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!11372 [Numpy-Native] Api docstring bug fix 

From: @yanglf1121
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2021-01-23 11:13:07 +08:00 committed by Gitee
parent ba6c885962 eab5ecf21a
commit b8ba05333e
14 changed files with 100 additions and 208 deletions
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2
mindspore/_checkparam.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

2
mindspore/_extends/parse/standard_method.py
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@ -1,6 +1,6 @@
# This is the Python adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
#
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

2
mindspore/common/tensor.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

10
mindspore/numpy/__init__.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -19,10 +19,10 @@ Examples:
>>> import mindspore.numpy as np
Note:
- array_ops.py define all the array generation and operation interfaces.
- math_ops.py define all the math operations on tensors.
- dtypes.py define all the mindspore.numpy dtypes (mainly redirected from mindspore)
- random/ defines all the random operations.
- array_ops.py defines all the array operation interfaces.
- array_creations.py defines all the array generation interfaces.
- math_ops.py defines all the math operations on tensors.
- dtypes.py defines all the mindspore.numpy dtypes (mainly redirected from mindspore)
"""
from .array_ops import (transpose, expand_dims, squeeze, rollaxis, swapaxes, reshape,

18
mindspore/numpy/array_creations.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -153,7 +153,7 @@ def asarray(a, dtype=None):
elif a.dtype is onp.dtype('float'):
dtype = mstype.float32
elif a.dtype is onp.dtype('object'):
raise TypeError(f"For Tensor convertion, the input_data is {a} that contains unsupported element.")
raise TypeError(f"For Tensor conversion, the input_data is {a} that contains unsupported element.")
a = Tensor.from_numpy(a)
# If a is already a tensor and we don't need to cast dtype, return a
@ -208,7 +208,7 @@ def asfarray(a, dtype=mstype.float32):
a = _deep_tensor_to_nparray(a)
a = onp.asarray(a)
if a.dtype is onp.dtype('object'):
raise TypeError(f"For Tensor convertion, the input_data is {a} that contains unsupported element.")
raise TypeError(f"For Tensor conversion, the input_data is {a} that contains unsupported element.")
if isinstance(a, onp.ndarray):
a = Tensor.from_numpy(a)
@ -952,7 +952,7 @@ def tril(m, k=0):
Returns a copy of an array with elements above the k-th diagonal zeroed.
Args:
m(array_like): The shape and data-type of a define these same
m(array_like): The shape and data-type of m define these same
attributes of the returned array.
k(int, optional): Diagonal above which to zero elements. k = 0 (the default)
is the main diagonal, k < 0 is below it and k > 0 is above.
@ -987,16 +987,16 @@ def triu(m, k=0):
"""
Returns an upper triangle of an array.
Returns a copy of an array with elements above the k-th diagonal zeroed.
Returns a copy of an array with elements below the k-th diagonal zeroed.
Args:
m(array_like): The shape and data-type of a define these same
m(array_like): The shape and data-type of m define these same
attributes of the returned array.
k(int, optional): Diagonal above which to zero elements. k = 0 (the default)
k(int, optional): Diagonal below which to zero elements. k = 0 (the default)
is the main diagonal, k < 0 is below it and k > 0 is above.
Returns:
triu(Tensor): Lower triangle of m, of same shape and data-type as m.
triu(Tensor): Upper triangle of m, of same shape and data-type as m.
Raises:
TypeError: If input arguments have types not specified above.
@ -1175,7 +1175,7 @@ def trace(a, offset=0, axis1=0, axis2=1):
>>> print(output)
[6 8]
>>> a = np.arange(24).reshape((2,2,2,3))
>>> output = np.trace.shape
>>> output = np.trace(a).shape
>>> print(output)
(2, 3)
"""

