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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from layers.Autoformer_EncDec import series_decomp | |
| class Model(nn.Module): | |
| """ | |
| Paper link: https://arxiv.org/abs/2308.11200.pdf | |
| """ | |
| def __init__(self, configs): | |
| super(Model, self).__init__() | |
| # get parameters | |
| self.seq_len = configs.seq_len | |
| self.enc_in = configs.enc_in | |
| self.d_model = configs.d_model | |
| self.dropout = configs.dropout | |
| self.task_name = configs.task_name | |
| if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation': | |
| self.pred_len = configs.seq_len | |
| else: | |
| self.pred_len = configs.pred_len | |
| self.seg_len = configs.seg_len | |
| self.seg_num_x = self.seq_len // self.seg_len | |
| self.seg_num_y = self.pred_len // self.seg_len | |
| # building model | |
| self.valueEmbedding = nn.Sequential( | |
| nn.Linear(self.seg_len, self.d_model), | |
| nn.ReLU() | |
| ) | |
| self.rnn = nn.GRU(input_size=self.d_model, hidden_size=self.d_model, num_layers=1, bias=True, | |
| batch_first=True, bidirectional=False) | |
| self.pos_emb = nn.Parameter(torch.randn(self.seg_num_y, self.d_model // 2)) | |
| self.channel_emb = nn.Parameter(torch.randn(self.enc_in, self.d_model // 2)) | |
| self.predict = nn.Sequential( | |
| nn.Dropout(self.dropout), | |
| nn.Linear(self.d_model, self.seg_len) | |
| ) | |
| if self.task_name == 'classification': | |
| self.act = F.gelu | |
| self.dropout = nn.Dropout(configs.dropout) | |
| self.projection = nn.Linear( | |
| configs.enc_in * configs.seq_len, configs.num_class) | |
| def encoder(self, x): | |
| # b:batch_size c:channel_size s:seq_len s:seq_len | |
| # d:d_model w:seg_len n:seg_num_x m:seg_num_y | |
| batch_size = x.size(0) | |
| # normalization and permute b,s,c -> b,c,s | |
| seq_last = x[:, -1:, :].detach() | |
| x = (x - seq_last).permute(0, 2, 1) # b,c,s | |
| # segment and embedding b,c,s -> bc,n,w -> bc,n,d | |
| x = self.valueEmbedding(x.reshape(-1, self.seg_num_x, self.seg_len)) | |
| # encoding | |
| _, hn = self.rnn(x) # bc,n,d 1,bc,d | |
| # m,d//2 -> 1,m,d//2 -> c,m,d//2 | |
| # c,d//2 -> c,1,d//2 -> c,m,d//2 | |
| # c,m,d -> cm,1,d -> bcm, 1, d | |
| pos_emb = torch.cat([ | |
| self.pos_emb.unsqueeze(0).repeat(self.enc_in, 1, 1), | |
| self.channel_emb.unsqueeze(1).repeat(1, self.seg_num_y, 1) | |
| ], dim=-1).view(-1, 1, self.d_model).repeat(batch_size,1,1) | |
| _, hy = self.rnn(pos_emb, hn.repeat(1, 1, self.seg_num_y).view(1, -1, self.d_model)) # bcm,1,d 1,bcm,d | |
| # 1,bcm,d -> 1,bcm,w -> b,c,s | |
| y = self.predict(hy).view(-1, self.enc_in, self.pred_len) | |
| # permute and denorm | |
| y = y.permute(0, 2, 1) + seq_last | |
| return y | |
| def forecast(self, x_enc): | |
| # Encoder | |
| return self.encoder(x_enc) | |
| def imputation(self, x_enc): | |
| # Encoder | |
| return self.encoder(x_enc) | |
| def anomaly_detection(self, x_enc): | |
| # Encoder | |
| return self.encoder(x_enc) | |
| def classification(self, x_enc): | |
| # Encoder | |
| enc_out = self.encoder(x_enc) | |
| # Output | |
| # (batch_size, seq_length * d_model) | |
| output = enc_out.reshape(enc_out.shape[0], -1) | |
| # (batch_size, num_classes) | |
| output = self.projection(output) | |
| return output | |
| def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None): | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| dec_out = self.forecast(x_enc) | |
| return dec_out[:, -self.pred_len:, :] # [B, L, D] | |
| if self.task_name == 'imputation': | |
| dec_out = self.imputation(x_enc) | |
| return dec_out # [B, L, D] | |
| if self.task_name == 'anomaly_detection': | |
| dec_out = self.anomaly_detection(x_enc) | |
| return dec_out # [B, L, D] | |
| if self.task_name == 'classification': | |
| dec_out = self.classification(x_enc) | |
| return dec_out # [B, N] | |
| return None | |