可以零表面 normal 降

brep2sdf/IsoSurfacing.py

@@ -35,23 +35,28 @@ def predict_sdf(model, points, device, use_bk=False):
     """
     points_t = torch.from_numpy(points).float().to(device)
     logger.print_tensor_stats("input poitns", points_t)
+    #logger.info(f"points_t:{points_t.shape}")
 
     with torch.no_grad():
         if use_bk:
             print("only background")
             sdf = model.forward_background(points_t)
         else:
-            batch_size = 8192*4  # 定义批量大小
+            batch_size = 8192*128  # 定义批量大小
             sdf_list = []  # 用于存储批量预测结果
+            model.octree_module = model.octree_module.to(points_t.device)
             for i in range(0, len(points), batch_size):
                 batch_points = points[i:i + batch_size]
                 points_t = torch.from_numpy(batch_points).float().to(device)
-                logger.print_tensor_stats("input points", points_t)
+                logger.print_tensor_stats("points_t", points_t)
                 batch_sdf = model(points_t)
+                logger.print_tensor_stats("batch_sdf", batch_sdf)
                 sdf_list.append(batch_sdf.cpu())
             sdf = torch.cat(sdf_list)  # 合并所有批量结果
+    #logger.info(f"sdf:{sdf.shape}")
     logger.print_tensor_stats("sdf", sdf)
     sdf = sdf.cpu().numpy().flatten()
+    #logger.info(f"sdf:{sdf.shape}")
     return sdf
 
 def extract_surface(sdf, xx, yy, zz, method='MC', bbox_size=1.0,feature_angle=30.0, voxel_size=0.01):

brep2sdf/config/default_config.py

@@ -49,9 +49,9 @@ class TrainConfig:
     # 基本训练参数
     batch_size: int = 8
     num_workers: int = 4
-    num_epochs1: int = 10000
-    num_epochs2: int = 0000
-    num_epochs3: int = 0000
+    num_epochs1: int = 0000
+    num_epochs2: int = 000
+    num_epochs3: int = 100
     learning_rate: float = 0.1
     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

brep2sdf/data/sampler.py

@@ -438,7 +438,7 @@ def sample_grid(trimesh_mesh_ncs: trimesh.Trimesh):
     np.ndarray | None: 形状为 (N, 7) 的数组 [x, y, z, nx, ny, nz, sdf]，
                          如果采样或计算失败则返回 None。
     """
-    grid_size = 2**5 + 1
+    grid_size = 2**4 + 1
     start = -0.5
     end = 0.5 
     x = np.linspace(start, end, grid_size)

brep2sdf/networks/decoder.py

@@ -54,12 +54,8 @@ class Decoder(nn.Module):
             self.norm_layers.append(nn.LayerNorm(dim))
         
         if geometric_init:
-            self.activation = nn.Sequential(
-                nn.LayerNorm(out_dim),  # 添加层归一化
-                nn.Softplus(beta=beta)
-            )
             if beta > 0:
-                self.activation = nn.SiLU() 
+                self.activation = nn.Softplus(beta=beta)
             # vanilla relu
             else:
                 self.activation = nn.ReLU()
@@ -68,7 +64,7 @@ class Decoder(nn.Module):
             self.activation = Sine()
         self.final_activation = nn.Tanh()
 
-    def forward(self, feature_matrix: torch.Tensor) -> torch.Tensor:
+    def forward_patch(self, feature_matrix: torch.Tensor) -> torch.Tensor:
         '''
         :param feature_matrix: 形状为 (B, P, D) 的特征矩阵
             B: 批大小
@@ -98,7 +94,7 @@ class Decoder(nn.Module):
         return f_i
 
