285 lines
11 KiB
Python
285 lines
11 KiB
Python
# -*- encoding:utf-8 -*-
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'''
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@Author : dingjiawen
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@Date : 2023/11/15 14:44
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@Usage :
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@Desc :
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'''
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import torch
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import torch.nn as nn
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from RUL.otherIdea.adaRNN.loss_transfer import TransferLoss
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import torch.nn.functional as F
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from RUL.baseModel.dctChannelAttention import dct_channel_block
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class AdaRNN(nn.Module):
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"""
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model_type: 'Boosting', 'AdaRNN'
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bottleneck_list: (dim,is_BatchNorm,is_ReLu,drop_out)
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"""
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def __init__(self, use_bottleneck=False, bottleneck_list=[(64, False, False, 0), (64, True, True, 0.5)],
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n_input=128, n_hiddens=[64, 64], n_output=6,
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dropout=0.0, len_seq=9, model_type='AdaRNN',
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trans_loss='mmd',mdw=1):
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super(AdaRNN, self).__init__()
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self.use_bottleneck = use_bottleneck
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self.n_input = n_input
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self.num_layers = len(n_hiddens)
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self.hiddens = n_hiddens
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self.n_output = n_output
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self.model_type = model_type
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self.trans_loss = trans_loss
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self.len_seq = len_seq
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in_size = self.n_input
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features = nn.ModuleList()
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dctAttention = nn.ModuleList()
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for hidden in n_hiddens:
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rnn = nn.GRU(
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input_size=in_size,
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num_layers=1,
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hidden_size=hidden,
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batch_first=True,
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dropout=dropout
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)
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attention = dct_channel_block(channel=hidden)
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features.append(rnn)
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dctAttention.append(attention)
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in_size = hidden
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self.features = nn.Sequential(*features)
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self.dctAttention = nn.Sequential(*dctAttention)
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if use_bottleneck == True: # finance
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bottleneck = []
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for i in range(len(bottleneck_list)):
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cur_input_dim = self.hiddens[-1] if i == 0 else bottleneck_list[i - 1][0]
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bottleneck.append(
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nn.Linear(cur_input_dim, bottleneck_list[i][0])
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)
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### 不加初始权重会让Hard predict更不稳定，振幅更大
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# 初始权重越大，振幅越大
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# bottleneck[-1].weight.data.normal_(0, 0.03)
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# bottleneck[-1].bias.data.fill_(0.1)
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if bottleneck_list[i][1]:
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bottleneck.append(nn.BatchNorm1d(bottleneck_list[i][0]))
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if bottleneck_list[i][2]:
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bottleneck.append(nn.ReLU())
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if bottleneck_list[i][3] != 0:
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bottleneck.append(nn.Dropout(bottleneck_list[i][3]))
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self.bottleneck = nn.Sequential(*bottleneck)
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self.fc = nn.Linear(bottleneck_list[-1][0], n_output)
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torch.nn.init.xavier_normal_(self.fc.weight)
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else:
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self.fc_out = nn.Linear(n_hiddens[-1], self.n_output)
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# 平均值权重
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self.mean_weight = nn.Sequential(
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nn.Conv1d(in_channels=2, out_channels=64, kernel_size=5, padding='same'),
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nn.BatchNorm1d(64),
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nn.ReLU(),
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nn.AdaptiveAvgPool1d(self.len_seq//2),
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nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, padding='same'),
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nn.BatchNorm1d(64),
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nn.ReLU(),
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nn.AdaptiveAvgPool1d(self.len_seq//4),
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nn.Flatten(),
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nn.Dropout(0.2),
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nn.Linear(len_seq // 4 * 64, 64),
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nn.BatchNorm1d(64),
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nn.ReLU(),
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nn.Linear(64, self.n_output)
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)
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self.mdw = mdw
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# 方差权重
