def test_neibs_bad_shape_wrap_centered(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(numpy.arange(
numpy.prod(shape), dtype=dtype
).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=self.mode)
self.assertRaises(TypeError, f)
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=self.mode)
self.assertRaises(TypeError, f)
# Test a valid shapes
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
mode=self.mode)
f()
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_bad_shape():
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 2))
try:
f = function([], images2neibs(images, neib_shape),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False, "An error was expected"
except TypeError:
pass
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2, 3))
try:
f = function([], images2neibs(images, neib_shape),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False, "An error was expected"
except TypeError:
pass
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_bad_shape():
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 2))
try:
f = function([],
images2neibs(images, neib_shape),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False, "An error was expected"
except TypeError:
pass
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((2, 3))
try:
f = function([],
images2neibs(images, neib_shape),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False, "An error was expected"
except TypeError:
pass
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 [((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(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_bad_shape(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(numpy.arange(
numpy.prod(shape), dtype=dtype
).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
f = function([], images2neibs(images, neib_shape),
mode=self.mode)
self.assertRaises(TypeError, f)
#Test that ignore border work in that case.
f = function([],
images2neibs(images, neib_shape,
mode='ignore_borders'),
mode=self.mode)
f()
def speed_neibs(self):
shape = (100, 40, 18, 18)
images = shared(
numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape), mode=self.mode)
for i in range(1000):
f()
def speed_neibs_wrap_centered():
shape = (100, 40, 18, 18)
images = shared(numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"))
for i in range(1000):
f()
def speed_neibs_wrap_centered():
shape = (100,40,18,18)
images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
from theano.sandbox.cuda.basic_ops import gpu_from_host
f = function([], images2neibs(images,neib_shape,mode="wrap_centered"))#, mode=mode_without_gpu)
for i in range(1000):
f()
def test_neibs_bad_shape(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
f = function([],
images2neibs(images, neib_shape),
mode=self.mode)
self.assertRaises(TypeError, f)
#Test that ignore border work in that case.
f = function([],
images2neibs(images,
neib_shape,
mode='ignore_borders'),
mode=self.mode)
f()
def test_neibs_bad_shape_warp_centered():
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
try:
f = function([],
images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
f()
assert False, "An error was expected"
except TypeError:
pass
shape = (2, 3, 2, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
try:
f = function([],
images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
f()
assert False, "An error was expected"
except TypeError:
pass
# Test a valid shapes
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([],
