forked from mindspore-Ecosystem/mindspore
72 lines
2.3 KiB
Python
72 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.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.ops import operations as P
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def soft_shrink_op_np_bencmark(input_x, lambd):
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result = input_x.asnumpy().copy()
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size = input_x.size
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result = result.reshape(size)
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for index in range(size):
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if result[index] > lambd:
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result[index] = result[index] - lambd
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elif result[index] < -lambd:
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result[index] = result[index] + lambd
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else:
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result[index] = 0
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result = result.reshape(input_x.shape)
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return result
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class SoftShrinkNet(nn.Cell):
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def __init__(self, lambd):
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super(SoftShrinkNet, self).__init__()
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self.soft_shrink = P.SoftShrink(lambd)
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def construct(self, input_x):
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return self.soft_shrink(input_x)
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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.float32])
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@pytest.mark.parametrize("data_shape", [(3, 4), (4, 5, 6, 7)])
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@pytest.mark.parametrize("lambd", [0.5])
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def test_soft_shrink(dtype, data_shape, lambd):
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"""
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Feature: SoftShrink cpu kernel
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Description: test the rightness of SoftShrink cpu kernel
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Expectation: the output is same as soft_shrink_op_np_bencmark output
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"""
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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data = np.random.uniform(low=-1, high=1, size=data_shape).astype(dtype)
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input_tensor = Tensor(data)
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benchmark_output = soft_shrink_op_np_bencmark(input_tensor, lambd)
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soft_shrink_net = SoftShrinkNet(lambd)
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output = soft_shrink_net(input_tensor)
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np.testing.assert_array_almost_equal(output.asnumpy(), benchmark_output)
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