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提交 23adf69e authored 作者: Frederic's avatar Frederic
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theano/tensor/tests/test_opt.py
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......@@ -1495,11 +1495,11 @@ def test_log1p():
f = function([x], T.log(1 + (x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
f = function([x], T.log(1 + (-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [T.neg,
inplace.log1p_inplace]
assert [node.op for node in f.maker.fgraph.toposort()] == [
T.neg, inplace.log1p_inplace]
f = function([x], -T.log(1 + (-x)), mode=m)
assert [node.op for node in f.maker.fgraph.toposort()] == [T.neg,
inplace.log1p_inplace, inplace.neg_inplace]
assert [node.op for node in f.maker.fgraph.toposort()] == [
T.neg, inplace.log1p_inplace, inplace.neg_inplace]
# check trickier cases (and use different dtype)
y = fmatrix()
......@@ -1507,12 +1507,12 @@ def test_log1p():
print f.maker.fgraph.toposort()
# the first three ops are Shape_i, Shape_i, and Dimshuffle
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()][3:] \
== [T.log1p, tensor.alloc]
assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
T.log1p, tensor.alloc]
f = function([x, y], T.log(0 + (x) + tensor.fill(y, 1.0)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()][3:] \
== [T.log1p, tensor.alloc]
assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
T.log1p, tensor.alloc]
f = function([x, y], T.log(2 + (x) - tensor.fill(y, 1.0)), mode=m)
theano.printing.debugprint(f)
assert [node.op for node in f.maker.fgraph.toposort()][3:] \
......@@ -1611,7 +1611,7 @@ def test_local_useless_subtensor():
prog = f.maker.fgraph.toposort()
if res:
assert isinstance(prog[0].op, theano.tensor.basic.
SpecifyShape), dims
SpecifyShape), dims
assert prog[1].op == tensor.exp, dims
assert len(prog) == 2, dims
else:
......@@ -1628,7 +1628,7 @@ def test_local_useless_subtensor():
((slice(0, x.shape[1]), slice(0, x.shape[1]), ), False),
((slice(0, x.shape[1]), 2), False),
((slice(0, x.shape[1]), slice(x.shape[0] - x.shape[0],
x.shape[1]),), False),
x.shape[1]),), False),
((slice(0, T.scalar_from_tensor(x.shape[0])), ), True),
]):
f = function([x], tensor.exp(x).__getitem__(dims), mode=mode_opt)
......@@ -1710,7 +1710,7 @@ class test_local_subtensor_lift(unittest.TestCase):
assert isinstance(prog[1].op, tensor.Subtensor) # first subtensor
assert isinstance(prog[2].op, tensor.Subtensor) # first subtensor
assert isinstance(prog[3].op.scalar_op, theano.scalar.
Composite) # Composite{add,add}
Composite) # Composite{add,add}
assert len(prog) == 4
f([[0, 1], [2, 3]], 4, [[4, 5], [6, 7]])
# let debugmode test something
......@@ -1727,7 +1727,7 @@ class test_local_subtensor_lift(unittest.TestCase):
assert isinstance(prog[1].op, tensor.Subtensor) # first subtensor
assert isinstance(prog[2].op, tensor.Subtensor) # first subtensor
assert isinstance(prog[3].op.scalar_op, theano.scalar.
Composite) # Composite{add,add}
Composite) # Composite{add,add}
assert len(prog) == 4
f([[0, 1], [2, 3]], 4, [[4, 5], [6, 7]])
# let debugmode test something
......@@ -1767,12 +1767,12 @@ class test_local_subtensor_lift(unittest.TestCase):
x = tensor.matrix('x')
y = tensor.vector('y')
f = function([x, y], [tensor.exp(x + y)[0], tensor.exp(x + y) + x],
mode=mode_opt)
mode=mode_opt)
prog = f.maker.fgraph.toposort()
assert isinstance(prog[0].op, tensor.DimShuffle)
assert isinstance(prog[1].op.scalar_op, theano.scalar.
