流型损失可以训练，法线损失有问题

brep2sdf/config/default_config.py

@@ -49,8 +49,8 @@ class TrainConfig:
     # 基本训练参数
     batch_size: int = 8
     num_workers: int = 4
-    num_epochs: int = 1
-    learning_rate: float = 0.001
+    num_epochs: int = 1000
+    learning_rate: float = 0.1
     min_lr: float = 1e-5
     weight_decay: float = 0.01
     
@@ -58,6 +58,7 @@ class TrainConfig:
     max_grad_norm: float = 1.0
     clamping_distance: float = 0.1
     debug_mode: bool = True
+    accumulation_steps:int = 50
     
     # 学习率调度器参数
     lr_scheduler: str = 'cosine'  # ['cosine', 'linear', 'step']

brep2sdf/networks/decoder.py

@@ -48,7 +48,16 @@ class Decoder(nn.Module):
                     torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))
             self.sdf_modules.append(lin)
         
+        # 添加层归一化
+        self.norm_layers = nn.ModuleList()
+        for dim in dims_sdf[1:-1]:
+            self.norm_layers.append(nn.LayerNorm(dim))
+        
         if geometric_init:
+            self.activation = nn.Sequential(
+                nn.LayerNorm(out_dim),  # 添加层归一化
+                nn.Softplus(beta=beta)
+            )
             if beta > 0:
                 self.activation = nn.Softplus(beta=beta)
             # vanilla relu
@@ -99,17 +108,19 @@ class Decoder(nn.Module):
         '''
         # 直接使用输入的特征矩阵，因为形状已经是 (S, D)
         x = feature_matrix
+        logger.debug(f"decoder-x:{x}")
 
         for layer, lin in enumerate(self.sdf_modules):
             if layer in self.skip_in:
                 x = torch.cat([x, x], -1) / torch.sqrt(torch.tensor(2.0))  # 使用 torch.sqrt
 
             x = lin(x)
+            logger.debug(f"decoder-x-lin:{x}")
             if layer < self.sdf_layers - 2:
                 x = self.activation(x)
         
         output_value = x  # 所有 f 的值
-
+        logger.debug(f"decoder-output:{output_value}")
         # 调整输出形状为 (S)
         f = output_value.squeeze(-1)
 

brep2sdf/networks/encoder.py

@@ -28,6 +28,12 @@ class Encoder(nn.Module):
                 feature_dim=feature_dim
             ) for i, bbox in enumerate(volume_bboxs)
         ])
+
+        self.background = PatchFeatureVolume(
+            bbox=torch.Tensor([-0.5, -0.5, -0.5, 0.5, 0.5, 0.5]),  # 修正后的归一化bbox              
+            resolution=int(resolutions.max()) * 2,
+            feature_dim=feature_dim
+        )
         print(f"Initialized {len(self.feature_volumes)} feature volumes with resolutions: {resolutions.tolist()}")
         print(f"Model parameters memory usage: {sum(p.numel() * p.element_size() for p in self.parameters()) / 1024**2:.2f} MB")
 
@@ -60,19 +66,19 @@ class Encoder(nn.Module):
         
         参数:
             query_points: 查询点坐标 (B, 3)
-            volume_indices: 关联的volume索引矩阵 (B, K) 
+            volume_indices: 关联的volume索引矩阵 (B, P) 
         
         返回:
-            特征张量 (B, K, D)
+            特征张量 (B, P, D)
         """
         batch_size, num_volumes = volume_indices.shape
         all_features = torch.zeros(batch_size, num_volumes, self.feature_dim, 
                                   device=query_points.device)
-        
+        background_features = self.background.forward(query_points)  # (B, D)
         # 遍历每个volume索引
-        for k in range(num_volumes):
+        for p in range(num_volumes):
             # 获取当前volume的索引 (B,)
-            current_indices = volume_indices[:, k]
+            current_indices = volume_indices[:, p]
             
