背景场训练

brep2sdf/batch_train.py

@@ -70,7 +70,8 @@ def batch_train(args):
     common_train_args = [
         "--use-normal",
         "--only-zero-surface",
-        #"--force-reprocess",
+        "--octree-cuda",
+        "--force-reprocess",
         # 可以添加更多通用参数
     ]
     if args.train_args:
@@ -249,8 +250,8 @@ def batch_Iso(args):
 
     logger.info(f"处理完成。成功: {success_count}, 失败: {failure_count}")
 def main(args):
-    #batch_train(args)
-    batch_Iso(args)
+    batch_train(args)
+    #batch_Iso(args)
 
 
 if __name__ == '__main__':

brep2sdf/networks/encoder.py

@@ -29,10 +29,17 @@ class Encoder(nn.Module):
             ) 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
+        self.background = self.simple_encoder = nn.Sequential(
+            nn.Linear(3, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            nn.Linear(256, 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")
@@ -88,6 +95,20 @@ class Encoder(nn.Module):
     
         return all_features
 
+
+    def forward_background(self, query_points: torch.Tensor) -> torch.Tensor:
+        """
+        修改后的前向传播，返回所有关联volume的特征矩阵
+        
+        参数:
+            query_points: 查询点坐标 (B, 3)
+        
+        返回:
+            特征张量 (B, D)
+        """
+        background_features = self.background.forward(query_points)  # (B, D)
+        return background_features
+      
     @torch.jit.export
     def forward_training_volumes(self, surf_points: torch.Tensor, patch_id: int) -> torch.Tensor:
         """
@@ -138,3 +159,22 @@ class Encoder(nn.Module):
                 _move_node(child)
         _move_node(self.octree.root)
         return self
+
+    def freeze_stage1(self):
+        for volume in self.feature_volumes:
+            for param in volume.parameters():
+                param.requires_grad = False
+        for param in self.background.parameters():
+            param.requires_grad = False
+    def freeze_stage2(self):
+        for volume in self.feature_volumes:
+            for param in volume.parameters():
+                param.requires_grad = True
+        for param in self.background.parameters():
+            param.requires_grad = False
+    def unfreeze(self):
+        for volume in self.feature_volumes:
+            for param in volume.parameters():
+                param.requires_grad = True
+        for param in self.background.parameters():
+            param.requires_grad = True

brep2sdf/networks/feature_volume.py

@@ -4,7 +4,7 @@ import torch
 import torch.nn as nn
 
 class PatchFeatureVolume(nn.Module):
-    def __init__(self, bbox: torch.Tensor, resolution=64, feature_dim=64, padding_ratio=0.05):
+    def __init__(self, bbox: torch.Tensor, resolution=64, feature_dim=8, padding_ratio=0.05):
         super(PatchFeatureVolume, self).__init__()
         # 将输入bbox转换为[min, max]格式
         self.resolution = resolution
@@ -19,8 +19,9 @@ class PatchFeatureVolume(nn.Module):
         grid_x, grid_y, grid_z = torch.meshgrid(x, y, z)
         self.register_buffer('grid', torch.stack([grid_x, grid_y, grid_z], dim=-1))
         
-        # 初始化特征向量
-        self.feature_volume = nn.Parameter(torch.randn(resolution, resolution, resolution, feature_dim))
+        # 初始化特征向量为很小的值，使用较小的标准差
+        self.feature_volume = nn.Parameter(torch.empty(resolution, resolution, resolution, feature_dim))
+        torch.nn.init.normal_(self.feature_volume, mean=0.0, std=0.01)  # 标准差设置为 0.01，可根据需要调整
 
     def _expand_bbox(self, min_coords, max_coords, ratio):
         # 扩展包围盒范围

brep2sdf/networks/loss.py

@@ -5,14 +5,15 @@ from brep2sdf.utils.logger import logger
 class LossManager:
     def __init__(self, ablation, **condition_kwargs):
         self.weights = {
-            "manifold": 10,
+            "manifold": 1,
             "feature_manifold": 1, # 原文里面和manifold的权重是一样的
             "normals": 1,
             "eikonal": 1,
             "offsurface": 1,
             "consistency": 1,
             "correction": 1,
-            "psdf": 10
+            "psdf": 1,
+            "psdf_sign_loss": 0
         }
         self.condition_kwargs = condition_kwargs
         self.ablation = ablation # 消融实验用
@@ -111,6 +112,21 @@ class LossManager:
                 correction_loss = (a_correction * torch.abs(mnfld_pred - all_fi[:,0])[mismatch_id]).mean()  # 计算修正损失
         return correction_loss
 
