替换stage3 训练数据

brep2sdf/train.py

@@ -579,10 +579,10 @@ class Trainer:
 
     def train_epoch_stage3(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)
+        # 注意：假设 self.train_surf_ncs 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:
+        if self.train_surf_ncs is None:
-             logger.error(f"Epoch {epoch}: self.sdf_data is None. Cannot train.")
+             logger.error(f"Epoch {epoch}: self.train_surf_ncs is None. Cannot train.")
              return float('inf')
 
         self.model.train()
@@ -592,9 +592,9 @@ class Trainer:
 
         # 数据处理
         # manfld
-        _mnfld_pnts = self.sdf_data[:, 0:3].clone().detach() # 取前3列作为点
+        _mnfld_pnts = self.train_surf_ncs[:, 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
+        _normals = self.train_surf_ncs[:, 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
+        _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:
@@ -628,7 +628,7 @@ class Trainer:
 
         
         # 将数据分成多个batch
-        num_points = self.sdf_data.shape[0]
+        num_points = self.train_surf_ncs.shape[0]
         num_batches = (num_points + batch_size - 1) // batch_size
         
         for batch_idx in range(num_batches):
@@ -894,7 +894,7 @@ class Trainer:
             logger.info(f"Loaded model from {args.resume_checkpoint_path}")
         
         # stage1
-        self.model.freeze_stage2()
+        self.model.freeze_stage1()
         for epoch in range(start_epoch, self.config.train.num_epochs1 + 1):
             # 训练一个epoch
             train_loss = self.train_epoch_stage1(epoch)
@@ -921,8 +921,8 @@ class Trainer:
 
         #stage 3
         self.scheduler.reset()
-        #self.model.freeze_stage2()
+        self.model.freeze_stage2()
-        self.model.unfreeze()
+        #self.model.unfreeze()
         for epoch in range(cur_epoch + 1, max_stage2_epoch + self.config.train.num_epochs3 + 1):
             # 训练一个epoch
             train_loss = self.train_epoch_stage3(epoch)