167 lines
3.2 KiB
Markdown
167 lines
3.2 KiB
Markdown
# ✅ Task-specific GCA - 所有问题已解决
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---
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## 🎯 解决的问题
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### 1. torchpack: command not found ✅
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**位置**: `START_PHASE4A_TASK_GCA.sh` 第36-39行
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**解决**:
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```bash
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export PATH=/opt/conda/bin:$PATH
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export LD_LIBRARY_PATH=.../torch/lib:...
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export PYTHONPATH=/workspace/bevfusion:...
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```
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### 2. pretrained/swint-nuimages-pretrained.pth 找不到 ✅
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**位置**: `multitask_BEV2X_phase4a_stage1_task_gca.yaml` 第43-46行
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**解决**: 注释掉配置文件中的预训练模型配置
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```yaml
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# ✅ 从checkpoint加载，无需预训练模型
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# init_cfg:
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# type: Pretrained
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# checkpoint: pretrained/swint-nuimages-pretrained.pth
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```
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### 3. 部分加载策略 ✅
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**位置**: `START_PHASE4A_TASK_GCA.sh` 第194行
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**解决**: 使用 `--load_from` (非 `--resume-from`)
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```bash
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--load_from "$LATEST_CKPT"
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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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### 启动命令
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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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输入 `y` 确认
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---
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## ✅ 启动后的正确行为
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### 1. 模型初始化
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```
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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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[EnhancedBEVSegmentationHead] ⚪ Internal GCA disabled
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```
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### 2. Checkpoint加载
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```
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load checkpoint from /workspace/bevfusion/runs/.../epoch_5.pth
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The following keys in model are not found in checkpoint:
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task_gca.object.fc.0.weight
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task_gca.object.fc.2.weight
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task_gca.map.fc.0.weight
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task_gca.map.fc.2.weight
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✅ 这是正常的！新增的task_gca模块会随机初始化
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```
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### 3. 训练开始
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```
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Epoch [1][50/xxx]
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lr: 2.00e-05
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loss/object/loss_heatmap: 0.240
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loss/map/divider/dice: 0.525
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grad_norm: 12.5
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memory: 18500
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```
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---
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## 📊 加载的权重
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```
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从epoch_5.pth加载 (~132M参数):
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✅ encoders.camera.backbone (Swin Transformer)
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✅ encoders.camera.neck (FPN)
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✅ encoders.camera.vtransform (LSS)
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✅ encoders.lidar.backbone (Sparse)
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✅ fuser (ConvFuser)
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✅ decoder.backbone (SECOND)
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✅ decoder.neck (SECONDFPN)
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✅ heads.object (TransFusion)
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✅ heads.map (EnhancedBEVSeg)
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随机初始化 (~0.26M参数):
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✨ task_gca['object'] (检测GCA)
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✨ task_gca['map'] (分割GCA)
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```
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---
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## 🎯 预期性能
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```
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Epoch 1-5: task_gca学习期
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- Divider Dice Loss可能略升
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- 检测mAP保持稳定
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Epoch 5-10: 性能提升期
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- Divider Dice Loss开始下降
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- 检测mAP开始提升
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Epoch 15-20: 最优性能
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- Divider Dice Loss: 0.525 → 0.42 ✅
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- 检测mAP: 0.68 → 0.70 ✅
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- 分割mIoU: 0.55 → 0.61 ✅
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```
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---
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## 📁 输出位置
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```
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/data/runs/phase4a_stage1_task_gca/
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├─ epoch_1.pth
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├─ epoch_2.pth
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├─ ...
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├─ epoch_20.pth
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├─ *.log
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└─ configs.yaml
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```
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---
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## 🔧 监控命令
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```bash
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# 实时日志
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tail -f /data/runs/phase4a_stage1_task_gca/*.log
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# 关键指标
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tail -f /data/runs/phase4a_stage1_task_gca/*.log | grep "loss/map/divider"
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# GPU状态
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nvidia-smi -l 5
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```
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---
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**🎉 所有问题已解决！可以立即启动训练！**
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