perf: 修改损失值计算方式

ginka/dataset.py

@@ -31,8 +31,22 @@ def load_minamo_gan_data(data: list):
         res.append((one['map1'], one['map2'], one['visionSimilarity'], one['topoSimilarity'], True))
     return res
 
+def apply_curriculum_remove(
+    maps: torch.Tensor,
+    remove_classes: List[int],             # 要移除的类别索引
+):
+    C, H, W = maps.shape
+    device = maps.device
+    removed_maps = maps.clone()
+
+    remove_mask = removed_maps[remove_classes, :, :].sum(dim=0, keepdim=True) > 0
+    removed_maps[:, :, :][remove_mask.expand(C, -1, -1)] = 0
+    removed_maps[0][remove_mask[0, :, :]] = 1  # 设置为“空地”
+
+    return removed_maps.to(device)
+
 def apply_curriculum_mask(
-    maps: torch.Tensor,                    # [B, C, H, W]
+    maps: torch.Tensor,                    # [C, H, W]
     mask_classes: List[int],               # 要遮挡的类别索引
     remove_classes: List[int],             # 要移除的类别索引
     mask_ratio: float                      # 遮挡比例 0~1
@@ -73,6 +87,42 @@ class GinkaWGANDataset(Dataset):
 
     def __len__(self):
         return len(self.data)
+    
+    def handle_stage1(self, target):
+        removed1, masked1 = apply_curriculum_mask(target, STAGE1_MASK, STAGE1_REMOVE, self.mask_ratio1)
+        removed2, masked2 = apply_curriculum_mask(target, STAGE2_MASK, STAGE2_REMOVE, self.mask_ratio2)
+        removed3, masked3 = apply_curriculum_mask(target, STAGE3_MASK, STAGE3_REMOVE, self.mask_ratio3)
+        
+        return removed1, masked1, removed2, masked2, removed3, masked3
+    
+    def handle_stage2(self, target):
+        removed1, masked1 = apply_curriculum_mask(target, STAGE1_MASK, STAGE1_REMOVE, random.uniform(0.1, 0.9))
+        # 后面两个阶段由于会保留一些类别，所以完全随机遮挡即可
+        removed2, masked2 = apply_curriculum_mask(target, STAGE2_MASK, STAGE2_REMOVE, random.uniform(0.1, 1))
+        removed3, masked3 = apply_curriculum_mask(target, STAGE3_MASK, STAGE3_REMOVE, random.uniform(0.1, 1))
+    
+        if self.random_ratio > 0:
+            rd = random.uniform(0, self.random_ratio)
+            masked1 = random_smooth_onehot(masked1, min_main=1 - rd, max_main=1.0, epsilon=rd)
+            masked2 = random_smooth_onehot(masked2, min_main=1 - rd, max_main=1.0, epsilon=rd)
+            masked3 = random_smooth_onehot(masked3, min_main=1 - rd, max_main=1.0, epsilon=rd)
+            
+        return removed1, masked1, removed2, masked2, removed3, masked3
+    
+    def handle_stage3(self, target):
+        rd = random.uniform(0, self.random_ratio)
+        removed1, masked1 = apply_curriculum_mask(target, STAGE1_MASK, STAGE1_REMOVE, random.uniform(0.1, 0.9))
+        removed2 = apply_curriculum_remove(target, STAGE2_REMOVE)
+        removed3 = apply_curriculum_remove(target, STAGE3_REMOVE)
+        masked1 = random_smooth_onehot(masked1, min_main=1 - rd, max_main=1.0, epsilon=rd)
+        return removed1, masked1, removed2, torch.zeros_like(target), removed3, torch.zeros_like(target)
+    
+    def handle_stage4(self, target):
+        input1 = torch.rand((32, 13, 13))
+        removed1 = apply_curriculum_remove(target, STAGE1_REMOVE)
+        removed2 = apply_curriculum_remove(target, STAGE2_REMOVE)
+        removed3 = apply_curriculum_remove(target, STAGE3_REMOVE)
+        return removed1, input1, removed2, torch.zeros_like(target), removed3, torch.zeros_like(target)
 