     @torch.jit.export
-    def forward_training_volumes(self, feature_matrix: torch.Tensor) -> torch.Tensor:
+    def forward(self, feature_matrix: torch.Tensor) -> torch.Tensor:
         '''
         :param feature_matrix: 形状为(S, D) 的特征矩阵
             S: 采样数量

brep2sdf/networks/encoder.py

@@ -2,6 +2,7 @@ import torch
 import torch.nn as nn
 import numpy as np
 import time
+
 from .octree import OctreeNode
 from .feature_volume import PatchFeatureVolume,SimpleFeatureEncoder
 from brep2sdf.utils.logger import logger
@@ -89,38 +90,43 @@ class Encoder(nn.Module):
             volume_indices_mask: 关联的volume索引矩阵 (B, P) 
         
         返回:
-            特征张量 (B, P, D)
+            # 获取前两个有效特征索引
+            # 注意：当某行True数量小于2时：
+            # 1. 如果只有1个True，会重复获取该特征两次
+            # 2. 如果没有True，会获取背景特征两次（因为mask最后补充了一列True）
+            特征张量 (B, 2, D)
         """
         batch_size, num_volumes = volume_indices_mask.shape
-        all_features = torch.zeros(batch_size, num_volumes, self.feature_dim, 
+        all_features = torch.zeros(batch_size, num_volumes+1, self.feature_dim, 
                                   device=query_points.device)
         background_features = self.background.forward(query_points)  # (B, D)
-        start_time = time.time()
-        # 创建 CUDA 流
-        streams = [torch.cuda.Stream() for _ in range(len(self.feature_volumes))]
-        features_list = [None] * len(self.feature_volumes)
         
-        # 并行计算
+        # 遍历每个volume索引
         for vol_id, volume in enumerate(self.feature_volumes):
             mask = volume_indices_mask[:, vol_id].squeeze()
-            if not mask.any():
-                continue
-            with torch.cuda.stream(streams[vol_id]):
-                features = volume(query_points[mask])
-                features_list[vol_id] = (mask, features)
-        
-        # 同步流
-        torch.cuda.synchronize()
-        
-        # 写入结果
-        for vol_id, item in enumerate(features_list):
-            if item is None:
-                continue
-            mask, features = item
-            all_features[mask, vol_id] = 0.1 * background_features[mask] + 0.9 * features
-        end_time = time.time()
-        logger.debug(f"duration:{end_time-start_time}")
-        return all_features
+            #logger.debug(f"mask:{mask},shape:{mask.shape},mask.any():{mask.any()}")
+            if mask.any():
+                # 获取对应volume的特征 (M, D)
+                features = volume.forward(query_points[mask])
+                all_features[mask, vol_id] = features
+        # 最后一维度作为背景场
+        all_features[:,num_volumes] = background_features
+        #all_features[:, :] = background_features.unsqueeze(1) 
+        features = torch.zeros(batch_size, 2, self.feature_dim, 
+                                  device=query_points.device)
+
+        # mask从 volume_indices_mask(B,P) 变成 （B，P+1) ,True 补充
+        mask = torch.cat([
+            volume_indices_mask, 
+            torch.ones(batch_size, 1, dtype=torch.bool, device=volume_indices_mask.device)
+        ], dim=1)
+        # 对于每个样本，取前两个非零特征， 如果没有
+
+        _, valid_indices = torch.topk(mask.float(), 2, dim=1)  # (B, 2)
+        # 使用gather获取特征
+        features = all_features.gather(1, valid_indices.unsqueeze(-1).expand(-1, -1, self.feature_dim))
+
+        return features
 
     def forward_background(self, query_points: torch.Tensor) -> torch.Tensor:
         """
@@ -146,10 +152,10 @@ class Encoder(nn.Module):
         """
         # 获取 patch 特征
         patch_features = self.feature_volumes[patch_id].forward(surf_points)
-        background_features = self.background.forward(surf_points)  # (B, D)
+        #background_features = self.background.forward(surf_points)  # (B, D)
         #dot = make_dot(patch_features, params=dict(self.feature_volumes.named_parameters()))
         #dot.render("feature_extraction", format="png")  # 将计算图保存为 PDF 文件
-        return 0.1 * background_features + 0.9 * patch_features
+        return patch_features
         
