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- # Copyright 2019 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 pytest
- from mindspore import Tensor
- from mindspore.ops import operations as P
- import mindspore.nn as nn
- import numpy as np
- import mindspore.context as context
-
- class AssignAdd(nn.Cell):
- def __init__( self):
- super(AssignAdd, self).__init__()
- self.add = P.AssignAdd()
-
- def construct(self, x, y):
- res = self.add(x, y)
- return res
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_assign_add():
- expect1 = np.array([[[[ 0, 2, 4.],
- [ 6, 8, 10.],
- [12, 14, 16.]],
- [[18, 20, 22.],
- [24, 26, 28.],
- [30, 32, 34.]],
- [[36, 38, 40.],
- [42, 44, 46.],
- [48, 50, 52.]]]])
- expect2 = np.array([[[[ 0, 3, 6],
- [ 9, 12, 15],
- [18, 21, 24]],
- [[27, 30, 33],
- [36, 39, 42],
- [45, 48, 51]],
- [[54, 57, 60],
- [63, 66, 69],
- [72, 75, 78]]]])
- x = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
- y = Tensor(np.arange(1 * 3 * 3 * 3).reshape(1, 3, 3, 3).astype(np.float32))
-
- context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
- add = AssignAdd()
- output1 = add(x, y)
- assert (output1.asnumpy() == expect1).all()
- output2 = add(output1, y)
- assert (output2.asnumpy() == expect2).all()
-
- context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
- add = AssignAdd()
- output1 = add(x, y)
- assert (output1.asnumpy() == expect1).all()
- output2 = add(output1, y)
- assert (output2.asnumpy() == expect2).all()
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