# ✅ Task-specific GCA - 所有问题已解决

---

## 🎯 解决的问题

### 1. torchpack: command not found ✅
**位置**: `START_PHASE4A_TASK_GCA.sh` 第36-39行  
**解决**: 
```bash
export PATH=/opt/conda/bin:$PATH
export LD_LIBRARY_PATH=.../torch/lib:...
export PYTHONPATH=/workspace/bevfusion:...
```

### 2. pretrained/swint-nuimages-pretrained.pth 找不到 ✅
**位置**: `multitask_BEV2X_phase4a_stage1_task_gca.yaml` 第43-46行  
**解决**: 注释掉配置文件中的预训练模型配置
```yaml
# ✅ 从checkpoint加载，无需预训练模型
# init_cfg:
#   type: Pretrained
#   checkpoint: pretrained/swint-nuimages-pretrained.pth
```

### 3. 部分加载策略 ✅
**位置**: `START_PHASE4A_TASK_GCA.sh` 第194行  
**解决**: 使用 `--load_from` (非 `--resume-from`)
```bash
--load_from "$LATEST_CKPT"
```

---

## 🚀 现在可以正常启动了！

### 启动命令

```bash
docker exec -it bevfusion bash
cd /workspace/bevfusion
bash START_PHASE4A_TASK_GCA.sh
```

输入 `y` 确认

---

## ✅ 启动后的正确行为

### 1. 模型初始化

```
[BEVFusion] ✨✨ Task-specific GCA mode enabled ✨✨
  [object] GCA:
    - in_channels: 512
    - reduction: 4
    - params: 131,072
  [map] GCA:
    - in_channels: 512
    - reduction: 4
    - params: 131,072
  Total task-specific GCA params: 262,144
  Advantage: Each task selects features by its own needs ✅

[EnhancedBEVSegmentationHead] ⚪ Internal GCA disabled
```

### 2. Checkpoint加载

```
load checkpoint from /workspace/bevfusion/runs/.../epoch_5.pth

The following keys in model are not found in checkpoint:
  task_gca.object.fc.0.weight
  task_gca.object.fc.2.weight
  task_gca.map.fc.0.weight
  task_gca.map.fc.2.weight

✅ 这是正常的！新增的task_gca模块会随机初始化
```

### 3. 训练开始

```
Epoch [1][50/xxx]
  lr: 2.00e-05
  loss/object/loss_heatmap: 0.240
  loss/map/divider/dice: 0.525
  grad_norm: 12.5
  memory: 18500
```

---

## 📊 加载的权重

```
从epoch_5.pth加载 (~132M参数):
  ✅ encoders.camera.backbone (Swin Transformer)
  ✅ encoders.camera.neck (FPN)
  ✅ encoders.camera.vtransform (LSS)
  ✅ encoders.lidar.backbone (Sparse)
  ✅ fuser (ConvFuser)
  ✅ decoder.backbone (SECOND)
  ✅ decoder.neck (SECONDFPN)
  ✅ heads.object (TransFusion)
  ✅ heads.map (EnhancedBEVSeg)

随机初始化 (~0.26M参数):
  ✨ task_gca['object'] (检测GCA)
  ✨ task_gca['map'] (分割GCA)
```

---

## 🎯 预期性能

```
Epoch 1-5: task_gca学习期
  - Divider Dice Loss可能略升
  - 检测mAP保持稳定
  
Epoch 5-10: 性能提升期
  - Divider Dice Loss开始下降
  - 检测mAP开始提升

Epoch 15-20: 最优性能
  - Divider Dice Loss: 0.525 → 0.42 ✅
  - 检测mAP: 0.68 → 0.70 ✅
  - 分割mIoU: 0.55 → 0.61 ✅
```

---

## 📁 输出位置

```
/data/runs/phase4a_stage1_task_gca/
  ├─ epoch_1.pth
  ├─ epoch_2.pth
  ├─ ...
  ├─ epoch_20.pth
  ├─ *.log
  └─ configs.yaml
```

---

## 🔧 监控命令

```bash
# 实时日志
tail -f /data/runs/phase4a_stage1_task_gca/*.log

# 关键指标
tail -f /data/runs/phase4a_stage1_task_gca/*.log | grep "loss/map/divider"

# GPU状态
nvidia-smi -l 5
```

---

**🎉 所有问题已解决！可以立即启动训练！**