolve2d
if conv_mode=='valid':
fulloutshp = N.array(imshp) - N.array(kshp) + 1
fulloutshp = numpy.array(imshp) - numpy.array(kshp) + 1
else:
fulloutshp = N.array(imshp) + N.array(kshp) - 1
fulloutshp = numpy.array(imshp) + numpy.array(kshp) - 1
ntime1 = time.time()
refout = N.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
refout = numpy.zeros((bsize,)+tuple(fulloutshp)+(nkern,))
for b in range(bsize):
for n in range(nkern):
refout[b,...,n] = convolve2d(\
......@@ -82,7 +81,7 @@ class TestSP(unittest.TestCase):
bench1 += biasvals.reshape(1,1,nkern)
# swap the last two dimensions (output needs to be nkern x outshp)
bench1 = N.swapaxes(bench1,1,2)
bench1 = numpy.swapaxes(bench1,1,2)
ttime1 = time.time()
out1 = f(filters, biasvals, img1d)
ttot += time.time() - ttime1
......@@ -95,13 +94,13 @@ class TestSP(unittest.TestCase):
#downprop = function([kerns,input], vis, mode=mode)
#visval = downprop(filters,img1d)
## test downward propagation -- reference implementation
#pshape = (img1d.shape[0],N.prod(outshp[1:]),N.prod(kshp))
#patchstack = N.zeros(pshape)
#for bi in N.arange(pshape[0]): # batch index
#pshape = (img1d.shape[0],numpy.prod(outshp[1:]),numpy.prod(kshp))
#patchstack = numpy.zeros(pshape)
#for bi in numpy.arange(pshape[0]): # batch index
#abspos = 0
#for outy in N.arange(outshp[1]):
#for outx in N.arange(outshp[2]):
#for ni in N.arange(nkern):
#for outy in numpy.arange(outshp[1]):
#for outx in numpy.arange(outshp[2]):
#for ni in numpy.arange(nkern):
#print 'filters[n,:].shape = ', filters[n,:].shape
#print 'out1[bi,abspos].shape =',out1[bi,abspos].shape
#patchstack[bi,abspos,:] = filters[n,:]*out1[bi,abspos]
......@@ -109,13 +108,13 @@ class TestSP(unittest.TestCase):
#patchstack = patchstack.reshape(1,-1)
#indices, indptr, spmat_shape, sptype, outshp = \
#sp.convolution_indices.conv_eval(imshp,kshp,ss,conv_mode)
#spmat = sparse.csc_matrix((N.ones_like(indices),indices,indptr),spmat_shape)
#visref = N.dot(patchstack, spmat.todense())
#spmat = sparse.csc_matrix((numpy.ones_like(indices),indices,indptr),spmat_shape)
#visref = numpy.dot(patchstack, spmat.todense())
#print 'visval = ', visval
#print 'visref = ', visref
#assert N.all(visref==visval)
#assert numpy.all(visref==visval)
print '**** Convolution Profiling Results (',mode,') ****'
......@@ -143,7 +142,7 @@ class TestSP(unittest.TestCase):
bias = T.dvector()
kerns = T.dvector()
input = T.dmatrix()
rng = N.random.RandomState(3423489)
rng = numpy.random.RandomState(3423489)
import theano.gof as gof
#Mode(optimizer='fast_run', linker=gof.OpWiseCLinker(allow_gc=False)),):
......@@ -157,24 +156,24 @@ class TestSP(unittest.TestCase):
f = function([kerns, bias, input], output, mode=mode)
# build actual input images
img2d = N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize,-1)
zeropad_img = N.zeros((bsize,\
zeropad_img = numpy.zeros((bsize,\
img2d.shape[1]+2*(kshp[0]-1),\
img2d.shape[2]+2*(kshp[1]-1)))
zeropad_img[:, kshp[0]-1:kshp[0]-1+img2d.shape[1],
kshp[1]-1:kshp[1]-1+img2d.shape[2]] = img2d
# build kernel matrix -- flatten it for theano stuff
filters = N.arange(N.prod(outshp)*N.prod(kshp)).\
reshape(nkern,N.prod(outshp[1:]),N.prod(kshp))
filters = numpy.arange(numpy.prod(outshp)*numpy.prod(kshp)).\
reshape(nkern,numpy.prod(outshp[1:]),numpy.prod(kshp))
spfilt = filters.flatten()
biasvals = N.arange(N.prod(outshp))
biasvals = numpy.arange(numpy.prod(outshp))
# compute output by hand
ntime1 = time.time()
refout = N.zeros((bsize,nkern,outshp[1],outshp[2]))
patch = N.zeros((kshp[0],kshp[1]))
refout = numpy.zeros((bsize,nkern,outshp[1],outshp[2]))
patch = numpy.zeros((kshp[0],kshp[1]))
for b in xrange(bsize):
for k in xrange(nkern):
pixi = 0 # pixel index in raster order
......@@ -183,7 +182,7 @@ class TestSP(unittest.TestCase):
n = j * ss[0]
m = i * ss[1]
patch = zeropad_img[b,n:n+kshp[0],m:m+kshp[1]]
refout[b,k,j,i] = N.dot(filters[k,pixi,:],\
refout[b,k,j,i] = numpy.dot(filters[k,pixi,:],\
patch.flatten())
pixi += 1
refout = refout.reshape(bsize,-1) + biasvals
......@@ -206,8 +205,8 @@ class TestSP(unittest.TestCase):
indices, indptr, spmat_shape, sptype, outshp, kmap = \
sp.convolution_indices.sparse_eval(imshp,kshp,nkern,ss,conv_mode)
spmat = sparse.csc_matrix((spfilt[kmap],indices,indptr),spmat_shape)
visref = N.dot(out1,spmat.todense())
assert N.all(visref==visval), (visref, visval)
