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model.py
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model.py
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import torch
import torch.nn as nn
class CNNLSTMModel(nn.Module):
def __init__(self, window=5, dim=4, lstm_units=16, num_layers=2):
super(CNNLSTMModel, self).__init__()
self.conv1d = nn.Conv1d(dim, lstm_units, 1)
self.act1 = nn.Sigmoid()
self.maxPool = nn.MaxPool1d(kernel_size=window)
self.drop = nn.Dropout(p=0.01)
self.lstm = nn.LSTM(lstm_units, lstm_units, batch_first=True, num_layers=1, bidirectional=True)
self.act2 = nn.Tanh()
self.cls = nn.Linear(lstm_units * 2, 1)
self.act4 = nn.Tanh()
def forward(self, x):
x = x.transpose(-1, -2) # tf和torch纬度有点不一样
x = self.conv1d(x) # in： bs, dim, window out: bs, lstm_units, window
x = self.act1(x)
x = self.maxPool(x) # bs, lstm_units, 1
x = self.drop(x)
x = x.transpose(-1, -2) # bs, 1, lstm_units
x, (_, _) = self.lstm(x) # bs, 1, 2*lstm_units
x = self.act2(x)
x = x.squeeze(dim=1) # bs, 2*lstm_units
x = self.cls(x)
x = self.act4(x)
return x
class CNNLSTMModel_ECA(nn.Module):
def __init__(self, window=5, dim=4, lstm_units=16, num_layers=2):
super(CNNLSTMModel_ECA, self).__init__()
self.conv1d = nn.Conv1d(dim, lstm_units, 1)
self.act1 = nn.Sigmoid()
self.maxPool = nn.MaxPool1d(kernel_size=window)
self.drop = nn.Dropout(p=0.01)
self.lstm = nn.LSTM(lstm_units, lstm_units, batch_first=True, num_layers=1, bidirectional=True)
self.act2 = nn.Tanh()
self.attn = nn.Linear(lstm_units * 2, lstm_units * 2)
self.act3 = nn.Sigmoid()
self.cls = nn.Linear(lstm_units * 2, 1)
self.act4 = nn.Tanh()
def forward(self, x):
x = x.transpose(-1, -2) # tf和torch纬度有点不一样
x = self.conv1d(x) # in： bs, dim, window out: bs, lstm_units, window
x = self.act1(x)
x = self.maxPool(x) # bs, lstm_units, 1
x = self.drop(x)
x = x.transpose(-1, -2) # bs, 1, lstm_units
x, (_, _) = self.lstm(x) # bs, 1, 2*lstm_units
x = self.act2(x)
x = x.squeeze(dim=1) # bs, 2*lstm_units
attn = self.attn(x) # bs, 2*lstm_units
attn = self.act3(attn)
x = x * attn
x = self.cls(x)
x = self.act4(x)
return x
class CNNLSTMModel_SE(nn.Module):
def __init__(self, window=5, dim=4, lstm_units=16, num_layers=2):
super(CNNLSTMModel_SE, self).__init__()
self.conv1d = nn.Conv1d(dim, lstm_units, 1)
self.act1 = nn.Sigmoid()
self.maxPool = nn.MaxPool1d(kernel_size=window)
self.drop = nn.Dropout(p=0.01)
self.lstm = nn.LSTM(lstm_units, lstm_units, batch_first=True, num_layers=1, bidirectional=True)
self.act2 = nn.Tanh()
self.cls = nn.Linear(lstm_units * 2, 1)
self.act4 = nn.Tanh()
self.se_fc = nn.Linear(window, window)
def forward(self, x):
x = x.transpose(-1, -2) # tf和torch纬度有点不一样
x = self.conv1d(x) # in： bs, dim, window out: bs, lstm_units, window
