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提交 b18629bd authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Test img2neibs with only float32 on GPU, other dtypes on CPU

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正在显示 包含 167 行增加 和 138 行删除
theano/sandbox/cuda/tests/test_neighbours.py
浏览文件 @ b18629bd
......@@ -20,6 +20,7 @@ else:
class T_GpuImages2Neibs(theano.sandbox.test_neighbours.T_Images2Neibs):
mode = mode_with_gpu
op = GpuImages2Neibs
dtypes = ['float32']
if __name__ == '__main__':
unittest.main()
theano/sandbox/test_neighbours.py
浏览文件 @ b18629bd
......@@ -18,9 +18,11 @@ else:
if not theano.config.cxx:
raise SkipTest("G++ not available, so we need to skip this test.")
class T_Images2Neibs(unittest_tools.InferShapeTester):
mode = mode_without_gpu
op = Images2Neibs
dtypes = ['int64', 'float32', 'float64']
def test_neibs(self):
for shape, pshape in [((100, 40, 18, 18), (2, 2)),
......@@ -29,124 +31,143 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
((10, 40, 68, 66), (34, 33))
]:
for border in ['valid', 'ignore_borders']:
images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
neib_shape = T.as_tensor_variable(pshape)
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_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 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)
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):
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 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]])
#neibs2images do not seam to support step != neib_shape
#g = function([], neibs2images(neibs, neib_shape, images.shape),
# mode=self.mode)
neibs = f()
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())
#print g()
#assert numpy.allclose(images.get_value(borrow=True), g())
def test_neibs_bad_shape(self):
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)
f = function([], images2neibs(images, neib_shape), mode=self.mode)
self.assertRaises(TypeError, f)
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()
#Test that ignore border work in that case.
f = function([],
images2neibs(images, neib_shape,
mode='ignore_borders'),
mode=self.mode)
f()
def test_neibs_wrap_centered_step_manual(self):
......@@ -197,58 +218,65 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
[(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 dtype in self.dtypes:
f = function([], images2neibs(images, neib_shape, neib_step,
mode="wrap_centered"),
mode=self.mode)
neibs = f()
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), mode_idx
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()]
assert self.op in [type(node.op)
for node in f.maker.fgraph.toposort()]
#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())
#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())
def test_neibs_bad_shape_wrap_centered(self):
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)
for dtype in self.dtypes:
images = shared(numpy.arange(
numpy.prod(shape), dtype=dtype
).reshape(shape))
f = function([], images2neibs(images, neib_shape,
mode="wrap_centered"),
mode=self.mode)
self.assertRaises(TypeError, f)
for neib_shape in [(3, 2), (2, 3)]:
neib_shape = T.as_tensor_variable(neib_shape)
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))
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()
f = function([], images2neibs(images, neib_shape, mode="wrap_centered"),
mode=self.mode)
f()
def test_grad_wrap_centered(self):
# It is not implemented for now. So test that we raise an error.
......
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