     def to(self, device):
         super().to(device)

brep2sdf/networks/feature_volume.py

@@ -45,36 +45,53 @@ class PatchFeatureVolume(nn.Module):
         return self._batched_trilinear(normalized)
 
     def _batched_trilinear(self, normalized: torch.Tensor) -> torch.Tensor:
-        """批量处理的三线性插值"""
-        # 计算8个顶点的权重
+        """
+        修复后的批量三线性插值
+        Args:
+            normalized (Tensor): [B, 3]，归一化坐标范围 [0, 1]
+        Returns:
+            Tensor: [B, feature_dim]
+        """
+        B = normalized.shape[0]
+        device = normalized.device
+        
+        # 将归一化坐标映射到网格索引范围 [0, resolution - 1]
         uvw = normalized * (self.resolution - 1)
-        indices = torch.floor(uvw).long()  # (B,3)
-        weights = uvw - indices.float()    # (B,3)
-        
-        # 计算8个顶点的权重组合 (B,8)
-        weights = torch.stack([
-            (1 - weights[...,0]) * (1 - weights[...,1]) * (1 - weights[...,2]),
-            (1 - weights[...,0]) * (1 - weights[...,1]) * weights[...,2],
-            (1 - weights[...,0]) * weights[...,1] * (1 - weights[...,2]),
-            (1 - weights[...,0]) * weights[...,1] * weights[...,2],
-            weights[...,0] * (1 - weights[...,1]) * (1 - weights[...,2]),
-            weights[...,0] * (1 - weights[...,1]) * weights[...,2],
-            weights[...,0] * weights[...,1] * (1 - weights[...,2]),
-            weights[...,0] * weights[...,1] * weights[...,2],
-        ], dim=-1)  # (B,8)
-
-        # 获取8个顶点的特征 (B,8,D)
-        indices = indices.unsqueeze(1).expand(-1,8,-1) + torch.tensor([
-            [0,0,0], [0,0,1], [0,1,0], [0,1,1],
-            [1,0,0], [1,0,1], [1,1,0], [1,1,1]
-        ], device=indices.device)
-        indices = torch.clamp(indices, 0, self.resolution-1)
-        
-        features = self.feature_volume[indices[...,0], indices[...,1], indices[...,2]]  # (B,8,D)
-        
-        # 加权求和 (B,D)
-        return torch.einsum('bnd,bn->bd', features, weights)
+        indices = torch.floor(uvw).long()
+        weights = uvw - indices.float()
+
+        # 确保所有维度对齐
+        indices = torch.clamp(indices, 0, self.resolution - 2)  # 改为resolution-2防止越界
+        
+        # 获取8个顶点的坐标
+        x, y, z = indices.unbind(dim=-1)
+        w_x, w_y, w_z = weights.unbind(dim=-1)
+
+        # 确保权重维度正确
+        w_z = w_z.unsqueeze(-1)
+        w_y = w_y.unsqueeze(-1)
+        w_x = w_x.unsqueeze(-1)
+
+        # 获取特征值
+        c00 = self.feature_volume[x,     y,     z    ]
+        c01 = self.feature_volume[x,     y,     z + 1]
+        c10 = self.feature_volume[x,     y + 1, z    ]
+        c11 = self.feature_volume[x,     y + 1, z + 1]
+        c20 = self.feature_volume[x + 1, y,     z    ]
+        c21 = self.feature_volume[x + 1, y,     z + 1]
+        c30 = self.feature_volume[x + 1, y + 1, z    ]
+        c31 = self.feature_volume[x + 1, y + 1, z + 1]
+
+        # 插值计算
+        c0 = c00 * (1 - w_z) + c01 * w_z
+        c1 = c10 * (1 - w_z) + c11 * w_z
+        c2 = c20 * (1 - w_z) + c21 * w_z
+        c3 = c30 * (1 - w_z) + c31 * w_z
+
+        c_top = c0 * (1 - w_y) + c1 * w_y
+        c_bot = c2 * (1 - w_y) + c3 * w_y
 