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self.std_weight = nn.Linear(in_features=n_input, out_features=n_hiddens[-1])
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if self.model_type == 'AdaRNN':
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gate = nn.ModuleList()
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for i in range(len(n_hiddens)):
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gate_weight = nn.Linear(
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len_seq * self.hiddens[i] * 2, len_seq)
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gate.append(gate_weight)
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self.gate = gate
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bnlst = nn.ModuleList()
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for i in range(len(n_hiddens)):
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bnlst.append(nn.BatchNorm1d(len_seq))
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self.bn_lst = bnlst
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self.softmax = torch.nn.Softmax(dim=0)
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self.init_layers()
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def init_layers(self):
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for i in range(len(self.hiddens)):
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self.gate[i].weight.data.normal_(0, 0.05)
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self.gate[i].bias.data.fill_(0.0)
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def forward_pre_train(self, x, len_win=0):
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# TODO 与原始维度取平均之后加起来
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mean = x[:, :, :].mean(-1)
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std = x[:, :, :].std(-1)
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hand_feature = torch.stack([mean, std], dim=1)
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mean = self.mean_weight(hand_feature).squeeze()
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# std = x.std(1)
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out = self.gru_features(x)
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# 两层GRU之后的结果
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fea = out[0]
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if self.use_bottleneck == True:
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fea_bottleneck = self.bottleneck(fea[:, -1, :])
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fc_out = self.fc(fea_bottleneck).squeeze() + mean*self.mdw
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else:
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fc_out = self.fc_out(fea[:, -1, :]).squeeze() + mean*self.mdw
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# 每层GRU之后的结果,每层GRU前后权重归一化之后的结果
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out_list_all, out_weight_list = out[1], out[2]
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# 可以理解为前半段 和 后半段
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out_list_s, out_list_t = self.get_features(out_list_all)
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loss_transfer = torch.zeros((1,))
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for i in range(len(out_list_s)):
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criterion_transder = TransferLoss(
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loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])
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h_start = 0
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for j in range(h_start, self.len_seq, 1):
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i_start = max(j - len_win, 0)
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i_end = j + len_win if j + len_win < self.len_seq else self.len_seq - 1
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for k in range(i_start, i_end + 1):
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weight = out_weight_list[i][j] if self.model_type == 'AdaRNN' else 1 / (
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self.len_seq - h_start) * (2 * len_win + 1)
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loss_transfer = loss_transfer + weight * criterion_transder.compute(
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out_list_s[i][:, j, :], out_list_t[i][:, k, :])
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return fc_out, loss_transfer, out_weight_list
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def gru_features(self, x, predict=False):
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x_input = x
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out = None
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out_lis = []
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out_weight_list = [] if (
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self.model_type == 'AdaRNN') else None
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for i in range(self.num_layers):
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# GRU的输出
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out, _ = self.features[i](x_input.float())
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out = self.dctAttention[i](out.float())
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x_input = out
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out_lis.append(out)
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if self.model_type == 'AdaRNN' and predict == False:
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out_gate = self.process_gate_weight(x_input, i)
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out_weight_list.append(out_gate)
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# 两层GRU之后的结果,每层GRU之后的结果,每层GRU前后权重归一化之后的结果
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return out, out_lis, out_weight_list
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def process_gate_weight(self, out, index):
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x_s = out[0: int(out.shape[0] // 2)] # 可以理解为前一半个batch_size的分布 域Di
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x_t = out[out.shape[0] // 2: out.shape[0]] # 可以理解为后一半个batch_size的分布 域Dj
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# 对应着不同的域
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x_all = torch.cat((x_s, x_t), 2)
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x_all = x_all.view(x_all.shape[0], -1)
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weight = torch.sigmoid(self.bn_lst[index](
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self.gate[index](x_all.float())))
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weight = torch.mean(weight, dim=0)
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res = self.softmax(weight).squeeze()
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return res
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def get_features(self, output_list):
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fea_list_src, fea_list_tar = [], []
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for fea in output_list:
