import torch import torch.nn as nn import torch.nn.functional as F from layers.Embed import DataEmbedding from layers.AutoCorrelation import AutoCorrelationLayer from layers.FourierCorrelation import FourierBlock, FourierCrossAttention from layers.MultiWaveletCorrelation import MultiWaveletCross, MultiWaveletTransform from layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp class Model(nn.Module): """ FEDformer performs the attention mechanism on frequency domain and achieved O(N) complexity Paper link: https://proceedings.mlr.press/v162/zhou22g.html """ def __init__(self, configs, version='fourier', mode_select='random', modes=32): """ version: str, for FEDformer, there are two versions to choose, options: [Fourier, Wavelets]. mode_select: str, for FEDformer, there are two mode selection method, options: [random, low]. modes: int, modes to be selected. """ 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 self.version = version self.mode_select = mode_select self.modes = modes # Decomp self.decomp = series_decomp(configs.moving_avg) self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq, configs.dropout) self.dec_embedding = DataEmbedding(configs.dec_in, configs.d_model, configs.embed, configs.freq, configs.dropout) if self.version == 'Wavelets': encoder_self_att = MultiWaveletTransform(ich=configs.d_model, L=1, base='legendre') decoder_self_att = MultiWaveletTransform(ich=configs.d_model, L=1, base='legendre') decoder_cross_att = MultiWaveletCross(in_channels=configs.d_model, out_channels=configs.d_model, seq_len_q=self.seq_len // 2 + self.pred_len, seq_len_kv=self.seq_len, modes=self.modes, ich=configs.d_model, base='legendre', activation='tanh') else: encoder_self_att = FourierBlock(in_channels=configs.d_model, out_channels=configs.d_model, n_heads=configs.n_heads, seq_len=self.seq_len, modes=self.modes, mode_select_method=self.mode_select) decoder_self_att = FourierBlock(in_channels=configs.d_model, out_channels=configs.d_model, n_heads=configs.n_heads, seq_len=self.seq_len // 2 + self.pred_len, modes=self.modes, mode_select_method=self.mode_select) decoder_cross_att = FourierCrossAttention(in_channels=configs.d_model, out_channels=configs.d_model, seq_len_q=self.seq_len // 2 + self.pred_len, seq_len_kv=self.seq_len, modes=self.modes, mode_select_method=self.mode_select, num_heads=configs.n_heads) # Encoder self.encoder = Encoder( [ EncoderLayer( AutoCorrelationLayer( encoder_self_att, # instead of multi-head attention in transformer 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 self.decoder = Decoder( [ DecoderLayer( AutoCorrelationLayer( decoder_self_att, configs.d_model, configs.n_heads), AutoCorrelationLayer( decoder_cross_att, 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) seasonal_init, trend_init = self.decomp(x_enc) # x - moving_avg, moving_avg # decoder input trend_init = torch.cat([trend_init[:, -self.label_len:, :], mean], dim=1) seasonal_init = F.pad(seasonal_init[:, -self.label_len:, :], (0, 0, 0, self.pred_len)) # enc enc_out = self.enc_embedding(x_enc, x_mark_enc) dec_out = self.dec_embedding(seasonal_init, x_mark_dec) enc_out, attns = self.encoder(enc_out, attn_mask=None) # 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 output = self.act(enc_out) output = self.dropout(output) output = output * x_mark_enc.unsqueeze(-1) output = output.reshape(output.shape[0], -1) 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, 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