def f(x1, x2):
if x1.dtype == dtypes.bool:
assert x2.dtype == dtypes.bool
float_ = np_dtypes.default_float_type()
x1 = math_ops.cast(x1, float_)
x2 = math_ops.cast(x2, float_)
if not np_dtypes.is_allow_float64():
# math_ops.truediv in Python3 produces float64 when both inputs are int32
# or int64. We want to avoid that when is_allow_float64() is False.
x1, x2 = _avoid_float64(x1, x2)
return math_ops.truediv(x1, x2)
def _test(self, *args, **kw_args):
onp_dtype = kw_args.pop('onp_dtype', None)
allow_float64 = kw_args.pop('allow_float64', True)
old_allow_float64 = np_dtypes.is_allow_float64()
np_dtypes.set_allow_float64(allow_float64)
old_func = getattr(self, 'onp_func', None)
# TODO(agarwal): Note that onp can return a scalar type while np returns
# ndarrays. Currently np does not support scalar types.
self.onp_func = lambda *args, **kwargs: onp.asarray( # pylint: disable=g-long-lambda
old_func(*args, **kwargs))
np_out = self.np_func(*args, **kw_args)
onp_out = onp.asarray(self.onp_func(*args, **kw_args))
if onp_dtype is not None:
onp_out = onp_out.astype(onp_dtype)
self.assertEqual(np_out.shape, onp_out.shape)
self.assertEqual(np_out.dtype, onp_out.dtype)
np_dtypes.set_allow_float64(old_allow_float64)
def run_test(*args):
num_samples = 1000
tol = 0.1 # High tolerance to keep the # of samples low else the test
# takes a long time to run.
np_random.seed(10)
outputs = [np_random.randn(*args) for _ in range(num_samples)]
# Test output shape.
for output in outputs:
self.assertEqual(output.shape, tuple(args))
default_dtype = (np.float64 if np_dtypes.is_allow_float64()
else np.float32)
self.assertEqual(output.dtype.as_numpy_dtype, default_dtype)
if np.prod(args): # Don't bother with empty arrays.
outputs = [output.tolist() for output in outputs]
# Test that the properties of normal distribution are satisfied.
mean = np.mean(outputs, axis=0)
stddev = np.std(outputs, axis=0)
self.assertAllClose(mean, np.zeros(args), atol=tol)
self.assertAllClose(stddev, np.ones(args), atol=tol)
# Test that outputs are different with different seeds.
np_random.seed(20)
diff_seed_outputs = [
np_random.randn(*args).tolist() for _ in range(num_samples)
]
self.assertNotAllClose(outputs, diff_seed_outputs)
# Test that outputs are the same with the same seed.
np_random.seed(10)
same_seed_outputs = [
np_random.randn(*args).tolist() for _ in range(num_samples)
]
self.assertAllClose(outputs, same_seed_outputs)
