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提交 d9ee20e6 authored 作者: bergstra@ip05.m's avatar bergstra@ip05.m
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added more conv profiling tests

上级 2bdcd148
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正在显示 包含 100 行增加 和 33 行删除
theano/sandbox/test_conv.py
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...@@ -50,10 +50,10 @@ class TestConvOp(unittest.TestCase): ...@@ -50,10 +50,10 @@ class TestConvOp(unittest.TestCase):
nkern = 5 # nb kernel nkern = 5 # nb kernel
ssizes = ((1,1),(2,2),(3,3),(4,4))#step size ssizes = ((1,1),(2,2),(3,3),(4,4))#step size
convmodes = ('full','valid') convmodes = ('full','valid')
elif 0: elif 1:
# fixed parameters # fixed parameters like NORB JOB
bsize = 10 # batch size bsize = 4 # batch size
imshp = (50,50)# image shape imshp = (96,96)# image shape
print >> sys.stderr, "WARNING: only square shape tested" print >> sys.stderr, "WARNING: only square shape tested"
kshps = [(12,12), (12,12)] kshps = [(12,12), (12,12)]
nkern = 20 # nb kernel nkern = 20 # nb kernel
...@@ -102,6 +102,8 @@ class TestConvOp(unittest.TestCase): ...@@ -102,6 +102,8 @@ class TestConvOp(unittest.TestCase):
#profmode = wraplinker.ProfileMode(OpWiseCLinker(), 'fast_run') #profmode = wraplinker.ProfileMode(OpWiseCLinker(), 'fast_run')
tconvop, tscipy, tconv2 = [], [], [] tconvop, tscipy, tconv2 = [], [], []
tconvop_kern, tconvop_batch = [], []
tconvop_gemm = []
for conv_mode in convmodes: for conv_mode in convmodes:
for kshp in kshps: for kshp in kshps:
...@@ -112,7 +114,7 @@ class TestConvOp(unittest.TestCase): ...@@ -112,7 +114,7 @@ class TestConvOp(unittest.TestCase):
# now test with real values # now test with real values
img2d = 1 + N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp) img2d = 1 + N.arange(bsize*N.prod(imshp)).reshape((bsize,)+imshp)
print 'img2d', img2d #print 'img2d', img2d
img1d = img2d.reshape(bsize,-1) img1d = img2d.reshape(bsize,-1)
# create filters (need to be flipped to use convolve2d) # create filters (need to be flipped to use convolve2d)
...@@ -121,37 +123,100 @@ class TestConvOp(unittest.TestCase): ...@@ -121,37 +123,100 @@ class TestConvOp(unittest.TestCase):
# compute with new convolve2 (no timing info) # compute with new convolve2 (no timing info)
output4, outshp4 = convolve2(kerns, kshp, nkern, input,\ output4, outshp4 = convolve2(kerns, kshp, nkern, input,\
imshp, bsize, (1,1), bias=bias, mode=conv_mode) imshp, bsize, (1,1), bias=bias, mode=conv_mode)
print 'output4', output4 #print 'output4', output4
ttime1 = time.time() ttime1 = time.time()
f = function([kerns, bias, input], output4) f = function([kerns, bias, input], output4)
out4 = f(filtersflipped.reshape(nkern,-1), biasvals, img1d) out4 = f(filtersflipped.reshape(nkern,-1), biasvals, img1d)
print 'out4', out4, img1d, filtersflipped #print 'out4', out4, img1d, filtersflipped
tconv2 += [time.time() - ttime1] tconv2 += [time.time() - ttime1]
out4 = out4.reshape(bsize, nkern, outshp4[1], outshp4[2]) out4 = out4.reshape(bsize, nkern, outshp4[1], outshp4[2])
out4 = out4[:,:,0::ss[0],0::ss[1]] out4 = out4[:,:,0::ss[0],0::ss[1]]
out4 = out4.reshape(bsize, -1) out4 = out4.reshape(bsize, -1)
# compute with ConvOp if 1: # compute with ConvOp (code_a)
dmatrix3=T.TensorType('float64', (False, False, False)) dmatrix3=T.TensorType('float64', (False, False, False))
inputs=dmatrix3() inputs=dmatrix3()
kerns3=dmatrix3() kerns3=dmatrix3()
bia=T.dscalar() bia=T.dscalar()
conv_op = ConvOp(imshp, kshp, nkern, bsize, 1,1, conv_mode)(inputs, kerns3) conv_op = ConvOp(imshp, kshp, nkern, bsize, 1,1, conv_mode, use_gemm=False)(inputs, kerns3)
f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c")) f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c"))
f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py")) f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py"))
ttime1 = time.time() ttime1 = time.time()
out2_ = f2(img2d, filtersflipped) out2_ = f2(img2d, filtersflipped)
out2__ = out2_[:,:,0::ss[0],0::ss[1]] out2__ = out2_[:,:,0::ss[0],0::ss[1]]
tconvop += [time.time() - ttime1] tconvop += [time.time() - ttime1]
out2___ = out2__.copy() out2___ = out2__.copy()
out2 = out2___ + biasvals.reshape(1,nkern,1,1) out2 = out2___ + biasvals.reshape(1,nkern,1,1)
out3_ = f3(img2d, filtersflipped) out3_ = f3(img2d, filtersflipped)
out3__ = out3_[:,:,0::ss[0],0::ss[1]] out3__ = out3_[:,:,0::ss[0],0::ss[1]]
out3___ = out3__.copy() out3___ = out3__.copy()
