forked from mindspore-Ecosystem/mindspore
93 lines
3.1 KiB
Python
93 lines
3.1 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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import mindspore.ops as ops
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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 ops.tanhshrink(x)
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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_tanhshrink_normal(mode):
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"""
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Feature: Tanhshrink
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Description: Verify the result of Tanhshrink with normal input
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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a = Tensor(np.array([1, 2, 3, 2, 1]).astype(np.float16))
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output = net(a).asnumpy()
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expected_output = np.array([0.2383, 1.036, 2.004, 1.036, 0.2383]).astype(np.float16)
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assert np.allclose(output, expected_output, 1e-3, 1e-3)
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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_tanhshrink_negative(mode):
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"""
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Feature: Tanhshrink
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Description: Verify the result of Tanhshrink with negative input
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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a = Tensor(np.array([-1, -2, -3, -2, -1]).astype(np.float16))
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output = net(a).asnumpy()
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expected_output = np.array([-0.2383, -1.036, -2.004, -1.036, -0.2383]).astype(np.float16)
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assert np.allclose(output, expected_output, 1e-3, 1e-3)
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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_tanhshrink_zeros(mode):
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"""
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Feature: Tanhshrink
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Description: Verify the result of Tanhshrink with zeros
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Expectation: success
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"""
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ms.set_context(mode=mode)
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net = Net()
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a = Tensor(np.array([0, 0, 0, 0, 0]).astype(np.float16))
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output = net(a).asnumpy()
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expected_output = np.array([0, 0, 0, 0, 0]).astype(np.float16)
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assert np.allclose(output, expected_output, 1e-3, 1e-3)
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