1# Copyright 2020 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18 19import mindspore.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops.operations import _grad_ops as G 23 24 25class NetSqrtGrad(nn.Cell): 26 def __init__(self): 27 super(NetSqrtGrad, self).__init__() 28 self.sqrt_grad = G.SqrtGrad() 29 30 def construct(self, x, dx): 31 return self.sqrt_grad(x, dx) 32 33 34@pytest.mark.level0 35@pytest.mark.platform_x86_gpu_training 36@pytest.mark.env_onecard 37def test_sqrt_grad(): 38 x = Tensor(np.array([[[[-1, 1, 10], 39 [5.9, 6.1, 6], 40 [10, 1, -1]]]]).astype(np.float32)) 41 dx = Tensor(np.array([[[[1, 1, 1], 42 [2, 2, 2], 43 [3, 3, 3]]]]).astype(np.float32)) 44 expect = np.array([[[[-0.5, 0.5, 0.05,], 45 [0.16949153, 0.16393442, 0.16666667,], 46 [0.15, 1.5, -1.5,]]]]).astype(np.float32) 47 error = np.ones(shape=[3, 3]) * 1.0e-6 48 49 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 50 sqrt_grad = NetSqrtGrad() 51 output = sqrt_grad(x, dx) 52 diff = output.asnumpy() - expect 53 assert np.all(np.abs(diff) < error) 54