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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.
- # ============================================================================
- import numpy as np
- import pytest
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor, Parameter
- import mindspore.common.dtype as mstype
- from mindspore.ops import operations as P
-
- context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.unique = P.Unique()
- self.dynamic_assign = P.DynamicAssign()
- self.param = Parameter(
- Tensor(np.zeros((5,), np.int32)), name="assign_x")
-
- def construct(self, y):
- y, _ = self.unique(y)
- return self.dynamic_assign(self.param, y)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_arm_ascend_training
- @pytest.mark.platform_x86_ascend_training
- @pytest.mark.env_onecard
- def test_dynamic_assign():
- y = Tensor(np.array([2, 2, 3, 3, 4]), mstype.int32)
- dynamic_assign = Net()
- _ = dynamic_assign(y)
- expect1 = np.array([2, 3, 4])
- param_np = dynamic_assign.param.data.asnumpy()
- assert (param_np == expect1).all()
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