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提交 33bd55ef authored 作者: abergeron's avatar abergeron 提交者： GitHub
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Merge pull request #21 from lamblin/bool

Fix tests for CAReduceDtype with bool
上级 91333f00 c25f2cc0
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正在显示 包含 100 行增加 和 91 行删除
theano/tensor/elemwise.py
浏览文件 @ 33bd55ef
......@@ -1848,6 +1848,7 @@ class CAReduceDtype(CAReduce):
if dtype is None:
# If input has a discrete dtype, upcast it to 64
return dict(
bool='int64',
int8='int64',
int16='int64',
int32='int64',
......@@ -1863,6 +1864,7 @@ class CAReduceDtype(CAReduce):
acc_dtype = self.acc_dtype
if acc_dtype is None:
return dict(
bool='int64',
int8='int64',
int16='int64',
int32='int64',
......
theano/tensor/tests/test_elemwise.py
浏览文件 @ 33bd55ef
......@@ -113,42 +113,42 @@ class test_reduce_axes(unittest.TestCase):
[numpy.array(0), numpy.array(1)]]
for a in axes:
x = tensor.matrix()
m = x.sum(a)
x.sum(a)
def test_mean_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
for a in axes:
x = tensor.matrix()
m = x.mean(a)
x.mean(a)
def test_max_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
for a in axes:
x = tensor.matrix()
m = x.max(a)
x.max(a)
def test_min_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
for a in axes:
x = tensor.matrix()
m = x.min(a)
x.min(a)
def test_argmax_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
for a in axes:
x = tensor.matrix()
m = x.argmax(a)
x.argmax(a)
def test_var_axes(self):
axes = [None, 0, 1, [0, 1], numpy.array(1),
[numpy.array(0), numpy.array(1)]]
for a in axes:
x = tensor.matrix()
m = x.var(a)
x.var(a)
class test_Broadcast(unittest.TestCase):
......@@ -159,7 +159,7 @@ class test_Broadcast(unittest.TestCase):
ctype = TensorType
cop = Elemwise
openmp_minsize = 2*config.openmp_elemwise_minsize
openmp_minsize = 2 * config.openmp_elemwise_minsize
openmp_minsize_sqrt = int(math.ceil(math.sqrt(openmp_minsize)))
# The order is important if you change them.
......@@ -346,8 +346,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
((5, 0), (1, )),
((5, 0), ()),
((), None),
((), ())
]
((), ())]
type = TensorType
def with_linker(self, linker, scalar_op=scalar.add, dtype="floatX",
......@@ -371,7 +370,7 @@ class test_CAReduce(unittest_tools.InferShapeTester):
f = copy(linker).accept(FunctionGraph([x], [e])).make_function()
xv = numpy.asarray(numpy.random.rand(*xsh))
if not "int" in dtype:
if "int" not in dtype:
xv = numpy.asarray(xv, dtype=dtype)
else:
xv = numpy.asarray(xv < 0.5, dtype=dtype)
......@@ -610,18 +609,20 @@ class test_Prod(unittest.TestCase):
x = theano.tensor.dmatrix()
# sanity check
x2 = theano.tensor.dmatrix()
p = Prod(axis=1)(x)
p2 = Prod(axis=1)(x2)
fn = theano.function([x, x2], [p - p2], mode=self.mode)
# print "hand computed diff for each row"
x2_val = numpy.asarray([[1., 2., 3.003], [0.003, 5., 6], [
0., 0., 9.01]])
# print fn(x_val, x2_val)
fn2 = theano.function([x], [theano.tensor.grad(p.sum(), x)],
mode=self.mode)
# print "real grad"
# print fn2(x_val)
# Uncomment this for debugging if needed
# x2 = theano.tensor.dmatrix()
# p2 = Prod(axis=1)(x2)
# fn = theano.function([x, x2], [p - p2], mode=self.mode)
# print("hand computed diff for each row")
# x2_val = numpy.asarray([[1., 2., 3.003], [0.003, 5., 6], [
# 0., 0., 9.01]])
# print(fn(x_val, x2_val))
# fn2 = theano.function([x], [theano.tensor.grad(p.sum(), x)],
# mode=self.mode)
# print("real grad")
# print(fn2(x_val))
fn3 = theano.function([x], [p], mode=self.mode)
assert numpy.allclose(fn3(x_val), [6., 0., 0.])
