forked from mindspore-Ecosystem/mindspore
125 lines
3.6 KiB
Python
125 lines
3.6 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore
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from mindspore import Tensor, nn
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from mindspore.ops import operations as P
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class MulNet(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mul = P.Mul()
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def construct(self, x, y):
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x = self.mul(x, y)
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return x
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class SubNet1(nn.Cell):
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def __init__(self, in_channels=3, out_channels=12):
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super().__init__()
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self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1, weight_init="ones")
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self.bn = nn.BatchNorm2d(out_channels)
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self.relu = P.ReLU()
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self.neg = P.Neg()
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def construct(self, x):
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x = self.conv(x)
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x = self.bn(x)
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x1 = self.relu(x)
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x2 = self.neg(x)
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return x1, x2
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class SubNet2(nn.Cell):
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def __init__(self, in_channels=12, out_channels=12):
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super().__init__()
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self.dense = nn.Dense(in_channels, out_channels, weight_init="ones")
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self.bn = nn.BatchNorm1d(out_channels)
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self.relu = P.ReLU()
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self.mul = P.Mul()
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self.split = P.Split(axis=1, output_num=3)
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self.mean = P.ReduceMean(keep_dims=False)
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def construct(self, x, y):
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x = self.dense(x)
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x = self.bn(x)
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y, _, _ = self.split(y)
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y = self.mean(y, (2, 3))
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x = self.mul(x, y)
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x = self.relu(x)
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return x
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class SubNet3(nn.Cell):
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def __init__(self):
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super().__init__()
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self.mul = MulNet()
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self.add = P.Add()
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self.neg = P.Neg()
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def construct(self, x, y):
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z = self.mul(x, y)
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z_1 = self.add(z, y)
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z_2 = self.neg(z)
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return z_1, z_2
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class SubNet4(nn.Cell):
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def __init__(self):
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super().__init__()
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self.add = P.Add()
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self.neg = P.Neg()
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def construct(self, x, y):
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x = self.add(x, y)
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x = self.neg(x)
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return x
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class CompNet(nn.Cell):
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def __init__(self, mul_size, add_size):
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super().__init__()
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mul_np = np.full(mul_size, 0.1, dtype=np.float32)
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add_np = np.full(add_size, 0.1, dtype=np.float32)
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self.mul_weight = mindspore.Parameter(Tensor(mul_np), name="mul_weight")
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self.add_weight = mindspore.Parameter(Tensor(add_np), name="add_weight")
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self.mul = P.Mul()
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self.add = P.Add()
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self.relu = P.ReLU()
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self.mean = P.ReduceMean(keep_dims=False)
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self.split = P.Split(axis=1, output_num=3)
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self.sub_net_1 = SubNet1()
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self.sub_net_2 = SubNet2()
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self.sub_net_3 = SubNet3()
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self.sub_net_4 = SubNet4()
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def construct(self, inputs):
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x, y = self.sub_net_3(inputs, self.mul_weight)
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x_1, x_2 = self.sub_net_1(x)
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x_1 = self.mean(x_1, (2, 3))
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y_1 = self.add(y, self.add_weight)
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x_3 = self.sub_net_2(x_1, self.add_weight)
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y_1, _, _ = self.split(y_1)
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y_2 = self.add(x_2, y_1)
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y_2 = self.sub_net_4(x_2, y_2)
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y_2 = self.mean(y_2, (2, 3))
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z = self.mul(x_3, y_2)
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z_1 = self.relu(z)
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return z_1
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