like20
/
mindspore2022
forked from huawei/mindspore2022
0
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

!18233 fix numpy api errors 

Merge pull request !18233 from 杨林枫/numpy_fix_api_example_error
This commit is contained in:
i-robot 2021-06-17 19:56:51 +08:00 committed by Gitee
parent 494639ad8e a6325a15b8
commit 019e141191
8 changed files with 70 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
mindspore/_extends/parse/standard_method.py
View File

@ -38,8 +38,8 @@ shape_ = P.Shape()
dtype_ = P.DType()
abs_ = P.Abs()
ndim_ = P.Rank()
size_ = P.Size()
cumsum_ = P.CumSum()
size_op_ = P.Size()
_reduce_sum_default = P.ReduceSum()
_reduce_sum_keepdims = P.ReduceSum(True)
_mean_keepdims = P.ReduceMean(True)
@ -117,6 +117,24 @@ def any_(x, axis=(), keep_dims=False):
return reduce_any(x, axis)
def size_(x):
"""
Return the number of elements in tensor `x`.
Note:
To strictly follow Numpy's behaviour, return 1 for tensor scalar.
Args:
x (Tensor): Input tensor.
Returns:
size(int).
"""
if not shape_(x):
return size_op_(x) + 1
return size_op_(x)
def itemsize_(x):
"""
Return length of one tensor element in bytes.

4
mindspore/common/tensor.py
View File

@ -372,11 +372,11 @@ class Tensor(Tensor_):
return output
def itemset(self, *args):
"""
r"""
Insert scalar into a tensor (scalar is cast to tensor’s dtype, if possible).
There must be at least 1 argument, and define the last argument as item.
Then, tensor.itemset(*args) is equivalent to :math:`tensor[args] = item`.
Then, tensor.itemset(\*args) is equivalent to :math:`tensor[args] = item`.
Args:
args (Union[(Number), (int/tuple(int), Number)]): The arguments that

2
mindspore/numpy/array_creations.py
View File

@ -2448,7 +2448,7 @@ def pad(arr, pad_width, mode="constant", stat_length=None, constant_values=0,
[0. 0. 0. 1. 2. 3. 4. 5. 0. 0. 0. 0.]
>>> print(np.pad(tensor, (3, 4), mode="wrap"))
[3. 4. 5. 1. 2. 3. 4. 5. 1. 2. 3. 4.]
>>> >>> print(np.pad(tensor, (3, 4), mode="linear_ramp", end_values=(10, 10)))
>>> print(np.pad(tensor, (3, 4), mode="linear_ramp", end_values=(10, 10)))
[10. 7. 4. 1. 2. 3. 4. 5. 6.25 7.5 8.75 10. ]
"""
arr = _to_tensor(arr)

3
mindspore/numpy/array_ops.py
View File

@ -2148,8 +2148,7 @@ def choose(a, choices, mode='clip'):
Examples:
>>> import mindspore.numpy as np
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
... [20, 21, 22, 23], [30, 31, 32, 33]]
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13], [20, 21, 22, 23], [30, 31, 32, 33]]
>>> print(np.choose([2, 3, 1, 0], choices))
[20 31 12 3]
>>> print(np.choose([2, 4, 1, 0], choices, mode='clip'))

13
mindspore/numpy/logic_ops.py
View File

@ -371,9 +371,9 @@ def isposinf(x):
Examples:
>>> import mindspore.numpy as np
>>> output = np.isposinf(np.array([-np.inf, 0., np.inf], np.float32))
>>> output = np.isposinf(np.array([-np.inf, 0., np.inf, np.nan], np.float32))
>>> print(output)
[False False True]
[False False True False]
"""
_check_input_tensor(x)
return _is_sign_inf(x, F.tensor_gt)
@ -402,9 +402,9 @@ def isneginf(x):
Examples:
>>> import mindspore.numpy as np
>>> output = np.isneginf(np.array([-np.inf, 0., np.inf], np.float32))
>>> output = np.isneginf(np.array([-np.inf, 0., np.inf, np.nan], np.float32))
>>> print(output)
[ True False False]
[ True False False False]
"""
return _is_sign_inf(x, F.tensor_lt)
@ -750,6 +750,9 @@ def array_equal(a1, a2, equal_nan=False):
In mindpsore, a bool tensor is returned instead, since in Graph mode, the
value cannot be traced and computed at compile time.
Since on Ascend, :class:`nan` is treated differently, currently the argument
`equal_nan` is not supported on Ascend.
Args:
a1/a2 (Union[int, float, bool, list, tuple, Tensor]): Input arrays.
equal_nan (bool): Whether to compare NaN’s as equal.
@ -761,7 +764,7 @@ def array_equal(a1, a2, equal_nan=False):
TypeError: If inputs have types not specified above.
Supported Platforms:
``GPU`` ``CPU``
``GPU`` ``CPU`` ``Ascend``
Examples:
>>> import mindspore.numpy as np

