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update api parameters

This commit is contained in:
shaojunsong 2023-03-04 10:41:34 +08:00
parent ba0775661f
commit 0e8045898a
7 changed files with 23 additions and 24 deletions
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2
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.mean.rst
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@ -1,6 +1,6 @@
mindspore.Tensor.mean
=====================
.. py:method:: mindspore.Tensor.mean(axis=(), keep_dims=False)
.. py:method:: mindspore.Tensor.mean(axis=None, keep_dims=False)
详情请参考 :func:`mindspore.ops.mean`。

2
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.prod.rst
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@ -1,6 +1,6 @@
mindspore.Tensor.prod
=====================
.. py:method:: mindspore.Tensor.prod(axis=(), keep_dims=False)
.. py:method:: mindspore.Tensor.prod(axis=None, keep_dims=False)
详情请参考 :func:`mindspore.ops.prod`。

6
docs/api/api_python/ops/mindspore.ops.func_mean.rst
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@ -1,19 +1,19 @@
mindspore.ops.mean
==================
.. py:function:: mindspore.ops.mean(x, axis=(), keep_dims=False)
.. py:function:: mindspore.ops.mean(x, axis=None, keep_dims=False)
默认情况下，移除输入所有维度，返回 `x` 中所有元素的平均值。也可仅缩小指定维度 `axis` 大小至1。 `keep_dims` 控制输出和输入的维度是否相同。
参数：
- **x** (Tensor[Number]) - 输入Tensor，其数据类型为数值型。shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: None，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
- **keep_dims** (bool) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
返回：
Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个零维Tensor，表示输入Tensor中所有元素的平均值。
- 如果 `axis` 为None，且 `keep_dims` 为False，则输出一个零维Tensor，表示输入Tensor中所有元素的平均值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
- 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。

6
docs/api/api_python/ops/mindspore.ops.func_prod.rst
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@ -1,19 +1,19 @@
mindspore.ops.prod
==================
.. py:function:: mindspore.ops.prod(x, axis=(), keep_dims=False)
.. py:function:: mindspore.ops.prod(x, axis=None, keep_dims=False)
默认情况下，使用指定维度的所有元素的乘积代替该维度的其他元素，以移除该维度。也可仅缩小该维度大小至1。 `keep_dims` 控制输出和输入的维度是否相同。
参数：
- **x** (Tensor[Number]) - 输入Tensor，其数据类型为数值型。shape： :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: None，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
- **keep_dims** (bool) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
返回：
Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个零维Tensor，表示输入Tensor中所有元素的乘积。
- 如果 `axis` 为None，且 `keep_dims` 为False，则输出一个零维Tensor，表示输入Tensor中所有元素的乘积。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
- 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。

6
mindspore/python/mindspore/_extends/parse/standard_method.py
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@ -68,7 +68,7 @@ itemsize_map = {mstype.bool_: 1, mstype.int8: 1, mstype.uint8: 1,
nan_tensor = Tensor(float('nan'), dtype=mstype.float32)
def mean(x, axis=(), keep_dims=False):
def mean(x, axis=None, keep_dims=False):
"""
Reduces a dimension of a tensor by averaging all elements in the dimension.
@ -102,14 +102,14 @@ def ndimension(x):
return len(x.shape)
def prod(x, axis=(), keep_dims=False):
def prod(x, axis=None, keep_dims=False):
"""
Reduces a dimension of a tensor by product all elements in the dimension.
Args:
x (Tensor): Input Tensor.
axis (Union[None, int, tuple(int), list(int)]): Dimensions of reduction,
when axis is None or empty tuple, reduce all dimensions. Default: ().
when axis is None or empty tuple, reduce all dimensions. Default: None.
keep_dims (bool): Whether to keep the reduced dimensions. Default: False.
Returns:

8
mindspore/python/mindspore/common/tensor.py
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@ -1423,7 +1423,7 @@ class Tensor(Tensor_, metaclass=_TensorMeta):
self._init_check()
return tensor_operator_registry.get('log2')(self)
def mean(self, axis=(), keep_dims=False):
def mean(self, axis=None, keep_dims=False):
"""
For details, please refer to :func:`mindspore.ops.mean`.
"""
@ -1437,8 +1437,6 @@ class Tensor(Tensor_, metaclass=_TensorMeta):
For details, please refer to :func:`mindspore.ops.amin`.
"""
self._init_check()
if axis is None:
axis = ()
return tensor_operator_registry.get('amin')(self, axis, keep_dims)
def reverse(self, axis):
@ -1453,8 +1451,6 @@ class Tensor(Tensor_, metaclass=_TensorMeta):
For details, please refer to :func:`mindspore.ops.amax`.
"""
self._init_check()
if axis is None:
axis = ()
return tensor_operator_registry.get('amax')(self, axis, keep_dims)
def reverse_sequence(self, seq_lengths, seq_dim=0, batch_dim=0):
@ -1464,7 +1460,7 @@ class Tensor(Tensor_, metaclass=_TensorMeta):
self._init_check()
return tensor_operator_registry.get("reverse_sequence")(seq_dim, batch_dim)(self, seq_lengths)
def prod(self, axis=(), keep_dims=False):
def prod(self, axis=None, keep_dims=False):
"""
For details, please refer to :func:`mindspore.ops.prod`.
"""

17
mindspore/python/mindspore/ops/function/math_func.py
View File

@ -7117,7 +7117,7 @@ def amax(input, axis=None, keep_dims=False):
return _get_cache_prim(P.ReduceMax)(keep_dims)(input, axis)
def mean(x, axis=(), keep_dims=False):
def mean(x, axis=None, keep_dims=False):
r"""
Reduces all dimension of a tensor by averaging all elements in the dimension, by default.
And reduce a dimension of `x` along the specified `axis`. `keep_dims`
@ -7126,7 +7126,7 @@ def mean(x, axis=(), keep_dims=False):
Args:
x (Tensor[Number]): The input tensor. The dtype of the tensor to be reduced is number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: (), reduce all dimensions.
axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: None, reduce all dimensions.
Only constant value is allowed. Assume the rank of `x` is r, and the value range is [-r,r).
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: False.
@ -7134,7 +7134,7 @@ def mean(x, axis=(), keep_dims=False):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
- If `axis` is None, and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
@ -7191,11 +7191,12 @@ def mean(x, axis=(), keep_dims=False):
[ 8.]
[10.]]]
"""
if axis is None:
axis = ()
return _get_cache_prim(P.ReduceMean)(keep_dims)(x, axis)
def prod(x, axis=(), keep_dims=False):
def prod(x, axis=None, keep_dims=False):
r"""
Reduces a dimension of a tensor by multiplying all elements in the dimension, by default. And also can
reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
@ -7204,7 +7205,7 @@ def prod(x, axis=(), keep_dims=False):
Args:
x (Tensor[Number]): The input tensor. The dtype of the tensor to be reduced is number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: (), reduce all dimensions.
axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: None, reduce all dimensions.
Only constant value is allowed. Assume the rank of `x` is r, and the value range is [-r,r).
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: False.
@ -7212,7 +7213,7 @@ def prod(x, axis=(), keep_dims=False):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
- If `axis` is None, and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
@ -7268,6 +7269,8 @@ def prod(x, axis=(), keep_dims=False):
[2.62144e+05]
[5.31441e+05]]]
"""
if axis is None:
axis = ()
return _get_cache_prim(P.ReduceProd)(keep_dims)(x, axis)