qing
1cf428f384
fixed_range 线性 min-max 下常用值挤在低端（10Hz→0.002），闭环反馈 中小绝对误差=大相对误差（模型 C/D 发散根因）。新增 normalize_mode= log_range：spk.F/spk.delay 用 log10(1+x) 对数尺度（10Hz→0.27）， 舵机列保持线性。全链路接入（dataset/pretrain/predict/onnx/hailo/ calibration/export），与 v3 审计 none 模式/元数据自动识别合并。
327 lines
16 KiB
Python
327 lines
16 KiB
Python
import argparse
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from dataclasses import dataclass, asdict, field
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from typing import List, Optional
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@dataclass
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class DataConfig:
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image_width: int = 640
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image_height: int = 480
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action_dim: int = 18
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history_len: int = 5
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frame_len: int = 3
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data_dir: str = "./data"
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normalize_actions: bool = True
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action_stats_path: Optional[str] = None
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# 归一化方式（仅 normalize_actions=True 时生效）：
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# "zscore" 数据统计 z-score（现状，依赖 action_stats.npy）
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# "fixed_range" 固定物理范围 min-max → [0,1]（免 stats 文件，零状态部署，
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# 常量表见 action_norm.py；越界值 clamp 防御）
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# "log_range" 同上，但 spk.F/spk.delay 两列用 log10(1+x) 对数尺度，
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# 修正低端聚集导致的精度错配（见实验报告）
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normalize_mode: str = "zscore"
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# 逐列损失权重（仅训练损失用，推理端无感知）：None → action_norm
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# .default_column_weights(action_dim)（舵机 1.0 / spk 0.2 / 保留位 0.0）。
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# 显式指定时长度须等于 action_dim。
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column_weights: Optional[List[float]] = None
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# R2 方案一：MPU6050 姿态作为独立 IMU token 注入（序列 F+H+2）。
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# 0 = 关闭（默认，序列保持 F+H+1，旧 checkpoint 完全兼容）；
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# 8×N = N 个传感器的 roll,pitch,ax,ay,az,gx,gy,gz 按 pybot 脚本
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# @MPU6050_BIND 行顺序拼接（最多 4 个 → 32，pybot recorder 的 imu.npy 格式）。
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imu_dim: int = 0
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@dataclass
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class ModelConfig:
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cnn_backbone: str = "simple"
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cnn_feature_dim: int = 128
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fused_hidden_dim: int = 128
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dropout: float = 0.1
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attn_dim: int = 128
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attn_heads: int = 4
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attn_layers: int = 2
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@dataclass
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class TrainConfig:
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# 默认输入分辨率 640×480（1080p 采集的预处理缩放在实机测速中主导
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# 端到端延迟，见测速报告 §6.1）；更高分辨率训练时需调小 batch
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batch_size: int = 4
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epochs: int = 100
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lr: float = 1e-3
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weight_decay: float = 1e-4
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val_split: float = 0.15
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checkpoint_dir: str = "./checkpoints"
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patience: int = 15
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num_workers: int = 2
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seed: int = 42
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max_grad_norm: float = 1.0
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augment: bool = False
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# R1-C 残差预测：True 时 target 改为 Δ=actions[t]-actions[t-1]，
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# 推理端 â_t = a_{t-1} + Δ̂（惯性先验，平滑动作）。默认 False 兼容旧 checkpoint。
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predict_delta: bool = False
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# R1-B 多步展开训练：rollout_len>1 时 dataset 返回 K 步连续序列，
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# 训练用 scheduled sampling（前 ss_prob 比例步用真历史，其余用自预测回传），
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# 治 exposure bias。rollout_len=1 时退化为现状单步。
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rollout_len: int = 1
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ss_prob: float = 1.0
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@dataclass
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class Config:
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data: DataConfig = field(default_factory=DataConfig)
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model: ModelConfig = field(default_factory=ModelConfig)
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train: TrainConfig = field(default_factory=TrainConfig)
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def to_dict(self):
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return asdict(self)
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@classmethod
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def from_dict(cls, d):
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return cls(
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data=DataConfig(**d.get("data", {})),
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model=ModelConfig(**d.get("model", {})),
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train=TrainConfig(**d.get("train", {})),
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)
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def build_argparser(description: str = "") -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser(description=description)
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parser.add_argument("--image-width", type=int, default=None)
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parser.add_argument("--image-height", type=int, default=None)
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parser.add_argument("--action-dim", type=int, default=None)
