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 NetSigmoidGrad(nn.Cell): 26 def __init__(self): 27 super(NetSigmoidGrad, self).__init__() 28 self.sigmoid_grad = G.SigmoidGrad() 29 30 def construct(self, y, dy): 31 return self.sigmoid_grad(y, dy) 32 33 34@pytest.mark.level0 35@pytest.mark.platform_x86_gpu_training 36@pytest.mark.env_onecard 37def test_sigmoid_grad(): 38 y = Tensor(np.array([[[[-1, 1, 2], 39 [1, -1, 1], 40 [2, 1, -1]]]]).astype(np.float32)) 41 dy = Tensor(np.array([[[[-11, 2, 4], 42 [-1, 1, -1], 43 [-4, 4, -4]]]]).astype(np.float32)) 44 45 expect = np.array([[[[22, 0, -8], 46 [0, -2, 0], 47 [8, 0, 8]]]]).astype(np.float32) 48 49 error = np.ones(shape=[1, 1, 3, 3]) * 1.0e-6 50 51 context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU") 52 sigmoid_grad = NetSigmoidGrad() 53 output = sigmoid_grad(y, dy) 54 diff = output.asnumpy() - expect 55 assert np.all(abs(diff) < error) 56 57 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 58 sigmoid_grad = NetSigmoidGrad() 59 output = sigmoid_grad(y, dy) 60 diff = output.asnumpy() - expect 61 assert np.all(abs(diff) < error) 62