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import torch
import torch.nn as nn
from layers.Pyraformer_EncDec import Encoder
class Model(nn.Module):
"""
Pyraformer: Pyramidal attention to reduce complexity
Paper link: https://openreview.net/pdf?id=0EXmFzUn5I
"""
def __init__(self, configs, window_size=[4,4], inner_size=5):
"""
window_size: list, the downsample window size in pyramidal attention.
inner_size: int, the size of neighbour attention
"""
super().__init__()
self.task_name = configs.task_name
self.pred_len = configs.pred_len
self.d_model = configs.d_model
if self.task_name == 'short_term_forecast':
window_size = [2,2]
self.encoder = Encoder(configs, window_size, inner_size)
if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
self.projection = nn.Linear(
(len(window_size)+1)*self.d_model, self.pred_len * configs.enc_in)
elif self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
self.projection = nn.Linear(
(len(window_size)+1)*self.d_model, configs.enc_in, bias=True)
elif self.task_name == 'classification':
self.act = torch.nn.functional.gelu
self.dropout = nn.Dropout(configs.dropout)
self.projection = nn.Linear(
(len(window_size)+1)*self.d_model * configs.seq_len, configs.num_class)
def long_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
dec_out = self.projection(enc_out).view(
enc_out.size(0), self.pred_len, -1)
return dec_out
def short_forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
# 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
enc_out = self.encoder(x_enc, x_mark_enc)[:, -1, :]
dec_out = self.projection(enc_out).view(
enc_out.size(0), self.pred_len, -1)
dec_out = dec_out * std_enc + mean_enc
return dec_out
def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
enc_out = self.encoder(x_enc, x_mark_enc)
dec_out = self.projection(enc_out)
return dec_out
def anomaly_detection(self, x_enc, x_mark_enc):
enc_out = self.encoder(x_enc, x_mark_enc)
dec_out = self.projection(enc_out)
return dec_out
def classification(self, x_enc, x_mark_enc):
# enc
enc_out = self.encoder(x_enc, x_mark_enc=None)
# 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, 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, x_mark_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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