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 17 18import mindspore.context as context 19import mindspore.nn as nn 20from mindspore import Tensor 21from mindspore.common.api import ms_function 22from mindspore.ops import operations as P 23from mindspore.ops.composite import GradOperation 24 25context.set_context(mode=context.GRAPH_MODE, device_target='CPU') 26 27 28class Grad(nn.Cell): 29 def __init__(self, network): 30 super(Grad, self).__init__() 31 self.grad = GradOperation(get_all=True, sens_param=True) 32 self.network = network 33 34 @ms_function 35 def construct(self, input_, output_grad): 36 return self.grad(self.network)(input_, output_grad) 37 38 39class Net(nn.Cell): 40 def __init__(self): 41 super(Net, self).__init__() 42 self.ops = P.Neg() 43 44 def construct(self, x): 45 return self.ops(x) 46 47@pytest.mark.level0 48@pytest.mark.platform_x86_cpu 49@pytest.mark.env_onecard 50def test_net(): 51 x = np.random.randn(2, 3, 3, 4).astype(np.float32) 52 y_expect = -x 53 net = Net() 54 out = net(Tensor(x)) 55 assert (out.asnumpy() == y_expect).all() 56 sens = np.random.randn(2, 3, 3, 4).astype(np.float32) 57 backword_net = Grad(Net()) 58 output = backword_net(Tensor(x), Tensor(sens)) 59 print(len(output)) 60 print(output[0].asnumpy()) 61