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- # 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.
- # ============================================================================
- """ test super"""
- import numpy as np
- import pytest
-
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore import context
-
- context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
-
-
- class FatherNet(nn.Cell):
- def __init__(self, x):
- super(FatherNet, self).__init__(x)
- self.x = x
-
- def construct(self, x, y):
- return self.x * x
-
- def test_father(self, x):
- return self.x + x
-
-
- class MatherNet(nn.Cell):
- def __init__(self, y):
- super(MatherNet, self).__init__()
- self.y = y
-
- def construct(self, x, y):
- return self.y * y
-
- def test_mather(self, y):
- return self.y + y
-
-
- class SingleSubNet(FatherNet):
- def __init__(self, x, z):
- super(SingleSubNet, self).__init__(x)
- self.z = z
-
- def construct(self, x, y):
- ret_father_construct = super().construct(x, y)
- ret_father_test = super(SingleSubNet, self).test_father(x)
- ret_father_x = super(SingleSubNet, self).x
- ret_sub_z = self.z
-
- return ret_father_construct, ret_father_test, ret_father_x, ret_sub_z
-
-
- class MulSubNet(FatherNet, MatherNet):
- def __init__(self, x, y, z):
- super(MulSubNet, self).__init__(x)
- super(FatherNet, self).__init__(y)
- self.z = z
-
- def construct(self, x, y):
- ret_father_construct = super().construct(x, y)
- ret_father_test = super(MulSubNet, self).test_father(x)
- ret_father_x = super(MulSubNet, self).x
- ret_mather_construct = super(FatherNet, self).construct(x, y)
- ret_mather_test = super(FatherNet, self).test_mather(y)
- ret_mather_y = super(FatherNet, self).y
- ret_sub_z = self.z
-
- return ret_father_construct, ret_father_test, ret_father_x, \
- ret_mather_construct, ret_mather_test, ret_mather_y, ret_sub_z
-
-
- class Net(nn.Cell):
- def __init__(self, x):
- super(Net, self).__init__()
- self.x = x
-
- def construct(self, x, y):
- ret = super(Net, self).construct(x, y)
- return ret
-
-
- def test_single_super():
- single_net = SingleSubNet(2, 3)
- context.set_context(mode=context.GRAPH_MODE)
- x = Tensor(np.ones([1, 2, 3], np.int32))
- y = Tensor(np.ones([1, 2, 3], np.int32))
- single_net(x, y)
-
-
- def test_mul_super():
- mul_net = MulSubNet(2, 3, 4)
- context.set_context(mode=context.GRAPH_MODE)
- x = Tensor(np.ones([1, 2, 3], np.int32))
- y = Tensor(np.ones([1, 2, 3], np.int32))
- mul_net(x, y)
-
-
- def test_super_cell():
- net = Net(2)
- context.set_context(mode=context.GRAPH_MODE)
- x = Tensor(np.ones([1, 2, 3], np.int32))
- y = Tensor(np.ones([1, 2, 3], np.int32))
- with pytest.raises(RuntimeError) as er:
- net(x, y)
- assert "Unsupported syntax 'Raise'" in str(er.value)
-
-
- def test_single_super_in():
- class FatherNetIn(nn.Cell):
- def __init__(self, x):
- super(FatherNetIn, self).__init__(x)
- self.x = x
-
- def construct(self, x, y):
- return self.x * x
-
- def test_father(self, x):
- return self.x + x
-
- class SingleSubNetIN(FatherNetIn):
- def __init__(self, x, z):
- super(SingleSubNetIN, self).__init__(x)
- self.z = z
-
- def construct(self, x, y):
- ret_father_construct = super().construct(x, y)
- ret_father_test = super(SingleSubNetIN, self).test_father(x)
- ret_father_x = super(SingleSubNetIN, self).x
- ret_sub_z = self.z
-
- return ret_father_construct, ret_father_test, ret_father_x, ret_sub_z
-
- single_net_in = SingleSubNetIN(2, 3)
- context.set_context(mode=context.GRAPH_MODE, save_graphs=True)
- x = Tensor(np.ones([1, 2, 3], np.int32))
- y = Tensor(np.ones([1, 2, 3], np.int32))
- single_net_in(x, y)
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