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| import torch | |
| import torch.nn as nn | |
| from layers.Embed import DataEmbedding | |
| from layers.ETSformer_EncDec import EncoderLayer, Encoder, DecoderLayer, Decoder, Transform | |
| class Model(nn.Module): | |
| """ | |
| Paper link: https://arxiv.org/abs/2202.01381 | |
| """ | |
| def __init__(self, configs): | |
| super(Model, self).__init__() | |
| self.task_name = configs.task_name | |
| self.seq_len = configs.seq_len | |
| self.label_len = configs.label_len | |
| if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation': | |
| self.pred_len = configs.seq_len | |
| else: | |
| self.pred_len = configs.pred_len | |
| assert configs.e_layers == configs.d_layers, "Encoder and decoder layers must be equal" | |
| # Embedding | |
| self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq, | |
| configs.dropout) | |
| # Encoder | |
| self.encoder = Encoder( | |
| [ | |
| EncoderLayer( | |
| configs.d_model, configs.n_heads, configs.enc_in, configs.seq_len, self.pred_len, configs.top_k, | |
| dim_feedforward=configs.d_ff, | |
| dropout=configs.dropout, | |
| activation=configs.activation, | |
| ) for _ in range(configs.e_layers) | |
| ] | |
| ) | |
| # Decoder | |
| self.decoder = Decoder( | |
| [ | |
| DecoderLayer( | |
| configs.d_model, configs.n_heads, configs.c_out, self.pred_len, | |
| dropout=configs.dropout, | |
| ) for _ in range(configs.d_layers) | |
| ], | |
| ) | |
| self.transform = Transform(sigma=0.2) | |
| if self.task_name == 'classification': | |
| self.act = torch.nn.functional.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): | |
| with torch.no_grad(): | |
| if self.training: | |
| x_enc = self.transform.transform(x_enc) | |
| res = self.enc_embedding(x_enc, x_mark_enc) | |
| level, growths, seasons = self.encoder(res, x_enc, attn_mask=None) | |
| growth, season = self.decoder(growths, seasons) | |
| preds = level[:, -1:] + growth + season | |
| return preds | |
| def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask): | |
| res = self.enc_embedding(x_enc, x_mark_enc) | |
| level, growths, seasons = self.encoder(res, x_enc, attn_mask=None) | |
| growth, season = self.decoder(growths, seasons) | |
| preds = level[:, -1:] + growth + season | |
| return preds | |
| def anomaly_detection(self, x_enc): | |
| res = self.enc_embedding(x_enc, None) | |
| level, growths, seasons = self.encoder(res, x_enc, attn_mask=None) | |
| growth, season = self.decoder(growths, seasons) | |
| preds = level[:, -1:] + growth + season | |
| return preds | |
| def classification(self, x_enc, x_mark_enc): | |
| res = self.enc_embedding(x_enc, None) | |
| _, growths, seasons = self.encoder(res, x_enc, attn_mask=None) | |
| growths = torch.sum(torch.stack(growths, 0), 0)[:, :self.seq_len, :] | |
| seasons = torch.sum(torch.stack(seasons, 0), 0)[:, :self.seq_len, :] | |
| enc_out = growths + seasons | |
| output = self.act(enc_out) # the output transformer encoder/decoder embeddings don't include non-linearity | |
| output = self.dropout(output) | |
| # 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' 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 | |