62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
import torch
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import torch.nn as nn
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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 einops import rearrange
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class Model(nn.Module):
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"""
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Paper link: https://arxiv.org/abs/2406.16964
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"""
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def __init__(self, configs, patch_len=16, stride=8):
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super().__init__()
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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self.patch_size = patch_len
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self.stride = stride
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self.d_model = configs.d_model
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self.patch_num = (configs.seq_len - self.patch_size) // self.stride + 2
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self.padding_patch_layer = nn.ReplicationPad1d((0, self.stride))
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self.in_layer = nn.Linear(self.patch_size, self.d_model)
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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(1)
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],
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norm_layer=nn.LayerNorm(configs.d_model)
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)
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self.out_layer = nn.Linear(self.d_model * self.patch_num, configs.pred_len)
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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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(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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B, _, C = x_enc.shape
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x_enc = x_enc.permute(0, 2, 1)
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x_enc = self.padding_patch_layer(x_enc)
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x_enc = x_enc.unfold(dimension=-1, size=self.patch_size, step=self.stride)
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enc_out = self.in_layer(x_enc)
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enc_out = rearrange(enc_out, 'b c m l -> (b c) m l')
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dec_out, _ = self.encoder(enc_out)
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dec_out = rearrange(dec_out, '(b c) m l -> b c (m l)' , b=B , c=C)
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dec_out = self.out_layer(dec_out)
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dec_out = dec_out.permute(0, 2, 1)
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
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return dec_out |