chore: 增大 RNN 参数量

ginka/generator/rnn.py

@@ -8,14 +8,13 @@ class RNNConditionEncoder(nn.Module):
         super().__init__()
         
         # 条件编码
-        
         self.val_fc = nn.Sequential(
-            nn.Linear(val_dim, output_dim),
-            nn.LayerNorm(output_dim),
+            nn.Linear(val_dim, output_dim * 2),
+            nn.LayerNorm(output_dim * 2),
             nn.ReLU(),
         )
         self.fusion = nn.Sequential(
-            nn.Linear(output_dim, output_dim)
+            nn.Linear(output_dim * 2, output_dim)
         )
         
     def forward(self, val_cond: torch.Tensor):
@@ -70,7 +69,7 @@ class GinkaMapPatch(nn.Module):
         return self.patch_cnn(result)
     
 class GinkaTileEmbedding(nn.Module):
-    def __init__(self, tile_classes=32, embed_dim=128):
+    def __init__(self, tile_classes=32, embed_dim=256):
         super().__init__()
         
         # 图块编码，上一次画的图块
@@ -81,7 +80,7 @@ class GinkaTileEmbedding(nn.Module):
         return self.embedding(tile)
     
 class GinkaPosEmbedding(nn.Module):
-    def __init__(self, width=13, height=13, embed_dim=128):
+    def __init__(self, width=13, height=13, embed_dim=256):
         super().__init__()
         
         # 位置编码
@@ -99,7 +98,7 @@ class GinkaPosEmbedding(nn.Module):
         return row, col
     
 class GinkaInputFusion(nn.Module):
-    def __init__(self, d_model=128):
+    def __init__(self, d_model=256):
         super().__init__()
         
         # 使用 Transformer 进行信息整合
@@ -116,19 +115,19 @@ class GinkaInputFusion(nn.Module):
         row_embed: torch.Tensor, col_embed: torch.Tensor, patch_vec: torch.Tensor
     ):
         """
-        tile_embed: [B, 128]
+        tile_embed: [B, 256]
         cond_vec: [B, 256]
-        row_embed: [B, 128]
-        col_embed: [B, 128]
+        row_embed: [B, 256]
+        col_embed: [B, 256]
         patch_vec: [B, 512]
         """
         vec = torch.cat([tile_embed, cond_vec, row_embed, col_embed, patch_vec], dim=1)
-        vec = torch.stack(torch.split(vec, 128, dim=1), dim=1)
+        vec = torch.stack(torch.split(vec, 256, dim=1), dim=1)
         feat = self.transformer(vec)
         return torch.mean(feat, dim=1)
 
 class GinkaRNN(nn.Module):
-    def __init__(self, tile_classes=32, input_dim=128, hidden_dim=1024):
+    def __init__(self, tile_classes=32, input_dim=256, hidden_dim=2048):
         super().__init__()
         
         # GRU
@@ -153,16 +152,16 @@ class GinkaRNNModel(nn.Module):
         self.height = height
         self.start_tile = start_tile
         
-        self.rnn_hidden = 1024
+        self.rnn_hidden = 2048
         self.tile_classes = 32
         
         # 模型结构
         self.cond = RNNConditionEncoder()
-        self.tile_embedding = GinkaTileEmbedding()
+        self.tile_embedding = GinkaTileEmbedding(tile_classes=self.tile_classes)
         self.pos_embedding = GinkaPosEmbedding()
-        self.map_patch = GinkaMapPatch()
+        self.map_patch = GinkaMapPatch(tile_classes=self.tile_classes)
         self.feat_fusion = GinkaInputFusion()
-        self.rnn = GinkaRNN(hidden_dim=self.rnn_hidden)
+        self.rnn = GinkaRNN(tile_classes=self.tile_classes, hidden_dim=self.rnn_hidden)
         
     def forward(self, val_cond: torch.Tensor, target_map: torch.Tensor, use_self=False):
         """