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parser.add_argument("--history-len", type=int, default=None)
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parser.add_argument("--frame-len", type=int, default=None)
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parser.add_argument("--data-dir", type=str, default=None)
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parser.add_argument("--normalize-actions", action=argparse.BooleanOptionalAction, default=None)
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parser.add_argument("--action-stats-path", type=str, default=None)
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parser.add_argument("--normalize-mode", type=str, default=None,
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choices=["zscore", "fixed_range", "log_range"],
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help="动作归一化方式：zscore=数据统计（依赖 action_stats.npy）；"
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"fixed_range=固定物理范围 min-max→[0,1]（免 stats 文件）；"
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"log_range=spk 两列对数尺度（免 stats 文件）")
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parser.add_argument("--column-weights", type=str, default=None,
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help="逐列损失权重（逗号分隔 action_dim 个浮点），"
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"缺省用默认表（舵机1.0/spk0.2/保留位0.0）；仅影响训练损失")
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parser.add_argument("--imu-dim", type=int, default=None,
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help="R2 方案一：MPU6050 姿态维度（0=关闭，默认；"
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"8×N=N 个传感器拼接（≤4 个），序列变 F+H+2）")
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parser.add_argument("--backbone", type=str, default=None, choices=["simple", "mobilenet"])
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parser.add_argument("--cnn-feature-dim", type=int, default=None)
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parser.add_argument("--fused-hidden-dim", type=int, default=None)
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parser.add_argument("--dropout", type=float, default=None)
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parser.add_argument("--attn-dim", type=int, default=None)
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parser.add_argument("--attn-heads", type=int, default=None)
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parser.add_argument("--attn-layers", type=int, default=None)
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parser.add_argument("--batch-size", type=int, default=None)
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parser.add_argument("--epochs", type=int, default=None)
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parser.add_argument("--lr", type=float, default=None)
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parser.add_argument("--weight-decay", type=float, default=None)
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parser.add_argument("--val-split", type=float, default=None)
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parser.add_argument("--checkpoint-dir", type=str, default=None)
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parser.add_argument("--patience", type=int, default=None)
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parser.add_argument("--num-workers", type=int, default=None)
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parser.add_argument("--seed", type=int, default=None)
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parser.add_argument("--max-grad-norm", type=float, default=None)
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parser.add_argument("--augment", action=argparse.BooleanOptionalAction, default=None,
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help="训练时启用安全图像增强（ColorJitter：亮度/对比度/饱和度/色调）")
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parser.add_argument("--predict-delta", action=argparse.BooleanOptionalAction, default=None,
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help="R1-C 残差预测：target 改为 Δ=actions[t]-actions[t-1]，"
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"推理端 â_t=a_{t-1}+Δ̂（惯性先验平滑动作，默认关兼容旧 checkpoint）")
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parser.add_argument("--rollout-len", type=int, default=None,
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help="R1-B 多步展开训练步数 K（默认 1=单步；K>1 时 scheduled sampling）")
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parser.add_argument("--ss-prob", type=float, default=None,
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help="scheduled sampling 真实历史概率（仅 rollout-len>1 生效，默认 1.0=全真）")
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parser.add_argument("--init-from", type=str, default=None,
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help="预训练 checkpoint 路径，作为微调起点（strict=False 加载兼容权重）")
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parser.add_argument("--resume", type=str, default=None,
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help="从 last.pt 断点续训（恢复 model/optimizer/scheduler/epoch）")
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parser.add_argument("--save-config", type=str, default=None, help="保存配置到 JSON 文件")
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return parser
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def validate_config_values(data_cfg: DataConfig, model_cfg: ModelConfig,
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train_cfg: TrainConfig = None):
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"""校验从 checkpoint 恢复或 CLI 传入的配置值域合理性
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防止恶意/损坏 checkpoint 注入异常值（如超大维度导致 OOM），
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也防止训练 CLI 越界参数（如 --history-len 0 / --lr -1）静默进入训练。
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"""
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checks = [
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("image_width", data_cfg.image_width, 1, 4096, int),
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("image_height", data_cfg.image_height, 1, 4096, int),
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("history_len", data_cfg.history_len, 1, 1024, int),
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("frame_len", data_cfg.frame_len, 1, 1024, int),
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("action_dim", data_cfg.action_dim, 1, 1024, int),
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("imu_dim", data_cfg.imu_dim, 0, 64, int),
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("cnn_feature_dim", model_cfg.cnn_feature_dim, 1, 4096, int),
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("fused_hidden_dim", model_cfg.fused_hidden_dim, 1, 4096, int),
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("dropout", model_cfg.dropout, 0.0, 1.0, float),
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("attn_dim", model_cfg.attn_dim, 1, 4096, int),
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("attn_heads", model_cfg.attn_heads, 1, 64, int),