+        return c_top * (1 - w_x) + c_bot * w_x
 
 class SimpleFeatureEncoder(nn.Module):
     def __init__(self, input_dim=3, feature_dim=64):

brep2sdf/networks/network.py

@@ -79,36 +79,25 @@ class Net(nn.Module):
             dims_sdf=[decoder_hidden_dim] * decoder_num_layers,
             #skip_in=(3,),
             geometric_init=True,
-            beta=5
+            beta=100
         )
 
         #self.csg_combiner = CSGCombiner(flag_convex=True)
     
-    def process_sdf(self,f_i, face_indices_mask, operator):
+    def process_sdf(self, f_i, 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)
-        masked_f_i = torch.where(face_indices_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)
         mask_convex = (operator == 1)
 
-        # 对 operator == 0 的样本取最大值
+        # 对 operator == 0 的样本取最小值
         if mask_concave.any():
-            output[mask_concave] = torch.min(padded_f_i[mask_concave], dim=1).values
+            output[mask_concave] = torch.min(f_i[mask_concave], dim=1).values
 
-        # 对 operator == 1 的样本取最小值
+        # 对 operator == 1 的样本取最大值
         if mask_convex.any():
-            output[mask_convex] = torch.max(padded_f_i[mask_convex], dim=1).values
+            output[mask_convex] = torch.max(f_i[mask_convex], dim=1).values
 
-        #logger.gpu_memory_stats("combine后")
         return output
 
     @torch.jit.export
@@ -125,18 +114,7 @@ class Net(nn.Module):
         #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 f_i[:,0]
-        return self.process_sdf(f_i, face_indices_mask, operator)
+        return self.forward_without_octree(query_points,face_indices_mask,operator)
 
     @torch.jit.export
     def forward_background(self, query_points):
@@ -152,31 +130,36 @@ class Net(nn.Module):
         # 编码
         feature_vectors = self.encoder.forward_background(query_points)
         # 解码
-        h = self.decoder.forward_training_volumes(feature_vectors) # (B, D)
+        h = self.decoder.forward(feature_vectors) # (B, D)
         return h
 
-    @torch.jit.ignore
-    def forward_without_octree(self, query_points,face_indices_mask,operator):
+    @torch.jit.export
+    def forward_without_octree(self, query_points, face_indices_mask, operator):
         """
         前向传播
         
         参数:
-            query_point: 查询点的位置坐标
+            query_points: 查询点的位置坐标 (B, 3)
+            face_indices_mask: 面索引掩码 (B, P)
+            operator: 操作符 (B,)
+        
         返回:
-            output: 解码后的输出结果
+            output: 解码后的SDF值 (B,)
         """
-        # 批量查询所有点的索引和bbox
-        #logger.debug("step encode")
         # 编码
-        feature_vectors = self.encoder(query_points,face_indices_mask)
+        feature_vectors = self.encoder(query_points,face_indices_mask)  # (B, 2, D)
+        feature_dim = feature_vectors.size(-1)  # 获取特征维度
+        flatten_feature_vectors = feature_vectors.reshape(-1, feature_dim)  # (B*2, D)
         #print("feature_vector:", feature_vectors.shape)
         # 解码
-        f_i = self.decoder(feature_vectors) # (B, P)
+        f_i = self.decoder(flatten_feature_vectors) # (B*2, )
+        B = feature_vectors.size(0)  # 获取batch size
+        f_i = f_i.reshape(B, 2)  # 将输出reshape为(B, 2)
         #logger.gpu_memory_stats("decoder farward后")
 
