1# Copyright 2021 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.common.api import ms_function 23from mindspore.ops import operations as P 24from mindspore.ops.composite import GradOperation 25 26context.set_context(mode=context.GRAPH_MODE, device_target="CPU") 27 28 29class Grad(nn.Cell): 30 def __init__(self, network): 31 super(Grad, self).__init__() 32 self.grad = GradOperation(get_all=True, sens_param=True) 33 self.network = network 34 35 @ms_function 36 def construct(self, input_, output_grad): 37 return self.grad(self.network)(input_, output_grad) 38 39 40class Net(nn.Cell): 41 def __init__(self): 42 super(Net, self).__init__() 43 self.HSwish = P.HSwish() 44 45 def construct(self, x): 46 return self.HSwish(x) 47 48 49@pytest.mark.level0 50@pytest.mark.platform_x86_cpu 51@pytest.mark.env_onecard 52def test_net(): 53 x = np.array([-1, -2, 0, 2, 1]).astype(np.float32) 54 hswish = Net() 55 y = hswish(Tensor(x)) 56 expect = np.array([-0.33333334, -0.33333334, 0., 1.6666666, 0.6666667]).astype(np.float32) 57 assert np.all(y.asnumpy() == expect) 58 sens = np.random.randn(5).astype(np.float32) 59 backword_net = Grad(Net()) 60 output = backword_net(Tensor(x), Tensor(sens)) 61 print(len(output)) 62 print(output[0].asnumpy()) 63