+    def psdf_loss(self, pred_sdfs, gt_sdfs):
+        # 定义符号相同和不同时的权重
+        weight_same_sign = 1.0  # 符号相同时的权重
+        weight_different_sign = 10.0  # 符号不同时的权重
+
+        # 判断符号是否相同
+        same_sign = (pred_sdfs * gt_sdfs) >= 0
+
+        # 根据符号设置权重
+        weights = torch.where(same_sign, weight_same_sign, weight_different_sign)
+
+        squared_diff = torch.pow(pred_sdfs - gt_sdfs, 2)
+        weighted_squared_diff = weights * squared_diff
+        return torch.mean(weighted_squared_diff)
+
 
     def compute_loss(self, 
                 mnfld_pnts,
@@ -150,7 +166,7 @@ class LossManager:
         # 计算修正损失
         #correction_loss = self.correction_loss(mnfld_pnts, mnfld_pred, all_fi)
 
-        psdf_loss = self.position_loss(nonmnfld_pred, psdfs)
+        psdf_loss = self.psdf_loss(nonmnfld_pred, psdfs)
 
         # 汇总损失
         loss_details = {
@@ -158,7 +174,7 @@ class LossManager:
             "normals": self.weights["normals"] * normals_loss,
             "eikonal": self.weights["eikonal"] * eikonal_loss,
             "offsurface": self.weights["offsurface"] * offsurface_loss,
-            "psdf":self.weights["psdf"] * psdf_loss
+            "psdf":self.weights["psdf"] * psdf_loss,
         }
         
         # 计算总损失

brep2sdf/networks/network.py

@@ -56,7 +56,7 @@ class Net(nn.Module):
     def __init__(self, 
                  octree,
                  volume_bboxs,
-                 feature_dim=64, 
+                 feature_dim=8, 
                  decoder_output_dim=1, 
                  decoder_hidden_dim=256, 
                  decoder_num_layers=4, 
@@ -87,8 +87,7 @@ class Net(nn.Module):
         output = f_i[:,0]
         # 提取有效值并填充到固定大小 (B, max_patches)
         padded_f_i = torch.full((f_i.shape[0], 2), float('inf'), device=f_i.device)  # (B, max_patches)
-        valid_mask = face_indices_mask.bool()  # 确保是布尔类型 (B, P)
-        masked_f_i = torch.where(valid_mask, f_i, torch.tensor(float('inf'), device=f_i.device))  # 将无效值设置为 inf
+        masked_f_i = torch.where(face_indices_mask, f_i, torch.tensor(float('inf'), device=f_i.device))  # 将无效值设置为 inf
 
         # 对每个样本取前 max_patches 个有效值 (B, max_patches)
         valid_values, _ = torch.topk(masked_f_i, k=2, dim=1, largest=False)  # 提取前两个有效值
@@ -108,7 +107,6 @@ class Net(nn.Module):
         if mask_convex.any():
             output[mask_convex] = torch.min(padded_f_i[mask_convex], dim=1).values
 
-        logger.debug("step over")
         #logger.gpu_memory_stats("combine后")
         return output
 
@@ -138,6 +136,45 @@ class Net(nn.Module):
         #logger.debug("step combine")
         return self.process_sdf(f_i, face_indices_mask, operator)
 
