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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
-
- import numpy as np
- import pytest
-
- import mindspore.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops.operations import _grad_ops as G
-
-
- class NetSqrtGrad(nn.Cell):
- def __init__(self):
- super(NetSqrtGrad, self).__init__()
- self.sqrt_grad = G.SqrtGrad()
-
- def construct(self, x, dx):
- return self.sqrt_grad(x, dx)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_sqrt_grad():
- x = Tensor(np.array([[[[-1, 1, 10],
- [5.9, 6.1, 6],
- [10, 1, -1]]]]).astype(np.float32))
- dx = Tensor(np.array([[[[1, 1, 1],
- [2, 2, 2],
- [3, 3, 3]]]]).astype(np.float32))
- expect = np.array([[[[-0.5, 0.5, 0.05,],
- [0.16949153, 0.16393442, 0.16666667,],
- [0.15, 1.5, -1.5,]]]]).astype(np.float32)
- error = np.ones(shape=[3, 3]) * 1.0e-6
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
- sqrt_grad = NetSqrtGrad()
- output = sqrt_grad(x, dx)
- diff = output.asnumpy() - expect
- assert np.all(np.abs(diff) < error)
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