mindspore/tests/syntax/simple_expression/test_hyper_param.py

81 lines
2.7 KiB
Python

# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# 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.
# ============================================================================
import mindspore as ms
from mindspore import Tensor, Parameter
from mindspore.nn import Cell
def test_hyper_param():
"""
Feature: Resolve parameter.
Description: The name of parameter in construct is the same with the name of parameter of class init.
Expectation: self.a is different from a in construct.
"""
class HyperParamNet(Cell):
def __init__(self):
super(HyperParamNet, self).__init__()
self.a = Parameter(Tensor(1, ms.float32), name="a")
self.b = Parameter(Tensor(5, ms.float32), name="param_b")
self.c = Parameter(Tensor(9, ms.float32), name="param_c")
def func_inner(self, c):
return self.a + self.b + c
def construct(self, a, b):
self.a = a
self.b = b
return self.func_inner(self.c)
x = Tensor(11, ms.float32)
y = Tensor(19, ms.float32)
net = HyperParamNet()
output = net(x, y)
output_expect = Tensor(39, ms.float32)
assert output == output_expect
def test_hyper_param_with_control_sink():
"""
Feature: Resolve parameter.
Description: Parameters whose name are the same between different graphs do not affect each other.
Expectation: self.a is different from a in construct.
"""
class HyperParamNet(Cell):
def __init__(self):
super(HyperParamNet, self).__init__()
self.a = Parameter(Tensor(1, ms.float32), name="a")
self.b = Parameter(Tensor(5, ms.float32), name="b")
self.c = Parameter(Tensor(9, ms.float32), name="c")
def func_inner(self, c):
return self.a + self.b + c
def func_inner_2(self, a, c):
return a - self.b + c
def construct(self, a, b):
self.b = b
if a > self.b:
return self.func_inner_2(a, self.c)
return self.func_inner(self.c)
x = Tensor(11, ms.float32)
y = Tensor(19, ms.float32)
net = HyperParamNet()
output = net(x, y)
output_expect = Tensor(29, ms.float32)
assert output == output_expect