179 lines
8.4 KiB
Python
179 lines
8.4 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from layers.Embed import DataEmbedding
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from layers.AutoCorrelation import AutoCorrelationLayer
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from layers.FourierCorrelation import FourierBlock, FourierCrossAttention
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from layers.MultiWaveletCorrelation import MultiWaveletCross, MultiWaveletTransform
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from layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
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class Model(nn.Module):
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"""
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FEDformer performs the attention mechanism on frequency domain and achieved O(N) complexity
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Paper link: https://proceedings.mlr.press/v162/zhou22g.html
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"""
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def __init__(self, configs, version='fourier', mode_select='random', modes=32):
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"""
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version: str, for FEDformer, there are two versions to choose, options: [Fourier, Wavelets].
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mode_select: str, for FEDformer, there are two mode selection method, options: [random, low].
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modes: int, modes to be selected.
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"""
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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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self.pred_len = configs.pred_len
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self.version = version
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self.mode_select = mode_select
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self.modes = modes
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# Decomp
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self.decomp = series_decomp(configs.moving_avg)
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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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self.dec_embedding = DataEmbedding(configs.dec_in, configs.d_model, configs.embed, configs.freq,
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configs.dropout)
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if self.version == 'Wavelets':
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encoder_self_att = MultiWaveletTransform(ich=configs.d_model, L=1, base='legendre')
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decoder_self_att = MultiWaveletTransform(ich=configs.d_model, L=1, base='legendre')
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decoder_cross_att = MultiWaveletCross(in_channels=configs.d_model,
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out_channels=configs.d_model,
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seq_len_q=self.seq_len // 2 + self.pred_len,
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seq_len_kv=self.seq_len,
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modes=self.modes,
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ich=configs.d_model,
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base='legendre',
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activation='tanh')
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else:
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encoder_self_att = FourierBlock(in_channels=configs.d_model,
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out_channels=configs.d_model,
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n_heads=configs.n_heads,
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seq_len=self.seq_len,
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modes=self.modes,
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mode_select_method=self.mode_select)
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decoder_self_att = FourierBlock(in_channels=configs.d_model,
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out_channels=configs.d_model,
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n_heads=configs.n_heads,
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seq_len=self.seq_len // 2 + self.pred_len,
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modes=self.modes,
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mode_select_method=self.mode_select)
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decoder_cross_att = FourierCrossAttention(in_channels=configs.d_model,
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out_channels=configs.d_model,
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seq_len_q=self.seq_len // 2 + self.pred_len,
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seq_len_kv=self.seq_len,
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modes=self.modes,
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mode_select_method=self.mode_select,
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num_heads=configs.n_heads)
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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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AutoCorrelationLayer(
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encoder_self_att, # instead of multi-head attention in transformer
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configs.d_model, configs.n_heads),
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configs.d_model,
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configs.d_ff,
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moving_avg=configs.moving_avg,
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dropout=configs.dropout,
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activation=configs.activation
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) for l in range(configs.e_layers)
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],
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norm_layer=my_Layernorm(configs.d_model)
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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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AutoCorrelationLayer(
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decoder_self_att,
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configs.d_model, configs.n_heads),
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AutoCorrelationLayer(
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decoder_cross_att,
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configs.d_model, configs.n_heads),
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configs.d_model,
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configs.c_out,
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configs.d_ff,
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moving_avg=configs.moving_avg,
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dropout=configs.dropout,
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activation=configs.activation,
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)
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for l in range(configs.d_layers)
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],
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norm_layer=my_Layernorm(configs.d_model),
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projection=nn.Linear(configs.d_model, configs.c_out, bias=True)
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)
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if self.task_name == 'imputation':
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self.projection = nn.Linear(configs.d_model, configs.c_out, bias=True)
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if self.task_name == 'anomaly_detection':
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self.projection = nn.Linear(configs.d_model, configs.c_out, bias=True)
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if self.task_name == 'classification':
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self.act = F.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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# decomp init
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mean = torch.mean(x_enc, dim=1).unsqueeze(1).repeat(1, self.pred_len, 1)
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seasonal_init, trend_init = self.decomp(x_enc) # x - moving_avg, moving_avg
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# decoder input
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trend_init = torch.cat([trend_init[:, -self.label_len:, :], mean], dim=1)
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seasonal_init = F.pad(seasonal_init[:, -self.label_len:, :], (0, 0, 0, self.pred_len))
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# enc
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enc_out = self.enc_embedding(x_enc, x_mark_enc)
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dec_out = self.dec_embedding(seasonal_init, x_mark_dec)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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# dec
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seasonal_part, trend_part = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None, trend=trend_init)
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# final
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dec_out = trend_part + seasonal_part
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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
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enc_out = self.enc_embedding(x_enc, x_mark_enc)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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# final
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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):
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# enc
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enc_out = self.enc_embedding(x_enc, None)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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# final
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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.enc_embedding(x_enc, None)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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# Output
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output = self.act(enc_out)
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output = self.dropout(output)
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output = output * x_mark_enc.unsqueeze(-1)
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output = output.reshape(output.shape[0], -1)
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output = self.projection(output)
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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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