......@@ -633,14 +634,14 @@ class test_Prod(unittest.TestCase):
# def fn5(x5):
# return theano.tensor.sqr(Prod(axis=1)(x5))
#x4 = theano.tensor.dmatrix()
#p4 = theano.tensor.sqr(Prod(axis=1)(x4))
#fn4 = theano.function([x4], p4)
# print "with sqr"
# print fn4(x_val)
# print fn4(x2_val)
# x4 = theano.tensor.dmatrix()
# p4 = theano.tensor.sqr(Prod(axis=1)(x4))
# fn4 = theano.function([x4], p4)
# print("with sqr")
# print(fn4(x_val))
# print(fn4(x2_val))
#unittest_tools.verify_grad(fn5, [x_val])
# unittest_tools.verify_grad(fn5, [x_val])
@attr('slow')
def test_prod_no_zeros_in_input(self):
......@@ -691,30 +692,33 @@ class test_Prod(unittest.TestCase):
x = theano.tensor.dmatrix()
pwz_a1 = ProdWithoutZeros(axis=0)(x)
pwz_grad = theano.grad(theano.tensor.sum(pwz_a1), x)
fn_a1 = theano.function([x], pwz_grad, mode=self.mode)
theano.function([x], pwz_grad, mode=self.mode)
@attr('slow')
def test_other_grad_tests(self):
x = theano.tensor.dmatrix()
x_val1 = numpy.array([[1, 2, 3], [0, 5, 6], [0, 0, 9]],
dtype='float32')
dtype='float32')
x_val2 = numpy.array([[1, 2, 0], [0, 5, 6], [7, 8, 9], [9, 10, 0]],
dtype='float32')
dtype='float32')
rng = rng = numpy.random.RandomState(43)
p = Prod(axis=1)
grad_p = theano.tensor.grad(p(x).sum(), x)
grad_fn = theano.function([x], grad_p, mode=self.mode)
assert numpy.allclose(grad_fn(x_val1), [[6., 3., 2.], [30., 0.,
0.], [0., 0., 0.]])
assert numpy.allclose(grad_fn(x_val2), [[0., 0., 2.], [30.,
0., 0.], [72., 63., 56.], [0., 0., 90.]])
assert numpy.allclose(
grad_fn(x_val1),
[[6., 3., 2.], [30., 0., 0.], [0., 0., 0.]])
assert numpy.allclose(
grad_fn(x_val2),
[[0., 0., 2.], [30., 0., 0.], [72., 63., 56.], [0., 0., 90.]])
p_axis0 = Prod(axis=0)
grad_p_axis0 = theano.tensor.grad(p_axis0(x).sum(), x)
grad_fn_axis0 = theano.function([x], grad_p_axis0, mode=self.mode)
assert numpy.allclose(grad_fn_axis0(x_val2), [[0., 400.,
0.], [63., 160., 0.], [0., 100., 0.], [0., 80., 0.]])
assert numpy.allclose(
grad_fn_axis0(x_val2),
[[0., 400., 0.], [63., 160., 0.], [0., 100., 0.], [0., 80., 0.]])
tensor.verify_grad(p, [x_val1], rng=rng, mode=self.mode)
......@@ -764,7 +768,7 @@ class test_IsInf_IsNan(unittest.TestCase):
numpy_isfunc = getattr(numpy, isfunc)
for x in self.test_vals:
if ((x.ndim == 0 and input is not self.scalar) or
(x.ndim == 1 and input is not self.vector)):
(x.ndim == 1 and input is not self.vector)):
# We only test with the appropriate input type.
continue
t_out = theano_isfunc(x)
......@@ -795,6 +799,7 @@ class T_reduce_dtype(unittest.TestCase):
x = tensor.matrix(dtype=dtype)
s = getattr(x, method)(axis=axis)
assert s.dtype == dict(
bool='int64',
int8='int64',
int16='int64',
int32='int64',
......@@ -820,6 +825,7 @@ class T_reduce_dtype(unittest.TestCase):
x = tensor.matrix(dtype=dtype)
s = getattr(x, method)(axis=axis)
assert s.owner.op.acc_dtype == dict(
bool='int64',
int8='int64',
int16='int64',
int32='int64',
......@@ -848,11 +854,11 @@ class T_reduce_dtype(unittest.TestCase):
for input_dtype in self.dtypes:
x = tensor.matrix(dtype=input_dtype)
for output_dtype in self.dtypes:
