102 lines
2.8 KiB
Python
102 lines
2.8 KiB
Python
#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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"""
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@author:andrew
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@file:Hybrid_Net003.py
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@email:admin@marques22.com
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@email:2021022362@m.scnu.edu.cn
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@time:2022/09/30
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"""
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import os
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import torch
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from torch import nn
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from torchinfo import summary
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from torch import cat
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os.environ["CUDA_VISIBLE_DEVICES"] = "1"
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# 修改激活函数
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# 提高呼吸采样率
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# 输入时长
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WHOLE_SEGMENT_SECOND = 30
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# 呼吸采样率
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RESPIRATORY_FRE = 10
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# BCG 时频图大小
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BCG_GRAPH_SIZE = (26, 121)
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class HYBRIDNET003(nn.Module):
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def __init__(self, num_classes=2, init_weights=True):
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super(HYBRIDNET003, self).__init__()
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self.lstm = nn.LSTM(input_size=26,
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hidden_size=16,
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num_layers=1,
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bidirectional=True,
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batch_first=True)
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self.right = nn.Sequential(
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nn.Conv1d(in_channels=1, out_channels=8, kernel_size=20,
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stride=2, padding=10),
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nn.GELU(),
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nn.MaxPool1d(kernel_size=3, stride=2, padding=1),
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nn.BatchNorm1d(8),
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nn.Conv1d(in_channels=8, out_channels=16, kernel_size=3,
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stride=1, padding=1),
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nn.GELU(),
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nn.MaxPool1d(kernel_size=3, stride=2, padding=1),
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nn.BatchNorm1d(16),
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nn.Conv1d(in_channels=16, out_channels=32, kernel_size=3,
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stride=1, padding=1),
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nn.GELU(),
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nn.MaxPool1d(kernel_size=3, stride=2, padding=1),
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nn.BatchNorm1d(32)
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)
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self.classifier = nn.Sequential(
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# nn.Dropout(p=0.5),
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nn.Linear(4480, 512),
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nn.GELU(),
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nn.Linear(512, num_classes),
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)
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if init_weights:
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self.initialize_weights()
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def initialize_weights(self):
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for m in self.modules():
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if isinstance(m, (nn.Conv2d, nn.Conv1d)):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') # 何教授方法
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.normal_(m.weight, 0, 0.01) # 正态分布赋值
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nn.init.constant_(m.bias, 0)
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def forward(self, x1, x2):
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# x1 = x1.view(x1.shape[0], -1, 5)
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x1 = self.right(x1)
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x2, (_, _) = self.lstm(x2)
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# print(x1.shape)
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# print(x2.shape)
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x1 = torch.flatten(x1, start_dim=1)
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x2 = torch.flatten(x2, start_dim=1)
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x = torch.cat((x1, x2), dim=1)
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# print(x.shape)
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x = self.classifier(x)
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return x
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if __name__ == '__main__':
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model = HYBRIDNET003().cuda()
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summary(model, [(32, 1, 30 * RESPIRATORY_FRE), (32, 121, 26)])
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