21
mindspore/numpy/math_ops.py
View File

@ -2505,6 +2505,8 @@ def nanmin(a, axis=None, dtype=None, keepdims=False):
Note:
Numpy arguments `out` is not supported.
For all-NaN slices, a very large number is returned instead of NaN.
On Ascend, since checking for NaN is currently not supported, it is not recommended to
use np.nanmin. If the array does not contain NaN, np.min should be used instead.
Args:
a (Union[int, float, list, tuple, Tensor]): Array containing numbers whose minimum
@ -4038,8 +4040,6 @@ def sum_(a, axis=None, dtype=None, keepdims=False, initial=None):
>>> import mindspore.numpy as np
>>> print(np.sum([0.5, 1.5]))
2.0
>>> print(np.sum(np.array([-1, 0, 1], np.int32)))
0
>>> x = np.arange(10).reshape(2, 5).astype('float32')
>>> print(np.sum(x, axis=1))
[10. 35.]
@ -4121,6 +4121,7 @@ def multi_dot(arrays):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> A = np.ones((10000, 100))
>>> B = np.ones((100, 1000))
>>> C = np.ones((1000, 5))
@ -5093,13 +5094,13 @@ def polyval(p, x):
Examples:
>>> import mindspore.numpy as np
>>> print(np.polyval([3,0,1], 5))
76
>>> print(np.polyval([3.,0.,1.], 5.))
76.0
"""
p = _to_poly1d(p)
x = _to_tensor(x)
shape = F.shape(x)
exp_p = arange(_type_convert(int, p.size) - 1, -1, -1)
exp_p = arange(_type_convert(int, p.size) - 1, -1, -1).astype(mstype.float32)
var_p = (x.reshape(shape + (1,)))**exp_p
return F.reduce_sum(p*var_p, -1)
@ -5159,7 +5160,7 @@ def polymul(a1, a2):
ValueError: if the input array has more than 1 dimensions.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
``GPU``
Examples:
>>> import mindspore.numpy as np
@ -5446,6 +5447,9 @@ def ravel_multi_index(multi_index, dims, mode='clip', order='C'):
>>> output = np.ravel_multi_index(arr, (7, 6))
>>> print(output)
[22. 41. 37.]
>>> output = np.ravel_multi_index((3, 1, 4, 1), (6, 7, 8, 9))
>>> print(output)
1621.0
"""
if isinstance(dims, int):
dims = (dims,)
@ -5548,7 +5552,7 @@ def norm(x, ord=None, axis=None, keepdims=False): # pylint: disable=redefined-bu
Examples:
>>> import mindspore.numpy as np
>>> print(np.norm(np.arange(9)))
>>> print(np.norm(np.arange(9).astype(np.float32)))
14.282857
"""
if not isinstance(ord, (int, float)) and not _in(ord, (None, 'fro', 'nuc', inf, -inf)):
@ -5787,6 +5791,9 @@ def correlate(a, v, mode='valid'):
>>> output = np.correlate([1, 2, 3], [0, 1, 0.5], mode="same")
>>> print(output)
[2. 3.5 3. ]
>>> output = np.correlate([1, 2, 3, 4, 5], [1, 2], mode="same")
>>> print(output)
[ 2. 5. 8. 11. 14.]
"""
a, v = _to_tensor(a, v)
if a.ndim != 1 or v.ndim != 1:

8
mindspore/ops/composite/multitype_ops/_compile_utils.py
View File

@ -922,6 +922,14 @@ def reduce_(a, reduce_fn, cmp_fn=None, axis=None, keepdims=False, initial=None,
if initial is None:
const_utils.raise_value_error('initial value must be provided for where masks')
ndim_orig = F.rank(a)
# broadcasts input tensors
shape_out = const_utils.infer_out_shape(F.shape(where), F.shape(a), F.shape(initial))
broadcast_to = P.BroadcastTo(shape_out)
where = where.astype(mstype.float32)
where = broadcast_to(where)
where = where.astype(mstype.bool_)
a = broadcast_to(a)
initial = broadcast_to(initial)
a = F.select(where, a, initial)
axes = const_utils.real_axes(ndim_orig, F.rank(a), axes)

18
mindspore/ops/composite/multitype_ops/_constexpr_utils.py
View File

@ -14,8 +14,9 @@
# ============================================================================
"""constexpr util"""
from itertools import compress
from itertools import compress, zip_longest
from functools import partial
from collections import deque
import operator
import numpy as np
@ -810,3 +811,18 @@ def compute_slice_shape(slice_shape, broadcast_shape_len, slice_cnt, fancy_posit
shape[slice_cnt] = slice_shape[slice_cnt]
shape = shape[:fancy_position] + [1] * broadcast_shape_len + shape[fancy_position:]
return shape
@constexpr
def infer_out_shape(*shapes):
"""
Returns shape of output after broadcasting. Raises ValueError if shapes cannot be broadcast.
"""
shape_out = deque()
reversed_shapes = map(reversed, shapes)
for items in zip_longest(*reversed_shapes, fillvalue=1):
max_size = 0 if 0 in items else max(items)
if any(item not in (1, max_size) for item in items):
raise ValueError(f'operands could not be broadcast together with shapes {*shapes,}')
shape_out.appendleft(max_size)
return tuple(shape_out)