# If the output is a complex, the gradient of the reduce will
# cast the complex to the input dtype. We can't call the normal
# cast on a complex to a not complex as this is ambiguous.
# If the output is a complex, the gradient of the reduce will
# cast the complex to the input dtype. We can't call the normal
# cast on a complex to a not complex as this is ambiguous.
if (not input_dtype.startswith('complex') and
output_dtype.startswith('complex')):
output_dtype.startswith('complex')):
continue
axis = self.axes[idx % len(self.axes)]
......@@ -861,8 +867,8 @@ class T_reduce_dtype(unittest.TestCase):
f = theano.function([x], var, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo,
dtype)
assert [n for n in topo if isinstance(n.op, self.op)], \
(topo, output_dtype)
data = numpy.random.rand(3, 4) * 10
data = data.astype(input_dtype)
f(data)
......@@ -882,21 +888,20 @@ class T_reduce_dtype(unittest.TestCase):
for input_dtype in self.dtypes:
x = tensor.matrix(dtype=input_dtype)
for acc_dtype in self.dtypes:
# If the accumulator is a complex, the gradient of the reduce will
# cast the complex to the input dtype. We can't call the normal
# cast on a complex to a not complex as this is ambiguous.
# If the accumulator is a complex, the gradient of the reduce will
# cast the complex to the input dtype. We can't call the normal
# cast on a complex to a not complex as this is ambiguous.
if (not input_dtype.startswith('complex') and
acc_dtype.startswith('complex')):
acc_dtype.startswith('complex')):
continue
axis = self.axes[idx % len(self.axes)]
# If output_dtype would force a downcast, we expect a TypeError
# We always allow int/uint inputs with float/complex outputs.
# If output_dtype would force a downcast, we expect a TypeError
# We always allow int/uint inputs with float/complex outputs.
upcasted_dtype = scalar.upcast(input_dtype, acc_dtype)
if (acc_dtype == upcasted_dtype or
(input_dtype in tensor.discrete_dtypes and
acc_dtype in tensor.continuous_dtypes)
):
acc_dtype in tensor.continuous_dtypes)):
var = getattr(x, method)(acc_dtype=acc_dtype,
axis=axis)
assert var.owner.op.acc_dtype == acc_dtype
......@@ -921,8 +926,7 @@ class T_reduce_dtype(unittest.TestCase):
s = getattr(x, method)()
f = theano.function([], s, mode=self.mode)
topo = f.maker.fgraph.toposort()
assert [n for n in topo if isinstance(n.op, self.op)], (topo,
dtype)
assert [n for n in topo if isinstance(n.op, self.op)], topo
s_val = f()
# Use extra precision in NumPy to compute the good answer.
ret = getattr(numpy.asarray([1e8, 1, -1e8], dtype='float64'),
......@@ -976,7 +980,7 @@ class T_mean_dtype(unittest.TestCase):
(mean_var.dtype, sum_dtype))
if (('complex' in input_dtype or
'complex' in sum_dtype) and
input_dtype != sum_dtype):
input_dtype != sum_dtype):
continue
f = theano.function([x], mean_var)
data = numpy.random.rand(3, 4) * 10
......@@ -1017,6 +1021,7 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
axis = axes[idx % len(axes)]
x = ProdWithoutZeros(axis=axis)(tensor.matrix(dtype=dtype))
assert x.dtype == dict(
bool='int64',
int8='int64',
int16='int64',
int32='int64',
......@@ -1035,16 +1040,17 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
x = tensor.matrix(dtype=dtype)
p = ProdWithoutZeros(axis=axis)(x)
assert p.owner.op.acc_dtype == dict(
int8='int64',
int16='int64',
int32='int64',
uint8='uint64',
uint16='uint64',
uint32='uint64',
float16='float32',
float32='float64',
complex64='complex128'
).get(dtype, dtype)
bool='int64',
int8='int64',
int16='int64',
int32='int64',
uint8='uint64',
uint16='uint64',
uint32='uint64',
float16='float32',
float32='float64',
complex64='complex128'
).get(dtype, dtype)
if 'complex' in dtype:
continue
......@@ -1065,11 +1071,11 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
