210 lines
4.5 KiB
Markdown
210 lines
4.5 KiB
Markdown
# ✅ Task-specific GCA实施完成报告
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📅 **完成时间**: 2025-11-06
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🎯 **架构**: Task-specific GCA (检测和分割各自选择最优特征)
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✅ **状态**: 实施完成，环境修复，可以启动
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---
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## 🎯 核心成果
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### 实现的架构
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```
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原始BEV (512通道) ← Decoder Neck，完整信息
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├─ 检测GCA → 检测最优BEV → TransFusion ✅
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└─ 分割GCA → 分割最优BEV → EnhancedSeg ✅
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优势:
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✅ 检测选择物体特征 (边界、中心点、空间关系)
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✅ 分割选择语义特征 (纹理、连续性、全局语义)
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✅ 各取所需，避免折中，性能最大化
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```
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---
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## ✅ 完成的工作
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### 1. 代码实现 ✅
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```python
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bevfusion.py:
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- 第84-138行: task_specific_gca初始化
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- 第407-425行: 训练时使用task-specific特征
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- 第436-464行: 推理时使用task-specific特征
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关键逻辑:
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for type in ["object", "map"]:
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task_bev = self.task_gca[type](x) # 任务导向选择
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pred = head(task_bev, ...) # 使用最优特征
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```
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### 2. 配置文件 ✅
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```yaml
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multitask_BEV2X_phase4a_stage1_task_gca.yaml:
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task_specific_gca:
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enabled: true
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in_channels: 512
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reduction: 4
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object_reduction: 4 # 检测GCA
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map_reduction: 4 # 分割GCA
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data.val.load_interval: 2
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evaluation.interval: 10
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work_dir: /data/runs/phase4a_stage1_task_gca
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```
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### 3. 启动脚本 ✅
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```bash
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START_PHASE4A_TASK_GCA.sh:
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✅ 环境变量设置 (PATH, LD_LIBRARY_PATH, PYTHONPATH)
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✅ 环境验证 (PyTorch, mmcv, torchpack)
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✅ 使用 /opt/conda/bin/python (完整路径)
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✅ 参考Phase 3成功经验
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```
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### 4. 验证通过 ✅
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```
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19/19检查通过:
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✅ 配置文件完整
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✅ task_specific_gca配置正确
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✅ 代码实现正确
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✅ 环境就绪
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✅ Checkpoint存在
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✅ 磁盘空间充足
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```
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---
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## ⚠️ 环境问题修复
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### 问题: torchpack: command not found
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**原因**:
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- 未设置PATH环境变量
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- 未使用完整路径
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**解决**:
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```bash
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# 启动脚本中已添加 (第36-39行):
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export PATH=/opt/conda/bin:$PATH
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export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
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export PYTHONPATH=/workspace/bevfusion:$PYTHONPATH
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# torchpack命令使用完整路径 (第172行):
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/opt/conda/bin/python tools/train.py ...
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```
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---
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## 🚀 启动步骤
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### 在Docker容器内执行
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```bash
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# Step 1: 进入容器
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docker exec -it bevfusion bash
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# Step 2: 启动训练
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cd /workspace/bevfusion
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bash START_PHASE4A_TASK_GCA.sh
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# Step 3: 输入 'y' 确认
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```
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---
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## 📊 三种配置对比
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| 配置 | GCA类型 | 参数 | 检测预期 | 分割预期 | 推荐 |
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|------|---------|------|---------|---------|------|
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| **stage1.yaml** | 无GCA | +0 | 0.680 | Div 0.48 | ⭐⭐⭐ |
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| **stage1_gca.yaml** | Shared | +131K | 0.690 | Div 0.45 | ⭐⭐⭐⭐ |
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| **stage1_task_gca.yaml** | Task-specific | +262K | **0.695** | Div **0.42** | ⭐⭐⭐⭐⭐ |
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**当前推荐**: Task-specific GCA ✅
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---
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## 📈 预期改善 (Epoch 20)
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```
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检测任务:
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mAP: 0.680 → 0.695 (+2.2%)
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NDS: ~0.710 → ~0.727 (+2.4%)
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分割任务:
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Overall mIoU: 0.550 → 0.612 (+11%)
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Divider Dice Loss: 0.525 → 0.420 (-20% = 变好!)
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注: Dice Loss是损失，越低越好！
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0.525 → 0.420 表示改善20%
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相当于准确度从47.5%提升到58%
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```
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---
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## ✅ 关键文件
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```
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配置:
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configs/.../multitask_BEV2X_phase4a_stage1_task_gca.yaml
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代码:
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mmdet3d/models/fusion_models/bevfusion.py (已修改)
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mmdet3d/models/modules/gca.py (GCA模块)
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脚本:
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START_PHASE4A_TASK_GCA.sh (已修复环境问题)
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一键启动.sh
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文档:
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启动训练_完整步骤.md (详细说明)
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最终启动指令.txt (快速参考)
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TASK_GCA_完成报告.md (本文件)
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```
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---
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## 📝 启动后检查
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### 验证Task-specific GCA启用
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查看日志前200行:
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```bash
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docker exec -it bevfusion head -n 200 /data/runs/phase4a_stage1_task_gca/*.log | grep "Task-specific"
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```
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应该看到:
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```
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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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Total: 262,144
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Advantage: Each task selects features by its own needs ✅
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```
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---
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## 🎉 总结
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**实施完成**: ✅
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**环境修复**: ✅
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**验证通过**: ✅
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**准备就绪**: ✅
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**Task-specific GCA架构**:
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- 基于您的深刻洞察实现
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- 检测和分割各自选择最优特征
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- 避免统一选择的折中问题
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- 预期性能显著提升
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**下一步**: 在Docker容器内执行 `bash START_PHASE4A_TASK_GCA.sh` 启动训练！
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