images2neibs(images, neib_shape, mode="wrap_centered"),
mode=mode_without_gpu)
f()
def test_neibs_step_manual():
shape = (2, 3, 5, 5)
images = shared(
numpy.asarray(numpy.arange(numpy.prod(shape)).reshape(shape),
dtype='float32'))
neib_shape = T.as_tensor_variable((3, 3))
neib_step = T.as_tensor_variable((2, 2))
modes = [mode_without_gpu]
if cuda.cuda_available:
modes.append(mode_with_gpu)
for mode_idx, mode in enumerate(modes):
f = function([],
images2neibs(images, neib_shape, neib_step),
mode=mode)
#print images.get_value(borrow=True)
neibs = f()
if mode_idx == 0:
assert Images2Neibs in [
type(node.op) for node in f.maker.env.toposort()
]
elif mode_idx == 1:
assert GpuImages2Neibs in [
type(node.op) for node in f.maker.env.toposort()
]
assert numpy.allclose(
neibs,
[[0, 1, 2, 5, 6, 7, 10, 11, 12], [2, 3, 4, 7, 8, 9, 12, 13, 14],
[10, 11, 12, 15, 16, 17, 20, 21, 22],
[12, 13, 14, 17, 18, 19, 22, 23, 24],
[25, 26, 27, 30, 31, 32, 35, 36, 37],
[27, 28, 29, 32, 33, 34, 37, 38, 39],
[35, 36, 37, 40, 41, 42, 45, 46, 47],
[37, 38, 39, 42, 43, 44, 47, 48, 49],
[50, 51, 52, 55, 56, 57, 60, 61, 62],
[52, 53, 54, 57, 58, 59, 62, 63, 64],
[60, 61, 62, 65, 66, 67, 70, 71, 72],
[62, 63, 64, 67, 68, 69, 72, 73, 74],
[75, 76, 77, 80, 81, 82, 85, 86, 87],
[77, 78, 79, 82, 83, 84, 87, 88, 89],
[85, 86, 87, 90, 91, 92, 95, 96, 97],
[87, 88, 89, 92, 93, 94, 97, 98, 99],
[100, 101, 102, 105, 106, 107, 110, 111, 112],
[102, 103, 104, 107, 108, 109, 112, 113, 114],
[110, 111, 112, 115, 116, 117, 120, 121, 122],
[112, 113, 114, 117, 118, 119, 122, 123, 124],
[125, 126, 127, 130, 131, 132, 135, 136, 137],
[127, 128, 129, 132, 133, 134, 137, 138, 139],
[135, 136, 137, 140, 141, 142, 145, 146, 147],
[137, 138, 139, 142, 143, 144, 147, 148, 149]])
def test_neibs_bad_shape_warp_centered():
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
try:
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
f()
assert False, "An error was expected"
except TypeError:
pass
shape = (2, 3, 2, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
try:
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
f()
assert False, "An error was expected"
except TypeError:
pass
# Test a valid shapes
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
mode=mode_without_gpu)
f()
def test_neibs_valid_with_inconsistent_borders(self):
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
f = theano.function([images],
T.sqr(images2neibs(images, (2, 2), mode='valid')),
mode=self.mode)
self.assertRaises(TypeError, f, images_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_valid_with_inconsistent_borders(self):
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
f = theano.function([images],
T.sqr(images2neibs(images, (2, 2), mode='valid')),
mode=self.mode)
self.assertRaises(TypeError, f, images_val)
def test_neibs_bad_shape_wrap_centered(self):
shape = (2, 3, 10, 10)
for dtype in self.dtypes:
images = shared(
numpy.arange(numpy.prod(shape), dtype=dtype).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
f = function([],
images2neibs(images,
neib_shape,
mode="wrap_centered"),
mode=self.mode)
self.assertRaises(TypeError, f)
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([],
images2neibs(images,
neib_shape,
mode="wrap_centered"),
mode=self.mode)
self.assertRaises(TypeError, f)
# Test a valid shapes
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([],
images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=self.mode)
f()
