加保存checkpoint

brep2sdf/config/default_config.py

@@ -38,20 +38,24 @@ class DataConfig:
     # 数据路径
     pkl_path = "/home/wch/brep2sdf/test_data/pkl/train/bathtub_0004.pkl"
     sdf_path = "/home/wch/brep2sdf/test_data/sdf/train/bathtub_0004.npz"
-    brep_dir: str = '/home/wch/brep2sdf/test_data/pkl'
-    sdf_dir: str = '/home/wch/brep2sdf/test_data/sdf'
-    valid_data_dir: str = '/home/wch/brep2sdf/test_data/result/pkl'
 
+    # 基础路径配置
+    base_data_dir: str = '/home/wch/brep2sdf/test_data'  # 基础数据目录
 
-    # 保存路径
-    save_dir: str = '/home/wch/brep2sdf/checkpoints'                    # 模型保存基础目录
-    model_save_dir: str = '/home/wch/brep2sdf/checkpoints/models'       # 模型文件保存目录
-    result_save_dir: str = '/home/wch/brep2sdf/checkpoints/results'     # 结果保存目录
     
-    # 文件命名
+    @property
-    model_name: str = 'brep2sdf'                     # 模型名称，用于文件命名
+    def brep_dir(self) -> str:
-    checkpoint_format: str = '{model_name}_epoch_{epoch:03d}.pth'  # 检查点文件名格式
+        return os.path.join(self.base_data_dir, 'pkl')
-    best_model_name: str = '{model_name}_best.pth'   # 最佳模型文件名格式
+    
+    @property
+    def sdf_dir(self) -> str:
+        return os.path.join(self.base_data_dir, 'sdf')
+    
+    @property
+    def valid_data_dir(self) -> str:
+        return os.path.join(self.base_data_dir, 'result/pkl')
+    
+    
 
 @dataclass
 class TrainConfig:
@@ -77,6 +81,15 @@ class TrainConfig:
     save_freq: int = 10  # 每多少个epoch保存一次
     val_freq: int = 1    # 每多少个epoch验证一次
 
+    # 保存路径
+    checkpoint_dir: str = '/home/wch/brep2sdf/checkpoints'   # 结果保存目录
+    
+    # 文件命名
+    model_name: str = 'brep2sdf'                     # 模型名称，用于文件命名
+    checkpoint_format: str = '{model_name}_epoch_{epoch:03d}.pth'  # 检查点文件名格式
+    best_model_name: str = '{model_name}_best.pth'   # 最佳模型文件名格式
+
+
 @dataclass
 class TestConfig:
     vis_freq: int = 100

brep2sdf/train.py

@@ -1,5 +1,7 @@
 import torch
 import torch.optim as optim
+import time
+import os
 
 from brep2sdf.config.default_config import get_default_config
 from brep2sdf.data.data import load_brep_file,load_sdf_file
@@ -103,30 +105,69 @@ class Trainer:
     def train(self):
         best_val_loss = float('inf')
         logger.info("Starting training...")
+        start_time = time.time()
         
         for epoch in range(1, self.config.train.num_epochs + 1):
+            # 训练一个epoch
             train_loss = self.train_epoch(epoch)
+            
+            # 验证
             '''
-            # 定期验证
             if epoch % self.config.train.val_freq == 0:
                 val_loss = self.validate(epoch)
+                logger.info(f'Epoch {epoch}: Train Loss = {train_loss:.6f}, Val Loss = {val_loss:.6f}')
                 
                 # 保存最佳模型
                 if val_loss < best_val_loss:
                     best_val_loss = val_loss
                     self._save_model(epoch, val_loss)
+                    logger.info(f'New best model saved at epoch {epoch} with val loss {val_loss:.6f}')
+            else:
+                logger.info(f'Epoch {epoch}: Train Loss = {train_loss:.6f}')
             '''
-            # 定期保存检查点
+            # 保存检查点
             if epoch % self.config.train.save_freq == 0:
                 self._save_checkpoint(epoch, train_loss)
+                logger.info(f'Checkpoint saved at epoch {epoch}')
+        
+        # 训练完成
+        total_time = time.time() - start_time
+        logger.info(f'Training completed in {total_time//3600:.0f}h {(total_time%3600)//60:.0f}m {total_time%60:.2f}s')
+        logger.info(f'Best validation loss: {best_val_loss:.6f}')
+    
     
     def _save_model(self, epoch: int, val_loss: float):
-        # 保存最佳模型的逻辑
+        """保存最佳模型"""
-        pass
+        save_path = os.path.join(
+            self.config.train.checkpoint_dir,
+            self.config.train.best_model_name.format(
+                model_name=config.train.model_name
+            )
+        )
+        torch.save({
+            'epoch': epoch,
+            'model_state_dict': self.model.state_dict(),
+            'optimizer_state_dict': self.optimizer.state_dict(),
+            'loss': val_loss,
+            'config': self.config
+        }, save_path)
     
     def _save_checkpoint(self, epoch: int, train_loss: float):
-        # 保存检查点的逻辑
+        """保存训练检查点"""
-        pass
+        checkpoint_path = os.path.join(
+            self.config.train.checkpoint_dir,
+            self.config.train.checkpoint_format.format(
+                model_name=self.config.train.model_name,
+                epoch=epoch
+            )
+        )
+        torch.save({
+            'epoch': epoch,
+            'model_state_dict': self.model.state_dict(),
+            'optimizer_state_dict': self.optimizer.state_dict(),
+            'loss': train_loss,
+            'config': self.config
+        }, checkpoint_path)
 
 def main():
     # 这里需要初始化配置