# Task-specific GCA训练启动 - 完整步骤

📅 **日期**: 2025-11-06  
⚠️ **重要**: 必须在Docker容器内执行

---

## ⚠️ 环境问题解决

### 问题: torchpack: command not found

**原因**: 未在Docker容器内，或环境变量未设置

**解决**: 启动脚本已自动设置环境变量 ✅

---

## 🚀 正确的启动方式

### 方式1: 在Docker容器内执行 (推荐)

```bash
# Step 1: 从宿主机进入Docker容器
docker exec -it bevfusion bash

# Step 2: 在容器内执行启动脚本
cd /workspace/bevfusion
bash START_PHASE4A_TASK_GCA.sh

# 看到提示时输入 'y'
```

### 方式2: 一行命令（自动进入容器）

```bash
# 在宿主机执行
docker exec -it bevfusion bash /workspace/bevfusion/一键启动.sh
```

---

## ✅ 环境配置说明

启动脚本会自动设置以下环境变量：

```bash
export PATH=/opt/conda/bin:$PATH
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PYTHONPATH=/workspace/bevfusion:$PYTHONPATH
```

并验证：
```
✅ PyTorch: 1.10.1
✅ mmcv: 1.4.0  
✅ torchpack: /opt/conda/bin/torchpack
```

---

## 📊 完整启动流程

```
1. 进入容器
   docker exec -it bevfusion bash

2. 脚本自动执行:
   ├─ 设置环境变量 ✅
   ├─ 验证Python环境 ✅
   ├─ 检查磁盘空间 ✅
   ├─ 确认checkpoint ✅
   ├─ 清理.eval_hook ✅
   └─ 显示配置摘要

3. 用户确认:
   输入 'y' 启动

4. 训练启动:
   torchpack dist-run -np 8 /opt/conda/bin/python tools/train.py ...

5. 日志输出:
   /data/runs/phase4a_stage1_task_gca/*.log
```

---

## ✅ 启动后验证

### 检查Task-specific GCA是否启用

```bash
# 查看日志前100行
docker exec -it bevfusion head -n 200 /data/runs/phase4a_stage1_task_gca/*.log | grep -A 10 "Task-specific"
```

应该看到:

```
[BEVFusion] ⚪ Shared BEV-level GCA disabled
[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 ✅
```

### 查看训练loss

```bash
# 实时监控
docker exec -it bevfusion tail -f /data/runs/phase4a_stage1_task_gca/*.log

# 查看divider改善
docker exec -it bevfusion tail -f /data/runs/phase4a_stage1_task_gca/*.log | grep "loss/map/divider/dice"
```

---

## 📊 监控指标

### 每50次迭代关注

```
检测:
  loss/object/loss_heatmap  # 应该稳定或下降
  stats/object/matched_ious # 应该上升

分割:
  loss/map/divider/dice     # 应该从0.52→0.45→0.42 (降低是好事!)
  loss/map/drivable_area/dice

通用:
  grad_norm                 # 8-15正常
  memory                    # <20000
```

---

## 🎯 预期性能 (Epoch 20)

```
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!)
```

**重要**: Dice Loss越低越好！

---

## 📁 输出位置

```
Checkpoints:
  /data/runs/phase4a_stage1_task_gca/epoch_*.pth

日志:
  /data/runs/phase4a_stage1_task_gca/*.log

配置快照:
  /data/runs/phase4a_stage1_task_gca/configs.yaml
```

---

## ⏰ 时间预估

```
剩余epochs: 15 (epoch 6-20)
每epoch时间: ~11小时
总时间: ~7天
预计完成: 2025-11-13
```

---

## 🔧 故障排查

### 如果torchpack仍未找到

```bash
# 手动设置环境
export PATH=/opt/conda/bin:$PATH
which torchpack

# 或使用完整路径
/opt/conda/bin/torchpack --version
```

### 如果Python导入错误

```bash
export LD_LIBRARY_PATH=/opt/conda/lib/python3.8/site-packages/torch/lib:/opt/conda/lib:/usr/local/cuda/lib64:$LD_LIBRARY_PATH
export PYTHONPATH=/workspace/bevfusion:$PYTHONPATH
```

---

## 📋 快速命令参考

```bash
# 进入容器
docker exec -it bevfusion bash

# 启动训练
cd /workspace/bevfusion
bash START_PHASE4A_TASK_GCA.sh

# 监控日志
tail -f /data/runs/phase4a_stage1_task_gca/*.log

# 检查GPU
nvidia-smi

# 检查磁盘
df -h /workspace /data
```

---

**🎉 环境问题已修复！现在可以正确启动了！**

**在Docker容器内执行**: `bash START_PHASE4A_TASK_GCA.sh`