forked from mindspore-Ecosystem/mindspore
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
# Copyright 2021 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.initializer import initializer
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from mindspore.common.parameter import Parameter
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from mindspore.ops.operations import _grad_ops as G
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context.set_context(mode=context.GRAPH_MODE, device_target='CPU')
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class NetReluGrad(nn.Cell):
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def __init__(self):
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super(NetReluGrad, self).__init__()
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self.relu6_grad = G.ReLU6Grad()
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self.x = Parameter(initializer(Tensor(np.array([[[[1, 0, 6],
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[-2, 3, 6],
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[-3, 1, 8]]]]).astype(np.float32)), [1, 1, 3, 3]), name='x')
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self.dy = Parameter(initializer(Tensor(np.array([[[[1, 2, 3],
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[4, 5, 6],
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[7, 8, 9]]]]).astype(np.float32)), [1, 1, 3, 3]), name='dy')
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def construct(self):
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return self.relu6_grad(self.dy, self.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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def test_relu_grad():
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relu_grad = NetReluGrad()
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output = relu_grad()
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expect = np.array([[[[1, 0, 3], [0, 5, 6], [0, 8, 0]]]]).astype(np.float32)
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error = np.ones(shape=[3, 3]) * 1.0e-6
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diff = np.abs(output.asnumpy() - expect)
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assert np.all(diff < error)
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