forked from mindspore-Ecosystem/mindspore
68 lines
2.2 KiB
Python
68 lines
2.2 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.nn as nn
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from mindspore import Tensor, context
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from mindspore.ops import operations as P
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class MishNet(nn.Cell):
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def __init__(self):
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super(MishNet, self).__init__()
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self.mish = P.Mish()
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def construct(self, x):
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output = self.mish(x)
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return output
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def mish_np_bencmark(x):
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"""
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Feature: generate a mish numpy benchmark.
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Description: The input shape match to input.
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Expectation: match to np mindspore mish.
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"""
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result = np.zeros_like(x, dtype=x.dtype)
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for index, _ in np.ndenumerate(x):
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result[index] = x[index] * np.tanh(np.log(np.exp(x[index]) + 1))
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return result
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@pytest.mark.level0
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@pytest.mark.env_onecard
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@pytest.mark.platform_x86_cpu
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@pytest.mark.parametrize("data_shape", [(4,), (3, 4), (4, 5, 7)])
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@pytest.mark.parametrize("data_type", [np.float32, np.float16])
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def test_mish(data_shape, data_type):
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"""
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Feature: Test Mish.
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Description: The output shape match to input shape.
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Expectation: match to np benchmark.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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x = np.random.random(data_shape).astype(data_type)
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error = 1e-6
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if data_type == np.float16:
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error = 1e-3
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benchmark_output = mish_np_bencmark(x)
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mish = MishNet()
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output = mish(Tensor(x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error)
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context.set_context(mode=context.PYNATIVE_MODE)
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output = mish(Tensor(x))
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np.testing.assert_allclose(output.asnumpy(), benchmark_output, rtol=error)
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