fix: network 测试脚本 tofix: some

norm =0

brep2sdf/networks/network.py

@@ -173,19 +173,24 @@ def train_step(model, batch, optimizer, criterion):
     # 确保模型处于训练模式
     model.train()
     
-    # 检查并设置所有参数的requires_grad
+    # 1. 深拷贝输入数据，确保每次都是新的张量
+    batch = {
+        k: v.clone().detach().requires_grad_(True) if isinstance(v, torch.Tensor) and v.dtype != torch.bool
+        else v
+        for k, v in batch.items()
+    }
+    
+    # 2. 确保所有模型参数都需要梯度
     for name, param in model.named_parameters():
         if not param.requires_grad:
             logger.warning(f"参数 {name} 的requires_grad为False，现在设置为True")
             param.requires_grad = True
     
-    # 将所有输入转为requires_grad=True
-    batch['query_points'].requires_grad_(True)
-    
-    # 清零梯度
-    optimizer.zero_grad()
+    # 3. 清零梯度（同时清除优化器和模型的梯度）
+    model.zero_grad(set_to_none=True)  # 使用set_to_none=True更彻底地清除梯度
+    optimizer.zero_grad(set_to_none=True)
     
-    # 前向传播
+    # 4. 前向传播
     pred_sdf = model(
         edge_ncs=batch['edge_ncs'],
         edge_pos=batch['edge_pos'],
@@ -196,33 +201,43 @@ def train_step(model, batch, optimizer, criterion):
         query_points=batch['query_points']
     )
     
-    # 计算损失
+    # 5. 检查预测输出是否需要梯度
+    if not pred_sdf.requires_grad:
+        logger.warning("预测输出不需要梯度！")
+        pred_sdf.requires_grad_(True)
+    
+    # 6. 计算损失
     loss = criterion(pred_sdf, batch['gt_sdf'])
     
-    # 检查损失是否有效
+    # 7. 检查损失
+    logger.info(f"Loss value: {loss.item()}")
+    logger.info(f"Loss requires grad: {loss.requires_grad}")
+    
     if not torch.isfinite(loss):
         logger.error(f"损失值无效: {loss.item()}")
         raise ValueError("损失值无效")
     
-    # 反向传播
-    loss.backward()
+    # 8. 反向传播
+    loss.backward(retain_graph=False)  # 使用retain_graph=False确保清理计算图
     
-    # 检查梯度
-    total_norm = 0
+    # 9. 详细的梯度检查
+    logger.info("\n梯度信息:")
     for name, param in model.named_parameters():
         if param.grad is not None:
-            param_norm = param.grad.data.norm(2)
-            total_norm += param_norm.item() ** 2
-            logger.info(f"  {name}: grad_norm = {param_norm.item()}")
+            grad_norm = param.grad.norm().item()
+            grad_mean = param.grad.mean().item()
+            grad_std = param.grad.std().item()
+            logger.info(f"  {name}:")
+            logger.info(f"    norm: {grad_norm:.6f}")
+            logger.info(f"    mean: {grad_mean:.6f}")
+            logger.info(f"    std: {grad_std:.6f}")
         else:
-            logger.warning(f"  {name}: No gradient!")
-    total_norm = total_norm ** 0.5
-    logger.info(f"梯度总范数: {total_norm}")
+            logger.warning(f"  {name}: 没有梯度!")
     
-    # 梯度裁剪（可选）
-    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+    # 10. 梯度裁剪
+    torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0)
     
-    # 更新参数
+    # 11. 更新参数
     optimizer.step()
     
     return loss.item()
@@ -260,7 +275,7 @@ def train(model, config, num_epochs=10):
     }
     
     # 训练循环
-    for epoch in range(2):
+    for epoch in range(num_epochs):
         try:
             loss = train_step(model, batch, optimizer, criterion)
             logger.info(f"Epoch {epoch+1}/{num_epochs}, Loss: {loss:.6f}")