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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from layers.Embed import DataEmbedding, DataEmbedding_wo_pos | |
| from layers.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer | |
| from layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp | |
| import math | |
| import numpy as np | |
| class Model(nn.Module): | |
| """ | |
| Autoformer is the first method to achieve the series-wise connection, | |
| with inherent O(LlogL) complexity | |
| Paper link: https://openreview.net/pdf?id=I55UqU-M11y | |
| """ | |
| def __init__(self, configs): | |
| super(Model, self).__init__() | |
| self.task_name = configs.task_name | |
| self.seq_len = configs.seq_len | |
| self.label_len = configs.label_len | |
| self.pred_len = configs.pred_len | |
| # Decomp | |
| kernel_size = configs.moving_avg | |
| self.decomp = series_decomp(kernel_size) | |
| # Embedding | |
| self.enc_embedding = DataEmbedding_wo_pos(configs.enc_in, configs.d_model, configs.embed, configs.freq, | |
| configs.dropout) | |
| # Encoder | |
| self.encoder = Encoder( | |
| [ | |
| EncoderLayer( | |
| AutoCorrelationLayer( | |
| AutoCorrelation(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), | |
| configs.d_model, configs.n_heads), | |
| configs.d_model, | |
| configs.d_ff, | |
| moving_avg=configs.moving_avg, | |
| dropout=configs.dropout, | |
| activation=configs.activation | |
| ) for l in range(configs.e_layers) | |
| ], | |
| norm_layer=my_Layernorm(configs.d_model) | |
| ) | |
| # Decoder | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| self.dec_embedding = DataEmbedding_wo_pos(configs.dec_in, configs.d_model, configs.embed, configs.freq, | |
| configs.dropout) | |
| self.decoder = Decoder( | |
| [ | |
| DecoderLayer( | |
| AutoCorrelationLayer( | |
| AutoCorrelation(True, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), | |
| configs.d_model, configs.n_heads), | |
| AutoCorrelationLayer( | |
| AutoCorrelation(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), | |
| configs.d_model, configs.n_heads), | |
| configs.d_model, | |
| configs.c_out, | |
| configs.d_ff, | |
| moving_avg=configs.moving_avg, | |
| dropout=configs.dropout, | |
| activation=configs.activation, | |
| ) | |
| for l in range(configs.d_layers) | |
| ], | |
| norm_layer=my_Layernorm(configs.d_model), | |
| projection=nn.Linear(configs.d_model, configs.c_out, bias=True) | |
| ) | |
| if self.task_name == 'imputation': | |
| self.projection = nn.Linear( | |
| configs.d_model, configs.c_out, bias=True) | |
| if self.task_name == 'anomaly_detection': | |
| self.projection = nn.Linear( | |
| configs.d_model, configs.c_out, bias=True) | |
| if self.task_name == 'classification': | |
| self.act = F.gelu | |
| self.dropout = nn.Dropout(configs.dropout) | |
| self.projection = nn.Linear( | |
| configs.d_model * configs.seq_len, configs.num_class) | |
| def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): | |
| # decomp init | |
| mean = torch.mean(x_enc, dim=1).unsqueeze( | |
| 1).repeat(1, self.pred_len, 1) | |
| zeros = torch.zeros([x_dec.shape[0], self.pred_len, | |
| x_dec.shape[2]], device=x_enc.device) | |
| seasonal_init, trend_init = self.decomp(x_enc) | |
| # decoder input | |
| trend_init = torch.cat( | |
| [trend_init[:, -self.label_len:, :], mean], dim=1) | |
| seasonal_init = torch.cat( | |
| [seasonal_init[:, -self.label_len:, :], zeros], dim=1) | |
| # enc | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| # dec | |
| dec_out = self.dec_embedding(seasonal_init, x_mark_dec) | |
| seasonal_part, trend_part = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None, | |
| trend=trend_init) | |
| # final | |
| dec_out = trend_part + seasonal_part | |
| return dec_out | |
| def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask): | |
| # enc | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| # final | |
| dec_out = self.projection(enc_out) | |
| return dec_out | |
| def anomaly_detection(self, x_enc): | |
| # enc | |
| enc_out = self.enc_embedding(x_enc, None) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| # final | |
| dec_out = self.projection(enc_out) | |
| return dec_out | |
| def classification(self, x_enc, x_mark_enc): | |
| # enc | |
| enc_out = self.enc_embedding(x_enc, None) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| # Output | |
| # the output transformer encoder/decoder embeddings don't include non-linearity | |
| output = self.act(enc_out) | |
| output = self.dropout(output) | |
| # zero-out padding embeddings | |
| output = output * x_mark_enc.unsqueeze(-1) | |
| # (batch_size, seq_length * d_model) | |
| output = output.reshape(output.shape[0], -1) | |
| output = self.projection(output) # (batch_size, num_classes) | |
| 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, x_mark_enc, x_dec, x_mark_dec) | |
| return dec_out[:, -self.pred_len:, :] # [B, L, D] | |
| if self.task_name == 'imputation': | |
| dec_out = self.imputation( | |
| x_enc, x_mark_enc, x_dec, x_mark_dec, mask) | |
| 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, x_mark_enc) | |
| return dec_out # [B, N] | |
| return None | |