         #logger.debug("step combine")
-        return f_i[:,0]
-        return self.process_sdf(f_i, face_indices_mask, operator)
+
+        return self.process_sdf(f_i, operator)
       
     @torch.jit.ignore
     def forward_training_volumes(self, surf_points, patch_id:int):
@@ -186,7 +169,7 @@ class Net(nn.Module):
         return (P, S)
         """
         feature_mat = self.encoder.forward_training_volumes(surf_points,patch_id)
-        f_i = self.decoder.forward_training_volumes(feature_mat)
+        f_i = self.decoder.forward(feature_mat)
         return f_i.squeeze()
 
 

brep2sdf/scripts/farward_speed.py

@@ -0,0 +1,81 @@
+import re
+
+for_log_data = """
+2025-05-21 17:35:04,438 | DEBUG    | encoder.py:forward:108 - duration:0.02291393280029297
+2025-05-21 17:35:04,487 | DEBUG    | encoder.py:forward:108 - duration:0.013659954071044922
+2025-05-21 17:35:05,096 | DEBUG    | encoder.py:forward:108 - duration:0.013151884078979492
+2025-05-21 17:35:05,128 | DEBUG    | encoder.py:forward:108 - duration:0.012245893478393555
+2025-05-21 17:35:05,667 | DEBUG    | encoder.py:forward:108 - duration:0.012324810028076172
+2025-05-21 17:35:05,698 | DEBUG    | encoder.py:forward:108 - duration:0.011858940124511719
+2025-05-21 17:35:06,247 | DEBUG    | encoder.py:forward:108 - duration:0.013593196868896484
+2025-05-21 17:35:06,278 | DEBUG    | encoder.py:forward:108 - duration:0.012105226516723633
+2025-05-21 17:35:06,829 | DEBUG    | encoder.py:forward:108 - duration:0.012081146240234375
+2025-05-21 17:35:06,859 | DEBUG    | encoder.py:forward:108 - duration:0.011334419250488281
+2025-05-21 17:35:07,404 | DEBUG    | encoder.py:forward:108 - duration:0.013489246368408203
+2025-05-21 17:35:07,436 | DEBUG    | encoder.py:forward:108 - duration:0.01230931282043457
+2025-05-21 17:35:07,983 | DEBUG    | encoder.py:forward:108 - duration:0.01315164566040039
+2025-05-21 17:35:08,015 | DEBUG    | encoder.py:forward:108 - duration:0.012539148330688477
+2025-05-21 17:35:08,569 | DEBUG    | encoder.py:forward:108 - duration:0.014146566390991211
+2025-05-21 17:35:08,602 | DEBUG    | encoder.py:forward:108 - duration:0.013015508651733398
+2025-05-21 17:35:09,156 | DEBUG    | encoder.py:forward:108 - duration:0.01263570785522461
+2025-05-21 17:35:09,186 | DEBUG    | encoder.py:forward:108 - duration:0.011255264282226562
+2025-05-21 17:35:09,722 | DEBUG    | encoder.py:forward:108 - duration:0.014206647872924805
+2025-05-21 17:35:09,754 | DEBUG    | encoder.py:forward:108 - duration:0.012360095977783203
+2025-05-21 17:35:10,307 | DEBUG    | encoder.py:forward:108 - duration:0.013350963592529297
+2025-05-21 17:35:10,339 | DEBUG    | encoder.py:forward:108 - duration:0.012225151062011719
+2025-05-21 17:35:10,894 | DEBUG    | encoder.py:forward:108 - duration:0.014019250869750977
+2025-05-21 17:35:10,925 | DEBUG    | encoder.py:forward:108 - duration:0.012645483016967773
+2025-05-21 17:35:11,477 | DEBUG    | encoder.py:forward:108 - duration:0.010942935943603516
+2025-05-21 17:35:11,494 | DEBUG    | encoder.py:forward:108 - duration:0.010617733001708984