+    @torch.jit.export
+    def forward_background(self, query_points):
+        """
+        前向传播
+        
+        参数:
+            query_point: 查询点的位置坐标
+        返回:
+            output: 解码后的输出结果
+        """
+        # 批量查询所有点的索引和bbox
+        # 编码
+        feature_vectors = self.encoder.forward_background(query_points)
+        # 解码
+        h = self.decoder.forward_training_volumes(feature_vectors) # (B, D)
+        return h
+
+    @torch.jit.export
+    def forward_without_octree(self, query_points,face_indices_mask,operator):
+        """
+        前向传播
+        
+        参数:
+            query_point: 查询点的位置坐标
+        返回:
+            output: 解码后的输出结果
+        """
+        # 批量查询所有点的索引和bbox
+        #logger.debug("step encode")
+        # 编码
+        feature_vectors = self.encoder.forward(query_points,face_indices_mask)
+        #print("feature_vector:", feature_vectors.shape)
+        # 解码
+        f_i = self.decoder(feature_vectors) # (B, P)
+        #logger.gpu_memory_stats("decoder farward后")
+
+        #logger.debug("step combine")
+        return self.process_sdf(f_i, face_indices_mask, operator)
+      
     @torch.jit.export
     def forward_training_volumes(self, surf_points, patch_id:int):
         """
@@ -154,12 +191,27 @@ class Net(nn.Module):
 
 
 def gradient(inputs, outputs):
+    # 问题点1：inputs可能包含非坐标特征
+    # 问题点2：未处理batch维度特殊情况
     d_points = torch.ones_like(outputs, requires_grad=False, device=outputs.device)
+    
+    # 改进计算方式
     points_grad = grad(
         outputs=outputs,
         inputs=inputs,
         grad_outputs=d_points,
         create_graph=True,
         retain_graph=True,
-        only_inputs=True)[0][:, -3:]
-    return points_grad
+        only_inputs=True,
+        allow_unused=True  # 新增异常处理
+    )[0]
+    
+    # 修正维度切片方式
+    if points_grad is None:
+        return torch.zeros_like(inputs[:, -3:])  # 处理空梯度情况
+    
+    # 添加安全截取和归一化
+    coord_grad = points_grad[:, -3:] if points_grad.shape[1] >=3 else points_grad
+    coord_grad = coord_grad / (coord_grad.norm(dim=-1, keepdim=True) + 1e-6)  # 安全归一化
+    
+    return coord_grad

brep2sdf/networks/sample.py

@@ -3,7 +3,7 @@ import torch
 
 class NormalPerPoint():
 
-    def __init__(self, global_sigma, local_sigma=0.5):
+    def __init__(self, global_sigma, local_sigma=0.001):
         self.global_sigma = global_sigma
         self.local_sigma = local_sigma
 

brep2sdf/train.py

@@ -75,6 +75,8 @@ class Trainer:
             self.data = process_single_step(step_path=input_step, output_path=data_path, sample_normal_vector=args.use_normal,sample_sdf_points=not args.only_zero_surface)
         
         logger.gpu_memory_stats("数据预处理后")
+
+        self.train_surf_ncs = torch.tensor(self.data["train_surf_ncs"],dtype=torch.float32,device=self.device) #
         # 将曲面点云列表转换为 (N*M, 4) 数组
         surfs = self.data["surf_ncs"]
         
@@ -98,6 +100,7 @@ class Trainer:
         else:
             self.sdf_data = surface_sdf_data
         print_data_distribution(self.sdf_data)
+        logger.debug(self.sdf_data.shape)
         logger.gpu_memory_stats("SDF数据准备后")
         # 初始化数据集
         #self.brep_data = load_brep_file(self.config.data.pkl_path)
@@ -200,7 +203,7 @@ class Trainer:
         # # 返回合并后的边界框
         # return torch.cat([global_min, global_max])
         # return [-0.5,] # 这个是错误的
-    def train_epoch_stage1(self, epoch: int):
+    def train_epoch_stage1_(self, epoch: int):
         total_loss = 0.0
         total_loss_details = {
             "manifold": 0.0,
@@ -293,8 +296,303 @@ class Trainer:
         return total_loss  # 返回平均损失而非累计值
     
