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!13977 fix numpy docstring errors 

From: @yanglf1121
Reviewed-by: @liangchenghui,@c_34
Signed-off-by: @liangchenghui,@c_34
This commit is contained in:
mindspore-ci-bot 2021-03-25 19:53:21 +08:00 committed by Gitee
parent dd33bccc33 6c6c9ebd6c
commit c008eb0aa1
5 changed files with 188 additions and 80 deletions
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113
RELEASE.md
View File

@ -106,6 +106,119 @@ def construct(self, *inputs):
</tr>
</table>
###### `mindspore.numpy.array()`, `mindspore.numpy.asarray()`, `mindspore.numpy.asfarray()`, `mindspore.numpy.copy()` now support GRAPH mode, but cannot accept `numpy.ndarray` as input arguments anymore([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))
Previously, these interfaces can accept numpy.ndarray as arguments and convert numpy.ndarray to Tensor, but cannot be used in GRAPH mode.
However, currently MindSpore Parser cannot parse numpy.ndarray in JIT-graph. To support these interfaces in graph mode, we have to remove `numpy.ndarray` support. With that being said, users can still use `Tensor` to convert `numpy.ndarray` to tensors.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0-rc1 </td>
</tr>
<tr>
<td>
```python
>>> import mindspore.numpy as mnp
>>> import numpy
>>>
>>> nd_array = numpy.array([1,2,3])
>>> tensor = mnp.asarray(nd_array) # this line cannot be parsed in GRAPH mode
```
</td>
<td>
```python
>>> import mindspore.numpy as mnp
>>> import numpy
>>>
>>> tensor = mnp.asarray([1,2,3]) # this line can be parsed in GRAPH mode
```
</td>
</tr>
</table>
###### mindspore.numpy interfaces remove support for keyword arguments `out` and `where`([!12726](https://gitee.com/mindspore/mindspore/pulls/12726))
Previously, we have incomplete support for keyword arguments `out` and `where` in mindspore.numpy interfaces, however, the `out` argument is only functional when `where` argument is also provided, and `out` cannot be used to pass reference to numpy functions. Therefore, we have removed these two arguments to avoid any confusion users may have. Their original functionality can be found in [np.where](https://www.mindspore.cn/doc/api_python/zh-CN/master/mindspore/numpy/mindspore.numpy.where.html#mindspore.numpy.where)
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0-rc1 </td>
</tr>
<tr>
<td>
```python
>>> import mindspore.numpy as np
>>>
>>> a = np.ones((3,3))
>>> b = np.ones((3,3))
>>> out = np.zeros((3,3))
>>> where = np.asarray([[True, False, True],[False, False, True],[True, True, True]])
>>> res = np.add(a, b, out=out, where=where) # `out` cannot be used as a reference, therefore it is misleading
```
</td>
<td>
```python
>>> import mindspore.numpy as np
>>>
>>> a = np.ones((3,3))
>>> b = np.ones((3,3))
>>> out = np.zeros((3,3))
>>> where = np.asarray([[True, False, True],[False, False, True],[True, True, True]])
>>> res = np.add(a, b)
>>> out = np.where(where, x=res, y=out) # instead of np.add(a, b, out=out, where=where)
```
</td>
</tr>
</table>
#### Deprecations
##### Python API
###### `nn.MatMul` is now deprecated in favor of `ops.matmul` ([!12817](https://gitee.com/mindspore/mindspore/pulls/12817))
[ops.matmul](https://www.mindspore.cn/doc/api_python/zh-CN/master/mindspore/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) follows the API of [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html) as closely as possible. As a function interface, [ops.matmul](https://www.mindspore.cn/doc/api_python/zh-CN/master/mindspore/ops/mindspore.ops.matmul.html#mindspore.ops.matmul) is applied without instantiation, as opposed to `nn.MatMul`, which should only be used as a class instance.
<table>
<tr>
<td style="text-align:center"> 1.1.1 </td> <td style="text-align:center"> 1.2.0-rc1 </td>
</tr>
<tr>
<td>
```python
>>> import numpy as np
>>> from mindspore import Tensor, nn
>>>
>>> x = Tensor(np.ones((2, 3)).astype(onp.float32)
>>> y = Tensor(np.ones((3, 4)).astype(onp.float32)
>>> nn.MatMul()(x, y)
```
</td>
<td>
```python
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>>
>>> x = Tensor(np.ones((2, 3)).astype(onp.float32)
>>> y = Tensor(np.ones((3, 4)).astype(onp.float32)
>>> ops.matmul(x, y)
```
</td>
</tr>
</table>
# MindSpore 1.1.1 Release Notes
## MindSpore

