可以加载和保存

brep2sdf/networks/encoder.py

@@ -8,7 +8,7 @@ from .octree import OctreeNode
 from brep2sdf.config.default_config import get_default_config
 from brep2sdf.utils.logger import logger
 import numpy as np
-
+'''
 class Encoder:
     def __init__(self, surf_bbox: torch.Tensor, origin_bbox: torch.Tensor, max_depth: int, feature_dim:int = 64):
         """
@@ -59,11 +59,129 @@ class Encoder:
 
         
 
+'''
+class Encoder(nn.Module):
+    def __init__(self, octree: OctreeNode, feature_dim: int = 32):
+        """
+        分离后的编码器，接收预构建的八叉树
+        
+        参数:
+            octree: 预构建的八叉树结构
+            feature_dim: 特征维度
+        """
+        super().__init__()
+        self.octree = octree
+        self.feature_dim = feature_dim
+        
+        # 为所有叶子节点注册可学习参数
+        self._init_parameters()
+        
+    def _init_parameters(self):
+        """为所有叶子节点初始化特征参数"""
+        # 使用字典保存所有参数，避免动态属性
+        self._leaf_parameters = nn.ParameterDict()
+        
+        # 递归遍历树结构
+        def _register_params(node, path=""):
+            #logger.debug(node.is_leaf())
+            if node.is_leaf():
+                param_name = f"leaf_{path}"
+                self._leaf_parameters[param_name] = nn.Parameter(
+                    torch.randn(8, self.feature_dim)  # 8个顶点的特征
+                )
+                node.set_param_key(param_name)  # 为节点存储参数键
+                #logger.debug(param_name)
+                #logger.debug(node.param_key)
+            else:
+                for i, child in enumerate(node.child_nodes):
+                    _register_params(child, f"{path}_{i}")
+        
+        _register_params(self.octree, "root")
+    
+    def get_leaf_parameter(self, node):
+        """获取叶子节点的特征参数"""
+        return self._leaf_parameters[node.param_key]
+    
+    def forward(self, query_points: torch.Tensor) -> torch.Tensor:
+        """
+        前向传播，处理批量查询点
+        
+        参数:
+            query_points: 查询点的位置坐标，形状为(batch_size, 3)
+        返回:
+            feature_vectors: 查询点的特征向量，形状为(batch_size, feature_dim)
+        """
+        batch_size = query_points.shape[0]
+        features = []
+        
+        for i in range(batch_size):
+            # 1. 在八叉树中查找包含该点的叶子节点
+            leaf_node = self.octree.find_leaf(query_points[i])
+            #logger.debug(leaf_node.param_key)
             
+            # 2. 获取该节点的特征参数
+            bbox = leaf_node.bbox
+            node_features = self.get_leaf_parameter(leaf_node)
             
+            # 3. 使用三线性插值计算特征
+            # (这里需要实现你的插值逻辑)
+            interpolated = self.trilinear_interpolation(
+                query_points[i], bbox, node_features)
             
+            features.append(interpolated)
             
+        return torch.stack(features, dim=0)
     
+    def trilinear_interpolation(self, query_point: torch.Tensor, bbox, features) -> torch.Tensor:
+        """
+        实现三线性插值
+        :param query_point: 待插值的点，格式为 (x, y, z)
+        :return: 插值结果，形状为 (D,)
+        """
+        # 确保 query_point 和 bbox 在同一设备上
+        #query_point = query_point.to(self.bbox.device)
+
+        # 获取包围盒的最小和最大坐标
+        min_coords = bbox[:3]  # [min_x, min_y, min_z]
+        max_coords = bbox[3:]  # [max_x, max_y, max_z]
+
+        # 计算归一化坐标
+        normalized_coords = (query_point - min_coords) / (max_coords - min_coords + 1e-8)  # 防止除零错误
+        x, y, z = normalized_coords.unbind(dim=-1)
+        
+        # 使用torch.stack避免Python标量转换
+        wx = torch.stack([1 - x, x], dim=-1)  # 保持自动微分
+        wy = torch.stack([1 - y, y], dim=-1)
+        wz = torch.stack([1 - z, z], dim=-1)
+
+        # 获取8个顶点的特征向量
+        c = features  # 形状为 (8, D)
+
+        # 执行三线性插值
+        # 先对 x 轴插值
+        c00 = c[0] * wx[0] + c[1] * wx[1]
+        c01 = c[2] * wx[0] + c[3] * wx[1]
+        c10 = c[4] * wx[0] + c[5] * wx[1]
+        c11 = c[6] * wx[0] + c[7] * wx[1]
+
+        # 再对 y 轴插值
+        c0 = c00 * wy[0] + c10 * wy[1]
+        c1 = c01 * wy[0] + c11 * wy[1]
+
+        # 最后对 z 轴插值
+        result = c0 * wz[0] + c1 * wz[1]
+
+        return result
+
+    def to(self, device):
+        super().to(device)
+        def _move_node(node):
+            if isinstance(node.bbox, torch.Tensor):
+                node.bbox = node.bbox.to(device)
+            for child in node.children:
+                _move_node(child)
+        _move_node(self.octree.root)
+        return self
 