 
+    def train_epoch_stage1(self, epoch: int) -> float:
+        # --- 1. 检查输入数据 ---
+        # 注意：假设 self.sdf_data 包含 [x, y, z, nx, ny, nz, sdf] (7列) 或 [x, y, z, sdf] (4列)
+        # 并且 SDF 值总是在最后一列
+        if self.train_surf_ncs is None:
+             logger.error(f"Epoch {epoch}: self.train_surf_ncs is None. Cannot train.")
+             return float('inf')
+
+        self.model.train()
+        total_loss = 0.0
+        step = 0 # 如果你的训练是分批次的，这里应该用批次索引
+        batch_size = 8192  # 设置合适的batch大小
+
+        # 数据处理
+        # manfld
+        _mnfld_pnts = self.train_surf_ncs[:, 0:3].clone().detach() # 取前3列作为点
+        _normals = self.train_surf_ncs[:, 3:6].clone().detach() # 取中间3列作为法线
+        _gt_sdf = self.train_surf_ncs[:, -1].clone().detach()  # 取最后一列作为SDF真值
+
+        # 检查是否需要重新计算缓存
+        if epoch % 10 == 1 or self.cached_train_data is None:
+            # 计算流形点的掩码和操作符
+            # 生成非流形点
+            _nonmnfld_pnts, _psdf = self.sampler.get_norm_points(_mnfld_pnts, _normals)
+            
+            # 更新缓存
+            self.cached_train_data = {
+                "nonmnfld_pnts": _nonmnfld_pnts,
+                "psdf": _psdf,
+            }
+        else:
+            # 从缓存中读取数据
+            _nonmnfld_pnts = self.cached_train_data["nonmnfld_pnts"]
+            _psdf = self.cached_train_data["psdf"]
+        
+        logger.debug((_mnfld_pnts, _nonmnfld_pnts, _psdf))
+
+
+        
+        # 将数据分成多个batch
+        num_points = self.train_surf_ncs.shape[0]
+        num_batches = (num_points + batch_size - 1) // batch_size
+        
+        for batch_idx in range(num_batches):
+            start_idx = batch_idx * batch_size
+            end_idx = min((batch_idx + 1) * batch_size, num_points)
+            
+            # 获取当前batch的数据
+            mnfld_pnts = _mnfld_pnts[start_idx:end_idx]  # 流形点
+            gt_sdf = _gt_sdf[start_idx:end_idx]  # SDF真值
+            normals = _normals[start_idx:end_idx] if args.use_normal else None  # 法线
+            
+            # 非流形点使用缓存数据（整个batch共享）
+            nonmnfld_pnts = _nonmnfld_pnts[start_idx:end_idx]
+            psdf = _psdf[start_idx:end_idx]
+
+            # --- 准备模型输入，启用梯度 ---
+            mnfld_pnts.requires_grad_(True) # 在检查之后启用梯度
+            nonmnfld_pnts.requires_grad_(True) # 在检查之后启用梯度
+
+            # --- 前向传播 ---
+            mnfld_pred = self.model.forward_background(
+                mnfld_pnts
+            )
+            nonmnfld_pred = self.model.forward_background(
+                nonmnfld_pnts
+            )
+
+
+
+            # --- 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:
+                # --- 3. 检查损失计算前的输入 ---
+                # (points 已经启用梯度，不需要再次检查inf/nan，但检查pred_sdf和gt_sdf)
+                #if check_tensor(pred_sdf, "Predicted SDF (Loss Input)", epoch, step): raise ValueError("Bad pred_sdf before loss")
+                #if check_tensor(gt_sdf, "GT SDF (Loss Input)", epoch, step): raise ValueError("Bad gt_sdf before loss")
+                if args.use_normal:
+                    # 检查法线和带梯度的点
+                    #if check_tensor(normals, "Normals (Loss Input)", epoch, step): raise ValueError("Bad normals before loss")
+                    #if check_tensor(points, "Points (Loss Input)", epoch, step): raise ValueError("Bad points before loss")
+                    logger.gpu_memory_stats("计算损失前")
+                    loss, loss_details = self.loss_manager.compute_loss(
+                        mnfld_pnts,
+                        nonmnfld_pnts,
+                        normals, # 传递检查过的 normals
+                        gt_sdf,
+                        mnfld_pred,
+                        nonmnfld_pred,
+                        psdf
+                    )
+                else:
+                    loss = torch.nn.functional.mse_loss(pred_sdf, gt_sdf)
+
+                # --- 4. 检查损失计算结果 ---
+                if self.debug_mode:
+                    logger.print_tensor_stats("psdf",psdf)
+                    logger.print_tensor_stats("nonmnfld_pred",nonmnfld_pred)
+                    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
+            logger.gpu_memory_stats("损失计算后")
 