43
mindspore/numpy/array_creations.py
View File

@ -1293,14 +1293,14 @@ def meshgrid(*xi, sparse=False, indexing='xy'):
>>> y = np.linspace(0, 1, 2)
>>> xv, yv = np.meshgrid(x, y)
>>> print(xv)
[[0. , 0.5, 1. ],
[0. , 0.5, 1. ]]
[[0. 0.5 1. ]
[0. 0.5 1. ]]
>>> print(yv)
[[0., 0., 0.],
[1., 1., 1.]]
[[0. 0. 0.],
[1. 1. 1.]]
>>> xv, yv = np.meshgrid(x, y, sparse=True)
>>> print(xv)
[[0. , 0.5, 1. ]]
[[0. 0.5 1. ]]
>>> print(yv)
[[0.],
[1.]
@ -1426,19 +1426,19 @@ class mGridClass(nd_grid):
>>> from mindspore.numpy import mgrid
>>> output = mgrid[0:5, 0:5]
>>> print(output)
[[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]]
[[[0 0 0 0 0]
[1 1 1 1 1]
[2 2 2 2 2]
[3 3 3 3 3]
[4 4 4 4 4]]
[[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]
[0 1 2 3 4]]]
>>> output = mgrid[-1:1:5j]
>>> print(output)
[-1. , -0.5, 0. , 0.5, 1. ]
[-1. -0.5 0. 0.5 1. ]
"""
def __init__(self):
super(mGridClass, self).__init__(sparse=False)
@ -1473,13 +1473,13 @@ class oGridClass(nd_grid):
[Tensor(shape=[5, 1], dtype=Int32, value=
[[0],
[1],
[2],
[2]
[3],
[4]]), Tensor(shape=[1, 5], dtype=Int32, value=
[[0, 1, 2, 3, 4]])]
>>> output = ogrid[-1:1:5j]
>>> print(output)
[-1. , -0.5, 0. , 0.5, 1. ]
[-1. -0.5 0. 0.5 1. ]
"""
def __init__(self):
super(oGridClass, self).__init__(sparse=True)
@ -1684,10 +1684,10 @@ def ix_(*args):
>>> import mindspore.numpy as np
>>> ixgrid = np.ix_(np.array([0, 1]), np.array([2, 4]))
>>> print(ixgrid)
[Tensor(shape=[2, 1], dtype=Int32, value=
(Tensor(shape=[2, 1], dtype=Int32, value=
[[0],
[1]]), Tensor(shape=[1, 2], dtype=Int32, value=
[[2, 4]])]
[[2, 4]]))
"""
# TODO boolean mask
_check_input_tensor(*args)
@ -1784,8 +1784,9 @@ def indices(dimensions, dtype=mstype.int32, sparse=False):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> grid = np.indices((2, 3))
>>> print(indices)
>>> print(grid)
[Tensor(shape=[2, 3], dtype=Int32, value=
[[0, 0, 0],
[1, 1, 1]]), Tensor(shape=[2, 3], dtype=Int32, value=

