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[TF-numpy] Adds @np_doc to finfo, result_type and promote_types.

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PiperOrigin-RevId: 318944304
Change-Id: I7c37f744725c8da1e5ca58fd79640354bee05302
This commit is contained in:
Peng Wang 2020-06-29 19:55:10 -07:00 committed by TensorFlower Gardener
parent 8562a13cca
commit aed5e3ea00
1 changed files with 38 additions and 61 deletions
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99
tensorflow/python/ops/numpy_ops/np_utils.py
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@ -82,22 +82,6 @@ def _to_numpy_type(dtype):
return np.dtype(dtype)
def finfo(dtype):
"""Returns properties of floating point types.
Note that currently it just forwards to the numpy namesake, while tensorflow
and numpy dtypes may have different properties.
Args:
dtype: Could be a python type, a numpy type or a TF DType.
Returns:
A class describing properties of `dtype`, as described by
https://docs.scipy.org/doc/numpy/reference/generated/numpy.finfo.html
"""
return np.finfo(_to_numpy_type(dtype))
def isscalar(val):
"""Returns whether `val` is a scalar value or scalar Tensor."""
if isinstance(val, np_arrays.ndarray):
@ -112,51 +96,6 @@ def isscalar(val):
return np.isscalar(val)
# Can't use np_doc because np.result_type is a builtin function.
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, 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 promote_types(type1, type2):
"""Returns the type resulting from applying NumPy type promotion.
Args:
type1: A numpy type.
type2: A numpy type.
Returns:
A numpy type.
"""
type1 = _to_numpy_type(type1)
type2 = _to_numpy_type(type2)
return np_dtypes.canonicalize_dtype(np.promote_types(type1, type2))
def _has_docstring(f):
return (f and hasattr(f, '__doc__') and isinstance(f.__doc__, str) and
f.__doc__)
@ -360,6 +299,44 @@ def np_doc_only(np_fun_name, np_fun=None):
return decorator
# pylint: disable=g-short-docstring-punctuation,g-no-space-after-docstring-summary,g-docstring-missing-newline,g-doc-return-or-yield,g-doc-args
@np_doc('finfo')
def finfo(dtype):
"""Note that currently it just forwards to the numpy namesake, while
tensorflow and numpy dtypes may have different properties."""
return np.finfo(_to_numpy_type(dtype))
# pylint: enable=g-short-docstring-punctuation,g-no-space-after-docstring-summary,g-docstring-missing-newline,g-doc-return-or-yield,g-doc-args
# Can't use np_doc because np.result_type is a builtin function.
@np_doc_only('result_type')
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
@np_doc('promote_types')
def promote_types(type1, type2): # pylint: disable=missing-function-docstring
type1 = _to_numpy_type(type1)
type2 = _to_numpy_type(type2)
return np_dtypes.canonicalize_dtype(np.promote_types(type1, type2))
def tf_broadcast(*args):
"""Broadcast tensors.