forked from mindspore-Ecosystem/mindspore
55 lines
1.9 KiB
Python
55 lines
1.9 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.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.ops import operations as P
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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@pytest.mark.level0
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@pytest.mark.platform_x86_cpu
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@pytest.mark.env_onecard
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@pytest.mark.parametrize('dtype', [np.float16, np.float32, np.float64, np.int32])
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def test_relu_v2(dtype):
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"""
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Feature: ReLUV2 cpu kernel
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Description: test the rightness of ReLUV2 cpu kernel, note: the mask output is useless.
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Expectation: the output[0] is same as numpy
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"""
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class Net4DReLUV2(nn.Cell):
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def __init__(self):
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super(Net4DReLUV2, self).__init__()
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self.reluv2 = P.ReLUV2()
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self.x = Parameter(Tensor(np.array([[[[-1, 1, 10],
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[1, -1, 1],
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[10, 1, -1]]]], dtype=dtype)), name='x')
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def construct(self):
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return self.reluv2(self.x)
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reluv2 = Net4DReLUV2()
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output, _ = reluv2()
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expect = np.array([[[[0, 1, 10],
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[1, 0, 1],
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[10, 1, 0]]]], dtype=dtype)
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assert np.allclose(output.asnumpy(), expect)
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