# Copyright 2022 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# pylint: disable=unused-import
"""Defines math operators with functional form."""
import collections
from functools import cmp_to_key
import math
import numbers
import numpy as np
import mindspore as ms
from mindspore import log as logger
import mindspore.ops as ops
from mindspore.common import dtype as mstype
from mindspore.common.generator import default_generator
from mindspore.ops import operations as P
from mindspore.ops import composite as C
from mindspore.ops.composite.multitype_ops import _constexpr_utils as const_utils
from mindspore.ops.primitive import constexpr, _primexpr
from mindspore.ops.operations._inner_ops import TileSize
from mindspore.ops.auto_generate import Cummin, BatchMatMul, BernoulliExt, lin_space_ext_op, BitwiseAndScalar,\
BitwiseAndTensor, BitwiseOrScalar, BitwiseOrTensor, BitwiseXorScalar, BitwiseXorTensor, RemainderTensorTensor,\
RemainderTensorScalar, RemainderScalarTensor, std_mean_op, var_mean_op, InplaceErfinv
from mindspore.ops import auto_generate
from mindspore.ops.operations.math_ops import STFT
from mindspore.ops.operations.math_ops import LuUnpack
from mindspore.ops.auto_generate.pyboost_inner_prim import roll_impl, cross_impl
from mindspore.ops.auto_generate.pyboost_inner_prim import reduce_max_impl, reduce_min_impl
from mindspore.ops.operations.math_ops import Ormqr
from mindspore.ops.operations.math_ops import DivMod
from mindspore.ops.operations.array_ops import MatrixSetDiagV3, Transpose
from mindspore.ops.auto_generate import (minimum, maximum, mul, sin, sinc, sinh, cummax, real, conj, add, sub, cos,
cosh, nan_to_num, norm_op, lp_norm_v2_op, linalg_vector_norm_op, std_op,
matrix_exp, sqrt, rsqrt, square, trace, nextafter, abs, acos, acosh, angle,
asin, asinh, atan, atan2, atanh, ceil, equal, erf, erfc, erfinv, exp, expm1,
floor, floor_divide, floor_mod, gcd, greater, greater_equal, less, less_equal,
log, log1p, neg, not_equal, round_op, isfinite, argmax_ext, mean_ext_op,
sum_ext_op, prod_ext_op, all, matrix_inverse_ext, atan2_ext, sign, acos_ext,
acosh_ext, asin_ext, asinh_ext, atan_ext, tan, median_ext_op, median_dim_op,
xlogy_op, xlogy_scalar_other_op, xlogy_scalar_self_op, trunc, histc_ext,
bincount_ext, rotated_iou_op, cat, narrow, var_op, pow, pow_scalar_tensor_op,
frac_ext, pow_tensor_scalar_op, not_equal_op, isinf, addmv_op, cdist,
addbmm_op, addmm_op)
from mindspore.ops.auto_generate.gen_ops_def import add_ext, sub_ext, bmm_ext
from mindspore.ops.auto_generate import tanh
from mindspore.nn import layer
from mindspore._checkparam import check_is_number
from mindspore import _checkparam as validator
from mindspore.ops.operations.math_ops import (
Bernoulli,
BesselI0,
BesselI1,
BesselJ0,
BesselJ1,
BesselK0,
BesselK0e,
BesselY0,
BesselY1,
BesselK1,
BesselK1e,
CumulativeLogsumexp,
LuSolve,
MatrixExp,
MatrixSolve,
Median,
Fmax,
Orgqr,
Fmin,
Renorm,
Hypot,
Heaviside,
Lcm,
Gcd,
Quantile,
NanToNum,
SparseSegmentMean,
TrilIndices,
TriuIndices,
InplaceIndexAdd,
InplaceUpdateV2,
Igamma,
Igammac,
Polar,
Angle,
FFTWithSize,
)
from mindspore.common.tensor import Tensor
from mindspore.ops._primitive_cache import _get_cache_prim
from mindspore._c_expression import Tensor as Tensor_
import mindspore.ops.function as F
from mindspore.ops.operations._sequence_ops import TupleToTensor
@constexpr
def _make_tensor(val, dtype):
"""Returns the tensor with value `val` and dtype `dtype`."""
return Tensor(val, dtype)
def get_x_shape(x_shape):
s = 1
for i in x_shape:
s = s * i
return (s,)
#####################################
# Public Operation Functions.
#####################################
absolute_ = P.Abs()
cast_ = P.Cast()
tensor_add = P.Add()
tensor_ceil = P.Ceil()
tensor_div = P.RealDiv()
tensor_exp = P.Exp()
tensor_expm1 = P.Expm1()
tensor_floordiv = P.FloorDiv()
floordiv = tensor_floordiv
tensor_ge = P.GreaterEqual()
tensor_gt = greater
tensor_le = P.LessEqual()
tensor_lt = P.Less()
tensor_mod = P.FloorMod()
floormod = tensor_mod
tensor_mul = P.Mul()
tensor_pow = P.Pow()
pows = tensor_pow
tensor_sub = P.Sub()
transpose_ = P.Transpose()
xdivy_ = P.Xdivy()
tensor_div_ = P.Div()
tensor_divmod_ = DivMod()
generator_step_ = Tensor(12, mstype.int64)
#####################################
# Private Operation Functions.
#####################################
accumulate_ = P.AccumulateNV2()
acos_ = P.ACos()
acosh_ = P.Acosh()
addcdiv_ = P.Addcdiv()
addcuml_ = P.Addcmul()
addn_ = P.AddN()
angle_ = Angle()
asin_ = P.Asin()
asinh_ = P.Asinh()
atan2_ = P.Atan2()
atan_ = P.Atan()
atanh_ = P.Atanh()
batch_matmul_ = BatchMatMul()
bernoulli_ext_ = BernoulliExt()
bessel_i0_ = BesselI0()
bessel_i0e_ = P.BesselI0e()
bessel_i1_ = BesselI1()
bessel_i1e_ = P.BesselI1e()
bessel_j0_ = BesselJ0()
bessel_j1_ = BesselJ1()
bessel_k0_ = BesselK0()
bessel_k0e_ = BesselK0e()
bessel_k1_ = BesselK1()
bessel_k1e_ = BesselK1e()
bessel_y0_ = BesselY0()
bessel_y1_ = BesselY1()
bitwise_and_ = P.BitwiseAnd()
bitwise_or_ = P.BitwiseOr()
bitwise_xor_ = P.BitwiseXor()
conj_ = P.Conj()
cumprod_ = P.CumProd()
cumsum_ = P.CumSum()
cumulative_logsumexp_ = CumulativeLogsumexp()
digamma_ = P.Digamma()
dtype_ = P.DType()
eps_ = P.Eps()
erf_ = P.Erf()
erfc_ = P.Erfc()
erfinv_ext_ = P.Erfinv()
inplace_erfinv_ = InplaceErfinv()
exp2_ = P.Pow()
expand_dims_ = P.ExpandDims()
fill_v2_ = P.FillV2()
floor_ = P.Floor()
gcd_ = Gcd()
igamma_ = Igamma()
igammac_ = Igammac()
imag_ = P.Imag()
inv_ = P.math_ops.Inv()
invert_ = P.Invert()
isnan_ = P.IsNan()
lcm_ = Lcm()
lerp_ = P.Lerp()
lerp_scalar_ = P.LerpScalar()
lgamma_ = P.Lgamma()
linspace_ = P.LinSpace()
log1p_ = P.Log1p()
log_ = P.Log()
log_matrix_determinant_ = P.LogMatrixDeterminant()
logical_and_ = P.LogicalAnd()
logical_not_ = P.LogicalNot()
logical_or_ = P.LogicalOr()
logical_xor_ = P.LogicalXor()
lu_solve_ = LuSolve()
lu_unpack_ = LuUnpack()
matmul_ = P.MatMul()
matrix_determinant_ = P.MatrixDeterminant()
matrix_inverse_ = P.MatrixInverse()
mod_ = P.Mod()
nextafter_ = P.NextAfter()
ones_ = P.Ones()
polar_ = Polar()
poly_gamma_ = P.Polygamma()
rank_ = P.Rank()
reciprocal_ = P.Reciprocal()
reduce_sum_ = P.ReduceSum()
reshape_ = P.Reshape()
select_ = P.Select()
slice_ = P.Slice()
size_ = P.Size()
scalar_to_tensor_ = P.ScalarToTensor()
shape_ = P.Shape()
sparse_segment_mean_ = SparseSegmentMean()
tanh_ = P.Tanh()
tensor_round_ = P.Round()
tile_ = P.Tile()
tile_size_ = TileSize()
trunc_ = P.Trunc()
truncate_div_ = P.TruncateDiv()
truncate_mod_ = P.TruncateMod()
xlogy_ = P.Xlogy()
zeros_ = P.Zeros()
zeta_ = P.Zeta()
bitwise_and_scalar_ = BitwiseAndScalar()
bitwise_and_tensor_ = BitwiseAndTensor()
bitwise_or_scalar_ = BitwiseOrScalar()
bitwise_or_tensor_ = BitwiseOrTensor()
bitwise_xor_scalar_ = BitwiseXorScalar()
bitwise_xor_tensor_ = BitwiseXorTensor()
#####################################
# Element-wise Operation Functions.
#####################################
remainder_tensor_tensor_ = RemainderTensorTensor()
remainder_tensor_scalar_ = RemainderTensorScalar()
remainder_scalar_tensor_ = RemainderScalarTensor()
[文档]def addn(x):
"""
Computes addition of all input tensors element-wise.
All input tensors must have the same shape.
Args:
x (Union(tuple[Tensor], list[Tensor])): A tuple or list composed of Tensor.
Returns:
Tensor, has the same shape and dtype as each Tensor of `x`.
Raises:
TypeError: If `x` is neither tuple nor list.
ValueError: If there are Tensors with different shapes in `x`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> y = Tensor(np.array([4, 5, 6]), mindspore.float32)
>>> output = ops.addn([x, y, x, y])
>>> print(output)
[10. 14. 18.]
"""
return addn_(x)
[文档]def absolute(input):
"""
Alias for :func:`mindspore.ops.abs` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return abs(input)
[文档]def addcdiv(input, tensor1, tensor2, value=1):
r"""
Performs the element-wise division of tensor tensor1 by tensor tensor2,
multiply the result by the scalar value and add it to input data.
.. math::
y[i] = input[i] + value[i] * (tensor1[i] / tensor2[i])
Args:
input (Tensor): The tensor to be added.
tensor1 (Tensor): The numerator tensor.
tensor2 (Tensor): The denominator tensor.
value (Union[Tensor, Number]): The multiplier for tensor1/tensor2. Default: ``1`` .
Returns:
Tensor, has the same shape and dtype as tensor1/tensor2.
Raises:
TypeError: If dtype of `tensor1`, `tensor2`, `input` is not tensor.
ValueError: If `tensor1` could not be broadcast to a tensor with shape of `tensor2`.
ValueError: If `value` could not be broadcast to tensors with shapes of `tensor1/tensor2`.
ValueError: If `input` could not be broadcast to tensors with shapes of `value*(tensor1/tensor2)`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_data = Tensor(np.array([1, 1, 1, 1]), mindspore.float32)
>>> x1 = Tensor(np.array([1, 2, 3, 4]), mindspore.float32)
>>> x2 = Tensor(np.array([4, 3, 2, 1]), mindspore.float32)
>>> value = Tensor([1], mindspore.float32)
>>> y = ops.addcdiv(input_data, x1, x2, value)
>>> print(y)
[1.25 1.6666667 2.5 5. ]
"""
return addcdiv_(input, tensor1, tensor2, Tensor(value))
[文档]def addcmul(input, tensor1, tensor2, value=1):
r"""
Performs the element-wise product of tensor tensor1 and tensor tensor2,
multiply the result by the scalar value and add it to input data.
.. math::
output[i] = input[i] + value[i] * (tensor1[i] * tensor2[i])
Args:
input (Tensor): The tensor to be added.
tensor1 (Tensor): The tensor to be multiplied.
tensor2 (Tensor): The tensor to be multiplied.
value (Union[Tensor, Number]): The multiplier for tensor1*tensor2. Default: ``1`` .
Returns:
Tensor, has the same shape and dtype as tensor1*tensor2.
Raises:
TypeError: If dtype of `tensor1`, `tensor2`, `input` is not Tensor.
TypeError: If dtype of `input` is not one of: float32, float16, int32.
TypeError: If dtype of `tensor1` or `tensor2` is not one of: float32, float16, int32.
TypeError: If dtype of `value` is not one of: float32, float16, int32.
ValueError: If `tensor1` could not be broadcast to a tensor with shape of `tensor2`.
ValueError: If `value` could not be broadcast to tensors with shapes of `tensor1` * `tensor2`.
ValueError: If `input` could not be broadcast to tensors with shapes of `value*(tensor1*tensor2)`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_data = Tensor(np.array([1, 1, 1]), mindspore.float32)
>>> x1 = Tensor(np.array([[1], [2], [3]]), mindspore.float32)
>>> x2 = Tensor(np.array([[1, 2, 3]]), mindspore.float32)
>>> value = Tensor([1], mindspore.float32)
>>> y = ops.addcmul(input_data, x1, x2, value)
>>> print(y)
[[ 2. 3. 4.]
[ 3. 5. 7.]
[ 4. 7. 10.]]
"""
return addcuml_(input, tensor1, tensor2, Tensor(value))
[文档]def bincount(input, weights=None, minlength=0):
"""
Counts the number of occurrences of each value in `input`.
If you don't specify `minlength`, the length of output Tensor will be
the maximum value of the input `input` plus one.
If `minlength` is specified, the length of output Tensor is the value of maximum of `input` plus 1 and `minlength`.
Each value in the output Tensor marks the number of occurrences of that index in `input`.
If 'weights' is specified, the output results are weighted, i.e ``out[n] += weight[i]`` instead of ``out[n] += 1``.
Note:
If `input` contains negative value, the result will be undefined.
Args:
input (Tensor): 1-d input tensor.
weights (Tensor, optional): Weights, a tensor of the same shape as `input`. Default: ``None`` .
minlength (int, optional): A minimum number of bins for the output tensor. Default: ``0`` .
Returns:
Tensor, a tensor of shape [max(input)+1] if input is non-empty, otherwise, the shape is [0].
Raises:
TypeError: If `input` or `weights` is not a tensor.
ValueError: If `input` is not one-dimensional, or if `input` and `weights` do not have the same shape.
ValueError: If `minlength` is a negative integer.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor([2, 4, 1, 0, 0], dtype=mstype.int64)
>>> print(ops.bincount(x, minlength=7))
[2. 1. 1. 0. 1. 0. 0.]
>>> weights = Tensor([0, 0.25, 0.5, 0.75, 1], dtype=mstype.float32)
>>> print(ops.bincount(x, weights=weights))
[1.75 0.5 0. 0. 0.25]
"""
if not isinstance(input, Tensor):
raise TypeError("For math function 'bincount', 'input' must be Tensor.")
if weights is not None and not isinstance(weights, Tensor):
raise TypeError(f"For math function 'bincount', 'weights' must be Tensor, but got {type(weights)}.")
if not isinstance(minlength, int) or isinstance(minlength, bool):
raise TypeError(f"For math function 'bincount', 'minlength' must be int but got {type(minlength)}.")
if rank_(input) != 1:
raise ValueError(f"For math function 'bincount', 'input' should be one-dimensional tensor.")
if input.shape[0] == 0:
return Tensor_([])
if minlength < 0:
raise ValueError(f"For 'bincount', 'minlength' should be >= 0 but got {minlength}.")
if max(input.astype(mstype.float32)) > minlength - 1:
length = (max(input.astype(mstype.float32)) + 1).astype(mstype.int32)
else:
length = cast_(minlength, mstype.int32)
idx = F.arange(length).expand_dims(-1)
idx_mapping = equal(input, idx.astype(input.dtype))
if weights is not None:
if input.shape != weights.shape:
raise ValueError('for bincount `input` and `weights` must have the same length')
idx_mapping *= weights
return reduce_sum_(idx_mapping.astype(mstype.float32), 1).ravel()
[文档]def bucketize(input, boundaries, *, right=False):
r"""
Bucketizes `input` based on `boundaries`. If `right` is ``False``, the left boundary is closed. For each element x
in `input`, the returned index satisfies the following rules:
.. math::
\begin{cases}
boundaries[i-1] < x <= boundaries[i], & \text{if right} = False\\
boundaries[i-1] <= x < boundaries[i], & \text{if right} = True
\end{cases}
Args:
input (Tensor): A tensor containing the search value(s).
boundaries (list): A sorted list of boundary values of the buckets.
Keyword Args:
right (bool, optional): if ``False``, gets the lower bound index for each value in input from boundaries;
If ``True``, gets the upper bound index instead. Default: ``False``.
Returns:
Tensor, the indexes Tensor, with the same shape as the input, and data type is int32.
Raises:
TypeError: If `boundaries` is not a list.
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([[3, 6, 9], [3, 6, 9]]))
>>> boundaries = list(np.array([1., 3., 5., 7., 9.]))
>>> output = ops.bucketize(input, boundaries, right=True)
>>> print(output)
[[2 3 5]
[2 3 5]]
"""
bucketize_op = _get_cache_prim(P.Bucketize)
epsilon_ = 0. if right else 1.e-6
boundaries = [boundary + epsilon_ for boundary in boundaries]
return bucketize_op(boundaries)(input)
[文档]def exp2(input):
"""
Computes base two exponential of Tensor `input` element-wise.
.. math::
out_i = 2^{input_i}
Args:
input (Tensor): Input tensor.
Returns:
Tensor, has the same shape and dtype as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2, 3, 4]), mindspore.float32)
>>> output = ops.exp2(x)
>>> print(output)
[ 4. 8. 16.]
"""
tensor_2 = Tensor(np.array(2.0).astype(np.float32))
if input.dtype == mstype.float16:
tensor_2 = Tensor(np.array(2.0).astype(np.float16))
return exp2_(tensor_2, input)
[文档]def argmin(input, axis=None, keepdims=False):
"""
Returns the indices of the minimum value of a tensor across the axis.
If the shape of input tensor is :math:`(x_1, ..., x_N)`, the shape of the output tensor is
:math:`(x_1, ..., x_{axis-1}, x_{axis+1}, ..., x_N)`.
Args:
input (Tensor): Input tensor.
axis (Union[int, None], optional): Axis where the Argmin operation applies to. Default: ``None`` .
keepdims (bool, optional): Whether the output tensor retains the specified
dimension. Ignored if `axis` is None. Default: ``False`` .
Returns:
Tensor, indices of the min value of input tensor across the axis.
Raises:
TypeError: If `axis` is not an int.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32)
>>> index = ops.argmin(input_x)
>>> print(index)
2
"""
if not input.shape:
return Tensor(0)
is_axis_none = False
if axis is None:
input = reshape_(input, (-1,))
axis = 0
is_axis_none = True
out = _get_cache_prim(P.Argmin)(axis)(input)
if keepdims and not is_axis_none:
out = expand_dims_(out, axis)
return out
[文档]def negative(input):
r"""
Alias for :func:`mindspore.ops.neg` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return neg(input)
[文档]def positive(input):
r"""
Return self Tensor.
Args:
input (Tensor): Input Tensor.
Returns:
Tensor, self input.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor(np.array([-5.0, 1.5, 3.0, 100.0]), mstype.float32)
>>> print(ops.positive(x))
[ -5. 1.5 3. 100. ]
"""
_check_is_tensor("input", input, "positive")
return input
[文档]def numel(input):
r"""
Returns a Scalar of type int that represents the total number of elements in the Tensor.
Args:
input (Tensor): Input Tensor.
Returns:
int. A scalar representing the total of elements in the Tensor.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[2, 2], [2, 2]]), mindspore.float32)
>>> print(ops.numel(input_x))
4
"""
_check_is_tensor("input", input, "numel")
return input.size
[文档]def permute(input, axis):
"""
Permutes the dimensions of the input tensor according to input `axis` .
Args:
input (Tensor): Input Tensor.
axis (tuple(int)): Permute will permute the tensor to the input `axis` order.
Returns:
Tensor, has the same dimension as input tensor, with `axis` suitably permuted.
Raises:
ValueError: If `axis` is None.
ValueError: If the number of elements of `axis` is not equal to `input` ndim.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]), mindspore.float32)
>>> input_perm = (0, 2, 1)
>>> print(ops.permute(input_x, input_perm))
[[[ 1. 4.]
[ 2. 5.]
[ 3. 6.]]
[[ 7. 10.]
[ 8. 11.]
[ 9. 12.]]]
"""
return transpose_(input, axis)
[文档]def subtract(input, other, *, alpha=1):
r"""
Performs the element-wise subtract of input tensors.
.. math::
output[i] = input[i] - alpha * other[i]
Args:
input (Union[Tensor, number.Number]): Tensor or Number involved in subtraction.
other (Union[Tensor, number.Number]): Tensor or Number involved in subtraction.
Keyword Args:
alpha (Number): The multiplier for :math:`other`. Default: ``1`` .
Returns:
Tensor, has the same shape and dtype as input tensors.
Raises:
TypeError: `input` or `other` is neither Tensor nor number.Number.
TypeError: Both `input` and `other` are not Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([4, 5, 6]), mindspore.float32)
>>> y = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> z = ops.subtract(input, y, alpha=1)
>>> print(z)
[3. 3. 3.]
"""
return tensor_sub(input, alpha * other)
[文档]def multiply(input, other):
r"""
Alias for :func:`mindspore.ops.asinh`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return tensor_mul(input, other)
[文档]def div(input, other, *, rounding_mode=None):
r"""
Divides the first input tensor by the second input tensor in floating-point type element-wise.
.. math::
out_{i} = input_{i} / other_{i}
Note:
- When the two inputs have different shapes, they must be able to broadcast to a common shape.
- The two inputs can not be bool type at the same time,
[True, Tensor(True, bool\_), Tensor(np.array([True]), bool\_)] are all considered bool type.
- The two inputs comply with the implicit type conversion rules to make the data types
consistent.
Args:
input (Union[Tensor, Number, bool]): The first input is a number or
a bool or a tensor whose data type is number or bool.
other (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.
Keyword Args:
rounding_mode (str, optional): Type of rounding applied to the result. Default: ``None`` .
Three types are defined as,
- None: Default behavior, which is the same as true division in Python or `true_divide` in NumPy.
- "floor": Rounds the division of the inputs down, which is the same as floor division in Python
or `floor_divide` in NumPy.
- "trunc": Rounds the division of the inputs towards zero, which is the same as C-style integer division.
Returns:
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 `input` and `other` is not one of the following: Tensor, Number, bool.
ValueError: If `rounding_mode` value is not None, "floor" or "trunc".
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([4.0, 5.0, 6.0]), mindspore.float32)
>>> output = ops.div(x, y)
>>> print(output)
[0.25 0.4 0.5]
"""
if rounding_mode is not None and rounding_mode not in ['floor', 'trunc']:
raise ValueError("For ops.div, rounding_mode value should be None, 'floor' or 'trunc'.")
if rounding_mode:
output = tensor_divmod_(input, other, rounding_mode)
else:
output = tensor_div_(input, other)
return output
[文档]def true_divide(dividend, divisor):
r"""
Alias for :func:`mindspore.ops.div` with :math:`rounding\_mode=None`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return div(dividend, divisor)
[文档]def divide(input, other, *, rounding_mode=None):
"""
Alias for :func:`mindspore.ops.div` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return div(input, other, rounding_mode=rounding_mode)
[文档]def float_power(input, exponent):
"""
Computes `input` to the power of the exponent.
For the real number type, cast `input` and `exponent` to mindspore.float64 to calculate.
Currently, complex type calculation is not supported.
Args:
input (Union[Tensor, Number]): The first input is a tensor or a number.
exponent (Union[Tensor, Number]): The second input, if the first input is Tensor,
the second input can be Number or Tensor. Otherwise, it must be a Tensor.
Returns:
Tensor, the shape is the same as the one after broadcasting. For the complex type,
the return value type is the same as the input type. For the real number type,
the return value type is mindspore.float64.
Raises:
TypeError: If neither `input` nor `exponent` is a Tensor.
TypeError: If the data type of `input` is not Tensor or Number.
TypeError: If the data type of `exponent` is not Tensor or Number.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([-1.5, 0., 2.]))
>>> output = ops.float_power(input, 2)
>>> print(output)
[2.25 0. 4. ]
"""
if not (isinstance(input, Tensor) or isinstance(exponent, Tensor)):
raise TypeError("At least one of the types of inputs must be tensor, " + \
f"but the type of 'input' got is {type(input)}, " + \
f"and the type of 'exponent' is {type(exponent)}.")
if not isinstance(input, (Tensor, numbers.Number)):
raise TypeError(f"The type of 'input' must be Tensor or Number, but got {type(input)}.")
if not isinstance(exponent, (Tensor, numbers.Number)):
raise TypeError(f"The type of 'exponent' must be Tensor or Number, but got {type(exponent)}.")
if (isinstance(input, Tensor) and is_complex(input)) or \
(isinstance(exponent, Tensor) and is_complex(exponent)) or \
isinstance(input, complex) or isinstance(exponent, complex):
input = cast_(input, mstype.complex128)
exponent = cast_(exponent, mstype.complex128)
else:
input = cast_(input, mstype.float64)
exponent = cast_(exponent, mstype.float64)
return pow(input, exponent)
def float_power_ext(input, exponent):
"""
Computes `input` to the power of `exponent` element-wise in double precision, and always
returns a mindspore.float64 tensor.
.. math::
out_{i} = input_{i} ^ {exponent_{i}}
.. warning::
This is an experimental API that is subject to change or deletion.
Note:
Unlike `ops.pow`, this function always uses double precision for calculations, while
the precision of `ops.pow` depends on type promotion.
Currently, this function does not support complex number calculations.
Since float64 calculations are significantly slower on ascend devices compared to other data
types, it is strongly recommended to use this function only in scenarios where double precision
is required and performance is not a priority. Otherwise, using `ops.pow` is a better choice.
Args:
input (Union[Tensor, Number]): The first input is a tensor or a number.
exponent (Union[Tensor, Number]): The second input, if the first input is Tensor,
the second input can be Number or Tensor. Otherwise, it must be a Tensor.
Returns:
Tensor, the shape is the same as the one after broadcasting, the return value type
is mindspore.float64.
Raises:
TypeError: If neither `input` nor `exponent` is a Tensor.
TypeError: If the data type of `input` or `exponent` is neither a tensor nor a number,
or it contains complex numbers.
ValueError: If `input` and `exponent` have different shapes and cannot be broadcasted
to each other.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([1, 2, 3])
>>> ops.float_power(input, 2)
Tensor(shape=[3], dtype=Float64, value= [ 1.00000000e+00, 4.00000000e+00, 9.00000000e+00])
>>>
>>> exp = Tensor([2, -3, -4])
>>> ops.float_power(input, exp)
Tensor(shape=[3], dtype=Float64, value= [ 1.00000000e+00, 1.25000000e-01, 1.23456790e-02])
"""
if not (isinstance(input, Tensor) or isinstance(exponent, Tensor)):
raise TypeError("At least one of the types of inputs must be tensor, " +
f"but the type of 'input' got is {type(input)}, " +
f"and the type of 'exponent' is {type(exponent)}.")
if (not isinstance(input, (Tensor, int, float, bool))) or \
(isinstance(input, Tensor) and is_complex(input)):
raise TypeError("The type of 'input' must be Tensor or Number (excluding complex), " +
f"but got {type(input)}.")
if (not isinstance(exponent, (Tensor, int, float, bool))) or \
(isinstance(exponent, Tensor) and is_complex(exponent)):
raise TypeError("The type of 'exponent' must be Tensor or Number (excluding complex), " +
f"but got {type(exponent)}.")
op = pow
if isinstance(input, Tensor) and isinstance(exponent, numbers.Number):
op = pow_tensor_scalar_op
if isinstance(input, numbers.Number) and isinstance(exponent, Tensor):
op = pow_scalar_tensor_op
if isinstance(input, Tensor) and input.dtype != mstype.float64:
input = cast_(input, mstype.float64)
if isinstance(exponent, Tensor) and exponent.dtype != mstype.float64:
exponent = cast_(exponent, mstype.float64)
return op(input, exponent)
[文档]def floor_div(x, y):
"""
Alias for :func:`mindspore.ops.floor_divide` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return tensor_floordiv(x, y)
[文档]def fmod(input, other):
"""
Computes the floating-point remainder of the division operation input/other.
.. math::
out = input - n * other
Where :math:`n` is :math:`input/other` with its fractional part truncated.
The returned value has the same sign as `input` and is less than `other` in magnitude.
Args:
input (Union[Tensor, Number]): the dividend.
other (Union[Tensor, Number]): the divisor.
Returns:
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 `input` nor `other` is a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([-4., -3.5, 0, 3.5, 4]), mindspore.float32)
>>> output = ops.fmod(input, 2.5)
>>> print(output)
[-1.5 -1. 0. 1. 1.5]
"""
if not (isinstance(input, (Tensor, Tensor_)) or isinstance(other, (Tensor, Tensor_))):
raise TypeError("At least one of the types of inputs must be tensor, " + \
f"but the type of 'input' got is {type(input)}, " + \
f"and the type of 'other' is {type(other)}.")
return input - div(input, other, rounding_mode="trunc") * other
[文档]def logdet(input):
r"""
Calculates log determinant of one or a batch of square matrices.
Args:
input (Tensor): Tensor of shape :math:`(*, N, N)` where :math:`*` means zero or more batch dimensions.
Returns:
Tensor, the log determinant of `input`. If the matrix determinant is smaller than 0, nan will be returned. If
the matrix determinant is 0, -inf will be returned.
Raises:
TypeError: If dtype of `input` is not float32, float64, Complex64 or Complex128.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> a = Tensor([[[8, 9], [1, 2]], [[5, 6], [3, 4]]], mindspore.float32)
>>> output = ops.logdet(a)
>>> print(output)
[1.9459091 0.6931454]
"""
det_x = det(input)
return log_(det_x)
[文档]def i0(input):
r"""
Alias for :func:`mindspore.ops.bessel_i0` .
Supported Platforms:
``GPU`` ``CPU``
"""
return bessel_i0(input)
[文档]def inplace_update(x, v, indices):
"""
Updates specified values in `x` to `v` according to `indices`.
.. warning::
This is an experimental API that is subject to change or deletion.
Note:
`indices` can only be indexed along the highest dimension.
Args:
x (Tensor): A tensor which to be inplace updated. It can be one of the following data types:
float32, float16 and int32.
v (Tensor): A tensor with the same type as `x` and the same dimension size as `x` except
the first dimension, which must be the same as the size of `indices`.
indices (Union[int, tuple[int], Tensor]): Determines which rows of `x` to update with `v`,
should be several int. It is an int or tuple or tensor with one dimension,
whose value is in [-x.shape[0], x.shape[0]).
If it is a tuple or Tensor, the size of 'indices' should be the same as the first dimension of 'v'.
Returns:
Tensor, with the same type and shape as the input `x`.
Raises:
TypeError: If `indices` is neither int nor tuple nor Tensor.
TypeError: If `indices` is a tuple or Tensor, but its element is not an int.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> indices = (0, 1)
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32)
>>> v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32)
>>> output = ops.inplace_update(x, v, indices)
>>> print(output)
[[0.5 1. ]
[1. 1.5]
[5. 6. ]]
"""
inplace_update_inner = InplaceUpdateV2()
return inplace_update_inner(x, indices, v)
[文档]def inplace_add(x, v, indices):
"""
Adds `v` into specified rows of `x`. Computes `y` = `x`; y[i,] += `v`.
Note:
`indices` refers to the left-most dimension.
Args:
x (Tensor): The tensor to be added. It has shape :math:`(N,*)` where :math:`*` means
any number of additional dimensions.
v (Tensor): The value tensor add to `x`. It has the same dimension sizes as `x` except
the first dimension, whose size must be the same as `indices`. It has the same data type with `x`.
indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of `x`
to add with `v`. It is an integer or a tuple, whose value is in [0, the first dimension size of `x`).
Returns:
Tensor, has the same shape and dtype as `x`.
Raises:
TypeError: If `indices` is neither int nor tuple.
TypeError: If `indices` is a tuple whose elements are not all int.
ValueError: If the rank of `x` is not equal to the rank of `v`.
ValueError: If the length of `indices` is not equal to `v.shape[0]`.
ValueError: If the values of `indices` are not in range of `[0, x.shape[0])`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> indices = (0, 1)
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32)
>>> input_v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32)
>>> output = ops.inplace_add(x, input_v, indices)
>>> print(output)
[[1.5 3. ]
[4. 5.5]
[5. 6. ]]
"""
inplace_add_inner = _get_cache_prim(P.InplaceAdd)(indices)
return inplace_add_inner(x, v)
[文档]def inplace_index_add(var, indices, updates, axis): # pylint: disable=redefined-outer-name
"""
Adds Tensor `updates` to specified axis and indices of Tensor `var` element-wise.
Args:
var (Parameter): The input Parameter to add to, with data type uint8, int8, int16, int32,
float16, float32, float64.
indices (Tensor): The indies along `axis` to perform the addition. A 1D Tensor
of shape :math:`(updates.shape[axis],)`, every value of it
should be in range :math:`[0, var.shape[axis])` with data type int32.
updates (Tensor): The input Tensor with the value to add. Must have same data type as `var`.
The shape must be the same as `var` except the `axis` th dimension.
axis (int): The dimension along which to index. It should be in range :math:`[0, len(var.dim))`.
Returns:
Tensor, updated result, has the same shape and dtype as `var`.
Raises:
TypeError: If `var` is not a Parameter.
TypeError: If neither `indices` nor `updates` is a Tensor.
ValueError: If `axis` is out of valid range.
ValueError: If `var` rank is not the same as `updates` rank.
ValueError: If shape of `indices` is not :math:`(updates.shape[axis],)`.
ValueError: If `updates`'s shape is not the same as `var` except the `axis` th dimension.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops, Parameter
>>> var = Parameter(Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32))
>>> indices = Tensor(np.array([0, 1]), mindspore.int32)
>>> updates = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32)
>>> var = ops.inplace_index_add(var, indices, updates, axis=0)
>>> print(var)
[[1.5 3. ]
[4. 5.5]
[5. 6. ]]
"""
inplace_index_add_ = InplaceIndexAdd(axis)
return inplace_index_add_(var, indices, updates)
[文档]def inplace_sub(x, v, indices):
r"""
Subtracts `v` into specified rows of `x`. Computes :math:`y = x`; :math:`y[i,] -= input\_v`.
Note:
`indices` refers to the left-most dimension.
Args:
x (Tensor): TThe tensor to be subtracted. It has shape :math:`(N,*)` where :math:`*` means
any number of additional dimensions.
v (Tensor): The value tensor subtract from `x`. It has the same dimension sizes as `x` except
the first dimension, whose size must be the same as `indices`. It has the same data type with `x`.
indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of `x`
to subtract with `v`. It is an int or tuple, whose value is in [0, the first dimension size of `x`).
Returns:
Tensor, has the same shape and dtype as `x`.
Raises:
TypeError: If `indices` is neither int nor tuple.
TypeError: If `indices` is a tuple whose elements are not all int.
ValueError: If the rank of `x` is not equal to the rank of `v`.
ValueError: If the length of `indices` is not equal to `v.shape[0]`.
ValueError: If the values of `indices` are not in range of `[0, x.shape[0])`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> indices = (0, 1)
>>> x = Tensor(np.array([[1, 2], [3, 4], [5, 6]]), mindspore.float32)
>>> input_v = Tensor(np.array([[0.5, 1.0], [1.0, 1.5]]), mindspore.float32)
>>> output = ops.inplace_sub(x, input_v, indices)
>>> print(output)
[[0.5 1. ]
[2. 2.5]
[5. 6. ]]
"""
inplace_sub_inner = _get_cache_prim(P.InplaceSub)(indices)
return inplace_sub_inner(x, v)
[文档]def logical_not(input):
"""
Computes the "logical NOT" of a tensor element-wise.
.. math::
out_{i} = \\neg input_{i}
Args:
input (Tensor): The input tensor.
Returns:
Tensor, the shape is the same as the `input`, and the dtype is bool.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([True, False, True]), mindspore.bool_)
>>> output = ops.logical_not(x)
>>> print(output)
[False True False]
"""
return logical_not_(input)
[文档]def logical_or(input, other):
"""
Computes the "logical OR" of two tensors element-wise.
Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one bool.
When the inputs are two tensors, the shapes of them could be broadcast.
When the inputs are one tensor and one bool, the bool object could only be a constant.
.. math::
out_{i} = input_{i} \\vee other_{i}
Note:
logical_or supports broadcasting.
Args:
input (Union[Tensor, bool]): The first input is a bool or a tensor whose data type can be implicitly
converted to bool.
other (Union[Tensor, bool]): The second input is a bool when the first input is a tensor or
a tensor whose data type can be implicitly converted to bool.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([True, False, True]), mindspore.bool_)
>>> y = Tensor(np.array([True, True, False]), mindspore.bool_)
>>> output = ops.logical_or(x, y)
>>> print(output)
[ True True True]
>>> x = Tensor(1, mindspore.bool_)
>>> y = Tensor(0, mindspore.bool_)
>>> output = ops.logical_or(x, y)
>>> print(output)
True
>>> x = True
>>> y = Tensor(0, mindspore.bool_)
>>> output = ops.logical_or(x, y)
>>> print(output)
True
>>> x = True
>>> y = Tensor(np.array([True, False]), mindspore.bool_)
>>> output = ops.logical_or(x, y)
>>> print(output)
[True True]
"""
return logical_or_(input, other)
[文档]def logical_and(input, other):
r"""
Computes the "logical AND" of two tensors element-wise.
Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one bool.
When the inputs are two tensors, the shapes of them could be broadcast.
When the inputs are one tensor and one bool, the bool object could only be a constant.
.. math::
out_{i} = input_{i} \wedge other_{i}
Note:
logical_and supports broadcasting.
Args:
input (Union[Tensor, bool]): The first input is a bool or a tensor whose data type can be implicitly
converted to bool.
other (Union[Tensor, bool]): The second input is a bool when the first input is a tensor or
a tensor whose data type can be implicitly converted to bool.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Raises:
TypeError: If neither `input` nor `other` is a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([True, False, True]), mindspore.bool_)
>>> y = Tensor(np.array([True, True, False]), mindspore.bool_)
>>> output = ops.logical_and(x, y)
>>> print(output)
[ True False False]
>>> x = Tensor(1, mindspore.bool_)
>>> y = Tensor(0, mindspore.bool_)
>>> output = ops.logical_and(x, y)
>>> print(output)
False
>>> x = True
>>> y = Tensor(0, mindspore.bool_)
>>> output = ops.logical_and(x, y)
>>> print(output)
False
>>> x = True
>>> y = Tensor(np.array([True, False]), mindspore.bool_)
>>> output = ops.logical_and(x, y)
>>> print(output)
[True False]
"""
return logical_and_(input, other)
[文档]def signbit(input):
r"""
Determine the symbol of each element. If the element value is less than 0,
the corresponding output position is True; otherwise, it is False.
Args:
input (Tensor): The input value.
Returns:
Tensor, the signbit of input.
Raises:
TypeError: If input is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> input = ms.Tensor([0.3, 1.2, 0., -2.5])
>>> output = ops.signbit(input)
>>> print(output)
[False False False True]
"""
if not isinstance(input, Tensor):
raise TypeError(f"For signbit, the input must be a Tensor, but got {type(input)}")
res = ops.less(input, 0)
return res
[文档]def sgn(input):
r"""
Extension of :func:`mindspore.ops.sign` in complex domain.
For real number input, this function is the same as :func:`mindspore.ops.sign`.
For complex input, this function is calculated according to the following formula.
.. math::
\text{out}_{i} = \begin{cases}
0 & |\text{input}_i| == 0 \\
\frac{{\text{input}_i}}{|{\text{input}_i}|} & \text{otherwise}
\end{cases}
Args:
input (Tensor): The input value.
Returns:
Tensor, the sgn of input.
Raises:
TypeError: If input is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> input = ms.Tensor([[3 + 4j, 7 - 24j, 0, 6 + 8j, 8], [15 + 20j, 7 - 24j, 0, 3 + 4j, 20]], dtype=ms.complex64)
>>> output = ops.sgn(input)
>>> print(output)
[[0.6 +0.8j 0.28-0.96j 0. +0.j 0.6 +0.8j 1. +0.j ]
[0.6 +0.8j 0.28-0.96j 0. +0.j 0.6 +0.8j 1. +0.j ]]
"""
if not isinstance(input, Tensor):
raise TypeError(f"For sgn, the input must be a Tensor, but got {type(input)}")
if not ops.is_complex(input):
return ops.sign(input)
modulus = ops.ComplexAbs()(input)
zeros_mask = modulus.equal(0)
non_zero_modulus = ops.masked_fill(modulus, zeros_mask, ops.cast(1, modulus.dtype))
zeros_modulus = ops.zeros_like(non_zero_modulus)
complex_modulus = ops.Complex()(non_zero_modulus, zeros_modulus)
res = tensor_div(input, complex_modulus)
return res
[文档]def cosine_similarity(x1, x2, dim=1, eps=1e-08):
r"""
Calculate cosine similarity between `x1` and `x2` along the axis, `dim`.
.. math::
\text{similarity} = \dfrac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
Note:
Currently, broadcast of input is not supported.
Args:
x1 (Tensor): The first input Tensor.
x2 (Tensor): The second input Tensor.
dim (int, optional): Axis for calculating cosine similarity. Default: ``1`` .
eps (float, optional): Minimal value to avoid division by zero. Default: ``1e-08`` .
Returns:
Tensor, cosine similarity between x1 and x2.
Raises:
TypeError: If the dtype of x1 or x2 is neither float16 nor float32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> x1 = ms.Tensor([[-0.0256, 0.0127, -0.2475, 0.2316, 0.8037],
... [0.5809, -1.2712, -0.7038, -0.2558, 0.7494]], dtype=ms.float32)
>>> x2 = ms.Tensor([[-0.6115, -0.1965, -0.8484, 0.2389, 0.2409],
... [1.8940, -2.1997, 0.1915, 0.0856, 0.7542]], dtype=ms.float32)
>>> output = ops.cosine_similarity(x1, x2)
>>> print(output)
[0.4843164 0.81647635]
"""
molecule = ops.sum(x1 * x2, dim=dim)
denominator = (ops.norm(x1, dim=dim, ord=2) * ops.norm(x2, dim=dim, ord=2)).clip(min=eps)
output = molecule / denominator
return output
def _check_cov_weights(weights, weights_name, num_observations, valid_type, valid_type_name):
"""check cov weights valid"""
if weights.ndim > 1:
raise ValueError(
f"For cov, the {weights_name} must have one or fewer dimensions, but got {weights.ndim} dimensions.")
if weights.dtype not in valid_type:
raise TypeError(
f"For cov, the dtype of {weights_name} must be {valid_type_name} type, but got type {weights.dtype}")
if ops.numel(weights) != num_observations:
raise ValueError(
f"For cov, the numel of {weights_name} must equal the number of columns of input, "
f"but got numel:{ops.numel(weights)}, number of columns of input:{num_observations}.")
return 0
def _get_default_div_type(param):
"""get the default type when div"""
if param.dtype == mstype.float64:
return param
return param.astype(mstype.float32)
[文档]def cov(input, *, correction=1, fweights=None, aweights=None):
r"""
Given the input and weights, returns the covariance matrix (the square matrix of the covariance of each pair of
variables) of input, where the input row is the variable and the column is the observation value.
The diagonal contains each variable and its own covariance. If input is a scalar or 1D vector of a single variable,
its variance will be returned.
The unbiased sample covariance of the variables :math:`a` and :math:`b` is given by the following formula:
.. math::
\text{cov}_w(a,b) = \frac{\sum^{N}_{i = 1}(a_{i} - \bar{a})(b_{i} - \bar{b})}{N~-~1}
where :math:`\bar{a}` and :math:`\bar{b}` are the simple means of the :math:`a` and :math:`b` respectively.
If `fweights` and/or `aweights` are provided, the unbiased weighted covariance
is calculated, which is given by:
.. math::
\text{cov}_w(a,b) = \frac{\sum^{N}_{i = 1}w_i(a_{i} - \mu_a^*)(b_{i} - \mu_b^*)}{\sum^{N}_{i = 1}w_i~-~1}
where :math:`w` denotes `fweights` or `aweights` based on whichever is provided, or
:math:`w = fweights \times aweights` if both are provided, and
:math:`\mu_x^* = \frac{\sum^{N}_{i = 1}w_ix_{i} }{\sum^{N}_{i = 1}w_i}` is the weighted mean of the variable.
.. warning::
The values of `fweights` and `aweights` cannot be negative, and the negative weight scene result is undefined.
.. note::
Currently, complex number is not supported.
Args:
input (Tensor): A 2D matrix, or a scalar or 1D vector of a single variable
Keyword Args:
correction (int, optional): The difference between sample size and sample degrees of freedom.
Defaults to Bessel's correction, `correction = 1` which returns the unbiased estimate,
even if both `fweights` and `aweights` are specified. `correction = 0`
will return the simple average. Default: ``1`` .
fweights (Tensor, optional): Scalar or one-dimensional Tensor containing integer frequency weight, indicating
the number of repetition of each observation vector. Its numel must equal the number of columns of `input`.
Ignored if `None`. Default: ``None`` .
aweights (Tensor, optional): A scalar or 1D Tensor containing float observation weights represents
the importance of each observation vector. The higher the importance, the greater the corresponding value.
Its numel must equal the number of columns of `input`. Must have floating point dtype. Ignored if `None`.
Default: ``None`` .
Returns:
Tensor, the covariance matrix Tensor of `input`.
Raises:
ValueError: If the dimensions of input is greater than 2.
ValueError: If the dimensions of fweights is greater than 1.
ValueError: If the numel of fweights not equal the number of columns of input.
ValueError: If the numel of aweights not equal the number of columns of input.
ValueError: If the dimensions of aweights is greater than 1.
TypeError: If the dtype of input is bool.
TypeError: If the dtype of fweights is not an integer type.
TypeError: If the dtype of aweights is not a floating point type.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> x = ms.Tensor([[0., 3.], [5., 5.], [7., 0.]]).T
>>> print(x)
[[0. 5. 7.]
[3. 5. 0.]]
>>> print(ops.cov(x))
[[13. -3.5 ]
[-3.5 6.3333335]]
>>> print(ops.cov(x, correction=0))
[[ 8.666667 -2.3333333]
[-2.3333333 4.2222223]]
>>> fw = ms.Tensor([5, 2, 4], dtype=ms.int64)
>>> aw = ms.Tensor([0.4588, 0.9083, 0.7616], ms.float32)
>>> print(ops.cov(x, fweights=fw, aweights=aw))
[[10.146146 -3.47241 ]
[-3.47241 4.716825]]
"""
if input.ndim > 2:
raise ValueError(f"For cov, the input must have two or fewer dimensions, but got {input.ndim} dimensions.")
if input.dtype == mstype.bool_:
raise TypeError(f"For cov, the input dtype can not be bool.")
# View input tensor as 2D
input_x = input.view((1, -1)) if input.ndim < 2 else input
num_observations = input_x.shape[1]
if fweights is not None:
_check_cov_weights(fweights, "fweights", num_observations, mstype.int_type, "an integer")
if aweights is not None:
_check_cov_weights(aweights, "aweights", num_observations, mstype.float_type, "a floating point")
if fweights is not None and aweights is None:
w = fweights
elif fweights is None and aweights is not None:
w = aweights
elif fweights is not None and aweights is not None:
w = fweights * aweights
else:
w = None
if w is not None:
w_sum = w.sum()
avg = (input_x * w).sum(1) / _get_default_div_type(w_sum)
else:
w_sum = ops.cast(num_observations, mstype.int64)
avg = input_x.sum(1) / _get_default_div_type(w_sum)
if w is not None and aweights is not None and correction != 0:
norm_factor = w_sum - correction * (w * aweights).sum() / w_sum
else:
norm_factor = w_sum - correction
norm_factor = norm_factor.clip(min=0)
input_x = input_x - avg.unsqueeze(1)
c = ops.mm(input_x, (input_x * w if w is not None else input_x).T)
norm_factor = norm_factor.astype(mstype.float32)
return ops.true_divide(c, _get_default_div_type(norm_factor)).squeeze()
[文档]def t(input):
r"""
Transposes a 2-D Tensor. 1-D Tensor are returned as it is.
Args:
input (Tensor): The input Tensor.
Returns:
Tensor, the transpose of `input` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor([[1, 2, 3], [2, 3, 4]], mstype.float32)
>>> output = ops.t(x)
>>> print(output)
[[1. 2.]
[2. 3.]
[3. 4.]]
"""
if input.ndim == 2:
return transpose_(input, (1, 0))
return input
[文档]def xlogy(input, other):
r"""
Computes the first input tensor multiplied by the logarithm of second input tensor element-wise.
Returns zero when `input` is zero.
.. math::
out_i = input_{i}\ln{other_{i}}
Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one scalar.
When the inputs are two tensors, the shapes of them could be broadcast.
When the inputs are one tensor and one scalar,
the scalar could only be a constant.
.. warning::
- On Ascend, the data type of `input` and `other` must be float16 or float32.
Args:
input (Union[Tensor, numbers.Number, bool]): The first input is a numbers.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
other (Union[Tensor, numbers.Number, bool]): The second input is a numbers.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\_.
Returns:
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 `input` and `other` is not a numbers.Number or a bool or a Tensor.
TypeError: If dtype of `input` and `other` is not in [float16, float32, float64, complex64, complex128].
ValueError: If `input` could not be broadcast to a tensor with shape of `other`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([-5, 0, 4]), mindspore.float32)
>>> other = Tensor(np.array([2, 2, 2]), mindspore.float32)
>>> output = ops.xlogy(input, other)
>>> print(output)
[-3.465736 0. 2.7725887]
"""
if isinstance(input, Tensor) and isinstance(other, Tensor) and input.dtype == mstype.bool_ \
and other.dtype == mstype.bool_:
input = input.astype(mstype.float32)
other = other.astype(mstype.float32)
return xlogy_(input, other)
[文档]def xlogy_ext(input, other):
r"""
Computes the first input multiplied by the logarithm of second input element-wise.
Returns zero when `input` is zero.
.. math::
out_i = input_{i}\log{other_{i}}
Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one scalar.
When the inputs are two tensors, the shapes of them could be broadcast.
