feat: Add LossManager for manifold loss computation

- Created `loss.py` with a `LossManager` class to handle loss calculation
- Integrated `LossManager` into the training pipeline in `train.py`
- Implemented a basic manifold loss computation using mean absolute value

code/conversion/loss.py

@@ -0,0 +1,20 @@
+
+
+
+
+class LossManager:
+    def __init__(self):
+        pass
+
+    def compute_loss(self, outputs):
+        """
+        计算流型损失的逻辑
+
+        :param outputs: 模型的输出
+        :return: 计算得到的流型损失值     
+        """
+
+        # 计算流型损失（这里使用均方误差作为示例）
+        manifold_loss = (outputs.abs()).mean()  # 计算流型损失
+        return manifold_loss
+

code/conversion/train.py

@@ -13,6 +13,7 @@ from torch.optim.lr_scheduler import StepLR
 from tqdm import tqdm
 from utils.logger import logger
 from data_loader import NHREP_Dataset
+from loss import LossManager
 from model.network import NHRepNet  # 导入 NHRepNet
 
 class NHREPNet_Training:
@@ -24,6 +25,7 @@ class NHREPNet_Training:
         d_in = 6  # 输入维度，例如 3D 坐标
         dims_sdf = [64, 64, 64]  # 隐藏层维度
         csg_tree, _ = self.dataset.get_csg_tree()
+        self.loss_manager = LossManager()
         self.model = NHRepNet(d_in, dims_sdf, csg_tree).to(self.device)  # 实例化模型并移动到设备
         
         self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)  # Adam 优化器
@@ -54,7 +56,7 @@ class NHREPNet_Training:
         outputs = self.model(input_data)  # 使用模型进行前向传播
         
         # 计算损失
-        loss = self.compute_loss(outputs)  # 计算损失
+        loss = self.loss_manager.compute_loss(outputs)  # 计算损失
         total_loss += loss.item()
 
         # 反向传播