114 lines
2.1 KiB
Markdown
114 lines
2.1 KiB
Markdown
# ✅ 修复 - decoder.neck返回列表问题
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---
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## 🎯 问题
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```
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AttributeError: 'list' object has no attribute 'size'
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位置: bevfusion.py line 417
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task_bev = self.task_gca[type](x)
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```
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---
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## 🔍 根本原因
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`SECONDFPN` (decoder.neck) 返回的是**列表**而不是单个tensor：
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```python
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x = self.decoder["neck"](x)
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# x 现在是 list: [tensor1, tensor2] ← 多尺度特征
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# 而不是单个 tensor: (B, 512, 360, 360)
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```
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GCA模块期望输入是tensor，所以报错。
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---
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## ✅ 解决方案
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在应用GCA之前，检查并拼接多尺度特征：
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```python
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x = self.decoder["neck"](x)
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# ✅ 处理neck可能返回的列表（多尺度特征）
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if isinstance(x, (list, tuple)):
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# SECONDFPN返回列表，需要拼接
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x = torch.cat(x, dim=1) # 拼接多尺度特征
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# 现在 x 是 tensor: (B, 512, 360, 360)
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# 然后应用GCA
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if type in self.task_gca:
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task_bev = self.task_gca[type](x) # ✅ 现在可以正常工作
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```
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---
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## 📊 修复位置
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```
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文件: mmdet3d/models/fusion_models/bevfusion.py
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行数: 第408-411行
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修改: 添加列表处理逻辑
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```
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---
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## ✅ 修复后的行为
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### decoder.neck返回单个tensor
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```python
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x = decoder.neck(x) # → tensor(B, 512, H, W)
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if isinstance(x, (list, tuple)): # False
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...
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# 直接使用 x
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```
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### decoder.neck返回列表
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```python
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x = decoder.neck(x) # → [tensor(B, 256, H, W), tensor(B, 256, H, W)]
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if isinstance(x, (list, tuple)): # True
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x = torch.cat(x, dim=1) # → tensor(B, 512, H, W)
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# 拼接后使用 x
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```
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---
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## 🚀 现在可以正常启动了！
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```bash
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docker exec -it bevfusion bash
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cd /workspace/bevfusion
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bash START_PHASE4A_TASK_GCA.sh
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```
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---
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## ✅ 启动后应该看到
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```
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[BEVFusion] ⚪ Skipping camera backbone init_weights
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[BEVFusion] ✨✨ Task-specific GCA mode enabled ✨✨
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[object] GCA: params: 131,072
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[map] GCA: params: 131,072
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load checkpoint from .../epoch_5.pth
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Epoch [1][50/xxx]
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lr: 2.00e-05
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loss/object/loss_heatmap: 0.XXX
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loss/map/divider/dice: 0.XXX
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grad_norm: XX.X
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```
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---
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**🎉 decoder.neck列表输出问题已修复！可以正常训练了！**
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