Args:
input (Union[Tensor, numbers.Number, bool]): The first input is a numbers.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
other (Union[Tensor, numbers.Number, bool]): The second input is a numbers.Number or
a bool or a tensor whose data type is number or bool when the first input is a tensor.
When the first input is Scalar, the second input must be a Tensor whose data type is number or bool.
Returns:
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 `input` and `other` is not a numbers.Number or a bool or a Tensor.
ValueError: If `input` could not be broadcast to a tensor with shape of `other`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([-5, 0, 4]), mindspore.float32)
>>> other = Tensor(np.array([2, 2, 2]), mindspore.float32)
>>> output = ops.xlogy_ext(input, other)
>>> print(output)
[-3.465736 0. 2.7725887]
"""
if isinstance(input, Tensor) and isinstance(other, Tensor):
return xlogy_op(input, other)
if isinstance(input, Tensor) and isinstance(other, (float, int, bool)):
return xlogy_scalar_other_op(input, other)
if isinstance(input, (float, int, bool)) and isinstance(other, Tensor):
return xlogy_scalar_self_op(input, other)
raise TypeError(f"For 'xlogy', at least one of input and other should be Tensor.")
[文档]def arccosh(input):
r"""
Alias for :func:`mindspore.ops.acosh`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> x = Tensor(np.array([1.0, 1.5, 3.0, 100.0]), mindspore.float32)
>>> output = ops.arccosh(x)
>>> print(output)
[0. 0.9624237 1.7627472 5.298292 ]
"""
return acosh_(input)
[文档]def arccosh_ext(input):
r"""
Alias for :func:`mindspore.ops.acosh_ext`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return acosh_ext(input)
[文档]def arcsin(x):
r"""
Alias for :func:`mindspore.ops.asin`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return asin_(x)
[文档]def arcsin_ext(x):
r"""
Alias for :func:`mindspore.ops.asin_ext`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return asin_ext(x)
[文档]def arctan(input):
r"""
Alias for :func:`mindspore.ops.atan`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> x = Tensor(np.array([1.0, 0.0]), mindspore.float32)
>>> output = ops.arctan(x)
>>> print(output)
[0.7853982 0. ]
"""
return atan_(input)
[文档]def arctan_ext(input):
r"""
Alias for :func:`mindspore.ops.atan_ext`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return atan_ext(input)
[文档]def arctan2(input, other):
r"""
Alias for :func:`mindspore.ops.atan2`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> x = Tensor(np.array([0, 1]), mindspore.float32)
>>> y = Tensor(np.array([1, 1]), mindspore.float32)
>>> output = ops.arctan2(x, y)
>>> print(output)
[0. 0.7853982]
"""
return atan2_(input, other)
[文档]def arctan2_ext(input, other):
r"""
Alias for :func:`mindspore.ops.atan2_ext`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> x = Tensor(np.array([0, 1]), mindspore.float32)
>>> y = Tensor(np.array([1, 1]), mindspore.float32)
>>> output = ops.arctan2_ext(x, y)
>>> print(output)
[0. 0.7853982]
"""
return atan2_ext(input, other)
[文档]def polar(abs, angle): # pylint: disable=redefined-outer-name
r"""
Converts polar coordinates to Cartesian coordinates.
Returns a complex tensor, its elements are Cartesian coordinates constructed with the polar
coordinates which is specified by radial distance `abs` and polar angle `angle`.
.. math::
y_{i} = abs_{i} * \cos(angle_{i}) + abs_{i} * \sin(angle_{i}) * j
Args:
abs (Tensor, float): Radial distance. Tensor of any dimension,
with dtype required to be float32.
angle (Tensor, float): Polar angle. It has the same shape and dtype as `abs`.
Returns:
Tensor, with the same shape as `abs` and the dtype is complex64.
Raises:
TypeError: If neither `abs` nor `angle` is a Tensor.
TypeError: If the dtype of input is not one of: float32.
TypeError: If the dtypes of `abs` and `angle` are not the same.
ValueError: If `abs`'s shape is not the same as `angle`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> abs = Tensor(np.array([1, 2]), mindspore.float64)
>>> angle = Tensor(np.array([np.pi / 2, 5 * np.pi / 4]), mindspore.float64)
>>> output = ops.polar(abs, angle)
>>> print(output)
[ 6.12323400e-17+1.j -1.41421356e+00-1.41421356j]
"""
return polar_(abs, angle)
[文档]def pow_ext(input, exponent):
"""
Calculates the `exponent` power of each element in `input`.
When `exponent` is a Tensor, the shapes of `input` and `exponent` must be broadcastable.
.. math::
out_{i} = input_{i} ^{ exponent_{i}}
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Union[Tensor, Number]): The first input is a Number or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
exponent (Union[Tensor, Number]): The second input is a Number or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
Returns:
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 types of `input` and `exponent` are bool.
TypeError: The `input` is tensor and of type int or bool, while the `exponent` is negative int.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> exponent = 3.0
>>> output = ops.pow(input, exponent)
>>> print(output)
[ 1. 8. 64.]
>>>
>>> input = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> exponent = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32)
>>> output = ops.pow(input, exponent)
>>> print(output)
[ 1. 16. 64.]
"""
if isinstance(input, Tensor) and isinstance(exponent, numbers.Number):
return pow_tensor_scalar_op(input, exponent)
if isinstance(input, numbers.Number) and isinstance(exponent, Tensor):
return pow_scalar_tensor_op(input, exponent)
return pow(input, exponent)
[文档]def arccos(input):
"""
Alias for :func:`mindspore.ops.acos` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return acos(input)
[文档]def arccos_ext(input):
"""
Alias for :func:`mindspore.ops.acos_ext` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return acos_ext(input)
[文档]def arcsinh(input):
r"""
Alias for :func:`mindspore.ops.asinh`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return asinh(input)
[文档]def arcsinh_ext(input):
r"""
Alias for :func:`mindspore.ops.asinh_ext`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return asinh_ext(input)
[文档]def arctanh(input):
r"""
Alias for :func:`mindspore.ops.atanh`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return atanh(input)
[文档]def bitwise_and(input, other):
r"""
Returns bitwise `and` of two tensors element-wise.
.. math::
out_i = input_{i} \wedge other_{i}
Args of `input` and `other` comply with the implicit type conversion rules to
make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
Args:
input (Tensor): The first input tensor with shape :math:`(N, *)` where :math:`*` means
any number of additional dimensions.
other (Tensor): The second input tensor with the same dtype as `input`.
Returns:
Tensor, has the same type as the `input`.
Raises:
TypeError: If `input` or `other` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> other = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_and(input, other)
>>> print(output)
[ 0 0 1 -1 1 0 1]
"""
return bitwise_and_(input, other)
[文档]def bitwise_and_ext(input, other):
r"""
Returns bitwise `and` of two tensors element-wise.
.. math::
out_i = input_{i} \wedge other_{i}
Note:
Args of `input` and `other` comply with the implicit type conversion rules to
make the data types consistent.
If they have different data types, the lower precision data type will be converted to
the relatively highest precision data type.
Args:
input (Tensor): The input tensor.
other (Tensor, Number.number): The input tensor or scalar. It has the same shape
with `input` or its shape is able to broadcast with `input`.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is same as `input`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> other = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_and_ext(input, other)
>>> print(output)
[ 0 0 1 -1 1 0 1]
"""
if not isinstance(other, Tensor):
return bitwise_and_scalar_(input, other)
return bitwise_and_tensor_(input, other)
[文档]def bitwise_or(input, other):
r"""
Returns bitwise `or` of two tensors element-wise.
.. math::
out_i = input_{i} \mid other_{i}
Args of `input` and `other` comply with the implicit type conversion rules to
make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
Args:
input (Tensor): The first input tensor with shape :math:`(N, *)` where :math:`*` means
any number of additional dimensions.
other (Tensor): The second input tensor with the same dtype as `input`.
Returns:
Tensor, has the same type as the `input`.
Raises:
TypeError: If `input` or `other` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> other = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_or(input, other)
>>> print(output)
[ 0 1 1 -1 -1 3 3]
"""
return bitwise_or_(input, other)
[文档]def bitwise_or_ext(input, other):
r"""
Returns bitwise `or` of two tensors element-wise.
.. math::
out_i = input_{i} \mid other_{i}
Note:
Args of `input` and `other` comply with the implicit type conversion rules to
make the data types consistent.
If they have different data types, the lower precision data type will be converted to
the relatively highest precision data type.
Args:
input (Tensor): The input tensor.
other (Tensor, Number.number): The input tensor or scalar. It has the same shape
with `input` or its shape is able to broadcast with `input`.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is same as `input`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> other = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_or_ext(input, other)
>>> print(output)
[ 0 1 1 -1 -1 3 3]
"""
if not isinstance(other, Tensor):
return bitwise_or_scalar_(input, other)
return bitwise_or_tensor_(input, other)
[文档]def bitwise_xor(input, other):
r"""
Returns bitwise `xor` of two tensors element-wise.
.. math::
out_i = input_{i} \oplus other_{i}
Args of `input` and `other` comply with the implicit type conversion rules to
make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
Args:
input (Tensor): The first input tensor with shape :math:`(N, *)` where :math:`*` means
any number of additional dimensions.
other (Tensor): The second input tensor with the same dtype as `input`.
Returns:
Tensor, has the same type as the `input`.
Raises:
TypeError: If `input` or `other` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> other = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_xor(input, other)
>>> print(output)
[ 0 1 0 0 -2 3 2]
"""
return bitwise_xor_(input, other)
[文档]def bitwise_xor_ext(input, other):
r"""
Returns bitwise `xor` of two tensors element-wise.
.. math::
out_i = input_{i} \oplus other_{i}
Note:
Args of `input` and `other` comply with the implicit type conversion rules to
make the data types consistent.
If they have different data types, the lower precision data type will be converted to
the relatively highest precision data type.
Args:
input (Tensor): The input tensor.
other (Tensor, Number.number): The input tensor or scalar. It has the same shape
with `input` or its shape is able to broadcast with `input`.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is same as `input`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0, 1, -1, 1, 1, 1]), mindspore.int16)
>>> other = Tensor(np.array([0, 1, 1, -1, -1, 2, 3]), mindspore.int16)
>>> output = ops.bitwise_xor_ext(input, other)
>>> print(output)
[ 0 1 0 0 -2 3 2]
"""
if not isinstance(other, Tensor):
return bitwise_xor_scalar_(input, other)
return bitwise_xor_tensor_(input, other)
[文档]def bitwise_left_shift(input, other):
r"""
Perform a left bitwise shift operation on the `input` element-wise, where the number of bits to shift is
specified by `other`.
.. math::
\begin{aligned}
&out_{i} =input_{i} << other_{i}
\end{aligned}
Args:
input (Union[Tensor, int, bool]): The input to be left shifted.
other (Union[Tensor, int, bool]): The number of bit to be applied on left arithmetic shift.
Returns:
Tensor, the result after bitwise left shift.
Raises:
TypeError: If neither `input` nor `other` is a tensor.
TypeError: If either `input` or `other` is not a bool, int or a tensor of dtype: int or uint.
TypeError: If `input` and `other` do not have the same dtype.
ValueError: If `input` and `other` could not be broadcast.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1024, 2]), mindspore.int16)
>>> other = Tensor(np.array([2]), mindspore.int16)
>>> output = ops.bitwise_left_shift(input, other)
>>> print(output)
[4096 8]
"""
if not isinstance(input, Tensor) and not isinstance(other, Tensor):
raise TypeError(f"For 'bitwise_left_shift', at least one of the inputs should be a Tensor.")
cast = ops.Cast()
if isinstance(input, numbers.Number):
if not isinstance(input, int):
raise TypeError(f"For 'bitwise_left_shift', 'input' must be an integer, but got input:{type(input)}.")
input = cast(input, other.dtype)
elif isinstance(other, numbers.Number):
if not isinstance(other, int):
raise TypeError(f"For 'bitwise_left_shift', 'other' must be an integer, but got other:{type(other)}.")
other = cast(other, input.dtype)
ls = ops.LeftShift()
return ls(input, other)
[文档]def bitwise_right_shift(input, other):
r"""
Perform a right bitwise shift operation on the `input` element-wise, where the number of bits to shift is
specified by `other`.
.. math::
\begin{aligned}
&out_{i} =input_{i} >> other_{i}
\end{aligned}
Args:
input (Union[Tensor, int, bool]): The input to be right shifted.
other (Union[Tensor, int, bool]): The number of bit to be applied on right arithmetic shift.
Returns:
Tensor, the result after bitwise right shift.
Raises:
TypeError: If neither `input` nor `other` is a tensor.
TypeError: If either `input` or `other` is not a bool, int or a tensor of dtype: int or uint.
TypeError: If `input` and `other` do not have the same dtype.
ValueError: If `input` and `other` could not be broadcast.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1024, 2]), mindspore.int16)
>>> other = Tensor(np.array([2]), mindspore.int16)
>>> output = ops.bitwise_right_shift(input, other)
>>> print(output)
[256 0]
"""
if not isinstance(input, Tensor) and not isinstance(other, Tensor):
raise TypeError(f"For 'bitwise_left_shift', at least one of the inputs should be a Tensor.")
cast = ops.Cast()
if isinstance(input, numbers.Number):
if not isinstance(input, int):
raise TypeError(f"For 'bitwise_left_shift', 'input' must be an integer, but got input:{type(input)}.")
input = cast(input, other.dtype)
elif isinstance(other, numbers.Number):
if not isinstance(other, int):
raise TypeError(f"For 'bitwise_left_shift', 'other' must be an integer, but got other:{type(other)}.")
other = cast(other, input.dtype)
rs = ops.RightShift()
return rs(input, other)
[文档]def inv(x):
r"""
Computes Reciprocal of input tensor element-wise.
.. math::
out_i = \frac{1}{x_{i} }
Args:
x (Tensor): Tensor of any dimension. Must be one of the following types: float16, float32 or int32.
Returns:
Tensor, has the same type and shape as input shape value.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If dtype of `x` is not one of float16, float32, int32.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.25, 0.4, 0.31, 0.52]), mindspore.float32)
>>> output = ops.inv(x)
>>> print(output)
[4. 2.5 3.2258065 1.923077 ]
"""
return inv_(x)
[文档]def inverse(input):
"""
Compute the inverse of the input matrix.
Args:
input (Tensor): A matrix to be calculated. Input `input` must be at least two dimensions, and the size of
the last two dimensions must be the same size. And the matrix must be invertible.
Returns:
Tensor, has the same type and shape as input `input`.
Raises:
TypeError: If `input` is not a Tensor.
ValueError: If the size of the last two dimensions of `input` is not the same.
ValueError: If the dimension of `input` is less than 2.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor([[1., 2.], [3., 4.]], mstype.float32)
>>> print(ops.inverse(x))
[[-2. 1. ]
[ 1.5 -0.5]]
"""
_check_is_tensor("input", input, "inverse")
return matrix_inverse_(input)
[文档]def inverse_ext(input):
"""
Compute the inverse of the input matrix.
Args:
input (Tensor): A matrix to be calculated. Input `input` must be at least two dimensions, and the size of
the last two dimensions must be the same size. And the matrix must be invertible.
Returns:
Tensor, has the same type and shape as input `input`.
Raises:
ValueError: If the size of the last two dimensions of `input` is not the same.
ValueError: If `input` is not empty and its dimensions are less than 2.
ValueError: If the dimensions of `input` are larger than 6.
Supported Platforms:
``Ascend``
Examples:
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor([[1., 2.], [3., 4.]], mstype.float32)
>>> print(ops.inverse_ext(x))
[[-2. 1. ]
[ 1.5 -0.5]]
"""
return matrix_inverse_ext(input)
[文档]def invert(x):
r"""
Flips all bits of input tensor element-wise.
.. math::
out_i = \sim x_{i}
Args:
x (Tensor): The input Tensor of shape :math:`(x_1, x_2, ..., x_R)`.
The data type should be one of the following types: int16, uint16.
Returns:
Tensor, has the same shape as `x`.
Raises:
TypeError: If dtype of `x` is neither int16 nor uint16.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([25, 4, 13, 9]), mindspore.int16)
>>> output = ops.invert(x)
>>> print(output)
[-26 -5 -14 -10]
"""
return invert_(x)
[文档]def bessel_j0(x):
r"""
Computes Bessel function of the first kind, order 0 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
J_{0}(x) = \frac{1}{\pi} \int_{0}^{\pi} \cos (x \sin \theta) d \theta
=\sum_{m=0}^{\infty} \frac{(-1)^{m} x^{2 m}}{2^{2 m} (m !)^2}
\end{array}
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_j0(x)
>>> print(output)
[0.93846981 0.76519769 0.22389078 -0.39714981]
"""
return bessel_j0_(x)
[文档]def bessel_j1(x):
r"""
Computes Bessel function of the first kind, order 1 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
J_{1}(x) = \frac{1}{\pi} \int_{0}^{\pi} \cos (x \sin \theta- \theta) d \theta
=\sum_{m=0}^{\infty} \frac{(-1)^{m} x^{2 m+1}}{2^{2 m+1} m !(m+1) !}
\end{array}
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_j1(x)
>>> print(output)
[0.24226846 0.44005059 0.57672481 -0.06604333]
"""
return bessel_j1_(x)
[文档]def bessel_i0(x):
r"""
Computes modified Bessel function of the first kind, order 0 element-wise.
.. math::
\begin{array}{ll} \\
I_{0}(x)=J_{0}(\mathrm{i} x)=\sum_{m=0}^{\infty}
\frac{x^{2 m}}{2^{2 m} (m !)^{2}}
\end{array}
where :math:`J_{0}` is Bessel function of the first kind, order 0.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-1, -0.5, 0.5, 1]), mindspore.float32)
>>> output = ops.bessel_i0(x)
>>> print(output)
[1.266066 1.0634835 1.0634835 1.266066]
"""
return bessel_i0_(x)
[文档]def bessel_i0e(x):
r"""
Computes exponential scaled modified Bessel function of the first kind, order 0 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
\text I_{0}e(x)=e^{(-|x|)} * I_{0}(x)=e^{(-|x|)} * \sum_{m=0}^
{\infty} \frac{x^{2 m}}{2^{2 m} (m !)^{2}}
\end{array}
where :math:`I_{0}` is modified Bessel function of the first kind, order 0.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-1, -0.5, 0.5, 1]), mindspore.float32)
>>> output = ops.bessel_i0e(x)
>>> print(output)
[0.46575961 0.64503527 0.64503527 0.46575961]
"""
return bessel_i0e_(x)
[文档]def bessel_k0(x):
r"""
Computes modified Bessel function of the second kind, order 0 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
K_{0}(x)= \lim_{\nu \to 0} \left(\frac{\pi}{2}\right) \frac
{I_{-\nu}(x)-I_{\nu}(x)}{\sin (\nu \pi)} = \int_{0}^{\infty} e^{-x \cosh t} d t
\end{array}
where :math:`I_{0}` is modified Bessel function of the first kind, order 0.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_k0(x)
>>> print(output)
[0.92441907 0.42102444 0.11389387 0.01115968]
"""
return bessel_k0_(x)
[文档]def bessel_k0e(x):
r"""
Computes exponential scaled modified Bessel function of the second kind, order 0 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
K_{0}e(x)= e^{(-|x|)} * K_{0}(x) = e^{(-|x|)} * \int_{0}^
{\infty} e^{-x \cosh t} d t
\end{array}
where :math:`K_{0}` is modified Bessel function of the second kind, order 0.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_k0e(x)
>>> print(output)
[1.52410939 1.14446308 0.84156822 0.60929767]
"""
return bessel_k0e_(x)
[文档]def bessel_y0(x):
r"""
Computes Bessel function of the second kind, order 0 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
Y_{0}(x)=\lim_{n \to 0} \frac{J_{n}(x) \cos n \pi-J_{-n}(x)}{\sin n \pi}
\end{array}
where :math:`J_{0}` is Bessel function of the first kind, order 0.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_y0(x)
>>> print(output)
[-0.44451874 0.08825696 0.51037567 -0.01694074]
"""
return bessel_y0_(x)
[文档]def bessel_y1(x):
r"""
Computes Bessel function of the second kind, order 1 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
Y_{1}(x)=\lim_{n \to 1} \frac{J_{n}(x) \cos n \pi-J_{-n}(x)}{\sin n \pi}
\end{array}
where :math:`J_{1}` is Bessel function of the first kind, order 1.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_y1(x)
>>> print(output)
[-1.47147239 -0.78121282 -0.10703243 0.39792571]
"""
return bessel_y1_(x)
[文档]def eps(x):
r"""
Create a Tensor with the same data type and shape as input, and the element value is the minimum value that the
corresponding data type can express.
Args:
x (Tensor): Tensor of any dimension used to obtain the minimum value that its data type can express.
The data type must be float16, float32 or float64.
Returns:
Tensor, has the same type and shape as `x`, but filled with `x` dtype minimum val.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If data type of `x` is neither float16, float32, nor float64.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> x = Tensor([4, 1, 2, 3], mindspore.float32)
>>> output = ops.eps(x)
>>> print(output)
[1.1920929e-07 1.1920929e-07 1.1920929e-07 1.1920929e-07]
"""
return eps_(x)
def erfinv_(input):
r"""
Update the `input` tensor in-place by computing the inverse error function with `input`, which is defined in the
range `(-1, 1)` as:
.. math::
erfinv(erf(input)) = input
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor to compute with.
Returns:
Tensor.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `input.dtype` is not one of: bool, int8, int16, int32, int64, uint8, float16, float32, bfloat16.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0, 0.5, -0.9]), mindspore.float32)
>>> output = ops.erfinv_(input)
>>> print(output)
[ 0. 0.47693613 -1.1630869 ]
"""
return inplace_erfinv_(input)
[文档]def linspace(start, end, steps):
r"""
Returns a Tensor whose value is `steps` evenly spaced in the interval `start` and `end` (including `start` and
`end`), and the length of the output Tensor is `steps`.
.. math::
\begin{aligned}
&step = (end - start)/(steps - 1)\\
&output = [start, start+step, start+2*step, ... , end]
\end{aligned}
Args:
start (Union[Tensor, int, float]): Start value of interval. The tensor data type must be float32 or float64
and with shape of 0-D.
end (Union[Tensor, int, float]): Last value of interval. The tensor data type must be float32 or float64
and with shape of 0-D.
steps (Union[Tensor, int]): Number of ticks in the interval, inclusive of start and end.
Must be positive int number or 0D int32/int64 Tensor.
Returns:
Tensor, has the same dtype as `start`, and the shape of :math:`(steps)`.
Raises:
TypeError: If `start` or `end` is not a Tensor.
TypeError: If dtype of `start` or dtype of `end` is not float32 or float64.
ValueError: If shape of `start` or shape of `end` is not 0-D.
TypeError: If `steps` is not int or 0D int32/int64 Tensor.
ValueError: If `steps` is not positive int number.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> start = Tensor(1, mindspore.float32)
>>> end = Tensor(10, mindspore.float32)
>>> steps = 5
>>> output = ops.linspace(start, end, steps)
>>> print(output)
[ 1. 3.25 5.5 7.75 10. ]
"""
if not isinstance(start, Tensor):
start = Tensor(start, mstype.float32)
if not isinstance(end, Tensor):
end = Tensor(end, mstype.float32)
return linspace_(start, end, steps)
[文档]def linspace_ext(start, end, steps, *, dtype=None):
r"""
Returns a Tensor whose value is `steps` evenly spaced in the interval `start` and `end` (including `start` and
`end`), and the length of the output Tensor is `steps`.
.. math::
\begin{aligned}
&step = (end - start)/(steps - 1)\\
&output = [start, start+step, start+2*step, ... , end]
\end{aligned}
.. warning::
Atlas training series does not support int16 dtype currently.
Args:
start (Union[float, int]): Start value of interval.
It can be a float or integer.
end (Union[float, int]): Last value of interval.
It can be a float or integer.
steps (int): Number of ticks in the interval, inclusive of start and end.
Must be positive integer.
Keyword Args:
dtype (mindspore.dtype, optional): The output Tensor data type. Default: ``None`` ,
in which case the data type of output Tensor is float32.
Returns:
Tensor, has the shape of :math:`(steps,)`, with dtype specified by `dtype`.
Raises:
TypeError: If type of `start` or dtype of `end` is not supported.
ValueError: If `steps` is not positive integer.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> start = 1
>>> end = 10
>>> steps = 5
>>> output = ops.function.math_func.linspace_ext(start, end, steps, dtype=ms.float32)
>>> print(output)
[ 1. 3.25 5.5 7.75 10. ]
"""
return lin_space_ext_op(start, end, steps, dtype)
def det(input):
r"""
Computes the determinant of one or more square matrices.
Args:
input (Tensor): A matrix to be calculated, its shape should be :math:`[..., M, M]` who must
have at least two dimensions, and the last two
dimensions must be the same size. Data type must be float32, float64, complex64 or complex128.
Returns:
Tensor. The shape is :math:`input.shape[:-2]`, and the dtype is same as `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` not float32, float64, complex64 or complex128.
ValueError: If the last two dimensions of `input` is not same size.
ValueError: If the dimension of `input` is less than 2.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]), mindspore.float32)
>>> output = ops.det(input)
>>> print(output)
[-16.5 21. ]
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return matrix_determinant_(input)
def matrix_determinant(input):
r"""
`matrix_determinant` is deprecated, please use `det` instead.
"""
logger.warning("matrix_determinant is deprecated, please use `det` instead.")
return matrix_determinant_(input)
def log_matrix_determinant(input):
r"""
`log_matrix_determinant` is deprecated, please use `matrix_solve` instead.
"""
logger.warning("`log_matrix_determinant` is deprecated, please use `matrix_solve` instead.")
return log_matrix_determinant_(input)
[文档]def lu_solve(b, LU_data, LU_pivots):
r"""
Computes the solution y to the system of linear equations :math:`Ay = b` ,
given LU decomposition :math:`A` and column vector :math:`b`.
LU decomposition of a matrix can be generated from :func:`mindspore.scipy.linalg.lu_factor` .
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
b (Tensor): Column vector `b` in the above equation. It has shape :math:`(*, m, k)`,
where :math:`*` is batch dimensions, with data type float32, float16.
LU_data (Tensor): LU decomposition. It has shape :math:`(*, m, m)`, where :math:`*` is batch
dimensions, that can be decomposed into an upper triangular matrix U and a lower triangular
matrix L, with data type float32, float16.
LU_pivots (Tensor): Permutation matrix P of LU decomposition. It has
shape :math:`(*, m)`, where :math:`*` is batch dimensions, that can be converted
to a permutation matrix P, with data type int32.
Returns:
Tensor, the same data type as the `b` and `LU_data`.
Raises:
TypeError: If dtype of `b` or `LU_data` is not one of: float32, float16.
TypeError: If dtype of `LU_pivots` is not: int32.
TypeError: If `b`, `LU_data` or `LU_pivots` is not Tensor.
TypeError: If dtype of `b` is not same as dtype of `LU_data`.
ValueError: If the batch dimensions of LU_pivots does not match the batch dimensions of LU_data.
ValueError: If `b` dimension less than 2, `LU_data` dimension less than 2 or `LU_pivots` dimension less than 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> b = Tensor(np.array([[1], [3], [3]]), mindspore.float32)
>>> LU_data = Tensor(np.array([[2, 1, 1], [0.5, 1, 1.5], [0.5, 0, 2.5]]), mindspore.float32)
>>> LU_pivots = Tensor(np.array([2, 2, 3]), mindspore.int32)
>>> y = ops.lu_solve(b, LU_data, LU_pivots)
>>> print(y)
[[ 1.9000002]
[-1.4000001]
[ 0.6 ]]
"""
out = lu_solve_(b, LU_data, LU_pivots)
return out
[文档]def matrix_solve(matrix, rhs, adjoint=False): # pylint: disable=redefined-outer-name
r"""
Solves systems of linear equations.
.. math::
\begin{aligned}
&matrix[..., M, M] * x[..., M, K] = rhs[..., M, K]\\
&adjoint(matrix[..., M, M]) * x[..., M, K] = rhs[..., M, K]
\end{aligned}
.. warning::
On GPU, if the matrix is irreversible, an error may be reported or an unknown result may be returned.
Args:
matrix (Tensor): The shape of tensor is :math:`(..., M, M)` .
rhs (Tensor): The shape of tensor is :math:`(..., M, K)` . `rhs` must have the same dtype as `matrix`.
adjoint(bool): Indicating whether to solve with matrix or its (block-wise) adjoint. Default: ``False`` .
Returns:
x (Tensor), The dtype and shape is the same as 'rhs'.
Raises:
TypeError: If adjoint is not the type of bool.
TypeError: If the type of matrix is not one of the following dtype:
mstype.float16, mstype.float32, mstype.float64, mstype.complex64, mstype.complex128.
TypeError: If the type of `matrix` is not the same as that of `rhs`.
ValueError: If the rank of `matrix` less than 2.
ValueError: If the dimension of `matrix` is not the same as `rhs`.
ValueError: If the inner-most 2 dimension of `matrix` is not the same.
ValueError: If the inner-most 2 dimension of `rhs` does not match `matrix`.
ValueError: If the `matrix` is irreversible.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> matrix = Tensor([[5, 4], [3, 1]], mindspore.float32)
>>> rhs = Tensor([[7], [2]], mindspore.float32)
>>> result = ops.matrix_solve(matrix, rhs)
>>> print(result)
[[0.14285707]
[1.5714287 ]]
"""
matrix_solve_ = _get_cache_prim(MatrixSolve)(adjoint=adjoint)
return matrix_solve_(matrix, rhs)
[文档]def slogdet(input):
r"""
Computes the sign and the log of the absolute value of the determinant of one or more square matrices.
Note:
The type of output always be real-value, even `input` is complex.
Args:
input (Tensor): A matrix to be calculated, its shape is :math:`(..., M, M)`.
The matrix must be at least two dimensions, and the last two
dimensions must be the same size. Data type must be float32, float64, complex64 or complex128.
Returns:
Tensor. The signs of the log determinants. The shape is :math:`input.shape[:-2]`.
Tensor. The absolute values of the log determinants. The shape is :math:`input.shape[:-2]`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` not float32, float64, complex64 or complex128.
ValueError: If the last two dimensions of `input` is not same size.
ValueError: If the dimension of `input` is less than 2.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]), mindspore.float32)
>>> sign, output = ops.slogdet(input_x)
>>> print(sign)
[-1. 1.]
>>> print(output)
[2.80336046e+00 3.04452229e+00]
"""
return log_matrix_determinant_(input)
[文档]def truncate_div(x, y):
"""
Divides the first input tensor by the second input tensor element-wise and rounds the results
of division towards zero. Equivalent to C-style integer division.
Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
When the inputs are two tensors,
dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.
When the inputs are one tensor and one scalar,
the scalar could only be a constant.
Note:
Broadcasting is supported.
Args:
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.
Returns:
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 `x` and `y` is not one of the following: Tensor, Number, bool.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2, 4, -1]), mindspore.int32)
>>> y = Tensor(np.array([3, 3, 3]), mindspore.int32)
>>> output = ops.truncate_div(x, y)
>>> print(output)
[0 1 0]
"""
return truncate_div_(x, y)
[文档]def truncate_mod(x, y):
r"""
Returns the remainder of division element-wise.
Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
When the inputs are two tensors,
dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.
When the inputs are one tensor and one scalar,
the scalar could only be a constant.
.. warning::
- The input data does not support 0.
- When the elements of input exceed 2048 , the accuracy of operator cannot guarantee the requirement of
double thousandths in the mini form.
- Due to different architectures, the calculation results of this operator on NPU and CPU may be inconsistent.
- If shape is expressed as (D1,D2... ,Dn), then D1\*D2... \*DN<=1000000,n<=8.
Args:
x (Union[Tensor, numbers.Number, bool]): The first input is a number, or a bool,
or a tensor whose data type is number or bool.
y (Union[Tensor, numbers.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.
Returns:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision among the two inputs.
Raises:
TypeError: If neither `x` nor `y` is one of the following: Tensor, number, bool.
TypeError: If neither `x` nor `y` is a Tensor.
ValueError: If the shape `x` and `y` cannot be broadcasted to each other.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2, 4, -1]), mindspore.int32)
>>> y = Tensor(np.array([3, 3, 3]), mindspore.int32)
>>> output = ops.truncate_mod(x, y)
>>> print(output)
[ 2 1 -1]
"""
return truncate_mod_(x, y)
[文档]def ldexp(x, other):
"""
Multiplies input Tensor by :math:`2^{other}` element-wise.
It takes two arguments, a mantissa `x` and an exponent `other`,
and returns their product as a floating-point number:
.. math::
out_{i} = x_{i} * ( 2 ^{other_{i}} )
Note:
This function is commonly used to construct
floating-point numbers from their component parts, or to scale a
floating-point number by a power of two.
Args:
x (Tensor): The input Tensor.
other (Tensor): A Tensor of integers that represent exponents.
Returns:
Tensor, the output Tensor.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `other` is not a Tensor.
ValueError: If shape of `x` and `other` can not broadcast.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> x = Tensor(np.array([1.]), mindspore.float32)
>>> other = Tensor(np.array([1, 2, 3, 4]), mindspore.int32)
>>> out = ops.ldexp(x, other)
>>> print(out)
[ 2. 4. 8. 16.]
>>> x = Tensor(np.array([[1.], [2]]), mindspore.float32)
>>> other = Tensor(np.array([[1.], [2]]), mindspore.int32)
>>> out = ops.ldexp(x, other)
>>> print(out)
[[2.]
[8.]]
"""
out = tensor_mul(x, tensor_pow(2.0, other))
return out
[文档]def logit(input, eps=None):
r"""
Calculate the logit of a tensor element-wise.
.. math::
\begin{align}
y_{i} & = \ln(\frac{z_{i}}{1 - z_{i}}) \\
z_{i} & = \begin{cases}
input_{i} & \text{if eps is None} \\
\text{eps} & \text{if } input_{i} \lt \text{eps} \\
input_{i} & \text{if } \text{eps} \leq input_{i} \leq 1 - \text{eps} \\
1 - \text{eps} & \text{if } input_{i} \gt 1 - \text{eps}
\end{cases}
\end{align}
Args:
input (Tensor): The input tensor of type float16, float32 or float64.
eps (float, optional): The epsilon. If eps is not None, the input clamp bound is defined as [eps, 1-eps],
otherwise, the `input` is not clamped. Default: ``None`` .
Returns:
Tensor, with the same shape and dtype as the `input`.
Raises:
TypeError: If `eps` is not a float.
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not float16, float32 or float64.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.1, 0.2, 0.3]).astype(np.float32))
>>> output = ops.logit(x, eps=1e-5)
>>> print(output)
[-2.1972246 -1.3862944 -0.8472978]
"""
if eps is None:
eps = -1.0
logit_ = _get_cache_prim(P.Logit)(eps)
return logit_(input)
#####################################
# Comparison Operation Functions.
#####################################
[文档]def lt(input, other):
"""
Alias for :func:`mindspore.ops.less` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return less(input, other)
[文档]def le(input, other):
r"""
Computes the boolean value of :math:`input <= other` element-wise.
.. math::
out_{i} =\begin{cases}
& \text{True, if } input_{i}<=other_{i} \\
& \text{False, if } input_{i}>other_{i}
\end{cases}
.. note::
- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types
consistent.
- The inputs must be two tensors or one tensor and one scalar.
- When the inputs are one tensor and one scalar, the scalar could only be a constant.
Args:
input (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/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
other (Union[Tensor, number.Number, bool]): The second input, when the first input is a Tensor,
the second input should be a number.Number or bool value, 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\_.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3]), mindspore.int32)
>>> y = Tensor(np.array([1, 1, 4]), mindspore.int32)
>>> output = ops.le(x, y)
>>> print(output)
[ True False True]
"""
return tensor_le(input, other)
[文档]def gt(input, other):
r"""
Compare the value of the input parameters :math:`input,other` element-wise, and the output result is a bool value.
.. math::
out_{i} =\begin{cases}
& \text{True, if } input_{i}>other_{i} \\
& \text{False, if } input_{i}<=other_{i}
\end{cases}
Note:
- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types
consistent.
- The inputs must be two tensors or one tensor and one scalar.
- When the inputs are two tensors, dtypes of them cannot be bool at the same time,
and the shapes of them can be broadcast.
- When the inputs are one tensor and one scalar, the scalar could only be a constant.
- Broadcasting is supported.
- If the input Tensor can be broadcast, the low dimension will be extended to the corresponding high dimension
in another input by copying the value of the dimension.
Args:
input (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/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ .
other (Union[Tensor, number.Number, bool]): The second input, when the first input is a Tensor,
the second input should be a number.Number or bool value, 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\_.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Raises:
TypeError: If neither `input` nor `other` is a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3]), mindspore.int32)
>>> y = Tensor(np.array([1, 1, 4]), mindspore.int32)
>>> output = ops.gt(x, y)
>>> print(output)
[False True False]
"""
return tensor_gt(input, other)
[文档]def ge(input, other):
r"""
Computes the boolean value of :math:`input >= other` element-wise.
Note:
- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types
consistent.
- The inputs must be two tensors or one tensor and one scalar.
- When the inputs are two tensors, dtypes of them cannot be bool at the same time,
and the shapes of them can be broadcast.
- When the inputs are one tensor and one scalar, the scalar could only be a constant.
- Broadcasting is supported.
- If the input Tensor can be broadcast, the low dimension will be extended to the corresponding high dimension
in another input by copying the value of the dimension.
.. math::
out_{i} =\begin{cases}
& \text{True, if } input_{i}>=other_{i} \\
& \text{False, if } input_{i}<other_{i}
\end{cases}
Args:
input (Union[Tensor, Number, bool]): The first input is a number or
a bool or a tensor whose data type is number or bool.
other (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.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Raises:
TypeError: If neither `input` nor `other` is a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3]), mindspore.int32)
>>> y = Tensor(np.array([1, 1, 4]), mindspore.int32)
>>> output = ops.ge(x, y)
>>> print(output)
[True True False]
"""
return tensor_ge(input, other)
[文档]def eq(input, other):
r"""
Computes the equivalence between two tensors element-wise.
The second argument can be a number or a tensor whose shape is broadcastable with the first argument and vise versa.
.. math::
out_{i} =\begin{cases}
& \text{True, if } input_{i} = other_{i} \\
& \text{False, if } input_{i} \ne other_{i}
\end{cases}
Note:
- `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
- The input must be two Tensors, or a Tensor and a Scalar.
- The shapes of the inputs can be broadcasted to each other.
Args:
input (Union[Tensor, Number]): The first input is a number or
a tensor whose data type is number.
other (Union[Tensor, Number]): The second input is a number when the first input is a tensor.
The data type is the same as the first input. If the first input is a number,
the second input should be a tensor.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Raises:
TypeError: If neither `input` nor `other` is a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> # case 1: The shape of two inputs are different
>>> x = Tensor([1, 2, 3], mindspore.float32)
>>> output = ops.eq(x, 2.0)
>>> print(output)
[False True False]
>>> # case 2: The shape of two inputs are the same
>>> x = Tensor([1, 2, 3], mindspore.int32)
>>> y = Tensor([1, 2, 4], mindspore.int32)
>>> output = ops.eq(x, y)
>>> print(output)
[ True True False]
"""
return equal(input, other)
[文档]def ne(input, other):
r"""
Computes the non-equivalence of two tensors element-wise.
Note:
- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types
consistent.
- When the inputs are two tensors, the shapes of them could be broadcast.
- When the inputs are one tensor and one scalar, the scalar could only be a constant.
- Broadcasting is supported.
.. math::
out_{i} =\begin{cases}
& \text{True, if } input_{i} \ne other_{i} \\
& \text{False, if } input_{i} = other_{i}
\end{cases}
Args:
input (Union[Tensor, Number, bool]): The first input is a number or
a bool or a tensor whose data type is number or bool.
other (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.
Returns:
Tensor, the shape is the same as the one after broadcasting,and the data type is bool.
Raises:
TypeError: If `input` and `other` is not one of the following: Tensor, Number, bool.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> x = Tensor([1, 2, 3], mindspore.float32)
>>> output = ops.ne(x, 2.0)
>>> print(output)
[ True False True]
>>>
>>> x = Tensor([1, 2, 3], mindspore.int32)
>>> y = Tensor([1, 2, 4], mindspore.int32)
>>> output = ops.ne(x, y)
>>> print(output)
[False False True]
"""
return not_equal(input, other)
[文档]def approximate_equal(x, y, tolerance=1e-5):
r"""
Returns ``True`` if abs(x-y) is smaller than tolerance element-wise, otherwise ``False`` .
.. math::
out_i = \begin{cases}
& \text{ if } \left | x_{i} - y_{i} \right | < \text{tolerance},\ \ True \\
& \text{ if } \left | x_{i} - y_{i} \right | \ge \text{tolerance},\ \ False
\end{cases}
where `tolerance` indicates Acceptable maximum tolerance.
Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
If they have different data types, the lower precision data type will be converted to
the relatively highest precision data type.
Args:
x (Tensor): A tensor. Must be one of the following types: float32, float16.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
y (Tensor): A tensor of the same type and shape as `x`.
tolerance (float): The maximum deviation that two elements can be considered equal. Default: ``1e-5`` .
Returns:
Tensor, the shape is the same as the shape of `x`, and the data type is bool.
Raises:
TypeError: If `tolerance` is not a float.
RuntimeError: If the data type of `x`, `y` conversion of Parameter is given
but data type conversion of Parameter is not supported.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> tol = 1.5
>>> x = Tensor(np.array([1, 2, 3]), mstype.float32)
>>> y = Tensor(np.array([2, 4, 6]), mstype.float32)
>>> output = ops.approximate_equal(Tensor(x), Tensor(y), tol)
>>> print(output)
[ True False False]
"""
return _get_cache_prim(P.ApproximateEqual)(tolerance)(x, y)
[文档]def isnan(input):
r"""
Determines which elements are NaN for each position.
.. math::
out_i = \begin{cases}
& \ True,\ \text{ if } input_{i} = \text{Nan} \\
& \ False,\ \text{ if } input_{i} \ne \text{Nan}
\end{cases}
where :math:`Nan` means not a number.
Args:
input (Tensor): The input tensor.
Returns:
Tensor, has the same shape of `input`, and the dtype is bool.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([np.log(-1), 1, np.log(0)]), mindspore.float32)
>>> output = ops.isnan(x)
>>> print(output)
[ True False False]
>>> x = Tensor(2.1, mindspore.float64)
>>> output = ops.isnan(x)
>>> print(output)
False
"""
return isnan_(input)
[文档]def isclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False):
"""
Returns a new Tensor with boolean elements representing if each element of `input`
is “close” to the corresponding element of `other`. Closeness is defined as:
.. math::
|input-other| ≤ atol + rtol × |other|
Args:
input (Tensor): First tensor to compare.
Support dtype: float16, float32, float64, int8, int16, int32, int64 and uint8.
On Ascend, more dtypes are support: bool and bfloat16.
other (Tensor): Second tensor to compare. Dtype must be same as `input`.
rtol (Union[float, int, bool], optional): Relative tolerance. Default: ``1e-05`` .
atol (Union[float, int, bool], optional): Absolute tolerance. Default: ``1e-08`` .
equal_nan (bool, optional): If ``True`` , then two NaNs will be considered equal. Default: ``False``.
Returns:
A bool Tensor, with the shape as broadcasted result of the input `input` and `other`.
Raises:
TypeError: `input` or `other` is not Tensor.
TypeError: `input` or `other` dtype is not support.
TypeError: `atol` or `rtol` is not float, int or bool.
TypeError: `equal_nan` is not bool.
TypeError: `input` and `other` have different dtypes.
ValueError: `input` and `other` cannot broadcast.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1.3, 2.1, 3.2, 4.1, 5.1]), mindspore.float16)
>>> other = Tensor(np.array([1.3, 3.3, 2.3, 3.1, 5.1]), mindspore.float16)
>>> output = ops.isclose(input, other)
>>> print(output)
[ True False False False True]
"""
is_close = _get_cache_prim(P.IsClose)(rtol=rtol, atol=atol, equal_nan=equal_nan)
return is_close(input, other)
[文档]def isreal(input):
"""
Tests element-wise for real number.
A complex value is considered real when its imaginary part is 0.
Args:
input (Tensor): The input tensor.
Returns:
Tensor, true where `input` is real number, false otherwise.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import ops, Tensor
>>> from mindspore import dtype as mstype
>>> x = Tensor([1, 1+1j, 2+0j], mstype.complex64)
>>> output = ops.isreal(x)
>>> print(output)
[ True False True]
"""
_check_is_tensor("input", input, "isreal")
# Note: Integral and Floating tensor values are always real
value = Tensor(1, mstype.bool_)
real_dtype = mstype.int_type + mstype.uint_type + mstype.float_type + (mstype.bool_,)
if input.dtype in real_dtype:
return fill_v2_(input.shape, value)
return imag_(input) == 0
[文档]def is_complex(input):
'''
Return True if the data type of the tensor is complex, otherwise return False.
Args:
input (Tensor): The input tensor.
Returns:
Bool, return whether the data type of the tensor is complex.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import ops, Tensor
>>> from mindspore import dtype as mstype
>>> input = Tensor([1, 1+1j, 2+2j], mstype.complex64)
>>> output = ops.is_complex(input)
>>> print(output)
True
'''
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("The input must be Tensor!")
return input.dtype in mstype.complex_type
[文档]def fmax(input, other):
r"""
Computes the maximum of input tensors element-wise.
.. math::
output_i = \max(x1_i, x2_i)
Note:
- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types
consistent.
- Shapes of `input` and `other` should be able to broadcast.
- If one of the elements to be compared is NaN, another element is returned.
Args:
input (Tensor): The first tensor. The supported dtypes are: float16, float32, float64, int32, int64.
other (Tensor): The second tensor. The supported dtypes are: float16, float32, float64, int32, int64.
Returns:
A 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 `input` or `other` is not Tensor.
TypeError: If dtype of `input` or `other` is not one of: float16, float32, float64, int32, int64.
ValueError: If the shape of `input` and `other` can not broadcast.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.array([1.0, 5.0, 3.0]), mindspore.float32)
>>> x2 = Tensor(np.array([4.0, 2.0, 6.0]), mindspore.float32)
>>> output = ops.fmax(x1, x2)
>>> print(output)
[4. 5. 6.]
"""
fmax_ = Fmax()
return fmax_(input, other)
def fmin(input, other):
r"""
Computes the minimum of input tensors element-wise.
.. math::
output_i = min(input_i, other_i)
Note:
- Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types
consistent.
- Shapes of `input` and `other` should be able to broadcast.
- If one of the elements to be compared is NaN, another element is returned.
Args:
input (Tensor): The first tensor. The supported dtypes are: float16, float32, float64, int32, int64.
other (Tensor): The second tensor. The supported dtypes are: float16, float32, float64, int32, int64.
Returns:
A 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 `input` or `other` is not Tensor.
TypeError: If dtype of `input` or `other` is not one of: float16, float32, float64, int32, int64.
ValueError: If the shape of `input` and `other` can not broadcast.
Supported Platforms:
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> input = Tensor(np.array([1.0, 5.0, 3.0]), mstype.float32)
>>> other = Tensor(np.array([4.0, 2.0, 6.0]), mstype.float32)
>>> output = ops.fmin(input, other)
>>> print(output)
[1. 2. 3.]
"""
fmin_ = Fmin()
return fmin_(input, other)
[文档]def nanmean(input, axis=None, keepdims=False, *, dtype=None):
r"""
Computes the mean of `input` in specified dimension, ignoring NaN.
If all elements in the specified dimensions are NaN, the result will be NaN.
Args:
input (Tensor): The input tensor to calculate the mean.
axis (int, optional): The dimension need to reduce. Default: ``None``, all dimensions are reduced.
keepdims (bool, optional): Whether the output tensor needs to retain dimension or not.
Default: ``False``, not to retain dimensions.
Keyword Args:
dtype (mindspore.dtype, optional): The output Tensor data type. Default: ``None`` , the data type of output
Tensor is same as the input.
Returns:
Tensor, the mean of input `input` in the given dimension axis, while ignoring NaNs.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not int.
TypeError: If `keepdims` is not bool.
TypeError: If `dtype` is not mindspore dtype.
ValueError: If `axis` is not in range of [-r, r) which `r` means the rank of `input`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> x = Tensor([[0.5, -1.1, float('nan')], [3.4, float('nan'), float('nan')]], mindspore.float32)
>>> y = ops.nanmean(x, axis=0, keepdims=False)
>>> print(y)
[ 1.95 -1.1 nan]
"""
_check_is_tensor("input", input, "nanmean")
_check_repeat_in_axis(axis, input.ndim, "nanmean")
if input.dtype not in mstype.float_type:
raise TypeError(f"For 'nanmean', input should be floating point dtype, but got {type(input)}.")
nan_sum = nansum(input, axis, keepdims)
is_num = isnan(input).logical_not()
is_num = is_num.sum(axis=axis, keepdims=keepdims)
out = nan_sum / is_num
if dtype is not None:
return out.astype(dtype)
return out
[文档]def orgqr(input, input2):
r"""
Calculates the explicit representation of the orthogonal matrix :math:`Q`
returned by :class:`mindspore.ops.Geqrf`.
Take the case of input without batch dimension as an example,
computes the first :math:`N` columns of a product of
`Householder <https://en.wikipedia.org/wiki/Householder_transformation#Householder_matrix>`_
matrices. Suppose input `input` is a matrix of size :math:`(M, N)` after householder transformation.
When the diagonal of `input` is set to 1, every colunm of lower triangular in `input` is
denoted as :math:`w_j` for :math:`j` for
:math:`j=1, \ldots, M`, this function returns the first :math:`N` columns of the matrix
.. math::
H_{1} H_{2} \ldots H_{k} \quad \text { with } \quad H_{j}=\mathrm{I}_{M}-\tau_{j} w_{j} w_{j}^{\mathrm{H}}
where :math:`\mathrm{I}_{M}` is the :math:`M`-dimensional identity matrix. And when :math:`w` is complex,
:math:`w^{\mathrm{H}}` is the conjugate transpose, otherwise the transpose.
The output matrix is the same size as the input matrix `input`.
:math:`tau` is corresponding to `input2`.
Args:
input (Tensor): Tensor of shape :math:`(*, M, N)`, indicating 2D or 3D matrices,
with float32, float64, complex64 and complex128 data type.
input2 (Tensor): Tensor of shape :math:`(*, K)`, where `K` is less than or equal to `N`, indicating the
reflecting coefficient in Householder transformation, which have the same type as `input`.
