first commit

models/PAttn.py

@@ -0,0 +1,62 @@
+import torch
+import torch.nn as nn
+from layers.Transformer_EncDec import Encoder, EncoderLayer
+from layers.SelfAttention_Family import FullAttention, AttentionLayer
+from einops import rearrange
+
+
+class Model(nn.Module):
+    """
+    Paper link: https://arxiv.org/abs/2406.16964
+    """
+    def __init__(self, configs, patch_len=16, stride=8):
+        super().__init__()
+        self.seq_len = configs.seq_len
+        self.pred_len = configs.pred_len
+        self.patch_size = patch_len 
+        self.stride = stride
+        
+        self.d_model = configs.d_model
+       
+        self.patch_num = (configs.seq_len - self.patch_size) // self.stride + 2
+        self.padding_patch_layer = nn.ReplicationPad1d((0,  self.stride)) 
+        self.in_layer = nn.Linear(self.patch_size, self.d_model)
+        self.encoder = Encoder(
+            [
+                EncoderLayer(
+                    AttentionLayer(
+                        FullAttention(False, configs.factor, attention_dropout=configs.dropout,
+                                      output_attention=False), configs.d_model, configs.n_heads),
+                    configs.d_model,
+                    configs.d_ff,
+                    dropout=configs.dropout,
+                    activation=configs.activation
+                ) for l in range(1)
+            ],
+            norm_layer=nn.LayerNorm(configs.d_model)
+        )
+        self.out_layer = nn.Linear(self.d_model * self.patch_num, configs.pred_len)
+            
+    def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec):
+        means = x_enc.mean(1, keepdim=True).detach()
+        x_enc = x_enc - means
+        stdev = torch.sqrt(
+            torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
+        x_enc /= stdev
+        
+        B, _, C = x_enc.shape
+        x_enc = x_enc.permute(0, 2, 1)
+        x_enc = self.padding_patch_layer(x_enc)
+        x_enc = x_enc.unfold(dimension=-1, size=self.patch_size, step=self.stride)
+        enc_out = self.in_layer(x_enc)
+        enc_out =  rearrange(enc_out, 'b c m l -> (b c) m l')
+        dec_out, _ = self.encoder(enc_out)
+        dec_out =  rearrange(dec_out, '(b c) m l -> b c (m l)' , b=B , c=C)
+        dec_out = self.out_layer(dec_out)
+        dec_out = dec_out.permute(0, 2, 1)
+        
+        dec_out = dec_out * \
+                  (stdev[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
+        dec_out = dec_out + \
+                  (means[:, 0, :].unsqueeze(1).repeat(1, self.pred_len, 1))
+        return dec_out