Skip to content

Commit

Permalink
fixing the pooling with stride plus adding a test.
Browse files Browse the repository at this point in the history
  • Loading branch information
Sina Honari committed Dec 18, 2014
1 parent f4110b5 commit b3a76f7
Show file tree
Hide file tree
Showing 2 changed files with 57 additions and 75 deletions.
68 changes: 19 additions & 49 deletions theano/tensor/signal/downsample.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -78,10 +78,11 @@ def out_shape(imgshape, ds, ignore_border=False, st=None):
scalar Theano variable.
:param ds: downsample factor over rows and columns
this parameter indicates the pooling region
this parameter indicates the size of the pooling region
:type ds: list or tuple of two ints
:param st: the stride size
:param st: the stride size. This is the distance between the pooling
regions. If it's set to None, in which case it equlas ds.
:type st: list or tuple of two ints
:param ignore_border: if ds doesn't divide imgshape, do we include an
Expand All @@ -97,42 +98,27 @@ def out_shape(imgshape, ds, ignore_border=False, st=None):
raise TypeError('imgshape must have at least two elements '
'(rows, cols)')

if st == None:
if st is None:
st = ds
r, c = imgshape[-2:]
if st[0] >= ds[0]:
nr = r // st[0]
else:
nr = (r - ds[0]) // st[0] + 1

if st[1] >= ds[1]:
nc = c // st[1]
else:
nc = (c - ds[1]) // st[1] + 1
nr = (r - ds[0]) // st[0] + 1
nc = (c - ds[1]) // st[1] + 1

rval = list(imgshape[:-2]) + [nr, nc]

if not ignore_border:
if st[0] >= ds[0]:
if isinstance(r, theano.Variable):
rval[-2] = tensor.switch(r % st[0], rval[-2] + 1, rval[-2])
elif r % ds[0]:
rval[-2] += 1
else:
if isinstance(r, theano.Variable):
rval[-2] = tensor.switch((r - ds[0]) % st[0], rval[-2] + 1, rval[-2])
elif (r - ds[0]) % st[0]:
rval[-2] += 1

if st[1] >= ds[1]:
if isinstance(c, theano.Variable):
rval[-1] = tensor.switch(c % st[1], rval[-1] + 1, rval[-1])
elif c % ds[1]:
rval[-1] += 1
else:
if isinstance(c, theano.Variable):
rval[-1] = tensor.switch((c - ds[1]) % st[1], rval[-1] + 1, rval[-1])
elif (c - ds[1]) % st[1]:
rval[-1] += 1
if isinstance(r, theano.Variable):
rr = r % st[0]
rval[-2] = tensor.switch(tensor.and_((rr % ds[0]), tensor.eq(rr // ds[0], 0)), rval[-2] + 1, rval[-2])
elif (r % st[0]) % ds[0]:
rval[-2] += 1
if isinstance(c, theano.Variable):
cr = c % st[1]
crn = cr - ds[1]
rval[-1] = tensor.switch(tensor.lt(crn, 0), rval[-1] + 1, rval[-1])
elif (c % st[1]) % ds[1]:
rval[-1] += 1
return rval

def __init__(self, ds, ignore_border=False, st=None):
Expand All @@ -158,7 +144,7 @@ def __init__(self, ds, ignore_border=False, st=None):
raise ValueError(
"DownsampleFactorMax downsample parameters must be ints."
" Got %s" % str(ds))
if st == None:
if st is None:
st = ds
self.st = tuple(st)
self.ignore_border = ignore_border
Expand Down Expand Up @@ -206,22 +192,6 @@ def perform(self, node, inp, out):
img_rows = x.shape[-2]
img_cols = x.shape[-1]

if self.ignore_border:
if st0 >= ds0:
x_usable2 = (x.shape[2] // ds0 * ds0)
else:
x_usable2 = (x.shape[2] - ds0) // st0 * st0 + ds0
else:
x_usable2 = x.shape[2]

if self.ignore_border:
if st1 >= ds1:
x_usable3 = (x.shape[3] // ds1 * ds1)
else:
x_usable3 = (x.shape[3] - ds1) // st1 * st1 + ds1
else:
x_usable3 = x.shape[3]

for n in xrange(x.shape[0]):
for k in xrange(x.shape[1]):
for r in xrange(pr):
Expand Down
64 changes: 38 additions & 26 deletions theano/tensor/signal/tests/test_downsample.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -52,41 +52,29 @@ def numpy_max_pool_2d_stride(input, ds, ignore_border=False, st=None):
st = ds
xi = 0
yi = 0
img_rows = input.shape[-2]
img_cols = input.shape[-1]

if not ignore_border:
if st[0] >= ds[0]:
if input.shape[-2] % st[0]:
xi += 1
else:
if (input.shape[-2] - ds[0]) % st[0]:
xi += 1
if st[1] >= ds[1]:
if input.shape[-1] % st[1]:
yi += 1
else:
if (input.shape[-1] % - ds[1]) % st[1]:
yi += 1
rr = (img_rows) % st[0]
cr = (img_cols) % st[1]
if rr > 0 and rr < ds[0]:
xi += 1
if cr > 0 and cr < ds[1]:
yi += 1

out_shp = list(input.shape[:-2])
if st[0] >= ds[0]:
out_shp.append(input.shape[-2] / ds[0] + xi)
else:
out_shp.append((input.shape[-2] - ds[0]) / st[0] + 1 + xi)

if st[1] >= ds[1]:
out_shp.append(input.shape[-1] / ds[1] + yi)
else:
out_shp.append((input.shape[-1] - ds[1]) / st[1] + 1 + yi)
out_shp.append((img_rows - ds[0]) / st[0] + 1 + xi)
out_shp.append((img_cols - ds[1]) / st[1] + 1 + yi)

output_val = numpy.zeros(out_shp)

img_rows = input.shape[-2]
img_cols = input.shape[-1]

for k in numpy.ndindex(*input.shape[:-2]):
for i in range(output_val.shape[-2]):
ii_st = i * ds[0]
ii_st = i * st[0]
ii_end = __builtin__.min(ii_st + ds[0], img_rows)
for j in range(output_val.shape[-1]):
jj_st = j * ds[1]
jj_st = j * st[1]
jj_end = __builtin__.min(jj_st + ds[1], img_cols)
patch = input[k][ii_st:ii_end, jj_st:jj_end]
output_val[k][i, j] = numpy.max(patch)
Expand Down Expand Up @@ -119,6 +107,30 @@ def test_DownsampleFactorMax(self):
output_val = f(imval)
assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()

def test_DownsampleFactorMaxStride(self):
rng = numpy.random.RandomState(utt.fetch_seed())
# generate random images
maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3))
stridesizes = ((1, 1), (2, 2), (3, 1), (2, 5), (5, 7))
imval = rng.rand(4, 10, 64, 64)
images = tensor.dtensor4()

for maxpoolshp in maxpoolshps:
for ignore_border in [True, False]:
for stride in stridesizes:
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)
maxpool_op = DownsampleFactorMax(maxpoolshp,
ignore_border=ignore_border, st=stride)(images)
f = function([images], maxpool_op)
output_val = f(imval)
assert (numpy.abs(output_val - numpy_output_val) < 1e-5).all()

def test_DownsampleFactorMax_grad(self):
rng = numpy.random.RandomState(utt.fetch_seed())
maxpoolshps = ((1, 1), (3, 2), (2, 3))
Expand Down

0 comments on commit b3a76f7

Please sign in to comment.