Composite) # Composite{add,exp}
Composite) # Composite{add,exp}
assert prog[2].op == tensor.add
assert isinstance(prog[3].op, tensor.Subtensor) # first subtensor
assert len(prog) == 4
......@@ -2039,10 +2039,10 @@ class test_local_subtensor_merge(unittest.TestCase):
# Some cases of merge: shape, (start, stop, step) of first,
# (start, stop, step) of second subtensor
cases = [
((2, 3), (None, None, None), (None, None, -1)),
((12, 1), (None, None, -4), (None, None, 1)),
((5, 3), (1, 4, 2), (None, None, -1)),
]
((2, 3), (None, None, None), (None, None, -1)),
((12, 1), (None, None, -4), (None, None, 1)),
((5, 3), (1, 4, 2), (None, None, -1)),
]
x = tensor.matrix('x')
for shape, sl1, sl2 in cases:
......@@ -2063,13 +2063,13 @@ class test_local_subtensor_merge(unittest.TestCase):
e2 = tensor.iscalar('e2')
s2 = tensor.iscalar('s2')
f = function([x, b1, e1, s1, b2, e2, s2], x[b1:e1:s1][b2:e2:s2],
mode=mode_opt)
mode=mode_opt)
#theano.printing.debugprint(f, print_type=True)
topo = f.maker.fgraph.toposort()
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.
Subtensor)]) == 1
Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, DeepCopyOp)
......@@ -2079,9 +2079,9 @@ class test_local_subtensor_merge(unittest.TestCase):
e2r = self.rng.permutation(range(-8, 8))[:2]
s1r = self.rng.permutation([-7, -6, -5, -4, -3, -2, -1, 1,
2, 3, 4, 5, 6, 7])[:2]
2, 3, 4, 5, 6, 7])[:2]
s2r = self.rng.permutation([-7, -6, -5, -4, -3, -2, -1, 1,
2, 3, 4, 5, 6, 7])[:2]
2, 3, 4, 5, 6, 7])[:2]
for x_s in self.x_shapes:
x_val = self.rng.uniform(size=x_s).astype(config.floatX)
......@@ -2152,7 +2152,7 @@ class test_local_subtensor_merge(unittest.TestCase):
topo = f.maker.fgraph.toposort()
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op, tensor.
Subtensor)]) == 1
Subtensor)]) == 1
#print topo[-1].op
assert isinstance(topo[-1].op, DeepCopyOp)
......@@ -2178,7 +2178,7 @@ class test_local_subtensor_merge(unittest.TestCase):
except IndexError:
n_index_err += 1
self.assertRaises(IndexError,
f, x_val, b_v, e_v, s_v, i_v)
f, x_val, b_v, e_v, s_v, i_v)
else:
# Executed if the "try" clause did not raise
# any exception
......@@ -2241,7 +2241,7 @@ class test_local_subtensor_merge(unittest.TestCase):
topo = f.maker.fgraph.toposort()
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op,
tensor.Subtensor)]) <= 1
tensor.Subtensor)]) <= 1
assert isinstance(topo[-1].op, DeepCopyOp)
for x_s in self.x_shapes:
......@@ -2298,7 +2298,7 @@ class test_local_subtensor_merge(unittest.TestCase):
topo = f.maker.fgraph.toposort()
#print [t for t in topo if isinstance(t.op, tensor.Subtensor)]
assert len([t for t in topo if isinstance(t.op,
tensor.Subtensor)]) <= 1
tensor.Subtensor)]) <= 1
assert isinstance(topo[-1].op, DeepCopyOp)
for x_s in self.x_shapes:
......@@ -2333,7 +2333,8 @@ class Test_alloc_zero(unittest.TestCase):
def setUp(self):
mode = theano.compile.mode.get_default_mode()
self.mode = mode.including("local_incsubtensor_of_allocs",
"local_setsubtensor_of_allocs", "local_0_dot_x")
"local_setsubtensor_of_allocs",
"local_0_dot_x")
def test_setsubtensor_allocs0(self):
x = tensor.matrix()
......@@ -2343,32 +2344,32 @@ class Test_alloc_zero(unittest.TestCase):