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_step(self):
shape = (2, 3, 5, 5)
for dtype in self.dtypes:
images = shared(
numpy.asarray(numpy.arange(numpy.prod(shape)).reshape(shape),
dtype=dtype))
neib_shape = T.as_tensor_variable((3, 3))
neib_step = T.as_tensor_variable((2, 2))
for border in ['valid', 'ignore_borders']:
f = function([],
images2neibs(images,
neib_shape,
neib_step,
mode=border),
mode=self.mode)
neibs = f()
if border in ['valid']:
assert self.op in [
type(node.op) for node in f.maker.fgraph.toposort()
]
assert numpy.allclose(
neibs, [[0, 1, 2, 5, 6, 7, 10, 11, 12],
[2, 3, 4, 7, 8, 9, 12, 13, 14],
[10, 11, 12, 15, 16, 17, 20, 21, 22],
[12, 13, 14, 17, 18, 19, 22, 23, 24],
[25, 26, 27, 30, 31, 32, 35, 36, 37],
[27, 28, 29, 32, 33, 34, 37, 38, 39],
[35, 36, 37, 40, 41, 42, 45, 46, 47],
[37, 38, 39, 42, 43, 44, 47, 48, 49],
[50, 51, 52, 55, 56, 57, 60, 61, 62],
[52, 53, 54, 57, 58, 59, 62, 63, 64],
[60, 61, 62, 65, 66, 67, 70, 71, 72],
[62, 63, 64, 67, 68, 69, 72, 73, 74],
[75, 76, 77, 80, 81, 82, 85, 86, 87],
[77, 78, 79, 82, 83, 84, 87, 88, 89],
[85, 86, 87, 90, 91, 92, 95, 96, 97],
[87, 88, 89, 92, 93, 94, 97, 98, 99],
[100, 101, 102, 105, 106, 107, 110, 111, 112],
[102, 103, 104, 107, 108, 109, 112, 113, 114],
[110, 111, 112, 115, 116, 117, 120, 121, 122],
[112, 113, 114, 117, 118, 119, 122, 123, 124],
[125, 126, 127, 130, 131, 132, 135, 136, 137],
[127, 128, 129, 132, 133, 134, 137, 138, 139],
[135, 136, 137, 140, 141, 142, 145, 146, 147],
[137, 138, 139, 142, 143, 144, 147, 148, 149]])
def test_neibs_step_manual():
shape = (2, 3, 5, 5)
images = shared(numpy.asarray(numpy.arange(numpy.prod(
shape)).reshape(shape), dtype='float32'))
neib_shape = T.as_tensor_variable((3, 3))
neib_step = T.as_tensor_variable((2, 2))
modes = [mode_without_gpu]
if cuda.cuda_available:
modes.append(mode_with_gpu)
for mode_idx, mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step),
mode=mode)
#print images.get_value(borrow=True)
neibs = f()
if mode_idx == 0:
assert Images2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
elif mode_idx == 1:
assert GpuImages2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
assert numpy.allclose(neibs,
[[ 0, 1, 2, 5, 6, 7, 10, 11, 12],
[ 2, 3, 4, 7, 8, 9, 12, 13, 14],
[ 10, 11, 12, 15, 16, 17, 20, 21, 22],
[ 12, 13, 14, 17, 18, 19, 22, 23, 24],
[ 25, 26, 27, 30, 31, 32, 35, 36, 37],
[ 27, 28, 29, 32, 33, 34, 37, 38, 39],
[ 35, 36, 37, 40, 41, 42, 45, 46, 47],
[ 37, 38, 39, 42, 43, 44, 47, 48, 49],
[ 50, 51, 52, 55, 56, 57, 60, 61, 62],
[ 52, 53, 54, 57, 58, 59, 62, 63, 64],
[ 60, 61, 62, 65, 66, 67, 70, 71, 72],
[ 62, 63, 64, 67, 68, 69, 72, 73, 74],
[ 75, 76, 77, 80, 81, 82, 85, 86, 87],
[ 77, 78, 79, 82, 83, 84, 87, 88, 89],
[ 85, 86, 87, 90, 91, 92, 95, 96, 97],
[ 87, 88, 89, 92, 93, 94, 97, 98, 99],
[100, 101, 102, 105, 106, 107, 110, 111, 112],
[102, 103, 104, 107, 108, 109, 112, 113, 114],
[110, 111, 112, 115, 116, 117, 120, 121, 122],
[112, 113, 114, 117, 118, 119, 122, 123, 124],
[125, 126, 127, 130, 131, 132, 135, 136, 137],
[127, 128, 129, 132, 133, 134, 137, 138, 139],
[135, 136, 137, 140, 141, 142, 145, 146, 147],
[137, 138, 139, 142, 143, 144, 147, 148, 149]])
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_grad():
shape = (2,3,4,4)
images = shared(numpy.arange(numpy.prod(shape), dtype='float32').reshape(shape))
cost = T.sum(T.sqr(images2neibs(images, (2,2))), axis=[0,1])
grad = T.grad(cost, images)
f = theano.function([], [cost, grad], mode=mode_without_gpu)
got = f()
