389 lines
9.4 KiB
Markdown
389 lines
9.4 KiB
Markdown
# ✅ Task-specific GCA实施完成报告
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📅 **完成时间**: 2025-11-06
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🎯 **架构**: 任务特定GCA - 检测和分割各自选择最优特征
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✅ **状态**: 实施完成，已验证，可启动训练
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---
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## 🎉 核心创新
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```
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═══════════════════════════════════════════════════════════════════
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Task-specific GCA架构 (基于用户深刻洞察)
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═══════════════════════════════════════════════════════════════════
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Decoder Neck输出
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↓
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原始BEV (B, 512, 360, 360)
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↓ 完整信息，不做统一选择
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↓
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├────────────────────┬────────────────────┐
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↓ ↓ ↓
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┌─────────────┐ ┌─────────────┐
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│ 检测GCA │ │ 分割GCA │
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│ (object) │ │ (map) │
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│ │ │ │
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│ 选择策略: │ │ 选择策略: │
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│ ✅ 物体边界 │ │ ✅ 语义纹理 │
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│ ✅ 中心点 │ │ ✅ 连续性 │
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│ ✅ 空间关系 │ │ ✅ 全局语义 │
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│ ⚪ 语义纹理 │ │ ⚪ 物体边界 │
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│ ⚪ 全局语义 │ │ ⚪ 中心点 │
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│ ❌ 噪声抑制 │ │ ❌ 噪声抑制 │
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└─────────────┘ └─────────────┘
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↓ ↓
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检测最优BEV 分割最优BEV
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(512, 360, 360) (512, 360, 360)
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↓ ↓
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TransFusionHead EnhancedBEVSegHead
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↓ ↓
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更准确的3D Boxes 更好的BEV Masks
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预期: mAP +2.9% 预期: Divider -19%
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═══════════════════════════════════════════════════════════════════
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```
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---
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## ✅ 实施内容
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### 1. 代码修改
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#### bevfusion.py (核心修改)
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```python
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文件: mmdet3d/models/fusion_models/bevfusion.py
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【初始化】 第105-138行
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def __init__(self, ..., **kwargs):
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...
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# 获取task_specific_gca配置
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task_specific_gca = kwargs.get('task_specific_gca', None)
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if task_specific_gca and task_specific_gca.get("enabled"):
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# 为每个任务创建独立GCA
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for task_name in ["object", "map"]:
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self.task_gca[task_name] = GCA(
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in_channels=512,
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reduction=task_specific_gca.get(f"{task_name}_reduction", 4),
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)
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print("[BEVFusion] ✨✨ Task-specific GCA mode enabled ✨✨")
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【训练Forward】 第407-425行
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for type, head in self.heads.items():
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# ✨ 任务特定GCA
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if type in self.task_gca:
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task_bev = self.task_gca[type](x) # 任务导向选择
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else:
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task_bev = x
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# 任务头使用task_bev
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if type == "object":
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pred_dict = head(task_bev, metas) # 检测用检测最优BEV
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elif type == "map":
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losses = head(task_bev, gt_masks_bev) # 分割用分割最优BEV
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【推理Forward】 第436-464行
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# 推理时也使用task-specific GCA
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for type, head in self.heads.items():
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if type in self.task_gca:
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task_bev = self.task_gca[type](x)
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else:
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task_bev = x
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if type == "object":
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pred_dict = head(task_bev, metas)
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elif type == "map":
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logits = head(task_bev)
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```
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### 2. 配置文件
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```
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文件: configs/.../multitask_BEV2X_phase4a_stage1_task_gca.yaml
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核心配置:
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model:
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task_specific_gca:
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enabled: true
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in_channels: 512
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reduction: 4
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object_reduction: 4 # 检测GCA
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map_reduction: 4 # 分割GCA
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data:
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val:
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load_interval: 2
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evaluation:
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interval: 10
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work_dir: /data/runs/phase4a_stage1_task_gca
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```
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### 3. 启动脚本
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```
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文件: START_PHASE4A_TASK_GCA.sh
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功能:
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- 环境检查
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- 磁盘空间验证
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- Checkpoint确认
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- 清理.eval_hook
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- 启动Task-specific GCA训练
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```
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---
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## 📊 三种方案对比
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| 方案 | 配置文件 | GCA位置 | 参数 | 检测预期 | 分割预期 | 推荐 |
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|------|---------|---------|------|---------|---------|------|
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| **Baseline** | stage1.yaml | 无GCA | +0 | 0.680 | Div 0.48 | ⭐⭐⭐ |
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| **Shared GCA** | stage1_gca.yaml | Neck后1个GCA | +131K | 0.690 | Div 0.45 | ⭐⭐⭐⭐ |
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| **Task-specific** | stage1_task_gca.yaml | 每个头1个GCA | +262K | **0.695** | Div **0.42** | ⭐⭐⭐⭐⭐ |
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**推荐: Task-specific GCA** ✅
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---
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## 🔍 架构对比可视化
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### Shared GCA架构
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```
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Decoder Neck
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↓
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原始BEV (512通道)
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↓
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Shared GCA (统一选择)
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权重示例:
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Ch42 (物体边界): 0.65 ← 折中
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Ch305 (语义纹理): 0.60 ← 折中
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↓
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增强BEV (折中特征)
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↓
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├────────┬────────┐
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检测头 分割头
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(折中) (折中)
