feat: Add TorchScript model tracing and export functionality

- Implemented `tracing()` method in training pipeline to export model as TorchScript
- Created dedicated TorchScript directory in checkpoints path
- Added model tracing with example input tensor
- Saved traced model as `model_h.pt` in the TorchScript directory

code/conversion/train.py

@@ -88,6 +88,7 @@ class NHREPNet_Training:
             except Exception as e:
                 logger.error(f"训练过程中发生错误: {str(e)}")
                 break
+        self.tracing()
 
     def train_one_epoch(self, epoch, patch_id, patch_id_n, n_patch_batch, n_patch_last, n_branch, n_batchsize):
         #logger.info(f"Epoch {epoch}/{self.nepochs} 开始")
@@ -211,10 +212,12 @@ class NHREPNet_Training:
         self.checkpoints_path = os.path.join("../exps/single_shape",name_prefix, "checkpoints")
         self.ModelParameters_path = os.path.join(self.checkpoints_path, "ModelParameters")
         self.OptimizerParameters_path = os.path.join(self.checkpoints_path, "OptimizerParameters")
+        self.TorchScript_path = os.path.join(self.checkpoints_path, "TorchScript")
 
         # 创建目录
         os.makedirs(self.ModelParameters_path, exist_ok=True)
         os.makedirs(self.OptimizerParameters_path, exist_ok=True)
+        os.makedirs(self.TorchScript_path, exist_ok=True)
 
     def save_checkpoints(self, epoch):
         torch.save(
@@ -230,6 +233,21 @@ class NHREPNet_Training:
             {"epoch": epoch, "optimizer_state_dict": self.scheduler.optimizer.state_dict()},
             os.path.join(self.OptimizerParameters_path, "latest.pth"))
         
+    def tracing(self):
+        csg_tree, flag_convex = self.dataset.get_csg_tree()
+        network = get_class(self.conf.get_string('train.network_class'))(
+                d_in=self.d_in,
+                n_branch=int(torch.max(self.feature_mask).item()),
+                csg_tree=csg_tree,
+                flag_convex=flag_convex,
+                **self.conf.get_config('network.inputs')
+            ).to(self.device)
+        #trace
+        example = torch.rand(224,3).to(self.device)
+        traced_script_module = torch.jit.trace(network, example)
+        traced_script_module.save(os.path.join(self.TorchScript_path, "model_h.pt"))
+
+
 if __name__ == "__main__":
     name_prefix = 'broken_bullet_50k'
     conf = ConfigFactory.parse_file('./conversion/setup.conf')