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 16 17import mindspore.context as context 18import mindspore.nn as nn 19from mindspore import Tensor 20from mindspore.ops import operations as P 21 22context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") 23 24 25class Net1(nn.Cell): 26 def __init__(self): 27 super(Net1, self).__init__() 28 self.relu1 = P.ReLU() 29 self.relu2 = P.ReLU() 30 self.mul = P.Mul() 31 self.depend = P.Depend() 32 33 def construct(self, x, y): 34 a = self.relu1(x) 35 y = self.depend(y, a) 36 b = self.relu2(y) 37 c = self.mul(a, b) 38 return c, a 39 40 41class Net2(nn.Cell): 42 def __init__(self): 43 super(Net2, self).__init__() 44 self.relu1 = P.ReLU() 45 self.relu2 = P.ReLU().add_prim_attr("primitive_target", "CPU") 46 self.mul = P.Mul() 47 self.depend = P.Depend() 48 49 def construct(self, x, y): 50 a = self.relu1(x) 51 y = self.depend(y, a) 52 b = self.relu2(y) 53 c = self.mul(a, b) 54 return c, a 55 56 57def test_net(): 58 x = np.random.randn(2, 3, 3, 4).astype(np.float32) 59 y = np.random.randn(2, 3, 3, 4).astype(np.float32) 60 net1 = Net1() 61 output1 = net1(Tensor(x), Tensor(y)) 62 63 net2 = Net2() 64 output2 = net2(Tensor(x), Tensor(y)) 65 assert np.allclose(output1[0].asnumpy(), output2[0].asnumpy()) 66 print("##success##") 67 68 69if __name__ == "__main__": 70 test_net() 71