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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/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 | |