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提交 f93b1c29 authored 作者: Frederic Bastien's avatar Frederic Bastien
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move speed test fct to its own and update it to test speed all case of unroll_patch

上级 304cbe3f
隐藏空白字符变更
内嵌 并排
正在显示 包含 71 行增加 和 63 行删除
theano/sandbox/test_conv.py
浏览文件 @ f93b1c29
......@@ -41,7 +41,7 @@ def flip(kern, kshp):
global_rng = N.random.RandomState(3423489)
dmatrix4=T.TensorType('float64', (False, False, False, False))
def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns, unroll_batch=0, unroll_kern=0, img=T.dmatrix(), validate=True, conv_op_py=False, do_convolve2=False, do_print=True, repeat=1, unroll_patch=0):
def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns, unroll_batch=0, unroll_kern=0, img=T.dmatrix(), validate=True, conv_op_py=False, do_convolve2=False, do_print=True, repeat=1, unroll_patch=False, unroll_patch_size=False, verbose=0):
# build actual input images
imgval = global_rng.rand(bsize, imshp[0], imshp[1], imshp[2])
......@@ -121,12 +121,12 @@ def exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp, kshps, nkerns, unroll
hidval1=outval.copy()
# ConvOp
if unroll_patch:
if unroll_patch and not unroll_patch_size:
conv_op = ConvOp(dx=ss[0],dy=ss[1], output_mode=conv_mode,
unroll_patch=unroll_patch)(inputs4, kerns4)
unroll_patch=unroll_patch, verbose=verbose)(inputs4, kerns4)
else:
conv_op = ConvOp(imshp, kshp, nkern, bsize, ss[0],ss[1], conv_mode,
unroll_batch=unroll_batch, unroll_kern=unroll_kern, unroll_patch=unroll_patch)(inputs4, kerns4)
unroll_batch=unroll_batch, unroll_kern=unroll_kern, unroll_patch=unroll_patch, verbose=verbose)(inputs4, kerns4)
l1shp=N.hstack((nkern,
getFilterOutShp(imshp, kshp, ss, conv_mode)))
propup2 = function([inputs4, kerns4], conv_op)
......@@ -319,55 +319,17 @@ class TestConvOp(unittest.TestCase):
d=N.asarray(tscipy)/tconvop
print 'speed up ConvOp vs convolve2d: %.3f'%d.mean(),d
def test_multilayer_conv(self):
print '\n\n*************************************************'
print ' TEST MULTILAYER CONVOLUTION'
print '*************************************************'
# fixed parameters
# test multiple configuration at the same time
bsizes = [6,6] # batch size
imshp_starts = [(1,13,14),(1,4,5)]
kshpss = ([[5,6],[7,4]],[[2,2],[2,2]])
nkernss = [[20,40],[2,2]] # per output pixel
ssizess = [[(1,1),(1,2)],[(1,1),(2,2)]]
convmodes = ['valid','full']
do_convolve2=True
unroll = [(0,0,True),(0,0,False),(1,1,False),(2,2,False),(3,2,False)]#(batch,kern,patch)
do_speed_test = False
# TODO: this version show a bug that was fixed
# the test is included in the upper test.
# imshp_start = (1,4,4)
# kshps = ([2,2],[2,2])#,[7,4])
# nkerns = [2,2] # per output pixel
# ssizes = [(1,1),(2,2)]#2,2)]
# bsizes = [1,1] # batch size
# imshp_starts = [(1,10,10),(1,5,6)]
# kshpss = ([[2,3],[3,2]],[[2,2],[2,2]])
# nkernss = [[1,1],[1,1]] # per output pixel
N.set_printoptions(threshold=N.nan)
# symbolic stuff
kerns = [T.matrix(),T.dmatrix()]
img = T.dmatrix()
rng = N.random.RandomState(3423489)
tctot, tpytot, ntot = [], [], []
for i in range(len(kshpss)):
assert len(kshpss[i])==len(nkernss[i])==len(kerns)
if do_speed_test:
def speed_multilayer_conv(self):
# calculate the speed up of different combination of unroll
# put the paramter to the same you will try.
validate=False# we don't validate the result to have it much faster!
verbose=1
unroll_batch = [1,2,4,5,10,20]
unroll_kern = [1,2,4,5,10,20]
unroll_batch = [1,4,5]
unroll_kern = [1,4,5]
unroll_patch = [True, False]
bsize = 20 # batch size
imshp_start = (1,48,48)#un square shape to test more corner case.
......@@ -381,15 +343,16 @@ class TestConvOp(unittest.TestCase):
assert len(kshps)==len(nkerns)==len(kerns)
timing = N.zeros((len(unroll_batch),len(unroll_kern),3))
timing = N.zeros((len(unroll_batch),len(unroll_kern),3,len(convmodes)*len(ssizes)))
t_b_k=[]
#calculate the timing with unrolling
print 'time unroll batch kern'
t_=[[ 7.60572791, 3.95069814, 3.74271464], [ 4.05631089, 2.90384555, 2.93613672], [ 3.90551591, 2.92595196, 3.00102282]]
best=[]
worst=[]
best=[0.52690219879150391, 2.4266397953033447]
worst=[0.92042708396911621, 6.8822150230407715]
best=[]
worst=[]
t_=[]
for unroll_b, n_b in zip(unroll_batch,range(len(unroll_batch))):
for unroll_k, n_k in zip(unroll_kern,range(len(unroll_kern))):
......@@ -398,30 +361,31 @@ class TestConvOp(unittest.TestCase):
tctot, tpytot, ntot=[],[],[]
for conv_mode, n_mode in zip(convmodes,range(len(convmodes))):
for ss, n_ss in zip(ssizes,range(len(ssizes))):
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=unroll_b, unroll_kern=unroll_k, validate=validate)
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=unroll_b, unroll_kern=unroll_k, validate=validate, verbose=verbose,do_print=False)
tctot+=[tctot_]
tpytot+=[tpytot_]
ntot+=[ntot_]
if unroll_b==4 and unroll_k==4:
print "unroll 4/4",tctot
#print "unroll 4/4",tctot
best=tctot
if unroll_b==1 and unroll_k==1:
print "unroll 1/1",tctot
#print "unroll 1/1",tctot
worst=tctot
timing[n_b,n_k]=[sum(tctot), sum(tpytot), sum(ntot)]
timing[n_b,n_k]=[tctot, tpytot, ntot]#[sum(tctot), sum(tpytot), sum(ntot)]
if not t_:
t=timing[:,:,0]#We select only the c timing.
