133 lines
5.6 KiB
Python
133 lines
5.6 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from layers.Transformer_EncDec import Encoder, EncoderLayer
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from layers.SelfAttention_Family import FullAttention, AttentionLayer
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from layers.Embed import DataEmbedding_inverted
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import numpy as np
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class Model(nn.Module):
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"""
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Paper link: https://arxiv.org/abs/2310.06625
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"""
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def __init__(self, configs):
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super(Model, self).__init__()
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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# Embedding
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self.enc_embedding = DataEmbedding_inverted(configs.seq_len, configs.d_model, configs.embed, configs.freq,
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configs.dropout)
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# Encoder
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self.encoder = Encoder(
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[
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EncoderLayer(
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AttentionLayer(
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FullAttention(False, configs.factor, attention_dropout=configs.dropout,
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output_attention=False), configs.d_model, configs.n_heads),
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configs.d_model,
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configs.d_ff,
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dropout=configs.dropout,
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activation=configs.activation
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) for l in range(configs.e_layers)
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],
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norm_layer=torch.nn.LayerNorm(configs.d_model)
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)
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# Decoder
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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self.projection = nn.Linear(configs.d_model, configs.pred_len, bias=True)
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if self.task_name == 'imputation':
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self.projection = nn.Linear(configs.d_model, configs.seq_len, bias=True)
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if self.task_name == 'anomaly_detection':
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self.projection = nn.Linear(configs.d_model, configs.seq_len, bias=True)
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if self.task_name == 'classification':
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self.act = F.gelu
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self.dropout = nn.Dropout(configs.dropout)
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self.projection = nn.Linear(configs.d_model * configs.enc_in, configs.num_class)
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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_, _, N = x_enc.shape
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# Embedding
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enc_out = self.enc_embedding(x_enc, x_mark_enc)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N]
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
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dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
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return dec_out
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def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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_, L, N = x_enc.shape
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# Embedding
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enc_out = self.enc_embedding(x_enc, x_mark_enc)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N]
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1))
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dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1))
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return dec_out
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def anomaly_detection(self, x_enc):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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_, L, N = x_enc.shape
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# Embedding
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enc_out = self.enc_embedding(x_enc, None)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N]
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * (stdev[:, 0, :].unsqueeze(1).repeat(1, L, 1))
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dec_out = dec_out + (means[:, 0, :].unsqueeze(1).repeat(1, L, 1))
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return dec_out
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def classification(self, x_enc, x_mark_enc):
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# Embedding
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enc_out = self.enc_embedding(x_enc, None)
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enc_out, attns = self.encoder(enc_out, attn_mask=None)
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# Output
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output = self.act(enc_out) # the output transformer encoder/decoder embeddings don't include non-linearity
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output = self.dropout(output)
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output = output.reshape(output.shape[0], -1) # (batch_size, c_in * d_model)
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output = self.projection(output) # (batch_size, num_classes)
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return output
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out[:, -self.pred_len:, :] # [B, L, D]
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if self.task_name == 'imputation':
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dec_out = self.imputation(x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
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return dec_out # [B, L, D]
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if self.task_name == 'anomaly_detection':
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dec_out = self.anomaly_detection(x_enc)
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return dec_out # [B, L, D]
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if self.task_name == 'classification':
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dec_out = self.classification(x_enc, x_mark_enc)
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return dec_out # [B, N]
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return None
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