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import torch
import torch.nn as nn
import torch.nn.functional as F
from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer, ConvLayer
from layers.SelfAttention_Family import FullAttention, AttentionLayer
from layers.Embed import DataEmbedding
import numpy as np
class Model(nn.Module):
"""
Vanilla Transformer
with O(L^2) complexity
Paper link: https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
"""
def __init__(self, configs):
super(Model, self).__init__()
self.task_name = configs.task_name
self.pred_len = configs.pred_len
# Embedding
self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model, configs.embed, configs.freq,
configs.dropout)
# Encoder
self.encoder = Encoder(
[
EncoderLayer(
AttentionLayer(
FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=False), configs.d_model, configs.n_heads),
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)
)
# Decoder
if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
self.dec_embedding = DataEmbedding(configs.dec_in, configs.d_model, configs.embed, configs.freq,
configs.dropout)
self.decoder = Decoder(
[
DecoderLayer(
AttentionLayer(
FullAttention(True, configs.factor, attention_dropout=configs.dropout,
output_attention=False),
configs.d_model, configs.n_heads),
AttentionLayer(
FullAttention(False, configs.factor, attention_dropout=configs.dropout,
output_attention=False),
configs.d_model, configs.n_heads),
configs.d_model,
configs.d_ff,
dropout=configs.dropout,
activation=configs.activation,
)
for l in range(configs.d_layers)
],
norm_layer=torch.nn.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):
# Embedding
enc_out = self.enc_embedding(x_enc, x_mark_enc)
enc_out, attns = self.encoder(enc_out, attn_mask=None)
dec_out = self.dec_embedding(x_dec, x_mark_dec)
dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None)
return dec_out
def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
# Embedding
enc_out = self.enc_embedding(x_enc, x_mark_enc)
enc_out, attns = self.encoder(enc_out, attn_mask=None)
dec_out = self.projection(enc_out)
return dec_out
def anomaly_detection(self, x_enc):
# Embedding
enc_out = self.enc_embedding(x_enc, None)
enc_out, attns = self.encoder(enc_out, attn_mask=None)
dec_out = self.projection(enc_out)
return dec_out
def classification(self, x_enc, x_mark_enc):
# Embedding
enc_out = self.enc_embedding(x_enc, None)
enc_out, attns = self.encoder(enc_out, attn_mask=None)
# Output
output = self.act(enc_out) # the output transformer encoder/decoder embeddings don't include non-linearity
output = self.dropout(output)
output = output * x_mark_enc.unsqueeze(-1) # zero-out padding embeddings
output = output.reshape(output.shape[0], -1) # (batch_size, seq_length * d_model)
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' 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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