 
 

brep2sdf/networks/network.py

@@ -53,9 +53,7 @@ from .decoder import Decoder
 
 class Net(nn.Module):
     def __init__(self, 
-                 surf_bbox,
+                 octree,
-                 origin_bbox,
-                 max_depth=4,
                  feature_dim=64, 
                  decoder_input_dim=64, 
                  decoder_output_dim=1, 
@@ -68,9 +66,7 @@ class Net(nn.Module):
         
         # 初始化 Encoder
         self.encoder = Encoder(
-            surf_bbox=surf_bbox,  # 使用传入的bbox作为表面包围盒
+            octree=octree,
-            origin_bbox=origin_bbox,  # 使用相同的bbox作为原点包围盒
-            max_depth=max_depth,
             feature_dim=feature_dim
         )
         

brep2sdf/networks/octree.py

@@ -28,21 +28,20 @@ def bbox_intersect(bbox1: torch.Tensor, bbox2: torch.Tensor) -> bool:
     # 向量化比较
     return torch.all((max1 >= min2) & (max2 >= min1))
 
-class OctreeNode:
+class OctreeNode(nn.Module):
-    feature_dim=None
     device=None
     surf_bbox = None
-    def __init__(self, bbox: torch.Tensor,face_indices: np.ndarray, max_depth: int = 5, feature_dim:int = None, surf_bbox:torch.Tensor = None):
+    def __init__(self, bbox: torch.Tensor,face_indices: np.ndarray, max_depth: int = 5, surf_bbox:torch.Tensor = None):
+        super().__init__()
         self.bbox = bbox  # 节点的边界框
         self.max_depth: int = max_depth  # 最大深度，当这个为0时，表示已经到达最大深度，不可再分子节点
-        self.children: List['OctreeNode'] = []  # 子节点列表
+        self.child_nodes: List['OctreeNode'] = []  # 子节点列表
         self.face_indices = face_indices
-        self.patch_feature_volume = None  # 补丁特征体积，only leaf has
+        self.param_key = None 
+        #self.patch_feature_volume = None  # 补丁特征体积，only leaf has
         self._is_leaf = True
         #print(f"box shape: {self.bbox.shape}")
 
-        if feature_dim is not None:
-            OctreeNode.feature_dim = feature_dim
         if  surf_bbox is not None:
             if not isinstance(surf_bbox, torch.Tensor):
                 raise TypeError(
@@ -56,13 +55,15 @@ class OctreeNode:
             OctreeNode.device = bbox.device
 
     def is_leaf(self):
-        # Check if self.children is None before calling len()
+        # Check if self.child——nodes is None before calling len()
         return self._is_leaf
 
+    def set_param_key(self, k):
+        self.param_key = k
+
     def conduct_tree(self):
         if self.max_depth <= 0 or self.face_indices.shape[0] <= 2:
             # 达到最大深度 or 一个单元格至多只有两个面
-            self.patch_feature_volume = nn.Parameter(torch.randn(8, OctreeNode.feature_dim, device=OctreeNode.device))
             return 
         self.subdivide()
 
@@ -102,7 +103,7 @@ class OctreeNode:
         ])
         
         # 为每个子包围盒创建子节点，并分配相交的面
-        self.children = []
+        self.child_nodes = []
         for bbox in child_bboxes:
             # 找到与子包围盒相交的面
             intersecting_faces = []
@@ -118,7 +119,7 @@ class OctreeNode:
                 max_depth=self.max_depth - 1
             )
             child_node.conduct_tree()
-            self.children.append(child_node)
+            self.child_nodes.append(child_node)
         
         self._is_leaf = False
 
@@ -143,6 +144,25 @@ class OctreeNode:
 
         return index.unsqueeze(0)
 
+    def find_leaf(self, query_point:torch.Tensor):
+        # 从根节点开始递归查找包含该点的叶子节点
+        if self._is_leaf:
+            return self
+        else:
+            index = self.get_child_index(query_point)
+            try:
+                # 直接访问子节点，不进行显式检查
+                return self.child_nodes[index].find_leaf(query_point)
+            except IndexError as e:
+                # 记录错误日志并重新抛出异常
+                logger.error(
+                    f"Error accessing child node: {e}. "
+                    f"Query point: {query_point.cpu().numpy().tolist()}, "
+                    f"Node bbox: {self.bbox.cpu().numpy().tolist()}, "
+                    f"Depth info: {self.max_depth}"
+                )
+                raise e
+
     def get_feature_vector(self, query_point:torch.Tensor):
         """
         预测给定点的 SDF 值
@@ -158,7 +178,7 @@ class OctreeNode:
             index = self.get_child_index(query_point)
             try:
                 # 直接访问子节点，不进行显式检查
-                return self.children[index].get_feature_vector(query_point)
+                return self.child_nodes[index].get_feature_vector(query_point)
             except IndexError as e:
                 # 记录错误日志并重新抛出异常
                 logger.error(
@@ -170,46 +190,7 @@ class OctreeNode:
                 raise e
 
