def test_neibs_gpu():
if cuda.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
for shape, pshape in [((100, 40, 18, 18), (2, 2)),
((100, 40, 6, 18), (3, 2)),
((10, 40, 66, 66), (33, 33)),
((10, 40, 68, 66), (34, 33))
]:
images = shared(numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
f = function([], images2neibs(images, neib_shape),
mode=mode_with_gpu)
f_gpu = function([], images2neibs(images, neib_shape),
mode=mode_with_gpu)
assert any([isinstance(node.op, GpuImages2Neibs)
for node in f_gpu.maker.env.toposort()])
#print images.get_value(borrow=True)
neibs = numpy.asarray(f_gpu())
assert numpy.allclose(neibs, f())
#print neibs
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=mode_with_gpu)
assert any([isinstance(node.op, GpuImages2Neibs)
for node in f.maker.env.toposort()])
#print numpy.asarray(g())
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_manual(self):
shape = (2, 3, 4, 4)
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape))
neib_shape = T.as_tensor_variable((2, 2))
for border in ['valid', 'ignore_borders']:
f = function([],
images2neibs(images, neib_shape, mode=border),
mode=self.mode)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
assert numpy.allclose(
neibs,
[[0, 1, 4, 5], [2, 3, 6, 7], [8, 9, 12, 13],
[10, 11, 14, 15], [16, 17, 20, 21], [18, 19, 22, 23],
[24, 25, 28, 29], [26, 27, 30, 31], [32, 33, 36, 37],
[34, 35, 38, 39], [40, 41, 44, 45], [42, 43, 46, 47],
[48, 49, 52, 53], [50, 51, 54, 55], [56, 57, 60, 61],
[58, 59, 62, 63], [64, 65, 68, 69], [66, 67, 70, 71],
[72, 73, 76, 77], [74, 75, 78, 79], [80, 81, 84, 85],
[82, 83, 86, 87], [88, 89, 92, 93], [90, 91, 94, 95]])
g = function([],
neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs(self):
for shape, pshape in [((10, 7, 18, 18), (2, 2)),
((10, 7, 6, 18), (3, 2)),
((5, 7, 66, 66), (33, 33)),
((5, 7, 68, 66), (34, 33))]:
for border in ['valid', 'ignore_borders']:
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape),
dtype=dtype).reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
f = function([],
images2neibs(images, neib_shape, mode=border),
mode=self.mode)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
g = function([],
neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
if border in ['valid']:
assert any([
isinstance(node.op, self.op)
for node in f.maker.fgraph.toposort()
])
#print g()
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs(self):
for shape, pshape in [((100, 40, 18, 18), (2, 2)),
((100, 40, 6, 18), (3, 2)),
((10, 40, 66, 66), (33, 33)),
((10, 40, 68, 66), (34, 33))
]:
for border in ['valid', 'ignore_borders']:
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype
).reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
f = function([],
images2neibs(images, neib_shape, mode=border),
mode=self.mode)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
g = function([],
neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
if border in ['valid']:
assert any([isinstance(node.op, self.op)
for node in f.maker.fgraph.toposort()])
#print g()
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_gpu():
if cuda.cuda_available == False:
raise SkipTest('Optional package cuda disabled')
for shape, pshape in [((100, 40, 18, 18), (2, 2)),
((100, 40, 6, 18), (3, 2)),
((10, 40, 66, 66), (33, 33)),
((10, 40, 68, 66), (34, 33))]:
images = shared(
numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
f = function([], images2neibs(images, neib_shape), mode=mode_with_gpu)
f_gpu = function([],
images2neibs(images, neib_shape),
mode=mode_with_gpu)
assert any([
isinstance(node.op, GpuImages2Neibs)
for node in f_gpu.maker.env.toposort()
])
#print images.get_value(borrow=True)