should_get = [numpy.asarray(290320.0, dtype=numpy.float32),
numpy.asarray([[[[ 0., 2., 4., 6.],
[ 8., 10., 12., 14.],
[ 16., 18., 20., 22.],
[ 24., 26., 28., 30.]],
[[ 32., 34., 36., 38.],
[ 40., 42., 44., 46.],
[ 48., 50., 52., 54.],
[ 56., 58., 60., 62.]],
[[ 64., 66., 68., 70.],
[ 72., 74., 76., 78.],
[ 80., 82., 84., 86.],
[ 88., 90., 92., 94.]]],
[[[ 96., 98., 100., 102.],
[ 104., 106., 108., 110.],
[ 112., 114., 116., 118.],
[ 120., 122., 124., 126.]],
[[ 128., 130., 132., 134.],
[ 136., 138., 140., 142.],
[ 144., 146., 148., 150.],
[ 152., 154., 156., 158.]],
[[ 160., 162., 164., 166.],
[ 168., 170., 172., 174.],
[ 176., 178., 180., 182.],
[ 184., 186., 188., 190.]]]], dtype=numpy.float32)]
assert numpy.allclose(got[0], should_get[0])
assert numpy.allclose(got[1], should_get[1])
def test_neibs_manual_step(self):
shape = (2, 3, 5, 5)
for dtype in self.dtypes:
images = shared(numpy.asarray(numpy.arange(numpy.prod(
shape)).reshape(shape), dtype=dtype))
neib_shape = T.as_tensor_variable((3, 3))
neib_step = T.as_tensor_variable((2, 2))
for border in ['valid', 'ignore_borders']:
f = function([],
images2neibs(images, neib_shape, neib_step,
mode=border),
mode=self.mode)
neibs = f()
if border in ['valid']:
assert self.op in [type(node.op)
for node in f.maker.fgraph.toposort()]
assert numpy.allclose(neibs,
[[ 0, 1, 2, 5, 6, 7, 10, 11, 12],
[ 2, 3, 4, 7, 8, 9, 12, 13, 14],
[ 10, 11, 12, 15, 16, 17, 20, 21, 22],
[ 12, 13, 14, 17, 18, 19, 22, 23, 24],
[ 25, 26, 27, 30, 31, 32, 35, 36, 37],
[ 27, 28, 29, 32, 33, 34, 37, 38, 39],
[ 35, 36, 37, 40, 41, 42, 45, 46, 47],
[ 37, 38, 39, 42, 43, 44, 47, 48, 49],
[ 50, 51, 52, 55, 56, 57, 60, 61, 62],
[ 52, 53, 54, 57, 58, 59, 62, 63, 64],
[ 60, 61, 62, 65, 66, 67, 70, 71, 72],
[ 62, 63, 64, 67, 68, 69, 72, 73, 74],
[ 75, 76, 77, 80, 81, 82, 85, 86, 87],
[ 77, 78, 79, 82, 83, 84, 87, 88, 89],
[ 85, 86, 87, 90, 91, 92, 95, 96, 97],
[ 87, 88, 89, 92, 93, 94, 97, 98, 99],
[100, 101, 102, 105, 106, 107, 110, 111, 112],
[102, 103, 104, 107, 108, 109, 112, 113, 114],
[110, 111, 112, 115, 116, 117, 120, 121, 122],
[112, 113, 114, 117, 118, 119, 122, 123, 124],
[125, 126, 127, 130, 131, 132, 135, 136, 137],
[127, 128, 129, 132, 133, 134, 137, 138, 139],
[135, 136, 137, 140, 141, 142, 145, 146, 147],
[137, 138, 139, 142, 143, 144, 147, 148, 149]])
def test_neibs_valid_with_inconsistent_borders():
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
f = theano.function([images],
T.sqr(images2neibs(images, (2, 2), mode='valid')),
mode=mode_without_gpu)
try:
f(images_val)
assert False, "An error was expected"
except TypeError, e:
# This is expected if the assert is there
pass
def test_neibs_valid_with_inconsistent_borders():
shape = (2, 3, 5, 5)
images = T.dtensor4()
images_val = numpy.arange(numpy.prod(shape),
dtype='float32').reshape(shape)
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
f = theano.function([images],
T.sqr(images2neibs(images, (2, 2), mode='valid')),
mode=mode_without_gpu)
try:
f(images_val)
assert False, "An error was expected"
except TypeError, e:
# This is expected if the assert is there
pass
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(images):
return images2neibs(images, (2, 2), (1, 1))
def fn(images):
return images2neibs(images, (3, 3), mode='wrap_centered')
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2))), axis=[0, 1])
def fn(images):
return T.sum(T.sqr(images2neibs(images, (3, 3), mode='wrap_centered')),
axis=[0, 1])
def test_neibs_wrap_centered_step_manual(self):
