forked from mindspore-Ecosystem/mindspore
71 lines
2.3 KiB
Python
71 lines
2.3 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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class Net(nn.Cell):
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def construct(self, x):
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return x.adjoint()
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_tensor_adjoint(mode):
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"""
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Feature: tensor.adjoint
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Description: Verify the result of adjoint
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Expectation: success, however, when running on Ascend, transpose does not support Complex numbers.
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"""
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ms.set_context(mode=mode)
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x = Tensor(np.array([[0., 1.], [2., 3.]]), ms.float32)
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net = Net()
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output = net(x)
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expect_output = np.array([[0., 2.],
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[1., 3.]])
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assert np.allclose(output.asnumpy(), expect_output)
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.platform_arm_cpu
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
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def test_tensor_adjoint_complex(mode):
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"""
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Feature: tensor.adjoint
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Description: Verify the result of adjoint
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Expectation: success.
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"""
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ms.set_context(mode=mode)
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x = Tensor(np.array([[0. + 0.j, 1. + 1.j], [2. + 2.j, 3. + 3.j]]), ms.complex128)
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net = Net()
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output = net(x)
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expect_output = np.array([[0. - 0.j, 2. - 2.j],
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[1. - 1.j, 3. - 3.j]])
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assert np.allclose(output.asnumpy(), expect_output)
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