148
mindspore/numpy/array_ops.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -20,12 +20,11 @@ from ..ops import functional as F
from ..ops.primitive import constexpr
from ..nn import Cell
from .utils import _covert_list_tensor_to_tuple_tensor, _expand, _broadcast_to, \
from .utils import _convert_list_tensor_to_tuple_tensor, _expand, _broadcast_to, \
_is_empty
from .utils_const import _check_is_int, _check_axes_range, _check_start_normalize, \
_check_is_tensor, _check_is_tuple, _check_is_list, _raise_type_error, _raise_value_error, \
_infer_out_shape, _get_index_for_unique, _get_counts_for_unique, _empty, _promote, \
_min, _check_same_type, _check_input_tensor
_infer_out_shape, _empty, _promote, _check_same_type, _check_input_tensor
# According to official numpy reference, the dimension of a numpy array must be less
# than 32
@ -336,7 +335,7 @@ def ravel(x):
Flattened tensor, has the same data type as the original tensor x.
Raises:
If x is not tensor.
TypeError: If x is not tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -450,7 +449,7 @@ def concatenate(arrays, axis=0):
return P.Concat(axis)(flattened_arrays)
# convert a list of tensor to a tuple of tensor
arrays = _covert_list_tensor_to_tuple_tensor(arrays)
arrays = _convert_list_tensor_to_tuple_tensor(arrays)
arr_shape = F.shape(arrays[0])
_check_axes_range((axis,), len(arr_shape))
@ -503,12 +502,11 @@ def column_stack(tup):
trans_tup = ()
for tensor in tup:
shape = F.shape(tensor)
if F.tuple_len(shape) == 1:
reshape_tensor = F.reshape(tensor, shape+(1,))
trans_tup += (reshape_tensor,)
else:
trans_tup += (tensor,)
if tensor.ndim < 1:
tensor = F.expand_dims(tensor, 0)
if tensor.ndim == 1:
tensor = F.expand_dims(tensor, 1)
trans_tup += (tensor,)
return P.Concat(axis=1)(trans_tup)
@ -552,12 +550,9 @@ def vstack(tup):
trans_tup = ()
for tensor in tup:
shape = F.shape(tensor)
if F.tuple_len(shape) == 1:
reshape_tensor = F.reshape(tensor, (1,)+shape)
trans_tup += (reshape_tensor,)
else:
trans_tup += (tensor,)
if tensor.ndim <= 1:
tensor = _expand(tensor, 2, 0)
trans_tup += (tensor,)
return P.Concat(axis=0)(trans_tup)
@ -600,13 +595,12 @@ def hstack(tup):
_raise_value_error("Need at least one tensor to concatenate.")
tuple_of_tensor = ()
if _check_is_list(tup):
for tensor in tup:
tuple_of_tensor += (tensor,)
else:
tuple_of_tensor = tup
for tensor in tup:
if tensor.ndim < 1:
tensor = F.expand_dims(tensor, 0)
tuple_of_tensor += (tensor,)
if F.tuple_len(F.shape(tup[0])) == 1:
if tuple_of_tensor[0].ndim <= 1:
return P.Concat(axis=0)(tuple_of_tensor)
return P.Concat(axis=1)(tuple_of_tensor)
@ -652,15 +646,11 @@ def dstack(tup):
trans_tup = ()
for tensor in tup:
shape = F.shape(tensor)
if F.tuple_len(shape) == 1:
reshape_tensor = F.reshape(tensor, (1,)+shape+(1,))
trans_tup += (reshape_tensor,)
elif F.tuple_len(shape) == 2:
reshape_tensor = F.reshape(tensor, shape+(1,))
trans_tup += (reshape_tensor,)
else:
trans_tup += (tensor,)
if tensor.ndim <= 1:
tensor = _expand(tensor, 2, 0)
if tensor.ndim == 2:
tensor = F.expand_dims(tensor, 2)
trans_tup += (tensor,)
return P.Concat(axis=2)(trans_tup)
@ -670,10 +660,6 @@ def where(condition, x=None, y=None):
Note:
As nonzero is not supported, neither x or y can be None.
On CPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
On GPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
Args:
condition (Tensor): where True, yield x, otherwise yield y.
@ -724,6 +710,9 @@ def where(condition, x=None, y=None):
shape_out = _infer_out_shape(F.shape(condition),
F.shape(x), F.shape(y))
ndim_out = len(shape_out)
if not _check_same_type(F.dtype(condition), mstype.float32):
# tiling with bool is not supported on GPU
condition = F.cast(condition, mstype.float32)
condition = _expand(condition, ndim_out)
x = _expand(x, ndim_out)
y = _expand(y, ndim_out)
@ -739,24 +728,16 @@ def where(condition, x=None, y=None):
return res
def _expand_atleast(arr, ndim):
"""Expands arr to at least ndim."""
arr = _expand(arr, _min(ndim, 2))
if ndim > 2:
arr = _expand(arr, ndim, axis=-1)
return arr
def _atleast_xd(ndim, arys):
"""Returns arys with at least ndim."""
for arr in arys:
_check_input_tensor(F.typeof(arr))
if F.tuple_len(arys) == 1:
return _expand_atleast(*arys, ndim)
res = []
for arr in res:
res.append(_expand_atleast(arr, ndim))