-    def train_epoch(self, epoch: int) -> float:
+            # --- 反向传播和优化 ---
+            try:
+                # 反向传播
+                self.scheduler.optimizer.zero_grad()  # 清空梯度
+                loss.backward()  # 反向传播
+                self.scheduler.optimizer.step()  # 更新参数
+                self.scheduler.step(loss,epoch)
+            except Exception as backward_e:
+                logger.error(f"Epoch {epoch} Step {step}: Error during backward pass or optimizer step: {backward_e}", exc_info=True)
+                # 如果你想看是哪个操作导致的，可以启用 anomaly detection
+                # torch.autograd.set_detect_anomaly(True) # 放在训练开始前
+                return float('inf') # 如果反向传播或优化出错，停止这个epoch
+
+
+            # --- 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')
+
+            total_loss += current_loss
+            del loss
+            torch.cuda.empty_cache()
+            
+        if epoch % 100 == 0:
+            # 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}")
+
+            return total_loss # 对于单批次训练，直接返回当前损失
+
+    def train_epoch_stage2(self, epoch: int) -> float:
+        # --- 1. 检查输入数据 ---
+        # 注意：假设 self.sdf_data 包含 [x, y, z, nx, ny, nz, sdf] (7列) 或 [x, y, z, sdf] (4列)
+        # 并且 SDF 值总是在最后一列
+        if self.sdf_data is None:
+             logger.error(f"Epoch {epoch}: self.sdf_data is None. Cannot train.")
+             return float('inf')
+
+        self.model.train()
+        total_loss = 0.0
+        step = 0 # 如果你的训练是分批次的，这里应该用批次索引
+        batch_size = 8192 * 2  # 设置合适的batch大小
+
+        # 数据处理
+        # manfld
+        _mnfld_pnts = self.sdf_data[:, 0:3].clone().detach() # 取前3列作为点
+        _normals = self.sdf_data[:, 3:6].clone().detach() # 取中间3列作为法线
+        _gt_sdf = self.sdf_data[:, -1].clone().detach()  # 取最后一列作为SDF真值
+
+        # 检查是否需要重新计算缓存
+        if epoch % 10 == 1 or self.cached_train_data is None:
+            # 计算流形点的掩码和操作符
+            _, _mnfld_face_indices_mask, _mnfld_operator = self.root.forward(_mnfld_pnts)
+            
+            # 生成非流形点
+            _nonmnfld_pnts, _psdf = self.sampler.get_norm_points(_mnfld_pnts, _normals)
+            _, _nonmnfld_face_indices_mask, _nonmnfld_operator = self.root.forward(_nonmnfld_pnts)
+            
+            # 更新缓存
+            self.cached_train_data = {
+                "mnfld_face_indices_mask": _mnfld_face_indices_mask,
+                "mnfld_operator": _mnfld_operator,
+                "nonmnfld_pnts": _nonmnfld_pnts,
+                "psdf": _psdf,
+                "nonmnfld_face_indices_mask": _nonmnfld_face_indices_mask,
+                "nonmnfld_operator": _nonmnfld_operator
+            }
+        else:
+            # 从缓存中读取数据
+            _mnfld_face_indices_mask = self.cached_train_data["mnfld_face_indices_mask"]
+            _mnfld_operator = self.cached_train_data["mnfld_operator"]
+            _nonmnfld_pnts = self.cached_train_data["nonmnfld_pnts"]
+            _psdf = self.cached_train_data["psdf"]
+            _nonmnfld_face_indices_mask = self.cached_train_data["nonmnfld_face_indices_mask"]
+            _nonmnfld_operator = self.cached_train_data["nonmnfld_operator"]
+        
+        logger.debug((_mnfld_pnts, _nonmnfld_pnts, _psdf))
+
+
+        
+        # 将数据分成多个batch
+        num_points = self.sdf_data.shape[0]
+        num_batches = (num_points + batch_size - 1) // batch_size
+        
+        for batch_idx in range(num_batches):
+            start_idx = batch_idx * batch_size
+            end_idx = min((batch_idx + 1) * batch_size, num_points)
+            
+            # 获取当前batch的数据
+            mnfld_pnts = _mnfld_pnts[start_idx:end_idx]  # 流形点
+            gt_sdf = _gt_sdf[start_idx:end_idx]  # SDF真值