Returns:
Tensor, has the same shape and data type as `input`.
Raises:
TypeError: If `input` or `input2` are not Tensors.
TypeError: If dtype of `input` and `input2` is not one of: float64, float32, complex64, complex128.
ValueError: If `input` and `input2` have different batch size.
ValueError: If input.shape[-2] < input.shape[-1].
ValueError: If input.shape[-1] < input2.shape[-1].
ValueError: If rank(input) - rank(input2) != 1.
ValueError: If rank(input) != 2 or 3.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62.]]),
... mindspore.float32)
>>> input2 = Tensor(np.array([1.55, 1.94, 0.0]), mindspore.float32)
>>> y = ops.orgqr(input, input2)
>>> print(y)
[[-0.54999995 -0.2128925 0.8137956 ]
[ 0.47119996 -0.8752807 0.08240613]
[ 0.69749993 0.42560163 0.57772595]]
"""
orgqr_ = Orgqr()
return orgqr_(input, input2)
[文档]def ormqr(input, tau, other, left=True, transpose=False):
r"""
Calculates two matrices multiplication of a product of a general matrix with Householder matrices.
Calculates the product of a matrix C(given by `other`) with dimensions (m, n) and a matrix Q which is represented
using Householder reflectors (`input`, `tau`). Returns a Tensor.
Args:
input (Tensor): Tensor of shape :math:`(*, mn, k)`, when `left` is True, mn equals to m,
otherwise, mn equals to n. And `*` is zero or more batch dimensions.
tau (Tensor): Tensor of shape :math:`(*, min(mn, k))` where `*` is zero or more batch dimensions,
and its type is the same as `input`.
other (Tensor): Tensor of shape :math:`(*, m, n)` where `*` is zero or more batch dimensions,
and its type is the same as `input`.
left (bool, optional): determines the order of multiplication. If True, computes op(Q) \* `other` ,
otherwise, compute `other` \* op(Q). Default: ``True`` .
transpose (bool, optional): If True, the matrix Q is conjugate transposed,
otherwise, not conjugate transposing matrix Q. Default: ``False`` .
Returns:
Tensor, with the same type and shape as `other`.
Raises:
TypeError: If `input` or `tau` or `other` is not Tensor.
TypeError: If dtype of `input` or `tau` or `other` is not one of: float64, float32, complex64, complex128.
ValueError: If the dimension of `input` or `other` is less than 2D.
ValueError: If rank(`input`) - rank(`tau`) != 1.
ValueError: If tau.shape[:-1] != input.shape[:-2]
ValueError: If other.shape[:-2] != input.shape[:-2]
ValueError: If left == true, other.shape[-2] < tau.shape[-1].
ValueError: If left == true, other.shape[-2] != input.shape[-2].
ValueError: If left == false, other.shape[-1] < tau.shape[-1].
ValueError: If left == false, other.shape[-1] != input.shape[-2].
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([[-114.6, 10.9, 1.1], [-0.304, 38.07, 69.38], [-0.45, -0.17, 62]]),
... mindspore.float32)
>>> tau = Tensor(np.array([1.55, 1.94, 3.0]), mindspore.float32)
>>> other = Tensor(np.array([[-114.6, 10.9, 1.1],
... [-0.304, 38.07, 69.38],
... [-0.45, -0.17, 62]]), mindspore.float32)
>>> output = ops.ormqr(input, tau, other)
>>> print(output)
[[ 63.82713 -13.823125 -116.28614 ]
[ -53.659264 -28.157839 -70.42702 ]
[ -79.54292 24.00183 -41.34253 ]]
"""
ormqr_ = _get_cache_prim(Ormqr)(left, transpose)
return ormqr_(input, tau, other)
[文档]def hypot(input, other):
r"""
Computes hypotenuse of input tensors element-wise as legs of a right triangle.
The shape of two inputs should be broadcastable, and data type of them should be
one of: float32, float64
.. math::
out_i = \sqrt{input_i^2 + other_i^2}
Args:
input (Tensor): The first input tensor.
other (Tensor): The second input tensor.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is one
with higher precision in the two inputs.
Raises:
TypeError: If data type `input` or `other` is not float32 or float64.
ValueError: If shape of two inputs are not broadcastable.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([3., 5., 7.]))
>>> other = Tensor(np.array([4., 12., 24.]))
>>> y = ops.hypot(input, other)
>>> print(y)
[ 5. 13. 25.]
"""
hypot_ = Hypot()
return hypot_(input, other)
[文档]def heaviside(input, values):
r"""
Computes the Heaviside step function for each element in input.
.. math::
\text { heaviside }(\text { input, values })=\left\{\begin{array}{ll}
0, & \text { if input }<0 \\
\text { values, } & \text { if input }=0 \\
1, & \text { if input }>0
\end{array}\right.
Args:
input (Tensor): The input tensor. With real number data type.
values (Tensor): The values to use where `input` is zero. Values can be broadcast with `input` .
`input` should have the same dtype with `values` .
Returns:
Tensor, has the same type as `input` and `values`.
Raises:
TypeError: If `input` or `values` is not Tensor.
TypeError: If data type `input` and `values` is different.
ValueError: If shape of two inputs are not broadcastable.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([-5., 1., 0., 2., 0.]))
>>> values = Tensor(np.array([3.]))
>>> y = ops.heaviside(input, values)
>>> print(y)
[0. 1. 3. 1. 3.]
"""
heaviside_ = Heaviside()
return heaviside_(input, values)
[文档]def histc(input, bins=100, min=0., max=0.):
r"""
Computes the histogram of a tensor.
The elements are sorted into equal width bins between `min` and `max`.
If `min` and `max` are both zero, the minimum and maximum values of the data are used.
Elements lower than min or higher than max are ignored.
Args:
input (Tensor): the input tensor, type support list :math:`[float16, float32, int32]`.
bins (int, optional): Number of histogram bins, optional. If specified, must be positive. Default: ``100`` .
min (int, float, optional): An optional float of the lower end of the range (inclusive). Default: ``0.0`` .
max (int, float, optional): An optional float of the upper end of the range (inclusive). Default: ``0.0`` .
Returns:
1-D Tensor. If the input is int32, the output returns int32, otherwise it returns float32.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `input` datetype not in support list.
TypeError: If attr `min` or `max` is not float or int.
TypeError: If attr `bins` is not int.
ValueError: If attr value `min` > `max`.
ValueError: If attr `bins` <= 0.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> x = Tensor([1., 2, 1])
>>> y = ops.histc(x, bins=4, min=0.0, max=3.0)
>>> print(y)
[0. 2. 1. 0.]
"""
if not isinstance(min, (int, float)):
raise TypeError(f"For 'histc', parameter 'min' must be an int or float, but got {type(min)}.")
if not isinstance(max, (int, float)):
raise TypeError(f"For 'histc', parameter 'max' must be an int or float, but got {type(max)}.")
histogram_op = _get_cache_prim(P.Histogram)(bins, float(min), float(max))
return histogram_op(input)
[文档]def logspace(start, end, steps, base=10, *, dtype=mstype.float32):
r"""
Returns a 1-D Tensor with size `steps` whose value is from :math:`base^{start}` to :math:`base^{end}`,
and use `base` as the base number.
.. math::
\begin{aligned}
&step = (end - start)/(steps - 1)\\
&output = [base^{start}, base^{start + 1 * step}, ... , base^{start + (steps-2) * step}, base^{end}]
\end{aligned}
Args:
start (Union[float, Tensor]): Start value of interval.
end (Union[float, Tensor]): End value of interval.
steps (int): The steps must be a non-negative integer.
base (int, optional): The base must be a non-negative integer. Default: ``10`` .
Keyword Args:
dtype (mindspore.dtype, optional): The dtype of output. Default: ``mstype.float32`` .
Returns:
Tensor has the shape as :math:`(step, )`. Its datatype is set by the attr 'dtype'.
Raises:
TypeError: If `start` is not a float or a Tensor.
TypeError: If `end` is not a float or a Tensor.
TypeError: If `steps` is not an int.
TypeError: If `base` is not an int.
ValueError: If `steps` is not a non-negative integer.
ValueError: If `base` is not a non-negative integer.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> start = Tensor(1, mindspore.float32)
>>> end = Tensor(10, mindspore.float32)
>>> output = ops.logspace(start, end, steps = 10, base = 10, dtype=mindspore.float32)
>>> print(output)
[1.e+01 1.e+02 1.e+03 1.e+04 1.e+05 1.e+06 1.e+07 1.e+08 1.e+09 1.e+10]
"""
if isinstance(start, float):
start = ops.cast(start, mstype.float32)
if isinstance(end, float):
end = ops.cast(end, mstype.float32)
logspace_ = _get_cache_prim(P.LogSpace)(steps, base, dtype)
return logspace_(start, end)
[文档]def logaddexp(input, other):
r"""
Computes the logarithm of the sum of exponentiations of the inputs.
This function is useful in statistics where the calculated probabilities of events may be
so small as to exceed the range of normal floating point numbers.
.. math::
out_i = \log(exp(input_i) + \exp(other_i))
Args:
input (Tensor): Input Tensor. The dtype of `input` must be float.
other (Tensor): Input Tensor. The dtype of `other` must be float.
If the shape of `input` is not equal to the shape of `other`,
they must be broadcastable to a common shape (which becomes the shape of the output).
Returns:
Tensor.
Raises:
TypeError: If `input`, `other` is not a Tensor.
TypeError: The dtype of `input` or `other` is not float.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.array([1, 2, 3]).astype(np.float16))
>>> x2 = Tensor(np.array(2).astype(np.float16))
>>> output = ops.logaddexp(x1, x2)
>>> print(output)
[2.312 2.693 3.312]
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError(f"For logaddexp, the input must be a Tensor, but got {type(input)}.")
if not isinstance(other, (Tensor, Tensor_)):
raise TypeError(f"For logaddexp, the other must be a Tensor, but got {type(other)}.")
if not ops.is_floating_point(input) or not ops.is_floating_point(other):
raise TypeError(f"For logaddexp, the dtype of 'input' and 'other' must be float,"
f"but got {input.dtype} and {other.dtype}.")
m = maximum(input, other)
abs_val = abs(input - other)
exp_val = tensor_exp(neg(abs_val))
y = m + log1p(exp_val)
return y
[文档]def logaddexp2(input, other):
r"""
Computes the logarithm of the sum of exponentiations in base of 2 of the inputs.
.. math::
out_i = \log_2(2^{input_i} + 2^{other_i})
Args:
input (Tensor): Input tensor. The dtype of `input` must be float.
other (Tensor): Input tensor. The dtype of `other` must be float.
If ``input.shape != other.shape``, they must be broadcastable to
a common shape (which becomes the shape of the output).
Returns:
Tensor.
Raises:
TypeError: If `input`, `other` is not a Tensor.
TypeError: If the dtype of `input`, `other` is not a float.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.array([2, 4, 8]).astype(np.float16))
>>> x2 = Tensor(np.array([2]).astype(np.float16))
>>> output = ops.logaddexp2(x1, x2)
>>> print(output)
[3. 4.32 8.02]
"""
_check_is_tensor("input", input, "logaddexp2")
_check_is_tensor("other", other, "logaddexp2")
if not ops.is_floating_point(input) or not ops.is_floating_point(other):
raise TypeError(f"For logaddexp2, the dtype of 'input' and 'other' must be float,"
f"but got {input.dtype} and {other.dtype}.")
m = maximum(input, other)
abs_val = abs(input - other)
exp2_val = pows(2., neg(abs_val))
y = m + log2(1. + exp2_val)
return y
@_primexpr
def _check_and_canonicalize_axes(axes, ndim):
"""Check whether the types and values of input axes are valid."""
return validator.check_and_canonicalize_axes(axes, ndim)
def _check_var_std_input(input, ddof, keepdims, axis, cls_name):
_check_is_tensor("input", input, cls_name)
_check_attr_dtype("ddof", ddof, [int, bool], cls_name)
_check_attr_dtype("keepdims", keepdims, [bool], cls_name)
if axis is None:
axis = ()
else:
axis = _check_and_canonicalize_axes(axis, input.ndim)
return axis
[文档]def vander(x, N=None):
"""
Generates a Vandermonde matrix. The columns of the output matrix are powers of the input vector.
The i-th output column is the input vector raised element-wise to the power of :math:`N - i - 1`.
Args:
x (Tensor): 1-D input array.
N (int, optional): Number of columns in the output. Default: ``None``,
`N` will be assigned as :math:`len(x)`.
Returns:
Tensor, the columns are :math:`x^0, x^1, ..., x^{(N-1)}`.
Raises:
TypeError: If input `x` is not Tensor.
ValueError: If `x` is not 1-D.
TypeError: If input `N` is not int.
ValueError: If `N` <= 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> a = Tensor([1., 2., 3., 5.])
>>> print(ops.vander(a, N=3))
[[1. 1. 1.]
[4. 2. 1.]
[9. 3. 1.]
[25. 5. 1.]]
>>> a = Tensor([1., 2., 3., 5.])
>>> print(ops.vander(a))
[[1. 1. 1. 1.]
[8. 4. 2. 1.]
[27. 9. 3. 1.]
[125. 25. 5. 1.]]
"""
if not isinstance(x, Tensor):
raise TypeError(
f"For vander, x must be Tensor, but got {type(x)}")
if x.ndim != 1:
raise ValueError(
f"For vander, x must be 1-D, but got dimension = {x.ndim}")
if N is None:
N = len(x)
if not isinstance(N, int):
raise TypeError(
f"For vander, N must be an integer but got {type(N)}.")
if N <= 0:
raise ValueError(
f"For vander, N must be greater than 0, but got {N}.")
exponent = ops.range(Tensor(N - 1), Tensor(-1), Tensor(-1))
x = F.expand_dims(x, 1)
exponent = F.expand_dims(exponent, 0)
return F.tensor_pow(x, exponent)
def var_ext(input, dim=None, *, correction=1, keepdim=False):
r"""
Calculates the variance over the dimensions specified by `dim`. `dim` can be a single dimension, list of
dimensions, or None to reduce over all dimensions.
The variance (:math:`\delta ^2`) is calculated as:
.. math::
\delta ^2 = \frac{1}{\max(0, N - \delta N)}\sum^{N - 1}_{i = 0}(x_i - \bar{x})^2
where :math:`x` is the sample set of elements, :math:`\bar{x}` is the sample mean, :math:`N` is the number
of samples and :math:`\delta N` is the `correction`.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The tensor used to calculate the variance.
dim (None or int or tuple of ints, optional): The dimension or dimensions to reduce. Defaults to ``None``.
If ``None``, all dimensions are reduced.
Keyword Args:
correction (int, optional): The difference between the sample size and sample degrees of freedom. Defaults
to Bessel’s correction. Defaults to ``1``.
keepdim (bool, optional): Whether the output tensor has dim retained or not. If ``True`` , keep these
reduced dimensions and the length is 1. If ``False``, don't keep these dimensions. Defaults to ``False``.
Returns:
Tensor, the variance.
Suppose the shape of `input` is :math:`(x_0, x_1, ..., x_R)`:
- If `dim` is () and `keepdim` is set to ``False`` , returns a 0-D Tensor, indicating the variance of all
elements in `input`.
- If `dim` is int, e.g. ``1`` and `keepdim` is set to ``False`` , then the returned Tensor has shape
:math:`(x_0, x_2, ..., x_R)`.
- If `dim` is tuple(int) or list(int), e.g. ``(1, 2)`` and `keepdim` is set to ``False`` , then the returned
Tensor has shape :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `input` is not in bfloat16, float16, flaot32.
TypeError: If `dim` is not one of the followings: None, int, tuple.
TypeError: If `correction` is not an int.
TypeError: If `keepdim` is not a bool.
ValueError: If `dim` is out of range :math:`[-input.ndim, input.ndim)`.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> input = Tensor([[8, 2, 1], [5, 9, 3], [4, 6, 7]], mindspore.float32)
>>> output = ops.var_ext(input, dim=0, correction=1, keepdim=True)
>>> print(output)
[[ 4.333333, 12.333333, 9.333333]]
"""
return var_op(input, dim, correction, keepdim)
def std_ext(input, dim=None, *, correction=1, keepdim=False):
r"""
Calculates the standard deviation over the dimensions specified by `dim`. `dim` can be a single dimension, list of
dimensions, or None to reduce over all dimensions.
The standard deviation (:math:`\sigma`) is calculated as:
.. math::
\sigma =\sqrt{\frac{1}{N-\delta N}\sum_{j-1}^{N-1}\left(s e l f_{i j}-\overline{x_{i}}\right)^{2}}
where :math:`x` is the sample set of elements, :math:`\bar{x}` is the sample mean, :math:`N` is the number
of samples and :math:`\delta N` is the `correction`.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The tensor used to calculate the standard deviation.
dim (None, int, tuple(int), optional): The dimension or dimensions to reduce. Defaults to ``None``.
If ``None``, all dimensions are reduced.
Keyword Args:
correction (int, optional): The difference between the sample size and sample degrees of freedom. Defaults
to Bessel’s correction. Defaults to ``1``.
keepdim (bool, optional): Whether the output tensor has dim retained or not. If ``True`` , keep these
reduced dimensions and the length is 1. If ``False``, don't keep these dimensions. Defaults to ``False``.
Returns:
Tensor, the standard deviation.
Suppose the shape of `input` is :math:`(x_0, x_1, ..., x_R)`:
- If `dim` is () and `keepdim` is set to ``False`` , returns a 0-D Tensor, indicating the standard deviation of
all elements in `input`.
- If `dim` is int, e.g. ``1`` and `keepdim` is set to ``False`` , then the returned Tensor has shape
:math:`(x_0, x_2, ..., x_R)`.
- If `dim` is tuple(int) or list(int), e.g. ``(1, 2)`` and `keepdim` is set to ``False`` , then the returned
Tensor has shape :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `input` is not in bfloat16, float16, float32.
TypeError: If `dim` is not one of the followings: None, int, tuple.
TypeError: If `correction` is not an int.
TypeError: If `keepdim` is not a bool.
ValueError: If `dim` is out of range :math:`[-input.ndim, input.ndim)`.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import mint, Tensor
>>> input = Tensor(np.array([[1, 2, 3], [-1, 1, 4]]).astype(np.float32))
>>> output = ops.std_ext(input, dim=1, correction=1, keepdim=False)
>>> print(output)
[1. 2.5166113]
"""
return std_op(input, dim, correction, keepdim)
[文档]def var(input, axis=None, ddof=0, keepdims=False):
r"""
Returns the variance of each row of the input Tensor by default, or it can calculate them
in specified dimension `axis`. If `axis` is a list of dimensions, reduce over all of them.
Note:
If ddof is 0, 1, True or False, the supported device is only Ascend and CPU. In other cases,
the supported device is Ascend, GPU and CPU.
Args:
input (Tensor[Number]): Input Tensor with a dtype of number.Number, its shape should be :math:`(N, *)`
where :math:`*` means any number of additional dims.
axis (Union[int, tuple(int)], optional): The dimensions to reduce. Only constant value is allowed.
Must be in the range [-rank(`input`), rank(`input`)). Default: ``None`` , reduce all dimensions.
ddof (Union[int, bool], optional): Means Delta Degrees of Freedom.
If ddof is an integer, the divisor used in calculations is :math:`N - ddof`,
where :math:`N` represents the number of elements.
If ddof is True, will use the Bessel correction unbiased estimation.
If ddof is False, will through the biased estimation to calculate variance.
Default: ``0`` .
keepdims (bool, optional): Whether the output Tensor has dim retained or not.
If ``true`` , keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: ``False`` .
Returns:
Tensor, the variance.
Suppose the shape of `input` is :math:`(x_0, x_1, ..., x_R)`:
- If `axis` is () and `keepdims` is set to ``False`` , returns a 0-D Tensor, indicating
the standard deviation of all elements in `input`.
- If `axis` is int 1 and `keepdims` is set to ``False`` , then the returned Tensor
has shape :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int) or list(int), e.g. (1, 2) and `keepdims` is set to ``False`` ,
then the returned Tensor has shape :math:`(x_0, x_2, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: None, int, tuple.
TypeError: If `keepdims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
>>> output = ms.ops.var(input, 1, 2, True)
>>> print(output)
[[ 2.5]
[54.5]]
"""
axis = _check_var_std_input(input, ddof, keepdims, axis, "var")
output = var_mean(input, axis, ddof, keepdims)
return output[0]
[文档]def var_mean(input, axis=None, ddof=0, keepdims=False):
r"""
Returns the variance and mean of each row of the input Tensor by default,
or it can calculate them in specified dimension `axis`.
If `axis` is a list of dimensions, reduce over all of them.
Note:
If ddof is 0, 1, True or False, the supported device is only Ascend and CPU. In other cases,
the supported device is Ascend, GPU and CPU.
Args:
input (Tensor[Number]): Input Tensor with a dtype of number.Number, its shape should be :math:`(N, *)`
where :math:`*` means any number of additional dims.
axis (Union[int, tuple(int)], optional): The dimensions to reduce. Only constant value is allowed.
Must be in the range [-rank(`input`), rank(`input`)). Default: ``None`` , reduce all dimensions.
ddof (Union[int, bool], optional): Means Delta Degrees of Freedom.
If ddof is an integer, the divisor used in calculations is :math:`N - ddof`,
where :math:`N` represents the number of elements.
If ddof is True, will use the Bessel correction unbiased estimation.
If ddof is False, will through the biased estimation to calculate the variance.
Default: ``0`` .
keepdims (bool, optional): Whether the output Tensor has dim retained or not.
If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: ``False`` .
Returns:
A tuple containing the variance and mean.
Suppose the shape of `input` is :math:`(x_0, x_1, ..., x_R)`:
- If `axis` is () and `keepdims` is set to ``False`` , returns a 0-D Tensor, indicating
the standard deviation of all elements in `input`.
- If `axis` is int 1 and `keepdims` is set to ``False`` , then the returned Tensor
has shape :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int) or list(int), e.g. (1, 2) and `keepdims` is set to ``False`` ,
then the returned Tensor has shape :math:`(x_0, x_2, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: None, int, tuple.
TypeError: If `keepdims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
>>> output_var, output_mean = ms.ops.var_mean(input, 1, 2, True)
>>> print(output_var)
[[ 2.5]
[54.5]]
>>> print(output_mean)
[[ 2.5]
[-1.5]]
"""
axis = _check_var_std_input(input, ddof, keepdims, axis, "var_mean")
if ddof in (0, 1):
output = _get_cache_prim(P.ReduceStd)(axis=axis, unbiased=bool(ddof), keep_dims=keepdims)(input)
return tensor_pow(output[0], 2), output[1]
x_mean = mean(input, axis, True)
x_sub = tensor_sub(input, x_mean)
x_pow = tensor_pow(x_sub, 2)
x_sum = sum(x_pow, axis, keepdims)
res_mean = mean(input, axis, keepdims)
nums = 1
if axis == ():
nums = input.size
else:
for ax in axis:
nums *= input.shape[ax]
return true_divide(x_sum, nums - ddof), res_mean
[文档]def std(input, axis=None, ddof=0, keepdims=False):
r"""
Returns the standard-deviation of each row of the input Tensor by default, or it can calculate them
in specified dimension `axis`. If `axis` is a list of dimensions, reduce over all of them.
Note:
If ddof is 0, 1, True or False, the supported device is only Ascend and CPU. In other cases,
the supported device is Ascend, GPU and CPU.
Args:
input (Tensor[Number]): Input Tensor with a dtype of number.Number, its shape should be :math:`(N, *)`
where :math:`*` means any number of additional dims.
axis (Union[int, tuple(int)], optional): The dimensions to reduce. Only constant value is allowed.
Must be in the range [-rank(`input`), rank(`input`)). Default: ``None`` , reduce all dimensions.
ddof (Union[int, bool], optional): Means Delta Degrees of Freedom.
If ddof is an integer, the divisor used in calculations is :math:`N - ddof`,
where :math:`N` represents the number of elements.
If ddof is True, will use the Bessel correction unbiased estimation.
If ddof is False, will through the biased estimation to calculate the standard deviation.
Default: ``0`` .
keepdims (bool, optional): Whether the output Tensor has dim retained or not.
If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: ``False`` .
Returns:
Tensor, the standard deviation.
Suppose the shape of `input` is :math:`(x_0, x_1, ..., x_R)`:
- If `axis` is () and `keepdims` is set to False, returns a 0-D Tensor, indicating
the standard deviation of all elements in `input`.
- If `axis` is int 1 and `keepdims` is set to False, then the returned Tensor
has shape :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int) or list(int), e.g. (1, 2) and `keepdims` is set to False,
then the returned Tensor has shape :math:`(x_0, x_2, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: None, int, tuple.
TypeError: If `keepdims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
>>> output = ms.ops.std(input, 1, 2, True)
>>> print(output)
[[1.5811388]
[7.3824115]]
"""
axis = _check_var_std_input(input, ddof, keepdims, axis, "std")
output = std_mean(input, axis, ddof, keepdims)
return output[0]
[文档]def std_mean(input, axis=None, ddof=0, keepdims=False):
r"""
Returns the standard-deviation and mean of each row of the input Tensor by default,
or it can calculate them in specified dimension `axis`.
If `axis` is a list of dimensions, reduce over all of them.
Note:
If ddof is 0, 1, True or False, the supported device is only Ascend and CPU. In other cases,
the supported device is Ascend, GPU and CPU.
Args:
input (Tensor[Number]): Input Tensor with a dtype of number.Number, its shape should be :math:`(N, *)`
where :math:`*` means any number of additional dims.
axis (Union[int, tuple(int)], optional): Specifies the dimensions from which to calculate the standard
deviation and mean. Only constant value is allowed. Must be in the range [-rank(`input`), rank(`input`)).
Default: ``None`` , reduce all dimensions.
ddof (Union[int, bool], optional): Means Delta Degrees of Freedom.
If ddof is an integer, the divisor used in calculations is :math:`N - ddof`,
where :math:`N` represents the number of elements.
If ddof is True, will use the Bessel correction unbiased estimation.
If ddof is False, will through the biased estimation to calculate the standard deviation.
Default: ``0`` .
keepdims (bool, optional): Whether the output Tensor has dim retained or not.
If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: ``False`` .
Returns:
A tuple containing the standard deviation and mean.
Suppose the shape of `input` is :math:`(x_0, x_1, ..., x_R)`:
- If `axis` is () and `keepdims` is set to ``False`` , returns a 0-D Tensor, indicating
the standard deviation of all elements in `input`.
- If `axis` is int 1 and `keepdims` is set to ``False`` , then the returned Tensor
has shape :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int) or list(int), e.g. (1, 2) and `keepdims` is set to ``False`` ,
then the returned Tensor has shape :math:`(x_0, x_2, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: None, int, tuple.
TypeError: If `keepdims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
>>> output_std, output_mean = ms.ops.std_mean(input, 1, 2, True)
>>> print(output_std)
[[1.5811388]
[7.3824115]]
>>> print(output_mean)
[[ 2.5]
[-1.5]]
"""
axis = _check_var_std_input(input, ddof, keepdims, axis, "std_mean")
if ddof in (0, 1):
return _get_cache_prim(P.ReduceStd)(axis=axis, unbiased=bool(ddof), keep_dims=keepdims)(input)
output = var_mean(input, axis, ddof, keepdims)
return tensor_pow(output[0], 0.5), output[1]
def std_mean_ext(input, dim=None, *, correction=1, keepdim=False):
r"""
By default, return the standard deviation and mean of each dimension in Tensor.
If dim is a dimension list, calculate the standard deviation and mean of the corresponding dimension.
The standard deviation (:math:`\sigma`) is calculated as:
.. math::
\sigma = \sqrt{\frac{1}{N - \delta N} \sum_{j=0}^{N-1} \left(self_{ij} - \overline{x_{i}}\right)^{2}}
where is :math:`x` the sample set of elements, :math:`\bar{x}` is the sample mean,
:math:`N` is the number of samples and :math:`\delta N` is the `correction` .
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor. Supported dtypes: float16, float32.
dim (Union[int, tuple(int), list(int)], optional):
Specify the dimensions for calculating standard deviation and mean. Default value: ``None``.
Keyword Args:
correction (int, optional): Difference between the sample size and sample degrees of freedom.
Defaults to Bessel's correction. Default: ``1``.
keepdim (bool, optional): Whether to preserve the dimensions of the output Tensor.
If True, retain the reduced dimension with a size of 1. Otherwise, remove the dimensions.
Default value: ``False``.
Returns:
A tuple of standard deviation and mean.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `dim` is not one of the following data types: int, tuple, list, or Tensor.
TypeError: If `keepdim` is not a bool.
ValueError: If `dim` is out of range.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
>>> output_std, output_mean = ms.mint.std_mean(input, 1, correction=2, keepdim=True)
>>> print(output_std)
[[1.5811388]
[7.3824115]]
>>> print(output_mean)
[[ 2.5]
[-1.5]]
"""
return std_mean_op(input, dim, correction, keepdim)
def var_mean_ext(input, dim=None, *, correction=1, keepdim=False):
r"""
By default, return the variance and mean of each dimension in Tensor.
If dim is a dimension list, calculate the variance and mean of the corresponding dimension.
The variance (:math:`\sigma ^2`) is calculated as:
.. math::
\sigma ^2 = \frac{1}{N - \delta N} \sum_{j=0}^{N-1} \left(self_{ij} - \overline{x_{i}}\right)^{2}
where is :math:`x` the sample set of elements, :math:`\bar{x}` is the sample mean,
:math:`N` is the number of samples and :math:`\delta N` is the `correction` .
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor. Supported dtypes: float16, float32.
dim (Union[int, tuple(int), list(int)], optional):
Specify the dimensions for calculating variance and mean. Default value: ``None``.
Keyword Args:
correction (int, optional): Difference between the sample size and sample degrees of freedom.
Defaults to Bessel's correction. Default: ``1``.
keepdim (bool, optional): Whether to preserve the dimensions of the output Tensor.
If True, retain the reduced dimension with a size of 1. Otherwise, remove the dimensions.
Default value: ``False``.
Returns:
A tuple of variance and mean.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `dim` is not one of the following data types: int, tuple, list, or Tensor.
TypeError: If `keepdim` is not a bool.
ValueError: If `dim` is out of range.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> input = ms.Tensor([[1, 2, 3, 4], [-1, 1, 4, -10]], ms.float32)
>>> output_var, output_mean = ms.mint.var_mean(input, 1, correction=2, keepdim=True)
>>> print(output_var)
[[ 2.5]
[54.5]]
>>> print(output_mean)
[[ 2.5]
[-1.5]]
"""
return var_mean_op(input, dim, correction, keepdim)
[文档]def reciprocal(input):
r"""
Returns reciprocal of a tensor element-wise.
.. math::
out_{i} = \frac{1}{x_{i}}
Args:
input (Tensor): The input tensor.
Returns:
Tensor, has the same shape as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> import numpy as np
>>> input = ms.Tensor(np.array([1.0, 2.0, 4.0]), ms.float32)
>>> output = ops.reciprocal(input)
>>> print(output)
[1. 0.5 0.25]
"""
return reciprocal_(input)
[文档]def outer(input, vec2):
"""
Return outer product of `input` and `vec2`. If `input` is a vector of size :math:`n`
and `vec2` is a vector of size :math:`m` , then output must be a matrix of shape :math:`(n, m)` .
Note:
This function does not broadcast.
Args:
input (Tensor): 1-D input vector.
vec2 (Tensor): 1-D input vector.
Returns:
out (Tensor, optional), 2-D matrix, the outer product of two vectors.
Raises:
TypeError: If `input` or `vec2` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> input = Tensor(np.array([7, 8, 9]), mindspore.int32)
>>> vec2 = Tensor(np.array([7, 10, 11]), mindspore.int32)
>>> out = ops.outer(input, vec2)
>>> print(out)
[[49 70 77]
[56 80 88]
[63 90 99]]
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("the input input must be Tensor!")
if not isinstance(vec2, (Tensor, Tensor_)):
raise TypeError("the input vec2 must be Tensor!")
input = input.reshape(-1, 1)
y = tensor_mul(input, vec2)
return y
[文档]def mv(mat, vec):
"""
Multiplies matrix `mat` and vector `vec`.
If `mat` is a Tensor with :math:`(N, M)`, `vec` is a 1-D Tensor of size :math:`M`,
out will be 1-D of size :math:`N`.
Args:
mat (Tensor): Input matrix of shape :math:`(N, M)`.
vec (Tensor): Input vector of shape :math:`(M,)`.
Returns:
Tensor, the shape of the output Tensor is :math:`(N,)`.
Raises:
TypeError: If `mat` or `vec` is not a Tensor.
ValueError: If `mat` is not a 2-D Tensor or `vec` is not a 1-D Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> mat = Tensor(np.array([[3., 4.], [1., 6.], [1., 3.]]).astype(np.float32))
>>> vec = Tensor(np.array([1., 2.]).astype(np.float32))
>>> output = ops.mv(mat, vec)
>>> print(output)
[11. 13. 7.]
"""
if not isinstance(mat, (Tensor, Tensor_)):
raise TypeError("The input mat must be Tensor.")
if not isinstance(vec, (Tensor, Tensor_)):
raise TypeError("The input vec must be Tensor.")
length_vec = get_x_shape(vec.shape)
vec = reshape_(vec, (length_vec[0], 1))
out = matmul_(mat, vec)
out = out.T
out = out[0]
return out
[文档]def addbmm(input, batch1, batch2, *, beta=1, alpha=1):
r"""
Applies batch matrix multiplication to `batch1` and `batch2`, with a reduced add step and add `input` to the result.
The optional values `alpha` and `beta` are the matrix-matrix product between `batch1` and `batch2` and the scale
factor for the added tensor `input` respectively. If `beta` is 0, then `input` will be ignored.
.. math::
output = \beta input + \alpha (\sum_{i=0}^{b-1} {batch1_i @ batch2_i})
Args:
input (Tensor): Tensor to be added.
batch1 (Tensor): The first batch of tensor to be multiplied.
batch2 (Tensor): The second batch of tensor to be multiplied.
Keyword Args:
beta (Union[int, float], optional): Multiplier for `input`. Default: ``1`` .
alpha (Union[int, float], optional): Multiplier for `batch1` @ `batch2`. Default: ``1`` .
Returns:
Tensor, has the same dtype as `input`.
Raises:
TypeError: If `alpha` or beta is not an int or float.
ValueError: If `batch1`, `batch2` cannot apply batch matrix multiplication.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> m = np.ones((3, 3)).astype(np.float32)
>>> arr1 = np.arange(24).astype(np.float32).reshape((2, 3, 4))
>>> arr2 = np.arange(24).astype(np.float32).reshape((2, 4, 3))
>>> a = Tensor(arr1)
>>> b = Tensor(arr2)
>>> c = Tensor(m)
>>> output = ops.addbmm(c, a, b)
>>> print(output)
[[ 949. 1009. 1069.]
[1285. 1377. 1469.]
[1621. 1745. 1869.]]
"""
if not isinstance(alpha, (int, float)):
raise TypeError(f"For 'addbmm', parameter 'alpha' must be an int or float, but got {type(alpha)}.")
if not isinstance(beta, (int, float)):
raise TypeError(f"For 'addbmm', parameter 'beta' must be an int or float, but got {type(beta)}.")
bmm_res = batch_matmul_(batch1, batch2)
return beta * input + alpha * (bmm_res.sum(axis=0))
def addbmm_ext(input, batch1, batch2, *, beta=1, alpha=1):
r"""
Applies batch matrix multiplication to `batch1` and `batch2`, with a reduced add step and add `input` to the result.
The optional values `alpha` and `beta` are the matrix-matrix product between `batch1` and `batch2` and the scale
factor for the added tensor `input` respectively. If `beta` is 0, then `input` will be ignored.
.. math::
output = \beta input + \alpha (\sum_{i=0}^{b-1} {batch1_i @ batch2_i})
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): Tensor to be added.
batch1 (Tensor): The first batch of tensor to be multiplied.
batch2 (Tensor): The second batch of tensor to be multiplied.
Keyword Args:
beta (Union[int, float], optional): Multiplier for `input`. Default: ``1`` .
alpha (Union[int, float], optional): Multiplier for `batch1` @ `batch2`. Default: ``1`` .
Returns:
Tensor, has the same dtype as `input`.
Raises:
TypeError: If `alpha` or `beta` is not an int or float.
ValueError: If `batch1`, `batch2` cannot apply batch matrix multiplication.
ValueError: If `batch1` and `batch2` are not 3-D tensors.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> m = np.ones((3, 3)).astype(np.float32)
>>> arr1 = np.arange(24).astype(np.float32).reshape((2, 3, 4))
>>> arr2 = np.arange(24).astype(np.float32).reshape((2, 4, 3))
>>> a = Tensor(arr1)
>>> b = Tensor(arr2)
>>> c = Tensor(m)
>>> output = ops.addbmm_ext(c, a, b)
>>> print(output)
[[ 949. 1009. 1069.]
[1285. 1377. 1469.]
[1621. 1745. 1869.]]
"""
return addbmm_op(input, batch1, batch2, beta, alpha)
def addmm_ext(input, mat1, mat2, *, beta=1, alpha=1):
r"""
Performs a matrix multiplication of the 2-D matrices mat1 and mat2. The matrix input is added to the final result.
The formula is defined as follows:
.. math::
output = \beta input + \alpha (mat1 @ mat2)
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): matrix to be added, the shape must be broadcastable with mat1 @ mat2.
mat1 (Tensor): the first matrix to be matrix multiplied, must be 2-D Tensor, with the same shape of the input.
mat2 (Tensor): the second matrix to be matrix multiplied, must be 2-D Tensor, with the same shape of the input.
Keyword Args:
beta (Union[float, int], optional): multiplier for input. Default: ``1`` .
alpha (Union[float, int], optional): multiplier for :math:`mat1 @ mat2`. Default: ``1`` .
Returns:
Tensor, with the same dtype as `input` and the same shape as mat1 @ mat2.
Raises:
TypeError: If the type of `input`, `mat1` or `mat2` is not Tensor.
TypeError: If the types of `input`, `mat1`, `mat2` are different.
ValueError: If `mat1` and `mat2` are not 2-D tensors.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.ones([3, 3]).astype(np.float32))
>>> mat1 = Tensor(np.ones([3, 4]).astype(np.float32))
>>> mat2 = Tensor(np.ones([4, 3]).astype(np.float32))
>>> output = ops.function.math_func.addmm_ext(input, mat1, mat2)
>>> print(output)
[[5. 5. 5.]
[5. 5. 5.]
[5. 5. 5.]]
"""
return addmm_op(input, mat1, mat2, beta, alpha)
[文档]def addmm(input, mat1, mat2, *, beta=1, alpha=1):
r"""
Multiplies matrix `mat1` and matrix `mat2`. The matrix `input` is added to the final result.
.. math::
output = \beta input + \alpha (mat1 @ mat2)
Args:
input (Tensor): Tensor to be added.
mat1 (Tensor): The first tensor to be multiplied.
mat2 (Tensor): The second tensor to be multiplied.
Keyword Args:
beta (Union[int, float], optional): Multiplier for `input`. Default: ``1`` .
alpha (Union[int, float], optional): Multiplier for `mat1` @ `mat2`. Default: ``1`` .
Returns:
Tensor, has the same dtype as `input`.
Raises:
ValueError: If `mat1`, `mat2` cannot apply matrix multiplication.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> m = np.ones((3, 3)).astype(np.float32)
>>> arr1 = np.arange(12).astype(np.float32).reshape((3, 4))
>>> arr2 = np.arange(12).astype(np.float32).reshape((4, 3))
>>> a = Tensor(arr1)
>>> b = Tensor(arr2)
>>> c = Tensor(m)
>>> output = ops.addmm(c, a, b)
>>> print(output)
[[ 43. 49. 55.]
[115. 137. 159.]
[187. 225. 263.]]
"""
if not isinstance(alpha, (int, float)):
raise TypeError(f"For 'addmm', parameter 'alpha' must be an int or float, but got {type(alpha)}.")
if not isinstance(beta, (int, float)):
raise TypeError(f"For 'addmm', parameter 'beta' must be an int or float, but got {type(beta)}.")
return beta * input + alpha * (matmul_(mat1, mat2))
[文档]def addmv(input, mat, vec, *, beta=1, alpha=1):
"""
Multiplies matrix `mat` and vector `vec`. The vector `input` is added to the final result.
If mat is a :math:`(N, M)` tensor, vec is a 1-D tensor of size :math:`M`, then `input` must be broadcastable
with a 1-D tensor of size :math:`N`.In this case `out` will be 1-D tensor of size :math:`N`.
The optional values `beta` and `alpha` are the matrix-vector product between `mat` and `vec` and the scale
factor for the added Tensor `input` respectively. If `beta` is 0, then `input` will be ignored.
.. math::
output = β input + α (mat @ vec)
Args:
input (Tensor): Vector to be added. The shape of the tensor is :math:`(N,)`.
mat (Tensor): The first tensor to be multiplied. The shape of the tensor is :math:`(N, M)`.
vec (Tensor): The second tensor to be multiplied. The shape of the tensor is :math:`(M,)`.
Keyword Args:
beta (scalar[int, float, bool], optional): Multiplier for `input` (β). The `beta` must be int or
float or bool. Default: ``1`` .
alpha (scalar[int, float, bool], optional): Multiplier for `mat` @ `vec` (α). The `alpha` must
be int or float or bool. Default: ``1`` .
Returns:
Tensor, the shape of the output tensor is :math:`(N,)`, has the same dtype as `input`.
Raises:
TypeError: If `mat`, `vec`, `input` is not a Tensor.
TypeError: If inputs `mat`, `vec` are not the same dtype.
ValueError: If `mat` is not a 2-D Tensor.
ValueError: If `vec` is not a 1-D Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([2., 3.]).astype(np.float32))
>>> mat = Tensor(np.array([[2., 5., 3.], [4., 2., 2.]]).astype(np.float32))
>>> vec = Tensor(np.array([3., 2., 4.]).astype(np.float32))
>>> output = ops.addmv(input, mat, vec)
>>> print(output)
[30. 27.]
"""
input_dtype = dtype_(input)
if not (isinstance(input, Tensor) and isinstance(mat, Tensor) and isinstance(vec, Tensor)):
raise TypeError("For Addmv, inputs must be all tensors.")
if dtype_(mat) != dtype_(vec):
raise TypeError("For Addmv, the mat and vec should be the same dtype.")
_check_input_dtype("input", input_dtype,
[mstype.float16, mstype.float32, mstype.float64,
mstype.int16, mstype.int32, mstype.int64], "Addmv")
_check_attr_dtype("alpha", alpha, [int, float, bool], "Addmv")
_check_attr_dtype("beta", beta, [int, float, bool], "Addmv")
if input_dtype in (mstype.int16, mstype.int32, mstype.int64):
alpha = ops.scalar_cast(alpha, mstype.int64)
beta = ops.scalar_cast(beta, mstype.int64)
out = beta * input + alpha * mv(mat, vec)
return out
def addmv_ext(input, mat, vec, *, beta=1, alpha=1):
"""
Performs a matrix-vector product of `mat` and `vec`, and add the input vector `input` to the final result.
If `mat` is a tensor of size :math:`(N, M)` , `vec` is a 1-D tensor of size :math:`M` , then `input` must be
broadcastable with a 1-D tensor of size :math:`N` . In this case, `output` is a 1-D Tensor of size :math:`N` .
.. math::
output = \beta input + \alpha (mat @ vec)
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): Vector to be added.
mat (Tensor): The first tensor needs to be multiplied.
vec (Tensor): The second tensor needs to be multiplied.
Keyword Args:
beta (Union[float, int], optional): Coefficient of `input`. Default: ``1``.
alpha (Union[float, int], optional): Coefficient of :math:`mat @ vec` . Default: ``1``.
Returns:
Tensor, with a shape of :math:`(N,)` , and its dtype is the same as `input`.
Raises:
TypeError: If dtype of `input`, `mat` or `vec` is not tensor.
TypeError: If dtypes of `mat` and `vec` are not the same.
ValueError: If `mat` is not a 2-D tensor.
ValueError: If `vec` is not a 1-D tensor.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, mint
>>> input = Tensor(np.array([2., 3.]).astype(np.float32))
>>> mat = Tensor(np.array([[2., 5., 3.], [4., 2., 2.]]).astype(np.float32))
>>> vec = Tensor(np.array([3., 2., 4.]).astype(np.float32))
>>> output = mint.addmv(input, mat, vec)
>>> print(output)
[30. 27.]
"""
return addmv_op(input, mat, vec, beta, alpha)
[文档]def adjoint(x):
r"""
Calculates the conjugation of Tensor element by element, and transposes the last two dimensions.
Args:
x (Tensor): Input Tensor.
Returns:
Tensor, the calculated result.
Raises:
TypeError: If `x` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> a = Tensor(np.array([[0. + 0.j, 1. + 1.j], [2. + 2.j, 3. + 3.j]]), mindspore.complex128)
>>> output = ops.adjoint(a)
>>> print(output)
[[0.-0.j 2.-2.j]
[1.-1.j 3.-3.j]]
"""
_dtype = x.dtype
_t = x.swapaxes(-1, -2)
if _dtype in mstype.complex_type:
return _t.conj()
return _t
[文档]def addr(x, vec1, vec2, *, beta=1, alpha=1):
"""
Computes the outer product of two vector `vec1` and `vec2`, and adds the resulting matrix to `x`.
Given `vec1` and `vec2` of sizes :math:`N` and :math:`M`,
`x` must be able to broadcast to a matrix of shape :math:`(N, M)`.
`beta` and `alpha` are optional scaling factors for the outer product of `vec1` and `vec2`,
and the matrix `x` respectively. Setting `beta` to 0 will exclude `x` from the computation.
.. math::
output = β x + α (vec1 ⊗ vec2)
Args:
x (Tensor): Vector to be added. The shape of the tensor is :math:`(N, M)`.
vec1 (Tensor): The first tensor to be multiplied. The shape of the tensor is :math:`(N,)`.
vec2 (Tensor): The second tensor to be multiplied. The shape of the tensor is :math:`(M,)`.
Keyword Args:
beta (scalar[int, float, bool], optional): Multiplier for `x` (β). The `beta` must be int or
float or bool. Default: ``1`` .
alpha (scalar[int, float, bool], optional): Multiplier for `vec1` ⊗ `vec2` (α). The `alpha` must
be int or float or bool. Default: ``1`` .
Returns:
Tensor, the shape of the output tensor is :math:`(N, M)`, has the same dtype as `x`.
Raises:
TypeError: If `x`, `vec1`, `vec2` is not a Tensor.
TypeError: If inputs `vec1`, `vec2` are not the same dtype.
ValueError: If `vec1`, `vec2` is not a 1-D Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[2., 2.], [3., 2.], [3., 4.]], np.float32))
>>> vec1 = Tensor(np.array([2., 3., 2.], np.float32))
>>> vec2 = Tensor(np.array([3, 4], np.float32))
>>> output = ops.addr(x, vec1, vec2)
>>> print(output)
[[ 8. 10.]
[12. 14.]
[ 9. 12.]]
"""
input_dtype = dtype_(x)
if not (isinstance(x, Tensor) and isinstance(vec1, Tensor) and isinstance(vec2, Tensor)):
raise TypeError("For Addr, inputs must be all tensors.")
if dtype_(vec1) != dtype_(vec2):
raise TypeError("For Addr, the vec1 and vec2 should be the same dtype.")
_check_input_dtype("x", input_dtype,
[mstype.float16, mstype.float32, mstype.float64,
mstype.int16, mstype.int32, mstype.int64], "Addr")
_check_attr_dtype("alpha", alpha, [int, float, bool], "Addr")
_check_attr_dtype("beta", beta, [int, float, bool], "Addr")
if input_dtype in (mstype.int16, mstype.int32, mstype.int64):
alpha = ops.scalar_cast(alpha, mstype.int64)
beta = ops.scalar_cast(beta, mstype.int64)
length_vec1 = get_x_shape(vec1.shape)
vec1 = reshape_(vec1, (length_vec1[0], 1))
length_vec2 = get_x_shape(vec2.shape)
vec2 = reshape_(vec2, (1, length_vec2[0]))
out = beta * x + alpha * matmul_(vec1, vec2)
return out
[文档]def lcm(input, other):
"""
Computes least common multiplier of input tensors element-wise.
The shape of two inputs should be broadcastable, and data type of them should be
one of: int32, int64
Args:
input (Tensor): The first input tensor.
other (Tensor): The second input tensor.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is one
with higher digits in the two inputs.
Raises:
TypeError: If data type `input` or `other` is not int32 or int64.
ValueError: If shapes of two inputs are not broadcastable.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([7, 8, 9]))
>>> other = Tensor(np.array([14, 6, 12]))
>>> y = ops.lcm(input, other)
>>> print(y)
[14 24 36]
"""
return lcm_(input, other)
[文档]def lerp(input, end, weight):
"""
Does a linear interpolation of two tensors input and end based on a float or tensor weight.
If `weight` is a tensor, the shapes of three inputs need to be broadcast;
If `weight` is a float, the shapes of `input` and `end` need to be broadcast.
.. math::
output_{i} = input_{i} + weight_{i} * (end_{i} - input_{i})
Args:
input (Tensor): The tensor with the starting points. Data type must be float16 or float32.
end (Tensor): The tensor with the ending points. Data type must be the same as `input`.
weight (Union[float, Tensor]): The weight for the interpolation formula. Must be a float
or a scalar tensor with float16 or float32 data type.
Returns:
Tensor, has the same type and shape as input `input`.
Raises:
TypeError: If `input` or `end` is not a tensor.
TypeError: If `weight` is neither scalar(float) nor tensor.
TypeError: If dtype of `input` or `end` is neither float16 nor float32.
TypeError: If dtype of `weight` is neither float16 nor float32 when it is a tensor.
TypeError: If `input` and `end` have different data types.
TypeError: If `input`, `end` and `weight` have different data types when `weight` is a tensor.
ValueError: If `end` could not be broadcast to a tensor with shape of `input`.
ValueError: If `weight` could not be broadcast to tensors with shapes of `input` and `end` when it is a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1., 2., 3., 4.]), mindspore.float32)
>>> end = Tensor(np.array([10., 10., 10., 10.]), mindspore.float32)
>>> output = ops.lerp(input, end, 0.5)
>>> print(output)
[5.5 6. 6.5 7. ]
"""
return lerp_(input, end, weight)
def lerp_ext(input, end, weight):
"""
Does a linear interpolation of two tensors input and end based on a float or tensor weight.