 
-    def trilinear_interpolation(self, query_point: torch.Tensor) -> torch.Tensor:
-        """
-        实现三线性插值
-        :param query_point: 待插值的点，格式为 (x, y, z)
-        :return: 插值结果，形状为 (D,)
-        """
-        # 确保 query_point 和 bbox 在同一设备上
-        #query_point = query_point.to(self.bbox.device)
-
-        # 获取包围盒的最小和最大坐标
-        min_coords = self.bbox[:3]  # [min_x, min_y, min_z]
-        max_coords = self.bbox[3:]  # [max_x, max_y, max_z]
-
-        # 计算归一化坐标
-        normalized_coords = (query_point - min_coords) / (max_coords - min_coords + 1e-8)  # 防止除零错误
-        x, y, z = normalized_coords.unbind(dim=-1)
-        
-        # 使用torch.stack避免Python标量转换
-        wx = torch.stack([1 - x, x], dim=-1)  # 保持自动微分
-        wy = torch.stack([1 - y, y], dim=-1)
-        wz = torch.stack([1 - z, z], dim=-1)
-
-        # 获取8个顶点的特征向量
-        c = self.patch_feature_volume  # 形状为 (8, D)
-
-        # 执行三线性插值
-        # 先对 x 轴插值
-        c00 = c[0] * wx[0] + c[1] * wx[1]
-        c01 = c[2] * wx[0] + c[3] * wx[1]
-        c10 = c[4] * wx[0] + c[5] * wx[1]
-        c11 = c[6] * wx[0] + c[7] * wx[1]
-
-        # 再对 y 轴插值
-        c0 = c00 * wy[0] + c10 * wy[1]
-        c1 = c01 * wy[0] + c11 * wy[1]
     
-        # 最后对 z 轴插值
-        result = c0 * wz[0] + c1 * wz[1]
-
-        return result
 
 
     def print_tree(self, depth: int = 0, max_print_depth: int = None) -> None:
@@ -231,46 +212,32 @@ class OctreeNode:
         # 打印面片信息（如果有）
         if self.face_indices is not None:
             print(f"{indent}  Face indices: {self.face_indices.tolist()}")
-            print(f"{indent}  len children: {len(self.children)}")
+            print(f"{indent}  len child_nodes: {len(self.child_nodes)}")
         
         # 递归打印子节点
-        for i, child in enumerate(self.children):
+        for i, child in enumerate(self.child_nodes):
             print(f"{indent}  Child {i}:")
             child.print_tree(depth + 1, max_print_depth)
 
-# 保存
+    def __getstate__(self):
-    def state_dict(self):
+        """支持pickle序列化"""
-        """返回节点及其子树的state_dict"""
+        return self._serialize_node(self)
-        state = {
-            'bbox': self.bbox,
-            'max_depth': self.max_depth,
-            'face_indices': self.face_indices,
-            'is_leaf': self._is_leaf
-        }
-        
-        if self._is_leaf:
-            state['patch_feature_volume'] = self.patch_feature_volume
-        else:
-            state['children'] = [child.state_dict() for child in self.children]
     
-        return state
+    def __setstate__(self, state):
+        """支持pickle反序列化"""
+        self = self._deserialize_node(state)
         
-    def load_state_dict(self, state_dict):
+    def _serialize_node(self, node):
-        """从state_dict加载节点状态"""
+        return {
-        self.bbox = state_dict['bbox']
+            'bbox': node.bbox,
-        self.max_depth = state_dict['max_depth']
+            'is_leaf': node._is_leaf,
-        self.face_indices = state_dict['face_indices']
+            'child_nodes': [self._serialize_node(c) for c in node.child_nodes],
-        self._is_leaf = state_dict['is_leaf']
+            'param_key': node.param_key
+        }
     