+            normals = _normals[start_idx:end_idx] if args.use_normal else None  # 法线
+            
+            # 非流形点使用缓存数据（整个batch共享）
+            nonmnfld_pnts = _nonmnfld_pnts[start_idx:end_idx]
+            psdf = _psdf[start_idx:end_idx]
+
+            # --- 准备模型输入，启用梯度 ---
+            mnfld_pnts.requires_grad_(True) # 在检查之后启用梯度
+            nonmnfld_pnts.requires_grad_(True) # 在检查之后启用梯度
+
+            # --- 前向传播 ---
+            mnfld_pred = self.model.forward_without_octree(
+                mnfld_pnts, 
+                _mnfld_face_indices_mask[start_idx:end_idx], 
+                _mnfld_operator[start_idx:end_idx]
+            )
+            nonmnfld_pred = self.model.forward_without_octree(
+                nonmnfld_pnts,
+                _nonmnfld_face_indices_mask[start_idx:end_idx],
+                _nonmnfld_operator[start_idx:end_idx]
+            )
+
+            #logger.print_tensor_stats("psdf",psdf)
+            #logger.print_tensor_stats("nonmnfld_pred",nonmnfld_pred)
+
+            # --- 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:
+                # --- 3. 检查损失计算前的输入 ---
+                # (points 已经启用梯度，不需要再次检查inf/nan，但检查pred_sdf和gt_sdf)
+                #if check_tensor(pred_sdf, "Predicted SDF (Loss Input)", epoch, step): raise ValueError("Bad pred_sdf before loss")
+                #if check_tensor(gt_sdf, "GT SDF (Loss Input)", epoch, step): raise ValueError("Bad gt_sdf before loss")
+                if args.use_normal:
+                    # 检查法线和带梯度的点
+                    #if check_tensor(normals, "Normals (Loss Input)", epoch, step): raise ValueError("Bad normals before loss")
+                    #if check_tensor(points, "Points (Loss Input)", epoch, step): raise ValueError("Bad points before loss")
+                    logger.gpu_memory_stats("计算损失前")
+                    loss, loss_details = self.loss_manager.compute_loss(
+                        mnfld_pnts,
+                        nonmnfld_pnts,
+                        normals, # 传递检查过的 normals
+                        gt_sdf,
+                        mnfld_pred,
+                        nonmnfld_pred,
+                        psdf
+                    )
+                else:
+                    loss = torch.nn.functional.mse_loss(pred_sdf, gt_sdf)
+
+                # --- 4. 检查损失计算结果 ---
+                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
+            logger.gpu_memory_stats("损失计算后")
+
+            # --- 反向传播和优化 ---
+            try:
+                # 反向传播
+                self.scheduler.optimizer.zero_grad()  # 清空梯度
+                loss.backward()  # 反向传播
+                self.scheduler.optimizer.step()  # 更新参数
+                self.scheduler.step(loss,epoch)
+            except Exception as backward_e:
+                logger.error(f"Epoch {epoch} Step {step}: Error during backward pass or optimizer step: {backward_e}", exc_info=True)
+                # 如果你想看是哪个操作导致的，可以启用 anomaly detection
+                # torch.autograd.set_detect_anomaly(True) # 放在训练开始前
+                return float('inf') # 如果反向传播或优化出错，停止这个epoch
+
+
+            # --- 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')
+
+            total_loss += current_loss
+            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}")
+
+        return total_loss # 对于单批次训练，直接返回当前损失
+
+    def train_epoch(self, epoch: int,resample:bool=True) -> float:
         # --- 1. 检查输入数据 ---
         # 注意：假设 self.sdf_data 包含 [x, y, z, nx, ny, nz, sdf] (7列) 或 [x, y, z, sdf] (4列)
         # 并且 SDF 值总是在最后一列
@@ -447,16 +745,19 @@ class Trainer:
         best_val_loss = float('inf')
         logger.info("Starting training...")
         start_time = time.time()
+        self.cached_train_data=None
 