If `weight` is a tensor, the shapes of three inputs need to be broadcast;
If `weight` is a float, the shapes of `input` and `end` need to be broadcast.
.. warning::
This is an experimental API that is subject to change or deletion.
.. math::
output_{i} = input_{i} + weight_{i} * (end_{i} - input_{i})
Args:
input (Tensor): The tensor with the starting points. Data type must be float16 or float32.
end (Tensor): The tensor with the ending points. Data type must be the same as `input`.
weight (Union[float, Tensor]): The weight for the interpolation formula. Must be a float
or a scalar tensor with float16 or float32 data type.
Returns:
Tensor, has the same type and shape as input `input`.
Raises:
TypeError: If `input` or `end` is not a tensor.
TypeError: If `weight` is neither scalar(float) nor tensor.
TypeError: If dtype of `input` or `end` is neither float16 nor float32.
TypeError: If dtype of `weight` is neither float16 nor float32 when it is a tensor.
TypeError: If `input` and `end` have different data types.
TypeError: If `input`, `end` and `weight` have different data types when `weight` is a tensor.
ValueError: If `end` could not be broadcast to a tensor with shape of `input`.
ValueError: If `weight` could not be broadcast to tensors with shapes of `input` and `end` when it is a tensor.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([1., 2., 3., 4.]), mindspore.float32)
>>> end = Tensor(np.array([10., 10., 10., 10.]), mindspore.float32)
>>> output = ops.lerp(input, end, 0.5)
>>> print(output)
[5.5 6. 6.5 7. ]
"""
if isinstance(weight, float):
return lerp_scalar_(input, end, weight)
return lerp_(input, end, weight)
[文档]def bernoulli(input, p=0.5, seed=None):
r"""
Randomly set the elements of output to 0 or 1 with the probability of `p`
which follows the Bernoulli distribution.
.. math::
out_{i} \sim Bernoulli(p_{i})
Args:
input (Tensor): Input Tensor. Data
type must be int8, uint8, int16, int32, int64, bool, float32 or float64.
p (Union[Tensor, float], optional): Success probability, representing the probability of setting 1 for the
corresponding position of the current Tensor. It has the same shape as `input`, the value of `p`
must be in the range `[0, 1]`. Default: ``0.5`` .
seed (Union[int, None], optional): The seed value for random generating. The value of `seed` must be a
positive integer. Default: ``None`` , means using the current timestamp.
Returns:
output (Tensor), with the same shape and type as `input`.
Raises:
TypeError: If dtype of `input` is not one of: int8, uint8, int16, int32, int64, bool, float32, float64.
TypeError: If dtype of `p` is not one of: float32, float64.
TypeError: If dtype of `seed` is not int or None.
ValueError: If `p` is not in range [0, 1].
ValueError: If `seed` is less than 0.
ValueError: If `p` is a Tensor but has different shape than `input`.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> input_x = Tensor(np.array([1, 2, 3]), mindspore.int8)
>>> output = ops.bernoulli(input_x, p=1.0)
>>> print(output)
[1 1 1]
>>> input_p = Tensor(np.array([0.0, 1.0, 1.0]), mindspore.float32)
>>> output = ops.bernoulli(input_x, input_p)
>>> print(output)
[0 1 1]
"""
if seed is None:
seed = -1
validator.check_is_int(seed, 'seed', 'bernoulli')
bernoulli_ = _get_cache_prim(Bernoulli)(seed)
if not isinstance(p, Tensor):
p = Tensor([p])
return bernoulli_(input, p)
def bernoulli_ext(input, *, generator=None):
r"""
Sample from the Bernoulli distribution and randomly set the i^{th} element of the `output` to (0 or 1) according to
the i^{th} probability value given in the `input`.
.. math::
output_{i} \sim Bernoulli(p=input_{i})
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor of Bernoulli distribution, where the i^{th} element 'input_{i}' represents the
probability that the corresponding output element 'output_{i}' is set to '1', therefore each element in
'input' have to be in the range '[0,1]'. Supported dtype: float16, float32, float64, bfloat16
(only supported by Atlas A2 training series products).
Keyword Args:
generator (:class:`mindspore.Generator`, optional): a pseudorandom number generator.
Default: ``None``, uses the default pseudorandom number generator.
Returns:
output (Tensor): The output tensor, with the same shape and dtype as `input`.
Raises:
TypeError: If dtype of `input` is not one of: float16, float32, float64, bfloat16.
ValueError: If any element of the `input` is not in the range [0, 1].
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> input_x = Tensor(np.ones((3, 3)), mindspore.float32)
>>> output = ops.bernoulli_ext(input_x)
>>> print(output)
[[ 1. 1. 1.]
[ 1. 1. 1.]
[ 1. 1. 1.]]
>>> input_x = Tensor(np.zeros((3, 3)), mindspore.float32)
>>> output = ops.bernoulli_ext(input_x)
>>> print(output)
[[ 0. 0. 0.]
[ 0. 0. 0.]
[ 0. 0. 0.]]
"""
if generator is None:
generator = default_generator
seed, offset = generator._step(generator_step_) # pylint: disable=protected-access
return bernoulli_ext_(input, seed, offset)
[文档]def bessel_i1(x):
r"""
Computes modified Bessel function of the first kind, order 1 element-wise.
.. math::
\begin{array}{ll} \\
I_{1}(x)=\mathrm{i}^{-1} J_{1}(\mathrm{i} x)=\sum_{m=0}^
{\infty} \frac{x^{2m+1}}{2^{2m+1} m ! (m+1) !}
\end{array}
where :math:`J_{1}` is Bessel function of the first kind, order 1.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-1, -0.5, 0.5, 1]), mindspore.float32)
>>> output = ops.bessel_i1(x)
>>> print(output)
[-0.5651591 -0.25789431 0.25789431 0.5651591]
"""
return bessel_i1_(x)
[文档]def bessel_i1e(x):
r"""
Computes exponential scaled modified Bessel function of the first kind, order 1 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
\text I_{1}e(x)=e^{(-|x|)} * I_{1}(x)=e^{(-|x|)} * \sum_{m=0}^
{\infty} \frac{x^{2m+1}}{2^{2m+1} m ! (m+1) !}
\end{array}
where :math:`I_{1}` is modified Bessel function of the first kind, order 1.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-1, -0.5, 0.5, 1]), mindspore.float32)
>>> output = ops.bessel_i1e(x)
>>> print(output)
[-0.20791042 -0.15642083 0.15642083 0.20791042]
"""
return bessel_i1e_(x)
[文档]def bessel_k1(x):
r"""
Computes modified Bessel function of the second kind, order 1 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
K_{1}(x)=\lim_{\nu \to 1} \left(\frac{\pi}{2}\right) \frac{I_{-\nu}(x)-
I_{\nu}(x)}{\sin (\nu \pi)} = \int_{0}^{\infty} e^{-x \cosh t} \cosh (t) d t
\end{array}
where :math:`I_{1}` is modified Bessel function of the first kind, order 1.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_k1(x)
>>> print(output)
[1.65644112 0.60190723 0.13986588 0.0124835]
"""
return bessel_k1_(x)
[文档]def bessel_k1e(x):
r"""
Computes exponential scaled modified Bessel function of the second kind, order 1 element-wise.
The formula is defined as:
.. math::
\begin{array}{ll} \\
K_{1}e(x)= e^{(-|x|)} * K_{1}(x) = e^{(-|x|)} * \int_{0}
^{\infty} e^{-x \cosh t} \cosh (t) d t
\end{array}
where :math:`K_{1}` is modified Bessel function of the second kind, order 1.
Args:
x (Tensor): The input tensor. The data type must be float16, float32 or float64.
Returns:
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:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 1., 2., 4.]), mindspore.float32)
>>> output = ops.bessel_k1e(x)
>>> print(output)
[2.73100971 1.63615349 1.03347685 0.68157595]
"""
return bessel_k1e_(x)
@constexpr
def _check_input_dtype(param_name, input_dtype, allow_dtypes, cls_name):
validator.check_type_name(param_name, input_dtype, allow_dtypes, cls_name)
[文档]def deg2rad(x):
"""
Converts angles in degrees to angles in radians element-wise.
Args:
x (Tensor): The input tensor.
With float16, float32 or float64 data type.
Returns:
Tensor, has the same dtype as the `x`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If dtype of `x` isn't float16, float32 or float64.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[90.0, -90.0], [180.0, -180.0], [270.0, -270.0]]).astype(np.float32))
>>> output = ops.deg2rad(x)
>>> print(output)
[[ 1.5707964 -1.5707964]
[ 3.1415927 -3.1415927]
[ 4.712389 -4.712389 ]]
"""
if not isinstance(x, (Tensor, Tensor_)):
raise TypeError("The input x must be tensor")
x_dtype = dtype_(x)
_check_input_dtype("x", x_dtype, [mstype.float16, mstype.float32, mstype.float64], "")
if x_dtype == mstype.float16:
out = x * (Tensor(math.pi / 180.0).astype(mstype.float16))
else:
out = x * math.pi / 180.0
return out
[文档]def rad2deg(x):
"""
Converts angles in radians to angles in degrees element-wise.
Args:
x (Tensor): The input tensor.
Returns:
Tensor, has the same shape and dtype as the `x`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If dtype of `x` isn't float16, float32 or float64.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> x = Tensor([[6.283, -3.142],[1.570, -6.283],[3.142, -1.570]], mindspore.float32)
>>> output = ops.rad2deg(x)
>>> print(output)
[[ 359.98935 -180.02333]
[ 89.95438 -359.98935]
[ 180.02333 -89.95438]]
"""
if not isinstance(x, (Tensor, Tensor_)):
raise TypeError("The input x must be tensor")
x_dtype = dtype_(x)
_check_input_dtype("x", x_dtype, [mstype.float16, mstype.float32, mstype.float64], "")
if x_dtype == mstype.float16:
out = x * (Tensor(180.0 / math.pi).astype(mstype.float16))
else:
out = x * 180.0 / math.pi
return out
[文档]def frac(x):
"""
Calculates the fractional part of each element in the input
Args:
x (Tensor): x is a tensor.
Returns:
Tensor, has the same shape and type as input.
Raises:
TypeError: If `x` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor
>>> from mindspore import dtype as mstype
>>> from mindspore import ops
>>> x = Tensor([2, 4.2, -2.5], mstype.float16)
>>> output = ops.frac(x)
>>> print(output)
[ 0. 0.1992 -0.5 ]
"""
return mod_(x, 1)
#####################################
# Reduction Operation Functions.
#####################################
@_primexpr
def _create_cummin_perm(axis, x_shape):
"""Insure axis is in [-len(x_shape),len(s_shape)-1]"""
def _check(axis, len_axis):
if not isinstance(axis, int):
raise TypeError(f"The date type of 'axis' must be Int, but got {axis}.")
if axis < -len_axis or axis > len_axis:
raise ValueError(f"The value of axis must be in [{-len_axis}, {len_axis}], but got {axis}.")
len_axis = len(x_shape)
_check(axis, len_axis)
prem = [i for i in range(len_axis)]
if axis < 0:
axis = axis + len_axis
prem[0], prem[axis] = axis, 0
prem = tuple(prem)
return prem
[文档]def cummin(input, axis):
r"""
Returns a tuple (values,indices) where 'values' is the cumulative minimum value of input Tensor `input`
along the dimension `axis`, and `indices` is the index location of each minimum value.
.. math::
\begin{array}{ll} \\
y_{i} = \min(x_{1}, x_{2}, ... , x_{i})
\end{array}
Args:
input (Tensor): The input Tensor, rank of `input` > 0.
axis (int): The dimension to do the operation over. The value of `axis` must be in the range
`[-input.ndim, input.ndim - 1]`.
Returns:
tuple [Tensor], tuple of 2 Tensors, containing the cumulative minimum of elements and the index.
The shape of each output tensor is the same as input `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `input` is a Tensor, but the type is complex or bool.
TypeError: If `axis` is not an int.
ValueError: If `axis` is out the range of `[-input.ndim, input.ndim - 1]`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> a = Tensor([-0.2284, -0.6628, 0.0975, 0.2680, -1.3298, -0.4220], mindspore.float32)
>>> output = ops.cummin(a, axis=0)
>>> print(output[0])
[-0.2284 -0.6628 -0.6628 -0.6628 -1.3298 -1.3298]
>>> print(output[1])
[0 1 1 1 4 4]
"""
if isinstance(axis, bool):
raise TypeError(f"For 'cummin', the date type of 'axis' must be Int, but got {axis}.")
cummin_op = _get_cache_prim(Cummin)(axis=0)
if axis == 0:
out1, out2 = cummin_op(input)
else:
x_shape = shape_(input)
prem = _create_cummin_perm(axis, x_shape)
input = transpose_(input, prem)
out1, out2 = cummin_op(input)
out1 = transpose_(out1, prem)
out2 = transpose_(out2, prem)
return [out1, out2]
[文档]def cumsum(x, axis, dtype=None):
"""
Computes the cumulative sum of input Tensor along `axis`.
.. math::
y_i = x_1 + x_2 + x_3 + ... + x_i
Note:
On Ascend, the dtype of `x` only support :int8, uint8, int32, float16 or float32 in case of static shape.
For the case of dynamic shape, the dtype of `x` only support int32, float16 or float32.
Args:
x (Tensor): The input Tensor of shape :math:`(N, *)` where :math:`*` means, any number
of additional dimensions.
axis (int): Axis along which the cumulative sum is computed.
dtype (:class:`mindspore.dtype`, optional): The desired dtype of returned Tensor. If specified,
the input Tensor will be cast to `dtype` before the computation. This is useful for preventing overflows.
If not specified, stay the same as original Tensor. Default: ``None`` .
Returns:
Tensor, the shape of the output Tensor is consistent with the input Tensor's.
Raises:
TypeError: If `x` is not a Tensor.
ValueError: If the axis is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> x = Tensor(np.array([[3, 4, 6, 10], [1, 6, 7, 9], [4, 3, 8, 7], [1, 3, 7, 9]]).astype(np.float32))
>>> # case 1: along the axis 0
>>> y = ops.cumsum(x, 0)
>>> print(y)
[[ 3. 4. 6. 10.]
[ 4. 10. 13. 19.]
[ 8. 13. 21. 26.]
[ 9. 16. 28. 35.]]
>>> # case 2: along the axis 1
>>> y = ops.cumsum(x, 1)
>>> print(y)
[[ 3. 7. 13. 23.]
[ 1. 7. 14. 23.]
[ 4. 7. 15. 22.]
[ 1. 4. 11. 20.]]
"""
if dtype is not None and x.dtype != dtype:
x = x.astype(dtype, copy=False)
return cumsum_(x, axis)
[文档]def sparse_segment_mean(x, indices, segment_ids):
r"""
Computes a Tensor such that :math:`output_i = \frac{\sum_j x_{indices[j]}}{N}` where mean is over :math:`j` such
that :math:`segment\_ids[j] == i` and :math:`N` is the total number of values summed. If the mean is empty for
a given segment ID :math:`i`, :math:`output[i] = 0`.
Note:
- On CPU, values in `segment_ids` are always validated to be sorted, and an error is thrown for indices that
are not increasing. Moreover, values in `indices` are validated to be bounded, and an error is thrown when
`indices` are out of range[0, x.shape[0]).
- On GPU, this does not throw an error for unsorted `segment_ids` and out-of-bound `indices`. Out-of-order
`segment_ids` result in safe but unspecified behavior, while out-of-range `indices` will be ignored.
Args:
x (Tensor): A Tensor, and its rank must be greater than or equal to 1.
indices (Tensor): A 1-D Tensor, with int32 or int64 data type.
segment_ids (Tensor): A 1-D Tensor, must have the same dtype as `indices`.
Values should be sorted and can be repeated.
Returns:
Tensor, whose dtype and rank is the same as `x`. The first dimension is equal to the value of the last element
of `segment_ids` plus one, and the other dimensions are the same as those of `x`.
Raises:
TypeError: If `x`, `indices` or `segment_ids` is not a Tensor.
TypeError: If the dtype of `x` is not one of the following dtype: float16, float32, float64.
TypeError: If the dtype of `indices` and `segment_ids` are not one of the following dtype: int32, int64.
TypeError: If the dtype of `indices` and `segment_ids` are not the same.
ValueError: If the shape of `x`, `indices` or `segment_ids` don't meet the parameter description.
ValueError: If the size of `indices` and `segment_ids` are not the same.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> x = Tensor([[0, 1, 2], [1, 2, 3], [3, 6, 7]], dtype=mindspore.float32)
>>> indices = Tensor([0, 1, 2], dtype=mindspore.int32)
>>> segment_ids = Tensor([1,2,2], dtype=mindspore.int32)
>>> out = ops.sparse_segment_mean(x, indices, segment_ids)
>>> print(out)
[[0. 0. 0.]
[0. 1. 2.]
[2. 4. 5.]]
"""
return sparse_segment_mean_(x, indices, segment_ids)
[文档]def block_diag(*inputs):
r"""
Creates a block diagonal matrix from the provided Tensor.
Args:
inputs (Tensor): One or more tensors, the dimension of Tensor should be 0, 1 or 2.
Returns:
Tensor, two-dimensional with all input tensors arranged in
order so that their top left and bottom right corners are
diagonally adjacent. All other elements are set to 0.
Raises:
TypeError: If the input is not a Tensor.
ValueError: If the dimension of Tensor is not 0, 1 or 2.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x1 = Tensor([[4], [3], [2]], mstype.int32)
>>> x2 = Tensor([7, 6, 5], mstype.int32)
>>> x3 = Tensor(1, mstype.int32)
>>> x4 = Tensor([[5, 4, 3], [2, 1, 0]], mstype.int32)
>>> x5 = Tensor([[8, 7], [7, 8]], mstype.int32)
>>> out = ops.block_diag(x1, x2, x3, x4, x5)
>>> print(out.asnumpy())
[[4 0 0 0 0 0 0 0 0 0]
[3 0 0 0 0 0 0 0 0 0]
[2 0 0 0 0 0 0 0 0 0]
[0 7 6 5 0 0 0 0 0 0]
[0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 5 4 3 0 0]
[0 0 0 0 0 2 1 0 0 0]
[0 0 0 0 0 0 0 0 8 7]
[0 0 0 0 0 0 0 0 7 8]]
"""
def to_col_block(arys, i, a):
return [
a if idx == i else ops.zeros((ary.shape[0], a.shape[1]), ary.dtype)
for idx, ary in enumerate(arys)
]
def to_2d(ary):
if not isinstance(ary, Tensor):
raise TypeError(
f"For 'block_diag', each element of 'inputs' must be a tensor, but got {type(ary)}"
)
if ary.ndim == 0:
return ops.expand_dims(ops.expand_dims(ary, 0), 0)
if ary.ndim == 1:
return ops.expand_dims(ary, 0)
if ary.ndim == 2:
return ary
raise ValueError(
"For 'block_diag', the dimension of each elements in 'inputs' must be 0, 1, or 2, but got "
f"{ary.ndim}"
)
if not inputs:
raise RuntimeError("For 'block_diag', the input is empty.")
arys = [to_2d(ary) for ary in inputs]
matrix = [ops.concat(to_col_block(arys, idx, ary)) for idx, ary in enumerate(arys)]
return ops.concat(matrix, 1)
[文档]def atleast_1d(inputs):
r"""
Reshapes Tensor in `inputs`, every Tensor has at least one dimension after this operation.
Scalar is converted to a 1-D Tensor, input tensor with one or more dimensions will be returned as it is.
Args:
inputs (Union[Tensor, list[Tensor]]): One or more input tensors.
Returns:
Tensor or list[Tensor]. If returned a list, every element `a` in that list satisfies `a.ndim >= 1`.
Raises:
TypeError: If the `inputs` is not a tensor or a list of tensors.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.ones((2, 3)))
>>> x2 = Tensor(np.ones(()))
>>> x3 = Tensor(np.ones(5))
>>> out = ops.atleast_1d([x1, x2, x3])
>>> print(out[0].asnumpy())
[[1. 1. 1.]
[1. 1. 1.]]
>>> print(out[1].asnumpy())
[1.]
>>> print(out[2].asnumpy())
[1. 1. 1. 1. 1.]
"""
if isinstance(inputs, Tensor):
return _expand(inputs, 1)
for tensor in inputs:
if not isinstance(tensor, Tensor):
raise TypeError(f"For 'atleast_1d', each element of 'inputs' must be a tensor, but got {type(tensor)}")
return tuple([_expand(arr, 1) for arr in inputs])
[文档]def dstack(tensors):
r"""
Stacks tensors along the third axis.
1-D tensors :math:`(N,)` should be reshaped to :math:`(1,N,1)`.
2-D tensors :math:`(M,N)` should be reshaped to :math:`(M,N,1)` before concatenation.
Args:
tensors (Union(List[Tensor], Tuple[Tensor])): A sequence of tensors.
The tensors must have the same shape along all but the third axis.
1-D or 2-D tensors must have the same shape.
Returns:
Stacked Tensor, will be at least 3-D.
The output shape is similar to the output of `numpy.dstack()` function.
Raises:
TypeError: If `tensors` is not tuple or list.
ValueError: If `tensors` is empty.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.arange(1, 7).reshape(2, 3))
>>> x2 = Tensor(np.arange(7, 13).reshape(2, 3))
>>> out = ops.dstack([x1, x2])
>>> print(out.asnumpy())
[[[ 1. 7.]
[ 2. 8.]
[ 3. 9.]]
[[ 4. 10.]
[ 5. 11.]
[ 6. 12.]]]
"""
if not isinstance(tensors, (tuple, list)):
raise TypeError(f"For 'dstack', 'tensors' must be list or tuple of tensors, but got {type(tensors)}")
if not tensors:
raise TypeError(f"For 'dstack', 'tensors' can not be empty.")
trans_tensors = ()
for tensor in tensors:
if not isinstance(tensor, Tensor):
raise TypeError(f"For 'dstack', each elements of 'tensors' must be Tensor, but got {type(tensor)}")
if tensor.ndim == 0:
tensor = reshape_(tensor, (1, 1, 1))
elif tensor.ndim == 1:
tensor = expand_dims_(expand_dims_(tensor, 0), 2)
elif tensor.ndim == 2:
tensor = expand_dims_(tensor, 2)
trans_tensors += (tensor,)
if not trans_tensors:
raise ValueError("For 'dstack', at least one tensor is needed to concatenate.")
return _get_cache_prim(P.Concat)(2)(trans_tensors)
@_primexpr
def _check_is_int(arg_value, arg_name, cls_name):
validator.check_is_int(arg_value, arg_name, cls_name)
[文档]def diff(x, n=1, axis=-1, prepend=None, append=None):
r"""
Computes the n-th discrete difference along a specified axis of a given input `x`.
The first difference is calculated as :math:`out[i] = x[i+1] - x[i]` along the specified `axis`.
To compute higher differences, the function is called recursively
using the output from the previous iteration as input.
Note:
Zero-shaped Tensor is not supported, a value error is raised if
an empty Tensor is encountered. Any dimension of a Tensor is 0, which is considered
an empty Tensor. Tensor with shape of :math:`(0,)`, :math:`(1, 2, 0, 4)` are all
empty Tensor.
Args:
x (Tensor): Input tensor.
Full support for signed integers, partial support for floats and complex numbers
n (int, optional): The number of times values are differenced. If zero,
the input is returned as-is. Currently only 1 is supported. Default: ``1`` .
axis (int, optional): The axis along which the difference is taken, default
is the last axis. Default: ``-1`` .
prepend (Tensor, optional): Values to prepend to `x` along
`axis` prior to performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of `axis` and the shape of the input
array along all other axis. Otherwise the dimension and shape must
match `x` except along `axis`. Default: ``None`` .
append (Tensor, optional): Values to append to `x` along
`axis` prior to performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of `axis` and the shape of the input
array along all other axis. Otherwise the dimension and shape must
match `x` except along `axis`. Default: ``None`` .
Returns:
Tensor, the n-th differences of input. The shape of the output is the same as `x`
except along `axis` where the size is reduced by `n`. The type of the output
is the same as `x`.
Raises:
TypeError: If the data type of the elementes in `x` is uint16, uint32 or uint64.
TypeError: If `x` is not a tensor.
ValueError: If `x` is an empty Tensor.
ValueError: If the dim of `x` is less than 1.
RuntimeError: If `n` is not 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> x = Tensor([1, 3, -1, 0, 4])
>>> out = ops.diff(x)
>>> print(out.asnumpy())
[ 2 -4 1 4]
"""
if not isinstance(x, Tensor):
raise TypeError(f"For 'diff', 'x' must be a tensor, but got {type(x)}")
if x.ndim < 1:
raise TypeError(f"For 'diff', the dimension 'x' must be at least 1, but got {x.ndim}")
if 0 in x.shape:
raise ValueError(f"For 'diff', 'x' can not be an empty Tensor.")
_check_is_int(n, 'n', 'diff')
if n != 1:
raise RuntimeError(f"For 'diff', 'n' must be 1, but got {n}")
if x.dtype in (mstype.uint16, mstype.uint32, mstype.uint64):
msg = f"For 'diff', the data type of the elements in 'x' cannot be uint16, uint32, uint64, but got {x.dtype}"
raise TypeError(msg)
if prepend is not None and append is not None:
x = ops.Concat(axis)((prepend, x, append))
elif append is not None:
x = ops.Concat(axis)((x, append))
elif prepend is not None:
x = ops.Concat(axis)((prepend, x))
a = ops.make_range(x.shape[axis])
a1 = x.gather(TupleToTensor()(a[:-1], mstype.int64), axis)
a2 = x.gather(TupleToTensor()(a[1:], mstype.int64), axis)
return a2 - a1
def _diff_is_scalar_or_scalar_tensor(value):
"""judge the value"""
if isinstance(value, int):
return True
if isinstance(value, ms.Tensor) and value.shape == ():
return True
return False
def _diff_check(input, n, dim):
"""judge the input n and dim"""
if not isinstance(input, Tensor):
raise TypeError("For 'diff', 'input' must be a tensor")
if not _diff_is_scalar_or_scalar_tensor(n):
raise TypeError("For 'diff', 'n' must be a int scalar or int scalar tensor")
if not _diff_is_scalar_or_scalar_tensor(dim):
raise TypeError("For 'diff', 'dim' must be a scalar or scalar tensor")
if input.dtype in (mstype.complex64, mstype.complex128, mstype.float64, mstype.int16):
raise TypeError("For 'diff', 'input' do not support complex64/complex128/float64/int16")
def _diff_helper(input, n, dim):
"""calculate the forward difference"""
out_len = input.shape[dim] - 1
is_bool = (input.dtype == mstype.bool_)
result = input
for i in range(n): # pylint: disable=unused-variable
if is_bool:
result = logical_xor(narrow(result, dim, 1, out_len), narrow(result, dim, 0, out_len))
else:
result = sub_ext(narrow(result, dim, 1, out_len), narrow(result, dim, 0, out_len))
if out_len == 0:
break
out_len -= 1
return result
def _diff_prepend_append_on_dim(input, prepend, append, dim):
"""append tensor on dim"""
if prepend is not None and append is None:
return cat((prepend, input), dim)
if prepend is None and append is not None:
return cat((input, append), dim)
return cat((prepend, input, append), dim)
def diff_ext(input, n=1, dim=-1, prepend=None, append=None):
r"""
Computes the n-th forward difference along the given dimension.
The first-order differences are given by :math:`out[i] = input[i+1] - input[i]`. Higher-order differences are
calculated by using `torch.diff()` recursively.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): the tensor to compute the differences on.
n (int, optional): the number of times to recursively compute the difference.
Default: ``1`` .
dim (Tensor, optional): the dimension to compute the difference along.
Default is the last dimension. Default: ``0`` .
prepend (Tensor, optional): values to prepend or append to `input` along `dim`
before computing the difference. Their dimensions must be equivalent to that of input,
and their shapes must match input's shape except on `dim`. Default: ``None`` .
append (Tensor, optional): values to prepend or append to `input` along `dim`
before computing the difference. Their dimensions must be equivalent to that of input,
and their shapes must match input's shape except on `dim`. Default: ``None`` .
Returns:
Tensor, the result of n-th forward difference computation.
Raises:
TypeError: If `input` is not a tensor.
TypeError: If `n` is not a scalar or scalar tensor.
TypeError: If `dim` is not a scalar or scalar tensor.
TypeError: If `input` type is complex64, complex128, float64, int16.
Supported Platforms:
``Ascend``
Examples:
>>> from mindspore import Tensor, ops
>>> x = Tensor([1, 3, -1, 0, 4])
>>> out = ops.diff_ext(x)
>>> print(out.asnumpy())
[ 2 -4 1 4]
"""
_diff_check(input, n, dim)
if (prepend is None and append is None) or n == 0:
return _diff_helper(input, n, dim)
input = _diff_prepend_append_on_dim(input, prepend, append, dim)
return _diff_helper(input, n, dim)
[文档]def tril_indices(row, col, offset=0, *, dtype=mstype.int64):
r"""
Calculates the indices of the lower triangular elements in a `row` * `col` matrix
and returns them as a 2-by-N Tensor. The first row of the Tensor contains
row coordinates, and the second row contains column coordinates. The coordinates are
sorted by row and then by column.
The lower triangular part of the matrix consists of all elements on and below the diagonal.
Note:
When running on CUDA, row * col must be less than 2^59 to prevent overflow during calculation.
Args:
row (int): number of rows in the 2-D matrix.
col (int): number of columns in the 2-D matrix.
offset (int, optional): diagonal offset from the main diagonal. Default: ``0`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): The specified type of output tensor.
An optional data type of `mindspore.int32` and `mindspore.int64`. Default: ``mstype.int64`` .
Returns:
- **y** (Tensor) - indices of the elements in lower triangular part of matrix. The type is specified by `dtype`.
The shape of output is :math:`(2, tril\_size)`, where :math:`tril\_size` is the number of elements in the
lower triangular matrix.
Raises:
TypeError: If `row`, `col` or `offset` is not an int.
TypeError: If `dtype` is neither int32 nor int64.
ValueError: If `row` or `col` < 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import ops
>>> output = ops.tril_indices(4, 3, -1, dtype=mindspore.int64)
>>> print(output)
[[1 2 2 3 3 3]
[0 0 1 0 1 2]]
>>> print(output.dtype)
Int64
"""
tril_indices_ = TrilIndices(row=row, col=col, offset=offset, dtype=dtype)
return tril_indices_()
[文档]def triu_indices(row, col, offset=0, *, dtype=mstype.int64):
r"""
Calculates the indices of the upper triangular elements in a `row` * `col` matrix
and returns them as a 2-by-N Tensor. The first row of the Tensor contains
row coordinates, and the second row contains column coordinates. The coordinates are
sorted by row and then by column.
The upper triangular part of the matrix consists of all elements on and above the diagonal.
Note:
When running on CUDA, row * col must be less than 2^59 to prevent overflow during calculation.
Args:
row (int): number of rows in the 2-D matrix.
col (int): number of columns in the 2-D matrix.
offset (int, optional): diagonal offset from the main diagonal. Default: ``0`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): The specified type of output tensor.
An optional data type of `mindspore.int32` and `mindspore.int64`. Default: ``mstype.int64``.
Returns:
- **y** (Tensor) - indices of the elements in upper triangular part of matrix. The type is specified by `dtype`.
The shape of output is :math:`(2, triu\_size)`, where :math:`triu\_size` is the number of elements in the
upper triangular matrix.
Raises:
TypeError: If `row`, `col` or `offset` is not an int.
TypeError: If `dtype` is neither int32 nor int64.
ValueError: If `row` or `col` < 0.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import ops
>>> output = ops.triu_indices(4, 4, 2, dtype=mindspore.int64)
>>> print(output)
[[0 0 1]
[2 3 3]]
>>> print(output.dtype)
Int64
"""
triu_indices_ = TriuIndices(row=row, col=col, offset=offset, dtype=dtype)
return triu_indices_()
[文档]def atleast_2d(inputs):
r"""
Reshapes Tensor in `inputs`, every Tensor has at least 2 dimension after this operation.
Scalar or 1-D Tensor is converted to 2-D Tensor, tensor with higher dimensions will be returned as it is.
Args:
inputs (Union[Tensor, list[Tensor]]): One or more input tensors.
Returns:
Tensor or list[Tensor]. If returned a list, every element `a` in that list satisfies `a.ndim >= 2` .
Raises:
TypeError: If the `inputs` is not a tensor or a list of tensors.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> from mindspore import ops
>>> x1 = np.ones((2, 3))
>>> x2 = np.ones(())
>>> x3 = np.ones(5)
>>> out = ops.atleast_2d([x1, x2, x3])
>>> print(out)
(Tensor(shape=[2, 3], dtype=Float32, value=
[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00],
[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]), Tensor(shape=[1, 1], dtype=Float32, value=
[[ 1.00000000e+00]]), Tensor(shape=[1, 5], dtype=Float32, value=
[[ 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00, 1.00000000e+00]]))
"""
if isinstance(inputs, Tensor):
return _expand(inputs, 2)
for tensor in inputs:
if not isinstance(tensor, Tensor):
msg = "expect Tensor or list of tensors, but got " + f"{type(tensor)}"
raise TypeError(msg)
return tuple([_expand(arr, 2) for arr in inputs])
def cartesian_prod(*inputs):
r"""
Performs a Cartesian product for a given tensor sequence.
The behavior is similar to Python's `itertools.product`.
Args:
inputs (List[Tensor]): Tensor sequence.
Returns:
Tensor, a Cartesian product for a given tensor sequence.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> x1 = Tensor([1, 2])
>>> x2 = Tensor([5])
>>> out = ops.cartesian_prod(x1, x2)
>>> print(out.asnumpy())
[[1 5]
[2 5]]
>>> x1 = Tensor([1, 2, 3, 4])
>>> x2 = Tensor([5, 6, 7])
>>> x3 = Tensor([8, 9, 0, 1, 2])
>>> out = ops.cartesian_prod(x1, x2, x3)
>>> print(len(out))
60
"""
meshgrid = _get_cache_prim(P.Meshgrid)(indexing="ij")
meshgrid_output = meshgrid(inputs)
stack = _get_cache_prim(P.Stack)(axis=-1)
stack_output = stack(meshgrid_output)
return reshape_(stack_output, (-1, len(inputs)))
[文档]def atleast_3d(inputs):
r"""
Reshapes Tensor in `inputs`, every Tensor has at least 3 dimension after this operation.
Scalar, 1-D or 2-D Tensor is converted to 3-D Tensor,
tensor with higher dimensions will be returned as it is.
Args:
inputs (Union[Tensor, list[Tensor]]): One or more input tensors.
Returns:
Tensor or list[Tensor]. If returned a list, every element `a` in that list satisfies `a.ndim >= 3`.
For example, a 1-D Tensor of shape :math:`(N,)` becomes a Tensor of shape :math:`(1, N, 1)`, and
a 2-D Tensor of shape :math:`(M, N)` becomes a tensor of shape :math:`(M, N, 1)`.
Raises:
TypeError: If the `inputs` is not a tensor or a list of tensors.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x1 = Tensor(np.ones((2, 3)))
>>> x2 = Tensor(np.ones(()))
>>> x3 = Tensor(np.ones(5))
>>> out = ops.atleast_3d([x1, x2, x3])
>>> print(out[0].asnumpy())
[[[1.]
[1.]
[1.]]
<BLANKLINE>
[[1.]
[1.]
[1.]]]
>>> print(out[1].asnumpy())
[[[1.]]]
>>> print(out[2].asnumpy())
[[[1.]
[1.]
[1.]
[1.]
[1.]]]
"""
def _expand3(arr):
ndim = rank_(arr)
if ndim == 0:
return reshape_(arr, (1, 1, 1))
if ndim == 1:
return reshape_(arr, (1, size_(arr), 1))
if ndim == 2:
return reshape_(arr, shape_(arr) + (1,))
return arr
if isinstance(inputs, Tensor):
return _expand3(inputs)
for tensor in inputs:
if not isinstance(tensor, Tensor):
raise TypeError(f"For 'atleast_3d', each element of 'inputs' must be a tensor, but got {type(tensor)}")
return tuple([_expand3(arr) for arr in inputs])
[文档]def view_as_real(input):
r"""
View a complex Tensor as a real Tensor.
The size of last dimension of the returned real Tensor is 2, and the last dimension is composed of
the real and imaginary components of complex numbers.
Args:
input (Tensor): the input must be a complex Tensor.
Returns:
A real Tensor.
Raises:
TypeError: If the input Tensor is not a complex Tensor.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> x = Tensor([2+1j,2+3j,2-1j,2], mstype.complex64)
>>> print(ops.view_as_real(x))
[[ 2. 1.]
[ 2. 3.]
[ 2. -1.]
[ 2. 0.]]
"""
if not is_complex(input):
raise TypeError("For view_as_real, the dtype of input Tensor must be complex.")
real_part = input.real().expand_dims(-1)
imag_part = input.imag().expand_dims(-1)
con = _get_cache_prim(ops.Concat)(-1)
return con((real_part, imag_part))
[文档]def vstack(inputs):
r"""
Stacks tensors in sequence vertically.
This is equivalent to concatenation along the first axis.
1-D tensors :math:`(N,)` should firstly be reshaped to :math:`(1, N)`,
and then be concatenated along the first axis.
Args:
inputs (Union(List[tensor], Tuple[tensor])): A sequence of 1-D or 2-D tensors.
The tensors must have the same shape along all but the first axis.
1-D tensors must have the same shape.
Returns:
Tensor, formed by stacking the given tensors, will be at least 3-D.
The output shape is similar to the output of `numpy.vstack()` function.
Raises:
TypeError: If `inputs` is not list or tuple.
ValueError: If `inputs` is empty.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> x1 = np.array([3, 1, 4])
>>> x2 = np.array([1, 5, 9])
>>> out = ops.vstack([x1, x2])
>>> print(out)
[[3 1 4]
[1 5 9]]
"""
if not isinstance(inputs, (tuple, list)):
msg = f"For 'vstack', list or tuple of tensors are required, but got {type(inputs)}"
raise TypeError(msg)
if not inputs:
msg = "For 'vstack', inputs can not be empty"
raise TypeError(msg)
trans_tup = ()
for tensor in inputs:
if not isinstance(tensor, Tensor):
msg = f"For 'vstack', Tensor is required, but got {type(tensor)}"
raise TypeError(msg)
if tensor.ndim <= 1:
shape = shape_(tensor)
if isinstance(shape, int):
shape = (shape,)
ndim_diff = 2 - len(shape)
if ndim_diff > 0:
shape = [1] * ndim_diff + [i for i in shape]
tensor = reshape_(tensor, tuple(shape))
trans_tup += (tensor,)
if not trans_tup:
raise ValueError("For 'vstack', need at least one tensor to concatenate.")
out = _get_cache_prim(P.Concat)(0)(trans_tup)
return out
[文档]def row_stack(tensors):
"""
Alias for :func:`mindspore.ops.vstack` .
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
"""
return vstack(tensors)
[文档]def combinations(input, r=2, with_replacement=False):
r"""
Returns all r-length subsequences of input Tensor.
When `with_replacement` is set to ``False``, it works similar to Python's
`itertools.combinations`, and when `with_replacement` is set to ``True``,
it behaves like `itertools.combinations_with_replacement`.
Args:
input (Tensor): One-dimensional tensors.
r (int, optional): Number of elements to perform combination. Default: ``2`` .
with_replacement (bool, optional): Allow duplication or not. Default: ``False`` .
Returns:
Tensor, contains all possible combinations of elements sampled from input Tensor.
Raises:
TypeError: If `input` is not a tensor.
TypeError: If `r` is not an int.
TypeError: If `with_replacement` is not bool.
ValueError: If `input` is not one-dimensional.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([1, 3, -1, 0, 4])
>>> output = ops.combinations(input)
>>> print(output.asnumpy())
[[ 1 3]
[ 1 -1]
[ 1 0]
[ 1 4]
[ 3 -1]
[ 3 0]
[ 3 4]
[-1 0]
[-1 4]
[ 0 4]]
"""
def _combinations(iterable, r):
lst = ops.StridedSlice()(ops.zeros(r), (0,), (0,), (1,))
pool = tuple(iterable)
n = len(pool)
if r > n:
return lst
indices = list(range(r))
lst = ops.concat([ops.reshape(pool[i], (1,)) for i in indices])
while True:
stop = True
i = 0
for index in range(r)[::-1]:
if indices[index] != index + n - r:
stop = False
i = index
break
if stop:
return lst
indices[i] += 1
for j in range(i + 1, r):
indices[j] = indices[j - 1] + 1
item = ops.concat([ops.reshape(pool[i], (1,)) for i in indices])
lst = ops.concat((lst, item), -1)
return None
def _combinations_with_replacement(iterable, r):
lst = Tensor_([])
pool = tuple(iterable)
n = len(pool)
if not n and r:
return lst
indices = [0] * r
lst = ops.concat([ops.reshape(pool[i], (1,)) for i in indices])
while True:
stop = True
i = 0
for index in range(r)[::-1]:
if indices[index] != n - 1:
stop = False
i = index
break
if stop:
return lst
indices[i:] = [indices[i] + 1] * (r - i)
item = ops.concat([ops.reshape(pool[i], (1,)) for i in indices])
lst = ops.concat((lst, item), -1)
return None
if not isinstance(input, Tensor):
raise TypeError(f"For 'combinations', 'x' must be a tensor, but got {type(input)}")
if input.ndim != 1:
raise ValueError(f"For 'combinations', the dimension 'x' must be 1, but got {input.ndim}")
if not isinstance(r, int):
raise TypeError(f"For 'combinations', 'r' must be an integer, but got {type(r)}")
comb_func = _combinations_with_replacement if with_replacement else _combinations
ret = comb_func(input, r)
if ret.size == 0:
return ret
return ops.reshape(ret, (-1, r))
[文档]def dist(input, other, p=2):
r"""
Computes batched the :math:`p`-norm distance between each pair of the two collections of row vectors.
Note:
Since only normalization for integer :math:`p`-normal form is supported in MindSpore,
a type error will be raised if :math:`p` is not an integer.
Args:
input (Tensor): The first input tensor. The dtype must be float16 or float32.
other (Tensor): The second input tensor. The dtype must be float16 or float32.
p (int, optional): The order of norm. `p` is greater than or equal to 0. Default: ``2`` .
Returns:
Tensor, has the same dtype as `input`, which shape is :math:`(1)`.
Raises:
TypeError: If `input` or `other` is not a Tensor.
TypeError: If dtype of `input` or `other` is neither float16 nor float32.
TypeError: If `p` is not a non-negative integer.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input_x = Tensor([[[1.0, 1.0], [2.0, 2.0]]])
>>> input_y = Tensor([[[3.0, 3.0], [3.0, 3.0]]])
>>> out = ops.dist(input_x, input_y)
>>> print(out.asnumpy())
3.1622777
"""
if not isinstance(input, Tensor):
raise TypeError(f"For 'dist', 'input' must be a tensor, but got {type(input)}")
if not isinstance(other, Tensor):
raise TypeError(f"For 'dist', 'other' must be a tensor, but got {type(other)}")
z = input - other
if z.ndim == 0:
return ops.abs(z)
# the types of p will expend once ops.LpNorm supports float
return ops.LpNorm(axis=0, p=p)(ops.reshape(z, (-1,)))
[文档]def copysign(x, other):
r"""
Create a new floating-point tensor with the magnitude of `x` and the sign of `other`, element-wise.
Args:
x (Union[Tensor]): Values to change the sign of.
other (Union[int, float, Tensor]): The sign of `other` is copied to `x`. If `x.shape != other.shape`,
`other` must be broadcastable to the shape of `x` (which is also the shape of the output).
Returns:
Tensor. The dtype of the tensor is float.
The values of `x` with the sign of `other`, the shape is the same as `x`.
Raises:
TypeError: If dtype of the input is not in the given types or
the input can not be converted to tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore.numpy as np
>>> from mindspore import ops
>>> x = np.array([[0.3, -0.7], [0.5, 0.5]])
>>> other = np.array([[-0.4, 0.6], [0.4, -0.6]])
>>> out = ops.copysign(x, other)
>>> print(out)
[[-0.3 0.7]
[ 0.5 -0.5]]
"""
def _broadcast_to_shape(x, shape):
"""Broadcasts x from current shape to shape"""
ndim_to = len(shape)
x = _expand(x, ndim_to)
return _broadcast_to(x, shape_(x), shape, ndim_to)
if not isinstance(x, Tensor):
raise TypeError("Tensor is expected, but got " + f"{type(x)}")
if not isinstance(other, (int, float, Tensor)):
raise TypeError(
"integer, float or Tensor is expected, but got " + f"{type(other)}"
)
if not isinstance(other, Tensor):
other = _type_convert(Tensor, other)
other = _broadcast_to_shape(other, shape_(x))
if _check_same_type(dtype_(x), mstype.bool_):
raise TypeError("copysign does not accept dtype bool.")
if _check_same_type(dtype_(x), mstype.complex64):
raise TypeError("copysign does not accept dtype complex64.")
if _check_same_type(dtype_(other), mstype.complex64):
raise TypeError("copysign does not accept dtype complex64.")
if _check_same_type(dtype_(x), mstype.complex128):
raise TypeError("copysign does not accept dtype complex128.")
if _check_same_type(dtype_(other), mstype.complex128):
raise TypeError("copysign does not accept dtype complex128.")
x_float = (
x
if x.dtype in (mstype.float16, mstype.float32, mstype.float64)
else x.astype("float32")
)
pos_tensor = absolute_(x_float)
less_zero = tensor_lt(other, 0)
return select_(less_zero, neg(pos_tensor), pos_tensor)
[文档]def hann_window(window_length, periodic=True, *, dtype=None):
r"""
Generates a Hann Window.
The Hann window is defined as
.. math::
w(n) = \frac{1}{2} - \frac{1}{2} \cos\left(\frac{2\pi{n}}{M-1}\right),\qquad 0 \leq n \leq M-1
Args:
window_length (int): Length of window.
periodic (bool, optional): When set to ``True`` , generates a periodic window for spectral analysis.
When set to ``False`` , generates a symmetric window for filter design.Default: ``True`` .
Keyword Args:
dtype (mindspore.dtype, optional): The output window data type, it must be float. Default: ``None`` .
Returns:
Tensor, a Hann window.
Raises:
TypeError: If `window_length` is not an integer.
TypeError: If `periodic` is not a variable of Boolean type.
ValueError: If `window_length` is negative.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import ops
>>> window_length = 5
>>> out = ops.hann_window(window_length)
>>> print(out.asnumpy())
[0. 0.3454915 0.9045085 0.9045085 0.3454915]
"""
if not isinstance(window_length, int):
raise TypeError(
f"For 'hann_window', 'window_length' must be a non-negative integer, but got {type(window_length)}"
)
if window_length < 0:
raise ValueError(
f"For 'hann_window', 'window_length' must be a non-negative integer, but got {window_length}"
)
if window_length <= 1:
return Tensor(np.ones(window_length))
if not isinstance(periodic, (bool, np.bool_)):
raise TypeError(
f"For 'hann_window', 'periodic' must be a variable of Boolean type, but got {type(periodic)}"
)
if dtype is not None and dtype not in mstype.float_type:
raise TypeError(f"For 'hann_window', 'dtype' must be floating point dtypes, but got {dtype}.")
if periodic:
window_length = window_length + 1
n = np.arange(0, window_length)
w = 0.5 - 0.5 * np.cos(2 * math.pi / (window_length - 1) * n)
if dtype is not None:
w = cast_(ms.tensor(w), dtype)
return Tensor(w[:-1]) if periodic else Tensor(w)
@constexpr
def _type_convert(force, obj):
"""
Convert type of `obj` to `force`.
"""
return force(obj)
[文档]def logcumsumexp(input, axis):
"""
Compute the cumulative log-sum-exp of the input tensor `input` along `axis` .
For example, if `input` is a tensor [a, b, c] and `axis` is 0, the output will be [a, log(exp(a) + exp(b)),
log(exp(a) + exp(b) + exp(c))].
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor) - The input tensor. Must be one of the following types: float16, float32, float64.
axis (int) - Describing the dimension to compute the cumulative product.
Must be in the range [-rank(input), rank(input)).
Returns:
Tensor, has the same dtype and shape as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not in [float16, float32, float64].
TypeError: If dtype of `axis` is not int.
ValueError: If `axis` is out of range [-rank(input), rank(input)).
Supported Platforms:
``Ascend`` ``CPU`` ``GPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> import numpy as np
>>> x = ms.Tensor(np.array([1.0, 2.0, 3.0]).astype(np.float32))
>>> output = ops.logcumsumexp(x, 0)
>>> print(output)
[1. 2.3132617 3.407606 ]
"""
if not isinstance(axis, int):
raise TypeError(
f"For 'logcumsumexp', 'axis' must be int type, but got {type(axis)}"
)
return cumulative_logsumexp_(input, Tensor(axis))
[文档]def logsumexp(input, axis, keep_dims=False):
r"""
Reduces a dimension of a tensor by calculating exponential for all elements in the dimension,
then calculate logarithm of the sum.
.. math::
logsumexp(input) = \log(\sum(e^{input-input_{max}})) + input_{max}
Args:
input (Tensor): The input tensor. With float16 or float32 data type.
axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Only constant value is allowed.
keep_dims (bool): If True, keep these reduced dimensions and the length is 1.
If ``False`` , don't keep these dimensions.
Default : ``False`` .
Returns:
Tensor, has the same dtype as the `input`.
- If axis is (), and keep_dims is False,
the output is a 0-D tensor representing the sum of all elements in the input tensor.
- If axis is int, set as 2, and keep_dims is False,
the shape of output is :math:`(input_1, input_3, ..., input_R)`.
- If axis is tuple(int), set as (2, 3), and keep_dims is False,
the shape of output is :math:`(input_1, input_4, ..., input_R)`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.logsumexp(x, 1, keep_dims=True)
>>> print(output.shape)
(3, 1, 5, 6)
"""
_reduce_sum = _get_cache_prim(P.ReduceSum)(keep_dims)
input_max = ops.ReduceMax(keep_dims=True)(input, axis)
input_exp = tensor_exp(input - input_max)
input_sumexp = _reduce_sum(input_exp, axis)
input_logsumexp = log_(input_sumexp)
if not keep_dims:
input_max = input_max.squeeze(axis=axis)
return input_logsumexp + input_max
[文档]def amin(input, axis=None, keepdims=False, *, initial=None, where=None):
r"""
Reduces all dimensions of a tensor by returning the minimum value in `input`, by default. And also can
reduce a dimension of `input` along specified `axis`. `keepdims` determines whether the dimensions of
output and input are the same.
