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!28651 optimize code docs about 8 issue items 

Merge pull request !28651 from chentangyu/code_docs_cty_master_I4PIJK_I4PILV_I4PIDN_I4PIG3_I4PIIK_I4OYFO_I4OYGE_I4OT8O
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i-robot 2022-01-07 09:35:18 +00:00 committed by Gitee
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mindspore/python/mindspore/ops/operations/math_ops.py
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@ -613,7 +613,7 @@ class ReduceMean(_Reduce):
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `axis` is not one of the following: int, tuple or list.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -701,7 +701,7 @@ class ReduceSum(_Reduce):
>>> output = op(x, 1)
>>> output.shape
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> # case 1: Reduces a dimension by summing all elements in the dimension.
>>> x = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]), mindspore.float32)
@ -754,8 +754,7 @@ class ReduceAll(_Reduce):
Args:
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions.
Default : False, don't keep these reduced dimensions.
If false, don't keep these dimensions. Default : False.
Inputs:
- **x** (Tensor[bool]) - The input tensor. The dtype of the tensor to be reduced is bool.
@ -776,7 +775,7 @@ class ReduceAll(_Reduce):
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `axis` is not one of the following: int, tuple or list.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -784,7 +783,7 @@ class ReduceAll(_Reduce):
Examples:
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> op = ops.ReduceAll(keep_dims=True)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> # case 1: Reduces a dimension by the "logicalAND" of all elements in the dimension.
>>> output = op(x)
>>> print(output)
[[False]]
@ -813,8 +812,7 @@ class ReduceAny(_Reduce):
Args:
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions.
Default : False, don't keep these reduced dimensions.
If false, don't keep these dimensions. Default : False.
Inputs:
- **x** (Tensor[bool]) - The input tensor. The dtype of the tensor to be reduced is bool.
@ -835,7 +833,7 @@ class ReduceAny(_Reduce):
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `axis` is not one of the following: int, tuple or list.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -843,7 +841,7 @@ class ReduceAny(_Reduce):
Examples:
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> op = ops.ReduceAny(keep_dims=True)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> # case 1: Reduces a dimension by the "logical OR" of all elements in the dimension.
>>> output = op(x)
>>> print(output)
[[ True]]
@ -872,8 +870,7 @@ class ReduceMax(_Reduce):
Args:
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions.
Default : False, don't keep these reduced dimensions.
If false, don't keep these dimensions. Default : False.
Inputs:
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
@ -894,7 +891,7 @@ class ReduceMax(_Reduce):
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `axis` is not one of the following: int, tuple or list.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -906,7 +903,7 @@ class ReduceMax(_Reduce):
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> # case 1: Reduces a dimension by the maximum value of all elements in the dimension.
>>> x = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]), mindspore.float32)
@ -959,8 +956,7 @@ class ReduceMin(_Reduce):
Args:
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions.
Default : False, don't keep these reduced dimensions.
If false, don't keep these dimensions. Default : False.
Inputs:
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
@ -981,7 +977,7 @@ class ReduceMin(_Reduce):
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `axis` is not one of the following: int, tuple or list.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -993,7 +989,7 @@ class ReduceMin(_Reduce):
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> # case 1: Reduces a dimension by the minimum value of all elements in the dimension.
>>> x = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]), mindspore.float32)
@ -1037,8 +1033,7 @@ class ReduceProd(_Reduce):
Args:
keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions.
Default : False, don't keep these reduced dimensions.
If false, don't keep these dimensions. Default : False.
Inputs:
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
@ -1059,7 +1054,7 @@ class ReduceProd(_Reduce):
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `x` is not a Tensor.
ValueError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `axis` is not one of the following: int, tuple or list.
Supported Platforms:
``Ascend`` ``GPU``
@ -1071,7 +1066,7 @@ class ReduceProd(_Reduce):
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> # case 1: Reduces a dimension by multiplying all elements in the dimension.
>>> x = Tensor(np.array([[[1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[7, 7, 7, 7, 7, 7], [8, 8, 8, 8, 8, 8], [9, 9, 9, 9, 9, 9]]]), mindspore.float32)
@ -1761,7 +1756,7 @@ class InplaceAdd(PrimitiveWithInfer):
class InplaceSub(PrimitiveWithInfer):
"""
Subtracts `v` into specified rows of `x`. Computes `y` = `x`; y[i,] -= `v.
Subtracts `v` into specified rows of `x`. Computes `y` = `x`; y[i,] -= `v`.
Args:
indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of `x`
@ -2361,14 +2356,16 @@ class Log(PrimitiveWithInfer):
is subject to change.
Inputs:
- **x** (Tensor) - The input tensor. The value must be greater than 0.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **x** (Tensor) - The input tensor. The data type must be float16, float32 or float64. The value must be
greater than 0. :math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should
be less than 8.
Outputs:
Tensor, has the same shape as the `x`.
Tensor, has the same shape and dtype as the `x`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If dtype of `x` is not float16, float32 or float64.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -2682,18 +2679,20 @@ class Div(_MathBinaryOp):
out_{i} = \frac{x_i}{y_i}
Inputs:
- **x** (Union[Tensor, Number, bool]) - The first input is a number or
a bool or a tensor whose data type is number or bool.