+"""
+
+parallel_log_data = """
+2025-05-21 17:25:30,716 | DEBUG    | encoder.py:forward:122 - duration:0.014799833297729492
+2025-05-21 17:25:30,748 | DEBUG    | encoder.py:forward:122 - duration:0.013928413391113281
+2025-05-21 17:25:31,318 | DEBUG    | encoder.py:forward:122 - duration:0.020897626876831055
+2025-05-21 17:25:31,352 | DEBUG    | encoder.py:forward:122 - duration:0.013567924499511719
+2025-05-21 17:25:31,929 | DEBUG    | encoder.py:forward:122 - duration:0.020887374877929688
+2025-05-21 17:25:31,965 | DEBUG    | encoder.py:forward:122 - duration:0.014947652816772461
+2025-05-21 17:25:32,550 | DEBUG    | encoder.py:forward:122 - duration:0.02316737174987793
+2025-05-21 17:25:32,586 | DEBUG    | encoder.py:forward:122 - duration:0.01513051986694336
+2025-05-21 17:25:33,172 | DEBUG    | encoder.py:forward:122 - duration:0.021285295486450195
+2025-05-21 17:25:33,207 | DEBUG    | encoder.py:forward:122 - duration:0.015576839447021484
+2025-05-21 17:25:33,790 | DEBUG    | encoder.py:forward:122 - duration:0.02099466323852539
+2025-05-21 17:25:33,826 | DEBUG    | encoder.py:forward:122 - duration:0.015471696853637695
+2025-05-21 17:25:34,406 | DEBUG    | encoder.py:forward:122 - duration:0.021028518676757812
+2025-05-21 17:25:34,441 | DEBUG    | encoder.py:forward:122 - duration:0.015815019607543945
+2025-05-21 17:25:35,034 | DEBUG    | encoder.py:forward:122 - duration:0.020988941192626953
+2025-05-21 17:25:35,070 | DEBUG    | encoder.py:forward:122 - duration:0.01592278480529785
+2025-05-21 17:25:35,662 | DEBUG    | encoder.py:forward:122 - duration:0.019669532775878906
+2025-05-21 17:25:35,698 | DEBUG    | encoder.py:forward:122 - duration:0.015323638916015625
+2025-05-21 17:25:36,276 | DEBUG    | encoder.py:forward:122 - duration:0.02336907386779785
+2025-05-21 17:25:36,311 | DEBUG    | encoder.py:forward:122 - duration:0.015668869018554688
+2025-05-21 17:25:36,896 | DEBUG    | encoder.py:forward:122 - duration:0.022051572799682617
+2025-05-21 17:25:36,932 | DEBUG    | encoder.py:forward:122 - duration:0.015897512435913086
+2025-05-21 17:25:37,526 | DEBUG    | encoder.py:forward:122 - duration:0.020981311798095703
+2025-05-21 17:25:37,560 | DEBUG    | encoder.py:forward:122 - duration:0.015113353729248047
+2025-05-21 17:25:38,137 | DEBUG    | encoder.py:forward:122 - duration:0.018566131591796875
+2025-05-21 17:25:38,157 | DEBUG    | encoder.py:forward:122 - duration:0.013988733291625977
+"""
+# 计算 duration 平均值
+
+def run(log_data):
+    # 使用正则表达式提取所有的 duration 数值
+    durations = re.findall(r'duration:(\d+\.\d+)', log_data)
+
+    # 转换为浮点数列表
+    durations = [float(d) for d in durations]
+
+    # 计算平均值
+    average_duration = sum(durations) / len(durations) if durations else 0
+
+    # 输出结果
+    print(f"共找到 {len(durations)} 个 duration")
+    print(f"平均 duration: {average_duration:.6f} 秒")
+    return average_duration
+
+
+speed1 = run(for_log_data)
+speed2 = run(parallel_log_data)
+print(f"for speed: {speed1:.6f} 秒")
+print(f"parallel speed: {speed2:.6f} 秒")