for output_dtype in imap(str, theano.scalar.all_types):
axis = axes[idx % len(axes)]
prod_woz_var = ProdWithoutZeros(
axis=axis, dtype=output_dtype)(x)
axis=axis, dtype=output_dtype)(x)
assert prod_woz_var.dtype == output_dtype
idx += 1
if ('complex' in output_dtype or
'complex' in input_dtype):
'complex' in input_dtype):
continue
f = theano.function([x], prod_woz_var)
data = numpy.random.rand(2, 3) * 3
......@@ -1092,23 +1098,23 @@ class T_prod_without_zeros_dtype(unittest.TestCase):
upcasted_dtype = scalar.upcast(input_dtype, acc_dtype)
if (acc_dtype == upcasted_dtype or
(input_dtype in tensor.discrete_dtypes and
acc_dtype in tensor.continuous_dtypes)
):
acc_dtype in tensor.continuous_dtypes)):
prod_woz_var = ProdWithoutZeros(
axis=axis, acc_dtype=acc_dtype)(x)
axis=axis, acc_dtype=acc_dtype)(x)
assert prod_woz_var.owner.op.acc_dtype == acc_dtype
if (acc_dtype.startswith('complex') and
input_dtype != acc_dtype):
input_dtype != acc_dtype):
continue
f = theano.function([x], prod_woz_var)
data = numpy.random.rand(2, 3) * 3
data = data.astype(input_dtype)
f(data)
else:
self.assertRaises(TypeError,
ProdWithoutZeros(axis=axis, acc_dtype=acc_dtype),
x)
self.assertRaises(
TypeError,
ProdWithoutZeros(axis=axis, acc_dtype=acc_dtype),
x)
idx += 1
......@@ -1148,24 +1154,26 @@ class TestElemwise(unittest_tools.InferShapeTester):
def test_infer_shape(self):
for s_left, s_right in [((5, 6), (5, 6)),
((5, 6), (5, 1)),
((5, 6), (1, 6)),
((5, 1), (5, 6)),
((1, 6), (5, 6)),
((2, 3, 4, 5), (2, 3, 4, 5)),
((2, 3, 4, 5), (2, 3, 1, 5)),
((2, 3, 4, 5), (1, 3, 4, 5)),
((2, 1, 4, 5), (2, 3, 4, 5)),
((2, 3, 4, 1), (2, 3, 4, 5))]:
for s_left, s_right in [
((5, 6), (5, 6)),
((5, 6), (5, 1)),
((5, 6), (1, 6)),
((5, 1), (5, 6)),
((1, 6), (5, 6)),
((2, 3, 4, 5), (2, 3, 4, 5)),
((2, 3, 4, 5), (2, 3, 1, 5)),
((2, 3, 4, 5), (1, 3, 4, 5)),
((2, 1, 4, 5), (2, 3, 4, 5)),
((2, 3, 4, 1), (2, 3, 4, 5))]:
dtype = theano.config.floatX
t_left = TensorType(dtype, [(entry == 1) for entry in s_left])()
t_right = TensorType(dtype, [(entry == 1) for entry in s_right])()
t_left_val = numpy.zeros(s_left, dtype=dtype)
t_right_val = numpy.zeros(s_right, dtype=dtype)
self._compile_and_check([t_left, t_right],
[Elemwise(scalar.add)(t_left, t_right)],
[t_left_val, t_right_val], Elemwise)
self._compile_and_check(
[t_left, t_right],
[Elemwise(scalar.add)(t_left, t_right)],
[t_left_val, t_right_val], Elemwise)
def test_input_dimensions_overflow(self):
# Elemwise.perform used to compute the product
......@@ -1174,7 +1182,7 @@ class TestElemwise(unittest_tools.InferShapeTester):
a, b, c, d, e, f = tensor.vectors('abcdef')
s = a + b + c + d + e + f
g = theano.function([a, b, c, d, e, f], s,
mode=theano.compile.Mode(linker='py'))
mode=theano.compile.Mode(linker='py'))
g(*[numpy.zeros(2 ** 11, config.floatX) for i in xrange(6)])
......@@ -1192,7 +1200,7 @@ def test_gt_grad():
input_ = T.vector(dtype=floatX)
random_values = numpy.random.RandomState(1234).uniform(
low=-1, high=1, size=(2, 2))
low=-1, high=1, size=(2, 2))
W_values = numpy.asarray(random_values, dtype=floatX)
W = theano.shared(value=W_values, name='weights')
correct_score = T.dot(input_, W)
......@@ -1223,7 +1231,7 @@ def test_clip_grad():
# use an x value less than y, an x value between y and z, and an x value
# greater than z
unittest_tools.verify_grad(func,
[numpy.asarray([-1., 0.5, 2.]), 0., 1.])
[numpy.asarray([-1., 0.5, 2.]), 0., 1.])
def test_clip_grad_int():
......
theano/tests/test_flake8.py
浏览文件 @ 33bd55ef
......@@ -56,7 +56,6 @@ whitelist_flake8 = [
"tensor/tests/test_opt.py",
"tensor/tests/test_basic.py",
"tensor/tests/test_blas.py",
"tensor/tests/test_elemwise.py",
"tensor/tests/test_merge.py",
"tensor/tests/test_gc.py",
"tensor/tests/test_complex.py",
......
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