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pytensor
提交 28bfa8d5 authored 作者: Frederic's avatar Frederic
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big refactoring and Images2Neibs tests and some update to it at the same time.

No need to review in detail. This is sandbox stuff.
上级 165ac99b
隐藏空白字符变更
内嵌 并排
正在显示 包含 270 行增加 和 329 行删除
theano/sandbox/test_neighbours.py
浏览文件 @ 28bfa8d5
...@@ -20,235 +20,196 @@ else: ...@@ -20,235 +20,196 @@ else:
mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu') mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
def test_neibs(): class T_Images2Neibs(unittest_tools.InferShapeTester):
shape = (100, 40, 18, 18) def __init__(self, name):
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) self.mode = mode_without_gpu
neib_shape = T.as_tensor_variable((2, 2)) self.op = Images2Neibs
return super(T_Images2Neibs, self).__init__(name)
f = function([], images2neibs(images, neib_shape), mode=mode_without_gpu)
def test_neibs(self):
#print images.get_value(borrow=True) for shape, pshape in [((100, 40, 18, 18), (2, 2)),
neibs = f() ((100, 40, 6, 18), (3, 2)),
#print neibs ((10, 40, 66, 66), (33, 33)),
g = function([], neibs2images(neibs, neib_shape, images.shape), ((10, 40, 68, 66), (34, 33))
mode=mode_without_gpu) ]:
for border in ['valid', 'ignore_borders']:
#print g() images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
assert numpy.allclose(images.get_value(borrow=True), g()) neib_shape = T.as_tensor_variable(pshape)
f = function([], images2neibs(images, neib_shape, mode=border),
def test_neibs_bad_shape(): mode=self.mode)
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) #print images.get_value(borrow=True)
neibs = f()
for neib_shape in [(3, 2), (2, 3)]: #print neibs
neib_shape = T.as_tensor_variable(neib_shape) g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=self.mode)
try: assert any([isinstance(node.op, self.op)
f = function([], images2neibs(images, neib_shape), for node in f.maker.fgraph.toposort()])
mode=mode_without_gpu)
f() #print g()
assert False, "An error was expected" assert numpy.allclose(images.get_value(borrow=True), g())
except TypeError:
pass def test_neibs_manual(self):
shape = (2, 3, 4, 4)
images = shared(numpy.arange(numpy.prod(shape)).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)
def test_neibs_bad_shape_warp_centered(): #print images.get_value(borrow=True)
shape = (2, 3, 10, 10) neibs = f()
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) #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_step(self):
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))
for border in ['valid', 'ignore_borders']:
f = function([], images2neibs(images, neib_shape, neib_step, mode=border),
mode=self.mode)
for neib_shape in [(3, 2), (2, 3)]: neibs = f()
neib_shape = T.as_tensor_variable(neib_shape) 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]])
#neibs2images do not seam to support step != neib_shape
#g = function([], neibs2images(neibs, neib_shape, images.shape),
# mode=self.mode)
#print g()
#assert numpy.allclose(images.get_value(borrow=True), g())
try: def test_neibs_bad_shape(self):
f = function([], images2neibs(images, neib_shape, shape = (2, 3, 10, 10)
mode="wrap_centered"), images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
mode=mode_without_gpu)
f()
assert False, "An error was expected"
except TypeError:
pass
shape = (2, 3, 2, 3) for neib_shape in [(3, 2), (2, 3)]:
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) neib_shape = T.as_tensor_variable(neib_shape)
neib_shape = T.as_tensor_variable((3, 3)) f = function([], images2neibs(images, neib_shape), mode=self.mode)
self.assertRaises(TypeError, f)
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]: #Test that ignore border work in that case.
try: f = function([], images2neibs(images, neib_shape, mode='ignore_borders'),
f = function([], images2neibs(images, neib_shape, mode=self.mode)
mode="wrap_centered"),
mode=mode_without_gpu)
f() 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_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 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.fgraph.toposort()]
elif mode_idx == 1:
assert GpuImages2Neibs 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]])
#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_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( def test_neibs_wrap_centered_step_manual(self):
shape)).reshape(shape), dtype='float32'))
neib_shape = T.as_tensor_variable(neib_shape) expected1 = [[24, 20, 21, 4, 0, 1, 9, 5, 6],
neib_step = T.as_tensor_variable(neib_step) [21, 22, 23, 1, 2, 3, 6, 7, 8],
expected = numpy.asarray(expected) [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, f = function([], images2neibs(images, neib_shape, neib_step,
mode="wrap_centered"), mode=mode) mode="wrap_centered"),
mode=self.mode)
neibs = f() neibs = f()
if expected.size > 1: if expected.size > 1:
...@@ -257,74 +218,54 @@ def test_neibs_wrap_centered_step_manual(): ...@@ -257,74 +218,54 @@ def test_neibs_wrap_centered_step_manual():
(i + 1) * expected.shape[0], :], (i + 1) * expected.shape[0], :],
expected + 25 * i), mode_idx expected + 25 * i), mode_idx
if mode_idx == 0: assert self.op in [type(node.op)
assert Images2Neibs in [type(node.op) for node in f.maker.fgraph.toposort()]
for node in f.maker.fgraph.toposort()]
elif mode_idx == 1:
assert GpuImages2Neibs in [type(node.op)
for node in f.maker.fgraph.toposort()]
#g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)
#g = function([], neibs2images(neibs, neib_shape, images.shape), mode=self.mode)
#TODO: why this is commented?
