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提交 ca55d2f9 authored 作者: nouiz's avatar nouiz
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Merge pull request #908 from lamblin/fix_test_neib

Fix tests for image2neibs
上级 7478227f 1d4f75a7
隐藏空白字符变更
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正在显示 包含 172 行增加 和 139 行删除
theano/sandbox/cuda/neighbours.py
浏览文件 @ ca55d2f9
...@@ -404,6 +404,7 @@ def gpu_images2neibs(ten4, neib_shape, neib_step=None, mode='valid'): ...@@ -404,6 +404,7 @@ def gpu_images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
@local_optimizer() @local_optimizer()
def use_gpu_images2neibs(node): def use_gpu_images2neibs(node):
if (type(node.op) is Images2Neibs and if (type(node.op) is Images2Neibs and
node.inputs[0].dtype == 'float32' and
node.op.mode in ['valid', 'wrap_centered']): node.op.mode in ['valid', 'wrap_centered']):
return [host_from_gpu(gpu_images2neibs(gpu_from_host(node.inputs[0]), return [host_from_gpu(gpu_images2neibs(gpu_from_host(node.inputs[0]),
node.inputs[1], node.inputs[2], node.inputs[1], node.inputs[2],
......
theano/sandbox/cuda/nvcc_compiler.py
浏览文件 @ ca55d2f9
...@@ -139,6 +139,7 @@ class NVCC_compiler(object): ...@@ -139,6 +139,7 @@ class NVCC_compiler(object):
default_to_move_computation_to_gpu=False, default_to_move_computation_to_gpu=False,
move_shared_float32_to_gpu=False, move_shared_float32_to_gpu=False,
enable_cuda=False) enable_cuda=False)
n = theano.sandbox.cuda.use.device_number
p = theano.sandbox.cuda.device_properties(n) p = theano.sandbox.cuda.device_properties(n)
flags.append('-arch=sm_' + str(p['major']) + str(p['minor'])) flags.append('-arch=sm_' + str(p['major']) + str(p['minor']))
......
theano/sandbox/cuda/tests/test_neighbours.py
浏览文件 @ ca55d2f9
...@@ -20,6 +20,7 @@ else: ...@@ -20,6 +20,7 @@ else:
class T_GpuImages2Neibs(theano.sandbox.test_neighbours.T_Images2Neibs): class T_GpuImages2Neibs(theano.sandbox.test_neighbours.T_Images2Neibs):
mode = mode_with_gpu mode = mode_with_gpu
op = GpuImages2Neibs op = GpuImages2Neibs
dtypes = ['float32']
if __name__ == '__main__': if __name__ == '__main__':
unittest.main() unittest.main()
theano/sandbox/test_neighbours.py
浏览文件 @ ca55d2f9
...@@ -18,9 +18,11 @@ else: ...@@ -18,9 +18,11 @@ else:
if not theano.config.cxx: if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.") raise SkipTest("G++ not available, so we need to skip this test.")
class T_Images2Neibs(unittest_tools.InferShapeTester): class T_Images2Neibs(unittest_tools.InferShapeTester):
mode = mode_without_gpu mode = mode_without_gpu
op = Images2Neibs op = Images2Neibs
dtypes = ['int64', 'float32', 'float64']
def test_neibs(self): def test_neibs(self):
for shape, pshape in [((100, 40, 18, 18), (2, 2)), for shape, pshape in [((100, 40, 18, 18), (2, 2)),
...@@ -29,124 +31,143 @@ class T_Images2Neibs(unittest_tools.InferShapeTester): ...@@ -29,124 +31,143 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
((10, 40, 68, 66), (34, 33)) ((10, 40, 68, 66), (34, 33))
]: ]:
for border in ['valid', 'ignore_borders']: for border in ['valid', 'ignore_borders']:
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) for dtype in self.dtypes:
neib_shape = T.as_tensor_variable(pshape) 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_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), f = function([], images2neibs(images, neib_shape, mode=border),
mode=self.mode) mode=self.mode)
#print images.get_value(borrow=True) #print images.get_value(borrow=True)
neibs = f() neibs = f()
#print neibs #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), g = function([], neibs2images(neibs, neib_shape, images.shape),
mode=self.mode) mode=self.mode)
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()) assert numpy.allclose(images.get_value(borrow=True), g())
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)
#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_step(self): def test_neibs_manual_step(self):
shape = (2, 3, 5, 5) shape = (2, 3, 5, 5)
images = shared(numpy.asarray(numpy.arange(numpy.prod( for dtype in self.dtypes:
shape)).reshape(shape), dtype='float32')) images = shared(numpy.asarray(numpy.arange(numpy.prod(
neib_shape = T.as_tensor_variable((3, 3)) shape)).reshape(shape), dtype=dtype))
neib_step = T.as_tensor_variable((2, 2)) neib_shape = T.as_tensor_variable((3, 3))
for border in ['valid', 'ignore_borders']: neib_step = T.as_tensor_variable((2, 2))
f = function([], images2neibs(images, neib_shape, neib_step, mode=border), for border in ['valid', 'ignore_borders']:
mode=self.mode) 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]])
#neibs2images do not seam to support step != neib_shape
#g = function([], neibs2images(neibs, neib_shape, images.shape),
# mode=self.mode)
neibs = f() #print g()
assert self.op in [type(node.op) #assert numpy.allclose(images.get_value(borrow=True), g())
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())
def test_neibs_bad_shape(self): def test_neibs_bad_shape(self):
shape = (2, 3, 10, 10) shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) for dtype in self.dtypes:
images = shared(numpy.arange(
for neib_shape in [(3, 2), (2, 3)]: numpy.prod(shape), dtype=dtype
neib_shape = T.as_tensor_variable(neib_shape) ).reshape(shape))
f = function([], images2neibs(images, neib_shape), mode=self.mode)
self.assertRaises(TypeError, f) 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. #Test that ignore border work in that case.
