mirror of
https://github.com/KimMeen/Time-LLM.git
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108 lines
4.4 KiB
Python
108 lines
4.4 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.Autoformer_EncDec import series_decomp
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class Model(nn.Module):
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"""
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Paper link: https://arxiv.org/pdf/2205.13504.pdf
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"""
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def __init__(self, configs, individual=False):
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"""
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individual: Bool, whether shared model among different variates.
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"""
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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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if self.task_name == 'classification' or self.task_name == 'anomaly_detection' or self.task_name == 'imputation':
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self.pred_len = configs.seq_len
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else:
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self.pred_len = configs.pred_len
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self.decompsition = series_decomp(configs.moving_avg)
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self.individual = individual
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self.channels = configs.enc_in
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if self.individual:
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self.Linear_Seasonal = nn.ModuleList()
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self.Linear_Trend = nn.ModuleList()
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for i in range(self.channels):
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self.Linear_Seasonal.append(
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nn.Linear(self.seq_len, self.pred_len))
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self.Linear_Trend.append(
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nn.Linear(self.seq_len, self.pred_len))
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self.Linear_Seasonal[i].weight = nn.Parameter(
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(1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]))
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self.Linear_Trend[i].weight = nn.Parameter(
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(1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]))
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else:
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self.Linear_Seasonal = nn.Linear(self.seq_len, self.pred_len)
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self.Linear_Trend = nn.Linear(self.seq_len, self.pred_len)
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self.Linear_Seasonal.weight = nn.Parameter(
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(1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]))
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self.Linear_Trend.weight = nn.Parameter(
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(1 / self.seq_len) * torch.ones([self.pred_len, self.seq_len]))
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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(
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configs.enc_in * configs.seq_len, configs.num_class)
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def encoder(self, x):
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seasonal_init, trend_init = self.decompsition(x)
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seasonal_init, trend_init = seasonal_init.permute(
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0, 2, 1), trend_init.permute(0, 2, 1)
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if self.individual:
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seasonal_output = torch.zeros([seasonal_init.size(0), seasonal_init.size(1), self.pred_len],
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dtype=seasonal_init.dtype).to(seasonal_init.device)
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trend_output = torch.zeros([trend_init.size(0), trend_init.size(1), self.pred_len],
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dtype=trend_init.dtype).to(trend_init.device)
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for i in range(self.channels):
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seasonal_output[:, i, :] = self.Linear_Seasonal[i](
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seasonal_init[:, i, :])
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trend_output[:, i, :] = self.Linear_Trend[i](
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trend_init[:, i, :])
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else:
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seasonal_output = self.Linear_Seasonal(seasonal_init)
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trend_output = self.Linear_Trend(trend_init)
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x = seasonal_output + trend_output
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return x.permute(0, 2, 1)
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def forecast(self, x_enc):
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return self.encoder(x_enc)
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def imputation(self, x_enc):
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return self.encoder(x_enc)
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def anomaly_detection(self, x_enc):
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return self.encoder(x_enc)
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def classification(self, x_enc):
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enc_out = self.encoder(x_enc)
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# Output
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# (batch_size, seq_length * d_model)
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output = enc_out.reshape(enc_out.shape[0], -1)
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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)
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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)
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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)
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return dec_out # [B, N]
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return None
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