z = tensor.set_subtensor(x0[:4], y0)
f = theano.function([x, y], z, mode=self.mode)
assert numpy.all([not isinstance(x.op, tensor.IncSubtensor) for x in
f.maker.fgraph.toposort()])
f.maker.fgraph.toposort()])
def test_setsubtensor_allocs1(self):
y = tensor.matrix()
x0 = tensor.constant(numpy.asarray(numpy.zeros((4, 4)),
dtype=config.floatX))
dtype=config.floatX))
y0 = tensor.zeros_like(y)
z = tensor.set_subtensor(x0[:4], y0)
f = theano.function([y], z, mode=self.mode)
assert numpy.all([not isinstance(x.op, tensor.IncSubtensor) for x in
f.maker.fgraph.toposort()])
f.maker.fgraph.toposort()])
def test_setsubtensor_allocs1t(self):
y = tensor.matrix()
x0 = tensor.constant(numpy.asarray(numpy.zeros((4, 4)),
dtype=config.floatX))
dtype=config.floatX))
y0 = tensor.zeros_like(y)
z = tensor.set_subtensor(x0[:4], y0.T)
f = theano.function([y], z, mode=mode_opt)
assert numpy.all([not isinstance(x.op, tensor.IncSubtensor) for x in
f.maker.fgraph.toposort()])
f.maker.fgraph.toposort()])
def test_setsubtensor_allocs2(self):
x = tensor.matrix()
y0 = tensor.constant(numpy.asarray(numpy.zeros_like((4, 4)),
dtype=config.floatX))
dtype=config.floatX))
x0 = tensor.zeros_like(x)
z = tensor.set_subtensor(x0[:4], y0)
f = theano.function([x], z, mode=self.mode)
......@@ -2382,7 +2383,7 @@ class Test_alloc_zero(unittest.TestCase):
z = tensor.inc_subtensor(x[:4], y0)
f = theano.function([x, y], z, mode=self.mode)
assert numpy.all([not isinstance(x.op, tensor.IncSubtensor) for x in
f.maker.fgraph.toposort()])
f.maker.fgraph.toposort()])
def test_incsubtensor_allocs0t(self):
x = tensor.matrix()
......@@ -2391,12 +2392,12 @@ class Test_alloc_zero(unittest.TestCase):
z = tensor.inc_subtensor(x[:4], y0.T)
f = theano.function([x, y], z, mode=mode_opt)
assert numpy.all([not isinstance(x.op, tensor.IncSubtensor) for x in
f.maker.fgraph.toposort()])
f.maker.fgraph.toposort()])
def test_incsubtensor_allocs1(self):
x = tensor.matrix()
y0 = tensor.constant(numpy.asarray(numpy.zeros_like((4, 4)),
dtype=config.floatX))
dtype=config.floatX))
z = tensor.inc_subtensor(x[:4], y0)
f = theano.function([x], z, mode=self.mode)
assert numpy.all([not isinstance(x.op, tensor.IncSubtensor) for x in
......@@ -2427,7 +2428,7 @@ class Test_alloc_zero(unittest.TestCase):
f(_e1[1], _e2[1])
f(_e1[2], _e2[2])
assert numpy.all([not isinstance(x.op, tensor.Dot) for x in
f.maker.fgraph.toposort() ])
f.maker.fgraph.toposort()])
#test that we don't remove shape errors
self.assertRaises((ValueError, AssertionError), f,
......@@ -2475,7 +2476,7 @@ def test_local_subtensor_of_alloc():
(slice(1, 3), slice(None, -1)),
(slice(None, None, 2)),
(slice(1, None, 2)),
]
]
for slices in slicess:
z = yx.__getitem__(slices)
f = theano.function([x], z)
......@@ -2809,8 +2810,8 @@ class test_assert(utt.InferShapeTester):
x = T.scalar()
y = T.scalar()
f = theano.function([x, y], theano.tensor.opt.assert_(x, y,
1), mode=mode)
f = theano.function([x, y], theano.tensor.opt.assert_(x, y, 1),
mode=mode)
assert f(1, 1) == 1
assert f(5, 1) == 5
topo = f.maker.fgraph.toposort()
......@@ -2827,8 +2828,8 @@ class test_assert(utt.InferShapeTester):
x = T.scalar()
y = T.scalar()
f = theano.function([x, y], theano.tensor.opt.assert_(x, y,