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("attn_layers", model_cfg.attn_layers, 1, 64, int),
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]
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for name, val, lo, hi, typ in checks:
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if typ is int:
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ok = isinstance(val, int) and not isinstance(val, bool) and lo <= val <= hi
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else:
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ok = isinstance(val, (int, float)) and not isinstance(val, bool) and lo <= val <= hi
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if not ok:
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raise ValueError(
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f"checkpoint config {name}={val} 超出合理范围 [{lo}, {hi}]")
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if model_cfg.attn_dim % model_cfg.attn_heads != 0:
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raise ValueError(
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f"attn_dim={model_cfg.attn_dim} 必须能被 attn_heads="
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f"{model_cfg.attn_heads} 整除")
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if not (data_cfg.normalize_mode in ("zscore", "fixed_range", "log_range")):
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raise ValueError(
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f"normalize_mode={data_cfg.normalize_mode!r} 非法，"
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f"应为 'zscore'、'fixed_range' 或 'log_range'")
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if data_cfg.column_weights is not None:
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w = data_cfg.column_weights
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if len(w) != data_cfg.action_dim:
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raise ValueError(
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f"column_weights 长度 {len(w)} 应等于 action_dim="
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f"{data_cfg.action_dim}")
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if any((not isinstance(v, (int, float))) or isinstance(v, bool)
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or not (0.0 <= v <= 1e6) for v in w):
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raise ValueError(
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f"column_weights 元素应为 [0, 1e6] 内的数值，实际: {w}")
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if not any(v > 0 for v in w):
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raise ValueError("column_weights 全 0：损失恒 0，训练无梯度")
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if train_cfg is not None:
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t_checks = [
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("batch_size", train_cfg.batch_size, 1, 65536, int),
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("epochs", train_cfg.epochs, 1, 100000, int),
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("lr", train_cfg.lr, 1e-9, 10.0, float),
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("weight_decay", train_cfg.weight_decay, 0.0, 1.0, float),
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("val_split", train_cfg.val_split, 0.0, 1.0, float),
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("patience", train_cfg.patience, 0, 100000, int),
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("num_workers", train_cfg.num_workers, 0, 256, int),
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("max_grad_norm", train_cfg.max_grad_norm, 0.0, 1e6, float),
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("rollout_len", train_cfg.rollout_len, 1, 1024, int),
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("ss_prob", train_cfg.ss_prob, 0.0, 1.0, float),
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]
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for name, val, lo, hi, typ in t_checks:
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if typ is int:
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ok = isinstance(val, int) and not isinstance(val, bool) and lo <= val <= hi
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else:
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ok = isinstance(val, (int, float)) and not isinstance(val, bool) and lo <= val <= hi
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if not ok:
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raise ValueError(
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f"配置 {name}={val} 超出合理范围 [{lo}, {hi}]")
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def warn_init_from_mismatch(init_cfg: dict, current: Config):
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"""--init-from 加载预训练权重前的兼容性警告（train.py / pretrain_mt.py 共用）
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normalize_actions 不一致时输入分布与权重不匹配，预训练收益大打折扣；
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action_dim 不一致时 act_proj 无法加载对应权重（strict=False 静默跳过，
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该层随机初始化）。
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"""
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if not isinstance(init_cfg, dict):
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return
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init_data = init_cfg.get("data") or {}
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init_norm = init_data.get("normalize_actions", False)
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if init_norm != current.data.normalize_actions:
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print(f"WARNING: init-from checkpoint 的 normalize_actions="
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f"{init_norm} 与当前运行 {current.data.normalize_actions} "
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f"不一致，输入分布不匹配会损失预训练收益（见 README 预训练说明）")
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init_mode = init_data.get("normalize_mode", "zscore") # 旧 checkpoint 无此键 → zscore
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if init_norm and current.data.normalize_actions and init_mode != current.data.normalize_mode:
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print(f"WARNING: init-from checkpoint 的 normalize_mode={init_mode} "
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f"与当前运行 {current.data.normalize_mode} 不一致，"
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f"归一化空间不同会损失预训练收益")
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init_dim = init_data.get("action_dim")
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if init_dim is not None and init_dim != current.data.action_dim:
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print(f"WARNING: init-from checkpoint 的 action_dim={init_dim} "
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f"与当前运行 {current.data.action_dim} 不一致，"
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f"act_proj 权重将随机初始化")
