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 hexone2086
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hexone2086
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hexone2086
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@@ -1,56 +1,49 @@
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# UZH-FPV Velocity Prediction
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从 DAVIS 事件相机灰度图像序列中预测**机体速度**（body-frame forward/lateral velocity）。
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从 DAVIS 事件相机灰度图像序列预测机体速度（body-frame forward/lateral velocity）。
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## 项目结构
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```
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uzh_fpv/
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├── AGENTS.md # ← 本文件
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├── requirements.txt # Python 依赖
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├── DATASET_FORMAT.md # 数据集格式详细说明
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├── rosbag2wds.py # ROS bag → WebDataset shard 转换脚本
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├── batch_convert.sh # 批量转换脚本
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├── dataset/ # 数据集（.gitignore 忽略）
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├── AGENTS.md
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├── requirements.txt
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├── DATASET_FORMAT.md
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├── rosbag2wds.py # ROS bag → WebDataset shard
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├── batch_convert.sh
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├── dataset/ # 数据集（.gitignore）
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│ └── <scene_name>/
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│ ├── shard_0000.tar # WebDataset shard（图像+GT）
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│ ├── imu_sequence.npz # 完整 IMU 序列
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│ └── metadata.json # 元信息
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│ ├── shard_0000.tar
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│ ├── imu_sequence.npz
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│ └── metadata.json
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├── src/
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│ ├── event_utils.py # EventProcessor: 帧间亮度变化 → 模拟事件帧
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│ ├── event_utils.py # 帧间亮度变化 → 模拟事件帧
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│ └── velocity_prediction/ # 主项目代码
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│ ├── __init__.py # 模块说明
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│ ├── config.py # 路径、模型架构、训练超参数
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│ ├── utils.py # 四元数运算（torch + numpy 封装）
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│ ├── transforms.py # 数据预处理管线
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│ ├── dataset.py # WebDataset 加载 + 序列采样
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│ ├── model.py # CNN + PoseMLP + GRU + Head
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│ ├── train.py # 训练循环
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│ └── evaluate.py # 评估 + 绘图
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│ ├── config.py
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│ ├── utils.py
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│ ├── transforms.py
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│ ├── dataset.py
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│ ├── model.py
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│ ├── train.py
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│ └── evaluate.py
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├── visualize/
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│ ├── __init__.py
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│ └── visualize_dataset.py # 数据集可视化：叠加位姿信息并生成视频
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├── benchmark/
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│ ├── __init__.py
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│ ├── config.py # 评估配置
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│ ├── evaluate.py # 完整评估管线
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│ └── benchmark.py # 统一评估入口
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├── checkpoints/ # 模型权重（.gitignore 忽略）
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├── logs/ # TensorBoard 日志（.gitignore 忽略）
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└── videos/ # 可视化输出视频
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│ └── visualize_dataset.py
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├── checkpoints/ # 模型权重（.gitignore）
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├── logs/ # TensorBoard 日志（.gitignore）
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└── videos/ # 可视化输出（.gitignore）
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```
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## 运行环境
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```bash
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uv run python -m <module> # 使用 uv 虚拟环境运行
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uv run python -m <module>
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```
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依赖见 `requirements.txt`，核心依赖：PyTorch、WebDataset、OpenCV、NumPy、Matplotlib。
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依赖：PyTorch, WebDataset, OpenCV, NumPy, Matplotlib。
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## 数据集
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UZH-FPV 数据集，由 DAVIS 事件相机采集。每个场景目录包含：
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UZH-FPV 数据集，DAVIS 事件相机采集。每个场景目录：
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| 文件 | 格式 | 内容 |
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|------|------|------|
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@@ -58,7 +51,7 @@ UZH-FPV 数据集，由 DAVIS 事件相机采集。每个场景目录包含：
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| `imu_sequence.npz` | NPZ | 完整 IMU 序列（加速度+角速度） |
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| `metadata.json` | JSON | 场景元信息 |
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shard 中每个样本的字段：
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shard 样本字段：
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| Key | 类型 | 说明 |
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|-----|------|------|
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@@ -67,8 +60,6 @@ shard 中每个样本的字段：
