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1# Copyright 2020-2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16import numpy as np
17import pytest
18
19import mindspore.context as context
20import mindspore.nn as nn
21from mindspore import Tensor, Parameter
22from mindspore.ops import operations as P
23
24
25class Net(nn.Cell):
26    def __init__(self, param):
27        super(Net, self).__init__()
28        self.var = Parameter(param, name="var")
29        self.assign = P.Assign()
30
31    def construct(self, param):
32        return self.assign(self.var, param)
33
34
35x = np.array([[1.2, 1], [1, 0]]).astype(np.float32)
36value = np.array([[1, 2], [3, 4.0]]).astype(np.float32)
37
38
39@pytest.mark.level0
40@pytest.mark.platform_x86_gpu_training
41@pytest.mark.env_onecard
42def test_assign():
43    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
44    var = Tensor(x)
45    assign = Net(var)
46    output = assign(Tensor(value))
47
48    error = np.ones(shape=[2, 2]) * 1.0e-6
49    diff1 = output.asnumpy() - value
50    diff2 = assign.var.data.asnumpy() - value
51    assert np.all(diff1 < error)
52    assert np.all(-diff1 < error)
53    assert np.all(diff2 < error)
54    assert np.all(-diff2 < error)
55
56
57@pytest.mark.level0
58@pytest.mark.platform_x86_gpu_training
59@pytest.mark.env_onecard
60def test_assign_float64():
61    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
62    var = Tensor(x.astype(np.float64))
63    assign = Net(var)
64    output = assign(Tensor(value.astype(np.float64)))
65
66    error = np.ones(shape=[2, 2]) * 1.0e-6
67    diff1 = output.asnumpy() - value
68    diff2 = assign.var.data.asnumpy() - value
69    assert np.all(diff1 < error)
70    assert np.all(-diff1 < error)
71    assert np.all(diff2 < error)
72    assert np.all(-diff2 < error)
73