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init_imu = init_data.get("imu_dim", 0) # 旧 checkpoint 无此键 → 0
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if init_imu != current.data.imu_dim:
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print(f"WARNING: init-from checkpoint 的 imu_dim={init_imu} "
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f"与当前运行 {current.data.imu_dim} 不一致，"
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f"imu_proj/pos_embed 权重将随机初始化")
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def parse_checkpoint_config(cfg_dict: dict) -> Config:
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"""从 checkpoint 恢复 Config，校验键集合与值域
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- 顶层缺 data/model 键 → ValueError（带明确信息）
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- data/model/train 段含未知键 → ValueError（列出未知键，避免裸 TypeError）
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- 值域非法 → ValueError（复用 validate_config_values）
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"""
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if not isinstance(cfg_dict, dict):
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raise ValueError(f"checkpoint config 应为 dict，实际为 {type(cfg_dict).__name__}")
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section_dataclasses = {
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"data": DataConfig,
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"model": ModelConfig,
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"train": TrainConfig,
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}
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for section, dataclass in section_dataclasses.items():
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if section not in cfg_dict:
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if section in ("data", "model"):
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raise ValueError(f"checkpoint config 缺少 '{section}' 段")
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continue
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section_dict = cfg_dict[section]
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if not isinstance(section_dict, dict):
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raise ValueError(
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f"checkpoint config['{section}'] 应为 dict，"
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f"实际为 {type(section_dict).__name__}")
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unknown = set(section_dict) - set(dataclass.__dataclass_fields__)
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if unknown:
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raise ValueError(
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f"checkpoint config['{section}'] 含未知键: {sorted(unknown)}")
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cfg = Config.from_dict(cfg_dict)
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validate_config_values(cfg.data, cfg.model, cfg.train)
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return cfg
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def apply_overrides(cfg: Config, args: argparse.Namespace) -> Config:
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mapping = {
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"image_width": ("data", "image_width"),
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"image_height": ("data", "image_height"),
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"action_dim": ("data", "action_dim"),
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"history_len": ("data", "history_len"),
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"frame_len": ("data", "frame_len"),
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"data_dir": ("data", "data_dir"),
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"normalize_actions": ("data", "normalize_actions"),
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"action_stats_path": ("data", "action_stats_path"),
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"normalize_mode": ("data", "normalize_mode"),
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"column_weights": ("data", "column_weights"),
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"imu_dim": ("data", "imu_dim"),
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"backbone": ("model", "cnn_backbone"),
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"cnn_feature_dim": ("model", "cnn_feature_dim"),
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"fused_hidden_dim": ("model", "fused_hidden_dim"),
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"dropout": ("model", "dropout"),
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"attn_dim": ("model", "attn_dim"),
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"attn_heads": ("model", "attn_heads"),
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"attn_layers": ("model", "attn_layers"),
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"batch_size": ("train", "batch_size"),
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"epochs": ("train", "epochs"),
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"lr": ("train", "lr"),
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"weight_decay": ("train", "weight_decay"),
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"val_split": ("train", "val_split"),
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"checkpoint_dir": ("train", "checkpoint_dir"),
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"patience": ("train", "patience"),
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"num_workers": ("train", "num_workers"),
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"seed": ("train", "seed"),
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"max_grad_norm": ("train", "max_grad_norm"),
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"augment": ("train", "augment"),
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"predict_delta": ("train", "predict_delta"),
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"rollout_len": ("train", "rollout_len"),
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"ss_prob": ("train", "ss_prob"),
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}
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args_dict = vars(args)
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for arg_name, (section, field) in mapping.items():
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val = args_dict.get(arg_name)
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if val is not None:
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if field == "column_weights" and isinstance(val, str):
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val = [float(v) for v in val.split(",")]
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setattr(getattr(cfg, section), field, val)
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return cfg
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