expected1 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[9, 5, 6, 14, 10, 11, 19, 15, 16],
[6, 7, 8, 11, 12, 13, 16, 17, 18],
[8, 9, 5, 13, 14, 10, 18, 19, 15],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[16, 17, 18, 21, 22, 23, 1, 2, 3],
[18, 19, 15, 23, 24, 20, 3, 4, 0]]
expected2 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[12, 13, 14, 17, 18, 19, 22, 23, 24]]
expected3 = [[19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected4 = [
[23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]
]
expected5 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[9, 5, 6, 14, 10, 11, 19, 15, 16],
[7, 8, 9, 12, 13, 14, 17, 18, 19],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected6 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[11, 12, 13, 16, 17, 18, 21, 22, 23],
[13, 14, 10, 18, 19, 15, 23, 24, 20]]
#TODO test discontinous image
for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
[(7, 8, 5, 5), (3, 3), (2, 2), expected1],
[(7, 8, 5, 5), (3, 3), (3, 3), expected2],
[(7, 8, 5, 5), (5, 3), (3, 3), expected3],
[(7, 8, 5, 5), (3, 5), (3, 3), expected4],
[(80, 90, 5, 5), (3, 3), (2, 3), expected5],
[(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
[(1, 1, 5, 1035), (3, 3), (3, 3), None],
[(1, 1, 1045, 5), (3, 3), (3, 3), None],
]):
for dtype in self.dtypes:
images = shared(
numpy.asarray(numpy.arange(
numpy.prod(shape)).reshape(shape),
dtype=dtype))
neib_shape = T.as_tensor_variable(neib_shape)
neib_step = T.as_tensor_variable(neib_step)
expected = numpy.asarray(expected)
f = function([],
images2neibs(images,
neib_shape,
neib_step,
mode="wrap_centered"),
mode=self.mode)
neibs = f()
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert numpy.allclose(
neibs[i * expected.shape[0]:(i + 1) *
expected.shape[0], :],
expected + 25 * i), "wrap_centered"
assert self.op in [
type(node.op) for node in f.maker.fgraph.toposort()
]
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2),
mode='ignore_borders')),
axis=[0, 1])
def fn(images):
return images2neibs(images, (2, 2), (1, 1))
def fn(images):
return images2neibs(images, (3, 3), mode='wrap_centered')
try:
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False, "An error was expected"
except TypeError, e:
pass
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
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
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2),
mode='ignore_borders')), axis=[0, 1])
def fn(images):
return T.sum(T.sqr(images2neibs(images, (3, 3), mode='wrap_centered')),
axis=[0, 1])
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2))), axis=[0, 1])
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
try:
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
neibs = f()
#print neibs
assert False,"An error was expected"
except TypeError,e:
pass
shape = (2,3,3,3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3,3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"), mode=mode_without_gpu)
neibs = f()
#print neibs
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],
def fn(images):
return images2neibs(images, (2, 2), mode='ignore_borders')
def test_neibs_wrap_centered_step_manual():
modes = [mode_without_gpu]
if cuda.cuda_available:
modes.append(mode_with_gpu)
expected1 = [[24, 20, 21, 4, 0, 1, 9, 5,
6], [21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[9, 5, 6, 14, 10, 11, 19, 15, 16],
[6, 7, 8, 11, 12, 13, 16, 17, 18],