Note:
The `axis` with tensor type is only used for compatibility with older versions and is not recommended.
Args:
input (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.
axis (Union[int, tuple(int), list(int), Tensor]): 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).
keepdims (bool): If ``True`` , keep these reduced dimensions and the length is 1. If ``False`` , don't keep
these dimensions. Default: ``False`` .
Keyword Args:
initial (scalar, optional): The minimum value of an output element. Must be present to allow computation
on empty slice. Default: ``None`` .
where (Tensor[bool], optional): A Tensor indicating whether to replace the primitive value in `input` with the
value in `initial`. If ``True`` , do not replace, otherwise replace. For the index of ``True`` in `where`,
the corresponding value in `initial` must be assigned. Default: ``None`` , which indicates ``True`` by
default.
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is ``None`` , and `keepdims` 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 `keepdims` is ``False`` ,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keepdims` is ``False`` ,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
- If `axis` is 1-D Tensor, set as [1, 2], and `keepdims` is ``False`` ,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple, list or Tensor.
TypeError: If `keepdims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.amin(x, 1, keepdims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # 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)
>>> output = ops.amin(x)
>>> print(output)
1.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.amin(x, 0, True)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]
[2. 2. 2. 2. 2. 2.]
[3. 3. 3. 3. 3. 3.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.amin(x, 1, True)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]]
[[4. 4. 4. 4. 4. 4.]]
[[7. 7. 7. 7. 7. 7.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = ops.amin(x, 2, True)
>>> print(output)
[[[1.]
[2.]
[3.]]
[[4.]
[5.]
[6.]]
[[7.]
[8.]
[9.]]]
"""
if axis is None:
axis = ()
input = _init_and_select_elem(input, initial, where, ops.minimum)
return _get_cache_prim(P.ReduceMin)(keepdims)(input, axis)
def _init_and_select_elem(input, initial, where, cmp_fn):
"""Initialize the input according to Initial, and select the element according to where."""
if initial is not None:
initial = ops.fill(input.dtype, input.shape, initial)
input = cmp_fn(input, initial)
if isinstance(where, Tensor):
if initial is None:
raise ValueError('initial value must be provided for where masks')
where = where.broadcast_to(input.shape)
initial = initial.broadcast_to(input.shape)
input = ops.select(where, input, initial)
return input
[文档]def amax(input, axis=None, keepdims=False, *, initial=None, where=None):
r"""
Reduces all dimensions of a tensor by returning the maximum value in `input`, by default. And also can
reduce a dimension of `input` along specified `axis`. `keepdims` determines whether the dimensions of
output and input are the same.
Note:
The `axis` with tensor type is only used for compatibility with older versions and is not recommended.
Args:
input (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.
axis (Union[int, tuple(int), list(int), Tensor]): 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).
keepdims (bool): If ``True`` , keep these reduced dimensions and the length is 1. If ``False`` , don't keep
these dimensions. Default: ``False`` .
Keyword Args:
initial (scalar, optional): The minimum value of an output element. Must be present to allow computation
on empty slice. Default: ``None`` .
where (Tensor[bool], optional): A Tensor indicating whether to replace the primitive value in `input` with the
value in `initial`. If ``True`` , do not replace, otherwise replace. For the index of ``True`` in `where`,
the corresponding value in `initial` must be assigned. Default: ``None`` , which indicates ``True`` by
default.
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is ``None`` , and `keepdims` 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 `keepdims` is ``False`` , the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keepdims` is ``False`` , the shape of output is
:math:`(x_0, x_3, ..., x_R)`.
- If `axis` is 1-D Tensor, set as [1, 2], and `keepdims` is ``False`` , the shape of output is
:math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple, list or Tensor.
TypeError: If `keepdims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.amax(x, 1, keepdims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # 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)
>>> output = ops.amax(x)
>>> print(output)
9.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.amax(x, 0, True)
>>> print(output)
[[[7. 7. 7. 7. 7. 7.]
[8. 8. 8. 8. 8. 8.]
[9. 9. 9. 9. 9. 9.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.amax(x, 1, True)
>>> print(output)
[[[3. 3. 3. 3. 3. 3.]]
[[6. 6. 6. 6. 6. 6.]]
[[9. 9. 9. 9. 9. 9.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = ops.amax(x, 2, True)
>>> print(output)
[[[1.]
[2.]
[3.]]
[[4.]
[5.]
[6.]]
[[7.]
[8.]
[9.]]]
"""
if axis is None:
axis = ()
input = _init_and_select_elem(input, initial, where, ops.maximum)
return _get_cache_prim(P.ReduceMax)(keepdims)(input, axis)
def amax_ext(input, dim=(), keepdim=False):
r"""
Computes the maximum value of of all elements along the specified `dim` dimension of the `input`,
and retains the dimension based on the `keepdim` parameter.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): Input tensor.
dim (Union[int, tuple(int), list(int)], optional): The dimension to be reduced,
the value should be within `[-len(input.shape), len(input.shape) - 1]`,
when the `dim` is `()`, all dimensions are reduced, default: ``()``.
keepdim (bool, optional): Whether the output tensor retains the dimension `dim`, default: ``False``.
Returns:
Tensor, has same type as `input`, and the shape changes according to the values of `dim` and `keepdim`.
- If `dim` is `()`, and `keepdim` is False, the output is a 0-D tensor representing the maximum value of
all elements in the `input` tensor.
- If `dim` is `1`, and `keepdim` is False, the shape of output is
:math:`(input.shape[0], input.shape[2], ..., input.shape[n])`.
- If `dim` is `(1, 2)`, and `keepdim` is False, the shape of output is
:math:`(input.shape[0], input.shape[3], ..., input.shape[n])`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `dim` is not an int or tuple(int) or list(int).
TypeError: If `keepdim` is not a bool.
ValueError: If the value of any elements of `dim` is not in the range
`[-len(input.shape), len(input.shape) - 1]`.
RuntimeError: If any element of `dim` is repeated.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.function.math_func.amax_ext(x, 1, keepdim=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # 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)
>>> output = ops.function.math_func.amax_ext(x)
>>> print(output)
9.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.function.math_func.amax_ext(x, 0, True)
>>> print(output)
[[[7. 7. 7. 7. 7. 7.]
[8. 8. 8. 8. 8. 8.]
[9. 9. 9. 9. 9. 9.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.function.math_func.amax_ext(x, 1, True)
>>> print(output)
[[[3. 3. 3. 3. 3. 3.]]
[[6. 6. 6. 6. 6. 6.]]
[[9. 9. 9. 9. 9. 9.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = ops.function.math_func.amax_ext(x, 2, True)
>>> print(output)
[[[1.]
[2.]
[3.]]
[[4.]
[5.]
[6.]]
[[7.]
[8.]
[9.]]]
"""
return reduce_max_impl(input, dim, keepdim)
def amin_ext(input, dim=(), keepdim=False):
r"""
Computes the minimum value of of all elements along the specified `dim` dimension of the `input`,
and retains the dimension based on the `keepdim` parameter.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): Input tensor.
dim (Union[int, tuple(int), list(int)], optional): The dimension to be reduced,
the value should be within `[-len(input.shape), len(input.shape) - 1]`,
when the `dim` is `()`, all dimensions are reduced, default: ``()``.
keepdim (bool, optional): Whether the output tensor retains the dimension `dim`, default: ``False``.
Returns:
Tensor, has same type as `input`, and the shape changes according to the values of `dim` and `keepdim`.
- If `dim` is `()`, and `keepdim` is False, the output is a 0-D tensor representing the minimum value of
all elements in the `input` tensor.
- If `dim` is `1`, and `keepdim` is False, the shape of output is
:math:`(input.shape[0], input.shape[2], ..., input.shape[n])`.
- If `dim` is `(1, 2)`, and `keepdim` is False, the shape of output is
:math:`(input.shape[0], input.shape[3], ..., input.shape[n])`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `dim` is not an int or tuple(int) or list(int).
TypeError: If `keepdim` is not a bool.
ValueError: If the value of any elements of `dim` is not in the range
`[-len(input.shape), len(input.shape) - 1]`.
RuntimeError: If any element of `dim` is repeated.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor
>>> from mindspore import ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.function.math_func.amin_ext(x, 1, keepdim=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # 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)
>>> output = ops.function.math_func.amin_ext(x)
>>> print(output)
1.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.function.math_func.amin_ext(x, 0, True)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]
[2. 2. 2. 2. 2. 2.]
[3. 3. 3. 3. 3. 3.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.function.math_func.amin_ext(x, 1, True)
>>> print(output)
[[[1. 1. 1. 1. 1. 1.]]
[[4. 4. 4. 4. 4. 4.]]
[[7. 7. 7. 7. 7. 7.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = ops.function.math_func.amin_ext(x, 2, True)
>>> print(output)
[[[1.]
[2.]
[3.]]
[[4.]
[5.]
[6.]]
[[7.]
[8.]
[9.]]]
"""
return reduce_min_impl(input, dim, keepdim)
[文档]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`
determines whether the dimensions of the output and input are the same.
Note:
The `axis` with tensor type is only used for compatibility with older versions and is not recommended.
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.
axis (Union[int, tuple(int), list(int), Tensor]): 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`` .
Returns:
Tensor, has the same data type as input tensor.
- 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)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is ``False`` ,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple, list or Tensor.
TypeError: If `keep_dims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.mean(x, 1, keep_dims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> x = Tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]),
... mindspore.float32)
>>> output = ops.mean(x)
>>> print(output)
5.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along the axis 0
>>> output = ops.mean(x, 0, True)
>>> print(output)
[[[4. 4. 4. 4. 4. 4.]
[5. 5. 5. 5. 5. 5.]
[6. 6. 6. 6. 6. 6.]]]
>>> # case 3: Reduces a dimension along the axis 1
>>> output = ops.mean(x, 1, True)
>>> print(output)
[[[2. 2. 2. 2. 2. 2.]]
[[5. 5. 5. 5. 5. 5.]]
[[8. 8. 8. 8. 8. 8.]]]
>>> # case 4: Reduces a dimension along the axis 2
>>> output = ops.mean(x, 2, True)
>>> print(output)
[[[ 2.]
[ 2.]
[ 2.]]
[[ 4.]
[ 5.]
[ 6.]]
[[ 6.]
[ 8.]
[10.]]]
"""
if axis is None:
axis = ()
return _get_cache_prim(P.ReduceMean)(keep_dims)(x, axis)
def mean_ext(input, axis=None, keep_dims=False, dtype=None):
r"""
Reduces all dimension of a tensor by averaging all elements in the dimension, by default.
And reduce a dimension of `input` along the specified `axis`. `keep_dims`
determines whether the dimensions of the output and input are the same.
Note:
The `axis` with tensor type is only used for compatibility with older versions and is not recommended.
Args:
input (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.
axis (Union[int, tuple(int), list(int), Tensor]): The dimensions to reduce. Default: ``None`` ,
reduce all dimensions. Only constant value is allowed. Assume the rank of `input` 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`` .
dtype (:class:`mindspore.dtype`): The desired data type of returned Tensor. Default: ``None`` .
Returns:
Tensor, has the same data type as the `input`.
- 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:`(input_0, input_2, ..., input_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_0, input_3, ..., input_R)`.
- If `axis` is 1-D Tensor, set as [1, 2], and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_0, input_3, ..., input_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple, list or Tensor.
TypeError: If `keep_dims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.function.math_func.mean_ext(x, 1, keep_dims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # case 1: Reduces a dimension by averaging all elements in the dimension.
>>> x = Tensor(np.array([[[2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2], [2, 2, 2, 2, 2, 2]],
... [[4, 4, 4, 4, 4, 4], [5, 5, 5, 5, 5, 5], [6, 6, 6, 6, 6, 6]],
... [[6, 6, 6, 6, 6, 6], [8, 8, 8, 8, 8, 8], [10, 10, 10, 10, 10, 10]]]),
... mindspore.float32)
>>> output = ops.function.math_func.mean_ext(x)
>>> print(output)
5.0
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along the axis 0
>>> output = ops.function.math_func.mean_ext(x, 0, True)
>>> print(output)
[[[4. 4. 4. 4. 4. 4.]
[5. 5. 5. 5. 5. 5.]
[6. 6. 6. 6. 6. 6.]]]
>>> # case 3: Reduces a dimension along the axis 1
>>> output = ops.function.math_func.mean_ext(x, 1, True)
>>> print(output)
[[[2. 2. 2. 2. 2. 2.]]
[[5. 5. 5. 5. 5. 5.]]
[[8. 8. 8. 8. 8. 8.]]]
>>> # case 4: Reduces a dimension along the axis 2
>>> output = ops.function.math_func.mean_ext(x, 2, True)
>>> print(output)
[[[ 2.]
[ 2.]
[ 2.]]
[[ 4.]
[ 5.]
[ 6.]]
[[ 6.]
[ 8.]
[10.]]]
"""
return mean_ext_op(input, axis, keep_dims, dtype)
[文档]def prod(input, axis=None, keep_dims=False, dtype=None):
r"""
Reduces a dimension of a tensor by multiplying all elements in the dimension, by default. And also can
reduce a dimension of `input` along the `axis`. Determine whether the dimensions of the output and input are the
same by controlling `keep_dims`.
Note:
The `axis` with tensor type is only used for compatibility with older versions and is not recommended.
Args:
input (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.
axis (Union[int, tuple(int), list(int), Tensor]): 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`` .
dtype (:class:`mindspore.dtype`): The desired data type of returned Tensor. Default: ``None`` .
Returns:
Tensor, has the same data type as the `input`.
- 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:`(input_0, input_2, ..., input_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_0, input_3, ..., input_R)`.
- If `axis` is 1-D Tensor, set as [1, 2], and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_0, input_3, ..., input_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple, list or Tensor.
TypeError: If `keep_dims` is not a bool.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> output = ops.prod(x, 1, keep_dims=True)
>>> result = output.shape
>>> print(result)
(3, 1, 5, 6)
>>> # 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)
>>> output = ops.prod(x)
>>> print(output)
2.2833798e+33
>>> print(output.shape)
()
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.prod(x, 0, True)
>>> print(output)
[[[ 28. 28. 28. 28. 28. 28.]
[ 80. 80. 80. 80. 80. 80.]
[162. 162. 162. 162. 162. 162.]]]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.prod(x, 1, True)
>>> print(output)
[[[ 6. 6. 6. 6. 6. 6.]]
[[120. 120. 120. 120. 120. 120.]]
[[504. 504. 504. 504. 504. 504.]]]
>>> # case 4: Reduces a dimension along axis 2.
>>> output = ops.prod(x, 2, True)
>>> print(output)
[[[1.00000e+00]
[6.40000e+01]
[7.29000e+02]]
[[4.09600e+03]
[1.56250e+04]
[4.66560e+04]]
[[1.17649e+05]
[2.62144e+05]
[5.31441e+05]]]
"""
if not isinstance(axis, (tuple, list, Tensor)):
return prod_ext_op(input, axis, keep_dims, dtype)
if dtype is not None:
input = input.astype(dtype)
return _get_cache_prim(P.ReduceProd)(keep_dims)(input, axis)
def _multi_svd_norm(x, row_axis, col_axis, op):
"""_multi_svd_norm for norm."""
y = _moveaxis(x.astype(mstype.float32), (row_axis, col_axis), (-2, -1))
svd_res = ops.svd(y, compute_uv=False)
if op == 'amax':
return ops.amax(svd_res, axis=-1)
if op == 'amin':
return ops.amin(svd_res, axis=-1)
if op == 'sum':
return ops.sum(svd_res, dim=-1)
raise ValueError(f"For svd_norm, the op input must be one of ['amax', 'amin', 'sum'], but got f{op}")
def _reshape_matrix_norm(input, res, dim, keepdims):
"""reshape res of matrix_norm if keepdims is True."""
if keepdims:
res_shape = list(input.shape)
res_shape[dim[0]] = 1
res_shape[dim[1]] = 1
res = res.reshape(res_shape)
return res
def _normalize_axis_index(axis, ndim):
"""normalize_axis_index for norm."""
# pylint: disable=chained-comparison
if axis >= 0 and axis < ndim:
return axis
# pylint: disable=chained-comparison
if axis < 0 and axis >= -ndim:
return ndim + axis
raise ValueError('For norm, the dim is out of range.')
@_primexpr
def _get_perm_for_norm(x_ndim, source, destination):
destination = tuple([_normalize_axis_index(ax, x_ndim) for ax in destination])
source = tuple([_normalize_axis_index(ax, x_ndim) for ax in source])
perm = [n for n in range(x_ndim) if n not in source]
for dest, src in sorted(zip(destination, source)):
perm.insert(dest, src)
perm = tuple(perm)
return perm
def _moveaxis(x, source, destination):
perm = _get_perm_for_norm(x.ndim, source, destination)
return ops.transpose(x, perm)
@_primexpr
def _check_axis(axis, ord, ndim):
"""axis check"""
if axis is None:
axis = tuple(range(ndim))
if (ord is None) or (ord == 'fro' and ndim == 2) or (ord == 2 and ndim == 1):
return axis, True
return axis, False
if isinstance(axis, int):
axis = (axis,)
elif isinstance(axis, tuple):
if len(axis) > 2:
raise ValueError("For norm, the dimensions is out of range.")
else:
raise TypeError(f'For norm, the dim should be int or tuple of int, but got {type(axis)}')
return axis, False
@_primexpr
def _check_ord(ord, axis):
if len(axis) == 1:
if isinstance(ord, str):
raise TypeError(f"For norm, ord mode can not be str for vectors, but got {ord}.")
elif len(axis) == 2:
if ord not in [2, -2, 1, -1, float('inf'), -float('inf'), 'fro', 'nuc', None]:
raise ValueError(f"For norm, the ord mode must be in "
f"[2, -2, 1, -1, float('inf'), -float('inf'), 'fro', 'nuc', None] for matrices, "
f"but got {ord}.")
def _check_dtype(d1, d2):
if mstype.float32 in (d1, d2):
return mstype.float32
if d1 == d2:
return d1
raise ValueError('the dtype is not supported.')
@_primexpr
def _check_last_dim_shape_eq(a, b):
if a.shape[-1] != b.shape[-1]:
raise ValueError('shapes are not aligned')
def _complex_square(A):
"""calculate square with complex or not"""
if ops.is_complex(A):
return ops.conj(A) * A
return ops.square(A)
[文档]def norm(A, ord=None, dim=None, keepdim=False, *, dtype=None):
r"""
Returns the matrix norm or vector norm of a given tensor.
`ord` is the calculation mode of norm. The following norm modes are supported.
====================== ================================ ==========================================
`ord` norm for matrices norm for vectors
====================== ================================ ==========================================
`None` (default) Frobenius norm `2`-norm (see below)
`'fro'` Frobenius norm -- not supported --
`'nuc'` nuclear norm -- not supported --
`inf` :math:`max(sum(abs(x), dim=1))` :math:`max(abs(x))`
`-inf` :math:`min(sum(abs(x), dim=1))` :math:`min(abs(x))`
`0` -- not supported -- :math:`sum(x != 0)`
`1` :math:`max(sum(abs(x), dim=0))` as below
`-1` :math:`min(sum(abs(x), dim=0))` as below
`2` largest singular value as below
`-2` smallest singular value as below
other `int` or `float` -- not supported -- :math:`sum(abs(x)^{ord})^{(1 / ord)}`
====================== ================================ ==========================================
Args:
A (Tensor): Tensor of shape :math:`(*, n)` or :math:`(*, m, n)` where * is zero or more batch dimensions.
ord (Union[int, float, inf, -inf, 'fro', 'nuc'], optional): norm's mode. refer to the table above for
behavior. Default: ``None`` .
dim (Union[int, Tuple(int)], optional): calculate the dimension of vector norm or matrix norm.
Default: ``None`` .
- When `dim` is int, it will be calculated by vector norm.
- When `dim` is a 2-tuple, it will be calculated by matrix norm.
- If `dim` is None and `ord` is None, `A` will be flattened to 1D and the 2-norm
of the vector will be calculated.
- If `dim` is None and `ord` is not None, `A` must be 1D or 2D.
keepdim (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `A` will be converted to the specified type,
`dtype`, before execution, and dtype of returned Tensor will also be `dtype`. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `dim`, has the same dtype as `A`.
Raises:
ValueError: If `dim` is out of range.
TypeError: If `dim` is neither an int nor a tuple of int.
TypeError: If `A` is a vector and `ord` is a str.
ValueError: If `A` is a matrices and `ord` is not in valid mode.
ValueError: If `A` is a matrices and `ord` is an integer but not in [1, -1, 2, -2].
ValueError: If two elements of `dim` is same after normalize.
ValueError: If any elements of `dim` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Note:
Currently, complex numbers are not supported.
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> data_range = ops.arange(-13, 13, dtype=ms.float32)
>>> # Exclude 0 from original data for 0 is invalid input when `ord` is negative.
>>> x = data_range[data_range != 0]
>>> y = x.reshape(5, 5)
>>> print(ops.norm(x))
38.327538
>>> print(ops.norm(x, float('inf')))
13.0
>>> print(ops.norm(x, float('-inf')))
1.0
>>> print(ops.norm(x, 0))
25.0
>>> print(ops.norm(x, 1))
169.0
>>> print(ops.norm(x, -1))
0.15915091
>>> print(ops.norm(x, 2))
38.327538
>>> print(ops.norm(x, -2))
0.5647041
>>> print(ops.norm(x, 3))
24.309084
>>> print(ops.norm(x, -3))
0.74708974
>>> print(ops.norm(y))
38.327538
>>> print(ops.norm(y, 'fro'))
38.327538
>>> print(ops.norm(y, 'nuc'))
45.56681
>>> print(ops.norm(y, float('inf')))
55.0
>>> print(ops.norm(y, float('-inf')))
9.0
>>> print(ops.norm(y, 1))
35.0
>>> print(ops.norm(y, -1))
33.0
>>> print(ops.norm(y, 2))
37.57774
>>> print(ops.norm(y, -2))
1.590545e-07
>>> m = ms.Tensor([[1., -1., 2.], [-2., 3., -4.]])
>>> print(ops.norm(m, dim=0))
[2.236068 3.1622777 4.472136 ]
>>> print(ops.norm(m, dim=1))
[2.4494898 5.3851647]
>>> print(ops.norm(m, ord=1, dim=1))
[4. 9.]
>>> print(ops.norm(m, ord=-2, dim=0))
[0.8944272 0.94868326 1.7888544 ]
>>> print(ops.norm(m, ord=2, dim=1))
[2.4494898 5.3851647]
>>> n = ops.arange(27, dtype=ms.float32).reshape(3, 3, 3)
>>> print(ops.norm(n, dim=(1, 2)))
[14.282857 39.76179 66.45299 ]
>>> print(ops.norm(n[0, :, :]), ops.norm(n[1, :, :]), ops.norm(n[2, :, :]))
14.282857 39.76179 66.45299
"""
ndim = A.ndim
dim, immediate = _check_axis(dim, ord, ndim)
_check_ord(ord, dim)
if dtype is not None:
A = ops.cast(A, dtype)
# Immediately handle some default, simple, fast, and common cases.
if immediate:
ret = ops.sqrt(ops.reduce_sum(_complex_square(A), dim))
if keepdim:
ret = ret.reshape(ndim * [1])
return ret
if isinstance(ord, int):
if len(dim) == 2:
row_axis, col_axis = dim
row_axis = _normalize_axis_index(row_axis, ndim)
col_axis = _normalize_axis_index(col_axis, ndim)
if ord == 1:
if col_axis > row_axis:
col_axis -= 1
ret = ops.max(A.abs().sum(row_axis), axis=col_axis)[0]
elif ord == -1:
if col_axis > row_axis:
col_axis -= 1
ret = ops.min(A.abs().sum(row_axis), axis=col_axis)[0]
elif ord == 2:
ret = _multi_svd_norm(A, row_axis, col_axis, 'amax')
elif ord == -2:
ret = _multi_svd_norm(A, row_axis, col_axis, 'amin')
else:
raise ValueError(f"For norm, the ord {ord} are not support for matrices.")
if keepdim:
ret_shape = list(A.shape)
ret_shape[dim[0]] = 1
ret_shape[dim[1]] = 1
ret = ret.reshape(ret_shape)
return ret
if len(dim) == 1:
if ord == 0:
return (A != 0).astype(A.dtype).sum(axis=dim, keepdims=keepdim)
if ord > 0:
_lp_norm = _get_cache_prim(ops.LpNorm)(dim, ord, keepdim)
return _lp_norm(A)
return ops.sum(ops.abs(A).pow(ord), dim=dim, keepdim=keepdim).pow(1.0 / ord)
if len(dim) == 1:
if ord == float('inf'):
return ops.max(ops.abs(A), axis=dim[0], keepdims=keepdim)[0]
if ord == -float('inf'):
return ops.min(ops.abs(A), axis=dim[0], keepdims=keepdim)[0]
if ord == 0:
# Zero norm
return (A != 0).astype(A.dtype).sum(axis=dim, keepdims=keepdim)
if ord is None:
# special case for speedup
s = _complex_square(A)
reduce_sum = _get_cache_prim(ops.ReduceSum)(keepdim)
return ops.sqrt(reduce_sum(s, dim))
# None of the str-type keywords for ord ('fro', 'nuc')
# are valid for vectors
absx = ops.abs(A)
absx **= ord
reduce_sum = _get_cache_prim(ops.ReduceSum)(keepdim)
ret = reduce_sum(absx, dim)
if isinstance(ord, Tensor):
ret **= ops.reciprocal(ord)
else:
ret **= 1 / ord
return ret
if len(dim) == 2:
row_axis, col_axis = dim
row_axis = _normalize_axis_index(row_axis, ndim)
col_axis = _normalize_axis_index(col_axis, ndim)
if row_axis == col_axis:
raise ValueError('For norm, the elements of dim can not be duplicate.')
if ord == float('inf'):
if row_axis > col_axis:
row_axis -= 1
ret = ops.max(ops.reduce_sum(abs(A), col_axis), axis=row_axis)[0]
elif ord == -float('inf'):
if row_axis > col_axis:
row_axis -= 1
ret = ops.min(ops.reduce_sum(abs(A), col_axis), axis=row_axis)[0]
elif ord == 'fro':
ret = ops.sqrt(ops.reduce_sum(_complex_square(A), dim))
elif ord == 'nuc':
ret = _multi_svd_norm(A, row_axis, col_axis, 'sum')
else:
ret = ops.sqrt(ops.reduce_sum(_complex_square(A), dim))
if keepdim:
ret_shape = list(A.shape)
ret_shape[dim[0]] = 1
ret_shape[dim[1]] = 1
ret = ret.reshape(ret_shape)
return ret
return None
@_primexpr
def _check_vector_norm_axis(axis, ndim):
"""vector_norm axis check"""
if (not isinstance(axis, int)) and (not isinstance(axis, tuple)) and (axis is not None):
raise TypeError(f'For vector_norm , the dim must be tuple or int, but got {type(axis)}')
if axis is None:
axis = tuple(range(ndim))
if isinstance(axis, int):
axis = (axis,)
dim = []
for elem_dim in axis:
elem_dim = _normalize_axis_index(elem_dim, ndim)
if elem_dim in dim:
raise ValueError('For vector_norm, the elements of axis can not be duplicate.')
dim.append(elem_dim)
tuple_dim = tuple(dim)
return tuple_dim
@_primexpr
def _check_vector_norm_ord(ord):
"""vector_norm ord check"""
if ord not in [0, 2, float('inf'), -float('inf')] and not isinstance(ord, (int, float)):
raise ValueError(f"For vector_norm, the ord mode must be in [0, 2, float('inf'), -float('inf')] "
f"or must be int or float, but got {ord}.")
def _compute_vector_norm_inf(x, dim, keepdims, norm_func):
"""compute vector norm of `x` when ord is ``inf`` or ``-inf`` """
if len(dim) == 1:
ret_norm = norm_func(ops.abs(x), axis=dim[0], keepdims=keepdims)[0]
else:
start_dim = min(dim)
end_dim = max(dim)
flatten_x = ops.flatten(x, start_dim=start_dim, end_dim=end_dim)
ret_norm = norm_func(ops.abs(flatten_x), axis=start_dim, keepdims=False)[0]
if keepdims is True:
ret_shape = list(x.shape)
for i in dim:
ret_shape[i] = 1
ret_norm = ret_norm.reshape(ret_shape)
return ret_norm
@_primexpr
def _check_vector_norm_inputs(x, ord):
"""vector_norm inputs check"""
if not isinstance(x, (Tensor, Tensor_)):
raise TypeError(f"For `vector_norm`, the `x` must be Tensor!, but get {type(x)}.")
if not isinstance(ord, (bool, int, float)):
raise ValueError(f"For `vector_norm`, the ord mode must be one of [bool, int, float, inf, -inf], "
f"but got {ord}.")
def vector_norm_ext(x, ord=2, dim=None, keepdim=False, *, dtype=None):
r"""
Returns the vector norm of the given tensor on the specified dimensions.
`ord` is the calculation mode of norm. The following norm modes are supported.
========================== ==========================================
`ord` norm for vectors
========================== ==========================================
``2`` (Default) ``2``-norm (see below)
``inf`` :math:`max(abs(x))`
``-inf`` :math:`min(abs(x))`
``0`` :math:`sum(x!=0)`
other ``int`` or ``float`` :math:`sum(abs(x)^{ord})^{(1 / ord)}`
========================== ===========================================
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
x (Tensor): Tensor of shape :math:`(*)` where * is zero s more batch dimensions.
ord (Union[bool, int, float, inf, -inf], optional): norm's mode. refer to the table above for
behavior. Default: ``2`` .
dim (Union[int, List(int), Tuple(int)], optional): The dimensions along which to perform the vector norm
calculation. Default: ``None`` .
- When `dim` is an integer, a list or a tuple, the norm calculation will be performed across these specified
dimensions, while the remaining dimensions will be considered as batch dimensions.
- When `dim` is None, the norm will be calculated after flattening the Tensor `x` .
keepdim (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `x` will be converted to the specified type,
`dtype` before execution, and dtype of returned Tensor will also be `dtype`.
When `dtype` is ``None`` , the dtype of `x` is preserved. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `dim`.
Raises:
TypeError: If `dim` is not an int or tuple.
ValueError: If `ord` is not in [bool, int, float, inf, -inf].
ValueError: The elements of `dim` are duplicate.
ValueError: If any elements of `dim` is out of range.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> x = ms.ops.arange(0, 12, dtype=ms.float32) - 6
>>> print(ms.ops.vector_norm_ext(x, ord=2))
12.083046
>>> print(ms.ops.vector_norm_ext(x, ord=float('inf')))
6.0
>>> print(ms.ops.vector_norm_ext(x, ord=float('-inf')))
0.0
>>> print(ms.ops.vector_norm_ext(x, ord=0))
11.0
>>> print(ms.ops.vector_norm_ext(x, ord=4.5))
7.2243643
"""
_check_vector_norm_inputs(x, ord)
if float(ord) in [0.0, 1.0, 2.0, 3.0]:
return linalg_vector_norm_op(x, float(ord), dim, keepdim, dtype)
if x.dtype in [mstype.bfloat16, mstype.float16, mstype.float32]:
if dtype is None:
return lp_norm_v2_op(x, ord, dim, keepdim, 0.0)
return ops.cast(lp_norm_v2_op(x, ord, dim, keepdim, 0.0), dtype)
cast_dtype = x.dtype if dtype is None else dtype
x = ops.cast(x, mstype.float32)
return ops.cast(lp_norm_v2_op(x, ord, dim, keepdim, 0.0), cast_dtype)
def matrix_norm_ext(A, ord='fro', dim=(-2, -1), keepdim=False, *, dtype=None):
r"""
Returns the matrix norm of a given tensor on the specified dimensions.
`ord` is the calculation mode of norm. The following norm modes are supported.
====================== ================================
`ord` norm for matrix
====================== ================================
``'fro'`` (Default) Frobenius norm
``'nuc'`` nuclear norm
``inf`` :math:`max(sum(abs(x), dim=1))`
``-inf`` :math:`min(sum(abs(x), dim=1))`
``1`` :math:`max(sum(abs(x), dim=0))`
``-1`` :math:`min(sum(abs(x), dim=0))`
``2`` largest singular value
``-2`` smallest singular value
====================== ================================
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
A (Tensor): Tensor of shape :math:`(*, m, n)` where * is zero or more batch dimensions.
ord (Union[int, inf, -inf, 'fro', 'nuc'], optional): norm's mode. refer to the table above for
behavior. Default: ``'fro'`` .
dim (Tuple(int, int), optional): calculate the dimension of the matrix norm.
Default: ``(-2, -1)`` .
keepdims (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `A` will be converted to the specified type,
`dtype`, before execution, and dtype of returned Tensor will also be `dtype`.
When `dtype` is ``None`` , the dtype of `A` is preserved. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `dim`.
Raises:
TypeError: If `dim` is not a tuple of int.
ValueError: If the length of `dim` is not equal to 2.
ValueError: If `ord` is not in [2, -2, 1, -1, float('inf'), float('-inf'), 'fro', 'nuc'].
ValueError: If two elements of `dim` is same after normalize.
ValueError: If any elements of `dim` is out of range.
Note:
Dynamic shape, Dynamic rank and mutable input is not supported in `graph mode (mode=mindspore.GRAPH_MODE)
<https://www.mindspore.cn/docs/en/master/model_train/program_form/static_graph.html>`_.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> A = ms.ops.arange(0, 12, dtype=ms.float32).reshape(3, 4)
>>> print(ms.ops.matrix_norm_ext(x, ord='fro'))
22.494444
>>> print(ms.ops.matrix_norm_ext(x, ord='nuc'))
24.364643
>>> print(ms.ops.matrix_norm_ext(x, ord=float('inf')))
38.0
>>> print(ms.ops.matrix_norm_ext(x, ord=float('-inf')))
6.0
"""
ndim = A.ndim
row_axis, col_axis = _check_matrix_norm_axis(dim, ndim)
_check_matrix_norm_ord(ord)
if ord == 'fro':
return vector_norm_ext(A, 2, dim, keepdim, dtype=dtype)
if ord == 'nuc':
res = _multi_svd_norm(A, row_axis, col_axis, 'sum')
return _reshape_matrix_norm(A, res, dim, keepdim)
if ord == 2:
res = _multi_svd_norm(A, row_axis, col_axis, 'amax')
return _reshape_matrix_norm(A, res, dim, keepdim)
if ord == -2:
res = _multi_svd_norm(A, row_axis, col_axis, 'amin')
return _reshape_matrix_norm(A, res, dim, keepdim)
if ord in [float('inf'), -float('inf')]:
row_axis, col_axis = col_axis, row_axis
if not keepdim and col_axis > row_axis:
col_axis -= 1
if ord < 0:
return ops.amin(vector_norm_ext(A, 1, row_axis, keepdim, dtype=dtype), col_axis, keepdim)
return ops.amax(vector_norm_ext(A, 1, row_axis, keepdim, dtype=dtype), col_axis, keepdim)
@_primexpr
def _check_linalg_norm_input(dim, ord, ndim):
"""dim check"""
if dim is None:
if ord is not None and ndim > 2:
raise ValueError("For `linalg.norm`, the input must be 1D or 2D when `ord` is specified but `dim` is None.")
dim = tuple(range(ndim))
if (ord is None) or (ord == 'fro' and ndim == 2) or (ord == 2 and ndim == 1):
return dim, True
return dim, False
if isinstance(dim, int):
dim = (dim,)
elif isinstance(dim, (list, tuple)):
if len(dim) > 2:
raise ValueError(f"For `linalg.norm`, the length of `dim` must be 1 or 2 when dim is not None",
f"but got {len(dim)}.")
else:
raise TypeError(f'For `linalg.norm`, the dim should be int, list of int or tuple of int, but got {type(dim)}')
return dim, False
def linalg_norm(A, ord=None, dim=None, keepdim=False, *, dtype=None):
r"""
Returns the matrix norm or vector norm of a given tensor.
`ord` is the calculation mode of norm. The following norm modes are supported.
====================== ================================ ==========================================
`ord` norm for matrices norm for vectors
====================== ================================ ==========================================
`None` (default) Frobenius norm `2`-norm (see below)
`'fro'` Frobenius norm -- not supported --
`'nuc'` nuclear norm -- not supported --
`inf` :math:`max(sum(abs(x), dim=1))` :math:`max(abs(x))`
`-inf` :math:`min(sum(abs(x), dim=1))` :math:`min(abs(x))`
`0` -- not supported -- :math:`sum(x != 0)`
`1` :math:`max(sum(abs(x), dim=0))` as below
`-1` :math:`min(sum(abs(x), dim=0))` as below
`2` largest singular value as below
`-2` smallest singular value as below
other `int` or `float` -- not supported -- :math:`sum(abs(x)^{ord})^{(1 / ord)}`
====================== ================================ ==========================================
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
A (Tensor): Tensor of shape :math:`(*, n)` or :math:`(*, m, n)` where * is zero or more batch dimensions.
ord (Union[int, float, inf, -inf, 'fro', 'nuc'], optional): norm's mode. refer to the table above for
behavior. Default: ``None`` .
dim (Union[int, Tuple(int)], optional): calculate the dimension of vector norm or matrix norm.
Default: ``None`` .
- When `dim` is int, it will be calculated by vector norm.
- When `dim` is a 2-tuple, it will be calculated by matrix norm.
- If `dim` is None and `ord` is None, `A` will be flattened to 1D and the 2-norm
of the vector will be calculated.
- If `dim` is None and `ord` is not None, `A` must be 1D or 2D.
keepdim (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `A` will be converted to the specified type,
`dtype`, before execution, and dtype of returned Tensor will also be `dtype`. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `dim`, has the same dtype as `A`.
Raises:
ValueError: If `dim` is out of range.
TypeError: If `dim` is neither an int nor a tuple of int.
TypeError: If `A` is a vector and `ord` is a str.
ValueError: If `A` is a matrices and `ord` is not in valid mode.
ValueError: If two elements of `dim` is same after normalize.
ValueError: If any elements of `dim` is out of range.
Note:
Dynamic shape, Dynamic rank and mutable input is not supported in `graph mode (mode=mindspore.GRAPH_MODE)
<https://www.mindspore.cn/docs/en/master/model_train/program_form/static_graph.html>`_.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> data_range = ops.arange(-13, 13, dtype=ms.float32)
>>> x = data_range[data_range != 0]
>>> print(ops.function.math_func.linalg_norm(x))
38.327538
>>> print(ops.function.math_func.linalg_norm(x, 1))
169.0
>>> n = ops.arange(27, dtype=ms.float32).reshape(3, 3, 3)
>>> print(ops.function.math_func.linalg_norm(n, dim=(1, 2)))
[14.282857 39.76179 66.45299 ]
>>> print(ops.function.math_func.linalg_norm(n[0, :, :]), ops.function.math_func.linalg_norm(n[1, :, :]))
14.282857 39.76179
"""
dim, immediate = _check_linalg_norm_input(dim, ord, A.ndim)
if immediate:
return vector_norm_ext(A, 2, dim, keepdim, dtype=dtype)
if ord is not None:
if ord in ['fro', 'nuc'] or (dim is not None and len(dim) == 2) or (dim is None and A.ndim == 2):
return matrix_norm_ext(A, ord, dim, keepdim, dtype=dtype)
return vector_norm_ext(A, ord, dim, keepdim, dtype=dtype)
return vector_norm_ext(A, 2, dim, keepdim, dtype=dtype)
def norm_ext(input, p='fro', dim=None, keepdim=False, *, dtype=None):
r"""
Returns the matrix norm or vector norm of a given tensor.
`p` is the calculation mode of norm. The following norm modes are supported.
====================== ================================ ==========================================
`p` norm for matrices norm for vectors
====================== ================================ ==========================================
`'fro'` Frobenius norm -- not supported --
`'nuc'` nuclear norm -- not supported --
other `int` or `float` -- not supported -- :math:`sum(abs(x)^{p})^{(1 / p)}`
====================== ================================ ==========================================
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input of norm with data type of bfloat16, float16 or float32.
The shape is :math:`(*)` where :math:`*` means, any number of additional dimensions.
p (Union[int, float, inf, -inf, 'fro', 'nuc'], optional): norm's mode. refer to the table above for
behavior. Default: ``fro`` .
dim (Union[int, List(int), Tuple(int)], optional): calculate the dimension of vector norm or matrix norm.
Default: ``None`` .
keepdim (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `input` will be converted to the specified type,
`dtype`, before execution, and dtype of returned Tensor will also be `dtype`. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `dim`.
Raises:
TypeError: If `input` is not a Tensor
ValueError: If `dim` is out of range.
TypeError: If `dim` is neither an int nor a tuple of int.
ValueError: If two elements of `dim` is same after normalize.
ValueError: If any elements of `dim` is out of range.
Note:
Dynamic shape, Dynamic rank and mutable input is not supported in `graph mode (mode=mindspore.GRAPH_MODE)
<https://www.mindspore.cn/docs/en/master/model_train/program_form/static_graph.html>`_.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> data_range = ops.arange(-13, 13, dtype=ms.float32)
>>> x = data_range[data_range != 0]
>>> y = x.reshape(5, 5)
>>> print(ops.function.math_func.norm_ext(x, 2.0))
38.327538
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError(f"For `norm_ext`, the `input` must be Tensor!, but get {type(input)}.")
if isinstance(p, (bool, int, float)):
return vector_norm_ext(input, p, dim, keepdim, dtype=dtype)
if p == 'fro':
if isinstance(dim, (list, tuple)) and len(dim) > 2:
raise ValueError(f"For `norm_ext`, the size of `dim` cannot be greater than 2 "
f"when the norm mode is `fro`.")
return linalg_vector_norm_op(input, 2.0, dim, keepdim, dtype)
if p == 'nuc':
dim = tuple(range(input.ndim)) if dim is None else dim
return matrix_norm_ext(input, p, dim, keepdim, dtype=dtype)
raise ValueError(f"For `norm_ext`, the value of `p` must be one of [int, float, inf, -inf, 'fro', 'nuc',] "
f"but got `{p}`.")
def vector_norm(x, ord=2, axis=None, keepdims=False, *, dtype=None):
r"""
Returns the vector norm of the given tensor on the specified dimensions.
`ord` is the calculation mode of norm. The following norm modes are supported.
========================== ==========================================
`ord` norm for vectors
========================== ==========================================
``2`` (Default) ``2``-norm (see below)
``inf`` :math:`max(abs(x))`
``-inf`` :math:`min(abs(x))`
``0`` :math:`sum(x!=0)`
other ``int`` or ``float`` :math:`sum(abs(x)^{ord})^{(1 / ord)}`
========================== ===========================================
Args:
x (Tensor): Tensor of shape :math:`(*, n)` where * is zero s more batch dimensions.
ord (Union[int, float, inf, -inf], optional): norm's mode. refer to the table above for
behavior. Default: ``2`` .
axis (Union[int, Tuple(int)], optional): The dimensions along which to perform the vector norm calculation.
Default: ``None`` .
- When `axis` is int or a tuple, the norm calculation will be performed across these specified dimensions,
while the remaining dimensions will be considered as batch dimensions.
- When `dim` is None, the norm will be calculated after flattening the Tensor `x` .
keepdims (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `x` will be converted to the specified type,
`dtype` before execution, and dtype of returned Tensor will also be `dtype`.
When `dtype` is ``None`` , the dtype of `A` is preserved. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `axis`, has the same dtype as `x`.
Raises:
TypeError: If `axis` is not an int or tuple.
ValueError: If `ord` is not in [int, float, inf, -inf].
ValueError: The elements of `axis` are duplicate.
ValueError: If any elements of `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> x = ms.ops.arange(0, 12, dtype=ms.float32) - 6
>>> print(ms.ops.vector_norm(x, ord=2))
12.083046
>>> print(ms.ops.vector_norm(x, ord=float('inf')))
6.0
>>> print(ms.ops.vector_norm(x, ord=float('-inf')))
0.0
>>> print(ms.ops.vector_norm(x, ord=0))
11.0
>>> print(ms.ops.vector_norm(x, ord=4.5))
7.2243643
"""
ndim = x.ndim
dim = _check_vector_norm_axis(axis, ndim)
_check_vector_norm_ord(ord)
if dtype is not None:
x = ops.cast(x, dtype)
if ord == 2:
s = _complex_square(x)
reduce_sum = _get_cache_prim(ops.ReduceSum)(keepdims)
return ops.sqrt(reduce_sum(s, dim))
if ord == float('inf'):
inf_norm = _compute_vector_norm_inf(x, dim, keepdims, ops.max)
return inf_norm
if ord == float('-inf'):
inf_norm = _compute_vector_norm_inf(x, dim, keepdims, ops.min)
return inf_norm
if ord == 0:
return (x != 0).astype(x.dtype).sum(axis=dim, keepdims=keepdims)
# ord is other int or float
abs_x = ops.abs(x)
abs_x **= ord
reduce_sum = _get_cache_prim(ops.ReduceSum)(keepdims)
ret = reduce_sum(abs_x, dim)
ret **= 1 / ord
return ret
@_primexpr
def _check_matrix_norm_axis(axis, ndim):
"""matrix_norm axis check"""
if not isinstance(axis, (list, tuple)):
raise TypeError(f'For matrix_norm , the axis should be tuple of int, but got {type(axis)}')
if len(axis) != 2:
raise ValueError(f'For matrix_norm, the length of axis should be 2, but got {len(axis)}.')
row_axis, col_axis = axis
row_axis = _normalize_axis_index(row_axis, ndim)
col_axis = _normalize_axis_index(col_axis, ndim)
if row_axis == col_axis:
raise ValueError('For matrix_norm, the elements of axis can not be duplicate.')
return row_axis, col_axis
@_primexpr
def _check_matrix_norm_ord(ord):
"""matrix_norm ord check"""
if ord not in [2, -2, 1, -1, float('inf'), float('-inf'), 'fro', 'nuc']:
raise ValueError(f"For matrix_norm, the ord mode must be in "
f"[2, -2, 1, -1, float('inf'), float('-inf'), 'fro', 'nuc'] "
f"but got {ord}.")
def matrix_norm(A, ord='fro', axis=(-2, -1), keepdims=False, *, dtype=None):
r"""
Returns the matrix norm of a given tensor on the specified dimensions.
`ord` is the calculation mode of norm. The following norm modes are supported.
====================== ================================
`ord` norm for matrix
====================== ================================
``'fro'`` (Default) Frobenius norm
``'nuc'`` nuclear norm
``inf`` :math:`max(sum(abs(x), dim=1))`
``-inf`` :math:`min(sum(abs(x), dim=1))`
``1`` :math:`max(sum(abs(x), dim=0))`
``-1`` :math:`min(sum(abs(x), dim=0))`
``2`` largest singular value
``-2`` smallest singular value
====================== ================================
Args:
A (Tensor): Tensor of shape :math:`(*, m, n)` where * is zero or more batch dimensions.
ord (Union[int, inf, -inf, 'fro', 'nuc'], optional): norm's mode. refer to the table above for
behavior. Default: ``'fro'`` .
axis (Tuple(int, int), optional): calculate the dimension of the matrix norm.
Default: ``(-2, -1)`` .
keepdims (bool): whether the output Tensor retains the original dimension. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): When set, `A` will be converted to the specified type,
`dtype`, before execution, and dtype of returned Tensor will also be `dtype`.
When `dtype` is ``None`` , the dtype of `A` is preserved. Default: ``None`` .
Returns:
Tensor, the result of norm calculation on the specified dimension, `axis`, has the same dtype as `A`.
Raises:
TypeError: If `axis` is not a tuple of int.
ValueError: If the length of `axis` is not equal to 2.
ValueError: If `ord` is not in [2, -2, 1, -1, float('inf'), float('-inf'), 'fro', 'nuc'].
ValueError: If two elements of `axis` is same after normalize.
ValueError: If any elements of `axis` is out of range.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> A = ms.ops.arange(0, 12, dtype=ms.float32).reshape(3, 4)
>>> print(ms.ops.matrix_norm(x, ord='fro'))
22.494444
>>> print(ms.ops.matrix_norm(x, ord='nuc'))
24.364643
>>> print(ms.ops.matrix_norm(x, ord=float('inf')))
38.0
>>> print(ms.ops.matrix_norm(x, ord=float('-inf')))
6.0
>>> print(ms.ops.vector_norm(x, ord=1))
21.0
>>> print(ms.ops.vector_norm(x, ord=-1))
12.0
>>> print(ms.ops.vector_norm(x, ord=2))
22.409302
>>> print(ms.ops.vector_norm(x, ord=-2))
1.672928e-07
"""
ndim = A.ndim
row_axis, col_axis = _check_matrix_norm_axis(axis, ndim)
_check_matrix_norm_ord(ord)
if dtype is not None:
A = ops.cast(A, dtype)
ret = None
if ord == 'fro':
ret = ops.sqrt(ops.reduce_sum(_complex_square(A), axis))
if ord == 'nuc':
ret = _multi_svd_norm(A, row_axis, col_axis, 'sum')
if ord == float('inf'):
if row_axis > col_axis:
row_axis -= 1
ret = ops.max(ops.reduce_sum(abs(A), col_axis), axis=row_axis)[0]
if ord == float('-inf'):
if row_axis > col_axis:
row_axis -= 1
ret = ops.min(ops.reduce_sum(abs(A), col_axis), axis=row_axis)[0]
if ord == 1:
if col_axis > row_axis:
col_axis -= 1
ret = ops.max(A.abs().sum(row_axis), axis=col_axis)[0]
if ord == -1:
if col_axis > row_axis:
col_axis -= 1
ret = ops.min(A.abs().sum(row_axis), axis=col_axis)[0]
if ord == 2:
ret = _multi_svd_norm(A, row_axis, col_axis, 'amax')
if ord == -2:
ret = _multi_svd_norm(A, row_axis, col_axis, 'amin')
if keepdims:
ret_shape = list(A.shape)
ret_shape[axis[0]] = 1
ret_shape[axis[1]] = 1
ret = ret.reshape(ret_shape)
return ret
[文档]def lu_unpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True):
r"""
Converts `LU_data` and `LU_pivots` back into P, L and U matrices, where
P is a permutation matrix, L is a lower triangular matrix, and U is an
upper triangular matrix. Typically, `LU_data` and `LU_pivots` are generated
from the LU decomposition of a matrix.