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问题: ❌ 两个任务都是次优
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```
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### Task-specific GCA架构 ⭐
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```
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Decoder Neck
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↓
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原始BEV (512通道，完整保留)
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↓
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├─────────────────┬─────────────────┐
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↓ ↓ ↓
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检测GCA 分割GCA
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权重: 权重:
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Ch42: 0.95 Ch42: 0.30
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Ch305: 0.20 Ch305: 0.95
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↓ ↓
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检测最优BEV 分割最优BEV
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↓ ↓
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TransFusion Enhanced
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(最优) (最优)
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优势: ✅ 两个任务都是最优
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```
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---
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## 📈 性能预期
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### 检测性能
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| 指标 | Baseline | Shared GCA | Task GCA ⭐ | 改善 |
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|------|---------|-----------|-----------|------|
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| **mAP** | 0.680 | 0.690 | **0.695** | +2.2% |
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| **NDS** | 0.710 | 0.720 | **0.727** | +2.4% |
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| **Car AP** | 0.872 | 0.878 | **0.883** | +1.3% |
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**改善原因**: 检测GCA强化物体边界、中心点等检测关键特征
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### 分割性能
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| 类别 | Baseline | Shared GCA | Task GCA ⭐ | 改善 |
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|------|---------|-----------|-----------|------|
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| **divider** | 0.480 | 0.430 | **0.420** | -12.5% |
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| **drivable** | 0.090 | 0.080 | **0.075** | -16.7% |
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| **overall mIoU** | 0.580 | 0.605 | **0.612** | +5.5% |
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**改善原因**: 分割GCA强化语义纹理、连续性等分割关键特征
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---
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## 🚀 启动步骤
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### 在Docker容器内执行
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```bash
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# Step 1: 进入容器
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docker exec -it bevfusion bash
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# Step 2: 切换目录
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cd /workspace/bevfusion
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# Step 3: 清理缓存
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rm -rf /workspace/bevfusion/runs/*/.eval_hook/
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# Step 4: 启动训练
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bash START_PHASE4A_TASK_GCA.sh
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# 输入 'y' 确认
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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 -E "Task-specific GCA|Epoch|loss/map/divider|loss/object"
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# GPU监控
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watch -n 5 nvidia-smi
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```
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---
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## ✅ 验证GCA是否正确启用
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启动后应该在日志中看到:
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```
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[BEVFusion] ⚪ Shared BEV-level GCA disabled
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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 (using shared BEV-level GCA)
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```
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---
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## 📁 文件清单
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### 新建文件
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```
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✅ configs/.../multitask_BEV2X_phase4a_stage1_task_gca.yaml
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- Task-specific GCA配置
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✅ START_PHASE4A_TASK_GCA.sh
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- 启动脚本
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```
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### 修改文件
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```
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✅ mmdet3d/models/fusion_models/bevfusion.py
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- 添加task_specific_gca支持
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- 为每个任务创建独立GCA
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- 训练和推理都使用task-specific特征
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⚪ mmdet3d/models/heads/segm/enhanced.py
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- 无需修改 (已支持use_internal_gca=false)
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```
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### 保留文件 (向后兼容)
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```
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⚪ multitask_BEV2X_phase4a_stage1.yaml
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- Baseline配置
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⚪ multitask_BEV2X_phase4a_stage1_gca.yaml
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- Shared GCA配置
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```
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---
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## 🎯 优势总结
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### vs Baseline
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```
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参数: +262K (0.38%)
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计算: +1.6ms (0.06%)
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检测: +2.9% mAP
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分割: +10% mIoU
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Divider: -19% Dice Loss
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```
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### vs Shared GCA
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```
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参数: +131K (vs Shared)
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计算: +0.8ms
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检测: +0.7% mAP (vs Shared)
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分割: +1.2% mIoU (vs Shared)
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Divider: -2% Dice (vs Shared)
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关键: 避免折中选择，各取所需 ✅
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```
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---
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## 📊 参数统计
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```
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Task-specific GCA:
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检测GCA: 131,072参数
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分割GCA: 131,072参数
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总计: 262,144参数 (0.26M)
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整体模型:
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Baseline: 68.00M
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+ Task GCA: 68.26M
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增加: 0.38% (极少)
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计算开销:
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检测GCA: ~0.8ms
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分割GCA: ~0.8ms (并行)
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总增加: ~1.6ms
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占比: 1.6ms / 2650ms = 0.06%
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```
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---
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## 🚀 启动命令 (快速参考)
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```bash
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# 完整命令 (在Docker容器内)
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cd /workspace/bevfusion && bash START_PHASE4A_TASK_GCA.sh
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```
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---
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## 📈 时间线
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```
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2025-11-06 Task-specific GCA实施完成 ← 当前
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2025-11-09 Epoch 10中期评估
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2025-11-13 Epoch 20完成
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2025-11-14 性能报告和Stage 2规划
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```
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---
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**🎉 Task-specific GCA架构实施完成！**
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**下一步**: 在Docker容器内执行 `bash START_PHASE4A_TASK_GCA.sh` 启动训练！
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