visref = numpy.dot(out1,spmat.todense())
assert numpy.all(visref==visval), (visref, visval)
print '**** Sparse Profiling Results (',mode,') ****'
print 'Numpy processing time: ', ntot
......@@ -227,10 +226,10 @@ class TestSP(unittest.TestCase):
# symbolic stuff
kerns = [T.dvector(),T.dvector()]
input = T.dmatrix()
rng = N.random.RandomState(3423489)
rng = numpy.random.RandomState(3423489)
# build actual input images
img2d = N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize,-1)
for mode in ('FAST_COMPILE','FAST_RUN'):
......@@ -246,8 +245,8 @@ class TestSP(unittest.TestCase):
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])
l1kernvals = numpy.arange(numpy.prod(l1outshp)*numpy.prod(kshp[0]))
l2kernvals = numpy.arange(numpy.prod(l2outshp)*numpy.prod(kshp[1])*nkerns[0])
l1hidval = l1propup(l1kernvals,img1d)
l2hidval = l2propup(l2kernvals,l1hidval)
......@@ -265,10 +264,10 @@ class TestSP(unittest.TestCase):
# symbolic stuff
kerns = [T.dmatrix(),T.dmatrix()]
input = T.dmatrix()
rng = N.random.RandomState(3423489)
rng = numpy.random.RandomState(3423489)
# build actual input images
img2d = N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
img2d = numpy.arange(bsize*numpy.prod(imshp)).reshape((bsize,)+imshp)
img1d = img2d.reshape(bsize,-1)
for mode in ('FAST_COMPILE','FAST_RUN'):
......@@ -279,8 +278,8 @@ class TestSP(unittest.TestCase):
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]))
#l1kernvals = numpy.random.rand(nkerns[0],numpy.prod(kshp[0]))
l1kernvals = numpy.arange(nkerns[0]*numpy.prod(kshp[0])).reshape(nkerns[0],numpy.prod(kshp[0]))
l1hidval = l1propup(l1kernvals,img1d)
# actual values
......@@ -288,10 +287,10 @@ class TestSP(unittest.TestCase):
nkerns[1], l1hid, l1shp, ss[1], mode=conv_mode)
l2propup = function([kerns[1], l1hid], l2hid, mode=mode)
#l2kernvals = N.random.rand(nkerns[1],N.prod(kshp[1])*nkerns[0])
l2kernvals = N.arange(nkerns[1]*N.prod(kshp[1])*nkerns[0]).reshape(nkerns[1],N.prod(kshp[1])*nkerns[0])
#l2kernvals = numpy.random.rand(nkerns[1],numpy.prod(kshp[1])*nkerns[0])
l2kernvals = numpy.arange(nkerns[1]*numpy.prod(kshp[1])*nkerns[0]).reshape(nkerns[1],numpy.prod(kshp[1])*nkerns[0])
# for debugging, we bring things back to integers
l1hidval = N.arange(N.size(l1hidval)).reshape(l1hidval.shape)
l1hidval = numpy.arange(numpy.size(l1hidval)).reshape(l1hidval.shape)
l2hidval = l2propup(l2kernvals,l1hidval)
......@@ -300,7 +299,7 @@ class TestSP(unittest.TestCase):
def test_maxpool(self):
# generate flatted images
maxpoolshps = ((2,2),(3,3),(4,4),(5,5),(6,6))
imval = N.random.rand(4,5,10,10)
imval = numpy.random.rand(4,5,10,10)
images = T.dmatrix()
for maxpoolshp in maxpoolshps:
......@@ -311,10 +310,10 @@ class TestSP(unittest.TestCase):
output_val = f(imval.reshape(imval.shape[0],-1))
# numeric verification
my_output_val = N.zeros((imval.shape[0], imval.shape[1],
my_output_val = numpy.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])
assert numpy.prod(my_output_val.shape[1:]) == numpy.prod(numpy.r_[imval.shape[1],outshp])
for n in range(imval.shape[0]):
for k in range(imval.shape[1]):
......@@ -322,9 +321,9 @@ class TestSP(unittest.TestCase):
for j in range(imval.shape[3]/maxpoolshp[1]):
ii,jj = i*maxpoolshp[0], j*maxpoolshp[1]
patch = imval[n,k,ii:ii+maxpoolshp[0],jj:jj+maxpoolshp[1]]
my_output_val[n,k,i,j] = N.max(patch)
my_output_val[n,k,i,j] = numpy.max(patch)
my_output_val = my_output_val.reshape(imval.shape[0],-1)
assert N.all(output_val == my_output_val)
assert numpy.all(output_val == my_output_val)
def mp(input):
output, outshp = sp.max_pool(input, imval.shape[1:], maxpoolshp)
......@@ -351,7 +350,7 @@ class TestSP(unittest.TestCase):
for ss in ssizes:
indvals, indptrvals, spshapevals, sptype, outshp, kmap = \
sp.convolution_indices.sparse_eval(imshp,kshp,nkern,ss,conv_mode)
kvals = N.random.random(nkern*N.prod(kshp)*N.prod(outshp)).flatten()
kvals = numpy.random.random(nkern*numpy.prod(kshp)*numpy.prod(outshp)).flatten()
def d(kerns):
return theano.sparse.dense_from_sparse(
......@@ -409,11 +408,11 @@ def test_diagonal():
f = theano.function([d], sd)
n = N.zeros((K,K), dtype='int32')
n = numpy.zeros((K,K), dtype='int32')
for i in range(K):
n[i,i] = i
assert N.all(n == f(range(K)).toarray())
assert numpy.all(n == f(range(K)).toarray())
def test_diagonal_grad():
def d(x):
......
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