brep2sdf/scripts/npz2points.py

@@ -11,10 +11,10 @@ def load_brep_file(brep_path):
 
 
 if __name__ == "__main__":
-    data=load_brep_file("/home/wch/brep2sdf/data/output_data/00000003.xyz")
-    surfs =data["train_surf_ncs"]
+    data=load_brep_file("/home/wch/brep2sdf/data/output_data/00000031.xyz")
+    surfs =data["sampled_points_normals_sdf"]
     print(surfs)
-    with open("0003_t.xyz","w") as f:
+    with open("0031_t.xyz","w") as f:
         for point in surfs:
             #f.write(f"{point[0]} {point[1]} {point[2]}\n")
             f.write(f"{point[0]} {point[1]} {point[2]} {point[3]} {point[4]} {point[5]}\n")

brep2sdf/test.py

@@ -164,7 +164,7 @@ def sample_grid(trimesh_mesh_ncs: trimesh.Trimesh):
     np.ndarray | None: 形状为 (N, 7) 的数组 [x, y, z, nx, ny, nz, sdf]，
                          如果采样或计算失败则返回 None。
     """
-    grid_size = 2**5 + 1
+    grid_size = 2**4 + 1
     start = -1
     end = 1
     x = np.linspace(start, end, grid_size)
@@ -199,6 +199,7 @@ def test2(obj_file):
 
     # 将点坐标和SDF值转换为网格格式
     grid_size = int(np.cbrt(len(points)))  # 假设采样点是立方体网格
+    print(f"grid size:{grid_size}")
     sdf_grid = sdf_values.reshape((grid_size, grid_size, grid_size))
 
     # 使用Marching Cubes提取零表面
@@ -243,5 +244,5 @@ def main():
 if __name__ == "__main__":
     #main()
     #test()
-    test2("/home/wch/brep2sdf/data/gt_mesh/00000003.obj")
+    test2("/home/wch/brep2sdf/data/gt_mesh/00000031.obj")
 # python test.py -i /home/wch/brep2sdf/data/gt_mesh/00000003.obj -o output.ply --depth 6 --box_size 2.0 --method MC

brep2sdf/train.py

@@ -604,7 +604,7 @@ class Trainer:
         self.model.train()
         total_loss = 0.0
         step = 0 # 如果你的训练是分批次的，这里应该用批次索引
-        batch_size = 4096*5  # 设置合适的batch大小
+        batch_size = 50000 # 设置合适的batch大小
 
         # 数据处理
         # manfld
@@ -640,7 +640,7 @@ class Trainer:
             _nonmnfld_face_indices_mask = self.cached_train_data["nonmnfld_face_indices_mask"]
             _nonmnfld_operator = self.cached_train_data["nonmnfld_operator"]
         
-        logger.debug((_mnfld_pnts, _nonmnfld_pnts, _psdf))
+        #logger.debug((_mnfld_pnts, _nonmnfld_pnts, _psdf))
 
 
         
@@ -747,10 +747,11 @@ class Trainer:
             
 
         # 记录训练进度 (只记录有效的损失)
+        if epoch % 10 == 0:
             logger.info(f'Train Epoch: {epoch:4d}]\t'
                         f'Loss: {total_loss:.6f}')
             if loss_details: logger.info(f"Loss Details: {loss_details}")
-        #self.validate(epoch,total_loss)
+        self.validate(epoch,total_loss)
 
         return total_loss # 对于单批次训练，直接返回当前损失
 
@@ -932,8 +933,8 @@ class Trainer:
 
         #stage 3
         self.scheduler.reset()
-        self.model.freeze_stage2()
-        #self.model.unfreeze()
+        #self.model.freeze_stage2()
+        self.model.unfreeze()
         for epoch in range(cur_epoch + 1, max_stage2_epoch + self.config.train.num_epochs3 + 1):
             # 训练一个epoch
             train_loss = self.train_epoch_stage3(epoch)