         start_epoch = 1
         if args.resume_checkpoint_path:
             start_epoch = self._load_checkpoint(args.resume_checkpoint_path)
             logger.info(f"Loaded model from {args.resume_checkpoint_path}")
         
+        self.model.encoder.freeze_stage1()
         for epoch in range(start_epoch, self.config.train.num_epochs + 1):
             # 训练一个epoch
-            #train_loss = self.train_epoch_stage1(epoch)
-            train_loss = self.train_epoch(epoch)
+            train_loss = self.train_epoch_stage1(epoch)
+            #train_loss = self.train_epoch_stage2(epoch)
+            #train_loss = self.train_epoch(epoch)
             
             # 验证
             '''
@@ -476,7 +777,7 @@ class Trainer:
             if epoch % self.config.train.save_freq == 0:
                 self._save_checkpoint(epoch, train_loss)
                 logger.info(f'Checkpoint saved at epoch {epoch}')
-        
+        self.model.encoder.unfreeze()
         # 训练完成
         total_time = time.time() - start_time
         
@@ -555,8 +856,6 @@ class Trainer:
             logger.error(f"加载checkpoint失败: {str(e)}")
             raise
 
-    # ... existing code ...
-
     def _save_octree(self):
         """
         保存八叉树到文件。
@@ -566,6 +865,7 @@ class Trainer:
             self.config.train.checkpoint_dir,
             self.model_name 
         )
+        os.makedirs(checkpoint_dir, exist_ok=True)
         octree_path = os.path.join(checkpoint_dir, "octree.pth")
         
         try:

