import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from layers.Crossformer_EncDec import scale_block, Encoder, Decoder, DecoderLayer from layers.Embed import PatchEmbedding from layers.SelfAttention_Family import AttentionLayer, FullAttention, TwoStageAttentionLayer from models.PatchTST import FlattenHead from math import ceil class Model(nn.Module): """ Paper link: https://openreview.net/pdf?id=vSVLM2j9eie """ def __init__(self, configs): super(Model, self).__init__() self.enc_in = configs.enc_in self.seq_len = configs.seq_len self.pred_len = configs.pred_len self.seg_len = 12 self.win_size = 2 self.task_name = configs.task_name # The padding operation to handle invisible sgemnet length self.pad_in_len = ceil(1.0 * configs.seq_len / self.seg_len) * self.seg_len self.pad_out_len = ceil(1.0 * configs.pred_len / self.seg_len) * self.seg_len self.in_seg_num = self.pad_in_len // self.seg_len self.out_seg_num = ceil(self.in_seg_num / (self.win_size ** (configs.e_layers - 1))) self.head_nf = configs.d_model * self.out_seg_num # Embedding self.enc_value_embedding = PatchEmbedding(configs.d_model, self.seg_len, self.seg_len, self.pad_in_len - configs.seq_len, 0) self.enc_pos_embedding = nn.Parameter( torch.randn(1, configs.enc_in, self.in_seg_num, configs.d_model)) self.pre_norm = nn.LayerNorm(configs.d_model) # Encoder self.encoder = Encoder( [ scale_block(configs, 1 if l == 0 else self.win_size, configs.d_model, configs.n_heads, configs.d_ff, 1, configs.dropout, self.in_seg_num if l == 0 else ceil(self.in_seg_num / self.win_size ** l), configs.factor ) for l in range(configs.e_layers) ] ) # Decoder self.dec_pos_embedding = nn.Parameter( torch.randn(1, configs.enc_in, (self.pad_out_len // self.seg_len), configs.d_model)) self.decoder = Decoder( [ DecoderLayer( TwoStageAttentionLayer(configs, (self.pad_out_len // self.seg_len), configs.factor, configs.d_model, configs.n_heads, configs.d_ff, configs.dropout), AttentionLayer( FullAttention(False, configs.factor, attention_dropout=configs.dropout, output_attention=False), configs.d_model, configs.n_heads), self.seg_len, configs.d_model, configs.d_ff, dropout=configs.dropout, # activation=configs.activation, ) for l in range(configs.e_layers + 1) ], ) if self.task_name == 'imputation' or self.task_name == 'anomaly_detection': self.head = FlattenHead(configs.enc_in, self.head_nf, configs.seq_len, head_dropout=configs.dropout) elif self.task_name == 'classification': self.flatten = nn.Flatten(start_dim=-2) self.dropout = nn.Dropout(configs.dropout) self.projection = nn.Linear( self.head_nf * configs.enc_in, configs.num_class) def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): # embedding x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1)) x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d = n_vars) x_enc += self.enc_pos_embedding x_enc = self.pre_norm(x_enc) enc_out, attns = self.encoder(x_enc) dec_in = repeat(self.dec_pos_embedding, 'b ts_d l d -> (repeat b) ts_d l d', repeat=x_enc.shape[0]) dec_out = self.decoder(dec_in, enc_out) return dec_out def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask): # embedding x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1)) x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d=n_vars) x_enc += self.enc_pos_embedding x_enc = self.pre_norm(x_enc) enc_out, attns = self.encoder(x_enc) dec_out = self.head(enc_out[-1].permute(0, 1, 3, 2)).permute(0, 2, 1) return dec_out def anomaly_detection(self, x_enc): # embedding x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1)) x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d=n_vars) x_enc += self.enc_pos_embedding x_enc = self.pre_norm(x_enc) enc_out, attns = self.encoder(x_enc) dec_out = self.head(enc_out[-1].permute(0, 1, 3, 2)).permute(0, 2, 1) return dec_out def classification(self, x_enc, x_mark_enc): # embedding x_enc, n_vars = self.enc_value_embedding(x_enc.permute(0, 2, 1)) x_enc = rearrange(x_enc, '(b d) seg_num d_model -> b d seg_num d_model', d=n_vars) x_enc += self.enc_pos_embedding x_enc = self.pre_norm(x_enc) enc_out, attns = self.encoder(x_enc) # Output from Non-stationary Transformer output = self.flatten(enc_out[-1].permute(0, 1, 3, 2)) output = self.dropout(output) 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