Args:
LU_data (Tensor): The packed LU factorization data. A Tensor of shape :math:`(*, M, N)`, where :math:`*` is
batch dimensions. The dim of `LU_data` must be equal to or greater than 2.
LU_pivots (Tensor): The packed LU factorization pivots. A Tensor of shape :math:`(*, min(M, N))`,
where :math:`*` is
batch dimensions, with data type int8, uint8, int16, int32, int64.
unpack_data (bool, optional): A flag indicating if the `LU_data` should be unpacked. If ``False`` ,
then the returned L and U are None. Default: ``True`` .
unpack_pivots (bool, optional): A flag indicating if the `LU_pivots` should be unpacked into
a permutation matrix P. If ``False`` , then the returned P is None. Default: ``True`` .
Returns:
- pivots(Tensor) - The permutation matrix of LU factorization.
The shape is :math:`(*, M, M)`, the dtype is same as `LU_data`.
- L (Tensor) - The L matrix of LU factorization. The dtype is same as `LU_data`.
- U (Tensor) - The U matrix of LU factorization. The dtype is same as `LU_data`.
Raises:
TypeError: If the dtype of `LU_data` is int, uint or float.
TypeError: If the dtype of `LU_pivots` is not one of the following: int8, uint8, int16, int32, int64.
ValueError: If the dimension of `LU_data` is less than 2.
ValueError: If the dimension of `LU_pivots` is less than 1.
ValueError: If the size of the last dimension of LU_pivots is not equal to the minimum of the sizes of the last
two dimensions of LU_data.
ValueError: If the batch dimensions of LU_data's does not match LU_pivots's batch dimensions.
ValueError: On the CPU platform, if the value of `LU_pivots` are out of range :math:`[1, LU\_data.shape[-2])`.
RuntimeError: On the Ascend platform, if the value of `LU_pivots` are
out of range :math:`[1, LU\_data.shape[-2])`.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> LU_data = Tensor(np.array([[[-0.3806, -0.4872, 0.5536],
... [-0.1287, 0.6508, -0.2396],
... [ 0.2583, 0.5239, 0.6902]],
... [[ 0.6706, -1.1782, 0.4574],
... [-0.6401, -0.4779, 0.6701],
... [ 0.1015, -0.5363, 0.6165]]]), mstype.float64)
>>> LU_pivots = Tensor(np.array([[1, 3, 3],
... [2, 3, 3]]), mstype.int32)
>>> pivots, L, U = ops.lu_unpack(LU_data, LU_pivots)
>>> print(pivots)
[[[1. 0. 0.]
[0. 0. 1.]
[0. 1. 0.]]
[[0. 0. 1.]
[1. 0. 0.]
[0. 1. 0.]]]
>>> print(L)
[[[ 1. 0. 0.]
[-0.1287 1. 0.]
[ 0.2583 0.5239 1.]]
[[ 1.0000 0. 0.]
[-0.6401 1. 0.]
[ 0.1015 -0.5363 1.]]]
>>> print(U)
[[[-0.3806 -0.4872 0.5536]
[ 0. 0.6508 -0.2396]
[ 0. 0. 0.6902]]
[[ 0.6706 -1.1782 0.4574]
[ 0. -0.4779 0.6701]
[ 0. 0. 0.6165]]]
"""
pivots, l, u = lu_unpack_(LU_data, LU_pivots)
if unpack_data:
if unpack_pivots:
return pivots, l, u
return None, l, u
if unpack_pivots:
return pivots, None, None
return None, None, None
[文档]def renorm(input, p, axis, maxnorm):
"""
Renormalizes the sub-tensors along dimension `axis`, and each sub-tensor's p-norm should not exceed the
`maxnorm`. The values of current sub-tensor don't need change if the p-norm of the sub-tensor is less than
`maxnorm`. Otherwise the sub-tensor needs to be modified to the original value of the corresponding position
divided by the p-norm of the substensor and then multiplied by `maxnorm`.
Args:
input (Tensor): A Tensor, types: float32 or float16.
p (int): Power of norm calculation.
axis (int): The dimension that expected to get the slice-tensor.
maxnorm (float32): Max norm.
Returns:
Tensor, has the same dtype and shape as input.
Raises:
TypeError: If dtype of `p` is not int.
TypeError: If dtype of `axis` is not int.
TypeError: If dtype of `maxnorm` is not float32.
ValueError: If the value of `p` less than 1.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3]]), mindspore.float32)
>>> y = ops.renorm(x, p=1, axis=0, maxnorm=5.)
>>> print(y)
[[1. 1. 1. ]
[1.6666666 1.6666666 1.6666666 ]
[1.6666667 1.6666667 1.6666667 ]]
"""
renorm_ = _get_cache_prim(Renorm)(p, axis, maxnorm)
return renorm_(input)
@constexpr
def _check_attr_dtype(param_name, input_dtype, allow_dtypes, cls_name):
validator.check_value_type(param_name, input_dtype, allow_dtypes, cls_name)
@_primexpr
def _check_positive_float(arg_value, arg_name, cls_name):
validator.check_positive_float(arg_value, arg_name, cls_name)
@_primexpr
def _check_int_range(arg_value, lower_limit, upper_limit, arg_name=None, prim_name=None):
validator.check_int_range(arg_value, lower_limit,
upper_limit, validator.INC_LEFT, arg_name, prim_name)
def _check_logits_tensor(logits):
if not isinstance(logits, (Tensor, Tensor_)):
raise TypeError("The input logits must be tensor")
def _check_logits_shape(logits):
if not logits.shape:
raise ValueError("For gumbel_softmax, the 0-D input is not supported.")
[文档]def gumbel_softmax(logits, tau=1.0, hard=False, dim=-1):
r"""
Returns the samples from the Gumbel-Softmax distribution and optionally discretizes. If `hard = True`, the returned
samples will be one-hot, otherwise it will be probability distributions that sum to 1 across `dim`.
Args:
logits (Tensor): Unnormalized log probabilities. The data type must be float16 or float32.
tau (float): The scalar temperature, which is a positive number. Default: ``1.0`` .
hard (bool): if `True`, the returned samples will be discretized as one-hot vectors, but will be differentiated
as if it is the soft sample in autograd. Default: ``False`` .
dim (int): Dim for softmax to compute. Default: ``-1`` .
Returns:
Tensor, has the same dtype and shape as `logits`.
Raises:
TypeError: If `logits` is not a Tensor.
TypeError: If dtype of `logits` is not one of: float16, float32.
TypeError: If `tau` is not a float.
TypeError: If `hard` is not a bool.
TypeError: If `dim` is not an int.
ValueError: If If `tau` is not positive.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mindspore.float32)
>>> output = ops.gumbel_softmax(input_x, 1.0, True, -1)
>>> print(output.shape)
(2, 3)
"""
_check_logits_tensor(logits)
_check_logits_shape(logits)
logits_dtype = dtype_(logits)
_check_input_dtype("logits", logits_dtype, [mstype.float16, mstype.float32], "gumbel_softmax")
valid_types = [mstype.float16, mstype.float32]
if logits_dtype not in valid_types:
names = [t.__name__ if hasattr(t, "__name__") else t for t in valid_types]
logits_dtype = logits_dtype.__name__ if hasattr(logits_dtype, '__name__') else repr(logits_dtype)
raise TypeError(f"For 'gumbel_softmax', the 'logits' should be one of '{names}', but got type '{logits_dtype}'")
_check_attr_dtype("tau", tau, [float], "gumbel_softmax")
_check_attr_dtype("hard", hard, [bool], "gumbel_softmax")
_check_attr_dtype("dim", dim, [int], "gumbel_softmax")
_check_positive_float(tau, "tau", "gumbel_softmax")
if hard:
_check_int_range(dim, -1, len(logits.shape), 'dim', "gumbel_softmax")
else:
_check_int_range(dim, -len(logits.shape),
len(logits.shape), 'dim', "gumbel_softmax")
sample_shape = shape_(logits)
uniform = C.uniform(sample_shape, scalar_to_tensor_(
0.0, mstype.float32), scalar_to_tensor_(1.0, mstype.float32))
uniform = cast_(uniform, logits_dtype)
gumbel = neg(log_(neg(log_(uniform))))
gumbel = (logits + gumbel) / tau
y_soft = _get_cache_prim(P.Softmax)(dim)(gumbel)
if hard:
index = y_soft.argmax(axis=dim)
y_hard = _get_cache_prim(P.OneHot)(dim)(index, sample_shape[dim], Tensor(1, logits_dtype),
Tensor(0, logits_dtype))
ret = ops.stop_gradient(y_hard - y_soft) + y_soft
else:
ret = y_soft
return ret
[文档]def kaiser_window(window_length, periodic=True, beta=12.0, *, dtype=None):
r"""
Generates a Kaiser window, which is also known as the Kaiser-Bessel window.
The Kaiser window is defined as
.. math::
w(n) = \frac{I_{0}\left( \beta\sqrt{1 - \frac{4n^{2}}{(M - 1)^{2}}} \right)}{I_{0}(\beta)}
with
.. math::
- \frac{M - 1}{2} \leq n \leq \frac{M - 1}{2}
where :math:`I_0` is the modified zeroth-order Bessel function.
Args:
window_length (int): Length of window.
periodic (bool, optional): When set to ``True`` , generates a periodic window for spectral analysis.
When set to ``False`` , generates a symmetric window for filter design. Default: ``True`` .
beta (float, optional): Shape parameter, when `beta` gets large, the window narrows. Default: ``12.0`` .
Keyword Args:
dtype (mindspore.dtype, optional): The output window data type, it must be float. Default: ``None`` .
Returns:
Tensor, a Kaiser window.
Raises:
TypeError: If `window_length` is not an integer.
TypeError: If `periodic` is not a variable of Boolean type.
ValueError: If `window_length` is negative.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import ops
>>> window_length = 5
>>> out = ops.kaiser_window(window_length)
>>> print(out.asnumpy())
[5.27734413e-05 1.01719688e-01 7.92939834e-01 7.92939834e-01
1.01719688e-01]
"""
if not isinstance(window_length, int):
raise TypeError(
f"For 'kaiser_window', 'window_length' must be a non-negative integer, but got {type(window_length)}"
)
if window_length < 0:
raise ValueError(
f"For 'kaiser_window', 'window_length' must be a non-negative integer, but got {window_length}"
)
if window_length <= 1:
return Tensor(np.ones(window_length))
if not isinstance(periodic, bool):
raise TypeError(
f"For 'kaiser_window', 'periodic' must be a variable of Boolean type, but got {type(periodic)}"
)
if dtype is not None and dtype not in mstype.float_type:
raise TypeError(f"For 'kaiser_window', 'dtype' must be floating point dtypes, but got {dtype}.")
if periodic:
window_length = window_length + 1
n = np.arange(0, window_length)
alpha = (window_length - 1) / 2.0
w = np.i0(
beta * np.sqrt(1 - ((n - alpha) / alpha) ** 2.0)
) / np.i0(float(beta))
if dtype is not None:
w = cast_(ms.tensor(w), dtype)
out = Tensor(w[:-1]) if periodic else Tensor(w)
return out
def stft(x, n_fft, hop_length=None, win_length=None, window=None, center=True,
pad_mode="REFLECT", normalized=False, onesided=None, return_complex=None):
r"""
STFT segments the signal into narrow time intervals and takes the Fourier transform
of each segment to quantify the change of a nonstationary signal's frequency
and phase content over time.
Ignoring the optional batch dimension, this operation computes the following expression:
.. math::
X[\omega, m]=\sum_{k=0}^{\text {win_length-1 }}
\text { window }[k] \text { input }[m \times \text { hop_length }+
k] \exp \left(-j \frac{2 \pi \cdot \omega k}{\text { win_length }}\right)
where :math:`m` is the index of the sliding window, and
:math:`ω` is the frequency in range :math:`0 \leq \omega < \text{n\_fft}0≤ω<n\_fft`.
Args:
x (Tensor): Time sequences of stft, must be either a 1-D time tensor or a 2-D tensor.
n_fft (int): The size of Fourier transform.
hop_length (int, optional): The distance between neighboring sliding window
frames. Default: ``None``(treated as equal to :math:`floor(n\_fft / 4)`).
win_length (int, optional): the size of window frame and STFT filter.
Default: ``None``(treated as equal to `n_fft`).
window (Tensor, optional): the optional window function, 1-D tensor of size `win_length`.
Default: ``None``(treated as window of all :math:`1` s). If `win_length` < `n_fft`,
`window` will be padded on both sides with ones to length `n_fft` before it takes effect.
center (bool, optional): whether to pad `x` on both sides. Default: ``True``.
pad_mode (str, optional): controls the padding method used when
`center` is True. Default: 'REFLECT'.
normalized (bool, optional): controls whether to return the normalized STFT results
Default: ``False``.
onesided (bool, optional): controls whether to return half of results to
avoid redundancy for real inputs.
Default: ``None``. True for real `x` and `window`, False otherwise.
return_complex (bool, optional): whether to return a complex tensor, or
a real tensor with an extra last dimension for the real and
imaginary components.
Default: ``None``. True for complex `x` or `window`, False otherwise.
Returns:
- **output** (Tensor) - A tensor containing the STFT result.
If `return_complex` is True, it returns a complex Tensor with shape :math:`(*, N, T)`.
If `return_complex` is False, it returns a real Tensor with shape :math:`(*, N, T, 2)`.
`N` is size of Fourier transform, it depends on parameter `onesided`:
- If `onesided` is False, :math:`N = n\_fft`.
- If `onesided` is True, :math:`N = n\_fft // 2 + 1`.
`T` is the total number of frames used, calculated by this formula:
:math:`T = 1 + (len - n\_fft) / hop\_length`, where `len` depends on parameter `center`:
- If `center` is False, :math:`len = signal\_length`.
- If `center` is True, :math:`len = signal\_length + (n\_fft // 2) * 2`.
where :math:`signal\_length` is the signal length, it equals to :math:`x.shape[-1]`.
Raises:
TypeError: If `x` is not a 1-D or 2-D tensor.
TypeError: If `window` is not a 1-D tensor.
TypeError: If any one of `center` , `normalized` , `onesided`
and `return_complex` is assigned a nonboolean value.
TypeError: If `pad_mode` is is assigned a value that is not string.
TypeError: If `n_fft` , `hop_length` or `hop_length` is not an int.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> import numpy as np
>>> x = ms.Tensor(np.random.rand(2,7192), ms.float32)
>>> output = ops.stft(n_fft=64, x=x)
>>> print(output.shape)
(2, 33, 450, 2)
"""
if hop_length is None:
hop_length = int(n_fft // 4)
if win_length is None:
win_length = int(n_fft // 1)
if window is None:
window = ops.ones(win_length, mstype.float32)
def _is_complex(x):
return dtype_(x) in [mstype.complex64, mstype.complex128]
if onesided is None:
onesided = (not _is_complex(x)) and (not _is_complex(window))
if return_complex is None:
return_complex = _is_complex(x) or _is_complex(window)
if center:
_check_attr_dtype("center", center, [bool], "stft")
signal_dim = len(x.shape)
pad = n_fft // 2
if signal_dim == 1:
x = layer.Pad(((pad, pad),), pad_mode)(x)
elif signal_dim == 2:
x = layer.Pad(((0, 0), (pad, pad)), pad_mode)(x)
else:
raise ValueError(
f"Expected a 1-D tensor or a 2-D tensor, but got {signal_dim}")
stft_ = STFT(n_fft, hop_length, win_length,
normalized, onesided, return_complex)
return stft_(x, window)
def _check_same_type(dtype1, dtype2):
return dtype1 == dtype2
@constexpr
def _max(*args):
"""Returns the maximum value."""
return max(*args)
@constexpr
def _min(*args):
"""Returns the minimum value."""
return min(*args)
@_primexpr
def _infer_shape_rem(shape1, shape2, ndim1, ndim2, transpose_b):
"""Infers the shape of the last two dimensions after performing matmul."""
shape_rem = []
if ndim1 >= 2:
shape_rem.append(shape1[-2])
if transpose_b:
if ndim2 >= 2:
shape_rem.append(shape2[-2])
else:
if ndim1 >= 1:
shape_rem.append(shape2[-1])
return tuple(shape_rem)
def _check_value(items, max_size, msg_prefix, shape1, shape2):
for item in items:
if item not in (1, max_size):
raise ValueError(f"{msg_prefix} operands could not be broadcast together with shape1 {shape1} and "
f"shape2 {shape2}.")
@_primexpr
def _check_matmul_shapes(shape1, shape2, prim_name=None):
"""Checks shape1 and shape2 are valid to perform matmul, and returns output shape after broadcasting."""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
shape_out = list()
r_shape1 = shape1[:-2]
r_shape2 = shape2[:-2]
max_len = max(len(r_shape1), len(r_shape2))
for i in range(max_len):
items = [it[i - max_len + len(it)] if i - max_len + len(it) >= 0 else 1 for it in (r_shape1, r_shape2)]
max_size = max(items)
_check_value(items, max_size, msg_prefix, shape1, shape2)
shape_out.append(max_size)
return tuple(shape_out)
@_primexpr
def _check_need_broadcast(shape1, shape2):
"""Returns True if broadcast is necessary for batchmatmul."""
return shape1[:-2] != shape2[:-2]
@_primexpr
def _expand(x, ndim):
"""Expand x to ndim from axis, which can be 0 or -1."""
while rank_(x) < ndim:
x = expand_dims_(x, 0)
return x
def _broadcast_to(x, shape_cur, shape_to, ndim_to):
"""Broadcasts x from shape_cur to shape_to."""
size = tile_size_(shape_cur, shape_to, ndim_to)
F.stop_gradient(size)
return tile_(x, size)
[文档]def matmul(input, other):
"""
Returns the matrix product of two tensors.
Note:
Numpy arguments `out`, `casting`, `order`, `subok`, `signature`, and `extobj` are
not supported.
The dtype of `input` and `other` must be same.
On Ascend, the rank of `input` or `other` must be between 1 and 6.
Args:
input (Tensor): Input tensor, scalar not allowed.
The last dimension of `input` must be the same size as the second last dimension of `other`.
And the shape of input and other could be broadcast.
other (Tensor): Input tensor, scalar not allowed.
The last dimension of `input` must be the same size as the second last dimension of `other`.
And the shape of input and other could be broadcast.
Returns:
Tensor or scalar, the matrix product of the inputs. This is a scalar only
when both `input`, `other` are 1-d vectors.
Raises:
TypeError: If the dtype of `input` and the dtype of `other` are not the same.
ValueError: If the last dimension of `input` is not the same size as the
second-to-last dimension of `other`, or if a scalar value is passed in.
ValueError: If the shape of `input` and `input` could not broadcast together.
RuntimeError: If the rank of `input` or `other` is less than 1 or greater than 6 on the Ascend platform.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> # case 1 : Reasonable application of broadcast mechanism
>>> input = Tensor(np.arange(2*3*4).reshape(2, 3, 4), mindspore.float32)
>>> other = Tensor(np.arange(4*5).reshape(4, 5), mindspore.float32)
>>> output = ops.matmul(input, other)
>>> print(output)
[[[ 70. 76. 82. 88. 94.]
[ 190. 212. 234. 256. 278.]
[ 310. 348. 386. 424. 462.]]
[[ 430. 484. 538. 592. 646.]
[ 550. 620. 690. 760. 830.]
[ 670. 756. 842. 928. 1014.]]]
>>> print(output.shape)
(2, 3, 5)
>>> # case 2 : the rank of `input` is 1
>>> input = Tensor(np.ones([1, 2]), mindspore.float32)
>>> other = Tensor(np.ones([2,]), mindspore.float32)
>>> output = ops.matmul(input, other)
>>> print(output)
[2.]
>>> print(output.shape)
(1,)
"""
return auto_generate.matmul_ext(input, other)
[文档]def inner(input, other):
r"""
Returns the inner product of two tensors.
For 1-D tensors (without complex conjugation), returns the ordinary inner product of vectors.
For higher dimensions, returns a sum product over the last axis.
Note:
If `input` or `other` is a Tensor scalar, :func:`mindspore.ops.inner` will be the same as
:func:`mindspore.ops.mul` .
Args:
input (Tensor): First input.
other (Tensor): Second input.
Returns:
Tensor, the result of the inner product.
Raises:
ValueError: If neither `input` nor `other` is scalar, and the last dimension of the two input tensors do not
match.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> # case1: 2 1D tensors
>>> input = ms.Tensor([1, 2, 3], mstype.float32)
>>> y = ms.Tensor([4, 5, 6], mstype.float32)
>>> output = ops.inner(input, y)
>>> print(output)
32
>>> # case2: Tensor scalar and tensor
>>> input = ms.Tensor([[[1, 2, 3], [3, 2, 1]], [[4, 5, 6], [4, 5, 6]]], mstype.float32)
>>> y = ms.Tensor(2, mstype.float32)
>>> output = ops.inner(input, y)
>>> print(output)
[[[ 2. 4. 6.]
[ 6. 4. 2.]]
[[ 8. 10. 12.]
[ 8. 10. 12.]]]
>>> # case3: Two tensors
>>> input = ms.Tensor([[[1, 2, 3], [3, 2, 1]], [[4, 5, 6], [4, 5, 6]]], mstype.float32)
>>> y = ms.Tensor([[2, 3, 4], [4, 3, 2]], mstype.float32)
>>> output = ops.inner(input, y)
>>> print(output)
[[[20. 16.]
[16. 20.]]
[[47. 43.]
[47. 43.]]]
"""
x_dim = input.ndim
other_dim = other.ndim
if x_dim == 0 or other_dim == 0:
output = input * other
return output
x_shape = input.shape
other_shape = other.shape
if x_shape[-1] != other_shape[-1]:
raise ValueError(f"For 'inner', the last dimension of 'input' and 'other' must be the same, \
but got input.shape: {x_shape} and other.shape: {other_shape}.")
return ops.tensor_dot(input, other, axes=(-1, -1))
[文档]def bmm(input_x, mat2):
r"""
Computes matrix multiplication between two tensors by batch.
.. math::
\text{output}[..., :, :] = \text{matrix}(input_x[..., :, :]) * \text{matrix}(mat2[..., :, :])
The dim of `input_x` can not be less than `3` and the dim of `mat2` can not be less than `2`.
Args:
input_x (Tensor): The first tensor to be multiplied. The shape of the tensor is :math:`(*B, N, C)`,
where :math:`*B` represents the batch size which can be multidimensional, :math:`N` and :math:`C` are the
size of the last two dimensions.
mat2 (Tensor): The second tensor to be multiplied. The shape of the tensor is :math:`(*B, C, M)`.
Returns:
Tensor, the shape of the output tensor is :math:`(*B, N, M)`.
Raises:
ValueError: If dim of `input_x` is less than `3` or dim of `mat2` is less than `2`.
ValueError: If the length of the third dim of `input_x` is not equal to
the length of the second dim of `mat2`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> input_x = Tensor(np.arange(24).reshape((2, 4, 1, 3)), ms.float32)
>>> mat2 = Tensor(np.arange(72).reshape((2, 4, 3, 3)), ms.float32)
>>> output = ops.bmm(input_x, mat2)
>>> print(output)
[[[[ 15. 18. 21.]]
[[ 150. 162. 174.]]
[[ 447. 468. 489.]]
[[ 906. 936. 966.]]]
[[[1527. 1566. 1605.]]
[[2310. 2358. 2406.]]
[[3255. 3312. 3369.]]
[[4362. 4428. 4494.]]]]
"""
return batch_matmul_(input_x, mat2)
def quantile(input, q, axis=None, keepdims=False):
r"""
Computes the q-th quantiles of all elements in `input`, when the
q-th quantile lies between two data points, a linear interpolation is implemented between them.
Args:
input (Tensor): The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
Supported dtypes: float32, float64.
q (Union[float, Tensor]): A scalar or 1D tensor of quantile values in the range [0, 1].
Supported dtypes: float32, float64.
axis (int, optional): The dimension to reduce. By default, `axis` is None resulting in the
input tensor being flattened before computation. Default: ``None``.
keepdims (bool, optional): Whether the output tensor has dim retained or not. Default: ``False``.
Returns:
Tensor, has the same dtype as the `input`.
Suppose the shape of `input` is :math:`(m, x_0, x_1, ..., x_i, ..., X_R)`, `axis` = :math:`i` and m is
the element count of input `q`.
- If `q` is scalar and `keepdims` is True, the shape of output is :math:`(x_0, x_1, ..., 1, ..., X_R)`.
- If `q` is scalar and `keepdims` is False, the shape of output is :math:`(x_0, x_1, ..., X_R)`.
- If `q` is 1D Tensor and `keepdims` is True, the shape of output is :math:`(m, x_0, x_1, ..., 1, ..., X_R)`.
- If `q` is 1D Tensor and `keepdims` is False, the shape of output is :math:`(m, x_0, x_1, ..., X_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `q` is not a Tensor or float.
TypeError: If dtype of `input` is not float32 or float64.
TypeError: If dtype of `q` is not float32 or float64.
TypeError: If dtype of `input` and the dtype of `q` is different.
ValueError: If the `q` values not in the range [0, 1].
ValueError: If the `axis` values out of range.
Supported Platforms:
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-0.7832, 0.8003, 0.8111]), mindspore.float32)
>>> q = Tensor(np.array([0.1, 0.7, 0.9]), mindspore.float32)
>>> output = ops.quantile(x, q)
>>> print(output.asnumpy())
[-0.4665 0.80462 0.80894]
"""
if axis is not None:
_check_attr_dtype("axis", axis, [int], "quantile")
if keepdims is not None:
_check_attr_dtype("keepdims", keepdims, [bool], "quantile")
quantile_ = _get_cache_prim(Quantile)(dim=axis, keep_dims=keepdims)
return quantile_(input, q)
def nanquantile(input, q, axis=None, keepdims=False):
r"""
This operator is derived from mindspore.ops.quantile() that 'ignores' NaN values.
It computes quantiles as though the input has no NaN values. If all values in a
reduced dimension are NaN then the quantiles for that reduction will be NaN.
Refer to :func:`mindspore.ops.quantile` for more details.
Args:
input (Tensor): The shape of tensor is :math:`(x_1, x_2, ..., x_R)`.
Supported dtypes: float32, float64.
q (Union[float, Tensor]): A scalar or 1D tensor of quantile values in the range [0, 1].
Supported dtypes: float32, float64.
axis (int, optional): The dimension to reduce. By default, `axis` is None resulting in the
input tensor being flattened before computation. Default: ``None``.
keepdims (bool, optional): Whether the output tensor has dim retained or not. Default: ``False``.
Returns:
Tensor, has the same dtype as the `input`.
Suppose the shape of `input` is :math:`(m, x_0, x_1, ..., x_i, ..., X_R)`, `axis` = :math:`i` and m is
the element count of input `q`.
- If `q` is scalar and `keepdims` is True, the shape of output is :math:`(x_0, x_1, ..., 1, ..., X_R)`.
- If `q` is scalar and `keepdims` is False, the shape of output is :math:`(x_0, x_1, ..., X_R)`.
- If `q` is 1D Tensor and `keepdims` is True, the shape of output is :math:`(m, x_0, x_1, ..., 1, ..., X_R)`.
- If `q` is 1D Tensor and `keepdims` is False, the shape of output is :math:`(m, x_0, x_1, ..., X_R)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `q` is not a Tensor or float.
TypeError: If dtype of `input` is not float32 or float64.
TypeError: If dtype of `q` is not float32 or float64.
TypeError: If dtype of `input` and the dtype of `q` is different.
ValueError: If the `q` values not in the range [0, 1].
ValueError: If the `axis` values out of range.
Supported Platforms:
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([float('nan'), 0.8003, 0.8111]), mindspore.float32)
>>> q = Tensor(np.array([0.1, 0.7, 0.9]), mindspore.float32)
>>> output = ops.nanquantile(x, q)
>>> print(output.asnumpy())
[0.80138 0.80786 0.81002]
"""
if axis is not None:
_check_attr_dtype("axis", axis, [int], "nanquantile")
if keepdims is not None:
_check_attr_dtype("keepdims", keepdims, [bool], "nanquantile")
quantile_ = _get_cache_prim(Quantile)(dim=axis, keep_dims=keepdims, ignore_nan=True)
return quantile_(input, q)
[文档]def baddbmm(input, batch1, batch2, beta=1, alpha=1):
r"""
The result is the sum of the input and a batch matrix-matrix product of matrices in batch1 and batch2.
The formula is defined as follows:
.. math::
\text{out}_{i} = \beta \text{input}_{i} + \alpha (\text{batch1}_{i} \mathbin{@} \text{batch2}_{i})
Args:
input (Tensor): The input Tensor. When batch1 is a :math:`(C, W, T)` Tensor and batch2 is a
:math:`(C, T, H)` Tensor, input must be broadcastable with :math:`(C, W, H)` Tensor.
batch1 (Tensor): :math:`batch1` in the above formula. Must be 3-D Tensor, dtype is same as input.
batch2 (Tensor): :math:`batch2` in the above formula. Must be 3-D Tensor, dtype is same as input.
beta (Union[float, int], optional): multiplier for input. Default: ``1`` .
alpha (Union[float, int], optional): multiplier for :math:`batch1 @ batch2`. Default: ``1`` .
Arguments beta and alpha must be integers when inputs of type not FloatTensor, otherwise they should
be a real number.
Returns:
Tensor, has the same dtype as input, shape will be :math:`(C, W, H)`.
Raises:
TypeError: The type of `input`, `batch1`, `batch2` is not Tensor.
TypeError: The types of `input`, `batch1`, `batch2` are different.
TypeError: For inputs of type FloatTensor or DoubleTensor, \
arguments beta and alpha not be real numbers, otherwise not be integers.
TypeError: For Baddbmm, attributes alpha and beta are not real numbers
ValueError: If `batch1` and `batch2` are not 3-D tensors.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.ones([1, 3, 3]).astype(np.float32))
>>> batch1 = Tensor(np.ones([1, 3, 4]).astype(np.float32))
>>> batch2 = Tensor(np.ones([1, 4, 3]).astype(np.float32))
>>> output = ops.baddbmm(input, batch1, batch2)
>>> print(output)
[[[5. 5. 5.]
[5. 5. 5.]
[5. 5. 5.]]]
"""
bmmop = _get_cache_prim(BatchMatMul)(False, False)
if not (isinstance(input, Tensor) and isinstance(batch1, Tensor) and isinstance(batch2, Tensor)):
raise TypeError("For Baddbmm, inputs must be all tensors.")
input_dtype = dtype_(input)
if not (input_dtype == dtype_(batch1) and input_dtype == dtype_(batch2)):
raise TypeError("For Baddbmm, the inputs should be the same dtype.")
if input_dtype in (mstype.float16, mstype.float32, mstype.float64):
if not (isinstance(alpha, (int, float)) and isinstance(beta, (int, float))):
raise TypeError("For attributes alpha and beta should be real numbers.")
check_is_number(alpha, (int, float))
check_is_number(beta, (int, float))
else:
if not (isinstance(alpha, int) and isinstance(beta, int)):
raise TypeError("For inputs of type not FloatTensor or DoubleTensor, "
"arguments beta and alpha must be integers.")
y = beta * input + alpha * (bmmop(batch1, batch2))
return y
[文档]def baddbmm_ext(input, batch1, batch2, *, beta=1, alpha=1):
r"""
The result is the sum of the input and a batch matrix-matrix product of matrices in batch1 and batch2.
The formula is defined as follows:
.. math::
\text{out}_{i} = \beta \text{input}_{i} + \alpha (\text{batch1}_{i} \mathbin{@} \text{batch2}_{i})
Args:
input (Tensor): The input Tensor. When batch1 is a :math:`(C, W, T)` Tensor and batch2 is a
:math:`(C, T, H)` Tensor, input must be broadcastable with :math:`(C, W, H)` Tensor.
batch1 (Tensor): :math:`batch1` in the above formula. Must be 3-D Tensor, dtype is same as input.
batch2 (Tensor): :math:`batch2` in the above formula. Must be 3-D Tensor, dtype is same as input.
Keyword Args:
beta (Union[float, int], optional): multiplier for input. Default: ``1`` .
alpha (Union[float, int], optional): multiplier for :math:`batch1 @ batch2`. Default: ``1`` .
Returns:
Tensor, has the same dtype as input, shape will be :math:`(C, W, H)`.
Raises:
TypeError: If the type of `input`, `batch1`, `batch2` is not Tensor.
TypeError: If the types of `input`, `batch1`, `batch2` are different.
ValueError: If `batch1` and `batch2` are not 3-D tensors.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.ones([1, 3, 3]).astype(np.float32))
>>> batch1 = Tensor(np.ones([1, 3, 4]).astype(np.float32))
>>> batch2 = Tensor(np.ones([1, 4, 3]).astype(np.float32))
>>> output = ops.baddbmm_ext(input, batch1, batch2)
>>> print(output)
[[[5. 5. 5.]
[5. 5. 5.]
[5. 5. 5.]]]
"""
return ops.auto_generate.baddbmm(input, batch1, batch2, beta, alpha)
[文档]def log2(input):
r"""
Returns a new Tensor by taking the base 2 logarithm of the elements in the input Tensor.
.. math::
y_i = \log_2(input_i)
.. warning::
If the input value of operator log2 is within the range (0, 0.01] or [0.95, 1.05], the output accuracy may
be affected.
Args:
input (Tensor): Input Tensor of any dimension. The value must be greater than 0.
Returns:
Tensor, has the same shape and dtype as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not float16 or float32 or float64 on CPU and GPU, if dtype of `input`
is not float16 or float32 on Ascend.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2, 4, 8]).astype(np.float16))
>>> output = ops.log2(x)
>>> print(output)
[1. 2. 3.]
"""
x_dtype = dtype_(input)
denominator = log_(_make_tensor(2, x_dtype))
frac_log = log_(input)
output = frac_log / denominator
return output
def arrange(x):
lists = []
for i in range(0, x):
lists.append(i)
return lists
[文档]def rot90(input, k, dims):
"""
Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
Rotation direction is from the first towards the second axis if k > 0,
and from the second towards the first for k < 0.
Args:
input (Tensor): Input tensor.
k (int): Number of times to rotate.
dims (Union[list(int), tuple(int)]): Axis to rotate.
Returns:
Tensor.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `k` is not integer.
TypeError: If `dims` is not tuple of integers or list of ints.
ValueError: If the length of `dims` is not `2`.
ValueError: If any dims is out of Tensor's range [-input.ndim, input.ndim).
RuntimeError: If rotation dims are not different.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[0, 1], [2, 3]])).astype(np.float32)
>>> k = 1
>>> dims = [0, 1]
>>> output = ops.rot90(x, k, dims)
>>> print(output)
[[1. 3.]
[0. 2.]]
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError(f"For `rot90`, the `input` must be Tensor!, but get {type(input)}.")
if not isinstance(k, int):
raise TypeError(f"For `rot90`, the `k` must be int!, but get {type(k)}.")
if not isinstance(dims, (list, tuple)):
raise TypeError(f"For `rot90`, the `dims` must be list or tuple!, but get {type(dims)}.")
total_dims = input.ndim
total_rot_dims = len(dims)
if total_rot_dims != 2:
raise ValueError(f"For `rot90`, total rotation dims must be 2, but get {total_rot_dims}.")
if dims[0] == dims[1] or (dims[0] - dims[1]) == total_dims or (dims[1] - dims[0]) == total_dims:
raise RuntimeError(f"For `rot90`, rotation dims must be different, but get dim0={dims[0]}, dim1={dims[1]}.")
if dims[0] >= total_dims or dims[0] < -total_dims:
raise ValueError(f"For `rot90`, rotation dim0 is out of range, dim0={dims[0]}.")
if dims[1] >= total_dims or dims[1] < -total_dims:
raise ValueError(f"For `rot90`, rotation dim1 is out of range, dim1={dims[1]}.")
k = (4 + (k % 4)) % 4
if k == 0:
out = input
return out
if k == 2:
op1 = P.ReverseV2(axis=[dims[0]])
output = op1(input)
op2 = P.ReverseV2(axis=[dims[1]])
out = op2(output)
return out
axes_list = arrange(total_dims)
(axes_list[dims[0]], axes_list[dims[1]]) = (axes_list[dims[1]],
axes_list[dims[0]])
if k == 1:
op = P.ReverseV2(axis=[dims[1]])
output = op(input)
out = output.transpose(axes_list)
else:
output = input.transpose(axes_list)
op = P.ReverseV2(axis=[dims[1]])
out = op(output)
return out
[文档]def roll(input, shifts, dims=None):
"""
Rolls the elements of a tensor along an axis.
Args:
input (Tensor): Input tensor.
shifts (Union[list(int), tuple(int), int]): Specifies the number of places by which elements are shifted
positively (towards larger indices) along the specified dimension. Negative shifts will roll the elements
in the opposite direction.
dims (Union[list(int), tuple(int), int], optional): Specifies the dimension indexes of shape to be rolled.
Default: ``None``. If dims is None, the Tensor will be flattened before rolling and then restored to the
original shape.
Returns:
Tensor, has the same shape and type as `input`.
Raises:
TypeError: If `shifts` is not an int, a tuple or a list.
TypeError: If `dims` is not an int, a tuple or a list.
Supported Platforms:
``GPU``
Examples:
>>> import numpy as np
>>> import mindspore as ms
>>> from mindspore import ops
>>> from mindspore import Tensor
>>> input = Tensor(np.array([0, 1, 2, 3, 4]).astype(np.float32))
>>> output = ops.roll(input, shifts=2, dims=0)
>>> print(output)
[3. 4. 0. 1. 2.]
"""
return roll_impl(input, shifts, dims)
[文档]def xdivy(x, y):
"""
Divides the first input tensor by the second input tensor element-wise. Returns zero when `x` is zero.
Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
When the inputs are two tensors,
dtypes of them cannot be bool at the same time, and the shapes of them could be broadcast.
If one of the inputs is scalar, the scalar could only be a constant.
.. note::
When `x` and `y` are both of datatype complex, they should be both complex64 or complex128 at the same time.
Args:
x (Union[Tensor, Number, bool]): Tensor of datatype number.Number or bool, or it can be a bool or number.
y (Union[Tensor, Number, bool]): Tensor of datatype number.Number or bool, or it can be a bool or number.
`x` and `y` can not be both bool at the same time.
Returns:
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 `x` and `y` is not one of the following: Tensor, Number, bool.
TypeError: If dtype of `x` and `y` is not in [float16, float32, float64, complex64, complex128, bool].
ValueError: If `x` could not be broadcast to a tensor with shape of `y`.
RuntimeError: If the data type of `x`, `y` conversion of Parameter is given
but data type conversion of Parameter is not supported.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2, 4, -1]), mindspore.float32)
>>> y = Tensor(np.array([2, 2, 2]), mindspore.float32)
>>> output = ops.xdivy(x, y)
>>> print(output)
[ 1. 2. -0.5]
"""
return xdivy_(x, y)
[文档]def log10(input):
r"""
Returns a new Tensor by taking the base 10 logarithm of the elements in the input Tensor.
.. math::
y_i = \log_{10}(input_i)
.. warning::
If the input value of operator log10 is within the range (0, 0.01] or [0.95, 1.05], the output accuracy may
be affected.
Args:
input (Tensor): Input Tensor of any dimension. The each element in Tensor must be greater than 0.
Returns:
Tensor, has the same shape and dtype as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not float16 or float32 or float64 on CPU and GPU, if dtype of `input`
is not float16 or float32 on Ascend.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2, 4, 10]).astype(np.float16))
>>> output = ops.log10(x)
>>> print(output)
[0.301 0.602 1. ]
"""
x_dtype = dtype_(input)
denominator = log_(_make_tensor(10, x_dtype))
frac_log = log_(input)
output = frac_log / denominator
return output
[文档]def kron(input, other):
"""
Computes the Kronecker product :math:`input ⊗ other`, denoted by ⊗, of `input` and `other`.
If `input` is a :math:`(a_{0}` input :math:`a_{1}` input ... input :math:`a_{n})` Tensor
and `other` is a :math:`(b_{0}` input :math:`b_{1}` input ... input :math:`b_{n})` Tensor,
the result will be a :math:`(a_{0}*b_{0}` input :math:`a_{1}*b_{1}` input ... input :math:`a_{n}*b_{n})`
Tensor with the following entries:
.. math::
(input ⊗ other)_{k_{0},k_{1},...k_{n}} =
input_{i_{0},i_{1},...i_{n}} * other_{j_{0},j_{1},...j_{n}},
where :math:`k_{t} = i_{t} * b_{t} + j_{t}` for 0 ≤ `t` ≤ `n`. If one
Tensor has fewer dimensions than the other it is unsqueezed
until it has the same number of dimensions.
Note:
Supports real-valued and complex-valued inputs.
Args:
input (Tensor): Input Tensor, has the shape :math:`(r0, r1, ... , rN)`.
other (Tensor): Input Tensor, has the shape :math:`(s0, s1, ... , sN)`.
Returns:
Tensor, has the shape :math:`(r0 * s0, r1 * s1, ... , rN * sN)`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `other` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, nn
>>> from mindspore import ops
>>> input = Tensor(np.array([[0, 1, 2], [3, 4, 5]])).astype(np.float32)
>>> other = Tensor(np.array([[-1, -2, -3], [-4, -6, -8]])).astype(np.float32)
>>> output = ops.kron(input, other)
>>> print(output)
[[ 0. 0. 0. -1. -2. -3. -2. -4. -6.]
[ 0. 0. 0. -4. -6. -8. -8. -12. -16.]
[ -3. -6. -9. -4. -8. -12. -5. -10. -15.]
[-12. -18. -24. -16. -24. -32. -20. -30. -40.]]
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("the input must be Tensor!")
if not isinstance(other, (Tensor, Tensor_)):
raise TypeError("the other must be Tensor!")
if input is None or other is None:
return None
if input.ndim == 0 or other.ndim == 0:
return input * other
if input.ndim >= other.ndim:
maxdim = input.ndim
else:
maxdim = other.ndim
pad_x = maxdim - input.ndim
pad_y = maxdim - other.ndim
x_reshape = [0 for _ in range(2 * maxdim)]
y_reshape = [0 for _ in range(2 * maxdim)]
result_shape = [0 for _ in range(maxdim)]
for i in range(maxdim):
if i >= pad_x:
x_reshape[2 * i] = input.shape[i - pad_x]
else:
x_reshape[2 * i] = 1
x_reshape[2 * i + 1] = 1
y_reshape[2 * i] = 1
if i >= pad_y:
y_reshape[2 * i + 1] = other.shape[i - pad_y]
else:
y_reshape[2 * i + 1] = 1
result_shape[i] = x_reshape[2 * i] * y_reshape[2 * i + 1]
input = input.reshape(x_reshape)
other = other.reshape(y_reshape)
result = (input * other).reshape(result_shape)
return result
def _check_is_tensor(param_name, input, cls_name):
"""Returns True if input is Tensor."""
if not isinstance(input, Tensor):
raise TypeError(f"For {cls_name}, {param_name} must be a Tensor, but got {type(input)}.")
[文档]def any(input, axis=None, keep_dims=False):
r"""
Reduces a dimension of `input` by the "logical OR" of all elements in the dimension, by default. And also can
reduce a dimension of `input` along the `axis`. Determine whether the dimensions of the output and input are the
same by controlling `keep_dims`.
Note:
The `axis` with tensor type is only used for compatibility with older versions and is not recommended.
Args:
input (Tensor): Input Tensor(bool),has the shape :math:`(N, *)` where :math:`*` means,
any number of additional dimensions.
axis (Union[int, tuple(int), list(int), Tensor], optional): The dimensions to reduce.
Suppose the rank of `input` is r, `axis` must be in the range [-rank(input), rank(input)).
Default: ``None`` , all dimensions are reduced.
keep_dims (bool, optional): If ``True`` , keep these reduced dimensions and the length is 1.
If ``False`` , don't keep these dimensions. Default : ``False`` .
Returns:
Tensor, the dtype is bool.
- If `axis` is ``None`` , and `keep_dims` is ``False`` ,
the output is a 0-D Tensor representing the "logical OR" of all elements in the input Tensor.
- If `axis` is int, such as 2, and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_1, input_3, ..., input_R)`.
- If `axis` is tuple(int), such as (2, 3), and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_1, input_4, ..., input_R)`.
- If `axis` is 1-D Tensor, such as [2, 3], and `keep_dims` is ``False`` ,
the shape of output is :math:`(input_1, input_4, ..., input_R)`.
Raises:
TypeError: If `keep_dims` is not a bool.
TypeError: If `input` is not a Tensor(bool).
TypeError: If `axis` is not one of the following: int, tuple, list or Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[True, False], [True, True]]))
>>> # case 1: Reduces a dimension by the "logical OR" of all elements in the dimension.
>>> output = ops.any(x, keep_dims=True)
>>> print(output)
[[ True]]
>>> print(output.shape)
(1, 1)
>>> # case 2: Reduces a dimension along axis 0.
>>> output = ops.any(x, axis=0)
>>> print(output)
[ True True]
>>> # case 3: Reduces a dimension along axis 1.
>>> output = ops.any(x, axis=1)
>>> print(output)
[ True True]
"""
if axis is None:
axis = ()
return _get_cache_prim(P.ReduceAny)(keep_dims)(input, axis)
[文档]def remainder(input, other):
r"""
Computes the remainder of dividing the first input tensor by the second input tensor element-wise.
Inputs of `input` and `other` comply with the implicit type conversion rules to make the data types consistent.
The inputs must be two tensors or one tensor and one scalar. When the inputs are two tensors,
both dtypes cannot be bool, and the shapes of them could be broadcast. When the inputs are one tensor
and one scalar, the scalar could only be a constant.
.. math::
remainder(input, other) = input - input.div(other, rounding\_mode="floor") * other
.. warning::
- When the elements of input exceed 2048, there might be accuracy problems.
- The calculation results of this operator on Ascend and CPU might be inconsistent.
- If shape is expressed as (D1,D2... ,Dn), then D1\*D2... \*DN<=1000000,n<=8.
Args:
input (Union[Tensor, numbers.Number, bool]): The first input is a number, a bool
or a tensor whose data type is number.
other (Union[Tensor, numbers.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.
Returns:
Tensor, the shape is the same as the one after broadcasting,
and the data type is the one with higher precision.
Raises:
TypeError: If neither `input` nor `other` is one of the following: Tensor, number, bool.
ValueError: If the shape `input` and `other` cannot be broadcasted to each other.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-4.0, 5.0, 6.0]).astype(np.float16))
>>> y = Tensor(np.array([3.0, 2.0, 3.0]).astype(np.float16))
>>> output = ops.remainder(x, y)
>>> print(output)
[2. 1. 0.]
"""
out = input - tensor_floordiv(input, other) * other
return out
def remainder_ext(input, other):
r"""
Computes the remainder of `input` divided by `other` element-wise. The result has the same sign as the divisor and
its absolute value is less than that of `other`.
Supports broadcasting to a common shape and implicit type promotion.
.. math::
remainder(input, other) = input - input.div(other, rounding\_mode="floor") * other
Note:
Complex inputs are not supported. At least one input need to be tensor, but not both are bool tensors.
Args:
input (Union[Tensor, numbers.Number, bool]): The dividend is a numbers.Number or
a bool or a tensor whose data type is
`number <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_ or
`bool_ <https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.dtype.html>`_.
other (Union[Tensor, numbers.Number, bool]): The divisor is a numbers.Number or
a bool or a tensor whose data type is number or bool\_ when the dividend is a tensor.
When the dividend is Scalar, the divisor must be a Tensor whose data type is number or bool\_.
Returns:
Tensor, with dtype promoted and shape broadcasted.
Raises:
TypeError: If `input` and `other` are not of types: (tensor, tensor), (tensor, number), (tensor, bool),
(number, tensor) or (bool, tensor).
ValueError: If `input` and `other` are not broadcastable.
Supported Platforms:
``Ascend``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([-4.0, 5.0, 6.0]).astype(np.float32))
>>> y = Tensor(np.array([3.0, 2.0, 3.0]).astype(np.float64))
>>> output = ops.remainder_ext(x, y)
>>> print(output)
[2. 1. 0.]
"""
if isinstance(input, Tensor) and isinstance(other, Tensor):
return remainder_tensor_tensor_(input, other)
if isinstance(input, Tensor) and isinstance(other, (float, int, bool)):
return remainder_tensor_scalar_(input, other)
if isinstance(input, (float, int, bool)) and isinstance(other, Tensor):
return remainder_scalar_tensor_(input, other)
raise TypeError(f"For 'remainder', inputs should either be two tensors, or a tensor and a scalar.")
[文档]def accumulate_n(x):
r"""
Computes accumulation of all input tensors element-wise.
:func:`mindspore.ops.accumulate_n` is similar to :func:`mindspore.ops.addn`,
but there is a significant difference between them: accumulate_n will not wait
for all of its inputs to be ready before summing. That is to say, accumulate_n is able to save memory when inputs
are ready at different time since the minimum temporary storage is proportional to the output size rather than the
input size.
Args:
x (Union(tuple[Tensor], list[Tensor])): The input tuple or list is made up of multiple tensors whose dtype is
number to be added together. Each element of tuple or list should have the same shape.
Returns:
Tensor, has the same shape and dtype as each entry of `x`.
Raises:
TypeError: If `x` is neither tuple nor list.
ValueError: If there is an input element with a different shape.
Supported Platforms:
``Ascend`` ``GPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3]), mindspore.float32)
>>> y = Tensor(np.array([4, 5, 6]), mindspore.float32)
>>> output = ops.accumulate_n([x, y, x, y])
>>> print(output)
[10. 14. 18.]
"""
return accumulate_(x)
[文档]def iou(anchor_boxes, gt_boxes, mode='iou'):
r"""
Calculates intersection over union for boxes.
Computes the intersection over union (IOU) or the intersection over foreground (IOF) based on the ground-truth and
predicted regions.
.. math::
\text{IOU} = \frac{\text{Area of Overlap}}{\text{Area of Union}}
\text{IOF} = \frac{\text{Area of Overlap}}{\text{Area of Ground Truth}}
.. warning::
In Ascend, only computation of float16 data is supported. To avoid overflow, the input length
and width are scaled by 0.2 internally.
