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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer, ConvLayer | |
| from layers.SelfAttention_Family import ProbAttention, AttentionLayer | |
| from layers.Embed import DataEmbedding | |
| class Model(nn.Module): | |
| """ | |
| Informer with Propspare attention in O(LlogL) complexity | |
| Paper link: https://ojs.aaai.org/index.php/AAAI/article/view/17325/17132 | |
| """ | |
| def __init__(self, configs): | |
| super(Model, self).__init__() | |
| self.task_name = configs.task_name | |
| self.pred_len = configs.pred_len | |
| self.label_len = configs.label_len | |
| # Embedding | |
| 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) | |
| # Encoder | |
| self.encoder = Encoder( | |
| [ | |
| EncoderLayer( | |
| AttentionLayer( | |
| ProbAttention(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) | |
| ], | |
| [ | |
| ConvLayer( | |
| configs.d_model | |
| ) for l in range(configs.e_layers - 1) | |
| ] if configs.distil and ('forecast' in configs.task_name) else None, | |
| norm_layer=torch.nn.LayerNorm(configs.d_model) | |
| ) | |
| # Decoder | |
| self.decoder = Decoder( | |
| [ | |
| DecoderLayer( | |
| AttentionLayer( | |
| ProbAttention(True, configs.factor, attention_dropout=configs.dropout, output_attention=False), | |
| configs.d_model, configs.n_heads), | |
| AttentionLayer( | |
| ProbAttention(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.d_layers) | |
| ], | |
| norm_layer=torch.nn.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 long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) | |
| dec_out = self.dec_embedding(x_dec, x_mark_dec) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None) | |
| return dec_out # [B, L, D] | |
| def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec): | |
| # Normalization | |
| mean_enc = x_enc.mean(1, keepdim=True).detach() # B x 1 x E | |
| x_enc = x_enc - mean_enc | |
| std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() # B x 1 x E | |
| x_enc = x_enc / std_enc | |
| enc_out = self.enc_embedding(x_enc, x_mark_enc) | |
| dec_out = self.dec_embedding(x_dec, x_mark_dec) | |
| enc_out, attns = self.encoder(enc_out, attn_mask=None) | |
| dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None) | |
| dec_out = dec_out * std_enc + mean_enc | |
| return dec_out # [B, L, D] | |
| 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) # the output transformer encoder/decoder embeddings don't include non-linearity | |
| output = self.dropout(output) | |
| output = output * x_mark_enc.unsqueeze(-1) # zero-out padding embeddings | |
| output = output.reshape(output.shape[0], -1) # (batch_size, seq_length * 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': | |
| dec_out = self.long_forecast(x_enc, x_mark_enc, x_dec, x_mark_dec) | |
| return dec_out[:, -self.pred_len:, :] # [B, L, D] | |
| if self.task_name == 'short_term_forecast': | |
| dec_out = self.short_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 | |