for arr in arys:
arr = _expand(arr, ndim)
res.append(arr)
if len(res) == 1:
return res[0]
return res
@ -770,10 +751,6 @@ def atleast_1d(*arys):
Note:
In graph mode, returns a tuple of tensor instead of a list of
tensors.
On CPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
On GPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
Args:
arys1, arys2, … (Tensor): one or more input tensors.
@ -810,10 +787,6 @@ def atleast_2d(*arys):
Note:
In graph mode, returns a tuple of tensor instead of a list of
tensors.
On CPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
On GPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
Args:
arys1, arys2, … (Tensor): one or more input tensors.
@ -850,10 +823,7 @@ def atleast_3d(*arys):
Note:
In graph mode, returns a tuple of tensor instead of a list of
tensors.
On CPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
On GPU, the supported dtypes are np.float16, np.float32, np.int16,
and np.int32.
Args:
arys1, arys2, … (Tensor): one or more input tensors.
@ -882,7 +852,19 @@ def atleast_3d(*arys):
value= [[[1.00000000e+000], [1.00000000e+000], [1.00000000e+000],
[1.00000000e+000], [1.00000000e+000]]]))
"""
return _atleast_xd(3, arys)
res = []
for arr in arys:
ndim = F.rank(arr)
if ndim == 0:
arr = F.reshape(arr, (1, 1, 1))
elif ndim == 1:
arr = F.reshape(arr, (1, F.size(arr), 1))
elif ndim == 2:
arr = F.reshape(arr, F.shape(arr) + (1,))
res.append(arr)
if len(res) == 1:
return res[0]
return res
def stack(arrays, axis=0):
@ -960,31 +942,24 @@ class UniqueNet(Cell):
return self.unique(x)
def unique(x, return_index=False, return_inverse=False, return_counts=False):
def unique(x, return_inverse=False):
"""
Finds the unique elements of a tensor. The input tensor will be flattened first
when it has more than one dimension.
Note:
The operation is derived from mindspore.ops.Unique.
Numpy arguments `axis` is not supported.
Numpy arguments `axis`, `return_index` and `return_counts` are not supported.
This operator must be executed in graph mode.
Args:
x (Tensor): The input tensor to be processed.
return_index (bool): If True, also return the indices of tensor x (along
the specified axis, if provided, or in the flattened tensor) that result
in the unique tensor. Default: False.
return_inverse (bool): If True, also return the indices of the unique tensor.
Default: False.
return_counts (bool): If True, also return the number of times each unique
item appears in input tensor `x`. Default: False.
Returns:
Tensor or tuple of Tensors.
- If all of the three bool arguments (`return_index`, `return_inverse`, `return_counts`)
are False, just return the unique tensor.
- If parts of the three bool arguments are True, the corresponding results (Tensor)
will be added in the tuple.
- If `return_inverse` is False, just return the unique tensor.
- If `return_inverse` is True, return tuple of tensors.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
@ -995,14 +970,12 @@ def unique(x, return_index=False, return_inverse=False, return_counts=False):
Examples:
>>> import mindspore.numpy as mnp
>>> import numpy as onp
>>> from mindspore import context
>>> context.set_context(mode=context.GRAPH_MODE)
>>> input_x = mnp.asarray(onp.array([1, 2, 2, 2, 3, 4, 5]).astype('float32'))
>>> output_x = mnp.unique(input_x)
>>> print(output_x)
[1. 2. 3. 4. 5.]
>>> output_x = mnp.unique(input_x, return_index=True)
>>> print(output_x)
(Tensor(shape=[5], dtype=Float32, value= [ 1. 2. 3. 4. 5.]), Tensor(shape=[5], dtype=Float32,
value= [ 0. 1. 4. 5. 6.]))
>>> output_x = mnp.unique(input_x, return_inverse=True)
>>> print(output_x)
(Tensor(shape=[5], dtype=Float32, value= [ 1. 2. 3. 4. 5.]), Tensor(shape=[7], dtype=Int32,
@ -1013,16 +986,7 @@ def unique(x, return_index=False, return_inverse=False, return_counts=False):
if F.tuple_len(F.shape(x)) > 1:
x = ravel(x)
uniq = UniqueNet()
unique_x, inverse_index = uniq(x)
if not return_index and not return_inverse and not return_counts:
return unique_x
res_tup = (unique_x,)
if return_index:
res_index = _get_index_for_unique(x, unique_x)
res_tup += (res_index,)
if return_inverse:
res_tup += (inverse_index,)
if return_counts:
res_counts = _get_counts_for_unique(x, unique_x)
res_tup += (res_counts,)
return res_tup
res = uniq(x)
if not return_inverse:
return res[0]
return res

