config/AEPSA/v2_PEMS-BAY.yaml

@@ -19,11 +19,9 @@ data:
   val_ratio: 0.2
 
 model:
-  chebyshev_order: 3
   d_ff: 128
   d_model: 64
   dropout: 0.2
-  graph_hidden_dim: 32
   gpt_layers: 9
   gpt_path: ./GPT-2
   input_dim: 1

model/AEPSA/aepsav3.py

@@ -190,7 +190,7 @@ class AEPSA(nn.Module):
         # 图编码
         H_t = self.graph_encoder(x_enc)  # [B,N,1,T] -> [B, N, hidden_dim*(K+1)]
         X_t_1 = self.graph_projection(H_t)  # [B,N,d_model]
-        X_enc = torch.cat([H_t, X_t_1], dim = -1) # [B, N, d_model + hidden_dim*(K+1)]
+        enc_out = torch.cat([H_t, X_t_1], dim = -1) # [B, N, d_model + hidden_dim*(K+1)]
         
         # 词嵌入处理
         self.mapping_layer(self.word_embeddings.permute(1, 0)).permute(1, 0)
@@ -198,9 +198,9 @@ class AEPSA(nn.Module):
         source_embeddings = self.word_embeddings[masks==1]  # [selected_words,d_llm]
    
         # 重编程与预测
-        X_enc = self.reprogramming_layer(X_enc, source_embeddings, source_embeddings)
+        enc_out = self.reprogramming_layer(enc_out, source_embeddings, source_embeddings)
-        X_enc = self.gpts(inputs_embeds=X_enc).last_hidden_state  # [B,N,d_llm]
+        enc_out = self.gpts(inputs_embeds=enc_out).last_hidden_state  # [B,N,d_llm]
-        dec_out = self.out_mlp(X_enc)  # [B,N,pred_len]
+        dec_out = self.out_mlp(enc_out)  # [B,N,pred_len]
         
         # 维度调整
         outputs = dec_out.unsqueeze(dim=-1)  # [B,N,pred_len,1]