t=timing[:,:,0,:]#We select only the c timing.
else:
t=t_
t=N.asarray(t)
#calculate the old timing
print 'time old version'
tctot_=[0.52555489540100098, 6.6634182929992676]
# tctot_=[]
tctot,tpytot,ntot=[],[],[]
tctot_=[]
if not tctot_:
for conv_mode, n_mode in zip(convmodes,range(len(convmodes))):
for ss, n_ss in zip(ssizes,range(len(ssizes))):
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate)
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate, verbose=verbose,do_print=False)
tctot+=[tctot_]
tpytot+=[tpytot_]
ntot+=[ntot_]
......@@ -432,29 +396,73 @@ class TestConvOp(unittest.TestCase):
print "timing for unrolled version"
print t_b_k
print t
t_detail=t
t = t.sum(axis=2)
print "max %.3fs"%t.max(), "max param(batch unloop size/kernel unloop size)", t_b_k[t.argmax()]
print "min %.3fs"%t.min(), "min param(batch unloop size/kernel unloop size)", t_b_k[t.argmin()]
print "speedup vs (1/1)%.3fx, vs old %.3fx"% (t.max()/t.min(),sum(tctot)/t.min())
print worst/best,tctot/best
#calculate the timing of unroll_patch
print 'time unroll_patch'
tctot_patch = []
tctot_patch_size = []
for conv_mode, n_mode in zip(convmodes,range(len(convmodes))):
for ss, n_ss in zip(ssizes,range(len(ssizes))):
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate,unroll_patch=2)
tctot_patch += [tctot_]
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate,unroll_patch=True,verbose=verbose,do_print=False)
tctot_patch += [tctot_]
tctot_, tpytot_, ntot_ = exec_multilayer_conv_nnet(conv_mode, ss, bsize, imshp_start, kshps, nkerns, unroll_batch=0, unroll_kern=0, validate=validate,unroll_patch=True,verbose=verbose,do_print=False,unroll_patch_size=True)
tctot_patch_size += [tctot_]
t_patch=sum(tctot_patch)
print "unroll_patch time", tctot_patch
print "unroll_patch without shape time", tctot_patch
print "speedup vs (1/1)%.3fx, vs old %.3fx"% (t.max()/t_patch,sum(tctot)/t_patch)
print best/tctot_patch, worst/tctot_patch
t_patch_size=sum(tctot_patch_size)
print "unroll_patch with shape time", tctot_patch_size
print "speedup vs (1/1)%.3fx, vs old %.3fx"% (t.max()/t_patch_size,sum(tctot)/t_patch_size)
print best/tctot_patch_size, worst/tctot_patch_size
print best
print worst
print tctot
print tctot_patch
return
def test_multilayer_conv(self):
print '\n\n*************************************************'
print ' TEST MULTILAYER CONVOLUTION'
print '*************************************************'
# fixed parameters
# test multiple configuration at the same time
bsizes = [6,6] # batch size
imshp_starts = [(1,13,14),(1,4,5)]
kshpss = ([[5,6],[7,4]],[[2,2],[2,2]])
nkernss = [[20,40],[2,2]] # per output pixel
ssizess = [[(1,1),(1,2)],[(1,1),(2,2)]]
convmodes = ['valid','full']
do_convolve2=True
unroll = [(0,0,True),(0,0,False),(1,1,False),(2,2,False),(3,2,False)]#(batch,kern,patch)
# TODO: this version show a bug that was fixed
# the test is included in the upper test.
# imshp_start = (1,4,4)
# kshps = ([2,2],[2,2])#,[7,4])
# nkerns = [2,2] # per output pixel
# ssizes = [(1,1),(2,2)]#2,2)]
# bsizes = [1,1] # batch size
# imshp_starts = [(1,10,10),(1,5,6)]
# kshpss = ([[2,3],[3,2]],[[2,2],[2,2]])
# nkernss = [[1,1],[1,1]] # per output pixel
N.set_printoptions(threshold=N.nan)
# symbolic stuff
kerns = [T.matrix(),T.dmatrix()]
img = T.dmatrix()
rng = N.random.RandomState(3423489)
tctot, tpytot, ntot = [], [], []
for i in range(len(kshpss)):
assert len(kshpss[i])==len(nkernss[i])==len(kerns)
for i in range(len(kshpss)):
for conv_mode, n_mode in zip(convmodes,range(len(convmodes))):
for ss, n_ss in zip(ssizess[i],range(len(ssizess[i]))):
......
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