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pytensor
提交 c7ba692d authored 作者: Pascal Lamblin's avatar Pascal Lamblin
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Make tests more robust to random samples

上级 99e32f41
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正在显示 包含 63 行增加 和 38 行删除
theano/tensor/tests/test_basic.py
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......@@ -73,8 +73,34 @@ if config.mode == "FAST_COMPILE":
else:
mode_opt = get_default_mode()
### seed random number generator so that unittests are deterministic ###
# Use a seeded random number generator so that unittests are deterministic
utt.seed_rng()
test_rng = numpy.random.RandomState(seed=utt.fetch_seed())
# A helpful class to check random values close to the boundaries
# when designing new tests
class MockRandomState:
def __init__(self, val):
self.val = val
def rand(self, *shape):
return numpy.zeros(shape, dtype='float64') + self.val
def randint(self, minval, maxval=None, size=1):
if maxval is None:
minval, maxval = 0, minval
out = numpy.zeros(size, dtype='int64')
if self.val == 0:
return out + minval
else:
return out + maxval - 1
# Examples of use:
# test_rng = MockRandomState(0)
# test_rng = MockRandomState(0.99999982)
# test_rng = MockRandomState(1)
if PY3:
def L(i):
......@@ -83,6 +109,7 @@ else:
def L(i):
return long(i)
def inplace_func(inputs, outputs, mode=None, allow_input_downcast=False,
on_unused_input='raise', name=None):
if mode is None:
......@@ -546,14 +573,14 @@ def makeTester(name, op, expected, checks=None, good=None, bad_build=None,
def rand(*shape):
r = numpy.random.rand(*shape) * 2 - 1
r = test_rng.rand(*shape) * 2 - 1
return numpy.asarray(r, dtype=config.floatX)
def rand_nonzero(shape, eps=3e-4):
"""Like rand, but the absolute value has to be at least eps"""
# covers [0, 1)
r = numpy.asarray(numpy.random.rand(*shape), dtype=config.floatX)
r = numpy.asarray(test_rng.rand(*shape), dtype=config.floatX)
# covers [0, (1 - eps) / 2) U [(1 + eps) / 2, 1)
r = r * (1 - eps) + eps * (r >= 0.5)
# covers [-1, -eps) U [eps, 1)
......@@ -562,17 +589,20 @@ def rand_nonzero(shape, eps=3e-4):
def randint(*shape):
return numpy.random.randint(-5, 6, shape)
return test_rng.randint(-5, 6, shape)
def randuint32(*shape):
return numpy.array(numpy.random.randint(5, size=shape), dtype=numpy.uint32)
return numpy.array(test_rng.randint(5, size=shape), dtype=numpy.uint32)
def randuint16(*shape):
return numpy.array(numpy.random.randint(5, size=shape), dtype=numpy.uint16)
return numpy.array(test_rng.randint(5, size=shape), dtype=numpy.uint16)
# XXX: this so-called complex random array as all-zero imaginary parts
def randcomplex(*shape):
r = numpy.asarray(numpy.random.rand(*shape), dtype=config.floatX)
r = numpy.asarray(test_rng.rand(*shape), dtype=config.floatX)
return numpy.complex128(2 * r - 1)
......@@ -581,21 +611,21 @@ def randcomplex_nonzero(shape, eps=1e-4):
def randint_nonzero(*shape):
r = numpy.random.randint(-5, 5, shape)
r = test_rng.randint(-5, 5, shape)
return r + (r == 0) * 5
def rand_ranged(min, max, shape):
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min,
return numpy.asarray(test_rng.rand(*shape) * (max - min) + min,
dtype=config.floatX)
def randint_ranged(min, max, shape):
return numpy.random.randint(min, max+1, shape)
return test_rng.randint(min, max+1, shape)
def randc128_ranged(min, max, shape):
return numpy.asarray(numpy.random.rand(*shape) * (max - min) + min,
return numpy.asarray(test_rng.rand(*shape) * (max - min) + min,
dtype='complex128')
......@@ -609,6 +639,8 @@ def rand_of_dtype(shape, dtype):
else:
raise TypeError()
# Used to exclude random numbers too close to certain values
_eps = 1e-2
def makeBroadcastTester(op, expected, checks=None, name=None, **kwargs):
if checks is None:
