增加伪sdf loss

brep2sdf/config/default_config.py

@@ -1,5 +1,5 @@
-from dataclasses import dataclass
-from typing import Tuple
+from dataclasses import dataclass, field
+from typing import Tuple,List
 
 @dataclass
 class ModelConfig:
@@ -49,15 +49,21 @@ class TrainConfig:
     # 基本训练参数
     batch_size: int = 8
     num_workers: int = 4
-    num_epochs: int = 100
-    learning_rate: float = 0.1
+    num_epochs: int = 50
+    learning_rate: float = 0.005
+    learning_rate_schedule: List = field(default_factory=lambda: [{
+        "Type": "Step",  # 学习率调度类型。"Step"表示在指定迭代次数后将学习率乘以因子
+        "Initial": 0.005,
+        "Interval": 2000,
+        "Factor": 0.5
+    }])
     min_lr: float = 1e-5
     weight_decay: float = 0.01
     
     # 梯度和损失相关
     max_grad_norm: float = 1.0
     clamping_distance: float = 0.1
-    debug_mode: bool = True
+    debug_mode: bool = False
     accumulation_steps:int = 50
     
     # 学习率调度器参数
@@ -65,7 +71,7 @@ class TrainConfig:
     warmup_epochs: int = 5
     
     # 保存和验证
-    save_freq: int = 100  # 每多少个epoch保存一次
+    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

brep2sdf/networks/loss.py

@@ -5,13 +5,14 @@ from brep2sdf.utils.logger import logger
 class LossManager:
     def __init__(self, ablation, **condition_kwargs):
         self.weights = {
-            "manifold": 1,
+            "manifold": 10,
             "feature_manifold": 1, # 原文里面和manifold的权重是一样的
             "normals": 1,
             "eikonal": 1,
             "offsurface": 1,
             "consistency": 1,
             "correction": 1,
+            "psdf": 10
         }
         self.condition_kwargs = condition_kwargs
         self.ablation = ablation # 消融实验用
@@ -117,7 +118,8 @@ class LossManager:
                 normals,
                 gt_sdfs,
                 mnfld_pred,
-                nonmnfld_pred):
+                nonmnfld_pred,
+                psdfs):
         """
         计算流型损失的逻辑
 
@@ -148,12 +150,15 @@ class LossManager:
         # 计算修正损失
         #correction_loss = self.correction_loss(mnfld_pnts, mnfld_pred, all_fi)
 
+        psdf_loss = self.position_loss(nonmnfld_pred, psdfs)
+
         # 汇总损失
         loss_details = {
             "manifold": self.weights["manifold"] * manifold_loss,
             "normals": self.weights["normals"] * normals_loss,
             "eikonal": self.weights["eikonal"] * eikonal_loss,
-            "offsurface": self.weights["offsurface"] * offsurface_loss
+            "offsurface": self.weights["offsurface"] * offsurface_loss,
+            "psdf":self.weights["psdf"] * psdf_loss
         }
         
         # 计算总损失

brep2sdf/networks/sample.py

@@ -3,7 +3,7 @@ import torch
 
 class NormalPerPoint():
 
-    def __init__(self, global_sigma, local_sigma=0.01):
+    def __init__(self, global_sigma, local_sigma=0.1):
         self.global_sigma = global_sigma
         self.local_sigma = local_sigma
 
@@ -20,3 +20,24 @@ class NormalPerPoint():
         sample = torch.cat([sample_local, sample_global], dim=1)
 
         return sample
+        
+    def get_norm_points(self, pc_input, normals, local_sigma=None):
+        """
+        返回沿法线方向偏移的点以及对应的伪 SDF 值（PSDF）
+        :param pc_input: 输入点云，形状为 (sample_size, 3)
+        :param normals: 点云的法线，形状为 (sample_size, 3)
+        :param local_sigma: 局部偏移的标准差
+        :return: 偏移后的点 (sample_size, dim), 伪 SDF 值 
+        """
+        sample_size, dim = pc_input.shape
+
+        # 生成随机位移值
+        if local_sigma is not None:
+            psdf = torch.randn(sample_size, device=pc_input.device) * local_sigma
+        else:
+            psdf = torch.randn(sample_size, device=pc_input.device) * self.local_sigma
+
+        # 沿法线方向偏移
+        sample = pc_input + normals * psdf.unsqueeze(-1)
+
+        return sample, psdf

brep2sdf/train.py

@@ -315,7 +315,7 @@ class Trainer:
             start_idx = batch_idx * batch_size
             end_idx = min((batch_idx + 1) * batch_size, num_points)
             mnfld_pnts = self.sdf_data[start_idx:end_idx, 0:3].clone().detach() # 取前3列作为点
-            nonmnfld_pnts = self.sampler.get_points(mnfld_pnts.unsqueeze(0)).squeeze(0)  # 生成非流形点
+            
             gt_sdf = self.sdf_data[start_idx:end_idx, -1].clone().detach()  # 取最后一列作为SDF真值
             normals = None
             if args.use_normal:
@@ -323,7 +323,11 @@ class Trainer:
                     logger.error(f"Epoch {epoch}: --use-normal is specified, but sdf_data has only {self.sdf_data.shape[1]} columns.")
                     return float('inf')
                 normals = self.sdf_data[start_idx:end_idx, 3:6].clone().detach() # 取中间3列作为法线
+                nonmnfld_pnts,psdf = self.sampler.get_norm_points(mnfld_pnts,normals)  # 生成非流形点
+                logger.debug((mnfld_pnts,nonmnfld_pnts,psdf))
 
+            else:
+                nonmnfld_pnts = self.sampler.get_points(mnfld_pnts.unsqueeze(0)).squeeze(0)  # 生成非流形点
             # 执行检查
             if  self.debug_mode:
                 if check_tensor(mnfld_pnts, "Input Points", epoch, step): return float('inf')
@@ -372,7 +376,8 @@ class Trainer:
                         normals, # 传递检查过的 normals
                         gt_sdf,
                         mnfld_pred,
-                        nonmnfld_pred
+                        nonmnfld_pred,
+                        psdf
                     )
                 else:
                     loss = torch.nn.functional.mse_loss(pred_sdf, gt_sdf)