Args:
anchor_boxes (Tensor): Anchor boxes, tensor of shape :math:`(N, 4)` . :math:`N` indicates the number of
anchor boxes, and the value :math:`4` refers to four boundary coordinates of the predicted area
"x0", "y0", "x1", and "y1". Data type must be either float16, float32 or float64.
gt_boxes (Tensor): Ground truth boxes, tensor of shape :math:`(M, 4)` . :math:`M` indicates the number
of ground truth boxes, and the value :math:`4` refers to four boundary coordinates of the truth
area "x0", "y0", "x1", and "y1". Data type must be either float16, float32 or float64.
mode (string): The mode is used to specify the calculation method,
now supporting 'iou' (intersection over union) or 'iof' (intersection over foreground) mode.
Default: ``'iou'`` .
Returns:
Tensor, the IOU/IOF values, tensor of shape :math:`(M, N)` , with the same data type as `anchor_boxes`.
Raises:
KeyError: When `mode` is not ``'iou'`` or ``'iof'``.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> anchor_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
>>> gt_boxes = Tensor(np.random.randint(1.0, 5.0, [3, 4]), mindspore.float16)
>>> mode = 'iou'
>>> output = ops.iou(anchor_boxes, gt_boxes, mode)
>>> print(output.shape)
(3, 3)
"""
return _get_cache_prim(P.IOU)(mode)(anchor_boxes, gt_boxes)
def _check_is_float(dtype):
return dtype in (mstype.float16, mstype.float32)
def _list_comprehensions(obj, item):
return tuple([item for _ in range(obj)])
def _tuple_setitem(tup, idx, value):
tup = list(tup)
tup[idx] = value
return tuple(tup)
@_primexpr
def _check_dim_in_range(dim, ndim):
def _check(dim, ndim):
if not isinstance(dim, int):
raise TypeError(f'axes should be integers, not {type(dim)}')
if -ndim > dim or dim >= ndim:
raise ValueError(f'dim {dim} is out of bounds for array of dimension {ndim}')
_check(dim, ndim)
return dim % ndim
def dotrapezoid(y, dx, dim):
y_left = _select(y, dim, 0)
y_right = _select(y, dim, -1)
y_sum = y.sum(dim)
return (y_sum - (y_left + y_right) * 0.5) * dx
def dotrapezoid_tensor(y, dx, dim):
y_start_dim_left = [0 for _ in range(dim)]
y_start_dim_left = tuple(y_start_dim_left)
y_start_dim_right = [0 for _ in range(y.ndim - dim - 1)]
y_start_dim_right = tuple(y_start_dim_right)
y_slice_size = _tuple_setitem(shape_(y), dim, shape_(y)[dim] - 1)
y_slice_left = slice_(y, y_start_dim_left + (0,) + y_start_dim_right, y_slice_size)
y_slice_right = slice_(y, y_start_dim_left + (1,) + y_start_dim_right, y_slice_size)
return (tensor_add(y_slice_left, y_slice_right) * dx).sum(dim) / 2.
def add_padding_to_shape(curr_shape, target_n_dim):
curr_size = len(curr_shape)
if curr_size >= target_n_dim:
target_n_dim = curr_size
new_shape = [1 for _ in range(target_n_dim)]
for i in range(curr_size):
new_shape[target_n_dim - i - 1] = curr_shape[curr_size - i - 1]
return new_shape
def zeros_like_except(y, dim):
_check_dim_in_range(dim, y.ndim)
dim = dim + y.ndim if dim < 0 else dim
sizes = y.shape[:dim] + y.shape[dim + 1:]
zeros = F.zeros(sizes, y.dtype)
return zeros
def trapezoid_tensor(y, x, dim):
r"""
add trapezoid implementation when x is not None.
"""
if y.shape[dim] == 0:
return zeros_like_except(y, dim)
if x.ndim < y.ndim and x.ndim != 1:
x_start_dim_left = [0 for _ in range(dim)]
x_start_dim_left = tuple(x_start_dim_left)
x_start_dim_right = [0 for _ in range(x.ndim - dim - 1)]
x_start_dim_right = tuple(x_start_dim_right)
x_slice_size = _tuple_setitem(x.shape, dim, x.shape[dim] - 1)
x_left = slice_(x, x_start_dim_left + (0,) + x_start_dim_right, x_slice_size)
x_right = slice_(x, x_start_dim_left + (1,) + x_start_dim_right, x_slice_size)
dx = x_right - x_left
new_sizes = add_padding_to_shape(dx.shape, y.ndim)
dx = dx.view(tuple(new_sizes))
return dotrapezoid_tensor(y, dx, dim)
if x.ndim == 1:
if x.shape[0] != y.shape[dim]:
raise RuntimeError("There must be one `x` value for each sample point")
new_sizes = [1 for _ in range(y.ndim)]
new_sizes[dim] = x.shape[0]
x_viewed = x.view(tuple(new_sizes))
else:
x_viewed = x
x_start_dim_left = [0 for _ in range(dim)]
x_start_dim_left = tuple(x_start_dim_left)
x_start_dim_right = [0 for _ in range(x_viewed.ndim - dim - 1)]
x_start_dim_right = tuple(x_start_dim_right)
x_slice_size = _tuple_setitem(x_viewed.shape, dim, x_viewed.shape[dim] - 1)
x_left = slice_(x_viewed, x_start_dim_left + (0,) + x_start_dim_right, x_slice_size)
x_right = slice_(x_viewed, x_start_dim_left + (1,) + x_start_dim_right, x_slice_size)
dx = x_right - x_left
return dotrapezoid_tensor(y, dx, dim)
def trapezoid(y, dx, dim):
if y.shape[dim] == 0:
return zeros_like_except(y, dim)
return dotrapezoid(y, dx, dim)
def get(ts, depth, dim, index, r):
if depth == dim:
r.append(ts[index])
return 0
for item in ts:
return get(item, depth + 1, dim, index, r)
def _select(feat, dim, index):
select_shape = feat.shape
select_shape = list(select_shape)
select_shape[dim] = 1
new_shape = feat.shape[:dim] + feat.shape[dim + 1:]
indexes = ones_(tuple(select_shape), mstype.int32) * (index)
return feat.gather_elements(dim, indexes).reshape(new_shape)
[文档]def trapz(y, x=None, *, dx=1.0, dim=-1):
r"""
Integrates `y(x)` along given dim using trapezoidal rule.
By default x-dim distances between points will be 1.0,
alternatively they can be provided with `x` array or with `dx` scalar.
.. math::
\mathop{ \int }\nolimits_{{}}^{{}}{y}{ \left( {x} \right) } \text{d} x
Args:
y (Tensor): Input tensor to integrate.
x (Tensor, optional): The sample points corresponding to the `y` values. If `x` is None,
the sample points are assumed to be evenly spaced `dx` apart. Default: ``None`` .
If `x` is not None, after subtracting 1 from the axis specified by `dim`, the shape of `x`
should be same as `y` or can broadcast to `y`.
Keyword Args:
dx (float, optional): The spacing between sample points when `x` is None. If `x` is specified,
`dx` does not take effect. Default: ``1.0`` .
dim (int, optional): The dim along which to integrate. Default: ``-1`` .
Returns:
Tensor of float, definite integral as approximated by trapezoidal rule.
If `y` is a one-dimensional array, the result is a floating-point number. If `y` is
an n-dimensional array, the result is an N-1 dimensional array.
Raises:
RuntimeError: If dim of `x` is 1, and x.shape[0] is not equal to y.shape[dim].
ValueError: If `dim` is out of range of :math:`[-y.ndim, y.ndim)`.
TypeError: If `y` is not a Tensor.
TypeError: If `x` is not None and is not a Tensor.
TypeError: If `dx` is not a float number.
TypeError: If `dim` is not a Integer.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> y = Tensor(np.array([[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]).astype(np.float32))
>>> x = Tensor(np.array([[1, 2, 3], [1, 3, 5], [1, 4, 7]]).astype(np.float32))
>>> output = ops.trapz(y, x)
>>> print(output)
[2. 4. 6.]
"""
if not isinstance(y, (Tensor, Tensor_)):
raise TypeError(f"For `trapz`, the input `y` must be Tensor, but get {type(y)}.")
if not isinstance(dx, float):
raise TypeError(f"For `trapz`, the input `dx` must be float, but get f{type(dx)}.")
if not isinstance(dim, int):
raise TypeError(f"For `trapz`, the input `dim` must be int, but get {type(dim)}.")
if not _check_is_float(y.dtype):
y = cast_(y, mstype.float32)
_check_dim_in_range(dim, y.ndim)
dim = dim + y.ndim if dim < 0 else dim
if x is None:
return trapezoid(y, dx, dim)
if not isinstance(x, (Tensor, Tensor_)):
raise TypeError(f"For `trapz`, the input `x` must be Tensor, but get {type(x)}.")
x = cast_(x, mstype.float32)
return trapezoid_tensor(y, x, dim)
[文档]def cholesky(input_x, upper=False):
r"""
Returns the Cholesky decomposition of zero or more batch dimensions consisting of symmetric positive-definite
matrices.
If `upper` is `True`, returns an upper-triangular matrix, :math:`U`, and the decomposition has the form:
.. math::
A = U^TU
If `upper` is `False`, returns a lower-triangular matrix, :math:`L`, and the decomposition has the form:
.. math::
A = LL^T
where `A` is the symmetric positive-definite matrix.
Args:
input_x (Tensor): Tensor of shape :math:`(*, N, N)`, where :math:`*` is zero or more batch dimensions
consisting of symmetric positive-definite matrices, with float32 or float64 data type.
upper (bool): If `upper` is `True`, returns an upper-triangular matrix. If `upper` is `False`, returns
a lower-triangular matrix. Default: ``False`` .
Returns:
Tensor, has the same shape and data type as `input_x`.
Raises:
TypeError: If `upper` is not a bool.
TypeError: If dtype of `input_x` is not one of: float64, float32.
TypeError: If `input_x` is not a Tensor.
ValueError: If `input_x` is not a or a batch of square matrix.
ValueError: If `input_x` is not symmetric positive definite.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[1.0, 1.0], [1.0, 2.0]]), mindspore.float32)
>>> output = ops.cholesky(input_x, upper=False)
>>> print(output)
[[1. 0.]
[1. 1.]]
"""
cholesky_op = _get_cache_prim(P.Cholesky)(upper=upper)
return cholesky_op(input_x)
def cholesky_inverse(input_x, upper=False):
r"""
Returns the inverse of the positive definite matrix using cholesky matrix factorization by its Cholesky factor.
If `upper` is `True`, :math:`U` is an upper triangular such that the output tensor is
.. math::
inv = (U^{T}U)^{-1}
If `upper` is `False`, :math:`U` is a lower triangular such that the output tensor is
.. math::
inv = (UU^{T})^{-1}
Note:
The input must be either an upper-triangular matrix or a lower-triangular matrix from Cholesky decomposition.
Args:
input_x (Tensor): The input tensor with a rank of 2. Supported dtypes: float32, float64.
upper (bool): If `upper` is `True`, return an upper triangular matrix. If `upper` is `False`, return
a lower-triangular matrix. Default: ``False``.
Returns:
Tensor, has the same shape and dtype as `input_x`.
Raises:
TypeError: If `input_x` is not a Tensor.
TypeError: If dtype of `input_x` is not one of: float32, float64.
ValueError: If the dimension of `input_x` is not equal to 2.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input_x = Tensor(np.array([[2,0,0], [4,1,0], [-1,1,2]]), mindspore.float32)
>>> output = ops.cholesky_inverse(input_x)
>>> print(output)
[[ 5.8125 -2.625 0.625 ]
[-2.625 1.25 -0.25 ]
[ 0.625 -0.25 0.25 ]]
"""
cholesky_inv_op = _get_cache_prim(P.CholeskyInverse)(upper=upper)
return cholesky_inv_op(input_x)
[文档]def cholesky_solve(input, input2, upper=False):
r"""
Computes the solution of a set of linear equations with a positive definite matrix,
according to its Cholesky decomposition factor `input2` .
If `upper` is set to ``True`` and `input2` is upper triangular, the output tensor is that:
.. math::
output = (input2^{T} * input2)^{{-1}}input
If `upper` is set to ``False`` and `input2` is lower triangular, the output is that:
.. math::
output = (input2 * input2^{T})^{{-1}}input
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): Tensor of shape :math:`(*, N, M)`, indicating 2D or 3D matrices,
with float32 or float64 data type.
input2 (Tensor): Tensor of shape :math:`(*, N, N)`, indicating 2D or 3D square matrices composed of
upper or lower triangular Cholesky factor, with float32 or float64 data type.
`input` and `input2` must have the same type.
upper (bool, optional): A flag indicates whether to treat the Cholesky factor
as an upper or a lower triangular matrix. Default: ``False``, treating the Cholesky factor
as a lower triangular matrix.
Returns:
Tensor, has the same shape and data type as `input`.
Raises:
TypeError: If `upper` is not a bool.
TypeError: If dtype of `input` and `input2` is not float64 or float32.
TypeError: If `input` is not a Tensor.
TypeError: If `input2` is not a Tensor.
ValueError: If `input` and `input2` have different batch size.
ValueError: If `input` and `input2` have different row numbers.
ValueError: If `input` is not 2D or 3D matrices.
ValueError: If `input2` is not 2D or 3D square matrices.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input1 = Tensor(np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]), mindspore.float32)
>>> input2 = Tensor(np.array([[2, 0, 0], [4, 1, 0], [-1, 1, 2]]), mindspore.float32)
>>> out = ops.cholesky_solve(input1, input2, upper=False)
>>> print(out)
[[ 5.8125 -2.625 0.625 ]
[-2.625 1.25 -0.25 ]
[ 0.625 -0.25 0.25 ]]
"""
return _get_cache_prim(P.CholeskySolve)(upper)(input, input2)
[文档]def cross(input, other, dim=None):
r"""
Computes the cross product of `input` and `other` in dimension `dim`.
`input` and `other` must have the same shape, and the size of their `dim` dimension should be `3`.
If `dim` is not specified, it is set to be the first dimension found with the size `3`.
Args:
input (Tensor): input is a tensor.
other (Tensor): The other Tensor, `other` must have the same shape and type as input `input`, and
the size of their `dim` dimension should be `3`.
dim (int, optional): dimension to apply cross product in. if `dim` is None, it is set to be the first dimension
found with the size `3`. Default: ``None``.
Returns:
Tensor, has the same shape and type as input `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `other` is not a Tensor.
TypeError: If the type of `input` is not the same as that of `other`.
ValueError: If `input` and `other` not have the same size, and the size of their `dim` dimension not be `3`.
ValueError: If `input` and `other` not have the same shape.
ValueError: If `dim` is out of range, `dim` should be [-len(input.shape), len(input.shape)-1].
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> # case 1: dim=None.
>>> x = Tensor([[1, 2, 3], [1, 2, 3]])
>>> other = Tensor([[4, 5, 6], [4, 5, 6]])
>>> output = ops.cross(x, other)
>>> print(output)
[[-3 6 -3]
[-3 6 -3]]
>>> # case 2: dim=1.
>>> x = Tensor([[1, 2, 3], [1, 2, 3]])
>>> other = Tensor([[4, 5, 6], [4, 5, 6]])
>>> output = ops.cross(x, other, dim=1)
>>> print(output)
[[-3 6 -3]
[-3 6 -3]]
"""
if dim is None:
dim = -65530
return cross_impl(input, other, dim)
def _einsum_convert_num_to_char(num):
"""For einsum, convert number into char."""
if [num] == [Ellipsis]:
return '...'
# pylint: disable=chained-comparison
if num >= 0 and num < 26:
return chr(num + ord('A'))
# pylint: disable=chained-comparison
if num >= 26 and num < 52:
return chr(num - 26 + ord('a'))
raise ValueError(f"For Einsum, the number in sublist should be in range [0, 52), but got {num}")
[文档]def einsum(equation, *operands):
r"""
According to the Einstein summation Convention (Einsum),
the product of the input tensor elements is summed along the specified dimension.
You can use this operator to perform diagonal, reducesum, transpose, matmul, mul, inner product operations, etc.
Note:
The sublist format is also supported. For example, ops.einsum(op1, sublist1, op2, sublist2, ..., sublist_out).
In this format, equation can be derived by the sublists which are made up of Python's Ellipsis and list of
integers in [0, 52). Each operand is followed by a sublist and an output sublist is at the end.
Args:
equation (str): Notation based on the Einstein summation convention, represent the operation you want to do.
the value can contain only letters, commas, ellipsis and arrow.
The letters represent input tensor dimension, commas represent separate tensors, ellipsis indicates
the tensor dimension that you do not care about, the left of the arrow indicates the input tensors,
and the right of it indicates the desired output dimension.
operands (Tensor): Input tensor used for calculation. The dtype of the tensor must be the same.
Returns:
Tensor, the shape of it can be obtained from the `equation` , and the dtype is the same as input tensors.
Raises:
TypeError: If `equation` is invalid, or the `equation` does not match the input tensor.
ValueError: If the number in sublist is not in [0, 52) in sublist format.
Supported Platforms:
``GPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> equation = "i->"
>>> output = ops.einsum(equation, x)
>>> print(output)
[7.]
>>> x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
>>> y = Tensor(np.array([2.0, 4.0, 3.0]), mindspore.float32)
>>> equation = "i,i->i"
>>> output = ops.einsum(equation, x, y)
>>> print(output)
[ 2. 8. 12.]
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> y = Tensor(np.array([[2.0, 3.0], [1.0, 2.0], [4.0, 5.0]]), mindspore.float32)
>>> equation = "ij,jk->ik"
>>> output = ops.einsum(equation, x, y)
>>> print(output)
[[16. 22.]
[37. 52.]]
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> equation = "ij->ji"
>>> output = ops.einsum(equation, x)
>>> print(output)
[[1. 4.]
[2. 5.]
[3. 6.]]
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> equation = "ij->j"
>>> output = ops.einsum(equation, x)
>>> print(output)
[5. 7. 9.]
>>> x = Tensor(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), mindspore.float32)
>>> equation = "...->"
>>> output = ops.einsum(equation, x)
>>> print(output)
[21.]
>>> x = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32)
>>> y = Tensor(np.array([2.0, 4.0, 1.0]), mindspore.float32)
>>> equation = "j,i->ji"
>>> output = ops.einsum(equation, x, y)
>>> print(output)
[[ 2. 4. 1.]
[ 4. 8. 2.]
[ 6. 12. 3.]]
>>> x = mindspore.Tensor([1, 2, 3, 4], mindspore.float32)
>>> y = mindspore.Tensor([1, 2], mindspore.float32)
>>> output = ops.einsum(x, [..., 1], y, [..., 2], [..., 1, 2])
>>> print(output)
[[1. 2.]
[2. 4.]
[3. 6.]
[4. 8.]]
"""
if isinstance(equation, Tensor):
equ_tmp = ''
for i, lst in enumerate(operands):
if i % 2 == 0:
for _, num in enumerate(lst):
equ_tmp += _einsum_convert_num_to_char(num)
if i in (len(operands) - 1, len(operands) - 2):
continue
equ_tmp += ','
if len(operands) % 2 == 0:
equ_tmp += '->'
for _, num in enumerate(operands[-1]):
equ_tmp += _einsum_convert_num_to_char(num)
operands_tmp = list([equation]) + list(operands[1:-1:2])
else:
operands_tmp = list([equation]) + list(operands[1::2])
equation = equ_tmp
operands = tuple(operands_tmp)
return _get_cache_prim(P.Einsum)(equation)(operands)
[文档]def cumprod(input, dim, dtype=None):
r"""
Computes the cumulative product of the `input` tensor along dimension `dim`.
For example, if `input` is a vector of size `N`, the result will also be a vector of size `N`, with elements.
.. math::
y_i = x_1 * x_2 * x_3 * ... * x_i
Args:
input (Tensor[Number]): The input tensor.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions.
dim (int): The dimensions to compute the cumulative product. Only constant value is allowed.
dtype (:class:`mindspore.dtype`, optional): The desired data type of output.
If not specified, remains the same as the original Tensor. Default: ``None`` .
Returns:
Tensor, has the same shape and dtype as the `input` unless `dtype` is specified.
Raises:
TypeError: If `dim` is not an int.
TypeError: If `dtype` conversion is not acceptable.
ValueError: If `dim` is None.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1, 2, 3], np.float32))
>>> output = ops.cumprod(x, 0)
>>> print(output)
[1. 2. 6.]
"""
output = cumprod_(input, dim)
if dtype:
output = cast_(output, dtype)
return output
[文档]def igamma(input, other):
r"""
Calculates lower regularized incomplete Gamma function.
If we define `input` as `a` and `other` as `x`, the lower regularized incomplete Gamma function is defined as:
.. math::
P(a, x) = Gamma(a, x) / Gamma(a) = 1 - Q(a, x)
where
.. math::
Gamma(a, x) = \int_0^x t^{a-1} \exp^{-t} dt
is the lower incomplete Gamma function.
Above :math:`Q(a, x)` is the upper regularized complete Gamma function.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The first input tensor. With type of float32 or float64.
other (Tensor): The second input tensor. With float32 or float64 type. `other` should have
the same dtype with `input`.
Returns:
Tensor, has the same dtype as `input` and `other`.
Raises:
TypeError: If `input` or `other` is not a Tensor.
TypeError: If dtype of input `other` and a is not float32 nor float64.
TypeError: If `other` has different dtype with `input`.
ValueError: If `input` could not be broadcast to a tensor with shape of `other`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> a = Tensor(np.array([2.0, 4.0, 6.0, 8.0]).astype(np.float32))
>>> x = Tensor(np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32))
>>> output = ops.igamma(a, x)
>>> print(output)
[0.593994 0.35276785 0.21486944 0.13337152]
"""
return igamma_(input, other)
[文档]def igammac(input, other):
r"""
Calculates upper regularized incomplete Gamma function.
If we define `input` as `a` and `other` as `x`, the upper regularized incomplete Gamma function is defined as:
.. math::
Q(a, x) = Gamma(a, x) / Gamma(a) = 1 - P(a, x)
where
.. math::
Gamma(a, x) = \int_{x}^{\infty} t^{a-1} exp(-t) dt
is the upper incomplete Gama function.
Above :math:`P(a, x)` is the lower regularized complete Gamma function.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The first input tensor. With type of float32 or float64.
other (Tensor): The second input tensor. With float32 or float64 type. `other` should have
the same dtype with `input`.
Returns:
Tensor, has the same dtype as `input` and `other`.
Raises:
TypeError: If `input` or `other` is not a Tensor.
TypeError: If dtype of input `other` and a is not float32 nor float64.
TypeError: If `other` has different dtype with `input`.
ValueError: If `input` could not be broadcast to a tensor with shape of `other`.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> a = Tensor(np.array([2.0, 4.0, 6.0, 8.0]).astype(np.float32))
>>> x = Tensor(np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32))
>>> output = ops.igammac(a, x)
>>> print (output)
[0.40600586 0.6472318 0.7851304 0.8666283]
"""
return igammac_(input, other)
def lgamma(input):
r"""
Computes the natural logarithm of the absolute value of the gamma function on input.
.. math::
\text{out}_{i} = \ln \Gamma(|\text{input}_{i}|)
Args:
input (Tensor): The input tensor. With type of float16 or float32 or float64.
Returns:
Tensor, has the same dtype as `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not float16 or float32 or float64.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([0.5, 3.2, 8.5]), mindspore.float32)
>>> output = ops.lgamma(x)
>>> print(output)
[0.5723649 0.8854049 9.549267 ]
>>> x = Tensor(2.1, mindspore.float32)
>>> output = ops.lgamma(x)
>>> print(output)
0.045437694
"""
return lgamma_(input)
[文档]def digamma(input):
r"""
Computes the derivative of the lgamma function on input.
.. math::
P(x) = \frac{d}{dx}(\ln (\Gamma(x)))
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor. With type of float16 or float32 or float64.
Returns:
Tensor, has the same dtype as `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not float16 or float32 or float64.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([1.5, 0.5, 9]).astype(np.float16))
>>> output = ops.digamma(x)
>>> print(output)
[ 0.0365 -1.964 2.14 ]
"""
return digamma_(input)
[文档]def polygamma(n, input):
r"""
Computes the :math:`n`-th derivative of the polygamma function on `input`.
.. math::
\psi^{(a)}(x) = \frac{d^{(a)}}{dx^{(a)}} \psi(x)
where :math:`\psi(x)` is the digamma function.
Args:
n (Tensor): The order of the polygamma function.
Supported dtypes: int32, int64. The shape of `n` is :math:`()`.
input (Tensor): The tensor to compute the :math:`n`-th derivative of the polygamma function with.
Returns:
Tensor, has the same dtype as `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not one of: float16, float32, float64.
TypeError: If dtype of `n` is not one of: int32, int64.
TypeError: If shape of `n` is not :math:`()`.
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([3.14, -2.71]), mindspore.float64)
>>> a = Tensor(np.array(1), mindspore.int64)
>>> output = ops.polygamma(a, x)
>>> print(output)
[ 0.37446456 15.49884838]
"""
return poly_gamma_(n, input)
def _is_sign_inf(x, fn):
"""Tests element-wise for infinity with sign."""
shape = x.shape
zeros_tensor = zeros_(shape, mstype.float32)
ones_tensor = ones_(shape, mstype.float32)
is_inf = isinf(x)
is_sign = fn(x, zeros_tensor)
res = ops.select(is_inf, ones_tensor, zeros_tensor)
res = ops.select(is_sign, res, zeros_tensor)
return cast_(res, mstype.bool_)
[文档]def isposinf(input):
"""
Tests element-wise for positive infinity.
Args:
input (Tensor): Input values.
Returns:
Tensor, true where `input` is positive infinity, false otherwise.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import ops, Tensor
>>> from mindspore import dtype as mstype
>>> output = ops.isposinf(Tensor([[-float("inf"), float("inf")], [1, float("inf")]], mstype.float32))
>>> print(output)
[[False True]
[False True]]
"""
_check_is_tensor("input", input, "isposinf")
return _is_sign_inf(input, tensor_gt)
[文档]def isneginf(input):
"""
Tests element-wise for negative infinity.
Args:
input (Tensor): Input Tensor.
Returns:
Tensor, true where `input` is negative infinity, false otherwise.
Raises:
TypeError: If the input is not a tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import ops, Tensor
>>> from mindspore import dtype as mstype
>>> output = ops.isneginf(Tensor([[-float("inf"), float("inf")], [1, -float("inf")]], mstype.float32))
>>> print(output)
[[ True False]
[False True]]
"""
_check_is_tensor("input", input, "isneginf")
return _is_sign_inf(input, tensor_lt)
[文档]def logical_xor(input, other):
r"""
Computes the "logical XOR" of two tensors element-wise.
.. math::
out_{i} = input_{i} \oplus other_{i}
Args:
input (Tensor): The first input is a tensor whose data type can be implicitly converted to bool.
other (Tensor): The second input is a tensor whose data type can be implicitly converted to bool
to compute XOR with the first input.
Returns:
Tensor, the shape is the same as the one after broadcasting, and the data type is bool.
Raises:
TypeError: If the dtype of `input` or `other` is not bool or can not be implicitly converted to bool.
ValueError: If the shape of two inputs cannot be broadcast.
Supported Platforms:
``Ascend`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([True, False, True]), mindspore.bool_)
>>> y = Tensor(np.array([True, True, False]), mindspore.bool_)
>>> output = ops.logical_xor(x, y)
>>> print(output)
[False True True]
>>> x = Tensor(1, mindspore.bool_)
>>> y = Tensor(0, mindspore.bool_)
>>> output = ops.logical_xor(x, y)
>>> print(output)
True
>>> x = True
>>> y = Tensor(0, mindspore.bool_)
>>> output = ops.logical_xor(x, y)
>>> print(output)
True
>>> x = True
>>> y = Tensor(np.array([True, False]), mindspore.bool_)
>>> output = ops.logical_xor(x, y)
>>> print(output)
[False True]
"""
return logical_xor_(input, other)
[文档]def imag(input):
r"""
Returns a new tensor containing imaginary value of the `input`.
If `input` is real, it will return zeros.
Args:
input (Tensor): The input tensor to compute to.
Returns:
Tensor, the shape is the same as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.asarray(np.complex(1.3 + 0.4j)), mindspore.complex64)
>>> output = ops.imag(x)
>>> print(output)
0.4
"""
return imag_(input)
@_primexpr
def _check_repeat_in_axis(axis, x_ndim, prim_name):
"""check repeat dim in axis"""
if isinstance(axis, (list, tuple)):
axis_deal = [dim + x_ndim if dim < 0 else dim for dim in axis]
for dim in axis_deal:
if axis_deal.count(dim) > 1:
raise RuntimeError(f"For {prim_name}, dim {dim} appears multiple times in axis.")
[文档]def nansum(input, axis=None, keepdims=False, *, dtype=None):
"""
Computes sum of `input` over a given dimension, treating NaNs as zero.
Args:
input (Tensor): The input Tensor.
axis (Union[int, tuple(int)], optional): The dimensions to reduce. Supposed the rank of `input` is r,
axis must be in the range [-rank(input), rank(input)). Default: ``None``, all dimensions are reduced.
keepdims (bool, optional): Whether the output Tensor keeps dimensions or not. Default: ``False``.
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): The dtype of output Tensor. Default: ``None``.
Returns:
Tensor, the sum of input `input` in the given dimension dim, treating NaNs as zero.
- If axis is None, keepdims is False,
the output is a 0-D Tensor representing the sum of all elements in the input Tensor.
- If axis is int, set as 2, and keepdims is False,
the shape of output is :math:`(input_1, input_3, ..., input_R)`.
- If axis is tuple(int) or list(int), set as (2, 3), and keepdims is False,
the shape of output is :math:`(input_1, input_4, ..., input_R)`.
Raises:
TypeError: If `input` is not Tensor.
TypeError: If `keepdims` is not a bool.
TypeError: If the dtype of `input` or `dtype` is complex type.
ValueError: If 'axis' not in [-rank(`input`), rank(`input`)).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([[float("nan"), 2, 3], [1, 2, float("nan")]]), mindspore.float32)
>>> output1 = ops.nansum(x, axis=0, keepdims=False, dtype=mindspore.float32)
>>> output2 = ops.nansum(x, axis=0, keepdims=True, dtype=mindspore.float32)
>>> print(output1)
[1. 4. 3.]
>>> print(output2)
[[1. 4. 3.]]
"""
_check_is_tensor("input", input, "nansum")
_check_repeat_in_axis(axis, input.ndim, "nansum")
if input.is_complex():
raise TypeError(f'For nansum, input are not supported complex type, but got {input.dtype}.')
if dtype is not None and dtype in mstype.complex_type:
raise TypeError(f'For nansum, dtype not supported complex type, but got {dtype}.')
if axis is None:
axis = ()
if input.dtype == mstype.bool_:
input = input.astype(mstype.int64)
is_nan = isnan_(input)
input = ops.masked_fill(input, is_nan, ops.cast(0, input.dtype))
input = _get_cache_prim(P.ReduceSum)(keepdims)(input, axis)
if dtype is not None and input.dtype != dtype:
input = input.astype(dtype)
return input
[文档]def diag_embed(input, offset=0, dim1=-2, dim2=-1):
r"""
Creates a tensor with diagonals filled by `input`. The remaining elements are filled by 0.
If the shape of `input` is :math:`[x_{0}, x_{1}, ..., x_{n-1}, x_{n}]`, the output shape is: the vector obtained
by inserting :math:`x_{n}+|offset|` into the vector :math:`[x_{0}, x_{1}, ..., x_{n-1}]`
at position `dim1` and `dim2`.
Args:
input (Tensor): Values to fill diagonal.
offset (int, optional): Offset of the diagonal. :math:`offset=0` refers to the main diagonal. Default: ``0`` .
- If :math:`offset>0`, fill the diagonals that are `offset` units upward from the main diagonal.
- If :math:`offset<0`, fill the diagonals that are `|offset|` units downward from the main diagonal.
dim1 (int, optional): The first dimension in `input` with respect to which to fill diagonal. Default: ``-2`` .
dim2 (int, optional): The second dimension in `input` with respect to which to fill diagonal. Default: ``-1`` .
Returns:
Tensor, has the same dtype as `input`, but the shape of output is one dimension higher than the `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If dtype of `input` is not supported.
TypeError: If `offset` is not an int.
TypeError: If `dim1` or `dim2` is not an int.
ValueError: If the dimension of `input` is not 1D-6D.
ValueError: If `dim1` is not in range of [-len(input.shape) - 1, len(input.shape)].
ValueError: If `dim2` is not in range of [-len(input.shape) - 1, len(input.shape)].
ValueError: If `dim1` and `dim2` are identical.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([2,3,4]), mindspore.float32)
>>> output = ops.diag_embed(x)
>>> print(output)
[[2. 0. 0.]
[0. 3. 0.]
[0. 0. 4.]]
"""
transpose_op = Transpose()
matrix_set_diag_op = MatrixSetDiagV3(align="LEFT_RIGHT")
zeros = ops.Zeros()
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("For 'diag_embed', 'input' must be Tensor.")
input_dtype = dtype_(input)
if not (input_dtype in (mstype.int8, mstype.int16, mstype.int32, mstype.int64, mstype.uint8, mstype.uint16,
mstype.uint32, mstype.uint64, mstype.float16, mstype.float32, mstype.float64)):
raise TypeError("For 'diag_embed', the dtype of 'input' must be int8, int16, int32, int64, "
f"uint8, uint16, uint32, uint64, float16, float32 or float64, but got '{input_dtype}'.")
_check_attr_dtype("offset", offset, [int], "diag_embed")
_check_attr_dtype("dim1", dim1, [int], "diag_embed")
_check_attr_dtype("dim2", dim2, [int], "diag_embed")
if len(input.shape) > 6:
raise ValueError("For 'diag_embed', the dimension of 'input' must be 1-6D.")
x_shape = input.shape
output_dim = len(x_shape) + 1
if dim1 < -output_dim or dim1 > (output_dim - 1):
raise ValueError(f"For 'diag_embed', 'dim1' must be in range of [{-output_dim}, {output_dim - 1}], "
f"but got {dim1}.")
if dim2 < -output_dim or dim2 > (output_dim - 1):
raise ValueError(f"For 'diag_embed', 'dim2' must be in range of [{-output_dim}, {output_dim - 1}], "
f"but got {dim2}.")
if dim1 < 0:
dim1_ = dim1 + output_dim
else:
dim1_ = dim1
if dim2 < 0:
dim2_ = dim2 + output_dim
else:
dim2_ = dim2
if dim1_ == dim2_:
raise ValueError("For 'diag_embed', 'dim1' must not be identical to 'dim2'.")
batch_shape = x_shape[:-1]
if offset > 0:
dsize = x_shape[-1] + offset
else:
dsize = x_shape[-1] - offset
diag_plane = (dsize, dsize)
output_shape_trans = batch_shape + diag_plane
output = zeros(output_shape_trans, input.dtype)
k = cast_(offset, mstype.int32)
output = matrix_set_diag_op(output, input, k)
dim = 0
perm = ()
for i in range(output_dim):
if i == dim1_:
perm = perm + (output_dim - 2,)
elif i == dim2_:
perm = perm + (output_dim - 1,)
else:
perm = perm + (dim,)
dim = dim + 1
return transpose_op(output, perm)
[文档]def sum(input, dim=None, keepdim=False, *, dtype=None):
"""
Calculate sum of Tensor elements over a given dim.
Note:
The `dim` with tensor type is only used for compatibility with older versions and is not recommended.
Args:
input (Tensor): The input tensor.
dim (Union[None, int, tuple(int), list(int), Tensor]): Dimensions along which a sum is performed.
If ``None`` , sum all the elements of the input tensor.
If the `dim` is a tuple or list of ints, a sum is performed on all the dimensions specified in the tuple.
Must be in the range :math:`[-input.ndim, input.ndim)` . Default: ``None`` .
keepdim (bool): Whether the output tensor has dim retained or not.
If ``True`` , keep these reduced dimensions and the length is 1.
If ``False`` , don't keep these dimensions. Default: ``False`` .
Keyword Args:
dtype (:class:`mindspore.dtype`, optional): The desired data type of returned Tensor. Default: ``None`` .
Returns:
A Tensor, sum of elements over a given dim in `input`.
Raises:
TypeError: If `input` is not a Tensor.
TypeError: If `dim` is not an int, tulpe(int), list(int), Tensor or None.
ValueError: If `dim` is not in the range :math:`[-input.ndim, input.ndim)` .
TypeError: If `keepdim` is not a bool.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> 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]]]), mstype.float32)
>>> out = ops.sum(x)
>>> print(out)
270.0
>>> out = ops.sum(x, dim=2)
>>> print(out)
[[ 6. 12. 18.]
[24. 30. 36.]
[42. 48. 54.]]
>>> out = ops.sum(x, dim=2, keepdim=True)
>>> print(out)
[[[ 6.]
[12.]
[18.]]
[[24.]
[30.]
[36.]]
[[42.]
[48.]
[54.]]]
"""
return sum_ext_op(input, dim, keepdim, dtype)
[文档]def tanhshrink(input):
'''
Tanhshrink Activation, :math:`Tanhshrink(x)=x-Tanh(x)` , where :math:`x` corresponds to `input` .
See :class:`mindspore.nn.Tanhshrink` for more details.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> from mindspore import Tensor
>>> import numpy as np
>>> input = Tensor(np.array([1, 2, 3, 2, 1]), ms.float16)
>>> output = ops.tanhshrink(input)
>>> print(output)
[0.2383 1.036 2.004 1.036 0.2383]
'''
if not isinstance(input, Tensor):
raise TypeError(f"For tanhshrink, the input must be a Tensor, but got {type(input)}.")
if input.dtype in mstype.int_type + mstype.uint_type:
input = input.astype(mstype.float32)
return input - tanh_(input)
[文档]def zeta(input, other):
r"""
Elemental-wise compute the Hurwitz zeta output.
.. math::
\zeta(x, q) = \sum_{k=0}^{\infty} \frac{1}{(k + q)^x}
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Union[Tensor, int, float]): Input Tensor. Represented as :math:`x` in the formula. If it's a Tensor, its
dtype must be either float32 or float64.
other (Union[Tensor, int, float]): Input Tensor must have the same dtype as `input`.
Represented as :math:`q` in the formula.
Returns:
Tensor, The result of Hurwitz zeta function.
Raises:
TypeError: If neither `input` nor `other` is not tensor.
TypeError: If dtype of `input` is neither float32 nor float64.
TypeError: If dtype of `other` is neither float32 nor float64.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> x = Tensor(np.array([10.]), mindspore.float32)
>>> q = Tensor(np.array([1.]), mindspore.float32)
>>> z = ops.zeta(x, q)
>>> print(z)
[1.0009946]
"""
if isinstance(input, (int, float)):
if not isinstance(other, Tensor):
raise TypeError(f"For 'zeta', at least one of the inputs should be Tensor.")
_dtype = other.dtype
input = cast_(input, _dtype)
if isinstance(other, (int, float)):
if not isinstance(input, Tensor):
raise TypeError(f"For 'zeta', at least one of the inputs should be Tensor.")
_dtype = input.dtype
other = cast_(other, _dtype)
if input.size < other.size:
input = _get_cache_prim(P.BroadcastTo)(other.shape)(input)
elif input.size > other.size:
other = _get_cache_prim(P.BroadcastTo)(input.shape)(other)
output = zeta_(input, other)
return output
def matrix_power(input, n):
"""
Raises a square matrix to the (integer) power `n` .
- When :math:`n=0` , returns the identity matrix, which has the same shape as `input` .
- When :math:`n<0` and `input` is invertible, returns the inverse of `input` to the power of :math:`-n` .
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): A 3-D Tensor. Supported data types are float16 and float32.
The shape is :math:`(b, m, m)` , represents b m-D square matrices.
n (int): The exponent, a required int.
Returns:
A 3-D Tensor. Data type and shape are the same as `input` 's.
Raises:
TypeError: If the data type of `n` is not int.
TypeError: If the data type of `input` is neither float32 nor float16.
TypeError: If `input` is not a Tensor.
ValueError: If `input` is not a 3-D tensor.
ValueError: If shape[1] and shape[2] of `input` are not the same.
ValueError: If `n` is negative but got input `input` has singular matrices.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore as ms
>>> from mindspore import Tensor, ops
>>> input = Tensor([[[0, 1], [-1, 0]], [[1, 0], [0, -1]]], dtype=ms.float32)
>>> y = ops.matrix_power(input, 2)
>>> print(y)
[[[-1. 0.]
[-0. -1.]]
[[ 1. 0.]
[ 0. 1.]]]
"""
matrix_power_ops = _get_cache_prim(P.MatrixPower)(n=n)
return matrix_power_ops(input)
def _maybe_wrap_dims_n(ret_dim, input_dim):
"""Check the dim"""
if input_dim <= 0:
input_dim = 1
min = -input_dim
max = input_dim - 1
for i, value in enumerate(ret_dim):
dim = value
if dim < min or dim > max:
raise ValueError(f"Dimension out of range, it must be in range of [{min}, {max}], "
f"but got {dim}.")
if dim < 0:
ret_dim[i] = dim + input_dim
return ret_dim
def _canonicalize_fft_shape_and_dim(input, shape, dim):
"""Check the input's shape and dim"""
input_dim = input.ndim
input_sizes = input.shape
ret_dim = None
ret_shape = None
if dim is not None:
ret_dim = list(dim)
ret_dim = _maybe_wrap_dims_n(ret_dim, input_dim)
# check if dim is duplicated
set_ret_dim = set(ret_dim)
if len(set_ret_dim) != len(ret_dim):
raise ValueError(f"FFT dims must be unique.")
if shape is not None:
if dim is not None and len(dim) != len(shape):
raise ValueError(f"shape and dim must have the same length, but now they are "
f"{len(dim)} and {len(shape)}.")
if len(shape) > input_dim:
raise ValueError(f"Got shape with {len(shape)} values but input tensor only "
f"has {input_dim} dimensions.")
transform_ndim = len(shape)
if dim is None:
ret_dim = [0] * transform_ndim
value = input_dim - transform_ndim
for i in range(transform_ndim):
ret_dim[i] = value + i
ret_shape = [0] * transform_ndim
for i in range(transform_ndim):
if shape[i] == -1:
ret_shape[i] = input_sizes[ret_dim[i]]
else:
ret_shape[i] = shape[i]
elif dim is None:
ret_dim = list(range(input_dim))
ret_shape = [0] * input_dim
for i in range(input_dim):
ret_shape[i] = input_sizes[i]
else:
ret_shape = [0] * len(ret_dim)
for i in range(len(ret_dim)):
value = ret_dim[i]
ret_shape[i] = input_sizes[value]
for value in ret_shape:
if value <= 0:
raise ValueError(f"The value of ret_shape must be greater than 0, "
f"but got '{value}'.")
return ret_shape, ret_dim
def as_strided(x, shape=None, strides=None):
n = np.dtype(mstype.dtype_to_nptype(x.dtype)).itemsize
strides = tuple(np.array(strides) * n)
if x.dtype == mstype.bfloat16:
return Tensor(np.lib.stride_tricks.as_strided(x.float().asnumpy(), shape, strides, False, True), dtype=x.dtype)
return Tensor(np.lib.stride_tricks.as_strided(x.asnumpy(), shape, strides, False, True), dtype=x.dtype)
def _resize_input(input, input_dim, ret_dim, ret_shape, input_sizes):
"""Resize the input"""
paddings = [0] * input_dim * 2
must_copy = False
for i in range(len(ret_dim)):
value = ret_dim[i]
# resize input based on n & dim
if ret_shape[i] == -1:
continue
if input_sizes[value] < ret_shape[i]:
pad_idx = len(paddings) - 2 * value - 1
paddings[pad_idx] = ret_shape[i] - input_sizes[value]
must_copy = True
if input_sizes[value] > ret_shape[i]:
start_index = [0] * input_dim
input_sizes[value] = ret_shape[i]
input = slice_(input, start_index, input_sizes)
if must_copy:
paddings = np.reshape(paddings, (input_dim, 2)).tolist()
paddings.reverse()
paddings = (*paddings,)
input = _get_cache_prim(P.Pad)(paddings)(input)
return input
def _permute_input(input, input_dim, ret_dim):
"""Permute input based on dim"""
dim_permute = list(range(input_dim))
# is_transformed_dim
is_transformed_dim = [0] * input_dim
for value in ret_dim:
is_transformed_dim[value] = True
# partition dim_permute
dim_permute_a, dim_permute_b = [], []
for i in range(len(dim_permute)):
value = dim_permute[i]
(dim_permute_a if not is_transformed_dim[i] else dim_permute_b).append(value)
# strides
type_size = np.dtype(mstype.dtype_to_nptype(input.dtype)).itemsize
input_strides = [int(x / type_size) for x in input.strides]
def cmp(x, y):
if input_strides[x] > input_strides[y]:
return -1
if input_strides[x] < input_strides[y]:
return 1
return 0
# sort
if dim_permute_a:
dim_permute_a = sorted(dim_permute_a, key=cmp_to_key(cmp))
# copy
if dim_permute_b:
ret_dim = sorted(ret_dim, key=cmp_to_key(cmp))
for i in range(len(ret_dim)):
value = ret_dim[i]
dim_permute_b[i] = value
# merge
dim_permute = dim_permute_a + dim_permute_b
# permute
input = transpose_(input, tuple(dim_permute))
return input, dim_permute
def _reshape_input(input, signal_ndim, batch_dims):
"""Reshape input"""
# Collapse batch dimensions into a single dimension
batched_sizes = [0] * (signal_ndim + 1)
batched_sizes[0] = -1
i = batch_dims
j = 1
while i < len(input.shape):
batched_sizes[j] = input.shape[i]
j += 1
i += 1
if j >= len(batched_sizes):
break
input = reshape_(input, tuple(batched_sizes))
return input
def _check_fftwithsize_input(input, s, dim, norm, fft_func_name): # pylint: disable=redefined-outer-name
"""Check the input of fftwithsize"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("For '{fft_func_name}', 'input' must be Tensor.")
input_dtype = dtype_(input)
if fft_func_name in ('FFTN', 'IFFTN'):
if not input_dtype in (mstype.complex64, mstype.complex128):
raise TypeError("For '{fft_func_name}', the dtype of 'input' must be complex64, complex128, "
f"but got '{input_dtype}'.")
else:
raise TypeError("For '{fft_func_name}', it is not supported now.")
if s is not None:
if isinstance(s, int):
s = (s,)
elif not isinstance(s, tuple):
raise TypeError("For '{fft_func_name}', 's' must be tuple(int).")
for ele in s:
if not isinstance(ele, int):
raise TypeError(f"For '{fft_func_name}', each elements of 's' must be int, but got {type(ele)}")
if dim is not None:
if isinstance(dim, int):
dim = (dim,)
elif not isinstance(dim, tuple):
raise TypeError("For '{fft_func_name}', 'dim' must be tuple(int).")
for ele in dim:
if not isinstance(ele, int):
raise TypeError(f"For '{fft_func_name}', each elements of 'dim' must be int, but got {type(ele)}")
ret_shape, ret_dim = _canonicalize_fft_shape_and_dim(input, s, dim)
input_dim = input.ndim
signal_ndim = len(ret_dim)
batch_dims = input_dim - signal_ndim
input_sizes = list(input.shape)
if fft_func_name in ('FFTN', 'IFFTN'):
input = _resize_input(input, input_dim, ret_dim, ret_shape, input_sizes)
out_sizes = input.shape
input, dim_permute = _permute_input(input, input_dim, ret_dim)
input = _reshape_input(input, signal_ndim, batch_dims)
if norm is None:
norm = "backward"
else:
_check_attr_dtype("norm", norm, [str], fft_func_name)
FFTInput = collections.namedtuple('FFTInput', ['input', 'signal_ndim', 'norm', 'input_dim',
'batch_dims', 'dim_permute', 'out_sizes'])
return FFTInput(input=input, signal_ndim=signal_ndim, norm=norm, input_dim=input_dim,
batch_dims=batch_dims, dim_permute=dim_permute, out_sizes=out_sizes)
def _handle_fftwithsize_output(out, input_dim, batch_dims, dim_permute, out_sizes):
"""Handle the output of fftwithsize"""
out_strides = [0] * input_dim
batch_numel = 1
for i in range(batch_dims - 1, -1, -1):
out_strides[dim_permute[i]] = batch_numel * out.strides[0]
batch_numel *= out_sizes[dim_permute[i]]
for i in range(batch_dims, input_dim):
out_strides[dim_permute[i]] = out.strides[1 + (i - batch_dims)]
type_size = np.dtype(mstype.dtype_to_nptype(out.dtype)).itemsize
if out.shape != out_sizes or out.strides != out_strides:
out = as_strided(out, out_sizes, [int(i / type_size) for i in out_strides])
return out
def fft(input, n=None, dim=-1, norm=None): # pylint: disable=redefined-outer-name
r"""
Calculates the one dimensional discrete Fourier transform of `input`.
Args:
input (Tensor): The input tensor.
n (int, optional): Signal length.
If given, the input will either be zero-padded or trimmed to this length before computing the FFT.
Default: ``None``.
dim (int, optional): The dimension along which to take the one dimensional FFT.
Default: -1.
norm (string, optional): Normalization mode. Three modes are defined as,
``"forward"`` (normalize by :math `1/n`), ``"backward"``(no normalization),
``"ortho"`` (normalize by :math:`1/\sqrt{n}`).
Default: ``None`` that means ``"backward"``.
Returns:
Tensor, The result of `fft()` function.
Raises:
TypeError: If the `input` type is not Tensor.
TypeError: If the `input` data type is not one of: complex64, complex128.
TypeError: If `n` or `dim` type is not int32.
ValueError: If `input` dimension is less than 1.
ValueError: If `n` is less than 1.
ValueError: If `dim` is not in the range of "[ `-input_dim` , `input_dim-1` ]".
ValueError: If norm is none of "backward", "forward" or "ortho".
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([ 1.6243454+0.j, -0.6117564+0.j, -0.5281718+0.j, -1.0729686+0.j])
>>> y = ops.fft(input)
>>> print(y)
[-0.5885514+0.j 2.1525173-0.46121222j 2.7808986+0.j
2.1525173+0.46121222j]
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("For 'FFT', 'input' must be Tensor.")
input_dtype = dtype_(input)
if not input_dtype in (mstype.complex64, mstype.complex128):
raise TypeError("For 'FFT', the dtype of 'input' must be complex64, complex128, "
f"but got '{input_dtype}'.")
