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bev-project/最终启动指令.txt

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Complete project state snapshot: Phase 4B RMT-PPAD Integration 🎯 Training Status: - Current Epoch: 2/10 (13.3% complete) - Segmentation Dice: 0.9594 - Detection IoU: 0.5742 - Training stable with 8 GPUs 🔧 Technical Achievements: - ✅ RMT-PPAD Transformer segmentation decoder integrated - ✅ Task-specific GCA architecture optimized - ✅ Multi-scale feature fusion (180×180, 360×360, 600×600) - ✅ Adaptive scale weight learning implemented - ✅ BEVFusion multi-task framework enhanced 📊 Performance Highlights: - Divider segmentation: 0.9793 Dice (excellent) - Pedestrian crossing: 0.9812 Dice (excellent) - Stop line: 0.9812 Dice (excellent) - Carpark area: 0.9802 Dice (excellent) - Walkway: 0.9401 Dice (good) - Drivable area: 0.8959 Dice (good) 🛠️ Code Changes Included: - Enhanced BEVFusion model (bevfusion.py) - RMT-PPAD integration modules (rmtppad_integration.py) - Transformer segmentation head (enhanced_transformer.py) - GCA module optimizations (gca.py) - Configuration updates (Phase 4B configs) - Training scripts and automation tools - Comprehensive documentation and analysis reports 📅 Snapshot Date: Fri Nov 14 09:06:09 UTC 2025 📍 Environment: Docker container 🎯 Phase: RMT-PPAD Integration Complete
2025-11-14 17:06:09 +08:00
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Task-specific GCA训练启动 - 环境问题已修复
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⚠️ 问题: torchpack: command not found
✅ 解决: 启动脚本已添加环境变量设置
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正确的启动方式 (在Docker容器内)
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在宿主机执行:
docker exec -it bevfusion bash
然后在容器内执行:
cd /workspace/bevfusion
bash START_PHASE4A_TASK_GCA.sh
输入 'y' 确认启动
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或者一行命令 (推荐)
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docker exec -it bevfusion bash /workspace/bevfusion/一键启动.sh
(需要手动输入'y'确认)
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脚本会自动:
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✅ 设置环境变量 (PATH, LD_LIBRARY_PATH, PYTHONPATH)
✅ 验证PyTorch和mmcv
✅ 检查磁盘空间
✅ 确认checkpoint
✅ 清理.eval_hook
✅ 启动训练
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监控训练
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新开终端:
docker exec -it bevfusion tail -f /data/runs/phase4a_stage1_task_gca/*.log
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预期性能
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检测: mAP 0.68 → 0.70 (+2.9%)
分割: mIoU 0.55 → 0.61 (+11%)
Divider: Dice Loss 0.525 → 0.420 (-20% = 变好!)
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