可以torch jit script

brep2sdf/networks/decoder.py

@@ -30,6 +30,9 @@ class Decoder(nn.Module):
 
         dims_sdf = [d_in] + dims_sdf + [1] #[self.n_branch]
         self.sdf_layers = len(dims_sdf)
+        
+        # 使用 ModuleList 存储 sdf 层
+        self.sdf_modules = nn.ModuleList()
         for layer in range(0, len(dims_sdf) - 1):
             if layer + 1 in skip_in:
                 out_dim = dims_sdf[layer + 1] - d_in
@@ -43,7 +46,8 @@ class Decoder(nn.Module):
                 else:
                     torch.nn.init.constant_(lin.bias, 0.0)
                     torch.nn.init.normal_(lin.weight, 0.0, np.sqrt(2) / np.sqrt(out_dim))
-            setattr(self, "sdf_"+str(layer), lin)
+            self.sdf_modules.append(lin)
+        
         if geometric_init:
             if beta > 0:
                 self.activation = nn.Softplus(beta=beta)
@@ -55,10 +59,6 @@ class Decoder(nn.Module):
             self.activation = Sine()
         self.final_activation = nn.ReLU()
 
-    # composite f_i to h
-    
-
-
     def forward(self, feature_matrix: torch.Tensor) -> torch.Tensor:
         '''
         :param feature_matrix: 形状为 (B, P, D) 的特征矩阵
@@ -73,10 +73,10 @@ class Decoder(nn.Module):
         # 展平处理 (B*P, D)
         x = feature_matrix.view(-1, D)
         
-        for layer in range(0, self.sdf_layers - 1):
+        # 使用枚举遍历 sdf_modules
-            lin = getattr(self, "sdf_" + str(layer))
+        for layer, lin in enumerate(self.sdf_modules):
             if layer in self.skip_in:
-                x = torch.cat([x, x], -1) / np.sqrt(2)  # Fix undefined 'input'
+                x = torch.cat([x, x], -1) / torch.sqrt(torch.tensor(2.0))  # 使用 torch.sqrt
 
             x = lin(x)
             if layer < self.sdf_layers - 2:
@@ -100,10 +100,9 @@ class Decoder(nn.Module):
         # 直接使用输入的特征矩阵，因为形状已经是 (S, D)
         x = feature_matrix
 
-        for layer in range(0, self.sdf_layers - 1):
+        for layer, lin in enumerate(self.sdf_modules):
-            lin = getattr(self, "sdf_" + str(layer))
             if layer in self.skip_in:
-                x = torch.cat([x, x], -1) / np.sqrt(2)
+                x = torch.cat([x, x], -1) / torch.sqrt(torch.tensor(2.0))  # 使用 torch.sqrt
 
             x = lin(x)
             if layer < self.sdf_layers - 2:

brep2sdf/networks/network.py

@@ -100,9 +100,9 @@ class Net(nn.Module):
         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")
+        #logger.gpu_memory_stats("encoder farward后")
         f_i = self.decoder(feature_vectors) # (B, P)
-        logger.gpu_memory_stats("decoder farward后")
+        #logger.gpu_memory_stats("decoder farward后")
 
 
         output = f_i[:, 0]
@@ -127,7 +127,7 @@ class Net(nn.Module):
             output[mask_convex] = torch.min(padded_f_i[mask_convex], dim=1).values
 
 
-        logger.gpu_memory_stats("combine后")
+        #logger.gpu_memory_stats("combine后")
         return output
 
     @torch.jit.export
@@ -137,6 +137,7 @@ class Net(nn.Module):
         surf_points (P, S): 
         return (P, S)
         """
+        logger.debug(surf_points)
         feature_mat = self.encoder.forward_training_volumes(surf_points,patch_id)
         f_i = self.decoder.forward_training_volumes(feature_mat)
         return f_i.squeeze()