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f9d3aeb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | 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,
n_heads=configs.n_heads,
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,
n_heads=configs.n_heads,
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
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