5
mindspore/numpy/dtypes.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -146,6 +146,9 @@ promotion_rule = {
(int32, float16): float16,
(int32, float32): float32,
(int32, float64): float64,
(int64, float16): float16,
(int64, float32): float32,
(int64, float64): float64,
(float16, float32): float32,
(float16, float64): float64,
(float32, float64): float64,

37
mindspore/numpy/math_ops.py
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@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -72,7 +72,8 @@ def absolute(x, out=None, where=True, dtype=None):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x = np.asarray([1, 2, 3, -4, -5], np.float64)
>>> import mindspore.numpy as np
>>> x = np.asarray([1, 2, 3, -4, -5], np.float32)
>>> output = np.absolute(x)
>>> print(output)
[1. 2. 3. 4. 5.]
@ -97,10 +98,6 @@ def add(x1, x2, out=None, where=True, dtype=None):
Argument out is not supported for storing the result, however it can be
used in combination with argument where to set the value at indices for
which where is set to False.
On GPU, the supported dtypes are np.float16, np.float32, np.int32,
and np.int64.
On CPU, the supported dtypes are np.float16, np.float32, np.float64,
np.int16, np.int32, and np.int64.
Args:
x1 (Tensor): input to be added.
@ -154,10 +151,6 @@ def subtract(x1, x2, out=None, where=True, dtype=None):
Argument out is not supported for storing the result, however it can be
used in combination with argument where to set the value at indices for
which where is set to False.
On GPU, the supported dtypes are np.float16, np.float32, np.int32,
and np.int64.
On CPU, the supported dtypes are np.float16, np.float32, np.float64,
np.int16, np.int32, and np.int64.
Args:
x1 (Tensor): the input to be subtracted from.
@ -207,10 +200,6 @@ def multiply(x1, x2, out=None, where=True, dtype=None):
Argument out is not supported for storing the result, however it can be
used in combination with argument where to set the value at indices for
which where is set to False.
On GPU, the supported dtypes are np.float16, np.float32, np.int32,
and np.int64.
On CPU, the supported dtypes are np.float16, np.float32, np.float64,
np.int16, np.int32, and np.int64.
Args:
x1 (Tensor): input tensor to be multiplied.
@ -273,8 +262,6 @@ def divide(x1, x2, out=None, where=True, dtype=None):
used in combination with argument where to set the value at indices for
which where is set to False.
On GPU, the supported dtypes are np.float16, and np.float32.
On CPU, the supported dtypes are np.float16, np.float32, np.float64,
np.int16, np.int32, and np.int64.
Args:
x1 (Tensor): the divident.
@ -325,12 +312,10 @@ def power(x1, x2, out=None, where=True, dtype=None):
Numpy arguments casting, order, dtype, subok, signature, and extobj are
not supported.
On GPU, the supported dtypes are np.float16, and np.float32.
On CPU, the supported dtypes are np.float16, np.float32, np.float64,
np.int16, np.int32, and np.int64.
Args:
x1 (Tensor): the bases.
x2 (Tensor): the exponenets.
x2 (Tensor): the exponents.
out (Tensor or None): optional, defaults to None.
where (Tensor or None): optional. For any non-default value of type other
than Tensor or None, the output retains its original value.
@ -345,7 +330,7 @@ def power(x1, x2, out=None, where=True, dtype=None):
Returns:
Tensor or scalar, the bases in x1 raised to the exponents in x2. This
is a scalarif both x1 and x2 are scalars.
is a scalar if both x1 and x2 are scalars.
Raises:
TypeError: if the input is not a tensor.
@ -354,8 +339,8 @@ def power(x1, x2, out=None, where=True, dtype=None):
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> x1 = np.full((3, 2), [1, 2])
>>> x2 = np.full((3, 2), [3, 4])
>>> x1 = np.full((3, 2), [1, 2]).astype('float32')
>>> x2 = np.full((3, 2), [3, 4]).astype('float32')
>>> output = np.power(x1, x2)
>>> print(output)
[[ 1, 16],
@ -548,8 +533,8 @@ def dot(a, b):
Examples:
>>> import mindspore.numpy as np
>>> a = np.full((1, 3), 7)
>>> b = np.full((2, 3, 4), 5)
>>> a = np.full((1, 3), 7).astype('float32')
>>> b = np.full((2, 3, 4), 5).astype('float32')
>>> output = np.dot(a, b)
>>> print(output)
[[[105, 105, 105, 105],
@ -597,8 +582,8 @@ def outer(a, b):
Examples:
>>> import mindspore.numpy as np
>>> a = np.full(7, 2)
>>> b = np.full(4, 3)
>>> a = np.full(7, 2).astype('float32')
>>> b = np.full(4, 3).astype('float32')
>>> output = np.outer(a, b)
>>> print(output)
[[6, 6, 6, 6],

