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/pdf/2205.13504.pdf """ def __init__(self, configs, individual=False): """ individual: Bool, whether shared model among different variates. """ super(Model, self).__init__() self.task_name = configs.task_name self.seq_len = configs.seq_len 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 # Series decomposition block from Autoformer self.decompsition = series_decomp(configs.moving_avg) self.individual = individual self.channels = configs.enc_in if self.individual: self.Linear_Seasonal = nn.ModuleList() self.Linear_Trend = nn.ModuleList() for i in range(self.channels): self.Linear_Seasonal.append( nn.Linear(self.seq_len, self.pred_len)) self.Linear_Trend.append( nn.Linear(self.seq_len, self.pred_len)) self.Linear_Seasonal[i].weight = nn.Parameter( (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])) self.Linear_Trend[i].weight = nn.Parameter( (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])) else: self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len) self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len) self.Linear_Seasonal.weight = nn.Parameter( (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])) self.Linear_Trend.weight = nn.Parameter( (1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len])) if self.task_name == 'classification': self.projection = nn.Linear( configs.enc_in * configs.seq_len, configs.num_class) def encoder(self, x): seasonal_init, trend_init = self.decompsition(x) seasonal_init, trend_init = seasonal_init.permute( 0, 2, 1), trend_init.permute(0, 2, 1) if self.individual: seasonal_output = torch.zeros([seasonal_init.size(0), seasonal_init.size(1), self.pred_len], dtype=seasonal_init.dtype).to(seasonal_init.device) trend_output = torch.zeros([trend_init.size(0), trend_init.size(1), self.pred_len], dtype=trend_init.dtype).to(trend_init.device) for i in range(self.channels): seasonal_output[:, i, :] = self.Linear_Seasonal[i]( seasonal_init[:, i, :]) trend_output[:, i, :] = self.Linear_Trend[i]( trend_init[:, i, :]) else: seasonal_output = self.Linear_Seasonal(seasonal_init) trend_output = self.Linear_Trend(trend_init) x = seasonal_output + trend_output return x.permute(0, 2, 1) 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