[8, 9, 5, 13, 14, 10, 18, 19, 15],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[16, 17, 18, 21, 22, 23, 1, 2, 3],
[18, 19, 15, 23, 24, 20, 3, 4, 0]]
expected2 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[12, 13, 14, 17, 18, 19, 22, 23, 24]]
expected3 = [[19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected4 = [[23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]]
expected5 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[9, 5, 6, 14, 10, 11, 19, 15, 16],
[7, 8, 9, 12, 13, 14, 17, 18, 19],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected6 = [[24, 20, 21, 4, 0, 1, 9, 5,
6], [21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[11, 12, 13, 16, 17, 18, 21, 22, 23],
[13, 14, 10, 18, 19, 15, 23, 24, 20]]
#TODO test discontinous image
for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
[(7, 8, 5, 5), (3, 3), (2, 2), expected1],
[(7, 8, 5, 5), (3, 3), (3, 3), expected2],
[(7, 8, 5, 5), (5, 3), (3, 3), expected3],
[(7, 8, 5, 5), (3, 5), (3, 3), expected4],
[(80, 90, 5, 5), (3, 3), (2, 3), expected5],
[(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
[(1, 1, 5, 1035), (3, 3), (3, 3), None],
[(1, 1, 1045, 5), (3, 3), (3, 3), None],
]):
images = shared(
numpy.asarray(numpy.arange(numpy.prod(shape)).reshape(shape),
dtype='float32'))
neib_shape = T.as_tensor_variable(neib_shape)
neib_step = T.as_tensor_variable(neib_step)
expected = numpy.asarray(expected)
for mode_idx, mode in enumerate(modes):
f = function([],
images2neibs(images,
neib_shape,
neib_step,
mode="wrap_centered"),
mode=mode)
neibs = f()
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert numpy.allclose(
neibs[i * expected.shape[0]:(i + 1) *
expected.shape[0], :],
expected + 25 * i), mode_idx
if mode_idx == 0:
assert Images2Neibs in [
type(node.op) for node in f.maker.env.toposort()
]
elif mode_idx == 1:
assert GpuImages2Neibs in [
type(node.op) for node in f.maker.env.toposort()
]
def test_neibs_wrap_centered_step_manual():
modes = [mode_without_gpu]
if cuda.cuda_available:
modes.append(mode_with_gpu)
expected1 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
[ 6, 7, 8, 11, 12, 13, 16, 17, 18],
[ 8, 9, 5, 13, 14, 10, 18, 19, 15],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[16, 17, 18, 21, 22, 23, 1, 2, 3],
[18, 19, 15, 23, 24, 20, 3, 4, 0]]
expected2 = [[ 24, 20, 21, 4, 0, 1, 9, 5, 6],
[ 22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 14, 10, 11, 19, 15, 16, 24, 20, 21],
[ 12, 13, 14, 17, 18, 19, 22, 23, 24]]
expected3 = [[19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected4 = [[23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]]
expected5 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected6 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[11, 12, 13, 16, 17, 18, 21, 22, 23],
[13, 14, 10, 18, 19, 15, 23, 24, 20]]
#TODO test discontinous image
for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
[(7, 8, 5, 5), (3, 3), (2, 2), expected1],
[(7, 8, 5, 5), (3, 3), (3, 3), expected2],
[(7, 8, 5, 5), (5, 3), (3, 3), expected3],
[(7, 8, 5, 5), (3, 5), (3, 3), expected4],
[(80, 90, 5, 5), (3, 3), (2, 3), expected5],
[(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
[(1, 1, 5, 1035), (3, 3), (3, 3), None],
[(1, 1, 1045, 5), (3, 3), (3, 3), None],
]):
images = shared(numpy.asarray(numpy.arange(numpy.prod(
shape)).reshape(shape), dtype='float32'))
neib_shape = T.as_tensor_variable(neib_shape)
neib_step = T.as_tensor_variable(neib_step)
expected = numpy.asarray(expected)
for mode_idx, mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step,
