111 lines
4.5 KiB
Python
111 lines
4.5 KiB
Python
import torch
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import torch.nn as nn
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from layers.Embed import DataEmbedding
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from layers.ETSformer_EncDec import EncoderLayer, Encoder, DecoderLayer, Decoder, Transform
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class Model(nn.Module):
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"""
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Paper link: https://arxiv.org/abs/2202.01381
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"""
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def __init__(self, configs):
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super(Model, self).__init__()
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.label_len = configs.label_len
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if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation':
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self.pred_len = configs.seq_len
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else:
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self.pred_len = configs.pred_len
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assert configs.e_layers == configs.d_layers, "Encoder and decoder layers must be equal"
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# Embedding
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self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
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configs.dropout)
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# Encoder
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self.encoder = Encoder(
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[
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EncoderLayer(
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configs.d_model, configs.n_heads, configs.enc_in, configs.seq_len, self.pred_len, configs.top_k,
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dim_feedforward=configs.d_ff,
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dropout=configs.dropout,
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activation=configs.activation,
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) for _ in range(configs.e_layers)
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]
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)
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# Decoder
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self.decoder = Decoder(
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[
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DecoderLayer(
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configs.d_model, configs.n_heads, configs.c_out, self.pred_len,
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dropout=configs.dropout,
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) for _ in range(configs.d_layers)
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],
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)
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self.transform = Transform(sigma=0.2)
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if self.task_name == 'classification':
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self.act = torch.nn.functional.gelu
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self.dropout = nn.Dropout(configs.dropout)
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self.projection = nn.Linear(configs.d_model * configs.seq_len, configs.num_class)
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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with torch.no_grad():
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if self.training:
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x_enc = self.transform.transform(x_enc)
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res = self.enc_embedding(x_enc, x_mark_enc)
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level, growths, seasons = self.encoder(res, x_enc, attn_mask=None)
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growth, season = self.decoder(growths, seasons)
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preds = level[:, -1:] + growth + season
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return preds
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def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
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res = self.enc_embedding(x_enc, x_mark_enc)
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level, growths, seasons = self.encoder(res, x_enc, attn_mask=None)
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growth, season = self.decoder(growths, seasons)
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preds = level[:, -1:] + growth + season
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return preds
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def anomaly_detection(self, x_enc):
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res = self.enc_embedding(x_enc, None)
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level, growths, seasons = self.encoder(res, x_enc, attn_mask=None)
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growth, season = self.decoder(growths, seasons)
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preds = level[:, -1:] + growth + season
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return preds
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def classification(self, x_enc, x_mark_enc):
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res = self.enc_embedding(x_enc, None)
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_, growths, seasons = self.encoder(res, x_enc, attn_mask=None)
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growths = torch.sum(torch.stack(growths, 0), 0)[:, :self.seq_len, :]
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seasons = torch.sum(torch.stack(seasons, 0), 0)[:, :self.seq_len, :]
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enc_out = growths + seasons
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output = self.act(enc_out) # the output transformer encoder/decoder embeddings don't include non-linearity
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output = self.dropout(output)
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# Output
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output = output * x_mark_enc.unsqueeze(-1) # zero-out padding embeddings
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output = output.reshape(output.shape[0], -1) # (batch_size, seq_length * d_model)
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output = self.projection(output) # (batch_size, num_classes)
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return output
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out[:, -self.pred_len:, :] # [B, L, D]
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if self.task_name == 'imputation':
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dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
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return dec_out # [B, L, D]
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if self.task_name == 'anomaly_detection':
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dec_out = self.anomaly_detection(x_enc)
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return dec_out # [B, L, D]
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if self.task_name == 'classification':
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dec_out = self.classification(x_enc, x_mark_enc)
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return dec_out # [B, N]
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return None
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