feat: 提高参数量

cycle2.sh

@@ -1,4 +1,6 @@
-for i in {$1...$2}
+start=$1
+end=$2
+for ((i=start; i<=end; i=i+1))
 do
     sh gan.sh "$i"
     echo "第 $i 次循环完成"

data/src/minamo.ts

@@ -95,8 +95,8 @@ function generateTransformData(
             types.push([rot, flip]);
         }
     }
-    // 随机抽取最多两个
-    const trans = chooseFrom(types, Math.floor(Math.random() * 2));
+    // 随机抽取最多一个
+    const trans = chooseFrom(types, Math.floor(Math.random() * 1));
     return trans
         .map(([rot, flip]) => {
             const com1 = `${id1}.${rot}.${flip}:${id1}`;
@@ -167,10 +167,10 @@ function generateTransformData(
 }
 
 function generateSimilarData(id: string, map: number[][]) {
-    // 生成最多五个微调地图
+    // 生成最多两个微调地图
     const width = map[0].length;
     const height = map.length;
-    const num = Math.floor(Math.random() * 3);
+    const num = Math.floor(Math.random() * 2);
     const res: [id: string, data: MinamoTrainData][] = [];
 
     for (let i = 0; i < num; i++) {
@@ -241,7 +241,7 @@ function generatePair(
     // 自身与自身对比的训练集，保证模型对相同地图输出 1
     const self1 = `${id1}:${id1}`;
     const self2 = `${id2}:${id2}`;
-    const selfTrain = chooseFrom([self1, self2], Math.floor(Math.random() * 3));
+    const selfTrain = chooseFrom([self1, self2], Math.floor(Math.random() * 1));
     if (selfTrain.includes(self1) && !data[`${id1}:${id1}`]) {
         const selfTrain1: MinamoTrainData = {
             map1: map1,

gan.sh

@@ -8,10 +8,10 @@ python3 -m ginka.validate
 mv "minamo-dataset.json" "datasets/minamo-dataset-$1.json"
 mv "minamo-eval.json" "datasets/minamo-eval-$1.json"
 cd data
-pnpm minamo "../minamo-dataset.json" "../result/ginka_val.json" "../../Apeiria/project" assigned:100:40
+pnpm minamo "../minamo-dataset.json" "../result/ginka_val.json" "../../Apeiria/project" assigned:100:30
 pnpm minamo "../minamo-eval.json" "../result/ginka_val.json" "../../Apeiria-eval/project" assigned:100:10
-pnpm review "../minamo-dataset.json" "../datasets/minamo-dataset-merged.json"
-pnpm review "../minamo-eval.json" "../datasets/minamo-eval-merged.json"
 pnpm merge "../datasets/minamo-dataset-merged.json" "../datasets/minamo-dataset-merged.json" "../datasets/minamo-dataset-$1.json"
 pnpm merge "../datasets/minamo-eval-merged.json" "../datasets/minamo-eval-merged.json" "../datasets/minamo-eval-$1.json"
+pnpm review "../minamo-dataset.json" "../datasets/minamo-dataset-merged.json"
+pnpm review "../minamo-eval.json" "../datasets/minamo-eval-merged.json"
 cd ..

ginka/dataset.py

@@ -3,7 +3,8 @@ import torch
 import torch.nn.functional as F
 from torch.utils.data import Dataset
 from minamo.model.model import MinamoModel
-from shared.graph import convert_soft_map_to_graph
+from shared.graph import differentiable_convert_to_data
+from shared.utils import random_smooth_onehot
 
 def load_data(path: str):
     with open(path, 'r', encoding="utf-8") as f:
@@ -28,8 +29,9 @@ class GinkaDataset(Dataset):
     def __getitem__(self, idx):
         item = self.data[idx]
         