     def __getitem__(self, idx):
         item = self.data[idx]
@@ -80,18 +130,16 @@ class GinkaWGANDataset(Dataset):
         target = F.one_hot(torch.LongTensor(item['map']), num_classes=32).permute(2, 0, 1).float()  # [32, H, W]
 
         if self.train_stage == 1:
-            removed1, masked1 = apply_curriculum_mask(target, STAGE1_MASK, STAGE1_REMOVE, self.mask_ratio1)
+            return self.handle_stage1(target)
-            removed2, masked2 = apply_curriculum_mask(target, STAGE2_MASK, STAGE2_REMOVE, self.mask_ratio2)
+            
-            removed3, masked3 = apply_curriculum_mask(target, STAGE3_MASK, STAGE3_REMOVE, self.mask_ratio3)
         elif self.train_stage == 2:
-            removed1, masked1 = apply_curriculum_mask(target, STAGE1_MASK, STAGE1_REMOVE, random.uniform(0.1, 0.9))
+            return self.handle_stage2(target)
-            removed2, masked2 = apply_curriculum_mask(target, STAGE2_MASK, STAGE2_REMOVE, random.uniform(0.1, 0.9))
-            removed3, masked3 = apply_curriculum_mask(target, STAGE3_MASK, STAGE3_REMOVE, random.uniform(0.1, 0.9))
         
-        if self.random_ratio > 0:
+        elif self.train_stage == 3:
-            removed1 = random_smooth_onehot(removed1, min_main=1 - self.random_ratio, max_main=1.0, epsilon=self.random_ratio)
+            return self.handle_stage3(target)
-            removed2 = random_smooth_onehot(removed2, min_main=1 - self.random_ratio, max_main=1.0, epsilon=self.random_ratio)
-            removed3 = random_smooth_onehot(removed3, min_main=1 - self.random_ratio, max_main=1.0, epsilon=self.random_ratio)
 
-        return removed1, masked1, removed2, masked2, removed3, masked3
+        elif self.train_stage == 4:
+            return self.handle_stage4(target)
+
+        raise RuntimeError(f"Invalid train stage: {self.train_stage}")
         

ginka/model/loss.py

@@ -327,7 +327,7 @@ def immutable_penalty_loss(
         target_mask = F.one_hot(target_mask, num_classes=len(not_allowed)).permute(0, 3, 1, 2).float()
 
     # 差异区域（模型试图改变的地方）
-    penalty = F.cross_entropy(input_mask, target_mask)
+    penalty = F.l1_loss(input_mask, target_mask)
 
     return penalty
 
@@ -405,13 +405,13 @@ class WGANGinkaLoss:
         
         fake_scores, _, _ = critic(fake, fake_graph, stage)
         minamo_loss = -torch.mean(fake_scores)
-        ce_loss = F.cross_entropy(fake, real)
+        ce_loss = F.l1_loss(fake, real)
         immutable_loss = immutable_penalty_loss(fake, input, STAGE_ALLOWED[stage])
         constraint_loss = outer_border_constraint_loss(fake) + inner_constraint_loss(fake)
         
         losses = [
             minamo_loss * self.weight[0],
-            ce_loss * self.weight[1] / mask_ratio * (1 - mask_ratio), # 蒙版越大，交叉熵损失权重越小
+            ce_loss * self.weight[1] * (1 - mask_ratio), # 蒙版越大，交叉熵损失权重越小
             immutable_loss * self.weight[2],
             constraint_loss * self.weight[3]
         ]
@@ -423,4 +423,25 @@ class WGANGinkaLoss:
         
