231 lines
10 KiB
Python
231 lines
10 KiB
Python
import torch
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import torch.nn as nn
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from layers.Transformer_EncDec import Decoder, DecoderLayer, Encoder, EncoderLayer
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from layers.SelfAttention_Family import DSAttention, AttentionLayer
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from layers.Embed import DataEmbedding
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import torch.nn.functional as F
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class Projector(nn.Module):
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'''
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MLP to learn the De-stationary factors
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Paper link: https://openreview.net/pdf?id=ucNDIDRNjjv
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'''
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def __init__(self, enc_in, seq_len, hidden_dims, hidden_layers, output_dim, kernel_size=3):
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super(Projector, self).__init__()
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padding = 1 if torch.__version__ >= '1.5.0' else 2
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self.series_conv = nn.Conv1d(in_channels=seq_len, out_channels=1, kernel_size=kernel_size, padding=padding,
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padding_mode='circular', bias=False)
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layers = [nn.Linear(2 * enc_in, hidden_dims[0]), nn.ReLU()]
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for i in range(hidden_layers - 1):
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layers += [nn.Linear(hidden_dims[i], hidden_dims[i + 1]), nn.ReLU()]
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layers += [nn.Linear(hidden_dims[-1], output_dim, bias=False)]
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self.backbone = nn.Sequential(*layers)
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def forward(self, x, stats):
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# x: B x S x E
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# stats: B x 1 x E
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# y: B x O
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batch_size = x.shape[0]
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x = self.series_conv(x) # B x 1 x E
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x = torch.cat([x, stats], dim=1) # B x 2 x E
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x = x.view(batch_size, -1) # B x 2E
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y = self.backbone(x) # B x O
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return y
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class Model(nn.Module):
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"""
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Paper link: https://openreview.net/pdf?id=ucNDIDRNjjv
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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.pred_len = configs.pred_len
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self.seq_len = configs.seq_len
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self.label_len = configs.label_len
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# Embedding
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self.enc_embedding = DataEmbedding(configs.enc_in, 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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DSAttention(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.dec_embedding = DataEmbedding(configs.dec_in, configs.d_model, configs.embed, configs.freq,
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configs.dropout)
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self.decoder = Decoder(
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[
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DecoderLayer(
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AttentionLayer(
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DSAttention(True, configs.factor, attention_dropout=configs.dropout,
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output_attention=False),
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configs.d_model, configs.n_heads),
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AttentionLayer(
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DSAttention(False, configs.factor, attention_dropout=configs.dropout,
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output_attention=False),
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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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)
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for l in range(configs.d_layers)
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],
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norm_layer=torch.nn.LayerNorm(configs.d_model),
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projection=nn.Linear(configs.d_model, configs.c_out, bias=True)
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)
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if self.task_name == 'imputation':
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self.projection = nn.Linear(configs.d_model, configs.c_out, bias=True)
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if self.task_name == 'anomaly_detection':
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self.projection = nn.Linear(configs.d_model, configs.c_out, 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.seq_len, configs.num_class)
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self.tau_learner = Projector(enc_in=configs.enc_in, seq_len=configs.seq_len, hidden_dims=configs.p_hidden_dims,
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hidden_layers=configs.p_hidden_layers, output_dim=1)
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self.delta_learner = Projector(enc_in=configs.enc_in, seq_len=configs.seq_len,
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hidden_dims=configs.p_hidden_dims, hidden_layers=configs.p_hidden_layers,
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output_dim=configs.seq_len)
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
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x_raw = x_enc.clone().detach()
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# Normalization
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mean_enc = x_enc.mean(1, keepdim=True).detach() # B x 1 x E
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x_enc = x_enc - mean_enc
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std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() # B x 1 x E
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x_enc = x_enc / std_enc
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# B x S x E, B x 1 x E -> B x 1, positive scalar
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tau = self.tau_learner(x_raw, std_enc)
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threshold = 80.0
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tau_clamped = torch.clamp(tau, max=threshold) # avoid numerical overflow
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tau = tau_clamped.exp()
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# B x S x E, B x 1 x E -> B x S
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delta = self.delta_learner(x_raw, mean_enc)
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x_dec_new = torch.cat([x_enc[:, -self.label_len:, :], torch.zeros_like(x_dec[:, -self.pred_len:, :])],
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dim=1).to(x_enc.device).clone()
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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, tau=tau, delta=delta)
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dec_out = self.dec_embedding(x_dec_new, x_mark_dec)
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dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None, tau=tau, delta=delta)
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dec_out = dec_out * std_enc + mean_enc
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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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x_raw = x_enc.clone().detach()
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# Normalization
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mean_enc = torch.sum(x_enc, dim=1) / torch.sum(mask == 1, dim=1)
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mean_enc = mean_enc.unsqueeze(1).detach()
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x_enc = x_enc - mean_enc
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x_enc = x_enc.masked_fill(mask == 0, 0)
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std_enc = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) / torch.sum(mask == 1, dim=1) + 1e-5)
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std_enc = std_enc.unsqueeze(1).detach()
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x_enc /= std_enc
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# B x S x E, B x 1 x E -> B x 1, positive scalar
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tau = self.tau_learner(x_raw, std_enc)
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threshold = 80.0
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tau_clamped = torch.clamp(tau, max=threshold) # avoid numerical overflow
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tau = tau_clamped.exp()
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# B x S x E, B x 1 x E -> B x S
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delta = self.delta_learner(x_raw, mean_enc)
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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, tau=tau, delta=delta)
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dec_out = self.projection(enc_out)
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dec_out = dec_out * std_enc + mean_enc
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return dec_out
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def anomaly_detection(self, x_enc):
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x_raw = x_enc.clone().detach()
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# Normalization
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mean_enc = x_enc.mean(1, keepdim=True).detach() # B x 1 x E
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x_enc = x_enc - mean_enc
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std_enc = torch.sqrt(torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() # B x 1 x E
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x_enc = x_enc / std_enc
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# B x S x E, B x 1 x E -> B x 1, positive scalar
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tau = self.tau_learner(x_raw, std_enc)
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threshold = 80.0
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tau_clamped = torch.clamp(tau, max=threshold) # avoid numerical overflow
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tau = tau_clamped.exp()
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# B x S x E, B x 1 x E -> B x S
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delta = self.delta_learner(x_raw, mean_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, tau=tau, delta=delta)
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dec_out = self.projection(enc_out)
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dec_out = dec_out * std_enc + mean_enc
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return dec_out
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def classification(self, x_enc, x_mark_enc):
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x_raw = x_enc.clone().detach()
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# Normalization
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mean_enc = x_enc.mean(1, keepdim=True).detach() # B x 1 x E
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std_enc = torch.sqrt(
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torch.var(x_enc - mean_enc, dim=1, keepdim=True, unbiased=False) + 1e-5).detach() # B x 1 x E
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# B x S x E, B x 1 x E -> B x 1, positive scalar
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tau = self.tau_learner(x_raw, std_enc)
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threshold = 80.0
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tau_clamped = torch.clamp(tau, max=threshold) # avoid numerical overflow
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tau = tau_clamped.exp()
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# B x S x E, B x 1 x E -> B x S
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delta = self.delta_learner(x_raw, mean_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, tau=tau, delta=delta)
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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 * x_mark_enc.unsqueeze(-1) # zero-out padding embeddings
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# (batch_size, seq_length * d_model)
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output = output.reshape(output.shape[0], -1)
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# (batch_size, num_classes)
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output = self.projection(output)
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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, L, D]
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return None
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