-        target = F.one_hot(torch.LongTensor(item['map']), num_classes=32).permute(2, 0, 1).float().to(self.device)  # [32, H, W]
-        graph = convert_soft_map_to_graph(target).to(self.device)
+        target = F.one_hot(torch.LongTensor(item['map']), num_classes=32).permute(2, 0, 1).float()  # [32, H, W]
+        target = random_smooth_onehot(target).to(self.device)
+        graph = differentiable_convert_to_data(target).to(self.device)
         vision_feat, topo_feat = self.minamo(target.unsqueeze(0), graph)
         
         return {

ginka/model/model.py

@@ -10,8 +10,22 @@ class GinkaModel(nn.Module):
         """
         super().__init__()
         self.base_ch = base_ch
+        fc_dim = base_ch * 8 * 4 * 4
         self.fc = nn.Sequential(
-            nn.Linear(feat_dim, 32 * 32 * base_ch)
+            nn.Linear(feat_dim, fc_dim),
+            nn.BatchNorm1d(fc_dim),
+            nn.ReLU()
+        )
+        self.deconv_layers = nn.Sequential(
+            nn.ConvTranspose2d(base_ch*8, base_ch*4, kernel_size=4, stride=2, padding=1),  # Upsample 2x
+            nn.BatchNorm2d(base_ch*4),
+            nn.ReLU(),
+            nn.ConvTranspose2d(base_ch*4, base_ch*2, kernel_size=4, stride=2, padding=1),   # Upsample 2x
+            nn.BatchNorm2d(base_ch*2),
+            nn.ReLU(),
+            nn.ConvTranspose2d(base_ch*2, base_ch, kernel_size=4, stride=2, padding=1),   # Upsample 2x
+            nn.BatchNorm2d(base_ch),
+            nn.ReLU(),
         )
         self.unet = GinkaUNet(base_ch, num_classes)
         self.down_sample = MapDownSample(num_classes, num_classes)
@@ -25,7 +39,8 @@ class GinkaModel(nn.Module):
             logits: 输出logits [BS, num_classes, H, W]
         """
         x = self.fc(feat)
-        x = x.view(-1, self.base_ch, 32, 32)
+        x = x.view(-1, self.base_ch*8, 4, 4)
+        x = self.deconv_layers(x)
         x = self.unet(x)
         x = F.interpolate(x, (13, 13), mode='bilinear')
         return x, F.softmax(x, dim=1)

ginka/train.py

@@ -48,7 +48,7 @@ def train():
     )
     
     # 设定优化器与调度器
-    optimizer = optim.AdamW(model.parameters(), lr=5e-3)
+    optimizer = optim.AdamW(model.parameters(), lr=1e-3)
     scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=10, T_mult=2, eta_min=1e-6)
     criterion = GinkaLoss(minamo)
     
@@ -72,7 +72,7 @@ def train():
             target = batch["target"].to(device)
             target_vision_feat = batch["target_vision_feat"].to(device)
             target_topo_feat = batch["target_topo_feat"].to(device)
-            feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device)
+            feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device).squeeze(1)
             # 前向传播
             optimizer.zero_grad()
             _, output_softmax = model(feat_vec)
@@ -84,6 +84,10 @@ def train():
             scaled_losses.backward()
             optimizer.step()
             total_loss += losses.item()
+            # for name, param in model.named_parameters():
+            #     if param.grad is not None:
+            #         print(f"{name}: grad_mean={param.grad.abs().mean():.3e}, max={param.grad.abs().max():.3e}")
+
             
         avg_loss = total_loss / len(dataloader)
         tqdm.write(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Epoch: {epoch + 1} | loss: {avg_loss:.6f} | lr: {(optimizer.param_groups[0]['lr']):.6f}")
@@ -112,7 +116,7 @@ def train():
                     target = batch["target"].to(device)
                     target_vision_feat = batch["target_vision_feat"].to(device)
                     target_topo_feat = batch["target_topo_feat"].to(device)
-                    feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device)
+                    feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device).squeeze(1)
                     
                     # 前向传播
                     output, output_softmax = model(feat_vec)

ginka/validate.py

@@ -106,7 +106,7 @@ def validate():
             target = batch["target"].to(device)
             target_vision_feat = batch["target_vision_feat"].to(device)
             target_topo_feat = batch["target_topo_feat"].to(device)
-            feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device)
+            feat_vec = torch.cat([target_vision_feat, target_topo_feat], dim=-1).to(device).squeeze(1)
             # 前向传播
             output, output_softmax = model(feat_vec)
             map_matrix = torch.argmax(output, dim=1)