neibs = numpy.asarray(f_gpu())
assert numpy.allclose(neibs, f())
#print neibs
g = function([],
neibs2images(neibs, neib_shape, images.shape),
mode=mode_with_gpu)
assert any([
isinstance(node.op, GpuImages2Neibs)
for node in f.maker.env.toposort()
])
#print numpy.asarray(g())
assert numpy.allclose(images.get_value(borrow=True), g())
def tes_neibs2images_crash_on_grad():
# say we had images of size (2, 3, 20, 20)
# then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4)
neibs = T.dmatrix()
neibs_val = numpy.random.rand(600, 4)
to_images = T.sum(neibs2images(neibs, (2, 2), (2, 3, 20, 20)))
g = T.grad(to_images, neibs)
fn = theano.function([neibs], to_images, mode=mode_without_gpu)
#print "Compiled"
fn(neibs_val)
def tes_neibs2images_crash_on_grad():
# say we had images of size (2, 3, 20, 20)
# then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4)
neibs = T.dmatrix()
neibs_val = numpy.random.rand(600, 4)
to_images = T.sum(neibs2images(neibs, (2, 2), (2, 3, 20, 20)))
g = T.grad(to_images, neibs)
fn = theano.function([neibs], to_images, mode=mode_without_gpu)
print "Compiled"
fn(neibs_val)
def test_neibs():
shape = (100,40,18,18)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2,2))#(array((2,2), dtype='float32'))
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)
#print g()
assert numpy.allclose(images.get_value(borrow=True),g())
def test_neibs():
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2, 2))
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
g = function([],
neibs2images(neibs, neib_shape, images.shape),
mode=mode_without_gpu)
#print g()
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_manual(self):
shape = (2, 3, 4, 4)
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype
).reshape(shape))
neib_shape = T.as_tensor_variable((2, 2))
for border in ['valid', 'ignore_borders']:
f = function([], images2neibs(images, neib_shape, mode=border),
mode=self.mode)
assert any([isinstance(node.op, self.op)
for node in f.maker.fgraph.toposort()])
#print images.get_value(borrow=True)
neibs = f()
#print neibs
assert numpy.allclose(neibs,
[[ 0, 1, 4, 5],
[ 2, 3, 6, 7],
[ 8, 9, 12, 13],
[10, 11, 14, 15],
[16, 17, 20, 21],
[18, 19, 22, 23],
[24, 25, 28, 29],
[26, 27, 30, 31],
[32, 33, 36, 37],
[34, 35, 38, 39],
[40, 41, 44, 45],
[42, 43, 46, 47],
[48, 49, 52, 53],
[50, 51, 54, 55],
[56, 57, 60, 61],
[58, 59, 62, 63],
[64, 65, 68, 69],
[66, 67, 70, 71],
[72, 73, 76, 77],
[74, 75, 78, 79],
[80, 81, 84, 85],
[82, 83, 86, 87],
[88, 89, 92, 93],
[90, 91, 94, 95]])
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_manual():
shape = (2, 3, 4, 4)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2, 2))
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
#print images.get_value(borrow=True)
neibs = f()
#print neibs
assert numpy.allclose(neibs,[[ 0, 1, 4, 5],
[ 2, 3, 6, 7],
[ 8, 9, 12, 13],
[10, 11, 14, 15],
[16, 17, 20, 21],
[18, 19, 22, 23],
[24, 25, 28, 29],
[26, 27, 30, 31],
[32, 33, 36, 37],
[34, 35, 38, 39],
[40, 41, 44, 45],
[42, 43, 46, 47],
[48, 49, 52, 53],
[50, 51, 54, 55],
[56, 57, 60, 61],
[58, 59, 62, 63],
[64, 65, 68, 69],
[66, 67, 70, 71],
[72, 73, 76, 77],
[74, 75, 78, 79],
[80, 81, 84, 85],
[82, 83, 86, 87],
[88, 89, 92, 93],
[90, 91, 94, 95]])
g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=mode_without_gpu)
#print g()
assert numpy.allclose(images.get_value(borrow=True), g())
def fn(neibs):
return neibs2images(neibs, (2, 2), (2, 3, 10, 10))
def fn(neibs):
return neibs2images(neibs, (2, 2), (2, 3, 20, 20))
def fn(neibs):
return neibs2images(neibs, (2, 2), (2, 3, 10, 10))
def fn(neibs):
return neibs2images(neibs, (2, 2), (2, 3, 20, 20))