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| `pose` | float32[7] | `[x, y, z, qx, qy, qz, qw]` 世界→机体四元数 |
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| `vel` | float32[6] | `[vx, vy, vz, wx, wy, wz]` 世界线速度 + 角速度 |
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坐标系：z 轴与重力对齐（水平坐标系）。
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### 场景列表
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| 场景 | 帧数 | 类型 |
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@@ -78,62 +69,11 @@ shard 中每个样本的字段：
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| outdoor_forward_1/3/5 | 907~13299 | 室外前飞 |
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| outdoor_45_1 | 799 | 室外 45° 飞行 |
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## 模型
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### 架构
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```
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Event frame (1, 240, 320) ──► CNN (4 Conv+Pool+GAP, 256-d)
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│
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Body up (3,) ──► PoseMLP (3→32→64, 64-d) ────────────────────────┤
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│
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concat (320-d) ← per-frame
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│
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GRU (hidden=128)
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│
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Head MLP (128→64→2)
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│
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[v_right, v_forward]
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```
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**注意**：当前 CNN 编码器被禁用（输出全零），模型仅依赖 `PoseMLP + GRU + Head`。
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### 输入
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- `events`: `(B, S, 1, H, W)` — 模拟事件帧，值域 `{-1, 0, +1}`
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- `tilt`: `(B, S, 3)` — body up 向量（世界 up 旋转到机体坐标系），仅含 pitch/roll，不含 yaw，单位向量
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### 输出
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- `v_body`: `(B, 2)` — 机体坐标系 `[v_right, v_forward]` 速度 (m/s)
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### 数据预处理管线
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```
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shard_*.tar → DecodeSample → SimulateEvents → ComputeTilt → ComputeBodyVelocity → NormalizeVelocity
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```
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1. **DecodeSample**: JPEG → 灰度图 uint8 (H,W)；bytes → float32 数组
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2. **SimulateEvents**: 帧间亮度变化 → 二值事件帧 `{-1, 0, +1}`
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3. **ComputeTilt**: 四元数 (world→odom) → 应用 R_odom_to_body → 旋转 world-up [0,0,1] → body up 向量 (3,)
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4. **ComputeBodyVelocity**: 世界速度 → 应用 R_odom_to_body → yaw 补偿（仅去除偏航，保留 tilt）→ 水平面 `[v_right, v_forward]`
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5. **NormalizeVelocity**: 归一化
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### 训练配置
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- seq_len=8, batch_size=32, epochs=100
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- lr=1e-3, AdamW, StepLR (step=30, gamma=0.5)
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- Loss: MSELoss
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- 训练/验证/测试场景见 `config.py`
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## 关键命令
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```bash
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# 训练
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uv run python -m src.velocity_prediction.train --device cuda:0
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# 评估
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uv run python -m src.velocity_prediction.evaluate --checkpoint checkpoints/best.pt
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# 训练（GPU 优先 cuda:7）
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uv run python -m src.velocity_prediction.train --device cuda:7
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# 数据集可视化（单场景）
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uv run python -m visualize.visualize_dataset --scene indoor_forward_3 --output videos/scene.mp4
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@@ -143,49 +83,4 @@ uv run python -m visualize.visualize_dataset --all --output videos/
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# 数据集可视化（实时显示）
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uv run python -m visualize.visualize_dataset --scene indoor_forward_3 --show
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# Benchmark 评估
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uv run python -m benchmark.benchmark --checkpoint checkpoints/best.pt
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```
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## 可视化说明
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`visualize/visualize_dataset.py` 在每帧图像上叠加：
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- 帧号、时间戳、世界坐标位置
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- 欧拉角 `[roll, pitch, yaw]`（从 body 四元数计算）
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- Body up 向量 `[x, y, z]`
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- 机体速度 `v_body [forward, lateral]`
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- 世界速度 `v_world [vx, vy, vz]`
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- 机体坐标系三轴箭头（左下角）
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- 机体速度方向箭头（图像中心）
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## 关键约定
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- 四元数格式：`[x, y, z, w]`（不是 `[w, x, y, z]`）
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- GT 四元数表示 **world→odom**（不是 world→body），通过静态 R_odom_to_body 校正
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- Body 坐标系（ROS 右手系）：`body_x=右, body_y=前, body_z=上`
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- R_odom_to_body = R_y(45°) @ R_x(90°)：先绕 odom_x 转 +90°，再绕 odom_y 转 +45°
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- 速度归一化统计量：待重新计算
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- 模型预测 `[v_right, v_forward]`（右向和前向速度）
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- 所有代码在项目根目录下以 `uv run python -m <module>` 运行
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- GPU 优先使用 `cuda:7`，训练时添加 `--device cuda:7`