minamo/dataset.py

@@ -3,22 +3,7 @@ import torch
 import torch.nn.functional as F
 from torch.utils.data import Dataset
 from shared.graph import differentiable_convert_to_data
-
-def random_smooth_onehot(onehot_map, min_main=0.75, max_main=1.0, epsilon=0.25):
-    """
-    生成随机平滑的 one-hot 编码，使主类别概率不再固定，而是随机波动
-    """
-    C, H, W = onehot_map.shape
-    # 生成主类别的随机概率 (min_main, max_main)
-    main_prob = torch.rand(H, W) * (max_main - min_main) + min_main  
-    
-    # 计算剩余概率并随机分配到其他类别
-    noise = torch.rand(C, H, W) * epsilon  # 随机噪声
-    noise = noise / noise.sum(dim=1, keepdim=True)  # 归一化到总和为 epsilon
-    
-    # 计算最终平滑 one-hot 结果
-    smooth_onehot = onehot_map * main_prob + (1 - onehot_map) * noise
-    return smooth_onehot
+from shared.utils import random_smooth_onehot
 
 def load_data(path: str):
     with open(path, 'r', encoding="utf-8") as f:

minamo/train.py

@@ -110,7 +110,7 @@ def train():
             total_loss += loss.item()
             
         ave_loss = total_loss / len(dataloader)
-        print(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Epoch: {epoch + 1} | loss: {ave_loss:.6f} | lr: {(optimizer.param_groups[0]['lr']):.6f}")
+        tqdm.write(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Epoch: {epoch + 1} | loss: {ave_loss:.6f} | lr: {(optimizer.param_groups[0]['lr']):.6f}")
         
         # total_norm = 0
         # for p in model.parameters():
@@ -128,7 +128,7 @@ def train():
         scheduler.step()
         
         # 每十轮推理一次验证集
-        if (epoch + 1) % 1 == 0:
+        if (epoch + 1) % 5 == 0:
             model.eval()
             val_loss = 0
             with torch.no_grad():
@@ -152,7 +152,7 @@ def train():
                     val_loss += loss_val.item()
                     
             avg_val_loss = val_loss / len(val_loader)
-            print(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Validation::loss: {avg_val_loss:.6f}")
+            tqdm.write(f"[INFO {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] Validation::loss: {avg_val_loss:.6f}")
             torch.save({
                 "model_state": model.state_dict(),
                 "optimizer_state": optimizer.state_dict(),

minamo/validate.py

@@ -20,7 +20,7 @@ def validate():
     print(f"Total parameters: {total_params}")
 
     # 准备数据集
-    val_dataset = MinamoDataset("minamo-eval.json")
+    val_dataset = MinamoDataset("datasets/minamo-eval-1.json")
     val_loader = DataLoader(
         val_dataset,
         batch_size=32,
@@ -44,6 +44,8 @@ def validate():
             vision_feat1, topo_feat1 = model(map1_val, graph1)
             vision_feat2, topo_feat2 = model(map2_val, graph2)
             
+            print(vision_feat1.isnan().any().item(), topo_feat1.isnan().any().item(), vision_feat2.isnan().any().item(), topo_feat2.isnan().any().item())
+            
             vision_pred_val = F.cosine_similarity(vision_feat1, vision_feat2, -1).unsqueeze(-1)
             topo_pred_val = F.cosine_similarity(topo_feat1, topo_feat2, -1).unsqueeze(-1)
             loss_val = criterion(

shared/utils.py

@@ -0,0 +1,17 @@
+import torch
+
+def random_smooth_onehot(onehot_map, min_main=0.75, max_main=1.0, epsilon=0.25):
+    """
+    生成随机平滑的 one-hot 编码，使主类别概率不再固定，而是随机波动
+    """
+    C, H, W = onehot_map.shape
+    # 生成主类别的随机概率 (min_main, max_main)
+    main_prob = torch.rand(H, W) * (max_main - min_main) + min_main  
+    
+    # 计算剩余概率并随机分配到其他类别
+    noise = torch.rand(C, H, W) * epsilon  # 随机噪声
+    noise = noise / noise.sum(dim=1, keepdim=True)  # 归一化到总和为 epsilon
+    
+    # 计算最终平滑 one-hot 结果
+    smooth_onehot = onehot_map * main_prob + (1 - onehot_map) * noise
+    return smooth_onehot