47
mindspore/numpy/array_ops.py
View File

@ -492,16 +492,14 @@ def column_stack(tup):
ValueError: If `tup` is empty.
Examples:
>>> import mindspore.numpy as mnp
>>> import numpy as onp
>>> from mindspore import Tensor
>>> x1 = Tensor(onp.array([1, 2, 3]).astype('int32'))
>>> x2 = Tensor(onp.array([4, 5, 6]).astype('int32'))
>>> output = mnp.column_stack((x1, x2))
>>> import mindspore.numpy as np
>>> x1 = np.array([1, 2, 3]).astype('int32')
>>> x2 = np.array([4, 5, 6]).astype('int32')
>>> output = np.column_stack((x1, x2))
>>> print(output)
[[1, 4],
[2, 5],
[3, 6]]
[[1 4]
[2 5]
[3 6]]
"""
if isinstance(tup, Tensor):
return tup
@ -541,15 +539,13 @@ def vstack(tup):
ValueError: If `tup` is empty.
Examples:
>>> import mindspore.numpy as mnp
>>> import numpy as onp
>>> from mindspore import Tensor
>>> x1 = Tensor(onp.array([1, 2, 3]).astype('int32'))
>>> x2 = Tensor(onp.array([4, 5, 6]).astype('int32'))
>>> output = mnp.vstack((x1, x2))
>>> import mindspore.numpy as np
>>> x1 = np.array([1, 2, 3]).astype('int32')
>>> x2 = np.array([4, 5, 6]).astype('int32')
>>> output = np.vstack((x1, x2))
>>> print(output)
[[1, 2, 3],
[4, 5, 6]]
[[1 2 3]
[4 5 6]]
"""
if isinstance(tup, Tensor):
return tup
@ -690,12 +686,13 @@ def where(condition, x=None, y=None):
>>> y = np.full((2, 1, 1), 7)
>>> output = np.where(condition, x, y)
>>> print(output)
[[[7, 5],
[7, 5],
[7, 5]],
[[7, 5],
[7, 5],
[7, 5]]]
[[[7 5]
[7 5]
[7 5]]
[[7 5]
[7 5]
[7 5]]]
"""
# type promotes input tensors
dtype1 = F.dtype(x)
@ -978,7 +975,7 @@ def unique(x, return_inverse=False):
>>> input_x = np.asarray([1, 2, 2, 2, 3, 4, 5]).astype('int32')
>>> output_x = np.unique(input_x)
>>> print(output_x)
[1, 2, 3, 4, 5]
[1 2 3 4 5]
>>> output_x = np.unique(input_x, return_inverse=True)
>>> print(output_x)
(Tensor(shape=[5], dtype=Int32, value= [ 1, 2, 3, 4, 5]), Tensor(shape=[7], dtype=Int32,
@ -1055,7 +1052,7 @@ def roll(a, shift, axis=None):
Tensor, with the same shape as a.
Supported Platforms:
``GPU``
``Ascend`` ``GPU`` ``CPU``
Raises:
TypeError: If input arguments have types not specified above.

7
mindspore/numpy/logic_ops.py
View File

@ -57,8 +57,8 @@ def not_equal(x1, x2, dtype=None):
>>> a = np.asarray([1, 2])
>>> b = np.asarray([[1, 3],[1, 4]])
>>> print(np.not_equal(a, b))
>>> [[False True]
[False True]]
[[False True]
[False True]]
"""
_check_input_tensor(x1, x2)
return _apply_tensor_op(F.not_equal, x1, x2, dtype=dtype)
@ -253,9 +253,6 @@ def isfinite(x, dtype=None):
>>> output = np.isfinite(np.array([np.inf, 1., np.nan]).astype('float32'))
>>> print(output)
[False True False]
>>> output = np.isfinite(np.log(np.array(-1.).astype('float32')))
>>> print(output)
False
"""
return _apply_tensor_op(F.isfinite, x, dtype=dtype)