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## 已知问题
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### 1. 滑窗跨 shard 边界
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`dataset.py` 中滑窗实现基于 WebDataset 串联后的连续流，不感知 shard 边界。当样本恰好处于 shard 末尾时，序列会跨越到下一个 shard 的起始帧。
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- 影响：每个 shard 边界处约有 `seq_len` 个序列包含跨 shard 样本（占总数 <1%）
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- 修复思路：在 `_sliding_window_fn` 中注入 shard 边界标记，遇到边界时清空缓冲区
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- 严重程度：低。若 shard 内帧数远大于 seq_len，可忽略
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### 2. `SimulateEvents` 跨 shard 状态残留
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`EventProcessor` 内部维护 `_prev_frame` 用于帧差计算。跨 shard 时，新 shard 的第一帧会与上一个 shard 最后一帧计算差，产生错误的事件帧。
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- 影响：每个 shard 的第 1 帧事件帧错误，涉及该帧的所有滑窗序列均受影响
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- 每 shard 错误帧数：1 帧（加上滑窗放大，约 `seq_len` 个序列各包含此帧）
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- 修复：在 shard 边界处调用 `EventProcessor.reset()`。需在 `_build_pipeline` 中插入边界信号或改用按 shard 独立处理的方案
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- 严重程度：低。每 shard 仅 1 帧，训练数据量大时可忽略
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@@ -39,10 +39,10 @@ VAL_SCENES = [
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# "indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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]
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TEST_SCENES = [
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"indoor_forward_9",
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# "indoor_forward_9",
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# "indoor_forward_9","indoor_forward_3",
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# "indoor_forward_7", # Hard 室内
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# "outdoor_forward_1", # Easy 室外
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"outdoor_forward_1", # Easy 室外
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# "outdoor_forward_5" # Hard 室外
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# "indoor_forward_3", "indoor_forward_9", "indoor_forward_10", # Easy
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]
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@@ -53,9 +53,9 @@ TEST_SCENES = [
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@dataclass
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class CNNConfig:
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in_channels: int = 1
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channels: tuple = (32, 64, 128, 256) # per-layer output channels
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channels: tuple = (32, 64, 128) # per-layer output channels
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kernel_size: int = 3
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pool_size: int = 2
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stride: int = 2 # strided conv replaces conv+pool
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use_bn: bool = True
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@@ -68,7 +68,7 @@ class PoseMLPConfig:
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@dataclass
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class GRUConfig:
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input_size: int = 320 # CNN(256) + PoseMLP(64)
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input_size: int = 192 # CNN(128) + PoseMLP(64)
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hidden_size: int = 128
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num_layers: int = 1
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dropout: float = 0.0
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@@ -104,7 +104,7 @@ class TrainConfig:
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seed: int = 42
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# Sliding window: stride=1 → full overlap, stride=seq_len → non-overlapping
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sliding_window_stride: int = 1
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sliding_window_stride: int = 64
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# Event simulation
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event_threshold: float = 0.1
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@@ -166,7 +166,11 @@ def main():
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ckpt = torch.load(args.checkpoint, map_location="cpu")
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model.load_state_dict(ckpt["model_state_dict"])
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model.to(device)
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# Restore event threshold from checkpoint, fall back to config default
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event_threshold = ckpt.get("event_threshold", train_cfg.event_threshold)
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print(f"Loaded checkpoint from {args.checkpoint} (epoch={ckpt.get('epoch', '?')})")
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print(f"Event threshold: {event_threshold} (checkpoint={ckpt.get('event_threshold', 'not saved')}, config={train_cfg.event_threshold})")
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# Evaluate each scene independently → NaN gaps prevent plot mixing
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from src.velocity_prediction.config import TEST_SCENES
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@@ -180,7 +184,7 @@ def main():
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stride=1,
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batch_size=1,
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num_workers=0, # strict temporal order
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event_threshold=train_cfg.event_threshold,
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event_threshold=event_threshold,
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event_use_log=train_cfg.event_use_log,
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)
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results = evaluate_stateful(model, loader, device)
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@@ -14,7 +14,10 @@ from src.velocity_prediction.config import model_cfg
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class CNNEncoder(nn.Module):
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"""
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4-layer ConvNet with BatchNorm, ReLU, MaxPool, ending with Global Avg Pool.