0), mode=mode)
f = theano.function([x, y], theano.tensor.opt.assert_(x, y, 0),
mode=mode)
self.assertRaises(AssertionError, f, 1, 0)
topo = f.maker.fgraph.toposort()
assert len(topo) == 2
......@@ -2843,14 +2844,14 @@ class test_assert(utt.InferShapeTester):
bdscal_val = numpy.random.rand() + 1
out = theano.tensor.opt.assert_(adscal, bdscal)
self._compile_and_check([adscal, bdscal], [out],
[adscal_val, bdscal_val], Assert)
[adscal_val, bdscal_val], Assert)
admat = dmatrix()
admat_val = numpy.random.rand(3, 4)
adscal_val += 1
out = theano.tensor.opt.assert_(admat, adscal, bdscal)
self._compile_and_check([admat, adscal, bdscal], [out],
[admat_val, adscal_val, bdscal_val], Assert)
[admat_val, adscal_val, bdscal_val], Assert)
def test_local_mul_specialize():
......@@ -3177,8 +3178,9 @@ def test_constant_get_stabilized():
class T_local_switch_sink(unittest.TestCase):
def setUp(self):
# condition values
self.condm = numpy.asarray([[0.1, 0, 1, -1], [0., 0., 0.,
0.], [1, 1, 1, 1]])
self.condm = numpy.asarray([[0.1, 0, 1, -1],
[0., 0., 0., 0.],
[1, 1, 1, 1]])
self.condv = numpy.asarray([0.1, 0, 1, -1])
self.conds = [0.1, 0, 1, -1]
......@@ -3256,14 +3258,14 @@ class T_local_erf(unittest.TestCase):
f = theano.function([x], 1 + T.erf(x), mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.mul, T.
erfc], f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [
T.mul, T.erfc], f.maker.fgraph.toposort()
f(val)
f = theano.function([x], T.erf(x) + 1, mode=self.mode)
print f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [T.mul, T.
erfc], f.maker.fgraph.toposort()
assert [n.op for n in f.maker.fgraph.toposort()] == [
T.mul, T.erfc], f.maker.fgraph.toposort()
f(val)
f = theano.function([x], T.erf(x) + 2, mode=self.mode)
......@@ -3277,7 +3279,7 @@ class T_local_erf(unittest.TestCase):
def test_local_one_minus_erf(self):
val = numpy.asarray([-30, -3, -2, -1, 0, 1, 2, 3, 30],
dtype=config.floatX)
dtype=config.floatX)
x = T.vector()
f = theano.function([x], 1 - T.erf(x), mode=self.mode)
......@@ -3305,7 +3307,7 @@ class T_local_erf(unittest.TestCase):
assert topo[0].op == T.erf, f.maker.fgraph.toposort()
assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
assert isinstance(topo[1].op.scalar_op, scal.Add)\
or isinstance(topo[1].op.scalar_op,scal.Sub), f.maker.fgraph.toposort()
or isinstance(topo[1].op.scalar_op, scal.Sub), f.maker.fgraph.toposort()
print f(val)
def test_local_erf_minus_one(self):
......@@ -3342,10 +3344,11 @@ class T_local_erf(unittest.TestCase):
class T_local_erfc(unittest.TestCase):
def setUp(self):
self.mode_fusion = theano.compile.mode.get_default_mode().including(
'canonicalize').including('fast_run').excluding('gpu')
'canonicalize').including('fast_run').excluding('gpu')
self.mode = self.mode_fusion.excluding('fusion')
self.mode._optimizer.position_cutoff = 1.50001
if theano.config.cxx == '' and not theano.scalar.basic_scipy.imported_scipy_special:
if (theano.config.cxx == '' and
not theano.scalar.basic_scipy.imported_scipy_special):
raise SkipTest("erfc need a c++ compiler or scipy")
def test_local_one_minus_erfc(self):
......
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