4
mindspore/numpy/utils.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -77,7 +77,7 @@ def _get_device():
return context.get_context('device_target')
def _covert_list_tensor_to_tuple_tensor(list_of_tensor):
def _convert_list_tensor_to_tuple_tensor(list_of_tensor):
"""Convert a list of tensor to a tuple of tensor"""
if isinstance(list_of_tensor, list):
tuple_of_tensor = ()

67
mindspore/numpy/utils_const.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -15,10 +15,7 @@
"""internal graph-compatible utility functions"""
from functools import partial
import numpy as onp
import mindspore.context as context
from ..common import Tensor
from ..ops import functional as F
from ..ops.primitive import constexpr
from ..common import dtype as mstype
@ -262,65 +259,6 @@ def _empty(dtype, shape):
return Tensor_(dtype, shape)
def _get_index_for_unique(input_x, unique_x):
"""
Return the indices of the first occurrences of the unique values in the original array.
Args:
input_x (Tensor): The flattened input tensor of `mindspore.numpy.unique`.
unique_x (Tensor): The tensor contains the unique elements in `input_x`, sorted in ascending order.
Returns:
Tensor. The indices of the unique values in the original array. Has the same shape as `unique_x`.
"""
o_array = input_x.asnumpy()
dic = {}
for idx in range(o_array.size):
val = o_array[idx]
if val not in dic:
dic[val] = idx
index_lst = []
u_array = unique_x.asnumpy()
for idx in range(u_array.size):
index_lst.append(dic[u_array[idx]])
return Tensor(onp.array(index_lst), input_x.dtype)
@constexpr
def _get_counts_for_unique(input_x, unique_x):
"""
Return the number of times each of the unique values comes up in the original tensor.
Args:
input_x (Tensor): The flattened input tensor of `mindspore.numpy.unique`.
unique_x (Tensor): The tensor contains the unique elements in `input_x`, sorted in ascending order.
Returns:
Tensor. The number of times each of the unique values comes up in the original tensor.
"""
dic = {}
o_array = input_x.asnumpy()
for idx in range(o_array.size):
val = o_array[idx]
if val not in dic:
dic[val] = 1
else:
dic[val] += 1
u_array = unique_x.asnumpy()
counts_lst = [dic[val] for val in u_array]
return Tensor(onp.array(counts_lst), input_x.dtype)
@constexpr
def _get_max_value(x):
"""Returns the maximum value of the input tensor `x`. """
return int(max(x.asnumpy()))
@constexpr
def _promote(dtype1, dtype2):
if dtype1 == dtype2:
@ -355,7 +293,8 @@ def _check_same_type(dtype1, dtype2):
@constexpr
def _check_is_float(dtype):
return dtype in mstype.float_type
"""Returns whether dtype is float16 or float32."""
return dtype in (mstype.float16, mstype.float32)
@constexpr

2
tests/st/numpy_native/__init__.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.

4
tests/st/numpy_native/test_array_creations.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -194,7 +194,7 @@ def match_all_arrays(mnp_res, onp_res, error=0):
def match_meta(actual, expected):
# float64 and int64 are not supported, and the defualt type for
# float64 and int64 are not supported, and the default type for
# float and int are float32 and int32, respectively
if expected.dtype == onp.float64:
expected = expected.astype(onp.float32)

5
tests/st/numpy_native/test_array_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
@ -168,6 +168,7 @@ def match_res(mnp_fn, onp_fn, *arrs, **kwargs):
def match_all_arrays(mnp_res, onp_res, error=0):
if isinstance(mnp_res, (tuple, list)):
assert len(mnp_res) == len(onp_res)
for actual, expected in zip(mnp_res, onp_res):
match_array(actual.asnumpy(), expected, error)
else:
@ -175,7 +176,7 @@ def match_all_arrays(mnp_res, onp_res, error=0):
def match_meta(actual, expected):
# float64 and int64 are not supported, and the defualt type for
# float64 and int64 are not supported, and the default type for
# float and int are float32 and int32, respectively
if expected.dtype == onp.float64:
expected = expected.astype(onp.float32)

2
tests/st/numpy_native/test_math_ops.py
View File

@ -1,4 +1,4 @@
# Copyright 2021 Huawei Technologies Co., Ltd
# Copyright 2020-2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.