refactor(SeasonPatch): unify encoder and head initialization

layers/SeasonPatch.py

@@ -19,6 +19,7 @@ class SeasonPatch(nn.Module):
                  patch_len: int,
                  stride: int,
                  k_graph: int = 8,
+                 encoder_type: str = "Transformer",
                  d_model: int = 128,
                  n_layers: int = 3,
                  n_heads: int = 16,
@@ -36,53 +37,46 @@ class SeasonPatch(nn.Module):
         # Calculate patch number
         patch_num = (seq_len - patch_len) // stride + 1  # patch_num: int
 
+        self.encoder_type = encoder_type
+        # 只初始化需要的encoder
+
+        if encoder_type == "Transformer":
             # Transformer (PatchTST) 编码器（channel independent）
             self.encoder = TSTiEncoder(
-            c_in=c_in, 
-            patch_num=patch_num, 
-            patch_len=patch_len,
-            d_model=d_model,
-            n_layers=n_layers,
-            n_heads=n_heads
+                c_in=c_in, patch_num=patch_num, patch_len=patch_len,
+                d_model=d_model, n_layers=n_layers, n_heads=n_heads
             )
-
-        # Mamba2 编码器（channel independent），返回 [B, C, d_model]
-        self.mamba_encoder = Mamba2Encoder(
-            c_in=c_in,
-            patch_num=patch_num,
-            patch_len=patch_len,
-            d_model=d_model,
-            d_state=d_state,
-            d_conv=d_conv,
-            expand=expand,
-            headdim=headdim,
-        )
-
-        # Cross-channel mixer（仅 Transformer 路径使用）
             self.mixer = HierarchicalGraphMixer(c_in, dim=d_model, k=k_graph)
-
             # Prediction head（Transformer 路径用到，输入维度为 patch_num * d_model）
-        self.head_tr = nn.Sequential(
+            self.head = nn.Sequential(
                 nn.Linear(patch_num * d_model, patch_num * d_model),
                 nn.SiLU(),
                 nn.Linear(patch_num * d_model, pred_len)
             )
-
+        elif encoder_type == "Mamba2":
+        # Mamba2 编码器（channel independent），返回 [B, C, d_model]
+            self.encoder = Mamba2Encoder(
+                c_in=c_in, patch_num=patch_num, patch_len=patch_len,
+                d_model=d_model, d_state=d_state, d_conv=d_conv,
+                expand=expand, headdim=headdim
+            )
             # Prediction head（Mamba2 路径用到，输入维度为 d_model）
-        self.head_mamba = nn.Sequential(
+            self.head = nn.Sequential(
                 nn.Linear(d_model, d_model),
                 nn.SiLU(),
                 nn.Linear(d_model, pred_len)
             )
+        else:
+            raise ValueError(f"Unsupported encoder_type: {encoder_type}")
 
-    def forward(self, x, encoder="Transformer"):
+    def forward(self, x ):
         # x: [B, L, C]
         x = x.permute(0, 2, 1)  # x: [B, C, L]
 
         # Patch the input
         x_patch = x.unfold(-1, self.patch_len, self.stride)  # x_patch: [B, C, patch_num, patch_len]
 
-        if encoder == "Transformer":
+        if self.encoder_type == "Transformer":
             # Encode patches (PatchTST)
             z = self.encoder(x_patch)  # z: [B, C, d_model, patch_num]
 
@@ -95,14 +89,11 @@ class SeasonPatch(nn.Module):
 
             # Flatten and predict
             z_mix = z_mix.view(B, C, N * D)  # z_mix: [B, C, patch_num * d_model]
-            y_pred = self.head_tr(z_mix)  # y_pred: [B, C, pred_len]
+            y_pred = self.head(z_mix)  # y_pred: [B, C, pred_len]
 
-        elif encoder == "Mamba2":
+        elif self.encoder_type == "Mamba2":
             # 使用 Mamba2 编码器（不使用 mixer）
-            z_last = self.mamba_encoder(x_patch)  # z_last: [B, C, d_model]（仅最后一个时间步）
-            y_pred = self.head_mamba(z_last)      # y_pred: [B, C, pred_len]
-
-        else:
-            raise ValueError(f"Unsupported encoder type: {encoder}. Use 'Transformer' or 'Mamba2'.")
+            z_last = self.encoder(x_patch)  # z_last: [B, C, d_model]（仅最后一个时间步）
+            y_pred = self.head(z_last)      # y_pred: [B, C, pred_len]
 
         return y_pred  # [B, C, pred_len]

models/xPatch_SparseChannel.py

@@ -37,8 +37,6 @@ class Model(nn.Module):
         beta = getattr(configs, 'beta', torch.tensor(0.1))
         self.decomp = DECOMP(ma_type, alpha, beta)
         
-        # 读取选择的编码器类型（'Transformer' 或 'Mamba2'）
-        self.season_encoder = getattr(configs, 'season_encoder', 'Transformer')
         # Season network (PatchTST + Graph Mixer)
         self.season_net = SeasonPatch(
             c_in=self.enc_in,
@@ -49,7 +47,9 @@ class Model(nn.Module):
             k_graph=getattr(configs, 'k_graph', 8),
             d_model=getattr(configs, 'd_model', 128),
             n_layers=getattr(configs, 'e_layers', 3),
-            n_heads=getattr(configs, 'n_heads', 16)
+            n_heads=getattr(configs, 'n_heads', 16),
+            # 读取选择的编码器类型（'Transformer' 或 'Mamba2'）
+            encoder_type = getattr(configs, 'season_encoder', 'Transformer')
         )
         
         # Trend network (MLP)