x = self.act1(x)
# se
avg = x.mean(dim=1) # bs, window
se_attn = self.se_fc(avg).softmax(dim=-1) # bs, window
x = torch.einsum("bnd,bd->bnd", x, se_attn)
x = self.maxPool(x) # bs, lstm_units, 1
x = self.drop(x)
x = x.transpose(-1, -2) # bs, 1, lstm_units
x, (_, _) = self.lstm(x) # bs, 1, 2*lstm_units
x = self.act2(x)
x = x.squeeze(dim=1) # bs, 2*lstm_units
x = self.cls(x)
x = self.act4(x)
return x
class CNNLSTMModel_CBAM(nn.Module):
def __init__(self, window=5, dim=4, lstm_units=16, num_layers=2):
super(CNNLSTMModel_CBAM, self).__init__()
self.conv1d = nn.Conv1d(dim, lstm_units, 1)
self.act1 = nn.Sigmoid()
self.maxPool = nn.MaxPool1d(kernel_size=window)
self.drop = nn.Dropout(p=0.01)
self.lstm = nn.LSTM(lstm_units, lstm_units, batch_first=True, num_layers=1, bidirectional=True)
self.act2 = nn.Tanh()
self.cls = nn.Linear(lstm_units * 2, 1)
self.act4 = nn.Tanh()
self.se_fc = nn.Linear(window, window)
self.hw_fc = nn.Linear(lstm_units, lstm_units)
def forward(self, x):
x = x.transpose(-1, -2) # tf和torch纬度有点不一样
x = self.conv1d(x) # in： bs, dim, window out: bs, lstm_units, window
x = self.act1(x)
# chanal
avg = x.mean(dim=1) # bs, window
se_attn = self.se_fc(avg).softmax(dim=-1) # bs, window
x = torch.einsum("bnd,bd->bnd", x, se_attn)
# wh
avg = x.mean(dim=2) # bs, lstm_units
hw_attn = self.hw_fc(avg).softmax(dim=-1) # bs, lstm_units
x = torch.einsum("bnd,bn->bnd", x, hw_attn)
x = self.maxPool(x) # bs, lstm_units, 1
x = self.drop(x)
x = x.transpose(-1, -2) # bs, 1, lstm_units
x, (_, _) = self.lstm(x) # bs, 1, 2*lstm_units
x = self.act2(x)
x = x.squeeze(dim=1) # bs, 2*lstm_units
x = self.cls(x)
x = self.act4(x)
return x
class CNNLSTMModel_HW(nn.Module):
def __init__(self, window=5, dim=4, lstm_units=16, num_layers=2):
super(CNNLSTMModel_HW, self).__init__()
self.conv1d = nn.Conv1d(dim, lstm_units, 1)
self.act1 = nn.Sigmoid()
self.maxPool = nn.MaxPool1d(kernel_size=window)
self.drop = nn.Dropout(p=0.01)
self.lstm = nn.LSTM(lstm_units, lstm_units, batch_first=True, num_layers=1, bidirectional=True)
self.act2 = nn.Tanh()
self.cls = nn.Linear(lstm_units * 2, 1)
self.act4 = nn.Tanh()
self.hw_fc = nn.Linear(lstm_units, lstm_units)
def forward(self, x):
x = x.transpose(-1, -2) # tf和torch纬度有点不一样
x = self.conv1d(x) # in： bs, dim, window out: bs, lstm_units, window
x = self.act1(x)
# wh
avg = x.mean(dim=2) # bs, lstm_units
hw_attn = self.hw_fc(avg).softmax(dim=-1) # bs, lstm_units
x = torch.einsum("bnd,bn->bnd", x, hw_attn)
x = self.maxPool(x) # bs, lstm_units, 1
x = self.drop(x)
x = x.transpose(-1, -2) # bs, 1, lstm_units
x, (_, _) = self.lstm(x) # bs, 1, 2*lstm_units
x = self.act2(x)
x = x.squeeze(dim=1) # bs, 2*lstm_units
x = self.cls(x)
x = self.act4(x)
return x