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| 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 is 0 else self.win_size, configs.d_model, configs.n_heads, configs.d_ff, | |
| 1, configs.dropout, | |
| self.in_seg_num if l is 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 |