- **y** (Union[Tensor, Number, bool]) - When the first input is a tensor, The second input
could be a number, a bool, or a tensor whose data type is number or bool. When the first input
is a number or a bool, the second input must be a tensor whose data type is number or bool.
- **x** (Union[Tensor, number.Number, bool]) - The first input is a number.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_.
- **y** (Union[Tensor, number.Number, bool]) - The second input is a number.Number or
a bool when the first input is a tensor or a tensor whose data type is number or bool_.
When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.
Outputs:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.
Raises:
TypeError: If neither `x` nor `y` is a Tensor.
TypeError: If `x` and `y` is not a number.Number or a bool or a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -2743,17 +2742,21 @@ class DivNoNan(_MathBinaryOp):
\end{cases}
Inputs:
- **x** (Union[Tensor, Number, bool]) - The first input is a number or
a bool or a tensor whose data type is number or bool.
- **y** (Union[Tensor, Number, bool]) - The second input is a number or
a bool when the first input is a tensor or a tensor whose data type is number or bool.
- **x** (Union[Tensor, number.Number, bool]) - The first input is a number.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_ or
`bool_ <https://www.mindspore.cn/docs/api/en/master/api_python/mindspore.html#mindspore.dtype>`_.
- **y** (Union[Tensor, number.Number, bool]) - The second input is a number.Number or
a bool when the first input is a tensor or a tensor whose data type is number or bool_.
When the first input is Scalar, the second input must be a Tensor whose data type is number or bool_.
Outputs:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision or higher digits among the two inputs.
Raises:
TypeError: If neither `x` nor `y` is a Tensor.
TypeError: If `x` and `y` is not a number.Number or a bool or a Tensor.
Supported Platforms:
``Ascend`` ``GPU``
@ -4142,7 +4145,7 @@ class NPUAllocFloatStatus(PrimitiveWithInfer):
The flag is a tensor whose shape is `(8,)` and data type is `mindspore.dtype.float32`.
Note:
Please refer to the Examples of class: `mindspore.ops.NPUAllocFloatStatus`.
Please refer to the Examples of :class:`mindspore.ops.NPUGetFloatStatus`.
Outputs:
Tensor, has the shape of `(8,)`.
@ -4254,9 +4257,9 @@ class NPUClearFloatStatus(PrimitiveWithInfer):
`NPUClearFloatStatus` is called.
In addition, there are strict sequencing requirements for use, i.e., before using the NPUGetFloatStatus
operator, need to ensure that the NPUClearFlotStatus and your compute has been executed.
We use depend on ensure the execution order.
We use :class:`mindspore.ops.Depend` on ensure the execution order.
Please refer to the Examples of class: `mindspore.ops.NPUGetFloatStatus`.
Please refer to the Examples of :class:`mindspore.ops.NPUGetFloatStatus`.
Inputs:
- **x** (Tensor) - The output tensor of `NPUAllocFloatStatus`.
@ -4500,9 +4503,9 @@ class NMSWithMask(PrimitiveWithInfer):
valid output bounding boxes.
Raises:
ValueError: If the `iou_threshold` is not a float number, or if the first dimension
of input Tensor is less than or equal to 0, or if the data type of the input
Tensor is not float16 or float32.
ValueError: If the `iou_threshold` is not a float number.
ValueError: if the first dimension of input Tensor is less than or equal to 0.
TypeError: if the dtype of the `bboxes` is not float16 or float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -5225,8 +5228,8 @@ class Eps(PrimitiveWithInfer):
class LinSpace(PrimitiveWithInfer):
r"""
The OP returns a Tensor whose value is num evenly spaced in the interval start and stop (including start and stop),
and the length of the output Tensor is num.
Returns a Tensor whose value is `num` evenly spaced in the interval `start` and `stop` (including `start` and
`stop`), and the length of the output Tensor is `num`.
.. math::
\begin{aligned}
@ -5235,12 +5238,17 @@ class LinSpace(PrimitiveWithInfer):
\end{aligned}
Inputs:
- **start** (Tensor[float32]) - Start value of interval, With shape of 0-D.
- **stop** (Tensor[float32]) - Last value of interval, With shape of 0-D.
- **start** (Tensor) - The data type must be float32. Start value of interval, With shape of 0-D.
- **stop** (Tensor) - The data type must be float32. Last value of interval, With shape of 0-D.
- **num** (int) - Number of ticks in the interval, inclusive of start and stop.
Outputs:
Tensor, has the same shape as `start`.
Tensor, has the same shape and dtype as `start`.
Raises:
TypeError: If `start` or `stop` is not a Tensor.
TypeError: If dtype of `start` or dtype of `stop` is not float32.
TypeError: If `num` is not a int.
Supported Platforms:
``Ascend`` ``GPU``
@ -5476,13 +5484,14 @@ class Real(PrimitiveWithInfer):
If input is real, it is returned unchanged.
Inputs:
- **input** (Tensor) - The input tensor to compute to.
-**input** (Tensor) - The input tensor to compute to, the type of the input should be complex64 or complex128.
Outputs:
Tensor, the shape is the same as the input.
Tensor, the type is the same as the real part of input.
Raises:
TypeError: If the input is not a Tensor.
TypeError: If the type of the real part of input is not complex64 or complex128.
Supported Platforms:
``GPU``