         # print(losses[2].item())
         
-        return sum(losses), minamo_loss, ce_loss / mask_ratio, immutable_loss
+        return sum(losses), minamo_loss, ce_loss, immutable_loss
+    
+    def generator_loss_total(self, critic, stage, fake) -> torch.Tensor:
+        fake_graph = batch_convert_soft_map_to_graph(fake)
+        
+        fake_scores, _, _ = critic(fake, fake_graph, stage)
+        minamo_loss = -torch.mean(fake_scores)
+        immutable_loss = immutable_penalty_loss(fake, fake, STAGE_ALLOWED[stage])
+        constraint_loss = outer_border_constraint_loss(fake) + inner_constraint_loss(fake)
+        
+        losses = [
+            minamo_loss * self.weight[0],
+            immutable_loss * self.weight[2],
+            constraint_loss * self.weight[3]
+        ]
+        
+        if stage == 1:
+            # 第一个阶段检查入口存在性
+            entrance_loss = entrance_constraint_loss(fake)
+            losses.append(entrance_loss * self.weight[4])
+            
+        return sum(losses)

ginka/train_wgan.py

@@ -46,14 +46,19 @@ def gen_curriculum(gen, masked1, masked2, masked3, detach=False) -> tuple[torch.
     else:
         return fake1, fake2, fake3
     
-def gen_total(gen, input, detach=False) -> torch.Tensor:
+def gen_total(gen, input, progress_detach=True, result_detach=False) -> torch.Tensor:
-    fake1 = gen(input, 1)
+    if progress_detach:
-    fake2 = gen(fake1, 2)
+        fake1 = gen(input.detach(), 1)
-    fake3 = gen(fake2, 3)
+        fake2 = gen(fake1.detach(), 2)
-    if detach:
+        fake3 = gen(fake2.detach(), 3)
-        return fake3.detach()
     else:
-        return fake3
+        fake1 = gen(input, 1)
+        fake2 = gen(fake1, 2)
+        fake3 = gen(fake2, 3)
+    if result_detach:
+        return fake1.detach(), fake2.detach(), fake3.detach()
+    else:
+        return fake1, fake2, fake3
 
 def train():
     print(f"Using {'cuda' if torch.cuda.is_available() else 'cpu'} to train model.")
@@ -67,6 +72,7 @@ def train():
     train_stage = 1
     mask_ratio = 0.1 # 蒙版区域大小，每次增加 0.1，到达 0.9 之后进入阶段 2 的训练
     random_ratio = 0
+    stage3_epoch = 0 # 第三阶段 epoch 数，100 轮后进入第四阶段
     
     ginka = GinkaModel()
     minamo = MinamoScoreModule()
@@ -109,6 +115,9 @@ def train():
         if data_ginka.get("random_ratio") is not None:
             random_ratio = data_ginka["random_ratio"]
             
+        if data_ginka.get("stage_epoch3") is not None:
+            stage3_epoch = data_ginka["stage_epoch3"]
+            
         if data_ginka.get("stage") is not None:
             train_stage = data_ginka["stage"]
             
@@ -151,18 +160,19 @@ def train():
                 if train_stage == 1 or train_stage == 2:
                     fake1, fake2, fake3 = gen_curriculum(ginka, masked1, masked2, masked3, True)
                     
-                    loss_d1, dis1 = criterion.discriminator_loss(minamo, 1, real1, fake1)
+                elif train_stage == 3 or train_stage == 4:
-                    loss_d2, dis2 = criterion.discriminator_loss(minamo, 2, real2, fake2)
+                    fake1, fake2, fake3 = gen_total(ginka, masked1, True, True)
-                    loss_d3, dis3 = criterion.discriminator_loss(minamo, 3, real3, fake3)
+                
-                    
+                loss_d1, dis1 = criterion.discriminator_loss(minamo, 1, real1, fake1)
-                    dis_avg = (dis1 + dis2 + dis3) / 3.0
+                loss_d2, dis2 = criterion.discriminator_loss(minamo, 2, real2, fake2)
-                    loss_d_avg = (loss_d1 + loss_d2 + loss_d3) / 3.0
+                loss_d3, dis3 = criterion.discriminator_loss(minamo, 3, real3, fake3)
+                
+                dis_avg = (dis1 + dis2 + dis3) / 3.0
+                loss_d_avg = (loss_d1 + loss_d2 + loss_d3) / 3.0
 
