268 lines
5.7 KiB
Markdown
268 lines
5.7 KiB
Markdown
# Phase 4A GCA优化总结
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📅 **日期**: 2025-11-06
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🎯 **目标**: 集成GCA模块 + 优化evaluation配置
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---
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## ✅ 已完成任务
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### 1. 清理磁盘空间
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```bash
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删除: /workspace/bevfusion/runs/run-326653dc-2334d461/.eval_hook/
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释放: 75GB
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当前可用: 61GB ✅
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```
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### 2. 优化Evaluation配置 (双策略)
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#### 策略1: 减少样本数量 (load_interval=2)
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```yaml
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data:
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val:
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load_interval: 2 # 均匀采样50%
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```
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- **原样本数**: 6,019个
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- **新样本数**: 3,010个 (减少50%)
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- **效果**: .eval_hook从75GB → 37.5GB
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#### 策略2: 降低评估频率 (interval: 5→10)
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```yaml
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evaluation:
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interval: 10 # 从5改为10
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```
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- **原频率**: 20 epochs × 4次评估 = 4次
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- **新频率**: 20 epochs × 2次评估 = 2次
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- **减少**: 50%评估次数
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#### 组合效果
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```
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评估开销 = 样本数 × 评估次数
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原方案: 6,019 × 4 = 24,076次样本评估
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新方案: 3,010 × 2 = 6,020次样本评估
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减少: 75% ✅
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```
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### 3. GCA模块集成
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#### 模块位置
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```
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/workspace/bevfusion/mmdet3d/models/modules/gca.py
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```
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#### 集成到分割头
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```python
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# mmdet3d/models/heads/segm/enhanced.py
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class EnhancedBEVSegmentationHead(nn.Module):
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def __init__(self, ...):
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# ASPP for multi-scale features
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self.aspp = ASPP(in_channels, decoder_channels[0])
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# ✨ GCA (Global Context Attention)
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self.gca = GCA(in_channels=decoder_channels[0], reduction=4)
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# Channel and Spatial Attention
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self.channel_attn = ChannelAttention(decoder_channels[0])
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self.spatial_attn = SpatialAttention()
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def forward(self, x, target=None):
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# 1. BEV Grid Transform
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x = self.transform(x)
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# 2. ASPP Multi-scale Features
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x = self.aspp(x)
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# 2.5. ✨ GCA Global Context Attention
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x = self.gca(x) # ⬅️ 新增
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# 3. Channel Attention
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x = self.channel_attn(x)
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# 4. Spatial Attention
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x = self.spatial_attn(x)
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...
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```
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#### GCA模块特性
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- **位置**: ASPP之后，Channel Attention之前
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- **作用**: 聚合全局上下文，增强语义特征
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- **参数**: reduction=4 (轻量级)
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- **预期效果**: divider性能提升5-10%
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---
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## 📊 配置对比
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| 配置项 | 原配置 | 新配置 (GCA优化) | 改善 |
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|--------|--------|-----------------|------|
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| **Validation样本** | 6,019 | 3,010 | ⬇️ 50% |
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| **Evaluation频率** | 每5 epochs | 每10 epochs | ⬇️ 50% |
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| **.eval_hook大小** | 75GB | 37.5GB | ⬇️ 50% |
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| **总评估开销** | 24,076次 | 6,020次 | ⬇️ 75% |
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| **分割头模块** | ASPP+Attention | +GCA | ✨ 新增 |
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---
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## 🚀 启动训练
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### 脚本路径
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```bash
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/workspace/bevfusion/START_PHASE4A_WITH_GCA.sh
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```
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### 训练参数
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```yaml
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起始epoch: 5
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目标epoch: 20
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剩余epochs: 15
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学习率: 2.0e-5
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BEV分辨率: 600×600
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Validation: 3,010样本
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Evaluation: 每10 epochs (epoch 10, 20)
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特性: GCA全局上下文模块
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```
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### 预期时间
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```
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FP32单卡: ~7天 (15 epochs)
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预计完成: 2025-11-13
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```