mode="wrap_centered"), mode=mode)
neibs = f()
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert numpy.allclose(neibs[i * expected.shape[0]:
(i + 1) * expected.shape[0], :],
expected + 25 * i), mode_idx
if mode_idx == 0:
assert Images2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
elif mode_idx == 1:
assert GpuImages2Neibs in [type(node.op)
for node in f.maker.env.toposort()]
try:
f = function([],
images2neibs(images, neib_shape, mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
assert False, "An error was expected"
except TypeError, e:
pass
shape = (2, 3, 3, 3)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([],
images2neibs(images, neib_shape, mode="wrap_centered"),
mode=mode_without_gpu)
neibs = f()
#print neibs
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
def fn(images):
return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
axis=[0, 1])
def fn(images):
return images2neibs(images, (2, 2),
mode='ignore_borders')
def test_neibs_wrap_centered_step_manual(self):
expected1 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
[ 6, 7, 8, 11, 12, 13, 16, 17, 18],
[ 8, 9, 5, 13, 14, 10, 18, 19, 15],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[16, 17, 18, 21, 22, 23, 1, 2, 3],
[18, 19, 15, 23, 24, 20, 3, 4, 0]]
expected2 = [[ 24, 20, 21, 4, 0, 1, 9, 5, 6],
[ 22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 14, 10, 11, 19, 15, 16, 24, 20, 21],
[ 12, 13, 14, 17, 18, 19, 22, 23, 24]]
expected3 = [[19, 15, 16, 24, 20, 21, 4, 0, 1, 9, 5, 6, 14, 10, 11],
[17, 18, 19, 22, 23, 24, 2, 3, 4, 7, 8, 9, 12, 13, 14],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16, 24, 20, 21, 4, 0, 1],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected4 = [[23, 24, 20, 21, 22, 3, 4, 0, 1, 2, 8, 9, 5, 6, 7],
[21, 22, 23, 24, 20, 1, 2, 3, 4, 0, 6, 7, 8, 9, 5],
[13, 14, 10, 11, 12, 18, 19, 15, 16, 17, 23, 24, 20, 21, 22],
[11, 12, 13, 14, 10, 16, 17, 18, 19, 15, 21, 22, 23, 24, 20]]
expected5 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[22, 23, 24, 2, 3, 4, 7, 8, 9],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
[ 7, 8, 9, 12, 13, 14, 17, 18, 19],
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[17, 18, 19, 22, 23, 24, 2, 3, 4]]
expected6 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[14, 10, 11, 19, 15, 16, 24, 20, 21],
[11, 12, 13, 16, 17, 18, 21, 22, 23],
[13, 14, 10, 18, 19, 15, 23, 24, 20]]
#TODO test discontinous image
for shp_idx, (shape, neib_shape, neib_step, expected) in enumerate([
[(7, 8, 5, 5), (3, 3), (2, 2), expected1],
[(7, 8, 5, 5), (3, 3), (3, 3), expected2],
[(7, 8, 5, 5), (5, 3), (3, 3), expected3],
[(7, 8, 5, 5), (3, 5), (3, 3), expected4],
[(80, 90, 5, 5), (3, 3), (2, 3), expected5],
[(1025, 9, 5, 5), (3, 3), (3, 2), expected6],
[(1, 1, 5, 1035), (3, 3), (3, 3), None],
[(1, 1, 1045, 5), (3, 3), (3, 3), None],
]):
for dtype in self.dtypes:
images = shared(numpy.asarray(numpy.arange(numpy.prod(
shape)).reshape(shape), dtype=dtype))
neib_shape = T.as_tensor_variable(neib_shape)
neib_step = T.as_tensor_variable(neib_step)
expected = numpy.asarray(expected)
f = function([], images2neibs(images, neib_shape, neib_step,
mode="wrap_centered"),
mode=self.mode)
neibs = f()
if expected.size > 1:
for i in range(shape[0] * shape[1]):
assert numpy.allclose(
neibs[i * expected.shape[0]:
(i + 1) * expected.shape[0], :],
expected + 25 * i), "wrap_centered"
assert self.op in [type(node.op)
for node in f.maker.fgraph.toposort()]