58
mindspore/numpy/math_ops.py
View File

@ -258,9 +258,9 @@ def add(x1, x2, dtype=None):
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.add(x1, x2)
>>> print(output)
[[4, 6],
[4, 6],
[4, 6]]
[[4 6]
[4 6]
[4 6]]
"""
# broadcast is not fully supported in tensor_add on CPU,
# so we use tensor_sub as a substitute solution
@ -297,9 +297,9 @@ def subtract(x1, x2, dtype=None):
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.subtract(x1, x2)
>>> print(output)
[[-2, -2],
[-2, -2],
[-2, -2]]
[[-2 -2]
[-2 -2]
[-2 -2]]
"""
return _apply_tensor_op(F.tensor_sub, x1, x2, dtype=dtype)
@ -331,9 +331,9 @@ def multiply(x1, x2, dtype=None):
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.multiply(x1, x2)
>>> print(output)
[[3, 8],
[3, 8],
[3, 8]]
[[3 8]
[3 8]
[3 8]]
"""
if _get_device() == 'CPU':
_check_input_tensor(x1, x2)
@ -374,9 +374,9 @@ def divide(x1, x2, dtype=None):
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.divide(x1, x2)
>>> print(output)
[[0.33333333, 0.5],
[0.33333333, 0.5],
[0.33333333, 0.5]]
[[0.33333334 0.5 ]
[0.33333334 0.5 ]
[0.33333334 0.5 ]]
"""
if not _check_is_float(F.dtype(x1)) and not _check_is_float(F.dtype(x2)):
x1 = F.cast(x1, mstype.float32)
@ -413,9 +413,9 @@ def true_divide(x1, x2, dtype=None):
>>> x2 = np.full((3, 2), [3, 4])
>>> output = np.true_divide(x1, x2)
>>> print(output)
[[0.33333333, 0.5],
[0.33333333, 0.5],
[0.33333333, 0.5]]
[[0.33333334 0.5 ]
[0.33333334 0.5 ]
[0.33333334 0.5 ]]
"""
return divide(x1, x2, dtype=dtype)
@ -450,9 +450,9 @@ def power(x1, x2, dtype=None):
>>> x2 = np.full((3, 2), [3, 4]).astype('float32')
>>> output = np.power(x1, x2)
>>> print(output)
[[ 1, 16],
[ 1, 16],
[ 1, 16]]
[[ 1 16]
[ 1 16]
[ 1 16]]
"""
return _apply_tensor_op(F.tensor_pow, x1, x2, dtype=dtype)
@ -708,8 +708,8 @@ def dot(a, b):
>>> b = np.full((2, 3, 4), 5).astype('float32')
>>> output = np.dot(a, b)
>>> print(output)
[[[105, 105, 105, 105],
[105, 105, 105, 105]]]
[[[105. 105. 105. 105.]
[105. 105. 105. 105.]]]
"""
ndim_a, ndim_b = F.rank(a), F.rank(b)
if ndim_a > 0 and ndim_b >= 2:
@ -760,13 +760,13 @@ def outer(a, b):
>>> b = np.full(4, 3).astype('float32')
>>> output = np.outer(a, b)
>>> print(output)
[[6, 6, 6, 6],
[6, 6, 6, 6],
[6, 6, 6, 6],
[6, 6, 6, 6],
[6, 6, 6, 6],
[6, 6, 6, 6],
[6, 6, 6, 6]]
[[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]
[6. 6. 6. 6.]]
"""
_check_input_tensor(a, b)
if F.rank(a) != 1:
@ -1478,7 +1478,7 @@ def amin(a, axis=None, keepdims=False, initial=None, where=True):
[0. 1.]
>>> output = np.amin(a, axis=1)
>>> print(output)
[0, 2]
[0. 2.]
>>> output = np.amin(a, where=np.array([False, True]), initial=10, axis=0)
>>> print(output)
[10. 1.]
@ -3733,7 +3733,7 @@ def promote_types(type1, type2):
>>> import mindspore.numpy as np
>>> output = np.promote_types(np.float32, np.float64)
>>> print(output)
np.float64
Float64
"""
type1 = _check_dtype(type1)
type2 = _check_dtype(type2)