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54 lines
1.8 KiB
54 lines
1.8 KiB
# Copyright 2020 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.operations import _grad_ops as G
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class NetSqrtGrad(nn.Cell):
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def __init__(self):
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super(NetSqrtGrad, self).__init__()
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self.sqrt_grad = G.SqrtGrad()
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def construct(self, x, dx):
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return self.sqrt_grad(x, dx)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_sqrt_grad():
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x = Tensor(np.array([[[[-1, 1, 10],
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[5.9, 6.1, 6],
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[10, 1, -1]]]]).astype(np.float32))
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dx = Tensor(np.array([[[[1, 1, 1],
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[2, 2, 2],
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[3, 3, 3]]]]).astype(np.float32))
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expect = np.array([[[[-0.5, 0.5, 0.05,],
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[0.16949153, 0.16393442, 0.16666667,],
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[0.15, 1.5, -1.5,]]]]).astype(np.float32)
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error = np.ones(shape=[3, 3]) * 1.0e-6
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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sqrt_grad = NetSqrtGrad()
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output = sqrt_grad(x, dx)
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diff = output.asnumpy() - expect
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assert np.all(np.abs(diff) < error)
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