228 lines
8.9 KiB
Python
228 lines
8.9 KiB
Python
import torch
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from torch import 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 layers.Embed import PatchEmbedding
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class Transpose(nn.Module):
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def __init__(self, *dims, contiguous=False):
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super().__init__()
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self.dims, self.contiguous = dims, contiguous
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def forward(self, x):
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if self.contiguous: return x.transpose(*self.dims).contiguous()
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else: return x.transpose(*self.dims)
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class FlattenHead(nn.Module):
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def __init__(self, n_vars, nf, target_window, head_dropout=0):
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super().__init__()
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self.n_vars = n_vars
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self.flatten = nn.Flatten(start_dim=-2)
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self.linear = nn.Linear(nf, target_window)
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self.dropout = nn.Dropout(head_dropout)
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def forward(self, x): # x: [bs x nvars x d_model x patch_num]
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x = self.flatten(x)
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x = self.linear(x)
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x = self.dropout(x)
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return x
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class Model(nn.Module):
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"""
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Paper link: https://arxiv.org/pdf/2211.14730.pdf
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"""
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def __init__(self, configs, patch_len=16, stride=8):
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"""
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patch_len: int, patch len for patch_embedding
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stride: int, stride for patch_embedding
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"""
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super().__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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padding = stride
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# patching and embedding
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self.patch_embedding = PatchEmbedding(
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configs.d_model, patch_len, stride, padding, 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=nn.Sequential(Transpose(1,2), nn.BatchNorm1d(configs.d_model), Transpose(1,2))
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)
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# Prediction Head
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self.head_nf = configs.d_model * \
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int((configs.seq_len - patch_len) / stride + 2)
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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self.head = FlattenHead(configs.enc_in, self.head_nf, configs.pred_len,
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head_dropout=configs.dropout)
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elif self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
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self.head = FlattenHead(configs.enc_in, self.head_nf, configs.seq_len,
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head_dropout=configs.dropout)
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elif self.task_name == 'classification':
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self.flatten = nn.Flatten(start_dim=-2)
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self.dropout = nn.Dropout(configs.dropout)
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self.projection = nn.Linear(
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self.head_nf * 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(
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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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# do patching and embedding
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x_enc = x_enc.permute(0, 2, 1)
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# u: [bs * nvars x patch_num x d_model]
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enc_out, n_vars = self.patch_embedding(x_enc)
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# Encoder
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# z: [bs * nvars x patch_num x d_model]
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enc_out, attns = self.encoder(enc_out)
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# z: [bs x nvars x patch_num x d_model]
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enc_out = torch.reshape(
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enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
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# z: [bs x nvars x d_model x patch_num]
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enc_out = enc_out.permute(0, 1, 3, 2)
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# Decoder
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dec_out = self.head(enc_out) # z: [bs x nvars x target_window]
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dec_out = dec_out.permute(0, 2, 1)
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# De-Normalization from Non-stationary Transformer
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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
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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 = torch.sum(x_enc, dim=1) / torch.sum(mask == 1, dim=1)
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means = means.unsqueeze(1).detach()
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x_enc = x_enc - means
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x_enc = x_enc.masked_fill(mask == 0, 0)
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stdev = torch.sqrt(torch.sum(x_enc * x_enc, dim=1) /
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torch.sum(mask == 1, dim=1) + 1e-5)
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stdev = stdev.unsqueeze(1).detach()
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x_enc /= stdev
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# do patching and embedding
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x_enc = x_enc.permute(0, 2, 1)
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# u: [bs * nvars x patch_num x d_model]
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enc_out, n_vars = self.patch_embedding(x_enc)
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# Encoder
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# z: [bs * nvars x patch_num x d_model]
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enc_out, attns = self.encoder(enc_out)
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# z: [bs x nvars x patch_num x d_model]
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enc_out = torch.reshape(
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enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
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# z: [bs x nvars x d_model x patch_num]
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enc_out = enc_out.permute(0, 1, 3, 2)
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# Decoder
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dec_out = self.head(enc_out) # z: [bs x nvars x target_window]
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dec_out = dec_out.permute(0, 2, 1)
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 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(
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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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# do patching and embedding
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x_enc = x_enc.permute(0, 2, 1)
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# u: [bs * nvars x patch_num x d_model]
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enc_out, n_vars = self.patch_embedding(x_enc)
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# Encoder
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# z: [bs * nvars x patch_num x d_model]
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enc_out, attns = self.encoder(enc_out)
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# z: [bs x nvars x patch_num x d_model]
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enc_out = torch.reshape(
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enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
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# z: [bs x nvars x d_model x patch_num]
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enc_out = enc_out.permute(0, 1, 3, 2)
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# Decoder
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dec_out = self.head(enc_out) # z: [bs x nvars x target_window]
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dec_out = dec_out.permute(0, 2, 1)
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(1, self.seq_len, 1))
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return dec_out
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def classification(self, x_enc, x_mark_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(
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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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# do patching and embedding
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x_enc = x_enc.permute(0, 2, 1)
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# u: [bs * nvars x patch_num x d_model]
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enc_out, n_vars = self.patch_embedding(x_enc)
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# Encoder
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# z: [bs * nvars x patch_num x d_model]
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enc_out, attns = self.encoder(enc_out)
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# z: [bs x nvars x patch_num x d_model]
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enc_out = torch.reshape(
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enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1]))
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# z: [bs x nvars x d_model x patch_num]
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enc_out = enc_out.permute(0, 1, 3, 2)
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# Decoder
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output = self.flatten(enc_out)
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output = self.dropout(output)
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output = output.reshape(output.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, 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(
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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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