102 lines
4.1 KiB
Python
102 lines
4.1 KiB
Python
import torch
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import torch.nn as nn
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from layers.Pyraformer_EncDec import Encoder
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class Model(nn.Module):
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"""
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Pyraformer: Pyramidal attention to reduce complexity
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Paper link: https://openreview.net/pdf?id=0EXmFzUn5I
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"""
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def __init__(self, configs, window_size=[4,4], inner_size=5):
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"""
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window_size: list, the downsample window size in pyramidal attention.
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inner_size: int, the size of neighbour attention
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"""
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super().__init__()
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self.task_name = configs.task_name
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self.pred_len = configs.pred_len
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self.d_model = configs.d_model
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if self.task_name == 'short_term_forecast':
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window_size = [2,2]
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self.encoder = Encoder(configs, window_size, inner_size)
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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self.projection = nn.Linear(
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(len(window_size)+1)*self.d_model, self.pred_len * configs.enc_in)
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elif self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
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self.projection = nn.Linear(
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(len(window_size)+1)*self.d_model, configs.enc_in, bias=True)
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elif 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(
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(len(window_size)+1)*self.d_model * configs.seq_len, configs.num_class)
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def long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
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dec_out = self.projection(enc_out).view(
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enc_out.size(0), self.pred_len, -1)
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return dec_out
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def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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# Normalization
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mean_enc = x_enc.mean(1, keepdim=True).detach() # B x 1 x E
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x_enc = x_enc - mean_enc
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std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() # B x 1 x E
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x_enc = x_enc / std_enc
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enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
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dec_out = self.projection(enc_out).view(
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enc_out.size(0), self.pred_len, -1)
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dec_out = dec_out * std_enc + mean_enc
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return dec_out
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def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
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enc_out = self.encoder(x_enc, x_mark_enc)
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dec_out = self.projection(enc_out)
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return dec_out
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def anomaly_detection(self, x_enc, x_mark_enc):
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enc_out = self.encoder(x_enc, x_mark_enc)
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dec_out = self.projection(enc_out)
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return dec_out
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def classification(self, x_enc, x_mark_enc):
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# enc
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enc_out = self.encoder(x_enc, x_mark_enc=None)
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# Output
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# the output transformer encoder/decoder embeddings don't include non-linearity
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output = self.act(enc_out)
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output = self.dropout(output)
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# zero-out padding embeddings
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output = output * x_mark_enc.unsqueeze(-1)
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# (batch_size, seq_length * d_model)
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output = output.reshape(output.shape[0], -1)
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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':
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dec_out = self.long_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 == 'short_term_forecast':
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dec_out = self.short_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(
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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, x_mark_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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