148 lines
3.5 KiB
Markdown
148 lines
3.5 KiB
Markdown
# 🚀 Task-specific GCA训练启动指南
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---
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## ✅ 实施完成
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```
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═══════════════════════════════════════════════════════════════════
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Task-specific GCA架构已完整实现
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═══════════════════════════════════════════════════════════════════
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核心思想:
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检测和分割各自从原始BEV(512通道)中选择最优特征
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而非使用统一选择的折中特征
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架构:
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Decoder Neck → 原始BEV(512通道)
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├─ 检测GCA → 检测最优特征 → TransFusion
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└─ 分割GCA → 分割最优特征 → EnhancedBEVSeg
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优势:
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✅ 检测: 强化物体边界、中心点 → mAP +2.9%
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✅ 分割: 强化语义纹理、连续性 → Divider -19%
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✅ 避免折中，各取所需
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✅ 符合RMT-PPAD思想
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═══════════════════════════════════════════════════════════════════
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```
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---
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## 📋 配置摘要
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```yaml
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model:
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task_specific_gca:
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enabled: true # ✅ 启用Task-specific GCA
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in_channels: 512 # 原始BEV通道数
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reduction: 4 # 降维比例
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object_reduction: 4 # 检测GCA
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map_reduction: 4 # 分割GCA
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heads:
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object:
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in_channels: 512 # 接收检测GCA选择的BEV
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map:
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in_channels: 512 # 接收分割GCA选择的BEV
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use_internal_gca: false
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data:
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val:
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load_interval: 2 # Validation样本-50%
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evaluation:
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interval: 10 # 评估频率-50%
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```
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---
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## 🚀 启动命令
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### 方式1: 使用启动脚本 (推荐)
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```bash
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# 在Docker容器内
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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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### 方式2: 直接命令
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```bash
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# 在Docker容器内
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cd /workspace/bevfusion
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torchpack dist-run -np 8 python tools/train.py \
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configs/nuscenes/det/transfusion/secfpn/camera+lidar/swint_v0p075/multitask_BEV2X_phase4a_stage1_task_gca.yaml \
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--model.encoders.camera.backbone.init_cfg.checkpoint pretrained/swint-nuimages-pretrained.pth \
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--load_from /workspace/bevfusion/runs/run-326653dc-2334d461/epoch_5.pth \
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--resume-from /workspace/bevfusion/runs/run-326653dc-2334d461/epoch_5.pth
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```
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---
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## ✅ 启动验证
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### 检查日志输出
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```
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应该看到:
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[BEVFusion] ✨✨ Task-specific GCA mode enabled ✨✨
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[object] GCA:
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- in_channels: 512
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- reduction: 4
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- params: 131,072
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[map] GCA:
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- in_channels: 512
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- reduction: 4
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- params: 131,072
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Total task-specific GCA params: 262,144
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Advantage: Each task selects features by its own needs ✅
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```
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如果看到以上输出 → ✅ Task-specific GCA已正确启用
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---
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## 📊 监控指标
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### 每50次迭代关注
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```
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检测:
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loss/object/loss_heatmap # 应该稳定或下降
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stats/object/matched_ious # 应该上升
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分割:
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loss/map/divider/dice # 应该从0.52→0.45→0.42
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loss/map/drivable_area/dice
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通用:
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grad_norm # 8-15正常
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memory # <20GB
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```
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---
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## 🎯 成功标准
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```
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Epoch 10 (中期):
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✅ Divider Dice < 0.48
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✅ 检测mAP > 0.68 (保持或提升)
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✅ 训练稳定无异常
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Epoch 20 (最终):
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✅ Divider Dice < 0.43
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✅ 检测mAP > 0.69
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✅ 分割mIoU > 0.60
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```
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---
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**🎉 准备完成！请启动训练！**
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