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# ============================================================================ 15import numpy as np 16import pytest 17from cus_square import CusSquare 18 19import mindspore.context as context 20import mindspore.nn as nn 21from mindspore import Tensor 22from mindspore.ops import composite as C 23context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 24 25 26grad_with_sens = C.GradOperation(sens_param=True) 27 28 29class Net(nn.Cell): 30 """Net definition""" 31 32 def __init__(self): 33 super(Net, self).__init__() 34 self.square = CusSquare() 35 36 def construct(self, data): 37 return self.square(data) 38 39 40@pytest.mark.level0 41@pytest.mark.platform_x86_ascend_training 42@pytest.mark.platform_arm_ascend_training 43@pytest.mark.env_onecard 44def test_net(): 45 x = np.array([1.0, 4.0, 9.0]).astype(np.float32) 46 square = Net() 47 output = square(Tensor(x)) 48 expect = np.array([1.0, 16.0, 81.0]).astype(np.float32) 49 assert (output.asnumpy() == expect).all() 50 51@pytest.mark.level1 52@pytest.mark.platform_x86_ascend_training 53@pytest.mark.platform_arm_ascend_training 54@pytest.mark.env_onecard 55def test_grad_net(): 56 x = np.array([1.0, 4.0, 9.0]).astype(np.float32) 57 sens = np.array([1.0, 1.0, 1.0]).astype(np.float32) 58 square = Net() 59 dx = grad_with_sens(square)(Tensor(x), Tensor(sens)) 60 expect = np.array([2.0, 8.0, 18.0]).astype(np.float32) 61 assert (dx.asnumpy() == expect).all() 62