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