chore: 微调 rnn-vae

ginka/train_vae.py

@@ -54,10 +54,10 @@ from shared.image import matrix_to_image_cv
 # 29. 入口，不区分楼梯和箭头
 
 BATCH_SIZE = 128
-LATENT_DIM = 48
-KL_BETA = 0.1
-SELF_GATE = 0.5
-GATE_EPOCH = 5
+LATENT_DIM = 64
+KL_BETA = 0.01
+SELF_GATE = 0.3
+GATE_EPOCH = 10
 VAL_BATCH_DIVIDER = 128
 PROB_STEP = 0.05
 
@@ -96,10 +96,10 @@ def train():
     dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True)
     dataloader_val = DataLoader(dataset_val, batch_size=BATCH_SIZE // VAL_BATCH_DIVIDER, shuffle=True)
     
-    optimizer_ginka = optim.AdamW(vae.parameters(), lr=2e-4, weight_decay=1e-4)
+    optimizer_ginka = optim.AdamW(vae.parameters(), lr=3e-4, weight_decay=1e-4)
     # 自定义调度器允许在 self_prob 提高时重置调度器信息并提高学习率以适应学习
     scheduler_ginka = VAEScheduler(
-        optimizer_ginka, factor=0.9, increase_factor=2, patience=10, max_lr=2e-4, min_lr=1e-6
+        optimizer_ginka, factor=0.9, increase_factor=1.5, patience=20, max_lr=3e-4, min_lr=1e-6
     )
 
     criterion = VAELoss()
@@ -129,6 +129,7 @@ def train():
         reco_loss_total = torch.Tensor([0]).to(device)
         kl_loss_total = torch.Tensor([0]).to(device)
         
+        vae.train()
         for batch in tqdm(dataloader, leave=False, desc="Epoch Progress", disable=disable_tqdm):
             target_map = batch["target_map"].to(device)
             
@@ -182,6 +183,7 @@ def train():
 
         # 每若干轮输出一次图片，并保存检查点
         if (epoch + 1) % args.checkpoint == 0:
+            vae.eval()
             # 保存检查点
             torch.save({
                 "model_state": vae.state_dict(),

ginka/vae_rnn/decoder.py

@@ -105,7 +105,13 @@ class DecoderInputFusion(nn.Module):
         )
         self.norm = nn.LayerNorm(d_model)
         self.fusion = nn.Sequential(
+            nn.Linear(d_model * 2, d_model * 2),
+            nn.Dropout(0.2),
+            nn.LayerNorm(d_model * 2),
+            nn.GELU(),
+            
             nn.Linear(d_model * 2, d_model),
+            nn.Dropout(0.1),
             nn.LayerNorm(d_model),
             nn.GELU()
         )
@@ -138,6 +144,7 @@ class DecoderRNN(nn.Module):
         self.drop = nn.Dropout(0.2)
         self.fc = nn.Sequential(
             nn.Linear(hidden_dim, hidden_dim),
+            nn.Dropout(0.1),
             nn.LayerNorm(hidden_dim),
             nn.GELU(),
             
@@ -168,8 +175,10 @@ class VAEDecoder(nn.Module):
         # 模型结构
         self.map_vec_fc = nn.Sequential(
             nn.Linear(map_vec_dim, 128),
+            nn.Dropout(0.1),
             nn.LayerNorm(128),
             nn.GELU(),
+            
             nn.Linear(128, 256)
         )
         self.tile_embedding = DecoderTileEmbedding(tile_classes=self.tile_classes)
@@ -227,22 +236,22 @@ class VAEDecoder(nn.Module):
         return output_logits.permute(0, 3, 1, 2)
 
 if __name__ == "__main__":
-    device = torch.device("cpu")
+    device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
     
     input = torch.randint(0, 32, [1, 13, 13]).to(device)
     map_vec = torch.rand(1, 32).to(device)
     
     # 初始化模型
-    model = VAEDecoder("cpu").to(device)
+    model = VAEDecoder(device).to(device)
     
-    print_memory("初始化后")
+    print_memory(device, "初始化后")
     
     # 前向传播
     start = time.perf_counter()
     fake_logits = model(map_vec, input, 0)
     end = time.perf_counter()
     
-    print_memory("前向传播后")
+    print_memory(device, "前向传播后")
     
     print(f"推理耗时: {end - start}")
     print(f"输出形状: fake_logits={fake_logits.shape}")

ginka/vae_rnn/encoder.py

@@ -38,9 +38,10 @@ class EncoderGRU(nn.Module):
         
         # GRU
         self.gru = nn.GRUCell(input_dim, hidden_dim)
-        self.drop = nn.Dropout(0.1)
+        self.drop = nn.Dropout(0.2)
         self.fc = nn.Sequential(
             nn.Linear(hidden_dim, hidden_dim),
+            nn.Dropout(0.1),
             nn.LayerNorm(hidden_dim),
             nn.GELU(),
             
@@ -62,13 +63,14 @@ class EncoderFusion(nn.Module):
         
         self.transformer = nn.TransformerEncoder(
             nn.TransformerEncoderLayer(
-                d_model=d_model, dim_feedforward=d_model, nhead=2, batch_first=True
+                d_model=d_model, dim_feedforward=d_model*2, nhead=2, batch_first=True
             ),
-            num_layers=2
+            num_layers=3
         )
         self.norm = nn.LayerNorm(d_model)
         self.fc = nn.Sequential(
             nn.Linear(d_model * 2, d_model * 2),
+            nn.Dropout(0.1),
             nn.LayerNorm(d_model * 2),
             nn.GELU()
         )
@@ -91,11 +93,20 @@ class VAEEncoder(nn.Module):
         self.embedding = EncoderEmbedding(tile_classes, width, height, 128, 256)
         self.rnn = EncoderGRU(256, self.rnn_hidden, self.logits_dim)
         self.fusion = EncoderFusion(256)
-        self.fc = nn.Sequential(
+        self.fc_mu = nn.Sequential(
+            nn.Linear(512, 512),
+            nn.Dropout(0.1),
+            nn.LayerNorm(512),
+            nn.GELU(),
+            nn.Linear(512, latent_dim)
+        )
+        self.fc_logvar = nn.Sequential(
+            nn.Linear(512, 512),
+            nn.Dropout(0.1),
+            nn.LayerNorm(512),
+            nn.GELU(),
             nn.Linear(512, latent_dim)
         )
-        self.fc_mu = nn.Linear(512, latent_dim)
-        self.fc_logvar = nn.Linear(512, latent_dim)
         
         self.col_list = []
         self.row_list = []
@@ -126,21 +137,21 @@ class VAEEncoder(nn.Module):
         return mu, logvar
 
 if __name__ == "__main__":
-    device = torch.device("cpu")
+    device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
     
     input = torch.randint(0, 32, [1, 13, 13]).to(device)
     
     # 初始化模型
     model = VAEEncoder(device).to(device)
     
-    print_memory("初始化后")
+    print_memory(device, "初始化后")
     
     # 前向传播
     start = time.perf_counter()
     mu, logvar = model(input)
     end = time.perf_counter()
     
-    print_memory("前向传播后")
+    print_memory(device, "前向传播后")
     
     print(f"推理耗时: {end - start}")
     print(f"输出形状: mu={mu.shape}, logvar={logvar.shape}")