-                    # 反向传播
+                # 反向传播
-                    loss_d_avg.backward()
+                loss_d_avg.backward()
-                elif train_stage == 3:
+                
-                    pass
-                    
                 optimizer_minamo.step()
                 
                 loss_total_minamo += loss_d_avg.detach()
@@ -188,8 +198,17 @@ def train():
                     loss_total_ginka += loss_g.detach()
                     loss_ce_total += loss_ce.detach()
                     
-                elif train_stage == 3:
+                elif train_stage == 3 or train_stage == 4:
-                    pass
+                    fake1, fake2, fake3 = gen_total(ginka, masked1, True, False)
+                    
+                    loss_g1 = criterion.generator_loss_total(minamo, 1, fake1)
+                    loss_g2 = criterion.generator_loss_total(minamo, 2, fake2)
+                    loss_g3 = criterion.generator_loss_total(minamo, 3, fake3)
+                    
+                    loss_g = (loss_g1 + loss_g2 + loss_g3) / 3.0
+                    loss_g.backward()
+                    optimizer_ginka.step()
+                    loss_total_ginka += loss_g.detach()
             
         avg_loss_ginka = loss_total_ginka.item() / len(dataloader) / g_steps
         avg_loss_minamo = loss_total_minamo.item() / len(dataloader) / c_steps
@@ -202,12 +221,14 @@ def train():
             f"CE: {avg_loss_ce:.8f} | Mask: {mask_ratio:.2f}"
         )
         
-        if avg_loss_ce < 0.5:
+        if avg_loss_ce < 0.1:
             low_loss_epochs += 1
         else:
             low_loss_epochs = 0
             
         if low_loss_epochs >= 5 and train_stage == 2:
+            if random_ratio >= 0.5:
+                train_stage = 3
             random_ratio += 0.1
             random_ratio = min(random_ratio, 0.5)
             low_loss_epochs = 0
@@ -215,11 +236,20 @@ def train():
         if low_loss_epochs >= 5 and train_stage == 1:
             if mask_ratio >= 0.9:
                 train_stage = 2
-
             mask_ratio += 0.1
             mask_ratio = min(mask_ratio, 0.9)
             low_loss_epochs = 0
             
+        if train_stage == 3:
+            stage3_epoch += 1
+            if stage3_epoch >= 100:
+                train_stage = 4
+                stage3_epoch = 0
+                
+        if train_stage >= 2:
+            # 第二阶段后 L1 损失不再应该生效
+            mask_ratio = 1.0
+            
         dataset.train_stage = 2
         dataset_val.train_stage = 2
         dataset.random_ratio = random_ratio
@@ -235,8 +265,8 @@ def train():
         else:
             g_steps = 1
             
-        if avg_loss_ginka > 0 or avg_loss_minamo > 0:
+        if avg_loss_minamo > 0:
-            c_steps = int(max(min(5 + (avg_loss_ginka + avg_loss_minamo) * 5, 15), 1))
+            c_steps = int(min(5 + avg_loss_minamo * 5, 15))
         else:
             c_steps = 5
         
@@ -251,6 +281,7 @@ def train():
                 "stage": train_stage,
                 "mask_ratio": mask_ratio,
                 "random_ratio": random_ratio,
+                "stage3_epoch": stage3_epoch,
             }, f"result/wgan/ginka-{epoch + 1}.pth")
             torch.save({
                 "model_state": minamo.state_dict(),