优化net计算

brep2sdf/networks/network.py

@@ -83,35 +83,18 @@ class Net(nn.Module):
 
         #self.csg_combiner = CSGCombiner(flag_convex=True)
     
-    @torch.jit.export
-    def forward(self, query_points):
-        """
-        前向传播
-        
-        参数:
-            query_point: 查询点的位置坐标
-        返回:
-            output: 解码后的输出结果
-        """
-        # 批量查询所有点的索引和bbox
-        _,face_indices_mask,operator = self.octree_module.forward(query_points)
-        # 编码
-        feature_vectors = self.encoder.forward(query_points,face_indices_mask)
-        print("feature_vector:", feature_vectors.shape)
-        # 解码
-        #logger.gpu_memory_stats("encoder farward后")
-        f_i = self.decoder(feature_vectors) # (B, P)
-        #logger.gpu_memory_stats("decoder farward后")
-
-
+    def process_sdf(self,f_i, face_indices_mask, operator):
         output = f_i[:,0]
         # 提取有效值并填充到固定大小 (B, max_patches)
         padded_f_i = torch.full((f_i.shape[0], 2), float('inf'), device=f_i.device)  # (B, max_patches)
-        for i in range(f_i.shape[0]):
-            sample_valid_values = f_i[i][face_indices_mask[i]]  # (N,), N <= P
-            num_valid = min(len(sample_valid_values), 2)
-            padded_f_i[i, :num_valid] = sample_valid_values[:num_valid]
+        valid_mask = face_indices_mask.bool()  # 确保是布尔类型 (B, P)
+        masked_f_i = torch.where(valid_mask, f_i, torch.tensor(float('inf'), device=f_i.device))  # 将无效值设置为 inf
+
+        # 对每个样本取前 max_patches 个有效值 (B, max_patches)
+        valid_values, _ = torch.topk(masked_f_i, k=2, dim=1, largest=False)  # 提取前两个有效值
 
+        # 填充到固定大小 (B, max_patches)
+        padded_f_i[:, :2] = valid_values  # (B, max_patches)
         
         # 找到需要组合的行
         mask_concave = (operator == 0)
@@ -125,10 +108,36 @@ class Net(nn.Module):
         if mask_convex.any():
             output[mask_convex] = torch.min(padded_f_i[mask_convex], dim=1).values
 
-
+        logger.debug("step over")
         #logger.gpu_memory_stats("combine后")
         return output
 
+    @torch.jit.export
+    def forward(self, query_points):
+        """
+        前向传播
+        
+        参数:
+            query_point: 查询点的位置坐标
+        返回:
+            output: 解码后的输出结果
+        """
+        # 批量查询所有点的索引和bbox
+        #logger.debug("step octree")
+        _,face_indices_mask,operator = self.octree_module.forward(query_points)
+        #logger.debug("step encode")
+        # 编码
+        feature_vectors = self.encoder.forward(query_points,face_indices_mask)
+        #print("feature_vector:", feature_vectors.shape)
+        # 解码
+        #logger.debug("step decode")
+        #logger.gpu_memory_stats("encoder farward后")
+        f_i = self.decoder(feature_vectors) # (B, P)
+        #logger.gpu_memory_stats("decoder farward后")
+
+        #logger.debug("step combine")
+        return self.process_sdf(f_i, face_indices_mask, operator)
+      
     @torch.jit.export
     def forward_training_volumes(self, surf_points, patch_id:int):
         """