-        if self._is_leaf:
+    def _deserialize_node(self, data):
-            self.patch_feature_volume = nn.Parameter(state_dict['patch_feature_volume'])
+        node = OctreeNode(data['bbox'], 0)  # max_depth会在encoder中重建
-        else:
+        node._is_leaf = data['is_leaf']
-            self.children = []
+        node.param_key = data['param_key']
-            for child_state in state_dict['children']:
+        node.child_nodes = [self._deserialize_node(c) for c in data['child_nodes']]
-                child = OctreeNode(
+        return node
-                    bbox=child_state['bbox'],
-                    face_indices=child_state['face_indices'],
-                    max_depth=child_state['max_depth']
-                )
-                child.load_state_dict(child_state)
-                self.children.append(child)

brep2sdf/train.py

@@ -1,4 +1,5 @@
 import torch
+from torch.serialization import add_safe_globals
 import torch.optim as optim
 import time
 import os
@@ -9,6 +10,7 @@ from brep2sdf.config.default_config import get_default_config
 from brep2sdf.data.data import load_brep_file,load_sdf_file 
 from brep2sdf.data.pre_process import process_single_step
 from brep2sdf.networks.network import Net
+from brep2sdf.networks.octree import OctreeNode
 from brep2sdf.utils.logger import logger
 
 def prepare_sdf_data(surf_data, max_points=100000, device='cuda'):
@@ -62,10 +64,13 @@ class Trainer:
             dtype=torch.float32,
             device=self.device
         )
-        bbox = self._calculate_global_bbox(surf_bbox)
+        
+
+        self.build_tree(surf_bbox=surf_bbox, max_depth=4)
+
+
         self.model = Net(
-            surf_bbox=surf_bbox,
+            octree=self.root,
-            origin_bbox=bbox,
             feature_dim=64
         ).to(self.device)
         
@@ -77,6 +82,20 @@ class Trainer:
         )
 
     
+    def build_tree(self,surf_bbox, max_depth=6):
+        num_faces = surf_bbox.shape[0]
+        bbox = self._calculate_global_bbox(surf_bbox)
+        self.root = OctreeNode(
+            bbox=bbox,
+            face_indices=np.arange(num_faces),  # 初始包含所有面
+            max_depth=max_depth,
+            surf_bbox=surf_bbox
+        )
+        #print(surf_bbox)
+        logger.info("starting octree conduction")
+        self.root.conduct_tree()
+        logger.info("complete octree conduction")
+        #self.root.print_tree(0) 
 
     def _calculate_global_bbox(self, surf_bbox: torch.Tensor) -> torch.Tensor:
         """
@@ -103,6 +122,7 @@ class Trainer:
         # 返回合并后的边界框
         return torch.cat([global_min, global_max])
 
+    
     def train_epoch(self, epoch: int) -> float:
         self.model.train()
         total_loss = 0.0
@@ -153,7 +173,7 @@ class Trainer:
         best_val_loss = float('inf')
         logger.info("Starting training...")
         start_time = time.time()
-        
+        """
         for epoch in range(1, self.config.train.num_epochs + 1):
             # 训练一个epoch
             train_loss = self.train_epoch(epoch)
@@ -182,7 +202,18 @@ class Trainer:
         logger.info(f'Training completed in {total_time//3600:.0f}h {(total_time%3600)//60:.0f}m {total_time%60:.2f}s')
         logger.info(f'Best validation loss: {best_val_loss:.6f}')
         self._tracing_model()
+        """
+        self.test_load()
         
+    def test_load(self):
+        model = self._load_checkpoint("/home/wch/brep2sdf/brep2sdf/00000054.pt")
+        model.eval()
+        logger.debug(model)
+        example_input = torch.rand(10, 3, device=self.device)
+        #logger.debug(model.encoder.octree.bbox)
+        logger.debug(f"points: {example_input}")
+        sdfs= model(example_input)
+        logger.debug(f"sdfs:{sdfs}")
     
     def _tracing_model(self):
         """保存模型"""
@@ -195,13 +226,8 @@ class Trainer:
     
     def _load_checkpoint(self, checkpoint_path):
         """从检查点恢复训练状态"""
-        checkpoint = torch.load(checkpoint_path)
+        model = torch.load(checkpoint_path)
-        self.model.load_state_dict(checkpoint['model_state_dict'])
+        return model
-        self.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-        start_epoch = checkpoint['epoch'] + 1  # 从下一轮开始
-        best_loss = checkpoint['loss']
-        logger.info(f"Loaded checkpoint from epoch {checkpoint['epoch']}")
-        return start_epoch, best_loss
 
     def _save_checkpoint(self, epoch: int, train_loss: float):
         """保存训练检查点"""
@@ -211,6 +237,7 @@ class Trainer:
         )
         os.makedirs(checkpoint_dir, exist_ok=True)
         checkpoint_path = os.path.join(checkpoint_dir,f"epoch_{epoch:03d}.pth")
+        '''
         torch.save({
             'epoch': epoch,
             'model_state_dict': self.model.state_dict(),
@@ -218,6 +245,8 @@ class Trainer:
             'loss': train_loss,
             'config': self.config
         }, checkpoint_path)
+        '''
+        torch.save(self.model,checkpoint_path)
 
 def main():
     # 这里需要初始化配置