# 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.
# ============================================================================
"""inner_ops"""
import numbers
from ..._checkparam import Validator as validator
from ..._checkparam import Rel
from ...common.dtype import tensor, dtype_to_pytype
from ..primitive import prim_attr_register, PrimitiveWithInfer
[docs]class ScalarCast(PrimitiveWithInfer):
"""
Cast the input scalar to another type.
Inputs:
- **input_x** (scalar) - The input scalar. Only constant value is allowed.
- **input_y** (mindspore.dtype) - The type should cast to be. Only constant value is allowed.
Outputs:
Scalar. The type is the same as the python type corresponding to `input_y`.
Examples:
>>> scalar_cast = P.ScalarCast()
>>> output = scalar_cast(255.0, mindspore.int32)
"""
@prim_attr_register
def __init__(self):
pass
def __infer__(self, x, t):
validator.check_integer('x shape', len(x['shape']), 0, Rel.EQ, self.name)
value, to = x['value'], t['value']
if value is not None:
validator.check_value_type("value", value, [numbers.Number, bool], self.name)
if isinstance(to, type(tensor)):
to = to.element_type()
np_type = dtype_to_pytype(to)
value = np_type(value)
out = {'shape': x['shape'],
'dtype': t['value'],
'value': value}
return out