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提交 b3a48f4d authored 作者: Sina Honari's avatar Sina Honari
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fixing the stride computation case and adding test cases for issue #2196

上级 ad22f517
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正在显示 包含 115 行增加 和 28 行删除
theano/tensor/signal/downsample.py
浏览文件 @ b3a48f4d
...@@ -102,23 +102,43 @@ class DownsampleFactorMax(Op): ...@@ -102,23 +102,43 @@ class DownsampleFactorMax(Op):
st = ds st = ds
r, c = imgshape[-2:] r, c = imgshape[-2:]
nr = (r - ds[0]) // st[0] + 1 out_r = (r - ds[0]) // st[0] + 1
nc = (c - ds[1]) // st[1] + 1 out_c = (c - ds[1]) // st[1] + 1
nr = 0
rval = list(imgshape[:-2]) + [nr, nc] nc = 0
if isinstance(r, theano.Variable):
nr = tensor.switch(tensor.ge(r - ds[0], 0), out_r, 0)
elif r - ds[0] >= 0:
nr = out_r
if isinstance(c, theano.Variable):
nr = tensor.switch(tensor.ge(c - ds[1], 0), out_c, 0)
elif c - ds[1] >= 0:
nc = out_c
out_re = 0
out_ce = 0
if not ignore_border: if not ignore_border:
re = r - ((out_r - 1) * st[0] + ds[0])
rr = r - out_r * st[0]
ce = c - ((out_c - 1) * st[1] + ds[1])
cr = c - out_c * st[1]
if isinstance(r, theano.Variable): if isinstance(r, theano.Variable):
rr = r % st[0] out_re = tensor.switch(tensor.gt(nr, 0), tensor.switch(tensor.gt(re, 0), tensor.switch(tensor.gt(rr, 0), 1, 0), 0), tensor.switch(tensor.gt(r, 0), 1, 0))
rval[-2] = tensor.switch(tensor.and_((rr % ds[0]), tensor.eq(rr // ds[0], 0)), rval[-2] + 1, rval[-2]) elif nr > 0:
elif (r % st[0]) % ds[0]: if re > 0 and rr > 0:
rval[-2] += 1 out_re = 1
elif r > 0:
out_re = 1
if isinstance(c, theano.Variable): if isinstance(c, theano.Variable):
cr = c % st[1] out_ce = tensor.switch(tensor.gt(nc, 0), tensor.switch(tensor.gt(ce, 0), tensor.switch(tensor.gt(cr, 0), 1, 0), 0), tensor.switch(tensor.gt(c, 0), 1, 0))
crn = cr - ds[1] elif nc > 0:
rval[-1] = tensor.switch(tensor.lt(crn, 0), rval[-1] + 1, rval[-1]) if ce > 0 and cr > 0:
elif (c % st[1]) % ds[1]: out_ce = 1
rval[-1] += 1 elif c > 0:
out_ce = 1
nr += out_re
nc += out_ce
rval = list(imgshape[:-2]) + [nr, nc]
return rval return rval
def __init__(self, ds, ignore_border=False, st=None): def __init__(self, ds, ignore_border=False, st=None):
......
theano/tensor/signal/tests/test_downsample.py
浏览文件 @ b3a48f4d
...@@ -55,20 +55,37 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -55,20 +55,37 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
img_rows = input.shape[-2] img_rows = input.shape[-2]
img_cols = input.shape[-1] img_cols = input.shape[-1]
out_r = 0
out_c = 0
if img_rows - ds[0] >= 0:
out_r = (img_rows - ds[0]) // st[0] + 1
if img_cols - ds[1] >= 0:
out_c = (img_cols - ds[1]) // st[1] + 1
if not ignore_border: if not ignore_border:
rr = (img_rows) % st[0] if out_r > 0:
cr = (img_cols) % st[1] if img_rows - ((out_r - 1) * st[0] + ds[0]) > 0 :
if rr > 0 and rr < ds[0]: rr = img_rows - out_r * st[0]
xi += 1 if rr > 0:
if cr > 0 and cr < ds[1]: out_r += 1
yi += 1 else:
if img_rows > 0:
out_r += 1
if out_c > 0:
if img_cols - ((out_c - 1) * st[1] + ds[1]) > 0 :
cr = img_cols - out_c * st[1]
if cr > 0:
out_c +=1
else:
if img_cols > 0:
out_c += 1
out_shp = list(input.shape[:-2]) out_shp = list(input.shape[:-2])
out_shp.append((img_rows - ds[0]) / st[0] + 1 + xi) out_shp.append(out_r)
out_shp.append((img_cols - ds[1]) / st[1] + 1 + yi) out_shp.append(out_c)
output_val = numpy.zeros(out_shp)
output_val = numpy.zeros(out_shp)
for k in numpy.ndindex(*input.shape[:-2]): for k in numpy.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]): for i in range(output_val.shape[-2]):
ii_st = i * st[0] ii_st = i * st[0]
...@@ -109,15 +126,22 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -109,15 +126,22 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
def test_DownsampleFactorMaxStride(self): def test_DownsampleFactorMaxStride(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = numpy.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (3, 3), (5, 3))
