loss删除无用代码

brep2sdf/networks/loss.py

@@ -4,74 +4,6 @@ from .network import gradient
 from brep2sdf.config.default_config import get_default_config
 from brep2sdf.utils.logger import logger
 
-class Brep2SDFLoss(nn.Module):
-    def __init__(self, batch_size:float=1, enforce_minmax: bool=True, clamping_distance: float=0.1,
-                 grad_weight=0.1, warmup_epochs=10):
-        super().__init__()
-        self.batch_size = batch_size
-        self.enforce_minmax = enforce_minmax
-        self.minT = -clamping_distance
-        self.maxT = clamping_distance
-        self.grad_weight = grad_weight
-        self.warmup_epochs = warmup_epochs
-        
-        self.l1_loss = nn.L1Loss(reduction='mean')
-        self.mse_loss = nn.MSELoss(reduction='mean')
-    
-    def forward(self, pred_sdf, gt_sdf, points=None, epoch=None):
-        """计算SDF预测的损失
-        
-        Args:
-            pred_sdf: 预测的SDF值 [B, N, 1]
-            gt_sdf: 真实的SDF值 [B, N, 1]
-            points: 查询点坐标 [B, N, 3]，用于计算梯度损失
-            epoch: 当前训练轮次，用于损失权重调整
-        """
-        # 1. 对SDF值进行clamp
-        if self.enforce_minmax:
-            pred_sdf = torch.clamp(pred_sdf, self.minT, self.maxT)
-            gt_sdf = torch.clamp(gt_sdf, self.minT, self.maxT)
-        
-        # 2. 计算基础L1损失
-        l1_loss = self.l1_loss(pred_sdf, gt_sdf)
-        #mse_loss = self.mse_loss(pred_sdf, gt_sdf)
-        
-        # 3. 计算梯度损失（如果提供了points）
-        grad_loss = 0
-        if points is not None:
-            grad_loss = self.gradient_loss(pred_sdf, points)
-        
-        # 4. 根据epoch调整权重
-        if epoch is not None:
-            weight = min(1.0, epoch / self.warmup_epochs)
-        else:
-            weight = 1.0
-        
-        # 5. 组合损失
-        total_loss = l1_loss + self.grad_weight * grad_loss * weight
-        
-        return total_loss
-    
-    def gradient_loss(self, pred_sdf, points):
-        """计算SDF梯度损失"""
-        grad_pred = torch.autograd.grad(
-            pred_sdf.sum(), 
-            points, 
-            create_graph=True
-        )[0]
-        
-        grad_norm = torch.norm(grad_pred, dim=-1)
-        grad_loss = torch.mean((grad_norm - 1.0) ** 2)
-        
-        return grad_loss
-
-
-
-
-
-
-
-
 
 class LossManager:
     def __init__(self, ablation, **condition_kwargs):