out3 = out3___ + biasvals.reshape(1,nkern,1,1) out3 = out3___ + biasvals.reshape(1,nkern,1,1)
assert (N.abs(out2_-out3_)<1e-5).all() assert (N.abs(out2_-out3_)<1e-5).all()
if 1: # compute with ConvOp with gemm if possible
dmatrix3=T.TensorType('float64', (False, False, False))
inputs=dmatrix3()
kerns3=dmatrix3()
bia=T.dscalar()
conv_op = ConvOp(imshp, kshp, nkern, bsize, 1,1, conv_mode, use_gemm=True)(inputs, kerns3)
f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c"))
f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py"))
ttime1 = time.time()
out2_ = f2(img2d, filtersflipped)
out2__ = out2_[:,:,0::ss[0],0::ss[1]]
tconvop_gemm += [time.time() - ttime1]
out2___ = out2__.copy()
out2 = out2___ + biasvals.reshape(1,nkern,1,1)
out3_ = f3(img2d, filtersflipped)
out3__ = out3_[:,:,0::ss[0],0::ss[1]]
out3___ = out3__.copy()
out3 = out3___ + biasvals.reshape(1,nkern,1,1)
assert (N.abs(out2_-out3_)<1e-5).all()
if 1: # compute with ConvOp with unroll_batch
dmatrix3=T.TensorType('float64', (False, False, False))
inputs=dmatrix3()
kerns3=dmatrix3()
bia=T.dscalar()
conv_op = ConvOp(imshp, kshp, nkern, bsize, 1,1, conv_mode, use_gemm=False, unroll_batch=bsize)(inputs, kerns3)
f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c"))
f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py"))
ttime1 = time.time()
out2_ = f2(img2d, filtersflipped)
out2__ = out2_[:,:,0::ss[0],0::ss[1]]
tconvop_batch += [time.time() - ttime1]
out2___ = out2__.copy()
out2 = out2___ + biasvals.reshape(1,nkern,1,1)
out3_ = f3(img2d, filtersflipped)
out3__ = out3_[:,:,0::ss[0],0::ss[1]]
out3___ = out3__.copy()
out3 = out3___ + biasvals.reshape(1,nkern,1,1)
assert (N.abs(out2_-out3_)<1e-5).all()
if 1: # compute with ConvOp with unroll_kern
dmatrix3=T.TensorType('float64', (False, False, False))
inputs=dmatrix3()
kerns3=dmatrix3()
bia=T.dscalar()
conv_op = ConvOp(imshp, kshp, nkern, bsize, 1,1, conv_mode, use_gemm=False, unroll_kern=bsize)(inputs, kerns3)
f2 = function([inputs, kerns3], conv_op, mode=Mode(linker="c"))
f3 = function([inputs, kerns3], conv_op, mode=Mode(linker="py"))
ttime1 = time.time()
out2_ = f2(img2d, filtersflipped)
out2__ = out2_[:,:,0::ss[0],0::ss[1]]
tconvop_kern += [time.time() - ttime1]
out2___ = out2__.copy()
out2 = out2___ + biasvals.reshape(1,nkern,1,1)
out3_ = f3(img2d, filtersflipped)
out3__ = out3_[:,:,0::ss[0],0::ss[1]]
out3___ = out3__.copy()
out3 = out3___ + biasvals.reshape(1,nkern,1,1)
assert (N.abs(out2_-out3_)<1e-5).all()
# REFERENCE IMPLEMENTATION: compute output with convolve2d # REFERENCE IMPLEMENTATION: compute output with convolve2d
fulloutshp = N.array(imshp) - N.array(kshp) + 1 if conv_mode=='valid'\ fulloutshp = N.array(imshp) - N.array(kshp) + 1 if conv_mode=='valid'\
...@@ -179,12 +244,14 @@ class TestConvOp(unittest.TestCase): ...@@ -179,12 +244,14 @@ class TestConvOp(unittest.TestCase):
assert (temp < 1e-5).all() assert (temp < 1e-5).all()
print '**** Convolution Profiling Results ****' print '**** Convolution Profiling Results ****'
print 'Scipy convolve2d processing time: %.3fs'%sum(tscipy),tscipy print 'Scipy convolve2d processing time: %.3fs'%sum(tscipy)#,tscipy
print 'ConvOp processing time: %.3fs'%sum(tconvop),tconvop print 'ConvOp processing time: %.3fs'%sum(tconvop)#,tconvop
print 'convolve2 processing time: %.3fs'%sum(tconv2),tconv2 print 'convolve2 processing time: %.3fs'%sum(tconv2)#,tconv2
d=N.asarray(tscipy)/tconvop print 'speed up ConvOp vs convolve2d: %.3f'% (N.asarray(tscipy)/tconvop).mean()
print 'speed up ConvOp vs convolve2d: %.3f'%d.mean(),d print 'speed up use_gemm : %.3f'% (N.asarray(tconvop)/tconvop_gemm).mean()
print 'speed up unroll_batch : %.3f'% (N.asarray(tconvop)/tconvop_batch).mean()
print 'speed up unroll_kern : %.3f'% (N.asarray(tconvop)/tconvop_kern).mean()
def test_multilayer_conv(self): def test_multilayer_conv(self):
# causes an atexit problem # causes an atexit problem
...@@ -350,7 +417,7 @@ class TestConvOp(unittest.TestCase): ...@@ -350,7 +417,7 @@ class TestConvOp(unittest.TestCase):
visdim = 1 if len(imshp)!=3 else imshp[0] visdim = 1 if len(imshp)!=3 else imshp[0]
for kshp in kshps: for kshp in kshps:
imgvals = N.random.random(N.hstack((bsize,imshp))) imgvals = N.random.random(N.hstack((bsize,imshp)))
print 'imgvals.shape = ', imgvals.shape #print 'imgvals.shape = ', imgvals.shape
imgvals = imgvals.reshape(bsize,-1) imgvals = imgvals.reshape(bsize,-1)
if visdim == 1: if visdim == 1:
......
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