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Strided-convolution encoder with BatchNorm and LeakyReLU.
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Each layer uses stride=2 to downsample, replacing the traditional
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conv+pool pattern. Ends with Global Avg Pool.
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Input: (B, S, 1, H, W) — processed per-frame (flattened to (B*S, 1, H, W))
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Output: (B, S, C_out) — per-frame feature vectors
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@@ -24,15 +27,15 @@ class CNNEncoder(nn.Module):
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super().__init__()
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channels = cfg.channels
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in_ch = cfg.in_channels
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stride = cfg.stride
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layers = []
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for out_ch in channels:
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layers.extend([
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nn.Conv2d(in_ch, out_ch, kernel_size=cfg.kernel_size, padding=cfg.kernel_size // 2),
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nn.Conv2d(in_ch, out_ch, kernel_size=cfg.kernel_size,
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stride=stride, padding=cfg.kernel_size // 2),
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nn.BatchNorm2d(out_ch) if cfg.use_bn else nn.Identity(),
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nn.Identity(),
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nn.LeakyReLU(inplace=True),
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nn.MaxPool2d(cfg.pool_size),
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])
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in_ch = out_ch
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@@ -102,7 +105,7 @@ class VelocityPredictionModel(nn.Module):
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self.cnn = CNNEncoder(cnn_cfg)
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self.pose_mlp = PoseMLP(pose_cfg)
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fused_dim = self.cnn.out_dim + self.pose_mlp.out_dim # 256 + 64 = 320
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fused_dim = self.cnn.out_dim + self.pose_mlp.out_dim # 128 + 64 = 192
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self.gru = nn.GRU(
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input_size=fused_dim,
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@@ -139,14 +142,13 @@ class VelocityPredictionModel(nn.Module):
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B, S = events.shape[:2]
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# Per-frame encoding
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cnn_feat = self.cnn(events) # (B, S, 256)
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cnn_feat = self.cnn(events) # (B, S, 128)
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# B, S = events.shape[:2]
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# cnn_feat = events.new_zeros(B, S, self.cnn.out_dim) # 全零替代
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pose_feat = self.pose_mlp(tilt) # (B, S, 64)
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# Fuse per frame
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fused = torch.cat([cnn_feat, pose_feat], dim=-1) # (B, S, 320)
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fused = torch.cat([cnn_feat, pose_feat], dim=-1) # (B, S, 192)
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# GRU temporal modelling — accepts external hidden state for TBPTT
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gru_out, h_new = self.gru(fused, h) # (B, S, 128), (num_layers, B, 128)