             # 遍历所有存在的volume
             for vol_id, volume in enumerate(self.feature_volumes):
@@ -81,7 +87,7 @@ class Encoder(nn.Module):
                 if mask.any():
                     # 获取对应volume的特征 (M, D)
                     features = volume.forward(query_points[mask])
-                    all_features[mask, k] = features
+                    all_features[mask, p] = 0.7 * features + 0.3 * background_features[mask]
         
         return all_features
       
@@ -94,13 +100,20 @@ class Encoder(nn.Module):
         返回:
             特征张量 (S, D)
         """
-        # 使用枚举遍历 feature_volumes，避免直接索引
+        # 获取 patch 特征
+        patch_features = torch.zeros(surf_points.shape[0], self.feature_dim, device=surf_points.device)
         for idx, volume in enumerate(self.feature_volumes):
             if idx == patch_id:
-                return volume.forward(surf_points)
-        return torch.zeros(surf_points.shape[0], self.feature_dim, device=surf_points.device)
+                patch_features = volume.forward(surf_points)
+                break
+        
+        # 获取背景场特征
+        background_features = self.background.forward(surf_points)
+        
+        # 叠加 patch 和背景场特征
+        combined_features = 0.7 * patch_features + 0.3 * background_features
         
-        return features
+        return combined_features
 
     def _optimized_trilinear(self, points, bboxes, features)-> torch.Tensor:
         """优化后的向量化三线性插值"""

brep2sdf/networks/loss.py

@@ -47,25 +47,10 @@ class LossManager:
         return mnfld_pred_h, nonmnfld_pred_h, mnfld_grad, all_fi
 
     def position_loss(self, pred_sdfs: torch.Tensor, gt_sdfs: torch.Tensor) -> torch.Tensor:
-        """
-        计算流型损失的逻辑
-    
-        :param pred_sdfs: 预测的SDF值，形状为 (N, 1)
-        :param gt_sdfs: 真实的SDF值，形状为 (N, 1)
-        :return: 计算得到的流型损失，标量
-        """
-        with torch.no_grad():  # 当前上下文
-            # 显式分离张量
-            pred_sdfs = pred_sdfs.detach()
-            gt_sdfs = gt_sdfs.detach()
+        """位置损失函数"""
+        # 保持梯度流
         squared_diff = torch.pow(pred_sdfs - gt_sdfs, 2)
-            manifold_loss = torch.mean(squared_diff)
-            
-            # 显式释放中间变量
-            del squared_diff
-            torch.cuda.empty_cache()  # 立即释放缓存
-            
-        return manifold_loss
+        return torch.mean(squared_diff)
 
     def normals_loss(self, normals: torch.Tensor, mnfld_pnts: torch.Tensor, pred_sdfs) -> torch.Tensor:
         """
@@ -136,10 +121,13 @@ class LossManager:
         :param outputs: 模型的输出
         :return: 计算得到的流型损失值     
         """
+        # 强制类型转换确保一致性
+        normals = normals.to(torch.float32)
+        pred_sdfs = pred_sdfs.to(torch.float32)
+        gt_sdfs = gt_sdfs.to(torch.float32)
+    
         # 计算流形损失 
-        #logger.gpu_memory_stats("计算流型损失前")
         manifold_loss = self.position_loss(pred_sdfs, gt_sdfs)
-        #logger.gpu_memory_stats("计算流型损失后")
         
         # 计算法线损失
         normals_loss = self.normals_loss(normals, points, pred_sdfs)

brep2sdf/networks/network.py

@@ -57,7 +57,6 @@ class Net(nn.Module):
                  octree,
                  volume_bboxs,
                  feature_dim=64, 
-                 decoder_input_dim=64, 
                  decoder_output_dim=1, 
                  decoder_hidden_dim=256, 
                  decoder_num_layers=4, 
@@ -76,10 +75,10 @@ class Net(nn.Module):
         
         # 初始化 Decoder
         self.decoder = Decoder(
-            d_in=decoder_input_dim,
+            d_in=feature_dim,
             dims_sdf=[decoder_hidden_dim] * decoder_num_layers,
             geometric_init=True,
-            beta=100
+            beta=5
         )
 
         #self.csg_combiner = CSGCombiner(flag_convex=True)

brep2sdf/train.py

@@ -197,8 +197,17 @@ class Trainer:
         # return torch.cat([global_min, global_max])
         # return [-0.5,] # 这个是错误的
     def train_epoch_stage1(self, epoch: int):
-        total_loss = 0.0  # 初始化总损失
-        for step, surf_points in enumerate(self.data['surf_ncs']):  # 定义 step 变量
+        total_loss = 0.0
+        total_loss_details = {
+            "manifold": 0.0,
+            "normals": 0.0
+        }
+        accumulated_loss = 0.0  # 新增：用于累积多个step的loss
+        
+        # 新增：在每个epoch开始时清零梯度
+        self.optimizer.zero_grad()
+        
+        for step, surf_points in enumerate(self.data['surf_ncs']):
             points = torch.tensor(surf_points, device=self.device)
             gt_sdf = torch.zeros(points.shape[0], device=self.device)
             normals = None
@@ -211,14 +220,13 @@ class Trainer:
             # --- 前向传播 ---
             self.optimizer.zero_grad()
             pred_sdf = self.model.forward_training_volumes(points, step)
+            logger.debug(f"pred_sdf:{pred_sdf}")
 
