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import torch
import torch.nn as nn
import torch.nn.functional as F
from layers.Transformer_EncDec import Encoder, EncoderLayer
from layers.SelfAttention_Family import ReformerLayer
from layers.Embed import DataEmbedding
class Model(nn.Module):
"""
Reformer with O(LlogL) complexity
Paper link: https://openreview.net/forum?id=rkgNKkHtvB
"""
def __init__(self, configs, bucket_size=4, n_hashes=4):
"""
bucket_size: int,
n_hashes: int,
"""
super(Model, self).__init__()
self.task_name = configs.task_name
self.pred_len = configs.pred_len
self.seq_len = configs.seq_len
self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
configs.dropout)
# Encoder
self.encoder = Encoder(
[
EncoderLayer(
ReformerLayer(None, configs.d_model, configs.n_heads,
bucket_size=bucket_size, n_hashes=n_hashes),
configs.d_model,
configs.d_ff,
dropout=configs.dropout,
activation=configs.activation
) for l in range(configs.e_layers)
],
norm_layer=torch.nn.LayerNorm(configs.d_model)
)
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)
else:
self.projection = nn.Linear(
configs.d_model, configs.c_out, bias=True)
def long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
# add placeholder
x_enc = torch.cat([x_enc, x_dec[:, -self.pred_len:, :]], dim=1)
if x_mark_enc is not None:
x_mark_enc = torch.cat(
[x_mark_enc, x_mark_dec[:, -self.pred_len:, :]], dim=1)
enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C]
enc_out, attns = self.encoder(enc_out, attn_mask=None)
dec_out = self.projection(enc_out)
return dec_out # [B, L, D]
def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
# Normalization
mean_enc = x_enc.mean(1, keepdim=True).detach() # B x 1 x E
x_enc = x_enc - mean_enc
std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() # B x 1 x E
x_enc = x_enc / std_enc
# add placeholder
x_enc = torch.cat([x_enc, x_dec[:, -self.pred_len:, :]], dim=1)
if x_mark_enc is not None:
x_mark_enc = torch.cat(
[x_mark_enc, x_mark_dec[:, -self.pred_len:, :]], dim=1)
enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C]
enc_out, attns = self.encoder(enc_out, attn_mask=None)
dec_out = self.projection(enc_out)
dec_out = dec_out * std_enc + mean_enc
return dec_out # [B, L, D]
def imputation(self, x_enc, x_mark_enc):
enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C]
enc_out, attns = self.encoder(enc_out)
enc_out = self.projection(enc_out)
return enc_out # [B, L, D]
def anomaly_detection(self, x_enc):
enc_out = self.enc_embedding(x_enc, None) # [B,T,C]
enc_out, attns = self.encoder(enc_out)
enc_out = self.projection(enc_out)
return enc_out # [B, L, D]
def classification(self, x_enc, x_mark_enc):
# enc
enc_out = self.enc_embedding(x_enc, None)
enc_out, attns = self.encoder(enc_out)
# Output
# the output transformer encoder/decoder embeddings don't include non-linearity
output = self.act(enc_out)
output = self.dropout(output)
# zero-out padding embeddings
output = output * x_mark_enc.unsqueeze(-1)
# (batch_size, seq_length * d_model)
output = output.reshape(output.shape[0], -1)
output = self.projection(output) # (batch_size, num_classes)
return output
def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
if self.task_name == 'long_term_forecast':
dec_out = self.long_forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
return dec_out[:, -self.pred_len:, :] # [B, L, D]
if self.task_name == 'short_term_forecast':
dec_out = self.short_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)
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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