_check_attr_dtype("dim", dim, [int], "FFT")
input_dim = input.ndim
signal_ndim = 1
batch_dims = input_dim - signal_ndim
input_sizes = list(input.shape)
dim = _maybe_wrap_dims_n([dim], input_dim)[0]
n_opt = n
if n is None:
n = input.shape[dim]
else:
_check_attr_dtype("n", n, [int], "FFT")
if n < 1:
raise ValueError("For 'FFT', the value of 'n' must be greater than or equal to 1, "
f"but got '{n}'.")
if n_opt is not None:
input = _resize_input(input, input_dim, [dim], [n], input_sizes)
out_sizes = input.shape
input, dim_permute = _permute_input(input, input_dim, [dim])
input = _reshape_input(input, signal_ndim, batch_dims)
if norm is None:
norm = "backward"
else:
_check_attr_dtype("norm", norm, [str], "FFT")
fft_ = FFTWithSize(signal_ndim=1, inverse=False, real=False, norm=norm)
out = fft_(input)
return _handle_fftwithsize_output(out, input_dim, batch_dims, dim_permute, out_sizes)
def fft2(input, s=None, dim=(-2, -1), norm=None): # pylint: disable=redefined-outer-name
r"""
Calculates the two dimensional discrete Fourier transform of `input`.
Args:
input (Tensor): The input tensor.
s (Tuple[int], optional): Signal size in the transformed dimensions.
If given, each dimension `dim[i]` will either be zero-padded or trimmed to the length `s[i]` before
computing the FFT. If a length `-1` is specified, no padding is done in that dimension.
Default: `s = [input.size(d) for d in dim]`
dim (Tuple[int], optional): Dimensions to be transformed.
Default: last two dimensions.
norm (string, optional): Normalization mode. Three modes are defined as,
``"forward"``(normalize by :math `1/n`), ``"backward"``(no normalization),
``"ortho"``(normalize by :math:`1/\sqrt{n}`). Where :math `n = prod(s)` is the logical FFT size.
Default: ``None`` that means ``"backward"``.
Returns:
Tensor, The result of `fft2()` function.
Raises:
TypeError: If the `input` type is not Tensor.
TypeError: If the `input` data type is not one of: complex64, complex128.
TypeError: If the `s` or `dim` is not tuple(int).
ValueError: If `input` dimension is less than 2.
ValueError: If the length of `s` and `dim` are not the same.
ValueError: If the value in `dim` is not in the range of "[ `-input_dim` , `input_dim-1` ]".
ValueError: If norm is none of "backward", "forward" or "ortho".
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([[ 1.6243454+0.j, -0.6117564+0.j], [-0.5281718+0.j, -1.0729686+0.j]])
>>> y = ops.fft2(input)
>>> print(y)
[[-0.5885514+0.j 2.7808986+0.j]
[ 2.6137294+0.j 1.691305 +0.j]]
"""
return fftn(input, s, dim, norm)
def fftn(input, s=None, dim=None, norm=None): # pylint: disable=redefined-outer-name
r"""
Calculates the N dimensional discrete Fourier transform of `input`.
Args:
input (Tensor): The input tensor.
s (Tuple[int], optional): Signal size in the transformed dimensions.
If given, each dimension `dim[i]` will either be zero-padded or trimmed to the length `s[i]` before
computing the FFT. If a length `-1` is specified, no padding is done in that dimension.
Default: `s = [input.size(d) for d in dim]`
dim (Tuple[int], optional): Dimensions to be transformed.
Default: all dimensions, or the last `len(s)` dimensions if `s` is given.
norm (string, optional): Normalization mode. Three modes are defined as,
``"forward"``(normalize by :math `1/n`), ``"backward"``(no normalization),
``"ortho"``(normalize by :math:`1/\sqrt{n}`). Where :math `n = prod(s)` is the logical FFT size.
Default: ``None`` that means ``"backward"``.
Returns:
Tensor, The result of `fftn()` function.
Raises:
TypeError: If the `input` type is not Tensor.
TypeError: If the `input` data type is not one of: complex64, complex128.
TypeError: If the `s` or `dim` is not tuple(int).
ValueError: If the length of `s` and `dim` are not the same.
ValueError: If `input` dimension is less than 1.
ValueError: If the value in `dim` is not in the range of "[ `-input_dim` , `input_dim-1` )".
ValueError: If norm is none of "backward", "forward" or "ortho".
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([[[ 1.6243454 +0.j, -0.6117564 +0.j, -0.5281718 +0.j],
... [-1.0729686 +0.j, 0.86540765+0.j, -2.3015387 +0.j]]])
>>> y = ops.fftn(input)
>>> print(y)
[[[-2.02468245+0.j 1.83940642-2.6702696j
1.83940642+2.6702696j ]
[ 2.99351685+0.j 2.54921257+2.81504238j
2.54921257-2.81504238j]]]
"""
fftninput = _check_fftwithsize_input(input, s, dim, norm, "FFTN")
fftn_ = FFTWithSize(signal_ndim=fftninput.signal_ndim, inverse=False, real=False, norm=fftninput.norm)
out = fftn_(fftninput.input)
return _handle_fftwithsize_output(out, fftninput.input_dim, fftninput.batch_dims,
fftninput.dim_permute, fftninput.out_sizes)
def ifft(input, n=None, dim=-1, norm=None): # pylint: disable=redefined-outer-name
r"""
Calculates the inverse of `fft()`.
Args:
input (Tensor): The input tensor.
n (int, optional): Signal length.
If given, the input will either be zero-padded or trimmed to this length before computing the IFFT.
Default: ``None``.
dim (int, optional): The dimension along which to take the one dimensional IFFT.
Default: -1.
norm (string, optional): Normalization mode. Three modes are defined as,
``"forward"``(normalize by :math `1/n`), ``"backward"``(no normalization),
``"ortho"``(normalize by :math:`1/\sqrt{n}`).
Default: ``None`` that means ``"backward"``.
Returns:
Tensor, The result of `ifft()` function.
Raises:
TypeError: If the `input` type is not Tensor.
TypeError: If the `input` data type is not one of: complex64, complex128.
TypeError: If `n` or `dim` type is not int32.
ValueError: If `input` dimension is less than 1.
ValueError: If `n` is less than 1.
ValueError: If `dim` is not in the range of "[ `-input_dim` , `input_dim-1` ]".
ValueError: If norm is none of "backward", "forward" or "ortho".
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([ 1.6243454+0.j, -0.6117564+0.j, -0.5281718+0.j, -1.0729686+0.j])
>>> y = ops.ifft(input)
>>> print(y)
[-0.14713785+0.j 0.5381293 +0.11530305j 0.69522465+0.j
0.5381293 -0.11530305j]
"""
if not isinstance(input, (Tensor, Tensor_)):
raise TypeError("For 'IFFT', 'input' must be Tensor.")
input_dtype = dtype_(input)
if not input_dtype in (mstype.complex64, mstype.complex128):
raise TypeError("For 'IFFT', the dtype of 'input' must be complex64, complex128, "
f"but got '{input_dtype}'.")
_check_attr_dtype("dim", dim, [int], "IFFT")
input_dim = input.ndim
signal_ndim = 1
batch_dims = input_dim - signal_ndim
input_sizes = list(input.shape)
dim = _maybe_wrap_dims_n([dim], input_dim)[0]
n_opt = n
if n is None:
n = input.shape[dim]
else:
_check_attr_dtype("n", n, [int], "IFFT")
if n < 1:
raise ValueError("For 'IFFT', the value of 'n' must be greater than or equal to 1, "
f"but got '{n}'.")
if n_opt is not None:
input = _resize_input(input, input_dim, [dim], [n], input_sizes)
out_sizes = input.shape
input, dim_permute = _permute_input(input, input_dim, [dim])
input = _reshape_input(input, signal_ndim, batch_dims)
if norm is None:
norm = "backward"
else:
_check_attr_dtype("norm", norm, [str], "IFFT")
fft_ = FFTWithSize(signal_ndim=1, inverse=True, real=False, norm=norm)
out = fft_(input)
return _handle_fftwithsize_output(out, input_dim, batch_dims, dim_permute, out_sizes)
def ifft2(input, s=None, dim=(-2, -1), norm=None): # pylint: disable=redefined-outer-name
r"""
Calculates the inverse of `fft2()`.
Args:
input (Tensor): The input tensor.
s (Tuple[int], optional): Signal size in the transformed dimensions.
If given, each dimension `dim[i]` will either be zero-padded or trimmed to the length `s[i]` before
computing the FFT. If a length `-1` is specified, no padding is done in that dimension.
Default: `s = [input.size(d) for d in dim]`
dim (Tuple[int], optional): Dimensions to be transformed.
Default: (-2, -1).
norm (string, optional): Normalization mode. Three modes are defined as,
``"forward"``(normalize by :math `1/n`), ``"backward"``(no normalization),
``"ortho"``(normalize by :math:`1/\sqrt{n}`). Where :math `n = prod(s)` is the logical IFFT size.
Default: ``None`` that means ``"backward"``.
Returns:
Tensor, The result of `ifft2()` function.
Raises:
TypeError: If the `input` type is not Tensor.
TypeError: If the `input` data type is not one of: complex64, complex128.
TypeError: If the `s` or `dim` is not tuple(int).
ValueError: If the length of `s` and `dim` are not the same.
ValueError: If `input` dimension is less than 2.
ValueError: If the value in `dim` is not in the range of "[ `-input_dim` , `input_dim-1` )".
ValueError: If norm is none of "backward", "forward" or "ortho".
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([[ 1.6243454+0.j, -0.6117564+0.j], [-0.5281718+0.j, -1.0729686+0.j]])
>>> y = ops.ifft2(input)
>>> print(y)
[[-0.14713785+0.j 0.69522465+0.j]
[ 0.65343235+0.j 0.42282625+0.j]]
"""
return ifftn(input, s, dim, norm)
def ifftn(input, s=None, dim=None, norm=None): # pylint: disable=redefined-outer-name
r"""
Calculates the inverse of `fftn()`.
Args:
input (Tensor): The input tensor.
s (Tuple[int], optional): Signal size in the transformed dimensions.
If given, each dimension `dim[i]` will either be zero-padded or trimmed to the length `s[i]` before
computing the FFT. If a length `-1` is specified, no padding is done in that dimension.
Default: `s = [input.size(d) for d in dim]`
dim (Tuple[int], optional): Dimensions to be transformed.
Default: all dimensions, or the last `len(s)` dimensions if `s` is given.
norm (string, optional): Normalization mode. Three modes are defined as,
``"forward"``(normalize by :math `1/n`), ``"backward"``(no normalization),
``"ortho"``(normalize by :math:`1/\sqrt{n}`). Where :math `n = prod(s)` is the logical IFFT size.
Default: ``None`` that means ``"backward"``.
Returns:
Tensor, The result of `ifftn()` function.
Raises:
TypeError: If the `input` type is not Tensor.
TypeError: If the `input` data type is not one of: complex64, complex128.
TypeError: If the `s` or `dim` is not tuple(int).
ValueError: If the length of `s` and `dim` are not the same.
ValueError: If `input` dimension is less than 1.
ValueError: If the value in `dim` is not in the range of "[ `-input_dim` , `input_dim-1` )".
ValueError: If norm is none of "backward", "forward" or "ortho".
Supported Platforms:
``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> input = Tensor([[[ 1.6243454 +0.j, -0.6117564 +0.j, -0.5281718 +0.j],
... [-1.0729686 +0.j, 0.86540765+0.j, -2.3015387 +0.j]]])
>>> y = ops.ifftn(input)
>>> print(y)
[[[-0.33744708+0.j 0.30656774+0.44504493j
0.30656774-0.44504493j]
[ 0.49891948+0.j 0.42486876-0.46917373j
0.42486876+0.46917373j]]]
"""
ifftninput = _check_fftwithsize_input(input, s, dim, norm, "IFFTN")
ifftn_ = FFTWithSize(signal_ndim=ifftninput.signal_ndim, inverse=True, real=False, norm=ifftninput.norm)
out = ifftn_(ifftninput.input)
return _handle_fftwithsize_output(out, ifftninput.input_dim, ifftninput.batch_dims,
ifftninput.dim_permute, ifftninput.out_sizes)
@_primexpr
def _check_validate_axis(axis, name):
def _check(axis):
if isinstance(axis, (tuple, list)):
for idx, item in enumerate(axis):
validator.check_value_type("axis[%d]" % idx, item, [int], name)
_check(axis)
axis = validator.check_value_type('axis', axis, [int, tuple, list], name)
return axis
@constexpr
def _check_validate_keepdims(keep_dims, name):
keep_dims = validator.check_value_type('keep_dims', keep_dims, [bool], name)
return keep_dims
[文档]def count_nonzero(x, axis=(), keep_dims=False, dtype=mstype.int32):
r"""
Count number of nonzero elements across axis of input tensor.
Args:
x (Tensor): Input data is used to count non-zero numbers. With shape
:math:`(*)` where :math:`*` means, any number of additional dimensions.
axis (Union[int, tuple(int), list(int)], optional): The dimensions to reduce.
Default: ``()`` , reduce all dimensions.
keep_dims (bool, optional): Whether to maintain dimensions specified by `axis`.
If true, keep these reduced dimensions and the length is 1.
If false, don't keep these dimensions. Default: ``False`` .
dtype (Union[Number, mindspore.bool\_], optional): The data type of the output tensor.
Default: ``mstype.int32`` .
Returns:
Tensor, number of nonzero element across axis specified by `axis`.
The data type is specified by `dtype`.
Raises:
TypeError: If `axis` is not int, tuple or list.
ValueError: If any value in `axis` is not in range [-x.ndim, x.ndim).
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> import mindspore
>>> # case 1: each value specified.
>>> x = Tensor(np.array([[0, 1, 0], [1, 1, 0]]).astype(np.float32))
>>> nonzero_num = ops.count_nonzero(x=x, axis=[0, 1], keep_dims=True, dtype=mindspore.int32)
>>> print(nonzero_num)
[[3]]
>>> # case 2: all value is default.
>>> nonzero_num = ops.count_nonzero(x=x)
>>> print(nonzero_num)
3
>>> # case 3: axis value was specified 0.
>>> nonzero_num = ops.count_nonzero(x=x, axis=[0,])
>>> print(nonzero_num)
[1 2 0]
>>> # case 4: axis value was specified 1.
>>> nonzero_num = ops.count_nonzero(x=x, axis=[1,])
>>> print(nonzero_num)
[1 2]
>>> # case 5: keep_dims value was specified.
>>> nonzero_num = ops.count_nonzero(x=x, keep_dims=True)
>>> print(nonzero_num)
[[3]]
>>> # case 6: keep_dims and axis value was specified.
>>> nonzero_num = ops.count_nonzero(x=x, axis=[0,], keep_dims=True)
>>> print(nonzero_num)
[[1 2 0]]
"""
const_utils.check_type_valid(dtype_(x), mstype.number_type, 'input x')
axis = _check_validate_axis(axis, "count_nonzero")
keep_dims = _check_validate_keepdims(keep_dims, "count_nonzero")
const_utils.check_type_valid(dtype, mstype.number_type + (mstype.bool_,), 'dtype')
reduce_sum = _get_cache_prim(P.ReduceSum)(keep_dims)
tensor_0 = ops.zeros(x.shape, x.dtype)
nonzero_bool = not_equal(x, tensor_0)
# ReduceSum only support float16 or float32 tensor.
nonzero_val = cast_(nonzero_bool, mstype.float32)
nonzero_num = cast_(reduce_sum(nonzero_val, axis), dtype)
return nonzero_num
@_primexpr
def _int_to_tuple_conv(axes):
"""
Converts ints to tuples in input axes, expected by most validation checks.
"""
for x in [0, 1]:
if isinstance(axes[x], int):
axes[x] = (axes[x],)
return axes
@_primexpr
def _check_axes(axes, prim_name=None):
"""
Check for validity and type of axes passed to function.
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
validator.check_value_type('axes', axes, [int, tuple, list], "tensor dot")
if not isinstance(axes, int):
axes = list(axes) # to avoid immutability issues
if len(axes) != 2:
raise ValueError(f"{msg_prefix} dimension of 'axes' must be 2, but got 'axes': {axes}.")
axes = _int_to_tuple_conv(axes) # convert before length checks
if len(axes[0]) != len(axes[1]):
raise ValueError(f"{msg_prefix} first and second dim of 'axes' have to be the same size/length, "
f"but got 'axes': {axes}.")
if len(axes[0]) != len(set(axes[0])) or len(axes[1]) != len(set(axes[1])):
raise ValueError(f"{msg_prefix} 'axes' cannot have duplicating values, but got {axes}.")
return axes
@constexpr
def _typecheck_input(x1_type, x2_type, prim_name=None):
"""
Check input tensor types to be valid and confirm they are the same type.
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
const_utils.check_type_valid(x1_type, [mstype.float32, mstype.float16], 'x1')
const_utils.check_type_valid(x2_type, [mstype.float32, mstype.float16], 'x2')
if x1_type != x2_type:
raise TypeError(f"{msg_prefix} inputs must be the same type, but got x1_type: {x1_type} "
f"and x2_type: {x2_type}.")
@_primexpr
def _axes_int_check(x1_shape, x2_shape, axes, prim_name=None):
"""
Convert from single int axes to 2d tuple if required
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
def _check_lt_zero(axes):
if axes < 0:
raise ValueError(f"{msg_prefix} 'axes' must be at least 0, but got {axes}.")
def _check_len(axes, x1_shape, x2_shape):
if axes > len(x1_shape) or axes > len(x2_shape):
raise ValueError(f"{msg_prefix} 'axes' cannot be greater than the length of 'x1_shape' and 'x2_shape', "
f"but got 'axes': {axes}, 'x1_shape': {x1_shape}, 'x2_shape': {x2_shape}.")
if isinstance(axes, int):
_check_lt_zero(axes)
if axes == 0:
# outer product, no input validation required
return [], []
_check_len(axes, x1_shape, x2_shape)
x1_ind = tuple(range(len(x1_shape))[-1 * axes:])
x2_ind = tuple(range(len(x2_shape))[:axes])
axes = tuple((x1_ind, x2_ind))
axes = _int_to_tuple_conv(axes)
return axes
@_primexpr
def _validate_axes(x1_shape, x2_shape, axes, prim_name=None):
"""
Checks for axes having the correct length according to input, for any value in axis
being out of range with given shape and also checking for compatible axes values
with given inputs.
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
def _check_len(axes_len, shape_dim_len, x_axes):
if axes_len > shape_dim_len:
raise ValueError(f"{msg_prefix} length of element {x_axes} in 'axes' must be less than or equal to "
f"{shape_dim_len}, but got {axes_len}.")
def _check_axes_value(x_axes, min_val, max_val):
for _, x_value in enumerate(x_axes):
if x_value > max_val or x_value < min_val:
raise ValueError(f"{msg_prefix} value in 'axes' must be in range: [{min_val}, {max_val}], "
f"but got {x_value}.")
shapes = [x1_shape, x2_shape]
# axis length check
for ix_input, x_axes in enumerate(axes):
axes_len = len(x_axes)
shape_dim_len = len(shapes[ix_input])
_check_len(axes_len, shape_dim_len, x_axes)
# axis values range check
for ix_input, x_axes in enumerate(axes):
comp_shape = shapes[ix_input]
max_val = len(comp_shape) - 1
min_val = -1 * len(comp_shape)
_check_axes_value(x_axes, min_val, max_val)
# check axis value with input shape - both ways for axis valid
invalid_a = False
invalid_b = False
for i in range(len(axes[0])): # sizes already validated
if x1_shape[axes[0][i]] != x2_shape[axes[1][i]]:
invalid_a = True
if x1_shape[axes[0][i]] != x2_shape[axes[1][len(axes[0]) - 1 - i]]:
invalid_b = True
def _check(invalid_a, invalid_b, x1_shape, x2_shape, axes):
if invalid_a and invalid_b:
raise ValueError(f"{msg_prefix} 'i' should exist such that 'x1_shape[axes[0][i]]' is equal to "
f"'x2_shape[axes[1][i]]' or 'x2_shape[axes[1][len(axes[0])-1-i]]', but got "
f"'x1_shape': {x1_shape}, 'x2_shape': {x2_shape}, 'axes': {axes}.")
_check(invalid_a, invalid_b, x1_shape, x2_shape, axes)
@_primexpr
def _calc_new_shape(shape, axes, position=0):
"""
Calculate transpose and reshape parameters for input transformations,
'position' refers to whether tensor is first or second in the op.
"""
contraction_axes = tuple(i if i >= 0 else i + len(shape) for i in axes[position])
prod_contraction = 1
for i in contraction_axes:
prod_contraction *= shape[i]
free_axes = tuple(i for i in range(len(shape)) if i not in contraction_axes)
free_dims = tuple(shape[i] if shape[i] is not None else -1 for i in free_axes)
prod_free = 1
for free_dim in free_dims:
prod_free *= free_dim
transpose_perm = contraction_axes + free_axes if position else free_axes + contraction_axes
new_shape = (prod_contraction, prod_free) if position else (prod_free, prod_contraction)
return new_shape, transpose_perm, free_dims
[文档]def tensor_dot(x1, x2, axes):
"""
Computation of Tensor contraction on arbitrary axes between tensors `a` and `b`.
Contraction allows for the summation of products of elements of `a` and `b` on specified axes.
The same number of axes must be specified for both x1 and x2, and values must be within range
of number of dims of both `a` and `b`.
Selected dims in both inputs must also match.
axes = 0 leads to outer product.
axes = 1 leads to normal matrix multiplication when inputs both 2D.
axes = 1 is the same as axes = ((1,),(0,)) where both `a` and `b` are 2D.
axes = 2 is the same as axes = ((1,2),(0,1)) where both `a` and `b` are 3D.
Args:
x1 (Tensor): First tensor in tensor_dot with datatype float16 or float32
x2 (Tensor): Second tensor in tensor_dot with datatype float16 or float32
axes (Union[int, tuple(int), tuple(tuple(int)), list(list(int))]): Single value or
tuple/list of length 2 with dimensions specified for `a` and `b` each. If single value `N` passed,
automatically picks up last N dims from `a` input shape and first N dims from `b` input shape in order
as axes for each respectively.
Returns:
Tensor, the shape of the output tensor is :math:`(N + M)`, where :math:`N` and :math:`M` are the free axes not
contracted in both inputs.
Raises:
TypeError: If `x1` or `x2` is not a Tensor.
TypeError: If `axes` is not one of the following: int, tuple, list.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> from mindspore import Tensor, ops
>>> import mindspore
>>> import numpy as np
>>> input_x1 = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32)
>>> input_x2 = Tensor(np.ones(shape=[3, 1, 2]), mindspore.float32)
>>> output = ops.tensor_dot(input_x1, input_x2, ((0,1),(1,2)))
>>> print(output)
[[2. 2. 2]
[2. 2. 2]
[2. 2. 2]]
"""
matmul_op = _get_cache_prim(P.MatMul)(False, False)
# input validity checks
x1_shape = shape_(x1)
x2_shape = shape_(x2)
axes = _check_axes(axes, 'tensor_dot')
# input compatibility check & axes format update
axes = _axes_int_check(x1_shape, x2_shape, axes, 'tensor_dot')
_validate_axes(x1_shape, x2_shape, axes, 'tensor_dot')
x1_reshape_fwd, x1_transpose_fwd, x1_ret = _calc_new_shape(x1_shape, axes, 0)
x2_reshape_fwd, x2_transpose_fwd, x2_ret = _calc_new_shape(x2_shape, axes, 1)
output_shape = x1_ret + x2_ret # combine free axes from both inputs
# run tensor_dot op
x1_transposed = transpose_(x1, x1_transpose_fwd)
x2_transposed = transpose_(x2, x2_transpose_fwd)
x1_reshaped = reshape_(x1_transposed, x1_reshape_fwd)
x2_reshaped = reshape_(x2_transposed, x2_reshape_fwd)
mul_result = matmul_op(x1_reshaped, x2_reshaped)
final_result = reshape_(mul_result, output_shape)
return final_result
[文档]def vecdot(x, y, *, axis=-1):
r"""
Calculates the dot product of two batches of vectors across the specified dimension.
The formula of calculation is as follows.
:math:`\bar{x_{i}}` represents the conjugate for complex vectors,
and :math:`\bar{x_{i}}` is the raw value for real vectors.
.. math::
\sum_{i=1}^{n} \bar{x_{i}}{y_{i}}
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
x (Tensor): First batch of vectors. The shape of Tensor is :math:`(*,N)`
where :math:`*` means, any number of additional dimensions. Supporting broadcasting.
The dtype of Tensor should be one of the following types: float, double, int, complex64 and complex128.
y (Tensor): Second batch of vectors. The shape of Tensor is :math:`(*,N)`
where :math:`*` means, any number of additional dimensions. Supporting broadcasting.
The dtype of Tensor should be one of the following types: float, double, int, complex64 and complex128.
Keyword Args:
axis (int): Dimension across which to calculate the dot product. Default: ``-1`` .
Returns:
Tensor, the shape is almost same as the shape of Tensor after broadcasting,
while the specified dimension `axis` in shape has been removed.
Raises:
TypeError: If `x` or `y` is not a Tensor.
TypeError: If type of `axis` is not int.
ValueError: If `axis` is out of range.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
.. note::
Currently, complex numbers are not supported on GPU.
Examples:
>>> import mindspore as ms
>>> from mindspore import ops
>>> x = ms.Tensor([[1, 3], [5, 7], [9, 8]], dtype=ms.float32)
>>> y = ms.Tensor([[4, 5], [6, 7], [3, 2]], dtype=ms.float32)
>>> output = ops.vecdot(x, y, axis=-1)
>>> print(output)
[19. 79. 43.]
"""
if (not isinstance(x, Tensor)) or (not isinstance(y, Tensor)):
raise TypeError("For vecdot, x or y must be Tensor.")
if not isinstance(axis, int):
raise TypeError(f"For vecdot, the dim should be int, but got {type(axis)}.")
ndim = x.ndim if x.ndim > y.ndim else y.ndim
if (axis < -ndim) or (axis >= ndim):
raise ValueError(f"For vecdot, the dim is out of range.")
if x.dtype in mstype.complex_type:
x = x.conj()
result = x * y
result = result.sum(axis=axis)
return result
@_primexpr
def _check_invalid_input(x1_shape, x2_shape, prim_name=None):
msg_prefix = f"For \\\'{prim_name}\\\', the" if prim_name else "The"
if len(x1_shape) < 2 or len(x2_shape) < 2:
raise ValueError(f"{msg_prefix} inputs x1, x2 should have \\\'dimension >= 2\\\',"
f"but got \\\'len(x1_shape)\\\': ({len(x1_shape)})"
f" and \\\'len(x2_shape)\\\': ({len(x2_shape)}).")
@constexpr
def _typecheck_input_dot(x1_type, x2_type, prim_name=None):
"""
Check input tensor types to be valid and confirm they are the same type for dot and batch dot ops.
"""
msg_prefix = f"For \\\'{prim_name}\\\', the" if prim_name else "The"
const_utils.check_type_valid(x1_type, [mstype.float16, mstype.float32], 'x1')
const_utils.check_type_valid(x2_type, [mstype.float16, mstype.float32], 'x2')
if x1_type != x2_type:
raise TypeError(f"{msg_prefix} inputs must be the same type, but got "
f"x1_type: {x1_type} and x2_type: {x2_type}.")
@_primexpr
def _get_transpose_shape(x2_shape):
x2_shape_range = tuple(range(len(x2_shape)))
x2_shape_transpose = x2_shape_range[-2:-1] + x2_shape_range[:-2] + x2_shape_range[-1:]
return x2_shape_transpose
[文档]def dot(input, other):
"""
Computation a dot product between samples in two tensors.
Args:
input (Tensor): First tensor in Dot op with datatype float16 or float32.
The rank must be greater than or equal to 2.
other (Tensor): Second tensor in Dot op with datatype float16 or float32.
The rank must be greater than or equal to 2.
Returns:
Tensor, dot product of input and other.
Raises:
TypeError: If type of input and other are not the same.
TypeError: If dtype of input or other is not float16 or float32.
ValueError: If rank of input or other less than 2.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import numpy as np
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.ones(shape=[2, 3]), mindspore.float32)
>>> other = Tensor(np.ones(shape=[1, 3, 2]), mindspore.float32)
>>> output = ops.dot(input, other)
>>> print(output)
[[[3. 3.]]
[[3. 3.]]]
>>> print(output.shape)
(2, 1, 2)
>>> input = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32)
>>> other = Tensor(np.ones(shape=[1, 3, 2]), mindspore.float32)
>>> output = ops.dot(input, other)
>>> print(output)
[[[[3. 3.]]
[[3. 3.]]]]
>>> print(output.shape)
(1, 2, 1, 2)
>>> input = Tensor(np.ones(shape=[1, 2, 3]), mindspore.float32)
>>> other = Tensor(np.ones(shape=[2, 3, 2]), mindspore.float32)
>>> output = ops.dot(input, other)
>>> print(output)
[[[[3. 3.]
[3. 3.]]
[[3. 3.]
[3. 3.]]]]
>>> print(output.shape)
(1, 2, 2, 2)
>>> input = Tensor(np.ones(shape=[3, 2, 3]), mindspore.float32)
>>> other = Tensor(np.ones(shape=[2, 1, 3, 2]), mindspore.float32)
>>> output = ops.dot(input, other)
>>> print(output)
[[[[[3. 3.]]
[[3. 3.]]]
[[[3. 3.]]
[[3. 3.]]]]
[[[[3. 3.]]
[[3. 3.]]]
[[[3. 3.]]
[[3. 3.]]]]
[[[[3. 3.]]
[[3. 3.]]]
[[[3. 3.]]
[[3. 3.]]]]]
>>> print(output.shape)
(3, 2, 2, 1, 2)
"""
matmul_op = _get_cache_prim(P.MatMul)(False, False)
input_shape = shape_(input)
other_shape = shape_(other)
input_type = dtype_(input)
other_type = dtype_(other)
_typecheck_input_dot(input_type, other_type, 'dot')
_check_invalid_input(input_shape, other_shape, 'dot')
if len(input_shape) > 2 or len(other_shape) > 2:
other_shape_transpose = _get_transpose_shape(other_shape)
other_transpose = transpose_(other, other_shape_transpose)
input_reshape = reshape_(input, (-1, input_shape[-1]))
other_reshape = reshape_(other_transpose, (other_shape[-2], -1))
mul_result = matmul_op(input_reshape, other_reshape)
reshape_shape = input_shape[:-1] + other_shape[:-2] + other_shape[-1:]
reshape_shape = (-1,) + reshape_shape[1:]
return reshape_(mul_result, reshape_shape)
return matmul_op(input, other)
@_primexpr
def _get_batch_size(x1_shape, x2_shape, prim_name=None):
"""
Get batch sizes from two inputs
"""
def _check():
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if len(x1_shape) < 2 or len(x2_shape) < 2:
raise ValueError(f"{msg_prefix} inputs x1, x2 should have 'dimension >= 2', "
f"but got 'len(x1_shape)': ({len(x1_shape)}) and 'len(x2_shape)': ({len(x2_shape)}).")
_check()
return x1_shape[0], x2_shape[0]
@constexpr
def _typecheck_input_batch_dot(x1_type, x2_type, prim_name=None):
"""
Check input tensor types to be valid and confirm they are the same type for batch dot ops.
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
const_utils.check_type_valid(x1_type, [mstype.float32], 'x1')
const_utils.check_type_valid(x2_type, [mstype.float32], 'x2')
if x1_type != x2_type:
raise TypeError(f"{msg_prefix} inputs must be the same type, but got x1_type: {x1_type} and "
f"x2_type: {x2_type}.")
@_primexpr
def _check_axes_for_batch_dot(x1_shape, x2_shape, axes, prim_name=None):
"""
Check whether axes are valid and cast axes from tuple to list
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
def _check_1(axes):
if 0 in axes:
raise ValueError(f"{msg_prefix} 'axes' cannot contain 0, but got axes: {axes}.")
if len(axes) != 2:
raise ValueError(f"{msg_prefix} length of 'axes' must be equal to 2, but got {len(axes)}.")
def _check_2(axes, x1_shape, x2_shape):
if axes[0] > len(x1_shape) or axes[1] > len(x2_shape):
raise ValueError(f"{msg_prefix} axes[0] must be less than or equal to len(x1_shape), "
f"and axes[1] must be less than or equal to len(x2_shape)."
f"But got 'axes': {axes}, 'x1_shape': {x1_shape}, 'x2_shape': {x2_shape}.")
def _check_3(axes, x1_shape, x2_shape):
if axes == 0:
raise ValueError(f"{msg_prefix} 'axes' should not be equal to 0, but got {axes}.")
if axes > len(x1_shape) or axes > len(x2_shape):
raise ValueError(f"{msg_prefix} 'axes' cannot be greater than the length of 'x1_shape' and 'x2_shape', "
f"but got 'axes': {axes}, 'x1_shape': {x1_shape}, 'x2_shape': {x2_shape}.")
if axes is None:
if len(x2_shape) == 2:
axes = [len(x1_shape) - 1, len(x2_shape) - 1]
else:
axes = [len(x1_shape) - 1, len(x2_shape) - 2]
if isinstance(axes, (list, tuple)):
_check_1(axes)
if isinstance(axes, tuple):
axes = list(axes)
validator.check_value_type('axes[0]', axes[0], [int], 'batch_dot')
validator.check_value_type('axes[1]', axes[1], [int], 'batch_dot')
# Reverse if axis < 0
if axes[0] < 0:
axes[0] += len(x1_shape)
if axes[1] < 0:
axes[1] += len(x2_shape)
validator.check_non_negative_int(axes[0], 'reversed axes[0]', 'batch_dot')
validator.check_non_negative_int(axes[1], 'reversed axes[1]', 'batch_dot')
_check_2(axes, x1_shape, x2_shape)
elif isinstance(axes, int):
_check_3(axes, x1_shape, x2_shape)
if axes < 0:
axes = [axes + len(x1_shape), axes + len(x2_shape)]
validator.check_non_negative_int(axes[0], 'reversed axes', 'batch_dot')
else:
axes = [axes, axes]
else:
raise ValueError(f"{msg_prefix} type of 'axes' must be one of those: int, tuple(int), list(int), "
f"but got {type(axes).__name__}.")
return axes
@_primexpr
def _calc_new_shape_batchdot(shape, axes, position=0):
"""
Calculate transpose and reshape parameters for input transformations,
'position' refers to whether tensor is first or second in the op.
"""
axis = axes[position]
contraction_axes = tuple([axis])
prod_contraction = 1
for i in contraction_axes:
prod_contraction *= shape[i]
free_axes = tuple(i for i in range(1, len(shape)) if i not in contraction_axes)
free_dims = tuple(shape[i] for i in free_axes)
prod_free = 1
for free_dim in free_dims:
prod_free *= free_dim
transpose_perm = contraction_axes + free_axes if position else free_axes + contraction_axes
transpose_perm = tuple([0]) + transpose_perm
new_shape = (prod_contraction, prod_free) if position else (prod_free, prod_contraction)
new_shape = tuple([shape[0]]) + new_shape
return new_shape, transpose_perm, free_dims
@_primexpr
def _check_batch_size(x1_batch_size, x2_batch_size, prim_name=None):
"""
Check whether batch size of two inputs are the same
"""
msg_prefix = f"For '{prim_name}', the" if prim_name else "The"
if x1_batch_size != x2_batch_size:
raise ValueError(f"{msg_prefix} inputs 'x1', 'x2' should have the same batch sizes, but got "
f"'x1_batch_size': {x1_batch_size} and 'x2_batch_size': {x2_batch_size}.")
@_primexpr
def _get_output_shape(batch_size, x1_ret, x2_ret):
"""
Compute output shape for batch dot
"""
output_shape = tuple([batch_size]) + x1_ret + x2_ret
return output_shape
[文档]def batch_dot(x1, x2, axes=None):
"""
Computation of batch dot product between samples in two tensors containing batch dims, i.e. `x1` or `x2` 's
first dimension is batch size.
.. math::
output = x1[batch, :] * x2[batch, :]
Args:
x1 (Tensor): First tensor in Batch Dot op with datatype float32 and the rank of `x1` must be greater
than or equal to 2.
x2 (Tensor): Second tensor in Batch Dot op with datatype float32. The datatype of `x2` should
be same as `x1` and the rank of `x2` must be greater than or equal to 2.
axes (Union[int, tuple(int), list(int)]): Single value or tuple/list of length 2 with dimensions
specified for `a` and `b` each. If single value `N` passed, automatically picks up last N dims from
`a` input shape and last N dimensions from `b` input shape in order as axes for each respectively.
Default: ``None`` .
Returns:
Tensor, batch dot product of `x1` and `x2`. For example, the Shape of output
for input `x1` shapes :math:`(batch, d1, axes, d2)` and
`x2` shapes :math:`(batch, d3, axes, d4)` is :math:`(batch, d1, d2, d3, d4)`,
where d1 and d2 means any number.
Raises:
TypeError: If type of x1 and x2 are not the same.
TypeError: If dtype of x1 or x2 is not float32.
ValueError: If rank of x1 or x2 less than 2.
ValueError: If batch dim used in axes.
ValueError: If len(axes) less than 2.
ValueError: If axes is not one of those: None, int, (int, int).
ValueError: If axes reversed from negative int is too low for dimensions of input arrays.
ValueError: If axes value is too high for dimensions of input arrays.
ValueError: If batch size of x1 and x2 are not the same.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> from mindspore import Tensor, ops
>>> import numpy as np
>>> x1 = Tensor(np.ones(shape=[2, 2, 3]), mindspore.float32)
>>> x2 = Tensor(np.ones(shape=[2, 3, 2]), mindspore.float32)
>>> axes = (-1, -2)
>>> output = ops.batch_dot(x1, x2, axes)
>>> print(output)
[[[3. 3.]
[3. 3.]]
[[3. 3.]
[3. 3.]]]
>>> x1 = Tensor(np.ones(shape=[2, 2]), mindspore.float32)
>>> x2 = Tensor(np.ones(shape=[2, 3, 2]), mindspore.float32)
>>> axes = (1, 2)
>>> output = ops.batch_dot(x1, x2, axes)
>>> print(output)
[[2. 2. 2.]
[2. 2. 2.]]
>>> print(output.shape)
(2, 3)
>>> x1 = Tensor(np.ones(shape=[6, 2, 3, 4]), mindspore.float32)
>>> x2 = Tensor(np.ones(shape=[6, 5, 4, 8]), mindspore.float32)
>>> output = ops.batch_dot(x1, x2)
>>> print(output.shape)
(6, 2, 3, 5, 8)
>>> x1 = Tensor(np.ones(shape=[2, 2, 4]), mindspore.float32)
>>> x2 = Tensor(np.ones(shape=[2, 5, 4, 5]), mindspore.float32)
>>> output = ops.batch_dot(x1, x2)
>>> print(output.shape)
(2, 2, 5, 5)
"""
squeeze_one_op = _get_cache_prim(P.Squeeze)(1)
squeeze_minus_one_op = _get_cache_prim(P.Squeeze)(-1)
# input validity checks
x1_shape = shape_(x1)
x2_shape = shape_(x2)
x1_dim_num = len(x1_shape)
x2_dim_num = len(x2_shape)
x1_type = dtype_(x1)
x2_type = dtype_(x2)
x1_batch_size, x2_batch_size = _get_batch_size(x1_shape, x2_shape, 'batch_dot')
_typecheck_input_batch_dot(x1_type, x2_type, 'batch_dot')
_check_batch_size(x1_batch_size, x2_batch_size, 'batch_dot')
axes = _check_axes_for_batch_dot(x1_shape, x2_shape, axes, 'batch_dot')
if x1_dim_num == 2:
x1 = F.expand_dims(x1, 1)
axes[0] += 1
if x2_dim_num == 2:
x2 = F.expand_dims(x2, 2)
x1_shape = shape_(x1)
x2_shape = shape_(x2)
x1_reshape_fwd, x1_transpose_fwd, x1_ret = _calc_new_shape_batchdot(x1_shape, axes, 0)
x2_reshape_fwd, x2_transpose_fwd, x2_ret = _calc_new_shape_batchdot(x2_shape, axes, 1)
output_shape = _get_output_shape(x1_batch_size, x1_ret, x2_ret)
x1_transposed = transpose_(x1, x1_transpose_fwd)
x2_transposed = transpose_(x2, x2_transpose_fwd)
x1_reshaped = reshape_(x1_transposed, x1_reshape_fwd)
x2_reshaped = reshape_(x2_transposed, x2_reshape_fwd)
# Batch matmal op part
mul_result = batch_matmul_(x1_reshaped, x2_reshaped)
final_result = reshape_(mul_result, output_shape)
# if the original dims are expanded, restore them from 3 to 2
if x1_dim_num == 2:
final_result = squeeze_one_op(final_result)
elif x2_dim_num == 2:
final_result = squeeze_minus_one_op(final_result)
return final_result
[文档]def round(input, *, decimals=0):
r"""
Returns half to even of a tensor element-wise.
.. math::
out_i \approx input_i
.. note::
The input data types supported by the Ascend platform include
bfloat16 (Atlas training series products are not supported), float16, float32, float64, int32, and int64.
Args:
input (Tensor): The input tensor.
Keyword Args:
decimals (int, optional): Number of decimal places to round to (default: 0). If decimals is negative,
it specifies the number of positions to the left of the decimal point. It supports converting the
single-element tensor to an int.
Returns:
Tensor, has the same shape and type as the `input`.
Raises:
TypeError: If `input` is not a Tensor.
Supported Platforms:
``Ascend`` ``GPU`` ``CPU``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> input = Tensor(np.array([0.8, 1.5, 2.3, 2.5, -4.5]), mindspore.float32)
>>> output = ops.round(input)
>>> print(output)
[ 1. 2. 2. 2. -4.]
>>> input = Tensor(np.array([0.81, 1.52, 2.35, 2.53, -4.57]), mindspore.float32)
>>> output = ops.round(input, decimals=1)
>>> print(output)
[ 0.8 1.5 2.4 2.5 -4.6]
"""
return round_op(input, decimals)
def isnan_ext(tensor):
r"""
Returns a new tensor with boolean elements representing if each element of input is :math:`Nan` or not.
Complex values are considered NaN when either their real and/or imaginary part is :math:`Nan`.
.. warning::
This is an experimental API that is subject to change or deletion.
Args:
input (Tensor): The input tensor.
Returns:
Tensor, return a Boolean Tensor. If the input is :math:`Nan`, the value is ``True``.
Otherwise, the value is ``False``.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore as ms
>>> import numpy as np
>>> input1 = Tensor([np.nan, 2, 3, 4])
>>> output = ms.mint.isnan(input1)
>>> print(output)
[ True False False False]
"""
return not_equal_op(tensor, tensor)
def rotated_iou(boxes, query_boxes, trans=False, mode=0, is_cross=True, v_threshold=0.0, e_threshold=0.0):
r"""
Calculate the overlap area between rotated rectangles.
.. note::
The input data types supported by the Ascend platform include
bfloat16, float16, float32.
Args:
boxes (Tensor): The first set of rectangles which has a
shape of :math:`(B, N, 5)`.
query_boxes (Tensor): The second set of rectangles which
has a shape of :math:`(B, K, 5)`.
trans (bool): Distinguish the rectangles representations
of boxes and query_boxes. If ``True``, the format of boxes
and query_boxes is ``'xyxyt'``, else the format is ``'xywht'``.
The default value is ``False``.
mode (int): Distinguish the calculation mode. If the value
is ``1``, the calculation mode is ``'iof'``, else the
calculation mode is ``'iou'``. The default value is ``0``.
is_cross (bool): If ``True``, use cross-calculation, else use
one-to-one calculation. The default value is ``True``.
v_threshold (float): Provide condition relaxation for
intersection calculation. The default value is ``0.0``.
e_threshold (float): Provide condition relaxation for
intersection calculation. The default value is ``0.0``.
Returns:
Tensor, the shape is :math:`(B, N, K)`.
Raises:
TypeError: If `boxes` is not a Tensor.
TypeError: If `query_boxes` is not a Tensor.
ValueError: If `boxes` and `query_boxes` do not has same first dim.
ValueError: If the third dimension of `boxes` or `query_boxes` is not ``5``.
Supported Platforms:
``Ascend``
Examples:
>>> import mindspore
>>> import numpy as np
>>> from mindspore import Tensor, ops
>>> a = np.random.uniform(0,1,(2,2,5)).astype(np.float16)
>>> b = np.random.uniform(0,1,(2,3,5)).astype(np.float16)
>>> box1 = Tensor(a)
>>> box2 = Tensor(b)
>>> output = ops.rotated_iou(box1, box2, trans=False, mode=0, is_cross=True)
"""
origin_dtype = boxes.dtype
if (origin_dtype != mstype.float16 and origin_dtype != mstype.float32
and origin_dtype != mstype.bfloat16):
raise ValueError(f"input boxes type is illegal.")
if (query_boxes.dtype != mstype.float16 and query_boxes.dtype != mstype.float32
and query_boxes.dtype != mstype.bfloat16):
raise ValueError(f"input query_boxes type is illegal.")
boxes_perm = (0, 2, 1)
boxes_cp = permute(boxes, boxes_perm)
if boxes_cp.dtype == mstype.float16 or boxes_cp.dtype == mstype.bfloat16:
boxes_cp = cast_(boxes_cp, mstype.float32)
query_boxes_perm = (0, 2, 1)
query_boxes_cp = permute(query_boxes, query_boxes_perm)
if query_boxes_cp.dtype == mstype.float16 or query_boxes_cp.dtype == mstype.bfloat16:
query_boxes_cp = cast_(query_boxes_cp, mstype.float32)
iou = rotated_iou_op(boxes_cp, query_boxes_cp, trans, mode, is_cross, v_threshold, e_threshold)
return cast_(iou, origin_dtype)
__all__ = [
'addn',
'absolute',
'abs',
'bucketize',
'tensor_add',
'add',
'addbmm',
'addcdiv',
'addcmul',
'angle',
'argmin',
'arccosh',
'arcsin',
'arctan',
'arctan2',
'bincount',
'neg',
'negative',
'tensor_lt',
'less',
'lt',
'logaddexp2',
'tensor_le',
'lcm',
'le',
'lerp',
'norm',
'vector_norm',
'matrix_norm',
'tensor_gt',
'logaddexp',
'mv',
'addmm',
'addmv',
'adjoint',
'outer',
'gt',
'tensor_ge',
'ge',
'addr',
'tensor_sub',
'sub',
'subtract',
'tensor_mul',
'mul',
'multiply',
'nan_to_num',
'nansum',
'nanmean',
'nanmedian',
'digamma',
'lgamma',
'tensor_div',
'div',
'divide',
'true_divide',
'tensor_floordiv',
'floor_div',
'floor_divide',
'floordiv',
'float_power',
'fmod',
'xdivy',
'tensor_pow',
'pow',
'pows',
'renorm',
'tensor_mod',
'floor_mod',
'floormod',
'tensor_exp',
'exp',
'tensor_expm1',
'expm1',
'eq',
'equal',
'not_equal',
'ne',
'numel',
'permute',
'inplace_update',
'inplace_add',
'inplace_sub',
'isfinite',
'isnan',
'isnan_ext',
'isclose',
'isreal',
'isneginf',
'isposinf',
'is_complex',
'log',
'logdet',
'log_matrix_determinant',
'matrix_determinant',
'det',
'linspace',
'logspace',
'lu_solve',
'matrix_solve',
'std',
'maximum',
'minimum',
'median',
'positive',
'floor',
'logical_not',
'logical_or',
'logical_and',
'logit',
'gcd',
'logcumsumexp',
'logsumexp',
'ldexp',
'rsqrt',
'reciprocal',
'real',
'sqrt',
'square',
't',
'sin',
'cos',
'tan',
'asin',
'acos',
'arccos',
'atan',
'sinc',
'sinh',
'cosh',
'tanh',
'tanhshrink',
'asinh',
'arcsinh',
'acosh',
'atanh',
'arctanh',
'atan2',
'round',
'bitwise_and',
'bitwise_or',
'bitwise_xor',
'bitwise_left_shift',
'bitwise_right_shift',
'inv',
'inverse',
'invert',
'erf',
'erfc',
'cdist',
'ceil',
'bernoulli',
'heaviside',
'hypot',
'i0',
'bessel_j0',
'bessel_j1',
'bessel_i0',
'bessel_i0e',
'bessel_k0',
'bessel_k0e',
'bessel_y0',
'bessel_y1',
'bessel_i1',
'bessel_i1e',
'bessel_k1',
'bessel_k1e',
'exp2',
'deg2rad',
'stft',
'rad2deg',
'truncate_div',
'truncate_mod',
'trunc',
'gumbel_softmax',
'kaiser_window',
'matmul',
'inner',
'cummin',
'cummax',
'cumsum',
'amin',
'amax',
'mean',
'prod',
'all',
'any',
'sparse_segment_mean',
'block_diag',
'atleast_1d',
'dstack',
'diff',
'diff_ext',
'atleast_2d',
'cartesian_prod',
'atleast_3d',
'view_as_real',
'vstack',
'vander',
'row_stack',
'var',
'var_mean',
'std_mean',
'combinations',
'dist',
'copysign',
'hann_window',
'log2',
'slogdet',
'trace',
'xlogy',
'log10',
'log1p',
'approximate_equal',
'frac',
'kron',
'rot90',
'remainder',
'sgn',
'sign',
'signbit',
'accumulate_n',
'iou',
'rotated_iou',
'baddbmm',
'baddbmm_ext',
'bmm',
'trapz',
'cholesky',
'cholesky_inverse',
'cholesky_solve',
'conj',
'cosine_similarity',
'cov',
'cross',
'einsum',
'erfinv',
'less_equal',
'cumprod',
'greater',
'greater_equal',
'igamma',
'igammac',
'isinf',
'logical_xor',
'imag',
'roll',
'sum',
'matrix_exp',
'matrix_power',
'orgqr',
'ormqr',
'diag_embed',
'fmax',
'fmin',
'inplace_index_add',
'lu_unpack',
'nanquantile',
'polar',
'polygamma',
'quantile',
'tril_indices',
'histc',
'nextafter',
'triu_indices',
'zeta',
'fft',
'fft2',
'fftn',
'ifft',
'ifft2',
'ifftn',
'count_nonzero',
'tensor_dot',
'vecdot',
'dot',
'batch_dot',
'eps',
]
__all__.sort()