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@@ -174,9 +176,9 @@ class VelocityPredictionModel(nn.Module):
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v_body: (B, 2) body-frame [v_right, v_forward]
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h_new: (num_layers, B, hidden_size)
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"""
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cnn_feat = self.cnn(events) # (B, 1, 256)
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cnn_feat = self.cnn(events) # (B, 1, 128)
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pose_feat = self.pose_mlp(tilt) # (B, 1, 64)
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fused = torch.cat([cnn_feat, pose_feat], dim=-1) # (B, 1, 320)
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fused = torch.cat([cnn_feat, pose_feat], dim=-1) # (B, 1, 192)
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_, h_new = self.gru(fused, h) # (num_layers, B, 128)
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last_hidden = h_new[-1] # (B, 128)
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v_body = self.head(last_hidden) # (B, 2)
|
||||
@@ -195,10 +197,11 @@ if __name__ == "__main__":
|
||||
print(f"Total trainable parameters: {total:,} ({total/1e6:.3f} M)")
|
||||
|
||||
# Forward pass test
|
||||
B, S, H, W = 4, 8, 240, 320
|
||||
B, S, H, W = 4, 8, 60, 80
|
||||
events = torch.randn(B, S, 1, H, W)
|
||||
tilt = torch.randn(B, S, 3)
|
||||
out, h = model(events, tilt)
|
||||
print(f"Input events: {events.shape}")
|
||||
print(f"Input tilt: {tilt.shape}")
|
||||
print(f"Output: {out.shape} (should be [4, 8, 2])")
|
||||
print(f"CNN out_dim: {model.cnn.out_dim}")
|
||||
|
||||
@@ -191,6 +191,10 @@ def main():
|
||||
global_step = ckpt.get("global_step", 0)
|
||||
best_val_loss = ckpt.get("best_val_loss", float("inf"))
|
||||
run_id = ckpt.get("run_id", None)
|
||||
# Restore event threshold from checkpoint if present
|
||||
if "event_threshold" in ckpt:
|
||||
event_threshold = ckpt["event_threshold"]
|
||||
print(f"Event threshold restored from checkpoint: {event_threshold}")
|
||||
|
||||
print(f"Resumed from checkpoint: {ckpt_path}")
|
||||
print(f" Resumed epoch={ckpt.get('epoch', '?')}, global_step={global_step}, "
|
||||
@@ -253,6 +257,7 @@ def main():
|
||||
"model_state_dict": model.state_dict(),
|
||||
"optimizer_state_dict": optimizer.state_dict(),
|
||||
"scheduler_state_dict": scheduler.state_dict(),
|
||||
"event_threshold": event_threshold,
|
||||
"global_step": global_step,
|
||||
"best_val_loss": best_val_loss,
|
||||
"run_id": run_id,
|
||||
@@ -269,6 +274,7 @@ def main():
|
||||
"model_state_dict": model.state_dict(),
|
||||
"optimizer_state_dict": optimizer.state_dict(),
|
||||
"scheduler_state_dict": scheduler.state_dict(),
|
||||
"event_threshold": event_threshold,
|
||||
"global_step": global_step,
|
||||
"best_val_loss": best_val_loss,
|
||||
"run_id": run_id,
|
||||
|
||||
@@ -20,11 +20,16 @@ from src.velocity_prediction.config import VELOCITY_MEAN, VELOCITY_STD
|
||||
|
||||
|
||||
class DecodeSample:
|
||||
"""Decode raw bytes from WebDataset tar entry into numpy arrays."""
|
||||
"""Decode raw bytes from WebDataset tar entry into numpy arrays, resize to 80×60."""
|
||||
|
||||
def __init__(self, height: int = 60, width: int = 80):
|
||||
self.height = height
|
||||
self.width = width
|
||||
|
||||
def __call__(self, sample: dict) -> dict:
|
||||
# Image: JPEG bytes → grayscale uint8 (H, W)
|
||||
# Image: JPEG bytes → grayscale uint8 (H, W) → resize
|
||||
img = cv2.imdecode(np.frombuffer(sample["jpg"], np.uint8), cv2.IMREAD_GRAYSCALE)
|
||||
img = cv2.resize(img, (self.width, self.height), interpolation=cv2.INTER_AREA)
|
||||
|
||||
# Timestamp
|
||||
ts = np.frombuffer(sample["ts"], dtype=np.float64).item()
|
||||
|
||||
Reference in New Issue
Block a user