#assert numpy.allclose(images.get_value(borrow=True), g()) #assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_bad_shape_wrap_centered(self):
shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
def test_neibs_gpu(): for neib_shape in [(3, 2), (2, 3)]:
if cuda.cuda_available == False: neib_shape = T.as_tensor_variable(neib_shape)
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), f = function([], images2neibs(images, neib_shape,
dtype='float32').reshape(shape)) mode="wrap_centered"),
neib_shape = T.as_tensor_variable(pshape) mode=self.mode)
self.assertRaises(TypeError, f)
f = function([], images2neibs(images, neib_shape), for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
mode=mode_with_gpu) images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
f_gpu = function([], images2neibs(images, neib_shape), neib_shape = T.as_tensor_variable((3, 3))
mode=mode_with_gpu) f = function([], images2neibs(images, neib_shape,
assert any([isinstance(node.op, GpuImages2Neibs) mode="wrap_centered"),
for node in f_gpu.maker.fgraph.toposort()]) mode=self.mode)
#print images.get_value(borrow=True) self.assertRaises(TypeError, f)
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.fgraph.toposort()])
#print numpy.asarray(g())
assert numpy.allclose(images.get_value(borrow=True), g())
def speed_neibs():
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))
for i in range(1000):
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))
def speed_neibs_wrap_centered(): f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
shape = (100, 40, 18, 18) mode=self.mode)
images = shared(numpy.arange(numpy.prod(shape), f()
dtype='float32').reshape(shape))
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered")) def test_grad_wrap_centered(self):
# It is not implemented for now. So test that we raise an error.
shape = (2, 3, 6, 6)
images_val = numpy.random.rand(*shape).astype('float32')
for i in range(1000): def fn(images):
f() return images2neibs(images, (3, 3), mode='wrap_centered')
self.assertRaises(NotImplementedError, unittest_tools.verify_grad,
fn, [images_val], mode=self.mode)
class T_Images2Neibs(unittest_tools.InferShapeTester):
def test_grad_valid(self): def test_grad_valid(self):
shape = (2, 3, 4, 4) shape = (2, 3, 4, 4)
images_val = numpy.random.rand(*shape).astype('float32') images_val = numpy.random.rand(*shape).astype('float32')
...@@ -332,11 +273,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester): ...@@ -332,11 +273,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
def fn(images): def fn(images):
return images2neibs(images, (2, 2)) return images2neibs(images, (2, 2))
unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu, unittest_tools.verify_grad(fn, [images_val], mode=self.mode,
eps=0.1) eps=0.1)
if cuda.cuda_available:
unittest_tools.verify_grad(fn, [images_val], mode=mode_with_gpu,
eps=0.1)
# The grad is not defined when the neib_shape and neib_step # The grad is not defined when the neib_shape and neib_step
# are not the same. # are not the same.
...@@ -344,22 +282,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester): ...@@ -344,22 +282,7 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
return images2neibs(images, (2, 2), (1, 1)) return images2neibs(images, (2, 2), (1, 1))
self.assertRaises(NotImplementedError, self.assertRaises(NotImplementedError,
unittest_tools.verify_grad, fn, [images_val], unittest_tools.verify_grad, fn, [images_val],
mode=mode_without_gpu) mode=self.mode)
def test_grad_warp_centered(self):
# It is not implemented for now. So test that we raise an error.
shape = (2, 3, 6, 6)
images_val = numpy.random.rand(*shape).astype('float32')
def fn(images):
return images2neibs(images, (3, 3), mode='wrap_centered')
self.assertRaises(NotImplementedError, unittest_tools.verify_grad,
fn, [images_val], mode=mode_without_gpu)
if cuda.cuda_available:
self.assertRaises(NotImplementedError, unittest_tools.verify_grad,
fn, [images_val], mode=mode_with_gpu)
def test_grad_ignore_border(self): def test_grad_ignore_border(self):
shape = (2, 3, 5, 5) shape = (2, 3, 5, 5)
...@@ -370,47 +293,65 @@ class T_Images2Neibs(unittest_tools.InferShapeTester): ...@@ -370,47 +293,65 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
return images2neibs(images, (2, 2), return images2neibs(images, (2, 2),
mode='ignore_borders') mode='ignore_borders')
unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu, unittest_tools.verify_grad(fn, [images_val], mode=self.mode,
eps=0.1) eps=0.1)
# GPU code not implemented in that case, but is should still def test_neibs2images_grad(self):
# not crash.
if cuda.cuda_available:
unittest_tools.verify_grad(fn, [images_val], mode=mode_with_gpu,
eps=0.1)
def test_neibs2images_crash_on_grad(self):
# say we had images of size (2, 3, 20, 20) # say we had images of size (2, 3, 20, 20)
# then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4) # then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4)
neibs = T.dmatrix() neibs = T.dmatrix()
neibs_val = numpy.random.rand(600, 4) 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_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 fn(neibs):
return neibs2images(neibs, (2, 2), (2, 3, 20, 20))
unittest_tools.verify_grad(fn, [neibs_val], mode=self.mode,
eps=0.1)
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 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(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="wrap_centered"),
mode=self.mode)
for i in range(1000):
f()
class T_GpuImages2Neibs(T_Images2Neibs):
def __init__(self, name):
self.mode = mode_with_gpu
self.op = GpuImages2Neibs
return super(T_GpuImages2Neibs, self).__init__(name)
if __name__ == '__main__': if __name__ == '__main__':
#test_neibs_gpu() #test_neibs_gpu()
......
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