brep2sdf/utils/load.py

@@ -0,0 +1,133 @@
+
+import os
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from tqdm import tqdm
+import logging
+
+# 假设 logger 是通过 logging 模块配置的
+logger = logging.getLogger(__name__)
+
+# utils
+def get_namelist(path):
+    try:
+        with open(path, 'r') as f:
+            names = [line.strip() for line in f if line.strip()]
+        logger.info(f"从 '{path}' 读取了 {len(names)} 个名称。")
+        return names
+    except FileNotFoundError:
+        logger.error(f"错误: 文件 '{path}' 未找到。")
+        return
+    except Exception as e:
+        logger.error(f"读取文件 '{path}' 时出错: {e}")
+        return
+
+
+def get_step_paths(names, step_root_dir, file_extensions, name_filter=None):
+    """
+    根据名称列表文件路径，获取所有匹配的 STEP 文件路径。
+
+    Args:
+        namelist_path (str): 名称列表文件的路径，该文件包含要处理的名称。
+        step_root_dir (str): 步骤文件的根目录，每个名称对应一个子目录。
+        file_extensions (list): 要匹配的文件扩展名列表，例如 ['.step', '.stp']。
+        name_filter (callable, optional): 文件名过滤函数，接受文件名和名称作为参数，返回布尔值。
+
+    Returns:
+        list: 匹配的 STEP 文件路径列表。
+    """
+    # 获取名称列表
+    if names is None:
+        logger.error("无法获取名称列表，终止任务。")
+        return []
+
+    step_file_paths = []
+    skipped_count = 0
+
+    # 遍历每个名称，查找匹配的 STEP 文件
+    for name in names:
+        step_dir = os.path.join(step_root_dir, name)
+        if not os.path.isdir(step_dir):
+            logger.warning(f"目录 '{step_dir}' 不存在。跳过 '{name}'。")
+            skipped_count += 1
+            continue
+
+        step_files = []
+        try:
+            # 查找匹配的文件
+            step_files = [
+                os.path.join(step_dir, f)
+                for f in os.listdir(step_dir)
+                if f.lower().endswith(tuple(file_extensions)) and (not name_filter or name_filter(f, name))
+            ]
+        except OSError as e:
+            logger.warning(f"无法访问目录 '{step_dir}': {e}。跳过 '{name}'。")
+            skipped_count += 1
+            continue
+
+        if len(step_files) == 0:
+            logger.warning(f"在目录 '{step_dir}' 中未找到匹配的文件。跳过 '{name}'。")
+            skipped_count += 1
+        elif len(step_files) > 1:
+            logger.warning(f"在目录 '{step_dir}' 中找到多个匹配的文件，将使用第一个: {step_files[0]}。")
+            step_file_paths.append(step_files[0])
+        else:
+            step_file_paths.append(step_files[0])
+
+    logger.info(f"成功获取 {len(step_file_paths)} 个文件路径，跳过了 {skipped_count} 个名称。")
+    return step_file_paths
+
+def run_batch_task(task_function, args, common_args_func, file_extensions, name_filter=None):
+    """
+    通用批量任务处理函数。
+
+    Args:
+        task_function: 要执行的任务函数，接受文件路径、脚本路径和通用参数作为参数。
+        args: 命令行参数对象。
+        common_args_func: 生成通用参数的函数。
+        file_extensions: 要匹配的文件扩展名列表。
+        name_filter: 文件名过滤函数，可选。
+
+    Returns:
+        None
+    """
+    # 获取任务文件路径
+    tasks = get_step_paths(args.name_list_path, args.step_root_dir, file_extensions, name_filter)
+    if not tasks:
+        logger.info("没有找到需要处理的有效文件。")
+        return
+
+    # 准备通用参数
+    common_args = common_args_func(args)
+
+    success_count = 0
+    failure_count = 0
+    skipped_count = len(get_namelist(args.name_list_path) or []) - len(tasks)
+
+    # 使用 ProcessPoolExecutor 进行并行处理
+    with ProcessPoolExecutor(max_workers=args.workers) as executor:
+        # 提交所有任务
+        futures = {
+            executor.submit(task_function, task_path, args.train_script, common_args): task_path
+            for task_path in tasks
+        }
+
+        # 使用 tqdm 显示进度并处理结果
+        for future in tqdm(as_completed(futures), total=len(tasks), desc="运行任务"):
+            input_path = futures[future]
+            try:
+                input_file, success, stdout, stderr = future.result()
+                if success:
+                    success_count += 1
+                    # 可以选择记录成功的 stdout/stderr，但通常只记录失败的更有用
+                    # logger.debug(f"成功处理 '{input_file}'. STDOUT:\n{stdout}\nSTDERR:\n{stderr}")
+                else:
+                    failure_count += 1
+                    logger.error(f"处理 '{input_file}' 失败。STDOUT:\n{stdout}\nSTDERR:\n{stderr}")
+            except Exception as e:
+                failure_count += 1
+                logger.error(f"处理任务 '{input_path}' 时获取结果失败: {e}")
+
+    logger.info(f"处理完成。成功: {success_count}, 失败: {failure_count}, 跳过: {skipped_count}")
+
+
+

data/scripts/pre_processing1.py

@@ -5,6 +5,7 @@ import multiprocessing
 from concurrent.futures import ProcessPoolExecutor, as_completed
 import argparse
 import time
+from brep2sdf.utils.load import get_namelist,get_step_paths
 from brep2sdf.utils.logger import logger
 
 import numpy as np
@@ -335,12 +336,23 @@ def test_single_step(step_path, output_obj_path=None, linear_deflection=0.01):
         print(f"\n处理失败: {str(e)}")
         return None
 
+def process_for_namelist():
+    names = get_namelist("/home/wch/brep2sdf/data/name_list.txt")
+
+    for name in names:
+        # 使用 glob 获取匹配的文件列表
+        step_files = glob.glob(f"/home/wch/brep2sdf/data/step/{name}/{name}*.step")
+        output =  f"/home/wch/brep2sdf/data/gt_mesh/{name}.obj"
+        test_single_step(step_files[0], output_obj_path=output)
+
+    # 准备任务（主进程执行）
 if __name__ == "__main__":
-    main()
+    #main()
+    process_for_namelist()
     '''
     test_single_step(
-        "/home/wch/brep2sdf/data/step/00002736/00002736_82034c87704b46a891e498d6_step_004.step",
-        "/home/wch/brep2sdf/data/gt_mesh/00002736.obj"
+        "/home/wch/brep2sdf/data/step/00000010/00000010_b4b99d35e04b4277931f9a9c_step_000.step",
+        "/home/wch/brep2sdf/data/gt_mesh/00000031.obj"
     )
     '''