stridesizes = ((1, 1), (3, 3), (5, 7))
# generate random images # generate random images
maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3)) imval = rng.rand(4, 10, 16, 16)
stridesizes = ((1, 1), (2, 2), (3, 1), (2, 5), (5, 7)) outputshps = ((4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3), (4, 10, 16, 16), \
imval = rng.rand(4, 10, 64, 64) (4, 10, 6, 6), (4, 10, 4, 3), (4, 10, 14, 14), (4, 10, 5, 5), \
(4, 10, 3, 2), (4, 10, 14, 14), (4, 10, 6, 6), (4, 10, 4, 3), \
(4, 10, 12, 14), (4, 10, 4, 5), (4, 10, 3, 2), (4, 10, 12, 14), \
(4, 10, 5, 6), (4, 10, 4, 3))
images = tensor.dtensor4() images = tensor.dtensor4()
indx = 0
for maxpoolshp in maxpoolshps: for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]: for ignore_border in [True, False]:
for stride in stridesizes: for stride in stridesizes:
outputshp = outputshps[indx]
indx += 1
print 'maxpoolshp =', maxpoolshp print 'maxpoolshp =', maxpoolshp
print 'ignore_border =', ignore_border print 'ignore_border =', ignore_border
print 'stride =', stride print 'stride =', stride
...@@ -125,12 +149,55 @@ class TestDownsampleFactorMax(utt.InferShapeTester): ...@@ -125,12 +149,55 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
#DownsampleFactorMax op #DownsampleFactorMax op
numpy_output_val = self.numpy_max_pool_2d_stride(imval, maxpoolshp, numpy_output_val = self.numpy_max_pool_2d_stride(imval, maxpoolshp,
ignore_border, stride) ignore_border, stride)
print "outshape is %s " %(outputshp, )
print "calculated shape is %s " %(numpy_output_val.shape,)
assert numpy_output_val.shape == outputshp
maxpool_op = DownsampleFactorMax(maxpoolshp, maxpool_op = DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border, st=stride)(images) ignore_border=ignore_border, st=stride)(images)
f = function([images], maxpool_op) f = function([images], maxpool_op)
output_val = f(imval) output_val = f(imval)
print "numpy_output_val shape is %s" %(numpy_output_val.shape,)
assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all() assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()
def test_DownsampleFactorMaxStrideExtra(self):
rng = numpy.random.RandomState(utt.fetch_seed())
maxpoolshps = ((5, 3), (5, 3), (5, 3), (5, 5), (3, 2), (7, 7), (9, 9))
stridesizes = ((3, 2), (7, 5), (10, 6), (1, 1), (2, 3), (10, 10), (1, 1))
imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5), (8, 5), (8, 5), (8, 5))
outputshps = ((4, 10, 4, 7), (4, 10, 5, 8), (4, 10, 2, 3), (4, 10, 3, 4), \
(4, 10, 2, 3), (4, 10, 2, 3), (4, 10, 4, 1), (4, 10, 4, 1), \
(4, 10, 3, 2), (4, 10, 4, 2), (4, 10, 1, 0), (4, 10, 1, 1), \
(4, 10, 0, 0), (4, 10, 1, 1))
images = tensor.dtensor4()
indx = 0
for indx in numpy.arange(len(maxpoolshps)):
imvsize = imvsizs[indx]
imval = rng.rand(4, 10 , imvsize[0], imvsize[1])
stride = stridesizes[indx]
maxpoolshp = maxpoolshps[indx]
for ignore_border in [True, False]:
indx_out = indx * 2
if not ignore_border:
indx_out += 1
outputshp = outputshps[indx_out]
print 'maxpoolshp =', maxpoolshp
print 'ignore_border =', ignore_border
print 'stride =', stride
#DownsampleFactorMax op
numpy_output_val = self.numpy_max_pool_2d_stride(imval, maxpoolshp,
ignore_border, stride)
print "outshape is %s " %(outputshp, )
print "calculated shape is %s " %(numpy_output_val.shape,)
assert numpy_output_val.shape == outputshp
maxpool_op = DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border, st=stride)(images)
f = function([images], maxpool_op)
output_val = f(imval)
print "numpy_output_val shape is %s" %(numpy_output_val.shape,)
print "output_val shape is %s " %(output_val.shape,)
assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()
def test_DownsampleFactorMax_grad(self): def test_DownsampleFactorMax_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed()) rng = numpy.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (3, 2), (2, 3)) maxpoolshps = ((1, 1), (3, 2), (2, 3))
......
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