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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from layers.Transformer_EncDec import Encoder, EncoderLayer | |
| from layers.SelfAttention_Family import FullAttention, AttentionLayer | |
| from layers.Embed import DataEmbedding_inverted | |
| import numpy as np | |
| class Model(nn.Module): | |
| """ | |
| Paper link: https://arxiv.org/abs/2310.06625 | |
| """ | |
| def __init__(self, configs): | |
| super(Model, self).__init__() | |
| self.task_name = configs.task_name | |
| self.seq_len = configs.seq_len | |
| self.pred_len = configs.pred_len | |
| # Embedding | |
| self.enc_embedding = DataEmbedding_inverted(configs.seq_len, configs.d_model, configs.embed, configs.freq, | |
| configs.dropout) | |
| # Encoder | |
| self.encoder = Encoder( | |
| [ | |
| EncoderLayer( | |
| AttentionLayer( | |
| FullAttention(False, configs.factor, attention_dropout=configs.dropout, | |
| output_attention=False), configs.d_model, configs.n_heads), | |
| configs.d_model, | |
| configs.d_ff, | |
| dropout=configs.dropout, | |
| activation=configs.activation | |
| ) for l in range(configs.e_layers) | |
| ], | |
| norm_layer=torch.nn.LayerNorm(configs.d_model) | |
| ) | |
| # Decoder | |
| if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast': | |
| self.projection = nn.Linear(configs.d_model, configs.pred_len, bias=True) | |
| if self.task_name == 'imputation': | |
| self.projection = nn.Linear(configs.d_model, configs.seq_len, bias=True) | |
| if self.task_name == 'anomaly_detection': | |
| self.projection = nn.Linear(configs.d_model, configs.seq_len, 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.enc_in, configs.num_class) | |
| def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): | |
| # Normalization from Non-stationary Transformer | |
| means = x_enc.mean(1, keepdim=True).detach() | |
| x_enc = x_enc - means | |
| stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) | |
| x_enc /= stdev | |
| _, _, N = x_enc.shape | |
| # Embedding | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N] | |
| # De-Normalization from Non-stationary Transformer | |
| dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) | |
| dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1)) | |
| return dec_out | |
| def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask): | |
| # Normalization from Non-stationary Transformer | |
| means = x_enc.mean(1, keepdim=True).detach() | |
| x_enc = x_enc - means | |
| stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) | |
| x_enc /= stdev | |
| _, L, N = x_enc.shape | |
| # Embedding | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N] | |
| # De-Normalization from Non-stationary Transformer | |
| dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1)) | |
| dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1)) | |
| return dec_out | |
| def anomaly_detection(self, x_enc): | |
| # Normalization from Non-stationary Transformer | |
| means = x_enc.mean(1, keepdim=True).detach() | |
| x_enc = x_enc - means | |
| stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5) | |
| x_enc /= stdev | |
| _, L, N = x_enc.shape | |
| # Embedding | |
| enc_out = self.enc_embedding(x_enc, None) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N] | |
| # De-Normalization from Non-stationary Transformer | |
| dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1)) | |
| dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1)) | |
| return dec_out | |
| def classification(self, x_enc, x_mark_enc): | |
| # Embedding | |
| enc_out = self.enc_embedding(x_enc, None) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| # Output | |
| output = self.act(enc_out) # the output transformer encoder/decoder embeddings don't include non-linearity | |
| output = self.dropout(output) | |
| output = output.reshape(output.shape[0], -1) # (batch_size, c_in * d_model) | |
| 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 | |