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fea_list_src.append(fea[0: fea.size(0) // 2])
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fea_list_tar.append(fea[fea.size(0) // 2:])
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return fea_list_src, fea_list_tar
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# For Boosting-based
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def forward_Boosting(self, x, weight_mat=None):
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# TODO 与原始维度取平均之后加起来
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mean = x[:,:, :].mean(-1)
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std = x[:,:, :].std(-1)
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hand_feature = torch.stack([mean, std], dim=1)
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mean = self.mean_weight(hand_feature).squeeze()
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out = self.gru_features(x)
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fea = out[0]
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if self.use_bottleneck:
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fea_bottleneck = self.bottleneck(fea[:, -1, :])
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fc_out = self.fc(fea_bottleneck).squeeze() + mean*self.mdw
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else:
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fc_out = self.fc_out(fea[:, -1, :]).squeeze() + mean*self.mdw
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out_list_all = out[1]
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# 可以理解为前半段和后半段
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out_list_s, out_list_t = self.get_features(out_list_all)
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loss_transfer = torch.zeros((1,))
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if weight_mat is None:
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weight = (1.0 / self.len_seq *
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torch.ones(self.num_layers, self.len_seq))
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else:
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weight = weight_mat
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dist_mat = torch.zeros(self.num_layers, self.len_seq)
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for i in range(len(out_list_s)):
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criterion_transder = TransferLoss(
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loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])
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for j in range(self.len_seq):
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loss_trans = criterion_transder.compute(
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out_list_s[i][:, j, :], out_list_t[i][:, j, :])
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loss_transfer = loss_transfer + weight[i, j] * loss_trans
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dist_mat[i, j] = loss_trans
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return fc_out, loss_transfer, dist_mat, weight
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# For Boosting-based
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def update_weight_Boosting(self, weight_mat, dist_old, dist_new):
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epsilon = 1e-12
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dist_old = dist_old.detach()
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dist_new = dist_new.detach()
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ind = dist_new > dist_old + epsilon
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weight_mat[ind] = weight_mat[ind] * \
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(1 + torch.sigmoid(dist_new[ind] - dist_old[ind]))
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weight_norm = torch.norm(weight_mat, dim=1, p=1)
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weight_mat = weight_mat / weight_norm.t().unsqueeze(1).repeat(1, self.len_seq)
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return weight_mat
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def predict(self, x):
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# TODO 与原始维度取平均之后加起来
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x = x.to(torch.float32)
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mean = x[:, :, :].mean(-1)
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std = x[:, :, :].std(-1)
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hand_feature = torch.stack([mean, std], dim=1)
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mean = self.mean_weight(hand_feature).squeeze()
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out = self.gru_features(x, predict=True)
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fea = out[0]
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if self.use_bottleneck:
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fea_bottleneck = self.bottleneck(fea[:, -1, :])
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fc_out = self.fc(fea_bottleneck).squeeze() + mean*self.mdw
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else:
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fc_out = self.fc_out(fea[:, -1, :]).squeeze() + mean*self.mdw
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return fc_out
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if __name__ == '__main__':
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x = torch.rand((64, 2, 20))
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x=nn.Conv1d(in_channels=2, out_channels=64, kernel_size=5, padding='same')(x)
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x=nn.BatchNorm1d(64)(x)
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x=nn.ReLU()(x)
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x=nn.AdaptiveAvgPool1d(2)(x)
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x=nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, padding='same')(x)
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x=nn.BatchNorm1d(64)(x)
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x=nn.ReLU()(x)
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x=nn.AdaptiveAvgPool1d(2)(x)
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x=nn.Flatten()(x)
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x=nn.Dropout(0.2)(x)
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x=nn.Linear(320, 64)(x)
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x=nn.BatchNorm1d(64)(x)
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x=nn.ReLU()(x)
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x=nn.Linear(64, 1)(x)
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# y = x.mean(1)
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# z = x.std(1)
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# print(y.size())
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# print(z.size())
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pass
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