1# Copyright 2019 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 16 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.common.api import ms_function 21from mindspore.ops import operations as P 22from mindspore.ops.composite import GradOperation 23 24context.set_context(device_target="Ascend") 25 26 27class Grad(nn.Cell): 28 def __init__(self, network): 29 super(Grad, self).__init__() 30 self.grad = GradOperation(get_all=True, sens_param=True) 31 self.network = network 32 33 @ms_function 34 def construct(self, input_, output_grad): 35 return self.grad(self.network)(input_, output_grad) 36 37 38class Net(nn.Cell): 39 def __init__(self): 40 super(Net, self).__init__() 41 self.maxpool = P.MaxPool(pad_mode="SAME", window=3, stride=2) 42 43 @ms_function 44 def construct(self, x): 45 output = self.maxpool(x) 46 return output 47 48 49def test_net(): 50 x = np.random.randn(32, 64, 112, 112).astype(np.float32) 51 output_grad = np.random.randn(32, 64, 56, 56).astype(np.float32) 52 net = Grad(Net()) 53 output = net(Tensor(x), Tensor(output_grad)) 54 if isinstance(output, (tuple, list)): 55 output = output[0] 56 print(output.asnumpy()) 57