def testAllowF64TruePreferF32True(self):
np_dtypes.set_allow_float64(True)
np_dtypes.set_prefer_float32(True)
self.assertEqual(dtypes.float32, np_dtypes.default_float_type())
self.assertEqual(dtypes.float32, np_dtypes._result_type(1.1))
self.assertEqual(dtypes.float64,
np_dtypes._result_type(np.zeros([], np.float64), 1.1))
def result_type(*arrays_and_dtypes):
"""Returns the type resulting from applying NumPy type promotion to arguments.
Args:
*arrays_and_dtypes: A list of array_like objects or dtypes.
Returns:
A numpy dtype.
"""
def maybe_get_dtype(x):
# Don't put np.ndarray in this list, because np.result_type looks at the
# value (not just dtype) of np.ndarray to decide the result type.
if isinstance(x, (np_arrays.ndarray, ops.Tensor,
indexed_slices.IndexedSlices)):
return _to_numpy_type(x.dtype)
elif isinstance(x, dtypes.DType):
return _to_numpy_type(x)
return x
arrays_and_dtypes = [
maybe_get_dtype(x) for x in nest.flatten(arrays_and_dtypes)
]
if not arrays_and_dtypes:
# If arrays_and_dtypes is an empty list, let numpy decide what the dtype is.
arrays_and_dtypes = [np.asarray([])]
return np_dtypes._result_type(*arrays_and_dtypes) # pylint: disable=protected-access
def _result_type_binary(t1, t2): # pylint: disable=missing-function-docstring
"""A specialization of result_type for 2 arguments for performance reasons."""
try:
return np_dtypes._result_type(_maybe_get_dtype(t1), # pylint: disable=protected-access
_maybe_get_dtype(t2)) # pylint: disable=protected-access
except ValueError:
return result_type(t1, t2)
def result_type(*arrays_and_dtypes): # pylint: disable=missing-function-docstring
arrays_and_dtypes = [
_maybe_get_dtype(x) for x in nest.flatten(arrays_and_dtypes)
]
if not arrays_and_dtypes:
# If arrays_and_dtypes is an empty list, let numpy decide what the dtype is.
arrays_and_dtypes = [np.asarray([])]
return np_dtypes._result_type(*arrays_and_dtypes) # pylint: disable=protected-access
def result_type(*arrays_and_dtypes): # pylint: disable=missing-function-docstring
def maybe_get_dtype(x):
# Don't put np.ndarray in this list, because np.result_type looks at the
# value (not just dtype) of np.ndarray to decide the result type.
if isinstance(
x,
(np_arrays.ndarray, core.Tensor, indexed_slices.IndexedSlices)):
return _to_numpy_type(x.dtype)
elif isinstance(x, dtypes.DType):
return _to_numpy_type(x)
return x
arrays_and_dtypes = [
maybe_get_dtype(x) for x in nest.flatten(arrays_and_dtypes)
]
if not arrays_and_dtypes:
# If arrays_and_dtypes is an empty list, let numpy decide what the dtype is.
arrays_and_dtypes = [np.asarray([])]
return np_dtypes._result_type(*arrays_and_dtypes) # pylint: disable=protected-access
def testAllowF64TruePreferF32False(self):
np_dtypes.set_allow_float64(True)
np_dtypes.set_prefer_float32(False)
self.assertEqual(dtypes.float64, np_dtypes.default_float_type())
self.assertEqual(dtypes.float64, np_dtypes._result_type(1.1))
self.assertEqual(dtypes.complex128, np_dtypes._result_type(1.j))
def testAllowF64False(self, prefer_f32):
np_dtypes.set_allow_float64(False)
np_dtypes.set_prefer_float32(prefer_f32)
self.assertEqual(dtypes.float32, np_dtypes.default_float_type())
self.assertEqual(dtypes.float32,
np_dtypes._result_type(np.zeros([], np.float64), 1.1))