f = function([], images2neibs(images, neib_shape, mode='ignore_borders'), f = function([],
mode=self.mode) images2neibs(images, neib_shape,
f() mode='ignore_borders'),
mode=self.mode)
f()
def test_neibs_wrap_centered_step_manual(self): def test_neibs_wrap_centered_step_manual(self):
...@@ -197,58 +218,65 @@ class T_Images2Neibs(unittest_tools.InferShapeTester): ...@@ -197,58 +218,65 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
[(1, 1, 1045, 5), (3, 3), (3, 3), None], [(1, 1, 1045, 5), (3, 3), (3, 3), None],
]): ]):
images = shared(numpy.asarray(numpy.arange(numpy.prod( for dtype in self.dtypes:
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)
f = function([], images2neibs(images, neib_shape, neib_step, images = shared(numpy.asarray(numpy.arange(numpy.prod(
mode="wrap_centered"), shape)).reshape(shape), dtype=dtype))
mode=self.mode) neib_shape = T.as_tensor_variable(neib_shape)
neibs = f() 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: if expected.size > 1:
for i in range(shape[0] * shape[1]): for i in range(shape[0] * shape[1]):
assert numpy.allclose(neibs[i * expected.shape[0]: assert numpy.allclose(
(i + 1) * expected.shape[0], :], neibs[i * expected.shape[0]:
expected + 25 * i), mode_idx (i + 1) * expected.shape[0], :],
expected + 25 * i), "wrap_centered"
assert self.op in [type(node.op) assert self.op in [type(node.op)
for node in f.maker.fgraph.toposort()] for node in f.maker.fgraph.toposort()]
#g = function([], neibs2images(neibs, neib_shape, images.shape), mode=self.mode) #g = function([], neibs2images(neibs, neib_shape, images.shape), mode=self.mode)
#TODO: why this is commented? #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): def test_neibs_bad_shape_wrap_centered(self):
shape = (2, 3, 10, 10) shape = (2, 3, 10, 10)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
for neib_shape in [(3, 2), (2, 3)]: for dtype in self.dtypes:
neib_shape = T.as_tensor_variable(neib_shape) images = shared(numpy.arange(
numpy.prod(shape), dtype=dtype
).reshape(shape))
f = function([], images2neibs(images, neib_shape, for neib_shape in [(3, 2), (2, 3)]:
mode="wrap_centered"), neib_shape = T.as_tensor_variable(neib_shape)
mode=self.mode)
self.assertRaises(TypeError, f)
for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]: 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)) images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable((3, 3)) 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 f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
shape = (2, 3, 3, 3) mode=self.mode)
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape)) f()
neib_shape = T.as_tensor_variable((3, 3))
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
mode=self.mode)
f()
def test_grad_wrap_centered(self): def test_grad_wrap_centered(self):
# It is not implemented for now. So test that we raise an error. # It is not implemented for now. So test that we raise an error.
......
theano/tests/unittest_tools.py
浏览文件 @ ca55d2f9
...@@ -166,9 +166,11 @@ class T_OpContractMixin(object): ...@@ -166,9 +166,11 @@ class T_OpContractMixin(object):
class InferShapeTester(unittest.TestCase): class InferShapeTester(unittest.TestCase):
def setUp(self): def setUp(self):
seed_rng() seed_rng()
# Take into account any mode that may be defined in a child class
mode = getattr(self, 'mode', theano.compile.get_default_mode())
# This mode seems to be the minimal one including the shape_i # This mode seems to be the minimal one including the shape_i
# optimizations, if we don't want to enumerate them explicitly. # optimizations, if we don't want to enumerate them explicitly.
self.mode = theano.compile.get_default_mode().including("canonicalize") self.mode = mode.including("canonicalize")
def _compile_and_check(self, inputs, outputs, numeric_inputs, cls, def _compile_and_check(self, inputs, outputs, numeric_inputs, cls,
excluding=None): excluding=None):
......
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