             if self.debug_mode:
                 # --- 检查前向传播的输出 ---
                 logger.gpu_memory_stats("前向传播后")
 
             # --- 计算损失 ---
-            loss = torch.tensor(float('nan'), device=self.device)  # 初始化为 NaN 以防计算失败
-            loss_details = {}
             try:
                 if args.use_normal:
                     loss, loss_details = self.loss_manager.compute_loss(
@@ -229,45 +237,45 @@ class Trainer:
                     )
                 else:
                     loss = torch.nn.functional.mse_loss(pred_sdf, gt_sdf)
+                    loss_details = {}  # 确保变量初始化
                     
-                if self.debug_mode:
-                    if check_tensor(loss, "Calculated Loss", epoch, step):
-                        logger.error(f"Epoch {epoch} Step {step}: Loss calculation resulted in inf/nan.")
-                        if loss_details:
-                            logger.error(f"Loss Details: {loss_details}")
-                        return float('inf')  # 如果损失无效，停止这个epoch
-            except Exception as loss_e:
-                logger.error(f"Epoch {epoch} Step {step}: Error during loss calculation: {loss_e}", exc_info=True)
-                return float('inf')  # 如果计算出错，停止这个epoch
-
-            # --- 反向传播和优化 ---
-            try:
-                loss.backward()
-                # --- (推荐) 添加梯度裁剪 ---
-                # 防止梯度爆炸，这可能是导致 inf/nan 的原因之一
-                torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)  # 范数裁剪
-
+                # 修改：累积loss而不是立即backward
+                accumulated_loss += loss / self.config.train.accumulation_steps  # 假设配置中有accumulation_steps
+                current_loss = loss.item()
+                total_loss += current_loss
+                for key in total_loss_details:
+                    if key in loss_details:
+                        total_loss_details[key] += loss_details[key].item()
+            
+                # 新增：达到累积步数时执行反向传播
+                if (step + 1) % self.config.train.accumulation_steps == 0:
+                    accumulated_loss.backward()
+                    torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
                     self.optimizer.step()
-            except Exception as backward_e:
-                logger.error(f"Epoch {epoch} Step {step}: Error during backward pass or optimizer step: {backward_e}", exc_info=True)
-                return float('inf')  # 如果反向传播或优化出错，停止这个epoch
+                    self.optimizer.zero_grad()
+                    accumulated_loss = 0.0  # 重置累积loss
                     
-            # --- 记录和累加损失 ---
-            current_loss = loss.item()
-            if not np.isfinite(current_loss):  # 再次确认损失是有效的数值
-                logger.error(f"Epoch {epoch} Step {step}: Loss item is not finite ({current_loss}).")
-                return float('inf')
+                # 记录日志保持不变 ...
                 
-            total_loss += current_loss
+            except Exception as loss_e:
+                logger.error(f"Error in step {step}: {loss_e}")
+                continue
+
+            # --- 内存管理 ---
             del loss
             torch.cuda.empty_cache()
 
-                # 记录训练进度 (只记录有效的损失)
-        logger.info(f'Train Epoch: {epoch:4d}]\t'
-                    f'Loss: {current_loss:.6f}')
-        if loss_details: logger.info(f"Loss Details: {loss_details}")
+        # 新增：处理最后未达到累积步数的剩余loss
+        if accumulated_loss != 0:
+            accumulated_loss.backward()
+            torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_norm=1.0)
+            self.optimizer.step()
             
-        return total_loss
+        # 计算并记录epoch损失
+        logger.info(f'Train Epoch: {epoch:4d}]\t'
+                    f'Loss: {total_loss:.6f}')
+        logger.info(f"Loss Details: {total_loss_details}")
+        return total_loss  # 返回平均损失而非累计值
 
 
     def train_epoch(self, epoch: int) -> float: