# Copyright 2020 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.
# ============================================================================
"""math"""
import math
from mindspore.ops import operations as P
from mindspore.ops.operations import _inner_ops as inner
from mindspore.common.tensor import Tensor
from ..cell import Cell
from ...common import dtype as mstype
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
__all__ = ['ReduceLogSumExp', 'Range', 'LinSpace']
[docs]class ReduceLogSumExp(Cell):
r"""
Reduce a dimension of a tensor by calculating exponential for all elements in the dimension,
then calculate logarithm of the sum.
The dtype of the tensor to be reduced is number.
Args:
keep_dims (bool): If True, keep these reduced dimensions and the length is 1.
If False, don't keep these dimensions.
Default : False.
Inputs:
- **input_x** (Tensor[Number]) - The input tensor.
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
Only constant value is allowed.
Outputs:
Tensor, has the same dtype as the 'input_x'.
- 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:`(x_1, x_3, ..., x_R)`.
- If axis is tuple(int), set as (2, 3), and keep_dims is false,
the shape of output is :math:`(x_1, x_4, ..., x_R)`.
Examples:
>>> input_x = Tensor(np.random.randn(3, 4, 5, 6).astype(np.float32))
>>> op = P.ReduceLogSumExp(keep_dims=True)
>>> output = op(input_x, 1)
"""
def __init__(self, axis, keep_dims=False):
super(ReduceLogSumExp, self).__init__()
validator.check_value_type('axis', axis, [int, list, tuple], self.cls_name)
validator.check_value_type('keep_dims', keep_dims, [bool], self.cls_name)
self.axis = axis
self.exp = P.Exp()
self.sum = P.ReduceSum(keep_dims)
self.log = P.Log()
def construct(self, input_x):
exp = self.exp(input_x)
sumexp = self.sum(exp, self.axis)
logsumexp = self.log(sumexp)
return logsumexp
[docs]class Range(Cell):
r"""
Creates a sequence of numbers.
Args:
start (Union[int, float]): If `limit` is `None`, the value acts as limit in the range and first entry
defaults to `0`. Otherwise, it acts as first entry in the range.
limit (Union[int, float]): Acts as upper limit of sequence. If `None`, defaults to the value of `start`
while set the first entry of the range to `0`. It can not be equal to `start`.
delta (Union[int, float]): Increment of the range. It can not be equal to zero. Default: 1.
Outputs:
Tensor, the dtype is int if the dtype of `start`, `limit` and `delta` all are int. Otherwise, dtype is float.
Examples:
>>> net = nn.Range(1, 8, 2)
>>> out = net()
[1, 3, 5, 7]
"""
def __init__(self, start, limit=None, delta=1):
super(Range, self).__init__()
validator.check_value_type("start", start, [int, float], self.cls_name)
validator.check_value_type("delta", delta, [int, float], self.cls_name)
if delta == 0:
raise ValueError("The input of `delta` can not be equal to zero.")
if limit is not None:
validator.check_value_type("limit", limit, [int, float], self.cls_name)
if isinstance(start, int) and isinstance(limit, int) and isinstance(delta, int):
self.dtype = mstype.int32
else:
self.dtype = mstype.float32
else:
if isinstance(start, int) and isinstance(delta, int):
self.dtype = mstype.int32
else:
self.dtype = mstype.float32
if isinstance(start, int):
start = float(start)
if isinstance(limit, int):
limit = float(limit)
if isinstance(delta, int):
delta = float(delta)
self.range_x = inner.Range(start, limit, delta)
if limit is None:
length_input = math.ceil(start / delta)
else:
length_input = math.ceil((limit - start) / delta)
self.input_tensor = Tensor(list(range(length_input)), self.dtype)
def construct(self):
range_out = self.range_x(self.input_tensor)
return range_out
[docs]class LinSpace(Cell):
r"""
Generates values in an interval. And return the corresponding interpolation accroding to assist.
Args:
- **start** (Union[int, float]) - The start of interval, With shape of 0-D.
- **stop** (Union[int, float]) - The end of interval, With shape of 0-D.
- **num** (int) - ticks number in the interval, the ticks include start and stop value.
With shape of 0-D.
Outputs:
Tensor, With type same as `start`. The shape is 1-D with length of `num`.
Examples:
>>> linspace = nn.LinSpace()
>>> start = Tensor(1, mindspore.float32)
>>> stop = Tensor(10, mindspore.float32)
>>> num = Tensor(5, mindspore.int32)
>>> output = linspace(start, stop, num)
[1, 3.25, 5.5, 7.75, 10]
"""
def __init__(self, start, stop, num):
super(LinSpace, self).__init__()
validator.check_value_type("start", start, [int, float], self.cls_name)
validator.check_value_type("stop", stop, [int, float], self.cls_name)
validator.check_value_type("num", num, [int], self.cls_name)
validator.check_integer("num", num, 0, Rel.GT, self.cls_name)
self.is_single = bool(num == 1)
self.lin_space = inner.LinSpace()
self.start = Tensor(start, mstype.float32)
self.stop = Tensor(stop, mstype.float32)
self.assist = Tensor(list(range(num)), mstype.float32)
self.num = Tensor(num, mstype.int32)
self.start_array = Tensor([start], mstype.float32)
def construct(self):
if self.is_single:
return self.start_array
lin_space_out = self.lin_space(self.assist, self.start, self.stop, self.num)
return lin_space_out