......@@ -1144,6 +1176,11 @@ _grad_broadcast_unary_normal = dict(
# empty = [numpy.asarray([])] # XXX: should this be included?
)
# Avoid epsilon around integer values
_grad_broadcast_unary_normal_noint = dict(
normal=[(rand_ranged(_eps, 1 - _eps, (2, 3)) + randint(2, 3))
.astype(floatX)])
_grad_broadcast_unary_normal_small_neg_range = dict(
normal=[numpy.asarray(rand_ranged(-2, 5, (2, 3)), dtype=floatX)],
corner_case=[corner_case_grad])
......@@ -1153,12 +1190,12 @@ _grad_broadcast_unary_normal_no_complex_no_corner_case = copymod(
without=['corner_case'])
_grad_broadcast_unary_abs1_no_complex = dict(
normal=[numpy.asarray(rand_ranged(-1, 1, (2, 3)), dtype=floatX)],
normal=[numpy.asarray(rand_ranged(-1 + _eps, 1 - _eps, (2, 3)), dtype=floatX)],
)
_grad_broadcast_unary_0_2_no_complex = dict(
# Don't go too close to 2 for tests in float32
normal=[numpy.asarray(rand_ranged(0, 1.9, (2, 3)), dtype=floatX)],
# Don't go too close to 0 or 2 for tests in float32
normal=[numpy.asarray(rand_ranged(_eps, 1 - _eps, (2, 3)), dtype=floatX)],
)
# inplace ops when the input is integer and the output is float*
......@@ -1222,9 +1259,7 @@ IntDivInplaceTester = makeBroadcastTester(
CeilTester = makeBroadcastTester(op=tensor.ceil,
expected=upcast_float16_ufunc(numpy.ceil),
good=_good_broadcast_unary_normal_no_complex,
grad=copymod(_grad_broadcast_unary_normal,
without=['corner_case'],
# corner_case includes ints where ceil is not differentiable
grad=copymod(_grad_broadcast_unary_normal_noint,
extra=[numpy.asarray([-2.5, -1.5, -1.51, 0.49, .98, 1.02],
dtype=floatX)]))
......@@ -1233,9 +1268,7 @@ CeilInplaceTester = makeBroadcastTester(op=inplace.ceil_inplace,
good=_good_broadcast_unary_normal_no_complex,
# corner cases includes a lot of integers: points where Ceil is not
# continuous (not differentiable)
grad=copymod(_grad_broadcast_unary_normal,
without=['corner_case'],
# corner_case includes ints where ceil is not differentiable
grad=copymod(_grad_broadcast_unary_normal_noint,
extra=[numpy.asarray([-2.5, -1.5, -1.51, 0.49, .98, 1.02],
dtype=floatX)]),
inplace=True)
......@@ -1243,16 +1276,12 @@ CeilInplaceTester = makeBroadcastTester(op=inplace.ceil_inplace,
FloorTester = makeBroadcastTester(op=tensor.floor,
expected=upcast_float16_ufunc(numpy.floor),
good=_good_broadcast_unary_normal_no_complex,
# XXX: why does grad of floor not give huge values at
# the integer points in the 'corner_case' in
# _grad_broadcast_unary_normal? It seems this test should fail,
# yet it does not...
grad=_grad_broadcast_unary_normal)
grad=_grad_broadcast_unary_normal_noint)
FloorInplaceTester = makeBroadcastTester(op=inplace.floor_inplace,
expected=upcast_float16_ufunc(numpy.floor),
good=_good_broadcast_unary_normal_no_complex,
grad=_grad_broadcast_unary_normal,
grad=_grad_broadcast_unary_normal_noint,
inplace=True)
TruncInplaceTester = makeBroadcastTester(
......@@ -1362,7 +1391,7 @@ _good_broadcast_unary_positive_float = copymod(
_good_broadcast_unary_positive,
without=['integers', 'uint8'])
_grad_broadcast_unary_positive = dict(normal=(rand_ranged(0.001, 5, (2, 3)),),)
_grad_broadcast_unary_positive = dict(normal=(rand_ranged(_eps, 5, (2, 3)),),)
LogTester = makeBroadcastTester(op=tensor.log,
expected=upcast_float16_ufunc(numpy.log),
......@@ -1629,7 +1658,7 @@ _good_broadcast_unary_arccosh = dict(
uint8=[numpy.arange(1, 256, dtype='uint8')],
complex=(randc128_ranged(1, 1000, (2, 3)),),
empty=(numpy.asarray([], dtype=config.floatX),),)
_grad_broadcast_unary_arccosh = dict(normal=(rand_ranged(1, 1000, (2, 3)),),)
_grad_broadcast_unary_arccosh = dict(normal=(rand_ranged(1 + _eps, 1000, (2, 3)),),)
ArccoshTester = makeBroadcastTester(
op=tensor.arccosh,
......@@ -1681,7 +1710,6 @@ TanhInplaceTester = makeBroadcastTester(
grad=_grad_broadcast_unary_normal,
inplace=True)
_eps = 1e-2
_good_broadcast_unary_arctanh = dict(
normal=(rand_ranged(-1 + _eps, 1 - _eps, (2, 3)),),
integers=(randint_ranged(-1 + _eps, 1 - _eps, (2, 3)),),
......@@ -4768,9 +4796,6 @@ class T_mean(unittest.TestCase):
class test_matinv(unittest.TestCase):
def setUp(self):
utt.seed_rng()
def mat_reciprocal(self, dim):
# symbolic program
# broadcastable=[False,False] means that the shape of matrix is two dimensional,
......@@ -4778,7 +4803,7 @@ class test_matinv(unittest.TestCase):
# Note that TensorType's constructor does not actually allocate any memory.
# TODO: Make TensorType syntax more explicit, and maybe give shape or number of dimensions.
utt.seed_rng()
rng = numpy.random.RandomState(seed=utt.fetch_seed())
a, b = matrices('ab')
ab = a * b
......@@ -4795,8 +4820,8 @@ class test_matinv(unittest.TestCase):
fn = inplace_func([a, b], [ssdiff, g_b])
# use the function
x = rand(dim, dim) + 0.1 # Initialized s.t. x is not too tiny
w = rand(dim, dim)
x = rng.rand(dim, dim) + 0.1 # Initialized s.t. x is not too tiny
w = rng.rand(dim, dim)
x = numpy.asarray(x, dtype=config.floatX)
w = numpy.asarray(w, dtype=config.floatX)
......@@ -4813,10 +4838,10 @@ class test_matinv(unittest.TestCase):
"""Matrix reciprocal by gradient descent"""
ssd0, ssd = self.mat_reciprocal(3)
utt.seed_rng()
rng = numpy.random.RandomState(seed=utt.fetch_seed())
# hand-coded numpy implementation for verification
x = rand(3, 3) + 0.1
w = rand(3, 3)
x = rng.rand(3, 3) + 0.1
w = rng.rand(3, 3)
x = numpy.asarray(x, dtype=config.floatX)
w = numpy.asarray(w, dtype=config.floatX)
ones = numpy.ones((3, 3), dtype=config.floatX)
......
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