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---
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## 📈 预期性能提升
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### Divider类别 (最难类别)
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```
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Phase 4A Epoch 5 (无GCA):
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- Dice Loss: 0.52 ± 0.04
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预期 Epoch 20 (GCA):
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- Dice Loss: 0.42-0.45 ✅
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- 改善: ~15-20%
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```
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### 其他类别
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```
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Drivable Area: 0.11 → 0.08-0.09
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Ped Crossing: 0.24 → 0.18-0.20
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Walkway: 0.22 → 0.16-0.18
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Stop Line: 0.34 → 0.25-0.28
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Carpark Area: 0.20 → 0.15-0.17
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```
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---
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## 🔍 GCA模块原理
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### 架构
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```
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Input (B, C, H, W)
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↓
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AdaptiveAvgPool2d(1) → (B, C, 1, 1)
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↓
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Conv2d(C → C//4) + ReLU (降维)
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↓
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Conv2d(C//4 → C) + Sigmoid (升维 + 归一化)
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↓
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Attention (B, C, 1, 1)
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↓
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Input * Attention → Output (B, C, H, W)
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```
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### 优势
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1. **轻量级**: 参数量 < 1% (reduction=4)
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2. **全局感知**: 聚合整个特征图的上下文
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3. **通道重标定**: 突出重要通道，抑制无关通道
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4. **即插即用**: 无需修改backbone
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### RMT-PPAD验证
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- 在2D图像分割任务上验证有效
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- 特别对细长结构(lane lines)有提升
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- 与BEVFusion的divider任务高度相关
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---
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## 📝 修改文件清单
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```
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✅ 已修改:
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1. configs/nuscenes/det/.../multitask_BEV2X_phase4a_stage1.yaml
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- 添加 data.val.load_interval: 2
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- 修改 evaluation.interval: 5 → 10
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2. mmdet3d/models/heads/segm/enhanced.py
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- 导入 GCA
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- 添加 self.gca = GCA(...)
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- 在forward中调用 x = self.gca(x)
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3. mmdet3d/models/modules/__init__.py
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- 创建空__init__.py
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✅ 已创建:
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1. START_PHASE4A_WITH_GCA.sh
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- GCA优化版训练启动脚本
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2. EVALUATION_OPTIMIZATION_STRATEGIES.md
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- Evaluation优化策略详解
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3. GCA_OPTIMIZATION_SUMMARY.md (本文件)
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- GCA集成总结
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```
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---
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## 🎯 下一步
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### 立即执行
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```bash
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cd /workspace/bevfusion
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chmod +x START_PHASE4A_WITH_GCA.sh
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bash START_PHASE4A_WITH_GCA.sh
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```
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### 监控指标
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```bash
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# 实时查看训练日志
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tail -f /workspace/bevfusion/runs/run-326653dc-2334d461/*.log
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# 重点关注 (每50 iters)
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- loss/map/divider/dice (目标: <0.45)
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- loss/map/divider/focal
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- grad_norm (健康: 8-15)
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- memory (不超过23GB)
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```
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### Epoch 10评估 (预计3天后)
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- 检查divider性能是否改善
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- 对比Epoch 5的性能
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- 决定是否继续或调整
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### Epoch 20完成 (预计7天后)
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- 完整validation评估
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- 性能报告
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- 规划Stage 2 (800×800)
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---
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## ✅ 总结
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**已完成**:
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- ✅ 清理.eval_hook缓存 (释放75GB)
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- ✅ 优化evaluation配置 (减少75%开销)
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- ✅ 集成GCA模块到分割头
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- ✅ 创建启动脚本
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**当前状态**:
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- 🚀 准备启动训练 (epoch 6-20)
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- 💾 磁盘空间充足 (61GB可用)
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- 🎯 目标: divider dice < 0.45
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**预期收益**:
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- 📉 Evaluation开销减少75%
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- 📈 Divider性能提升15-20%
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- 💾 磁盘占用减少50%
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- ⚡ 训练更稳定高效
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